Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek)
IntroductionResearch and development in high-potential agricultural areas are of great importance for ensuring the food needs of the population and livestock. Neglecting these regions can lead to increased food prices and food shortages, which can have a negative impact on the economy and public hea...
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Ferdowsi University of Mashhad
2024-09-01
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| author | F. Jannati F. Sarmadian |
| author_facet | F. Jannati F. Sarmadian |
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| collection | DOAJ |
| description | IntroductionResearch and development in high-potential agricultural areas are of great importance for ensuring the food needs of the population and livestock. Neglecting these regions can lead to increased food prices and food shortages, which can have a negative impact on the economy and public health. Land suitability maps provide essential information for agricultural planning and are vital for reducing land degradation and evaluating sustainable land use. The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. These methods have been widely employed as primary tools for mapping and evaluating land suitability in various regions worldwide. Materials and MethodsIn this study, a total of 288 soil profiles were utilized to compute the land suitability index for wheat, barley, and alfalfa crops. Various environmental variables were included, such as topographic factors derived from the digital elevation model and spectral indices obtained from Landsat 8 satellite imagery. Eight key factors, namely slope percentage, climate, texture, gypsum content, equivalent calcium carbonate, electrical conductivity (EC), and sodium absorption ratio (SAR), were identified as influential in the assessment of land suitability. To quantify the degrees of land suitability for the target crops, a parametric approach based on the square root method was employed. Moreover, the random forest machine learning model was utilized for spatial modeling, zoning mapping, and determining the significance of environmental variables in the land suitability evaluation process. By incorporating these comprehensive methodologies, a more detailed and accurate understanding of the land suitability for wheat, barley, and alfalfa cultivation can be achieved, facilitating informed decision-making in agricultural planning and land management strategies. Results and DiscussionThe spatial prediction results demonstrated the effectiveness of the random forest model in classifying land suitability for wheat, barley, and alfalfa. The model achieved high accuracy, with Kappa coefficients of 81%, 84%, and 85% for wheat, barley, and alfalfa, respectively. The overall accuracies were also impressive, reaching 86% for wheat, 88% for barley, and 89% for alfalfa. Analyzing the land suitability assessment results, it was found that barley had the highest land suitability class, covering a significant portion of 40% in class S1. Alfalfa followed closely with 35.5% of the total area, and wheat occupied 32% in the same class. Delving into the predictive environmental variables for barley, Diffuse, SHt, and MrVBF emerged as the most influential factors. These variables played a crucial role in assessing the suitability of land for barley cultivation. Similarly, for wheat, the variables Diffuse, MrVBF, and TWI were identified as significant indicators, contributing to the accurate prediction of wheat performance. Regarding alfalfa, the variables MrVBF, Diffuse, and Valley_depth stood out as the most important variables, providing valuable insights into land suitability for alfalfa cultivation. In general, the limiting factors for irrigated cultivation of these crops were primarily associated with soil properties. In the northern regions, soil texture was identified as a significant limiting factor, impacting the suitability of the land for crop cultivation. On the other hand, in the southern regions, soil characteristics such as the percentage of lime, gypsum, salinity, and alkalinity were recognized as the most influential limiting factors, affecting the suitability of the land for successful crop production. These findings provide valuable information for land planners, farmers, and decision-makers in determining suitable areas for wheat, barley, and alfalfa cultivation. By considering the identified influential factors and addressing the limiting soil properties, agricultural practices can be optimized to maximize crop productivity and ensure sustainable land use. ConclusionThe research aimed to evaluate land suitability for wheat, barley, and alfalfa crops under irrigation. Data selection focused on the most limiting factors for these crops. The model achieved acceptable predictions for wheat, barley, and alfalfa, with Kappa coefficients of 0.81, 0.85, and 0.84, and overall accuracies of 0.86, 0.89, and 0.88, respectively. Barley had the highest percentage of suitable land (40%), followed by alfalfa (39.5%) and wheat (32%). Soil constraints varied across the study area, including texture, stoniness, lime, gypsum, salinity, and alkalinity. The analysis identified 31 soil types, and the random forest model yielded a digital soil map with a Kappa coefficient of 0.76 and overall accuracy of 0.81. The findings support effective land management and agricultural planning. |
| format | Article |
| id | doaj-art-fd863b8644f34da79fd73ebd1b900968 |
| institution | DOAJ |
| issn | 2008-4757 2423-396X |
| language | fas |
| publishDate | 2024-09-01 |
| publisher | Ferdowsi University of Mashhad |
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| spelling | doaj-art-fd863b8644f34da79fd73ebd1b9009682025-08-20T03:01:46ZfasFerdowsi University of Mashhadمجله آب و خاک2008-47572423-396X2024-09-0138447949310.22067/jsw.2024.87916.140745390Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek)F. Jannati0F. Sarmadian1Soil Engineering Department, Faculty of Agricultural, University College of Agriculture and Natural Resources, University of Tehran, Karaj, IranSoil Engineering Department, Faculty of Agricultural, University College of Agriculture and Natural Resources, University of Tehran, Karaj, IranIntroductionResearch and development in high-potential agricultural areas are of great importance for ensuring the food needs of the population and livestock. Neglecting these regions can lead to increased food prices and food shortages, which can have a negative impact on the economy and public health. Land suitability maps provide essential information for agricultural planning and are vital for reducing land degradation and evaluating sustainable land use. The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. These methods have been widely employed as primary tools for mapping and evaluating land suitability in various regions worldwide. Materials and MethodsIn this study, a total of 288 soil profiles were utilized to compute the land suitability index for wheat, barley, and alfalfa crops. Various environmental variables were included, such as topographic factors derived from the digital elevation model and spectral indices obtained from Landsat 8 satellite imagery. Eight key factors, namely slope percentage, climate, texture, gypsum content, equivalent calcium carbonate, electrical conductivity (EC), and sodium absorption ratio (SAR), were identified as influential in the assessment of land suitability. To quantify the degrees of land suitability for the target crops, a parametric approach based on the square root method was employed. Moreover, the random forest machine learning model was utilized for spatial modeling, zoning mapping, and determining the significance of environmental variables in the land suitability evaluation process. By incorporating these comprehensive methodologies, a more detailed and accurate understanding of the land suitability for wheat, barley, and alfalfa cultivation can be achieved, facilitating informed decision-making in agricultural planning and land management strategies. Results and DiscussionThe spatial prediction results demonstrated the effectiveness of the random forest model in classifying land suitability for wheat, barley, and alfalfa. The model achieved high accuracy, with Kappa coefficients of 81%, 84%, and 85% for wheat, barley, and alfalfa, respectively. The overall accuracies were also impressive, reaching 86% for wheat, 88% for barley, and 89% for alfalfa. Analyzing the land suitability assessment results, it was found that barley had the highest land suitability class, covering a significant portion of 40% in class S1. Alfalfa followed closely with 35.5% of the total area, and wheat occupied 32% in the same class. Delving into the predictive environmental variables for barley, Diffuse, SHt, and MrVBF emerged as the most influential factors. These variables played a crucial role in assessing the suitability of land for barley cultivation. Similarly, for wheat, the variables Diffuse, MrVBF, and TWI were identified as significant indicators, contributing to the accurate prediction of wheat performance. Regarding alfalfa, the variables MrVBF, Diffuse, and Valley_depth stood out as the most important variables, providing valuable insights into land suitability for alfalfa cultivation. In general, the limiting factors for irrigated cultivation of these crops were primarily associated with soil properties. In the northern regions, soil texture was identified as a significant limiting factor, impacting the suitability of the land for crop cultivation. On the other hand, in the southern regions, soil characteristics such as the percentage of lime, gypsum, salinity, and alkalinity were recognized as the most influential limiting factors, affecting the suitability of the land for successful crop production. These findings provide valuable information for land planners, farmers, and decision-makers in determining suitable areas for wheat, barley, and alfalfa cultivation. By considering the identified influential factors and addressing the limiting soil properties, agricultural practices can be optimized to maximize crop productivity and ensure sustainable land use. ConclusionThe research aimed to evaluate land suitability for wheat, barley, and alfalfa crops under irrigation. Data selection focused on the most limiting factors for these crops. The model achieved acceptable predictions for wheat, barley, and alfalfa, with Kappa coefficients of 0.81, 0.85, and 0.84, and overall accuracies of 0.86, 0.89, and 0.88, respectively. Barley had the highest percentage of suitable land (40%), followed by alfalfa (39.5%) and wheat (32%). Soil constraints varied across the study area, including texture, stoniness, lime, gypsum, salinity, and alkalinity. The analysis identified 31 soil types, and the random forest model yielded a digital soil map with a Kappa coefficient of 0.76 and overall accuracy of 0.81. The findings support effective land management and agricultural planning.https://jsw.um.ac.ir/article_45390_eea85204e86b75d8eee5cbc2d33b3d2d.pdfdigital soil mappingparametric methodrandom forestsuitability and digital assessment |
| spellingShingle | F. Jannati F. Sarmadian Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek) مجله آب و خاک digital soil mapping parametric method random forest suitability and digital assessment |
| title | Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches
(Case Study: Qazvin-Abyek) |
| title_full | Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches
(Case Study: Qazvin-Abyek) |
| title_fullStr | Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches
(Case Study: Qazvin-Abyek) |
| title_full_unstemmed | Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches
(Case Study: Qazvin-Abyek) |
| title_short | Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches
(Case Study: Qazvin-Abyek) |
| title_sort | digital land suitability assessment for irrigated cultivation of some agricultural crops using machine learning approaches case study qazvin abyek |
| topic | digital soil mapping parametric method random forest suitability and digital assessment |
| url | https://jsw.um.ac.ir/article_45390_eea85204e86b75d8eee5cbc2d33b3d2d.pdf |
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