Development of Agrometeorological Models for Estimation of Cotton Yield

An attempt was made to predict American cotton (Gossypium barbadense) yield over Punjab region (Ludhiana, Bathinda and Ferozepur) by regression models. Three statistical models were developed for forecasting the yield of the American cotton using the yield data and weekly weather variable viz. maximum and minimum temperature, morning and evening relative humidity, sunshine hours, rainfall and number of rainy days. In the first model (Basic model) sensitive period for American cotton yield with respect to weather parameters were identified for different weather parameters by using correlation and selected windows were taken for further regression analysis. The second model is Modified model, where composite index was taken as one of the extra variable in multiple regressions. In the third model multiple regression analysis was done by using SPSS software. Regression equations were developed separately for all the three models and were used to predict the yield of American cotton. The historical weather data for the period of 1971–2009, 1978–2009 and 2001–2009 were used to develop forecast models for Ludhiana, Bathinda and Ferozepur, respectively. The recent three year meteorological data (2010–2012) was used to validate the models. For Ludhiana, among all the three models, Basic model explained up to 69 per cent variation, modified model explained 75 per cent and the highest i.e. 89 per cent variation was explained by SPSS model. For Bathinda district, basic model, modified model and SPSS model explained 50, 57 and 68 per cent variation, respectively. For Ferozepur, basic model explained up to 67 per cent, modified model explained 68 per cent and SPSS model explained 93 per cent variation in cotton yield. The results revealed that SPSS model fits better for all the three districts as far as American cotton yield is concerned.