enteric methane; machine learning; prediction models; proxies for methane; Food Science; Animal Science and Zoology; Genetics
Abstract :
[en] Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Negussie, Enyew ; Animal Genomics and Breeding, Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland. Electronic address: enyew.negussie@luke.fi
González-Recio, Oscar ; Department of Animal Breeding, Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA-CSIC), 28040 Madrid, Spain
Battagin, Mara ; Italian Brown Cattle Breeders' Association, Verona, Italy
Bayat, Ali-Reza ; Animal Nutrition, Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland
Boland, Tommy ; Agriculture and Food Science Centre, School of Agriculture and Food Science, University College Dublin, Belfield, Belfield, Dublin 4, Ireland
de Haas, Yvette ; Animal Breeding and Genomics, Wageningen University and Research, 6700 AH Wageningen, the Netherlands
Garcia-Rodriguez, Aser ; Department of Animal Production, NEIKER-Basque Institute for Agricultural Research and Development, 01192 Arkaute, Spain
Garnsworthy, Philip C ; School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
Gengler, Nicolas ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Kreuzer, Michael ; ETH Zurich, Institute of Agricultural Sciences, Universitaetstrasse 2, 8092 Zurich, Switzerland
Kuhla, Björn ; Research Institute for Farm Animal Biology (FBN), Institute of Nutritional Physiology "Oskar Kellner," Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
Lassen, Jan ; VikingGenetics, Ebeltoftvej 16, 8960 Randers, Denmark
Peiren, Nico ; Institute for Agricultural and Fisheries Research (ILVO), Merelbeke, Belgium
Pszczola, Marcin ; Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wołynska 33, 60-637 Poznan, Poland
Schwarm, Angela ; Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway
Soyeurt, Hélène ; Université de Liège - ULiège > Département GxABT
Vanlierde, Amélie ; Productions in Agriculture Department, Walloon Agricultural Research Centre (CRA-W), BEL-5030 Gembloux, Belgium
Yan, Tianhai ; Livestock Production Science Branch, Agri-Food and Biosciences Institute, Hillsborough, Co. Down BT26 6DR, United Kingdom
Biscarini, Filippo ; National Research Council, Institute of Agricultural Biology and Biotechnology (CNR-IBBA), Via Bassini 15, 20133 Milan, Italy
Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle.
Alternative titles :
[fr] Intégration de données hétérogènes multi-pays pour confectionner un indicateur des émissions de méthane des vaches laitères au moyen d'une random forest
This paper is the result of the concerted effort of all participants and support from the networks of COST Action FA1302 “METHAGENE: Large-scale methane measurements on individual ruminants for genetic evaluations.” The authors thank all individuals and groups who have directly or indirectly contributed to this work; special thanks are due to the technical and financial support from the COST Action FA1302 of the European Union. In addition, all financial and technical supports from all participating countries and research centers involved in this work are greatly acknowledged. The authors have not stated any conflicts of interest.
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