Effect of Planting Date on Yield and Water Productivity of Sunflower Using AquaCrop Model

آب و خاک

Article 2, Volume 36, Issue 2 - Serial Number 82, May and June 2022, Pages 185-196   

Document Type: Research Article

DOI: 10.22067/jsw.2022.74628.1133

Authors

B. Sadeghi¹; B. Farhadi Bansouleh^{* 1}; A. Bafkar¹; M. Ghobadi²

¹Water Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran

²Department of Plant Production and Genetic Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran

Abstract

Introduction
The rapid growth of the world's population, followed by an increase in the need for water, has put great pressure on water resources, so it is necessary to plan for the optimal use and increase of efficiency of this vital resource. Sunflower is one of the most important oilseed crops that is mainly cultivated in Kermanshah province. Therefore, determining the appropriate sowing time of this crop for maximum production and water use efficiency is of particular importance. Because field experiments are costly and time-consuming, researchers use crop growth simulation models to determine the optimal planting time for each crop in a specific environment and climate. The use of simulation models minimizes the limitations of field experiments and allows the analysis of plant responses to environmental stresses and management scenarios. The objective of this study was to determine the optimal planting date of the Farrokh sunflower cultivar in four regions of Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) in order to maximize yield and water use efficiency using the AquaCrop model.
Materials and Methods
A field experiment was conducted at the Research Farm of Razi University, Kermanshah, Iran in order to calibrate and validate the crop parameters in the AquaCrop model. The experiment was performed in a randomized complete block design with eight irrigation treatments in three replications. The irrigation treatments were the application of 60, 80, 100, and 120% of irrigation requirement (T1, T2, T3, and T4), 20 and 40% deficit irrigation in vegetative phase (T5 and T6), and 20 and 40% deficit irrigation in reproductive phase (T7 and T8). The crop water requirement was calculated based on the daily weather data collected from an automated meteorological station at the Research Farm using the FAO Penman-Monteith equation. During the growing season, canopy cover, biomass, and soil moisture were measured weekly. The crop parameters were calibrated based on the measured data in treatments T1, T3, T6, and T7 and validated with four treatments T2, T4, T6, and T8. In the calibration and validation stages, the statistical indices including compatibility index (d) and root mean square error (RMSE) were used to evaluate the model outputs. The calibrated model was used to simulate crop growth based on daily weather data for 30 years (1988-2017) in four synoptic stations in Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) and for several different planting dates. The crop water productivity was calculated based on simulated grain yield and seasonal crop evapotranspiration. Finally, the model outputs under different planting dates were analyzed to determine the most appropriate planting time from the perspective of maximum production and maximum water use efficiency.
Results and Discussion
 Statistical indicators show that the model has simulated the parameters of biomass, crop canopy, and soil moisture in the calibration stage with good accuracy. T1 and T6 treatments in biomass simulation, T7, T6, and T3 treatments in crop canopy simulation, and T3 and T7 treatments in soil moisture simulation had the highest accuracy. The accuracy of the model outputs in the validation stage for biomass and canopy cover was as accurate as in the calibration stage, while the accuracy of the simulated soil moisture in the validation stage was not high except in T4 treatment. Based on the model results, grain yield, seasonal evapotranspiration and water productivity were determined. According to the results, it can be said that in the study period (1988 -2017), grain yield has generally increased with a slight slope. The results showed that the planting date, which maximizes grain yield and water productivity, varies in the studied regions.  According to the model results, planting in the second decade of May and the second decade of June will lead to the highest grain yield and water productivity in Kermanshah, respectively. Planting in the third decade of May showed the highest grain yield and crop water productivity in Islam Abad. In Sarpol Zahab, which has the highest temperature among the studied stations, planting in the last decade of March and the first decade of April has the highest grain yield and water productivity, respectively. In Kangavar, which is located in the east of Kermanshah province and has the coldest climate, by cultivating sunflower in the last decade of May and the first decade of June, respectively, the highest grain yield and water productivity can be achieved.
Conclusion
Due to the fact that some crop parameters of crop growth simulation models are variety specific, in this study, the crop parameters of the AquaCrop model for Farrokh sunflower cultivar were calibrated and validated. The accuracy of the calibrated model for estimating biomass and canopy cover was higher than soil moisture. The simulation results showed that the values of the studied parameters (grain yield and seasonal evapotranspiration) have changes according to the planting time in each region. The highest crop yield can be obtained in Sarpol Zahab, Islam Abad, Kermanshah, and Kangavar regions (west to east of the province) by cultivation in the last decade of March, last decade of April, the second decade of May, and last decade of May, respectively. In all study areas except Islamabad, planting date that resulted in maximum water productivity was different from the planting date that had maximum grain yield station and delayed planting had the highest water productivity.

Keywords

AquaCrop; Cultivation date; Productivity; Simulation; Sunflower

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