Reducing food-related economic loss to improve food security and cattle trade in the Sahel: the case of agropastoral systems in Senegal

From Firenze University Press Journal: Bio-based and Applied Economics (BAE)

Abdrahmane Wane, ILRI Regional Representative for West Africa

Mballo Aliou Diouf, International Fund for Agricultural Development (IFAD)

Dzoukou Homsi Cabrelle Lauriane, Université Cheikh Anta Diop de Dakar

Diakhate Pathe, Université Cheikh Anta Diop de Dakar

Memboup Rahimatou, Université Laval

A growing local and regional demand for meat and milk has provided opportunities for pastoral producers in the Sahel. However, several factors make it difficult for Sahelian producers and other actors in the livestock value chain to take full advantage of this positive trend. Ickow-icz et al. (2012), Hollinger and Staaz (2015), and Diawara et al. (2017) all highlight low herd productivity as a criti-cal constraint. Together with structural constraints relat-ing to logistics, infrastructure, public policy and enabling environment, low productivity contributes to sub-optimal performance in the livestock sector. The Sahelian live-stock sector is vulnerable to multifaceted shocks, mainly relating to climate, disease, natural disasters and market fluctuations. Some of these shocks are severe, leading to quantitative, qualitative and economic loss (IFAD 2016).There is ongoing interest in this issue, even though there is scarcity of information and the evidence on the extent of the losses is mixed with estimates ranging from 2% up to 27% (FAO, 2011; Blanchard et al., 2016).A quantitative evaluation of the different types of loss remains challenging for several reasons. While pro-ductivity gaps have been documented, food loss in the livestock sector has received far less attention. Compre-hensive modelling methods are needed to clarify spa-tial and temporal fluctuations in loss rates and to make credible estimates of the quantitative, qualitative and economic losses. Further complicating the situation in the Sahel is the perceived dualism between commercial and communal livestock keepers and between modern and traditional systems (Lyet et al., 2010). The structure of the livestock value chain is extremely nuanced. There are considerable differences in the levels of market inte-gration, motivation and vulnerability among value chain actors, and this influences the nature and perceptions of loss. Furthermore, small-scale producers have a ‘produc-er–consumer model,’ as articulated by Chayanov (1926, 1990). The goal of pastoralists in a changing environ-ment, such as the Sahel, is to balance short-term con-sumption needs with long-term herd-building strategies to meet future consumption demands (Fadiga, 2013). Consequently, an understanding of the motivation to increase sales is key to understanding decision-making strategies. Moreover, the parameters of the livestock market remain relatively rigid, with a low supply of ani-mals and high price levels. Food loss has adverse effects on food safety and security, particularly for poor and vulnerable people (Sheahan and Barrett, 2016), on the livestock market (Wane and Mballo, 2016) and on sustainable develop-ment (Gustavsson, 2011). Concerns about food loss frequently give rise to quantitative and qualitative estimates, which tend to be followed by remediation (in a ‘zero loss’ approach). However, when considered from a different econom-ic perspective, not all loss is undesirable. This opens the opportunity to explore an exciting loss assessment method in which mitigation is the goal (an ‘optimal loss’ approach). The optimal loss approach is based on two key assumptions. First, the cost of total elimination of loss is prohibitive, regardless of the availability of tech-nology and institutional arrangements. Second, a certain level of loss is inevitable and not necessarily undesirable, particularly in the agricultural sector. This paper contributes to a long-standing debate on risk in the agricultural sector (Wane and Mballo, 2016; Chavas et al., 2021) and decision-making related to risk perception (Wane et al., 2020).To address the complex issue of loss in the Sahe-lian pastoral areas, we used a sequential approach by which qualitative data was collected from approximately 15 people in each of the three targeted sites, and these results guided the subsequent collection of quantitative data from 202 households. This paper pioneers the idea that it is possible to improve food security in the Sahelian region by mini-mizing losses in the production stage of the live animal value chain. The paper contributes by developing an opti-misation model that determines the optimum numbers of different animal species to be sold to counteract losses while also being subject to the farmers’ constraints. Our methodology is pragmatic in that the recommended opti-misation approach aims for loss reduction rather than illusory loss eradication. In addition, unlike measures of loss in the crop sector, which focus entirely on post-harvest losses, in our analysis of loss in the livestock sec-tor, we consider both pre-market and market losses to be equally significant. Second, our model shows the ideal sales volume, age at sale and price at sale that will allow the livestock farmer to generate sufficient income to cover his expenditure. Third, we simulate loss reduction scenarios, with their effects on volume and market price parameters, and we show how these scenarios can result in a decline in average market prices, with the result that buyers can access more affordable live animals. Overall, our paper demonstrates that addressing economic losses offers a more impactful perspective than focusing solely on the more commonly emphasized physical losses.This paper is organized as follows. Section 2 reviews the literature discussing the issues and challenges faced by people living shock-prone dryland areas. It analyses the relevance of the optimal loss approach by empha-sising the effect of multifaceted exogenous shocks on producer market behaviours. Section 3 describes the economic loss model to the Senegalese Sahelian agropas-toral production system. Section 4 describes the study area and data used in our analysis. Section 5 presents the main results of the optimization, identifying optimal quantity and price in different loss reduction scenarios. Section 6 discusses the main results and concludes.

DOI: https://doi.org/10.36253/bae-13521

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