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9 results for “Cropland expansion”
Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions
<p>The datasets contain original data from the article titled" Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions "</p>
Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols
<p><span>This is the dataset to support our paper title of “Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols”. Cropland expansion has been the most significant global land use change since industrialization. However, evaluations of radiative forcing from land use changes have often neglected the radiative effects of secondary organic aerosols (SOA) linked to cropland expansion. Sensitivity experiments using an Earth system model that incorporates advanced SOA processes reveal approximately a 10% reduction in the global biogenic SOA burden due to cropland expansion since industrialization. This reduction weakens SOA</span><span>’</span><span>s role in scattering radiation and forming clouds, leading to a decline in its cooling effect by 146 mW m⁻², which is equivalent to 8% of the warming caused by CO₂ emissions since industrialization. This effect is expected to increase by nearly half under future climate warming and reduced emissions scenarios. Therefore, policies addressing food security and climate change must consider the radiative impacts of biogenic SOA associated with cropland expansion.</span></p> <p><span> </span></p> <p><span>The dataset consists of three zip files, which include model code and output from sensitivity simulations conducted with the Community Earth System Model (CESM) version 1.2.2, using the IMPACT aerosol module and an offline radiative model. The files are described as follows:</span></p> <p><span> </span></p> <p><strong><span>Model code.zip:</span></strong><span> Contains the source code for the IMPACT aerosol module, which was integrated as an additional aerosol module within CESM version 1.2.2, available from the NCAR repository.</span></p> <p><span> </span></p> <p><strong><span>PD_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in present-day cases. The Concentration folder includes 13 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 13 additional subfolders, which share the same names as those in the Concentration folder. These 13 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E20C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions and climate conditions; </span></p> <p><strong><span>20L20E20C</span></strong><span> represents simulations with present-day land use, emissions, and climate conditions.</span></p> <p><span>Eight subfolders for single vegetation type transition experiments include model output for cases where land use transitions from deciduous broadleaf forest to cropland (<strong>DBF2CRO</strong>), evergreen broadleaf forest to cropland (<strong>EBF2CRO</strong>), evergreen needleleaf forest to cropland (<strong>ENF2CRO</strong>), grassland to cropland (<strong>GRA2CRO</strong>), shrubland to cropland (<strong>SHR2CRO</strong>), deciduous broadleaf forest to grassland (<strong>DBF2GRA</strong>), evergreen broadleaf forest to grassland (<strong>EBF2GRA</strong>), and evergreen needleleaf forest to grassland (<strong>ENF2GRA</strong>).</span></p> <p><span>Three subfolders for latitude-specific experiments cover conversions for all vegetation types in tropical (20</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>N, <strong>LLAT</strong>), mid-latitude (50</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>S and 20</span><span>°</span><span>N</span><span>–</span><span>50</span><span>°</span><span>N, <strong>MLAT</strong>), and high-latitude (south of 50</span><span>°</span><span>S and north of 50</span><span>°</span><span>N, <strong>HLAT</strong>) regions.</span></p> <p><span> </span></p> <p><strong><span>FU_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in future cases. The Concentration folder includes 4 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 4 additional subfolders, which share the same names as those in the Concentration folder. These 4 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E21C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>18L21E21C</span></strong><span> represents simulations with pre-industrial land use, future emissions, and future climate conditions;</span></p> <p><strong><span>20L20E21C</span></strong><span> represents simulations with present-day land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>20L21E21C</span></strong><span> represents simulations with present-day land use, future emissions, and future climate conditions.</span></p>
The super-rich and cropland expansion via direct investments in agriculture
<p>Cropland expansion represents an important cause of tropical deforestation, contributing to the loss of ecosystems' functions. Flex-crops (e.g., oil palm, soy, sugar cane) account for an increasing share of cropland and contribute significantly to carbon emissions and biodiversity loss. Various forms of inequality have been shown to impact on agricultural expansion, yet the effect of wealth concentration among the super-rich is understudied. Here I show how, over the period 1991-2014, the large amount of wealth in the hands of high net worth individuals (HNWI) stimulated foreign direct investments in agriculture in Latin America and South-East Asia. This, in turn, drove the expansion of flex-crops areas. The combination of these two effects implies that, a 1% increase in the wealth of HNWI generated an expansion of the flex-crops area share of up to 2.4-10%. The results point to the urgency of addressing wealth inequality to protect the remaining forests.</p>
Global dataset of areas under cropland expansion pressure
<p>To reconcile global sustainability goals, such as protecting biodiversity and the climate, with agricultural production, a spatial understanding of potential future cropland expansion and potentially resulting trade-offs is required. Assuming that the globally most profitable land for cropland expansion is also under the highest pressure to be converted into cropland, we provide a global dataset on the areas under globally highest expansion pressure until 2030 considering future socio-economic and environmental conditions.</p> <p>Using the integrative land-use change model iLANCE (integrative land-allocation sequencer), the relative profitability of cropland expansion is assessed globally at 0.5° spatial resolution. Thereby, future environmental conditions for crop growth (under SSP585) are considered by the crop model PROMET. Socio-economic drivers of land-use change, regional economic conditions and global trade are taken into account by the Computable General Equilibrium model DART-BIO. Thereon based, the areas under the globally highest expansion pressure up to a global cropland increase of +30% (as an upper benchmark) are identified. The data on the area under expansion pressure at each pixel is provided in km² at 0.5° spatial resolution. Based on the relative profitability ranking, we provide additional spatial data at 0.5° spatial resolution indicating the percentage of global cropland expansion under which each pixel is among the globally most profitable ones (from 1% to 30% global cropland expansion). Accordingly, by overlaying both datasets, various scenarios of an increase in future cropland extent from 1% to 30% global cropland expansion can be investigated.</p> <p>The areas under highest expansion pressure are assessed without any restrictions on cropland expansion (EXP scenario) and under a conservation policy scenario that prohibits cropland expansion into forests, wetlands and strictly protected areas (CON scenario), thereby reflecting key aims of the Sustainable Development goals and recent efforts to stop deforestation, protect the climate and preserve biodiversity.</p> <p>Additionally, information on the area under expansion pressure under both scenarios, EXP and CON, is provided in km² at country level for different global cropland expansion scenarios from 1% to 30% (in 1% increments).</p> <p>The provided data could be used in integrated assessment models or impact studies to investigate various potential effects of different future cropland expansion scenarios, for example regarding biodiversity, climate, hydrology, local or regional agricultural production or socio-economic effects. In the study associated with this dataset, potential impacts on agricultural markets, biodiversity intactness and carbon storage are assessed.</p> <p>Information on the spatial patterns of future expansion pressure and resulting trade-offs as well as co-benefits could contribute to improving the spatial planning of conservation measures and to creating more efficient conservation policies.</p> <p> </p> <p><strong>Further information:</strong></p> <p>A detailed description on the methods and underlying data is available in:</p> <p>Schneider, J.M., Delzeit, R., Neumann, C., Heimann, T., Seppelt, R., Schuenemann, F., Söder, M., Mauser, W., Zabel, F. (2024): Effects of profit-driven cropland expansion and conservation policies. Nature Sustainability. </p> <p><a href="https://www.nature.com/articles/s41893-024-01410-x">https://doi.org/10.1038/s41893-024-01410-x</a></p> <p> </p> <p><strong>Contact</strong>:</p> <p>Please contact: Julia M. Schneider (<a href="mailto:Schneider.ju@lmu.de">Schneider.ju@lmu.de</a>), Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.</p> <p>or</p> <p>Florian Zabel (<a href="mailto:florian.zabel@unibas.ch">florian.zabel@unibas.ch</a>), Departement of Environmental Sciences, University of Basel, Basel, Switzerland.</p> <p> </p> <p><strong>Funding</strong>:</p> <p>This project was supported by the German Federal Ministry of Education and Research (grant 031B0230B and grant 031B0788B).</p>
The super-rich and cropland expansion via direct investments in agriculture
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Natural potential for future cropland expansion
<p><strong>Natural potentials for future cropland expansion </strong></p> <p>The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions.</p> <p>We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel.</p> <p>In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure.</p> <p>In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops.</p> <p>Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (I<sub>exp</sub>) that expresses the natural potential for an area to be converted into cropland as follows:</p> <p>I<sub>exp</sub> = S * A<sub>av</sub></p> <p>The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (A<sub>av</sub>) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future.</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publication:</p> <p>Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017).<strong> Addressing future trade-offs between biodiversity and cropland expansion to improve food security</strong>. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1</p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department für Geographie, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</p>
Data from: Comparing the impact of future cropland expansion on global biodiversity and carbon storage across models and scenarios
<p>Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land-use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socio-economic scenarios (SSP1-3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritise future protection of biodiverse and carbon-rich areas.</p>
Data from: Comparing the impact of future cropland expansion on global biodiversity and carbon storage across models and scenarios
Open the record for dataset details and reuse information.
U.S. annual cultivated extent and maps of cropland expansion and cropland abandonment
<p>This repository will eventually contain the data layers of several connected products related to U.S. cropland extent and dynamics, including:</p> <p><br> A set of 30-m resolution maps of <strong>annual cultivated extent </strong>for the conterminous US (CONUS) for the years 1986-2018</p> <p>A 30-m resolution nationwide map of <strong>abandoned croplands </strong>in the U.S., as described by Xie et al. (in review).</p> <p>A 30-m resolution nationwide map of <strong>stable croplands</strong> in the U.S., as described by Uludere-Aragon et al. (in review).</p> <p>A 30-m resolution nationwide map of <strong>cropland expansion</strong> in the U.S. for the period 1986-2018 (in prep). </p> <p> </p> <p> </p> <p>These data were also utilized in related publications, including:</p> <p>Xie and Lark (2021). Mapping annual irrigation from Landsat imagery and environmental variables across the conterminous United States. https://doi.org/10.1016/j.rse.2021.112445</p> <p>Xie et al. (2021). Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017. https://doi.org/10.5194/essd-13-5689-2021</p> <p>O'neil et al. (in prep) Effect of marginal land definitions on biofuel supply chain optimization outcomes.</p>
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