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Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development. 

The purpose of this paper is to leverage the growth of AI-enabled tools to support the democratization of mine observation (MO) research. Mining is essential to meet projected demand for renewable energy technologies crucial to global climate mitigation objectives, but all mining activities pose local and regional challenges to environmental sustainability. Such challenges can be mitigated by good governance, but unequal access among stakeholders to accurately interpreted satellite imagery can weaken good governance. Using readily available software—QGIS, and Segment Anything Model (SAM)—this paper develops and tests the reliability of MO-SAM, a new method to identify and delineate features within the spatially-explicit mine extent at a high level of detail. It focuses on dry tailings, waste dumps, and stockpiles in above-ground mining areas. While we intend for MO-SAM to be used generally, this study tested it on mining areas for energy-critical materials: lithium (Li), cobalt (Co), rare earth elements (REE), and platinum group elements (PGE), selected for their importance to the global transition to renewable energy. MO-SAM demonstrates generalizability through prompt engineering, but performance limitations were observed in imagery with complex mining landscape scenarios, including spatial variations in image morphology and boundary sharpness. Our analysis provides data-driven insights to support advances in the use of MO-SAM for analyzing and monitoring large-scale mining activities with greater speed than methods that rely on manual delineation, and with greater precision than practices that focus primarily on changes in the spatially-explicit mine extent. It also provides insights into the importance of multidisciplinary human expertise in designing processes for and assessing the accuracy of AI-assisted remote sensing image segmentation as well as in evaluating the significance of the land use and land cover changes identified. This has widespread potential to advance the multidisciplinary application of AI for scientific and public interest, particularly in research on global scale human-environment interactions such as industrial mining activities. This is methodologically significant because the potential and limitations of using large pre-trained image segmentation models such as SAM for analyzing remote sensing data is an emergent and underexplored issue. The results can help advance the utilization of large pre-trained segmentation models for remote sensing imagery analysis to support sustainability research and policy. 

Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, changes in mineral exploitation in China and Myanmar have garnered considerable attention in the past decade. The prevailing assumption is that mining in China has decreased while mining in Myanmar has increased, but the dynamic in border regions is more complex. Our empirical study used Google Earth Engine (GEE) to characterize changes in mining surface footprints between 2005 and 2020 in two rare earth mines located on either side of the Myanmar–China border, within Kachin State in northern Myanmar and Nujiang Prefecture in Yunnan Province in China. Our results show that the extent of the mining activities increased by 130% on China’s side and 327% on Myanmar’s side during the study period. We extracted surface reflectance images from 2005 and 2010 from Landsat 5 TM and 2015 and 2020 images from Landsat 8 OLI. The Normalized Vegetation Index (NDVI) was applied to dense time-series imagery to enhance landcover categories. Random Forest was used to categorize landcover into mine and non-mine classes with an overall accuracy of 98% and a Kappa Coefficient of 0.98, revealing an increase in mining extent of 2.56 km2, covering the spatial mining footprint from 1.22 km2 to 3.78 km2 in 2005 and 2020, respectively, within the study area. We found a continuous decrease in non-mine cover, including vegetation. Both mines are located in areas important to ethnic minority groups, agrarian livelihoods, biodiversity conservation, and regional watersheds. The finding that mining surface areas increased on both sides of the border is significant because it shows that national-level generalizations do not align with local realities, particularly in socially and environmentally sensitive border regions. The quantification of such changes over time can help researchers and policymakers to better understand the shifting geographies and geopolitics of rare earth mining, the environmental dynamics in mining areas, and the particularities of mineral extraction in border regions. 

Food production data — such as crop, livestock, aquaculture and fisheries statistics — are critical to achieving multiple sustainable development goals. However, the lack of reliable, regularly collected, accessible, usable and spatially disaggregated statistics limits an accurate picture of the state of food production in many countries and prevents the implementation of effective food system interventions. In this Review, we take stock of national and international food production data to understand its availability and limitations. Across databases, there is substantial global variation in data timeliness, granularity (both spatially and by food category) and transparency. Data scarcity challenges are most pronounced for livestock and aquatic food production. These challenges are largely concentrated in Central America, the Middle East and Africa owing to a combination of inconsistent census implementation and a global reliance on self-reporting. Because data scarcity is the result of technical, institutional and political obstacles, solutions must include technological and policy innovations. Fusing traditional and emerging data-gathering techniques with coordinated governance and dedicated long-term financing will be key to overcoming current obstacles to sustained, up-to-date and accurate food production data collection, foundational in promoting and monitoring progress towards healthier and more sustainable food systems worldwide. 

Abstract The ongoing agrarian transition from smallholder farming to large-scale commercial agriculture promoted by transnational large-scale land acquisitions (LSLAs) often aims to increase crop yields through the expansion of irrigation. LSLAs are playing an increasingly prominent role in this transition. Yet it remains unknown whether foreign LSLAs by agribusinesses target areas based on specific hydrological conditions and whether these investments compete with the water needs of existing local users. Here we combine process-based crop and hydrological modelling, agricultural statistics, and georeferenced information on individual transnational LSLAs to evaluate emergence of water scarcity associated with LSLAs. While conditions of blue water scarcity already existed prior to land acquisitions, these deals substantially exacerbate blue water scarcity through both the adoption of water-intensive crops and the expansion of irrigated cultivation. These effects lead to new rival water uses in 105 of the 160 studied LSLAs (67% of the acquired land). Combined with our findings that investors target land with preferential access to surface and groundwater resources to support irrigation, this suggests that LSLAs often appropriate water resources to the detriment of local users. 

Foreign investors have acquired approximately 90 million hectares of land for agriculture over the past two decades. The effects of these investments on local food security remain unknown. While additional cropland and intensified agriculture could potentially increase crop production, preferential targeting of prime agricultural land and transitions toward export-bound crops might affect local access to nutritious foods. We test these hypotheses in a global systematic analysis of the food security implications of existing land concessions. We combine agricultural, remote sensing, and household survey data (available in 11 sub-Saharan African countries) with georeferenced information on 160 land acquisitions in 39 countries. We find that the intended changes in cultivated crop types generally imply transitions toward energy-rich, but nutrient-poor, crops that are predominantly destined for export markets. Specific impacts on food production and access vary substantially across regions. Deals likely have little effect on food security in eastern Europe and Latin America, where they predominantly occur within agricultural areas with current export-oriented crops, and where agriculture would have both expanded and intensified regardless of the land deals. This contrasts with Asia and sub-Saharan Africa, where deals are associated with both an expansion and intensification (in Asia) of crop production. Deals in these regions also shift production away from local staples and coincide with a gradually decreasing dietary diversity among the surveyed households in sub-Saharan Africa. Together, these findings point to a paradox, where land deals can simultaneously increase crop production and threaten local food security.