2009 Soybean Yield Prospects Improving
The purpose of this brief is to update our previous evaluation of yield potential for soybeans in Illinois, Indiana, and Iowa in 2009. (Irwin, Good, and Tannura, 2009). This update makes use of a crop weather model that estimates the impact of technology (trend), state average monthly weather variables, and portion of the crop planted late on state average yield. Previously, that model was used to evaluate 2009 yield potential based on planting progress, state average precipitation through June 2009, and alternative 2009 July-August weather scenarios. This update incorporates preliminary state average precipitation and temperature for July and alternative weather scenarios for August 2009. The yield forecasts for the three states are then used to project the U.S. average yield. Trend yields for 2009 for each of the three states and the U.S. are also presented. It should be noted at the outset that average July temperatures for 2009 were well below the coldest July in the sample of historical observations used to
2009 Final Corn and Soybean Yield Forecasts
The purpose of this brief is to update our previous evaluation of yield potential for corn and soybeans in Illinois, Indiana, and Iowa in 2009. (Irwin, Good, and Tannura, August 2009c,d). This update makes use of a crop weather model that estimates the impact of technology (trend), state average monthly weather variables, and portion of the crop planted late on state average yield. Previously, that model was used to evaluate 2009 yield potential based on planting progress, state average precipitation through July 2009, and alternative 2009 August weather scenarios. This update incorporates preliminary state average precipitation and temperature for August, the final month used in the model. The yield forecasts for the three states are then used to project the U.S. average yield. Trend yields for 2009 for each of the three states and the U.S. are also presented. It should be noted at the outset that average July temperatures for 2009 were well below the coldest July in the sample of historical observations used to estimate the crop weather model. In addition August temperatures were at the low end of historical experience. These conditions
Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt
The purpose of this study was to investigate the relationship between weather, technology, and corn and soybean yields in the U.S. Corn Belt. Corn and soybean yields, monthly temperature, and monthly precipitation observations were collected over 1960 through 2006 for Illinois, Indiana, and Iowa. Multiple regression models were developed based on specifications found in studies by Thompson (1962 1963 1969 1970 1985 1986 1988). Estimated models explained at least 94% and 89% of the variation in corn and soybean yields for each state, respectively. Analysis of the regression results showed that corn yields were particularly affected by technology, the magnitude of precipitation during June and July, and the magnitude of temperatures during July and August. The effect of temperatures during May and June appeared to be minimal. Soybean yields were most affected by technology and the magnitude of precipitation during June through August (and especially during August). Structural change tests were performed on each model to test for changes in any of the regression model parameters. Some breakpoints were identified, but were difficult to explain since the results were not consistent across states and crops. Additional tests for structural change were directed specifically at the trend variable in corn models. The tests did not indicate a notable change in the technology trend for corn since the mid-1990s. Corn and soybean yield forecasts from the regression models on June 1 and July 1 were no more accurate then trend yield forecasts. Regression model forecasts for corn improved on August 1, while model forecasts for soybeans improved by September 1. U.S. Department of Agriculture (USDA) corn and soybean forecasts were always more accurate than those from the regression models. Nonetheless, encompassing tests showed that the accuracy of USDA yield forecasts could be significantly improved by the information contained in regression model forecasts. Across states and forecast months, combining regression model forecasts with USDA forecasts improved accuracy an average of 10% for corn and 6% for soybeans. In sum, this research provided strong evidence that precipitation, temperature, and a linear time trend to represent technological improvement explained all but a small portion of the variation in corn and soybean yields in the U.S. Corn Belt. An especially important finding was that relatively benign weather for the development of corn since the mid-1990s should not be discounted as an explanation for seemingly “high” yields. The potential impact of this finding on the agricultural sector is noteworthy. Trend yield forecasts based on perceptions of a rapid increase in technology may eventually lead to poor forecasts. Unfavorable weather in the future may lead to unexpectedly low corn yields that leave producers, market participants, and policymakers wondering how such low yields could have occurred despite technological improvements.