عنوان مقاله [English]
Rice is one of the most important and popular plants that is cultivated in all six continents of the earth (Asia, Africa, Australia, Europe, North America and South America). In Iran, according to the statistics of the World Food Organization (FAO), in 2018, there were 580 thousand hectares of cultivated area and 1.99 million tons of rice production, and the import of rice in 2018 was about 1.2 million tons. Seventy percent of fresh water available in the world is consumed in the agricultural sector, of which 2 to 30 percent is dedicated to rice cultivation. Plant growth is significantly affected by climate change and subsequent water shortage. Basically, the low yield of crops is caused by deficiencies in the growing environment of plants that have not been addressed in crop management. In this regard, crop plant simulation models are used to conduct various studies, such as choosing the right plant and variety for planting, determining the best crop management, estimating regional production capacity, determining research priorities, technology transfer, classification. agricultural-ecological, predicting the effects of climate change and yield, as well as providing the basis for formulating and explaining the optimal pattern of water consumption. The ORYZA2000 model simulates the growth and development of rice plants under favorable conditions, water and nitrogen limitation. VSM model (Very Simple Model) is also a simple model for simulating plant growth and estimating product yield, which, while requiring little input information, has acceptable accuracy. Another new technology used in this direction is remote sensing. This technology can be fruitful in many agricultural decisions by predicting the performance of the plant before harvesting.
In order to evaluate the ORYZA2000 and VSM models in Golestan province, an experiment based on a randomized complete block design with three replications was conducted on Fajr rice during the 2020 and 2021 crop seasons in the suburbs of Gorgan city (Sorkhankalateh). The fields under study are located in the north of Iran with geographical coordinates of 28◦ 29' to 28◦ 32' east longitude and 40◦ 83' to 40◦ 97' north latitude with a level of 102 meters above sea level. Irrigation treatments include three levels, flood irrigation (TPR-FI) and the use of pressurized irrigation systems, including 1) band-type drip irrigation (DSR-DI) and 2) constant classic rain irrigation (DSR-SI) and compression There are three levels including D1 = 15 * 15, D2 = 20 * 20 and D3 = 30 * 30. The experimental plots of this research were considered to be 10 meters long and 5 meters wide, and the distance between the plots was equal to half a meter. In the flooded farm, after transplanting the seedlings to the intended land (June), they were kept in flooded condition until the harvest. In pressure irrigation systems, rice cultivation was carried out as dry farming and the irrigation schedule (irrigation volume) during the plant growth period was calculated using the five-year hydrological and meteorological data of the study area and the AquaCrop model. Based on this, its watering cycle was considered every other day. To evaluate the validity of the models, the root mean square error (RMSE), normalized root mean square error (RMSEn), efficiency coefficient of Nash-Sutcliffe model (NS) and explanation coefficient (R2) were used.
In this research, rice yield was estimated using ORYZA2000 model and VSM along with remote sensing technology. As seen, the recalibrated models have been successful in estimating product performance for a wide range of test data and have been able to estimate performance with acceptable accuracy. So that both models based on the values of the root mean square of the normalized error less than 10% and the efficiency of the model and the coefficient of explanation above 0.65 and the structural deviation less than 2%, have an acceptable accuracy in simulating the yield during the calibration and validation of the models. ORYZA2000 model has a higher accuracy in simulating performance than the VSM model due to the explanation factor and high efficiency of the model (0.91 and 0.84, respectively) along with the micro-scaled images with the explanation factor and efficiency of the model, It showed respectively 0.86 and 0.72. Therefore, this model has a suitable ability to estimate the yield of rice in the study area. Therefore, this model has a suitable ability to estimate the yield of rice in the study area. According to its practical results, this research can be used in the region in the coming years and the product estimation can be done based on it, which is very important in saving costs in addition to reducing the volume of field operations.