A dynamic simulation model for planning and controlling grazing systems : a thesis presented in partial fulfilment of the requirements for the degree of Master of Applied Science in Agricultural Systems and Management at Massey University - Barioni, Luis G (Massey University, 1997)
This study reports on the development of a stochastic dynamic model to simulate a pastoral sheep enterprise. The event driven model was constructed using the iconic simulation package, ExtendTM. Events corresponded to the shifting of animals from one paddock to another. Each paddock was represented as a single entity with inherent attributes such as grazing area, sward characteristics and pasture production potential. The rotation sequence for grazing was determined by always allocating the flock of ewes, flock replacements or lambs to the paddock with the greatest pasture mass. Herbage mass was divided into three fractions: leaf, stem and dead. Pasture growth and senescence rates for individual paddocks were calculated from pasture leaf mass. A Micherlich-type function was used to relate leaf mass to total pasture growth. Senescence was assumed to increase linearly with herbage mass. Deterministic or stochastic pasture growth rate data can be generated by the model. Pasture responses to nitrogen were estimated dynamically and moderated for the farm by entering a user-defined response for a standard 50 kg/ha nitrogen application. Animal performance was calculated using average attributes for ewes, ewe hoggets and rams, but lambs were simulated individually. Lamb performance is affected by its date of birth and sex, and this information was generated by a sub-model for mixed-age ewe and ewe hogget reproduction. The potential herbage intake of the sheep was defined by their rumen fill and physiological energy demand, and herbage availability which was defined by pre-grazing green herbage mass and green herbage allowance for rotational grazing and leaf mass for continuous grazing. The grazing time spent in each paddock was derived from a linear interpolation of user-defined herbage allowances for each month of the year. The proportion of leaf, stem and dead material in the diet was calculated according to the proportion of these fractions in the sward and herbage availability. If animals were supplemented they consumed all of the material offered. This caused pasture substitution by decreasing the physiological energy demand and utilising rumen space otherwise taken up by grazed pasture. The partitioning of nutrients by animals was estimated from the ratio between energy intake and energy demand in an animal growth sub-model. This was driven by the DNA, protein and fat content of individual lambs and the average for animals in other sheep classes. Lambs were drafted for sale and graded according to user-defined threshold drafting weights. Carcass weight and fatness (GR) were generated from the live weight and sex of individuals lambs. A genetic optimisation algorithm was developed to optimise the systems control variables incorporated in the model. These were pasture allowance, supplement fed, nitrogen applied and lamb drafting weight. The model was evaluated against three New Zealand "farmlet" grazing experiments. This validation suggested re-parameterisation of the physiological intake limit is needed and that the British equation used to relate intake to leaf mass availability is overly sensible to the pasture conditions found in New Zealand. The model was also used to test the effects of pasture measurement errors on the profitability of a grazing system. Significant differences in profitability occurred when a CV of 40% in measurement of pasture mass was assumed (Gross margin = $NZ 495 /ha vs. $NZ 542 /ha and $NZ 570 /ha for 20 and 0% CV in measurement estimations and normal variability in pasture accumulation rates and Gross margin = $NZ 587 /ha, $NZ 576/ha and $NZ 519/ha, respectively for 40,20 and 0% CV in measurement estimates and no pasture accumulation rate variability). It was concluded that low gains in system performance can be expected by improving the accuracy of measuring pre-grazing herbage mass beyond the level (13-16% CV) provided by the correct use of current measurement techniques.
Keywords: Range management; Grazing; Sheep; Economic aspects; Mathematical models; Simulation models