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1,425 results for “Agriculture”
Data sets for Interacting effects of cold snaps, rain, and agriculture on the fledging success of a declining aerial insectivore
<p>Climate change predicts the increased frequency, duration, and intensity of inclement weather periods, such as unseasonably low temperatures (i.e., cold snaps) and prolonged precipitation. Many migratory species have advanced the phenology of important life history stages, and as a result will likely be exposed to these periods of inclement spring weather more often, thus risking reduced fitness and population growth. For declining avian species, including aerial insectivores, anthropogenic landscape changes such as agricultural intensification are another driver of population declines. These landscape changes may affect the foraging ability of food provisioning parents, and reduce the survival of nestlings exposed to inclement weather, through for example pesticide exposure impairing thermoregulation and punctual anorexia. Breeding in agro-intensive landscapes may thus exacerbate the negative effects of inclement weather under climate change. We observed that a significant reduction in the availability of insect prey occurred when daily maximum temperatures fell below 18.3°C, and thereby defined any day where the maximum temperature fell below this value as a day witnessing a cold snap. We then combined daily information on the occurrence of cold snaps and measures of precipitation to assess their impact on the fledging success of Tree Swallows (<em>Tachycineta bicolor</em>) occupying a nest box system placed across a gradient of agricultural intensification. Estimated fledging success of this declining aerial insectivore was 36.2% lower for broods experiencing four cold snap days during the 12 days post hatching period versus broods experiencing none, and this relationship was worsened when facing more precipitation. We further found that the overall negative effects of a brood experiencing periods of inclement weather was exacerbated in more agro-intensive landscapes. Our results indicate that two of the primary hypothesized drivers of many avian population declines may interact to further increase the rate of declines in certain landscape contexts.</p>
Data for: Negative effects of agricultural intensification on the food provisioning rate of a declining aerial insectivore
<p>The historical rise of intensive agricultural practices is hypothesized to be related to declines of grassland and aerial insectivorous birds. Drivers of declines may also influence the overall abundance and spatial distribution of insects within agricultural landscapes. Subsequently, the food provisioning rate of birds breeding within more agro-intensive landscapes may be impacted. Lower provisioning rates in agro-intensive landscapes may lead to reduced growth rate, body condition or fledging success of nestlings but also to diminished body condition of food provisioning adults. Results from a previous study supported this hypothesis as the fledging success and proxies of nestling body condition were lowest for an aerial insectivore breeding in more agro intensive landscapes. Of the multiple hypotheses put forward to explain these correlations, one mechanism may act through variation in food provisioning rates. In this study, we expounded on this hypothesis using data derived from the aforementioned study system and assessed if provisioning rates to nestlings and food provisioning behavior of adults varied across a gradient of agricultural intensification in a declining aerial insectivore, the Tree Swallow (<em>Tachycineta bicolor)</em>. We found that the hourly provisioning rate was lower in agro-intensive landscapes, and yet travel distances were longest within less agro-intensive landscapes. Our results highlight that, in order to maximize long term average gain rates, Tree Swallows breeding within agro-intensive landscapes must forage with greater intensity, perhaps at a cost to themselves, or else costs will transfer to growing broods. Our work provides further evidence that agricultural intensification on the breeding grounds can contribute to the declines of aerial insectivores in part through a trophic pathway.</p>
Role of sustainable agricultural intensification in food and nutrition security of smallholder subsistence farmers: Evidence from cereal legume intercropping in Eastern Ethiopia
<p>This data set is a biological data generated through field experimentation on cereal legume intercropping. In general, the contents of the data set are type of treatment (experimental materials), site of experiment, biomass and grain yields, etc. There is no legal and ethical issues related to this data set and its process of generation. The data can be re-used with proper acknowledgement of the authors. </p>
Supplementary data: "Evaluación de Tierras: Elaboración de un modelo de aptitud de uso agrícola para Kernza (Thinopyrum intermedium) en agroecosistemas con distinto grado de artificialización en el Partido de Azul, Provincia de Buenos Aires, Argentina"/"Land Evaluation: an agricultural suitability model for Kernza (Thinopyrum intermedium) in agroecosystems with different land uses intensities in Azul distric, Buenos Aires, Argentina.
<p>This is the data generated for the achievement of the objectives of my thesis and publications.</p>
Supplementary data: "Evaluación de Tierras: Elaboración de un modelo de aptitud de uso agrícola para Kernza (Thinopyrum intermedium) en agroecosistemas con distinto grado de artificialización en el Partido de Azul, Provincia de Buenos Aires, Argentina" (II) /"Land Evaluation: an agricultural suitability model for Kernza (Thinopyrum intermedium) in agroecosystems with different land uses intensities in Azul distric, Buenos Aires, Argentina. (II)
<p>Decision trees for the land suitability model for Kernza.</p>
Supplementary material 2 from: Schmidt M, Weißhuhn P, Augustin J, Funk R, Häfner K, König H, Loft L, Merz C, Meyer C, Piorr A, Reutter M, Stachow U, Stein-Bachinger K, Matzdorf B (2017) Evaluation of the ecosystem services approach in agricultural literature. One Ecosystem 2: e11613. https://doi.org/10.3897/oneeco.2.e11613
Articles which were dropped out
Supplementary material 1 from: Schmidt M, Weißhuhn P, Augustin J, Funk R, Häfner K, König H, Loft L, Merz C, Meyer C, Piorr A, Reutter M, Stachow U, Stein-Bachinger K, Matzdorf B (2017) Evaluation of the ecosystem services approach in agricultural literature. One Ecosystem 2: e11613. https://doi.org/10.3897/oneeco.2.e11613
Evaluation of the ES concept implementation in agricultural scientific literature
Supplementary material 3 from: Schmidt M, Weißhuhn P, Augustin J, Funk R, Häfner K, König H, Loft L, Merz C, Meyer C, Piorr A, Reutter M, Stachow U, Stein-Bachinger K, Matzdorf B (2017) Evaluation of the ecosystem services approach in agricultural literature. One Ecosystem 2: e11613. https://doi.org/10.3897/oneeco.2.e11613
Bibliographic data file containing all articles found with the search query
Figure 3 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figure 3 - Principal component analysis (PCA) of the descriptor variables and the covariate pond area. Relationship between the PCA 1 and PCA 2, points were grouped according to the soybean (white triangle), corn (white circles) and non-agricultural lands (black circles). (TL) Tail Length, (% DNA) percentage of DNA in Tail, (OTM) Olive tail moment, (RNV) Remnant native vegetation.
Figures 4-6 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figures 4-6 - Relationship between comet assay parameters and the pond area according to soybean (gray squares), corn (white circles) and non-agricultural lands (black circles); 4) TL with pond area; 5)% DNA; 6) OTM with pond area. (TL) Tail Length, (% DNA) percentage of DNA in Tail, (OTM) Olive tail moment.
Figure 2 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figure 2 - Differences between the average scores of Soybean, Corn and Non-agricultural lands, generated by the discriminant function scores.
Calibrated Mathematical Programming Models In Agriculture: A Systematic and Bibliometric Review
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Datasets for "Leveraging Machine Learning and Natural Language Processing Techniques for Agriculture Experiment Station Project Classification"
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The environmental indicators and soil particle size of lakeside agricultural land vadose zone soil
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Agricultural Exudate Responsive Metagenome Database (ARM)
<p>Additional File 6: fasta file with concatenated MAGs (334) from the ARM genome resource. </p>
ANALYSIS OF THE STATE OF BANK FINANCING IN AGRICULTURAL PRODUCTION IN CENTRAL ASIA
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INTRODUCTION AND INVESTMENT PROVISION OF INNOVATIVE PROCESSES IN AGRICULTURAL DEVELOPMENT: PROBLEMS AND SOLUTIONS IN THE ECONOMY
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Dataset: Agriculture & Natural Solutions Acquisition Corporation (ANSCU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Agriculture and Livestock Metadata Elements Set (Almes Core)
<p>This document was published for citation purposes only. To access the current version of the Almes Core schema, please refer to <a href="https://w3id.org/AlmesCore/">https://w3id.org/AlmesCore#</a></p> <p>Serializations:</p> <p>HTML | SHACL | JSON-LD</p> <p>Related publication:</p> <p>Soares, F. M., Corrêa, F. E., Pires, L. F., da Silva Santos, L. O. B., Drucker, D. P., Braghetto, K. R., de Abreu Moreira, D., Delbem, A. C. B., da Silva, R. F., da Silva Lopes, C. O., & Saraiva, A. M. (2022). <a href="https://ceur-ws.org/Vol-3235/paper26.pdf">Building a Community-Based FAIR Metadata Schema for Brazilian Agriculture and Livestock Trading Data</a>. In SEMPDW 2022 (CEUR workshop proceedings; Vol. 3235). </p>
Fig. 7 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 7. Integrated biomarkers response index (IBR) values for each experimental site along Água das Araras stream (S1, S2, S3, S4, and S5) where in situ tests were performed. Biomarkers results are represented in relation to the reference site (0). The area above 0 reflects induction of the biomarker, and below 0 indicates reduction of the biomarker.
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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.