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1,425 results for “Agriculture”
Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.
Data from: Diet adaptation in dog reflects spread of prehistoric agriculture
Adaptations allowing dogs to thrive on a diet rich in starch, including a significant AMY2B copy number gain, constituted a crucial step in the evolution of the dog from the wolf. It is however not clear if this change was associated with the initial domestication or represents a secondary shift related to the subsequent development of agriculture. Previous efforts to study this process were based on geographically limited data sets and low-resolution methods and it is therefore not known to what extent the diet adaptations are universal among dogs and whether there are regional differences associated with alternative human subsistence strategies. Here we use droplet PCR to investigate worldwide AMY2B copy number diversity among indigenous as well as breed dogs and wolves to elucidate how a change in dog diet was associated with the domestication process and subsequent shifts in human subsistence. We find that AMY2B copy numbers are bimodally distributed with high copy numbers (median 2nAMY2B=11) in a majority of dogs but no, or few, duplications (median 2nAMY2B=3) in a small group of dogs originating mostly in Australia and the arctic. We show that this patterns correlates geographically to the spread of prehistoric agriculture and conclude that the diet change may not have been associated with initial domestication but rather the subsequent development and spread of agriculture to most, but not all regions of the globe.
Data from: Dispersal constraints for the conservation of the grassland herb Thymus pulegioides L. in a highly fragmented agricultural landscape
Species-rich grassland communities are one of the most important habitats for biodiversity and of high conservation priority in Europe. Restoration actions are mainly focused on the improvement of abiotic conditions, such as nutrient depletion techniques, and are generally based on the assumption that the target community will re-establish at the restored site when the target species exist in the neighborhood. Information on the contemporary seed-dispersal range is therefore crucial to develop effective conservation measures. Here, we investigated the contemporary long-distance seed dispersal and genetic structure of the grassland herb Thymus pulegioides in an intensively managed agricultural landscape in Flanders (Northern Belgium). Assignment tests based on amplified fragment length polymorphisms revealed very low levels of effective seed dispersal between populations although seed availability and seed viability was not a limiting factor. The process of fragmentation has resulted in a high population differentiation and without further incoming gene flow the remnant populations are prone to further genetic erosion and perhaps extinction. Our findings illustrate that restoring suitable abiotic habitat conditions in the neighborhood of existing populations does likely not guarantee colonization for this grassland specialist. For the survival of the species, existing populations should be functionally connected and seed addition may be necessary for successful conservation to overcome dispersal-limitation.
Data from: Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix
Species-area relationships have long been used to assess patterns of species diversity across scales. Here this concept is extended to spectral diversity using hyperspectral data collected by NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over western Michigan. This mixture of mesic forest and agricultural lands offers two end-points on the local-scale diversity continuum – one set of well mixed forest patches and one set of highly homogeneous agricultural patches. Using the sum of the first three principal component values and the principal components' convex hull volume, spectral diversity was compared within and among these plots and to null expectations for perfectly random and perfectly patchy landscapes. Overall the spectral diversity area relationship confirms the patterns that would be expected for this landscape, but this application suggests that this approach could be extended to less well understood landscapes and could reveal key insights about the relative importance of different drivers of community assembly, even in the absence of additional data about plant functional traits or species' identities.
Data from: Overlooked biodiversity loss in tropical smallholder agriculture
Smallholder agriculture is the main driver of deforestation in the western Amazon, where terrestrial biodiversity reaches its global maximum. Understanding the biodiversity value of the resulting mosaics of cultivations and secondary forest is therefore crucial for conservation planning. However, Amazonian communities are organized across multiple forest‐types that support distinct species assemblages, and little is known about smallholder impacts across the range of forest habitats that are essential for sustaining biodiversity. We address this issue with a large‐scale field inventory of birds and trees in primary forest and smallholder agriculture in northern Peru, spanning three key forest‐types that structure Amazonian biodiversity. For birds, smallholder agriculture supported species richness comparable to primary forest within each forest‐type, but biotic homogenization across forest‐types resulted in substantial losses of biodiversity overall. These overall losses are invisible to studies that focus solely on upland (terra firme) forest. For trees, biodiversity losses in upland forests dominated the signal across all habitats combined, and homogenization across habitats did not exacerbate biodiversity loss. Proximity to forest strongly predicted the persistence of forest‐associated bird and tree species in the smallholder mosaic, and because intact forest is ubiquitous in our study area, our results probably represent a best‐case scenario for biodiversity in Amazonian agriculture. Land‐use planning both inside and outside of protected areas should recognize that tropical smallholder agriculture has pervasive biodiversity impacts that are not apparent in typical single‐habitat studies. The full range of forest‐types must be surveyed to accurately assess biodiversity losses, and primary forests must be protected to prevent landscape‐scale biodiversity loss.
Data from: Spatial heterogeneity in landscape structure influences dispersal and genetic structure: empirical evidence from a grasshopper in an agricultural landscape
Dispersal may be strongly influenced by landscape and habitat characteristics that could either enhance or restrict movements of organisms. Therefore, spatial heterogeneity in landscape structure could influence gene flow and the spatial structure of populations. In the past decades, agricultural intensification has led to the reduction in grassland surfaces, their fragmentation and intensification. As these changes are not homogeneously distributed in landscapes, they have resulted in spatial heterogeneity with generally less intensified hedged farmland areas remaining alongside streams and rivers. In this study, we assessed spatial pattern of abundance and population genetic structure of a flightless grasshopper species, Pezotettix giornae, based on the surveys of 363 grasslands in a 430-km² agricultural landscape of western France. Data were analysed using geostatistics and landscape genetics based on microsatellites markers and computer simulations. Results suggested that small-scale intense dispersal allows this species to survive in intensive agricultural landscapes. A complex spatial genetic structure related to landscape and habitat characteristics was also detected. Two P. giornae genetic clusters bisected by a linear hedged farmland were inferred from clustering analyses. This linear hedged farmland was characterized by high hedgerow and grassland density as well as higher grassland temporal stability that were suspected to slow down dispersal. Computer simulations demonstrated that a linear-shaped landscape feature limiting dispersal could be detected as a barrier to gene flow and generate the observed genetic pattern. This study illustrates the relevance of using computer simulations to test hypotheses in landscape genetics studies.
Data from: Plant and insect microbial symbionts alter the outcome of plant-herbivore-parasitoid interactions: implications for invaded, agricultural and natural systems
1. Understanding how soil microbial communities influence plant interactions with other organisms, and how this varies with characteristics of the interacting organisms, is important for multiple systems. Solanum spp. are a suitable model for trophic interactions in studies of agricultural and natural systems and can also provide useful corollaries in invaded systems. This study examined the influence of soil mutualist arbuscular mycorrhizal (AM) fungi on growth of different Solanum types fed on by the potato aphid, Macrosiphum euphorbiae, in relation to presence of the aphid facultative endosymbiont Hamiltonella defensa. 2. Four Solanum types comprising two wild species, S. berthaultii and S. polyadenum, and two accessions of S. tuberosum, were grown with or without AM fungi and infested with one of four clonal lines of a single M. euphorbiae genotype (two with and two without H. defensa). Two experiments were conducted to i) characterise plant responses to AM fungi and aphids and ii) assess whether soil AM fungi could influence the success of the parasitoid wasp Aphidus ervi when attacking aphids reared on each Solanum type. 3. In both experiments, similar patterns of plant biomass were observed in relation to AM fungal and aphid treatments. Solanum biomass depended on plant type and aphid infection with H. defensa. Plants exposed to aphids harbouring H. defensa had smaller root biomass, and therefore total plant biomass, compared to plants infested with H. defensa-free aphids. M. euphorbiae performance varied with aphid clonal line, Solanum type and presence of AM fungi. 4. Parasitoid success, measured as the proportion of aphids from which a wasp emerged, was highest from aphids that had fed on plants colonised by AM fungi, although this result also varied with Solanum type and aphid clonal line. 5. Synthesis: The presence of soil AM fungi, combined with within-species plant and insect variation in key traits, can have subtle - but significant - effects on plant fitness and insect success. This study highlights the importance of exploring genotypic variation in plant and pest responses to soil microbiota to identify suitable biocontrol options.
Agriculture and the Third World
<p>In 1995, the polymerase chain reaction was 10 years old. Since its invention by Kary Mullis, it has become the bedrock of DNA research, gene discovery, diagnostics development, forensic investigation and environmental science.</p> <p>To mark the anniversary, a conference sponsored by the Perkin-Elmer Corporation was held at Cold Spring Harbor Laboratory in September 1994. Outstanding scientists from a variety of fields reviewed the impact of the technique on their specialties, discussing the present and future applications of PCR technology. Their talks have been captured in this unique videotape library, which will appeal to scientists who apply PCR to problems in human, animal and plant genetics, cell biology, diagnostics, forensic science and molecular evolution.</p> <p>Copyright 1995 Cold Spring Harbor Laboratory Press</p> <p>ISBN 0-87969-473-4 VHS</p> <p>ISBN 0-87969-474-2 PAL</p> <p> </p> <p> </p>
FIGURE 46. A in Jinhaku Sonan's skipper type collection deposited at Taiwan Agricultural Research Institute (Lepidoptera: Hesperiidae)
FIGURE 46. A map showing the distribution of Polytremis kiraizana Sonan and taxa closely related to the species: Circle denotes P. kiraizana, triangle P. suprema and square P. m a t s u i i.
FIGURES 43–45 in Jinhaku Sonan's skipper type collection deposited at Taiwan Agricultural Research Institute (Lepidoptera: Hesperiidae)
FIGURES 43–45. Male genitalia of Polytremis mencia (based on a specimen collected from "Tianmushan, Zheziang [=Zhejiang Province], [China], May–June 1982"): 43, dorsal view of tegumen, 44, lateral view of 9th + 10th sclerites with left valva attached, 45, dorsal view of phallus. Scale bar = 1 mm.
FIGURES 40–42 in Jinhaku Sonan's skipper type collection deposited at Taiwan Agricultural Research Institute (Lepidoptera: Hesperiidae)
FIGURES 40–42. Male genitalia of Polytremis kiraizana (based on a specimen collected from "Formosa [=Taiwan], Sept 1974"): 40, dorsal view of tegumen, 41, lateral view of 9th + 10th sclerites with left valva attached, 42, dorsal view of phallus. Scale bar = 1 mm.
FIGURES 19–39 in Jinhaku Sonan's skipper type collection deposited at Taiwan Agricultural Research Institute (Lepidoptera: Hesperiidae)
FIGURES 19–39. Types of skipper taxa described by Jinhaku Sonan: 19–21, paratype of Augiades bouddha niitakana, upperside, underside, labels; 22–24, holotype of Parnara kotoshona, upperside, underside, labels; 25–27, holotype of Parnara ranrunna, upperside, underside, labels; 28–30, holotype of Pamara kiraizana, upperside, underside, labels; 31– 33, holotype of Gangara thyrsis hainana, upperside, underside, labels; 34–36, holotype of Telicota palmarum hainanum, upperside, upperside, labels; 37–39, holotype of Tagiades menaka hainana, upperside, underside, labels.
FIGURES 1–18 in Jinhaku Sonan's skipper type collection deposited at Taiwan Agricultural Research Institute (Lepidoptera: Hesperiidae)
FIGURES 1–18. Types of skipper taxa described by Jinhaku Sonan: 1–3, holotype of Lobocla kodairai, upperside, underside, labels; 4–6, holotype of Coladenia sadakoe, upperside, underside, labels; 7–9, holotype of Tagiades menaka kotoshona, upperside, underside, labels; 10–12, holotype of Notocrypta arisana, upperside, underside, labels; 13–15, holotype of Ampittia maro matsumurai, upperside, underside, labels; 16–18. holotype of Augiades bouddha niitakana, upperside, underside, labels.
FIGURES 1–7 in A new species and key to species of the agriculturally important sharpshooter genus Sonesimia Young (Hemiptera: Cicadellidae: Cicadellini)
FIGURES 1–7. Sonesimia nessimiani, sp. nov. Male holotype: 1, dorsal habitus; 2, head, pronotum, and mesonotum, dorsal view; 3, pygofer, lateral view; 4, apical left portion of pygofer, dorsal view; 5, valve and subgenital plates, ventral view; 6, styles and connective, dorsal view; 7, aedeagus, lateral view. Scale bars in mm.
FIGURES 8–12 in A new species and key to species of the agriculturally important sharpshooter genus Sonesimia Young (Hemiptera: Cicadellidae: Cicadellini)
FIGURES 8–12. Sonesimia nessimiani, sp. nov. Female paratype: 8, abdominal sternite VII, ventral view; 9, pygofer, lateral view; 10, internal abdominal sternite VIII, dorsal view; 11, first valvula of ovipositor, apical portion and dorsal sculpturing in detail, lateral view; 12, second valvula of ovipositor, dorsal and apical portions in detail, lateral view. Scale bars in mm.
Supplementary Materials for: Discovery of a novel merbecovirus DNA clone contaminating agricultural rice sequencing datasets from Wuhan, China
<p>Supplementary Materials for</p><p><strong>Discovery of a novel merbecovirus DNA clone contaminating agricultural rice sequencing datasets from Wuhan, China</strong></p><p>Adrian Jones, Daoyu Zhang, Steven E. Massey, Yuri Deigin, Louis R. Nemzer, Steven C. Quay</p>
A systems analysis of sustainability impacts of agricultural policies in India - Supporting Information
<p>This Supporting Information provides additional details about the HTE framework applied to study sustainability challenges and interventions in the rice-wheat cropping system of Punjab, India. It includes: </p><p>Supp. Info Word document with: </p><ul><li>Text S1-S2 (Table S1-S3) on detailed quantitative model set-up and model validation and sensitivity analysis results</li><li>Text S3-S4 (Table S4-S5) on methods used to evaluate the impacts of interventions on interactions, specifying direct (structural) and indirect (quantitative) changes as well as sustainability benefits using the inclusive wealth approach.</li><li>Text S5 on expert interviews conducted to inform choice of policy options analyzed in this work </li></ul><p> </p><p>Supp. Info Excel spreadsheet Data Set S1 with the following tables:</p><p>Data Table SD1: List of system components and their attributes</p><p>Data Table SD2: Detailed interaction matrix between system components</p><p>Data Table SD3: Attributes of crops and residues: crop production, protein content and residue generation</p><p>Data Table SD4: Attributes of crops: use of agricultural inputs for crop production </p><p>Data Table SD5: Attributes of technical components: Emission factors and GWP</p><p>Data Table SD6: Values of system components' attributes at t=1 (year=2019)</p><p>Data Tables SD7-SD14: Detailed quantitative impacts of interventions on sustainability metrics</p>
Selection and application of agri-environmental indicators to assess potential technologies for nutrient recovery in agriculture (Data Sets)
Open the record for dataset details and reuse information.
Assessment of social aspects across Europe resulting from the insertion of technologies for nutrient recovery and recycling in agriculture (Data Sets)
Open the record for dataset details and reuse information.
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
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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.