Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle

Publication history: Accepted - 9 February 2022; Published online - 26 March 2022Direct 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.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 support from all participating countries and research centers involved in this work is greatly acknowledged

The Resilient Dairy Genome Project - a general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis

Short communication: Production performance and plasma metabolites of dairy ewes in early lactation as affected by chitosan

<p>The objective of this study was to evaluate the effects of chitosan (CHI) supplementation on production performance and blood parameters in dairy ewes. Twenty-four multiparous Latxa dairy ewes at d 16 of lactation were divided into two groups of 12 ewes each. Ewes were fed one of two experimental concentrates (0.840 kg dry matter/d), control or supplemented with 1.2% CHI, on a dry matter basis. Ewes also had free access to tall fescue hay, water, and mineral salts. The experimental period lasted for 25 d, of which the first 14 d were for treatment adaptation and the last 11 d for measurements and samplings. Supplementation with CHI decreased total (<em>p</em>=0.043) and fescue (<em>p</em>=0.035) dry matter intake (DMI), but did not affect concentrate DMI. Supplementation with CHI, moreover, increased plasma glucose (<em>p</em>=0.013) and BUN concentrations (<em>p</em>=0.035), but did not affect those of non-esterified fatty acids. Dietary supplementation with CHI, however, did not affect milk yield, 6.5% FCM, milk composition, or BW, but it improved dietary apparent efficiency by increasing the milk yield-to-DMI (<em>p</em>=0.055) and 6.5% FCM-to-DMI (<em>p</em>=0.045) ratios. In conclusion, dietary supplementation of chitosan maintained ewe performance while reducing feed intake and improving dietary apparent efficiency.</p

Use of Cold-Pressed Sunflower Cake in the Concentrate as a Low-Input Local Strategy to Modify the Milk Fatty Acid Profile of Dairy Cows

Cold-pressed sunflower cake (CPSC) is a cheap by-product of oil-manufacturing. Supplementing diets with CPSC, rich in fat and linoleic acid, could be an effective tool for increasing healthy fatty acids (FA) in milk. To test this hypothesis, 10 cows were used in a crossover design with two experimental diets fed during two 63-day periods. Cows&rsquo; milk production was recorded and samples were taken for fat, protein, lactose, and for FA composition analysis. Dry matter intake (DMI) and dry matter apparent digestibility (DMD) were estimated using two markers. Milk acceptance test was carried out. CPSC decreased milk C12:0 (10%, p = 0.023) and C16:0 (5%, p = 0.035) and increased C18:1 cis-12 (37%, p = 0.006), C18:1 trans-11 (32%, p = 0.005), C18:2 cis-9 cis-12 (13%, p = 0.004), and cis-9 trans-11 CLA (35%, p = 0.004). CPSC increased total trans-monounsaturated FA (21%, p = 0.003), total CLA (31%, p = 0.007), and PUFA:SFA ratio (18%, p = 0.006). CPSC did not affect milk production, DMD, DMI and milk composition, but reduced fat yield (9%, p = 0.013) and FCM (7%, p = 0.013). CPSC improved milk overall acceptability. In conclusion, CPSC could modify milk FA profile without a detrimental effect on digestibility, production performance, or milk acceptance

Comparison Between Non-Invasive Methane Measurement Techniques in Cattle

The aim of this trial was to study the agreement between the non-dispersive infrared methane analyzer (NDIR) method and the hand held laser methane detector (LMD). Methane (CH4) was measured simultaneously with the two devices totaling 164 paired measurements. The repeatability of the CH4 concentration was greater with the NDIR (0.42) than for the LMD (0.23). However, for the number of peaks, repeatability of the LMD was greater (0.20 vs. 0.14, respectively). Correlation was moderately high and positive for CH4 concentration (0.73 and 0.74, respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A moderate concordance correlation coefficient was observed for the CH4 concentration (0.62) and for the number of peaks (0.66). A moderate-high coefficient of individual agreement for the CH4 concentration (0.83) and the number of peaks (0.77) were observed. However, CH4 concentrations population means and all variance components differed between instruments. In conclusion, methane concentration measurements obtained by means of NDIR and LMD cannot be used interchangeably. The joint use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between-subject and within-subject variabilities, are identified and corrected for

Production performance and plasma metabolites of dairy ewes in early lactation as affected by chitosan

The objective of this study was to evaluate the effects of chitosan (CHI) supplementation on production performance and blood parameters in dairy ewes. Twenty-four multiparous Latxa dairy ewes at d 16 of lactation were divided into two groups of 12 ewes each. Ewes were fed one of two experimental concentrates (0.840 kg dry matter/d), control or supplemented with 1.2% CHI, on a dry matter basis. Ewes also had free access to tall fescue hay, water, and mineral salts. The experimental period lasted for 25 d, of which the first 14 d were for treatment adaptation and the last 11 d for measurements and samplings. Supplementation with CHI decreased total (p=0.043) and fescue (p=0.035) dry matter intake (DMI), but did not affect concentrate DMI. Supplementation with CHI, moreover, increased plasma glucose (p=0.013) and BUN concentrations (p=0.035), but did not affect those of non-esterified fatty acids. Dietary supplementation with CHI, however, did not affect milk yield, 6.5% FCM, milk composition, or BW, but it improved dietary apparent efficiency by increasing the milk yield-to-DMI (p=0.055) and 6.5% FCM-to-DMI (p=0.045) ratios. In conclusion, dietary supplementation of chitosan maintained ewe performance while reducing feed intake and improving dietary apparent efficiency

Characterisation of the rumen resistome in Spanish dairy cattle

Background Rumen microorganisms carry antimicrobial resistance genes which pose a threaten to animals and humans in a One Health context. In order to tackle the emergence of antimicrobial resistance it is vital to understand how they appear, their relationship with the host, how they behave as a whole in the ruminal ecosystem or how they spread to the environment or humans. We sequenced ruminal samples from 416 Holstein dairy cows in 14 Spanish farms using nanopore technology, to uncover the presence of resistance genes and their potential effect on human, animal and environmental health. Results We found 998 antimicrobial resistance genes (ARGs) in the cow rumen and studied the 25 most prevalent genes in the 14 dairy cattle farms. The most abundant ARGs were related to the use of antibiotics to treat mastitis, metritis and lameness, the most common diseases in dairy cattle. The relative abundance (RA) of bacteriophages was positively correlated to the ARGs RA. The heritability of the RA of the more abundant ARGs ranged between 0.10 (mupA) and 0.49 (tetW), similar to the heritability of the RA of microbes that carried those ARGs. Even though these genes are carried by the microorganisms, the host is partially controlling their RA by having a more suitable rumen pH, folds, or other physiological traits that promote the growth of those microorganisms. Conclusions We were able to determine the most prevalent ARGs (macB, msbA, parY, rpoB2, tetQ and TaeA) in the ruminal bacteria ecosystem. The rumen is a reservoir of ARGs, and strategies to reduce the ARG load from livestock must be pursued

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle

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

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle

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.ISSN:0022-0302ISSN:1525-319