Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere without the permission of the Author. A QUANTITATIVE ANALYSIS OF THE VARIABILITY IN THE ACTIVITY OF NITRIFYING ORGANISMS IN A SOIL UNDER PASTURE A thesis presented in partial fulfi lment of the requirements for the degree of Doctor of Phi losophy at Massey University Robert G.V. Bramley 1 9 89 i ABSTRACT variability in the inputs , outputs and transformations of mineral N under field conditions makes the predictive modelling of the leaching of soil nitrate very diff icult . In an attempt to understand and quantify this variability , the activity of nitrifying organisms in the Tokomaru silt loam ( a Typic fragiaqualf ) under pasture was measured using a short-term nitrif ication assay ( SNA ) . Spatial dependence of the variabi l i ty in SNA was examined using geostatistical methods , and the ef fect on SNA of soil pH change through l iming , and of seasonal changes in soil temperature and moisture were invest igated . Nitr i f ier activity and associated soi l properties such as the amount of exchangeable ammonium and the soi l ni trate concentration , were found to decrease in value with depth between 0-24 cm . The greatest decrease in SNA was observed between 0-9 cm depth , but due to the need for suf f icient quantities of soi l to make SNA measurements , and the desire to avoid the possibi l ity of inhibitory ef fects of grass roots on nitrif ication , soil was sampled from the 3 -9 cm depth range for the bulk of the work reported here . Results indicated that the technique of sieving and mixing samples was s at is factory f or removing depth-dependence from the results for spatial variability and other analyses . The spatial variability of SNA , soi l No3 - , soi l moisture content and the pH of the SNA incubation , which was assumed to approximate the f ield soil pH , was investigated over areas of 9 m2 and 625 m2 us ing a regular 1 1 x 1 1 square grid sampling design with minimum sample separations of 30 cm and 2 . 5 m respectively . However , the results of these analyses proved inconclusive , apparently due to the lack of samples separated. by lags that were suf f iciently short in relation to the overal l dimensions of the sampl ing area . Accordingly , spatial analysis of the above properties , together with exchangeable ammonium , was carried out over 625 m2 using a n es ted sampling design that permitted an adequate number of observation pain t s a t l ags r anging f rom 1 2 . 5 cm to 2 5 m . Thi s des i gn was a considerable improvement on the regular square design , although i t had a number of shortcomings , notably bias caused in the estimation of the sample vari ance due to the nes ting o f a large number of data points ii within a small area , and bias caused in the estimation of values of the semiva r i ance a t some l ags- due to m i s s ing sampl ing points at some positions in the sampling grid . The va lues o f SNA , N0 3 - and exchangeab le ammonium were all highly variable and conformed to lognormal distributions . The range of spatial dependence in the variabil ity of SNA , soil N03- and incubation pH was 2 . 4 , 5 . 4 and 6 . 1 m respectively . Exchangeable ammonium , SNA , soil No3- and incubation pH varied isotropically within the sampling area but Ex­ NH4• showed no spatial dependence . Soi l moisture content was strongly anisotropic , and showed no spatial dependence in one direction , but clear evidence o f dr i f t in a perpendicular direc t i on . These results are d i scus sed in r e l a t ion to t he mos t e f f i c ient s ampl ing strategy for estimation of the mean field N03 - concentration . I t was concluded that suf f icient small localized clusters of samples should be taken to give a low standard error of the mean , with each cluster separated by at least 5 m . In the case of the Tokomaru silt loam , 20 clusters , each comprising 5 samples ( bulked ) , would be required for estimation of the mean f ield nitrate concentration with 9 5 % probability of 0 being within ± 5% of �' the true mean . This represents a l arge sampling ef fort . The a c t iv i t y o f n i t r i f iers was s tudied in relation to soil pH and seasonal changes in soil moisture and temperature over two consecutive years in an attempt to explain the spatial variabil i ty in SNA values . The pH optimum for nitrif ier activity ( pHopt ) was def ined for four variates of the Tokomaru s i l t loam with dif ferent l iming hi stories . Values of pHopt which varied between the four soi ls in the range 5 . 92-6 . 4 5 did not vary markedly w i th s eason , and i t was found that the form of the rela t ionship between SNA and pH remained constant wi th t ime . I t was further observed that the addit ion of l ime in 1 9 87 had the effect of rais ing the mean soil pH and pHopt in previously-unlimed soi l , but had negligible ef fect on either the soi l pH or pHopt in soi l that had been l imed in 1 982 . The significance of heterotrophic relative to autotrophic nitrif ication could not be discerned . iii No signi f icant relationships could be found for the four soils between soil pH , pHopt , SNA, soil moisture content and soi l temperature at 3 0 cm depth . Va lues of SNA ( pmol N g-1 so i l h- 1 ) at pHopt ( SNAopt) were calcul ated from equations f i t ted t o plots of SNA vs . the pH of SNA incuba t ion , and these show a more obvious seasonal trend . SNA va lues calculated for the prevail ing soi l pH (SNApH) were never very dif ferent from values of SNAopt and follow a 1 : 1 relationship over a range of values from 0 . 0 1 5 -0 . 1 1 0 pmol g-1 h-1 ; that i s , the nitrifier activity in the s o i l , i rre spect ive o f va ria t ions t hat were ra ndom ( unknown inf luences ) or associa ted wi th seasonal var iables ( temperature and moisture ) , was near the optimum with respect to the soil pH a t the time of sampling . The ef fect of soil moisture variat ion on nitrif ier activity was further invest igated in an experiment in which soil samples were stored for 1 2 4 days a t dif ferent soi l moisture tensions . The optimum moisture conditions for nitrifier activity in the Tokomaru s i l t loam prevailed at pF 3 . 39 . However , this optimum was less clearly def ined than was the pHopt • S ince the soil moisture status changes considerably with season, whi lst soi l pH does not , it was concluded that nitrif iers were more tolerant of changes in pF than changes in pH . Comparison of these with published results indicates that not only is the soi l n i trif ier population dynamic , and changes in response to changes in its environment , but the degree to which nitrif ier activity i s a f fected by various soi l properties is soi l-specific . It is therefore concluded that the spatial variability of nitrif ier activity will also be soi l ­ speci f ic , and that dif ferent soils are likely to have dif ferent ranges o f spatial dependence for the parameters of mineral N. Furthermorer the fact that SNA is not the only factor governing the soi l No3- concentration , and that other f actors such as plant uptake and leaching are also importan t , indicates that SNA variabil ity i s not necessari ly a good e s t i m ator of s o i l N03- var i abi l i ty . This conclus ion i s certainly supported by the geostatistical aspects of this work . iv ACKNOWLEDGEMENTS I would like to thank the fol lowing people for their part in seeing this proj ect through to completion : P r o f es sor R . E . Whi te f or h i s superv i s ion and guidance throughout . The support given in l oco paren ti s by Bob and his w i fe , Annette , on arrival in this s trange ( ! ) country and their friendship thereafter has been very greatly appreciated . Dr A . N . Macgregor for his supervision . Drs D . R . Scotter , P . R . Darrah , A . B . McBratney and A . Swi f t , and Messrs L . D . Currie, M . Eggels , Mrs H . Murphy and Mrs A . Rouse for their assistance at various stages of the proj ect . The s ta f f and postgraduate s tudents of the S o i l S cience Department for providing the relaxed and friendly atmosphere which made l i fe at Massey so enjoyable . The Vice-Chancellor of Massey University , Dr T . N . M . Waters , for making the necessary funds available for this work . Finally I would l ike to thank my parents , John and Rosalind Bramley , for providing all that made starting this work possible , and Jo Tomp ki ns , who se suppor t and af f e ct i o n was the much needed inspiration for its completion. V A QUANTITATIVE ANALYSIS OF THE VARIABILITY IN THE ACTIVITY OF NITRIFYING ORGANISMS IN A SOIL UNDER PASTURE TABLE OF CONTENTS Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Table of contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List o f Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi SECTION I . INTRODUCTION CHAPTER 1. INTRODUCTION AND AIMS OF THE PROJECT . . . . . . . . . . . . . . . . 1 i . Background to the proj ect - the nitrate leaching problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 i i . Nitrate leaching models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 i i i . The aim of the proj ect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 CHAPTER 2 . vi NITRIFICATION IN SOILS: A REVIEW . . . . . . . . . . . . . . . . . . . . 7 i . The factors af fecting nitri f ication and mineral ization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 i i . Modell ing of nitrif ication . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 i i i . A comment on measured nitrif ication rates . . . . . . . . . . 2 3 iv . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 4 CHAPTER 3 . A THEORETICAL CONSIDERATION OF SPATIALLY DEPENDENT VARIABILITY . . . . . . . . . . -. . . . . . . . . . . . . . . . . . . . 2 5 i . Why do we need geostatistics ? . . . . . . . . . . . . . . . . . . . . . 2 5 i i . Some preliminary data analysis using class ical statist ics . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 28 i i i . Stationarity and the semi-variance . . . . . . . · . . . . . . . . . . 3 2 i v . The variogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 5 v . Spatial variation in two dimensions . . . . . . . . . . . . . . . . 40 vi . Other variogram models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 7 vii . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 vi i CHAPTER 4. EXPERIMENTAL METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 A . THE SHORT-TERM NITRIFICATION ASSAY . . . . . . . . . . . . . . . . . . . . . . . 5 2 i . Selection of incubation media for SNA analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 ii . Linearity of nitrification rate in the Tokomaru silt loam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 4 i i i . Selection o f ammonium substrate concentration for SNA analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 8 iv . Analysis of exchangeable ammonium . . . . . . . . . . . . . . . . . . 6 1 B . FIELD SAMPLING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 i . S ite details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 i i . Soil sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 i i i . Correlation between moisture contents o f sieved and unsieved soil . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 C . STORAGE OF SAMPLES PRIOR TO SNA MEASUREMENTS . . . . : . . . . . . . . 6 7 i . Ef fects of drying and storage on mineral nitrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 i i . Ef fects of drying and storage on soil biomass . . . . . . 69 D . CONCLUS IONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 6 vii i SECTION I I . AN ANALYSIS OF SPATIAL VARIABILITY IN NITRIFIER 'ACTIVITY CHAPTER 5 . VARIABILITY IN NITRIFIER ACTIVITY WITH DEPTH AND DISTANCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 8 A . DEPTH DEPENDENT VARIABILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 i . Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 i i . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1 i i i . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 B . SPATIALLY DEPENDENT VARIABILITY . . . . . , . . . . . . . . . . , . . . . . . . . . 9 6 i . Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 7 i i . Results . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 9 8 i i i . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 08 CHAPTER 6 . SPATIAL VARIABILITY OF NITRIFIER ACTIVITY - A MORE REFINED ANALYSIS . . . . . . , . . . . . . . . . . . . . . . . . . . . 1 1 6 i . Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 1 i i . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 1 i i i . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 5 iv . Conclusions and recommendations for future sampling strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 0 SECTION I I I . FACTORS AFFECTING NITRIFIER ACTIVITY ix CHAPTER 7 . THE EFFECT OF pH, MOISTURE AND TEMPERATURE ON NITRIFIER ACTIVITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 4 i . Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 4 i i . Results . . . . . . . . . . . . . . . ; . . . ; . . . . ·. ; . . . . . . . . . . . . . . . . . 1 4 9 i i i . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 68 iv . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 6 CHAPTER 8 . A FURTHER INVESTIGATION OF THE EFFECT OF MOISTURE ON NITRIFIER ACTIVITY . . . . . . . . . . . . . . . . . . . . 1 7 7 i . Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 8 i i . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 80 i i i . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 9 3 CHAPTER 9 . HETEROTROPHIC NITRIFICATION - EXACTLY WHAT HAS BEEN MEASURED BY THE SNA ? . . . . . . . . . . . . . . . . . . . . 1 98 i . Methods and Materials . . . . . . . . . . . . . . . . . . . . . , . . . . . . . 2 0 1 i i . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 0 3 i i i . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 0 9 iv . Conclus ion . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . 2 1 2 X SECTION IV . CHAPTER 10. GENERAL DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS FOR FURTHER WORK . . . . . . . . . . , . . . . . . . 2 1 3 A . A CRITIQUE OF THE GEOSTATISTICAL TECHNIQUES USED FOR THE ASSESSMENT OF SPATIAL VARIABILITY IN NITRIFIER ACTIVITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1 3 i . Problems caused by anisotropy . . . . . . . . . . . . . . . . . . . . . 2 1 4 i i . Problems caused by changes in the sample variance and variation in the variogram model with time and the scale of sampling . . . . . . . . . . . . . . . 2 1 8 i i i . The relationship between the sill and the sample variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 iv . Crossvariogram analysi s . . . . . . . . . . . . . . . . . . . : . . . . . . . 2 3 0 B . SOME CONCLUDING COMMENTS ON VARIABILITY IN NITRIFIER ACTIVITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 2 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 5 APPENDIX I . COMPUTER PROGRAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 52 GAMMAH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 COVGM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 58 Figure 3 . 1 Figure 3 . 2 Figure 3 . 3 Figure 3 . 4 Figure 3 . 5 Figure 3 . 6 Figure 3 . 7 Figure 3 . 8 Figure 3 . 9 Figure 3 . 1 0 x i LIST OF FIGURES Distribution of a set of 2 5 pH data . . . . . . . . . . . . . . . . . . . . . 2 9 Change in the mean and variance with increasing sample number for a set of 25 pH data . . . . . . . . . . . . . . . . . . . 3 1 Experimental variogram for the 2 5 pH. data , and the number of pairs of points separated by each lag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 6 A Change in the value of ¥ ( h ) as the number of pairs of data points used to calculate it increases . . . . . . . . . . . 3 8 Experimental variogram for soi l pH with linear models f itted by ordinary and least squares optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 9 1 2 1 equally spaced pH data sampled from a regular 1 1 x 1 1 square grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1 Distribution of the 1 2 1 pH data shown in Figure 3 . 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Change i n the mean and variance with increasing sample number for the set of 1 2 1 pH data . . . . . . . . . . . . . . . . 4 4 Change i n the number o f pairs o f points separated by each lag as the length of the grid side increases from 1 to 1 0 lag uni ts . . . . . . . . . . . . . . . . . . . . . . . . 4 5 Development o f an experimental variogram a s the number of samples used to estimate it increases as the length of the grid side increases from 2 ( 4 samples ) to 1 0 lag units ( 1 2 1 samples ) . . . . . . . . . . . . . . . 4 6 Figure 3 . 1 1 Figure 4 . 1 Figure 4 . 2 Figure 4 . 3 Figure 4 . 4 Figure 4 . 5 Figure 5 . 1 Figure 5 . 2 Figure 5 . 3 Experimental variogram for the 1 2 1 pH data f itted with a spherical model by weighted least squares x i i optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 0 Linearity o f nitrification rate over 1 9 hours i n the Tokomaru s i l t loam a t 2 2 oc . . . . . . . . . . . . . . . . . . . . . . . . . 5 6 Mean monthly weather data ( 1 928- 1 980 ) for the D . S . I . R Grasslands weather station , Palmerston North ( N . Z . Meteorological service , 1 98 3 ) . . . . . . . . . . . . . . . 6 4 Correlation between the gravimetric moisture contents of s ieved ( < 2mm ) and unsieved Tokomaru s i l t loam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 pH optima curves for nitri fier activity in fresh soil and soil that had been stored for 3 weeks . . . . . . . . . . 7 4 Common pH optimum curve fi tted to SNA data for fresh and stored soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 5 Change i n bulk density and mean volumetric moisture content with depth in the Tokomaru silt loam sampled in mid May . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Depth profiles of ( a ) SNA , ( b ) No3 - , ( c ) Ex-NH4 · , ( d ) incubation pH , ( e ) total carbon , ( f ) total nitrogen , ( g ) C/N ratio , ( h ) total phosphorus , and ( i ) % mineral N in the Tokomaru �ilt loam. sampled in mid May . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4 Distribution with depth of total carbon , ni trogen , phosphorus , C/N ratio and SNA expressed as a % of their maximum values . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 92 Figure 5 . 4 Figure 5 . 5 Figure 5 . 6 Figure 5 . 7 Figure 6 . 1 Figure 6 . 2 Figure 6 . 3 Distribution of ( a ) SNA , ( b ) No3 - , ( c ) incubation pH and ( d ) gravimetric moisture content sampled on a regular 1 1 x 1 1 square grid between 3-9 cm over xi i i 6 2 5 m2 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 99 Experimental variograms of ( a ) SNA , ( b ) No3- , ( c ) incubation pH and ( d ) moisture content sampled on a regular 1 1 x 1 1 square grid between 3-9 cm over 625 m2 with models f itted by weighted least squares optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 0 2 Distribution of ( a ) SNA , ( b ) No3 - , ( c ) incubation pH and ( d ) gravimetric moisture content sampled on a regular 1 1 x 1 1 square grid between 3-9 cm over 9 m2 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 1 06 Experimental variograms of (a ) SNA , (b ) N03 - , (c ) incubation pH and ( d ) moisture content sampled on a regular 1 1 x 1 1 square grid between 3 -9 cm over 9 m2 with models fi tted by weighted least squares optimization . . . . . . . . . . . . . . . : . . . . . . . . . : . . . . . . . . . . . . . . . . . 1 09 Sampling design for a nested spatial analysis over 625 m2 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 1 2 0 Distribution of ( a ) SNA , ( b ) N03 - , ( c ) Ex-NH4 · ( d ) incubation pH and ( e ) gravimetric moisture content sampled between 3- 1 2 cm over 625 m2 using a nested sampling strategy ( Figure 6 . 1 ) . . . . . . . . . . . . . . . . . . . . . . . . . 1 22 Experimental variograms of ( a ) SNA , ( b ) No3- , ( c ) Ex-NH4. ( d ) incubation pH and ( e ) gravimetric moisture content sampled between 3 - 1 2 cm over 625 m2 using a nested sampling strategy ( Figure 6 . 1 ) with models f itted by weighted least squares optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 26 Figure 6 . 4 Figure 7 . 1 Figure 7 . 2 Figure 7 . 3 Figure 7 . 4 Figure 7 . 5 Figure 7 . 6 Figure 7 . 7 Figure 8 . 1 F igure 8 . 2 Crossvariograms of ( a ) SNA and incubation pH and ( b ) SNA and No3- sampled between 3 - 1 2 cm over 625 m2 using a nested sampling stategy ( Figure 6 . 1 ) with l inear models f itted by weighted least xiv squares optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 3 7 Change in the pH with time of suspensions of soil in agar solution fol lowing addition of acid or alkali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 7 Mean monthly temperatures for the years 1 986/87 and 1 987/88 with sine curves f itted by ordinary least squares optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 48 pH optima curves for nitri fier activity in soi ls T and TL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . ' . . . . . . . 1 5 0 pH optima curves for nitrif ier activity in soi ls TX and TLX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5 6 Seasonal variation in pH , pHopt1 SNApH' SNAopt, soil moisture content and soil temperature in ( a ) soil T , ( b ) soi l TL , ( c ) soi l TX and ( d ) soi l TLX . . . . . . 1 62 Relationship between SNAopt and SNApH for soils T , TL , TX and TLX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 0 Relationship between pHopt and soi l pH for a range of soi ls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 1 Moisture characteristic curve for the Tokomaru silt loam ( s ieved < 2 mm ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 8 1 Levels of ( a ) soil moisture stress , ( b ) nitrif ier activity , ( c ) No3- , ( d ) Ex-NH4· , ( e ) gravimetric soi l moisture content and ( f ) incubation pH in the eight samples s tored for 1 2 4 days at di f ferent levels of soil moisture stress . . . . . . . . . . . . . . . . . . . . . . . . . 1 83 Figure 8 . 3 Figure 9 . 1 Figure 9 . 2 Figure 9 . 3 Figure 1 0 . 1 Figure 1 0 . 2 Figure 1 0 . 3 XV pF optimum curve for nitri f ier activity in the Tokomaru silt loam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 92 ( a ) Relative nitrification rate in the Tokomaru s i l t loam at dif ferent pH and ( b ) a pH optima curve for nitrif ier activity in the Patua soil . . . . . . . . . 200 Nitrif ier activities in soi l sampled in June incubated with a range of N-substrates . . . . . . . . . . . . . . . . . 204 Nitri f ier activities in soil sampled in October incubated with a range of N-substrates . . . . . . . . . . . . . . . . . 207 Experimental variogram of incubation pH of soi l sampled between 3 - 1 2 cm over 625 m2 using a nested sampling design ( Figure 6 . 1 ) with separate models f itted by weighted least squares optimization for the north-south and-Bast-west directions . . . . . . . . . . . . . _ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1 5 The main grid of the nested sampling design ( Figure 6 . 1 ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Distribution of the ln transformed values of initial No3 - sampled betweeri 3 - 1 � cm . depth over 625 m2 us ing a nested sampling design ( Figure 6 . 1 ) . . . . . 229 Table 3 . 1 Table 4 . 1 Table 5 . 1 Table 6 . 1 Table 6 . 2 Table 7 . 1 Table 7 . 2 Table 8 . 1 Table 8 . 2 Table 1 0 . 1 xvi LIST OF TABLES 2 5 values of soi l pH measured at equal spacings along a transect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Ef fect of ( NH4 ) 2S04 substrate concentration on SNA value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Correlation matrix for a range of soi l properties measured at di f ferent sampling depths . . . . . . . . . . . . . . . . . . . 9 1 The number o f pairs o f points separated by a given lag ( h ) under the two sampling designs used for spatial analysis over 625 m2 assuming isotropic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 8 Sample variance ( s2 ) and the parameters of models f itted by weighted least squares optimization to the experimental variograms of SNA , initial N03�, Ex-NH4 · , incubat ion pH and soil moisture content . . . . . . . 1 3 1 Summary of SNA results for soils T , TL , TX, and TLX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 67 Ef fect of l iming on nitrif ier activity in the Tokomaru Silt Loam ( 3 -9 cm depth ) under pasture . . . . . . . . 1 7 2 Mean values of SNA ( � mol N g- 1 h- 1 ) , initial No3 - and Ex-NH4• ( � mol N g- 1 ) in soil samples kept for 1 2 4 days at different moisture tensions and the s igni f icance of di fferences between the means . . . . . . . . . . 1 9 0 Summary of results for the suggested optimum moisture tension for nitrification . . . . . . . . . . . . . . . . . . . . . 1 9 6 Summary of results for the three spatial analyses of SNA and incubation pH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 SECTION I . INTRODUCTION CHAPTER 1 INTRODUCTION AND THE AIMS OF THE PROJECT i . Background to the project - the nitrate leaching problem 1 The annual world consumption of fert ilizer nitrogen has been variously estimated at 60 M .t ( Hauck, 1 9 88 ) and 73 M t ( Douglas & Cochrane , 1 989 ) . Douglas and Cochrane ( 1 989 ) estimated that an increase in consumption of fertil izer nitrogen of 1 7 - 1 8 % was needed annually to maintain world food supp l y , w h i l s t H auck ( 1 9 8 8 ) more cons erva t i vely e s t imated that consumption by the year 2000 would be 1 00 M t . On top of this , 9 0 M t of N i s added to agricul tural and pastoral systems annua l l y through biological f i xation of nitrogen ( White, 1 989b) which comprises 79 % by volume o f the Earth's atmosphere . The distribution on a global scale of this add ition of fertili zer N is by no means uni form , 58 % of world consumption being in developed western countries , 35 % in countries with central l y con trol led economies , and 7 % in the developing countries . Although the N-fertilizer market is stable in some parts of the world such as Europe , consumption in others is increasing rapidly - in China for example , by as much as 27 % for the year 1 987/88 ( Douglas & Cochrane , 1 989 ) . Against this background of increasing and uneven N consumption , the envi ronmenta l cons equences o f high f er t i l i zer app lication , in particular , are gaining increasing prominence in the publi� eye , to the extent that in Wes tern Europe , the eutroph ication o f s treams and waterways and nitrate pollution of potable water supplies due to leaching has become something of a cause celebre. Figures for N consumption and nitrate leaching in New Zealand are scarce , although the use of fertilizer-N in New Zealand i s only sma l l , being t ied to user-prosperity more than any other factor ( Douglas & Cochrane , 1 989 ) . Indeed , most N . Z bul l beef farmers , for example , do not as a rule apply N fert i l i z er to their pasture , and tend only to apply i t as a rescue 2 mechanism in t imes of low production ( Baars e t a l . , 1 989 ) . In contrast , during 1 9 78 in Britain , the total input of new nitrogen to agriculture was about 2 M t for the year, comprising 0 . 2 - 0 . 4 M t f rom biological N2 f ixation , 1 . 1 5 M t as fertil izer, at least 0 . 2 8 M t f rom precipitation and 0 . 1 8 M t from imported animal feedstuffs ( Thomson , 1 985 ) . All of this except the animal feedstuffs ( i . e . 9 1 % ) can be regarded as a direct application to l and ; the feedstuf fs represent an indirect application ( Ball & Ryden, 1 984 ) . In the U . K, the ferti l izer component applied to arab l e c rops i s a l mo s t a l l app l i ed in spring , whi lst pastures are ferti l ized through the summer ai�ell . It is therefore not surprising that the l evel of ni trate in water supplies of ten exceeds the so-ca l led a ccep tabl e concentration . This i s especially the case when one considers that 3 7 % of the grassland in England and Wales now receives more than 2 0 0 kg N ha- 1 , and 5 % receives more than 4 0 0 kg N ha- 1 ( Ryden et a l . , 1 98 4 ) . The acceptable range of N in drinking water according to the World Health Organi sation is 1 1 . 3 -22 . 6 mg N dm-3 ( W.H . O . , 1 9 7 0 ) whi lst the European Community treat 1 1 . 3 mg N dm- 3 as a permissible maximum but have a gui de l evel of 5 . 7 mg N dm- 3 ( E . E . C . , 1 980 ) . Al l these f igures were greatl y exceeded by the concentration of nitrate in water draining from the experimental plots of Ryden et a l . ( 1 984 ) and were somewhat similar to the mean annual concentration of N03-N i n water draining from the l y s ime ters o f Dowde l l e t a l . ( 1 9 8 4 ) of 1 1 . 8 - 2 6 . 7 m g N dm-3, a concentration which was unaffected by the rate of fertilizer application in the range 0 - 1 2 0 kg N ha- 1 • The latter suggests that the source of the leachable nitrate may not in fact be applied fertilizer. Ryden e t a l . ( 1 984 ) measured nitrate leaching from grazed and cut swards , and found that leaching losses of No3 - from the grazed ( uncut ) sward far exceeded ( by a factor of 5 ) the loss from cut swards despite a common input of fertilizer, and also exceeded the losses from arable land . The enhanced nitrate movement observed below grazed pastures was attributed to the return in urine and dung of as much as 90 % of the N in herbage consumed by the cattle . This casts considerable doubt on the previously held view that arable land was the main source of leached No:- (White , 1 98 4 ) . The problem of enhanced leaching due to the presence of ruminants i s e x a cerba ted by t he spat ia l h e t erogen e i ty o f dung and urine deposi tions . The result of this i s that ho tspots ( White , 1 98 4 ) of very 3 high inorganic N occur , and since the salt concentration in the urine is high , plants suffer from scorch . Plant uptake is low as a result and so does not significant ly reduce the pool of inorganic N ( Ball _& Ryden , 1 98 4 ) . Despite the many advances in agricultural practice , both technological and otherwise , the efficiency of use of fertilizer nitrogen by crops and pastures is poor and the consequent loss due to leaching i s l ikely to be high . White ( 1 989b ) noted that wet and dry depositional input� of N are insignif icant in New Zealand, and that the biological input of N is much more important than the fert i l izer input on a kg N ha-• basis . The results of Ryden et a l . ( 1 984 ) and Dowdell et a l . ( 1 98 4 ) suggested that fertilizers applied to arable land may not be the major source of leached nitrate , but that grasslands , especially those grazed by ruminant animals were the source of much of the leached nitrate . One is therefore led to conclude that since the use of N fertil izer in New Zealand is so low , i f a New Zealand s tudy were to show s ignif icant leaching o f No3 - from grasslands , it would be confirmed that the predominant source of leached No3 - was indeed grazed grassland , and the continuing debate on the source o f the leached nitrate could be ended . Thus , there is a very real need for a model ( or models ) for the accurate prediction of nitrate leaching , so that steps may be taken to minimise i ts ef fects . i i . Nitrate leaching models In the absence of a direct application of N03-N fertilizer , the s ize of - the nitrate pool available for leaching in a def ined volume of soil at a g iven time depends on the balance between the nitrif ication rate and the rate of nitrate removal by plants and micro-organisms . The nitrif ication rate is determined by the activity of the ammonium oxidase enzyme system in nitri fying organisms which in soil is affected primarily by the supply of NH 4 - subs trat e , s o i l tempera ture , moisture and pH ( Barber , 1 98 4 ; Gi lmour , 1 98 4 ) . These factors are discussed i n Chapter 2 . Given a pool of l eachable n i t rate , the l eaching process is governed by patterns of r a infa l l and evaporat ion , so i l t ex ture· and s tructure , i rrigat ion management ( i f any ) and land use ( White , 1 988 ) . 4 A f irst attempt at modelling nitrate leach ing was presented by Burns ( 1 980a ) . The model was based on the concept of the effective _roo t ing depth above which all the inorganic N in the soil was considered equally avai lable , and below which , all N was considered t otally unavailable . N03 - release from the soil organic matter was ignored and all of the ammonium fertilizer added was assumed to have been converted to nitrate , except for the condition when most of the drainage occurred within s i x weeks o f fertil izer application , i n which case only 5 0 % of the NH4 . was considered to have been nitrified . In addition , the leaching of nitrate was assumed to occur only over the 'winter period between fert i l izer application and the beginning of March , by which time all top dressings had been applied, that i s , it was assumed that most of the leaching had taken place before there was s igni f icant crop uptake . It was found that the slow release of mineral N from organic sources , and the differences between the distributions of mineralized and freshly applied-N , af fected the rate at which nitrate from the two sources was lost , and since no account was taken of mineral ization , it was clear that a more complex model was required . A s imi lar conclusion was drawn by Burns ( 1 980b) who tested his model against data from published N response experiments in the Netherlands and U.K. Predictions of the inf luence of winter rainfall on the ferti l izer residues in spring tended to overestimate the spring No3 - concentration observed in Dutch experiments , and underestimate that for the English data . However , the predictions were broadly correct , and the dif ference between the predicted and measured ef fects appeared to be caused by errors in the estimation of the amount and distribution of autumn nitrate in the so i l , and by other losses of nitrate ( e . g . by denitrification ) which may have occurred during the winter . Again, a more complex model was required . Bresler and Laufer ( 1 97 4 ) and Bresler e t al . ( 1 9 82 ) used the convec tion dispersion equa tion in conjuction with a nitrate product ion term to model nitrate movement , whilst White ( 1 985a ; 1 985a , b ; 1 987 ; 1 989a ) used the equations developed by Jury ( 1 9 82 ) and modelled nitrate leaching as a stochastic process using transfer functions . The former approach is based on the m echanisms governing the spread ( or di spersion ) o f a so lute inj ected into a liquid moving through homogenous porous media ( Dyson & 5 White , 1 987 ) . The latter approach acknowledges that the various processes governing solute transport are poorly known , especially in heterogeneous media such as soil, but it can be used to make predictions oe nitrate leaching based on probability , providing that the nitrate concentration prior to leaching i s known , and that estimates o f gains or losses o f mineral N by other processes can be made ( White , 1 987 ) . Dyson and White ( 1 98 7 ) compared these two approaches for the leaching of chloride through a s tructured c lay s o il , and on the bas i s o f compar i sons between experimental results and model predictions were unable to conclude that one model approach was better than the o ther . However , since the mechan i sms of solute transport are poorly understood , the transfer function approach.seems preferable from a philosophical point of view . In spite of the relative success of the t ransfer function .model for nitrate leaching ( White , 1 989a ) , spatial and temporal variability in the inputs , outputs and transformations of mineral N under field conditions make the predictive modelling of soil nitrate diff icult ( White & Bramley , 1 98 6 ; White , 1 988 ; 1 98 9 a ) . White ( 1 985a ) found that a change of ± s tandard deviation in the mean No3- concentration in the soil solution had a relatively large effect on the prediction o f the amount of nitrate leached . Thus , the estimate of this ( initial) nitrate concentrat ion is crucial to the success ful use of the model, but as indicated above is made very diff icult by the spatial variability of nitrate in the f ield . White ( 1 985a ) took soil cores over an area of 4 m2 and found that their nitrate concentrations ranged from 30 to 226 � g N cm- 3 , whilst White et a l . ( 1 98 7 ) conducted an elementary spatial analysis of soil nitrate and found that almost all the var iance was short-range , occurring within 0 . 4 m . i ii. The aim o f the project The aim of the work reported in this thesis was to try to quantitatively analyse spatial ( and other ) variability in soil mineral nitrogen , so that the nitrate concentration in the soil solution could be estimated more accurately over relatively large areas ( e . g . a f ield) . The areal average No3- concentration can then be used as an input parameter in f ield-scale 6 ni trate l eaching model s . In a departure from previous work on this problem ( e . g . White e t al . , 1 987 ) , the emphasis was placed on s tudying the system that generates the nitrate in an attempt to understand why the concentrat ion i s so var i able . In the f o l lowing two chapters , the l iterature on nitri fication and its modell ing i s reviewed and a precis of geostatistical theory i s presented . In further chapters , the variabil i ty of nitrifier activity over area , depth and time is investigated , together with the way in which factors . such as pH and soi l moisture content may affect soil nitrate concentrations . 7 CHAPTER 2 NITRIFICATION IN SOILS : A REVIEW The minerali zati on of nitrogen in soil comprises two separate processes - ammoni fi cati on , which is the release of NH4. from the soi l organic matter , and n i tri fi cati on , whi ch is the ox idation of NH4 • to N03-. A review of the b iochemistry o f these processes i s given by Focht and Verstraete ( 1 9 7 7 ) . �espite the pre-occupation o f the work reported in this thesis with nitri f ication , it was considered pertinent to review previous work on the whole mineralization process , in addition to the nitri f ication l iterature , prior to carrying out further research , for two reasons . Firstly , as Addiscott ( 1 983 ) pointed out , it is not possible to measure nitri f ica tion in the soi l using added ammonium substrates in i solation from either ammonification or immobi l i zati on ( the re-conversion of inorganic to organic N) . Secondly , nitrification depends on. a supply of NH4-N substrate , but because the source of N for the ammonification process , the soil organic matter, comprises many substances some of which are onl y l oosely def ined , ammoni f i cat ion has not been extensively researched in its own right . As a result , much of the earl ier work on soi l n i trogen transf orma tions dea lt instead w ith the minera l i za t i on process as a whole ( e . g . Stanford & Smith , 1 9 72 ) . S ince the rate-limiting step in soi l N mineralization is the conversion of organic N to NH4-N, under condit ions of adequate aeration over a broad range of temperatures and soi l moisture contents , soil derived NH4• is oxidized to No3- rapidly enough to prevent NH4• accumulation ( Stanford & Epstein , 1 9 7 4 ; Wild & Cameron , 1 9 80 ; Schmidt , 1 982 ) . N02-, the intermediary in the convers ion of NH4 • to N03- is not normally detected in excess of 1 I 3 ppm ( e . g . Reichman et a l . , 1 9 66 ) , and so the rate of N03- accumulation generally reflects the rate of N mineralization . Thus , in the fol lowing discuss ion both nitrif ication and mineralization as a whole are considered . Rosswa l l ( 1 9 82 ) sugges ted that there was a probl em regarding the ident i f i ca t i on of the spec i f ic funct ions of the various nitrifying bacteria . Ni trosomonas has always been accredited with the conversion of NH4 • to N02-, with Ni trobacter completing the transformation to N03-. 8 Howeve r , Ni trosol obu s and Ni trospora are a l so common in soi ls and Rosswal l ( 1 982 ) c laimed that Nitrosol obus was the dominant NH4 · oxidizer i n agricul tura l s o i l s . S chmidt ( 1 9 82 ) even suggested that methane oxidizing bacteria �ay be involved, despite acknowledging that the only micro-organisms known to be l inked directly to nitrif ication in natural environments are the gram - ve chemosynthetic autotrophs which comprise the family Ni t robact eriaceae. Since the purpose of this study was to i nvest igate the var i abi l i ty and magnitude of the ne t production of l eachable nitrat e , the t ypes and d i f ferences -between · the various nitrifying organisms was considered to be of secondary importance and it was assumed that autotrophic organisms were predominantly responsible for the production o f nitrate in the particular soi l studied ( see Chapter 9 ) . Throughout , the generic term nitrifiers will be used to denote a l l soil nitr ifying organisms ( both autotrophic and heterotrophic ) ; s imilar ly , ammon ifiers wi l l be used t o denote those organisms responsible for ammonification . Barber ( 1 98 4 ) suggested that 2 % of the total soil nitrogen could be mineralized each year . Dowdel l and Webster ( 1 9 84 ) applied '5N-label led fert i l izer to soi l s in lysimeters . From the amounts of fert i l izer derived '5N that remained at the beginning o f each cropping season , they estimated that 5 - 6 % o f the residual '5N applied turned over each year . Shen e t a l . ( 1 982 ) estimated that the amount of nitrogen contained in the soil microbial biomass represented about 6 % of the total soi l N , and that about 3 0 % of the '5N residual from labelled fertilizer applied to the soil was associated with the microbial biomass . Using these f igures , and their own results for the change in res idual '5N , Dowdell and Webster ( 1 984 ) suggested that about 2 0 % of the biomass turned over each year . They d i s t i nguished between more read i l y avai lable organi c mat ter ( microbial biomass and roots ) and the total soi l organic nitrogen , and calculated that i f the estimate of biomass N turnover v1as correct , and the total soil organic matter turned over at the same rate , it would be equivalent to a release of 1 0 0 - 1 2 0 kg N ha- ' per year . The output of total nitrogen from the fertilizer treated lysimeters exceeded inputs by 7 6 - 9 4 kg N ha- ' per year and in the unferti l ized lysimeters by 1 2 9 kg N ha- ' per year . Thus , i f these differences were due to net mineralization, then there is good agreement between them and the N turnover rates estimated from the 1 5N results . i . The factors affecting nitrification and mineral i zation 9 Macduff and White ( 1 985 ) found that the rate o f nitrif ication was never less than the rate of ammonif ication , whi lst Kowalenko ( 1 9 78 ) found that nitri f ication proceeded very rapidly after the addition o f NH4 -substrate with only background levels of extractable-NH4 remaining after a period o f 4 2 days . In view of these f indings , and those detai led above , it is a reasonable assumption that the factors affecting nitri f ication are those affecting mineral ization . A number o f f actors have been ident i f i ed as rate-determining for ammoni f icat i on and nitri f icat ion . These inc lude the amount , type and avai labi l ity of substrate , the s ize o f the ammoni f ier and nitri fier populations , which in the case of the ammonif iers is very diverse and therefore difficult to investigate , and the envi ronmental condit ions under which these organisms l ive ; i . e . mineral nutrients , temperature , aeration , soil moisture content and pH must a l l be at satisfactory levels ( Harmsen & Kolenbrander , 1 96 5 ; Legg & Meisinger, 1 982 ) . Organi c substrate In a series of f ield and laboratory experiments , Campbell and Biederbeck ( 1 9 8 2 ) ident i f i ed the importance o f_ c rop r es idue s f o r m icrob i a l - pro l i f eration . For an appreciable net minera lization o f soi l organic nitrogen to occur , the C : N ratio of the decomposing substrate must be below 2 0 - 2 5 ( Harmsen & Kolenbrander , 1 96 5 ) or 20 ( Barber , 1 984 ) . When plant residues with a C : N ratio o f greater than 2 0 were added to soil , nitrate and ammonium levels in the soi l decreased as micro-organisms used up carbon from the residues , and nitrogen was immobil ized as a result ( Bartholomew , 1 9 65 ) . If the C : N ratio was l ess than 20 , nitrogen was released at the rate of decomposition as micro-organisms decomposed the residues . In the f ield , this rate is usually between 1 and 3 % per year when calculations are based on the total amount of soil organic matter ( Barber , 1 984 ) . Thus , providing soil organic matter has a C : N ratio of l ess than 2 0 , under otherwise non-l imiting soil conditions , the supply of 1 0 ammonium for n itri f ication wi l l depend on the size o f the ammoni f ier population and the amount of decomposable substrate . The effect of the concentration of avai lable ammonium is discussed in Chapter 4 . pH There have been a number o f studies on the ef fect of pH on nitrif ication ( e . g . Frederick , 1 95 6 ; Aleem & Alexander , 1 9 6 0 ; Morri l l & Dawson , 1 9 6 1 ; Dancer et a l . , 1 97 3 ; Darrah e t a l . , 1 98 6b ) , but commonly these have involved either long-term perfusion experiments or pure culture studies of ni trif iers growing under laboratory condit ions . However , Schmidt ( 1 982 ) noted that nitri f ication "proceeds at soil reactions far below the pH l imits observed for the nitrifying bacteria in pure culture" , and that mos t observa t i on s had indicated an "arb i t ra r y lower 1 i m i t " for nitrif ication of pH 4 , with obvious nitrif ication between pH 4 and 6 , and pH independent nitrif ication in the range pH 6 to 8 . The optimum pH for m i nera l i za t i on o f so i l organic n i t rogen was stated by Harmsen and Kolenbrander ( 1 9 6 5 ) to be on the alkal ine side of neutrality , fol lowing an increase of 1 00-1 5 0 0 kg N ha_ , per year when acid sandy soils with h i gh humus c o n t e n t w e r e l i med . D ancer e t a l . ( 1 9 7 3 ) s tudi ed ammonif ication and nitrification over a range of soil pH values using l imed plots in which the soil pH had been constant for a number of years , and found that soil pH did not af fect rates of ammonif ication appreciably but s ignif icantl y affected nitrification ; Weier and Gil li am ( 1 98 6 ) had s imilar results . Frederick ( 1 9 5 6 ) found that there was a marked decrease in the nitr i f ication rate as the pH dropped below neutra l i ty , whilst G i l mo u r ( 1 9 8 4 ) f ound t h a t t h e r e was a 1 in ear increas e in the nitrif ication rate over the range pH 4 . 9 to 7 . 2 , and by setting relative No3- production to unity at pH 7 . 2 , derived the fol lowing equation to describe the effects of pH on the level of soil N03-N: N03- = ( E X pH ) - F ( 2 . 1 ) 1 1 where E and F are constant s to whi ch the values 0 . 3 3 and 1 . 3 6 were assigned respectively . Bhat et a l . ( 1 9 80 ) used 0 . 4 and 1 . 6 suggest ing that such a relationship is unlikely to be the same for a l l soi ls . Whi lst equation ( 2 . 1 ) will ( correctly ) predict the cessation o f nitrification below pH 4 , i t does not al low f or the effects of an a lka l ine pH on n i t r i f icat i on ( Darrah e t a l . , 1 9 8 6b ) . Evidence from the l iterature suggests that pH has a maj or controll ing effect on soi l nitrification but i t a lso appears that nitri f icat ion rates in d i f ferent so i l s will be affected to dif fering degrees by soil pH . The work of Pang et a l . ( 1 9 7 5 ) conf irmed that the di fference in nitri fying capacity amongst soils was related to the initial nitri fier numbers whose activities were affected by the initial soil pH . It therefore seems likely with respect to this study , that variability in the nitri f ication rate may be in part due to variability in the soi l pH . This is investigated in Chapter 7 . Al l the above relates to the ef fects on nitrif ication rates of pH as measured i n bul k so lut ions . L i tt le account has been taken in the l i terature of the possibil ity that the pH of the immediate environment o f the nitri f iers may be rather dif ferent to that of the bulk solut ion . Evidence suggests ( Fletcher , 1 98 5 ) that soil bacteria tend to be attached to solid surfaces and do not just dri ft about in the soi l solution . Keen and Presser ( 1 98 7 ) found that the degree of attachment of Ni trobacter to an anion-exchange resin increased with pH over the range 5 . 5 - 8 . 0 . No such increase was observed when the attachment was to glass coverslips , but attached cel l s grew approximately 20 % faster than free cel ls . Fletcher ( 1 985 ) attributed this to dif ferent H. ion concentrations in the mucilage of attached organisms compared to th� bulk solution , although, why this should be different to the H. concentration in the mucilage of unattached bacteria in the same medium i s unc lear . Nevertheless , a ssuming that n itri f iers exist in the soi l in clusters of cel ls ( Mol ina , 1 98 5 ; Darrah e t a l . , 1 987b - see below ) , and that the clusters of cells are attached to surfaces in the soi l , the idea that nitri fying organisms can generate a pH env ironment d i f f erent to that of the bulk s o i l s o lut ion is acceptab l e . The e f f ects o f change in the pH of bulk solutions on nitrif iers must therefore be due to altering the pH gradient between the bulk soluti on and the microbial muci lage ; when this gradient is steep , the pH of the mucilage may be altered sufficiently to af fect the activity of nitri f iers covered by it . 1 2 Temperature Very l ittle work has l ooked at the effect of temperature on nitrogen mineralization per se a lthough Harmsen and Kolenbrander ( 1 9 6 5 ) attributed the seasona l f luc tuat ion in m inera lization rate to changes in soil temperature . Kowa lenko and Cameron ( 1 9 7 8 ) f ound an increase in soil mineral N in spring and considered that it was due to higher temperatures enabling mineralization of substrates which had presumably accumulated during winter when low temperatures prevailed . Anderson and Purvis ( 1 9 5 5 ) studied the effects of low temperatures on nitrif ication in incubated soi l s , and found that whi lst nitri f ication began sooner in some soils than others fol lowing the addition o f ammonium , and maximum rates varied, the dif ferences tended to decrease with increasing temperature . In all but one of their soi ls , the accumulation of N03 - at least doubled between 5 . 5° and 8 . 3 oc ( 4 2°-4 7 °F ) , a lthough Stanford et al . ( 1 9 7 3 ) found that Q , 0 for the mineralization process as a whole was approximately equal to 2 . Frederick ( 1 9 5 6 ) found the greatest increase in nitri f ication between 7° and 1 5 oc , but noted that temperatures f luctuating in a 2 4 hour cycle general ly resulted in an increased rate of nitrif ication at temperatures below 1 5 . 5 oc . Harmsen and Kolenbrander ( 1 96 5 ) found that nitrif ication was inhibited at the upper end of the mesophil ic range and that none took place above 4 5 oc , whilst below their stated optimum range o f 2 5°- 30 oc , i t decreased s lowly and practically ceased near freezing point . Schmidt ( 1 982 ) stated that co ld and wet soi l s were e f fectively inactive with respect to n i tri f i ca t i on , whi l s t Mahli and W'Gi l l ( 1 982 ) thought it l ikely that microbes in cool c l imates would adapt to those conditions . Schmidt ( 1 982 ) noted that there were indeed geographical di f ferences in the optimum temperature for nitr i f ication - 20°-25 oc in northwes tern U . S . A ; 3 0°- 4 0 oc in southwestern U . S . A and 60 oc in tropical Australia . Mahendrappa et al . ( 1 9 6 6 ) added ( NH4 ) 2S04 to different soi ls from western U . S . A and incubated them at a range of temperatures between 2 0° and 40 oc at 0 . 3 bar moisture tension . In a l l the soi l s from northern regions , nitrification was faster at 20° and 2 5 oc than at 3 5° and 4 0 oc , whi lst the reverse was true in the case of the southern soi l s whi ch nitrif ied f a s t e s t a t 3 5 o c . Under c ond i t i ons where the t emperature was "unfavourable" , nitrite accumulated . These results suggest that microbial populations in different soils w i l l adapt to their speci f ic environmental conditions . The work of Nakos ( 1 98 4 ) further supports this . 1 3 Gilmour ( 1 9 84 ) described the effect o f temperature on nitri f ication by the equation : Kmax = exp { A/T + B } ( 2 . 2 ) where T i s the tempera ture , Kmax i s the zero-order rate constant at opt imum soil moisture , and A and B are constants . The exp ( B ) term is the frequency factor and A is equal to the ratio of the activation energy of the reaction to the gas constant in the original form of the Arrhenius equation . However , Macduff and White ( 1 9 85 ) found that nitri f ication was limited by the supply of NH4. irrespective of temperature and Rosswal l ( 1 9 8 2 ) agreed that this wa s probably the "main factor" control l ing nitrif ication . With respect to experimental technique , it seems l ikely from the above , that when studying soi ls from a temperate cl imate ( such as New Zealand ) , incubation temperature is not going to be crit ical , so long as it is kept constant and extreme temperatures are avoided . Moisture content , aeration and osmotic stress It has been wel l known for many years that the autotrophic nitri f iers are s trictly aerobic organisms (Meiklej ohn , 1 9 5 3 ) , and it is therefore to be expected that the soil moisture content and degree of aeration would be of fundamental importance to nitrif ication . Indeed , Bresler and Laufer ( 1 97 4 ) found that the rate of NH4· oxidation was directly related to the degree of oxygen availability by gaseous dif fusion , which was in turn inversely proportional to soi l moisture content . Much of the work on the ef fects of moisture content on mineralization was carried out in the context of the ef fects of -drying { and s toring ) soils on their rates of mineralization ; this is discussed in Chapter 4 . With respect to n i t r i f i ca t i on , Bres ler e t a l . ( 1 9 8 2 ) stated that under i sothermal conditions when the soil water content varies considerably during inf i ltration , redistribution and evaporation , the e f fect of soil mois ture content on n i trate production wi l l be of prime importance . Yadvinder - S i ngh and Beauchamp { 1 9 8 8 ) f ound that nitr i fier activity 1 4 increased with increasing soil water potential , and S indhu and Cornf ield ( 1 9 6 7a ) found that the optimum moisture content for ammonif ication and nitr i f ication in the soil s they studied was equivalent to 5 0 % of the maximum water holding capacity . Reichman et al . ( 1 9 6 6 ) . found that the rate of both ammoni f icat ion and nitrif ication of soi l N were almost direct ly proportional to the soil water content at suctions between 0 . 2 and 1 5 bars . At 1 5 bars there was still measurable nitrif ication . Dubey ( 1 9 6 8 ) found that the nitri f ication rate in a sandy loam increased as the soi l moisture tension decreased from 1 5 to 2 bars and then decreased at lower tensions , al though marked nitri fication ( presumably heterotrophic ) occurred even under f looded conditions . In contrast , he found practically no di f ference in the nitri f icat ion rate between 1 5 and 0 . 3 bars in a loamy sand . Justice and Smith ( 1 96 2 ) found that at the optimum incubation temperature f or ni tri f icat ion in the ir ca lcareous soi l , the start of nitr i f i cation f al lowing addit ion of substrate was delayed at tensions greater than 7 bars , al though nitrif ication did occur at the permanent wi l t ing point . A maximum level of nitrif ier activity was noted by Mil ler and Johnson ( 1 9 6 4 ) in the range 0 . 1 - 0 . 2 bars , a l though there was variation in microbial behaviour with respect to the ammonifiers - those producing NH4 + a t z ero t ens i on did not function either when under tension, or with more aeration than was found at zero tension . Those producing NH4 + at higher tensions did not function with less aeration . S tanford and Smith ( 1 9 72 ) stated that the moisture content after vacuum suction at 60 cm Hg was near optimal for mineral izat·ion and they regarded the o xygen concentration under these condi t i ons as similarly near optima l . Sanchez ( 1 9 7 6 ) reported that accumulation of N03- in the upper hori zons of some tropical soi l s could be explained by the existence of nitri f ication at soil moisture tensions of 1 5 -80 bars s ince the crumbs of such soi l s can ho ld water a t these very high tensions due to their mi croa ggrega t e s tructure . Al l this sugges t s that the response of nitrif iers to changing moisture stress is s6i l-specific . Khyder and Cho ( 1 9 83 ) measured the partial pressure of 02 in the soil atmosphere at several depths and found that when the air-porosity was 1 0 . 5 % ( 3 0 % moisture ) the boundary between the aerobic and anaerobic soil layers occurred at approximately 20 cm depth , but when the a ir­ porosi ty was increased to 1 6 % ( 2 5 % moisture ) , thi s boundary occurred at 1 5 4 0 c m depth . i . e . a sma 1 1 change in t h e a i r poro s i ty led t o a considerable change in the position of the boundary between the aerobic and anaerobic zones . It may wel l be therefore , that N f lushes . ( Birch , 1 9 5 8 ; 1 9 60 ) may occur on a micro-scale as the groundwater table rises and fal l s , allowing for s ignif icant oxidation of soi l organic matter below the surface horizon . Gilmour ( 1 984 ) observed a linear decl ine in nitrif ication rate as the soi l moisture content decreased over the range 0 . 2 -0 . 1 2 g g_ , and he described the moisture relations of nitrif ication by the equation : K/Kmax = ( C X WC ) + D ( 2 . 3 ) where Kmax is the maximum nitrification rate , K i s the nitr i f ication rate at a speci f ic gravimetric moisture content , WC , and C and D are constants which Gi lmour ( 1 984 ) found to be equal to 4 . 8 and 0 . 3 respectively . There is no theoretical basis for C and D , however , and it seems l ikely that a l i near model i s inappropri ate to decribe the relationship between moisture content and nitri f ication rate s ince the evidence is that both very high and very low moisture contents are inhibitory to nitri f ication . Thi s is investigated in Chapter 8 . The factors discussed above relate primarily to matric potential effects on nitrif ier act ivity . In addition , the osmotic ef fects of particular s o i l so lutes may be important . The e f fects o f osmo t i c s tress on n i t r i f i ca t ion and m inera l i za t ion have not been extensively studied a lthough Darrah et a l . ( 1 985a ; 1 9 8 6c ; 1 9 87a ) looked at the ef fects of high concentrations of ( NH 4 ) 2S04 and NH4Cl on nitrification rates . This work is discussed in Chapter 4 . Sindhu and Cornfield ( 1 9 67b ) studied the effects of chlorides and sulphates of Na , K , ea and Mg added in solution at concentrations of 0 . 1 -2 . 0 % ( Na equivalent ) on N mineral i zation and nitrification . cl - in concentrations between 0 . 5 and 1 % caused almost complete suppress i on of nitr i f icat ion , but mineralization was only reduced when a concentration of more than % salt was added . S04 2- only reduced mineralization and nitrif ication when added as 2 % Na2S04 . In some cases , both sulphates and chlor ides of all cations �xcept Na resulted in small but s ignificant increases in N mineralization . Whether 1 6 o r not the sodium response i s a toxic effec t i s unclear , but Harris ( 1 98 0 ) noted that Na as NaCl ( along with sucrose ) was the most important solute with respect to osmotic stress in soils . Here it is enough to say that high osmotic stress is inhibitory to nitri fiers and presumably ammoni f i ers too . One might therefore conclude that equa tion ( 2 . 3 ) is either incomplete , or that C and D are in some way dependent on either osmotic stress , degree of aeration or both . Other factors In addition to the factors discussed above which may be thought of as most importan t , there may be other less obvious factors which will i n f l uence n i tr i f icat ion rates in certain s i tua t i ons . For example , Purchase ( 1 9 7 4 ) found that P defi ciency affected nitrif ication t o the extent that N02- would accumulate i f P levels were low enough . Loveless and P a inter ( 1 9 6 7 ) demonstrated that the effect of def iciencies of copper , s odium , ca lcium and magnes ium on the growth of Ni trosomona s europaea were such that the effect of pH was dependent on the metal ion concentrat ion . pH was also found to strongly influence copper toxicity . The l iterature on the ef fects of pH on nitri f ication rates suggests that the nitrif ication rate is highest at neutral or mi ldly alkaline pH ( see above ) . Some of the mi ldly acidic New Zealand soils ( Yel low brown learns ) studied by Steele e t a l . ( 1 980 ) were found t o have surprisingly h igh nitri f ier activity ( 2 -3 � g N g_ , h- ' ) , this activity being of the order o f , or h igher than , that o f so ils of near neutrq, l pH . Sarathchandra ( 1 9 7 8 ) noted that the dominant clay mineral in these soils is al lophane , and thought it probable that at pH 5 . 5 while negative charges on the a l lophane surface retain some NH4+ , the positive charges present may in fact repel H+ ions , establishing a micro-site containing fewer H+ ions than the bulk soi l , and thus a higher pH than the bulk solution . This explanation is based on the assumption that pH 5 . 5 is close to the point of zero charge , that is , at this pH al lophane has approximately equal numbers of positive and negative surface charges ( Sarathchandra , 1 9 78 ) . However , at the point of zero charge the pH measured in water is similar to that measured in salt solution , and so the difference between the pH 17 at adsorpt ion sur faces and the bulk solution is likely to be small . Sarathchandras ' explanation for high ni tri f ier act ivity in all ophanic soi ls may therefore not be correct . ii . Modelling o f Nitri fication At the s implest level , Gilmour ( 1 98 4 ) took equations ( 2 . 1 ) to ( 2 . 3 ) and by combining them and adding an expression for the ef fect of substrate concentration, Nt , ( see Chapter 4 ) , calculated the absolute nitrif ication rate NR , according to the equation : NR { [ exp (A/TB ) ] X [ ( C X WC ) + D ] X [ ( E ll' pH ) - F ] } Nt 0 . 9 5 ( 2 . 4 ) where the symbols are as before . However, this seems little more than an elaborate curve f i tting exercise; in any case , the value of equations ( 2 . 1 ) t o ( 2 . 3 ) is doubtful . C learly something more sophisticated is required . In a l l the models describing the nitrif ication process which are outlined below , i t has been assumed that the oxidations involved are zero-order react ions ( e . g . W"Laren, 1 9 7 6 ) . Addi scot t ( 1 9 8 3 ) quest ioned whether nitri f ication was truly zero-order on the grounds that nitrif ication rates are not independent of the initial NH4 . concentration ( see Chapter 4 ) . Molina et a l . ( 1 9 79 ) suggested that the kinetics of NH4 . oxidation are the resultant averages of pulses of activity from small isolated and asynchronous clusters of NH4 . oxidizers , and a theoretical consideration o f cell clustering ( Darrah e t a l . , 1 9 87b ) supported this . The oxidation o f ammonium around each c luster , which may comprise a few hundred cells ( Mol ina e t a l . ( 1 979 ) , i s very rapid and the exponential ( f irst-order ) mode l applies . Thus , i f the soi l can be regarded as a single large aggregate with only one cluster , in the non-steady state the kinetics of n i t r i f icat ion wi l l be f i r s t - order and wi l l f ollow the kinetics of m i crob i a l growth , and it may be as sumed that the rate of ammonium o x ida t i on w i l l not be cons tant unt i l steric saturation is achieved ( Mo l ina et a l . , 1 979 ) . At this point , steady s tate conditions apply and 1 8 N0 3 - product ion i s constant . Thus , zero- order kine t i cs apply . In addition , Molina et al . ( 1 9 79 ) found that there was no synchronization of the beginning of nitri f ication amongst the aggregates tested , despite the f act that they a l l c ame from the same f i eld s ample ; indeed , some particles took eight weeks to exhibit their nitri fying potential . Thus , even under non-steady state conditions , zero-order kinetics will appear to apply due to the averaging ef fect of cluster asynchronicity . M0Laren ( 1 9 69 ) ignored this problem on the assumption that the whole microbial population will grow unifor�ly until a maximum population is achieved , and that this population continues to carry out nitrif ication with very little multiplication . As outl ined below , this assumption is not really acceptable , a lthough i t enabl ed init ial progress to be made in the modelling of nitri f ication . Burns ( 1 980a ) modelled with a deliberate lack of analysis of N interactions so that the " main e f fects of the s low release of nitrate and the distribution o f rainfall on the leaching of nitr i f ied-N" could be considered . Whether or not this is a helpful approach is debatable . M0Laren ( 1 969 ) modelled the concentrations of NH4 · , N02- and No3 - under cond i t i ons o f m i crob i a l s teady s tate ( i . e . w i t h no growth in the microbial population ) as NH4 · was applied to the top of a soil column by the equations : ( N02- 1 = { k , . ( NH4• ) o } { exp ( -k , x ) - exp ( -k2x ) } ( k2 - k , ) ( 2 . 5 ) ( 2 . 6 ) ( 2 . 7 ) where x is the distance of f low down the colu�n , k , is th� rate of the NH4 · to N02- oxidation divided by the flow rate down the column , k2 is the rate of the N02- to No3 - oxidation divided by the f low rate down the column , and [ NH4 · ] o is the concentration of ammonium in solution applied at the top of the column . M0Laren ( 1 9 7 0 , 1 9 7 1 ) used these equations as inputs to the main model which gave the rate of either oxidation at small substrate concentrations by : -o [ S ] = A¥m + am + ( � � m [ S ] ) ot ( km + [ S l l 1 9 ( 2 . 8 ) where S i s the substrate concentration , m is the biomass , A is a proportional ity constant ( the reciprocal of growth yie ld ) equal to N oxidized per unit weight of biomass synthesized , a is the N oxidized per unit weight of biomass per uni t time for cel l main tenance, � is the amount of enzyme per unit biomass involved in waste me tabol i sm , ¥m = om/ot for growth me tabol ism, � is a proportionality constant and km i s a saturation constant . Thus , the f irst term relates the disappearance of subs trate due to microbial gro wth , the second term provides f or maintenance of the population in the absence of growth , and the third term represents the rate of change of substrate in addition to growth and maintenance , the waste metabolism . According to M0Laren ( 1 9 7 0 ; 1 97 1 ) it is this which provides N03- for plant uptake and may be regarded as occurr ing s i mply because the enzyme system i s present and active . However , this parti tioning of NH4 � oxidation into growth , maintenance and waste metaboli sm i s misleading because the only reason that nitrifying organisms oxidize NH4 • is to gain energy for growth . According to Wild ( 1 988 ) , the Ni trosomonas group of bacteria oxidize 3 5-70 moles of NH4• for every mole of carbon assi milated, and Ni trobacter ox idize 7 0- 1 0 0 moles of N02 - for every mole of carbon assimilated . If we assume that the C : N ratio of nitrifying bacteria is approximately 6 ( Brady , 1 984 ) , it fol lows that the amount of N that is assimilated by the organisms is so small in relation to the amount oxidized , that virtually a l l the N03- produced is available for release to the medium ( i . e . assuming for the sake of argument that the mass of a mole of C and N is approximately the same , 6 moles of C assimi lated gives 420 moles of oxidized N of which only 1 i s retained for protein synthesis ) . Nevertheless , M0Laren ( 1 9 7 0 ) persisted with the a , � and ¥ terms and presented sub-models for a range o f scenarios w i th vary ing degrees of substrate concentra t ion and microbial enrichment . 2 0 M0Laren ( 1 9 7 6 ) acknowledged that , in fact , the steady state situation is never achieved due to ion exchange and leaching of nutrients . In a f lowing solution , hydrodynamic dispersion, equations for which were given by K irkham and Powers ( 1 9 7 2 ) , may a lso be important . Thus , for a microbial oxidation in a column of soil with the substrate moving at a f low rate f, the change in concentration with time is described by the equation ( M0Laren , 1 97 6 ) : o [ S l ot -f (o [ S ] ) + D ( o2 [ S ] - � [ S ] ox ( 2 . 9 ) where D is hydrodynamic dispersion and � [ S ] is some function o f [ S ] that represents change , i . e . loss of substrate by microbial oxidation . M0Laren ( 1 97 6 ) presented equations to describe � [ S ] under conditions of different growth , f low and substrate concentration in terms of a , � and ¥ . Despite the fact that M0Laren ( 1 9 70 ; 1 9 7 1 ; 1 9 76 ) presented a nice theoretical model with respect to these terms , _ they cannot in practice be in any way separated or identi f ied , and as indicated above , there seems to be little j ustif ication in distinguishing between them . An alternat ive treatment was therefore required . The rate of growth of nitrifying biomass , m, was modelled by Darrah e t a l . ( 1 9 85b ) as : om = J,J max { [ C , l t } m ( 2 . 1 0 ) -a t [ C , J t + k .. where J,J max i& the maximum specific growth rate ( h- 1 ) , ka i s an affinity constant ( J.J moles cm-3 ) , and c , is the ammonium concentration in solution, where at any time t : [ c , l t = [ C , l o - I oC2 + J· ( o [ CN l ) ot o t o t ( 2 . 1 1 ) where [ C , ] o is the NH4 . concentration at time zero and C2 is the No3- concentration per unit soil volume . The last term describes the rate of ammonif ication for native soil organic N . 2 1 The rate of No3 - formation is given by the equation ( Da rrah e t al . , 1 98Sb ) : { 1 } {am} Y a t ( 2 . 1 2 ) where Y is the yield constant defined as � g biomass formed per � mole o f NH4 - transformed . Substitution from equation ( 2 . 1 0 ) gives : oC2 = � max { [ C , ] t {m } ( 2 . 1 3 ) ot [ C , ] t + ka Y Then dividing both sides of equation ( 2 . 1 0 ) by Y gives ( Darrah e t al . , 1 9 8Sb ) : o ( m/Y ) = IJ ma.x { [ C , ] t ------ ot [ C , ] t + ka {m } y ( 2 . 1 4 ) and thus the formation of N03 - can be expressed in terms of equations ( 2 . 1 3 ) and ( 2 . 1 4 ) containing the three parameters ( m/Y ) , � ma.>< and k .. . When C , is less than , or of simi lar magnitude to k .. , equations ( 2 . 1 3 ) and ( 2 . 1 4 ) are solved numerically , but where c , is greater than k .. , they can be integrated to give the nitrate formed in terms of the two constants ( mo/Y ) and � max ( Darrah et al . , 1 98Sb ) . U s ing this theory as a bas i s , in addi tion to equations describing dif fusion of NH4- and No3- in soi l ( Darrah et al . , 1 983 ) , adjustments were made to enable the model ling of simultaneous nitri f ication and dif fus ion in soil with respect to the addit ion of ammonium sulphate ( Darrah et al . , 1 98 6a ) , pH ( Darrah et al . , 1 986b ) , and osmotic potential ( Darrah et al . , 1 987a ) . A simplif ication of the model is presented by Darrah et al . ( 1 98 6d ) . 2 2 Models such as those o f M':Laren ( 1 970 ; 1 97 1 ; 1 9 7 6 ) and Darrah e t a l . ( 1 9 8 5b ) require some estimat ion of the size o f the microbial population . Many workers have used the most probable number ( MPN ) technique of estimating microbial populations ( Cochran , 1 9 5 0 ; Schmidt , 1 982 ) . However , this technique shows a high degree of variability and cannot be regarded as accurate ( Schmidt , 1 982 ) . Morri l l and Dawson ( 1 9 6 1 ) initiated the development of an alternative indirect method of measuring the nitri f ier population when they noted that advantage could be " taken of the facts that chemoautotrophs are the maj or , if not the sole agents concerned with N03 - production in nature , and that the oxidation of ammonium and nitrite compounds i s growth linked . Hence by measuring the quantity of ammonium­ or nitrite-N oxidized , the rate of growth of the respective bacteria can be ascertained . " However , i f the time period over which NH4· oxidation occurs i s l imited , then the assumption can be made that microbial growth is minimal and the nitri fication rate measured is an index of the size and activity of the nitrifier population . By optimizing conditions in terms of substrate , 02 , temperature and moisture , so that each organism can f unc t i on o pt i ma l l y , the n i t r i f ica t ion ra t e in a short - t erm n i tri fi ca t i on assay, SNA , should be an index of the number of organisms presen t . This was the bas is o f the work o f Sarathchandra ( 1 978 ) and Steele e t a l . ( 1 980 ) who found that the main benefit of the SNA was that the short t ime of perfusion or incubation meant that the results were unaf fect ed by microbial pro l i feration , and thus a good ref lection of nitrif ication activity was obtained . The technique employed ip the SNA { Sarathchandra , 1 9 78 ; Steele et a l . , 1 98 0 , Darrah et a l . , 1 986b; 1 987a ) g ives a h i gh degree o f reproduc ib i l i t y ( P . R . Da rrah persona l communication ; see also chapters 4 -9 ) and may thus be used to give useful input data to nitrif ication models . Mol ina { 1 98 5 ) looked at nitri f ication in a completely di f ferent and novel way . As already mentioned , he assumed that nitrification proceeded from pulses of ammonium oxidation generated by microbial clusters , and in this connect i on carr ied out his e xper iments with individual soil micro­ aggregates rather than soil columns . He noted that for every NH4· ion oxidized , two H. ions are released , resulting in a pH decrease : 2 3 ( 2 . 1 5 ) and therefore used an experimental procedure involving bromothymol blue indicator which al lowed the pH change to be monitored by transmittance through a spectrophotometer . This method may be potentially use ful in invest igat ions of variabil ity of ni tri f ica t i on on a micro - s ca l e , especially in relation to the differences in nitri f ier activity between the inside and outside o f clods . iii . A comment on measured nitrification rates Brandt e t al . ( 1 9 6 3 ) f ound that there were large discrepancies in their results between NH4 . disappearance and No3- accumulation and as a result , had to distinguish between ni tri fi ca ti on as the biological oxidation of reduced forms of N and net ni tri fi ca tion as the observed accumulation o f No3- · With respect t o experimental techniques such a s the SNA , this is of great importance . I t was stated at the beginning of this chapter that it is not possible to measure nitrif ication in soi l in isolation from either mineralization or immobili zat ion ( Addiscott , 1 9 83 ) and it is implied above that the SNA measures net nitrate production irrespective of the mode of production . It is not within the scope of either this thesis or this review to cons id er the vast l i terature on deni tr i f i ca t i o n , immobilization , and volatilization of soil N . However , it is wel l known ( e . g . Starr et a l . , 1 9 7 4 ; Kowalenko , 1 978 ) that nitri f ication can occur simultaneous ly w ith these processes . Indeed , Colbourn et a l . ( 1 9 8 4 ) demonstrated that in a drying soil , nitri f ication was rate-determining for denitri f icat ion . Furthermore , when NH4 • substra tes a re added in incubation exper iments , some may be f i xed by - clays ( Mogi levkina & Lebedeva , 1 982 ) depending on the amount of available NH4 . and the clay percentage ; Darrah e t al . ( 1 985b ) attributed their incomplete N recovery to the fixation of NH 4• by mica-type clays . Thus , until such time that measurements of these various processes can be made in isolation f rom one another , net measurements will have to suffice . There is no indication in the l iterature to suggest that any interpretation is lost as a result . Moreover , if abundant NH4· is added , the f ixation of NH4 · by clays will 2 4 be unimportant ; i f the system is wel l aerated , denitrif ication will be insign i f icant ; if incuba tion times are short so tha t growth o f the o rga n i sms i s m i n ima l , then N03 - i mmob i l i z a t i on s ho u l d a ls o be insignif icant , and the concentrat ion of NH4 • should ensure that the requirements of both the autotrophic nitrif iers and the heterotrophs are satisf ied . iv . Conclusion Overal l , it appears that the variability in nitri fi er activity may be a function of variabilty in the factors discussed in Section i ( above ) , most importantly soi l pH , mo is ture content and availabil ity of NH4 • substrate . Since the ef fects o f the f irst two of these factors appear to be soi l -specific , they clearly merit attention in this study . For a study o f variabil ity in nitrifier activity , the SNA appears to have the most potent ial since it is much quicker than the other techniques , gives readil y reproducible results , and in view of the small amount of soil required ( Darrah et a l . , 1 986b) may be very suitable for spatial studies involving large numbers of samples . In addition , the problem of dif ferent nitrifying species can be ignored with thi s technique ( P . R . Darrah - personal communication ) . 2 5 CHAPTER 3 A THEORETICAL CONSIDERATION OF SPATIALLY DEPENDENT VARIABILITY The main obj ective of the work reported in this thesis was to investigate the v ar iab i l i ty o f ni tr i f i e r act ivi t y . As was i nd icated in the introductory chapter , in addition to investigating this variability in relation to factors which might be expected to control nitrif ication , it was of maj or interest to investigate and quantify the spatial variability of nitrif ier activity so as to improve estimates o f the initial nitrate concentration as an input parameter for nitrate leaching models . This can not be readily done using classical statistics . The following discussion expl ains why this is so , and outlines the means by which spatial variabi lity may be investigated . i . Why do we need geostatistics ? The traditional means of statistical analysis that soil scientists have used to corroborate the hypotheses which inspired their experiments - described by Nielsen ( 1 987 ) as " aggie statistics" - involved calculation of the mean and variance of sets of data col lected from regions , or under conditions , that were perceived to be horr.ogeneous ,. Often this was done , and sti l l is today , with total disregard for the distribution of the data about the mean , and with results explained in terms of cause and ef fect ; when an ef fect could not be attributed to a cause , i t was explained away by an error term ( usually the residual mean square or variance ) which was commonly ascribed to the inadequacy of sampling , the inaccuracy of an analytical technique or simply to random variation . In fact , a large part of such error is most likely to be due to measurements being made in non ­ homogenous areas , but this possibility has either been ignored , or the worker has been ignorant of the possibility of some spatial dependence in the data . Given this background of an experimental ahd statistical sta tus quo, D . R . Nielsen speaking to the Dutch Soil Science Society on their 5Qth anniversary , argued that " . . . . [ incorporated into ] the next page of soil science should be regional ized variable theory . . . . If we are to achieve greater succes s , we mus t take advantage o f spa t i a l and temporal vari ab i l i t y instead of avoiding it . I f we acknowl edge i t s existence , it w i l l enhance our research e fforts even when we subj ect experimental sites to selected treatments . " He commented further : " . . . . The mean value of a soi l property , which we have become so accustomed to seek and appreciat e , may not , in the f inal analysis , be as important as its spatial and temporal variance or the identi f ication and possible signi f icance o f its per turbed values . " ( Nielsen , 1 987 ) . 2 6 Thus , the need has arisen for quantitative spatial and temporal analysis of soil properties , to complement the investigation of cause and ef fect relationships between them . In t he f o l lowing sec t i ons , the rat iona le behi nd geos ta t i s t ics is presented . Since much of the theory is not new and has been extensively detailed in a soil science context previously ( Webster , 1 98 5 ; Trangmar et al . , 1 98 5 ; Ol iver, 1 987 ) , the topic will be developed here in relation to the work presented i n this thes i s , and thus some aspects , such as interpolation by kriging , are omitted . In this discussion , geostatistical techniques are looked at from a dif ferent angle to that presented in the l iterature with the intention of explaining to the uninitiated , and the non-mathema t i c i an in part icular , the poten t i a l o f these power ful statistical tools . For the purpose of this discussion, a data set comprising 2 5 soil pH values wi l l be considered ; the data are l isted in Table 3 . 1 . I t will be assumed that they represent measurements taken along a transect within an area which is assumed to be homogeneous , wi th equa l spacing between samples ; samples No . 1 and 2 5 represent the two ends of the transect . Table 3 . 1 2 5 values of soi l pH measured at equal spacings along a transect Sample pH Sample pH No . No . 5 . 38 1 4 4 . 9 3 2 4 . 38 1 5 4 . 8 0 3 5 . 00 1 6 4 . 88 4 5 . 1 0 1 7 4 . 88 5 4 . 65 1 8 4 . 9 0 6 5 . 2 0 1 9 4 . 9 3 7 4 . 78 2 0 5 . 0 5 8 5 . 38 2 1 4 . 8 5 9 4 . 95 2 2 4 . 8 5 1 0 4 . 78 2 3 4 . 9 5 1 1 4 . 70 2 4 4 . 80 1 2 4 . 5 3 2 5 4 . 8 5 1 3 4 . 68 i i . Some pre l iminary data analysis using classical s tatist ics 2 8 Any kind of analysis requires a starting point , and one useful way to begin to analyse a data set is to f ind out about its distribution ; i . e . one needs to know the mean and variance , and the way the data are distributed about the mean . It i s assumed that the sample has been drawn from a population with true mean � ' and variance o2 ( Clarke , 1 980 ) . The true mean value of a property z , is estimated by P , __ the ari thmetic mean , where ( Clarke , 1 980 ) : A � "' r. Z ( x:�. ) ( 3 . 1 ) n and the population variance o2 is estimated by s 2 , the sample variance , by ( Clarke , 1 980 ) : ( 3 . 2 ) n - 1 where n i s the number of realizations or values of the property Z , and x def ines the location , in cartesian coordinates with i "' 1 , 2 , 3 . . . n , at which the individual value s of the property Z a re observed or measured . Applying these equations to the data in Table 3 . 1 , values of 4 . 89 and 0 . 0 5 1 7 are obt a ined f or the ari thmetic mean and s ampl e variance respectively . White e t a l . ( 1 987 ) noted the dangers of assuming that the s imp le ar ithmetic mean and variance were the best es t ima tes o f the population mean and variance when s ample data are skewed and do not conform to a normal distribution . When this i s the case , an estimate of the ar i thmet i c mean derived f rom the parameters of a l og -norma l distribution, o r one based on Sichel ' s estimator ( Siche l , 1 9 5 2 ) may give better estimates of the population mean . Thus , the distribution of the data must be checked . The 2 5 pH data were grouped into classes ( 0 . 2 pH uni ts wide ) , the f r equency o f each c l a s s normal i z ed , and the resultant distribution plotted ( Figure 3 . 1 ) . This was shown by means of a least-squares f itting Normal ised frequency 2.5 2 1 .5 1 0 .5 0+-��--�----+---�--�----+-��-A�--� 4 4.2 4.4 4.6 4.8 5 pH Figure 3 . 1 Distribution of a set of 2 5 pH data 5.2 5 .4 5 .6 5.8 3 0 procedure ( CFIT - Dept . Soi l Science , Massey University ) t o conform to the normal distribution f( x ) ( R2 = 0 . 9 4 , p< 0 . 1 % ) where ( Clarke , 1 980 ) : f( x ) = ( 3 . 3 ) Here , Ql and "C' 2 are best est imat es of the population mean and s ample variance , IJ and and w i s the mid-value for each pH c lass . 0 is calculated as an average of a l l the values of Z ( xi ) , and it should give a good est imate of the property z , in this case soil pH , when that is measured at any point xi along the transect . We therefore say that the expec t ed val u e of z at any point x i along the transect i s given by ( Webster , 1 985 ) : E [ Z ( xi ) ] = 1J ( 3 . 4 ) where E denotes expectation . Taking this idea to its logical extension and considering just the first point on the transect , it can be argued that i f the value of z at t his f irst point is unknown , i t could be expected to be equal to � which i s an estimate of v in equation ( 3 . 4 ) ; i . e 4 . 89 . Conversely , i f the mean was unknown but the value of z at this point was known , and assuming that the distribution of pH values along ,\ this transect was normal , IJ would be expected to be equal to the value of z i . e . 5 . 38 . Using this argument , and equation ( 3 . 1 ) to calculate the value o f 0 when n i s greater than 1 , the change in the est imated mean and variance with increasing sample number was investigated by starting at one end of the transect and moving along i t , one sample separation at a t ime to include the other points , unti l the whole data set was included . The results are shown in Figure 3 . 2 as a plot of the mean and variance vs . the number o f va lues used to ca lculate them . Since the sample variance of a single real ization is zero , the change in s2 was calculated for n=2 to n=2 5 . By def inition , the variance o2 i s a measure of the scatter or dispersion A of the values of Z ( xi ) about the mean IJ ( Clarke , 1 980 ) . 52 and IJ are also assumed t o be good e st imates of the true variance and mean of the population from which the sample data are drawn . The s ize of s2 tel ls us 5.6 5 .2 Mean 4.8 (�) 4.4 4 0.5 0 .4 Variance 0.3 0 . 1 0�-------+--------�------�--------�------� 0 Figure 3 . 2 5 1 0 1 5 20 25 No. of samples . A 2 Change �n the mean ( � ) and variance ( s ) with i ncreas ing sample number for a set of 2 5 pH data 3 2 1\ something about the precision with which � is measured and we can assume " that the more precisely � is measured , the nearer i t will approach to � · I t is therefore interesting to note from Figure 3 . 2 that as n increases , " the change in the value of lJ calculated for n and n+ 1 realizations of Z ( x.._ ) decreases , to the extent that for values of n greater than 1 5 , there i s very l ittle f luctuation in the estimated value of � relative to the f luctuation when n is less than 9 . That is , the greater the value of n, the lower the value of 52 ( Figure 3 . 2 ) , and consequently the more 1\ precise the est imate of lJ by lJ . This idea of precision will be seen to be important in the fol lowing sections when the spatial distribution of the Z ( x.._ ) i s · considered . In addit ion to the concepts of the mean and vari anc e , i t is a lso an important pre-requisite of spatial analysis to understand the concept of covariance . Supposing that in addition to soi l pH , the C . E . C had been measured at each s i te , x .._ , a long the transect . As part of the data analysi s , it may be useful to have a measure of the correlation between the two properties , denoted here by Z and Y . This can be estimated by the covariance , COV , where ( Clarke , 1 980 ) : ( 3 . 5 ) n - 1 1\ ;\ where l.l z and lJ v are the mean values of the sample data of Z and Y . It is shown below that the concept of covariance is important to geostatistical theory . iii . Stationarity and the semi-variance Although equation ( 3 . 4 ) states that the expected value of Z at any point x .._ is lJ , it is clear from Table 3 . 1 and implicit in Figure 3 . 2 that the value o f z wi ll in fa ct vary from place to place . Thi s would more obviousl y be the case i f , for example , the transect crossed the boundary between two distinct soi l types . Thus , Z ( x.._ ) is cal led a random vari abl e - geostatistics are concerned with identifying its spatial structure . By spatial structure is meant the spatial correlation of the variable with i ts e l f , . which can be described by propert ies of i t s probabi l i ty distribution . The f irst property is the mean , def ined by equation ( 3 . 4 ) ; the second i s the spatial covariance , defined below . 3 3 When the mean value o f z ( x:1. ) does not vary along the transect , the condition of firs t-order sta tionari ty is said to hold ( Webster , 1 985 ) : E [ Z ( x:t. ) l = � = constant ( 3 . 6 ) and i t would be expected that a plot l ike Figure 3 . 2 would show a straight horizontal l ine corresponding to a pH of 4 . 89 . I f equation ( 3 . 6 ) holds , the expected di f ference between any two values of Z ( x:t. ) separated by a distance or l ag, h , would be zero ( Trangmar et al . , 1 985 ) : E [ Z ( x:t. ) - Z ( x;�. + h ) ] = 0 ( 3 . 7 ) If the mean does vary , dri ft is said to be present and the changing value of the mean can be described by the dri ft function , d (x;�. ) ( Starks & Fang , 1 982 ) , and equation ( 3 . 6 ) can be re-written more generally as : ( 3 . 8 ) where w ( x:t. ) is a random function of zero mean and finite , f ixed variance . w ( x1 ) depends on the variation between values of Z ( x:t. ) and Z ( x:t. + h ) , for all values of h . One of the aims o f geostatistics i s to quanti fy the degree o f spatial correlation between the values of Z ( x:t. ) and Z ( x;�. + h ) . This can be done using the concept of covariance , expressed mathematical ly in equation ( 3 . 5 ) . Thus , the spa t i a l covari ance of Z ( x:t. ) , C ( h ) , is given by : C ( h ) ( 3 . 9 ) Unlike ( 3 . 5 ) , equation ( 3 . 9 ) has no denominator because 1 / ( n- 1 ) = 1 when there are only two obs ervation points , X;�. and ( x:1. + h ) . Second-order sta t i onari ty exists i f the value of C ( h ) for each pair of property values Z ( x;�. ) and Z ( x:t. + h ) is _ the same , and independent of its position in the sampling region ; that i s , C ( h ) depends only on h ( Trangmar et a l . , 1 985 ) , and the variabil i ty of z is the same throughout the region ( Russo & Bresler , 1 98 1 ) . By implication, when h is zero , C ( h ) must be equivalent 3 4 to the variance of z , of ten denoted by C ( O ) . The ratio of the spatial covariance to the sample variance is cal led the spa tial a uto-correl a tion coeffi cien t , P (h ) given by : P ( h ) = C ( h ) I C ( O ) ( 3 . 1 0 ) Thus , under second order stationarity , the mean and variance do not vary . P ( h ) = 1 when h = 0 and the spatial covariance decreases as h increases and so P ( h ) becomes a useful geostatistical tool s ince a plot of P ( h ) against h will give an indication o f the size of h for which values o f z remain correlated , or are spa t i a l ly dependen t . The a ssumption of s econd-order s tationarity upon which P ( h ) and C ( h ) depend i s regarded by many geostatist icians as too s trong for many spatial variables because of the tendency of estimates of the variance to vary w i t hout 1 im i t a s the s i ze of the area under investigation is extended ( O l i ver , 1 9 87 ) . As an al ternative to assum ing second-order stationari ty , the in trinsi c hypo th esi s of regi onal i zed vari abl e theory may be used . This assumes that equation ( 3 . 4 ) holds and that for a given value of h , the di f ference between Z ( x"- ) and Z ( x"- - + h ) has a f inite vari ance which i s independent of x "- , the position of the sample ( Webster , 1 98 5 ) : VAR [ Z ( x"- ) - Z ( x"- + h ) ] = E { [ Z ( x"- ) - Z ( x"- + h ) ] 2 } ( 3 . 1 1 ) = 2 ¥ ( h ) where ¥ i s the semi - variance . Implicit in the assumptions underlying equations ( 3 . 4 ) and ( 3 . 1 1 ) is that the soi l property fol lows the following model of variation : ( 3 . 1 2 ) where � � i s the mean value of Z in a region , v , and � ( x"- ) is a spatially dependent random component with zero mean , and a variance def ined by : 3 5 VAR [ � ( x� ) - � ( x� + h ) ] = E { [ � ( x� ) - � ( x� + h ) ] 2 } ( 3 . 1 3 ) = 2 ¥ ( h ) Thus , under the constraints o f the intrinsic hypothesis , variables need only be l o ca l l y sta t i onary . It wi ll be assumed for the rest o f the analysis of the 25 pH data that local stationarity applies . iv . The variogram A The semi-variance , ¥ ( h ) , is estimated by ¥ ( h ) for each value o f h where ( Webster , 1 985 ) : A ¥ ( h ) = 2: { Z ( x� ) - Z ( x� + h ) } 2 ( 3 . 1 4 ) 2m ( h ) 1\ ¥ (h ) is equivalent· to hal f the sum of the squared di f ference between pairs of values of Z ( x� ) and Z ( x� + h) averaged according to the number of pairs , m , at each value of the lag h . A. A plot of ¥ ( h ) against h for a range of separation distances i s the semi - vari ogram, which for simplicity will henceforth be cal led the vari ogram . The variogram represents the average rate of change of a property with distance ( Ol iver , 1 987 ) . Figure 3 . 3 shows the experimental variogram for the 25 pH data from the transect . Although there is much fluctuation in A A the value of ¥ ( h ) , i t can be seen that the trend i s for ¥ ( h ) to increase as the lag increases ; i . e . samples closer together have a lower semi­ variance than those farther apart , such that the var iance of the property is s aid to be spatially dependent . Figure 3 . 3 also shows how the number of pairs of points decl ines with increasing lag . From Figure 3 . 2 and the prel iminary analysis described in " section ( i i ) , it would seem likely that the values of ¥ ( h ) at large lags have low precision compared with those at small lags . Ol iver ( 1 987 ) noted that the precision of the variogram depends on the effective degrees o f 0. 1 6 0. 1 4 0. 1 2 I\ 0. 1 y (h) 0.08 0 .06 0 .04 0 .02 24 1 8 No: of 1 2 pa1rs 6 0 0 5 1 0 1 5 No. of units of lag 20 25 B- Experimental variogram Sample variance Figure 3 . 3 Experimental variogram for the 2 5 pH data , and the number of pairs of points separated by each lag 3 7 freedom a t each lag, which are a function o f the number o f pairs at each po int , and a l s o on the s ampl ing interva l , and degree o f s pa t i al variation . The dependence o f variogram precision on the ef fective degrees " of freedom is demonstrated by Figure 3 . 4 , which shows the change in ¥ ( h ) as the number o f pairs used to calculate i t increases , From Figure 3 . 3 i t can be concluded that as the distance separating samples increases , so ,.. does the value of ¥ ( h ) ; i . e . the variance between close samples i s less than the variance between points far apart . This , as has already been " suggested , could be due to the decreased precision of ¥ ( h ) at large h , so 1\ there may be a double effect ; increased ¥ ( h ) due to increased h ( Figure " 3 . 3 ) , and increased ¥ ( h ) due to a decrease in m ( Figure 3 . 4 ) . The former is the spa tial effect and one could reasonably conclude that the value o f 1\ h at which ¥ ( h ) ceases to increase , called the range, marks the l imit o f spa t i a l dependence , the f i nd ing o f which in many cases may be the " obj ective o f the geostatistical analysis . The value of ¥ ( h ) at this and greater values of h is known as the si l l . The s ignif icance of both s ill and range i s discussed more ful ly in section ( vi ) . Nevertheles s , to f ind the s i l l and range , a model must be f itted to the experimental variogram A so that its value can be interpolated . However , the increase in ¥ ( h ) due a possible lack of precision at large values of h presents a problem " 1\ since clearly , ¥ ( h ) at large h should carry less weight than ¥ ( h ) at small h . Thus , to account for the di f ferent value of m at diff erent values of h, weighted least squares must be used for the f i tt ing of variogram models ( the types of which are outl ined below ) . This point i s discus sed extens ive ly by K i tanidi s ( 1 983 ) , Armstrong ( 1 98 4 ) , Cressie ( 1 98 5 ) , and McBratney and Webster ( 1 986 ) . Figure 3 . 5 shows the experimental variogram for pH along the transect with two l inear models f i t ted - one , by weighted , and the other by ordinary least squares . As the models are linear , the range cannot be A identif ied s ince no point i s reached where ¥ ( h ) ceases to increase with increasing h . Given that the slope of the variogram is a measure of the degree or intensity of spatial dependence ( Ol iver , 1 98 7 ) , it c'an be seen that weighted and non-weighted models dif fer markedly ; the inte�sity of spatial dependence is shown by the weighted model to be much less than is indicated by the model fitted by ordinary least squares . The weighted variogram model takes the form : 0.5 0.45 0.4 0.35 0 .3 1\ y (h) 0 .25 0.2 0 . 1 5 0 . 1 0 .05 0 Figure 3 . 4 0 5 1 0 1 5 20 25 No. of pairs ,. Change in the value of Y ( h ) as the number of pairs of data points used to calculate it increases . Here h = 1 lag uni t ; the maximum number of pairs separated by this value of h is 2 4 . 0 . 1 6 0. 1 4 0. 1 2 0 . 1 1\ y (h) 0 .08 0 .06 0.04 0.02 0 5 0 ---- --o ---- 0 1 0 1 5 No. of units of lag o o 0 -- ­-- -- 0 20 25 0 Experimental variogram - - Sample variance - -Ordinary least squares Weighted least squares Figure 3 . 5 Experimental variogra� for soil pH with l inear models fi tted by ordinary and weighted least squares optimization 4 0 1\ ¥ ( h ) = 0 . 0 4 7 0 + 0 . 00 0 6 h ( 3 . 1 8 ) By def inition , ¥ ( h ) at h = 0 must be zero ( Webster , 1 985 ) . However , as can be seen in Figure 3 . 5 , the fitted model used here to approximate the sample semi-variance does not pass through the origin . Thi s fact that the A f itted variogram intersects the ordinate at a value of ¥ ( h ) greater than zero i s one of the more important aspects of the experimental variogram . " The value of ¥ ( h ) at this point , denoted by Ca is known as the nugget vari ance , and corresponds to either unexplained error or variabil ity of z which i s undetected at the scale of sampling , or both . In Figure 3 . 5 , the value of Co ( 0 . 0 4 7 0 ) i s not much less than either 52 ( 0 . 0 5 1 7 ) or the " value of ¥ ( h ) at h = 2 5 units of lag ( 0 . 0620 ) , and thus , this variogram i s s a id to have a h igh nugget variance ; i . e . the degree of spatial dependence is low . This point i s discussed in more detail in sect ion ( vi ) . v . Spatial variation in two dimensions S ince a transect will only show variation in one dimension , data sampled in the same area in two dimensions might be expected to show di fferent spatial varia tion, and it is therefore use ful to repeat the analysis outl ined above on data sampled on a grid . Figure 3 . 6 shows the spatial arrangement of a data set compris ing 1 2 1 pH measurements made on soil samples taken from the same area as the transect , this time on an 1 1 x 1 1 sampling grid with a grid spacing of 2 . 5 m . The 1 2 1 data points were found to be normally distributed ( Figure 3 . 7 ; R2 = 0 . 9 1 , p < 0 . 1 % ) . The mean , � ' was 4 . 92 and the sample variance , s2 , was 0 . 0 3 3 5 . Instead of analysing the data one point at a t ime as for the transect ( F igure 3 . 2 ) , th<:! data were ana lysed i n two dimens ions by s tarting in the top left corner and moving in stages a long both the north - so u th and ea s t - wes t axes s imultaneously , one lag at · a time . Throughout thi s analysis , iso tropy was assumed , that is , i t was assumed that the spatial variation ( i f any ) is the same in both the north-south - - and east-west directions ( and in a l l other directions ) . When the data are i sotropic the lag becomes a scalar ( Ol iver & Webster , 1 9 87 ) , and the data for a l l directions may be grouped , which is what has been done here . A B c D E F G H I J K a 5 . 38 4 . 5 3 4 . 9 5 4 . 90 4 . 7 5 4 . 80 4 . 7 5 4 . 80 5 . 0 0 5 . 1 8 4 . 98 b 4 . 38 4 . 68 4 . 8 0 4 . 98 4 . 65 4 . 90 4 . 80 4 . 85 5 . 0 0 4 . 9 5 4 . 9 5 c 5 . 00 4 . 9 3 4 . 85 4 . 68 4 . 68 4 . 7 0 4 . 98 4 . 80 5 . 0 3 4 . 8 3 4 . 9 5 d 5 . 1 0 4 . 80 4 . 9 0 4 . 53 4 . 8 0 4 . 7 3 4 . 9 3 4 . 9 0 4 . 9 8 4 . 63 4 . 58 e 4 . 65 4 . 88 4 . 7 0 4 . 85 4 . 9 0 4 . 9 5 4 . 9 3 5 . 05 5 . 0 5 4 . 7 8 4 . 7 0 f 5 . 2 0 4 . 88 5 . 0 8 5 . 0 5 4 . 78 4 . 9 3 4 . 9 0 4 . 9 0 5 . 20 5 . 3 0 5 . 08 g 4 . 7 8 4 . 9 0 4 . 83 5 . 20 4 . 80 5 . 2 0 5 . 1 0 4 . 7 0 5 . 1 3 5 . 1 3 5 . 28 h 5 . 38 4 . 9 3 4 . 7 5 5 . 1 0 4 . 98 5 . 2 0 5 . 0 5 4 . 9 3 5 . 0 5 5 . 1 0 5 . 3 5 i 4 . 9 5 5 . 0 5 5 . 0 3 5 . 0 5 5 . 0 3 4 . 9 3 4 . 9 3 4 . 9 5 4 . 75 4 . 78 5 . 0 5 j 4 . 78 4 . 8 5 4 . 9 0 5 . 0 0 5 . 0 5 5 . 0 5 4 . 83 4 . 80 5 . 1 3 5 . 0 5 5 . 20 k 4 . 7 0 4 . 8 5 4 . 7 8 4 . 78 4 . 98 5 . 0 5 4 . 7 0 4 . 7 0 4 . 70 5 . 00 4 . 93 Figure 3 . 6 1 2 1 equally spaced pH data . ( N . B . The ak and AK axes were of equal length in the f ield sampling grid . ) Normalised frequency 2 .5 2 1 .5 1 0 .5 0 0 +-�-=��----�-----+------�-----+----� 4.2 4.4 4.6 4.8 pH 5 Figure 3 . 7 Distribution of the 12 1 pH data shown in Figure 3 . 6 5.2 5.4 4 3 The data were assessed for changing mean and variance as the number of samples increased w i th increas ing lag, th is time in a progression dependent on the square of the lag ; 1 , 4 , 9 , 1 6 , 25 . . . . 1 2 1 , rather than in a s imple ari thmetic progress ion as was the case with the transect . As F i gure 3 . 8 shows , the precis ion o f the mean again increased with increasing sample number . Figure 3 . 9 shows the change in . the number of pairs of points , m, with increasing lag, h . As in Figure 3 . 3 , m decreases markedly as the lag increases . However , the degree . of precision is seen to be much greater at high lags in the case of the grid compared to the transect ( Figure 3 . 1 0 ) . In the case of the transect , there were only 2 4 pairs of points a t the smallest lag . With the grid, there are 22 pairs a t the largest , and 2 2 0 pairs a t the smal lest lag . Obviously , a much greater sampling ef fort is needed for 1 2 1 samples as compared to 2 5 , but these two figures i l lustrate the value of sampl ing in two dimensions ; in the case o f a transect -based sampl ing strategy , this could be done by sampling along two intersecting transects . Assuming isotropy , this would have the effect of doubl ing the precision of a variogram based on a s ingle transect since there would be twice as many pairs of observations . i . e . samples would have to be taken from 49 points instead of 2 5 ( the point at which the transects intersect would occur on both transects ) . The effect of changing the point of intersection on the variogram is not c lear and wi l l not be i nvestigated here , al though where the data are aniso tropi c the point of intersection and the angle of one transect to the other may be important . Figure 3 . 1 0 shows how the variogram changes as more samples are included by increasing the length of the side of the square grid to include more " l ags . In Figure 3 . 4 , ¥ ( h ) was shown to decrease as m increased when A samples separated by a s ingle unit of lag were considered . Here , ¥ ( h ) is shown to decrease as m at all lags increases , as is indicated by the development of the variogram from 2 lags ( 4 samples ) to 1 0 lags ( 1 2 1 ) samples . It is clear that the precision of the variogram for a l l 1 0 l ags is signif icantly greater than when h is less than 1 0 ; one infers , because ,... ¥ ( h ) decreases at intermediate lags as m for any one lag increases , that the precis ion of the variogram i s increasing . If there were spatial dependence , increas ing m through bringing in more data points might be 1\. expected to increase ¥ (h ) at intermediate lags . 5.4 5.2 Mean 5

4.8 4.6 0.2 0. 1 6 Variance 0· 1 2 0 .08 0.04 I I �e -o- -{)- - <:T - - o- - - e- - - -o- - - - e- - - - -o 0 �------+-------+-------�------�------�-------+ 0 20 40 60 80 1 00 1 20 No. of samples Figure 3 . 8 Change in the mean ( �) and variance ( s 2 ) with increasing sample number for the set of 12 1 pH data 250 *... Length of grid side ' 200 '*... 1 Jag unit ' + G. '*... * 2 Jag units ' "R ' 1 i0 '*... 3 Jag units ' '& ' No. of ' '*..... -e- 4 Jag units pairs 'u.. ' X.. ' � -+ S lag units 1 00 ...... "0, 'X, ' 6 Jag units '*..... ......_ ' -7( 7 Jag units ' � 50 "R ' 8 Jag units ' ' *..... "0, ' -e 9 lag units ' ...... 0 '* 'X -« 1 0 lag units 0 0 1 2 3 4 5 6 7 8 9 1 0 No. of units of Jag Figure 3 . 9 Change in the number of pairs of points separated by each lag as the length of the grid side increases from 1 to 10 lag units 0. 1 2 Length of grid side 0 . 1 2 1ag units -+ 3 1ag units 0.08 / ..... * 4 1ag units / B S lag units 1\ y (h) 0.06 ....,_ 6 Jag units -K 7 1ag units 0.04 -+- 8 lag units "'* 9 Jag units 0.02 -e- 1 0 Jag units 0 1 2 3 4 5 6 7 8 9 1 0 No. of units of Jag Figure 3 . 10 Development of an experimental variogra� as the number of samples used to estimate it increases as the length of the grid s ide increases from 2 ( 4 s amples ) to 10 lag units ( 12 1 samples ) 4 7 vi . Other variogram models Unlike the variogram in Figure 3 . 5 , the complete variogram for the grided data ( Figure 3 . 1 0 ) has a curvil inear form . It i s therefore appropriate to investigate some other possible variogram models . Armstrong ( 1 984 ) noted that the eff icacy of geostatistics was essentially dependent on the qual ity of the estimate obtained for the variogram . Thus , getting the best fit for the variogram i s v'ry important , whether it i s to be used for interpo lating va lues of z at unsampl ed s i tes ( kriging ) , or simply to f ind the values of h for which values of z are related . It is not intended here to discuss kriging s ince it was not necessary to use this technique in any of the work presented in this thesis . Suf f ice to say that kriging involves minimizing the estimation variance of the interpolated values of a property . One of its principle advantages over other interpolation methods is that it gives a measure of precision , but this is only good if the model for the spatial structure ( ie . the variogram ) is at least approximately correct ( Starks & Fang , 1 982 ) . Kriging therefore rel ies heavily on the form and goodness of f it of the variogram model , especially at small lags , since interpolation i s invariably used to g ive inf ormat ion on the spaces between existing sampling points . The rationale which determines whether a particular model i s suitable to describe a variogram is complicated and will not be dealt with here . It i s d i s cussed extens ively by Armstrong and Jabin ( 1 98 1 ) , Christakos ( 1 9 84 ) , and W''Bratney & Webster ( 1 986 ) . Here it is enough to say that l inear , spherical , and exponential models are permissible functions for variograms and will describe most . As indicated by equation ( 3 . 1 8 ) , the l inear model takes the general form : ¥ ( h ) = Co + kh for h > 0 ( 3 . 1 9 ) where Co i s the nugget variance , and k i s the s lope . Note that this model has no s i l l , and therefore no range . In the case where k = 0 , the variogram i s said to show a pure nugge t effect ( Webster , 1 985 ) ; that i s , there i s no spatial dependence a t the scale .of sampling . 4 8 The spherical model takes the form : ¥ ( h ) = Co + C { [ 3h/2a ] - [ (h/a ) 3 /2 ] } for 0 < h < a ( 3 . 2 0 ) ¥ ( h ) = Co + C for h > a and its tangent at h = 0 cuts the s i l l at 2a/3 . Here , a is the range , and h , and Co are the lag , and nugget variance respectively . Just as Co represents variabil ity which i s not detected at the scale of sampling , C represents variabil ity which is detected at the scale of sa�pl ing; the s i l l variance i s given by Co + C . In theory , the spherical model is three-dimensional , yet Webster ( 1 98 5 ) noted that i t nearly always f its exper imenta l results f rom s o i l s ampl ing be tter than one and two­ dimensional analogs , such as the circular model ( which is not recommended for describing variograms ) . The exponential model takes the form : ¥ ( h ) = Co + C [ 1 - exp ( -h/r ) ] for h > 0 ( 3 . 2 1 ) Here , r i s a distance parameter which controls the spatial extent of the function ( Webster , 1 985 ) . In the exponential mode l , ¥ ( h ) approaches the s i l l asymptotically and there i s no such thing as a def inable finite range . However , since the semi-variance must cease to increase beyond a certain point , the range is taken to be equa l to 3r , where ¥ ( h ) is approximately equal to Co + 0 . 9 5C ( Webster , 1 98 5 ) . Ol iver and Webster ( 1 9 87 ) have noted however , that taking the range as equal to 3r tends to overestimate i t , and where this �ppears to be the case, the spherical model will probably give a better fit because it curves more tightly . Whichever model is chosen , it is pertinent to bear in mind that " to serve us wel l , the model has to adequately portray the behaviour of the measurements as they really are . I t is not enough to represent how we wish the measurements had been ( but were not ) . " ( Tukey ( 1 973 ) quoted by Armstrong ( 1 98 4 ) . ) 49 According l y , w=Br a t ney and Webs t er ( 1 9 8 6 ) recommend the Aka i ke Information Criterion ( AIC ) for determining which is the best model for the experimental variogram . The A IC is calculated according to the equation : AIC n [ ln ( R ) ] + 2p ( 3 . 22 ) where n i s the number of observations ( or points on the variogram ) , p is the number of estimated parameters ( s i l l , range etc . . . ) , and R is the residual mean square of deviations from the f itted model . The model with the smal lest AIC value is the best . Where the models being compared have the same number of parameters ( spherical and exponential ) , there is no need to calculate the AIC s ince a simple comparison of the residual sum o f squares wi l l reveal which model has the best f i t . The three v a r i ogram mode l s des er ibed above were f i t ted to the experimental variogram for the 1 2 1 grided data and the AIC calculated for each . The value of AIC was lowest when the spherical model was used ; this model is shown fitted to the experimental variogram by weighted least A squares ·in Figure 3 . 1 1 . Here, ¥ ( h ) increases from a nugget variance of 0 . 0 1 83 to a s i l l of 0 . 0 5 09 at h = 3 . 9 6 lag units , the range . Thus , the pH data show spatial dependence at sample separations less than 3 . 9 6 lag units which is equivalent to 9 . 9 m . In Figure 3 . 1 1 , and also in the other f igures showing the variograms for both the transect and grided data , the sample variance has been plotted as wel l as �he experimental variogram , and in Figure 3 . 1 1 i t i s seen to be approximately equivalent to the s i l l . Trangmar et a l . ( 1 98 5 ) stated that the s i l l corresponds to the maximum variabilty of z which i s detected at the scale o f sampling . When the sample variance i s almost pure nugge t , that is Co � s2 , the s i l l will be approximately equal to the sample variance ( Webster , 1 985 ) . Generally the s i ll is expected to be somewhat l arger than the sample variance ( Webster , 1 985 ) . One reason that this might be expected to be so is that the f itted model represents the bes t fit to a set of experimen tal data , although it might equally be expected that the s i l l would under-estimate the sample variance for the 0.05 0 .045 0.04 0 .035 ,... y (h) 0.03 0 .025 0 .02 0 .01 5 0 * * * * - - - -;r - - - --*- - - - - - 1 2 3 * 4 5 6 Lag (h) * Range = 3.96 lag units (9.9 m) CO = 0.01 83; C = 0.0326 7 8 9 1 0 - - Sample variance - Spherical model Figure 3 . 11 Experimental variogram for the 1 2 1 pH data f itted with a spherical model by weighted least squares optimization 5 1 same reason . Another is that the estimated variance s 2 must be smaller when more samples are taken within a given area , especially when there i s some spatially dependent variability . This point is discussed in some detail by Webster ( 1 98 5 ) and will be returned to in Chapter 1 0 . For now , i t may be concluded that the spherical variogram shown in Figure 3 . 1 1 i s a good model o f the experimental data as sampled on the grided design as described . vii . Conclusions Using " aggie" s tatist ics ( Nielsen , 1 987 ) , the conclusions made as to the 1 2 1 pH data would have been conf ined to comments on the estimated mean and sample variance , and in isolation these would tell very l i ttle . Of course , if measurements of another property had also been made at each xi , then correlations , comparisons and relationships between them could have been investigated , but the often large error term may have occurred . By performing a s patial anal ysis us ing geostatis tica l techniques as detailed above , a large portion of this error term can be accounted for in terms of the spatial effect . When the nugget - variance is low as a proportion of the sample variance , then the spatial ef fect at the scale o f sampling is large and may merit more intensive investigation . I f Co is large in comparison with 52 , the error term may not be due to a high degree of spatial dependence ; at least , no t at the sca le of sampl ing used . This would either merit further spatial analysis at a di f ferent sampling scale , or al ternatively may point to an inadequacy in the analytical techniques used or to the use· of · an insufficient number of samples . The other important information gained from a spatial analysis i s the identi f ication o f the range ( i f present ) since i t indicates the minimum sampl e separat ion for which samples are unrelated . In other words , knowing the range to be equal to � , any further sampling should be done with samples taken at intervals of h > � so as to avoid spatial dependence in the data , and thus to e l im inate error due to sampling design . This is the obj ect of the analysis o f spatial variabilit y in nitri f ier activity which forms the bulk of the work described in this thesis . CHAPTER 4 EXPERIMENTAL METHODOLOGY A . THE SHORT-TERM NITRIFICATION ASSAY 5 2 Studies of nitrification and the ef fects on i t of various soi l parameters such as pH ( e . g . Frederick, 1 9 5 6 ; Aleem & Alexander , 1 9 6 0 ; Morri l l & Dawson, 1 96 1 ; Darrah e t a l . , 1 986b) , or soil moisture and temperature ( e . g . S indhu & Cornfield, 1 9 67a ; Kowalenko & Cameron , 1 9 7 6 ; Addiscott , 1 98 3 ; Nakos , 1 984 ; Macduff & Whi te, 1 98 5 ) have been undertaken f or a number of years . Commonly these have involved either long-term perfus ion experiments , pure culture studies of nitrifiers growing under laboratory conditions , or incubation experiments of several days or weeks duration . Thus , l ittle attention has been paid to monitoring the in si tu nitrifier activity , either in i solation, or in response to changes in one or more of the s o i l parameter s . The short-term n itri f i cation assay , SNA, of Sarathchandra ( 1 9 78 ) and Schmidt and Belser ( 1 982 ) , as modif ied by Darrah et a l . ( 1 986b ) , permits the study of in si tu nitri fier activi'ty without the complication of microbial growth . Consequently , this technique , w ith the modi f ications de scr ibed be low , was chosen as the basis of the experimental work reported in this thesis . The rate of nitrate production by nitrif iers growing under non-l imit ing substrate conditions can be described by the equation ( Darrah et a l . , 1 9 85b ) : CIN03 = Jl\.1,. ... ,. • exp ( � ...... x t ) Cl t y ( 4 . 1 ) where CIN03/Ci t is the instantaneous rate of nitrate production , � ...... x is the maximum specific growth rate ( h- 1 ) , m is the nitrif ier biomass ( � g g- 1 soi l ) and Y is the growth yield constant ( � g biomass formed per � mol of ammonium oxidized ) . When the incubation t ime t is short , m may be assumed to be constant , and equation ( 4 . 1 ) simpl i f ies to ( Darrah e t a l . , 1 9 86b ) : oN03 = Jl1)max a t Y J J ( 4 . 2 ) This constant rate of nitrate production can be measured in the short­ term nitrif ication assay . The rate of nitrate production in a s teady­ state population should depend on the size of the nitrifier population and t he phys i o l og i ca l a ct iv i ty o f the organ i sm s mak ing u p that population . Thus , the measured nitri fication rate in a short-term assay reflects these features of the population and w i l l be referred to by the a l l-encompasing term , ni trifier acti vi ty; the SNA value is an index of nitri fier activity . S ince the test of no measurable growth i n a n itr i f ier population i s l inearity of nitri f i cat ion , the succes sful use of equation ( 4 . 2 ) in l aboratory incubation experiments depends upon the selection o f a t ime interval for incubation such that nitrif ication proceeds linearly over the who l e period t . I t a l so depends on the use of a non-limiting incubation medium and substrate concentration . Thus , the SNA technique has to be tailored to suit the particular soil under investigation . i . Selection o f incubation medium for SNA analyses The observations of D arrah et a l . ( 1 98 7 a ) of the adverse effect on nitrif iers of solutions of low osmotic potential suggest that incubation media should have an ionic strength approximately equivalent to that of soil solutions in the f ield . Edmeades et a l . ( 1 98 5 ) studied the chemical composition and ionic strength of a range of New Zealand topsai l s under grassland and found that ionic strengths ranged from 0 . 0 0 3 - 0 . 0 1 6 mol dm- 3 with a mean value of 0 . 00 5 . Dol l ing and Ritchie ( 1 985 ) found that the ionic strengths of soil solutions from 2 0 soils from Western Australia had very similar values . They also noted the marked ef fect of dif fering ionic s trength on the measurement of soi l pH . pH measurements made in s o l u t i on s w it h a n i on i c s t reng th of 0 . 0 0 5 d i f fered leas t f rom measurements made at the ionic strength of so il solutions at f ield capaci ty , whi l s t the d i f ferences that o ccurred in comparisons with 5 4 dist i l led water or CaCl2 a t an ionic strength o f 0 . 0 3 ( 0 . 0 1 M ) were much greater ( ;:: 0 . 4 pH units ) . In view of the dependence ·of nitrif ication on pH ( Darrah et al . , 1 9 86b ) and the fact that the relationship between pH and nitrif ier activity was to be a maj or area of interest in this study , it was clear that all SNA measurements had to be made in solutions of ionic s trength close to that of the soil solution in the field . The mean ionic s trength of the topsoi l of Tokomaru s i lt loam was measured by Edmeades e t al . ( 1 985 ) as 5 . 4 x 1 0- 3 mol dm-3 ( wi th a range of 2 . 1 - 1 1 . 3 x 1 0- 3 mol dm-3 ) . On this basis , it was decided that 0 . 0 05 M KCl would be sui t ab le for SNA ana lyses and as a medium for all the experiments reported here ( unless otherwise stated ) ; the dif ference in ionic strength between 0 . 005 M KCl and the expected extremes in ionic s trength of f ield soi l solutions was assumed to be suf f iciently small to cause nei ther dispersion of the soil nor inhibition of the nitrif iers . ii . Linearity of nitrification rate in the Tokomaru Silt Loam Aleem and Alexander ( 1 960 ) found that the minimum generation time for Ni trobacter agi l i s was about 7 hours , whilst Sarathchandra ( 1 9 78 ) found no s ignif icant change in the most probable number of soil nitrif iers during 1 7 hours incubation . Accordingly , Sarathchandra ( 1 978 ) and Steele et a l . ( 1 9 80 ) sampled incuba ting med ia a f ter and 1 7 hours and cal cul ated SNA values as the dif ference between the amount . o f N03 -N produced between these times per g soil per hour . Darrah et al . ( 1 986b) cal cul ated SNA values in the same way but used shorter incubations , sampling a fter 1 and 8 hours fol lowing the addition of NH4-N substrate . In v iew o f this range of incubat ion t imes for SNA measurements , an e x p e r i men t w a s c onduc t ed t o i nv e s t i g a t e the 1 ineari ty of the nitrif ication rate in the Tokomaru silt loam . 5 5 Methods and Materials A bulk soil sample ( approx . 5 kg ) was dug from the 3 -9 cm depth range of a randomly selected s i te in f ield No . 6 ( see Section B , below ) . The soil was sieved ( < 2 mm ) , thoroughly mixed , and a 200 g subsample was placed in a Buchner funnel f itted with a Whatman No . 1 f i lter paper , and leached overnight with 1 dm3 0 . 005 M KCl to remove any nitrate present . At the end of leaching , excess moisture was removed from the ·soil by suction f i ltration for 9 0 minutes , after which 3 8 replicate 5 g samples ( oven-dry equivalent - determined by oven-drying overnight at 1 05 °C ) were placed into 5 0 cm3 incubation tubes containing 20 cm3 0 . 00 5 M KCl with 0 . 3 % �;v agar . In l ater exper iments the d i lute agar suspension a l lowed the incubating media to be sub-sampled after 1 hour without affecting the soil : solut ion rat io for the rest of the i ncubat ion ( Darrah et al . , 1 98 7a ) . 1 0 cm3 0 . 0 1 M ( NH4 ) 2S04 was added to each tube and the tubes were shaken at 2 2 oc in an enclosed end-over-end shaker f itted with a thermostat . ( The choice of 2 2 oc as a suitable incubation temperature was entirely arbitrary , and was governed by the fact that s ince the temperature inside the shaker was mai nta ined by two 1 00 wat t l ight bulbs , 2 2 oc was a temperature that was easily maintained at a constant , at all t imes of the year . ) At hourly intervals up to 1 9 hours after the start of incubation , two tubes were removed , shaken and dupl icate 5 cm3 samples of suspension were quickly taken by pipette from each tube . These were centrifuged at 3 0 0 0 r . p . m for 1 0 minutes and the supernatant frozen and s tored . The solutions were later analysed for N03 -N on a Technicon Autoanalyser fol lowing the method of Downes ( 1 9 78 ) . Results and Discussion Figure 4 . 1 shows the amount of N03 -N produced in each incubation plotted as a function of the t ime of incubation . By expressing the data on an hourly basis , i t was clear that the nitrif ication rate over the f irst hour was significantly higher than it was during the remaining eighteen hours of the incubation . Thus , the assumption of l inear nitrif ication did 1 .6 1 .4 0 � 1 .2 �� / 1 o�'S pmol § N03-N 0.8 � z o g-1 / 0 .6 A5 �6 0 0.4 0 .2 0 0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 20 Time (hours) after addition of NH4-N Figure 4 . 1 N itrification rate in the Toko�aru silt loam measured at 2 2 °C for 1 9 hours after the addition of a��oniu� substrate 5 7 not s eem to be a good one . In f act the data were best-f i tted by an upwards curvil inear model ( Figure 4 . 1 , dotted l ine ; R2 = 0 . 98 , p< 0 . 1 % ) whi ch i s what would be e xpec ted for a growing population . _ It was therefore concluded that the incubation time selected would have to be a compromise which satis f ied three basic requirements ; ( a ) that ni trate production should be approximately l inear with time ; ( b ) the incubation t ime would have to be long enough to al low for production of a measurable increase in No3 - concentration ; and ( c ) not too long that s ignif icant popu l a t i on gr owth occurred , or the experi ment be came ph ys ical l y impossible t o do . A l inear model was f itted to the data between 1 and 8 hours ( Darrah e t al . , 1 98 6b ) and s ince this gave a good f i t ( Figure 4 . 1 s o l id l ine ; R2 = 0 . 8 2 , p < 0 . 1 % ) these times seemed sui table for SNA measurements on the Tokomaru silt l oam . As indicated above , the time taken to complete an SNA analysis was an important consideration . I t was apparent that the maximum number · of tubes that could be dealt with at a time without incurring a t ime error whilst sampl ing the suspensions , was between 1 5 and 20 . ( In fact with practice , 1 5 tubes could be sampled in 2 minutes . i . e . the t ime o f sampl ing given as 1 or 8 hours is accurate to ± 3 % after 1 hour and ± 0 . 4 % after 8 hours . ) By starting the Buchner suction at 6 . 30 am ( and allowing for a period of 5 hours for the samples to equil ibrate fol lowing the addition of acid or a l ka l i - s ee Chapter 7 and Darrah e t a l . ( 1 9 8 6b ) ) , and separating the tubes into four groups of 1 5 with a stagger of half an hour between the f irst and second and the third and fourth groups , and 4 5 minutes between the second and third , i t was possible to complete 6 0 SNA analyses by . midnight i f an incubation of 8 hours was used . Accordingly , in a l l SNA anal yses reported here , the incubating media were sampled after 1 and 8 hours , and the SNA value calculated as the difference between N03 -N produced at these t imes per g soil per hour . 5 8 iii . Selection o f ammonium substrate concentration for SNA analyses Macduff and Whi te ( 1 985 ) measured nitrif ication rates over a range of so i l mo i s ture cont ents and incubation t emperatures , and found that irrespective of temperature, nitrification was l imited by the supply of NH4 -N . Gilmour ( 1 9 8 4 ) predicted that nitri f ication rates fol lowing zero­ order kinetics w i l l increase as the i n i t i a l c.<:mcentration of NH4 · increases according t o the equation : NR = k )I NXt ( 4 . 3 ) where NR is the absolute nitri fication rate , k is the rate constant , and NXt is the initial NH4 • concentration at the start of the t ime period t . Mo l ina ( 1 9 8 5 ) a s serted that n itr i f icat ion proceeded from pulses of ammonium oxidation generated by microbial c lusters . The size of the pulse must be subj ect to a negative feedback system , however , because if large app l icat ions o f ammonium are suppl ied , the e f f ect of l ow osmotic poten t i a l w i l l inhibit ni tri f i ca t i on ( Darrah et al . , 1 98 7 a ) . Thus equation ( 4 . 3 ) must be treated with circumspection because i t suggests that NR will continue to increase l inearly with increasing initial NH 4 concentration even at very high concentrations . Furthermore , Clay e t al . ( 1 985 ) noted that i f clusters of NH4 · oxidizers were to generate a pulse of N02- large enough , their microni che may be acidi fied to toxic levels and nitrificat ion would be reduced ( Chapter 7 ) . Neverthe less equation ( 4 . 3 ) does indicate that nitri fication rates can be limited by inadequate NH 4 -N substrate concentrat ions . Thus , the concentrat ion of NH 4 -N subs trate suppl ied i n SNA incubations must be such that i t is non­ l imiting in the sense that it is in excess , but not so much so as to generate conditions which are toxic to the nitrif iers . The form in which the ammonium was to be supplied for SNA measurements was also an important considerat ion , the obvious choice being between NH4Cl and ( NH4 ) ;so4 since these are readily available forms of ammonium . Darrah et al . ( 1 9 8 5 a ) monitored the response of soil ni tr i f iers to additions of both NH4Cl and (NH4 ) 2S04 . Additions of more than 7 . 3 �moles N g_ , soil as NH4Cl were found to inhibit nitrif ication , but . a s imi lar e ffect was not found with (NH 4 ) 2S04 sugges ting that the chloride ion 59 rather than osmotic potential was the cause of the inhibition . Further work ( Darrah et al . , 1 987a ) demonstrated that the inhibitory ef fect of the cl - ion was disproportionate to its contribut ion to the . o smotic pot en t i a l of the s o i 1 s o lu t i on . i . e . C l - i s t ox ic to nitri fiers . Accordingly , ( NH4 ) 2S04 was chosen as the substrate to be used for SNA mea su r ement s , bu t an experi men t was requ ired t o e stabl i sh what concentration should be used, s ince Darrah et al . ( 1 985a ; 1 987a ) had shown that this salt was also inhibitory to nitr i f ier activity in high concentrations . Methods and Materials A bulk soil sample was col lected as for the l inearity experiment ( Section i i . above ) and leached overnight with 0 . 0 05 M KC! . After the removal of exce s s m o i stur e , 6 0 incuba t i ons were set up f o l l owing the method described above , except that the tubes were spl it into 6 groups and ( NH4 ) 2S04 substrate added as 1 0 cm3 of 0 . 0 0 1 , 0 . 0 0 5 , 0 . 0 08 , 0 . 0 1 0 , 0 . 0 1 5 or 0 . 0 2 0 M ( 1 0 repl ica tes each ) . The incuba tions were carried out as described , with sampling of the suspension after 1 and 8 hours . Result s and D iscussion The mean SNA values for each concentration of ( NH4 ) 2 S04 added to the incubations are shown in Table 4 . 1 . An analysis of variance showed that there was no signi f icant di f ference in SNA value ( p < 0 . 1 % ) over the range of NH4 -N concentrations tested . i . e . there was either no inhibition of nitr i f ier activity in any treatment , or the amount of inhibition was the same for e ach treatment . Direct comparison between thes e results and those o f Darrah et al . ( 1 987a ) i s made diff icult by the fact that ( a ) in this experiment there was no means of measuring the osmotic potential ( an osmometer was not available ) ; ( b ) the ionic strengths of the incubat ion media were quite dif ferent ( 0 . 0 1 M CaCl2 compared to 0 . 00 5 M KC! ) ; and most importantly ( c ) the ionic s trength of the soil solution in the f ield in the soil studied by Darrah et al . ( 1 987a ) was probably different from that in the Tokomaru s i l t loam , wi th the consequence that the soil Table 4 . 1 Effect of ( NH4 ) 2S04 substrate concentration on SNA value Concentration of ( NH4 ) 2S04 0 . 00 1 M 0 . 005 M 0 . 0 08 M 0 . 0 1 0 M 0 . 0 1 5 M 0 . 020 M Mean SNA ( IJ mol N03 -N g- 1 h_ , ) 0 . 0 1 6 0 . 0 1 5 0 . 0 1 5 0 . 0 1 5 0 . 0 1 3 0 . 0 1 5 Standard e:r.ror 0 . 00 1 0 0 . 0008 O . OO J 1 0 . 00 1 5 0 . 00 1 0 0 . 00 1 7 6 1 ni t r i f ier response to changes in osmotic potential might be quite d i f ferent between the two soi ls , certainly in terms of i ts magnitude . Nevertheless , i f one assumes that the dif ferences between the incubating media were insignif icant in terms of their ef fect on the overal l osmotic potential in the two experiments , s imple comparisons can be made . Osmotic pressures were calculated for each substrate concentration using the equation ( Hi l lel , 1 9 80 ) : r = MRT ( 4 . 4 ) where r is the osmotic pressure ( Pa ; 1 bar = 1 0 5 Pa ) , M is the total mol ar concentration of solutes ( mol m- 3 ) , T is the temperature ( degrees Kelvin ) and R is the gas constant ( 8 . 3 1 4 J K_ , mol - ' ) . Using equation ( 4 . 4 ) , it was apparent that the osmotic potential of the substrates used was high ( osmotic potential = - osmotic pressure ) in relation to those of Darrah et al . ( 1 987a ) , and therefore one was led to conclude that there was no inhibition of nitrif ication in any of these incubations . In view o f thi s , and mindful that no account had so far been taken of the pos s ible seasonal f l uc tuat ion in SNA value ( see Chapter 7 ) and the consequent possibilty of much higher values at other times of the year than those measured here , 0 . 0 1 M ( NH4 ) 2S04 was chosen as the substrate to be used in a l l future SNA analyses . S ince the SNA value for 0 . 0 1 M ( NH 4 ) 2 S04 was 0 . 0 1 5 ± 0 . 00 1 5 � mol N g- ' h_ , indications were that this concentration of NH4- was wel l in excess of the nitrif ier requirements , but not too high to cause toxicity problems . iv . Analysi s o f exchangeable ammonium In several o f the experiments described in the fol lowing chapters , exchangeable ammonium was analysed . This was a particularly important ana l ys i s in the spatial vari abil ity experiment ( Chapter 6 ) s ince one po s s ib i l ty w a s t ha t v a r i ab i l i ty in n i tr i f ie r act ivi ty f o l lowed variability in Ex-NH4- · Ex-NH4 - was analysed by the fol lowing method ( A . D . A . S . , 1 98 1 ) : 62 6 g soil ( oven-dry equivalent , s ieved < 2 mm ) was weighed into 50 cm3 plastic tubes ( the same as those used for SNA measurements ) . To these , 3 0 cm3 2 M KCl was added and the tubes were shaken i n an end-over-end shaker for 2 hours . The suspensions were then f i ltered through Whatman No . 3 2 f i l ter paper , and the f i l trate frozen and stored . This was l ater analysed for NH 4 -N on a Technicon autoanalys er following the standard method ( Technicon Users Manual , 1 9 7 6 ) . B . FIELD SAMPLING i . S ite Details Unless otherwise indicated , a l l the experimental work reported here was carried out using soi l sampled from the Massey University No . 4 Dairy Farm , specifically from two adj acent f ields in the Soil Science Research Area - No . 6 , which had no previous history of ferti l izer trials or other experimental work , was used for the bulk of the study , and No . 2 , a f ormer lime tria l , whose history is detai led in Chapter 7 , was used for the pH related work . Both f ields had been under a ryegrass-white clover pa s tur e for severa l years and for the duration of thi s study were periodically grazed by sheep ( 3 - 4 days each grazing ) at approximately 1 40 s tock units ha- ' ; one stock unit is equivalent to a 5 5 kg ewe ( weight at mating ) which consumes suf ficient dry matter to produce one weaned lamb per year ( Cornforth & Sinclair , 1 984 ) . The soil at this site , the Tokomaru silt loam ( Cowie , 1 9 7 4 ) i s classi f ied as a Yellow Grey Earth ( Taylor & Pohlen , 1 968 ) or Typic fragiaqualf ( So i l Survey Staf f , 1 97 4 ) . I t i s a poorly drained so i l of low nutrient status and was not recommended by Cowie ( 1 97 4 ) for cropping or horticultural use . He recommended regular dressings of phosphate , l ime and potash to achi eve opt imum pasture growth . Accordingl y , land on this soil is typical ly used for town dairy supply ( serving Palmerston North ) and the f a ttening of sheep , a l though product ion can be l imited by the wet conditions in winter and spring , and by the drying out of the soil in summer . 63 Mean annual rainfal l at the s ite , which is approximately 7 5 m above sea level , is 9 9 5 mm and mean monthly temperatures range from 8 oc in. Jul y to 1 7 oc in February ( New Zealand Meteorological Service ;· Figure 4 . 2 ) . ii . Soil Sampling Soil samples for a l l experiments except the pH work ( Chapter 7 ) were taken in sections using a core auger 3 cm deep with a diameter of 5 cm . For a l l SNA measurements ( see below ) , the top 3 cm layer was discarded to minimise any inhibitory ef fects that grass roots may have on the rate of nitrif ication ( Mol ina & Rovira , 1 9 6 4 ; Neal , 1 9 69 ; Moore & Waid , 1 9 7 1 ) , and the 3 - 9 cm layer retained for analysis . The rationale behind using this depth range for experimental work is more ful ly explained in Chapter 5 . On a l l sampling occasions , samples were sieved ( < 2 mm ) as soon as possible a fter sampling and stored in sealed plastic bags at 3 oc ( see Section C below ) . Prior to soil sampling , there was always a period of three weeks during which there was no grazing . This was done in an attempt to minimise grazing effects such as ho tspo ts of high nitrate concentration caused by the urine and excreta of grazing animals ( Ryden e t al . , 1 98 4 ; Bal l & Ryden, 1 9 8 4 ; White , 1 98 4 ) . iii . Correlation between moisture contents of sieved and rinsieved soil As out l ined in section A ( above ) , the amount of s oi l required for a s ingle SNA analysis was approximately 5 g ( oven-dry equivalent ) of s ieved soi l . Each soil sample was analysed in duplicate for nitri fer activity , and in a ddi t i o n , t wo f urther sub - samples o f 6 g e ach ( oven - dry equivalent ) were needed for analysis of exchangeable ammonium , and a further 1 0 g needed for measurement ( in duplicate ) o f the soil moisture content so that results could be calculated on a per g dry soil basis . Overall , approximately 3 5 g sieved soil was needed for analysis . S ince the purpose of this work was to study field n itrif ier activity , e ach a. Precipitation 200 1 50 -e Evaporation mm 1 00 -w- Rainfall 50 0 �--P---P---+---+---+---+---+---+---+---+-� b. Temperature 20 1 6 OC 1 2 8 4 �--P---P---+---+---+---+---�--�--+---+-� JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Figure 4 . 2 Mean monthly weather data ( 1 9 2 8 - 1 9 8 0 ) for the D . S . I . R Grass lands weather station , Palrnerston North ( N . Z . Meteorological S ervice , 1 9 8 3 ) 65 samp le had to be s ieved in the f i eld-moist s tate to ma intain field conditions , and as a resul t , the amount of sieved soil gained from a 5 cm diameter core from the 3-9 cm depth range was often not far in excess of the amount needed for analysis , although this depended on the moisture content of the soi l . Therefore in order to conserve soi l , i t was decided to investigate the possibility of a relationship between the mois ture content o f s ieved and unsieved so i l samples i n t he hope that field moisture contents could be inferred from the s ieved moisture content . This was parti cularl y important for the spatial vari abi l ity analyses s ince the variability in moisture content was considered a possible cause of variabil ity in nitrif ier activity . Methods and Materials Fifty soil samples were taken from sampling sites randomly arranged at roughly 5 m intervals in field No . 6 at a range of depths to 24 cm using the core auger . Sub-samples were assayed immedia tely for gravimetric moisture content by oven drying overnight at 1 0 5 oc . The remainder of each sample was s i eved ( < 2 mm ) and this too , was dried by the same method . Results and Discussion The moi sture content of s ieved samples was plotted aga inst that of unsieved samples and an equation fitted by l inear regression ( Figure 4 . 3 ; R2 = 0 . 9 1 , p < 0 . 1 % ) . The equation took the form : S = 0 . 0624 + 0 . 7 49 1 1 U ( 4 . 5 ) where S and U were the moisture contents of the s ieved and uns ieved samples respectively . In view of the good fit of equation ( 4 . 5 ) to the data over a wide range of mois ture contents ·( 0 . 2-0 . 5 g g- , ) , this equat ion was used in all subsequent experiments to calculate the field soil moisture content on the basis. of the moisture content of the sieved soi l . 9g (gg-1 ) <2mm SEfVED 0.45 0 .4 0.35 0 0 0.3 0 0.25 0 .2 0 . 1 5 +---+----+----t----'t---�1----t------f 0 . 1 5 0.2 0 .25 0.3 0.35 0.4 9g (gg-1 ) UNSEfVED 0.45 0 .5 Figure 4 . 3 Correlation between the gravimetric moisture contents of s ieved ( <2 �� ) and unsieved Tokomaru s i lt loam C . STORAGE OF SAMPLES PRIOR TO SNA MEASUREMENTS 67 It was frequently necessary to col lect soi l samples several days before laboratory measurements were made . Therefore the samples had to be stored . The term s torage often encompasses drying, pulverization and sieving ( Bartlett & James , 1 980 ) , and soils are often stored with l ittle regard for the possible ef fects of storage on the soi l ' s properties . The physical properties of soils are well known to be affected by drying ( and wet t in g ) ; the contract ion o f pores i s .known to . occur and further structural al terati ons may also arise . In extreme cases , such as in Vertisols , the development of large cracks and fissures upon drying i s l i ke l y , a nd w i l l be espec i a l l y s ignificant in larger , more massive samples . In view of these known physical changes , i t appears unlikely that the microbial and chemical characteristics of a soil would remain unaltered from the field state during storage . Since the pur�ose of this study was to investigate the activity of ni tri f iers in the f ield , the method o f s ample s torage and pr eparat ion was obvi ous l y of gre a t importance . i . Effects o f drying and storage on mineral nitrogen It has o ften been observed that variation in pretreatments , particularly degree of drying , affects the amount of soil nitrogen mineral ized during short periods , thereby complicating the use of the results as indices of soil nitrogen availabi lity ( Stanford & Smith , 1 972 ) . Patten e t al . ( 1 980 ) s tudied the effects of drying and a ir-dry storage on the soi l ' s capacity for denitrif ication under anaerobic incubation . Their results indicated that the d ry ing o f s o i ls markedly increased the i r capac i ty for deni tr i f icat ion , and this e f fect increased a s the drying temperature increased . Partial dry ing and air-drying had a similar but slightly sma l ler ef fect . Agarwal et a l . ( 1 9 7 1 ) found that t he · t emperature of drying as wel l as the drying-rewetting cycles enhanced both nitrogen and carbon mineralization in "practically all soils" studied . The N release increased i f incubation followed drying . In all but one of their soi l s , air-drying caused a greater N release than heating at 6 0 oc regardless o f 68 the number of rewetting-dry ing cycles . Ross et al . ( 1 9 79 ) found that sample s ieving led to a sl ight increase in all mineral N fractions as did storage of the sieved samples for 24 hours at 4° or -20 oc . · Frye and Hutchinson ( 1 98 1 ) f ound a pronounced increase in exchangeable NH 4 ... on dry ing , this increase being greater a fter oven-drying . However , the source of this NH4 ... was unknown although the effect was less in the subsoi l suggesting that the NH4 "'" was released in the topsoi l . Thus , either humus or mi crobes kil led by drying ( or both ) were a possible source of mineralizable N . These results agree with those of Soul ides and Al l i son ( 1 9 6 1 ) , who f ound a s imi lar increase in available ammonium . Gasser ( 1 9 6 1 ) found that drying led to an increase in both NH4 "'" and No3- and found that on rewetting , most extra mineralization had occurred after 1 0 days and all by 42 days . He also found that the increase in nitrogen mineralization became more marked with time of storage although a clear trend was not e s tabl ished f o r 1 2 - 1 6 we eks be fore whi ch , v a lues fluctuated . Munro and Mackay ( 1 9 6 4 ) found that incubation of soils at a humidity of less than 85 % severely restric ted N03 - production . This effect was due to drying as the soil moisture content at 85 % relative humidity was hal f that a t a rel ative humidity of 1 0 0 % . However , on rewetting the drier samples , the N03- production increased signi ficantly . They also found that air-drying the soil from field capacity to wilting point had little or no ef fect on No3- production , but further drying due to a ir-dry storage caused a marked increase on rewetting . Tests on C02 evolved during the experiments of Pat ten e t a l . ( 1 9 80 ) indicated that the increase in the soil ' s capacity for denitrification was due to an increase in soil organic matter which was readily utilised by denitrifying organisms . This was thought to be due to humus breakdown although there i s good evidence ( Jenkinson , 1 9 6 6 ; Jenkinson & Powlson , 1 97 6 a ; 1 976b ) to suggest that much of this increase - in organic matter is in the form of dead organisms that were kil led off by drying . This is supported by the results of Agarwal e t al . ( 1 9 7 1 ) , who with respect to mineralization , proposed that in addition to microbial s timulat ion in rewetted samples fol lowing drying , heat was directly responsible for the amount of N and C released i n unincubated samples through chem ical alteration of otherwise unavailable organic matter , and by the "kill ing o f f " o f organisms . When incubat ion followed the drying and heating 69 treatment , the direct effect of heat together with increased microbial act ivity and associated changes during incubation accounted for C and N mineral ization . Addiscott ( 1 983 ) noted that the net amount of N ammoni f ied in t ime t , Nt , measured as the increase in the sum of ammonium and nitrate , increased approximately l inearly with t . However , this zero-order relationship was found to be dependent to some extent on whether the soi l was pre-dried . Many authors ( e . g . Stanford & Smith , 1 9 7 2 ; Tabor e t a l . , 1 985 ; Clay et al . , 1 9 85 ) used soils that had been dried and sieved and onl y a very few ( e . g . Addi scott , 1 98 3 ) used soi ls in the f i eld state . It was thought conceivable that the f lush o f mineral N caused by rewetting the dry soil cou l d change a l i near re l a t i on sh ip wi th t i n t o an apparent .ft relationsh ip, or even a f irst -order ( exp ( t ) ) relati onship ( Addiscott , 1 98 3 ) . Despite a l l this apparently conclusive work , it has been found that the e f f e c t o f storing s o i l s on the ir subsequent abil ity to mineralise n i t rogen depends on the indiv i dual so i l ( Harding & Ross , 1 9 6 4 ) . Investigations of four soils showed the effect to be much more pronounced in three but not so great in the fourth . These d i f ferences were related to the moisture and carbon contents of the soils but i t was found that for a g iven drying period , the amounts of carbon and nitrogen mineralised were proportional to the carbon content of the soi l , whi le for a given so i l , they were f ound to be a s i gn i f icant l inear function of the logari thm o f the t ime the s o i l was in an a ir -dry s t a te prior to moi stening . i i . The e ffects o f drying and storage on soil biomass From the above , i t is clear that the major effects o f soil drying and s torage are governed by the e f fects of s torage on the microbial popul a t ions s ince dead organi sms add to the pool of mineralizable substrate . Thus , the mode and t ime of storage were especially important to this study , given that it was the activity of the nitrifiers that was under investigation . 7 0 Chao and Alexander ( 1 982 ) studied the inf luence o f drying on the survival of Rhi zobi um . They found that the numbers of both R. mel i l oti and R . pha seo l i f el l markedly a s the soils dried , but the ir abundance only dec l ined s lowly in soi ls maintained in the air-dry state . Further , the number of surviving cel ls increased i f the bacteria were added to sterile soil and allowed to grow before desiccation was extensive . The' work of stevenson ( 1 9 5 6 ) on the respiration of air-dried and fresh soils showed that a higher level of metabolic activity is attained in air-dried soi ls on remois tening than occurs in fresh soi l . The degree by which the metabolic activity increased varied directly with the concentration o f free amino acids and other nitrogenous materials released by the air­ drying process . Salonius ( 1 9 83 ) allowed microbial populations from dried , remoistened and undried forest organic horizons to recolonise steri l ised forest hor i z ons and conc luded that samples o f forest organic soi l material designated for the study of microbially driven processes should not be air-dried . White ( 1 9 6 4 ) reached a simi lar conclusion with regard to the study of soil phosphate potential . He found that when air-dried samples were used for measurements of phosphate potential , microbial uptake of phosphate interfered with the resul ts if the samples were shaken f or more than two hours . i . e . re-wett ing led to increased microbial activity and thus uptake of P , this effect being significant when samples were shaken for more than two hours . Harding and Ross ( 1 9 6 4 ) added ammonium to dried stored soi ls and obtained results suggesting that numbers of nitrifying organisms decreased after six months of storage . Soul ides and Al l ison ( 1 9 6 1 ) found that drying was more destructive to organisms than freezing and so the latter would seem preferable if storage is required . Far more desirable however , would be to use fresh soi l . Salonius ( 1 983 ) reported the work of Sneath ( 1 9 62 ) who studied dormant populations in dry soil stored for up to three hundred years and estimated that these soils would reach steri l ity in a thousand years at ambient temperature and humidity . The d i f ference between s ix months and a thousand years storage is obvious , but this hypothesis would nevertheless seem both reasonable , and with clear implications . 7 1 Invest igations on the ef fec t of storage on soil biomass estimated by biochemi cal techniques were carried out by Ross e t a l . ( 1 9& 0 ) . For determination of biomass by C02 evolution from chloroform fumigated soils incubated for f ixed periods , the differences in patterns of C02 evolution between soils s tored for 2 8 and 5 6 days at 2 5° , 4° , and -20 oc were negligible and not s igni ficantly different from those calculated from individual ly determined incubation periods for each _ _ treatment and soi l . However , biomass C va lues could change s igni f i cantly at all storage temperatures but general ly least at -20 oc , the temperature of storage which was bes t for maintaining ATP content s . Overa l l , no storage temperature was satis factory for a l l indices of microbial biomass tested , but 4 oc was adequate for short periods . I t is clear therefore that assays of fresh soil are preferable . Mention has been made of the effect of drying on soil organic components such as amino acids . B irch ( 1 9 58 ) showed that C and N mineralizat ion occurred rapidly on rewetting dried soi l , the ef fect being greater for oven-dried than for air-dried soil . He therefore concluded that �rying of any kind leads to humus decomposition . The work of Stevenson ( 1 9 56 ) has already been discussed , but this assertion of Birch ( 1 9 5 8 ) would explain S tevenson ' s correlation of metabol ic activity increase on remoistening dried soi l , with avai labil i ty of free amino acids and "other nitrogenous materials " re leased by the air-drying process . S oulides and All ison ( 1 9 6 1 ) showed that when e i ther drying or freez ing was followed by a period of incubation , there was an increase in the decomposition of soil organic matter , this be ing substanti ally greater for drying than for free zing . Pro longed drying increa sed the rate of decompos ition , and mult iple dryings had a cumul at ive e f fect . Mult iple freezings had no e f fect . I t would appear that the increased decomposition of organic matter following intermittent drying or freezing is due primarily to the re lease o f nutrients , e specially energy s ources that can be rapidly oxidized by the soil f lora . From the above , much of these are likely to be derived from microbes ki l led by the drying process . Providing the C/N ratio is suf ficiently low to al low for the net ·mineralization of N , a snowbal l effect result s , leading to further breakdown , and thus the burst of C02 production and release of NH4 . following drying is enhanced by the 7 2 youthful state o f a growing ( feeding ) microbial population ( Soul ides & Al l i son , 1 9 6 1 ) ; the microbes which survive the drying process are provided with an excess of substrate, and so grow rapidly . The key to succesful storage of soils therefore l ies in the maintenance of microbial populations at f ield levels . From the above i t is obvious that experiments - especially those such as SNA measurements - should be carried out on soil in the f ield state . If storage is necessary , then the period of storage should be as short as poss ible and under condit ions of low temperature and high humidity . Conditions of high humidity are easily attained when samples are stored in sealed plastic bags at low temperature , especially when the soil in the f ield is moist or even wet . Accordingly, all soil samples col lected in this study were stored at 3 oc in sealed plastic bags . However , it was considered desirable to check on the ef fects of a period of storage under these conditions on SNA values , and gain some idea as to how long samples could be stored . Methods and Materials This experiment was carried out in conjunction with the second experiment of the f irst year of the work on the pH relations of nitrif ier activity ( Chapter 7 ) . Despite modif ication of the SNA technique to accommodate the adjustment of incubation pH , the experimental procedure lent itself to the study of storage effects s ince in addit ion to investigat ing the ef fect of storage on the SNA value , it was also of interest to see i f the relationship between nitrif ier activity and pH was affected by storage . A bulk soil sample ( approx . 5 kg ) was dug from one of the control plots from f ield No . 2 on Augus t 2 5 , 1 9 86 . The so i l was s ieved ( < 2 mm ) , thoroughly mixed, and a 200 g sub-sample leached overnight with . 1 dm3 0 . 00 5 M KCl a s bef ore . At the end of leaching , excess moisture was removed from the soi l by suction filtration for 90 minutes after which , 3 0 repl icate 5 g samples ( oven-dry equivalent ) · were placed into 5 0 cm3 incubation tubes containing 20 cm3 0 . 005 M KCl with 0 . 3 % �;v agar . The pH of the suspensions was then adju:;ted in tripl icate by adding sma l l 7 3 amounts o f 0 . 1 M H C l or KOH . A pre l im inary experiment ( Chapter 7 ) established that the pH of the suspens ions attained an approx imately steady value within f ive hours , and accordingly , after f ive hours , 1 0 cm3 0 . 0 1 M ( NH4 ) 2S04 was added to each tube and the incubations begun as before . After the eight hour sampling, the incubation pH was measured by glass electrode and pH meter . Three weeks after this experiment , a further 200 g sub-sample of soi l was taken from the original bulk sample which had been stored in a sealed plastic bag at 3 oc and the experiment was repeated . Results and Discuss ion For both experiments , SNA values were plotted against pH and the data were f i t ted w i th a quadrat ic equat ion by a l east- squares f i tting procedure ( Figure 4 . 4 ) . By differentiating the . f itted equations , a pH optimum for nitri f ication pHopt was calculated . The f i tted equations for fresh soil ( Figure 4 . 4a ; R2 = 0 . 8 5 , p < 0 . 1 % ) and stored soil ( Figure 4 . 4b ; R2 = 0 . 5 3 , p < 0 . 1 % ) were : SNA = - 0 . 1 7 622 + 0 . 06864pH - 0 . 0 0 583pH2 ( 4 . 6 ) for fresh soi l , and SNA = - 0 . 1 5 60 0 + 0 . 0 6 49 4pH - 0 . 0 0577pH2 ( 4 . 7 ) for stored soi l . Predicted values of pHopt were 5 . 89 and 5 . 63 for the fresh and stored samples respectively . The cal culated SNA values at pHopt 1 SNAopt were 0 . 02 6 v mol N03-N g- 1 h_ , for the fresh, and 0 . 02 7 v mol N03-N g- 1 h_ , f or the stored soil sample . Looking at Figure 4 . 4 , there appeared to be very l i ttle di f ference between the f itted curves for fresh and stored soi l s . By combining analysi s of variance and analysis of covariance ( Freund & Minton , 1 979 ) it was found that the SNA and pH data for both experiments could be grouped and f i tted with a common quadratic equation ( Figure 4 . 5 , R2 = 0 . 62 , p < 0 . 1 % ) , which predicted values of pHopt and SNAopt of 5 . 76 and 0 . 0 2 6 respectively . i . e . the 3 week period of storage had no ef fect on the SNA or the response of the nitrifiers to pH . Accordingly it was concluded that samples could be stored for 3 weeks without affecting experimental results . a. Fresh soil 0.03 0.028 0.026 0.024 SNA 0.022 �mol 03-N g-1 h-1 } 0.02 0.01 8 0 .01 6 0.01 4 0 .0 1 2 4 4.5 5 0 .03 0.028 0 .026 0 .024 0 .022 0 .02 0 .01 8 0 .01 6 0 .01 4 0 .01 2 5.5 6 6.5 7 pH b. Stored soi l * * * * * * * 4 4.5 5 5.5 6 6 .5 7 pH Figure 4 . 4 pH optima curves for nitrifier activity in ( a ) fresh soil and ( b ) soil that had been s tored for 3 weeks 0.03 * 0.028 * * "li(D {!) 00 0 0.026 * ** 0 0.024 0 SNA 0.022 0 * (JJmol * N03-N 0.02 g-1 h-1 ) 0 0.01 8 * CD * 0.01 6 0.0 1 4 * 0.01 2 4 4 .5 5 . 5 .5 6 6 .5 7 pH Figure 4 . 5 Co��on pH optimum curve fitted to SNA data for fresh and s tored soil 0 Fresh soi l * Stored soil 7 6 A s mentioned above , Ross e t a l . ( 1 9 8 0 ) found no temperature to be satisfactory for s torage of soil where maintenance of the biomass was a priority , although 4 oc was adequate for short periods . In the l ight of this and other f indings (detai led above ) , storage periods were clearly to be avoided if possible . The period of storage for samples used in the work reported in this thesis was never longer than 1 0 days , and was generally conf ined to 3 days . Thus , on the assumption that any microbial , chemical or biochemi cal changes undergone by the soi l as a resul t of sieving and storage were either negligible , or would have been manifested over periods of s torage much longer than three weeks , it was concluded that SNA me asurement s were una f fected by s torage , and therefore represented f ield nitrif ier activity . Of course , there may have been an immediate ef fect on nitri fer activity caused by sieving ; this was not investigated , but wa s unavoidable since the soil . . had to be s ieved to obtain a homogeneous bulk sample for the incubation experiments . D . CONCLUSIONS The prel iminary experimental work outl ined in this chapter was done with the aim of tailoring the SNA technique to the Tokomaru s i l t loam , and ensuring that the logist ics of the various parts of the technique were compat ible w i th the intended l ines o f research . As a result , the following was drawn up as a standard procedure for SNA measurements on the Tokomaru s i l t loam , and was used as the basis of experimental technique for the research reported in the fol lowing chapters of this thesis : 1 . Following sampling, each soil sample was sieved ( < 2 mm ) and stored at 3 oc in a sealed plastic bag . The period of storage was never greater than 1 0 days . 2 . Each sample was subsampled for moisture content and Ex-NH4. ( where applicable ) , and leached with 0 . 00 5 M KCl in a Buchner funnel fitted with a Whatman No . 1 fi lter paper at a soi l : solution ratio of 1 : 5 . ( In the spatial variabil ity experiments ( Chapters 5 & 6 ) the leachate was retained and analysed for N03-N ) 3 . Excess moisture was removed by suction f i ltration for 90 minutes . 7 7 4 . 5 g samples ( oven-dry equivalent ) were placed into 50 cm3 plastic tubes containing 20 cm3 0 . 00 5 M KCl with 0 . 3 % -!� agar . 5 . To each tube , 1 0 cm3 0 . 0 1 M ( NH 4 l 2S04 was added . The tubes were sealed and placed in an enclosed end-over-end shaker f itted with a thermostat , and incubated at 22 oc for 8 hours . 6 . Af ter 1 and 8 hours , a 5 cm3 sample of suspension was taken from each tube , centrifuged at 3 0 0 0 r . p . m for 1 0 minutes and the supernatant frozen and stored for N03 -N analysis at a later date . 7 . After the 8 hour sampling , the pH of the suspension was measured by glass electrode and pH meter . SECTION I I . AN ANALYSIS OF SPATIAL VARIABILITY IN NITRIFIER ACTIVITY CHAPTER 5 VARIABILITY IN NITRIFIER ACTIVITY WITH DEPTH ��D D ISTANCE A . DEPTH DEPENDENT VARIABILITY 7 8 It i s generally accepted that microbial activity i s higher in the upper relative to the lower layers of soil profi les ( e . g . Speir et al . , 1 984 , Higashida & Takao , 1 98 5 ) . This is due to the f act that organic matter ( i . e . s ubs tra te ) enters the soi l system at or near the surface , and consequently occurs predominantly in the surface layers , and the oxygen needed by aerobic micro-organisms decreases in availabil i ty with depth ( Khyder & Cho , 1 983 , Colbourn et al . , 1 98 4 ) . When N is being mineralized at a rate in excess of that of No3 - loss by leaching , one might expect the distribution of N03 - down a prof ile to fol low that of NH4 + which , becaus e i t i s read i l y adsorbed by s o i l col loids , might i n turn be expected to fol low the distribution of organic-N substrate . Cameron et al . ( 1 97 8 , 1 9 79 ) found that both No3- and NH4+ tended to decrease with depth in a clay loam in the range 0-60 cm , whi lst Young and Aldag ( 1 9 82 ) noted that only a very small proportion of the total soi l N occurred as readily available mineral N . Stevenson ( 1 982a ) s tated that over 9 0 % of the N in the surface layer of most soils was organically combined , much of i t as amino acid-N or amino sugar-N ( Khan & Sowden , 1 97 1 , Stevenson , 1 982b ) , al though the amount of amino-N as a proportion of the total N tends to decrease with depth due to greater humif ication . For the purposes of the prel iminary experimental work outlined in Chapter 4 , soi l samples were taken from the 3-9 cm depth range . As explained previously , the top 3 cm were avoided to minimise any inhibitory effects that grass roots may have on the rate of nitrification ( Molina & Rovira , 1 9 64 , Neal , 1 969 , Moore & Waid , 1 97 1 ) . However , the assumption that this would be the most suitable depth range of sampling for an invest igat ion of spatial variability in nitri f ier activity in the Tokomaru s i l t loam 79 may not have been a good one becaus e ( a ) grass roots may not a f fect nitr i f ication in this soi l ; ( b ) nitrification may occur at higher ( or lower ) rates in the 0 - 3 cm range regardless of the effect of grass roots ; and ( c ) the degree of any spatial dependence in �itrif ier activity may not be the same at a l l depths . As a consequence , a f ield-scale estimate of the mean so i l n i trate concent rat ion , made for use as an input parameter to a nitrate leaching model may not be a bes t es tima te due to depth dependence . Thus , i t was considered important to investigate the vertica l distribution of nitrif ier activity and i ts associated parameters in addition to any spatial analysis . i . Methods and Materials Soil sampling Ten sampl ing sites were selected in f ield No . 6 us ing random numbers to generate the coordinates o f each s i te . Fol lowing adjustment ( where necessary ) of the posit ion of some of the si tes to ensure a minimum site separation of 3 m , the soi l at each site was sampled to a depth of 24 cm in 3 cm layers using the corer described in Chapter 4 . Two immediately adjacent cores were taken at each site . The soi l from the two cores was sieved and bulked on a depth basis to give 80 samples representing the 1 0 sites at 8 depths . The samples were stored as described previously prior to analysis for SNA and exchangeable-NH4 . The leachate from the SNA pre­ leaching was retained for analysis of N03-N to give the quantity of N03 -N present initially per g soil . A further f ive sampl ing s i tes were randomly sampled in the manner described above ( only one core per site ) and the soil sieved and stored as before . These samples were analysed for their total nitrogen , carbon and phosphorus contents following the methods described below . S i x addit ional sites were randomly selected for the measurement of soi l bul k dens i t y . ( A m in i mum of f our samples a re requi red f or thi s measur emen t ( D . R . S co t ter , Dept . S o i l Science , Massey University - personal communication ) , but six si tes gave an easily manageable number 8 0 and a l lowed for more precise estimation o f mean values - see Chapter 3 . ) . At each of these sites , cores measuring 5 cm deep with a diameter of 4 . 8 cm ( i . e . 90 . 4 8 cm 3 soi l ) were taken to a depth of 25 cm , oven-dried overnight at 1 05 oc , and the bulk density calculated on a g dry soi l cm- 3 basis . Sampling was carried out during mid-May ; there was a period of 9 days between the sampling for SNA measurements and that for ana lysis of C , N and P . I t was assumed that any change in the latter soil properties between the two sampling dates was insignif icant . Analysi s SNA , exchangeable-NH4 and N03 -N were analysed by the methods described in Chapter 4 . The ana lysis of total nitrogen and phosphorus ( 4 replicates per sample ) was carried out by Kj eldahl digestion fol lowing the method o f Bolan & Hedley ( 1 987 ) . 1 g f inely- ground air-dry soil was placed in a pyrex tube and 4 cm3 digest acid ( 25 0 g K2S04 and 2 . 5 g Se powder dissolved in 2 . 5 dm3 cone . H2S04 ) were added , and the tube heated at 350 oc for 4 hours . After cooling , the contents of each tube were di luted to 5 0 cm3 wi th deionised water , thoroughly mixed in a vortex shaker , and the solutions s imu ltaneous ly analysed by autoanalyser for the ir N and P contents , following the method of Twine and Will iams ( 1 9 7 1 ) . The carbon content of each sample was analysed by dry combustion in a stream of 02 using a Leco furnace fol lowing the method of Bol an and Hedley ( 1 987 ) ; a copper oxide catalyst was used to promote the conversion of CO to C02 , and an Mn02 trap used to remove any halogens present . The amount of carbon in the soi l was calculated as the mass of C02 produced ( mg ) x 0 . 2729 ( the proportion by mass of C in C02 ) per mg air-dry soil . 8 1 i i . Results Figure 5 . 1 a shows the bulk density at each s i te plotted as a function of the depth at the centre of each core ( the centre of the 0 -5 cm depth core for example , was taken to be at 2 . 5 cm depth ) . Since the dimensions of the bulk density corer were dif ferent from those of the corer used for all other soil sampling , it was necessary to f ind an expression relating bulk density to soil depth so that the bulk density at the depth at which the other samples were taken could be interpolated . The data followed a curvil inear trend with depth and were best f itted using least squares optimization by the equation ( R2 = 0 . 88 ; p < 0 . 1 % ) : 0 . 9 3 6 1 1 + 0 . 0 355D - 0 . 0 0 0 64D2 ( 5 . 1 ) where p b is the bulk density and D denotes depth ( cm ) . It should be noted that this equation is valid only over the depth range 0- 2 5 cm . The soil moisture content at the time of sampl ing for SNA analysis was calculated from the moisture content of the sieved soil us ing equation ( 4 . 5 ) , and these gravimetric data were converted to volumetric moisture contents , S v , using values of p b calculated from equation ( 5 . 1 ) . The data at each depth were assumed to be normally distributed ( see section B , and also Chapter 6 ) and the mean values are plotted against depth in Figure 5 . 1 b . The spatial analysis ( see section B and also Chapter 6 ) indicated that SNA , in it ial N03- and Ex- NH4- conformed to log -norma 1 distributions . White e t a l . ( 1 9 87 ) suggested that for lognormally distributed data , when the variance of the natural logarithms of the property values i s less than 0 . 5 , and the number of samples is large , I< the best estimate , � ' of the mean , � ' of the population from which the sample is drawn is given by : " � = x .. exp (� + l) / 2 ) ( 5 . 2 ) a. Bulk density b. Moisture content g cm-3 cm3 cm-3 0 .9 1 1 . 1 1 .2 1 .3 1 .4 1 .5 1 .6 0 .2 0 .3 0 .4 0 .5 0 .6 0 0 3 5 6 1 0 9 Depth 1 2 (cm) 1 5 1 5 1 8 20 2 1 25 24 Figure 5 . 1 Change in ( a ) bulk density and ( b ) mean volumetric moisture content with depth in the Tokomaru silt loam sampled in mid May 8 3 where Xa i s the estimate of the mean value o f the sampled property , and � and V are the arithmetic mean and variance of the natural logarithms of the property . Both SNA and soil No 3 - f u l f i l l e d the criteria for use of equation ( 5 . 2 ) with respect to V and despite there being only 1 0 samples for each depth , this equation was used to est imate the mean values . In contrast , the va lue of l) in the cas e of Ex-NH4 ... exceeded 0 . 5 at all depths and so the mean value was estimated using Sichel ' s est imator , XQ ( Sichel , 1 9 5 2 ) where : x.. = { exp ( � ) } { 1 + l) + ( n - 1 ) V 2 2 2 22 ! ( n + 1 ) + ( n 1 ) 2 V 3 233 ! ( n + 1 ) ( n + 3 ) + . • • } ( 5 . 3 ) The data f or C , N , P and C / N were as sumed t o con form t o normal distributions as did the incubation pH ( see section B and also Chapter 6 ) . The distributions of all the measured properties and their standard error a t each depth are shown in F igure 5 . 2a -h . In the case of the normally distributed properties , the standard error , S . E , was calculated using the equation ( Clarke , 1 98 0 ) : ( 5 . 4 ) where S2 i s the sample variance , and n is the number of samples ; the vari ance of the s ampl e mean i s g iven by s2 /n . Whi te e t al . ( 1 9 87 ) presented equations for the variance of both Xa and XQ and used these to infer the rel iability of Xs relative to Xa . However , it i s suggested that for the purpose of est imating standard errors of lognormally distributed properties , dist inguishing between the variance of Xe and Xs may not be necessary . S ichel ( 1 95 2 ) stated that the variance of the estimate of the arithmetic mean Xa , denoted here by Var ( X ) , could be calculated using the equation : Var ( X ) = { exp ( 2� + V ) } { [ exp ( V ) ] [ 1 - 2V ] - < n.- , > / 2 - [ 1 - V ] - < r> - ' > } ( 5 . 5 ) n n n n a. SNA b. In itial N03 0 3 6 9 Depth 1 2 {cm) 1 5 1 8 2 1 24 0 Figure· 5 . 2 pmol N g-1 h-1 pmol N g-1 0.01 0.02 0.03 0.04 0 0.2 0 .4 0.6 0 � 3 / I / _.. 6 oil ... 6 I ... ... J. qY ..... 9 ..... I ..... ..... @ ..... ..... 1 2 ..... ..... ..... $ .,... I I 1 5 I 9 rf / 1 8 \ / � � \ 21 I � L-..j..... 24 Depth profi les of ( a ) SNA , ( b ) N03 - , ( c ) Ex-NH4 + , ( d ) incubation pH , ( e ) total carbon , ( f ) total nitrogen , ( g ) C /N ratio , ( h ) total phosphorus and ( i ) % mineral N i n the Tokomaru s i lt loam sampled in mid May c. Ex-NH4 ------ --- -- -- pmol N g-1 0 3 6 9 Depth 1 2 (cm) 1 5 1 8 2 1 24 0 0 .5 / ' ' ' / / ....... ....... ..... ..... Figure 5 . 2 ( Contd ) 1 1 .5 d. Incubat ion pH pH 5 5.2 5.4 5.6 0 -""" o @ 0 0 0 Soil T 5 .75 6 .25 6.75 7.25 pH 0.04 * 0.035 * * * * * 0.025 0.02 0.01 5 0.01 Soil TL * 0.005 5 5 .5 . 6 6 . 5 7 pH f. 12-04-87 0 .07 0 .06 0.05 0 SNA 0.04 (umol N g-1 h-1 ) 0.03 0 0.02 0 0.01 0 4.25 4.75 5 .25 Figure 7 . 3 ( Contd ) � 0 0 0 0 § 9Jo cP O c}) 0 0 0 Soil T 5.75 6.25 6.75 7.25 pH 0 . 1 6 * * * 0. 1 4 * * 0 . 1 2 * 0 . 1 * 0 .08 * 0.06 * * 0.04 * 0.02 Soi l TL 0 5 5 .5 6 6 .5 7 7 .5 pH a. 1 9-05-87 0.05 0 0 .08 0 .07 0 0.04 0 0 0.06 0 0 0.05 0.03 SNA (j.Jmol N 0 0.04 g-1 h-1 ) 0.02 0 0 0.03 0 0 .02 0.01 0.01 Soii TX 0 0 4.5 5 5.5 6 6 .5 7 7.5 5 pH Figure 7 . 4 pH optima curves for nitrifier activity in soils TX and TLX * * * * * * Soi l TLX 6 7 8 pH b. 20-07-87 Figure 7 . 4 ( Contd ) c. 1 4-09-87 0.06 0.05 0.04 SNA (J.Jmol N 0.03 g-1 h-1 ) 0.02 0.01 0 4 5 Figure 7 . 4 ( Contd ) 0 0 6 7 pH 0.05 * * 0.04 * 0.03 0.02 SoiJ TX Soi l TLX 0.01 8 4 5 6 7 8 pH d. 27-1 1 -87 0.025 0 .02 0 .01 5 SNA (J,Jmol N g-1 h-1 ) 0.01 0.005 0 4 .5 5 Figure 7 . 4 ( Contd ) 0 8 - - -.... . -er ... - - 0P---+---�--�--�---P--�---+--�--�--��--�--P 0 30 60 90 1 20 1 50 1 80 21 0 240 270 300 330 360 Number of days after June 1, 1 986 -w- pH B pH opt -w- SNA pH B SNA opt Figure 7 . 5 Seasonal variation in pH , pHopt • SNApH • SNAopt • soil moisture content and soil temperature D. :SOl i I L 7 6.5 t::--=-�-=-=-=---..a.._- _ e- _ - - a .... .... pH .... 6 - -o .... 5.5 ...._--t�--+--+--....... -...,__.,.._��----�---+---+---+---+- 0 .6 8g 0 .4 �---�---.Q. (gg-1 ) 0 .2 OP---������--�---+---+---+---+---+---+---+ 20 Temp 1 5 (OC) 1 0 p-----� 5�--������--�---+---+---+---+---+---+---+ 0 . 1 2 30 60 90 1 20 1 50 1 80 2 1 0 240 270 300 330 360 Figure 7 . 5 ( Contd ) Number of days after June 1 , 1 986 -*- pH B pH opt -*- SNA pH -8 SNA opt 7 6.5 pH 6 5.5 c. Soi l TX r.:..__ • - -o- - - 0 -o- .. - - \7 ... ... - ' e- -- - .... ... ... .... ::::£)... ,... h 5 P---+---+---+---+---+---*---*-__ *-__ *-__ *---�--� 0.6 Sg 0.4 (gg-1 ) o.2 o�-----Ao-- ---� ���--.....0 - 8 O P---+---+---+---+---+---*-__ *-__ *-__ *-__ *---�--� 20 Temp 1 5 (OC) 1 0 5 P---+---+---+---*---*-__ *-__ *---�--�--�--�---P 0.04 SNA 0.03 �0��� 0 .02 g-1 h-1 ) 0.01 - - - 0�--�--�--�--��������--�--�--�---+ 0 30 Figure 7 . 5 ( C ontd ) 60 90 1 20 1 50 1 80 2 1 0 240 270 300 330 360 Number of days after May 1 , 1 987 ...._ pH B pH opt ...._ SNA pH B SNA opt 6.6 6.4 pH 6.2 6 d . Soi l TL X - - -G- -.. � ..() -------=-=-:"""=�� - - - e - - -.. -.. � -- e � 5.8�--�--�--�--������--�--�--�---+---+ 0 .6 eg 0.4 e­ 0 (gg-1 ) o.2 OP---P---�--P---�--������--�--�--�---P 20 Temp 1 5 (OC) 1 0 5 �--�--�--�--�--������--�--�---*---+- 0.06 SNA (pmol 0.04 N03-N o.o2 g-1 h-1 ) OP---P---�--P---P---�--��������--�---P 0 30 Figure 7 . 5 ( Contd ) 60 90 1 20 1 50 1 80 2 1 0 240 270 300 330 360 Number of days after May 1 , 1 987 -*- pH B pH opt -*- SNA pH B SNA opt 1 6 6 approximately constant over the year , averaging 6 . 2 1 ± 0 . 09 and 6 . 45 ± 0 . 0 5 respectively . Thus , the addition of 2 5 0 0 kg CaC03 ha- 1 on 1 4 April , 1 98 7 had the ef feet of rai sing the mean soi l pH and optimum pH for nitrification in the unlimed soil by 0 . 3 3 and 0 . 29 pH units respectively , but had a smaller ef fect on the mean soil pH and pHopt in the soil limed 5 years previously in which the values of soil pH and pHopt rose by 0 . 0 4 and 0 . 1 4 pH units respectively . This seems to suggest that although the nitrifiers in soil T had adjusted to low pH , addition of l ime to raise the so i l pH s t i l l l i f ts the value of pHopt . There was no obvious correlation between pHopt and either 89 or soil temperature at 3 0 cm depth ( Figure 7 . 5c , d ) . Seasonal variation in nitrif ier activity The value of SNA ( IJ mol N g- 1 soil h- 1 ) at pHopt f or each sampling occasion ( SNAopt ) could be calculated from the equations given in Table 7 . 1 . These values , plotted in Figure 7 . 5a-d, show more obvious seasonal variation than the pHopt values . Also plotted are the values of SNA at the soi l pH for each sampl ing time ( SNApH ) , whi ch show very s imi lar seasonal trends to the SNAopt values ( Figure 7 . Sa-d ) . They demonstrate that the SNA value at the soil pH was generally close to the estimated SNA value at the optimum pH for n i tr i f i er act iv i t y , the greatest divergence being for soil T between 28 November, 1 98 6 , and 1 2 Apr i l , 1 987 . Indeed , the plot o f SNAopt against SNApH for all soil treatments at a l l s ampl ing t imes showed a near 1 : 1 relationship , except for the aberrant observation on 1 2 April , 1 987 ( Figure 7 . 6 ) . The f itted l ine had the equation ( R2 = 0 . 9 1 , p< 0 . 1 % ) : SNAopt = 0 . 005 + 0 . 9 75SNApH ( 7 . 1 ) The small positive intercept ( 0 . 0 05 IJ mol N g- 1 h- 1 ) indicates that at low SNA values , that i s , generally at low soil pH , SNApH was s l ightly less than SNAopt 1 but at high SNA values , that is, higher soil pH , SNApH and SNAopt were approximately equal . Table 7 . 1 Summary of SNA results for soils T , TL , TX , and TLX Soi l Date of Sampling T T T T T T TL TL TL TL TL TL TX TX TX TX TX TX TLX TLX TLX TLX TLX TLX 0 3 - 0 6-86 2 5 - 08-86 0 4 - 1 0 -86 2 8- 1 1 -8 6 0 2 - 0 2 -87 1 2 - 0 4 -87 Mean S . E . 0 3 - 0 6-86 2 5- 0 8 -8 6 0 4 - 1 0 -8 6 2 8- 1 1 -86 0 2 -0 2-87 1 2 - 0 4-87 Mean S . E . 1 9 -0 5 -87 2 0 - 0 7 -87 1 4 - 09 - 87 2 7 - 1 1 -8 7 0 3 -0 2 -88 3 0 - 0 3 - 88 Mean S . E . 1 9 - 0 5 -87 2 0 - 0 7 -87 1 4 - 0 9 -8 7 2 7 - 1 1 -87 0 3 - 0 2 - 88 3 0 - 0 3 -88 Mean S . E . Soil pHopt pH 5 . 3 0 5 . 1 0 5 . 30 5 . 0 5 5 . 2 5 5 . 1 0 5 . 1 8 0 . 0 5 6 . 50 6 . 3 0 6 . 0 0 6 . 0 0 6 . 0 0 6 . 0 5 6 . 1 4 0 . 09 5 . 5 0 5 . 50 5 . 38 5 . 80 5 . 50 5 . 4 0 5 . 5 1 0 . 0 6 6 . 4 0 6 . 50 6 . 0 0 6 . 20 6 . 0 0 6 . 0 0 6 . 1 8 0 . 09 5 . 7 3 5 . 7 6 5 . 9 1 6 . 1 7 n . d 5 . 9 7 5 . 92 0 . 08 6 . 2 5 6 . 3 7 6 . 39 6 . 59 5 . 9 1 6 . 3 3 6 . 3 1 0 . 09 6 . 00 6 . 1 9 6 . 32 5 . 9 5 6 . 5 1 6 . 3 0 6 . 2 1 0 . 09 6 . 3 6 6 . 4 7 6 . 4 0 6 . 59 6 . 3 1 6 . 58 6 . 4 5 0 . 05 Equations for SNAopt & SNApH SNA ::; -0 . 5865 1 SNA ::; -0 . 1 5995 SNA ::; -0 . 4 5 47 1 SNA ::; -0 . 3 4973 + 0 . 2 27 63pH - + 0 . 0 6 4 5 5pH - + 0 . 1 7 5 39pH 0 . 0 1 988pH2 0 . 0 0 5 6 0pH2 0 . 0 1 4 82pH2 + 0 . 1 2 7 1 7pH - 0 . 0 1 0 3 0pH2 0 . 86 0 . 62 0 . 80 0 . 7 5 No curve f itted n . d . SNA ::; -0 . 6 3 1 39 + 0 . 2 2 63 1 pH - 0 . 0 1 89 5pH2 0 . 60 SNA ::; -0 . 52 582 + 0 . 1 8 6 1 8pH - 0 . 0 1 489pH2 SNA ::; -0 . 65247 + 0 . 2 2 6 4 6pH - 0 . 0 1 7 78pH2 SNA ::; -0 . 7 4 308 + 0 . 2 5 587pH - 0 . 0200 1 pH2 aSNA ::; -0 . 5 3658 + 0 . 1 8 9 58pH - 0 . 0 1 4 37pH2 SNA = -0 . 5 5348 + 0 . 1 9 72 8pH - 0 . 0 1 6 68pH2 aSNA -2 . 0 7 1 89 + 0 : 688�2pH - 0 . 0 5 4 3 3pH2 SNA -0 . 3 7 253 + 0 . 1 3 4 7 3pH - 0 . 0 1 1 2 2pH2 SNA ::; -0 . 1 9 5 00 + 0 . 0 697 9pH - 0 . 0 0 5 6 4pH2 SNA = -0 . 3 3 405 + 0 . 1 1 8 1 0pH - 0 . 0 0 9 3 5pH2 SNA ::; -0 . 1 5 877 + 0 . 0 5 882pH - 0 . 0 0 4 9 4pH2 bSNA = -0 . 1 5975 + 0 . 0 5668pH - 0 . 0 0 4 3 5pH2 SNA ::; -0 . 2 6 4 52 + 0 . 09482pH - 0 . 0 0 7 52pH2 bSNA ::; - 0 . 3 89 7 1 + 0 . 1 3 60 0pH - 0 . 0 1 0 69pH2 SNA ::; -0 . 28 1 1 6 + 0 . 1 0 2 5 6pH - 0 . 0 0792pH2 SNA = -0 . 3 5 368 + 0 . 1 2 4 7 7pH - 0 . 0 0975pH2 SNA = -0 . 2 3 0 65 + 0 . 0 7 66 2pH - 0 . 0 0 58 1 pH2 SNA ::; -0 . 1 38 1 9 + 0 . 0 4 9 49pH - 0 . 0 0 392pH2 SNA = -0 . 28753 + 0 . 09629pH - 0 . 0 0 7 3 2pH2 0 . 9 3 0 . 7 3 0 . 88 0 . 3 4 0 . 49 0 . 3 3 0 . 39 0 . 60 0 . 86 0 . 5 8 0 . 1 8 0 . 6 3 0 . 1 4 0 . 4 0 0 . 82 0 . 8 6 0 . 39 0 . 4 5 pHopt = pH optimum for nitrif ication found by dif ferentiating the f itted quadratic equations . All equations are s ignif icant at the 0 . 1 % level except as indicated by the superscripts : a significant at the 1 . 0 % level ; b signif icant at the 5 % level . 1 68 Compar i sons of the changes in SNA.,H wi th changes i n 6 "' and soil ' temperature during the winter and summer periods of 1 98 6-8 7 for soi ls T and TL ( Figure 7 . 5a , b ) do not suggest any obvious correlations , except for the inverse relationship between soi l temperature and soil moisture content , which ref lects the rise in the evapotranspiration rate during the warmer summer months . The same inverse relationship between soi l temperature and moisture held for the 1 987-88 period , but there was also a more def inite tendency for SNA.,H to vary directly with 6"' , particularly in soi l TLX ( Figure 7 . 5c , d ) . The cycl ical pattern of soil temperature and mo i s ture change was very s im i lar for the two observation periods , allowing for the fact that one started on 3 June , 1 986 , and the second on 1 9 M a y , 1 9 8 7 . However , t he se cond year ' s re sul t s i nd i ca t e more conclusively than the f irst that soil nitrifier activity may decl ine by as much as 1 0 0 % between mid-winter and mid- summer in l imed Tokomaru soi l . Evaluation of the effect of liming ( current or historic ) on the s oi l nitri fier activity is to some extent confounded by the variable seasonal trend in SNA.,H in soils T and TL during the 1 986-87 period . Nevertheless , i f the SNA.,H values are averaged over time , the results shown in Table 7 . 2 are obtained . As expected for this acid soi l , l iming increased the activity of nitrifying organisms , the ef fect being most marked due to the res idual effect of l ime appl ied in 1 982 which , even after 4 - 5 years , kept the pH of the limed soil at 6 . 1 4 compared to the unlimed soil pH of 5 . 1 8 . In the year fol lowing the application of �dditional l ime in April , 1 987� to b o th un l imed and l imed soi l s , t he pH rose to 5 . 5 1 and 6 . 1 8 respectively , but the dif ferential ef fect on the SNA value was smal ler than in the preceding year . iii . Discussion The f act tha t SNAo.,t and SNA.,H are so s imi lar ( Figure 7 . 6 ) over an approximatel y s even-fold range of va lues ( 0 . 0 1 5 -0 . 1 1 0 IJ mol g- 1 h- 1 ) suggests that the nitrif ier activity in the soi l , irrespective both of variations that are random and unknown and those associated with seasonal 0. 1 2 0 . 1 0.08 SNA opt (JJmOI 0 06 N03-N · · g-1 h-1 ) 0.04 0.02 0 <6 I 0 0 0 (§) 0 0 0 f) 0 �---+----+---�----�--��--� 0 0.02 0.04 0.06 0.08 0. 1 0. 1 2 SNA pH (J.Jmol N03-N g-1 h-1 ) Figure 7 . 6 Relationship between SNAopt and SNApH for soils T , TL , TX ,· and TLX \ \ \ I 1 7 0 variables ( temperature and moisture ) , is near the optimum with respect to pH on each sampling occasion . Given the l im ing history of the four soil s , i t seems that the nitrif ier population i s fairly adaptable t o changes in its environment and that as a result , pHopt for an indigenous population i s never far from the prevailing soil pH . Darrah et al . ( 1 9 86b ) studied a sandy loam soil ( Begbroke series ) with a pH of 6 . 1 and found a value for pHopt of 6 . 67 , whilst the heavy clay soil ( Evesham series ) s tudied by Whi t e e t a l . ( 1 9 8 3 ) which had a s o i l pH o f 7 . 3 , wa s found in a preliminary study ( Bramley, unpublished ) to have a pHopt of 6 . 72 . During the early stages of this work a pH optimum experiment was also done on the Patua soil , a very strongly l eached yel low brown loam ( N . z Soil Bureau , 1 968 ) , sampled from the lower slopes of Mt Egmont . This acid soil ( pH 4 . 9 ) had a pHopt of 5 . 09 . ( This result is discussed further in Chapter 9 . ) These data , together with those for soils T, TL , TX and TLX are shown in Figure 7 . 7 , which ind icat es a curvi linear relationship between pHopt and soil pH for this l imited range of soil types ( R2 = 0 . 9 2 , p < 0 . 1 % ) : pHopt = 6 . 6 1 - 1 . 49 exp { - 2 . 3 3 ( pH - 4 . 90 ) } ( 7 . 2 ) From Figure 7 . 7 and equation ( 7 . 2 ) , i t mi ght be concluded that the h ighes t pH opt imum l i kely to be observed for nitri f i er ac·tivi ty in pasture soils lies at a soil pH of approximately 6 . 6- 6 . 7 , even though popu l a t ions o f indigenous ni tr i f i ers in soils more acid than those included in Figure 7 . 7 appear to adapt , so that the optimum for short­ term measurements of nitrif ication act ivity is not much less than the prevai l ing soil pH . Comparison of SNA values between soils T and TL, and soi l s TX and TLX cannot be regarded as meaningful s ince the two pairs of soils were s tudied in different years , and as Figure 7 . 5 shows , the seasonal trends in SNA dif fered between 1 986-87 and 1 9 87-88 . Sarathchandra et a l . ( 1 9 88 ) reported a s imilar problem when comparing data for a range of microbial properties in a New Zealand Typic Vi trandept between 1 9 8 3 and 1 9 84 . However , the mean SNA values given i n Table 7 . 2 general ly show the benefi cial ef fect of l im ing ( i . e . rais ing the soi l pH ) on nitrif ier activity in this soi l , both when the ef fect of l ime is residual and when .----------- -- 6.8 6.6 6.4 6.2 6 pH opt 5.8 5.6 5.4 5.2 4.5 5 5.5 0 6 Soil pH 6.5 0 7 7.5 Figure 7 . 7 Relationship between pHopt and soil pH f or a range of soils ( see text ) L _ Table 7 . 2 Ef fect of liming on nitri fier activity in the Tokomaru Silt Loam ( 3 -9 cm depth ) under pasture Period 1 986-87 Period 1 987-88 Soi l T no l ime 0 . 04 P " ± 0 . 0 0 8 Soil TL St lime ha_ , 1 982 0 . 069 ± 0 . 0 1 0 Soi l TX no l ime 1 982 2 . 5 t ha_ , 1 987 0 . 0 2 4 ± 0 . 0 0 3 a Calculated from the equations given i n Table 7 . 1 b Mean of 5 ; others means of 6 Soil TLX St l ime ha_ , 1 982 2 . 5t l ime ha_ , 1 987 0 . 0 3 4 ± 0 . 0 0 6 17 3 it i s immediate, al though this result is not necessarily consistently reported in the literature . For example , Pang et a l . ( 1 9 7 5 ) found during a six week incubation that the addition of licie tb a Canadian $Oi l , to raise the pH from 5 . 4 to 6 . 5 , decreased the n itri f ication rate , this decrease being ascribed to the adverse effect of l ime on the nitrif ier population init ially present , and especially on the Ni trosomona s spp . Further , i t was found that the di f ference in nitrifying capacity among the so i l s studied was re lated to the s i ze of the i ni t i al nitrif ier popu l a t i on whose act iv i ty was a f fected by the initial pH prior to incubation . Hoj i to e t al . ( 1 987 ) found that the effects of liming on an orchard grass sward in Japan were to increase microbial numbers and activity in proport ion to the amount of l ime added up to 4 t ha_ , . At h igher rates o f l ime app l ica t ion , microbial activities and numbers decreased . These e f fects were confined to the top 5 cm of the profile since the pH did not change appreciably in the 5 - 1 0 cm layer during the s ix months after lime application . One would therefore presume that the reason why the ef fect of the 1 98 7 l ime application was small was probably that the soil at 3-9 cm was not markedly affected by the lime - certainly the pH data reflect this ( Table 7 . 1 ) . Regression analyses were performed on data for soil pH , SNApH , SNAapt , pHopt , soi l temperature and soil moisture content , but no signif i cant relationships were found cons istent ly for the four soi ls other than between SNAapt and SNApH ( Fi gure 7 . 6 ) , and the unsurprising inverse relationship between soi l temperature and moisture content . Morrill and Dawson ( 1 9 67 ) identi f ied three patterns of nitri f ication , and found that only those factors associated with soil pH were s igni f icantly correlated w i th n i t r i f i cat ion pat tern . Sarathchandra e t a l . ( 1 9 8 4 ) performed incubation experiments on a range of New Zealand soils and s tudied rates and amounts of C02 production , N-mineralized , total N, Org-C, microbial biomass , microbial P, mineral-N flush , SNA, soi l pH , and the activities of phosphatase , arylsulphatase , urease and protease . They did a· principal components analysis on these properties and found that two components explained 7 1 % of the total variance , but neither component was strongly correlated with pH . The work of Steele et a1 . ( 1 980 ) also fai led to show any close relationships between SNA , soil pH , Org-C , total N and C/N ratio . One of the soils studied by Sarathchandra e t a l . ( 1 984 ) was the Tokomaru. s ilt loam under a productive dairy pasture which had a soil pH of 6 . 4 and an SNA value of 0 . 0 5 4 � mol N g- ' . h_ , - values comparable to soil TL . 1 7 4 By combining analysis o f variance and analysis of covariance ( Freund & Minton , 1 979 ) , it was found that for each soil , the SNA and pH data for all s ampl ing dates could be grouped over the year and the relationship between the two variables described by a common quadratic equation, except that the i ntercept parameter C ( i . e . the SNA value at pH 0 ) changed for each sampling date . The fitted equations were as fol lows : Soil T; SNA = C + 0 . 1 7088pH - 0 . 0 1 4 4 3pH2 ( 7 . 3a ) Soil TL; SNA C + 0 . 2 1 99 3pH - 0 . 0 1 7 1 6pH2 ( 7 . 3b ) Soil TX; SNA C + 0 . 08340pH - 0 . 0 0 6 7 1 pH2 ( 7 . 3c ) Soil TLX; SNA = C + 0 . 07934pH - 0 . 00 6 1 4pH2 ( 7 . 3d ) The fact that these common equations could be f itted for each of the four soils leads to the important conclusion that the response of the soil nitrif ier population to pH change was cons tant and did not vary w ith season , though the level of activity was displaced up or down depending on environmental factors . Understanding the role of these is not helped by the results reported here , although despite the lack of signi f icant corre l a t ions , there i s a. suggest ion that moisture content exerts a contro l l ing inf luence on nitri f i er act ivity . Soil drying in summer appeared to depress nitrif ier activity and it is clear from Table 7 . 1 , that those sampling dates for which the quadratics f it least wel l tend to be in the summer , i . e . the controll ing ef fect of pH on nitri f ier activity is being outweighed by some other factor , most probably soil moisture content . However , the work of Kowalenko and Cameron ( 1 9 7 6 ) suggests that the interaction between soil temperature and moisture content makes i t very di f f icult to isolate the effect o f one of these factors alone . They found that due to the importance of the temperature-moisture interaction, the optimum moisture content for the nitrif iers appeared to be dependent on temperature . 1 7 5 Higashida and Takao ( 1 98 5 ) showed that meteorological factors were not the exclusive cause of changes in microbial numbers in the Japanese soil s they s tudied . Generally , microbial numbers fluctuated i n response to the supply of substrates from vegetation . Peaks in bacterial numbers occurred i n May when dead winter grass became available for decolliposi tion and m inerali zation , and in September when roots decayed fol lowing the cutting o f heading t il lers for forage . These resul ts seem to be confirmed by those of Steele et al . ( 1 980 ) who studied a range of New Zealand soi l s under pasture and suggested that the increased rate of ammonium oxidation in a perfusion experiment , as compared with rates measured by a field technique , was due to the correction of a nitr i f ication-limiting factor in the perfusion system . They suggested that the most probable l imit ing f actor was substra te . Robin son ( 1 9 6 3 ) noted that minera l-N tended to remain at low levels under grass and although this might be explained by the qui ck removal of m ineral -N by grass roots , the low nitrif ication rates m easured were not s o eas i ly understood . A series of per fusion experiments w i th Crai gi eburn soi 1 , a Yel low Brown Earth , which was amended in the laboratory and f ield with lime , urea and "enriched" garden soi l , i ndicated that lack of substrate was responsible f or the small nitrif ier population , and therefore the low nitrif ication rate . Robinson ( 1 9 63 ) commented further that since grass roots remove mineral-N from soi l very rapidly , the nitrif iers have to compete for an already low supply of NH4-N . Total soil N for soil T between 3 - 9 cm depth was 2 . 60 mg N g- 1 or 0 . 26 % N ( Chapter 5 ) with mineral-N amounting to 0 . 5 1 � g N g- 1 , or only 0 . 02 % of the total N . This compares , for example , with 0 . 9 4 % total N for the Evesham soil studied by Macduff and White ( 1 9 8 5 ) , so that the Tokomaru soil must be considered to be low in soil N, both organic and inorganic, i . e . low in substrate available for nitrif iers . Soil T had SNA,.,H va lues which r anged from 0 . 0 2 4 - 0 . 0 6 1 � mol N03 -N g- 1 h- 1 • In contras t , the Evesham soi l , sampled from under pasture on the Oxford University farm in England in January , had a soil pH of 7 . 30 and a SNA,.,H value o f 0 . 3 1 3 � mol N03 -N g- 1 h- 1 ( Bramley , unpubl ished ) : Thus , even ignoring seasonal ef fects , the Evesham soi l had a nitrif ication rate at least f ive times , and up to 1 3 times , that measured for the Tokomaru s i l t loam b y an identical technique . 1 7 6 iv . Conclusions Overal l it is clear that whilst the factors governing levels of mineral-N and nitrif ier act ivi ty in so ils are broadl y the same throughout the world, the degree to which one factor is important in relation to others can vary markedly from one soil to another . This , together with the fact that the ni t r i f i er ' popul at ion i s dynamic , l eads to the somewhat depressing conclusion that for progress to be made i n modelling various aspects of the soil nitrogen cycle , and more importantly , in order to test any models that are produced , specific information on the nitrifying characterist ics o f the part icular so ils being mode lled is needed . By implication it seems likely that it may not be possible to produce a def initive model which works for all soil types , a view supported by the results of the analysis of spatial variability { Chapter 5 & 6 ) , and a lso by many of the resu l t s reported in the 1 i t era ture { Chapter 2) of experiments whi ch examined the f actors known to control n itrif ication rates . 1 7 7 CHAPTER 8 A FURTHER INVESTIGATION OF THE EFFECT OF MOISTURE ON NITRIFIER ACTIVITY The results reported in the previ ous chapter confirmed the sugges tion from the l·i terature ( Chapter 2 ) that soil ni trif ier activity is largely dependent on the soil pH . However , the spat ial ana lys i s ( Chapter 6 ) indicated that SNA and pH were not , in fact , strongly correlated over space . This , together with the seasonality of SNA values ( Chapter 7 ) , suggested that variability in some other factor , ··most l ikely the soil moi sture status , inf luenced the variability in nitrif ier activity in the Tokomaru s i l t loam . The result s of the inve stigat ion of s pat i a l vari abi l ity in nitrif ier activity ( Chapter 6 ) suggested that there was l ittle to be gained from crossvariogram analysi s of SNA and 89 in view of the marked difference between their variogram models , and the strongly anisotropic nature of 89 variability in field No . 6 . It was therefore concluded that there was unlikely to be a · str6ng cioirelation over space between SNA and 8 9 • However, as indicated in Chapter 6 , the value of thes e various vari ogram models may be open to question , and it was therefore considered worthwhile to conduct an experiment to further investigate the effect of soil moisture on nitrif ier activity . I t was suggested in Chapter 2 that microbial populations in dif ferent soi l s are adapted to their specific environments . This appeared to be part icularly so in the case o f soil tempera ture (Mahendrappa et al . , 1 9 6 6 ; Nakos , 1 9 8 4 ) . In Chapter 7 i t was f ound that nitri fiers in dif f erent soils were adapted to the prevail ing pH of the soil which they inhabited ( Figures 7 . 6 and 7 . 7 ) to the extent that they operated at near­ opt imum levels at the amb ient so il pH , providing substrates were in sat isfactory supply . In view of these f indings , it seemed l ikely that the nitri f iers might readi ly adapt to changes in the soi l moi sture status . Mindful that the SNA technique involves wet incubat ion , it was considered that ra ther than i nve s t i gating the moi s ture relations of ni tri f ier activity using a new technique which would account for the poss ibil ity of a f lush of microbial activity upon rewetting dry soi l samples ( Birch , 1 95 8 ; 1 9 60 ) , more use ful inf ormation would be gained if soil samples 1 7 8 could be maintained a t speci f ic moisture . contents . for a considerable period of time before measuring their nitri fier activities by the usual SNA technique . By us ing such an experimental design , it was hoped to give the soi l nitrifiers long enough to come to equi librium with the moisture s tatus of soil samples which had been maintained at constant moisture contents for a considerable period of time . Thus , any differences between the measured nitrif ier act ivities in the various samples should ref lect the true response of the nitri fiers to their moisture environment . The l i terature contains a number o f reports of the ef fect of soil moisture content on nitrif ication and mineralization ( e . g . Kowalenko & Carneron , 1 97 6 ; Higashida & Takao, 1 9 85 ) but as has been pointed out by numerous authors ( e . g . Miller & Johnson , 1 96 4 ; Dubey , 1 9 68 ; Sabey , 1 9 69 ) , the usefulness of these to other workers i s l irni ted in the absence of accompanying information on the moisture characteristics of the speci f ic soils being studied . For this reason , in addition to the main experiment , the soil moisture characteristic curve for the Tokomaru silt loam , as sampled from f ield No . 6 , . was also determined . i . Methods and Materials Soil sampling On 6 July , 1 98 8 , a bulk soil sample ( approx . 1 0 kg ) was taken f rom a randomly chosen site in f ield No . 6 in the 3-9 cm depth range . The soil was sieved and stored as described in Chapter 4 . Analyses Soi l m o i s t ure charac teri s t i c . Approx imatel y 30 g sieved soi l was loosely packed into a Haynes apparatus and the head of water adjusted to 7 0 ern ( equivalent to - 0 . 07 bars or -7 kPa ) . Af ter equil ibration for 2 4 hours , duplicate samples ( approx . 5 g ) were taken , and their moisture contents were determined on a gravimetric basis by oven drying overnight at 1 0 5 oc . Further samples ( approx . 1 0 g each ) were placed on pressure 1 7 9 plates ( 3 replicates each ) . The pressure was adjusted to 1 , 2 , 5 , and 1 5 bars . The apparatus was left to equil ibrate for 2 days in the case of the 1 bar plate , for 4 days in the case of the 2 and 5 bar plates , and for a week i n the case of the 1 5 bar plate . Following equil ibration the gravimetric moisture content of each sample was determined as before . Moi s t ure rel a t i ons of n i tri fi ers . Immed i a tely f o l l ow ing f ield sampling, the nitri fier activity of a subsample ( St ) was determined by SNA measurement ( 1 0 repl icates ) fol lowing the s tandard method ( Chapter 4 ) . Two subsamples ( approx . 2 0 0 g ) were placed in plastic j ars . The soil in one of these was saturated ( Sat ) , and the soil in the other left in the f ield-moist state ( F ) . The mass of each j ar plus soil was recorded , and they were then tightly sealed . The lid of each j ar was pierced with two pin holes to allow for gaseous exchange , and both j ars were stored as deta i l ed below . The remainder of the bulk s ample was air-dried in a drying room kept at a cons tant temperature ( 20 °C ) . At intervals of approximately twelve hours , the drying process was stopped by placing the soil in a sealed plastic bag at 3 oc , and the mois ture content of a subsample was determined . When drying had taken place long enough to obta in moisture contents of approximately 0 . 3 0 , 0 . 2 1 , 0 . 1 9 , 0 . 1 7 and 0 . 1 6 g g_ , ( treatments 6 , 5 , 4 , 3 and 2 respectively ) , . a 2QO g sample of soil at each o f these moisture contents was placed in j ars as described . The remainder of the sample was al lowed to become completely air-dry , its moi sture content was determined and a further 2 0 0 g subsample placed i n a sealed j ar . Thus , there were eight j ars containing soi l s amples with moi sture contents ranging from saturation to air-dryness . The j ars were stored next to the outside wall of an unheated and little­ used laboratory for 1 2 4 days , which was assumed to be suf ficient time for the nitrif iers to adj ust to the moisture status of the various soil s a mp les . I t was further a s sumed that the temperature o f s torage approximated the f ield a ir temperature , and that there was no s ignif icant d i f f erence in the s o i l temperature between t reatments . At weekly i nterva l s the j ars were weigheq , and when necessary , the mass was adj usted to its original level by adding water drop-wise from a pipette . I t was never necessary to add more than 0 . 08 g water to any j ar . 1 8 0 A t the end o f the 1 2 4 day storage period , the moisture content of each sample was determined . Four subsamples ( approx . 6 g each ) were removed f rom each j ar , and were extracted w i th 2 M KCl f or ana l ys i s o f exchangeable ammonium ( Chapter 4 ) . The N03 -N concentration of each extract was also determined . The remainder of each · sample was leached overnight with 1 dm3 0 . 0 0 5 M KCl and fol lowing removal of the excess mois ture by suct ion f i l trat ion for 90 minutes , SNA measurements ( 1 5 replicates per sample ) were made in the usual way ( Chapter 4 ) . ii . Results Soil moisture characteristic Figure 8 . 1 shows the plot of moisture content vs . suction ( bars ) . The data were f i tted by least squares optimization with a power function of the form ( Figure 8 . 1 ; R2 = 0 . 99 ) : ( 8 . 1 ) where r is the suction ( bars ) and eq is the gravimetric moisture content as before . Moisture relations of nitrifiers In view of the good f i t of equation ( 8 . . 1 ) over the range .of values measured , it was used to calculate the moisture tension of the soil in the eight j ars on the basis of their gravimetric moisture content at the end of the 1 2 4 day period . However , because of the exponential nature of the curve , it did�could not give a good estimate of the moisture tension in the air-dry sample ( 8q = 0 . 03 5 g g- 1 ) , predicting a value of the order of -80 , 0 0 0 bars ! An estimate was made of the relative humidity of the drying room ( D . R . Scat ter , Dept . Soil Science , Massey University - personal communication ) , and the moisture tension of the air-dry sample was calculated using the equation : Moisture content (gg-1 ) 0 . 1 5 or---�----�==:c==�----��� 0.2 0.25 0 .3 0 .35 0 .4 0 .45 -2 -4 -6 Suction _8 (Bars) - 1 0 - 1 2 - 1 4 - 1 6 Figure 8 . 1 Moisture characteristic curve f or the Tokomaru s il t loa� ( s ieved < 2 !T':r: ) r = {p RT} { ln ( e/eo ) } M 1 8 2 ( 8 . 2 ) where p is the density of water ( 1 00 0 kg m- 3 ) , R i s the gas constant ( 8 . 3 1 J K_ , mol- 1 ) , T is the temperature ( Kelvin ) , M is the molar mass of water ( 0 . 0 1 8 kg mol- 1 ) , e/eo is the relative humidity and r i s the water potential ( Pascals ; 1 bar = 1 0 5 Pa ) . Using this equation , the moisture tension of the air-dry sample was estimated as _ approximately -980 bars . It was assumed that the moisture content of the air-dry sample did not change in the t ime between the completion of air-drying fol lowing f ield sampling and the end of the 1 2 4 day storage period ( there was no change in the mass of the j ar + soil over the 1 2 4 days ) . The values of the moisture tension ( bars ) for each of the eight samples were converted to units o f pF , where F denotes the free energy of the soi l water in cm ( 1 bar = 1 02 0 cm ) and pF = log , 0F . Va lues of pF for the dif ferent soi l moisture treatments are shown in Figure 8 . 2a together with the values for SNA , No3 - , Ex-NH4+ ' 89 and incubation pH ( Figure 8 . 2b- f ) . In view of the di f ference in incubation pH between the saturated sample and the others , the SNA values of all treatments were adjusted to account for the e f f ec t o f pH change us ing the c oncep t o f the re lat ive nitri fication rate RNR ( Darrah e t al . , 1 98 6b ) . A mean value for C ( the SNA value at pH 0 ) was calculated from the values obtained by f i tting equation ( 7 . 3a ) to the six pH experiments done on soil T ( Chapter 7 ) . RNR values were then determined by dividing the values of SNApH calculated over a range of pH using equation ( 7 . 3a ) , by the value of SNA at pH 5 . 92 , the pH optimum for soi l T , calculated from the same equation , that i s : RNRpH = SNApH SNAopt and thus : -0 . 4 5826 + 0 . 1 7088pH - 0 . 0 1 4 4 3pH2 0 . 0 47 63 RNRpH = -9 . 62 1 2 5 + 3 . 58765pH - 0 . 3029 6pH2 ( 8 . 3 ) ( 8 . 4 ) Measured SNA values were then divided by the value of RNR appropriate to the measured incubation pH . The mean values are shown in Figure 8 . 2b . a. Moisture stress 6 5 4 pF 3 2 1 0 St Figure 8 . 2 Air dry 2 3 4 Treatment 5 6 F Sat Levels of ( a ) soil moisture stress , ( b ) nitrif ier activity , ( c ) No3 - , ( d ) Ex-NH4 + , ( e ) gravimetric moisture content and ( f ) incubation pH in the eight samples stored f or 124 days b. SNA (pH corrected data) 0 .03 0 .025 0.02 SNA (}Jmol N 0 .01 5 g-1 h-1 ) 0 .01 0 .005 0 St Figure 8 . 2 ( Contd ) Air dry 2 -;.'\ 3 4 5 6 F Sat Treatment N03 (J,Jmol N g-1 ) 2 1 .6 1 .2 0 .8 OA : a c. N03 n.d St Figure 8 . 2 ( Contd ) Air dry tr 2 3 4 5 6 F Sat Treatment d. Exchangeable ammonium 1 .4 1 . 2 1 NH4 O.B (pmol N g-1 ) 0 .6 0 .4 0 .2 St Figure 8 . 2 ( C ontd ) Air dry 2 3 4 5 6 F Sat Treatment e. Moisture content 0 .7 0 .6 0 .5 0 .4 eg (gg-1 ) 0.3 0 .2 0 . 1 0 St Figure 8 . 2 ( Contd ) Air dry 2 3 4 5 6 F Sat Treatment :c a. c: 0 :;::: m .0 ::J 0 c: - :c a. -c: Q) E -m Q) ... ..... '0 .j.J c 0 u N . CO Q) � ;j trl ..... � 1 8 9 At the end of the 1 2 4 day period i t was observed that a l l the samples except the saturated one had maintained the appearance of sieved soi l . The saturated sample in contrast , had lost all structure and had become a pungent black mud . Ni tra te had accumul ated in all but the saturated sample . Since there could have been no leaching under the conditions of the experiment , the absence of No3 - in the saturated sample was probably due to a very low ni tr i f ier activity and a l s o the occurrence of denitrif ication , ref lecting the anaerobic nature of the sample . Indeed, the sugges tion o f low ni tri f ier ac t iv i ty ( re lat ive to ammonif ier activity ) in the saturated sample is supported by the accumulation of NH4. although this could also be due to low immobilization rates caused by the lack of microbial growth . In view of the lack of true replication of treatments in this experiment , paired s ample t-tests were used to identify any significant dif ferences between the mean values of SNA , No3- and NH4 · for each treatment . Mean values and the levels of s ignif icant di f ferences (p < 0 . 1 , 1 and 5% ) between them are given in Table 8 . 1 . Other than for the d i f ference between the mean SNA values of th� air-dry treatment and treatment 2 , which were signif icantly di f ferent at p < 1 . 0% , mean values of SNA , N0 3 - and Ex-NH4 • i n the air-dry and saturated treatments we re s i gn i f ican t l y d i f ferent ( p < 0 . 1 % ) f rom a l l other treatments in addition to each other . Mean SNA and N03- values were generally signi f icantly dif ferent between treatments , and in the case of No3 - the level of the s ignif icant dif ference was greatest between samples whos e d i f f erence in mo i s ture s tatus was grea test . Ex-NH4 · was not signi ficantly dif ferent ( p < 0 . 1 % ) between any treatments other than the saturated and air-dry samples , and differences amongst treatments tended to be either insignif icant or only significant at the p < S . O% l evel . This observation supports previous f indings ( Chapter 2 ) that the nitri f ication rate tends to fol low the rate of mineralization . If this were not the case , then those treatments which showed higher ( or lower ) SNA values might have been expected to have lower Ex-NH 4 · contents . Table 8. 1 Mean values of SNA ( IJ mol N g- 1 h- 1 ) , initial ·No 3 - and Ex-NH4 · ( IJ mol N g- 1 ) in so i l samples kept for 1 2 4 days at dif ferent moisture tensions and the signi ficance of di fferences between the means Treatment Air-dry 2 3 4 5 6 F Sat Mean SNA 0 . 0 1 2 0 . 0 1 6 0 . 020 0 . 02 4 0 . 02 9 0 . 0 1 9 0 . 020 0 . 0 0 9 Mean N o 3 - 0 . 22 5 1 . 0 5 4 1 . 1 9 0 0 . 935 1 . 4 1 5 1 . 7 1 8 1 . 9 3 6 0 . 0 4 0 Mean NH4 • 0 . 29 0 0 . 087 0 . 084 0 . 085 0 . 068 0 . 1 07 0 . 1 38 1 . 2 1 3 Air b a a a a a a dry a a a a a a a a a a a a a a b a a n . s b a 2 c c c a a a n . s n . s c n . s c a a b n . s n . s a 3 a n . s a a a n . s n . s n . s b a n . s b a a 4 c a a a n . s n . s c a b b a 5 n . s c a b b a n . s a 6 b a n . s a a F a a D i f ferences between the mean values are signi ficant at : a , p < 0 . 1 % b, p < 1 . 0 % c , p < S . O% n . s denotes no signi f icant difference at p< 5 % 1 9 1 The f inding that the SNA value in treatment 5 · ( 8 ..,. · =· 0 . 2 09 g g_ , , pF = 3 . 3 7 ) was s ignif icantly d i f ferent from the SNA va lues of al l other treatments ( except treatment 4 ) at a level of s igni ficance of at least p < 1 . 0 % ( the only treatment for which this was so ) , together with the observation from Figure 8 . 2b that treatment 5 appeared to approximate the optimum moisture conditions f or nitrifier activity , suggested that such an optimum could be identi f ied in the same way that it was possible to identify an optimum pH for nitri f ier activity ( Chapter 7 ) . Values of SNA were therefore plotted against pF for all treatments ( not including the SNA measurements made immediately af ter sampl ing ) , and the data were f itted by least squares optimization with the quadratic equation ( Figure 8 . 3 ; R2 ( adjusted for the degrees of freedom of the regression ) = 0 . 4 3 , p < 0 . 1 % ) : SNA 0 . 0 0 222 + 0 . 0 1 1 9 5pF - 0 . 0 0 1 7 6pF2 ( 8 . 5 ) which predicted maximum nitrif ier activity at pF 3 . 39 ( i . e . at a moisture status approximately equivalent to that of treatment 5 ) . The data for the S t s ample were not inc luded in f i t t ing equation ( 8 . 5 ) because the di f ference between its mean SNA value and that of treatment F suggested that s torage for 1 24 days had af fected the nitrif ier activities of the s tored sample s ; c learly thi s ef fect was not present in soil a ssayed immediately after f ield sampling . In spite of the strong significance ( p < 0 . 1 % ) of equation ( 8 . 5 ) , the wide range of SNA values at each value of pF ( Figure 8 . 3 ) suggested that soi l moisture stress was not as critical in regulating nitrif ier activity as was soi l pH ( Chapter 7 ) . Indeed , the low value of R2 indicated that only 4 3 % of the variance was accounted for in the f itting of equation ( 8 . 5 ) to the experimental data points . Furthermore , as Figure 8 . 3 shows , the nitrif ier act ivity a s predicted by equation ( 8 . 5 ) is expected to be appreciable at moisture levels wel l outside the l imits def ined by the wi lt ing point and field capacity . 0.05 Field 0 Wilt ing 0 .045 capacity 0 point 0 .04 0 0 0.035 0 .03 8 SNA � {J.Jmol N 0.025 g-1 h-1 ) 8 0.02 0 0.01 5 0 .01 0 .005 0 0 0 1 2 3 4 5 pF Figure 8 . 3 pF opti�um curve for nitrifier activity in the Toko�aru s ilt loam 0 6 1 9 3 The possibility of relationships between the various properties measured in this experiment was invest igated by mul tiple regress ion . In some ins tances s trong corre l at i ons were found , bu t the phys ical and biochemical s ignif icance of these appeared to be in doubt regardless of their statistical signif icance , on account of the extreme values measured in the a ir-dry and saturated soil samples . For example , as indicated above , the high NH4 - concentration in the saturated soil sample could have been due to a low rate of immobilization, with the result that net m ine ra l i zat i on appeared to be more e f f ic i ent than i n the other treatments . This effect is wel l known and together with the fact that autotrophic nitri fication ef fectively ceases under anaerobic conditions , forms the basis of a technique for estimating the nitrogen availabi lity in soi l s ( Bremner , 1 9 65 ) , in which the amount of N mineralized under anaerobic conditions is measured . When the extreme values of the air-dry and s aturated samples were omitted from the statistical analysis , no statis ticall y s igni f icant relationship was found between any pair of properties . iii . Discussion The f inding that the soi l sample which had been stored in the f ield-moist state for 1 2 4 days had a signif icantly lower SNA value than the fresh sample ( 0 . 0 1 5 compared to 0 . 0 1 9 � mol N g- 1 h- 1 ) , together with fact that there was more Ex-NH4- in the stored sample, a similar result to that of Ga s s er ( 1 9 6 1 ) , s ugge s t s t h a t t he e f f e c t s o f s to rage may caus e compl ications in the interpretation of the experimental results . This was especi a l ly l ikel y in the case of the dr ier samples . ( Gasser , 1 9 6 1 ; Bart lett and James , 1 98 0 ) . Bar tlett and James ( 1 9 80 ) warned against studying dried s tored soi l noting that the behaviour of a dry soil immediately after adding water is different to that of continuously moist soi l . There is l ittle to suggest that the results reported here show evidence of a microbial explosion , or fl ush ( Birch , 1 9 5 8 ; f960 ) , s ince the air-dry sample had a signi ficantly lower SNA value than all other treatments except treatment 2 , the next driest , and the saturated sample which had a lower nitri f ier activity . One could argue that the SNA value of the a ir -dry sample would have been expec ted to be even lower in 1 9 4 relation to the other samples , especially as Harris ( 1 98 0 ) quoted 4 0 bars ( pF 4 . 6 1 ) as the maximum s tress tolerated by nitrifiers . However , the results of Chapter 7 suggested that the initial nitrifier activity of so i l s ampl ed i n July i s expected to be relat ive l y low ( i . e . the population was small ) , probably due to the cold wet winter conditions . S ince the only factors which were a ltered under the conditions of the experiment were the soi l moisture status , and the soil temperature which changed according to season and was assumed t o be the same in al l treatments , any difference in the nitrif ier activity between treatments was assumed to be due to di f ferences in the soil moisture status . I f the popu l a t i on were i n i t i a l l y smal l , i t s characteri stics might not be expect ed to change very not i ceably , even i f i t were l iving under conditions of near-optimum pF . This would especially be the case i f some other f actor ( e . g . substrate ) was l imiting . It would therefore be of interest to repeat this experiment with soi l sampled at a t ime of high ni tr i f ie r act ivity , such as Apri l , when one presumes that substrate levels are relative l y high and that the population is both large and active , to see whether the di fference between the SNA value of air-dry soi l f o l lowing 1 2 4 days o f storage and that of soil kept at a near opt ima l moisture status was of a s im i lar order of magnitude to that observed here . Bartlett and James ( 1 980 ) commented that air-drying leads to an increase in the amount of water-soluble organic matter that may be extracted upon rewetting . Thus , the amount of substrate available to ammonif iers and ni tr i f iers on remo is tening would be expected to be higher following drying . However , s ince an excess of NH4• substrate is supplied in the SNA technique , it is unl ikely that a f lush of nitri fier activity would be observed under the cond i t ions of the i ncubat ion . Even i f a blank incubation had been carried out , a microbial flush would still probably not have been observed , as the pre-leachy{ing would have removed most of this new substrate , and s ince nitrifiers are slow to regenerate ( Aleem & Alexander , 1 9 60 , Sarathchandra , 1 9 78 ) , SNA values measured over an eight hour incubation such as used here would not be expected to reflect high activity immediat ely a fter rewetting . Thus , the SNA technique can be cons i dered to be ent i re l y s a t i s f ac t ory for use in thi s k i nd o f invest igation . 1 9 5 Mi l ler and Johnson ( 1 9 6 4 ) incubated soi ls a t 3 0 oc for 1 4 days under moisture tensions ranging from zero to air-dryness , and found max imum nitri f ication to occur between 0 . 5 and 0 . 1 5 bars (pF 2 . 7 0 - 2 . 1 8 ) . They also measured nitrif ication at tensions greater than 1 5 bars ( pF 4 . 2 ) but at very low rates , and noted that ammonif ication occurred at higher rates than nitrification at both high and low tensions ; NH4 -N built up in both air-dry and saturated samples . Sabey ( 1 9 69 ) found the optimum moisture tension for nitri fication in a 1 imed loessial s i lt loam to be of the order of 0 . 1 bar (pF 2 . 0 1 ) , whilst Dubey ( 1 968 ) studied two sandy learns and found nitri f ication to be greatest at 2 bars ( pF 3 . 3 1 ) . He also noted marked nitr i fica tion under f looded ( saturated ) conditions . Whilst the saturated sample tested here had the lowest SNA value , it was interesting to note that the anaerobic condi tions did not kill off the nitri f ier populat ion - a pos s ible reflect ion of some heterotrophic nitri f ier activity in this soil ( Focht & Verstraete , 1 9 77 ) . Mahli and McGil l ( 1 98 2 ) found that appreciable nitri fication occurred at the permanent wilting point in a loam , a s ilt loam, and a s i lty clay loam from Alberta , which is in agreement with the results of this experiment ( Figure 8 . 3 )· , and found ni trif ication to be at a max imum at - 3 3 kPa ( pF 3 . 5 3 ) for a l l soils . A summary of published results is given in Table 8 . 2 . The difference between the SNA values reported here and those calculated from the results of Yadvinder-Singh and Beauchamp ( 1 98 8 ) and Mahli and McGill ( 1 982 ) at comparable moisture tensions ( Table 8 . 2 ) i s further ev idence o f the marked d i f ference in ni tr i f i er activities between d i f ferent soils . The range in apparent pF optima , pFopt 1 for nitrifier activities in soi ls of dif fering texture (Table 8 . 2 ) suggests that some fa ctor re lated to the particle s i z e d istribution i s i mportant in controlling the moisture relations of ni trif ier activity . However , the results of Mahli and McGill ( 1 982 ) for sandy loam and s i lty loam soi l s ( pFopt = 3 . 5 3 ) contrast markedly with the result for the sandy loam of Reichmann e t a l . ( 1 9 6 6 ) and the si lty loam studied by Sabey ( 1 9 6 9 ) which i had values o f pFopt of 2 . 3 1 and 2 . 0 1 respect ively . It may wel l be therefore that i t is soi l structure which is o f greatest importance in contro l l ing the . n i tr i f i er response to changes in the soil moisture status . Brandt et al . ( 1 9 64 ) found that rates of NH4 • oxidation were Table 8. 2 Summary of results for the suggested optimum moisture tension for nitrif ication Reference Miller & Johnson ( 1 96 4 ) Reichmann e t a l . ( 1 9 66 ) Dubey ( 1 968 ) sabeY'-< 1 969 l Mahli & M0Gill ( 1 982 ) Yadvinder-Singh & Beauchamp ( 1 988 ) This Chapter ( Equation 8 . 5 ) Soi l texture Silt loam Clay loam Sandy loam Loam Sandy loam Loamy sand Silt loam Sandy loam Loam Si lty clay loam Clay Silt loam Silt loam Suggested Optimum pF range of 2 . 1 8-2 . 7 0 2 . 3 1 3 . 30 2 . 0 1 3 . 5 3 3 . 5 5 3 . 39 Nitri fication rate* ( !J mol N g_ , h_ , ) *Nitri f i cation rates estimated from the resul ts of incubations of "'6 days , b 1 6 hours fol lowing 35 days at 1 5 oc and 08 hours s tandard SNA 1 9 7 direct ly related to the l eve l of oxygen supply as determined by the oxygen d i f fus ion rate , a l t hough he also fot.ind that the · independent ef fects of aggregate size and moisture content on N transformations could not be explained by measured di f ferences in the oxygen dif fusion rate . The sieved soil samples used in this study could not be said to have structure sen su stri cto, al though one might argue that the observed difference between the saturated sample , which resembled a mud , and the soi l in the other treatments which retained its s ieved appearance , was a structura�i f ference . However , s ince all the treatments other than the saturated one maintained their s ieved s tructure, the di f ferences in SNA values between them could not be attributed to structural dif ferences , and accordingl y , the l ow SNA value of the saturated treatment was a t tr ibuted to the anaerobic condit ions caused by the high moisture content rather than any l oss of s tructure . I t would therefore be of interest to repeat this experiment with dispersed or ground soil samples , and see i f any di f ferences between measured nitri f ier activities were s imilar to those observed here . Macduf f and Whi te ( 1 9 8 5 ) concluded that their model for predicting nitri f ication and mineralization rates on the basis of soil temperature and moisture content was biased in terms of the weight attached to soi l moi sture . Kowalenko and Cameron ( 1 976 ) did not account f or the soil moisture characteristics of the soil they studied , but found that it was not pos s ible to di s t ingu i sh between the ef fects of temperature and mois ture on ni trif ication rates on account of t he strong interaction between these properties . In view of these findings , together with the sma l l range in mean SNA values between field capacity and the wilting point ( Figure 8 . 3 ) and the resultant weakly defined pF optimum for soi l T ( Figure 8 . 3 ) , i t appears tha t Macduff and Whi te ( 1 98 5 ) were probably correct in s ta t ing that the form o f the re lationship between soi l moisture and nitrif ication rate ( and by implication , nitrif ier · activty ) i s dependent on the moisture holding characteristics , porosity , organic matter content and pH , and that as a consequence , it varies considerably between soils . 1 9 8 CHAPTER 9 HETEROTRdPHIC NITRIFICATION - EXACTLY WHAT HAS BEEN MEASURED BY THE SNA ? As was stated in Chapter 1 , one of the attractions of the SNA technique to a project such as thi s , was that because SNA values are determined on the basis of the net rate of nitrate production , the poss ibility that the SNA value represents the activity of a range of species , rather than that o f a s ingl e one , c an be i gnored . I n terms o f the dif ficulties of mode l l ing nitrate leaching caused by the spatial variabil i ty of the concentration of soil nitrate , accounting for dif ferences in the activity of di f ferent species of ni trifiers is of l ittle importance , providing that the species present contribute towards the measured net activity in s imilar proportions to one another under the conditions of the SNA as they do in the field . This i s because it is the amount of nitrate which i s present and leachable that is crucial to the nitrate leaching model , and not necessari ly the means by which the nitrate is produced . The biochemistry and microbiology of nitrif ication in the field have not been a ma in concern at any s tage of this proj ect . However , t he broad assumption throughout has been that the activity measured by the SNA has been that of auto trophi c nitrifiers ; heterotroph i c organisms have been ignored . It follows from the preceding discussion that whether or not heterotrophic nitrif ication is signif icant is of l ittle consequence to the spatial variabil ity s tudies ( Chapters 5 and 6 ) providing it i s measured under the condi tions o f the SNA . Nevertheless , i t may have considerable implications in the study of the pH and moisture relations o f nitrif iers because the degree to which s oi l properties af fect the activity of nitr i f iers may not be the same f or he terotrophs as for autotrophs . As a consequence , one mode of n itri f i cation may mask the e f f e c t s o f unf avourab l e c ondi t i on s on t he o t her . F or e xa mp l e , n i t r i f icat ion i n acid forest s o i l s i s predominant l y heterotrophic ( Schimel et al . , 1 98 4 ; Adams , 1 986a ; 1 986b ) . Heterotbophic so i l m i c ro-organi sms such a s A spergi l l us spp . ( Focht & Vers traete , 1 97 7 ) 1 9 9 A r throba c t er spp . and will produce N03 - from organic substrates if the N content of these substrates is suf ficiently high ( Wi ld , 1 988 ) , presumably such that i t is in excess of the amount requi red f or prote in s ynthes i s . In add i t i on , he terotrophic micro­ organisms wi ll produce N02- from NH4+ if the latter is at a high enough concentration and providing that an appropriate carbon source i s a lso present ( Focht & Vers t raet e , 1 9 7 7 , W i l d , 1 9 8 8 ) . Assuming that the concentration of the added ammonium in the SNA is at least as great as i t i s in the f ield, and/or that the N content of the soil organic matter i s high enough , the activity o f heterotrophic nitrif iers ( i f present ) wi l l b e accounted for under the conditions o f the SNA . However , given that the amount of Ex-NH4+ in the Tokomaru silt loam is low ( Chapter 5 ) , it seems unl ikely that the heterotrophic production of N02- from NH4 + wi l l be s ignif icant . Furthermore , if the N content of the soil organic matter i s not high enough for N03 - production , then this form o f heterotrophic nitri f ication will a lso be insignificant in the f ield, and will therefore not be measured in the SNA . I shaque and Cornfield ( 1 9 7 2 ) studied nitrif ication in two " tea" soils in Pakistan with pH values of 4 . 1 and 4 . 2 . They observed an increase in the n i t r i f icat ion rate i n the pH r ange 4 -5 but a decl ine with further increases in soil pH , which they concluded was evidence of either an adaption of the nitrif iers to the acid nature of the soi ls ( c . f . Chapter 7 ) , or alternatively , was evidence of heterotrophic nitrif ier act ivity . Examination of the micro-organi sms present in these soils indica ted a complete absence of autotrophic nitrif iers leading to the conclusion that heterotrophs were entirely respons ible for the measured nitrif ication . The p lots of SNA vs . pH shown in F igures 7 . 3 and 7 . 4 s uggest that s ignif icant nitrif ication occurs in the Tokomaru s i lt loam at pH values bel ow that regarded i n the l i terature as the l i kely lower l imit for n i t r i f icat ion ( Chapter 2 ) . I ndeed , Schmidt ( 1 9 82 ) noted that most observat ions reported on the l iterature ind icated a lower l imit for nitrif ication of pH 4 . The cessation of nitri f ication in the Tokomaru s i l t loam is predicted by equation ( 8 . 4 ) a t a pH of approximately 4 . 1 ( Figure 9 . 1 a ) . Although this is in agreement with the summary conclusions of Schmidt ( 1 982 ) , it is not inconsistent with the results of Ishaque and Cornfield . ( 1 9 72 ) . Thus , there was a suspicion that some of the nitrif ier activity measured in the Tokomaru s i l t loam may have been heterotrophic . RNR 1 0 .8 0 .6 0 .4 0.2 a. Predicted relative nitrification rate for soil T OP-----+-----+-----+---� 4 5 6 pH 7 8 SNA (JJrnol N g-1 h-1 ) 0.06 0.05 0.04 0.03 0.02 b. Plot of SNA vs. pH for Patua soi l 0 0.01 ,.__ .,._ _ _._ _ _..,._.....,_� 4 4.5 5 5.5 pH 6 6 .5 Figure 9 . 1 ( a ) Realtive nitrif ication rate in the Tokomaru s ilt loam at different soil pH and ( b ) a pH optimum curve for nitrif ier activity in the Patua soil 2 0 1 When the pH work was started , the pH relations of nitrifier activity in the Patua soil were also investigated . This strongly leached yel low brown loam ( N . Z . Soil Bureau , 1 9 68 ) , which was sampled from the lower slopes of Mt Egmont , had a pH of 4 . 9 , and on the basis of the fitted quadratic equation ( Figure 9 . 1 b , l ight line ; R2 = 0 . 65 , p < 0 . 1 % ) had a pHopt of 5 . 0 9 . However , as can be seen from Figure 9 . 1 b , this soi l exhibited cons iderab l e n i t r i f ier ac t iv i ty a t low pH , s o much so t ha t the relationship between SNA and pH was better described by a cubic equation ( Figure 9 . 1 b , heavy l ine ; R2 = 0 . 79 , p < 0 . 1 % ) . Whilst this model may be of dubious theoretical standing due to the prediction of increasing SNA with increased acidity below a pH of about 4 , it does nevertheless suggest that at a certain critical pH , a minimum SNA value may be reached , and that further i ncreases in the level of acidity wil l have negl igible effect on the nitrif ier activity . This was . taken to .be an indication of heterotrophic ni tri f ier act ivity . Unfortunately the cost of obtaining samples of the Patua soil at regular intervals for a study s imi lar to that described in Chapter 7 meant that no further experiments were done using this soi l . However , in view of the possibi l ity of heterotrophic nitri f ier activity as indicated by both Figure 9 . 1 a and 9 . 1 b , it was cons idered important to try to es tablish the extent of heterotrophic nitrif ication ( if any ) in the Tokomaru silt loam . It was decided that a sui table means of doing this wou ld be to carry out a series of SNA incubations using a range of substrates including peptone ( Van de Dijk & Troe l s tra , 1 9 8 0 ; Schimel e t a l . , 1 9 84 ; De Beer e t al . , 1 988 ) . The assumption was made that any nitrifier activity measured in a medium with peptone as the onl y substrate , would be an indication of heterotrophic nitrif ication . i . Methods and materials On 1 1 June , 1 9 88 , two bulk soil samples ( approx . 5 kg each ) were taken from field No . 6 ; one between 0 and 3 cm depth and the other from the 1 2- 1 5 cm depth range . The samples were sieved and s tored as before . 6 g subsamp l e s ( oven-dry equiva l ent ) were ta ken f rom each s ample ( 5 replicates each ) and extracted with 2 M KCl for analysis of exchangeable ammonium ( Chapter 4 ) . 2 02 A 2 0 0 g subsample of soi l from each depth was prepared for SNA incubation by overnight leaching with 0 . 00 5 M KCl as before ( Chapter 4 ) . Fol lowing removal of the excess moisture by suction f iltration for 9 0 minutes , 5 g subsamples ( oven-dry equivalent ) were weighed into 64 incubation tubes ( 3 2 tubes per sample depth ) . To each tube , 1 0 cm3 of the fol lowing treatments were added ( 8 replicates per treatment per sample depth ) ; ( a ) 0 . 0 1 M (NH4 ) 2S04 , the s tandard SNA substrate ; ( b ) 0 . 0 1 M K2S04 , a blank substrate ; ( c ) peptone ( Gibco Laboratories ; Gelatin hydrolysate , peptone No . 1 9 0 ) ; and ( d ) a solut ion containing both 0 . 0 1 M ( NH4 bS04 and peptone . Al l solutions were made up in 0 . 00 5 M KCl as before . The total N content of the peptone according to the manufacturers speci f ication was 1 6 % . The amount of peptone dissolved in 0 . 0 0 5 M KCl ( 1 . 7 3 g dm- 3 ) to make up treatments ( c ) and ( d ) was calculated so as to give the same N content in the peptone solution as was in the standard treatment ( a ) . In the case of treatment ( d ) , it was assumed that the NH4• was ef fectively unavailable to the heterotrophs and the substrate for this treatment was made up by dissolving 1 . 7 3 g peptone in 1 dm-3 0 . 0 1 M ( NH4 ) 2S04 . Thus , the concentration of N as peptone and as NH4. was the same in treatment ( d ) as it was in ( a ) and ( c ) . Fol lowing addition of the substrate , the incubations were carried out as before , and were sampled after. 1 and 8 hours . The pH of the incubating media was recorded fol lowing the 8 hour sampling . A compl icating f actor in thi s experiment was that exce s s peptone remaining in the samples taken after 1 and 8 hours incubation was found to upset the chemistry of the analysis of N03 -N . Accordingly , to a 2 cm3 subsample of each supernatant , 0 . 2 cm3 2 % hydrogen peroxide ( H202 ) were added . The solutions were mixed and then dr ied overnight ( 1 0 5 "'C ) to complete dryness ( S . K . Saggar , Dept Soil Science , Massey Univers ity - personal communi cation ) . Fol lowing dry ing , 2 cm3 deionized double­ dis t illed water were added to each dry tube . The tubes were thoroughly shaken using a vortex shaker , and the solutions analysed for N03 -N in the usual way ( Downes , 1 97 8 ) . A series of blank ( no peptone ) treatments with H2 0 2 and standard N03-N solutions demonstrated that this procedure had no e f fect on the measured N03 -N concentration of solutions containing no peptone . 2 0 3 On 27 October , 1 988 , further bulk samples were taken from f ield No . 6 from both the 0 - 3 and 1 2 - 1 5 cm depth ranges , and the experiment was repeated . ii . Results As was the case in the moi sture s tress experiment ( Chapter 8 ) , the incubat i on pH was found to vary be tween treatments , the pH of the s tandard and peptone incubat ions being approximate l y 5 . 0 and 5 . 9 respectively . Accordingly , the SNA data were adjusted to account for this variation using equations ( 8 . 3 ) and ( 8 . 4 ) . The mean pH-adjusted values of SNA in each treatment are shown in Figure 9 . 2 . Generally , the nitrif ier activity between 0 and 3 cm was higher than in soil sampled between 1 2 and 1 5 cm depth, which i s consistent with the results reported in Chapter 5 . The dif ference between the 0 - 3 and 1 2- 1 5 cm samples in treatment ( b ) was not s igni f icant ( p < 5% ) , and i n the light o f the results for the other treatments , and those of the depth experiment ( Chapter 5 ) , this result was regarded as an anomaly . Treatment ( b ) showed s ignificantly lower ( p < 0 . 1 % ) nitri f ier activity in the 0- 3 cm sample than treatments ( a ) , ( c ) and (d ) whose SNA values were no t s i gn i f i cant ly di f f erent f rom each other at the p < 5% level of s ignif icance . The same applied for the 1 2- 1 5 cm samples . As might have been expected , the lowest SNA values were measured in the blank treatment ( b ) ( 0 . 0 0 6 ± 0 . 0 0 0 4 and 0 . 008 ± 0 . 0 0 08 � mol N g- 1 h-1 for the 0 - 3 and 1 2- 1 5 cm samples respectively ) . The fact that the SNA values measured for treatments ( c ) and ( d ) at 0 - 3 cm ( 0 . 02 1 ± 0 . 00 4 and 0 . 0 2 2 ± 0 . 002 � mol N g- 1 h- 1 ) were not signif icantly different from the standard incubation ( 0 . 02 1 ± 0 . 00 3 � mol N g- 1 h- 1 ) makes the interpretation of the results di f f icul t . Nevertheless , the result for treatment ( c ) suggests that peptone has stimulated heterotrophic nitri fier activity . In view of the low concentration of Ex-NH4 + in the f ield ( 0 . 32 ± 0 . 0 3 5 and 0 . 0 5 ± 0 . 00 7 � mol N g- 1 a t 0 - 3 and 1 2 - 1 5 cm respectively ) and the low content o f total ' N in the soil ( Chapter 5 ) , it was assumed that there was insuff icient substrate for measurable heterotrophic activity under f ield conditions . SNA (J,Jmol N g-1 h-1 ) 0 .0 1 5 0 .01 0 .005 (NH4)2S04 a K2S04 b Peptone c Treatment • 0-3 cm. D 1 2-1 5 cm. Soi l sampled 1 1 -06-88 Figure 9 . 2 N itrifier activities in soil sampled in June incubated with a range of N -substrates 2 05 The SNA value for treatment ( b ) was therefore considered to represent the activity of an autotrophic population which was surviving on the small amount o f subs tra te ava i l able in the f ie ld . In trea tment ( a ) the nitri f iers were stimulated by the addition of ( NH4 ) 2 S04 , but as the work reported in Chapter 4 indicated , the ni tri f iers d id not i ncrease in number during the time of the incubation . Assuming that there was no growth in the nitri f ier population in treatments ( a ) and ( b ) and that the concentration of enyzme in the ammonium oxidase system was the same , the idea of stimulated activity following addition of NH4 . is consistent with the theory o f Michael is -Menten kinetics . However , it i s not certain whether the activity measured in treatment j a ) was entirely autotrophic , especi a l ly s ince treatment ( d ) had a mean SNA value which was not s ignif i cantly di f ferent to that of treatment ( c ) . The latter result indicated that there was no increase in activity in treatment ( d ) following addition of ( NH4 ) 2S04 in addition to peptone , over and above that measured in treatment ( c ) where the measured activity was assumed to be predominantly heterotrophic . Assuming that the activity measured in treatment ( b ) e f fect ively occurs in all treatments , it fol lows that the difference between (b ) and ( c ) ( i . e . 0 . 0 1 5 � mol N g- 1 h- 1 ) represents the actua l amount of heterotrophic activity in ( c ) . I f the measured SNA value in treatment ( a ) was entirely due to autotrophic act ivity , then the SNA value of treatment ( d ) would be expected to be of the order of that measured for ( a ) plus the d i f ference between ( b ) and ( c ) , that i s , 0 . 0 3 6 � mol N g- 1 h- 1 • The fact that this was not the case suggests that there may be some heterotrophic activity in treatment ( a ) . The di f ference between treatment ( c ) and the expected value of ( d ) is equivalent to 0 . 0 1 5 � mol N g- 1 h- 1 • Therefore in treatment ( a ) , activity equivalent to 0 . 0 06 � mol N g- 1 h- 1 ( i . e . the di f ference between 0 . 02 1 and 0 . 0 1 5 ) may be due to heterotrophic nitrifiers ; that i s , approximately 1 / 3 o f the nitri f ier activity measured i n the standard incubation may have been het erotrophic . This s eems a reasonabl e interpretation of the experimental results because when 1 0 cm3 0 . 0 1 M ( NH4 ) 2S04 were added to media c ontain ing the equ ivalent of 5 g oven-dry so i l , the effective concentration of NH4 · substrate increased, in the case of the 0 - 3 cm sample , from 0 . 32 � mol N g- 1 , the f ield concentration, to 4 0 � mol N g- 1 in treatment ( a ) . Thus , the concentration of NH4 · may have been great 2 0 6 enough to st imulate the heterotrophic product ion of N02- f rom NH4 - . Howeve r , the anoma lous result for treatment ( b ) at 1 2 - 1 5 cm, the unexpected lack of any s igni ficant di f ference between treatments ( a ) , ( c ) and ( d ) , and the f act that the interpretation of these results is speculative and cannot be regarded as conclusive on the basis o f a single experiment , led to the conclusion that it was necessary to repeat the experiment . This was done in late October when , in contrast to June when the f irst experiment was carried out , nitrif ier activity was expected to be high ( Chapter 7 ) . The mean SNA value for treatment ( a ) in the second experiment ( Figure 9 . 3 ) reflects the higher nitri f ier activity in spring ( 0 . 0 3 6 ± 0 . 002 and 0 . 0 1 7 ± 0 . 00 1 � mol N g- ' h _ , for the 0 - 3 and 1 2- 1 5 cm depth ranges ) . This increased act i vi t y was mos t l i kely brought about by the great er concentration of Ex-NH4- in the soi l in October ( 1 . 588 ± 0 . 0 56 and 0 . 4 1 5 ± 0 . 0 6 4 for the 0 - 3 and 1 2 - 1 5 cm samples respectively ) compared to June . The higher SNA values for the 0-3 cm sample compared with the 1 2- 1 5 cm sample are again consistent with the f indings of Chapter 5 . However , as can be seen from Figures 9 . 2 and 9 . 3 , the pattern of di fferences between the mean SNA values of each treatment at 0-3 cm is dif ferent in the two experiments . In the f irst experiment , the trend in mean SNA value was ( d ) � ( a ) = ( c ) > ( b ) . In the second experiment the trend is ( a ) > ( c ) > ( d ) = ( b ) which, ignoring treatment ( d ) , may s imp ly be a reflection of differing seasonal effects on the various species which make up the total soi l nitrif ier population . If this is not the case, these results cast s e r i ou s doubt on the interpretat ion of the resul ts o f the f ir st experiment , espec ially with respect to the rel ative magnitude of the activity of autotrophs and heterotrophs . I n contrast to the f irst experiment , the mean adjusted SNA value of the s tandard i ncubation for 0 - 3 cm w a s s i gn i f i cant l y d i f f erent f rom treatments ( b ) and ( d ) a t the p < O . 1 % level o f s igni ficance and f rom treatment ( c ) a t p < 1 . 0 % . Treatments ( b ) and ( c ) were s igni f icantly different ( p < 0 . 1 % ) from each other as were treatments ( c ) and ( d ) . In the case o f the 1 2 - 1 5 cm sample , all treatments were s igni f icantly di f ferent ( p < O . 1 % ) except treatments ( b ) and ( c ) whi ch were not s ignif icantly dif ferent , and treatments ( c ) and (d ) which were signi ficantly dif ferent 0 .04 0 .03 SNA (pmol N 0.02 g-1 h-1 ) 0 .01 0 (NH4)2S04 a K2S04 b - Treatment Peptone c d • 0-3 cm. D 1 2-1 5 cm. Soil sampled 27-1 0-88 Figure 9 . 3 Nitrifier activities in soil s ampled in October incubated with a range of N -subs trates 2 08 a t the p < 1 % level of significance . The fact that the peptone treatment had a lower mean SNA value than the standard incubation ( 0 . 027 ± 0 . 0 0 1 compared to 0 . 0 36 ± 0 . 0 02 for the 0 - 3 cm depth range , and 0 . 0 08 ± 0 . 0 0 0 6 compared to 0 . 0 1 7 ± 0 . 0 0 1 � mol N g- 1 h- 1 at 1 2- 1 5 cm ) suggests that at this time o f year , the heterotrophic population may not be as act ive re lat ive t o the autotrophic population as was the case in winter , a s sumi ng that the activity measured in the standard incubation was predominan t l y autotrophi c . A l ternative ly , one could say that the autotrophic population was more active at this time of the year than the heterotrophic population . This would certainly be the expected result i f the soil had become much drier , s ince many soi l heterotrophic organisms are not obligate aerobes ( Focht & Verstraete , 1 97 7 ) and therefore drier , or more aerobic , conditions may not have a significant beneficial ef fect on their activity . In June the soi l was wet , with moisture contents for the 0 - 3 and 1 2- 1 5 cm samples of 0 . 49 5 and 0 . 290 g g- 1 respectively , equivalent to pF values of 1 . 1 7 and 2 . 5 4 . In October , the soil was wetter in the 0 -3 cm sample ( 0 . 5 46 g g- 1 , pF 0 . 9 1 ) following rain the day before sampling, and had approximately the same moisture content at 1 2 - 1 5 cm as it was in June ( 0 . 287 g g- 1 , pF 2 . 56 ) . Thus , on the basis of the results of the moi sture s tress experiments ( Chapter 8 ; F i gure 8 . 3 ) , t he d i f f erences in activit ies between the two sampling times cannot be attributed to di f ferences in the soil moisture content because the 0 - 3 cm sample in October would be expected to show a lower nitri fier activity than in June . The fact that the SNA value at 1 2- 1 5 cm in October was higher than in June despite the soil moisture status being similar at both s ampl ing t imes , indicates that mo i sture was not a factor in accounting for the di fference in ni tri f ier act ivity between the two sampling dates . I t is also unlikely that the increased temperature would have c aused an increase in autotrophic rel at ive to heterotrophic activi ty , s ince warmer tempera tures m igh t be expect ed to favour autotrophs and heterotrophs equally . The increased activity may therefore simply be a reflection of the increase in Ex-NH4. in the soi l , caused by the relative inactivity of the ni trif ier population during the winter which allowed the concentration of NH4 • to build up . On the evidence of these results , the build-up of Ex-NH4. was not sufficient to promote the heterotrophic production of N02 - from NH4· · 2 09 The explanation of seasonal differences between the two sets of results does not contr ibute to an explanation of the differences between the measured SNA values for treatments ( a ) , ( c ) and ( d ) . Indeed , following the a rgument used f or the f irst experiment , the 0 - 3 c m sample with treatment ( d ) would be expected to have an SNA value of 0 . 0 4 7 � mol N g_ , h- 1 ( a+ ( c-b ) ) , that i s , nearly three times as large as the measured value of 0 . 0 1 6 ± 0 . 0 0 2 � mol N g- 1 h- 1 • In fact the SNA value o f treatment ( d ) was identical t o that for treatment ( b ) . A possible explanation for this result is given below . iii . Discussion A number of authors have attempted to identify and quantify heterotrophic nitrification in forest and heath soils ( e . g . Van de Dij k & Troelstra , 1 9 8 0 ; Hynes & Know le s , 1 9 8 2 ; Schimel e t al . , 1 984 ) , but none have investigated heterotrophic activity in agricultural soils . No worker has succeeded in obtaining values for the actual amounts of heterotrophic n i t r i f icat ion in s i t u , and mos t have merel y conf irmed that it is occurr ing in their so i ls . Van de Dij k and Troelstra ( 1 9 80 ) measured nitrate reductase activity ( NRA ) in the leaves of two sub- species of Hypochaeris radi ca ta as an indicator of in si tu nitrate production in two heath soils . In one of these ( pH 4 . 3 ) , addi tion of ammonium did not af fect NRA in the leaves , and analysis of the microbial population fai led to identify any autotrophic nitrif iers . However, the addit ion of peptone led t o an increase i n NRA indicating the presence of heterotrophic nitri fying organi sms . In a s imil ar experiment in the l aboratory , the addition of NH4 - led to a decrease in the production of No3 - to zero , whereas the. addition of peptone led to a doubling of the amount of No3- produced . In the other soi l ( pH 6 . 3 ) , addition of both NH4- and peptone increased the amount of N03 - produced . These results are of interest with respect to the resul ts f or treatments ( c ) and ( d ) iri the second experiment . S ince treatment ( c ) showed an increase in nitri f ier activity o v e r t re a t m e n t ( b ) , i t appe a r ed tha t when peptone was added , heterotrophic activity increased , but when NH4 - was also added , nitrif ier activity was suppressed . However , this type of explanation is at odds :n u with the results of the f irst experiment when treatment ( d ) showed equal highest activity , and also the other results in the second experiment , s i nce treatment ( a ) showed autotrophic activity . to be greater than heterotrophic activity . One might have therefore expected treatment ( d ) t o show nitrif ier activity o f a similar magnitude , assuming that peptone does not suppress autotrophic activity , because the same concentration of NH4-N was present in treatment (d ) as was present in treatment ( a ) . I t should be noted however , that this kind o f argument is based on the assumption that the concentration of peptone used in these experiments was as optimal for heterotrophs as 0 . 0 1 M CNH4 ) 2S04 was shown to be for autotrophs ( Chapter 4 ) . Whether or not this assumption is correct is not known , and therefore both the results and their interpretation must be regarded as speculative . Adams ( 1 9 8 6a , b ) a l so noted suppress ion o f heterotroph i c act iv i ty following addition of ( NH4 ) 2S04 . He studied "strongly acid" forest litter ( pH 4 . 50 ) and humus (pH 3 . 88-4 . 2 3 ) beneath sitka spruce , heather , Scots pine and larch in Scotland , and found that net N03-N product ion during aerobic incubation was generally greater in l itter than in humus , and that it was correlated ( p < 0 . 1 % ) with the initial concentration o f organic N soluble in 1 M KCl , thus suggesting heterotrophic activity . However , Adams ( 1 986a ) argued that the very low amount of N03-N produced in most of the samples suggested that the factor determining whe ther or not nitrification occured in a particular forest f loor sample was the amount o f readily mineral izable carbon available for heterotrophic uti l ization and not the supply of a source of organic N . He supported thi s idea in the case of sites where the trees had received fertilizer , by suggesting that leaf litter falling from fertilized spruce contained less l ignin and more metabolizable carbon following fert i lization of the trees . Not a l l the samples tested exhibited nitrif ication , but of those that did , the rate of N03 -N production was increased by the addition of peptone , whi lst addition of ( NH4 ) 2 S04 generally had no ef fect . In larch humus , however , addi tion o f ( NH4 ) 2 S04 s igni f icant ly reduced N03 -N produc tion . Adams ( 1 9 86b ) found that whi lst the reason for this apparent suppression of heterotrophi c n i tri f icat ion by NH4 -N was unclear , it could not be attributed to any direct toxic ef fect of added NH4- . 2 1 1 Meiklej ohn ( 1 9 5 3 ) stated that peptone was very poisonous to autotrophic nitri f iers , especial ly i f it contained free amino acids . De Boer et a l . 1 988 ) also found peptone to be inhibitory to the nitrif ier population in a fertilized acid heath soil when added weekly at a rate of 4 mg peptone­ N dm- 3 soil suspension . This kind of result might go some way to explain the results of the second experiment , but seems an unlikely reason for the depressed SNA value in treatment ( d ) in the second relative to the f irst experiment since the same peptone was used in both . However, i f the winter conditions were suff iciently unfavourable to autotrophs that the nitr i f i er activity measured in the f irst experiment was predominantly heterotrophic , then the rel ative magn itude of the observed nitri f i er activities in the two experiments makes sense . In the f irst experiment , the SNA value was greatest in treatments ( a ) , ( c ) and ( d ) , and the lack of a s igni f icant increase in treatment (d ) compared to ( c ) or ( a ) might have been due to inhibition of autotrophic nitrification ( which was low anyway ) by peptone . In the s econd experiment the f ield conditions f avoured a larger autotrophic population so that treatment ( a ) exhibited greater nitri f ier activity than treatment ( c ) , but when both peptone and NH4 • were supplied as substrates as in treatment ( d ) , the activi� of the autotrophs was inhibited by the peptone . This explanation would appear reasonable but would be more convincing if treatments ( c ) and ( d ) in the second experiment had recorded s imilar SNA values , just as they did in the f irst experiment . Schimel e t a l . ( 1 98 4 ) used a variety of agents including acetylene and chlora te to block the ox idat ion o f NH4• and N02 - in an at tempt t o identi fy the presence o f heterotrophic nitri f ication i n a S ierran forest soil i n Cali fornia . They found that No3 - production was not inhibited by either acetyl ene or chlorate which sugges ted that the No 3- had been produced by heterotrophs . However , neither peptone nor NH4 • stimulated the production of No3 - , suggesting that either these were not suitable substrates , or that the rate of nitrif ication was not substrate l imited in thi s soi l . In fact , the rate of N03 - production decreased during the assay period, which Schimel et a l . ( 1 984 ) considered suf f icient evidence to conclude that neither peptone nor NH4 . were suitable substrates . This seems a curious result , but along with those of Adams ( 1 98 6a , b ) , Van de 2 1 2 D i j k and Troel s tra ( 1 9 8 0 ) and those reported here , reinforces the conclusion of Chapters 7 and 8, that the nitri fier population is dynamic and will change to suit particular soi l conditions . On the basis of the results of the work reported in this chapter , this adaption to specific environments mus t inc lude the pos s ibi l ity that the make up of the popula t ion , in terms o f the re lative number s of di f ferent species present , is also dynamic . iv . Conclusions Like those of other workers , this experiment has fai led to provide conc lus ive evidence a s to the rel ative importance of heterotrophic n i t r i f i c a t i on ; i nde ed much of the i nt e rpreta tion of resu l t s i s speculative . Nevertheless , it seems likely that there was a potential for heterotrophic activity in the Tokomaru silt loam at both sampling times , and that the activity of the autotrophic population ( and therefore the population size) was approximately twice as high in October as it was in June . The ef fect of adding both peptone and ( NH 4 ) 2S04 together is complex and the results for treatment ( d ) were ambiguous ; there was a suggestion that the concentration of peptone used inhibited autotrophic activity , but only at the October sampling . To further investigate the importance of heterotrophic activity in this soi l , one would have to do much more deta i l ed exper iments w i th di f f erent concentrat ions o f peptone and ( NH4 ) 2S04 , and examine any interactions between these substrates . Looking at the Patua soil ( Figure 9 . 1 b ) , one might think that about hal f t he nitrif ier activity at pH 4 -4 . 5 was heterotrophic j udgirig by the lack of a s teep decline in SNA value below pH 5 , such as that seen for the Tokomaru silt loam ( Figure 9 . 1 a ) . However , in the absence of extensive analysis of the s o i l m icrob i a l populat ion , and ident i f ication of all the soi l m icrobes present that might be capable of nitrif ication , it seems that the SNA will have to be assumed to be a good measure of in si tu nitrif ier activity . The fact that the very low pH values at which heterotrophic activity seemed l ikely to occur have not been measured in soil samples t aken from either Field No . 2 or No . 6 , suggests that it i s reasonable to assume that autotrophs either predominate or are at least as important as heterotrophs in the Tokomaru silt loam as sampled . If this is the case , the SNA . may be cons idered to be an ent irely satisfactory means of measuring in si tu nitrif ier activities . 2 1 3 SECTION IV CHAPTER 1 0 GENERAL DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS FOR FURTHER WORK A . A CRITIQUE OF THE GEOSTATISTICAL TECHNIQUES USED FOR THE ASSESSMENT OF SPATIAL VARIABILITY IN NITRIFIER ACTIVITY Geostatistics were f irst introduced to soil scientists at the beginning of the decade in a series of papers by Burgess and Webster ( 1 980a , b ) , Webster and Burgess ( 1 98 0 ) and Burgess e t al . ( 1 98 1 ) . Since then , the l i terature has contained reports of the spatial analysis of a range o f s o i l propert ies inc luding so i l tempera ture ( Davidoff e t a l . , 1 98.6 , Davidof f & Selim, 1 988 ) , soil moisture content ( Davidoff & Selim , 1 988 ) , soi l particle s ize ( Oliver and Webster , 1 987 ) , topsoil properties ( Xu & Webster , 1 984 ) , crop yield-soil water relationships ( Russo , 1 98 4b ) , soi l surf ace roughness ( Lehrsch e t al . , 1 9 88 ) , soil ferti lity ( Webster & M0Bratney , 1 987 ) , soil nitrate concentration ( Tabor_ . e t al . , . 1 98 5 , White e t a l . , 1 987 ) and the heavy metal content of soils ( Wopereis e t al . , 1 988 ) . Some authors have proceeded from the experimental variogram and used kriging to produce maps of the properties under investigation ( e . g . Xu & Webster , 1 98 4 , Webster & M0Bratney , 1 987 ) , whi lst others have used the variogram to design so-cal led optimal sampl ing schemes ( e . g . Burgess e t a l . , 1 98 1 , M0Bratney & Webster , 1 98 1 , M0Bratney & Webster , 1 98 3 , M0Bra tney e t a l . , 1 9 8 1 ) . H owever , because · so i l · scientists are not necessari ly also mathematicians , few have given theoretical consideration to the probl ems that may be encountered in the pract ical use o f geostatistics for soil science problems . In . many o f the instances listed above , this i s probably because workers have produced a single variogram based on a s ingle sampling occasion and therefore have been unaware o f the possibility of changes in the variogram model with time and sampling design ( Chapters 5 and 6 ) . Indeed , the selection of models for f itting to experimental variograms has been the only aspect of geostatistical theory to receive much attention in the soil science l i terature ( M0Bratney & Webster , 1 986 ) . 2 1 4 Some soil scientists might draw a "perverse comfort " from the notion that geostatistical theory may have outpaced its application to soi l science ( M0Bra tney & Webs ter , 1 9 8 6 ) s ince by and large , they have been left --· behind in the development of the subj ect by mathematicians . Nevertheless , i f better practical use is to be made of these techniques , which are seen by many ( e . g . Dudal , 1 98 6 , Nielsen , 1 9 87 ) as a potentially maj or avenue o f p r o g r e s s i n s o i l s e i enc e , t he p r o b l ems o f e s t ima t i on and interpreta t i on of the vari ogram must be resolved . In the fol lowing discussion, an attempt is made at addressi ng some of these problems , particularly in relation to the work reported in Chapters 5 and 6 . i . Problems caused by anisotropy With the exception of the variogram for moisture content in the nested experiment ( Figure 6 . 3e ) , spatial variation in the data was assumed to be i sotropic because as explained in Chapter 5 , anisotropy is di f f i cult to " identi fy given the tendency of values of ¥ ( h ) to be more variable at l arge lags . However , inspection of many of the variogram models , such as those for incubation pH ( Figure 5 . 5c ) and moisture content ( Figure 5 . 5d ) in the f irst experiment over 625 m2 , and SNA over 9 m2 ( Figure 5 . 7 a ) , suggested that i sotropy may not have been a good assumption . When f i tting models to the experimental variograms shown in Figure 6 . 3 , i t was apparent that by cons idering the north-south and east-west directions separately , direction-dependent values of the range , nugget variance , Co , and the spatially dependent variance, C , would be obtained . For example, in the case of incubation pH ( Figure 6 . 3d ) , in the north­ south direction the bes t f i t model was exponential with values of Co , C and r equal to 0 . 0 0 4 4 3 0 5 , 0 . 0 2 0 4 3 7 and 1 . 7 7 4 respectively ( Figure 1 0 . 1 ) . The range ( equa l to 3r ) was est ima ted a s 5 . 3 m . In the east-west direction, in contras t , the best fit model was spherical , with values for Co , C and the range of 0 . 0 088 1 4 , 0 . 0 1 1 469 , and 4 . 1 m ( Figure 1 ·0 . 1 ) . The difference between the s ill variances of the two models ( 0 . 0 2 4872 in the north-south direction compared to 0 . 0 2 0283 in the east-west direction ) might be taken to indicate zonal ani sotropy ( Webster , 1 98 5 ) . However , it 0.045 0 .04 0. 035 0 .03 A 0.025 'Y (h) 0 .02 0 .01 5 0 .01 0.005 0 0 Figure 1 0 . 1 0 0 0 * 0 0 * 0 0 N-S * @ * N-S model - - - .z _ - - - . * E-W 0 E-W model * * 0 5 1 0 1 5 20 25 h (m) Experimental variogram of incubation pH of soil s ampled between 3 -12 cm over 6 2 5 m2 us ing a nested sampling des ign ( Figure 6 . 1 ) with s eparate models f itted by weighted least squares optimization for the north-south and east-west d irections 2 1 6 seems unlike l y that these di fferences are signif icant i n view of the l arge error a s sociated with the es t imate o f the s i l l variance as 1\ indicated by the spread of values of ¥ ( h ) about the s i l l at values of h greater than the range . Thus , on the basis of di f ferences between the s i l l vari ance of the t wo models , the assumption of isotropy seemed satisfactory . However , by express ing the nugget variance as a percentage of the s i l l var i ance ( 1 7 . 8% in the north-south direction compared to 4 3 . 5 % in the east-west direction ) , the dif ferences between the models for the two directions seem more significant and might therefore suggest that the assumption of isotropic variation in incubation pH was incorrect . Many authors ( e . g . Burgess & Webster , 1 980a , Webster & Burgess , 1 980 ) have ca lculated anis otropy rat ios over the f i rst few lags where the variogram is approximately l inear using the equation ( Trangmar et al . , 1 985 ) : A ¥ ( h , 8 ) = Co + [ Acos2 ( 8 -Q ) + Bsin2 ( 8 -Q ) ] h ( 1 0 . 1 ) A where ¥ ( h , 8 ) i s the semivariance estimated for lag h in direction 8 , Co is the nugget variance , Q is the direction of maximum s lope A, and B is the slope of the variogram at 90° to Q . The anisotropy ratio is given by A /B . In the f i r s t instance , equa t i on ( 1 0 . 1 ) appears to have much potential as a descriptor of the difference in spatial dependence between two direct ions . However , deciding whether or not it is val id to use equation ( 1 0 . 1 ) presents a considerable problem . Firstly , equation ( 1 0 . 1 ) assumes that the value of Co is common to the variogram model in both directions which , from the above discussion , is c learly not the case . Secondly , a s igni f icant difference between l inear models f itted over the f irst few lags may not necess ari l y indicate a s i gnif icant di f ference between the variogram models fitted over all values of h . The relatively small dif ference between the sills of the models fitted to the north­ south and east-west directions ( Figure 1 0 . 1 ) , in spite of an apparent difference between the models over the first few lags , might indicate thi s . In addit ion , the si tuation could ari se where the slope of the variogram over the f ir s t f ew l ags was the s ame i n two ( or more ) directions , but values of Co and C , and therefore the value of the sill variance and range , were quite di fferent . In this event , equation ( 1 0 . 1 ) 2 1 7 would indicate i s o tropy desp i te the var iogram models showing large di f ferences in the variation in different directions . Thus , the use of equation ( 1 0 . 1 ) seems limited ; what is needed i s a test for anisotropy which applies over a l l values of h . Laslett e t al . ( 1 9 8 7 ) pointed out that there i s no statistical test for anisotropy , so whether or not the di f ferences between the north-south and east-west models ( Figure 1 0 . 1 ) are s ignif icant cannot be discerned . One might expect d i f ferences in the variogram mode l s fi tted in di f ferent directions in any case , simply on account o f experimental error and the error associa ted with the model f itting process . The use of weighted least squares prevents the use of a test for s igni f icant d i fferences between regressions ( Freund & M inton , 1 9 7 9 ) to di fferentiate between var iogram mode l s , due to the compl i c a t i on caused by the weighting factors . In l ight of the general ly held view that models must be f itted by weighted least squares , it therefore appears that f inding an obj ective test for anisotropy will prove very di f f icul t . Nevertheless , this should be an essential aspect of future geostatistical research . With regard to the aims of the work described in thi s thesis , i t is sugges ted that whether or not anisotropy occurs may be of l ittle consequence . The intention here was to identify the distance over which soi l mineral nitrogen and the factors af fecting it might be spatially dependent , wi th a v iew to improving the estimat ion of a f ield mean nitrat e concentration . Continuing wi th the example of incubation pH , whether the range i s taken to be 4 . 1 or 5 . 3 m i s probably only of academ ic interes t , and there f ore i rrelevant to further f ield-scale sampling . By taking samples at separations greater than 5 . 3 m, the effect of spatial dependence on measurements made in either direction will be A avoided s ince there i s no increase in the f i tted value of V ( h ) with h for values of h > a , where a denotes the range o f spatial dependence . Thus , for s tudies such this , it seems reasonable to ignore anisotropy unless it is obvious , as was the case for moisture content in the nested experiment . I t should be noted , however, that this approach may not be acceptable i f interpolation of unsampled sites by kriging is to be used because the presence of anisotropy wi l l necessitate consideration of the direction of separation between samples , as well as the distance of separation , and as 2 18 a result will af fect the weights given to near neighbours in the kriging procedure . It is therefore interesting to note that Laslett et a l . ( 1 987 ) found i sotropic kriging to be bet ter than anisotropic kriging despite clear evidence of anisotropy in their data . A similar result was found in the cas e of incubation pH ( Fi gure 6 . 3 ) . When the number of sampling points separated by each lag is the same in both directions , as i s the /1. case in a symmetrical sampling design , ¥ ( h ) in both directions combined A is equal to the average of ¥ ( h ) in the two directions separately ; that i s , the number o f po ints f i t ted to the model i s the same and so comparison of AIC values ( equation ( 3 . 22 ) ) is valid . Comparison of the AIC values for the pH variograms ( Figures 6 . 3d and 1 0 . 1 ) indicated that the model descr ibing both d i rections ( AIC = - 4 5 1 . 59 ) described the spatial variation better than either the north-south ( AIC = - 4 3 2 . 06 ) or east -wes t ( AIC = - 4 4 5 . 1 7 ) di rect ions separatel y . I t i s therefore concluded that the assumption of isotropy in this work was acceptable . i i . Problems caused �changes in the sample variance and variation in the variogram model with time and scale of sampling In Chapter 5 it was observed that the form of the model fitted to the experimenta l var iogram appeared to be a funct ion o f the scale of sampling . I t also appeared that the variogram model was affected by the t ime of sampling , a lthough in view of the fact th_at sampling was only carr ied out on two occas ions , thi s e f fec t was less conc lusive . The observation that the scale of sampling af fected the form of the variogram was supported by the finding in Chapter 6 that the variograms produced from data sampled on a nested gr id were more meaningful than those produced from regular grided dat a , in that the nugget var iance was reduced , and the range of spatial dependence was better def ined as a result . On this basis , it was concluded that the eff icacy of a sampling scheme was a function of the ratio of the smal lest : largest lag . However , the observation in Chapter 5 that both the sample variance and the form of the variogram model changed with time and size of the sampling area , and the similar f inding in Chapter 6 that the sample variance was also dependent on the size of the sampling area , raises serious questions as to the reliability of the variogram irrespective of the sampling design . 2 19 I t also suggests that a given variogram model may only give information that is of use in considering the spatial distribution of a soil property measured at the t ime and scale of sampling used for production of that variogram . In the case of soi l physical characteristics , the error due to t ime i s probably i ns igni f icant and so the reproducib i l ity of the variogram may be satis factory . However , in the case of biological soil properties such as those measured here , variation of the variogram model and its parameters with t ime may reflect either a serious shortcoming in geostatistical techniques , or may simply indicate that these techniques are not appropriate f or the s tudy of tempora l ly variable biological properties . The variat ion in s ampl e vari ance with the scale o f s ampl ing , as identi fied in Chapter 6 , may reflect a shortcoming of nested sampling designs . The sample variance of data in the nested grid ( 0 . 0 1 2 in the case of incubation pH ) was less than the sample variance for the whole data set ( 0 . 0 1 65 ) , and much less than the sample variance of the main grid alone ( 0 . 02 2 ) . This suggests that the estimate of o2 by s2 was not an unbiased estimate . 52 calculated for the whole data set underestimated 02 for the sampling area due to bias caused by the spatial dependence of sample values measured at sites within the nested grid on one another - a reasonable explanat ion given that the neste� data points were confined to an area of 2 . 5 m x 2 . 5 m, and the range of spatial dependence ( for incubation pH ) was 6 . 1 m . In view of the tendency for estimates of the variance to vary without l imit as the size of the sampling area increases ( Ol iver , 1 987 ) , the degree of this bias wi l l be a function of the ratio of the area covered by the nested grid to that covered by the whole grid . There may be further bias in the nested design because the location of the nested grid within the main grid was arbitrari ly decided . Overal l , it appears that the sampling design used for the experiment described in Chapter 6 was not as good as previously suggested . Indeed , in l ight of the above di scus s ion , i t seems that the problem of s2 varying with changing s ampl ing des i gn may be avoided by positioning the nested sampling points over the whole sampling area adjacent to points on a regular grid, in a s imi lar way to that used by Laslett e t al . ( 1 98 7 ) . 2 2 0 Changes i n the sample variance wi th time and s ca le o f sampling, as opposed to changes in the actual sampling design , present more difficult problems , especial ly s ince changes in the value of s2 will probably be mirrored by changes in the s i l l variance ( Co+C ) , given that both are a measure of the variation of the measured property over the area in which i t was sampled . Analysis of the di fferences in sample variance for the properties measured in the three experiments described in Chapters 5 and 6 i s made di f f icult in the case of the f irst experiment over 625 m2 , and the 9 m2 experiment , by the di f ferent times o f sampling ; and in the case of the two experiments over 625 m2 by the di f ference in the depth of sampling . In the case of incubation pH , which was not expected to vary much with season , the values of the mean and sample variance in the f irst 625 m2 experiment were 4 . 92 and 0 . 0 3 3 5 respectively , compared to 4 . 82 and 0 . 0 2 4 0 in the 9 m2 experiment . In l ight o f the discussion i n Chapter 6 , this di f ference is probably s imply a result of the reduction in the s ize of the sampling area . In the case of SNA , however , the mean value was 0 . 028 IJ mo l N g - 1 h - 1 i n both the f ir s t 625 m 2 exper iment and the 9 m2 experiment , yet the value of s2 ( ln transformed data in ng N g- 1 h- 1 ) changed from 0 . 2298 over 62 5 m2 to 0 . 3 4 1 5 over 9 m2 • A s imi lar increase in the sample variance was observed for soil No3 - ( ln transformed values ; 0 . 3 9 7 6 and 0 . 9 2 2 5 f o r 6 2 5 m2 and 9 m2 respectivel y ) . Both these dif ferences were probably a reflection of the di f ferent time of sampling . Thus , explanation of the di f ferences i n the value o f 52 between the three experiments i s complex , and is a problem that w i l l not be dealt w i th further here . However , the f act that the var i ance o f the sampled properties can change with the design and scale of sampling ( in spite of the l og transformation which is expected t o s tabi lize the variance ) indicates that both of these factors can markedly af fect the results of the data analysis . As indicated above , in the case of biological soil properties , temporal variation may also have to be accounted for . Other than by carrying out a series of identical spatial analyses over a period o f at leas t a year - which would require a massive sampling ef fort overal l - how temporal variation in the variogram should be accounted for i s unclear . 2 2 1 With regard to geostatistical data analysis , the change i n the variogram with time and scale of sampling is somewhat disturbing . Which variogram is the correct one ? In Chapter 6 , it was assumed that since the sampling design appeared good , the models fitted to the experimental variograms def ined the spatial variation of the properties measured for 1 / 1 6 ha ( the area of field No . 6 ) in the Tokomaru silt loa m . However , differences between the vari ogram mode l s , especi a l l y those f or the first two experiments where the only di f ference in sampling was in terms of the area sampled , require some examination . Table 1 0 . 1 gives a summary of the results of the three spatial analyses of SNA and incubation pH . The variogram for incubation pH over 625 m2 ( Figure 5 . 5c ) was best fi tted by a spherical model predicting a range of 9 . 9 m , yet over 9 m2 the variation was pure nugget ( Figure 5 . 7c ) . S ince the largest lag over 9 m2 was less than the range as defined over 625 m2 ( in both the regular and nested experiments ) one would not expect to identi fy a range when sampling pH over 9 m2 • However , it would be expected that the variogram model should be linear upwards , because as " the variogram over 62 5 m2 shows ( F igure 5 . Se ) , ¥ ( h ) i ncreases with A. increasing h between 2 . 5 and 9 . 9 m . S ince by definition ¥ ( h ) at h= O is A zero , one can say that ¥ ( h ) in this variogram should increase between h=O and h=9 . 9 even though no measurements were made at values of h < 2 . 5 m . Indeed , convention was followed and the model was plotted over this range of values of h . Thus , there is an inconsistency between these results which could perhaps reflect either seasonal variation , or more likely , an inadequacy of the 9 m2 sampling design . In the case of SNA , in the first experiment over 625 m2 , the variance was pure nugget ( Figure 5 . 5a ) . In the 9 m2 experiment , SNA showed spatial dependence within 0 . 6 m ( Figure 5 . 7a ) . In contrast to incubation pH ( see above ) , these results are not inconsistent with one another because the shor test lag in the f i rs t experiment was 2 . 5 m , and the o ther two experiments indicated that the range of spatial dependence was less than 2 . 5 m . However , the result for the 9 m2 experiment is at odds with that for the nested experiment ( Figure 6 . 3a ) . Given that the sampling design in the nested experiment was good , and that sample separations less than Table 1 0 . 1 Summary of results for the three spatial analyses of SNA and incubation pH 6 2 5 m2 ( regular grid ) ( Figure 5 . 5 ) 9 m2 ( regular grid ) ( Figure 5 . 7 ) 6 2 5 m2 ( Nested grid ) ( Figure 6 . 3 ) Variogram Model Spherical Nugget Exponential pH Range ( m ) 9 . 9 6 . 1 Variogram Model Nugget Spherical SNA Exponential Range ( m ) 0 . 6 2 . 4 2 2 3 0 . 6 m were covered under the nested desi gn , one wonders whether the difference in the range as def ined under the nested and 9 m2 experiments m ight not be a function of seasona l variation . I f thi s i s s o , and " assuming that only two values of ¥ ( h ) , those at h= 3 0 and 60 cm , can provide suf f i cient evidence of spatial dependence within 0 . 6 m, the question arises as to why the spatial dependence of nitrif ier activity in June should be so much more intense than it is in October, given that the s i l l i s greater , and the range shorter , in the 9 m2 ( June ) experiment compared to ei ther the nested experiment or indeed the regul ar grid experiment over 62 5 m2 where the variance was pure nugget ? Find ing answers to these questions is perhaps beyond the scope of this work and i n the absence o f an unders t anding o f why SNA should be spatially dependent within 2 . 4 m , may not be possible . However , the evidence f rom these experiments i s that SNA variability is predominantly short-range . With respect to estimating field mean values of such properties as SNA , No3 - and NH4· , which may be referred to as the parameters of soil mineral N, this is valuable information . O liver and Webster ( 1 986 ) and Russo and Jury ( 1 987a ) noted that the variability of a soil property may occur over a range of di f ferent scales which may change by several orders of magnitude , such that each scale of observation wi ll integrate the variabilities apparent at smaller scales . I t is tempting to explain the differences between the variograms for the experiments done in Chapters 5 and 6 in these terms . However , on t he basis o f the results for these three experiments , it seems more l ikely that the variograms are unreliable , certainly in the case of the regular grid experiments . This conclusion presumes that variograms should have a high degree of accuracy . In view of the wide scatter of the values o f " ¥ ( h ) , i t may be that in the case of some soil properties , this degree of accuracy cannot be expected . Nevertheless , i t would be of great interest , i f time were available , to investigate the ( temporal ) reproducibi lity of the var i ogram . Obvious l y , if it c annot be accurately reproduced , particularly with regard to the value of · the - range , its value may be l imited , especially if kriging , which depends on an accurate variogram , i s t o b e used . Consideration of this problem i s seen a s a n essenti al element of future research . 2 2 4 A further question of importance i s whether there i s any value t o the non-kri ger in f itting variogram models at all . In investigations such as this , where the aim was to def ine a minimum acceptable sample separation for future sampling, the increased precis ion of a range def ined by a f itted model over that defined by s imple inspect ion of the experimental variogram i s almost certainly not signif icant in terms of future sampl ing at the f ield scale . Furthermore , the f itted model is only a best fi t model . I t i s not necessari ly the perfect fi t , nor the only a cceptabl e bes t f i t ( A . Swi f t , Dept . Mathematics and Statistics , Massey University - personal communication ) ; and as Burrough ( 1 983 ) pointed out , j ust because variation in a soi l property can be defined in terms of a vari ogram model , it does not mean that the variation can be explained in physical terms . For example, ni tri fier activity has been found to be spatially dependent within 2 . 4 m , but in view of the microscopic s i ze of microbial c lusters , the physical s ignif icance of a range of spatially dependent var i ab i l ity i n nitri f i er activi ty of this magnitude i s di f f icult to d i scern . The l ack of any spa t i a l dependence in the variability of exchangeable ammonium suggests that variabil ity in the substrate supply was not responsible for the observed variability in nitrif ier . activity , and that variabil ity in some other factor ( s ) causes th� variabil ity in nitri f ier activity . On the basis of the results of Chapters 5 and 6, and without further extensive investigation into the variabil ity of nitri f ier activity , one is inclined to think that Burrough ' s observation was not only correct , but also points to a serious shortcoming in the use of geostatistical techniques . The fact that very l i ttle attempt has been made in the l iterature to account for the spatially dependent variabil ity that has been def ined for various soil properties , further supports this conclusion . 2 2 5 i i i . The relationship between the sill and the sample variance According to Webster ( 1 98 5 ) the population variance of a f inite region , known to geostati sticians a s the dispersi on va riance , i s the average semi variance wi thin that region . It therefore fol lows that the sample variance must be less than the sill unless the variogram is pure nugget . In Figures 5 . 5a-c, 5 . 7b-d and 6 . 3c , d the position of the sample variance " in re lation to the values o f ¥ ( h ) i s con sistent with the above . In A contrast , values of ¥ ( h ) in the variograms for moisture content in both experiments over 625 m2 ( Figures 5 . 5d and 6 . 3e ) , SNA over 9 m2 ( Figure 5 . 7 a ) and 625 m2 under the nested sampling design ( Figure 6 . 3 a ) , and initial No3- in the nested experiment ( Figure 6 . 3b ) are not donsistent with the value of the sample variance . Indeed, in all these cases , the 1\ values of ¥ ( h ) appear too low . In the other variograms , the values of A ¥ ( h ) and 52 , which were calculated in the same way as for the variograms l isted above ( i . e . using GAMMAH ) , were consistent with one another . Thus , A. the inconsistencies between values of ¥ ( h ) and s2 cannot be ascribed to mathematical error , and therefore require some explanation . In section ( ii ) above , a problem was identified with the nested sampling design with respect to bias in the estimation of the sample variance . S i nce the ef fect o f the nest ing was to reduce the value of s2 as estimated for the whole sampling area ( i . e . the whole data set ) , the nesting cannot be blamed for the apparently high value of 52 for SNA and "' N03 - in relation to the values of ¥ ( h ) . Indeed , since the sample variance is calculated independently of the position of the data points in the "' sampling area , one might conclude that it is the values of ¥ ( h ) which are too low . A simi lar bias to that observed in estimating s2 in the nested A grid may occur in the est imation of values of ¥ (h ) . It f ol lows from equation ( 3 . 1 4 ) that the semi variance at any given lag represents the variation associated with property values separated by that lag . Under the regular grid sampling design ( Chapter 5 ) , .data. from every sampl ing " point is considered at least once in the calculation of ¥ ( h ) for any value of h; at shorter lags a point may be considered twice as it may have neighbours separated by a given lag either side of it along the same plane . For example , every point in the regular grid over 625 m2 except those on the outside of the grid had two neighbours 2 . 5 m away in both 2 2 6 the north-south and east-west directions . Thus , every sampling point is A included in the calculation of ¥ ( h ) at any value o f h in any direction either once or twice . Under the nested design in contrast , this is not the case because some points do not have neighbours at a l l lags . Four po ints were randomly se lected from the ma in grid of the nested sampling design ( Figure 1 0 . 2 ) , and the grid was examined to investigate " how many t imes these points were considered in the calculation of ¥ (h ) at each value of h, and thus in the spatial analysis as a whole . The four po i n t s s e l ected ( hence forth denoted by A , B , C and D ) were those occurring at 5x 7 . 5y , 1 0x 1 5y , 1 2 . 5x 2 . 5y , and 1 7 . 5x 20y where x denotes eas t-west , and y denotes the north-south direction ( Figure 1 0 . 2 ) . Point A has one neighbour at lags of 1 7 . 5 , 1 0 , 7 . 5 and 5 m in the north-south direction , and in the east-west direction has a neighbour at 2 0 , 1 7 . 5 , 1 5 and 2 . 5 m . Thus , i t i s considered four times in the variogram for any one direction and eight t imes assuming isotropy . Similarl y , point B has a neighbour in bo th the north-south and east-west directions at distances of 1 5 , 1 2 . 5 , 1 0 , 7 . 5 , 5 and 2 . 5 m and so is referenced six t imes in either direction, or twelve times assuming i sotropy . Like point A, point D has a neighbour at four di f ferent lags ; at 2 0 , 1 7 . 5 , 1 5 and 2 . 5 m in the north-south direction , and at separations of 1 7 . 5 , 1 0 , 7 . 5 and 5 m in the east-west direction . Thus , assuming isotropy it is referenced e ight t imes . In marked contrast to the others , point C has a neighbour in the north-south direction at 22 . 5 , 2 0 , 1 7 . 5 , 1 2 . 5 , 1 0 and 7 . 5 m , but has two neighbours at a lag of 2 . 5 m , that i s , it is referenced eight _ times in the estimation of the north-south variogram . In the east-west direction , point C has two neighbours at lags of 1 2 . 5 , 5 and 2 . 5 m and thus i s referenced six times . Assuming i sotropy however , point C i s referenced fourteen times , that i s , two t imes more than point B , and six t imes more than points A and D . Thus , under the nested design , the est imation of the experimental variogram can be unequally inf luenced by some sampl ing s ites which , i f they represent extreme values of the soil property , could lead A to bias in the values of ¥ ( h ) in relation to 5 2 • In the case where the " data are anisotropic , values of ¥ ( h ) which involve reference to point C might be expected to differ in the north-south compared to the east-west direction . c: 01 ·rl U) Q) LO 'd C\.1 01 c: ·rl rl 0.. E 0 10 U) C\.1 'd Q) � U) Q) c: LO Q) ,..... ..c: � 3= 4-l • 0 w 'd 0 ·rl ,..... 1-l 01 c: ·rl 10 E LO Q) ..c: E-< N 0 0 rl 0 LO 0 LO 0 LO Q) ,..... ,..... C\.1 C\.1 1-l ::I (/) 01 I ·rl z ""' 2 2 8 A further complication t o the above i s that i n the case o f lognormal ly distributed data such as SNA and initial No3- which are highly skewed , most of the measured values occur in the low end of the range . However, extreme ( high ) values , whi ch may s t i l l be out l iers after the log transformation , are included in the calculation of S2 but may not carry the same weight as int ermediate and low val ues in . the calculation of A � values of ¥ ( h ) , thus making the values of ¥ ( h ) appear low in relation to "' 52 • The fact that the values of ¥ ( h ) in the case of incubation pH are not inconsistent with the values of 52 may be a ref lection of the lack of extreme values in the data . Indeed , incubation pH ranged between 4 . 4 and 5 . 3 yet the standard error was only 0 . 0 1 pH units which i s equivalent to only 0 . 2% of the mean value value of 5 . 0 4 . In contrast , the standard error for SNA values calculated f rom the ln transformed data us ing equations ( 5 . 5 ) and ( 5 . 6 ) , was equal to 0 . 0 0 0 4 � mol N g- 1 h- 1 , equivalent to 5 % of the mean value of 0 . 008 � mol N g- 1 h- 1 • In the case of initial No3- values , the standard error ( 0 . 0 1 0 � mol N g- 1 ) was equivalent to 8 . 1 % of the mean value ( 0 . 1 2 4 � mol N g- 1 ) . Thus , SNA and No3- had more extreme va lues than incuba t i on pH , and thi s was reflected by the relative A position of values of ¥ ( h ) and 52 in Figures 6 . 3a , b , and d . Indeed , No3- which had the grea test number o f e xtreme s a l so had the greatest ,... discrepancy between s2 and values o f ¥ ( h ) ( Figure 6 . 3b ) . Figure 1 0 . 3 shows the distribution of the ln transformed values of initial N03 - , f itted by ordinary least squares optimization with a normal distribution ( R2 = 0 . 68 , p < 1 % ) . Comparison of Figure 1 0 . 3 with Figures 5 . 4 c , d ; 5 . 6c , d and 6 . 2d , e suggests that the f it of the normal distribution to these log transformed data is not as godd as the fit of the normal distribution to the data for .incubation pH and moisture content , which did not require transformation . One might therefore conclude that the normalization of the lognormal ly distributed SNA and No 3- data has not been entirely satisfactory . The unexpected large number of ln No3 - values in the range 7 . 8-8 . 2 ( Figure 1 0 . 3 ) in relation to the expected small number of values in the range 5 . 8-6 . 2 suggests this to be the case . Normalised frequency Figure 1 0 . 3 0.7 0.6 0 0.5 0.4 0 .3 0.2 0. 1 0 5.5 6 6.5 7 7.5 8 8.5 9 In N03 Distribution of the ln transformed values of initial No3- sampled between 3 - 12 cm depth over 625 m2 us ing a nested sampling design ( F igure 6 . 1 ) 2 3 0 The fact that the above argument does not hold for the Ex-NH4- variogram 1\ ( Figure 6 . 3c ) , where the values of ¥ ( h ) were cons i._s tent with s2 , is no doubt due to the f act that values of Ex-NH4 - were highly variable and showed no spatial dependence . Thus , variation in Ex-NH4- was pure nugget and so the value of s2 and the s il l , in so far as one could be defined by the linear model , were in close agreement . In the light of the above discussion, i t appears that the nested sampling desi gn us ed was not in fact as good as . was thought earl ier . It is therefore suggested that designs of this type , where the nested sampling points are all closely grouped with each other , should be avoided . An alternative means of achieving a small ratio of smallest : largest lag is therefore required . In conclusion, it appears that in order to produce a variogram of good rel iabi l i ty and reproducibi lity , not only must the distribution of the number of pairs at each lag be as even as possible ( Russo , 1 9 84a , Cress ie , 1 98 5 ) , but the number of t imes the sampling points are referenced in the estimation of the variogram must also be approximately equal . The design of such a s ampling scheme i s ano ther avenue of important future research . iv . Crossvariogram analysis In Chapter 6 , the correlation of SNA with No3 - and incubation pH over space was investigated using crossvariogram analysi s , and on the bas is of the crossvariograms produced ( Figure 6 . 4a , b ) , i t was concluded that SNA was not closely correlated with either No3- or pH over space . However , it was not clear what information crossvariograms could provide over and above ordinary corre l a t i on . G iven that the SNA and No3- data , for example , were sampled at the same points in the f ield, it fol lows that any c orre l at i on b e tween them mus t occur over space . A s t a ndard correlation analysis was therefore carried out between the SNA , N03 - and incubation pH data from the nested 625 m2 experiment . I t was found that SNA was not correlated ( p < 5% ) with either pH or N03 - . This may explain why the models f i tted to the crossvariograms for these properties were hori zontal l inear , or pure nugget ; that i s , there was no spatial " dependence between them . However, the fact that values of ¥ z.,. ( h ) were 2 3 1 generally positive was taken to mean that these properties were general ly correlated over space ( Davidof f & Sel im , 1 988 ) , although no indication was given as to the level of signi f icance o f the correlations ( - a shortcoming of the analysis perhaps ? ) . That the results of these two analyses were at odds was further indica ted by the fact that the correlat ion coe f f icient for SNA and No3 - was negative , and although insigni f icant at the 5% level , this would suggest that the interpretation o f the crossvariogram was incorrect . "' A separate correlation analysis was carried out on the values of ¥ ( h ) for S NA , No 3 - a nd i ncubat ion pH , and i t w a s f ound tha t t hese were s igni f icantly correlated . In the case o f SNA and pH , the correlation was s igni f icant at the p < 0 . 1 % confidence level , whi lst that for SNA and No3 - was s igni f i cant at p< 1 % . Since SNA , No3 - and incubat ion pH are a l l spatially dependent , that i s , the variabil ity in their values depends on the dis tance o f separati on of sampl ing sites , it f ol lows from f irst principles tha t at l ags grea ter than the range , their values wi l l f luctuate more than at lags shorter than the range o f spatially dependent variabil ity . Their values may therefore not be expected to be closely correlated . By the s ame token , s ince the variabil ity in all these properties is spatially dependent within ranges of a s imi lar order of magnitude ( 2 . 4 , 5 . 4 and 6 . 1 m respectively for SNA , No3 - and incubation ,.. pH ) , close correlations between their values of ¥ ( h ) over a range of v a l ues of h wou ld be expec ted . Thus , it i s conc l uded tha t the crossvariogram will only fit an exponential or spherical model , that is , w i l l only show spatial dependence of one property on another , when the correlat ion between values of Z ( x:�.. ) and Y ( x:�.. ) , in addi t i on to that A � between ¥ z ( h ) and ¥y ( h ) , is significant . Al ternatively one can say that the crossvariogram is a true test of spa tial eo-dependence . It therefore s eems reasonable to conclude that crossvariogram analysis is potentially useful , but that time and ef fort wi l l be saved in the case o f spa t i a l ly i ndependen t covari abl es if estimation of the crossvariogram i s preceded by a classical test of correlation because this wi l l indicate whether estimation of the crossvariogram is l ikely to be worthwhile . 2 J J I n view of the conclusion that nitrifiers are suff iciently adapted to the prevai ling soil pH that their activity in the f ield is close to that when operating at the pH optimum for nitrif ication , small f luctuations in pH over space might not be expected to cause spatial variation in nitri f ier activity . Hence , the lack of any close correlation over space between SNA and pH i n the nested spatial vari abi lity experiment a s descr ibed in sec tion A ( iv ) above , should not be regarded as an anomalous result . Indeed , i t i s concluded that for the Tokomaru silt l oam , in any given area , a pH value within the range 5 - 6 . 5 would not be a good predictor of nitrif ier activity . The ind i c a t i ons f rom the 1 i terature are that the degree to which n i t r i f i e r a c t iv i t y i s a f fected by various soil properties i s soi l­ specific . In the case of soil pH and moisture ef fects , this conclusion i s supported by the di fferences between values of pHopt i n soils T, TL , TX, TLX and the Patua soi l , and also between these values and those for other soi ls ( Chapter 7 ) , and by the similar di fferences between pFopt for the Tokomaru s i l t loam and publi shed results f or othe r s o i ls . I t m ight therefore be concluded that the spatial variabil ity of nitrif ier activity wi l l also be soil -specific . Thus , dif ferent soi l s w i l l have di fferent ranges o f s pa t i a l dependence f or the paramet e r s o f mine ra l N . Furthermore , the fact that SNA i s not the only factor governing the soi l N03 - concentration , and that other factors such a s plant upt ake and leaching are a lso important , indicates that SNA variabi l i ty is probably not a good e s t imator o f so i l N03 - var iabi l ity . This conclusion i s certainly supported by the geostatistical aspects of the work reported in this thes i s . Thus , with respect to f ield scale estimates of the areal mean soil nitrate concentration , it is concluded that soils should be sampled in the manner described at the end of Chapter 6 , that i s , using clusters o f samples separated by a minimum distance o f 5 m , and analysed primari ly for No3 - concentration . Whether further spatial analysis of the parameters of mineral N in other regions and in other soi ls is w arranted , s o as to def ine an optimum distance for the separation between sampling clusters , w i l l probably be determined by the cost of such analysis . Nevertheless , in spite of the apparent shortcomings of geostatist ical techniques , they obviously have I I ) ' 2 3 4 the potential t o al low for the precise def inition of spatial variabil ity . Assuming that the range of spatial dependence ( if any ) of the other input parameters to the nitrate leaching model can be determined , in addition to that for the soil nitrate concentration , it should be possible to map soil nitrate l eachabi l i ty over large areas us ing kriging . However , the inferred soi l-specif icity of N variability , together with the possibi l ity that in addition to the so il nitrate concentration, the o ther input I parameters to the nitrate leaching model may also be soil-speci f ic , might necessitate sampling and measurement of a range of properties over a wide range of soils . Furthermore , the fact that the variability of mineral N is predominantly short-range will make i t necessary for sampl_ing to be very intensive for nitrate leachability maps to be of real value . The costs involved may be therefore be prohibitive . However , in view of the public concern about the environmental consequences of nitrate leaching , this i s an avenue of future research that should be pursued . 2 3 5 REFERENCES Adams , J . A . 1 986a . Nitrif ication and ammonif ication in acid forest l itter and humus as affected by peptone and ammonium-N ammendment . Soi l Biol ogy & Bi ochemis try 1 8 4 5 - 5 1 . Adams , J . A . 1 9 86b . Identi f ication of heterotrophi c nitri f ication in strongly acid larch humus . Soi l Bi ol ogy & Bi ochemi s try 1 8 3 39- 3 4 1 . A . D . A . S . 1 98 1 . The analysis of agricultural materials . 2nd Ed . H . M . S . O . London . Add i scot t , T . M . 1 9 8 3 . 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( Ed ) N i t rogen in Agr i cul tural Soils . American Society of Agronomy , Monograph No . 2 2 , Madison , Wisconsin . pp . 5 0 3 - 566 . APPENDIX I COMPUTER PROGRAMMES - GAMMAH CO VGM c C ****************************** GAMMAH ************************* c c Robert G . V . Bramley , Dept . Soil Science , Massey University c Palmerston North , New Zealand . c 2 5 3 c c c Thi s programme performs qll the necessary calculations for the spatial analysis of grided data in two perpendicular directions . / /� c The programme assumes a nested design whereby a 2 1 * 2 1 grid c with a grid spacing of 1 2 . 5 cm is nested at the centre of an 1 1 c * 1 1 grid with a 2 . 5 m grid spacing ; samples need not be taken c at every . node , but where there are gaps , a value of zero should c be entered in the data f ile . The data are read in from a data c f i le in which the data are arranged in two matrices - an 1 1 * c 1 1 matrix containing the main grid data , and a 2 1 * 2 1 grid c immediately below it containing the nested grid data . I . c c ( N . B . The size of the grid can be altered by changing the i , j , c lagA and lagB parameters , and the grid spacing may be changed by c altering the initial values of gapA and gapE . ) c c As well as calculating the semivariance at each lag , the c programme output also contains a check on the number of data c points used in the calculations , separated by each lag . c The mean , sample variance and total sum o f squares are also c calculated . c c ( N . B . The " call openfile" command must be replaced by the c longhand FORTRAN alternatives , Read and Write , i f the programme i s c to be run outside Massey University . Access to this routine may be c obtained from H . Murphy , Dept . Soi l Science . ) c c c c Declare Parameters c integer i , j , n , q integer lagA , lagB , mnsA , mnsB , mewA , mewB , mA , mB c Real ZA ( 1 1 , 1 1 ) Real ZB ( 2 1 , 2 1 ) Real mean , sum, var , zsqs , sq, squm , p , sumsqs Real gapA , totnsA , totewA, seminsA , semiewA , semiA , dif f Real gapE , totnsB , totewB , seminsB , semiewB , semiB c c Read in the data c call openfi le ( 5 , ' cal l openfi le ( 6 , ' ' , ' read ' ) ' , ' write ' ) c i corresponds to rows ; j to columns . c do 1 0 i = 1 , 1 1 read ( 5 , * ) ( ZA ( i , j ) , j = 1 , 1 1 ) 1 0 continue c do 2 0 i = 1 , 2 1 read ( 5 , * ) ( ZB ( i , j ) , j = 1 , 2 1 ) 2 0 continue c c c Calculate sample mean and variance c n = 0 sum = 0 . 0 zsqs = 0 . 0 do 3 0 i = 1 , 1 1 do 4 0 j = 1 , 1 1 i f ( i . eq . 5 . and . j . eq . 6 ) goto 4 0 i f ( i . eq . 6 . and . j . eq . 6 ) goto 4 0 i f ( i . eq . 5 . and . j . eq . 7 ) goto 40 if ( i . eq . 6 . and . j . eq . 7 ) goto 40 if ( ( ZA ( i , j ) . l t . 0 ) . or . ( ZA ( i , j ) . gt . 0 ) ) then sum = sJm + ZA ( i , j ) n = n + 1 sq = ( ZA ( i , j ) ) * ( ZA ( i , j ) ) zsqs = zsqs + sq end i f 4 0 continue 3 0 continue c do 5 0 i = 1 , 2 1 do 6 0 j = 1 , 2 1 i f ( ( Z B ( i , j ) . l t . 0 ) . or . ( Z B ( i , j ) . gt . 0 ) ) then sum = sum + ZB ( i , j ) n = n + 1 sq = ( ZB ( i , j ) ) * ( ZB ( i , j ) ) zsqs = zsqs + sq endif 60 continue 5 0 continue c 7 0 c c c mean = sum I n wri te ( 6 , 7 0 ) n , mean, sum format < 11 , ' Mean of ' , i4 , ' data points is ' , f8 . 4 , ' * 0 . 4 1 ) squm = sum * sum p = squm I n sumsqs = zsqs - p q = n - 1 var = sumsqs I q wri te ( 6 , 80 ) var , sumsqs 2 5 4 sum of z is ' , f 1 8 0 format ( ' Sample variance is ' , f 1 2 . 6 , ' * I > Sum of squares is ' , f 1 5 . 6 ll c c c c c c c c Calculate semivariances a ) Nested Grid mnsB = 0 mewB = 0 do 1 80 lags = 1 I 2 0 i f ( lagB . eq . 1 ) then gapB = 0 . 1 2 5 end i f i f ( lagB . ne . 1 ) then gapB = gapB + 0 . 1 25 end i f i f ( gapB . gt . 2 . 0 ) go to 1 80 totrisB = 0 . 0 totewB = 0 . 0 do 1 90 i = 1 1 2 1 - lags do 200 j = 1 1 2 1 2 5 5 i f ( ( ( Z B ( i 1 j ) . lt . 0 ) . or . ( Z B ( i 1 j ) . gt . 0 ) ) . and . ( ( Z B ( i + lagB 1 * j ) . lt . O ) . or . ( ZB ( i + lagB 1 j ) . gt . O ) ) ) then diff = ZB ( i 1 j ) - ZB ( i + lagB 1 j ) mnsB = mnsB + 1 totnsB = totnsB + ( di f f * dif f ) end i f 2 0 0 continue 1 9 0 continue c do 2 1 0 j = 1 1 2 1 - lagB do 220 i = 1 1 2 1 i f ( ( ( ZB ( i 1 j ) . 1 t . 0 ) . or . ( Z B ( i 1 j ) . gt . 0 ) ) . and . ( ( ZB ( i 1 j + lag *B ) . lt . O ) . or , ( ZB ( i 1 j + lagB ) . gt . O ) ) ) then diff = ZB ( i 1 j ) - ZB ( i 1 j + lagB ) mewB = mewB + 1 totewB = totewB + ( di f f * dif f ) end i f 2 2 0 continue 2 1 0 continue c c c c seminsB = totnsB I ( 2 . 0 * mnsB ) semiewB = totewB I ( 2 . 0 * mewB ) mB = mnsB + mewB semiB = ( totnsB + totewB ) I ( 2 . 0 * mB ) write ( 6 1 23 0 ) gapB write ( 6 1 24 0 ) mnsB 1 seminsB write ( 6 1 25 0 ) mewB 1 semiewB write ( 6 1 260 ) mB 1 semis 2 3 0 format ( ' With a lag o f ' 1 f6 . 3 1 ' m : ' I I > 2 4 0 format ( ' In a "north-south" direction with ' 1 i 3 1 ' pairs of point *5 1 gamma = ' 1 f 1 0 . 8l ) 2 5 0 format ( ' In an "east-west " direction with ' 1 i3 1 ' pairs of points *1 gamma = ' 1 f 1 0 . 8ll > 2 5 6 2 6 0 f ormat ( ' I f data are i sotropic , there are a total of ' , i S , ' pai r *s of points , ' , / , ' with gamma = ' , f 1 0 . 8/ // / / ) cc mnsB = 0 mewB = 0 1 80 continue c c c b ) Main Grid c c c mnsA = 0 mewA = 0 do 9 0 l agA = 1 , 1 0 i f ( lagA . eq . 1 ) then gapA = 2 . 5 endif · i f ( lagA . ne . 1 ) then gapA = gapA + 2 . 5 end i f totnsA = 0 . 0 totewA = 0 . 0 do 1 0 0 i = 1 , 1 1 - lagA do 1 1 0 j = 1 , 1 1 i f ( ( ( ZA ( i , j ) . lt . 0 ) . or . ( ZA ( i , j ) . gt . 0 ) ) . and . ( ( ZA ( i + lagA , * j ) . lt . O ) . or . ( ZA ( i + lagA , j ) . gt . O ) ) ) then di f f = ZA ( i , j ) - ZA ( i + lagA, j ) mnsA = mnsA + 1 totnsA = totnsA + ( di f f * di ff ) end i f 1 1 0 continue 1 0 0 continue c c do 1 2 0 j = 1 , 1 1 - lagA do 1 3 0 i = 1 , 1 1 i f ( ( ( ZA ( i , j ) . 1 t . 0 ) . or . ( ZA ( i , j ) . gt . 0 ) ) . and . ( ( ZA ( i , j + lag *A ) . lt . O ) . or . ( ZA ( i , j + lagA ) . gt . O ) ) ) then dif f = ZA ( i , j ) - ZA ( i , j + lagA ) mewA = mewA + 1 totewA = totewA + ( di f f * diff ) end i f 1 3 0 continue 1 20 continue c c c seminsA = totnsA I ( 2 . 0 * mnsA ) semiewA = totewA I ( 2 . 0 * mewA ) mA = mnsA + mewA semiA = ( totnsA + totewA ) I ( 2 . 0 * mA ) write ( 6 , 1 40 ) gapA wri te ( 6 , 1 50 ) mnsA, seminsA write ( 6 , 1 60 ) mewA , semiewA write ( 6 , 1 70 ) mA , semiA c 1 40 1 50 *s , 1 60 * , 1 7 0 *s c 2 5 7 format ( ' With a lag o f ' , f 4 . 1 , ' m : ' I l l f ormat ( ' In a "north-south" direction with ' , i3 , ' pairs o f point gamma = ' , f 1 0 . 8l l f ormat ( ' In an "east-west " direction with ' , i 3 , ' pairs of points gamma = ' , f 1 0 . 8l l l f ormat ( ' If data are i sotropic , there are a total of ' , iS , ' pair o f points , ' , I , ' wi th gamma = ' , f 1 0 . 8lll l mnsA = 0 mewA = 0 9 0 continue c c write ( 6 , 2 70 ) 2 70 f ormat ( I I , ' N . B . Formulae used in this programme are as fol lows : ' * , I I , ' Mean = Sum Z ( i , j ) I n ' , I I , ' Sum o f squares = Sum Z ( i , j ) - 2 - { [ *Sum Z ( i , j ) ] - 2 I n } I { n - 1 } ' , 11 , ' Variance = Sum of squares I {n ­ * 1 } ' I I ' Gamma ( h ) = { 1 I 2m ( h ) } * Sum { [ z ( x ) - z ( x + h ) ] - 2 } ' ) c stop end c c ****************************** COVGM ************************* c c Robert G . V . Brarnley , Dept . Soi l Science , Massey University c Palmerston North, New Zealand . c c Thi s programme i s an adapted version of GAMMAH and calculates c cross-semivariances for two properties arranged in a nested c grid as per GAMMAH . c c c Decl are Parameters c integer i , j integer l agA1 l agB 1 rnnsA1 mnsB 1 mewA 1 mewB 1 mA , rnB c Real ZA ( 1 1 1 1 1 ) Real ZB ( 2 1 ' 2 1 ) Real QA ( 1 1 1 1 1 ) Rea l QB ( 2 1 1 2 1 ) Real dif f Z , d i f fQ Rea l gapA1 totnsA , totewA , seminsA1 Rea l gapB , totnsB , totewB , seminsB1 c cal l openfile ( 6 1 ' c c Read in the data c ' 1 ' write ' ) c i corresponds to rows ; j to columns . c cal l openfi le ( 5 1 ' I I I read I ) c do 1 0 i = 1 1 1 1 read ( 5 1 * ) ( ZA ( i 1 j ) , j = 1 1 1 1 ) 1 0 continue c do 2 0 i = 1 1 2 1 read ( 5 , * ) ( ZB ( i I j ) I j = 1 1 2 1 ) 2 0 continue c cal l openfile ( 5 1 ' I I I read I ) c 1 1 c 2 1 c c c do 1 1 i re. ad continue do 2 1 i read continue = 1 ' ( 5 1 = 1 I ( 5 1 1 1 * ) 2 1 * ) ( QA ( i 1 j ) 1 ( QB ( i , j ) , j j = = 1 I 1 1 ) 1 I 2 1 ) semiewA 1 semi A semiewB , semiB 2 5 8 c Calculate cross semi-variances c c a ) Nested Grid c c c mnsB = 0 mewB = 0 do 1 80 lagB = 1 1 2 0 i f ( lagB . eq . 1 ) then gapB = 0 . 1 25 end i f i f ( lagB . ne . 1 ) then gapB = gapB + 0 . 1 2 5 end i f i f ( gapB . gt . 2 . 0 ) goto 1 80 totnsB = 0 . 0 totewB = 0 . 0 i do 1 9 0 i = 1 1 2 1 - lagB do 2 0 0 j = 1 1 2 1 2 5 9 i f ( ( ( ZB ( i 1 j ) . lt . O ) . or . ( ZB ( i , j ) . gt . O ) ) . and . ( ( ZB ( i + lagB , *j ) . l t . O ) . or . ( ZB ( i + lagB 1 j ) . gt . O ) ) ) then diffZ = ZB ( i 1 j ) - ZB ( i + lag8 1 j ) diffQ = QB ( i 1 j ) - QB ( i + lag8 1 j ) mnsB = mnsB + 1 totnsB = totnsB + ( di f f Z * diffQ ) end i f 2 0 0 continue 1 9 0 continue c do 2 1 0 j = 1 1 2 1 - lagB do 220 i = 1 , 2 1 i f ( ( ( ZB ( i , j ) . lt . 0 ) . or . ( Z B ( i 1 j ) . gt . 0 ) ) . and . ( ( ZB ( i 1 j + lag *B ) . lt . O ) . or . ( ZB ( i , j + lagB ) . gt . O ) ) ) then diffZ = ZB ( i 1 j ) - ZB ( i , j + lagB ) dirfQ = QB ( i 1 j ) - QB ( i 1 j + lagB ) mewB = mewB + 1 totewB = totewB + ( d iffZ * dif fQ ) endif 2 2 0 continue 2 1 0 continue c c c c seminsB = totnsB I ( 2 . 0 * mnsB ) semiewB = totewB I ( 2 . 0 * mewB ) mB = mnsB + mewB semiB = ( totnsB + totewB ) I ( 2 . 0 * mB ) write ( 6 1 23 0 ) gapB write ( 6 1 2 4 0 ) mnsB , seminsB write ( 6 1 25 0 ) mewB 1 semiewB write ( 6 1 2 60 ) m8 1 semiB 2 3 0 f ormat ( ' Wi th a lag of ' 1 f 6 . 3 1 ' m : ' I I ) 2 4 0 format ( ' In a "north-south" direction with ' , i3 1 ' pairs o f point *s 1 gamma = ' 1 f 1 0 . 8l ) 2 6 0 2 5 0 format ( ' In an "east-west" direction with ' , i3 , ' pairs o f points * , gamma = ' , f 1 0 . 8l l l 2 6 0 format ( ' I f data are i sotropic , there are a total o f ' , i S , ' pair cc *s of points , ' , I , ' wi th gamma = ' , f 1 0 . 8IIIII J mnsB = 0 mewB = 0 1 80 continue c c c b ) Main Grid c c c mnsA = 0 mewA = 0 do 9 0 lagA = 1 , 1 0 i f ( lagA . eq . 1 ) then gapA = 2 . 5 end i f i f ( lagA . ne . 1 ) then gapA = gapA + 2 . 5 end i f totnsA = 0 . 0 totewA = 0 . 0 do 1 00 i = 1 , 1 1 - lagA do 1 1 0 j = 1 , 1 1 i f ( ( ( ZA ( i , j ) . l t . 0 ) . or . ( ZA ( i , j ) . gt . 0 ) ) . and . ( ( ZA ( i + lagA , *j ) . lt . O ) . or . ( ZA ( i + lagA , j ) . gt . O ) ) ) then di f fZ = ZA ( i , j ) - ZA ( i + l agA, j ) diffQ = QA ( i , j ) - QA ( i + l agA , j ) mnsA = mnsA + 1 totnsA = totnsA + ( diffZ * di ffQ ) endif 1 1 0 continue 1 00 continue c c do 1 20 j = 1 , 1 1 - lagA do 1 3 0 i = 1 , 1 1 i f ( ( ( ZA ( i , j ) . l t . 0 ) . or . ( ZA ( i , j ) . gt . 0 ) ) . and . ( ( ZA ( i , j + lag *A ) . lt . O ) . or . ( ZA ( i , j + lagA ) . gt . O ) ) ) then di f fZ = ZA ( i , j ) - ZA ( i , j + lagA ) di f fQ = QA ( i , j ) - QA ( i , j + lagA ) mewA = mewA + 1 totewA = totewA + ( diffZ * diffQ ) end i f 1 3 0 continue 1 20 continue c c seminsA = totnsA I ( 2 . 0 * mnsA ) semiewA = totewA I ( 2 . 0 * mewA ) mA = mnsA + mewA semiA = ( totnsA + totewA ) I ( 2 . 0 * mA ) c c 1 4 0 1 50 1 60 1 70 c 9 0 c c *s , * I *s 2 6 1 write ( 6 , 1 40 ) gapA write ( 6 , 1 50 ) mnsA , seminsA write ( 6 , 1 60 ) mewA , semiewA write ( 6 , 1 7 0 ) mA , semi A format ( ' With a lag of ' , f4 . 1 , ' m : ' / / ) format ( ' In a "north-south" direction with ' , i 3 , ' pairs o f point gamma = ' , f 1 0 . 8/ ) format ( ' In an "east-west " direction with ' , i3 , ' pairs o f points gamma = ' , f 1 0 . 8// ) format ( ' I f data are i sotropic , there are a total of ' , iS , ' pair of points , ' , / , ' wi th gamma = ' , f 1 0 . 8/ / / ) mnsA = 0 mewA = 0 continue write ( 6 , 2 7 0 ) 270 format ( // , ' N . B . Formulae used in this programme are as fol lows : ' * , // ' Gamma ( h ) = { 1 /2m ( h ) } * Sum { [ Z ( x ) -Z ( x+h ) ] [ Q ( x ) -Q ( x+h ) ] } ' ) c stop end