Insuring crops from space: the potential of satellite-retrieved soil moisture to reduce farmers’ drought risk exposure

Vroege, Willemijn; Bucheli, Janic; Dalhaus, Tobias; Hirschi, Martin; Finger, Robert

doi:10.1093/erae/jbab010

Cited by 43 publications

(36 citation statements)

References 72 publications

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“…Following previous studies (Huang et al, 2015; Mukasa, 2018; Sarr et al, 2021; Vroege et al, 2021), we specify the banana production function as follows:

O_{i} = f false(I_{i} ; θ_{i} false) + ε_{i}

where O i refers to banana yield, measured at kg/mu (1 mu = 1/15 ha); I i refers to a vector of production inputs (e.g., land, labour, pesticides and fertilisers); θ i represents a vector of parameters to be estimated, capturing how (positively or negatively) and the magnitudes regarding the effects of production inputs on banana yield; and ε i is an error term.…”

Section: Estimation Strategiesmentioning

confidence: 63%

“…In practice, farmers have adopted various agricultural technologies (e.g., drought‐tolerant varieties and climate‐smart practices) to raise farm productivity and mitigate risk. A growing number of studies have examined the impacts of technology adoption on farm productivity (measured by yield or revenue) and production risk exposure (measured by variance and skewness) (e.g., Amondo et al, 2019; Di Falco & Chavas, 2009; Huang et al, 2015; Issahaku & Abdulai, 2020; Mukasa, 2018; Sarr et al, 2021; Shahzad & Abdulai, 2020; Vroege et al, 2021). These studies show how the adoption of new techniques and practices increases crop yield and reduces production risk.…”

Section: Introductionmentioning

confidence: 99%

“…First, we analyse cooperative membership effects on banana yield, measured by expected yield, yield variance (variability), and yield skewness (exposure to downside risk). We rely on a moment‐based specification of the production function to compute these three central moments (Amondo et al, 2019; Di Falco & Chavas, 2009; Shahzad & Abdulai, 2020; Vroege et al, 2021). Second, we apply an endogenous switching regression (ESR) model to address the selection bias issue.…”

Section: Introductionmentioning

confidence: 99%

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Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Zheng

Yuan

2021

J Agricultural Economics

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The yield‐increasing effects of agricultural cooperative membership have been widely examined in the literature. However, so far, little is known about whether cooperative membership has the potential to reduce farmers’ exposure to production risk. We address this gap by estimating the impacts of cooperative membership on expected yield, yield variance (variability), and yield skewness (exposure to downside risk), using data collected from a survey of 626 banana farmers in China. We employ an endogenous switching regression model to address the selectivity bias issue. The empirical results show that cooperative membership increases banana yield by 3% and reduces the variance and downside risk exposure by 60% and 114%, respectively. The results are supported by robustness check estimates, using propensity score matching and inverse probability weighted regression adjustment models. Additional analysis reveals that the treatment effects of cooperative membership vary among members with different household and farm characteristics. Our findings suggest that agricultural cooperatives can be an effective institutional arrangement for reducing production risk and crop failure and point to the need for policies and programmes in developing cooperatives and increasing membership involvements of smallholder farmers.

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“…Following previous studies (Huang et al, 2015; Mukasa, 2018; Sarr et al, 2021; Vroege et al, 2021), we specify the banana production function as follows:

O_{i} = f false(I_{i} ; θ_{i} false) + ε_{i}

Section: Estimation Strategiesmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Zheng

Yuan

2021

J Agricultural Economics

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“…It also assists design novel insurance policies to compensate farmers' losses at finer scales (Perry andYu 2020, Diffenbaugh et al 2021). For example, the emerging weather index insurance established by linking weather conditions (mainly precipitation-related index) and yield losses has been demonstrated to be a cost-efficient way to improve drought risk management (Dalhaus et al 2018, Bucheli et al 2021, Vroege et al 2021. For extreme precipitation, one might consider migrating susceptible crops to regions with higher optimal precipitation thresholds.…”

Section: Implications For Climate Change Adaptationmentioning

confidence: 99%

Divergent responses of maize yield to precipitation in the United States

Guan

et al. 2021

Environ. Res. Lett.

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How maize yield responds to precipitation variability in space and time over broader scales is largely unknown compared with the well-understood temperature response, even though precipitation change is more erratic with greater spatial heterogeneity. Here, we develop a method to quantify the spatially explicit precipitation response of maize yield using statistical data and crop models in the contiguous United States. We find the precipitation responses are highly heterogeneous with inverted-U (40.3%) being the leading response type, followed by unresponsive (30.39 %), and linear increase (28.6%). The optimal precipitation threshold derived from inverted-U response exhibits considerable spatial variations, which is higher under wetter, hotter, and well-drainage conditions but lower under drier and poor-drainage conditions. Irrigation alters precipitation response by making yield either unresponsive to precipitation or having lower optimal thresholds than rainfed conditions. We further find that the observed precipitation responses of maize yield are misrepresented in crop models, with a too high percentage of increase type (59.0% versus 29.6%) and an overestimation in optimal precipitation threshold by ~90 mm. These two factors explain about 30% and 85% of the inter-model yield overestimation biases under extreme rainfall conditions. Our study highlights the large spatial heterogeneity and the key role of human management in the precipitation responses of maize yield, which need to be better characterized in crop modeling and food security assessment under climate change.

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“…The presence of basis risk has triggered a large literature, extending now well beyond the economic realm, on a quest for the "perfect" index. Indices considered range from input-based indices such as precipitation, temperatures, soil moisture (Vroege et al, 2021) or satellitederived vegetation indices (Chantarat et al, 2013) to output-based indices using average yields, either from official statistics (Skees et al, 1997), sampling surveys (World Bank, 2015) or from satellite estimates (Flatnes et al, 2018); see Benami et al (2021) for a survey. With the proliferation of these studies, there is some confusion with the notion of basis risk.…”

Section: Introductionmentioning

confidence: 99%

Optimal index insurance and basis risk decomposition: an application to Kenya

Stigler¹,

Lobell²

2021

Preprint

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Index insurance is a promising tool to reduce the risk faced by farmers, but high basis risk, which arises from imperfect correlation between the index and individual farm yields, has limited its adoption to date. Basis risk arises from two fundamental sources: the intrinsic heterogeneity within an insurance zone (zonal risk ), and the lack of predictive accuracy of the index (design risk ). Whereas previous work has focused almost exclusively on design risk, a theoretical and empirical understanding of the role of zonal risk is still lacking.Here we investigate the relative roles of zonal and design risk, using the case of maize yields in Kenya. Our first contribution is to derive a formal decomposition of basis risk, providing a simple upper bound on the insurable basis risk that any index can reach within a given zone. Our second contribution is to provide the first large-scale empirical analysis of the extent of zonal versus design risk. To do so, we use satellite estimates of yields at 10m resolution across Kenya, and investigate the effect of using smaller zones versus using different indices. Our results show a strong local heterogeneity in yields, underscoring the challenge of implementing index insurance in smallholder systems, and the potential benefits of low-cost yield measurement approaches that can enable more local definitions of insurance zones.

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Insuring crops from space: the potential of satellite-retrieved soil moisture to reduce farmers’ drought risk exposure

Cited by 43 publications

References 72 publications

Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Divergent responses of maize yield to precipitation in the United States

Optimal index insurance and basis risk decomposition: an application to Kenya

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