Soil science
E. Fathi; M. Ekhtesasi; A. Talebi; J. Mosaffaie
Abstract
IntroductionWatersheds, as diverse ecosystems, play a fundamental role in water provision, soil conservation, biodiversity, and ecological sustainability. In addition to delivering environmental services, these areas serve as vital resources for supporting the livelihoods and well-being of local communities. ...
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IntroductionWatersheds, as diverse ecosystems, play a fundamental role in water provision, soil conservation, biodiversity, and ecological sustainability. In addition to delivering environmental services, these areas serve as vital resources for supporting the livelihoods and well-being of local communities. However, population growth, climate change, land-use changes, and overexploitation have imposed significant pressures on these ecosystems, jeopardizing their health and natural functionality. The degradation of these areas can lead to serious consequences for water resources, biodiversity, and environmental sustainability. Therefore, identifying and implementing effective strategies to preserve and enhance watershed health is essential. In this regard, the present study utilizes the strategic SWOT model to identify the strengths, weaknesses, opportunities, and threats within the Ilam Dam watershed and aims to propose practical solutions for improving and strengthening the health of these valuable ecosystems. Materials and MethodsTo achieve optimal strategies for resource management and improving the health of the study area, the SWOT analysis method was employed. This method provides a comprehensive framework for developing operational strategies by identifying existing strengths, weaknesses, opportunities, and threats. Data for this research were collected through field studies, specialized interviews with local experts, and a review of scientific resources and available information. To enhance accuracy and reliability in evaluating and weighting internal and external factors, the Analytic Hierarchy Process (AHP) and Expert Choice software were utilized. Subsequently, the collected data were analyzed using the Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices, leading to the formulation of appropriate strategies. These strategies were categorized into four main types: aggressive, conservative, competitive, and defensive. Finally, to ensure the selection of the best options, the Quantitative Strategic Planning Matrix (QSPM) was applied. At this stage, each strategy was scored and prioritized based on its attractiveness and feasibility, ensuring the identification of the most effective and actionable strategies. Results and DiscussionAccording to the results of this study, seven factors were identified as strengths and seven as weaknesses (internal factors), along with seven opportunities and seven threats (external factors). The total score for strengths was 3.33, and for weaknesses, it was 3.57. Additionally, the score for opportunities was calculated at 3.54, while threats scored 3.28. Based on these scores and the internal and external factors evaluation matrix analysis, the WO strategy position was recommended, with specific solutions determined for each strategy. In the SO strategy, the QSPM matrix analysis indicated that optimal management of surface and groundwater resources, along with the establishment of suitable infrastructure for water capture and storage (strategy SO2), was recognized as the top priority. Within the ST strategy, the strategy of leveraging high organizational and local capacity to address the negative impacts of climate change and sustainably engage stakeholders and local communities in decision-making and watershed resource protection (strategy ST4) was prioritized. For the WO strategy, enhancing water and soil conservation programs and developing research and management initiatives through encouragement, support, and both material and spiritual contributions for specialized studies (strategy WO2) was identified as the main priority. Likewise, under the WT strategy, expanding and diversifying educational programs, developing educational content on water crises and climate change, and addressing the consequences of natural resource degradation in the basin, along with planning and approving national and international projects on climate change and dust storm mitigation (strategy WT1), emerged as the top priority. These strategies can provide an effective framework for improving resource management in watersheds and addressing environmental challenges. Conclusion The findings of this study clearly demonstrate that strengthening protective, managerial, and educational programs plays a crucial role in improving the health of this watershed. These strategies, by optimizing available opportunities and minimizing weaknesses, can significantly contribute to sustainable development and effective natural resource conservation. In particular, the implementation of these programs requires collaboration and synergy among the local community, governmental and non-governmental organizations, and related agencies. It is recommended that conservation and management planning be accompanied by education and awareness initiatives for the local community, so residents understand the importance of preserving natural resources and are encouraged to participate in conservation efforts. This active community involvement not only enhances the effectiveness of these strategies but also contributes to achieving desirable outcomes and ecosystem sustainability, setting the stage for more effective management and long-term conservation of water and soil resources.
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.
J. Givi; A. Haghighi
Abstract
Introduction: Land suitability evaluation and land production potential estimation are considered as prerequisites for land use planning. In quantitative land suitability evaluation, land suitability is evaluated based on production per surface area unit. In this kind of evaluation, first, radiation ...
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Introduction: Land suitability evaluation and land production potential estimation are considered as prerequisites for land use planning. In quantitative land suitability evaluation, land suitability is evaluated based on production per surface area unit. In this kind of evaluation, first, radiation thermal production potential is calculated, using different models such as FAO model. This potential is a genetical one which is not under influence of water, soil and management limitations. If soil limitations are exerted in the radiation thermal production potential, land production potential is resulted. The difference between the land production potential and the farmer yield is that the first one is not under influence of management limitation but the second one is under influence of management. Management level is determined based on management index. Canola (Brassica napus) is one of the oil crops which is cultivated in Iran and provides more than 90% of the required oil of the country. This crop is effective in the control of pests, diseases and weeds. Oil of the edible varieties of canola has good quality. After extraction of the oil, the remained meal is full of protein and is appropriate for animal nutrition. The aims of this research have been land production potential prediction and quantitative land suitability evaluation for irrigated canola in the north of Shahrekord. In the present research, for the first time, canola growth requirements were rated for different suitability classes.
Materials and Methods: The studied land with a total surface area of 25 hectares is located north of Shahrekord, in the vicinity of the previous Saman industrial district. The average annual precipitation in the studied area is 370 mm and the mean annual temperature is 13.1 OC. 19 soil profiles were dug and described. Leaf area index, harvest index and canola grain moisture percentage were measured. Farmer yield was also measured for each profile and economic data were collected. Physical and chemical analyses of the soils were done according to the standard laboratory methods. For the first time, canola growth requirements were rated for different suitability classes. In a next step, the measured land characteristics were matched with the canola growth requirements (except climatic requirements) and depending on the limitation level of the land characteristics for canola, a suitability rating was considered for each land property. By using these ratings in the second root and story formulas, soil index was calculated. Radiation-thermal production potential was calculated, using FAO model, considering temperature, solar radiation, leaf area index and harvest index limitations. Land production potential was determined by multiplication of the radiation-thermal production potential and the soil index. Margin yield was calculated by dividing total costs to the price of one kilogram of canola in the market. The limits between quantitative land suitability classes of S1 and S2, S2 and S3 and S3 and N were considered to be 75% of the radiation-thermal production potential, 140% and 90% of the marginal yield, respectively. Management index was calculated by dividing the farmer yield to the land production potential. Management index of 0.75 and 0.50 was considered respectively to be the limits between management levels of high and intermediate and intermediate and low. To evaluate the accuracy of the used methods, the correlation between the land production potential and the farmer yield was investigated.
Results and Discussion: Canola radiation-thermal production potential was calculated as 7603 kg. ha.-1; mean land production potentials, using second root and story formulas were predicted respectively, as 3214 and 2291 kg. ha.-1 and mean farmer yield was measured as 1943 kg. ha.-1. Management level was determined as high to intermediate. The marginal yield was calculated as 2025 kg. ha.-1 The results of this study showed that 59 and 6 percent of the land is marginal (S3) and moderated (S2) suitable respectively. 35 percent of them are not suitable (N). Use of the second root formula is more appropriate than story formula as far as land production potential calculation is concerned.
Conclusion: Moderate limitation of slope and carbonate content and moderate to severe limitation of gravels in the soils are the origin of a difference of 4400 to 5300 kg. ha.-1 between the radiation-thermal production potential and the land production potential. A difference of 348 to 1271 kg. ha.-1 between the land production potential and the average farmer yield is due to the high to intermediate management level. Land physical limitations and management level have caused more than 50% of the lands to have marginal suitability and 35% of them become non-suitable.
