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Foods 2016, 5(3), 52; doi:10.3390/foods5030052

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

1
Department of Organic Food Quality and Food Culture, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany
2
Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn, Germany
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Wijitha Senadeera
Received: 13 April 2016 / Revised: 7 July 2016 / Accepted: 18 July 2016 / Published: 23 July 2016
(This article belongs to the Special Issue Food Modelling)
View Full-Text   |   Download PDF [3802 KB, uploaded 23 July 2016]   |  

Abstract

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 R2 (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 R2 (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. View Full-Text
Keywords: AIC; LASSO; climate indices; ESI; ETI; HLI; RRP; THI; linear regression model; milk components AIC; LASSO; climate indices; ESI; ETI; HLI; RRP; THI; linear regression model; milk components
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Marami Milani, M.R.; Hense, A.; Rahmani, E.; Ploeger, A. 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. Foods 2016, 5, 52.

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