Evaluating the SSM Model Efficiency in Simulating the Wheat Growth under Water Stress Conditions

IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress is extended to morphological, physiological and biochemical responses. Considering the rapid advancement in computer model development, plant growth models have emerged as a valuable tool to predict changes in production yield. These growth simulation models effectively incorporate the intricate influences of various factors, such as climate, soil characteristics, and management practices on crop yield. By doing so, they offer a cost-effective and time-efficient alternative to traditional field research methods. Material and MethodsThis research was conducted in the research farm of Varamin province, which has a silty loam soil texture. The latitude and longitude of the region are 35º 32ʹ N and 51º 64ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Varamin has a temperate humid climate. The long-term mean temperature of Varamin is 11.18 ° C and the total long-term rainfall is 780 mm. In this study, in order to simulate irrigated wheat cv. Mehregan growth under drought stress, an experimental based on completely randomized blocks (CRBD) including: non-stress as control (NS), water stress at booting stage (WSB), water stress at flowering stage (WSF), water stress at milking stage (WSM) and water stress at doughing stage (WSD) with three replications during growth season 2019-2020 was carried out in Varamin, Iran. Crop growth simulation was done using SSM-wheat model. This model simulates growth and yield on a daily basis as a function of weather conditions, soil characteristics and crop management (cultivar, planting date, plant density, irrigation regime). Results and DiscussionBased on the results, the simulation of the phenological stages of irrigated wheat cv. Mehregan under water stress condition using SSM-wheat model showed that there was no difference between observed and simulated values. Summary, the values of day to termination of seed growth (TSG) were observed under non- stress, stress in the booting stage, flowering, milking and doughing of the grains, 222, 219, 219, 221, 221 days, respectively andsimulation values with 224, 221, 220, 221, respectively. However, with their simulation values, there were slight differences with 224, 221, 220, 221, respectively. Acceptable values of RMSE (11.7 g.m-2) and CV (3.5) indexes showed the high ability of the SSM model in simulating the grain yield of irrigated wheat cv. Mehregan under water stress conditions. Grain yield values were observed in non-stress conditions of 5783, water stress in booting, flowering, milking and doughing of the grain stages in 5423, 5160, 5006 and 5100 kg. h-1, respectively. While the simulated values were 5630, 5220, 4920, 4680 and 4880 kg. h-1, respectively. Based on the findings, observed and simulated values of leaf area index (LAI) were observed under water stress condition in the booting, flowering, milking and doughing of the grain stages (4.3 and 4.47), (4.33) and 4.46), (4.4 and 4.57) and (4.4 and 4.58) cm-2, respectively. Evaluation of the 1000-grain weight of irrigated wheat cv. Mehregan under the water stress showed that the SSM model was highly accurate. RMSE (4.6 g.m-2) and CV (1.8) values indicate the ability of the SSM model to simulate the 1000-grain weight of irrigated wheat cv. Mehregan. Also, the simulated values of the harvest index were 34.7 % in non-stress conditions, which decreased by 6 % compared to the observed value. Harvest index values were observed under water stress conditions in the in the booting, flowering, milking and doughing of the grain stages in 30.2, 29.3, 29.9 and 29.5 %, respectively. Compared to its observed values, it was reduced by 3, 3.5, 5, and 5.5 %, respectively. ConclusionBased on the findings, the slight difference between the observed and simulated values demonstrates the SSM model's capability to accurately capture water stress impacts on the phenological stages, grain yield, and yield components of irrigated wheat cv. Mehregan during critical growth stages, including booting, flowering, milking, and doughing. The results indicate that the SSM model is effective in simulating wheat growth under water stress conditions, showcasing its potential as a valuable tool for modeling irrigated wheat growth. The model's ability to account for water stress and its effects on various growth parameters makes it a reliable and efficient tool for predicting crop performance in water-limited environments

Estimating actual evapotranspiration using OLI images and SEBAL algorithm (Case study: Dasht-e Naz, Sari)

Estimation of evapotranspiration is,one of the main components of the hydrological cycle, and is essential for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Unlike conventional methods remote sensing estimates evapotranspiration in different spatial scales. Therefore, the aim of this study is to estimate evapotranspiration using OLI sensor images and SEBAL algorithm in the Dasht-e-Naz area, Sari, Iran. To evaluate the results, evapotranspiration estimated by the reference method, Penman-Monteith-FAO, was compared with SEBAL algorithm. Strong and significant correlation was obtained between these two methods (R2=0.93), Low error of estimation by SEBAL compared with reference method (RMSE=1.14) and low absolute difference between the SEBAL and Penman-Monteith-FAO (MAE=0.96) indicates that there is a good match between estimated values by SEBAL algorithm and Penman-Monteith-FAO standard method. This study showed that OLI sensor images and SEBAL algorithm could satisfactorily estimate actual evapotranspiration in the Dasht-e-Naz area, Sari