Crop Yield and Price Distributional Effects on Revenue Hedging

The use of crop yield futures contracts is examined. The expectation being modeled here reflects that of an Illinois corn and soybeans producer at planting, of revenue realized at harvest. The effects of using price and crop yield contracts are measured by comparing the results of the expected distribution to the expected distribution found under five general alternatives: 1) a revenue hedge using just price futures, 2) a revenue hedge using crop yield futures, 3) an unhedged scenario where revenue is determined by realized prices and yields, 4) an unhedged scenario where revenue is determined by realized prices and yields and by participation in government support programs with deficiency payments, and 5) a no hedge scenario where revenue is determined by realized prices and yields and by participation in a proposed revenue-assurance program. We draw four major conclusions from the results. First, hedging effectiveness using the new crop yield contract depends critically on yield basis risk which presumably can be reduced considerably by covering large geographical areas. Second, crop yield futures can be used in conjunction with price futures to derive risk management benefits significantly higher than using either of the two alone. Third, hedging using price and crop yield futures has a potential to offer benefits larger than those from the simulated revenue assurance program. However, the robustness of the findings depends largely on whether yield basis risk varies significantly across regions. Finally, the qualitative results described by the above three conclusions do not change depending on whether yields are distributed according to the beta or lognormal distribution.published or submitted for publicationnot peer reviewe

Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics and Operations Research (Baltimore, MD, USA). One of the winning solutions to the 2018 Syngenta Crop Challeng

Elicitation of Subjective Crop Yield PDF for DSS Implementation

The aim of this research is to establish the persistence of annual crop yield point values subjective estimates, and the coherence and reliability of subjective crop yield probability density functions (PDF) elicited from a series of interviews carried out on a wide group of farmers, and then to determine whether they should be included or not in a decision support system (DSS). Three different elicitation techniques were used: a) The Two Step PDF estimation method b) Triangular distribution c) Beta distribution Although the results are noteworthy, further studies should be carried out to perfect the aforementioned techniques before crop yield PDF's are used in decision making processes.decision support systems, subjective crop yield PDF elicitation, two step PDF estimation method, triangular and beta distributions, Crop Production/Industries,

IT architecture of the MARS crop yield forecasting system

The Crop Growth Monitoring System (CGMS) provides operational services and analysis tools to the Joint Research Centre of the European Commission (JRC) in the area of crop monitoring and crop yield forecast, as part the MARS Crop Yield Forecasting System

Research activities in regional crop modelling and yield forecasting

CGMS is being applied successfully within the MARS Crop Yield Forecasting System for qualitative monitoring of the growing season and for making quantitative crop yield forecasts. Nevertheless, there are large uncertainties related to applying crop growth models over large areas

CROP YIELD AND PRICE DISTRIBUTIONAL EFFECTS ON REVENUE HEDGING

The use of crop yield futures contracts is examined. The expectation being modeled here reflects that of an Illinois corn and soybeans producer at planting, of revenue realized at harvest. The effects of using price and crop yield contracts are measured by comparing the results of the expected distribution to the expected distribution found under five general alternatives: 1) a revenue hedge using just price futures, 2) a revenue hedge using crop yield futures, 3) an unhedged scenario where revenue is determined by realized prices and yields, 4) an unhedged scenario where revenue is determined by realized prices and yields and by participation in government support programs with deficiency payments, and 5) a no hedge scenario where revenue is determined by realized prices and yields and by participation in a proposed revenue-assurance program. We draw four major conclusions from the results. First, hedging effectiveness using the new crop yield contract depends critically on yield basis risk which presumably can be reduced considerably by covering large geographical areas. Second, crop yield futures can be used in conjunction with price futures to derive risk management benefits significantly higher than using either of the two alone. Third, hedging using price and crop yield futures has a potential to offer benefits larger than those from the simulated revenue assurance program. However, the robustness of the findings depends largely on whether yield basis risk varies significantly across regions. Finally, the qualitative results described by the above three conclusions do not change depending on whether yields are distributed according to the beta or lognormal distribution.Marketing,

Spatial Patterns of Crop Yields in Latin America and the Caribbean

Because of the apparent slowdown in the growth of crop yield potential, the increasing share of farmers already using modern crop varieties, and the accelerating flow of knowledge on agricultural technology, one would expect to find gradual convergence inLatin america, crop yield, convergence, spillover, weather variability

Can crop yield risk be globally diversified?

In 2007 and 2008 world food markets observed a significant price boom. Crop failures simultaneously occurring in some of the world’s major production regions have been quoted as one factor among others for the price boom. Against this background, we analyse the stochasticity of crop yields in major production areas. The analysis is exemplified for wheat, which is one of the most important crops worldwide. Particular attention is given to the stochastic dependence of yields in different regions. Thereby we address the question of whether local fluctuations of yields can be smoothed by international agricultural trade, i.e. by global diversification. The analysis is based on the copula approach, which requires less restrictive assumptions compared with linear correlations. The use of copulas allows for a more reliable estimation of extreme yield shortfalls, which are of particular interest in this application. Our calculations reveal that a production shortfall, such as in 2007, is not a once in a lifetime event. Instead, from a statistical point of view, similar production conditions will occur every 15 years.crop yield risk, fully nested hierarchical Archimedean copulas (FNAC), price boom

Modeling Crop Yield Distributions from Small Samples

Accurately modeling crop yield distributions is important for estimation of crop insurance premiums and farm risk-management decisions. A major challenge in the modeling has been due to small sample size. This study evaluated potentials of L-moments, a recent concept in mathematical statistics, in modeling crop yield distribution. Five candidate distributions were ranked for describing the wheat yields. The selected distribution was robust for small sample and was invariant to de-trending. The result was consistent with that from the maximum likelihood and goodness-of-fit method.Crop Production/Industries,