August 10 Corn Yield Forecast

The USDA/NASS released the first 2012 corn yield forecast on August 10. The current Iowa corn yield forecast is for 141 bu/acre, down from both last year’s 172, and the 30-year trend line value of 180(Figure 1). The August forecast for Iowa yield is 22 percent below trend line; that of the U.S., 123, is 23 percent below trend line

August 2011 Iowa Corn Yield Forecast

USDA released the first corn yield forecast for 2011 on Aug. 11. Forecast U.S. yields of 153 bushels per acre are similar to last years\u27 and six bushels below the 30-year trend line (Figure 1). This is not surprising. On the other hand, Iowa’s forecast of 177 bushels per acre matches that of the 30-year trend line and lies 12 bushels above last year’s final yield estimate. This, if realized, would rank third highest behind 2004 (181) and 2009 (182) bushels per acre for Iowa. This seems unlikely

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

Honey Yield Forecast Using Radial Basis Functions

Honey yields are difficult to predict and have been usually associated with weather conditions. Although some specific meteorological variables have been associated with honey yields, the reported relationships concern a specific geographical region of the globe for a given time frame and cannot be used for different regions, where climate may behave differently. In this study, Radial Basis Function (RBF) interpolation models were used to explore the relationships between weather variables and honey yields. RBF interpolation models can produce excellent interpolants, even for poorly distributed data points, capable of mimicking well unknown responses providing reliable surrogates that can be used either for prediction or to extract relationships between variables. The selection of the predictors is of the utmost importance and an automated forward-backward variable screening procedure was tailored for selecting variables with good predicting ability. Honey forecasts for Andalusia, the first Spanish autonomous community in honey production, were obtained using RBF models considering subsets of variables calculated by the variable screening procedure

Corn Development and September Yield Forecast, 2010

The September 2010 USDA–NASS corn yield forecast remains at 179 bushels per acre (bu/acre) for Iowa – the same as the August report. If realized, 2010 will boast the third highest yield in Iowa’s history behind 2004 (181 bu/acre) and 2009 (182 bu/acre)

Yield Gap Analysis: What Limited Iowa Corn and Soybean Yields in 2015

To evaluate how good corn and soybean yields were in 2015, farmers and agronomists compare their yields to those obtained in previous years. To answer why yields were higher or lower than past years, they develop hypotheses to explain factors that limited yields based on their own experiences, anecdotal evidence from neighbors, knowledge of crop growth and development, and weather patterns. As a next step to the in-season Yield Forecast Project, we can provide an alternative analysis and a yield gap analysis of the 2015 growing season by using the explanatory power that a cropping systems model offers. Our analysis is focused on corn and soybean cropping systems used in the Yield Forecast Project (more information on the cropping systems and the Yield Forecast Project can be found in the June 17thICM News article). In total, we evaluated eight cropping systems; two locations, two crops, two planting dates

Evaluation of Near-Surface Air Temperature from Reanalysis over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2>0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season

Soybean Yield Forecast Application Based

Abstract: This article establishes the estimate's mathematics model of the soybean's yield, using the artificial nerve network's knowledge, and by the model we can increase accuracy of the Soybean Yield Forecast