Soil science
A. Farajnia; K. Moravej; P. Alamdari; M. Eslahi
Abstract
Introduction: FAO agro-ecological model determines the production capacity, creating a logical relationship between the natural potential of the environment, the needs of communities, human activities, and sustainable adaptation. With the development of plant growth simulation models, researchers have ...
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Introduction: FAO agro-ecological model determines the production capacity, creating a logical relationship between the natural potential of the environment, the needs of communities, human activities, and sustainable adaptation. With the development of plant growth simulation models, researchers have begun a large-scale effort to agroecological zoning of various crops on a regional scale. In this method, an area was divided into homogeneous units with maximum similarity in terms of climate and land characteristics. Then, the potential yield map predicted by a simulation model is used for zoning. Pistachio is a subtropical plant that has long been cultivated in the central areas of Iran. With the occurrence of drought in the last two decades, farmers cultivated Pistachio in East Azerbaijan province without considering this crop requirement. This study aimed to use the AHP model to evaluate the suitability of East Azerbaijan lands for cultivating pistachio.
Methods and Materials: East Azerbaijan province is located in the northwest of Iran, between the latitudes of 36˚ and 45' to 39˚ and 26' N and the longitudes of 45˚ and 5' to 48˚ and 22' E based on the geographic coordinate system. The area of the province is 45800 square kilometers. The climate is generally cold and semi-arid, but it has different climates due to its diverse and extensive topography. The area of agricultural lands is estimated to be 18,000 square kilometers, which is about 39% of the total area. In this research, climatic data were collected for 30 years from Tabriz, Jolfa, Mianeh, Sarab, Maraghe, and Malekan synoptic stations, and from four neighboring stations of Orumieh, Khoy, Miandoab, and Parsabad. Three criteria (i.e. climate, land, and soil) and 11 sub-criteria were studied. The sub-climatic criteria included the average temperature of the growing season, average temperature in the pollination stage, absolute minimum temperature in the coldest month of the year, and average percentage of relative humidity in the flowering stage. Land criteria were land use sub-criteria, land slope, and slope directions, and soil criteria were salinity (electrical conductivity of saturated extract), pH, soil texture, and soil lime content (CaCO3). The results of the analysis of about 9000 soil samples were prepared for zoning of soil factors from East Azerbaijan Agricultural and Natural Resources Research Center. Land characteristics of slope map and aspects were prepared from the digital elevation map of the study area and land use map was obtained base on the map provided by the Forests and Rangelands Research Institute of Iran. The parameters were then weighted upon AHP by the parameter importance for each region. Data were transferred to Expert Choice software and clustered, rated, integrated for producing the final layer.
Results and Discussion: According to the AHP model, there are no entirely suitable class areas for pistachio cultivation in East Azerbaijan province. Because one or more factors or sub-criteria created low restrictions for the cultivation of this crop. The results showed that 3887 square kilometers or 8.5% of the area was classified as a relatively suitable class. Although this area has low restrictions for pistachio planting, the profitability of this complex has increased the area of pistachio orchards rapidly. The suitable lands are mainly located by the agricultural lands and if water requirement could be met, they can be allocated for planting. The low water requirement and tolerance to salinity compared to other crops can be considered as the advantages of cultivating pistachio. Since 1998, droughts have occurred in different areas of the province. It caused a decrease in agricultural products by up to 35%. The declining water level of Lake Urmia is one of the consequences of the recent droughts, deteriorating the groundwater quantity and quality. The 6250 square kilometers (13.6%) of the province's lands was classified as the critically suitable class. Some of the sub-criteria studied in these lands such as the average temperature of pollination period, the average temperature of the growth period, amount and direction of slope, and soil texture were in the critical classes. The 35663 square kilometers (77.9%) of the studied lands were found to be unsuitable (N). The main reason for the unsuitability was the very high salinity of lands, as seen in the soil salinity map. Although it is a modifiable factor, the lack of quality for leaching, heavy soil texture, and the impossibility of draining drainage due to flatness, render the reclamation of these lands impossible. Under the current situation, East Azerbaijan province is much more capable of planting this crop. However, it is necessary to conduct more detailed studies to avoid pistachio cultivation in marginal suitable lands.
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.