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. Mohammadi; B. Ghahraman; K. Davary; H. Ansari; A. Shahidi
Abstract
Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop ...
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Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop model predicts crop productivity, water requirement, and water use efficiency under water-limiting and saline water conditions. This model has been tested and validated for different crops such as maize, sunflower and wheat (T. aestivum L.) under diverse environments. In most of arid and semi-arid regions water shortage is associated with reduction in water quality (i.e. increasing salinity). Plants in these regions in terms of water quality and quantity may be affected by simultaneous salinity and water stress. Therefore, in this study, the AquaCrop model was evaluated under simultaneous salinity and water stress. In this study, AquaCrop Model (v4.0) was used. This version was developed in 2012 to quantify the effects of salinity. Therefore, the objectives of this study were: i) evaluation of AquaCrop model (v4.0) to simulate wheat yield and water use efficiency under simultaneous salinity and water stress conditions in an arid region of Birjand, Iran and ii) Using different treatments for nested calibration and validation of AquaCrop model.
Materials and Methods: This study was carried out as split plot design (factorial form) in Birjand, east of Iran, in order to evaluate the AquaCrop model.Treatments consisted of three levels of irrigation water salinity (S1, S2, S3 corresponding to 1.4, 4.5, 9.6 dS m-1) as main plot, two wheat varieties (Ghods and Roshan), and four levels of irrigation water amount (I1, I2, I3, I4 corresponding to 125, 100, 75, 50% water requirement) as sub plot. First, AquaCrop model was run with the corresponding data of S1 treatments (for all I1, I2, I3, and I4) and the results (wheat grain yield, average of soil water content, and ECe) were considered as the “basic outputs”. After that and in the next runs of the model, in each step, one of the inputs was changed while the other inputs were kept constant. The interval of variation of the inputs was chosen from -25 to +25% of its median value. After changing the values of input parameters, the model outputs were compared with the “basic outputs” using the sensitivity coefficient (Sc) of McCuen, (1973). Since there are four irrigation treatments for each salinity treatment, the model was calibrated using two irrigation treatments for each salinity treatment and validated using the other two irrigation treatments. In fact, six different cases of calibration and validation for each salinity treatment were [(I3 and I4), (I2 and I4), (I1 and I4), (I2 and I3), (I1 and I3), and (I1 and I2) for calibration and (I1 and I2), (I1 and I3), (I2 and I3), (I1 and I4), (I2 and I4), and (I3 and I4) for validation, respectively]. The model was calibrated by changing the coefficients of water stress (i.e. stomata conductance threshold (p-upper) stomata stress coefficient curve shape, senescence stress coefficient (p-upper), and senescence stress coefficient curve shape) for six different cases. Therefore, the average relative error of the measured and simulated grain yield was minimized for each case of calibration. After calibrating the model for each salinity treatment, the model was simultaneously calibrated using six different cases for three salinity treatments as a whole.
Results and Discussion: Results showed that the sensitivity of the model to crop coefficient for transpiration (KcTr), normalized water productivity (WP*), reference harvest index (HIo), θFC, θsat, and maximum temperature was moderate. The average value of NRMSE, CRM, d, and R2 for soil water content were 11.76, 0.055, 0.79, and 0.61, respectively and for soil salinity were 24.4, 0.195, 0.72, and 0.57, respectively. The model accuracy for simulation of soil water content was more than for simulation of soil salinity. In general, the model accuracy for simulation yield and WP was better than simulation of biomass. The d (index of agreement) values were very close to one for both varieties, which means that simulated reduction in grain yield and biomass was similar to those of measured ones. In most cases the R2 values were about one, confirming a good correlation between simulated and measured values. The NRMSE values in most cases were lower than 10% which seems to be good. The CRM values were close to zero (under- and over-estimation were negligible). Based on higher WP under deficit irrigation treatments (e.g. I3) compared to full irrigation treatments (e.g. I1 and I2), it seems logical to adopt I3 treatment, especially in Birjand as a water-short region, assigning the remaining 25% to another piece of land. By such strategy, WP would be optimized at the regional scale.
Conclusion: The AquaCrop was separately and simultaneously nested calibrated and validated for all salinity treatments. The model accuracy under simultaneous case was slightly lower than that for separate case. According to the results, if the model is well calibrated for minimum and maximum irrigation treatments (full irrigation and maximum deficit irrigation), it could simulate grain yield for any other irrigation treatment in between these two limits. Adopting this approach may reduce the cost of field studies for calibrating the model, since only two irrigation treatments should be conducted in the field. AquaCrop model can be a valuable tool for modelling winter wheat grain yield, WP and biomass. The simplicity of AquaCrop, as it is less data dependent, made it to be user-friendly. Nevertheless, the performance of the model has to be evaluated, validated and fine-tuned under a wider range of conditions and crops.
Keywords: Biomass, Plant modeling, Sensitivity analysis
