Irrigation
E. Farrokhi; M. Nassiri Mahallati; A. Koocheki; alireza beheshti
Abstract
Introduction: Predicting yield is increasingly important to optimize irrigation under limited available water to enhance sustainable production. Calibrated crop simulation models therefore increasingly are being used an alternative means for rapid assessment of water-limited crop yield over a wide range ...
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Introduction: Predicting yield is increasingly important to optimize irrigation under limited available water to enhance sustainable production. Calibrated crop simulation models therefore increasingly are being used an alternative means for rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly-intuitive parameters to be defined compared to other crop models, and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic setting. This study was conducted as a two-year field experiment with the aim of the simulation of water productivity, above ground biomass and fresh and dry yield of tomato using AquaCrop model under different irrigation regimes applied at two growth stages in Mashhad climate conditions.Materials and Methods: A two-year field experiment was conducted during 2016-2017 growing seasons in the experimental field of Ferdowsi University of Mashhad located in Khorasan Razavi province, North East of Iran. The water-driven AquaCrop model developed by FAO was calibrated and validated to simulate water productivity, above-ground biomass and yield of tomato crop under varying irrigation regimes. AquaCrop was calibrated and validated for tomato under full (100% of water requirements) and deficit (75 and 50% of water requirements) irrigation regimes at vegetative (100V, 75V, and 50V) and reproductive stages (100R, 75R, and 50R). Model performance was evaluated in terms of the normalized root mean squared error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF), Willmott’s index of agreement (d) and coefficient of determination (R2). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. The 2016 and 2017 measured data sets were used for calibration and validation of AquaCrop model, respectively.Results and Discussion: Calibration results showed good agreement between simulated and observed data for water productivity in all treatments with high R2 value (0.93), good ME (0.23), low estimation errors (RMSE=0.09 kgm3) and high d value (0.85). The goodness of fit results showed that measured WP values were closer to simulated WP values for the validation season (2017) than for the calibration season (2016). During calibration, (2016), the model simulated the biomass with good accuracy. The simulated above ground biomass values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.92 to 0.99, NRMSE in range of 7.4 to 23%, d varying from 0.94 to 1, and ME ranging from 0.71 to 0.98. Validation results indicated good performance of model in simulating above ground biomass for most of the treatments (0.92 < R2 < 0.98, 6.5% < NRMSE < 21.3%, 0.76 < ME < 0.99). During validation (2017 growing season), overall, the trend of biomass growth (or accumulation) was captured well by model. However, the range of biomass of simulation errors was high, especially in treatments with higher stress. Accurate simulation of the response of yield to water is important for agricultural production, especially in an arid region where agriculture depends closely heavily on irrigation. During validation, the model predicted dry and fresh yield satisfactorily (NRMSE = 15.64% and 11.80% for dry and fresh yield, respectively).Conclusion: In general, the AquaCrop model was able to simulate the observed water productivity, above ground biomass and yield of tomato satisfactorily in both calibration and validation stage. However, the model performance was more accurate in non- and/or moderate stress conditions than in sever water-stress environments. In conclusion, the AquaCrop model could be calibrated to simulate growth and yield of tomato under temperate condition, reasonably well, and become a very useful tool to support decision on when and how much irrigate. This study provides the first estimate of the soil and plant parameter values of AquaCrop for simulation of tomato growth in Iran. Model parameterization is site specific, and thus the applicability of key calibrated parameters must be tested under different climate, soil, variety, irrigation methods, and field management.
Irrigation
E. Farrokhi; M. Nassiri Mahallati; A. Koocheki; alireza beheshti
Abstract
Introduction: The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the rapid assessment of water-limited crop yield over a wide range of environmental ...
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Introduction: The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly intuitive parameters to be defined compared to other crop models and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic settings. This study was conducted as a two-year field experiment with the aim of the simulation of soil water content, evapotranspiration, and green canopy cover of tomato using AquaCrop model under different irrigation regimes at two growth stages in Mashhad climate conditions. Materials and Methods: A field experiment was conducted over two consecutive seasons (2016-2017) in the experimental field of Ferdowsi University of Mashhad, located in Khorasan Razavi province, North East of Iran. The experiment was laid out in a split-plot design with different irrigation regimes at the vegetative and at the reproductive stage as the main and subplot factors, replicated thrice. In total, 27 plots of 4.5×3 m (13.5 m2) were used at a planting density of 2.7 plants per m2. Seedlings were planted in a zigzag pattern into twin rows, with a distance of 1.5 m between them, so there were four twin rows of three meters in each plot. The distance between tomato plants within each twin-row was 0.5 meters. A buffer zone spacing of 3 and 1.5 m was provided between the main plots and subplots, respectively. The following experimental factors were studied: three irrigation regimes (100= 100% of water requirement, 75= 75% of water requirement, 50= 50% of water requirement) and two crop growth stages (V= vegetative stage and R= Reproductive stage). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. Model accuracy was evaluated using statistical measures, e.g., R2, normalized root means square error (NRMSE), model efficiency (E.F.), and d-Willmott. The 2016 and 2017 measured soil and canopy data sets were used for calibration and validation of the AquaCrop model, respectively. Results and Discussion: For a water-driven model, such as AquaCrop, it is important to evaluate its effectiveness in simulating soil water content. During calibration (2016), the model simulated the soil water content with good accuracy. The simulated soil water content values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.90 to 0.97, NRMSE in range of 8.47 to 17.96%, d varying from 0.76 to 0.99, and M.E. ranging from 0.87 to 0.96. Validation results indicated the good performance of the model in simulating soil water content for most of the treatments (0.79<R2<0.99, 10.04%<NRMSE<18.65%, 0.77<ME<0.92). Appropriate parameterization of canopy cover curve is critical for the model to provide accurate estimates of soil evaporation, crop transpiration, biomass, and yield. In general, the calibration results showed good agreement between simulated and observed data for canopy cover development in all treatments with high R2 values (>0.87), good E.F. (>0.61), low estimation errors (RMSE, ranging from only 4.5 to 9.2) and high d values (>0.92). Conclusion: The AquaCrop model (version 6.1) was calibrated and validated for modeling soil water content, evapotranspiration, and green canopy cover for tomatoes under drought stress conditions. In general, soil water content, evapotranspiration, and green canopy cover of tomato were simulated by AquaCrop model with acceptable accuracy in both calibration and validation stages. However, the model performance was more accurate in no and/or moderate stress conditions than in severe water stress environments. In conclusion, the AquaCrop model could be calibrated to simulate the growth and soil water content of tomatoes under temperate conditions reasonably well and become a very useful tool to support the decision on when and how much irrigate. For R2, values > 0.90 were considered very well, while values between 0.70 and 0.90 were considered good. Values between 0.50 and 0.70 were considered moderately well, while values less than 0.50 were considered poor. Root mean square error ranges from 0 to positive infinity and expresses in the units of the studied variable. An RMSE approaching 0 indicates good model performance.
A. Mousavi; F. Shahabzi; Sh. Oustan; A.A. Jafarzadeh; B. Minasny
Abstract
Introduction: Soils are considered as one of the most important parameters to be achieved the sustainable agriculture at any place in the world. Additionally, the digital environment needs to have a soil continuous maps at local and regional scales. However, such information are always not available ...
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Introduction: Soils are considered as one of the most important parameters to be achieved the sustainable agriculture at any place in the world. Additionally, the digital environment needs to have a soil continuous maps at local and regional scales. However, such information are always not available at the required scale and mapping with high accuracy. Digital soil mapping (DSM) is a key for quantifying and assessing the variation of soil properties such as organic carbon (OC) especially in un-sampled and scarcely sampled areas. Using remotely sensed indices as an important auxiliary information relevant to the study area and data mining techniques were the pathway to create digital maps. Previous studies showed that digital elevation model (DEM) and remotely sensed data are the most commonly useful ancillary data for soil organic carbon prediction. the importance of DEM and derivative data in soil spatial modelling, it was not carried out in our research because there were no sharp differences in relief, and climate for that matter, across the study area. This research aims to investigate the spatial distribution of soil organic carbon (SOC) in a study area in north-western Iran using 21 remotely sensed indices as well as two data mining techniques namely Random Forests (RF) and Cubist.
Materials and Methods: This study was performed on the east shore of Urmia Lake located in the east Azerbaijan province, Iran. The area extension is about 500 km2. Based on the synoptic meteorological station report, the average annual precipitation and temperature of the study area is 345.37 mm and 10.83°C, respectively. Soil moisture and temperature regimes are Xeric and Mesic, respectively. Using stratified random soil sampling method, 131 soil samples (for the depth of 0-10 cm) were collected. Soil organic carbon (SOC) were then measured. The next step was to gather a suite of auxiliary data or environmental covariates thought to be useful (and available) for predicting SOC within a DSM framework for the region studied. Then, a number of remotely sensed imagery scenes from the Landsat 8-OLI acquired were collected in July 2017. The RF and Cubist models were applied to establish a relationship between soil organic carbon and auxiliary data. Both reflectance of the individual bands and indices derived from combinations of the individual bands were used. Fourteen spectral indices relevant to four types of data including: i) vegetation and soil; ii) water; iii) landscape; and iv) geology were gathered. Three different statistics was used for evaluating the performance of model in predicting SOC, namely the coefficient of determination (R2), mean error or bias (ME) and root mean square error (RMSE).
Results and Discussion: The results of the descriptive statistics of determined and calculated SOC for 131 soil samples showed that the mean and median values for SOC were 2.52% and 2.11%, respectively. Also, the CVs was recorded 57.94%. Minimum and maximum recorded values for SOC were 0.83% and 5.22%, respectively. The contents of SOC was left-skewed in the data set. The RF model prediction was quite good with calibration (R2= 0.89, MSE = 0.16 and ME = 0.01). While, in the Cubist calibration data set, the Valve of RMSE and ME were increased (R2= 0.85, MSE = 0.21 and ME = 0.03). In terms of R2, The RF model showed the higher value (0.89) compared with the Cubist model (0.85) for the validation dataset. Generally, the remote sensing (RS) spectral indices can successfully predict various SOC across the study area. The covariate importance rankings showed that VARI, NDVI, CRI2 and CRI1 were the four important covariates to predict SOC in the study area. Accordingly, the changes in SOC over space were not uniform across the study area and also it means that the study area is very dynamic and evolved over time.
Conclusion: The results of this study showed that although variables and auxiliary data had different importance in predicting the distribution of SOC, in general it can be found by modelling the relationship between them and SOC through the model. The results revealed that the RF model was suitable for the target variable. Accordingly, the auxiliary variables had different importance in predicting the spatial distribution of SOC. Remote sensing imagery, particularly those encompassing the combined indices played an important role in the prediction of SOC. The obtained results also indicated that the Visible Atmospherically Resistant Index (VARI) and Normalized Difference Vegetation Index (NDVI) were important to predict SOC. The current study revealed that DSM using important environmental covariates can be successfully used in Iran which there is no sufficient soil databases. This research also provided a pathway to start further works in the future such as DSM relevant to the soil erosion, soil ripening, trace elements and so on.
Ali Barikloo; Parisa Alamdari; kamran Moravej; Moslem Servati
Abstract
Introduction: In recent decades, the most important issue for agricultural activities is maximizing the productions. Today, wheat is grown on more lands than any other commercial crops and continues to be the most important food grain source for humans. Sustainable agriculture is a scientific activity ...
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Introduction: In recent decades, the most important issue for agricultural activities is maximizing the productions. Today, wheat is grown on more lands than any other commercial crops and continues to be the most important food grain source for humans. Sustainable agriculture is a scientific activity based on ecological principles with focus on achieving sustainable production. It requires a full understanding of the relationships between crop production with soil and land characteristics. Furthermore, one of the objectives of sustainable agriculture is enhancing the agricultural production efficiency through applying proper management, which requires a deep understanding of relationships between production rate, soil and environment characteristics. Hence, the first step in this process is finding appropriate methods which are able to determine the correct relationships between measured characteristics of soil and environment with performance rate. The aim of this study was evaluating the performance of neuro-genetic hybrid model in predicting wheat yield by using land characteristics in the west of Herris City.
Materials and Methods: The study area was located in the northwest of east Azarbaijan province, Heris region. In this study, 80 soil profiles were surveyed in irrigated wheat farms and soil samples were taken from each genetic horizon for physical and chemical analyses. In this region, soil moisture and temperature regimes are Aridic border to Xeric and Mesic, respectively. The soils were classified as Entisols and Aridisols. We used 1×1 m woody square plots in each profile to determine the amounts of yield. Because of nonlinear trend of yield, a nonlinear algorithm hybrid technique (neural-genetics) was used for modeling. At first step, the average weight of soil characteristics (from depth of 100 cm) and landscape parameters of selected profiles were measured for modeling according to the annual growing season of wheat. Then, land components and wheat yield were considered as inputs and output of model, respectively. For this reason, genetic algorithm was investigated to train neural network. Finally, estimated wheat yield was obtained using input data. Root mean square error (RMSE) and Coefficient of determination (r2), Nash-Sutcliffe Coefficient (NES) indices were used for assessing the method performance.
Results and Discussion: The sensitivity analysis of model showed that soil and land parameters such as total nitrogen, available phosphorus, slope percentage, content of gravel, soil reaction and organic matter percentage played an important role in determining wheat yield in the studied area. The soil organic matter and total nitrogen had the highest and lowest correlation with wheat yield quantity and quality, respectively, indicating the total nitrogen was the most important soil property for determination of wheat yield in our studied area. We found that network learning process based on genetic algorithms in the learning process had lower error. The findings showed that beside of confirming the desired results in the case of using sigmoid activation function in the hidden layer and linear activation function in the output layer of all neural networks, it is demonstrated that the proposed hybrid technique had much better results. These findings also confirm better prediction ability of neural network based on error back propagation algorithm or Levenberg-Marquardt training algorithm compared to other types of neural network confirms.
Conclusion: Using nonlinear techniques in modeling and forecasting wheat yield due to its nonlinear trend and influencing variables is inevitable. Recently, genetic algorithms and neural network techniques is considered as the most important tools to model nonlinear and complex processes. Despite the advantages of these techniques there are a lot of weaknesses. Imposing specific conditioned form by researchers in the techniques of genetic algorithms and stopping neural network learning at the optimal points are the main weaknesses of these techniques, while searching for global optimal point and not imposing a specific functional forms are the robustness of genetic algorithm techniques and neural networks, respectively. Results of this study indicated that the proposed hybrid technique had much better results. Correlation coefficient (0.87) and average deviation square error (473.5) were high and low, respectively. It can be concluded that the surveyed soil properties have very strong relationship with the yield. Implementation of appropriate land management practices is thus necessary for improving soil and land characteristics to maintain high yield, preventing land degradation and preserving it for future generations required for sustainable development.
seyed javad rasooli; Mohammad Taghi Naseri Yazdi; reza ghorbani
Abstract
Introduction: Environmental factors whichaffect crop yield areone of the most important factors in increasing yield.Accurate prediction of crop yield for economic management and farming systems is of particular importance.
Materials and Methods: This research was done in order to statistically model ...
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Introduction: Environmental factors whichaffect crop yield areone of the most important factors in increasing yield.Accurate prediction of crop yield for economic management and farming systems is of particular importance.
Materials and Methods: This research was done in order to statistically model and predict the canola growth and yield in Mashhad region based on 5 agricultural meteorology indicesand 12 climatic parameters during 1999 - 2014period. The date of planting determined with regard to the optimum temperature at planting with probability of 75% based on Weibull formula. Beginning and the end of the phenological stages of canola (germination, emergence, Single leaf, rosette, stemming, flower, poddingand ripening) were calculated on the basis of growing degree days (GDD) for each set. Calculation and statistical equations was done usingMinitab Ver. 13.0, 16.Ver SPSS and Excelsoftwares. Correlation analysis,statistical models andmultivariate models were used to determine the relationship between the annual yield of canolaand independent variables, includingclimaticparameters and agricultural meteorologyindices during the growing season between 1999- 2000 and2009-2010for each phenological stage (8stages).The bestmodel was selected with respect to the values of the coefficient of determination (R2) and root mean square error (RMSE).If the predictive power is estimated of the model RMSE values of less than 10% excellent, between 10 and 20% good, 20 to 30% average, and higher than 30% weak. The model tested by estimating the yield of canola for the 2010 to2014 years and the correction factor was calculated and the effect.
Results and Discussion: Canola planting date wascalculated for 23 September in Mashhad region. The phenology of canola was calculated based on growing degree days (GDD) above 5 ° C.Germination calculatedfor25 September, emergence in 3 October, appearance single leaf in 7 October, rosette in 6 March, stemming in 4 April, floweringin 21 April, podding in 15 May and ripening in 4 Jun. The time of the phenological stages of cereals is virtually the same time. Therefore, due to the water scarcity in the studied region -canola can be used in crop rotation. Average, the highest and the lowest yield of canola were1329.5, 2159 and 835.5 kg per hectare,respectively.Canola crop yield showed a rising trend during 1999 – 2014period due toimprovingfarming techniques and mechanization. All models are significant regression coefficients were tested normal, alignment and line.Each model in the absence of proof of any of these hypotheses was removed and the 9remaining models were compared.Model 1 predicted canola crop yield in the single leaf stagewith an average yield of canola evapotranspiration ((Mpet, absolute maximum wind speed (FFabsmax) and the sum of the vapor pressure deficit (VPD).Model 5 predicted canola yield in the floweringstage based on the absolute lowest temperature (Tabsmin), average daily wind speed (FF) and total sunshine hours (SH). Model 3 predicted canola yield in the rosette stage based on the average of daily minimum temperature (Tmin), the number of days with precipitation greater than 1 mm R (day) and total pressure loss water vapor (VPD). Model 7 predicted canola yield during the whole growing season based on the average of daily maximum temperature (Tmax) and total precipitation (R).After R2 models with higher coefficient of 1, 5, 7 and 3, respectively, with coefficients of determination 0.902, 0.902, 0.868 and 0.866 respectively.Then F and RMSE were evaluated forecasting models 1 and 7 excellent, 5 good model and version 3 was average. Model 7due to lower RMSE and the number of parameters during growing season was the most appropriate model. Model validatedby means ofrecordedcrop yieldsduring 2011 and2014 years. The simulated yieldswere 1470, 1639 and 1226 with average of 1445 kg per hectare. Error percent was 45.1, 9.3 and -7.1for the following years with an average of 15.7. RMSE was 9.4, 2.6 and 2.3 with average of 7.4. The predictive value of the model was excellent for all these years.
Conclusion: Model predicted the yield of canola based on the average maximum temperature (Tmax) and total precipitation (R)with error correction to reduce15.7. These variables described 86.8percent yield in the growing season and were significant at 5 percent. Canola planting date wascalculated for 23 September. Time phenology was germinated 25 September until ripening 4 Jun.
