Browsing by Author "Rollo M"

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    Estimating direct N2O emissions from sheep, beef, and deer grazed pastures in New Zealand hill country: accounting for the effect of land slope on N2O emission factors from urine and dung
    (Elsevier, 2015-03) Saggar SK; Giltrap DL; Davison R; Gibson R; de Klein CAM; Rollo M; Ettema P; Rys G
    Nearly one-half of New Zealand's ruminant livestock graze on hill country pastures where spatial differences in soil conditions are highly variable and excretal deposition is influenced by pasture production, animal grazing and resting behaviour that impact the nitrous oxide (N2O) emission factor from excreta (EF3). New Zealand currently uses country-specific EF3 values for urine and dung of 0.01 and 0.0025, respectively, to estimate direct N2O emissions from excreta. These values have largely been developed from trials on flat pastoral land. The use of the same EF3 for hill pasture with medium and steep slopes has been recognised as a possible source of overestimation of N2O emissions in New Zealand. The objectives of this study were to develop and describe an approach that takes into account the effects of slope in estimating hill country N2O emissions from the dung and urine of ruminant animals (sheep, beef cattle, and deer) across different slope classes, and then compare these estimates with current New Zealand inventory estimates. We use New Zealand as a case study to determine the direct N2O emissions between 1990 and 2012 from sheep, beef cattle and deer excreta using updated estimates of EF3 for sloping land, the area of land in different slope classes by region and farm type, and a nutrient transfer model to allocate excretal-N to the different slope classes, and compare the changes between these hill pastures-specific and current inventory estimates. Our findings are significant - the proposed new methodology using New Zealand specific EFs calculated from a national series of hill country experiments resulted in 52% lower N2O estimates relative to using current inventory emission factors, for the period between 1990 and 2012 and reduces New Zealand's total national agricultural N2O greenhouse inventory estimates by 16%. The improved methodology is transparent, and complete, and has improved accuracy of emission estimates. On this basis, the improved methodology of estimating N2O emission is recommended for adoption where hill land grasslands are grazed by sheep, beef cattle and deer.
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    Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting
    (John Wiley and Sons Inc on behalf of American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2022-09-30) Vibart R; Giltrap D; Saggar S; Mackay A; Betteridge K; Costall D; Rollo M; Draganova I; Zhu-Barker. X
    In New Zealand, nitrous oxide emissions from grazed hill pastures are estimated using different emission factors for urine and dung deposited on different slope classes. Allocation of urine and dung to each slope class needs to consider the distribution of slope classes within a landscape and animal behavior. The Nutrient Transfer (NT) model has recently been incorporated into the New Zealand Agricultural GHG Inventory Model to account for the allocation of excretal nitrogen (N) to each slope class. In this study, the predictive ability of the transfer function within the NT model was explored using urine deposition datasets collected with urine sensor and GPS tracker technology. Data were collected from three paddocks that had areas in low (<12°), medium (12-24°), and high slopes (>24°). The NT model showed a good overall predictive ability for two of the three datasets. However, if the urine emission factors (% of urine N emitted as N2 O-N) were to be further disaggregated to assess emissions from all three slope classes or slope gradients, more precise data would be required to accurately represent the range of landscapes found on farms. We have identified the need for more geospatial data on urine deposition and animal location for farms that are topographically out of the range used to develop the model. These new datasets would provide livestock urine deposition on a more continuous basis across slopes (as opposed to broad ranges), a unique opportunity to improve the performance of the NT model.