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Please use this identifier to cite or link to this item: http://nbn-resolving.de/urn:nbn:de:hebis:34-2016110951358

Title: 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
Authors: Marami Milani, Mohammad RezaHense, AndreasRahmani, ElhamPloeger, Angelika
???metadata.dc.subject.ddc???: 630 - Landwirtschaft, Veterinärmedizin (Agriculture)
Issue Date: 23-Jul-2016
Citation: In: Foods. - Basel : MDPI. - 2016, 5(3), 52
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.
URI: urn:nbn:de:hebis:34-2016110951358
additional URI: doi:10.3390/foods5030052OA-GEF
ISSN: 2304-8158
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