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Using grazing systems models to evaluate business options for fattening dairy bulls in a region with a highly variable feed supply

机译:使用放牧系统模型评估饲料供应高度变化地区的育肥公牛的商业选择

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A major challenge for profitable meat production in grazing systems is to take account of the effects of diet selection on intake, the substitution of pasture for supplements fed and the potential for compensatory weight gains, particularly if the pasture supply and quality varies throughout the production period. Economically successful production outcomes depend on the skill of the farm manager to integrate these nutritional issues within the constraints imposed by the environment. In south-eastern Australia the risk of financial failure is particularly high because the amount of dry matter (DM) and energy and protein content of rain-fed pastures, typically highly productive mixed swards of Lolium perenne and Trifolium subterreaneum, can vary widely due to the erratic timing of opening rains in autumn. Modelling is one way to manage this biophysical complexity and to quantify some of the uncertainties for managers of livestock businesses. This paper describes the use of a grazing systems decision support tool, GrassGro, to help a beef producer in south-eastern Australia make a decision about buying 12-month-old 300-350kg live weight (LW) Holstein dairy bulls for fattening to a market specification (530-750kgLW) over the ensuing 12 months. The initial business decision was to decide on the number of bulls to buy, a stocking rate issue and then to deal with the uncertainties of subsequent feed supply and meeting market specifications. To model this bull meat enterprise, a description of local soil and historical daily weather records were needed as inputs to GrassGro to simulate the growth of the main pasture species sown on the farm. GrassGro then computed the daily dry matter (DM) intake and weight gain of the bulls based on their metabolizable energy (ME) and protein requirements, which were specific for their genotype, maturity level, live weight and body condition. GrassGro includes models of diet selection and substitution, and the simulation of compensatory weight gain in making these assessments. Gross margin analysis that used this information from the simulations showed that the bull beef enterprise was potentially highly profitable but it also carried high risks of financial loss if the start of the pasture-growing season was delayed. These production and economic outcomes were very sensitive to stocking rate, so the number of bulls purchased was a key decision for financial success. In this environment, the best financial outcome was achieved when 2.5bulls/ha were purchased. Sensitivity analysis using GrassGro showed that purchase of similar numbers of younger, lighter bulls (2.5-3.5bulls/ha) increased the mean gross margin by up to AUD$600/ha, despite a higher risk of failure to achieve the target weight.
机译:放牧系统中肉类生产的盈利面临的主要挑战是要考虑饮食选择对摄入量的影响,用牧场代替饲喂的补品以及补偿体重增加的潜力,尤其是如果牧场的供应和质量在整个生产期间有所变化时。经济上成功的生产结果取决于农场经理的技能,以将这些营养问题整合到环境所约束的范围内。在澳大利亚东南部,财务危机的风险特别高,因为雨养牧场(通常是高产黑麦草和地下三叶草的高产混合草)的干物质(DM)量以及能量和蛋白质含量可能由于秋季开雨的时间不稳定。建模是管理这种生物物理复杂性并对牲畜企业管理者进行量化的一些不确定性的一种方法。本文介绍了放牧系统决策支持工具GrassGro的使用,以帮助澳大利亚东南部的牛肉生产商做出购买12个月大的300-350kg活重(LW)荷斯坦奶牛育肥的决定。随后十二个月内的市场规格(530-750kgLW)。最初的业务决策是确定要购买的多头数量,放养率问题,然后处理后续饲料供应的不确定性和满足市场规格。为了对这个牛市企业进行建模,需要对当地土壤和历史每日天气记录进行描述,作为GrassGro的输入,以模拟农场种植的主要牧场物种的生长。然后GrassGro根据公牛的代谢能(ME)和蛋白质需求量(它们的基因型,成熟度,活体体重和身体状况),计算了公牛的每日干物质(DM)摄入量和体重增加。 GrassGro包括饮食选择和替代模型,以及进行这些评估时补偿体重增加的模拟。使用来自模拟的此信息的毛利率分析显示,牛市企业可能具有很高的利润率,但是如果放牧季节的开始被推迟,它也将带来很高的财务损失风险。这些生产和经济结果对放养率非常敏感,因此购买多头牛的数量是财务成功的关键决定。在这种环境下,购买2.5公顷/公顷可获得最佳的财务结果。使用GrassGro进行的敏感性分析显示,尽管未能达到目标体重的风险较高,但购买相同数量的更年轻,更轻的公牛(2.5-3.5 bulls / ha)可使平均毛利率增加至AUD $ 600 / ha。

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