Land Evaluation and Suitability
Anahid Salmanpour; Mohammad Hassan Salehi; Jahangard Mohammadi; Abdolmohammad Mehnatkesh; Sayyed-Hassan Tabatabaei
Abstract
Introduction One of the objectives of land evaluation method is determining the land suitability degree and class in case of making any changes, including causing elimination or limitation. Thus, as an example, if it could be possible to predict changes in soil salinity for the future, any changes in ...
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Introduction One of the objectives of land evaluation method is determining the land suitability degree and class in case of making any changes, including causing elimination or limitation. Thus, as an example, if it could be possible to predict changes in soil salinity for the future, any changes in land suitability class can be investigated based on the predicted variations over time.The most important crops in Neyriz area are wheat and barley. Unfortunately, over the past two decades, improper agricultural management caused reduction and salinization of irrigation water in this region. To this end, the present study was performed to investigate the possibility of changes in the class or degree of land suitability in case of variations in soil electrical conductivity due to irrigation with saline water in Neyriz, for the next 10 years. Materials and Methods In three soil map units in three regions consisting of Deh-Fazel, Tal-Mahtabi and Nasir Abad, wheat and barley fields were selected and representative pedons were excavated, described and classified. Soil and water samples were obtained and necessary analyses and soil humidity and salinity, hydraulic conductivity and bulk density and water electrical conductivity were determined. Crop yields were evaluated by 1×1 quadrate, soil surface layer hydraulic conductivity was carried out by guelph permeameter and the volume of irrigation water was measured according to pipe discharge in each farm. Soil retention curve was calculated for all soil layers using sand box and pressure plate. van Genuchten equation parameters were gained using RETC software. Afterward, solute transport modeling was done using the software Hydrus and its results were validated using four statistical parameters including Coefficient of determination (R2), Root Mean Square Error (RMSE), Model efficiency (EF) and Coefficient of Residual Mass (CRM) to investigate the possible variation in soil salinity during the next 10 years, the data of the studied period of the crop year between 1392 and 1393 was repeated for 10 years. Qualitative and quantitative land evaluation was performed by standards methods. Finally, the Hydrus results were compared with salinity maps of Neyriz area which were calculated and obtained in the previous research from Landsat images bands for the past 20 years. Results and Discussion Based on the results, climate suitability class in Neyriz area was suitable (S1) for wheat and relatively suitable (S2) for barley. The limiting factor for barley was the average of maximum temperature in the coldest month for barley. The soil suitability class was suitable (S1) for both crops (wheat and barley) in all farms. Therefore, the land suitability in the studied farmlands was S1 for the wheat and S2 for the barley. Results also revealed that the values for potential production were 10723 and 8677.5 Kg(grain)ha-1 for wheat and barley and for critical production were 1167 and 1297.6 Kg(grain) ha-1 for wheat and barley, respectively in the farms. Amongst the farmlands, only a barley farm which was located in Tal-Mahtabi had the S1 quantitative suitability class and others had S2. The results also showed that if all other conditions like volume and the quality of the irrigation water, precipitation, temperature and evaporation remain constant over the next 10 years, land suitability class will not change but land suitability degree will decrease gradually over time. The validation of the Hydrus model, based on the RMSE values, revealed that the predicted soil salinity and the observed value were very similar and the model had good ability in estimating and modeling soil salinity in the studied area. Comparing the results of modeling and soil salinity maps over the last 20 years have confirmed this trend. Based on the satellite salinity maps, the soil salinity of the studied fields has increased slightly from 2 to 4 dSm-1 between the years 1374 and 1393. Hence it can be concluded that the prediction of Hydrus model about gradual rise in predicted soil salinity and land suitability degree during the next 10 years is acceptable. Conclusion The present study showed that climate and land suitability class in Neyriz area was suitable and relatively suitable for wheat and barley, respectively. Solute transport modeling showed that land suitability degree will decrease gradually and soil quality will decline over time by assuming constant irrigation and precipitation condition over the next 10 years. Therefore, preventing the expansion of soil salinity and degrading agricultural lands require serious considerations of the authorities in the crisis Managements.
Behrooz pourmohamadali; M.H. Salehi; S.J. Hosseinifard; H. Shirani; I. Esfandiarpour Borujeni
Abstract
Introduction In recent decades, due to a significant increase of pistachio cultivation and uncontrolled exploitation of groundwater resources as well as reducing rainfall precipitation, groundwater level has dropped and the quantity and the quality of water has also been reduced. Therefore, agricultural ...
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Introduction In recent decades, due to a significant increase of pistachio cultivation and uncontrolled exploitation of groundwater resources as well as reducing rainfall precipitation, groundwater level has dropped and the quantity and the quality of water has also been reduced. Therefore, agricultural producers, researchers and policy makers need to pay more attention to appropriate land management as an important strategy to achieve greater yield per unit area and optimal use of soil and water resources. Crop yield prediction regarding its temporal and spatial variations has an important role in developing proper management programs. However, few studies have been carried out in relation to pistachio yield prediction using an acceptable range of features on regional scale. In the present study, pistachio yield modeling was performed by multivariate linear regression and artificial neural networkbased on soil, water and management features. Materials and Methods 129 orchard plots in different areas of Rafsanjan and Anar were identified and selected. The study area is located between 54° 56′ and 56° 41′ E, 29° 54′ and 31° 13′ N. Soil sampling, was performed from the areas under pistachio canopy and three soil depths of 0 to 40, 40 to 80 and 80 to 120 cm in each plot, fully expanded sub-terminal leaflets were randomly collected from non-fruiting branches, during the late July through August. Irrigation water of all orchards was also sampled. Moreover, for each orchard, a questionnaire was prepared to collect management and yield data. Soil quality indicators including particle size distribution, pH in saturated soil paste, electrical conductivity of saturated extract, soluble sodium, soluble calcium, soluble magnesium, available phosphorus and available potassium were determined for soil samples. The concentrations of phosphorus, potassium, iron, zinc, copper, manganese, calcium and magnesium in leaf samples and electrical conductivity in water samples, were also calculated. Finally, a dependent variable (pistachio yield) and 50 independent variables including soil, water and plant characteristics were used for modeling. For this purpose, stepwise multiple linear regression and artificial neural network technique were applied. Then, the study area was divided into 4 parts with the highest pistachio orchards densities and regression models were run for each part, separately. The ability of models to yield prediction was evaluated using the root mean square error (RMSE), relative root mean square error (% RMSE), adjusted coefficient of determination (adj - R2) and Durbin - Watson statistic (D – W). Results and Discussion The average of yield in the study area is about 1,700 kilograms per hectare. Results indicated that multiple linear regression could explain only 26 percent of the pistachio yield variation, but its accuracy increased when data became more homogeneous via dividing the study area into four parts. The model adjusted-R2 for Noogh, Anar, eastern suburbs and western suburbs orchards rose to about 92.4, 81.5, 95 and 53.6 percent, respectively. In all regression models except the model of western suburbs, at least one of the characteristics associated with irrigation water was significant. Artificial neural network with 9 neurons in a hidden layer, Tangent - sigmoid activation function and Levenberg - Marquardt training function, has a 98.3 percent accuracy in predicting pistachio yield in the study area (% RMSE = 13.8). Conclusion Multivariate linear regression model did not accurately predict the pistachio yield for the whole of study area whereas increasing data homogeneity and decreasing sources of variations, reduced complexity of relationships between features which resulted in increasing of the efficiency of linear regression to modeling these relationships. These models were highly sensitive to irrigation water features. Therefore, special attention should be paid to modern irrigation techniques and proper management approaches in order to enhance water efficiency. Overall, artificial neural network had greater accuracy compared to multivariate linear regression for pistachio yield modeling. This indicates the existence of non-linear and complex relationships between pistachio yield and the factors affecting yield and also the necessity of using modern and robust data mining tools for crop yield estimating. It seems that artificial intelligence techniques can be used as an efficient tool for developing proper management programs.
I. Fazeli Farsani; M. H. Salehi; A. A. Besalatpour; M. R. Farzaneh
Abstract
Introduction In order to achieve sustainable management of water resources, integrated knowledge of water resources and modeling is essential, especially in arid and semi-arid regions where water resources have become scarcer with increasing demands from socioeconomic development and population growth. ...
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Introduction In order to achieve sustainable management of water resources, integrated knowledge of water resources and modeling is essential, especially in arid and semi-arid regions where water resources have become scarcer with increasing demands from socioeconomic development and population growth. In recent years, utilization of hydrological models has been increased to simulate watershed processes for cost saving purposes. Various hydrological models such as soil and water assessment tool (SWAT) have been developed to simulate runoff in the watersheds. In this study, SWAT was used to simulate monthly runoff in Bazoft watershed and the impact of springs discharge on the simulation accuracy was evaluated. Materials and Methods Bazoft is one of the watersheds in Karun basin, (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E) located in northern part of the Karun river basin in southwestern Iran. The area of the watershed is 2168 km2. The main river in the watershed is Ab Bazoft which is joined by the Karun River at the outlet of the watershed. The elevation ranges from 880 m in the south of the watershed to 4200 m on Zardkuh Mountain in the north eastern area. Because the topography is very high in the watershed, the rainfall distribution is different, the average of rainfall in the northern part of the watershed is 1400 mm, while the southern part is 500mm. SWAT was used because the model is a continuous time, spatially and semi-distributed and basin-scale model, in which hydrological processes and water quality are coupled with crop growth and agricultural management practices. Input data include digital Elevation Model (DEM), land use, soil type, meteorological and hydrological observed data were provided. After running the model, a sensitivity analysis was done using the one at time method and SUFI-2 program. For calibration and uncertainty analysis in this study, we used the PSO (particle swarm optimization) algorithm. SUFI-2 and PSO are linked to SWAT in the environment of SWAT-CUP software. We used about two-third of the observed data for calibration and the remaining for validation. The simulation period was from 1992 to 2008. The study period was 1998–2008 for calibration and 1992–1997 for validation. The calibration and validation period results were analyzed at monthly time scale. ). The objective function was the Nash– Sutcliffe coefficient. Two indices, the P-factor and the R-factor, are used to quantify the goodness of calibration performance. The P-factor is the percentage of data bracketed by the 95PPU band, and ideally we would like to bracket all measured data, except the outliers, in the 95PPU band, and the R-factor is the average thickness of the 95PPU band divided by the standard deviation of the corresponding measured variable. Theoretically, the value of P-factor ranges between 0 and 100%, while that of R-factor ranges between 0 and infinity. In ideal conditions when the uncertainty model is perfect, P-factor will be 1 and the R-factor will be 0. Results and Discussion The results showed that the simulated base flow, peak flow, and hydrograph trend by entrance of spring discharge data to the model were more in agreement to the observed runoff data than the model with no spring discharge data. Therefore, the constructed model with the spring discharge data was selected to calibrate the particle swarm optimization (PSO) algorithm. In the sensitivity analysis, the parameters of curve number for moisture condition II (CN2), groundwater delay time (GW_DELAY), deep aquifer percolation fraction (RCHRG_DP), snow pack temperature lag factor (TIMP), the average monthly precipitation during the prediction period (PCPMM), temperature and precipitation parameters and surface runoff lag time coefficient (SURLAG) were the most sensitive parameters in the watershed. Conclusion The calibration and validation results for the base period (1992-2008) showed that the accuracy of the simulations was satisfactory for the discharge and sediment values. The obtained evaluation criteria r-factor, p-factor, and R2 for the calibration period were 1.01, 76% and 0.79 and for the validation period were 0.76, 72% and 0.57, respectively. Therefore, due to the noticeable effects of spring discharge data and the input parameters on the runoff simulation in the study area, it appears that it is essential to consider these factors for the runoff simulation using SWAT in similar mountainous watersheds with high topography.
M.H. Salehi; H. Khademi; J. Givi; M. Karimiyan
Volume 27, Issue 2 , March 2005
