Multi-country investigation of factors influencing breeding decisions by smallholder dairy farmers in sub-Saharan Africa.

Artificial insemination (AI) and selective bull mating are considered as robust methods for dairy cattle breeding. Globally, these methods have been used to enhance productivity and realize rapid genetic gains. However, these technologies have had low adoption rates in sub-Saharan Africa (SSA). Even though available evidence suggests that this is due to various infrastructural and technical challenges. There is limited information about what drives this low uptake of AI from a farmer's perspective. Therefore, the main objective of this study was to determine and characterize factors that influence the choice by smallholder farmers between bull service and AI for dairy cow breeding. Further, the relationships between the breeding choices and the bio-physical elements of dairy farming, mainly, farmer characteristics, household income levels, farm management practices, and institutional support structures, were investigated. Data were collected through face-to-face interviews from a total of 16,308 small-scale dairy farmers in Ethiopia (n = 4679), Kenya (n = 5278), Tanzania (n = 3500), and Uganda (n = 2851). The questionnaire was coded in an electronic form using Open Data Kit (ODK) platform to allow for real-time data entry and management. Descriptive statistics, chi-square test, and a t-test were used to evaluate the independent and dependent variables, while logistic regression and factor analysis were used to identify factors that influenced farmers' breeding decisions. Results showed that there was a significant difference in animal husbandry practices between farmers who used artificial insemination (AI) and those who practiced bull mating. The majority of farmers who used AI kept records, purchased more animal feeds, had more labor by hiring workers whose average wages were higher than those of bull service farmers. However, farmers who used AI pay more for services such as water access and breeding while their service providers had to cover long distances compared to farmers who used bulls. This indicates limited access to services and service providers for AI farmers. The ratio of AI to bull service users was even for Ethiopia and Kenya, while in Uganda and Tanzania, more farmers preferred bull service to AI. It was established that the factors that influence farmers' breeding decision were not the same across the region. Factors such as farmer's experience in dairy farming, influence of the neighbor, farmer's ability to keep records, and management practices such as water provision and availability of feeds had a significant association (p < 0.001) with AI adoption among dairy farmers. In contrast, large herd and large land size negatively influenced AI adoption. Institutional settings including cost of AI service and the distance covered by the service provider negatively affected (p < 0.001) the choice of AI as a breeding option. There was a high probability of continued use of a specific breeding method when there was a previous conception success with that same method. Based on the results obtained, we recommend that improvement of institutional settings such as the availability of AI service providers, as well as better access to services such as water, animal feed, and animal health provision, be treated as critical components to focus on for enhanced AI adoption. Most importantly, there is a need to avail training opportunities to equip farmers with the necessary skills for best farm management practices such as record keeping, proper feeding, and selective breeding.

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 < 0.01) equations with R(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 R(2) = 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.

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.

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 single-marker 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 P-value <0.002 were retained. The 100 SNP were then analyzed using random regression BLUP, and only SNP that were jointly significant (P < 0.05) were included in the final marker panels. The marker effects from the selected panels were used to derive the molecular breeding values, which were calculated as a weighted sum of the number of copies of the more frequent allele at each SNP locus, with the weights being the allele substitution effects. The correlation between molecular breeding value and phenotype represented the accuracy of prediction. For all traits evaluated, accuracy across breeds was low, ranging between 0.007 and 0.414. Accuracy was least in data split 2, where the validation individuals had no pedigree relationship with animals in the training data. Given the low predictive ability observed, a large number of individuals may be needed for prediction when using such an admixed population. Further, these results suggest that breed composition of the target population in which the marker panels are likely to be used should be an important consideration when developing prediction equations across breeds, especially where an admixed population is used as the training data set.

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.

Feed efficiency differences and reranking in beef steers fed grower and finisher diets.

This 3-yr study used 490 steers to determine whether feedlot steers changed their feed efficiency (FE) ranking when fed a grower diet, then a finisher diet. The steers were crossbreds and were between 5 to 7 mo of age. There were 2 feeding periods each year. Within each year, approximately 90 steers had their diet switched from a grower to a finisher diet (feed-swap group), whereas another 90 steers were fed either the grower (grower-fed group) or the finisher (finisher-fed group) diet throughout the feeding trial. Each feeding test lasted for a minimum of 10 wk, and all steers were fed ad libitum. Individual animal feed intakes were collected using the GrowSafe feeding system, and BW were measured every 2 wk. Residual feed intake (RFI), G:F, and Kleiber ratio (KR) were computed at the end of each feeding period. For each measure of efficiency, animals were classified as low, medium, or high based on 0.5 SD from the mean. The majority of steers did not maintain the previous efficiency class in the second period. Approximately 58, 51, and 51% of steers in the feed-swap group, finisher-fed group, and the grower-fed group, respectively, changed their RFI measure by 0.5 SD. A low rank correlation occurred in all test groups but was less in the feed-swap group. Spearman rank correlations between the 2 feeding periods in the feed-swap group were 0.33, 0.20, and 0.31 for RFI, G:F, and KR, respectively. Classifications based on G:F and KR showed that a greater number of steers (P < 0.05) in the feed-swap group did not maintain their FE class from 1 feeding regimen to the other, whereas classification based on RFI did not show any difference (P > 0.05) between the proportions of individuals that changed or maintained their FE class. In the groups without a feed-swap, there was no difference (P > 0.05) in the proportion of steers that changed or maintained the same FE class for all FE measures. Our results suggest that diet type and feeding period affect the FE ranking in beef steers. A feedlot diet is ideal for evaluating the FE potential of steers for feedlot profitability; however, we suggest that tests involving less dense diets should be examined in an effort to understand the relationships between FE and feeder profitability.

Season of testing and its effect on feed intake and efficiency in growing beef cattle.

This study sought to assess whether residual feed intake (RFI) calculated by regressing feed intake (DMI) on growth rate (ADG) and metabolic mid-BW in 3 different ways led to similar estimates of genetic parameters and variance components for young growing cattle tested for feed intake in fall and winter seasons. A total of 378 beef steers in 5 cohorts were fed a typical high energy feedlot diet and had free-choice access to feed and water. Feed intake data were collected in fall or winter seasons. Climate data were obtained from the University of Alberta Kinsella meteorological station and Vikings AGCM station. Individual animal RFI was obtained by either fitting a regression model to each test group separately (RFI(C)), fitting a regression model to pooled data consisting of all cohorts but including test group as a fixed effect (RFI(O)), or fitting a regression to pooled data with test group as a fixed effect but within seasonal (fall-winter or winter-spring) groups (RFI(S)). Two animal models (M1 and M2) that differed by the inclusion of fixed effects of test group or season, respectively, were used to evaluate RFI measurements. Feed intake was correlated with air temperature, relative humidity, solar radiation, and wind speed (-0.26, 0.23, 0.30, -0.14 for fall-winter and 0.31, -0.04, 0.14, 0.16 for winter-spring, respectively), but the nature and magnitude of the correlations were different for the 2 seasons. Single trait direct heritability, model likelihood, direct genetic variance, and EBV accuracy estimates were greatest for RFI(C) and least for RFI(O) for both M1 and M2 models. A significant genetic correlation was also observed between RFI(O) and ADG, but not for RFI(C) and RFI(S). Including a season effect (M2) in the genetic evaluation of RFI(O) resulted in the smallest heritability, model LogL, EBV accuracy, and largest residual variance estimates. These results, though not conclusive, suggest a possible effect of seasonality on feed intake and thus feed efficiency.

Genetic parameters for calving ease, gestation length, and birth weight in Charolais cattle.

In this study, a 3-trait linear model was used to obtain genetic parameters for direct and maternal components of calving ease (CE), gestation length (GEST), and birth weight (BWT). Calving ease scores were transformed into Snell scores and expressed as percent unassisted calving (SC), ranging from 0 to 100% (least to greatest ease). A total of 40,420 records (n = 14,403 for CE) were obtained from the Canadian Charolais Association field database. The animal model included fixed effects of contemporary group (herd x year of birth combinations), age of heifer, and sex of calf (only for CE), whereas random effects included direct and maternal genetic effects, residual error, and permanent environmental effects (for CE). The BWT and GEST were preadjusted for age of dam and sex of calf effects. Variance components were estimated using REML. Mean SC was 83.31% (SD = 23.30) and ranged from 3.44 to 100%. Mean BWT was 46.54 kg (SD = 4.79), whereas mean GEST was 286.48 d (SD = 4.93). Direct heritability estimates for SC, BWT, and GEST were 0.14 +/- 0.02, 0.46 +/- 0.03, and 0.62 +/- 0.04, respectively, and maternal heritability estimates were 0.06 +/- 0.02, 0.14 +/- 0.02, and 0.10 +/- 0.02, respectively. The permanent environmental effect as a proportion of SC phenotypic variance was 0.35 +/- 0.11, indicating a large influence on CE. Genetic correlations of direct SC with direct BWT and GEST were -0.93 +/- 0.04 and -0.38 +/- 0.08, respectively, whereas maternal correlations were -0.69 +/- 0.14 and -0.49 +/- 0.17, respectively, illustrating the importance of including both traits in CE evaluations. Within trait direct x maternal genetic correlations were substantial and negative. Regression of average direct and average maternal EBV on year of birth yielded significant genetic trends for the direct effects of BWT, GEST, and CE, whereas no trends were detected for maternal effects. Even though CE is routinely analyzed, no study has evaluated transformed CE scores with 2 correlated traits. In these data, the large negative genetic correlation between BWT and CE suggests that increasing SC would also decrease BWT. Genetic improvement programs, therefore, should consider both CE and growth.