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Enhancing crop water productivity is crucial for regional water resource management and agricultural sustainability, particularly in arid regions. However, evaluating the spatial heterogeneity and temporal dynamics of crop water productivity in face of data limitations poses a challenge. In this study, we propose a framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian network (DBN), and optimization model to assess crop water productivity and optimize crop planting structure under limited water resources allocation in the Qira oasis. The results demonstrate that the combination of TimeGAN and DBN better improves the accuracy of the model for the dynamic prediction, particularly for short-term predictions with 4 years as the optimal timescale (R 2  > 0.8). Based on the spatial distribution of crop suitability analysis, wheat and corn are most suitable for cultivation in the central and eastern parts of Qira oasis while cotton is unsuitable for planting in the western region. The walnuts and Chinese dates are mainly unsuitable in the southeastern part of the oasis. Maximizing crop water productivity while ensuring food security has led to increased acreage for cotton, Chinese dates and walnuts. Under the combined action of the five optimization objectives, the average increase of crop water productivity is 14.97%, and the average increase of ecological benefit is 3.61%, which is much higher than the growth rate of irrigation water consumption of cultivated land. It will produce a planting structure that relatively reduced irrigation water requirement of cultivated land and improved crop water productivity. This proposed framework can serve as an effective reference tool for decision-makers when determining future cropping plans.

High levels of crop species diversity are considered beneficial. However, increasing diversity might be difficult because of environmental constraints and the reliance on a few major crops for most food supply. Here we introduce a theoretical framework of hierarchical levels of crop diversity, in which the environmental requirements of crops limit potential diversity, and the demand for agricultural products further constrain attainable crop diversity. We estimated global potential, attainable, and current crop diversity for grid cells of 86 km 2 . To do so, we first estimated cropland suitability values for each of 171 crops, with spatial distribution models to get estimations of relative suitability and with a crop model to estimate absolute suitability. We then used a crop allocation algorithm to distribute the required crop area to suitable cropland. We show that the attainable crop diversity is lower in temperate and continental areas than in tropical and coastal regions. The diversity gap (the difference between attainable and current crop diversity) is particularly large in most of the Americas and relatively small in parts of Europe and East Asia. By filling these diversity gaps, crop diversity could double on 84% of the world’s agricultural land without changing the aggregate amount of global food produced. It follows that while there are important regional differences in attainable diversity, specialization of farms and regions is the main reason for low levels of local crop diversity across the globe, rather than our high reliance on a few crops.

Crop suitability analysis plays an important role in identifying and utilizing the areas suitable for better crop growth and higher yield without deteriorating the natural resources. The present study aimed to identify suitable areas for rice and coconut cultivation across the coastal region of India using the analytic hierarchy process (AHP) integrated with geographic information systems (GIS) and remote sensing. A total of nine parameters were selected for suitability analysis including elevation, slope, soil depth, drainage, texture, pH, soil organic carbon, rainfall, temperature and a land use land cover (LULC) constraint map. This study represents the first-ever application of an integrated approach combining AHP, GIS, and remote sensing for crop suitability analysis in entire coastal region of India. The weights for the parameters and subclasses were assigned using the AHP method based on experts’ opinions. Subsequently, all the thematic maps were overlaid using the weighted overlay analysis to generate a land suitability map. Separately, the LULC crop mask map was used to extract suitable areas for rice and coconut cultivation to create crop-specific suitability maps. The final suitability maps were classified into four different classes: highly suitable, moderately suitable, marginally suitable, and not suitable for crop production. The findings revealed that approximately 13.68% of the study area was highly suitable, with around 19.26% and 18.35% being moderately and marginally suitable, respectively, and 13.76% was not suitable for rice cultivation. Similarly, for coconut cultivation, approximately 11% were highly suitable, with 27.40% and 18.34% being moderately and marginally suitable. However, about 35% of the total study region was deemed permanently unsuitable for any type of cultivation. The suitability maps were validated using area under receiver operating characteristic curve (AUROC). The AUROC values for rice and coconut were found to be 0.764 and 0.740 indicating high accuracy. By strategically cultivating rice and coconut in highly and moderately suitable locations identified in the current study, and utilizing marginally suitable areas for other crops, it is possible to achieve financial viability in agricultural production by increasing crop yield without causing harm to natural resources.

Climate change threatens global agriculture, food security and nutrition. Understanding its regional impacts is necessary to help agricultural sectors adapt and increase their resilience in these changing climate conditions. In New Zealand (NZ), studies on the impacts of climate change on agriculture have been conducted on a limited number of crops, using different methods and without assessing uncertainties. This study aims to bridge these gaps by applying a consistent method of Land Suitability Analysis (LSA) to four key crops for NZ agriculture – apple, cherry, maize and wheat – and assessing suitability robustness. The results show that historical suitability patterns are consistent with current production areas. An increase in suitability is projected for all the crops in almost all of the South Island of NZ, whereas in the North Island, some of the crops are less suitable, highlighting both constraints and opportunities for future NZ agriculture. The robustness of results varies depending on the climate scenario and period considered. In addition, an increase in net irrigation requirements is also projected for the crops, requiring critical management of future water supplies. The limits of the study include the climate data resolution and the omission of the water availability seasonality and other biotic factors, such as diseases. The framework and methodology developed and applied in this study to New Zealand can readily be adapted for use in other regions.

Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops. The objective of this work is to utilize modern computer vision technology to identify and map cultivable land for agricultural needs. With increasing population and demand for food, improving the farming sector is crucial. However, the challenge lies in the lack of suitable land for food crops cultivation. To tackle this issue, we propose to use sophisticated image processing techniques on satellite images of the land to determine the regions that are capable of growing food crops. The solution architecture includes enhancement of satellite imagery using sophisticated pan sharpening techniques, notably the Brovey transformation, aiming to transform dull satellite images into sharper versions, thereby improving the overall quality and interpretability of the visual data. Making use of the weather data on the location observed and taking into factors like the soil moisture, weather, humidity, wind, sunlight times and so on, this data is fed into a generative pre-trained transformer model which makes use of it and gives a set of crops that are suitable to be grown on this piece of land under the said conditions. The results obtained by the proposed fusion approach is compared with the dataset provided by the government for different states in India and the performance was measured. We achieved an accuracy of 80% considering the crop suggested by our model and the predominant crop of the region. Also, the classification report detailing the performance of the proposed model is presented.

Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for consistent and long-term mapping of shifting cultivation using Landsat data from 1975 to 2018. Widely used vegetation indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio (NBR) and its relative difference NBR (RdNBR) were integrated with the suitable thresholds in the classification, which yielded overall accuracy above 85%. A significant decrease in total shifting cultivation area was observed with an overall reduction of 75% from 1975–1976 to 2017–2018. The methodology presented in this study is reproducible with minimal inputs and can be useful to map similar changes by optimizing the index threshold values to accommodate relative differences for other landscapes. Furthermore, the crop-suitability maps generated by incorporating climate and soil factors prioritizes suitable land use of shifting cultivation plots. The Google Earth Engine (GEE) platform was employed for automatic mapping of the shifting cultivation areas at desired time intervals for facilitating seamless dissemination of the map products. Besides the novel DTMT method, the shifting cultivation and crop-suitability maps generated in this study, can aid in sustainable land management.

Barley (Hordeum vulgare) is a globally significant cereal crop, widely used in both food production and brewing. However, it is particularly vulnerable to climate change, especially extreme temperature fluctuations, which can severely reduce yields. To address this challenge, a detailed climate zoning study was conducted to assess the suitability of barley production areas across South America, considering both current conditions and future climate scenarios from the Intergovernmental Panel on Climate Change (IPCC). The study utilized historical climate data along with projections from the CMIP6 IPSL-CM6A-LR model for the period 2021–2100. Several indices, such as evapotranspiration, were calculated, and factors like soil composition and topography were integrated into the classification of regions based on their agricultural potential. Critical variables in this assessment included temperature, precipitation, and water or thermal excess. The results showed that 6.59% of South America's territory is currently suitable for barley cultivation without additional irrigation, with these regions concentrated primarily in temperate southern areas. In contrast, 18.62% of the region is already unsuitable due to excessive heat. Projections under future climate scenarios indicate a shrinking of suitable areas, alongside an expansion of unsuitable regions. In the worst-case scenario, only 1.48% of the territory would remain viable for barley farming. These findings emphasize the crop's vulnerability to climate change, underscoring the urgency of developing agricultural adaptation strategies. The predicted contraction in suitable barley cultivation areas demonstrates the profound impact of climate change on agriculture and highlights the need for proactive measures to ensure sustainable barley production in South America.

Quinoa ( Chenopodium quinoa Willd.) is an Andean crop of high nutritional value that is resistant to adverse climatic conditions. However, its sustainable expansion in Peru faces limitations due to the lack of detailed spatial information on areas suitable for cultivation, especially under climate change scenarios. The objective of this study was to define the agroclimatic and productive zones suitable for quinoa cultivation in Peru, taking into account current conditions (1970–2000) and projected climate change scenarios (2041–2060 and 2081–2100). Multicriteria evaluation techniques were integrated using the Fuzzy analytic hierarchy process (F-AHP) method to weight the criteria and the technique of Order of Preference by Similarity to the ideal Solution (TOPSIS) to classify the alternatives in a Geographic Information System (GIS) environment. Future projections were assessed under Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The results indicated that 45.71% of the national territory is marginally suitable and 10.95% is moderately suitable. Under future scenarios (SSP2-4.5 and SSP5-8.5), a slight reduction in marginal areas and an increase in moderately suitable and highly suitable areas are projected, especially towards the end of the period 2081–2100. For the SSP5-8.5 scenario, highly suitable areas could increase to 1.09% of the territory and moderately suitable areas to 14.08%. These results show a spatial redistribution of suitability, highlighting the need for adaptive agricultural policies, climate resilient planning strategies, and targeted territorial management to support sustainable quinoa expansion in Peru.

The application of nanomaterial-based soil sensing has been described as a solution to precision and sustainable agriculture because it can be hyper sensitive, fast responsive to applications and miniaturized. This paper reflects on a conceptual and narrative survey of nanomaterials as the semiconductors of metal oxide (ZnO and SnO2), carbon nanotubes, and graphene derivatives to sense soil parameters, e.g. moisture, pH, and nutrient availability. The importance of various synthesis techniques, methods of characterising materials, including FTIR (Fourier Transform Infrared Spectroscopy), XRD (X-Ray Diffraction), SEM (Scanning Electron Microscopy) and electrochemical analysis, and their effect on sensor performance and stability in the field are shown. Instead of referring to any new algorithms, the paper talks about the integration prospects of sensors made of nanomaterials with data-driven crop recommendation models. The existing drawbacks of sensor drift, calibration difficulty, environmental safety, and absence of protracted lobe evaluation are critically discussed. The paper is then concluded by identifying the research directions of stable, scalable, and environmentally sustainable soil sensing system.

The study aimed to assess the impact of climate change on the crop suitability index (CSI) of selected staple crops for current (1981–2010) and future (2021–2050 and 2051–2080) climates across Africa. Precipitation and mean temperature data from gridded observations, and 10 Global Climate Models (GCMs) were utilized to calculate the CSI for maize, soybean, wheat, plantain, cassava, rice, millet, sorghum, and yam. The Ecocrop model implemented in R, utilizing the FAO‐Ecocrop database alongside climatic variables for different climatic zones across the continent, was employed to compute the CSI. The results indicate that all crops, except rain‐fed rice, are suitable in parts of West and Central African regions, with wheat being inclusive in some parts of the Guinea Coast. The northern, eastern, and southern African regions are identified as the least suitable for any crop production based on the balance between the base climate parameters over the historical period. Analysis over this historical period reveals an increasing trend for major crops in most regions, except for wheat crop production, which demonstrates a decreasing trend in most areas. Projection analysis reveals that the Sahel region is expected to be the most affected by climate change, with a significant reduction in the suitability index for most crops. Conversely, the Southeastern Africa and the Guinea Coast regions are likely to be the least affected, as the suitability index increases for the considered crops. This analysis provides crucial information for effective agricultural planning and resource allocation, optimizing land use by identifying crops aligned with prevailing environmental conditions, including soil type, climate, and water availability. Such information enhances the understanding of crop suitability, contributing to improved agricultural productivity and sustainability. A figure illustrating the projected changes in the crop suitability index (CSI) for selected crops across various climatic sub‐regions in Africa under the Shared Socioeconomic Pathway (SSP) 5–8.5 climate projection. These changes are calculated for the period 2021–2050 relative to the baseline period (1981–2010), with asterisks (*) indicating statistically significant changes at the 0.05 significance level. The SSP5–8.5 scenario represents the upper boundary of possible future scenarios, with an additional radiative forcing of 8.5 W/m2 by the year 2100. The projected changes in CSI under this scenario vary significantly by region, with both increases and decreases expected. Regions such as the Atlantic, East Africa, north Central Africa, and South Africa are likely to become less suitable for most crops. Conversely, the Guinea Coast consistently shows a minimal yet significant increase in CSI for all crops, both during the mid‐century (2021–2050) and far‐century (2051–2080) periods. The CSI index for wheat, sorghum, maize, and rice shows a decrease of 0.2 or more in South Africa, East Africa, and the Sahel, indicating that major cereal crops will be adversely affected under both SSP2–4.5 and SSP5–8.5 scenarios during these periods. In contrast, Central Africa, the Guinea Coast, and the western Sahel show slight improvements or minimal decreases in CSI, particularly under the SSP5–8.5 scenario. This suggests that the effects of climate change under SSP5–8.5 could create favorable growing conditions for some crops in certain regions.