Irrigation
M.R. Emdad; A. Tafteh; N.A. Ebrahimipak
Abstract
Introduction
Quinoa (Chenopodium quinoa) is native plant in Bolivia, Chile and Peru, which is widely adapted to different climatic conditions and can grow in all soils. This plant has shown adequate adaptation to arid and semi-arid areas conditions and is planted from areas with low elevation ...
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Introduction
Quinoa (Chenopodium quinoa) is native plant in Bolivia, Chile and Peru, which is widely adapted to different climatic conditions and can grow in all soils. This plant has shown adequate adaptation to arid and semi-arid areas conditions and is planted from areas with low elevation (sea level) to areas with an altitude of 4000 meters above sea level. Quinoa is often cultivated in areas with limited water resources, and it is rare to find quinoa cultivation under full irrigation conditions. Some studies have shown that quinoa yields slightly better under full irrigation (without water restriction) than quinoa under deficit irrigation. Crop growth models are very important tools in the study of agricultural systems and they can be used to simulate the yield of crop in different conditions. Given that the study of performance limiting factors requires numerous and costly research and experiments in different areas, so finding a way to reduce the number, time and cost of these experiments is worthwhile. Aquacrop model is one of the applied models that are used to simulate yield variations in different water and soil management.
Materials and Methods
This investigation was carried out in two growing seasons of 2019 and 2020 to determine the efficiency of Aquacrop model for simulating Quinoa grain yield and biomass under imposing three stress treatments of 30, 50 and 70% of water consumption in development and mid-growth stages. Plant spacing was 40 cm between rows and 7 cm between plants within rows. Seeds of quinoa (Titicaca cultivar) were cultivated in the first decade of August 2019 and in the third decade of July 2020. The experiment was a randomized complete block design with three replications. Three deficit irrigation treatments including 30, 50 and 70% of available water were considered in two growth stages (development and mid-growth) in 18 experimental plots (3 × 4 m). Soil moisture in rooting depth (about 40 cm) was measured by TDR and after the soil moisture of the treatments reached the desired values, plots were irrigated until the soil moisture reached the field capacity. The results of grain and biomass yield in the first year were used to calibrate the Aquacrop model and the results of the second year were used to validate the model. Root mean square error (RMSE), normalized root mean square error (NRMSE), Willmott index (D), model efficiency (EF) and mean error deviation (MBE) were used to compare the simulated and observed values.
Results and Discussion
The results of the first and second year were used to calibrate and validate the model, respectively. The results of the first year showed that irrigation with 50 and 70% of available water in the development stage reduced quinoa grain yield by 17 and 33%, respectively, compared to the control treatment. The application of these two deficit irrigation treatments in the middle stage reduced the yield by about 12 and 28%, respectively. The results of comparing the statistical indices of grain yield, biomass and water use efficiency showed that the NRMSE for grain, biomass and water use efficiency were 9, 8 and 14% in the first year and 9, 6 and 9% in the second years. Furthermore, the EF for these traits were 0.81, 0.77 and 0.64 in the first year and 0.68, 0.71 and 0.62, in the second year, respectively.
Conclusion
The results of calibration and validation of the model showed the accuracy and efficiency of the Aquacrop model in simulating grain yield, biomass and water use efficiency of quinoa. This model can be used to provide the most appropriate scenario and irrigation management for different levels of deficit irrigation managements.
M. R. Emdad; A. Tafteh
Abstract
Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can ...
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Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can be used to make or predict appropriate management scenarios according to the region's conditions and to predict plant performance in the field. Since the performance of irrigation scenarios in field conditions are costly and time consuming, and due to the limited water resources in the country and the necessity of optimal water use in agriculture, using the efficient and generic models can be useful tool for simulating crop production and soil salinity variations. This research has been conducted in order to simulate soil salinity and yield production using SALTMED model in Azadegan Plain of Khuzestan province. Materials and Methods: This study was carried out in wheat fields of Azadegan plain in Khuzestan province during 2014-2015 in three regions including Ramseh (as saline soil), Atabieh (as very saline soil) and Hamidieh (as control, non-saline soil). Three 10-hectare plots were selected in each area and a pilot with area of 2000 m2 was used for evaluation and measurement in each plot. First year data were used to calibrate the SALTMED model and second year field data were used to validate the model and to achieve the results in three conditions. The dominant soil texture in the area was clay loam. The quality of used irrigation water with average salinity of 2 dSm-1 was classified as C3-S1(high salinity with low sodium absorption ratio) and had no effect on wheat yield loss. In this study, version 3-04-25(2018) of SALTMED model was used and after calibrating in the first year, the results of simulated wheat grain yield and soil salinity variation values were used for model validation in different regions and in soils with different degrees of salinity, in the second year. Results and Discussion: The average measured and simulated biomass yield in the first year were 6.6 and 6.1 t/ha, respectively. Furthermore, the average of measured and simulated of wheat grain yield was 2.9 and 2.6 t/ha, respectively. Some statistical indices including mean bias error, normalized root mean square error, and root mean square error for grain yield were 0.11, 0.04, and 0.12 t/ha, respectively. The values of the same statistical parameters for biomass were -0.49, 0.1, and 0.61t/ha, respectively. These results showed that the measured values of grain yield and wheat biomass were in good agreement with the simulated values using SALTMED model. The simulated and measured variations of soil salinity at three soil depths of 0-30, 30-60, and 60-90 cm, showed close agreement with each other in three layers. Root mean square error, normalized root mean square error, and mean bias error for soil salinity values were 1.3, 0.20, and -0.06, respectively. After calibrating the model in the first year, to validate this model in the second year, the results of three pilots locations in three regions of Ramseh (saline), Atabieh(very saline) and Hamidieh(non-saline) were used. Comparison of simulated and measured wheat grain yield and biomass values showed that there was no significant difference between simulated and measured values. The simulated values of grain yield and wheat biomass in the three non-saline, saline and very saline soils had high correlation with the measured values, indicating high accuracy and efficiency of this model in simulating grain and biomass yield in different degrees of soil salinity. Moreover, the trend of soil salinity changes simulated by the SALTMED model in three highly saline, saline and non-saline soils (for three soil layers) was close to the measured values. The SALTMED model with normalized root mean square error and mean bias error of 0.18 and -0.13, respectively, showed good accuracy in different salinity conditions. There was no significant difference (5% level) between the measured and simulated salinity values of the different soil layers. The mean standard error at the 0-30, 30-60, and 60-90 cm layers was 1.1, 1.05, and 0.81 dSm-1, respectively. Therefore, based on the results and statistical indices, it was found that SALTMED model had good accuracy and efficiency in simulating yield, biomass and soil salinity under different salinity conditions. Conclusion: According to the results and statistical indices, SALTMED model had good performance and accuracy in simulating grain yield, biomass and soil salinity variations in different soil salinity conditions and so it can be used to predict wheat yield, yield components and soil salinity in different soil condition with different degrees of soil salinity to sustain soil and water and improve water productivity in similar areas.
Mohammad Reza Emdad; arash tafteh; saeed ghalebi
Abstract
Introduction: Simulation models have been used for decades to analyse crop responses to environmental stresses. AquaCrop is a crop water productivity model developed by the Land and Water Division of FAO. It simulates yield response to water of herbaceous crops, and is particularly suited to address ...
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Introduction: Simulation models have been used for decades to analyse crop responses to environmental stresses. AquaCrop is a crop water productivity model developed by the Land and Water Division of FAO. It simulates yield response to water of herbaceous crops, and is particularly suited to address conditions where water is a key limiting factor in crop production. It is designed to balance simplicity, accuracy and robustness, and is particularly suited to address conditions where water is a key limiting factor in crop production. AquaCrop is a companion tool for a wide range of users and applications including yield prediction. Aquacrop has high accuracy and performance for yield prediction than other models regarding to irrigation and fertilizer management base foundation. Using Aquacrop model for crop yield simulation in different soil and water managements has high accuracy and its use requires calibration and validation. The use of models saves time and cost and, if calibrated and validated, acceptable results are expected.
Material and Methods: This research was carried out in order to calibrate and validate the Aquacrop model for simulating wheat grain yield in the three selected pilots in Hamidiyeh province of Khuzestan province in two years of cultivation.In this regard, three different plots with a total area of about 10 hectares were selected in Hamidyeh region. Sampling, measuring and determining the parameters of soil, water, plant, irrigation management (information required for the Aquacrop model) and the existing conditions of the area were carried out.The climatic data required in Aquacrop model was collected from synoptic meteorological weather station of Ahvaz. Irrigation water quality with mean water salinity of 1.9 dS/m has a good quality for irrigation. In the first year, 5 irrigation events (with a total volume of 9500 cubic meters per hectare) are available to the wheat plant at different stages. In this regard, based on meteorological data and field and vegetation data that was taken from the field level in the first year, the Aquacrop model calibration and performance variations were carried out at different times of irrigation using a simulation model. In order to validate the results simulated by the model, the best scenario provided by the model in the second year was implemented at selected farm level and its results were compared with the simulation results by the model.
Results and Discussion: Aquacrop model calibrated for the first year and then compared for different scenarios of irrigation timing (3-6 irrigation event).The amount of grain yield and total in 4 irrigation intervals are not different with the corresponding values in 5 irrigation intervals. Irrigation rotations were considered in accordance with routine irrigation rotations of the region during planting, tillering, stemming, flowering and seed filling (5 turns) for 4 steps of irrigation step and for 3 irrigation stages, the tiller and stem elongation was deleted. The model showed that, using four irrigation timing is the most appropriate irrigation scenario. Using the results of the model with considering 4 irrigation times, wheat was planted in the second year for model validation. In the second year, the average of measured and simulated wheat grain yield was 3.8 and 4.4 t/h (with 14% error).Average values of total yield and simulated wheat seeds in 4 and 5 irrigation intervals were not different, while the amount of water consumed in 4 irrigation intervals decreased by 20% compared to 5 irrigation intervals. On the other hand, water use efficiency increased by up to 21% in 4 irrigation intervals compared to 5 irrigation intervals. Also, according to the simulation, it was observed that increasing the irrigation interval at the arrival stage, while not significantly increasing the grain yield and the total, did not increase the water use efficiency in order to increase the water consumption (one irrigation interval) Reduced. Considering 3 irrigation timing, the grain yield decreased by 15%. Due to the reduced yield in three irrigation intervals than the more irrigation intervals, this scenario is not recommended for performance reasons. So, according to the simulation, at least 4 irrigation intervals (during planting, stemming, flowering and seed filling) are recommended to maintain proper production level in existing conditions. Comparison of statistical indices between measured and simulation values of wheat grain yield in both years showed that the coefficient of correlation, normalized root mean square error (RMSE) and agreement index were 0.9, 0.14, and 0.89 respectively, which indicates the proper performance of the model for simulating yield of wheat for two consecutive years. The average grain yield of simulated wheat has been estimated at 3.8 ton / ha, which estimates 14% of grain yield less than actual experimental conditions compared to its measured value, indicating the accuracy and efficiency of this model in simulating wheat yield in the present situation. With considering 4 irrigation events, the water use efficiency of wheat grain yield increased by 0.7 kg/m3, which confirms the ability and accuracy of the Aquacrop model for simulating grain yield of wheat and also improving water use efficiency.
Conclusions: The results of this study showed that the simulation of wheat yield in the first year (2.6 t/ha) has a close proximity to the measured values of yield (3 t/ha). Also, validation of the model with changing conditions in the second year showed that the simulated yield of wheat (4.4 t/ha) also had a good agreement with its measured value (3.8 t/ha), which indicates the high accuracy of this model in simulating wheat grain yields every two years. Therefore, this model has the efficiency and accuracy in simulating wheat yield in research conditions.