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United States Department of Agriculture

Agricultural Research Service

Title: Large-Area Maize Yield Forecasting Using Leaf Area Index Based Yield Model

Authors
item Baez-Gonzalez, Alma - INIFAP
item Kiniry, James
item Maas, Stephan - TEXAS TECH UNIV
item Tiscareno, Mario - INIFAP
item Macias, Jaime - INIFAP
item Mendoza, Jose - INIFAP
item Richardson, Clarence
item Salinas, Jaime - INIFAP
item Manjarrez, Juan - INIFAP

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 29, 2004
Publication Date: March 20, 2005
Citation: Baez-Gonzalez, A.D., Kiniry, J.R., Maas, S.J., Tiscareno, M.L., Macias, J.C., Mendoza, J.L., Richardson, C.W., Salinas, J., Manjarrez, J.R. 2005. Large-area maize yield forecasting using leaf area index based yield model. Agronomy Journal. 97(2):418-425.

Interpretive Summary: Large-area yield prediction early in the growing season is important in agricultural decision-making. The objectives of this study were to derive estimates for corn leaf area from satellite data and use these estimates with a simple computer model to forecast yield under irrigated conditions in Sinaloa, Mexico. Leaf area was derived from satellite data using an equation developed with measurements from farmers' fields. A yield model was developed using leaf area and yield data measured in farmers' fields during three growing seasons. The model using leaf area can forecast yield in large areas in Sinaloa early in the growing season with reasonable accuracy. Using satellite-derived leaf area values in place of ground measurements increases the simulation error. Nevertheless, it can reduce the effort in prediction for large areas early in the growing season.

Technical Abstract: Large-area yield prediction early in the growing season is important in agricultural decision-making. The objectives of this study were to derive maize (Zea mays L.) leaf area index (LAI) estimates from spectral data and to use these estimates with a simple LAI-based yield model to forecast yield under irrigated conditions in Sinaloa, Mexico. LAI was derived from satellite data using an equation developed with LAI measurements from farmers' fields. These measurements were correlated to the normalized difference vegetation index (NDVI) values from Landsat ETM+ data. A yield model was developed using maximum LAI and yield data measured in farmers' fields during three growing seasons. Validation was done using maximum LAI gathered in the field during 2002-2003 in 71 farmers' fields in Sinaloa, and satellite-derived LAI (sLAI). The deviation was examined for sLAI versus ground LAI (gLAI) collected close to the dates of the images used for sLAI, and sLAI versus the maximum LAI measured in each site. The mean and standard deviation was 5.36 ± 0.98 for maximum gLAI, and 4.95 ± 0.50 for sLAI. Grain yield was predicted with a mean error of -9.2 % using maximum gLAI and -11.2% using sLAI. Thus, the model using LAI can forecast yield in large areas in Sinaloa early in the growing season with reasonable accuracy. Using satellite-derived LAI in place of ground measurements increases the simulation error. Nevertheless, it can reduce the effort in prediction for large areas early in the growing season.

Last Modified: 9/22/2014
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