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ContextCharacterized by intensive urban sprawl and continuous cropland shrinkage, the unprecedented urbanization process has profoundly reshaped China’s landscape over the past four decades. However, the interaction between urban expansion and cropland loss in China at a finer spatiotemporal resolution remains unclear.ObjectivesThis study aims to quantify and compare the rates, patterns, dynamics, and interactions of urban expansion and cropland loss in 14 Chinese cities during 1980–2015.MethodsMultiple landscape metrics were calculated to quantify the magnitudes, rates, and patterns of urban expansion and cropland loss for each city. The standard deviation ellipse analysis and two quantitative indices (the dependence and the contribution of urban expansion on cropland loss) were used to characterize the relationship between urban expansion and cropland loss.ResultsThe pattern of rapid urban expansion and extensive cropland loss was observed across all selected cities (except for Harbin), with the averaged expansion area of 764.17 km2 and averaged loss area of 650.83 km2 per city. The primary mode of urbanization was the edge-expansion (6889.22 km2, 60.01%), followed by the infilling (2767.32 km2, 24,11%) and the outlying (1822.72 km2, 15.88%). Urban expansion was identified to be the dominant driver of cropland loss, accounting for 84.99% of the newly expanded urban land and 74.36% of the lost cropland in total, thus leading to a more spatially irregular and fragmented distribution of the cropland.ConclusionsThe balance between urbanization and land protection is still challenging. Here we advocate more effective policy-driven practices to protect China’s existing cropland for food security and sustainable development goals.

Cropland expansion is a common strategy for boosting agricultural production in sub-Saharan Africa (SSA) even though it often leads to economic, environmental, and social trade-offs. Ensuring sustainable cropland use and their management is critical for improving food security and preserving ecosystem services. To develop policies and approaches that support sustainable cropland management at national and sub-national scales, there is a need to understand the spatial distribution of cropland expansion (/loss), and any resultant changes in cropland productivity. This is especially important in SSA countries such as Malawi, where spatially explicit assessments of changes in cropland area and cropland productivity are lacking. To address this gap in Malawi, we used multi-source satellite data and socio-economic data, combined with satellite image classification and trend analysis, firstly to quantify spatial changes in cropland area and productivity, and secondly to evaluate potentially available cropland for future expansion. We found evidence of unsustainable cropland use in Malawi, which was demonstrated by: (a) rapid cropland expansion between 2010 and 2019 (increase 8.5% of land area), characterized by an expansion of crop farming into upland areas which indicate increased land scarcity in Malawi; (b) limited potential for future expansion, as approximately only 5% of the total land remained as potentially available cropland (corresponding to 4671 000 ha); and (c) an overall reduction in cropland productivity and a prevalence of increase in soil erosion. Our findings underscore the urgent need for taking measures to promote sustainable cropland use, including by protecting current cropland from further degradation (e.g. Southern Malawi) and improving cropland use planning (e.g. Northern Malawi).

Sustainable management of natural resources and green urbanization is crucial because it assists the use of resources wisely without unnecessary use and without affecting future generations’ needs. This research aims to examine the impact of the abundance of natural resources on China’s CO 2 emissions while moderating the roles of manufacturing value-added, urbanization, and permanent cropland from 1970 to 2016. This study developed a comprehensive empirical analysis, applied advanced econometric methodologies, and used the generalized linear model (GLM) and robust generalized estimating equation (GEE). Overall, the results conclude that natural resource abundance and permanent cropland are negatively associated with China’s CO 2 emissions. However, urbanization and manufacturing value-added are negatively related to those CO 2 emissions. Moreover, natural resource abundance and permanent cropland improve environmental sustainability while urbanization and manufacturing value-added deteriorate that environmental sustainability. It is suggested that policymakers should promote sustainable management of natural resources and encourage economic usage of natural resources to boost resilient ecosystems; shape sustainable places, lifestyles, and communities; and consume natural resources less. Additionally, policymakers should consider collaborating with landscape architects, urban planners, engineers, transport planners, ecologists, sociologists, physiologists, economists, physicists, and other specialists to develop green urban communities. The limitations of the study and directions for future research are discussed.

Core Ideas A current estimate of global P use efficiency for cereal production is not available. This study shows that world P use efficiency for cereal crops is low. Using the difference method, average world P use efficiency from 1961 to 2013 was 16%. A current estimate of global phosphorus use efficiency (PUE) for cereal production is not available. The objectives of this paper were to estimate PUE for cereal crops grown in the world and to review methods for improvement. Phosphorus use efficiency was determined using world cereal harvested area, total grain production, and P fertilizer consumption from 1961 to 2013, in addition to assumptions established from previous literature. World PUE of cereal crops was calculated using both balance and difference methods. Using the balance method, cereal grain P uptake is divided by the P fertilizer applied. Alternatively, the difference method accounts for P coming from the soil and that is subtracted from applied P. Utilized in this analysis is the estimate that cereal production accounts for 61% of the total harvested cropland. Cereal grain yields increased from 1.35 to 3.90 Mg h−1 between 1961 and 2013. In 1961, the world's fertilizer P consumption was 4,770,182 Mg and increased to 16,662,470 Mg of P fertilizer by 2013. This represents a 3.5× increase in P fertilizer consumption over 53 yr. Phosphorus use efficiency estimated using the balance method was 77%. Using the difference method, PUE for cereal production in the world was estimated to be 16%.

The international food trade is beneficial for enhancing global food security but also raises issues such as global cropland redistribution, land use efficiency, and environmental problems. While current studies have examined the impacts of the international food trade on these issues, its long-term effects on global cropland use efficiency remain unclear, especially when considering different crops and countries. Utilizing the international trade theory and the principle of virtual cropland, this study explores the relationship between international food trade and global cropland use efficiency from 2000 to 2022. The results illustrate that the global crop trade surged by 142%, outpacing the 102% increase in virtual cropland trade, which was attributed to crop yield enhancements. By 2022, the global virtual cropland trade encompassed 10.7% of the total croplands, with China emerging as the foremost importer, particularly due to soybean imports. Notably, the global crop trade led to substantial cropland savings and higher cropland use efficiency, totaling 1244.9 million hectares (Mha) between 2000 and 2020. These gains were largely attributed to the superior yields of major crop-exporting countries. Despite these gains, socio-economically vulnerable countries face significant challenges, potentially compromising their food security amidst the complexities of the global trade dynamics.

The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

Timely cropland information is crucial for ensuring food security and promoting sustainable development. Traditional field survey methods are time-consuming and costly, making it difficult to support rapid monitoring of large-scale cropland changes. Furthermore, most existing studies focus on cropland evaluation from a single aspect such as quantity or quality, and thus cannot comprehensively reveal spatiotemporal characteristics of cropland. In this study, a method for evaluating the quantity and quality of cropland using multi-source remote sensing-derived data was proposed and effectively applied in the black soil region in Northeast China. Evaluation results showed that the area of cropland increased significantly in the study area between 2010 and 2018, and the proportion of cropland increased by 1.17%. Simultaneously, cropland patches became larger and landscape connectivity improved. Most of the gained cropland was concentrated in the northeast and west, resulting in a shift in the gravity center of cropland to the northeast direction. Among land converted into cropland, unused land, grassland, and forest were the main sources, accounting for 36.38%, 31.47%, and 16.94% respectively. The quality of cropland in the study area generally improved. The proportion of low-quality cropland decreased by 7.17%, while the proportions of high-quality and medium-quality cropland increased by 5.65% and 5.17%, respectively. Specifically, the quality of cropland improved strongly in the east, improved slightly in the southwest, and declined in the north. Production capacity and soil fertility were key factors impacting cropland quality with obstacle degrees of 36.22% and 15.64%, respectively. Overall, the obtained results were helpful for a comprehensive understanding of spatiotemporal changes in cropland and driving factors and can provide guidance for cropland protection and management. The proposed method demonstrated promising reliability and application potential, which can provide a reference for other cropland evaluation studies.

The role of urban agriculture in global food security is a topic of increasing discussion. Existing research on urban and peri-urban agriculture consists largely of case studies that frequently use disparate definitions of urban and peri-urban agriculture depending on the local context and study objectives. This lack of consistency makes quantification of the extent of this practice at the global scale difficult. This study instead integrates global data on croplands and urban extents using spatial overlay analysis to estimate the global area of urban and peri-urban irrigated and rainfed croplands. The global area of urban irrigated croplands was estimated at about 24 Mha (11.0 percent of all irrigated croplands) with a cropping intensity of 1.48. The global area of urban rainfed croplands found was approximately 44 Mha (4.7 percent of all rainfed croplands) with a cropping intensity of 1.03. These values were derived from the MIRCA2000 Maximum Monthly Cropped Area Grids for irrigated and rainfed crops and therefore their sum does not necessarily represent the total urban cropland area when the maximum extent of irrigated and rainfed croplands occurs in different months. Further analysis of croplands within 20 km of urban extents show that 60 and 35 percent of, respectively, all irrigated and rainfed croplands fall within this distance range.

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (~80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (khat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (khat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (~90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ~30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms. The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.