Principal Component Analysis for weather based wheat yield modeling in Western Agro-climatic zone of Haryana
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Date
2019
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CCSHAU
Abstract
An efficient crop forecasting infrastructure is pre-requisite for information system about food supply especially export–import policies, procurement and price-fixation. Multiple linear regression and principal component analyses were used to obtain district-level wheat yield estimation in Hisar, Bhiwani, Sirsa and Fatehabad districts of Haryana. The zonal yield forecast models have been fitted using wheat yield/weather data for the period 1980-81 to 2011-12 of Hisar, Bhiwani and Sirsa districts and 1997-98 to 2011-12 of Fatehabad district. Models have been validated for the subsequent years i.e. 2012-13 to 2016-17, not included in the development of the models. The zonal models have been fitted by taking DOA yield as dependent variable and trend yield along with higher loading displaying weather variables obtained through PC analysis as regressors. The performance of zonal wheat yield forecast models has been compared on the basis of different statistics viz., Adj-R2, percent deviations of forecast yield(s) from the observed yield(s) and RMSEs.
The overall results indicate the preference of using model-1based on higher loading displaying weather variables obtained through PC analysis and trend yield. Trend yield has been observed an important parameter appearing in all the models, which is an indication of technological advancement, improvement in fertilizer/insecticide/pesticide/weedicide used and increased use of high yielding varieties. District-level wheat yield(s) forecasting have improved significantly using zonal weather-yield models. The developed models provide reliable forecasts of wheat yield at least one month in advance of the crop harvest, while on the other hand, DOA yield estimates are obtained quite late after the actual harvest of the crop. The average absolute percent deviations of post-sample period forecasts falling between 3.5- 6 percent favour the use of zonal models for district-level wheat yield forecasting in western agro-climatic zone of Haryana.
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