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This paper offers an integrated approach to contribute to the process of agricultural land suitability analysis using the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) methods. The paper addresses Cihanbeyli, the largest county in Turkey in terms of area, and focuses on determining sustainable strategies to activate/improve agriculture as a main source of income, thereby improving the economy of the region. The combined AHP and GIS methodology which consists of stages such as structuring AHP hierarchy, describing evaluation criteria, doing pairwise comparisons, and preparing criterion maps and land suitability maps has been applied to identify the areas suitable for irrigated and dry farm agriculture. A comparison of the final land suitability map with current land use has revealed that an area of 294.73 km 2 (7.18 %) is suitable for irrigation and an area of 2323.45 km 2 (56.77 %) is suitable for dry farm agriculture. Additionally, the analysis clearly shows the necessity of a decrease in irrigated agricultural land and an increase in dry farm agricultural land. The applied AHP and GIS based agricultural land suitability analysis is useful in (1) referring agricultural activities to the areas that have good physical and environmental conditions for agriculture, thus achieving maximum agricultural efficiency in countryside, (2) improving non-agricultural uses in the areas that are unsuitable for agriculture and have low efficiency, (3) avoiding the construction and environmental pressures on suitable farmland, so conducing to better land-use planning decisions.

Grapes are one of the world’s most widely distributed crops and are cultivated in more than 100 countries in the global scheme. Due to climate change and improper vine growth variable selection, production has significantly decreased across countries. Therefore, the primary purpose of this study was to develop a land suitability analysis method using a fuzzy expert system at a regional scale. The fuzzy membership function was used in the ArcGIS® environment to perform the spatial analysis, and the overlay function was used to generate the final suitability map for Afghanistan considering policy planning. The results indicated that 23% (15,760,144 ha) of the areas were potential and located in the highly suitable region for grape production; however, 11% (7,370,025 ha) of the regions were not suitable for vineyards throughout the country of Afghanistan. In the present study, it was observed that most of the vineyards were in highly suitable areas (90%, 80,466 ha), while 0.01% (5 ha) of the vineyards were in less suitable areas. The present analysis demonstrated that the significant extension of grape vines can be possible in highly suitable areas. The results of this research can support decision-makers, farm managers and land developers to find more prospective acreage for expanding vineyards in Afghanistan.

The process of determining whether a specific portion of land is suitable for a specific purpose is known as land suitability analysis (LSA). In order to promote sustainable development in semi-arid regions, the objective of this study is to analyse, evaluate, and identify the land for green growth based on topography, climate, and soil characteristics. Twelve thematic maps are prepared by using remote sensing satellite data. The Landsat 8 OLI/TIRS is used for the preparation of the thematic maps like land use land cover (LULC), normalized difference vegetation index (NDVI), top soil grain size index (TGSI), and geomorphology (GM), and DEM data is used for the preparation slope, and drainage density (DD). The collateral data is used to prepare geology and soil thematic maps. From the field work, we have collected soil samples for the compulsory physicochemical parameters such as soil EC and soil N-P-K which were taken into consideration and prepared thematic maps. The analytical hierarchy process (AHP) was used to generate the LSA of the research region, by assigning the appropriate weights to each criterion and sub-criterion for the thematic maps. Geographic information systems (GIS) and the multicriteria decision-making (MCDM) approach were used in the study’s methodology. The LSA of the study area has been categories in to four types, i.e., highly suitable, moderately suitable, marginally suitable, and not suitable. The results revealed that 421.31 sq.km (40.09%) is not suitable for agriculture green growth in the study region, whereas 89.58 sq.km (8.52%) is moderately suitable, 267.66 sq.km (25.47%) is marginally suitable, and 266.54 sq.km (25.36%) is highly suitable. Accuracy assessment has validated the LSA map’s accuracy (AA). The AA of LSA is 84.22%, which demonstrates a strong connection with the actual data. The research’s results could be helpful in locating productive agricultural areas in various parts of the world. The decision-making AHP tool paired with GIS provides a novel method.

The degradation of soil and water resources is the consequence of a mismatch between land suitability and land use. In this study, sustainable agricultural land suitability assessment (SALSA) for wheat and perennial horticultural crops was carried out, considering a conservation-use balance using the factors affecting the crop yield as well as indicating the degradation of lands. The study area was located in the Besni district in the Southeastern Anatolia Region of Turkey and covered 1330 km 2 of land. A total of 132 surface (0–30 cm) soil samples were collected and analyzed. Annual soil loss (RUSLE), gully erosion factor, soil properties and length of the growing period are included in the SALSA model. Fuzzy continuous classification was carried out using the Mamdani fuzzy inference system method. Fuzzy continuous classification-SALSA results for wheat using only the fuzzy soil layer showed that the ratios of moderately (S2), marginally (S3) and currently not suitable (N1) lands were 0.87, 72.2 and 26.9%, respectively. However, when the fuzzy erosion layer and fuzzy climate layer were integrated into the land suitability model, the ratios of sustainability classes for S1, S2, S3, N1 and N2 lands were 1.36, 3.8, 47.6, 18.6 and 28.6%, respectively. The results of perennial horticultural crops fuzzy soil layer indicated that 38.1% of the study area is S1, 57.8% is S2, and 4.02% is S3. However, when all three fuzzy layers were used in suitability assessment, the distributions of S1, S2, S3 and N1 classes changed to 2.72, 5.94, 48.86 and 42.48%, respectively.

This research aims at assessing land suitability for large-scale agriculture using multiple spatial datasets which include climate conditions, water potential, soil capabilities, topography and land management. The study case is in the Emirate of Abu Dhabi, in the UAE. The aridity of climate in the region requires accounting for non-renewable sources like desalination and treated sewage effluent (TSE) for an accurate and realistic assessment of irrigated agriculture suitability. All datasets were systematically aggregated using an analytical hierarchical process (AHP) in a GIS model. A hierarchal structure is built and pairwise comparisons matrices are used to calculate weights of the criteria. All spatial processes were integrated to model land suitability and different types of crops are considered in the analysis. Results show that jojoba and sorghum show the best capabilities to survive under the current conditions, followed by date palm, fruits and forage. Vegetables and cereals proved to be the least preferable options. Introducing desalinated water and TSE enhanced land suitability for irrigated agriculture. These findings have positive implications for national planning, the decision-making process of land alteration for agricultural use and addressing sustainable land management and food security issues.

The objective of this research was to detect changes in forest areas and, subsequently, the potential forest area that can be extended in the South Sumatra province of Indonesia, according to the Indonesian forest resilience classification zones. At first, multispectral satellite remote sensing datasets from Landsat 7 ETM+ and Landsat 8 OLI were classified into four classes, namely urban, vegetation, forest and waterbody to develop Land Use/Land Cover (LULC) maps for the year 2003 and 2018. Secondly, criteria, namely distance from rivers, distance from roads, elevation, LULC and settlements were selected and the reclassified maps were produced from each of the criteria for the land suitability analysis for forest extension. Thirdly, the Analytical Hierarchy Process (AHP) was incorporated to add expert opinions to prioritize the criteria referring to potential areas for forest extension. In the change detection analysis, Tourism Recreation Forest (TRF), Convertible Protection Forest (CPF) and Permanent Production Forest (PPF) forest zones had a decrease of 20%, 13% and 40% in area, respectively, in the forest class from 2003 to 2018. The Limited Production Forest (LPF) zone had large changes and decreased by 72% according to the LULC map. In the AHP method, the influential criteria had higher weights and ranked as settlements, elevation, distance from roads and distance from rivers. CPF, PPF and LPF have an opportunity for extension in the highly suitable classification (30%) and moderately suitable classification (41%) areas, to increase coverage of production forests. Wildlife Reserve Forests (WRFs) have potential for expansion in the highly suitable classification (30%) and moderately suitable classification (52%) areas, to keep biodiversity and ecosystems for wildlife resources. Nature Reserve Forests (NRFs) have an opportunity for extension in the highly suitable classification (39%) and moderately suitable classification (48%) areas, to keep the forests for nature and biodiversity. In case of TRF, there is limited scope to propose a further extension and is required to be managed with collaboration between the government and the community.

Assessing the quality of land is a very important step that precedes the planning of land use and taking management decisions; for example, in the agricultural field, it can be used to evaluate the suitability of the land for planting crops, determine the suitable irrigation system type, or adjust the agricultural inputs such as fertilizers and pesticides according to the requirements of each zone in the land. The spatial–temporal dynamic nature of land characteristics entails also updated evaluation process and updated management plan. The present paper tries to exploit the advances in information and communication technologies to develop a conceptual design of a dynamic system that accommodates the spatial–temporal dynamics of the agricultural soil characteristics to realize a land suitability assessment (LSA) based on a factor analysis method. The proposed design combines IoT technologies, web development, database, and digital mapping and tries to consolidate the system with other functionalities useful for decision support and suitable for different cases. The paper conducted a survey and made comparisons to select the best technologies that fit the current use case implementation and presents its reproducible conceptual modeling by developing the static and dynamic views through schemas, diagrams, message sequence charts, IoT messaging topic tree, pseudocode, etc. The functionality of the design was validated with a simple implementation of the system model. To our knowledge, there is no previous significant contribution that has addressed a LSA IoT use case. The proposed design automates the LSA process for more accurate decision-making, saving cost, time, and effort consumed in repeated field trips. It is characterized by flexibility and centralization in its offered services of spatial analysis, detection, visualizations, and status monitoring. The design also allows for remote control of field machinery.

Land valuation is essential for developing land use planning and achieving efficient land use, food security, and poverty reduction. This study aimed to assess land suitability for cassava and bean production using the multicriteria decision analysis AHP technique and GIS. Land suitability analysis considers factors affecting crop growth and development, particularly in producing beans and cassava. The Analytical Hierarchy Process (AHP) model was used to determine the importance of main and sub-criteria parameters. ArcGIS software was used to create crop suitability distribution maps for bean and cassava production. Each parameter was subjected to pair-wise comparison by employing the Analytical Hierarchy Process (AHP). The study found that the southern region displayed a vastly suitable level of 38% and 46% for beans and cassava, respectively. Farmers can use the comprehensive data to decide whether to plant beans and cassava on their lands, increasing revenue while preserving soil quality management. The GIS-AHP integration approach was suggested to determine optimal decisions based on chosen criteria.

Accurately predicting changes in the potential distribution of crops resulting from climate change has great significance for adapting to and mitigating the impacts of climate change and ensuring food security. After understanding the spatial and temporal suitability of wheat (Triticum aestivum), rice (Oryza sativa), and maize (Zea mays), as well as the main bioclimatic variables affecting crop growth, we used the MaxEnt model. The accuracy of the MaxEnt was extremely significant, with mean AUC (area under curve) values ranging from 0.876 to 0.916 for all models evaluated. The results showed that for wheat, annual mean temperature (Bio-1) and mean temperature of the coldest quarter (Bio-11) contributed 39.2% and 13.4%, respctively; for rice, precipitation of the warmest quarter (Bio-18) and elevation contributed 34.9% and 19.9%, respectively; and for maize, Bio-1 and precipitation of the driest quarter (Bio-17) contributed 36.3% and 14.3%, respectively. The map drawn indicates that the suitability of wheat, rice, and corn in South Asia may change in the future. Understanding the future distribution of crops can help develop transformative climate change adaptation strategies that consider future crop suitability. The study showed an average significant improvement in high-suitable areas of 8.7%, 30.9%, and 13.1%, for wheat, rice, and maize, respectively; moderate-suitable area increases of 3.9% and 8.6% for wheat and rice, respectively; and a decrease of −8.3% for maize as compared with the current values. The change in the unsuitable areas significantly decreases by −2.5%, −13.5%, and −1.7% for wheat, rice, and maize, respectively, compared to current land suitability. The results of this study are crucial for South Asia as they provide policy-makers with an opportunity to develop appropriate adaptation and mitigation strategies to sustain wheat, rice, and corn production in future climate scenarios.

The identification of mountainous areas suitable for chestnut stands for fruit production (CSFP) is raising increasing interest among researchers. This work aimed to (i) identify the areas suitable for CSFP shown in a land suitability map easy to read by land planners, and (ii) propose a remote-sensing-based methodology able to identify the lands currently under cultivation for CSFP. This study was conducted using the QGIS software for the Municipality of Castel del Rio, Emilia-Romagna Region, Italy. To obtain the land suitability map, topographic, lithological, and pedological data were acquired, and the areas located between 200 and 1000 m of altitude, with north exposition, a slope < 20°, sandstone-based lithology, and soils with dystric features were selected. The currently cultivated areas for CSFP were identified through remote-sensing images of the early spring period, which were delineated and georeferenced. The findings showed that only 10% of the whole study site area can be considered suitable for CSFP. Further, most of the currently cultivated CSFP (59%) are in non-suitable areas characterised by high slope gradients. The methodology applied in this study can easily provide detailed information about the suitable areas for CSFP and the areas currently cultivated with chestnut, thus allowing accurate land-use planning and land conservation.