Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review

The suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

This work aims to utilize Internet of Things (IoT) technology and the Artificial Neural Network - Cellular Automaton (ANN-CA) model to analyze the construction of indicators for territorial spatial planning and urban development suitability assessment. Firstly, the IoT technology is introduced, and its application potential in land planning is explored. Using the IoT technology, various data related to land use are collected, and these data are transmitted and summarized through IoT equipment to form a data base. Based on the collected data, the ANN-CA model and the "dual assessment" concept are employed to establish an indicator system for urban development suitability assessment, encompassing permanent basic farmland, ecological redlines, and current built-up areas. Through the combination of these two models, the future land use situation can be predicted more accurately. The trained model is evaluated, including simulation accuracy, error analysis, Kappa coefficient and other indicators. Compared with the actual data, the accuracy and credibility of the model are verified. Finally, according to the prediction results of the model, the land use situation is analyzed and interpreted to provide decision support for urban planning and development. The research results show that the combination of IoT technology and ANN-CA model can effectively analyze the evolution law of land use and the suitability of urban development. Through the reasonable setting and processing of model parameters and data, people can get high accuracy land use prediction results, which provides important reference and support for urban planning and sustainable development. The suitability for urban development within County M exhibits noticeable spatial disparities, with the central region being more suitable for development while the peripheral regions are relatively less favorable. This work provides valuable guidance for decision-makers and researchers in the field of territorial planning, and promotes orderly urban development and sustainable prosperity.

Geospatial technology in agroforestry: status, prospects, and constraints

Agroforestry has an indispensable role in food and livelihood security in addition to its capacity to combat the detrimental effects of climate change. However, agroforestry has not been properly promoted and exploited due to lack of precise extent, geographical distribution, and carbon sequestration (CS) assessment. The recent advent of geospatial technologies, as well as free availability of spatial data and software, can provide new insights into agroforestry resources assessment, decision-making, and policy development despite agroforestry's small spatial extent, isolated nature, and higher structural and functional complexity of agroforestry. In this review, the existing application of geospatial technologies together with its constraints and limitations as well as the potential future application for agroforestry has been discussed. The review reveals that the application of optical remote sensing in agroforestry includes spatial extent mapping, production of tree species spectral signature, CS assessment, and suitability mapping. Simultaneously, the recent surge in the use of synthetic aperture radar in conjunction with algorithms based on vegetation photosynthesis and optical data enables a more accurate estimation of gross primary productivity at different scales. However, unmanned aerial vehicles equipped with sensors, such as multispectral, LiDAR, hyperspectral, and thermal, offer a considerably higher potential and accuracy than satellite-based datasets. In the future, the health monitoring of agroforestry systems can be a key concern that may be addressed by utilizing hyperspectral and thermal datasets to analyze plant biochemistry, chlorophyll fluorescence, and water stress. Additionally, current (GEDI, ECOSTRESS) and future space agency missions (BIOMASS, FLEX, NISAR, TRISHNA) have enormous potential to shed fresh light on agroforestry systems.

Fuzzy-based models' performance on qualitative and quantitative land suitability evaluation for cotton cultivation in Sarayan County, South Khorasan Province, Iran

Using appropriate models in the land use planning process will help increase the accuracy and precision of decisions made by designers. The aim of this study was to investigate and compare fuzzy-based models (fuzzy set theory, fuzzy-AHP, and fuzzy-ANP) to evaluate the suitability of cotton cultivation in Sarayan region (located in eastern Iran). Twenty-eight land units were selected. Weighted arithmetic means of characteristics were performed in representative soil profiles of each unit. Landform-related characteristics were directly entered into the land suitability evaluation modeling. The land index was calculated using three selective qualitative land suitability model guidelines. Qualitative and quantitative land suitability was estimated. The validity of models was determined by r², RMSE, GMER, and MAPE indicators between predicted and actual production. Soil texture, pH, calcium carbonate equivalent, drainage, organic matter, salinity and sodicity, slope, and gypsum are the most important, respectively. Also, the fuzzy-ANP method is more efficient than other models due to its higher r² (0.98) and lower RMSE (4.31) and MAPE (0.56) and GMER (0.99) closer to 1. The value of cotton production using fuzzy, fuzzy-AHP, and fuzzy-ANP methods was calculated as 1085 to 4235, 1235 to 4318, and 1391 to 4452 tons per hectare, respectively. The high efficiency of the fuzzy-ANP model is due to the characteristics of the lands used in the evaluation process that are not independent of each other and this model considers them. Examining these models in different weather conditions and combining with the other computational intelligence methods in future experiments are recommended.

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach

Identifying available farmland suitable for agricultural machinery is the most promising way of optimizing agricultural production and increasing agricultural mechanization. Farmland consolidation suitable for agricultural machinery (FCAM) is implemented as an effective tool for increasing sustainable production and mechanized agriculture. By using the machine learning approach, this study assesses the suitability of farmland for agricultural machinery in land consolidation schemes based on four parameters, i.e., natural resource endowment, accessibility of agricultural machinery, socioeconomic level, and ecological limitations. And based on "suitability" and "potential improvement in farmland productivity", we classified land into four zones: the priority consolidation zone, the moderate consolidation zone, the comprehensive consolidation zone, and the reserve consolidation zone. The results showed that most of the farmland (76.41%) was either basically or moderately suitable for FCAM. Although slope was often an indicator that land was suitable for agricultural machinery, other factors, such as the inferior accessibility of tractor roads, continuous depopulation, and ecological fragility, contributed greatly to reducing the overall suitability of land for FCAM. Moreover, it was estimated that the potential productivity of farmland would be increased by 720.8 kg/ha if FCAM were implemented. Four zones constituted a useful basis for determining the implementation sequence and differentiating strategies for FCAM schemes. Consequently, this zoning has been an effective solution for implementing FCAM schemes. However, the successful implementation of FCAM schemes, and the achievement a modern and sustainable agriculture system, will require some additional strategies, such as strengthening farmland ecosystem protection and promoting R&D into agricultural machinery suitable for hilly terrain, as well as more financial support.

Assessment of land suitability using a soil-indicator-based approach in a geomatics environment

The study aims to develop new approach for soil suitability evaluation, Based on the fact that choosing the proper agricultural sites is a requirement for good ergonomic and financial feasibility. The AHP included a selection of different criteria used for analysis and categorized according to their usefulness in relation to the growth conditions/requirements of the selected crops. Lithology, soil physicochemical, topography (slope and elevation), climate (temperature and rainfall), and irrigation water were the main criteria selected for the study. The study indicated that the area is suitable for agricultural use, taking into account the quality of the water used to maintain the quality of the soil. According to the FAO the suitability result was for S1 (0.71%), S2 (19.81%), S3 (41.46%), N1 (18.33%) and N2 (19.68%) of the total area. While the results obtained from the new approach for the study 9.51%, 30.82%, 40.12% and 19.54 for very high, high, moderate, low and very low suitability respectively, Taking into account that the constraints units of FAO is located in very low suitability class with 0.69% of the total area which Not valid for crop production due to some restrictions. The findings of the study will help narrow the area to the suitable sites that may further be sustainably used for annual and/or perennial crops. The proposed approach has high potential in applications for assessing land conditions and can facilitate optimal planning for agricultural use.

Urban Green Space Planning and Development in Urban Cities Using Geospatial Technology: A Case Study of Noida

Urban planning, with special attention to green space development, offers a relatively simple and low-cost solution to the impacts of climate change and urbanization faced by urban centres. The present work examines the spatial variability of availability of adequate sites for the development of urban green amenities in Noida city. Multi-criteria assessment of potential locations has been accomplished using Analytical Hierarchical Process coupled with geospatial technology. Urban land use, physiographic factors (slope and elevation), accessibility (proximity to roads), and presence of grey, green and blue amenities (Normalised Difference Built-up Index, Normalised Difference Vegetation Index and proximity to water bodies, respectively) are the seven key criteria used to derive the final green space suitability map. A total of 46.47 % of the land was found to be in the category of highly and moderately suitable for greening the city, highlighting the potential of developing different forms of green spaces in the area. Such holistic city scale analysis of availability of potential sites for green space development can be utilised by the city administrators and urban planners for future land use planning and improving the distribution and spatial connectivity of the green spaces in the city with the common goals of better health, a cleaner environment, and climate change mitigation.

Land Suitability Analysis for Vineyard Cultivation in the Izmir Metropolitan Area

The grapevine, so-called Vitis vinifera L., is one of the most diffuse perennial crop plantations in the world due to a flourishing market that shaped the landscape and the societal values. Turkey has been a historical vine producer, counting on an overall vineyard extension of 550,000 hectares. Besides, Turkey has some favorable pre-requisites to be one of the most fertile lands for vineyard production: variegated topography, rich soil diversity, heterogeneous morphology, and several micro-climatic conditions. However, establishing a flourishing and fully productive vineyard requires many years, and therefore, the selection and management of sites should be considered with great attention. Within this work, a first land suitability analysis for vineyard production has been established for the entire metropolitan area of Izmir according to the most scientifically-agreed criteria: elevation, slope, aspect, land capability, and solar radiation. These criteria were superimposed through spatial overlay analysis using Esri ArcGIS (ver.10.8) and evaluated using the Principal Component Analysis technique. The first three bands were then extracted to define the most suitable areas for vineyard production in Izmir. The final layer has been used to define which areas can be considered for future strategic expansion and management. The discussion focuses on the Kozak plateau, where a new policy of vineyard plantation will be promoted with techniques that aim to maintain and revalorize the traditional vineyard landscapes and conserve traditional methods and practices that have evolved with the cultural values of the villagers and producers.

Optimized Land Use through Integrated Land Suitability and GIS Approach in West El-Minia Governorate, Upper Egypt

Land evaluation is imperative for its efficient use in agriculture. Therefore, this study aimed at assessing the suitability of a region in West El-Minia for cultivating some of the major crops using the geographical information system (GIS). The results focus on allocating space for cultivating sugar beet and utilizing the free period of sugar beet in other crops. This exploitation helps to maintain the quality of the land and increase its fertility by using crop rotation with integrated agricultural management. A machine learning technique was implemented using the random forest algorithm (RF) to predict soil suitability classes for sugar beet using geomorphology, terrain attribute and remote sensing data. Fifteen major crops were evaluated using a suitability multicriteria approach in GIS environment for crop rotation decisions. Soil parameters were determined (soil depth, pH, texture, CaCO3, drainage, ECe, and slope) to characterize the land units for soil suitability. Soils of the area were found to be Entisols; Typic Torrifluvents, Typic Torripsamments and Typic Torriorthents and Aridsols; Typic Haplocacids, Calcic Haplosalids and Sodic Haplocalcids. Overall, the studied area was classified into four suitability classes: high “S1”, moderate “S2”, marginal “S3”, and not suitable “N”. The area of each suitability class changed depending on the crop tested. The highest two crops that occupied S1 class were barley with 471.5 ha (representing 6.8% of the total study area) and alfalfa with 157.4 ha (2.3%). In addition, barley, sugar beet, and sorghum occupied the highest areas in S2 class with 6415.3 ha (92.5%), 6111.3 ha (88.11%) and 6111.3 ha (88.1%), respectively. Regarding the S3 class, three different crops (sesame, green pepper, and maize) were the most highly represented by 6151.8 ha (88.7%), 6126.3 ha (88.3%), and 6116.7 ha (88.2%), respectively. In the end, potato and beans occupied the highest areas in N class with 6916.9 ha (99.7%) and 6853.5 ha (98.8%), respectively. The results revealed that the integration of GIS and soil suitability system consists of an appropriate approach for the evaluation of suitable crop rotations for optimized land use planning and to prevent soil degradation. The study recommends using crop rotation, as it contributes to soil sustainability and the control of plant pests and diseases, where the succession of agricultural crops on a scientific basis aims at maintaining the balance of nutrients and fertilizers in the soil.