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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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References

Agresti, A 2013. Categorical data analysis, 3rd edition. Wiley, New York, NY, USA.Google Scholar
Alary, V, Corniaux, C and Gautier, D 2011. Livestock’s contribution to poverty alleviation: how to measure it? World Development 39, 1638–1648.CrossRefGoogle Scholar
Burnham, KP and Anderson, DR 2002. Model selection and multimodel inference: a practical information-theoretic approach, 2nd edition. Springer, New York, NY, USA.Google Scholar
Carletto, C, Jolliffe, D and Banerjee, R 2015. From tragedy to renaissance: improving agricultural data for better policies. The Journal of Development Studies 51, 1–16.CrossRefGoogle Scholar
Caswell, H 2001. Matrix population models: construction, analysis and interpretation, 2nd edition. Sinauer Associates, Sunderland, MS, USA.Google Scholar
Ezanno, P, Ickowicz, A and Lancelot, R 2005. Relationships between N’Dama cow body condition score and productive performance under extensive range management in Southern Senegal; calf weight gain milk production, probability of pregnancy and juvenile mortality. Livestock Production Science 92, 291–306.CrossRefGoogle Scholar
Faivre, R, Ioos, B, Mahévas, S, Makowski, D and Monod, H 2013. Analyse de sensibilité et exploration de modèles: Application aux sciences de la nature et de l’environnement. QUAE, Versaille, France.Google Scholar
FAOSTAT 2015. Food and Agriculture Organization (FAO). Retrieved March 5, 2015, from http://faostat.fao.org Google Scholar
Faugère, O, Merlin, P and Faugère, B 1991. Méthodologie d’évaluation de la santé et de la productivité des petits ruminants en Afrique: l’exemple du Sénégal. Revue scientifique et technique de l’Office International des Epizooties 10, 103–130.CrossRefGoogle Scholar
Holford, TR 1980. The analysis of rates and of survivorship using log-linear models. Biometrics 36, 299–305.CrossRefGoogle ScholarPubMed
Hooten, MB and Hobbs, NT 2015. A guide to Bayesian model selection for ecologists. Ecological Monographs 85, 3–28.CrossRefGoogle Scholar
Laird, N and Olivier, D 1981. Covariance analysis of censored survival data using log-linear analysis techniques. Journal of American Statistical Association 76, 231–240.CrossRefGoogle Scholar
Lesnoff, M 2014. Simulating dynamics and productions of tropical livestock populations – mmage: a R package for discrete time matrix models. CIRAD (French Agricultural Research Centre for International Development), Montpellier, France. Retrieved March 5, 2015, from http://livtools.cirad.fr/mmage Google Scholar
Lesnoff, M, Corniaux, C and Hiernaux, P 2012. Sensitivity analysis of the recovery dynamics of a cattle population following drought in the Sahel region. Ecological Modelling 232, 28–39.CrossRefGoogle Scholar
Lesnoff, M, Messad, S and Juanès, X 2013. 12MO: a cross-sectional retrospective method for estimating livestock demographic parameters in tropical small-holder farming systems – version 2. CIRAD (French Agricultural Research Centre for International Development), Montpellier, France. Retrieved March 5, 2015, from http://livtools.cirad.fr/12mo Google Scholar
Matthewman, RW and Perry, BD 1985. Measuring the benefits of disease control: relationship between herd structure, productivity and health. Tropical Animal Health and Production 17, 39–51.CrossRefGoogle ScholarPubMed
McCullagh, P and Nelder, JA 1989. Generalized linear models, 2nd edition. Chapman and Hall, New York, NY, USA.CrossRefGoogle Scholar
Otte, MJ and Chilonda, P 2002. Cattle and small ruminant production systems in sub-Saharan Africa – a systematic review. FAO, Rome, Italy.Google Scholar
Pica-Ciamarra, U, Tasciotti, L, Otte, J and Zezza, A 2015. Livestock in the household economy: cross-country evidence from microeconomic data. Development Policy Review 33, 61–81.CrossRefGoogle Scholar
Pradère, JF 2007. Performances et contraintes de l’élevage au Mali. Projet d’Appui à l’Agriculture Africaine (P3A) au Mali. MAE (Ministère des Affaires Etrangères), Paris, France.Google Scholar
Pradère, JF and Sidibe, S 1989. Etude du cheptel bovin malien. Evolution, structure des troupeaux, productivité. Projet d’Appui à l’Agriculture Africaine (P3A) au Mali. Cellule de suivi-évaluation, Direction Nationale de l’Elevage, Bamako, Mali.Google Scholar
R Core Team 2014. R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. Retrieved March 5, 2015, from http://www.R-project.org Google Scholar
Saltelli, A, Tarantola, S and KP-S, Chan 1999. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41, 39–56.CrossRefGoogle Scholar
Saltelli, A, Chan, K and Scott, EM ed. 2000. Sensitivity analysis. Wiley, New York, NY, USA.Google Scholar
Sarniguet, J, de Mieulle, JE, Blanc, P and Tyc, J 1975. Approvisionnement en viandes de l’Afrique de l’ouest. Tomes I-IV. SEDES, Paris, France.Google Scholar
Searle, SR, Speed, FM and Milliken, GA 1980. Population marginal means in the linear model: an alternative to least squares means. The American Statistician 34, 216–221.Google Scholar
Seré, C and Steinfeld, H 1996. World livestock production systems. Current status, issues and trends. FAO, Rome, Italy.Google Scholar
Sidibé-Anago, AG, Ouedraogo, GA and Ledin, I 2008. Effect of season and supplementation during late pregnancy and early lactation on the performance of Zebu cows and calves. African Journal of Agricultural Research 3, 640–646.Google Scholar
Stein, M 1987. Large sample properties of simulations using Latin hypercube sampling. Technometrics 29, 143–151.CrossRefGoogle Scholar
Tillard, E, Moulin, CH, Faugère, O and Faugère, B 1997. Le suivi individuel des petits ruminants au Sénégal: un mode d’étude des troupeaux en milieu villageois. INRA Productions Animales 10, 67–78.CrossRefGoogle Scholar
Wagenaar, KT, Diallo, A and Sayers, AR 1986. Productivity of transhumant Fulani cattle in the inner Niger delta of Mali. ILCA Research Report no. 13. ILCA (International Livestock Centre for Africa), Addis Ababa, Ethiopia.Google Scholar
Wilson, RT 1986. Livestock production in central Mali: long-term studies on cattle and small ruminants in the agropastoral system. ILCA Research Report no. 14. ILCA (International Livestock Centre for Africa), Addis Ababa, Ethiopia.Google Scholar