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Masoud Pourgholam-Amiji; Khaled Ahmadaali; Abdolmajid Liaghat
Abstract
This research aimed to select essential features for modeling the cost of pressurized irrigation systems using the data of 515 drip irrigation projects in four parts, including the cost of pumping station and central control system (TCP), cost of on-farm equipment (TCF), cost of installation and operation ...
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This research aimed to select essential features for modeling the cost of pressurized irrigation systems using the data of 515 drip irrigation projects in four parts, including the cost of pumping station and central control system (TCP), cost of on-farm equipment (TCF), cost of installation and operation on-farm and pumping station (TCI), and total cost (TCT). In the first stage, a database including 39 features influencing the cost of the mentioned sectors was prepared and the price of all projects (2006 to 2019) was updated for the base year of 2021. Then, feature selection was done with different algorithms in MATLAB environment and in two parts including (1) all features (39 features before and after the design stage) and (2) 18 features before the design phase (BD). The results showed that the amounts of RMSE and R2 for all the features were equal to 0.007 and 0.92, respectively, and for the BD section, they were equal to 0.003 and 0.89, respectively. Among the different algorithms for feature selection, support vector machine (SVM) and optimization algorithms (Wrapper) were identified as the best learner and feature selection method, respectively. The results of the evaluation criteria showed that the two LCA and FOA algorithms achieved the best estimation, and their error criterion in all the features were 0.0020 and 0.0018, respectively, while their correlations were 0.94 and 0.94. In the BD features, these criteria were 0.0006 and 0.95 for both algorithms, respectively. Finally, in the all features section, 10 out of 39 features and for BD section, 8 out of 18 were selected as the most effective features. The results of choosing the most effective features that affect the cost of different parts of the drip irrigation system can make the cost modeling of the systems simpler and faster and, while being useful for research works, it facilitates estimation and management of costs before implementation of each project.
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Sara bulukazari; Hossein Babazadeh; Nyazali Ebrahimipak; Seyed Habib Mousavi-Jahromi; Hadi Ramezani_etedali
Abstract
In exploitation of low-quality water in arid and semi-arid regions, irrigation management is essential to increase water use efficiency. Determination of crop-water-salinity production function is an essential tool for proper irrigation management. In this study, the AquaCrop model was first evaluated ...
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In exploitation of low-quality water in arid and semi-arid regions, irrigation management is essential to increase water use efficiency. Determination of crop-water-salinity production function is an essential tool for proper irrigation management. In this study, the AquaCrop model was first evaluated by considering 4 soil and water salinity levels and 4 deficit irrigation levels for the major cereal crops including wheat, barley, and corn in Qazvin Plain. The results showed that the coefficients of determination for wheat, barley, and corn yield were 0.97, 0.86 and 0.91, respectively. Therefore, the model can evaluate the performance in salinity and deficit irrigation conditions with a good approximation. To determine the optimal production functions of each crop, the results of the plant model were compared with three models of linear and nonlinear regression, and artificial neural network. The neural network model was able to estimate the performance compared to the AquaCrop model with lower error and higher correlation (0.99). These values in the linear function for wheat, barley, and corn were 0.98, 0.95, and 0.78 and in the nonlinear function as 0.92, 0.86 and 0.81, respectively. Also, the error calculated in the neural network method for wheat, barley, and maize was 40.16, 62.09, and 57.08 kg, respectively, which were less than the linear model by 75 %, 70 %, and 95 %; and less than the exponential model by 90 %, 85 %, and 93%, respectively. The best trained network for determining the water-salt production function for barley and wheat 5 Nero and for corn 7 Nero was introduced in the single layer structure. Sensitivity analysis on wheat and barley showed that this model had low sensitivity to irrigation and salinity parameters and only corn plant showed a moderate range sensitivity to salinity parameter.
mohammad norozian; seyed mehdi hosseini; Ahmad Akbari
Abstract
Presently, the country has a problem of water scarcity, therefore, development of alternative approaches with operational capabilities can be considered as a matter of utmost importance. In this study, using comparative advantage indexes (i.e. domestic resource cost (DRC), social cost benefit ratio (SCB) ...
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Presently, the country has a problem of water scarcity, therefore, development of alternative approaches with operational capabilities can be considered as a matter of utmost importance. In this study, using comparative advantage indexes (i.e. domestic resource cost (DRC), social cost benefit ratio (SCB) and net social profit (NSP)), the ranking of crops and adaptation of products with limited water resources and current cropping pattern with comparative advantage patterns in the district of Kashmar were studied. Then, by analyzing the cost of consuming water input at the rate of 15%, 35% and 60%, the sensitivity analysis of this input was analyzed. Agricultural and trade data were collected from the Ministry of Agriculture Jihad and the Customs, respectively, in the years 2016-2017. After determining the optimal values of the indices, the results showed that saffron and grapes had comparative advantage with both free market and official exchange rates, while wheat and barley had a comparative advantage only in the official exchange rate. Also, the sensitivity analysis of water input showed that comparative advantage and product ranking varied in the three different scenarios (15%, 35% and 60%). Finally in order to adapt the comparative advantages of the products with the region's conditions, enhancement of support and implementation of suitable research and promotion plans were suggested.
T H; M KH; M Z; A GH
Abstract
Crop models are suitable for simulation of crop yield by different scenarios of deficit irrigation and salinity. In this research, the AquaCrop model was evaluated to simulate the soybean grain yield and biomass under different levels of salinity and deficit irrigation in Gorgan County during 2011 and ...
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Crop models are suitable for simulation of crop yield by different scenarios of deficit irrigation and salinity. In this research, the AquaCrop model was evaluated to simulate the soybean grain yield and biomass under different levels of salinity and deficit irrigation in Gorgan County during 2011 and 2012 growing seasons. The model was calibrated by experimental data of 2011 and validated with data of 2012. The experiment included three irrigation levels of 100%, 75% and 55% water requirement and three salinity levels of 0.7, 5 and 10 dS/m. Statistical indices of the results of validated model including RMSE, E, and d for grain yield were 0.225 ton/ha, 0.88 and 0.97, respectively, and for biomass, they were 0.718 ton/ha, 0.77 and 0.95, respectively. Results showed that grain yield decreased with decrease in the amount of irrigation water and increase in salinity level. Further analysis showed that the sensitivity of AquaCrop model to the canopy decline coefficient (CDC) was more than the other parameters at senescence and maximum canopy cover stages.
m m; k d; b gh; h a; a h
Abstract
Crop growth simulation models have been developed for predicting the effects of water and salinity on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for different regions using the data generated from field. This study was carried in Mashhad ...
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Crop growth simulation models have been developed for predicting the effects of water and salinity on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for different regions using the data generated from field. This study was carried in Mashhad for two years (2010 and 2011) in order to evaluate the AquaCrop model under simultaneous salinity and water stress. Calibration was done using the data of 2009-2010 and validation with the data of 2010-2011.Results indicated that AquaCrop successfully simulated yield, biomass, water productivity, harvest index, soil moisture and salinity profiles for spring wheat under salinity and water-limiting treatments with high accuracy, although simulation of harvest index and soil salinity profiles were less accurate. Average value of normalized root mean square error (NRMSE), maximum error (ME), index of agreement (d), coefficient of the residual mass (CRM) and coefficient of determination (R2) in both the calibration and verification were 13.3 %, 36.1 %, 0.95, -0.072, and 0.87, respectively, for grain yield, while these measures were 12.59%, 34.46%, 0.92, 0.057, and 0.77, respectively, for biomass. Also, value of NRMSE, ME, d and CRM were 11.84 %, 25.72 %, 0.93, and 0.032, respectively, for soil moisture, while these measures were 26.25%, 58.5%, 0.91 and -0.12, respectively, for soil salinity. Sensitivity analysis revealed that crop transpiration coefficient (KC-Tr), normalized crop water productivity (WP*), reference harvest index (HIO), volumetric water content at field capacity, soil water content at saturation[S1] , and air temperature were the most sensitive parameters. Although the accuracy of the model simulation decreased with increasing salinity and water stress, AquaCrop can be a valuable model for simulating spring wheat yield and soil water content and salinity in Mashhad region, because the model requires few input data which can be readily available or easily collected. [S1]This is probably”initial conditions” and not saturation.
Alireza Tavakoli; Abdolmajid Liaghat; Amin Alizadeh
Abstract
Crops growth and production in rainfed systems is a function of changes in climatic parameters. Identification of the effective parameters and planning for their management and/or adapting agronomic practices to those changes will result in improving production baseline and yield prediction. In order ...
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Crops growth and production in rainfed systems is a function of changes in climatic parameters. Identification of the effective parameters and planning for their management and/or adapting agronomic practices to those changes will result in improving production baseline and yield prediction. In order to estimate climate-yield production functions, we analyzed eight crop seasons data (1998-2006) of 25 climate parameters and rainfed wheat grain yields of four cold and semi-cold regions of Lorestan province including Aleshtar, Khoram-Abad, Aligodarz, and Boroujerd. Correlation coefficients of linear and non-linear regressions were established between each weather parameter, as the independent variable, and wheat grain yield. By path analysis method, correlation coefficients were separated into direct and indirect effects. Results showed that, in local models of production functions, the role of vapor pressure deficit during crop growth was very important. Rain water productivity of all regions determined for eight crop seasons, and the amounts of maximum, minimum, and average rain water productivity were 0.341, 0.132, and 0.234 kg per cubic meter precipitation, with the average being 20 percent lower than the national average (0.292 kg.m-3). The maximum temperature of Oct-Nov, sunshine hours,, autumn precipitation, and maximum seasonal temperature were the most sensitive parameters with respect to grain yield prediction. Determination of the effective climatic factors and the degree of their effects will help farmers in adopting improved agronomic practices (such as proper planting dates, suitable cultivars, and improving soil water holding capacity), thereby controlling the negative factors affecting criop growth and yield and improving the effectiveness of the positive factors.
Mahsa Sameti; NOZAR GHAHREMAN; Khalil Ghorbani
Abstract
The capability of M5 model tree in estimating reference evapotranspiration in two stations, namely, Shiraz and Kermanshah, was studied. The daily weather data including mean air temperature, sunshine hours, precipitation, dew point temperature, mean relative humidity, wind speed and actual vapor pressure ...
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The capability of M5 model tree in estimating reference evapotranspiration in two stations, namely, Shiraz and Kermanshah, was studied. The daily weather data including mean air temperature, sunshine hours, precipitation, dew point temperature, mean relative humidity, wind speed and actual vapor pressure were collected and used as input variables for daily estimation of potential evapotranspiration by Penman-Montieth and Hargreaves-Samani equations. As the main goal of this study, the performance of M5 model tree in predicting reference evapotranspiration was evaluated. The results showed that the skill of M5 model in predicting ET by both methods was high but its performance in predicting Penman-Montieth values was relatively more, with R2 of 0.975 and 0.973 and RMSE values of 0.346 and 0.361 for Shiraz and Kermanshah, respectively, while the corresponding values for Hargreaves-Samani equation were Shiraz:R2=0.837, RMSE=0.844, and Kermanshah:R2=0.979, RMSE= 0.774. Sensitivity analysis revealed that the most significant variables in Penman-Montieth equation in Shiraz station were, respectively, air temperature, dew point, sunshine hours, and wind, while in Kermanshah station, important factors were air temperature, sunshine hours, wind, relative humidity, and dew point, respectively. Further studies are recommended in other climates for more scrutiny.