کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10977000 | 1108046 | 2014 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms
ترجمه فارسی عنوان
پیش بینی نتایج تلقیح در گاوهای شیری هلشتاین با استفاده از الگوریتم های یادگیری ماشین جایگزین
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کلمات کلیدی
فراگیری ماشین، مدیریت تولید مثل، احشام لبنیاتی،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم دامی و جانورشناسی
چکیده انگلیسی
When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Dairy Science - Volume 97, Issue 2, February 2014, Pages 731-742
Journal: Journal of Dairy Science - Volume 97, Issue 2, February 2014, Pages 731-742
نویسندگان
Saleh Shahinfar, David Page, Jerry Guenther, Victor Cabrera, Paul Fricke, Kent Weigel,