Cost-benefit analysis of prioritized climate-smart agricultural practices among smallholder farmers: evidence from selected value chains across sub-Saharan Africa

Abstract

Prioritization of adaptation options is complex. This study presents a multi-dimensional framework to evaluate how to allocate resources among competing alternatives. The main objectives of the study were to identify the prioritized climate-smart agricultural practices adopted among smallholder farmers in different value chains across sub-Saharan Africa (SSA) and to assess the economic feasibility of the practices using Cost-Benefit Analysis (CBA) to develop a portfolio of viable and cost-effective options. This study focused on selected five SSA countries and selected value chains. 153 smallholder farmers and stakeholders were interviewed. The Climate Smart Agriculture Prioritization Framework was applied for the assessment of economically viable adaptation options. The prioritization was based on standard ranks on the ability of the practice to improve productivity, increase resilience, and mitigation. Spearman's rank-order correlation was used to assess the independence of the ranks. A CBA was conducted as the final step. Smallholder farmers in the study areas prioritized the adoption of improved seed, good agricultural practices, and conservation agriculture practices. In the sweet potato value chain in Kenya, good agricultural practices was viable with an NPV of US$ 28,044, an IRR of 328%, and a one-year payback period. This is in comparison to the improved seed varieties (US$ 8,738, 111%, and two years payback period) respectively. In Nigeria, the most viable option was the improved seed in the potato value chain and good agricultural practices in the rice value chain. In Malawi, Ethiopia, and Zambia, the most viable practices were improved seed, and conservation agriculture in the soybean, faba beans, and peanut value chains respectively. The NPV was highly sensitive to changes in the discount rate, moderately to price, yield, and practice lifecycle, and least to changes in annual labour costs. The results elaborate on the most feasible adaptation practices that enable smallholder farmers to increase productivity and be economically efficient. The use of the CSA-PF consecutively with the CBA tool allows for the proper identification of best-bet CSA options.

Keywords: Climate-smart agriculture; Cost-benefit analysis; Prioritization; Sensitivity analysis; Sub-Saharan Africa.

Figure 1

Climate Smart Agriculture Prioritization framework (CSA-PF). CSA stands for Climate smart agriculture, CBA represents Cost-Benefit Analysis.

Figure 2

The proposed shape of the productivity response function (Adapted from Sain et al., 2017) (The productivity increases exponentially from t1 to t2 given one limiting factor and then stabilizes over time when other factors limit production).

Figure 3

(a) Sensitivity of the net present value to changes in different variables (drought tolerant sweet potato varieties in Kenya). USD stands for United States Dollars, ha represents hectares. The bars to the right show a positive change in the NPV while the bars to the left represent a negative change in NPV given the two scenarios, optimistic and pessimistic). (b) Sensitivity of the net present value to changes in different variables (The rest of the value chains). USD stands for United States Dollars, ha represents hectares. The bars to the right show a positive change in the NPV while the bars to the left represent a negative change in NPV given the two scenarios, optimistic and pessimistic In column two, on the second and third graphs, the writings on the y axis are not visible.