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dc.contributor.authorBarrientos, J.A.
dc.contributor.authorReed, K.F.
dc.description.abstractImplementing the current standard forage sampling practices may not yield samples that accurately represent the quality of the delivered forages in dairy diets. Sampling frequency, number of samples, and allowed deviation in the change of forage quality can be optimized according to the farm characteristics to improve diet accuracy and interpretation of forage samples. For a period of 16 weeks in the winter of 2020 and spring of 2021, we collected corn silage and haylage samples in duplicate 3 days per week at feedout from 8 NYS dairy farms and 3 silage storage methods (bunker, bag, and drive-over-pile). We are using these data as inputs in a renewal reward model and apply a genetic algorithm optimization method to estimate the optimum sampling practice of each farm. The objective of this study is to increase sampling efficiency and diet accuracy by optimizing sampling practices at eight different NYS dairy farms.en_US
dc.subjectCorn silageen_US
dc.subjectQuality control charten_US
dc.subjectGenetic algorithmen_US
dc.subjectRenewal reward modelen_US
dc.titleOptimizing Sampling Practices at NYS Dairy Farmsen_US
dc.typeconference papers and proceedingsen_US

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