MohammadTaghi Fayazikia; Mohammad Dadpasand; Hamideh Keshavarzi
Volume 25, Issue 2 , July 2023, , Pages 123-132
Abstract
Introduction Mastitis is one of the most frequent and costly diseases of the dairy cattle industry and causes many economic losses, which negatively affects milk yield and composition, fertility, longevity and welfare of cows. The best solution for reducing the economic and biological consequences is ...
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Introduction Mastitis is one of the most frequent and costly diseases of the dairy cattle industry and causes many economic losses, which negatively affects milk yield and composition, fertility, longevity and welfare of cows. The best solution for reducing the economic and biological consequences is early and accurate prediction of mastitis based on indicator factors. So far, various statistical methods have been used to predict mastitis such as linear and multiple regression, and threshold models. Machine learning is another method that has recently widely been used to predict farm profitability, reproductive traits, longevity and abortion in dairy cow. Machine learning is defined as a set of methods for automatically finding patterns in data and then using those patterns to predict possible future data.Material and Methods In this research, the performance of four machine learning algorithms including random forest, decision tree, Naïve Bayes and logistic regression and two sampling methods, over-sampling and under-sampling, were compared to predict risk of clinical mastitis based on data collected in two Holstein dairy herds in Isfahan province. Final dataset included 393504 records on cows calved during 2007 to 2017 of which 13653 cases (3.47%) were infected and 379851 cases (96.53%) were healthy. Factors related to mastitis, including parity, daily milk production, calving
Hamed Ahmadi; Vahid Rasoli Marivani; Yousef Mohammadi
Volume 22, Issue 2 , June 2020, , Pages 281-288
Abstract
The goal of this study was to determine regression equations to predict metabolizable energy of wheat samples given their chemical compositions using meta-analytical approach. A database compromising chemical compositions and apparent metabolizable energy corrected for the nitrogen (AMEn) ...
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The goal of this study was to determine regression equations to predict metabolizable energy of wheat samples given their chemical compositions using meta-analytical approach. A database compromising chemical compositions and apparent metabolizable energy corrected for the nitrogen (AMEn) of 111 published sources of wheat strains was used. Sample information contains crude protein (CP), ether extract (EE), crude fiber (CF), ash and AMEn. Average values for AMEn was calculated as 2917.46 (kcal/kg), while for the CP, EE, CF, ash was calculated as 12.53, 2.12, 1.61and 1.56 (% dry matter), respectively. Meta-regression equations for predicting AMEn wheat based on chemical composition were developed and evaluated by means of provided database. Best equation obtained as: AMEn (kcal/kg)=1648+45.8 %CP+175.8 %EE+ 185.4 %CF. This equation can be used for predicting energy of wheat variates in feed-factories and poultry farms.