Productive efficiency of beef cattle production in Botswana: a latent class stochastic meta-frontier analysis

Abstract

IntroductionEfficiency in food production is crucial for sustainable agriculture in developing countries. This paper contributes to the existing literature by presenting an innovative approach to modeling productive efficiency in beef cattle production. Treating farm performance across regions as unobserved heterogeneity, we determine technical efficiency of beef cattle production in Botswana. We aim to shed light on the factors influencing efficiency in this sector.MethodsThe study utilized block-level data from various annual agricultural surveys (2006–2014) covering 26 agricultural districts and six agro-ecological regions in Botswana. We employed a latent class stochastic frontier model complemented with the stochastic meta-frontier analysis.ResultsResults show that the best performing farming systems in terms of efficiency are districts with well-developed infrastructure and better access to output and input markets. In contrast, the farming systems that perform poorly consist of agricultural districts without access to livestock advisory centers, with higher average temperatures and foot and mouth disease, limiting access to export markets. The mean technical efficiency scores for beef production for agricultural districts in class one and two were 62 and 59%, respectively, implying high potential to improve beef production using the same level of agricultural inputs through efficiency-enhancing investments.DiscussionBased on our results, it is crucial for agricultural policies to prioritize regionally specific investments that address the needs of the under-performing districts. By targeting the lagging districts, policymakers can help beef producers improve their input efficiency and bridge the technological gaps to the meta-frontier. This can be achieved through investments in infrastructure, access to livestock advisory services, and disease control measures. Such efforts will not only enhance the efficiency of beef production but also contribute to the overall sustainability of the agricultural sector in Botswana.

Technical Efficiency Effects of agroecological practices use on crop-producing farms: Empirical Evidence from Northern Benin Republic using an endogeneity corrected stochastic frontier model.

“…Based on evidence of farming systems' heterogeneity of in the study area (Bernard et al 2010;MAEP, 2019;Akpatcho et al 2022), this study considers that production technologies of the farms are not homogenous. Studies found evidence that results of technical efficiency could be misleading if production technology heterogeneity is not considered (Sauer et al 2013;Bahta et al 2018). Although heterogeneity can be modelled using several methodological approaches.…”

ESTIMATION OF EFFICIENCY AND THE IMPACT OF COVID-19 ON THE EFFECT OF ACCESS TO FINANCE ON EFFICIENCY OF SMALL AND MEDIUM ENTERPRISES (SMEs) IN THE WESTERN AREA OF SIERRA LEONE

“…They also found that the determinants of inefficiency were soybean availability, production expenses, the width of the production area and the number of employees. Bahta and Baker (2015) in Botswana find an average profit efficiency of 58 percent for a sample of 556 small livestock producers in Botswana. The research showed that the factors that influenced the high degree of inefficiency (42 percent) were education level, distance to the commonly used market, herd size, access to information and income from crop production.…”

Factors Influencing Productivity and Sustainability of Small-Scale Beef Farms in Thailand

“…Cattle farm sustainability can be assessed through various dimensions, including nutrient surplus, land use, greenhouse gas emissions, and animal welfare (Sturaro et al, 2012). While research on livestock sustainability, especially in small-scale beef cattle farms in developing countries, is limited (Bahta et al, 2023;Honjo et al, 1989;Ogino et al, 2016), this case-control study focused on traditional parameters. Future research should integrate modern technologies such as smart farming, precision diets, reproductive biotechnologies, molecular genetics, and animal welfare management (Greenwood, 2021;Lyles & Calvo-Lorenzo, 2014;Pulina et al, 2021;Ventura et al, 2016).…”