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A study of the effect of inputs on level of production of dairy farms in Queensland - a comparative analysis of survey data

机译:投入对昆士兰州奶牛场生产水平的影响研究-调查数据的比较分析

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摘要

Summary. Multiple linear regression models able to estimate total farm milk production from nutritional inputs were developed from farm survey data provided by dairy farmers in Queensland, Australia. These models were specifically developed for inclusion in a decision support system that could provide dairy farmers with an annual milk production estimate, thus enabling them to compare their production with an average farm using the same inputs in their region. Separate models were developed for each of 4 regions in Queensland and an additional model was developed for farms producing greater than 750 kL of milk per farm per year. The models were tested on dairy farms in Queensland by using the decision support system on farms that were not involved with initial model development. The partial regression coefficients for the models were biologically sensible and, apart from some minor interactions between independent variables in 2 regions, were additive. These interactions were not included in the final model in the interests of parsimony, ease of explanation and a need to provide transparent models within the decision support system. The coefficients of determination (R2) for the models varied from 79.9 to 88.3%. Forward-feed artificial neural network models were also used to confirm the relative accuracy of the multiple linear regression models and to allow for any interactions or non-linear functions in the data and to show that the simple equations are more appropriate for a farmer-orientated decision support system.
机译:摘要。根据澳大利亚昆士兰州奶农提供的农场调查数据,开发了能够通过营养投入估算总牛奶产量的多个线性回归模型。这些模型是专门为包含在决策支持系统中而开发的,该系统可以为奶农提供每年的牛奶产量估算值,从而使他们可以将他们的产量与使用该地区相同投入的普通农场进行比较。为昆士兰州的四个地区分别开发了单独的模型,并为每个农场每年生产超过750 kL牛奶的农场开发了另一个模型。通过在不参与初始模型开发的农场上使用决策支持系统,在昆士兰州的奶牛场上对模型进行了测试。该模型的部分回归系数具有生物学敏感性,除了两个区域中自变量之间的一些小相互作用外,还具有累加性。出于简约性,易于解释以及需要在决策支持系统中提供透明模型的目的,这些交互未包括在最终模型中。模型的确定系数(R2)从79.9%到88.3%不等。还使用前馈人工神经网络模型来确认多元线性回归模型的相对准确性,并允许数据中存在任何相互作用或非线性函数,并表明简单方程更适合于以农民为导向的模型决策支持系统。

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  • 来源
    《Animal Production Science》 |1998年第5期|p.419-425|共7页
  • 作者单位

    A Queensland Department of Primary Industries, Australian Tropical Dairy Institute, Mutdapilly Research Station,Mail Service 825, Peak Crossing, Qld 4306, Australia;

    author for correspondence;

    e-mail: kerrd@dpi.qld.gov.auB Faculty of Environmental Sciences, Griffith University, Kessels Road, Nathan, Qld 4111, Australia.C Queensland Department of Primary Industries, Australian Tropical Dairy Institute, PO Box 6014,Rockhampton Mail Centre, Rockhampton, Qld 4702, Australia.D Dairy Research and Development Corporation, Level 3/84 William Street, Melbourne, Vic. 3000, Australia.E Queensland Department of Primary Industries, Australian Tropical Dairy Institute, PO Box 102,Toowoomba, Qld 4350, Australia.;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    regression models, decision support systems.;

    机译:回归模型;决策支持系统。;

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