• Korean Journal of Soil Science and Fertilizer
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• v.47 no.2
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• pp.92-99
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• 2014

The crop growing conditions make accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of crop required to be imported to meet the expected domestic shortfall. Corn and soybean especially are widely cultivated throughout the world and a staple food in many regions of the world. On the other hand, the CASA (Carnegie-Ames-Stanford Approach) model is a process-based model to estimate the land plant NPP (Net Primary Productivity) based on the plant growing mechanism. In this paper, therefore, a methodology for the estimation of corn/soybean yield ahead of harvest time is developed specifically for the growing conditions particular to Iowa and Illinois. The method is based on CASA model using MODIS data, and uses Net Primary Productivity (NPP) to predict corn/soybean yield. As a result, NPP at DOY 217 (in Illinois) and DOY 241 (in Iowa) tend to have high correlation with corn/soybean yields. The corn/soybean yields of Iowa in 2013 was estimated to be 11.24/3.55 ton/ha and Illinois was estimated to be 10.09/3.06 ton/ha. Errors were 6.06/17.58% and -10.64/-7.07%, respectively, compared with the yield forecast of the USDA. Crop yield distributions in 2013 were presented to show spatial variability in the state. This leads to the conclusion that NPP changes in the crop field were well reflected crop yield in this study.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.741-750
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• 2017

The objective of this study was to estimate corn and soybean yield in Illinois and Iowa in USA using satellite and meteorological data. MODIS products for NDVI were downloaded from a NASA website. Each layer was processed to convert projection and extract layers for NDVI. Relations of NDVI from 2002 to 2012 with corn and soybean yield were investigated to find informative days for rice yield estimation. Weather data for the county of study state duration from 2002 to 2012 to correlate crop yield. Multiple regression models based on MODIS NDVI and rainfall were made to estimate corn and soybean yields in study site. Corn yields estimated for 2013 were $10.17ton\;ha^{-1}$ in Illinois, $10.21ton\;ha^{-1}$ in Iowa and soybean yields estimated were $3.11ton\;ha^{-1}$ in Illinois, $2.58ton\;ha^{-1}$ in Iowa, respectively. Corn and Soybean yield distributions in 2013 were mapped to show spatial variability of crop yields of the Illinois and Iowa state.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1037-1039
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• 2005