Impact of cropland-urbanization dynamics on crop production in the United States

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We quantitatively assessed the impact of urbanization on crop production in the United States since 2000 by combining earth observation products and crop modelling. We found that urbanization has appropriated 2.1 M ha of highly productive cropland, leading to an overall decline in U.S. cropland area over time and a production loss of 15 million tons of maize, soybean, and wheat. Avoiding the negative impacts of urbanization on crop production and the environment will require proper land-use planning and urban design and yield intensification on existing cropland. Earth and environmental sciences/Ecology/Ecological modelling Biological sciences/Ecology/Ecological modelling Figures Figure 1 Figure 2 Figure 3 Main text Meeting food demand without massive land conversion for agriculture is one of the biggest challenges that humanity has ever faced (Cassman and Grassini, 2020 ). At the same time, urban population keeps increasing, currently exceeding the non-urban population (Seto et al., 2009, 2010). While crop demand keeps increasing due to population growth and dietary changes, cities are encroaching on millions of hectares of cropland, and this trend will persist as cities continue to expand (d’Amour et al, 2016 ; van Vilet, 2019). Prior to the establishment of global supply chains and modern transport and storage systems, major cities were in locations with good soils and sufficient water supply to ensure adequate and stable food supply. Thus, productivity of converted cropland near cities tends to be higher than that of new cropland (d’Amour et al., 2016 ; Zuo et al. 2018 ; van Vilet, 2019; Andrade et al., 2022 ). The United States (U.S.) plays a major role in the global food system, accounting for 35%, 34%, and 7% of maize, soybean, and wheat production (FAO, 2024). While urban growth is encroaching prime farmland near cities (Smidt et al., 2018 ; Islam et al., 2024 ), agriculture is expanding into less-productive environments with larger negative environmental footprint or even being abandoned (Yu and Lu, 2018 ; Xie et al, 2024 ). Previous studies reporting on trends in U.S. cropland over time have focused on specific types of land-use changes (e.g., expansion, abandonment, or loss due to urbanization) or impacts ( e.g. , land conversion, environmental impact), based on a few points in time or small regions, lacking crop-specificity, and/or using coarse approaches to assess changes in land productivity that do not separate the impact of land management from the inherent biophysical suitability of locations to produce crops (Supplementary Table S1) . Moreover, some of these studies focused on future scenarios impacts of urbanization and/or have a global scope ( e.g. , Huang et al., 2020 ; d’Amour et al., 2016 ; van Vilet, 2019). Only a series of reports developed by the American Farmland Trust non-profit organization (Freedgood et al., 2020 ) and a recent study by Xie et al. ( 2023 ) have explicitly assessed the national-level impact of urbanization on U.S croplands during recent decades. A major limitation of these studies is that they neither quantified the associated production loss nor assessed how cropland expansion has compensated for the negative impact of urbanization. Most of the literature about the negative impacts of urbanization on crop production has focused on developing populous countries experiencing fast urbanization rates (e.g., Gibson et al. 2015 ; d’Amour et al., 2016 ; Andrade et al., 2022 ; Kuang et al., 2022 ). Considering rapid global economic and demographic trends worldwide, assessing the impact of urbanization on crop production in high-income countries provides insights about future global scenarios, as countries continue to develop and their urban populations increase. Here, we investigated the impact of cropland-urbanization dynamics on the production of major U.S. crops after year 2000 through a novel approach that combined land use products derived from satellite imagery, crop modeling, and detailed soil, terrain, and climate databases (see Methods) . Urbanization has appropriated 2.4 M ha of cropland between 2000 and 2023, mostly in the Great Lakes, Heartland, and Upper Midwest ( Figs. 1 and 2 ; Supplementary Figure S1). During the same period, ca. 9.5 M ha of cropland were converted permanently into pastures, grasslands, and woodlands, mostly in the Northern and Southern Plains and the Southern region. However, this loss was offset by cropland expansion into these same land classes (9.9 M ha), especially in the Northern and Southern Plains and Heartland. Thus, even when urbanization was only ca. 20% of other land use changes explaining cropland loss, it accounted for most of the permanent cropland loss between 2000 and 2023, totaling 2.1 M ha and representing 2% of the existing cropland by year 2000 (inset in Fig. 1 ) . Three crops (maize, soybean, and wheat) accounted for more than 80% of the new cropland and that converted to urban and other uses (Supplementary Figure S2) . Thus, we focused on these crops to investigate differences in productivity between new, unchanged, and converted cropland into urban and non-urban (see Methods) . Similarity in land productivity between new and converted cropland to non-urban ( Fig. 3 ; Supplementary Fig. S3) , together with similar rates of conversion from one land use type to another ( Fig. 1 ) , led to a neutral effect on crop production. Conversely, converted cropland to urban showed 10% higher yield potential than the new cropland. Yield differences were larger for summer crops (i.e., maize and soybean) compared with wheat and consistent with those observed for average farmer yields. Likewise, yield stability of summer crops was substantially lower, and downside risk higher, in the new versus converted cropland. Yield differences were associated with larger water limitation, lower soil quality, and higher risk of water runoff and soil erosion in the new cropland relative to the unchanged cropland or that converted to urban ( Fig. 3 ) . Considering both cropland loss and associated yield potential, we estimated that urbanization has led to an aggregated production loss of ca . 15 Mt of maize, soybean, and wheat (Supplementary Table S2) . Such loss has been offset by yield improvement in the unchanged cropland over the same period, requiring ca. 13% of the historical yield gain to compensate for the negative impact of urbanization on crop production. Our study based on long-term trends in satellite imagery and crop modeling advances previous assessments of urbanization impact on U.S. farmland (Supplementary Table S1) . We found that urbanization has been the main driver for the observed decline in U.S. cropland since year 2000, encroaching ca. 0.11 M ha of highly productive cropland annually ( Figs. 1 and 3 ) . The magnitude of cropland conversion was largest in regions that combined relatively high population growth and large cropland area (Supplementary Figure S1) , which is consistent with findings from previous global and national studies (d’Amour et al., 2016 ; Freedgood et al., 2020 ; Xie et al., 2023 ). Conversely, the impact of cropland conversion to non-urban uses (pasture, grasslands, and woodlands) has been offset by cropland expansion into land with similar productivity ( Figs. 1 and 3 ) . If these trends persist in the future, we expect yield differences between new and converted cropland to urban to accentuate as cropland will expand into less-productive areas, ultimately leading to larger negative impacts on crop production and the environment ( e.g. , Zuo et al, 2018 ; Lark et al. 2020 ). Findings from our study highlight the importance of minimizing cropland conversion to urban through proper land-use planning and urban design while fostering yield improvement on existing cropland. For example, previous studies have shown that dense urban designs can serve to protect agricultural land (Güneralp et al., 2017 ; Mahtta et al., 2022 ) and that the productivity of existing croplands can be further improved through adoption of improved agronomic management together with continuous crop cultivar replacement (Rizzo et al., 2022 ). To be effective, however, these approaches need to be complemented with a comprehensive national strategy that protects and ensures the economic viability of current cropland, including agricultural and farm link programs, tax incentives and exemptions, and land use planning policies (Freedgood et al., 2020 ; Xie et al., 2023 , 2024 ; Yu and Lu, 2018 ). Our study provides key input to these efforts by identifying highly productive croplands that should be protected from conversion to urban, and those with largest room for yield improvement. Such approach would be useful not only for the U.S., but also for developing populous countries experiencing fast urbanization rates (Gibson et al. 2015 ; d’Amour et al., 2016 ; Andrade et al., 2021; Kuang et al., 2022 ). Since these countries usually prioritize a reasonable level of self-sufficiency for the staple crops that constitute the basis of their diet (Clapp, 2017 ), applying our crop-specific approach can provide key input to inform their land-use and agricultural policies. Likewise, our study has broader implications for global food security and environmental sustainability as our analysis for the U.S. provides important lessons to other crop exporting countries about the need for combining land-use planning to avoid loss of prime farmland and yield intensification on existing cropland to maintain export capacity while avoiding cropland expansion at expense of fragile ecosystems. Methods We used the annual land cover change data at 30-m spatial resolution of National Land Cover Database (NLCD) from USGS (2024) to track the conversion between cropland and other land cover types between 2000 and 2023. To focus on the impact of urbanization on crop production, we aggregated some of the land classes (Supplementary Tables S3-S4) . For example, grasslands, pasture, and hay were classified as ‘grassland’ and forests and shrubland as ‘woodland’. Likewise, all developed land was classified as ‘urban’, and cultivated crops was classified as ‘cropland’. Subsequently, we estimated the cropland converted into developed land and the cropland converted into non-urban (grassland, woodland, and wetland) over the period. To avoid episodic changes in land use, we only considered new or converted cropland that has not shifted back to the original land use more than two times during the study period. We assessed differences in yield between cropland classes (new, unchanged and converted to urban and non-urban) and investigated the underlying drivers (Supplementary Table S5) . We focused on maize, soybean, and wheat, which are the main field crops in the United States, accounting for ca. 85% of national cropland and > 80% of land use changes. First, we used the Cropland Data Layer (CDL) at 30-m spatial resolution published by USDA-NASS (2024) to identify croplands planted with a given crop. To do so, we aggregated crop sub-categories into crop-specific categories (e.g., winter, spring, and durum wheat were categorized as ‘wheat’) and selected pixels sown at least twice with the given crop between 2008 and 2023 (note that earlier years are not available in CDL). We note that most cropland is normally sown with more than one crop over time, so the same cropland could be considered for two or more crops. Second, for each crop, we compared the productivity of the new, converted, and unchanged cropland. Unfortunately, field-level yields are not available. Moreover, farmer yields do not reflect the potential land productivity as they are influenced by agronomic management and access to inputs, markets, and extension services, leading to ‘yield gaps’ (van Ittersum et al., 2013 ; Andrade et al., 2021). Hence, a better indicator of land productivity for rainfed crops is the water-limited yield potential (Yw), which is determined by solar radiation, temperature, precipitation, and soil properties influencing the crop water balance (van Ittersum et al., 2013 ). We retrieved crop-specific gridded (30 arc-second) estimates of water-limited yield potential for each crop (Aramburu-Merlos et al., 2024 ). Briefly, yield potential was estimated for individual sites based on well-validated process-based crop models (Hybrid Maize for maize, APSIM for soybean, and SSM for wheat) and local weather and soils and interpolated using a machine-learning algorithm that considers spatial variation in the parameters influencing yield potential (Aramburu-Merlos et al., 2024 ; Couedel et al., 2015 and references cited therein). These models have been validated on their performance to estimate yield potential using yield data collected from well-managed, high-yielding experiments across US producing area (Couëdel et al., 2025 ). Additionally, we retrieved the associated temporal coefficients of variation, which gives an indication of climate risk, from the Global Yield Gap Atlas ( www.yieldgap.org ). To understand drivers for differences in yield potential and its stability, we retrieved data on soil properties from gSSURGO (2024): pH, plant available soil water holding capacity (PAWHC), soil organic matter (SOM), and Kw erodibility factor. In addition, we estimated a water limitation index (WLI) as follows: $$\:WLI\:\left(\%\right)=\left(1-\frac{Yw}{Yp}\right)\times\:100\:\:\:\:\:\:\:\:\:(Eq.\:1)$$ where Yp is the simulated yield potential without water limitations ( www.yieldgap.org ). Large WLI indicates severe water limitation to crop productivity. Additionaly, we estimated the topographic water index (TWI) from the USGS elevation data (Gesch et al., 2002 ; Böhner and Selige, 2006 ). Low TWI values indicate steep slopes with higher water runoff, risk of erosion, and potentially shallower soils. Average WLI, TWI, and soil properties were estimated for each cropland type (new, converted to urban or non-urban, and unchanged). To validate the yield differences derived from crop modeling, we also included a comparison of land productivity based on county-level average farmer yields retrieved from USDA-NASS (2024). Details on data sources and spatial and temporal resolutions are shown in Supplementary Table S5. We computed separate averages for each cropland type (unchanged, new, and converted to urban and non-urban), separately for each crop, and expressed them as a ratio of the unchanged cropland. Finally, we retrieved the historical yield gain for each for the three crops from the slope of the yield versus year relationship between 1970 and 2023 and compared it against the yield gain needed to compensate for the negative effect of cropland conversion to urban on crop production potential (Supplementary Table S2) . Our study is subject to several limitations and uncertainties. First, land type may have been misclassified in some cases, leading to potential biases in the estimation of land-use changes. We used the most updated version of NLCD and CDL databases, which are the only publicly available and spatially explicit database on U.S. land-use type available on an annual basis. These databases have been extensively validated and used in the literature (e.g., Huang et al., 2020 ; Xie et al., 2023 , 2024 and references cited therein). Second, there is uncertainty in the simulation of water-limited yield potential and associated data inputs. Our simulations were based on process-based models that have been rigorously validated on their capacity to reproduced measured yields in experiments conducted across a wide range of environments where crops where explicitly managed to avoid nutrient limitations and yield reductions due to biotic stresses (Couëdel et al., 2025 ). Moreover, our simulations are based on measured weather data, fine-resolution soil maps, and local sowing dates and crop cultivars and a robust method to extrapolate resulting yield potential estimates over space (Aramburu-Merlos et al., 2024 ). Third, our analysis of land productivity did not account for all crop types. We note that our analysis accounted for the main crops, which altogether account for 85% of U.S. cropland and for more than 80% of the new cropland and that converted to urban and other uses. Finally, mismatches in spatial resolution between the databases used in our studies may influence some of the results. For example, data on average farmer yield were reported at county level whereas land-use changes were determined at a resolution of 30 meters. Fortunately, most of our databases have similar spatial resolutions and, for those cases in which it is not the case, we have complemented the analysis with other data sources. For example, our comparison of land productivity is based on county-level average yield and fine-resolution yield potential, in both cases arriving to similar findings. Thus, despite the limitations and uncertainties inherent to this type of the analysis, our results can be considered robust and the conclusions from the study valid. Declarations Data availability Data on land cover change from USGS are available at: https://www.usgs.gov/centers/eros/science/national-land-cover-database. Data on crop distribution from USDA are available at: https://nassgeodata.gmu.edu/CropScape/. Data on county average yields from USDA-NASS Quick Stats are available at https://www.nass.usda.gov/Quick_Stats/. Data on water-limited yield potential are available at https://doi.org/10.5281/zenodo.12209709. Data on yield potential fluctuation and water limitation are available at www.yieldgap.org. Elevation data from USGS are available at https://apps.nationalmap.gov/downloader/. Data on soil properties from USDA-NRCS are available at https://gdg.sc.egov.usda.gov/. Source data files are provided with this paper. The data that support the findings of this study are also available from the corresponding author upon request. Acknowledgments This study was funded by a joint program from the National Science Foundation of China (T2261129473 to L.Z.) and U.S. National Science Foundation (Grant #2214604 to P.G.), with additional funding from the International Research Center of Big Data for Sustainable Development Goals (CBAS) (CBASYX0906 to L.Z.), and the National Institute of Food and Agriculture of the United States Department of Agriculture (Hatch NEB-22-373 & AFRI Grant #12431808 to P.G.). Author Contributions P. G. and L. Z. designed the study; L.Q., F. L., F. A., X. W., and J. X. prepared the data and/or carried out modeling; L. Z., P. G., F. L., L. Q., Y. M, and F. A. T. analyzed the data, and P. G., L. Z., and F. A. wrote the paper. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7179033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":494746531,"identity":"51db8bb9-a5af-413f-9f1a-512589a58dc3","order_by":0,"name":"Patricio Grassini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYPACCQY29uYDDAwFQPYBorXwHEtgYDAgXgtIV44BcVrM+dc+/Pjjj0UeH0PON+kCAwY5vhsJ+LVYznhuLM3bJlHMxnB2m/QMAwZjSUJaDG4cY5BmbJBIbGPs3SbNY8CQuIEILcw/f/wBamHmeQbSUk9Yy/k2NgkeNqAWNh42kJYEA8J+YWOzBvolsY2Hzdiax0DCcOaZB/i1mPMfY775409d4vz5jx/e5qmwkec7TshhEqgKJPArB2vhP0BY0SgYBaNgFIxwAAA6kz6GkLHMQQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7501-842X","institution":"University of Nebraska-Lincoln","correspondingAuthor":true,"prefix":"","firstName":"Patricio","middleName":"","lastName":"Grassini","suffix":""},{"id":494746532,"identity":"f66c09d2-be01-436b-8159-36a5c9f6ccd6","order_by":1,"name":"Lijun Zuo","email":"","orcid":"https://orcid.org/0000-0001-6554-0815","institution":"Aerospace Information Research Institute, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Zuo","suffix":""},{"id":494746533,"identity":"aa31df79-cf5d-489f-a3e8-8abccb7c4599","order_by":2,"name":"Luoping Quan","email":"","orcid":"","institution":"Aerospace Information Research Institute, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Luoping","middleName":"","lastName":"Quan","suffix":""},{"id":494746534,"identity":"31628412-f28d-4e14-9072-68856a9a5b8c","order_by":3,"name":"Fernando Aramburu-Merlos","email":"","orcid":"https://orcid.org/0000-0003-0957-7482","institution":"University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Aramburu-Merlos","suffix":""},{"id":494746535,"identity":"afbe3d25-9441-47a2-8dc5-5d42319a2ed7","order_by":4,"name":"Fang Liu","email":"","orcid":"","institution":"Aerospace Information Research Institute, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Liu","suffix":""},{"id":494746536,"identity":"6583590a-5c1e-468b-b98d-84ade21d5d7f","order_by":5,"name":"Xiao Wang","email":"","orcid":"","institution":"Aerospace Information Research Institute, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Wang","suffix":""},{"id":494746537,"identity":"5d9718be-6fb9-4653-bd1b-b91c59ba2339","order_by":6,"name":"Yu Meng","email":"","orcid":"","institution":"Aerospace Information Research Institute, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Meng","suffix":""},{"id":494746538,"identity":"8afc0b78-25b1-4bad-aabd-015cf1d2f570","order_by":7,"name":"Fatima Tenorio","email":"","orcid":"","institution":"University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Fatima","middleName":"","lastName":"Tenorio","suffix":""},{"id":494746539,"identity":"1a7719ee-b2bc-45aa-a500-2111aa04cfca","order_by":8,"name":"Jinyong Xu","email":"","orcid":"","institution":"Aerospace Information Research Institute, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jinyong","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-07-21 15:27:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7179033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7179033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89551405,"identity":"f3cb3c8b-5639-4abb-9280-7476175f4132","added_by":"auto","created_at":"2025-08-21 08:25:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":163370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCumulative new and converted cropland into urban and non-urban areas (grassland, pasture, and woodland) in the United States between 2000 and 2023. Inset shows total cropland at the beginning and end of the time series.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7179033/v1/3fb5a2b2b81b52e0bd1e8d71.jpeg"},{"id":89550502,"identity":"06727189-7bee-47d0-807d-7695a8723aba","added_by":"auto","created_at":"2025-08-21 08:17:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2172220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCropland conversion to urban between 2000 and 2023 around large cities in the Great Lakes, Heartland, and Upper Midwest based on Landsat imagery (false color composite images), with red color representing cropland and blue and grey colors indicating urban areas. Large cities include (A) Chicago, (B) Indianapolis, (C) Des Moines, and (D) Minneapolis. The accumulated cropland converted to urban area around these cities between 2000 and 2023 was 0.46 (Chicago), 0.26 (Indianapolis), 0.12 (Des Moines), and 0.19 M ha (Minneapolis).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7179033/v1/8956549b7a7f42aa12fce1fc.jpeg"},{"id":89550506,"identity":"68757d93-1a7d-4871-8b7f-4553ea38dfe8","added_by":"auto","created_at":"2025-08-21 08:17:29","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":345746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of new and converted cropland to urban and non-urban relative to unchanged cropland. Radar charts comparing the ratio between each cropland class (new, lost to urban, and lost to non-urban) and unchanged cropland for average farmer yield (Ya), water-limited yield potential (Yw) and associated risk and coefficient of variation (CV), water limitation index (WLI), soil erodibility factor (kw), topographic wetness index (TWI), and soil pH, organic matter (SOM), and plant available water-holding capacity (PAWHC). Separate radar charts are shown for each crop and the unchanged cropland is shown as a reference (black line). See Methods for the description and calculation of each variable.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7179033/v1/976b024f6fd06856dec68ac9.jpeg"},{"id":91446678,"identity":"d9b63863-044a-43a4-93ea-426a7a2c2e6d","added_by":"auto","created_at":"2025-09-16 14:47:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3209672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7179033/v1/e67ec32c-d4f8-40e6-a50d-803b998a1d61.pdf"},{"id":89551400,"identity":"2100b9d3-78ab-493d-af64-d0e53a9e3390","added_by":"auto","created_at":"2025-08-21 08:25:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2374577,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7179033/v1/0abf041c4633f665433287cd.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Impact of cropland-urbanization dynamics on crop production in the United States","fulltext":[{"header":"Main text","content":"\u003cp\u003eMeeting food demand without massive land conversion for agriculture is one of the biggest challenges that humanity has ever faced (Cassman and Grassini, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the same time, urban population keeps increasing, currently exceeding the non-urban population (Seto et al., 2009, 2010). While crop demand keeps increasing due to population growth and dietary changes, cities are encroaching on millions of hectares of cropland, and this trend will persist as cities continue to expand (d’Amour et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; van Vilet, 2019). Prior to the establishment of global supply chains and modern transport and storage systems, major cities were in locations with good soils and sufficient water supply to ensure adequate and stable food supply. Thus, productivity of converted cropland near cities tends to be higher than that of new cropland (d’Amour et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zuo et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; van Vilet, 2019; Andrade et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe United States (U.S.) plays a major role in the global food system, accounting for 35%, 34%, and 7% of maize, soybean, and wheat production (FAO, 2024). While urban growth is encroaching prime farmland near cities (Smidt et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), agriculture is expanding into less-productive environments with larger negative environmental footprint or even being abandoned (Yu and Lu, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xie et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous studies reporting on trends in U.S. cropland over time have focused on specific types of land-use changes (e.g., expansion, abandonment, or loss due to urbanization) or impacts (\u003cem\u003ee.g.\u003c/em\u003e, land conversion, environmental impact), based on a few points in time or small regions, lacking crop-specificity, and/or using coarse approaches to assess changes in land productivity that do not separate the impact of land management from the inherent biophysical suitability of locations to produce crops \u003cb\u003e(Supplementary Table S1)\u003c/b\u003e. Moreover, some of these studies focused on future scenarios impacts of urbanization and/or have a global scope (\u003cem\u003ee.g.\u003c/em\u003e, Huang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; d’Amour et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; van Vilet, 2019). Only a series of reports developed by the American Farmland Trust non-profit organization (Freedgood et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and a recent study by Xie et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have explicitly assessed the national-level impact of urbanization on U.S croplands during recent decades. A major limitation of these studies is that they neither quantified the associated production loss nor assessed how cropland expansion has compensated for the negative impact of urbanization.\u003c/p\u003e\u003cp\u003eMost of the literature about the negative impacts of urbanization on crop production has focused on developing populous countries experiencing fast urbanization rates (e.g., Gibson et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; d’Amour et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Andrade et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kuang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Considering rapid global economic and demographic trends worldwide, assessing the impact of urbanization on crop production in high-income countries provides insights about future global scenarios, as countries continue to develop and their urban populations increase. Here, we investigated the impact of cropland-urbanization dynamics on the production of major U.S. crops after year 2000 through a novel approach that combined land use products derived from satellite imagery, crop modeling, and detailed soil, terrain, and climate databases \u003cb\u003e(see Methods)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eUrbanization has appropriated 2.4 M ha of cropland between 2000 and 2023, mostly in the Great Lakes, Heartland, and Upper Midwest \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eSupplementary Figure S1).\u003c/b\u003e During the same period, \u003cem\u003eca.\u003c/em\u003e 9.5 M ha of cropland were converted permanently into pastures, grasslands, and woodlands, mostly in the Northern and Southern Plains and the Southern region. However, this loss was offset by cropland expansion into these same land classes (9.9 M ha), especially in the Northern and Southern Plains and Heartland. Thus, even when urbanization was only ca. 20% of other land use changes explaining cropland loss, it accounted for most of the permanent cropland loss between 2000 and 2023, totaling 2.1 M ha and representing 2% of the existing cropland by year 2000 \u003cb\u003e(inset in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThree crops (maize, soybean, and wheat) accounted for more than 80% of the new cropland and that converted to urban and other uses \u003cb\u003e(Supplementary Figure S2)\u003c/b\u003e. Thus, we focused on these crops to investigate differences in productivity between new, unchanged, and converted cropland into urban and non-urban \u003cb\u003e(see Methods)\u003c/b\u003e. Similarity in land productivity between new and converted cropland to non-urban \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Fig. S3)\u003c/b\u003e, together with similar rates of conversion from one land use type to another \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, led to a neutral effect on crop production. Conversely, converted cropland to urban showed 10% higher yield potential than the new cropland. Yield differences were larger for summer crops (i.e., maize and soybean) compared with wheat and consistent with those observed for average farmer yields. Likewise, yield stability of summer crops was substantially lower, and downside risk higher, in the new \u003cem\u003eversus\u003c/em\u003e converted cropland.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eYield differences were associated with larger water limitation, lower soil quality, and higher risk of water runoff and soil erosion in the new cropland relative to the unchanged cropland or that converted to urban \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Considering both cropland loss and associated yield potential, we estimated that urbanization has led to an aggregated production loss of \u003cem\u003eca\u003c/em\u003e. 15 Mt of maize, soybean, and wheat \u003cb\u003e(Supplementary Table S2)\u003c/b\u003e. Such loss has been offset by yield improvement in the unchanged cropland over the same period, requiring \u003cem\u003eca.\u003c/em\u003e 13% of the historical yield gain to compensate for the negative impact of urbanization on crop production.\u003c/p\u003e\u003cp\u003eOur study based on long-term trends in satellite imagery and crop modeling advances previous assessments of urbanization impact on U.S. farmland \u003cb\u003e(Supplementary Table S1)\u003c/b\u003e. We found that urbanization has been the main driver for the observed decline in U.S. cropland since year 2000, encroaching \u003cem\u003eca.\u003c/em\u003e 0.11 M ha of highly productive cropland annually \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The magnitude of cropland conversion was largest in regions that combined relatively high population growth and large cropland area \u003cb\u003e(Supplementary Figure S1)\u003c/b\u003e, which is consistent with findings from previous global and national studies (d’Amour et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Freedgood et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, the impact of cropland conversion to non-urban uses (pasture, grasslands, and woodlands) has been offset by cropland expansion into land with similar productivity \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. If these trends persist in the future, we expect yield differences between new and converted cropland to urban to accentuate as cropland will expand into less-productive areas, ultimately leading to larger negative impacts on crop production and the environment (\u003cem\u003ee.g.\u003c/em\u003e, Zuo et al, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lark et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFindings from our study highlight the importance of minimizing cropland conversion to urban through proper land-use planning and urban design while fostering yield improvement on existing cropland. For example, previous studies have shown that dense urban designs can serve to protect agricultural land (Güneralp et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mahtta et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and that the productivity of existing croplands can be further improved through adoption of improved agronomic management together with continuous crop cultivar replacement (Rizzo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To be effective, however, these approaches need to be complemented with a comprehensive national strategy that protects and ensures the economic viability of current cropland, including agricultural and farm link programs, tax incentives and exemptions, and land use planning policies (Freedgood et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu and Lu, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our study provides key input to these efforts by identifying highly productive croplands that should be protected from conversion to urban, and those with largest room for yield improvement. Such approach would be useful not only for the U.S., but also for developing populous countries experiencing fast urbanization rates (Gibson et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; d’Amour et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Andrade et al., 2021; Kuang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since these countries usually prioritize a reasonable level of self-sufficiency for the staple crops that constitute the basis of their diet (Clapp, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), applying our crop-specific approach can provide key input to inform their land-use and agricultural policies. Likewise, our study has broader implications for global food security and environmental sustainability as our analysis for the U.S. provides important lessons to other crop exporting countries about the need for combining land-use planning to avoid loss of prime farmland and yield intensification on existing cropland to maintain export capacity while avoiding cropland expansion at expense of fragile ecosystems.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe used the annual land cover change data at 30-m spatial resolution of National Land Cover Database (NLCD) from USGS (2024) to track the conversion between cropland and other land cover types between 2000 and 2023. To focus on the impact of urbanization on crop production, we aggregated some of the land classes \u003cstrong\u003e(Supplementary Tables S3-S4)\u003c/strong\u003e. For example, grasslands, pasture, and hay were classified as \u0026lsquo;grassland\u0026rsquo; and forests and shrubland as \u0026lsquo;woodland\u0026rsquo;. Likewise, all developed land was classified as \u0026lsquo;urban\u0026rsquo;, and cultivated crops was classified as \u0026lsquo;cropland\u0026rsquo;. Subsequently, we estimated the cropland converted into developed land and the cropland converted into non-urban (grassland, woodland, and wetland) over the period. To avoid episodic changes in land use, we only considered new or converted cropland that has not shifted back to the original land use more than two times during the study period.\u003c/p\u003e\n\u003cp\u003eWe assessed differences in yield between cropland classes (new, unchanged and converted to urban and non-urban) and investigated the underlying drivers \u003cstrong\u003e(Supplementary Table S5)\u003c/strong\u003e. We focused on maize, soybean, and wheat, which are the main field crops in the United States, accounting for ca. 85% of national cropland and \u0026gt;\u0026thinsp;80% of land use changes. First, we used the Cropland Data Layer (CDL) at 30-m spatial resolution published by USDA-NASS (2024) to identify croplands planted with a given crop. To do so, we aggregated crop sub-categories into crop-specific categories (e.g., winter, spring, and durum wheat were categorized as \u0026lsquo;wheat\u0026rsquo;) and selected pixels sown at least twice with the given crop between 2008 and 2023 (note that earlier years are not available in CDL). We note that most cropland is normally sown with more than one crop over time, so the same cropland could be considered for two or more crops. Second, for each crop, we compared the productivity of the new, converted, and unchanged cropland. Unfortunately, field-level yields are not available. Moreover, farmer yields do not reflect the potential land productivity as they are influenced by agronomic management and access to inputs, markets, and extension services, leading to \u0026lsquo;yield gaps\u0026rsquo; (van Ittersum et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Andrade et al., 2021). Hence, a better indicator of land productivity for rainfed crops is the water-limited yield potential (Yw), which is determined by solar radiation, temperature, precipitation, and soil properties influencing the crop water balance (van Ittersum et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). We retrieved crop-specific gridded (30 arc-second) estimates of water-limited yield potential for each crop (Aramburu-Merlos et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Briefly, yield potential was estimated for individual sites based on well-validated process-based crop models (Hybrid Maize for maize, APSIM for soybean, and SSM for wheat) and local weather and soils and interpolated using a machine-learning algorithm that considers spatial variation in the parameters influencing yield potential (Aramburu-Merlos et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Couedel et al., 2015 and references cited therein). These models have been validated on their performance to estimate yield potential using yield data collected from well-managed, high-yielding experiments across US producing area (Cou\u0026euml;del et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, we retrieved the associated temporal coefficients of variation, which gives an indication of climate risk, from the Global Yield Gap Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.yieldgap.org\" target=\"_blank\"\u003ewww.yieldgap.org\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e). To understand drivers for differences in yield potential and its stability, we retrieved data on soil properties from gSSURGO (2024): pH, plant available soil water holding capacity (PAWHC), soil organic matter (SOM), and Kw erodibility factor. In addition, we estimated a water limitation index (WLI) as follows:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:WLI\\:\\left(\\%\\right)=\\left(1-\\frac{Yw}{Yp}\\right)\\times\\:100\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.\\:1)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere Yp is the simulated yield potential without water limitations (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.yieldgap.org\u003c/span\u003e\u003c/span\u003e). Large WLI indicates severe water limitation to crop productivity. Additionaly, we estimated the topographic water index (TWI) from the USGS elevation data (Gesch et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; B\u0026ouml;hner and Selige, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Low TWI values indicate steep slopes with higher water runoff, risk of erosion, and potentially shallower soils. Average WLI, TWI, and soil properties were estimated for each cropland type (new, converted to urban or non-urban, and unchanged).\u003c/p\u003e\n\u003cp\u003eTo validate the yield differences derived from crop modeling, we also included a comparison of land productivity based on county-level average farmer yields retrieved from USDA-NASS (2024). Details on data sources and spatial and temporal resolutions are shown in \u003cstrong\u003eSupplementary Table S5.\u003c/strong\u003e We computed separate averages for each cropland type (unchanged, new, and converted to urban and non-urban), separately for each crop, and expressed them as a ratio of the unchanged cropland. Finally, we retrieved the historical yield gain for each for the three crops from the slope of the yield versus year relationship between 1970 and 2023 and compared it against the yield gain needed to compensate for the negative effect of cropland conversion to urban on crop production potential \u003cstrong\u003e(Supplementary Table S2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eOur study is subject to several limitations and uncertainties. First, land type may have been misclassified in some cases, leading to potential biases in the estimation of land-use changes. We used the most updated version of NLCD and CDL databases, which are the only publicly available and spatially explicit database on U.S. land-use type available on an annual basis. These databases have been extensively validated and used in the literature (e.g., Huang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xie et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e and references cited therein). Second, there is uncertainty in the simulation of water-limited yield potential and associated data inputs. Our simulations were based on process-based models that have been rigorously validated on their capacity to reproduced measured yields in experiments conducted across a wide range of environments where crops where explicitly managed to avoid nutrient limitations and yield reductions due to biotic stresses (Cou\u0026euml;del et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, our simulations are based on measured weather data, fine-resolution soil maps, and local sowing dates and crop cultivars and a robust method to extrapolate resulting yield potential estimates over space (Aramburu-Merlos et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Third, our analysis of land productivity did not account for all crop types. We note that our analysis accounted for the main crops, which altogether account for 85% of U.S. cropland and for more than 80% of the new cropland and that converted to urban and other uses. Finally, mismatches in spatial resolution between the databases used in our studies may influence some of the results. For example, data on average farmer yield were reported at county level whereas land-use changes were determined at a resolution of 30 meters. Fortunately, most of our databases have similar spatial resolutions and, for those cases in which it is not the case, we have complemented the analysis with other data sources. For example, our comparison of land productivity is based on county-level average yield and fine-resolution yield potential, in both cases arriving to similar findings. Thus, despite the limitations and uncertainties inherent to this type of the analysis, our results can be considered robust and the conclusions from the study valid.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on land cover change from USGS are available at:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://www.usgs.gov/centers/eros/science/national-land-cover-database. Data on crop distribution from USDA are available at: https://nassgeodata.gmu.edu/CropScape/. Data on county average yields from USDA-NASS Quick Stats are available at https://www.nass.usda.gov/Quick_Stats/. Data on water-limited yield potential are available at https://doi.org/10.5281/zenodo.12209709. Data on yield potential fluctuation and water limitation are available at www.yieldgap.org. Elevation data from USGS are available at https://apps.nationalmap.gov/downloader/. Data on soil properties from USDA-NRCS are available at https://gdg.sc.egov.usda.gov/. Source data files are provided with this paper. The data that support the findings of this study are also available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by a joint program from the National Science Foundation of China (T2261129473 to L.Z.) and U.S. National Science Foundation (Grant #2214604 to P.G.), with additional funding from the International Research Center of Big Data for Sustainable Development Goals (CBAS) (CBASYX0906 to L.Z.), and the National Institute of Food and Agriculture of the United States Department of Agriculture (Hatch NEB-22-373 \u0026amp; AFRI Grant #12431808 to P.G.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP. G. and L. Z. designed the study; L.Q., F. L., F. A., X. W., and J. X. prepared the data and/or carried out modeling; L. Z., P. G., F. L., L. Q., Y. M, and F. A. T. analyzed the data, and P. G., L. Z., and F. A. wrote the paper. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAndrade, J., Cassman, K. G., Rattalino Edreira, J. I., Agus, F., Bala, A., Deng, N. \u0026amp; Grassini, P. Impact of urbanization trends on production of key staple crops. \u003cem\u003eAmbio\u003c/em\u003e 51, 1158-1167 (2022).\u003c/li\u003e\n \u003cli\u003eAramburu-Merlos, F., van Loon, M. P., van Ittersum, M. K. \u0026amp; Grassini, P. High-resolution global maps of yield potential with local relevance for targeted crop production improvement. \u003cem\u003eNat. Food\u003c/em\u003e. 5 (2024).\u003c/li\u003e\n \u003cli\u003eB\u0026ouml;hner, J. \u0026amp; Selige, T. 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Historical cropland expansion and abandonment in the continental US during 1850 to 2016. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e 27, 322-333 (2018).\u003c/li\u003e\n \u003cli\u003eZuo, L., Zhang, Z. \u0026amp; Carlson, K. M. \u003cem\u003eet al.\u003c/em\u003e Progress towards sustainable intensification in China challenged by land-use change. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;Sustain\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 1, 304\u0026ndash;313 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7179033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7179033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn a context of economic growth and increasing urban populations, assessing the impact of urban-cropland interactions on crop production in developed countries offers insights about future global scenarios. We quantitatively assessed the impact of urbanization on crop production in the United States since 2000 by combining earth observation products and crop modelling. We found that urbanization has appropriated 2.1 M ha of highly productive cropland, leading to an overall decline in U.S. cropland area over time and a production loss of 15\u0026nbsp;million tons of maize, soybean, and wheat. Avoiding the negative impacts of urbanization on crop production and the environment will require proper land-use planning and urban design and yield intensification on existing cropland.\u003c/p\u003e","manuscriptTitle":"Impact of cropland-urbanization dynamics on crop production in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 08:17:24","doi":"10.21203/rs.3.rs-7179033/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78b38f62-95aa-4577-9e2a-95f10897f456","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52545974,"name":"Earth and environmental sciences/Ecology/Ecological modelling"},{"id":52545975,"name":"Biological sciences/Ecology/Ecological modelling"}],"tags":[],"updatedAt":"2025-09-16T14:39:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 08:17:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7179033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7179033","identity":"rs-7179033","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}