Explore the vast range of titles available.

In agriculture, land use and land classification address questions such as “where”, “why” and “when” a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

Land evaluation is important for assessing environmental limitations that inhibit higher yield and productivity in tea. The aim of this research was to determine the suitable lands for sustainable tea production in the northeastern part of Bangladesh using phenological datasets from remote sensing, geospatial datasets of soil–plant biophysical properties, and expert opinions. Sentinel-2 satellite images were processed to obtain layers for land use and land cover (LULC) as well as the normalized difference vegetation index (NDVI). Data from the Shuttle Radar Topography Mission (SRTM) were used to generate the elevation layer. Other vector and raster layers of edaphic, climatic parameters, and vegetation indices were processed in ArcGIS 10.7.1® software. Finally, suitability classes were determined using weighted overlay of spatial analysis based on reclassified raster layers of all parameters along with the results from multicriteria analysis. The results of the study showed that only 41,460 hectares of land (3.37% of the total land) were in the highly suitable category. The proportions of moderately suitable, marginally suitable, and not suitable land categories for tea cultivation in the Sylhet Division were 9.01%, 49.87%, and 37.75%, respectively. Thirty-one tea estates were located in highly suitable areas, 79 in moderately suitable areas, 24 in marginally suitable areas, and only one in a not suitable area. Yield estimation was performed with the NDVI (R2 = 0.69, 0.66, and 0.67) and the LAI (R2 = 0.68, 0.65, and 0.63) for 2017, 2018, and 2019, respectively. This research suggests that satellite remote sensing and GIS application with the analytical hierarchy process (AHP) could be used by agricultural land use planners and land policy makers to select suitable lands for increasing tea production.

The study area was chosen in the district of the western Jadwal in Karbala governorate - Iraq, which is located between E44o05'10'' to  E44o13'03'' and  N32o38'30'' to N32o27'40'', as 100 locations were identified for the depth of 0-30 cm by auger hole  sampling method samples were obtained from each site, and kept laboratory measurements. The results of the study showed that the dominant soil texture is medium clay and silt are the predominant separates in the soil. As for the salinity of the soil represented by electrical conductivity, it was low of the dissolution the salts and the land use for cultivation besides the presence of a drainage network a percentage of the organic matter is good. As for assessing the suitability of the land for cultivation, Results showed the use of the standard addition method of land evaluation for the wheat crop by Sys,1993 is better and more accurate than the standard multiplication method for the wheat crop that was also suggested by Sys, 1980, where the very suitable class S1 and the suitable S2 were the predominant cultivars of the addition method, while the non- suitable class N and the least suitable S5 were classes when the methods of multiplication were used.

Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that “citrus grove” was a significant factor with a positive coefficient for all models, whereas “forest” was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves.

Environmental constraints are not only important aspects that affect the cultivated land quality but also necessary factors that shall be considered when evaluating the cultivated land quality scientifically. Moreover, identifying the quality condition of cultivated land accurately is the premise for guaranteeing food security. Based on the case study of diluvial fan terrain in Jimsar County, Xinjiang in the arid region of Northwest China, this study utilizes a geographic information system spatial analysis and a multifactor comprehensive evaluation method and constructs a comprehensive evaluation index system for cultivated land quality on account of three dimensions, namely soil properties, farming conditions, and natural environmental conditions. To reduce the Modifiable Areal Unit Problem (MAUP) effect and improve the accuracy of the quality evaluation results of cultivated land, this study compares the spatial interpolation methods of Inverse Distance Weighted Matrix (IDW), Ordinary Kriging (OK), and Spline Functions (Spline) based on different cultivated land evaluation units. Through the assessment on the comparison results, we finally adopted large-scale cultivated land as the quality evaluation unit of cultivated land and Ordinary Kriging (OK) as the spatial interpolation method. The results indicated that the average grade of the quality index of cultivated land in the diluvial fan terrain of Jimsar County is 6.66 at the middle or lower level; the quality of cultivated land and natural environment conditions reduce with the rise of elevation of the diluvial fan terrain, indicating a vertical zonality differentiation rule; the farming conditions keep sliding from the middle part of diluvial fan terrain to the edge of the diluvial fan terrain and the piedmont slope. The major factors affecting the quality of the cultivated land include the soil capacity, soil pH, soil organic matter, the quantity of straw returning to the field, source of irrigation water, water delivery method, part of the diluvial fan, groundwater level depth, and geomorphic type. Therefore, the measures to improve the quality of the cultivated land are put forward, mainly including improving the soil, carrying out land consolidation projects, and developing highly efficient water-saving irrigation agriculture. This study provides favorable references and directions for the sustainable utilization and quality improvement of cultivated land resources in arid regions.

Land is a finite resource that must be managed wisely to ensure its sustainability. Consequently, land evaluation has become essential. Identifying and utilizing productive capacity of land efficiently and profitably is crucial; otherwise, resource degradation can severely impact natural ecosystems and food production. Over the years, various methodologies have been employed to assess land resources, and one such method is the Land Capability Classification (LCC). LCC is a widely used and fundamental approach to land-use planning, traditionally assessing land based on its intrinsic qualities and climate. This study aims to highlight new approaches and developments in land evaluation techniques. It provides a brief overview of recent technological and scientific advancements integrated into land evaluation. The findings suggest that LCC alone is inadequate for precise land assessment, emphasizing the need to integrate new technologies. Remote Sensing (RS) and Geographic Information System (GIS) technologies are becoming increasingly significant. Integrating various software, decision-making systems, and mathematical models can also enhance the accuracy of land assessment results. The continuous advancement of GIS and remote sensing technologies is paving the way for new tools to facilitate natural resource mapping, appraisal, surveillance, and management. Utilizing these technologies for future projections will be highly beneficial in accurately assessing the long-term impacts of current land management practices.

Wheat is considered one of the most essential crops for Egypt. Nevertheless, it is also one of its largest imports. Therefore, it is important to develop an accurate wheat suitability model to define the most suitable areas for its production. This study aimed to develop a parametric-based Analytical Hierarchy Process (AHP) using Geographic Information Systems (GIS) for land suitability evaluation of wheat in a selected area in El-Beheira governorate, Egypt. The climatic and land parameters influencing wheat production in the studied area were selected and rated according to the parametric method. These parameters included slope, texture, calcium carbonate, sum of basic cations, pH, organic matter, salinity, exchangeable sodium percentage and mean temp. of the growing cycle. The rated parameters were processed according to the AHP. The results were compared with Storie and the Square root methods and field observations. When validated using field observations, the developed method had a higher accuracy suitability evaluation for wheat cultivation in the studied area than the other two methods. According to the developed method, almost all of the studied areas could be classified as very suitable (S1) for wheat cultivation. On the other hand, the wheat suitability evaluation according to the other two methods indicated that most of the studied area could be classified as moderately suitable (S2) and marginally suitable (S3), which contradicted the field survey.  

The research goal is to evaluate land suitability for geo-tourism focuses on geology and landscape. Most of the Citatah karst area is natural-based industries or mining in particular. The ecological disturbance is an impetus for decision-makers to choose new use of land to deal with the conservation issues. SMCE techniques that apply geographic information systems (GIS) and analytical hierarchy processes. The use of land is formulated based on policy and stakeholder analysis. The research benefit is the possibility to change the area from mining to a geo-tourism area. There are two important results of research in spatial analysis, namely: intensive and extensive tourism areas, and the rest is for protective or no suitable area of tourism. In conclusion, the land suitability analysis is important for tourism industry development.

Conducting arable land suitability evaluation (ALSE) is essential for identifying agricultural development opportunities and ensuring sustainable production and food security. Traditional ALSE methods, relying on suitability proportion functions, often encounter constraints due to land use structures. Therefore, it is necessary to develop new function methods to avoid the constraints imposed by land use structures, thus making ALSE more convenient. This study aims to propose a novel set of rules for constructing proportion functions, aiming to enhance the applicability of suitability functions in arable land suitability evaluation. The study findings reveal that: (1) In the Anning River Basin, the highly Suitable, Moderately Suitable, and Marginally Suitable current arable land (CAL) respectively account for 45.3%, 29.8%, and 18.9% of the arable land (AL). The proportion of areas deemed Temporarily Unsuitable and Permanently Unsuitable is only 6%. The distribution of suitability levels for the potential arable land (PAL) is relatively uniform, with a proportion of suitable areas reaching 66.1%, indicating substantial development potential. (2) The agricultural production conditions in the arid and warm river valley area of the Anning River Basin are exceptional. Highly Suitable CAL and Highly Suitable PAL cover 93.14% and 82.97% of this region, respectively, making it a focal point for regional agricultural development. (3) The spatial distribution patterns of ALSE results based on the original function and the improved function are essentially consistent. However, there are significant differences among the suitability levels. The correlation analysis results indicate that the evaluation results based on the improved function are closer to reality. This study enhanced the accuracy of ALSE results based on the suitability function. It provided a new approach for evaluating the suitability of AL and offers a beneficial reference for regional arable land resource utilization and sustainable agricultural development.