Postharvest loss reduction throughout commodity value chains is an important pathway to food and nutrition security in sub-Saharan Africa. However, lack of understanding of the location and share of the losses and associated factors along the postharvest value chains remains a major challenge to operationalizing postharvest loss mitigation strategies. This paper assesses the determinants of postharvest losses at each postharvest stage of maize and sweetpotato (white fleshed and orange fleshed) value chains for smallholder farmers using our cross-sectional field survey data from two districts in Uganda. An ordered probit model estimation reveals that self-reported perceptions of the level of quantitative postharvest losses at different stages of commodity value chains are influenced by socio-economic factors as well as existing postharvest handling and storage practices. Increased years of education and training received on postharvest management are related to lower perceived levels of postharvest losses at key stages of value chains. Lower perceived postharvest losses are also associated with: at transport to homestead the use of sacks and bicycles as opposed to the use of baskets or transporting by trucks; at drying the use of tarpaulins as opposed to use of plastic sheets; shelling using bare-hands as opposed to beating cobs in sack with sticks; storage in a brick and mortar store as opposed to storing in living room in the house.

Postharvest losses (PHLs) amplify food insecurity and reduce the amount of nutrients available to vulnerable populations, especially in the world's Low and Middle Income Countries (LMICs). However, little is known about nutrient loss at the various postharvest stages. The objective of our study was to develop a methodology and a tool to estimate nutritional postharvest losses (NPHLs) along food value chains for three distinct food commodities in sub-Saharan Africa. The study used a combination of literature, laboratory and field data to investigate NPHLs caused by both changes in quantity and quality of food material (quantitative and qualitative NPHLs, respectively). The method can be expanded to various other food value chains. A user-friendly predictive tool was developed for case studies involving maize and cowpea in Zimbabwe, and for sweet potato in Uganda. Quantitative and qualitative NPHLs were combined and converted into predicted nutrient loss and nutritional requirement lost due to postharvest losses. The number of people who may not meet their daily nutritional needs, as a result of the food and nutrient losses at country level, was estimated. The estimates consider nutritionally vulnerable groups such as children under five years and pregnant women. The nutrient density of the harvested food material, the level of food production, the postharvest stages along the food value chain, the levels of pest damage along the value chain, and the susceptibility of the nutrients to degradation e.g. during storage, are all important factors that affect NPHLs. Our modelling work suggests that reducing PHLs along food value chains could significantly improve access to nutritious food for populations in LMICs.

Initiatives on the sustainable intensification of agriculture have introduced improved technologies tailored to farmers’ local conditions by trial demonstration with free provision of improved seeds and fertilizers. It is not clear, though, whether smallholder farmers would be willing to pay for these technologies, and what factors determine their informed demand. Using a contingent valuation experiment, combined with information at baseline among 400 households in Northern Tanzania, this study measured farmers’ willingness to pay (WTP) for hybrid maize seed and local inorganic fertilizer. Farmers’ WTP was estimated using a dichotomous contingent valuation with follow-up model. Results showed that the average WTP was 61% higher for hybrid maize seed, and 15% lower for inorganic fertilizer, than their respective average local market prices during the reference period, suggesting that farmers were willing to pay a premium for hybrid maize seed, while they did not seem to be interested in fertilizer purchase at current market price. Moreover, since improved access to extension services was found to positively affect farmers’ WTP, strengthening extension services could be a suitable policy intervention to increase farmers’ demand for improved technologies. On the other hand, farmers’ risk aversion was negatively correlated with WTP for both technologies. This result suggests that encouraging risk reduction options, such as agricultural insurance, could be a useful policy strategy for boosting farmers’ demand for improved agricultural technologies.

Weather-related agricultural risks and limited access to credit are serious impediments to agricultural productivity and growth in developing countries. This paper describes a novel insurance linked credit model piloted in Kenya, where insurance markets are effectively absent, and farmers do not borrow because of the risk of losing their collateral. One of the challenges in deigning bundled credit products, in the absence of traded securities, is the actuarial pricing and risk rating of the insurance and the loan product. We develop a rainfall linked risk-contingent credit that transfers drought risk related perils from borrower to lender via insurance mechanism that provide a balance between business and credit risks for smallholder farmers. We describe the methodology used to design and rating of a risk-contingent structured operating agricultural credit instrument using CHIRPS rainfall data from 1981–2016 in Kenya. We illustrate the use of Monte Carlo methods to risk modelling that can be integrated within the general insurance and credit rating framework. The innovative design and methodology presented in this paper are as important as the product delivery mechanism and will be of interest to specialists in development economics and agricultural finance.

We employ a discrete choice experiment to elicit demand and supply side preferences for insurance‐linked credit, a promising market‐based tool for managing agricultural weather risks and providing access to credit for farmers. We estimate preference heterogeneity using primary data from smallholder farmers and managers of lenders/insurers combined with household socio‐economic survey data in Kenya. We analyse the choice data using maximum simulated likelihood and Hierarchical Bayes estimation of a mixed logit model. Although there are some similarities, we find that there is conflicting demand and supply side preferences for credit terms, collateral requirements, and loan use flexibility. We also analyse willingness to buy and willingness to offer for farmers and suppliers, respectively, for the risk premium for different attributes and their levels. Identifying the preferred attributes and levels for both farmers and financial institutions can guide optimal packaging of insurance and credit providing market participation and adoption motivation for insurance‐bundled credit product.

Climate risk financing programs in agriculture have caught the attention of researchers and policy makers over the last decade. Weather index insurance has emerged as a promising market-based risk financing mechanism. However, to develop a suitable weather index insurance mechanism it is essential to incorporate the distribution of underlying weather and climate risks to a specific event model that can minimize intraseasonal basis risk. In this paper we investigate the erratic nature of rainfall patterns in Kenya using Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall data from 1983 to 2017. We find that the patterns of rainfall are fractional, both erratic and persistent, which is consistent with the Noah and Joseph effects that are well known in mathematics. The erratic nature of rainfall emerges from the breakdown of the convergence to a normal distribution. Instead we find that the distribution about the average is approximately lognormal, with an almost 50% higher chance of deficit rainfall below the mean than adequate rainfall above the mean. We find that the rainfall patterns obey the Hurst law and that the measured Hurst coefficients for seasonal rainfall pattern across all years range from a low of 0.137 to a high above 0.685. To incorporate the erratic and persistent nature of seasonal rainfall, we develop a new approach to weather index insurance based upon the accumulated rainfall in any 21-day period falling below 60% of the long-term average for that same 21-day period. We argue that this approach is more satisfactory to matching drought conditions within and between various phenological stages of growth.