Agromet wheat app for estimation of phenology and yield of wheat under Punjab conditions.

Abstract

The field experiments were conducted at the Research Farm, Punjab Agricultural University, Ludhiana, during rabi seasons of 2017-18 and 2018-19. The wheat varieties, viz. PBW-725, PBW-677, and HD-3086, were sown on 25th October, 15th November, and 5th December during both the crop seasons. Regression equations between grain yield, dry matter, and growing degree days were developed, and their performance efficiency was evaluated using mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), Akaike information criteria (AIC), and Schwarz-Bayesian information criteria (SBC), and the lowest values of AIC and SBC were used for wheat yield prediction. Based on historical data, agroclimatic index-based regression models were also developed for grain yield prediction at vegetative and reproductive stages under different dates of sowing. At the reproductive stage, the accumulated growing degree day (AGDD)-based model for 25th October sowing that gave 16 percent error, followed by 15th November sowing with 5.7 percent error, is the best model for yield prediction having minimum error. "Agromet wheat app," which is a mobile-based android app, was developed. This app is simple and has easy user interface which gives information about wheat management practices, weather, and insect-disease warnings and calculation of growing degree days at different phenophases of wheat in English and regional language Punjabi for three agroclimatic zones, viz., submountain undulating zone (Gurdaspur), central plain zone (Ludhiana), and western zone (Bathinda).