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

Prediction of body weight of Curraleiro Pé-Duro cattle based on morphometric measurements

Cattle weight development is highly correlated with some body measurements. Based on the relationship between morphometric measurements and body mass, our aim was to develop regression equations to estimate the body weight of Curraleiro Pé-Duro (CPD) cattle to be used in farms that lack access to weighting scales. Data from 1023 animals from four farms on withers height (WH), body length (BL), body score (BS), heart girth (HG), permanent teeth (PT), scrotal perimeter (SP), and live weight were used. The animals were classified into five categories depending on age and/or sex: newborns (NB), calves, weaned animals, cows, and bulls. The best models are GLM with Gamma, Gamma, inverse Gaussian, Gaussian, and Gamma distributions for NB, calves, weaned animals, cows, and bulls, respectively. Predictive modeling for bulls was the best performing overall, with a correlation of 0.97 between the estimated by the model and the obtained with a weighting scale. For NB, calves, weaned animals, and cows, the correlation (r) was 0.85, 0.90, 0.95, and 0.87, respectively. The evaluated models are adequate to be used as a technical solution to estimate weight in a cattle production system.

Heart girth best predicts live weights of market-age pigs in Tanzania

The aim of this study was to use linear body measurements to develop and validate a regression-based model for prediction of live weights (LW) of pigs reared under smallholder settings in rural areas in the southern highlands of Tanzania. LW of 400 pigs (range 7 to 91 kg) was measured, along with their heart girths (HG) and body lengths (BL). BL was measured from the midpoint between the ears to the tail base. HG was measured as chest circumference just behind the front legs. LW was determined using a portable hanging scale. An analysis of covariance was performed to test for differences in LW between male and female pigs, including age, HG and BL as covariates. LW was regressed on HG and BL using simple and multiple linear regressions. Models were developed for all pig ages, and separately for market/breeding-age pigs and those below market/breeding age. Model validation was done using a split-samples approach, followed by PRESS-related statistics. Model efficiency and accuracy were assessed using the coefficient of determination, R2, and standard deviation of the random error, respectively. Model stability was determined by assessing 'shrinkage' of R2 value. Results showed that HG was the best predictor of LW in market/breeding-age pigs (model equation: LW = 1.22HG-52.384; R2 = 0.94, error = 3.7). BL, age and sex of pigs did not influence LW estimates. It is expected that LW estimation tools will be developed to enable more accurate estimation of LW in the pig value chain in the area.

A genome-wide association study of morphometric traits in dromedaries

BACKGROUND

Investigating genomic regions associated with morphometric traits in camels is valuable, because it allows a better understanding of adaptive and productive features to implement a sustainable management and a customised breeding program for dromedaries.

OBJECTIVES

With a genome-wide association study (GWAS) including 96 Iranian dromedaries phenotyped for 12 morphometric traits and genotyped-by-sequencing (GBS) with 14,522 SNPs, we aimed at identifying associated candidate genes.

METHODS

The association between SNPs and morphometric traits was investigated using a linear mixed model with principal component analysis (PCA) and kinship matrix.

RESULTS

With this approach, we detected 59 SNPs located in 37 candidate genes potentially associated to morphometric traits in dromedaries. The top associated SNPs were related to pin width, whither to pin length, height at whither, muzzle girth, and tail length. Interestingly, the results highlight the association between whither height, muzzle circumference, tail length, whither to pin length. The identified candidate genes were associated with growth, body size, and immune system in other species.

CONCLUSIONS

We identified three key hub genes in the gene network analysis including ACTB, SOCS1 and ARFGEF1. In the central position of gene network, ACTB was detected as the most important gene related to muscle function. With this initial GWAS using GBS on dromedary camels for morphometric traits, we show that this SNP panel can be effective for genetic evaluation of growth in dromedaries. However, we suggest a higher-density SNP array may greatly improve the reliability of the results.

Effects of Dietary Inclusion of Tannin-Rich Sericea Lespedeza Hay on Relationships among Linear Body Measurements, Body Condition Score, Body Mass Indexes, and Performance of Growing Alpine Doelings and Katahdin Ewe Lambs

The objective of this study was to assess the effects of the dietary level of a condensed tannin-rich forage on linear measures, body condition score (BCS), body mass indexes (BMI), and performance and relationships among these variables in growing dairy goats and hair sheep raised for meat. An experiment with a 2 × 3 factorial treatment arrangement was conducted, with two species and three diets. Diets were 25% concentrate and 75% forage, which were alfalfa hay, condensed tannin-containing Sericea lespedeza hay, and a 1:1 mixture of both hay sources. Twenty-four Alpine (ALP) doelings and 24 Katahdin (KAT) ewe lambs were used in the 173-day study, consisting of four measurement periods. Variables included BCS, linear measures, BMI, and performance variables such as average daily gain (ADG) and dry matter intake (DMI, g/day). Linear measures were length from the shoulder point to pin (Pin) and hook (Hook) bones, height at the withers (Wither), circumference from heart girth (Heart), and width at the hook bones (Rump). Different BMI were based on the Wither, Hook, Pin, Heart, and various combinations. Heart, Rump, and all BMI were affected by species, whereas linear measures and BMI based on Wither and the combination of height and length measures were influenced by diet. There were positive (p < 0.05) correlation coefficients (r) between BCS and body weight (BW) and linear measures as well as BMI for both species, and correlations were greater for KAT than for ALP. Body condition score was correlated with BW, ADG, and DMI for KAT (p < 0.05). For ALP, the correlation between BCS and BW (p < 0.015) was much lower than for KAT (0.49 vs. 0.91), and there were only tendencies for relationships between BCS and ADG and DMI (p < 0.10). Body weight and ADG were positively correlated (p < 0.05) with all BMI for both species, and most correlations were greater for KAT than for ALP. Correlations involving BMI based on combinations of two linear measures with BW and ADG were in most instances greater than for BMI calculated from one or three linear measures. For ALP, DMI was related to BMI based on Wither and Pin (r = 0.43), Heart and Hook (r = 0.44), and Heart and Pin (r = 0.61), whereas for KAT, correlations were similar (0.72−0.75) for each of the four BMI based on two linear measures (i.e., Wither and Hook, Wither and Pin, Heart and Hook, and Heart and Pin). Therefore, it appears that each of these four BMI, preferably based on Heart and length, could be appropriate for better livestock management over BCS and in predicting animal performance.

PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements

Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive. We introduce the PreciseEdge image segmentation algorithm to address these issues by employing a standard image collection protocol with a semi-automated image labeling method, and a highly precise image segmentation for automated body measurement extraction directly from each image. These elements, from image collection to extraction are designed to work together to yield values highly correlated to real-world body measurements. PreciseEdge adds a brief preprocessing step inspired by chromakey to a modified GrabCut procedure to generate image masks for data extraction (body measurements) directly from the images. Three hundred RGB (red, green, blue) image samples were collected uniformly per the African Goat Improvement Network Image Collection Protocol (AGIN-ICP), which prescribes camera distance, poses, a blue backdrop, and a custom AGIN-ICP calibration sign. Images were taken in natural settings outdoors and in barns under high and low light, using a Ricoh digital camera producing JPG images (converted to PNG prior to processing). The rear and side AGIN-ICP poses were used for this study. PreciseEdge and GrabCut image segmentation methods were compared for differences in user input required to segment the images. The initial bounding box image output was captured for visual comparison. Automated digital body measurements extracted were compared to manual measures for each method. Both methods allow additional optional refinement (mouse strokes) to aid the segmentation algorithm. These optional mouse strokes were captured automatically and compared. Stroke count distributions for both methods were not normally distributed per Kolmogorov-Smirnov tests. Non-parametric Wilcoxon tests showed the distributions were different (p< 0.001) and the GrabCut stroke count was significantly higher (p = 5.115 e-49), with a mean of 577.08 (std 248.45) versus 221.57 (std 149.45) with PreciseEdge. Digital body measures were highly correlated to manual height, length, and girth measures, (0.931, 0.943, 0.893) for PreciseEdge and (0.936, 0. 944, 0.869) for GrabCut (Pearson correlation coefficient). PreciseEdge image segmentation allowed for masks yielding accurate digital body measurements highly correlated to manual, real-world measurements with over 38% less user input for an efficient, reliable, non-invasive alternative to livestock hand-held direct measuring tools.

Models to predict live weight from heart girth in crossbred beef heifers

The objective of this study was to develop and evaluate linear, quadratic, and exponential mathematical models to predict live weight (LW) from heart girth (HG) in crossbred heifers raised in tropical humid conditions in Mexico. Live weight (363.32 ± 150.88 kg) and HG (166.83 ± 24.88 cm) were measured in 400 heifers aged between 3 and 24 months. Linear and non-linear regression was used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), the Bayesian information criterion (BIC), coefficient of determination (R²), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was evaluated based on the root mean square error of prediction (RMSEP), R², and mean absolute error (MAE). The correlation coefficient between LW and HG was r = 0.98 (P < 0.001). The quadratic model showed the lowest values of MAE (736.57), RMSEP (27.13), AIC (3783.95), and BIC (3799.91). Additionally, this model exhibited better goodness-of-fit values regarding external and internal validation criteria (higher R² and lower RMSEP and MAE), thus having better predictive performance. The RMSE represented about 8% of the observed LW. Heart girth is highly correlated (r = 0.98) with LW. The quadratic model showed a high predictive capacity for crossbred heifers kept in tropical conditions.

Assessment of Livestock Feed Resources and Coping Strategies with Dry Season Feed Scarcity in Mixed Crop–Livestock Farming Systems around the Gilgel Gibe Catchment, Southwest Ethiopia

In the current study area, livestock are an integral part of the mixed farming system, and play very important roles as sources of draught power, nutrition, cash income, employment and poverty alleviation. However, feed shortage, especially during the dry season, is the most important constraint to optimal productivity. This study aimed to investigate livestock feed resources and feeding practices, coping strategies with seasonal scarcity, and to identify major constraints to livestock production in a mixed farming system around the Gilgel Gibe catchment, southwest Ethiopia. Data were collected from 342 households using a structured questionnaire. The results showed natural pasture, crop residues, stubble grazing, and roadside grasses were the main feed resources, in that order. None of the respondents practiced improved forage cultivation due to insufficient land and lack of knowledge on forage production and utilization. Free grazing was the most predominant feeding system. Almost all respondents experienced dry season feed scarcity. Conserving crop residues and hay, purchasing roughages, reducing herd size and renting grazing land were the major coping strategies to feed scarcity. The farmers’ perceived major constraints to livestock production were feed shortage, animal diseases, and low productivity of local breeds. Institutional, technical and technological interventions are suggested to alleviate the constraints to livestock production in mixed crop-livestock systems in the study area and outside with similar settings.

Genomic prediction of crossbred dairy cattle in Tanzania: A route to productivity gains in smallholder dairy systems

Selection based on genomic predictions has become the method of choice for genetic improvement in dairy cattle. This offers huge opportunity for developing countries with little or no pedigree data, and preliminary studies have shown promising results. The African Dairy Genetic Gains (ADGG) project initiated a digital system of dairy performance data collection, accompanied by genotyping in Tanzania in 2016. Currently, ADGG has the largest body of dairy performance data generated in East Africa from a smallholder dairy system. This study examines the use of genomic best linear unbiased prediction (GBLUP) and single-step (ss)GBLUP for the estimation of genetic parameters and accuracy of genomic prediction for daily milk yield and body weight in Tanzania. The estimates of heritability for daily milk yield from GBLUP and ssGBLUP were essentially the same, at 0.12 ± 0.03. The heritability estimates for daily milk yield averaged over the whole lactation from random regression model (RRM) GBLUP or ssGBLUP were 0.22 and 0.24, respectively. The heritability of body weight from GBLUP was 0.24 ± 04 but was 0.22 ± 04 from the ssGBLUP analysis. Accuracy of genomic prediction for milk yield from a forward validation was 0.57 for GBLUP based on fixed regression model or 0.55 from an RRM. Corresponding estimates from ssGBLUP were 0.59 and 0.53, respectively. Accuracy for body weight, however, was much higher at 0.83 from GBLUP and 0.77 for ssGBLUP. The moderate to high levels of accuracy of genomic prediction (0.53-0.83) obtained for milk yield and body weight indicate that selection on the basis of genomic prediction is feasible in smallholder dairy systems and most probably the only initial possible pathway to implementing sustained genetic improvement programs in such systems.

Designing Statistical Models for Holstein Rearing Heifers' Weight Estimation from Birth to 15 Months Old Using Body Measurements

Simple Summary

The growth monitoring process represents an important part of rearing heifers. The use of a scale is not feasible in some breeding conditions; it may be interesting to investigate the possibility of evaluating body weight (BW) with body measurements. The aim of this study was to estimate heifers’ weight based on their body dimension characteristics. A total of 25 Holstein rearing heifers were monitored after birth, weekly until 2 months of life and monthly until 15 months of age. Animals were weighed, and their wither height (WH), shin circumference (SC), heart girth circumference (HG), body length (BL), hip width (HW) and body condition score (BCS) were measured using tape measure. Equations were built with a stepwise regression to estimate the BW at each time using body measures for the study group. Equations were able to estimate the BW of heifers under a 0.800 kg as an average weight gain target using different variables, representing an alternative method of BW evaluation without a scale. Three variables or fewer were needed for BW estimation at crucial growing times, making these models feasible for use in the field. Different growing rate target may be studied in order to evaluate possible modifications to our equations.

Abstract

Body measurements could be used to estimate body weight (BW) with no need for a scale. The aim was to estimate heifers weight based on their body dimension characteristics. Twenty-five Holstein heifers represent the study group (SG); another 13 animals were evaluated as a validation group (VG). All the heifers were weighed (BW) and their wither height (WH), shin circumference (SC), heart girth circumference (HG), body length (BL), hip width (HW) and body condition score (BCS) were measured immediately after birth, and then weekly until 2 months and monthly until 15 months old. Equations were built with a stepwise regression in order to estimate the BW at each time using body measures for the SG. A linear regression was applied to evaluate the relationship between the estimated BW and the real BW. Equations found were to be statistically significant (r² = 0.688 to 0.894; p < 0.0001). Three variables or fewer were needed for BW estimation a total of 11/23 times. Regression analysis indicated that the use of HG was promising in all the equations built for BW estimation. These models were feasible in the field; further studies will evaluate possible modifications to our equations based on different growing rate targets.

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

Environmental risk assessment of the anthelmintic albendazole in Eastern Africa, based on a systematic review

This study performs an environmental risk assessment (ERA) of the anthelmintic medicine albendazole (ABZ) in the eastern African region. A systematic literature search strategy was applied to obtain quantitative information on the physicochemical characteristics, the metabolization-fate, the ecotoxicity and the environmental occurrence in different countries worldwide serving as model regions. In addition, insilico tools were employed to obtain data on physicochemical characteristics and toxic hazards of ABZ and its metabolites. Moreover, ERA models were used to predict environmental concentrations in different compartments and compare them with the measured environmental concentrations. Finally, the environmental risk of ABZ in the eastern Africa was estimated by calculating the risk quotient (RQ), and its uncertainty estimated by Monte Carlo simulation. The predicted environmental concentrations of ABZ in surface water in the model region based on consumption (1.6-267 ng/L) were within the range of values obtained from the measured environmental concentrations of the same region (0.05-101,000 ng/L). Using these models with adapted input variables for eastern Africa, the predicted surface water concentration in that region was 19,600 ± 150 ng/L (95% CI). The calculated soil concentrations of ABZ in the model regions and the eastern Africa were found to be 0.057 ± 0.0 μg/kg and 0.022 ± 0.0 μg/kg, respectively. The environmental risk expressed as risk quotient of ABZ in eastern Africa estimated for the aquatic compartment (146 ± 1) indicated a significant environmental risk calling on appropriate actions from the competent authorities to reduce this risk in this region.

A multi-breed GWAS for morphometric traits in four Beninese indigenous cattle breeds reveals loci associated with conformation, carcass and adaptive traits

BACKGROUND

Specific adaptive features including disease resistance and growth abilities in harsh environments are attributed to indigenous cattle breeds of Benin, but these breeds are endangered due to crossbreeding. So far, there is a lack of systematic trait recording, being the basis for breed characterizations, and for structured breeding program designs aiming on conservation. Bridging this gap, own phenotyping for morphological traits considered measurements for height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL), including 449 cattle from the four indigenous Benin breeds Lagune, Somba, Borgou and Pabli. In order to utilize recent genomic tools for breed characterizations and genetic evaluations, phenotypes for novel traits were merged with high-density SNP marker data. Multi-breed genetic parameter estimations and genome-wide association studies (GWAS) for the six morphometric traits were carried out. Continuatively, we aimed on inferring genomic regions and functional loci potentially associated with conformation, carcass and adaptive traits.

RESULTS

SNP-based heritability estimates for the morphometric traits ranged between 0.46 ± 0.14 (HG) and 0.74 ± 0.13 (HW). Phenotypic and genetic correlations ranged from 0.25 ± 0.05 (HW-BL) to 0.89 ± 0.01 (HAW-SH), and from 0.14 ± 0.10 (HW-BL) to 0.85 ± 0.02 (HAW-SH), respectively. Three genome-wide and 25 chromosome-wide significant SNP positioned on different chromosomes were detected, located in very close chromosomal distance (±25 kb) to 15 genes (or located within the genes). The genes PIK3R6 and PIK3R1 showed direct functional associations with height and body size. We inferred the potential candidate genes VEPH1, CNTNAP5, GYPC for conformation, growth and carcass traits including body weight and body fat deposition. According to their functional annotations, detected potential candidate genes were associated with stress or immune response (genes PTAFR, PBRM1, ADAMTS12) and with feed efficiency (genes MEGF11 SLC16A4, CCDC117).

CONCLUSIONS

Accurate measurements contributed to large SNP heritabilities for some morphological traits, even for a small mixed-breed sample size. Multi-breed GWAS detected different loci associated with conformation or carcass traits. The identified potential candidate genes for immune response or feed efficiency indicators reflect the evolutionary development and adaptability features of the breeds.

Assessment of Phenotypic Characteristics and Work Suitability for Working Donkeys in the Central Highlands in Kenya

The study aimed to assess the phenotypic characteristics of donkeys and their suitability for work. Data were collected on age, sex, coat color, height at withers, body length, and heart girth from 360 randomly sampled donkeys raised in a highland agroecological system in Kenya between the months of June and September 2018. Data were analyzed using descriptive statistics and ANOVA with the sex of the donkey and age group treated as sources of variation. The weight of donkeys was estimated using a formulae incorporating body length and heart girth. The study reveals that the average weight of the working donkey in the central highlands of Kenya was 155.5 kgs ± SE 1.71. Their height at withers was 99.7 cm ± SEM 0.50, with a heart girth of 113.7 cm ± SEM 0.43 and a body length of 113.2 cm ± SEM 0.58. All these body measurements varied significantly by sex and age group (P < 0.001). Therefore, donkeys raised in Kenya had the same height but heavier, with longer body lengths and heart girth measurements when compared to other domesticated working donkeys in different parts of the world indicating genetic diversity, differences in ecogeographical conditions and husbandry practices. The majority (86%) of the donkeys were in good welfare conditions with moderate to ideal 86% body condition scores, minimal body lesions 5%, and lameness 18%. The results are useful for extension agents and donkey users when estimating optimal pack or cart loads in line with their welfare. The findings provide opportunities for future research on the reasons for phenotypic diversity between donkeys raised in Kenya and other parts of the world.

Correlation and path analysis of body weight and biometric traits of Nguni cattle breed

Objective:

This work was conducted to examine the association between body weight (BW) and biometric traits viz. head width (HW), head length (HL), ear length (EL), body length (BL), rump height (RH), withers height (WH), sternum height (SH), rump width (RW), and heart girth (HG) and to determine the direct and indirect effects of biometric traits on BW.

Materials and Methods:

Sixty female and twenty male Nguni cattle between the ages of one to four years were used. Pearson correlation and path analysis were used for data analysis.

Results:

Correlation results recognized that BW had a positive highly significant correlation with RW (r = 0.70**), RH (r = 0.90**), HG (r = 0.90**), SH (r = 0.90**), and WH (r = 0.93**) in male, whereas SH (r = 0.34**), WH (r = 0.55**), RH (r = 0.70**), and HG (r = 0.76**) had a positive highly significant correlation with BW of female Nguni cattle. Path analysis showed that RW (13.35) had the highest direct effect, whereas SH had an indirect effect on BW of male Nguni cattle. In female Nguni cattle, RH (4.87) had the highest direct effect, whereas HL had an indirect effect on BW.

Conclusion:

Association findings suggest that improvement of RW, RH, HG, SH, HG, and WH might result in the increase in BW of Nguni cattle. Path analysis results suggest that RW and RH might be used as a selection criterion during breeding to increase BW of Nguni cattle. The results of the current study might be used by cattle farmers to estimate BW using biometric traits.

Comparison of Reconstituted, Acidified Reconstituted Milk or Acidified Fresh Milk on Growth Performance, Diarrhea Rate, and Hematological Parameters in Preweaning Dairy Calves

Simple Summary

The preweaning phase is the period for the rapid growth and development of dairy calves. During this period, dairy calves receive their nutrients through milk. Feeding hygienic milk is of great benefit to optimum growth rate and health status of dairy calves. Previous studies focused on the effects of hygienic milk by acidification on dairy calves’ health and growth. Reconstituted milk, as the common source of milk, is being used in dairy calves feeding. However, no previous studies reported the effects of feeding acidified reconstituted milk on dairy calves’ health and growth. Our study will provide the evidence that the acidification of reconstituted milk had positive effects on growth performance and health status of preweaning dairy calves.

Abstract

The present experiment was carried out to assess the effects of reconstituted milk (RM), acidified reconstituted milk (ARM), and acidified fresh milk (AFM) on growth performance, diarrhea rate, and hematological parameters of preweaning dairy calves. For this purpose, a total of 27 Holstein female calves (one month of age) with initial body weight of (67.46 ± 4.08) kg were divided into three groups in such a way that each group contained nine calves. Calves were housed individually, and starter was offered ad libitum to each calf. The dietary treatments were RM, ARM, and AFM. The highest milk intake was observed in calves receiving AFM as compared to other treatments (p < 0.01). Calves fed AFM had more feed intake than those fed ARM and RM (p < 0.01). Feed efficiency was significantly lower for calves offered ARM than those offered RM and AFM (p < 0.01). A lower withers height growth was found for calves fed RM than those fed ARM and AFM (p <0.05). Diarrhea rate and white blood cell (WBC) and lymphocytes (LYM) counts were greater for calves fed RM than those fed ARM and AFM (p < 0.05). These findings suggested that ARM and AFM had positive effects on growth performance and health status of the preweaning dairy calves.

Impact of forage introduction on cattle grazing practices and crop–livestock systems: a case study in an upland village in northern Laos

Lao smallholders are experiencing livestock grazing land constraints due to resettlement, increasing cattle numbers and commercial cash crop plantations. In this paper we describe changes in cattle grazing systems in an upland village in northern Laos, including the role of forage crops and their effects on cattle productivity. We interviewed 92 Hmong and Khmu households about their migration history, cattle grazing practices, cattle productivity and other livelihood activities. In addition, we measured the heart girths of 231 cattle. We found that the traditional free-range cattle grazing has diverged into three distinct systems incorporating fields fenced to different degrees. Although none of the three systems increased cattle body size, the forage pasture and swidden-farming system successfully increased the grazing capacity compared with other systems. Thus, this method appeared to be the most suitable for Hmong smallholders to manage crop and cattle production in the context of land constraints. Efforts should be made to examine how the newly implemented systems could attenuate villager livelihood and pre-emptively address the problems associated with degrading fallow land.

Simple and robust algorithms to estimate liveweight in African smallholder cattle

Measurement of liveweight of stock is one of the most important production tools available to farmers – playing a role in nutrition, fertility management, health and marketing. Yet most farmers in sub-Saharan Africa do not have access to scales on which to weigh cattle. Heart girth measurements (and accompanying algorithms) have been used as a convenient and cost-effective alternative to scales, however despite a plethora of studies in the extant literature, the accuracy and sensitivity of such measures are not well described. Using three datasets from phenotypically and geographically diverse cattle populations, we developed and validated new algorithms with similar R2 to extant studies but lower errors of prediction over a full range of observed weights, than simple linear regression, that was valid for measurements in an unassociated animal population in sub-Saharan Africa. Our results further show that heart girth measurements are not sufficiently sensitive to accurately assess seasonal liveweight fluctuations in cattle and thus should not be relied on in situations where high precision is a critical consideration.