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Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

This paper explores the idea that a competitive seed system may systematically underserve farmers in small, agro-ecological niches, leaving potential yield gains on the table and farmers in these areas less productive and poorer than they need be. We develop a simple theoretical model that illustrates how a confluence of demand and supply factors can result in such an underserviced equilibrium. To study the empirical veracity of this model, we study the disruption of the maize seed market in Western Kenya that took place when a combination of public sector foundation breeding and social impact investment capital allowed a local seed company to expand and target a niche agro-ecological zone with adaptively-bred maize varieties. A three-year randomized controlled trial reveals that the impacts of these seed varieties on farmer yields and revenues in the niche market were substantial, both for better-resourced farmers (who used nonadapted hybrids and fertilizer prior to the intervention) as well less well-resourced farmers (who did not). Taken together, this theoretical and empirical evidence suggests news ways for thinking about seeds systems in areas typified by the high levels of agro-ecological heterogeneity found in important parts of sub-Saharan Africa.

Using a multi-year, spatially diversified randomised controlled trial spanning two African countries, this paper explores whether a complementary bundle of genetic and financial technologies can boost the resilience and productivity of small-scale farmers. The analysis shows that both moderate droughts and more severe yield losses undermine the resilience of control-group households, and that these shocks have long-lasting effects as farmers invest and produce less following shocks. Severe yield shocks also increase hunger and food insecurity. The genetic technology—drought-tolerant seeds—provides economically significant protection against mid-season drought and mitigates the long-term drop in farm productivity seen in the control group. The financial technology—satellite-based index insurance—offsets the long-term consequences of severe yield losses that are not mitigated by the drought-tolerant seeds. Finally, the analysis shows that treatment-group farmers who experienced shocks and saw the technologies in action subsequently increased their agricultural investment beyond pre-shock levels, an effect we call the resilience dividend. Unfortunately, this apparent experiential learning cuts both ways. Farmers who did not experience the efficacy of the risk management technologies backed away from using them. Our findings thus showcase how genetic and financial risk-mitigating technologies can offer farmers more complete protection, as well as the challenge of inducing sustained uptake of technologies that are midway between experience and credence goods and only infrequently reveal their benefits. [ABSTRACT FROM AUTHOR]

Background: Climate-smart agriculture (CSA) addresses the challenge of meeting the growing demand for food, fibre and fuel, despite the changing climate and fewer opportunities for agricultural expansion on additional lands. CSA focuses on contributing to economic development, poverty reduction and food security; maintaining and enhancing the productivity and resilience of natural and agricultural ecosystem functions, thus building natural capital; and reducing trade-offs involved in meeting these goals. Current gaps in knowledge, work within CSA, and agendas for interdisciplinary research and science-based actions identified at the 2013 Global Science Conference on Climate-Smart Agriculture (Davis, CA, USA) are described here within three themes: (1) farm and food systems, (2) landscape and regional issues and (3) institutional and policy aspects. The first two themes comprise crop physiology and genetics, mitigation and adaptation for livestock and agriculture, barriers to adoption of CSA practices, climate risk management and energy and biofuels (theme 1); and modelling adaptation and uncertainty, achieving multifunctionality, food and fishery systems, forest biodiversity and ecosystem services, rural migration from climate change and metrics (theme 2). Theme 3 comprises designing research that bridges disciplines, integrating stakeholder input to directly link science, action and governance. Outcomes: In addition to interdisciplinary research among these themes, imperatives include developing (1) models that include adaptation and transformation at either the farm or landscape level; (2) capacity approaches to examine multifunctional solutions for agronomic, ecological and socioeconomic challenges; (3) scenarios that are validated by direct evidence and metrics to support behaviours that foster resilience and natural capital; (4) reductions in the risk that can present formidable barriers for farmers during adoption of new technology and practices; and (5) an understanding of how climate affects the rural labour force, land tenure and cultural integrity, and thus the stability of food production. Effective work in CSA will involve stakeholders, address governance issues, examine uncertainties, incorporate social benefits with technological change, and establish climate finance within a green development framework. Here, the socioecological approach is intended to reduce development controversies associated with CSA and to identify technologies, policies and approaches leading to sustainable food production and consumption patterns in a changing climate.

By shrinking the available menu of loan contracts, asymmetric information can result in two types of nonprice rationing in credit markets. The first is conventional quantity rationing. The second is 'risk rationing.' Risk rationed agents are able to borrow, but only under relatively high collateral contracts that offer them lower expected well-being than a safe, reservation rental activity. Like quantity rationed agents, credit markets do not perform well for the risk rationed. While the incidence of conventional quantity rationing is straightforward (low wealth agents who cannot meet minimum endogenous collateral requirements are quantity rationed), the incidence of risk rationing is less straightforward. Increases in financial wealth, holding productive wealth constant, counter intuitively result in the poor becoming entrepreneurs and the wealthy becoming workers. While this counterintuitive puzzle has been found in the literature on wealth effects in principal-agent models, we show that a more intuitive pattern of risk rationing results if we consider increases in productive wealth. Empirical evidence drawn from four country studies corroborates the implications of the analysis, showing that agents with low levels of productive wealth are risk rationed, and that their input and output levels mimic those of low productivity quantity rationed firms.