Use of body linear measurements to estimate liveweight of crossbred dairy cattle in smallholder farms in Kenya

Body linear measurements, and specifically heart girth (HG), have been shown to be useful predictors of cattle liveweight. To test the accuracy of body linear measurements for predicting liveweight, crossbred dairy cattle of different genotypes were measured and weighed. A total of 352 mature cows and 100 heifers were weighed using an electronic weighing scale and measurements of HG, body length, height at withers were taken using an ordinary measuring tape and body condition scored (BCS) using a five-point scale. The animals were grouped according to genotype and age. Genotype classification was undertaken from farmer recall and by visual appraisal as 40–60, 61–80 or 81–100 % exotic (non-indigenous). Age classification was simply as mature cows or heifers. Liveweight of the animals ranged from 102 to 433 kg. Liveweight was strongly correlated with HG (r = 0.84) and body condition scores (r = 0.70) and moderately correlated with body length (r = 0.64) and height at withers (0.61). Regressing LW on HG measurements gave statistically significant (P 2 ranging from of 0.53 to 0.78 and residual standard deviation ranging from 18.11 to 40.50 kg. The overall model developed (adjusted R2 = 0.71) had a prediction error of 26 kg (or 11 % of the mean) and predicted LW of over 95 % of crossbred dairy cattle in the range of 100–450 kg, regardless of age and breed group. Including BCS in the model slightly improved the model fit but not the prediction error. It was concluded that the model can be useful in making general management decisions in smallholder farms

Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.

This research article published by Springer Nature Switzerland AG., 2020This study is concerned with developing predictive models using machine learning techniques to be used in identifying factors that influence farmers' decisions, predict farmers' decisions, and forecast farmers' demands relating to breeding service. The data used to develop the models comes from a survey of small-scale dairy farmers from Tanzania (n = 3500 farmers), Kenya (n = 6190 farmers), Ethiopia (n = 4920 farmers), and Uganda (n = 5390 farmers) and more than 120 variables were identified to influence breeding decisions. Feature engineering process was used to reduce the number of variables to a practical level and to identify the most influential ones. Three algorithms were used for feature selection, namely: logistic regression, random forest, and Boruta. Subsequently, six predictive models, using features selected by feature selection method, were tested for each country-neural network, logistic regression, K-nearest neighbor, decision tree, random forest, and Gaussian mixture model. A combination of decision tree and random forest algorithms was used to develop the final models. Each country model showed high predictive power (up to 93%) and are ready for practical use. The use of ML techniques assisted in identifying the key factors that influence the adoption of breeding method that can be taken and prioritized to improve the dairy sector among countries. Moreover, it provided various alternatives for policymakers to compare the consequences of different courses of action which can assist in determining which alternative at any particular choice point had a high probability to succeed, given the information and alternatives pertinent to the breeding decision. Also, through the use of ML, results to the identification of different clusters of farmers, who were classified based on their farm, and farmers' characteristics, i.e., farm location, feeding system, animal husbandry practices, etc. This information had significant value to decision-makers in finding the appropriate intervention for a particular cluster of farmers. In the future, such predictive models will assist decision-makers in planning and managing resources by allocating breeding services and capabilities where they would be most in demand

Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network—Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes

Introduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions.Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3 years, in 12 countries with over 12,000 images taken.Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day’s images, or even an entire sampling trip’s images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection

Use of Body Linear Measurements to Estimate Live Weight of Crossbred Dairy Cattle in Smallholder Farms in Kenya

Animal weight is an important tool in livestock research and management. The most direct method of measuring liveweight (LW) is using a calibrated electronic or mechanical scale. However such equipment is usually costly and not readily available to poor rural livestock keepers. Farmers and livestock traders have been found to underestimate or overestimate the weights of the cattle by an average of 46 and 25% respectively of their true L W (Machila et al 2008). Linear body measurements, in particular heart girth are useful predictors of liveweight in livestock. However, the predictive ability of models developed from these measurements is influenced by body condition, age, breed and sex (Lesosky et al 2012; Ozkaya and Bozkurt 2009). The present study tested the accuracy of linear body measurements for predicting L W of crossbred dairy cattle of different genotypes in smallholder farms in Kenya and developed predictive equations appropriate to this context

Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved

Genetic parameters and genotype x environment interaction for feed efficiency traits in steers fed grower and finisher diets

The objective of this study was to examine the genetic parameters and genetic correlations of feed efficiency traits in steers (n = 490) fed grower or finisher diets in 2 feeding periods. A bivariate model was used to estimate phenotypic and genetic parameters using steers that received the grower and finisher diets in successive feeding periods, whereas a repeated animal model was used to estimate the permanent environmental effects. Genetic correlations between the grower-fed and finisher-fed regimens were 0.50 ± 0.48 and 0.78 ± 0.43 for residual feed intake (RFI) and G:F, respectively. The moderate genetic correlation between the 2 feeding regimens may indicate the presence of a genotype × environment interaction for RFI. Permanent environmental effects (expressed in percentage of phenotypic variance) were detected in the grower-fed steers for ADG (38%), DMI (30%), RFI (18%), and G:F (40%) and also in the finisher-fed steers for ADG (28%), DMI (35%), metabolic mid-weight (23%), and RFI (10%). Heritability estimates were 0.08 ± 0.10 and 0.14 ± 0.15 for the grower-fed steers and 0.42 ± 0.16 and 0.40 ± 17 for the finisher-fed steers for RFI and G:F, respectively. The dependency of the RFI on the feeding regimen may have serious implications when selecting animals in the beef industry. Because of the higher cost of grains, feed efficiency in the feedlot might be overemphasized, whereas efficiency in the cow herd and the backgrounding segments may have less emphasis. These results may also favor the retention (for subsequent breeding) of cows whose steers were efficient in the feedlot sector. Therefore, comprehensive feeding trials may be necessary to provide more insight into the mechanisms surrounding genotype × environment interaction in steers

Associations of marker panel scores with feed intake and efficiency traits in beef cattle using preselected single nucleotide polymorphisms

Because of the moderate heritability and the expense associated with collecting feed intake data, effective selection for residual feed intake would be enhanced if marker-assisted evaluation were used for accurate estimation of genetic merit. In this study, a suite of genetic markers predictive of residual feed intake, DMI, and ADG were preselected using singlemarker regression analysis, and the top 100 SNP were analyzed further to provide prediction equations for the traits. The data used consisted of 728 spring-born beef steers, offspring of a cross between a composite dam line and Angus, Charolais, or University of Alberta hybrid bulls. Feed intake data were collected over a 5-yr period, with 2 groups (fall-winter and winter-spring) tested every year. Training and validation data sets were obtained by splitting the data into 2 distinct sets, by randomly splitting the data into training and testing sets based on sire family (split 1) in 5 replicates or by retaining all animals with no known pedigree relationships as the validation set (split 2). A total of 37,959 SNP were analyzed by single-marker regression, of which only the top 100 that corresponded to a Pvalu