Buda Mark, Raper Kellie Curry, Riley John Michael, Peel Derrell S
Department of Agribusiness and Bioresource Economics, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.
Department of Agricultural Economics, Oklahoma State University, Stillwater, OK, United States.
Front Vet Sci. 2023 Jan 30;10:1087080. doi: 10.3389/fvets.2023.1087080. eCollection 2023.
Industry reports and anecdotal evidence indicate that the death loss rate in cattle feedlots has increased over time. Such increases in death loss rates impact feedlot cost and thus profitability.
The primary objective of this study is to examine whether feedlot death loss rates in cattle have changed over time, to analyze the nature of any identified structural change, and to identify possible catalysts for that change.
Data from the Kansas Feedlot Performance and Feed Cost Summary from 1992 through 2017 is used to model feedlot death loss rate as a function of feeder cattle placement weight, days on feed, time, and seasonality in the form of monthly dummy variables. Commonly used tests of structural change, including the CUSUM, CUSUMSQ, and Bai and Perron methods, are implemented to examine the existence and nature of any structural changes in the proposed model. All tests indicate the presence of structural breaks in the model, including both systematic change and abrupt change. Following a synthesis of structural test results, the final model is modified to include a structural shift parameter for the period from December 2000 to September 2010.
Models indicate that days on feed has a significant positive influence on death loss rate. Trend variables indicate that death loss rates have increased systematically over the period studied. However, the structural shift parameter in the modified model is positive and significant for December 2000 to September 2010, indicating that death loss is higher on average during this period. Variance of death loss percentage is also higher during this period. Parallels between evidence of structural change and possible industry and environmental catalysts are also discussed.
Statistical evidence does indicate changes in the structure of death loss rates. Ongoing factors such as changes in feeding rations prompted by market forces and feeding technologies may have contributed to systematic change. Other events, such as weather events and beta agonist use could result in abrupt changes. No clear evidence directly connects these factors to death loss rates and disaggregated data would be required to facilitate such a study.
行业报告和传闻证据表明，随着时间的推移，肉牛饲养场的死亡率有所上升。死亡率的这种上升会影响饲养场成本，进而影响盈利能力。
本研究的主要目的是检验肉牛饲养场的死亡率是否随时间发生了变化，分析任何已识别出的结构变化的性质，并确定这种变化可能的促成因素。
使用1992年至2017年堪萨斯饲养场性能和饲料成本汇总的数据，将饲养场死亡率建模为育肥牛投放体重、饲养天数、时间以及以月度虚拟变量形式表示的季节性的函数。实施常用的结构变化检验，包括累积和（CUSUM）、累积平方和（CUSUMSQ）以及白和佩龙方法，以检验所提出模型中任何结构变化的存在和性质。所有检验均表明模型中存在结构断点，包括系统性变化和突变。综合结构检验结果后，对最终模型进行修改，纳入了2000年12月至2010年9月期间的结构转移参数。
模型表明，饲养天数对死亡率有显著的正向影响。趋势变量表明，在所研究的时间段内死亡率呈系统性上升。然而，修改后模型中的结构转移参数在2000年12月至2010年9月期间为正且显著，表明在此期间平均死亡率更高。在此期间死亡率百分比的方差也更高。还讨论了结构变化证据与可能相关的行业和环境促成因素之间的相似之处。
统计证据确实表明死亡率结构发生了变化。诸如市场力量和饲养技术引发的饲料配方变化等持续因素可能导致了系统性变化。其他事件，如天气事件和β-激动剂的使用可能导致突变。没有明确证据直接将这些因素与死亡率联系起来，需要分类数据来推动此类研究。
