Search

The article offers the authors' insights regarding the weather index insurance (WII) program in Mexico, which is one of the largest in the world. The authors state that the WII makes disincentives to invest in non-insured crops that leads to overspecialization and monoculture. Furthermore , they suggest that the threshold of WII be recalibrated to adjust for the creation of drought-resistant seeds.

Crop insurance programs rely on conditional predictive distributions of loss random variables (e.g., yield, revenue, loss costs, etc.) to determine probabilities and magnitudes of loss. The loss variables may be related to stochastic variables that are not known at the time the policy is priced. Such is the case for weather; weather is stochastic, realizations are not known when the crop insurance policy is sold, and there is often additional historical information on weather relative to the loss variable itself. We provide a Bayesian methodology for incorporating historical weather information in crop insurance rating. We apply the method in empirical applications to county‐level U.S. corn yields and loss cost ratios in the Midwest. The historical weather‐conditioned distributions differ from those based on shorter samples. In the yield distribution setting, additional temporal weather information leads to economic gains relative to other rating approaches; the magnitude of these gains increases with the amount of historical weather information included in the analysis. [ABSTRACT FROM AUTHOR]

We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%. [ABSTRACT FROM AUTHOR]

Sugarcane is a multipurpose crop whose components may be used, in addition to sugar production, for various energy carriers or end-products (electricity, liquid biofuels and heat) which enhance its economic potential. For many years, plant breeders and agronomists have focused on increasing sucrose yields per hectare and millers on increasing recoverable sucrose per ton of sugarcane in sugar mills. Attempting to exploit the energy potential of sugarcane more fully, calls for a more holistic approach focusing on both sucrose and lignocellulosic components of sugarcane biomass, and gaining some insight into the management practices required to optimize sugarcane cropping systems in these respects. Such options include genotype selection, harvest date with respect to the crop's growing cycle, crop type (plant crop vs. ratoon crops) and harvesting systems (mechanical vs. manual). The effects of these factors are strongly modulated by climate and soil properties, and these interactions are overall poorly known. Here, we set out to examine sugarcane infield management × environmental interactions with respect to (i) sugarcane yield and partitioning of the aboveground biomass; and (ii) sugarcane milling products (recoverable sucrose yield and amounts of coproducts) and their derived energy carriers. Three Saccharum cv. cultivars (R570, R579 and R585) were planted in three locations on La Reunion Island with contrasting management practices and climatological conditions. Quality characteristics of the samples were assessed by conventional and near infrared spectroscopy methods. Product, coproducts and potential energy production were measured and computed using transfer equations and a mill-operating model. Yields and quality characteristics from cultivars and harvesting systems were affected differently by environmental factors - low temperature and radiation, and water stress. The current study also provides valuable information on how combinations between environments, genotypes and practices affect yield and partitioning of the aboveground biomass, and food and energy production. [ABSTRACT FROM AUTHOR]