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154 results for “croplands”
Data from: Global trends of cropland phosphorus use and sustainability challenges
<p>To meet the growing food demand while addressing the multiple challenges of exacerbating phosphorus (P) pollution and depleting P rock reserves, P use efficiency (PUE, the ratio of productive P output to P input in a defined system) in crop production needs to be improved. Although many efforts have been devoted to improving nutrient management practices on farms, few studies have examined the historical trajectories of PUE and their socioeconomic and agronomic drivers on a national scale. Here we present a database of the P budget (the input and output of the crop production system) and PUE by country and by crop type for 1961–2019 and examine the substantial contribution of several drivers for PUE, such as economic development stages and crop portfolios. To address the P management challenges, we found that global PUE in crop production must increase to 68–81%, and recent trends indicate some meaningful progress towards this goal. However, P management challenges and opportunities in croplands vary widely among countries.</p>
Quantifying nitrogen deposition inputs to cropland: A national scale dataset from 1961 to 2020
<p>Nitrogen (N) deposition is one of the major inputs to cropland and consequently important for the estimation of N Use Efficiency (NUE) for crop production. However, the estimates for N deposition carry large uncertainty, and existing assessments of N budgets and NUE on agricultural land use different estimates of N deposition. To evaluate the uncertainties in existing methods for national scale N deposition estimation and assess their impacts on the resulting NUE estimation for countries around the world, we 1) reviewed existing methods and related data sources for quantifying N deposition inputs to crop production on a national scale; 2) identified the most up–to–date data sources and designed methods to quantify N deposition input to crop production on a national scale; 3) collected N deposition data from observation sites in major countries (e.g., UK, US, and China) to validate the estimated N deposition input; and 4) conducted sensitivity analysis to evaluate how the uncertainties in N deposition affect crop NUE assessment. As a result, we established four estimates for N deposition inputs on cropland for 251 countries around the world during 1961–2020 as combinations of two sets of N deposition maps (ACCMIP<sup>1</sup> and Wang <em>et al</em>.<sup>2-4</sup>) and two sets of cropland maps (HYDE<sup>5</sup> and LUH2<sup>6</sup>). The four products (1. <strong>AH</strong>: ACCMIP and HYDE, 2. <strong>AL</strong>: ACCMIP and LUH2, 3. <strong>WH</strong>: Wang <em>et al</em>. and HYDE, and 4. <strong>WL</strong>: Wang <em>et al</em>. and LUH2) show good agreement in N deposition estimates for the majority of countries, but have large differences in several Asian countries (e.g., China, India, and Pakistan), and the differences are mostly caused by the use of different N deposition maps. According to the comparison with the observation records in China, the deposition estimates based on Wang <em>et al</em>. show a better agreement with the observations. Hence, the authors recommend using product #4 <strong>WL</strong> (Wang <em>et al</em>. and LUH2) as the reference dataset for N deposition in the global assessments of N budgets by countries. </p> <p><strong>References:</strong></p> <ol> <li>Lamarque, J. F. <em>et al</em>. Multi-model mean nitrogen and sulfur deposition from the atmospheric chemistry and climate model intercomparison project (ACCMIP): Evaluation of historical and projected future changes. <em>Atmos. Chem. Phys</em>. <strong>13</strong>, 7997–8018 (2013).</li> <li>Shang, Z. <em>et al</em>. Weakened growth of cropland-N2O emissions in China associated with nationwide policy interventions. <em>Glob. Chang. Biol</em>. <strong>25</strong>, 3706–3719 (2019).</li> <li>Wang, Q. <em>et al</em>. Data-driven estimates of global nitrous oxide emissions from croplands. <em>Natl. Sci. Rev</em>. <strong>7</strong>, 441–452 (2020).</li> <li>Wang, R. <em>et al</em>. Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100. <em>Glob. Chang. Biol</em>. <strong>23</strong>, 4854–4872 (2017).</li> <li>Goldewijk, K. K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene - HYDE 3.2. <em>Earth Syst. Sci</em>. <em>Data</em> <strong>9</strong>, 927–953 (2017).</li> <li>Hurtt, G. C. <em>et al</em>. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6. <em>Geoscientific Model Development </em><strong>13</strong>, (2020).</li> </ol>
Climate mitigation potential and soil microbial response of cyanobacteria-fertilized bioenergy crops in a cool semi-arid cropland
<p>Bioenergy carbon capture and storage (BECCS) systems can serve as decarbonization pathways for climate mitigation. Perennial grasses are a promising second-generation lignocellulosic bioenergy feedstock, but optimizing their sustainability, productivity, and climate mitigation potential requires an evaluation of how nitrogen (N) fertilizer strategies interact with greenhouse gas (GHG) and soil organic carbon (SOC) dynamics. Further, crop and fertilizer choice can affect the soil microbiome which is critical to soil organic matter turnover, nutrient cycling, and sustaining crop productivity but these feedbacks are poorly understood due to the paucity of data from agroecosystems. Here, we examine the climate mitigation potential and soil microbiome response to establishing two functionally different perennial grasses, switchgrass (Panicum virgatum, C4), and tall wheatgrass (Thinopyrum ponticum, C3), in a cool semi-arid agroecosystem under two fertilizer applications, a novel cyanobacterial biofertilizer (CBF) and urea. Finally, we examine shifts in soil microbial composition resulting from crop establishment and fertilizer regime. We find that in contrast to the C4 crop, the C3 crop achieved 98% greater productivity and had a higher N use efficiency when fertilized and the CBF produced the same biomass enhancement as urea. Non-CO2 greenhouse gas fluxes across all treatments were low and we observed a three-year net loss of SOC under the C4 crop and a net increase under the C3 crop at a 0-30 cm soil depth regardless of fertilization. Further, we detected crop-specific changes in the soil microbiome, including an increased relative abundance of arbuscular mycorrhizal fungi under the C3, and potentially pathogenic fungi in the C4 grass. Taken together, these findings highlight the potential of CBF-fertilized C3 crops as a second-generation bioenergy feedstock in semiarid regions as a part of a climate mitigation strategy.</p>
Figure 2 in Morphometry And Eye Morphology Of Harpalus (Proteonus) Distinguendus (Duftschmid, 1812) And H. (Amblystus) Rufipalpis (Sturm, 1818) (Coleoptera: Carabidae), Two Congeners Inhabiting Abandoned Croplands
Figure 2. Measured traits of Harpalus rufipalpis female and male individuals. Trait units in
In-situ observations of nitrate loss factor for "Estimation method is the primary source of uncertainty in cropland nitrate leaching estimate in China"
<p>This database includes In-situ observations of nitrate loss factor. Details can be found in paper named "Estimation method is the primary source of uncertainty in cropland nitrate leaching estimate in China".</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>
Biological nitrogen fixation in cropland
<p>Methods are documented in the README.md of the source code repository:</p> <p><a href="https://doi.org/10.5281/zenodo.7133336">https://doi.org/10.5281/zenodo.7133336</a></p> <p>The version number of this dataset corresponds to the version number of the source code.</p>
National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets
<p>Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.</p>
Detecting ecological traps in human-altered landscapes: A case study of the thick-billed longspur nesting in croplands
<p>Conversion of the North American prairies to cropland remains a prominent threat to grassland bird populations. Yet, a few species nest in these vastly modified systems. The thick-billed longspur (<em>Rhynchophanes mccownii</em>) is an obligate grassland bird whose populations have declined 4% annually during the past 50 years. Thick-billed longspurs historically nested in recently disturbed or sparsely vegetated patches within native mixed-grass prairie, but observations of longspurs in spring cereal and pulse crop fields during the breeding season in northeastern Montana, USA suggest such fields also provide cues for habitat selection. Maladaptive selection for poor-quality habitat may contribute to ongoing declines in longspur populations, but information on thick-billed longspur breeding ecology in crop fields is lacking. We hypothesized that these crop fields may function as ecological traps; specifically, we expected that crop fields may provide cues for territory selection, but frequent human disturbance and increased exposure to weather and predators would have negative consequences for reproduction. To address this hypothesis, we compared measures of habitat selection (settlement patterns and trends in abundance) and productivity (nest density, nest survival, and number of young fledged) between crop fields and native grassland sites during 2020–21. Across both years, settlement patterns were similar between site types and occupancy ranged from 0.52 ± 0.17SE to 0.99 ± 0.01 on April 7 and 30, respectively. Early season abundance differed by year, and changes in abundance during the breeding season appeared to be associated with precipitation-driven vegetation conditions rather than habitat type. While an index of nest density was lower in crop than native sites, the number of young fledged per successful nest (2.9 ± 0.18SE) and nest survival (0.24 ± 0.03 SE; n=222 nests) were similar for crop and native sites. Collectively, the data did not support our ecological trap hypothesis: longspurs did not exhibit a clear preference for crop sites and reproductive output was not significantly reduced. Our results indicate that croplands may provide alternative breeding habitat within a human-dominated landscape.</p>
Global reference data set for validating ESA WorldCereal temporary cropland extent
<p>IIASA team has created a new validation data set, which is completely independent from all other existing maps or reference data sets, and which is in line with the cropland definitions and mapping period of the WorldCereal products. To decide if this is an active cropland in each period, the experts looked at very high-resolution Google historical imagery and Google Street level images, Microsoft Bing images, ESRI imagery, Planet historical data, Sentinel-2 time series, and Modis NDVI time series. The experts were asked to label 5 by 5 Sentinel pixels (each pixel 10m by 10m) either as winter crops, or as summer crops, or as maize (if this was possible), or as active crops (where it was not possible to confirm a growing season, e.g. overlap between seasons was too big or crop fields were too small in size), or as no crops, or as not sure where was too little information available for 2021. There was additional question on irrigation system, either circle, or other irrigation, or rainfed, or not sure. </p> <p>Fields:</p> <p>"rowid" -unique row identifier</p> <p>"submissionid" – unique submission id, which consists of 25 single pixels (rows)</p> <p>"timestamp" – time stamp of each submission</p> <p>"sampleid" – unique sample site, which could have a few submission ids</p> <p>"submission_itemid" – unique single pixel submission id</p> <p>"enhancement" – unique legend identifier in the Geo-Wiki database</p> <p>"question" – question asked (either on crop presence or irrigation)</p> <p>"answer" – answer to a question asked</p> <p>"sub_pixel_x","sub_pixel_y" – centroids of a single pixel in WGS84</p>
Validating NISAR's cropland mapping approach and the USDA/NASS Cropland Data Layer against ground truth data in a fragmented urban agricultural region
<p>Field data used in manuscript:</p> <p>1 shapefile containing the ROI outline for which Sentinel-1 data was cropped</p> <p>1 shapefile containing the 93 fields investigated with their names, types and sizes as attributes </p> <p>8 annual csv data for active fields, consisting of 3 harvest dates and 5 planting dates. This list is after translating data from Farmlogic Report (not conducive to analysis in the format) and filtering for fields greater 1 acre. The study lateron further screened to use only fields greater than 2 acreas (0.81 hectares). </p> <p> </p>
Cropland management impacts on soil organic carbon stock changes in US croplands from 1990 to 2015
<p>This geospatial dataset represents soil organic carbon stock changes estimated from a counterfactual analysis of climate-smart soil management practices that were adopted in U.S. croplands between 1990 and 2015. The counterfactual scenarios are relative to historical cropland management implemented in the U.S. for the temporal domain of this study. These data provide a large-scale overview of the carbon stock changes in US cropland agricultural soils associated with conservation tillage, manure amendments, cover crops terminated with cultivation, cover crop terminated with herbicide, hay and pasture in rotation with annual crops, set-aside/Conservation Reserve Program lands. Data were generated using the DayCent ecosystem model driven by cropping histories in the USDA National Resources Inventory (NRI) and associated agricultural management data. The average annual stock change was calculated for each management practice to determine the impact. Average rates of annual stock changes on a per-hectare basis (averaged from 1990 to 2015) are presented as a gridded dataset. Data are in a GeoTIFF format on a 5 km grid.</p>
Projections of Future Cropland Abandonment: Impacts to Biodiversity and Carbon Sequestration
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Aridity drives the response of soil organic carbon and inorganic carbon to drought in cropland
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A meta-analysis reveals increases in soil organic carbon following the restoration and recovery of croplands in Southwest China
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Data from: Global trends of cropland phosphorus use and sustainability challenges
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Plastic film residue concentrations in Chinese croplands
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Detecting ecological traps in human-altered landscapes: A case study of the thick-billed longspur nesting in croplands
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Net greenhouse gas balance in U.S. croplands: How can soils be a part of the climate solution?
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Climate mitigation potential and soil microbial response of cyanobacteria-fertilized bioenergy crops in a cool semi-arid cropland
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