Advances and Applications in Statistics
Volume 26, Issue 2, Pages 113 - 136
(February 2012)
|
|
DEVELOPMENT OF AGROMETEOROLOGICAL AND SPECTRAL WHEAT YIELD MODELS USING
VARIOUS STATISTICAL PROCEDURES IN HARYANA STATE (INDIA)
U. Verma, H. P. Piepho, M. H. Kalubarme and M. Goyal
|
Abstract: This study attempts to answer the question whether the observed climate changes along with spectral index have an additional impact in controlling percent deviation from real time data in context of wheat yield prediction. Agrometeorological (agromet)-trend and agromet-spectral-trend wheat yield models on agro-climatic zone basis in Haryana state have been developed using trend predicted yield, Normalized Difference Vegetation Index, agromet indices like Growing Degree Days (GDD), Temperature Difference (TD) and Accumulated Rainfall (ARF) calculated over critical growth phases of wheat crop. Regression analysis, principal component analysis and discriminant function technique have been used for the development of zonal yield models. A perusal of the results indicates the preference of using prediction equations based on principal component scores during the model development period and the model testing period. The overall results indicate that the integration of remote sensing data with trend and weather variables provides an immense scope to improve the efficiency and reliability of the district-level wheat yield forecasts in the state. Zonal yield models provided considerable improvement in district-level yield forecasting by showing good agreement with Department of Agriculture (DOA) yield estimates. The crop yield forecasts may be obtained about 4-5 weeks before the actual harvest of the crop. |
Keywords and phrases: agro-climatic indices, spectral index, step-wise regression, principal component scores, discriminant scores, agromet-spectral-trend wheat yield models. |
|
Number of Downloads: 350 | Number of Views: 1505 |
|