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

Abstract

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 R2 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.

Using body measurements to estimate live weight of dairy cattle in low-input systems in Senegal

“…The single explanatory variable that explained the most variation was HG, consistent with previous studies (Dodo et al 2001;Yan et al 2009;Lesosky et al 2012;Lukuyu et al 2016). Predicting LW from HG alone, the model for the overall data was LW = 4.81 HG-437.52, which had an adjusted R 2 of 0.85, and a RMSE of 34.25 kg corresponding to 11.10% of the mean LW.…”

“…The average LW, HG and HW measures for cows of high Bos Taurus found in this study were generally lower compared to 419-781 kg, 176-223 cm and 125-150 cm reported for Holstein Friesians cows in a developed country context (Yan et al 2009), likely due to the lower feeding level of animals in this study in comparison to that in the developed world, and because the high Bos Taurus group in this study were not always pure-bred (see Table 1). A study on dairy cattle in Kenya (Lukuyu et al 2016) where animals mainly comprised the Small East African Zebu (SEAZ) crossed at different levels to exotic breeds generally gave lower LW, HG and HW than that reported here, reflecting the different statures of the main Zebu types in the two studies. The high standard deviations observed for LW, HG and HW within a particular breed group are also in line with previous studies (Fall et al 1982;Dodo et al 2001;Yan et al 2009), and would largely reflect the diversity of cattle management practices within our study sites.…”

“…The adjusted coefficient of (multiple) determination (adjusted R 2 ) was also given for the models, as an indicator of the amount of variance in LW explained by the model. The root mean square error (RMSE; the same as the standard deviation of the residuals), and also the RMSE expressed as a percent of the real LW, was used an indicator of accuracy of the regression estimates (Yan et al 2009;Lukuyu et al 2016). Ninety-five percent prediction intervals were determined as the predicted value ± twice the RMSE.…”

“…Body weight is closely related to body measurements, with HG generally accepted as the most satisfactory single predictor of LW in cattle (Fall et al 1982;Dodo et al 2001;Yan et al 2009;Isik et al 2009;Lesosky et al 2012;Lukuyu et al 2016). The method is cheap, accurate and consistent; thus, many predictive regression equations are based on this parameter alone or in combination with others.…”

Simple and robust algorithms to estimate liveweight in African smallholder cattle

“…Therefore, our study had four objectives: (i) To determine the strongest relationship possible between HG and LW, by considering both PE and regression coefficients, rather than regression coefficients alone; (ii) To determine the extent to which disaggregation of data into more phenotypically homogenous populations is likely to strengthen the relationship between HG and LW; (iii) To assess whether such an algorithm may be used successfully to establish LW in novel populations; and (iv) To determine the applications for which HG measurements may validly be used as an alternative to weighing scales for LW determination. (Lukuyu et al 2016). Data from cattle from West Africa were collected between November 2013 and June 2015 on 84 farms in the Thiès and Diourbel regions of Senegal, (n = 621, LW: range: 31-604 kg; · = 262.7 kg, s.e.m.…”