• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Foods >Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk
【24h】

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

机译：应用最小绝对收缩选择算子和Akaike信息准则分析，找到最佳的气候指标与牛奶成分之间的多元线性回归模型

获取原文
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new ), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p -value < 0.001 and R 2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation ( p -value < 0.001) with R 2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.
机译：本研究着重于将六个气候指数（温度湿度THI，环境应力ESI，当量温度指数ETI，热负荷HLI，改良的HLI（HLI new）和呼吸速率预测值RRP）与牛奶的三个主要成分相关联的多个线性回归模型（产量，脂肪和蛋白质）在伊朗的奶牛。运用最小绝对收缩选择算子（LASSO）和Akaike信息标准（AIC）技术，为气候预测因子数量最少的牛奶预测因子选择最佳模型。通过重采样应用自举来进行不确定性估计。交叉验证用于避免过度拟合。气候参数是根据NASA-MERRA全球大气再分析得出的。使用2002年4月至9月至2010年9月的牛奶数据。春季发现最佳的线性回归模型是在产奶量之间进行预测，而在THI，​​ESI，ETI，HLI和RRP之间分别作为p值<0.001和R 2（0.50，0.49）的预测指标。在夏季，具有THI，ETI和ESI自变量的牛奶产量与R 2（0.69）的相关性最高（p值<0.001）。对于脂肪和蛋白质，结果只是微不足道的。当可获得短时间序列或不确定性较大的数据时，建议将该方法用于气候变化/变化对农业和食品科学领域的影响研究。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号