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Uncertainty analysis of the productivity of cattle populations in tropical drylands

Published online by Cambridge University Press:  20 July 2015

M. Lesnoff
M. Lesnoff*
Affiliation:
CIRAD, UMR SELMET, Campus International de Baillarguet, TA C-112/A, 34398 Montpellier Cedex 5, France
*
†E-mail: matthieu.lesnoff@cirad.fr
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Abstract

This article presents an uncertainty analysis of the productivity of cattle herds in traditional farming systems of West and Central African drylands. The study focused on productivity rates in animal numbers (RN) and meat weights (RW) estimated from a herd growth model, which were compared with FAOSTAT-based estimates. The uncertainty analysis contained the following two steps: uncertainty propagation and a global sensitivity analysis. The analysis was based on a state-of-the-art of the current knowledge and a set of available data on the herd performances. The calculations used Monte Carlo simulations to estimate the 95% confidence intervals (CI) of RN and RW and the standardized regression coefficients method to estimate the contribution of the input variables to the outputs variances. The mean rate RN was estimated to 0.127 animal/animal-year with a 95% CI of (0.091, 0.163) and the mean rate RW to 11.7 kg/animal-year with a 95% CI of (8.8, 14.7), corresponding to relative variation around the mean of about ±29% and ±25%, respectively. The input variables that contributed most to the variance of RN (almost 76% of the output variance) were the calving rate, the adult female mortality rate and the female proportion in the population (determined by the pattern of the male offtake in the herds). The input variables that contributed most to the variance of RW were the same as those for RN plus the adult live weights. The CI ranges that were estimated in this article indicate that productivity rates based on literature data or expert estimations of the herd performances should be considered with caution. Research efforts based on gold-standard herd monitoring protocols accounting for temporal and spatial variations should be undertaken in future to decrease the knowledge gaps on the input variables that contribute most to these ranges.

Keywords

cattledryland farmingtropical climateproductivitysensitivity analysis
Type
Research Article
Information
animal , Volume 9 , Issue 11 , November 2015 , pp. 1888 - 1896
Copyright
© The Animal Consortium 2015 

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