11 December 2006 Methodology for national wheat yield forecast using wheat growth model, WTGROWS, and remote sensing inputs
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Abstract
Wheat is an important food crop of the country. Its productivity lies in a very wide range due to diverse bio-physical and socio-economic conditions in the growing regions. Crop cutting and sample surveys are time consuming as well tedious, and procedure of forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC Research Programme, possibility of linking the dynamic wheat growth model with the remote sensing input and other relational database layers was tried. Use of WTGROWS, a wheat growth model developed at IARI, with the remote sensing and relational databases is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs are received. National wheat yield forecast was done for three seasons on meteorological sub-division scale by using WTGROWS, relational database layers and satellite image. WTGROWS was run for historic weather dataset (last 25 years), with the relational database inputs through their associated growth rates and compared with the productivity trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained to capture the other biotic and abiotic stresses and subsequently used to bring down the yields at each sub-division to realistic scale. The satellite image was used to compute the acreage with wheat in each sub-division. Meteorological data for each-subdivision was obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtained upto a given time, and weather normals use for subsequent period, and the forecast was prepared. This was updated on weekly basis, and the methodology could forecast the wheat yield well in advance with a great accuracy. This procedure shows the pathway for Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning and agri-production estimates, which although looks difficult for diverse agro-ecologies and wide range of bio-physical and socio-economic characters contributing to differential productivity trends.
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Naveen Kalra, Naveen Kalra, P. K. Aggarwal, P. K. Aggarwal, A. K. Singh, A. K. Singh, V. K. Dadhwal, V. K. Dadhwal, V. K. Sehgal, V. K. Sehgal, R. C. Harith, R. C. Harith, S. K. Sharma, S. K. Sharma, "Methodology for national wheat yield forecast using wheat growth model, WTGROWS, and remote sensing inputs", Proc. SPIE 6411, Agriculture and Hydrology Applications of Remote Sensing, 641106 (11 December 2006); doi: 10.1117/12.697698; https://doi.org/10.1117/12.697698
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