Search Results (21)

Almonds are one of the major products that are economically competent and compatible with the Mediterranean climate, a key characteristic that distinguishes Lebanon. The present study aims to examine the suitability of land use and land cover on the Lebanese territory for sustainable almond cultivation, based on the FAO land suitability criteria. The research explored the existing areas of almond cultivation and the land possessing the potential for almond cultivation in Lebanon using an analysis model developed on GIS. The evaluation of Land Suitability (LS) based on GIS and Multi-Criteria Evaluation methods (MCE) with Weighted Overlay (WO) was applied, and the almond suitability map was rendered using the seven following parameters: temperature, rainfall, slope, elevation, soil pH, soil texture, and soil depth. These variables were integrated through GIS and were allocated to different weights to each thematic layer, as per its relevance. Ultimately, the almond suitability map was established, comprising four categories: highly suitable, moderately suitable, marginally suitable, and not suitable. The obtained results indicated that almond cultivation areas were around 5500 ha in 2010, while more than 60% of the study area can be planted with almonds in accordance with the almond suitability map. In closing, the targeted decision-makers will potentially deem this study as a valid source of knowledge for planning land use, and a tool to mitigate land degradation. Full article

The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (Glycine max L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. The peak leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) from PROBA-V/Sentinel-3 data were calculated according to ground-truth soybean agricultural parcels in continental Croatia during 2015–2021. Four machine learning regression algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), as well as their combination, were evaluated for predicting the peak LAI and FAPAR on the entire agricultural land in the study area, with RF producing the highest prediction accuracy with an R² in the range of 0.250–0.590. The translation from K-means classes to the FAO land suitability standard was performed using a relative-based approach, ranking five resulting classes based on their relative mean sums of LAI and FAPAR values. The results of the proposed approach indicate that it is viable for major crops, while cropland suitability prediction for minor crops would require higher spatial resolution, such as vegetation indices from Sentinel-2 imagery. Full article

The systematic identification of appropriate sites for different rainwater harvesting (RWH) structures may contribute to better success of crop production in such areas. One approach to improving crop yields in North Kordofan, Sudan, that is mostly adaptable to the changing climate is in-field water harvesting. The main objective of this study is to employ a geographical information system (GIS) in order to identify the most suitable sites for setting in situ water harvesting structures, aiming to address climate change in this area. A GIS-based model was developed to generate suitability maps for in situ RWH using multi-criteria evaluation. Five suitability criteria (soil texture, runoff depth, rainfall surplus, land cover, and slope) were identified; then, five suitability levels were set for each criterion (excellent, good, moderate, poor, and unsuitable). Weights were assigned to the criteria based on their relative importance for RWH using the analytical hierarchy process (AHP). Using QGIS 2.6.1 and ArcGIS 10.2.2 software, all criterion maps and suitability maps were prepared. The obtained suitability map for the entire region showed that 40% of the region area fell within the “good” class, representing 7419.18 km², whereas 26% of the area was “excellent”, occupying 4863.75 km². However, only 8.9% and 15.6% of the entire region’s area were “poor” and “unsuitable” for RWH, respectively. The suitability map of the delineated pilot areas selected according to the attained FAO data revealed that one location, Wad_Albaga, was found to be in an excellent position, covering an area of 787.811 km², which represents 42.94% of the total area. In contrast, the Algabal location had 6.4% of its area classified as poor and the remaining portion classified as excellent. According to the findings from the validated trial, Wad_Albaga is located in a good site covering 844 km², representing 46.04%, while Algabal is classified as a moderate site, covering 341 km² or 18.6% of the area. This study concluded that the validation of the existing trial closely matched the suitability map derived using FAO data. However, ground data from field experiments provided more accurate results compared to the FAO suitability map. This study also concluded that using GIS is a time-saving and effective tool for identifying suitable sites and discovering the most appropriate locations for rainwater harvesting (RWH). Full article

Expanding projects to reclaim marginal land is the most effective way to reduce land use pressures in densely populated areas, such as Egypt’s Nile Valley and Delta; however, this requires careful, sustainable land use planning. This study assessed the agricultural potential of the El-Dabaa area in the northern region of the Western Desert, Egypt. It focused on assessing land capability, evaluating crop suitability, mapping soil variability, and calculating crop water requirements for twenty different crops. In this work, we evaluated land capability using the modified Storie index model and assessed soil suitability using the land use suitability evaluation tool (LUSET). We also calculated crop water requirements (CWRs) utilizing the FAO-CROPWAT 8.0 model. Additionally, we employed ArcGIS 10.8 to create spatial variability maps of soil properties, land capability classes, and suitability classes. Using a systematic sampling grid, 100 soil profiles were excavated to represent the spatial variability of the soil in the study area, and the physicochemical parameters of the soil samples were analyzed. The results indicated that the study area is primarily characterized by flat to gently sloping surfaces with deep soils. Furthermore, there are no restrictions on soil salinity or alkalinity, no sodicity hazards, and low CaCO₃ levels. On the other hand, the soils in the study area are coarse textured and have low levels of CEC and organic matter (OM), which are the major soil limiting factors. As a result, the land with fair capability (Grade 3) accounted for the vast majority of the study area (87.3%), covering 30599.4 ha. Land with poor capability (Grade 4) accounted for 6.5% of the total area, while non-agricultural land (Grade 5) accounted for less than 1%. These findings revealed that S2 and S3 are the dominant soil suitability classes for all the studied crops, indicating moderate and marginal soil suitabilities. Furthermore, there were only a few soil proportions classified as unsuitable (N class) for fruit crops, maize, and groundnuts. Among the crops studied, barley, wheat, sorghum, alfalfa, olives, citrus, potatoes, onions, tomatoes, sunflowers, safflowers, and soybeans are the most suitable for cultivation in the study area. The reference evapotranspiration (ETo) varied between 2.6 and 5.9 mm day⁻¹, with higher rates observed in the summer months and lower rates in the winter months. Therefore, the increase in summer ETo rates and the decrease in winter ones result in higher CWRs during the summer season and lower ones during the winter season. The CWRs for the crops we studied ranged from 183.9 to 1644.8 mm season⁻¹. These research findings suggest that the study area is suitable for cultivating a variety of crops. Crop production in the study area can be improved by adding organic matter to the soil, choosing drought-resistant crop varieties, employing effective irrigation systems, and implementing proper management practices. This study also provides valuable information for land managers to identify physical constraints and management needs for sustainable crop production. Furthermore, it offers valuable insights to aid investors, farmers, and governments in making informed decisions for agricultural development in the study region and similar arid and semiarid regions worldwide. Full article

Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km² in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps. Full article

Water, being the basic resource for life on earth, is of great importance in arid and semi-arid regions, which face the direct impacts of climate change. This study aims to solve water scarcity for Baranti Plain farmers by constructing concrete canals using modern technology. The Baranti Plain is located approximately 25 km north of Erbil in Iraq and spans an area of 445 km². The Great Zap River flows through its northern region, with an average discharge of about 400 m³ per second. In response to the challenges faced in this area, the Ministry of Agriculture and Water Resources collaborated with the Food and Agriculture Organization (FAO) to gather essential data. This extensive dataset, covering the period from 2000 to 2021, particularly focuses on ground-level monitoring in September. Notably, the region experienced a significant decline in groundwater levels, totaling 23 m on average. Additionally, there was a 7.8% increase of urban expansion, and the number of wells increased from 257 in 2006 to 600 in 2021. To counter the diminishing groundwater levels and facilitate agricultural irrigation, a proposal was introduced to harness the waters of the Great Zap River. This plan involves channeling the river waters to the plain through a network of concrete canals known as the Baranati Project Plain. For precise planning, a digital elevation model (DEM) with a 12.5 m resolution was procured to analyze the area using GIS. This investigation revealed a height difference of 130 m between the Great Zap River and the Baranti Plain. Subsequently, the area was segmented into four zones based on its suitability for the project: highest, medium, low, and unsuitable. Notably, the combined areas of high, medium, and low suitability encompass 68% of the entire study region. The project’s next phase used a flow calculator to determine the channel’s shape, area, slope, and water requirements. The final phase involved analyzing annual rainfall data from three meteorological stations (Bastora, Ankawa, and Khabat), showing an average annual rainfall of 396 mm. The project has the capacity to irrigate more than 30,000 hectares of land, benefiting more than 1200 farmers. It is expected to stop the use of over 600 wells for irrigation and potentially raise groundwater levels by about 2.5 m annually. Our work revealed that addressing groundwater depletion requires implementing canals, rainwater harvesting, farmer education, modern irrigation, drilling restrictions, and supporting water. Full article

Land Suitability Analysis (LSA), under the impact of climate change, is a fundamental approach to the design of appropriate land management strategies for sustainable crop production and food security. In this study, the FAO framework was used to assess the impact of climate change on land suitability for maize in Flanders, Belgium. The current LSA revealed the marginal suitability for maize cultivation, characterizing most of the agricultural land in Flanders and identifying precipitation as the most limiting factor for maize suitability. The LSA, under two climate change scenarios, was based on climate projections from several CMIP5 Global Circulation Models, transformed into future land suitability projections and assembled into a multi-model ensemble (MME) of projected suitability changes. The results indicate an average reduction in projected suitability of approximately 7% by 2099 under the high-emission scenario. The potential of the Soil-Improving Cropping System (SICS) to mitigate the impacts of climate change on land suitability was statistically significant under both low- and high-emission scenarios. This research provides valuable insights into the MME modeling of climate change impacts on land suitability and its associated uncertainty, with the application of SICS as a potential long-term mitigation measure to promote sustainable agricultural practices. Full article

Along with the concept of improving reforestation efforts in Lebanon, this study aimed to provide a land suitability analysis for forest species in Lebanon while considering the effect of climate change. Herein, the soil evaluation criteria developed by FAO (The Food and Agriculture Organization) for land suitability classification were implemented through the weighted overlay method to produce suitability maps based on natural variables (soil, climate, and topography) influencing the presence of the species on the land. Cedrus libani, Quercus calliprinos, Ceratonia siliqua, Eucalyptus globulus, and Pinus halepensis are the species considered in this study. The results of this study provide useful information to inform reforestation activities in Lebanon, considering the expected climate change projections for medium- (2050) and long-term (2070) periods, according to two different scenarios (RCP4.5 and RCP8.5) and three General Circulation Models: CCSM4, GFDL-CM3, and HadGEM2-ES. The suitability maps showed a generally critical situation for the spatial distribution of forest species under future climate change compared to the current situation (1970–2000). The distribution of thermophilic species, which tolerate high temperatures (over 20 °C), was projected to expand compared to the current situation. In contrast, the expansion of cold-adapted species may be limited by future climate change conditions. It is crucial to consider the expected effects of climate change to better select species for reforestation and, therefore, to maintain forest cover in Lebanon. Full article

This study presents a micro-scale approach for the cropland suitability assessment of permanent crops based on a low-cost unmanned aerial vehicle (UAV) equipped with a commercially available RGB sensor. The study area was divided into two subsets, with subsets A and B containing tangerine plantations planted during years 2000 and 2008, respectively. The fieldwork was performed on 27 September 2021 by using a Mavic 2 Pro UAV equipped with a commercial RGB sensor. The cropland suitability was performed in a two-step classification process, utilizing: (1) supervised classification with machine learning algorithms for creating a vegetation mask; and (2) unsupervised classification for the suitability assessment according to the Food and Agriculture Organization of the United Nations (FAO) land suitability standard. The overall accuracy and kappa coefficients were used for the accuracy assessment. The most accurate combination of the input data and parameters was the classification using ANN with all nine input rasters, managing to utilize complimentary information regarding the study area spectral and topographic properties. The resulting suitability levels indicated positive suitability in both study subsets, with 63.1% suitable area in subset A and 59.0% in subset B. Despite that, the efficiency of agricultural production can be improved by managing crop and soil properties in the currently non-suitable class (N1), providing recommendations for farmers for further agronomic inspection. Alongside low-cost UAV, the open-source GIS software and globally accepted FAO standard are expected to further improve the availability of its application for permanent crop plantation management. Full article

Nilgiri tea is a vital perennial beverage variety and is in high demand in global markets due to its quality and medicinal value. In recent years, the cultivation of tea plantations has decreased due to the extreme climate and prolonged practice of tea cultivation in the same area, decreasing its taste and quality. In this scenario, land suitability analysis is the best approach to evaluate the bio-physiochemical and ecological parameters of tea plantations. The present study aims to identify and delineate appropriate land best suited for the cultivation of tea within the Kallar watershed using the geographic information system (GIS) and multi-criteria evaluation (MCE) techniques. This study utilises various suitability criteria, such as soil (texture, hydrogen ion concentration, electrical conductivity, depth, base saturation, and drainability), climate (rainfall and temperature), topography (relief and slope), land use, and the normalised difference vegetation index (NDVI), to evaluate the suitability of the land for growing tea plantations based on the Food and Agricultural Organization (FAO) guidelines for rainfed agriculture. The resultant layers were classified into five suitability classes, including high (S1), moderate (S2), and marginal (S3) classes, which occupied 16.7%, 7.08%, and 16.3% of the land, whereas the currently and permanently not suitable (N1 and N2) classes covered about 18.52% and 29.06% of the total geographic area. This study provides sufficient insights to decision-makers and farmers to support them in making more practical and scientific decisions regarding the cultivation of tea plantations that will result in the increased production of quality tea, and prevent and protect human life from harmful diseases. Full article

Reclamation of salt-affected soil has been identified by the FAO as being critical to meet the needs to increase agricultural productivity. This paper reviews commonly used reclamation methods for salt-affected soils, and provides critical identifiers for an effective reclamation practice of salt-affected soil. There are widely used methods to reduce salinity and sodicity of salt-affected soils, including salt leaching, addition of amendments, revegetation using halophytes and salt scrapping. Not all reclamation techniques are suitable for salt-affected land. The reclamation strategy must be tailored to the site, and based on understanding the soil, plant and climate interactions. On some occasions, a combination of techniques may be required for reclamation. This can include salt scrapping to remove salts from the surface soil, the addition of physical amendments to improve soil pore systems and enhance salt leaching, followed by amelioration of soil by chemical amendments to preserve soil physical conditions, and then halophyte establishment to expand the desalinization zone. This study reveals that soil hydro-geochemical models are effective predictive tools to ascertain the best reclamation practice tailored to salt-affected land. However, models need to be calibrated and validated to the conditions of the land before being applied as a tool to combat soil salinity. Full article

The Food and Agriculture Organization of the United Nations (FAO) is building a land cover monitoring system in Lesotho in support of ReNOKA (‘we are a river’), the national program for integrated catchment management led by the Government of Lesotho. The aim of the system is to deliver land cover products at a national level on an annual basis that can be used for global reporting of official land cover statistics and to inform appropriate land restoration policies. This paper presents an innovative methodology that has allowed the production of five standardized annual land cover maps (2017–2021) using only a single in situ dataset gathered in the field for the reference year, 2021. A total of 10 land cover classes are represented in the maps, including specific features, such as gullies, which are under close monitoring. The mapping approach developed includes the following: (i) the automatic generation of training and validation datasets for each reporting year from a single in situ dataset; (ii) the use of a Random Forest Classifier combined with postprocessing and harmonization steps to produce the five standardized annual land cover maps; (iii) the construction of confusion matrixes to assess the classification accuracy of the estimates and their stability over time to ensure estimates’ consistency. Results show that the error-adjusted overall accuracy of the five maps ranges from 87% (2021) to 83% (2017). The aim of this work is to demonstrate a suitable solution for operational land cover mapping that can cope with the scarcity of in situ data, which is a common challenge in almost every developing country. Full article

Over the last decade, there has been increased interest in applying biomass as a raw material for producing biofuels used for thermochemical conversions. Extensive use of biomass could lead to controversial competition for arable land, water, and food; therefore, only waste materials and agricultural by-products and residues should be used to produce biofuels. One suitable by-product of agricultural production is crop residue from the harvest of maize for grain (corn stover). The harvest residues of corn stover consist of four fractions, i.e., husks, leaves, cobs, and stalks, which are structurally and morphologically distinct. The aim of the study was to determine the effect of selected maize cultivars with distinct FAO (Food and Agriculture Organization of the United Nations) earliness classifications on the chemical and energetic properties of their corn cob cores. We determined the chemical properties based on elemental analysis, and the energy properties based on the heat of combustion and calorific values. The content of ash and volatile compounds in the corn cobs were also determined. The results indicated that the heat of combustion of fresh and seasoned corn cob cores ranged from 7.62–10.79 MJ/kg and 16.19–16.53 MJ/kg, respectively. The heat of combustion and calorific value of corn cob cores in the fresh state differed significantly and were strongly correlated with maize cultivars with distinct FAO earliness. Full article

Carbon farming is a capable strategy for more sustainable production of food and other related products. It seeks to produce a diverse array of natural farming methods and marketable products simultaneously. According to the food and agriculture organization (FAO), agriculture, forestry, and other land-use practices account for 24% of global greenhouse gas (GHG) emissions and total global livestock emissions of 7.1 gigatons of CO₂-equivalent per year, representing 14.5% of total anthropogenic GHG emissions. For example, an agroforestry system that deliberately integrates trees and crops with livestock in agricultural production could potentially increase carbon sequestration and decrease GHG emissions from terrestrial ecosystems, thus helping to mitigate global climatic change. Also, agroforestry is capable of generating huge amounts of bio-mass and is believed to be particularly suitable for replenishing soil organic carbon (SOC). SOC is a crucial indicator for soil fertility since the change in SOC can explain whether the land use pattern degrades or improves soil fertility. Moreover, SOC found in soil in the form of soil organic matter (SOM) helps to improve soil health either directly or indirectly. Thus, efforts should be made to convince farmers to increase their resource-use efficiency and soil conserving ability to get maximum benefits from agriculture. Therefore, this review aimed at clarification about carbon farming, modifications in carbon cycle and carbon sequestration during agricultural development, and benefits of agroforestry. Full article

The determination of cropland suitability is a major step for adapting to the increased food demands caused by population growth, climate change and environmental contamination. This study presents a novel cropland suitability assessment approach based on machine learning, which overcomes the limitations of the conventional GIS-based multicriteria analysis by increasing computational efficiency, accuracy and objectivity of the prediction. The suitability assessment method was developed and evaluated for soybean cultivation within two 50 × 50 km subsets located in the continental biogeoregion of Croatia, in the four-year period during 2017–2020. Two biophysical vegetation properties, leaf area index (LAI) and a fraction of absorbed photosynthetically active radiation (FAPAR), were utilized to train and test machine learning models. The data derived from a medium-resolution satellite mission PROBA-V were prime indicators of cropland suitability, having a high correlation to crop health, yield and biomass in previous studies. A variety of climate, soil, topography and vegetation covariates were used to establish a relationship with the training samples, with a total of 119 covariates being utilized per yearly suitability assessment. Random forest (RF) produced a superior prediction accuracy compared to support vector machine (SVM), having the mean overall accuracy of 76.6% to 68.1% for Subset A and 80.6% to 79.5% for Subset B. The 6.1% of the highly suitable FAO suitability class for soybean cultivation was determined on the sparsely utilized Subset A, while the intensively cultivated agricultural land produced only 1.5% of the same suitability class in Subset B. The applicability of the proposed method for other crop types adjusted by their respective vegetation periods, as well as the upgrade to high-resolution Sentinel-2 images, will be a subject of future research. Full article