Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
17
datasets available to search
ShareScore release 0.9.0
Dataset results
17 results for “Agricultural expansion”
Proactive conservation to prevent habitat losses to agricultural expansion
<p>The projected loss of millions of square kilometres of natural ecosystems to meet future demand for food, animal feed, fibre, and bioenergy crops is likely to massively escalate threats to biodiversity. Reducing these threats requires a detailed knowledge of how and where they are likely to be most severe. We developed a geographically explicit model of future agricultural land clearance based on observed historic changes and combine the outputs with species-specific habitat preferences for 19,859 species of terrestrial vertebrates. We project that 87.7% of these species will lose habitat to agricultural expansion by 2050, with 1,280 species projected to lose ≥25% of their habitat. Proactive policies targeting how, where, and what food is produced could reduce these threats, with a combination of approaches potentially preventing almost all these losses while contributing to healthier human diets. As international biodiversity targets are set to be updated in 2021, these results highlight the importance of proactive efforts to safeguard biodiversity by reducing demand for agricultural land.</p>
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 maps of agricultural expansion potential at a 300 m resolution
<p><strong>Global </strong><strong>maps of agricultural expansion potential</strong><strong> at a 300 m resolution </strong></p> <p>This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.</p> <p><strong>Abstract:</strong></p> <p>The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.</p> <p><strong>Data description:</strong></p> <p>We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.</p> <p>Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*<em>"</em>. The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.</p> <p><strong>Repository content:</strong></p> <p>Full resolution layers:<br> - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds.<br> - "readme.txt" is the text file with the basic description and the metadata for the repository.</p> <p>Aggregated layers:<br> This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).</p> <p>File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”</p> <p>- "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.</p> <p>- "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.</p> <p>- "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.</p> <p>Raster metadata:</p> <p>Driver: GTiff<br> Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs</p> <p><strong>Notes on use:</strong></p> <p>Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.</p>
Proactive conservation to prevent habitat losses to agricultural expansion
Open the record for dataset details and reuse information.
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>
The super-rich and cropland expansion via direct investments in agriculture
Open the record for dataset details and reuse information.
Data from: Recent range expansion and agricultural landscape heterogeneity have only minimal effect on the spatial genetic structure of the plant pathogenic fungus Mycosphaerella fijiensis
Understanding how geographical and environmental features affect genetic variation at both the population and individual levels is crucial in biology, especially in the case of pathogens. However, distinguishing between these factors and the effects of historical range expansion on spatial genetic structure remains challenging. In the present study, we investigated the case of Mycosphaerella fijiensis-a plant pathogenic fungus that has recently colonized an agricultural landscape characterized by the presence of potential barriers to gene flow, including several commercial plantations in which disease control practises such as the use of fungicides are applied frequently, and low host density areas. We first genotyped 300 isolates sampled at a global scale on untreated plants in 2 dimensions over a 50x80 Km area. Using two different clustering algorithms, no genetic structure was detected in the studied area, suggesting expansion of large populations and/or no influ ence of potential barriers. Second, we investigated the potential effect of disease control practises on M. fijiensis diversity by comparing populations sampled in commercial vs. food-crop plantations. At this local scale, we detected significantly higher allelic richness inside commercial plantations compared to the surrounding food-crop plantation populations. Analysis of molecular variance (AMOVA) indicated that 99% of the total genetic variance occurred within populations. We discuss the suggestion that high population size and/or high migration rate between populations might be responsible for the absence of any effect of disease control practises on genetic diversity and differentiation.
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008). in Muridae
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008).
Supplementary Materials for: Agricultural expansion raises groundwater and increases flooding in the South American plains
<p>The present are the materials to reproduce the results obtained in the manuscript: "Agricultural expansion raises groundwater and increases flooding in the South American plains."</p>
Data from: Recent range expansion and agricultural landscape heterogeneity have only minimal effect on the spatial genetic structure of the plant pathogenic fungus Mycosphaerella fijiensis
Open the record for dataset details and reuse information.
Data from: Influence of historical land use and modern agricultural expansion on the spatial and ecological divergence of sugarcane borer, Diatraea saccharalis (Lepidoptera: Crambidae) in Brazil
Open the record for dataset details and reuse information.
Similarity between agricultural and natural land covers shapes how biodiversity responds to agricultural expansion at landscape scales
<p><span>The impact of agriculture on biodiversity depends on the extent and types of agriculture and the degree to which agricultural land contrasts with the natural ecosystem. Most research on the latter comes from studies on the influence of different agricultural types within a single ecosystem with far less study on how the natural ecosystem context shapes the response of biodiversity to agricultural production. We used citizen science data from agricultural areas in Canada's Eastern Hardwood-Boreal (forest ecosystem, n=108 landscapes) and Prairie Pothole (prairie ecosystem, n=99) regions to examine how ecosystem context shapes the response of avian species diversity, functional diversity and abundance to the amount of arable crop and pastoral agriculture at landscape scales. Avian surveys were conducted along 8km transects of Breeding Bird Survey routes with land cover assembled within a 20km2 landscape around each transect. The amount of agriculture at which species diversity peaked differed between the forest (15%) and prairie (51%) ecosystems, indicating that fewer species tolerated the expansion of agriculture in the former. In both ecosystems, functional diversity initially increased with agriculture and peaked at higher amounts (forest: 42%, prairie: 77 %) than species diversity suggesting that functional redundancy was lost first as agriculture increased. Species turnover with increasing agriculture was primarily among functional groups in forest where a shift from a low to a high agriculture landscape led to a decline in the percent of the community represented by Neotropical migrants, insectivores, upper foliage gleaners and bark foragers, and an increase in the percent of the community represented by short-distance migrants, granivores, omnivores and ground gleaners. There were few distinct shifts in the percent of the community represented by different functional groups in the prairie ecosystem. Total abundance was the least sensitive measure examined in both ecosystems and indicated that species losses with agriculture are likely followed by numerical compensation from agriculture tolerant species. Our results highlight the importance of ecosystem context for understanding how biodiversity is affected by agricultural production with declines in diversity occurring at lower agricultural extents in ecosystems with lower similarity between natural and agricultural land covers. These findings allow for more specific conservation recommendations including managing for species intolerant to agriculture in prairie ecosystems and limiting the expansion of high contrast agriculture and the loss of semi-natural habitat, such as hedge rows, in historically forested ecosystems.</span></p>
Data from: Exotic flies maintain pollination services as native pollinators decline with agricultural expansion
1.Globally, conversion of natural habitat to agricultural land is a primary driver of declines in critical ecosystem services, including pollination. However, exotic species are often well-adapted to human-modified environments and could compensate for ecosystem services that are lost when native species decline. 2.We measured pollination services (pollen delivery to stigma) provided by wild insects to a mass flowering crop, pak choi Brassica rapa at 12 sites across a gradient of increasing agricultural land use (agricultural expansion) in New Zealand. 3.We found that pollination services increased as the proportion of agricultural land in the surrounding landscape increased; pollination from exotic species exceeded the loss of pollination from native species. However, pollination service delivery became increasingly dominated by a few exotic fly species that were active throughout the day, compared to native species, which had more constrained activity patterns. 4.Synthesis and applications. The best way to ensure continued sufficient crop pollination is to protect and restore diverse natural habitats on or around farms, as species-rich pollinator communities are relatively resilient to further environmental change. However, we show that where human-driven disturbance has caused loss of native pollinator species, exotic pollinators can maintain sufficient pollination. Therefore, in areas where native species loss cannot easily be reversed, decisions about pesticide use and habitat provision that foster populations of beneficial exotic species are likely to maintain pollination service delivery, at least in the short term. This highlights the need for land managers to identify the pollinator communities that are present on their farms, whether native or exotic, and make decisions to support these important communities accordingly.
Data from: Widespread and major losses in multiple ecosystem services as a result of agricultural expansion in the Argentine Chaco
Open the record for dataset details and reuse information.
Data from: Exotic flies maintain pollination services as native pollinators decline with agricultural expansion
Open the record for dataset details and reuse information.
Similarity between agricultural and natural land covers shapes how biodiversity responds to agricultural expansion at landscape scales
Open the record for dataset details and reuse information.
Data from: The impact of income, land, and wealth inequality on agricultural expansion in Latin America
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.