Agricultural Index Insurance Research Articles

Envisaging Indian farmers' desires from agricultural index insurance integrating rank sum weighting method and MoSCoW technique: an approach to requirements prioritization

Envisaging Indian farmers' desires from agricultural index insurance integrating rank sum weighting method and MoSCoW technique: an approach to requirements prioritization

Short- and long-term weather prediction based on a hybrid of CEEMDAN, LMD, and ANN.

Agriculture is one of the major economic sectors in Africa, and it predominantly depends on the climate. However, extreme climate changes do have a negative impact on agricultural production. The damage resulting from extreme climate change can be mitigated if farmers have access to accurate weather forecasts, which can enable them to make the necessary adjustments to their farming practices. To improve weather prediction amidst extreme climate change, we propose a novel prediction model based on a hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), local mean decomposition (LMD), and artificial neural networks (NN). A detailed comparison of the performance metrics for the short- and long-term prediction results with other prediction models reveals that the three-phase hybrid CEEMDAN-LMD-NN model is optimal in terms of the evaluation metrics used. The study's findings demonstrate the efficiency of the three-phase hybrid CEEMDAN-LMD-NN prediction model in decision-system design, particularly for large-scale commercial farmers, small-holder farmers, and the agricultural index insurance industry that require reliable forecasts generated at multi-step horizons.

Возможные организационноэкономические схемы индексного страхования в сельском хозяйстве России

Agricultural risks, given climate change and the crisis of globalization, are increasingly considered as national security risks. In global practice, insurance has proven to be one of the most successful and sustainable ways to manage agricultural risks. In the Russian Federation, agricultural insurance is also state-supported, which emphasizes the importance of containment of agricultural sector hazards. However, the level of insurance penetration in Russian agriculture is insufficient. Indices in agricultural insurance, i.e. insurance in which insurance payments are made not on the basis of the assessment of damage to the insurance object, but on the basis of the behavior of a special index, are used in many countries around the world. The main idea of index insurance is that there is no need to establish the fact and calculate the amount of loss, which makes it especially demanded for those activities in the real sector of the economy where speed, transparency and simplicity of insurance payments are important, as well as the need to attract additional well-trained employees to carry out the insurance process and claims settlement. Reduced workload due to the absence of the need to calculate the amount of loss reduces the time required to process claims. The procedures of claim settlement in index insurance can be called the most simplified for the client and the least costly for the insurance company, but the advantages and positive features of index insurance do not end there. The authors have investigated and systematized the possibilities of index insurance, for which they also considered the successful experience of a country with similar socio-cultural codes, Mexico, in the field of agricultural index insurance. The article also suggests ways to overcome the difficulties of index insurance implementation in the Russian insurance realities. The positive features of the index insurance identified by the authors can make it a promising alternative to classical (traditional) insurance, including for the further development of agricultural insurance with state support.

A pricing model for agricultural insurance based on big data and machine learning

<p>Agricultural insurance is a crucial element of policies that promote and protect agriculture. It protects agriculture from risk and distributes agricultural hazards. The rural economy’s stabilization has been a significant stabilizer function. But as agriculture insurance has quickly advanced, a number of issues have unavoidably come to light. Agricultural insurance still offers a wide range of products and services available today. Big data will play a significant supporting role in the pressing need to innovate and improve goods and services. Other information supporting agricultural insurance includes agricultural data connected to it. The two previously most often utilized agricultural index insurances are regional yield insurance and weather index insurance. They struggle with risk pricing mostly due to a lack of appropriate empirical data, complicated dependence linkages between various hazards, and the prevalence of basis risk. A comprehensive study and review of pertinent research findings are carried out by modelling regional yield risk, building weather indicators and their distribution fitting, modelling agricultural dependence risk, and measuring and reducing basis risk. This article highlights the flaws in the current pricing models as well as the problems that need to be addressed in future studies. The need to further develop agricultural index insurance’s risk modelling techniques and increase the objectivity and precision of the pricing outcomes cannot be overstated in terms of their practical importance.</p>

The role of peer imitation in agricultural index insurance adoption: Findings from lab‐in‐the‐field experiments in Kyrgyzstan

AbstractFrequent climate shocks require farmers in developing countries to increase their resilience. Although index insurance is often discussed as a promising climate adaptation strategy, take‐up rates are still low. This study primarily explores the role of peer behavior as peer imitation in the demand for three marketable and unsubsidized crop index insurance options. Furthermore, the influence of trust and understanding is investigated. We collected data in lab‐in‐the‐field experiments among farmers in Kyrgyzstan, where index insurance is planned for imminent implementation. Applying ordered logit estimations, our results show significant and strong peer imitation effects. Imitation attitudes decrease with own insurance experience and received insurance payouts, but intensify with peer size, insurance trust and practical insurance understanding. While trust robustly increases index insurance adoption, understanding effects only gain significance in the dynamic perspective. These findings underline the importance of community‐based extension treatments and trust toward the uptake of innovative agricultural technologies in the first steps of dissemination.

Is Agricultural Insurance Fulfilling Its Promise for the Developing World? A Review of Recent Evidence

Innovations in agricultural index insurance have raised expectations that the private sector can overcome shortcomings associated with more traditional indemnity-based products like multiperil crop insurance and strengthen agricultural risk management at scale across developing countries. This article updates previous reviews on agricultural insurance but differs in that it goes beyond the prognosis that recent innovations can help make insurance more commercially viable. As such, it addresses two important challenges that have received limited attention. First, it distinguishes different types of farm households and recognizes that many are excluded from the insurance market, describing additional innovations that can help make insurance more accessible to these excluded groups. Second, it acknowledges that insurance for catastrophic risks is unaffordable for most farmers and summarizes new developments in disaster assistance and safety net programs that can provide broader protection against these risks. The review concludes that cost-benefit analyses of subsidized insurance programs will be crucial for guiding public spending decisions.

Linking farmers’ risk attitudes, livelihood diversification and adoption of climate smart agriculture technologies in the Nyando basin, South-Western Kenya

Climate smart agriculture (CSA) technologies are innovations meant to reduce the risks in agricultural production among smallholder farmers. Among the factors that influence farmer adoption of agricultural technologies are farmers' risk attitudes and household livelihood diversification. This study, focused on determining how farmers' risk attitudes and household livelihood diversification influenced the adoption of CSA technologies in the Nyando basin. The study utilized primary data from 122 households from two administrative regions of Kisumu and Kericho counties in Kenya. The study employed the multivariate probit (MVP) and ordered probit (OP) models and descriptive statistics in data analysis using Stata 14.0. Results from the study indicated that farmers’ risk attitudes had a significant negative influence in the adoption of terraces, ridges and bunds as well as the intensity of adoption of given CSA technologies. Household livelihood diversification had a significant negative influence in the adoption of stress tolerant livestock but did not have a significant effect on the intensity of adoption of given CSA technologies. The study recommends that relevant stakeholders should introduce an appropriate agricultural index insurance product to Nyando basin farmers to encourage the broader adoption of CSA technologies.

Evaluating the quality of remote sensing products for agricultural index insurance.

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.

Mapping weather risk – A multi-indicator analysis of satellite-based weather data for agricultural index insurance development in semi-arid and arid zones of Central Asia

Index insurance has been introduced as a solution to tackle several challenges that prevail in the agricultural insurance sector of developing countries. One of the main implementation challenges in these countries is the lack of reliable weather data for index development and implementation. The increasing availability of satellite data could ease the constraints of data access. Meanwhile, the suitability of various satellite products for yield estimation across world regions has to undergo a thorough assessment. This study contributes to the literature by systematically analyzing the accuracy of some globally available satellite data, namely the Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and the Global Land Data Assimilation System (GLDAS) compared to ground-level weather information for 14 different indicators for the case of Uzbekistan. Our analysis indicates that those sources may provide the necessary data for an accessible and adequate climate service. However, a considerable risk of overestimation and underestimation depending on the source of satellite data may exist, especially for precipitation data in the conditions of Central Asia. Among the tested datasets, GSMaP showed a relatively better performance than CHIRPS in precipitation estimation for drought and flood detection. In order to reduce detection inaccuracy, the application of satellite weather products for index insurance is possible when temporal aggregation (e.g., monthly, seasonal) is considered. Globally available climate data could serve as a good source to establish index insurance products in Central Asia; however, a careful selection of source and index is required.

Linking Agricultural Index Insurance with Factors That Influence Maize Yield in Rain-Fed Smallholder Farming Systems

Weather extremes pose substantial threats to food security in areas where the main source of livelihood is rain-fed crop production. In most of these areas, agricultural index insurance (AII) is recognized as being capable of securitizing food production by providing safety nets against weather-induced crop losses. Unfortunately, however, AII does not indemnify farmers for non-weather-related crop losses. This study investigates how this gap can be filled by exploring strategies through which AII can be linked with non-weather factors that influence crop production. We do this by using an improvised variable ranking methodology to identify these factors in the O.R. Tambo District Municipality, South Africa. Results show that key agrometeorological variables comprising surface moisture content, growing degree-days, and precipitation influence maize yield even under optimal weather conditions, while seed variety, fertilizer application rate, soil pH, and ownership of machinery play an equally important role. This finding is important because it demonstrates that although AII focuses more on weather elements, there are non-weather variables that may expose farmers to production risk even under optimal weather conditions. As such, linking AII with critical non-weather, yield-determining factors can be a better risk management strategy.

A market infrastructure for environmental intangibles: the materiality and challenges of index insurance for agriculture in Senegal

ABSTRACT In Senegal, various development pilot projects experimented with agricultural index insurance to make drought insurable. These interventions produced ‘rainfall deficit’ as an environmental problem suited for such market-based solutions. Investigating the materiality of this index insurance scheme, I explore the environmental market infrastructure, which produces risk conventions, articulates governance and market requirements, and fuels the promise of cheap, efficient, and automated environmental risk management in the Global South. I show that the production of such risk requires both a selective reduction of the physical reality of drought and the substantial reordering of material activities that organize many actors around rainfall deficit. Meteorological data produced by rain gauges placed in Senegalese fields were a central element in the initial infrastructure of index insurance. However, as reductionist technologies are often prone to failure, this index insurance scheme met many challenges. After documenting the design of this insurance scheme and the obstacles encountered, the paper depicts how actors in charge of its implementation made a shift in the insurance infrastructure from field rain gauges to remote sensing – in order to re-energize index insurance’s expectations.

Understanding farmers’ valuation of agricultural insurance: Evidence from Vietnam

Agricultural index insurance offers smallholder farmers the potential to manage pervasive weather and environmental risks, and smooth income over time. Yet despite the potential benefits, take-up of index insurance has been largely disappointing. We employ a novel approach to investigate the reasons for low demand and we confirm that farmers systematically undervalue agricultural index insurance. We find that access to private transfers acts as a substitute for insurance demand, while membership of a farmer’s union helps explain why farmers value index insurance. Finally, we find that individual riskiness is positively related to willingness to pay for agricultural index insurance and interpret this as further evidence that, within the context of basis risk and lack of trust in insurers, farmers valuation of agricultural index insurance can be better understood through behavioural theories such as ambiguity aversion or prospect theory.

Agricultural Index Insurance for Development

Index insurance is often promoted as a solution to many of the barriers that are thought to limit the supply of formal insurance coverage to smallholder farmers and livestock owners in developing countries. This manuscript summarizes the state of index insurance, paying special attention to the key challenges facing index insurance if it is to become a more effective tool for development. We then offer recommendations to tackle those challenges by strategically investing in a set of public goods and services geared towards addressing key informational gaps and improving the quality of index insurance products around the world.

Combining satellite observations to develop a global soil moisture product for near-real-time applications

Abstract. The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from 10 different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI product for daily global updates via satellite-derived near-real-time (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced Scatterometer on-board the two MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. In addition to adaptations of the ESA CCI SM processing chain for NRT datasets, the quality of the NRT datasets is a main source of uncertainty. Our findings indicate that, despite issues in arid regions, the new CCI NRT dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.80. An initial validation with 40 in situ observations in France, Spain, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT, respectively. In summary, the CCI NRT product is nearly as accurate as the existing ESA CCI SM product and, therefore, of significant value for operational applications such as drought and flood forecasting, agricultural index insurance or weather forecasting.

Weather insurance as a means of supporting vulnerable agriculture, and impacts of climate change.

Abstract In recent years a growing amount of resources has been allocated for the advocacy and promotion of agricultural index insurance projects. Three factors motivated such actions: (i) agricultural production accounts for a large fraction of the economy in these countries, (ii) farmers' output largely depend on weather's realization given the agricultural technologies adopted, and (iii) covariant shocks such as weather variations remain quite uninsured. In this literature review, we report the theoretical and empirical evidence about the need and demand for these insurance products. We first discuss the potential behavioural impact of the provision of insurance. We then focus on the modalities of distributions and consider the economic and behavioural factors that are identified in the literature as constraints to the insurance diffusion.

Environment, Technology, and the Social Articulation of Risk in West African Agriculture

two preoccupations are not mutually exclusive. Superficially, they share the common concern that liberalization is insufficient to resolve the subSaharan food crisis. At a deeper level, the two preoccupations are more intimately interrelated via the correspondence that links initial wealth to risk exposure and to behavior in both production and asset accumulation. A number of recent empirical studies of risk in low-income countries find that households are able to employ their accumulated assets to smooth consumption in the face of adverse agricultural production shocks. 3 Missing from these studies is explicit attention to the fact that the effective risk from which households insulate consumption is not that of production shocks directly, but of those shocks as socially articulated by institutions and property rights. This article explores the creation and distribution of effective risk, estimating how environment, technology, and social factors interact to construct it endogenously. 4 Put differently, this article develops the notion that the risk from which households ex post try to insulate consumption is not an immutable natural or technical feature of the landscape. As Michael Watt’s contrast between precolonial and colonial Nigeria forcefully demonstrates, the effective risk presented by an unchanged set of environmental and technical circumstances can