Abstract

Many factors contribute to the profitability of sheep farms. A sensitivity analysis, using a simulation model, was conducted to investigate the impact of changes in pasture growth, ewe live weight, conception rate, ewe prolificacy (EP), lamb survival, lamb growth rate and lamb price on farm gross margin (FGM). The model simulated herbage growth and flock energy requirement on a daily basis over a one-year period starting on the 1st January and calculated gross margins for a 500 ha farm, using average Manawatu herbage growth rates. Ewe energy requirements were calculated as a function of live weight, lambing percentage, week of pregnancy or lactation and milk production and lamb energy requirements as a function of live weight, growth rate and body composition. The pre-weaning survival rate and lamb daily growth rate were adjusted for litter size. For each simulation, stocking rates were adjusted so that the pasture cover at the beginning and the end of the year was 1200 kgDM/ha. Simulations were conducted for different farm productivity levels (‘Low’, ‘Medium’ and ‘High’). Results of the simulations show that, over all farm productivity levels, lamb price and growth rate had the largest impact on FGM followed by ewe liveweight, EP, conception rate and herbage growth. However, the ranking and the effect of these parameters on FGM varied across farm types. For example in "Low productivity farms" a 1 % increase in lamb growth rate has the same impact on FGM as a 1 % increase in EP (+$5.4 / ha vs +$5.9 / ha) and in "High productivity farms" a 1 % increase in lamb growth rate is equivalent to a 3 % increase in EP (+$11.2 / ha vs +$11.1 / ha). This illustrates that for each specific farm production level there are different management strategies to optimize FGM. It was also found that the relationship between EP and FGM was not linear. Across the liveweight ranges a one percent increase in EP between 140% and 160% increases FGM by $3.95 /ha, between 160% and 180% by $2.51 /ha, between 180% and 200 % by $1.33 /ha, between 200% and 220% by $2.83/ha, and between 220% and 240% by $2.33/ha. Simulation models can be useful tools when considering farm management decisions as they help identify optimal management systems and identify areas that may require further research.

PCH, Morel, and PR Kenyon

Proceedings of the New Zealand Society of Animal Production, Volume 66, Napier, 377-381, 2006