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1,425 results for “Agriculture”
Quantitative assessment of agricultural sustainability reveals divergent priorities among nations
<p><span>Agriculture is fundamental to all three pillars of sustainability, environment, society, and economy. However, the definition of sustainable agriculture and capacities to measure it remain elusive. Independent and transparent measurements of national sustainability are needed to gauge progress, encourage accountability, and inform policy. Here, we developed a Sustainable Agriculture Matrix (SAM) to quantify national performance indicators in agriculture and to investigate the tradeoffs and synergies based on historical data for most countries of the world. The results reveal priority areas for improvement by each country and show that the trade-offs and synergies among indicators often differ. Exceptions to common economic-versus-environmental trade-offs, for example, offer opportunities to learn from countries with synergistic pathways for multiple sustainability indicators. These SAM indicators will improve as data become more available, but this version offers a useful starting point for evaluating progress, identifying priorities for improvement, and informing national policies and actions towards sustainable agriculture.</span></p>
Structural implications of traditional agricultural landscapes on the functional diversity of birds near the Korean Demilitarized Zone
<p><span>Bird assemblages are sensitive to changes in landscape composition and the environment, such as those that result from drought. In this study, the relationship between landscape composition and avian functional diversity in traditional agricultural ecosystems in the Civilian Control Zone (CCZ) of Korea was examined. In addition, the resilience of biodiversity to changes in landscape elements resulting from drought conditions was investigated. The traditional agricultural landscape (TAL) of the sites studied was divided into three types: TAL 1 had a high proportion of rice paddies, TAL 2 included large forest areas, and TAL 3 represented areas with drylands. Of these, TAL 1 showed the highest species richness and functional richness, but these measures were most vulnerable to drought. Meanwhile, TAL 2 showed that the bird communities were more tolerant under drought event. This study shows that to conserve and enhance the diversity of birds in traditional agricultural landscapes of Northeast Asia, active management of forest areas is needed to protect bird populations. In addition, commercial pressures to develop this area will require urgent biodiversity conservation plans to protect the unique biodiversity of the Korean CCZ. This study thus provides landscape management guidance for conservation planning.Bird assemblages are sensitive to changes in landscape composition and the environment, such as those that result from drought. In this study, the relationship between landscape composition and avian functional diversity in traditional agricultural ecosystems in the Civilian Control Zone (CCZ) of Korea was examined. In addition, the resilience of biodiversity to changes in landscape elements resulting from drought conditions was investigated. The traditional agricultural landscape (TAL) of the sites studied was divided into three types: TAL 1 had a high proportion of rice paddies, TAL 2 included large forest areas, and TAL 3 represented areas with drylands. Of these, TAL 1 showed the highest species richness and functional richness, but these measures were most vulnerable to drought. </span>Meanwhile, TAL 2 showed that the bird communities were more tolerant under drought event. <span>This study shows that to conserve and enhance the diversity of birds in traditional agricultural landscapes of Northeast Asia, active management of forest areas is needed to protect bird populations. In addition, commercial pressures to develop this area will require urgent biodiversity conservation plans to protect the unique biodiversity of the Korean CCZ. This study thus provides landscape management guidance for conservation planning.</span></p>
A Spatiotemporal Dataset of Irrigated Agricultural Areas Across the Coastal Plain Region of South Carolina; USA
<p>A Spatiotemporal Dataset of Irrigated Agricultural Areas Across the Coastal Plain Region of South Carolina; USA</p>
Agriculture causes homogenization of plant-feeding nematode communities at the regional scale
<p>1. An emerging research line in conservation ecology addresses how environmental change drivers may cause the biotic homogenization of ecological communities by shifts in species diversity and community composition. While the drivers have been explored in unmanaged ecosystems and managed agricultural systems, this issue has received limited attention in regards to a key soil bioindicator organisms, soil nematodes.<br> <br> 2. In this study, we evaluated the effect of land-use change and intensification on the diversity of plant-feeding nematodes (PFN) thought taxonomic and functional measures of alpha and beta diversity. We selected olive tree farms in southern Spain as the study system, given the wide distribution of wild forms in unmanaged systems and cultivated forms in agricultural systems, thus providing the opportunity to assess the effects of land use intensity.<br> <br> 3. Notably, our study revealed that the conversion from natural to agricultural systems and even moderate increases in land-use intensity caused a significant biotic homogenization by enhancing the functional similarities of PFN communities. Our study emphasizes the key role of body size in structuring nematode communities in response to land-use type and intensity. <br> <br> 4. Synthesis and applications. The importance of soil nematodes in soil processes is well known. We show that land use intensification reduces soil nematode diversity. Our study has important implications for the development of management strategies that foster soil biodiversity conservation such as no or minimal tillage and logging, vegetative covers and the maintenance of natural habitat.</p>
Elephant agricultural use metrics in Mara-Serengeti ecosystem
<p>Agricultural use metrics were calculated for 66 elephants as part of a study to characterize crop use tactics in the Mara-Serengeti ecosystem in Kenya and Tanzania. Metrics were calculated to capture mean agricultural use, maximum use from a moving average, and the difference between mean and max use. These metrics were used to classify agricultural use tactics for each elephant using Gaussian mixture models. Tables are provided with metrics and tactic classifications for the lifetime track (TableS3) and individual years (TableS4). Data contained in these files can be used to reproduce and further investigate Guassian mixture model clustering and cutpoint calculation, agricultural use linear mixed models, and tactic change generalized logistic mixed models in Hahn et al. 2021. </p>
Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020: Data and software
<p>This dataset comprises spatial and temporal data related to our analysis on blue and green water consumption (WC) of global crop production in high spatial resolution (5 arc-minutes – approximately 10 km at the equator) for the years 2020, 2010 and 2000.</p> <p><strong>Modelling water consumption of SPAM data<br></strong></p> <p>We use SPAM (Spatial Production Allocation Model) data, released by the International Food Policy research Institute (IFPRI). We use SPAM2020 data for the year 2020 (46 crops), SPAM2010 data for the year 2010 (42 crops) and SPAM2000 data for the year 2000 (20 crops).</p> <p>We develop a Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater. </em>Operating on a daily time scale, CropGBWater dynamically simulates rootzone water balance and related fluxes. We provide this model open access as <a href="https://zenodo.org/api/records/17059989/draft/files/Data_S10_CropGBWater_v02_1c-clean.ipynb/content">Data_S10</a> </p> <p>SPAM2020 crop data are modelled for the years 2018-2022, SPAM2010 crop data for the years 2008-2012 and SPAM2000 crop data for the years 1998-2002. We compute WCbl (blue WC) and WCgn (green WC), with components WCgn,irr (green WC of irrigated area) and WCgn,rf (green WC of rainfed area)<br><br><strong>File description:</strong></p> <p>The data-set consists of the following files:</p> <ul> <li>Data_S4: <a href="https://zenodo.org/records/15779747/files/Data_S4_Y2020_WC_m3_gridded.zip" target="_blank" rel="noopener">Data_S4_Y2020_WC_m3_gridded.zip</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn,rf values in m3 in csv format, year 2020. Individual crop GIS-Rasters for annual m3 amounts are provided as Data_S17</li> <li>Data_S5: <a href="https://zenodo.org/records/15779747/files/Data_S5_Y2020_WC_mm_gridded.zip" target="_blank" rel="noopener">Data_S5_YR2020_WC_mm_gridded_csv</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn, rf in mm as well as SPAM harvested area values in csv format, year 2020. Individual crop GIS-Rasters for annual mm amounts are provided as Data_S18</li> <li>Data_S6: <a href="https://zenodo.org/records/15779747/files/Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx" target="_blank" rel="noopener">Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx</a><br>One grid file (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in m3, differentiating between individual crops, year 2020. </li> <li>Data_S7: <a href="https://zenodo.org/records/15779747/files/Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv" target="_blank" rel="noopener">Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv</a><br>One grid file (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in m3, for the sum of all crops, year 2020</li> <li>Data_S8: <a href="https://zenodo.org/records/15779747/files/Data_S8_YR2000_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S8_YR2000_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2000</li> <li>Data_S9: <a href="https://zenodo.org/records/17059989/files/Data_S9_YR2010_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S9_YR2010_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2010</li> <li>Data_S10: <a href="https://zenodo.org/records/17059989/files/Data_S10_CropGBWater_v02_1c-clean.ipynb">Data_S10_CropGBWater_v02_1c-clean.ipynb</a> Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater</em></li> </ul> <ul> <li>Data_S11: <a href="https://zenodo.org/records/15779747/files/Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xlsx" target="_blank" rel="noopener">Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xls</a><br>Grid (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in mm, for rice, year 2020, RiceAtlas calendar</li> <li>Data_S12: <a href="https://zenodo.org/records/15779747/files/Data_S12_INPUT_YR2020_cropcalendars.zip" target="_blank" rel="noopener">Data_S12_INPUT_YR2020_cropcalendars.zip</a><br>Modelling INPUT data for year 2020: Crop calendars</li> <li>Data_S13: <a href="https://zenodo.org/records/15779747/files/Data_S13_INPUT_YR2020_ET0.zip" target="_blank" rel="noopener">Data_S13_INPUT_YR2020_ET0.zip</a><br>Modelling INPUT data for year 2020: daily ET0</li> <li>Data_S14: <a href="https://zenodo.org/records/15779747/files/Data_S14_INPUT_YR2020_Precip.zip" target="_blank" rel="noopener">Data_S14_INPUT_YR2020_Precip.zip</a><br>Modelling INPUT data for year 2020: daily Precipitation</li> <li>Data_S15: <a href="https://zenodo.org/records/15779747/files/Soil.zip" target="_blank" rel="noopener">Data_S15_INPUT_YR2020_Soil.zip</a><br>Modelling INPUT data for year 2020: Soil</li> <li>Data_S16: <a href="https://zenodo.org/records/15779747/files/Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip" target="_blank" rel="noopener">Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip</a><br>Modelling INPUT for year 2020: Climate-SPAM-grid and different processing scripts</li> <li>Data_S17: <a href="https://zenodo.org/records/15779747/files/Data_S17_Y2020_WC_m3_GisRasters.zip" target="_blank" rel="noopener">Data_S17_Y2020_WC_m3_GisRasters</a><br>GIS-Rasters of individual crops for year 2020 (as well as the sum of all crops), values in m3 per year, differentiation between WCbl, WCgn,irr and WCgn</li> <li>Data_S18: <a href="https://zenodo.org/records/15779747/files/Data_S18_Y2020_WC_mm_GisRasters.zip" target="_blank" rel="noopener">Data_S18_Y2020_WC_mm_GisRasters</a><br>GIS-Rasters of individual crops for year 2020, values in mm per year, differentiation between WCbl, WCgn,irr and WCgn</li> </ul> <p><em>Please only use the latest version of this zenodo repository</em></p> <p><strong>Publication:</strong></p> <p>For all details, please refer to the open access paper:</p> <p>Chukalla, A.D., Mekonnen, M.M., Gunathilake, D., Wolkeba, F.T., Gunasekara, B., Vanham, D. (2025) Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020, Nature Food, Volume 6, <a href="https://doi.org/10.1038/s43016-025-01231-x" target="_blank" rel="noopener">https://doi.org/10.1038/s43016-025-01231-x</a></p> <p><strong>Funding:</strong></p> <p>This research, led by IWMI, a CGIAR centre, was carried out under the CGIAR Initiative on Foresight (<a href="www.cgiar.org/initiative/foresight/" target="_blank" rel="noopener">www.cgiar.org/initiative/foresight/</a>) as well as the CGIAR “Policy innovations” Science Program (<a href="www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations" target="_blank" rel="noopener">www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations</a>). The authors would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund (<a href="www.cgiar.org/funders" target="_blank" rel="noopener">www.cgiar.org/funders</a>).</p> <p><strong> </strong></p>
Social-ecological cascade effects of land-use on vertebrate pest dynamics in arid agricultural communities
<p>Extensive land conversion to agriculture in drylands and associated resource use have wide-ranging impacts on desert ecosystems globally. Incorporating the impacts of human-social aspects is thus imperative in examining ecological interactions. The provision of agricultural inputs in these resource-scarce regions supports invasive and pest species, negatively impacting both agricultural productivity and native desert ecosystems. Understanding the spatial dynamics of invasive and pest species requires analyzing both bottom-up resource availability factors underlying animal distributions and top-down biological controls. Here, we evaluate the social-ecological cascading effects of dryland agriculture on vertebrate pest communities in dryland agricultural communities of Israel. Our study region is characterized by 18 agricultural cooperatives with distinct crop regimes due to contrasting social decision-making and resource allocation schemes (i.e., communal Kibbutzim vs. privatized Moshavim). Crop choices further affect land management (e.g., enclosed vs. open farm systems) and resource intensity. This system is ideal to study trophic mechanisms underlying animal assemblages between agricultural regimes. We examine the role of agricultural land-use practices on pest spatial distributions based on multi-year vertebrate pest observations with agricultural datasets. We use structural equation modelling (SEM) to quantify the relative importance of added agricultural resources underlying bottom-up and top-down trophic processes regulating vertebrate pest assemblages. Results reveal that crop choices determine pest distributions through bottom-up processes directly, while simultaneously driving pest competitive interactions through indirect top-down cascades impacting pest communities. For example, due to the indirect negative effect of wolves on meso-predators (foxes and jackals) mediated by livestock, the total positive effect of livestock on the abundance of meso-predators is reduced. Our study illustrates the social-ecological cascading effects of agricultural regimes on pest community assemblages mediated by contrasting agricultural land-use practices. Considering the expansion of dryland agro-ecological systems globally, understanding the intricate cascading pathways of predator- and prey-pest communities has important implications for agricultural management, biological invasions in drylands and fragile desert environments.</p>
Replication Package for: Down the River: Glyphosate Use in Agriculture and Birth Outcomes of Surrounding Populations
<p>This package contains all the code necessary to reproduce the figures and tables in Dias, Rocha, and Soares (Forthcoming). “Down the River: Glyphosate Use in Agriculture and Birth Outcomes of Surrounding Populations”. Review of Economic Studies. We also provide the raw data and the codes necessary to generate the final datasets. The only raw datasets not provided are the births, mortality, and Census microdata due to their size. We provide the cleaned and aggregated data we use and indicate how to access these (and all other) datasets.</p>
Agricultural cost and benefit data of China at regional level
<p>Cost-benefit of Agricultural Products in China at regional level, published by China Statistics Press, China, 2011.</p>
Microfarms and computational agriculture: a future of farming?
<p><strong>The following video introduces the Robots for Microfarms (ROMI) project and describes the concept of Micro Farms and Computational Agriculture. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(MINCHIN) Micro-Farms are farms which are smaller than five hectares and that is a European commission designation of that type of farm which are very common and proliferate all across Europe and are growing in number.<br> <br> (HANAPPE) Micro-farms the way I see it now would be a small farm 1000 square meters maybe a bit more using poly-cropping to really optimise the space and situated next to the city, with the direct link to the city.<br> <br> (MINCHIN) But the Farms themselves are very interesting because being a small scale they cannot compete with a conventional agriculture and for that reason often turn to more diverse crops, polyculture systems and diversity as a solution for their markets. difference information<br> <br> (SERRA CANTí) An organic farm is a space where the coal is to cultivate the entire ecosystem, and to do so we focus on different crops that help and complement between them, and are not monocultures. Rotating crop beds, adding organic matter, structuring the soil and taking care of the life in the soil such as microorganisms, bacteria, fungi. All of this ultimately ensure that the plant is healthy and does not have so many pests and diseases. A conventional field can have many hectares with the same crop, they are monocultures. That means that if one disease comes, or one insect it can reproduce exponentially. But instead if you have different crops, this stops because the food that it has is limited.<br> <br> (MINCHIN) When we work with Organic agriculture we're really looking to a tradition of companion planting and polyculture which is much more complex than the conventional agriculture of today. So what we're seeing here is planting of tomatoes together with lettuce and together with a basil and the Basils give a natural defence against predators for the tomatoes but we can also use an intercrop lettuces in the gaps. But dealing with that polyculture becomes rather complex.<br> <br> (HANAPPE) And managing its complexity is difficult of course that's one of the reasons why we went to monoculture in the first place. But now with the digital tools that we have we can reintroduce some of this complexity and handle AI and Robotics to assist us in managing this complexity.<br> <br> (MINCHIN) Farmers working on a small scale with a large diversity of crop finds that the work is often very very labor intensive.<br> <br> (HANAPPE) For two months I've experienced myself at the Chatelain farm and in it it's quite taxing on the knees and the back. Ninety Five percent of the disorders in agriculture are muscular skeletal problems.<br> <br> (MINCHIN) The statistics show that after four years of starting a new Farm, many of these young Farmers actually end up with back problems and really struggle to manage the intensity of that biodiversity, of that diversity of crop. So for ROMI our challenge really is to try and support those young Farmers to deal with the complexity and to manage some of the menial tasks that they face day to day.<br> <br> (BAORI) If we can help a small organic micro-farm stay in business, because we're helping to replace a little bit of the manual labor and the the kind of hardship of that keep their costs down it means that we're encouraging smaller Farms to not use chemicals they can use a Rover for weeding, and it's better for for the land it's better for the farmers it's better for us all really in the end so it's a very worthwhile project.<br> <br> (CAMPRODON) Agricultural itself is technology right it's not lets say we don't wake up one day and suddenly the world looked like it is, no, we built it for thousands of years. And so for me I don't know if there's such a big disconnection or we should think as a big disconnection between traditional farming and this new farming. Things change but I like to see it more as a natural evolution of the way that humans use technology. Basically they develop this cultural practice, that helps them to modify their environment and they apply that to farming.<br> <br> (HANAPPE) What can we do with new technology to help these farms? And then it was at some point you know just sort of well, let's do something very provocative, let's do robotics for these farms. Which was a bit of a clash because many of these farmers actually started farming because they sort of wanted to go back to to Nature. And then saying to them look well we can introduce robotics for you, is a bit of a sort of edgy topic.<br> <br> (MINCHIN) And that really means that we might be able to take the best of the past and combine it with the tools of the future to forge a ‘computational agriculture’ which feeds the land, feeds the ecology and also feeds ourselves. The ROMI tools are really set up to be able to help farmers with the support of biologists and with the support of computation, but it's not there yet. That's why we have open sourced these tools to allow a future development towards those objectives.</p>
FIG. 1 in Factors Influencing Anuran Wetland Occupancy in an Agricultural Landscape
FIG. 1.—Site map: (a) depicts Iowa̕s location within the United States (indicated by gray shading), (b) depicts the location of counties where the study took place within Iowa (indicated by gray shading), and (c) depicts the locations of the 27 wetland study sites with land cover information. This map was created using ArcGIS (v.10.5.1; ESRI 2018).
FIGURE 2 in Earthworms in various agricultural and forest ecosystems in São Carlos-SP Brazil
FIGURE 2. Number of native and exotic species and total earthworm species richness found (including both quantitative and qualitative samples) in the integrated and conventional land use systems, at the experimental area of Embrapa Livestock Southeast and the Federal University of São Carlos (UFSCar) in São Carlos – SP. Refer to Figure 1 for meaning of the abbreviations.
FIGURE 3 in Earthworms in various agricultural and forest ecosystems in São Carlos-SP Brazil
FIGURE 3. Proportion of the total number of individuals of each species collected with the quantitative—TSBF (A) and the qualitative methods (B), in the integrated and conventional land use systems, at the experimental area of Embrapa Livestock Southeast and the Federal University of São Carlos (UFSCar) in São Carlos – SP. Refer to Figure 1 for the meaning of the abbreviations.
FIGURE 1 in Earthworms in various agricultural and forest ecosystems in São Carlos-SP Brazil
FIGURE 1. Location of the land use systems sampled at the Embrapa Livestock Southeast research station and at the Federal University of São Carlos (UFSCAR), in São Carlos county – SP.
Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field
<p>This repository contains the SAS model input data and the model results that can be used to reproduce the water age results for the three study tiles presented in Yu et al. Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field</p> <p><strong>File 1: SAS_model_input_TileX.csv</strong></p> <p>The input data for the SAS model calibration for the three tiles.</p> <p><strong>File 2: behavioral_parameter_sets_Model#1_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 3: behavioral_parameter_sets_Model#2_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 4: Cl_simulation_results_Model#1_TileX.csv</strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 5: Cl_simulation_results_Model#2_TileX.csv </strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 6: median_water_age_Model#1_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 7: median_water_age_Model#2_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 8: File_column_names.txt</strong></p> <p> A text file that explains the column names for each file</p>
Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field [Dataset]
<p>This repository contains the SAS model input data and the model results that can be used to reproduce the water age results for the three study tiles presented in Yu et al. Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field</p> <p><strong>File 1: SAS_model_input_TileX.csv</strong></p> <p>The input data for the SAS model calibration for the three tiles.</p> <p><strong>File 2: behavioral_parameter_sets_Model#1_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 3: behavioral_parameter_sets_Model#2_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 4: Cl_simulation_results_Model#1_TileX.csv</strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 5: Cl_simulation_results_Model#2_TileX.csv </strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 6: median_water_age_Model#1_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 7: median_water_age_Model#2_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 8: File_column_names.txt</strong></p> <p> A text file that explains the column names for each file</p>
Agriculture and Environment in the Swiss Cantons - Dataset
<p>Dataset presented in the Report (same title)</p>
Data from: In situ 15N-N2O site preference and O2 concentration dynamics disclose the complexity of N2O production processes in agricultural soil
<p class="MsoNormal"><span>Arable soil continues to be the dominant anthropogenic source of nitrous oxide (N<sub>2</sub>O) emissions owing to application of nitrogen (N) fertilizers </span><span>and manures across the world. Using</span><span> laboratory </span><span>and </span><em><span>in-situ</span></em><span> studies to elucidate the key factors controlling soil N<sub>2</sub>O emissions remains challenging due to the potential importance of multiple complex processes. We<em> </em>examined soil surface N<sub>2</sub>O fluxes in an arable soil, combined with<em> in-situ</em> high</span><span>-</span><span>frequency measurements of soil matrix oxygen (O<sub>2</sub>) and N<sub>2</sub>O concentrations, </span><em><span>in situ</span></em><span> <sup>15</sup>N labeling, and</span><span> N<sub>2</sub>O <sup>15</sup>N site preference (SP). The </span><em><span>in situ</span></em><span> O<sub>2</sub> concentration and further microcosm visualized spatiotemporal distribution of O<sub>2</sub> both suggested that O<sub>2</sub> dynamics were the proximal determining factor to matrix N<sub>2</sub>O concentration and fluxes due to quick O<sub>2</sub> depletion after N fertilization. Further SP analysis<em> </em>and<em> in situ</em> <sup>15</sup>N labeling experiment revealed </span><span>that the main source for N<sub>2</sub>O emissions was bacterial denitrification during the hot-wet summer with lower soil </span><span>O<sub>2</sub></span><span> concentration, while nitrification or fungal denitrification contributed about 50<span>.0</span>% to total emissions during the cold-dry winter with higher soil </span><span>O<sub>2</sub></span><span> concentration. </span><span>The robust positive correlation between O<sub>2</sub> concentration and SP values underpinned that the O<sub>2</sub> dynamics were the key factor to differentiate the composite processes of N<sub>2</sub>O production in <em>in situ</em> structured soil. Our findings deciphered the complexity of N<sub>2</sub>O production processes in real field conditions, and suggest that O<sub>2</sub> dynamics rather than stimulation of functional gene abundances play a key role in controlling soil N<sub>2</sub>O production processes in undisturbed structure soils. </span><span>Our results help to develop targeted N<sub>2</sub>O mitigation measures and to improve process models for constraining global N<sub>2</sub>O budget.</span></p>
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>
Key stakeholder groups in the agriculture sector
<p>The dataset shown identified stakeholders in the agriculture ecosystem within the literature. The papers included in the analysis are from databases Scopus and Web of Science. The query was conducted in 2020 year. </p>
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.