Phenology Information Contributes to Reduce Temporal Basis Risk in Agricultural Weather Index Insurance

Crop insurance loss data can illuminate variations in agricultural impacts from exposure to weather and climate-driven events, and can improve our understanding of agricultural vulnerabilities. Here we perform a retrospective analysis of weather and climate-driven reasons for crop loss (i.e. cause of loss) obtained from the Risk Management Agency of the United States Department of Agriculture. The federal crop insurance program has insured over $440 billion in liabilities representing farmers' crops from 2001 to 2016. Specifically, we examine the top ten weather and climate-driven causes of loss from 2001 to 2016 across the nation comprising at least 83% of total indemnities (i.e. insurance payouts provided to farmers after crop loss events). First, we analyzed the relative fraction of indemnities by causes of loss, over different spatial and temporal resolutions. We found that drought and excess precipitation comprised the largest sources of crop loss across the nation. However, these causes varied strongly over space and time. We applied two additional normalization techniques to indemnities using (1) insurance premia and the gross domestic product implicit price deflator, and (2) liabilities to calculate the loss cost. We conducted trend analyses using the Mann-Kendall statistical test on loss cost over time. Differential trends and patterns in loss cost demonstrated the importance of spatio-temporal resolution in assessing causes of loss. The majority of monthly significant trends (p<0.05) showed increasing loss cost (i.e. increasing indemnities or decreasing liabilities) in response to weather events. Finally, we briefly discuss an online portal (AgRisk Viewer) to make these data accessible at multiple spatial scales and sub-annual time steps to support both research and outreach efforts promoting adaptation and resilience in agricultural systems.

Section: Risk Management Implicationsmentioning

confidence: 99%

Spatio-temporal variation of crop loss in the United States from 2001 to 2016

Reyes

Elias

2019

“…The models in this study are based on seasonal totals for precipitation, GDDs and EDDs instead of the sub-seasonal totals used with most process-based models. Crop yields are known to be affected by intraseasonal timing  (Dalhaus et al 2018, Hufkens et al 2019 and intensity (Fishman 2016) of extreme weather events. Incorporating sub-seasonal weather variables is likely to exacerbate differences between models even further as individual gridded weather datasets are known to differ substantially in their ability to capture intraseasonal weather dynamics, for example the size and arrival of the South Asian monsoon (Ceglar et al 2017).…”

Section: Weather Dataset Choice Alters Implied Crop Yield Sensitivitymentioning

confidence: 99%

Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

Parkes

Higginbottom

Hufkens

et al. 2019

Weather shocks, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to these events and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983-2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to variability in growing season weather, which in turn, highlights the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.

“…This information may have implications for the time frame choice of the agricultural weather index insurance (WII). As suggested by Dalhaus et al (2018), it is important to consider occurrence dates and shifts of critical growth phases in space and time to improve agricultural weather index insurance. Future research on the implications of climate oscillations on crop insurance could incorporate these findings.…”

Section: Discussionmentioning

confidence: 99%

Changes of crop failure risks in the United States associated with large-scale climate oscillations in the Atlantic and Pacific Oceans

Schillerberg

Tian

2020

Regions that produce a large supply of agriculture commodities can be susceptible to crop failure, thus causing concern for global food security. The contiguous United States, as one of the major agricultural producers in the world, is influenced by several large-scale climate oscillations that contribute to climate variability: Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), El-Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Pacific-North American (PNA). Since local weather conditions are associated with these climate oscillations through teleconnections, they are potentially causing changes of crop failure risks. The objective of this study is to assess climate-induced changes of annual crop failure risks for maize and winter wheat from 1960 to 2016, by analyzing the associations of large-scale climate oscillations with the frequency of crop failure in the rainfed regions of the United States using a Bayesian approach. The analysis revealed that crop failure frequencies showed contrast spatial patterns and different extent under different climate oscillation phases. Among individual oscillations, the positive PNA and negative AMO resulted in the most substantial increase in maize and winter wheat crop failures over a high percentage of climate divisions, respectively. Among oscillation combinations, the positive AMO and negative PDO and the positive AMO and positive PDO resulted in the highest percentage of climate divisions experiencing significant increase of maize and winter wheat crop failures, respectively. Random forest models with climate oscillations accurately predicted probabilities of crop failure, with the inclusion of local surface climate variables decreased or increased the predictive accuracy, depending on regions. These results revealed the plausible drivers of long-term changes of U.S. crop failure risks and underscore the importance for improving climate oscillation forecasting for early warning of food insecurity.