Abstract
Crop models can provide daily estimates of soil water content in the root zone, potential and actual evapotranspiration, leaf area index (LAI), dry matter (DM) and finally grain yield. Forecasting of wheat yield under different irrigation management practices is possible with a wheat growth simulation model calibrated for the study area. In this study, a mathematical model is proposed to simulate the environmental and physiological processes involved in the growth of rainfed and irrigated wheat. Wheat yield was estimated for different soil moisture regimes by simulating crop response for irrigation management and climatic condition. The model can be used as a tool for analyzing growth and yield to help planning and management of wheat production. The model runs on a daily basis. The driving variables were daily pan evaporation, rainfall and irrigation. The development and growth of the crop based on accumulated pan evaporation has been predicted in this study. Soil properties, pan evaporation, rainfall and irrigation were the model inputs. The model was initially calibrated with a set of data for wheat in Jamalpur area during 2003-04 cropping season. Field data were also collected from a series of experiments conducted during three consecutive years (2003-04 to 2005-06). The collected data sets were used for model validation. A promising wheat variety, Shatabdi was used as the test crop. Fifteen treatments containing different irrigation options including one rainfed were selected for the study. Daily and cumulative pan evaporation values were used to calculate the phenological development (emergence, vegetative growth, dry matter) and period of growth. The predicted results were then compared with the results obtained from the field experiments. The model estimated satisfactorily the grain yield in case of irrigated conditions. Model predictions of almost all parameters at harvest agreed well with the measured values. Measured grain yields were slightly over estimated by the model (4.3 percent in 2003-04, 5.73 percent in 2004-05 and 7.67 percent in 2005-06 study years, respectively). However, all the variations between the measured and predicted values were below 10 percent. Total dry matter was slightly underestimated (by 3.53 percent) during 2003-04 cropping year and slightly over estimated during the following two study years (by 7.8 percent and 4.2 percent, respectively) by the model. The model slightly underestimated the measured leaf area index (LAI) during all the three study years (5.57 percent, 10.58 percent and 6.16 percent, respectively). Prediction of seasonal ET by the model was also satisfactory. The model slightly overestimated seasonal ET by only 2.20 percent and 1.47 percent during 2003-04 and 2004-05 study years, respectively. However, the proposed model slightly underestimated the seasonal ET by 4.29 percent during 2005-06 cropping period. The results showed that, the simulated seasonal ET, leaf area index (LAI), total dry matter and ultimate grain yield were in close agreement with the measured values. The simulated results were within ±10 percent accuracy. Regression analysis of actual and simulated results gave a satisfactory correlation (r2 > 0.75). Grain yield was substantially influenced by water use up to the flowering stage. Average yield obtained from the treatment irrigated at four sensitive stages after sowing until flowering (treatment Ts) was 4.068 t/ha. On the other hand, the treatment T6, in which irrigation was applied in five sensitive stages from sowing to grain formation stage, yielded 4.008 t/ha on average. The results indicate that, irrigation at grain formation stage is not very important with respect to grain yield, if the crop does not suffer a reasonable stress up to the flowering stage.