For Authors
Editorial Board
Submit article
Editor's selection process
Join as Reviewer/Editor
List of Reviewer
Indexing Information
Most popular articles
Purchase Single Articles
Free Articles
Current Issue
Recommend this journal to your library
Accepted Articles
Search Articles
Email Alerts
Contact Us
Indian Journal of Plant and Soil

Volume  1, Issue 2, July - December 2014, Pages 53-60


Original Article

Role of Meteorological Models in Estimating Yield of Sugarcane Based on Weather Variables
Rajeev Ranjan, A.S. Nain
 * Research Scholar, **Associate Professor, Dept. Of Agrometeorology, GBPUA&T., Pantnagar
Choose an option to locate / access this Article:
Check if you have access through your login credentials.             |



A field study was conducted at G.B.P.U.A.&T., Pantnagar to investigate the feasibility of estimating the yield of sugarcane crop based on weather variables. Five years (2004 to 2009) crop management data (sowing/harvest/irrigation etc.) for sugarcane were collected from Agricultural farm, Pantnagar. The development of multiple variable regression models employs that the dependent variable (yield) of multiyear is related with independent weather variables. SPSS (Statistical Package for the Social Sciences) software was used for the statistical analysis and development of multiple regression models based on fortnightly meteorological parameters. A total number of 8 models were developed using different combinations of fortnightly weather variables at different crop growth stages. Among all models, the performance of the model 8 was superior as compared to other models. The predicted yield by this model ranged between 349.39q ha-1 to 803.76q ha-1 with the value of R2 as 0.668, while the observed values ranged from 221.80q ha-1 to 824.16q ha-1. The RMSE between observed and predicted yield of sugarcane by model 8 was 15.49%, while the value of F test was 7.32 which is significant at 1% probability level. Hence, it can be concluded that the observed and predicted values were close enough in model 8 as compared to other meteorological models. The reason is obvious because model 8 used more number of weather variables.

Keywords: Weather parameters; Coefficient of correlation and Yield prediction models.

Corresponding Author : Rajeev Ranjan