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56 results for “Agricultural management”
The influence of inherent soil factors and agricultural management on soil organic matter
<p>The accumulation of soil organic matter (SOM) is vital to the agronomic and environmental functioning of agroecosystems, yet the relative influence of inherent soil properties and agricultural management practices on SOM dynamics are not often addressed in individual studies. Using a network of 218 operating farm fields across Wisconsin and southern Minnesota, USA, this research employs single variable analysis (ANOVA and regression) and regression tree analysis to assess the effects of soil properties (texture, drainage class, pH) and management variables related to crop rotation, tillage, cover cropping, and manure application on SOM, as well as total organic carbon (TOC) and total nitrogen (TN) in the upper 15 cm. Single variable analysis revealed that greater SOM, TOC, and TN were associated with poorly drained soil, tile-drained fields, high-clay content soil, and high biomass crop rotations. Soil organic matter (SOM) and TOC were strongly related (R<sup>2</sup>=0.71), but different regression trees were produced; SOM was most influenced by clay content, while TOC was most influenced by drainage class. Future assessment for the building of SOM or TOC should be conducted with drainage and texture class categories and on a regional basis, given that these factors influence the practices that occur within landscapes. A rapid building of data sets through unstructured sampling, including an abundance of meta-data, should be a research priority in agricultural science to identify practices to build SOM on a regional basis.</p>
Community reorganization stabilizes freshwater ecosystems in intensively managed agricultural fields
<ol> <li>Sustainable intensification may depend on associating precision farming with the harnessing of ecological principles in crop fields and with integrating farms and non-farmed land in productive landscapes. Small wetlands could play an important role in both pursuits for having high per-unit-area rates of element cycling and species richness while deeply penetrating crop fields. However, their potential for ecosystem service provisioning is unlikely to be met if land management intensification promotes ecosystem destabilization in biomass production. </li> <li>We tested the consequences of land use intensification on various dimensions of freshwater ecosystem stability by means of a large-scale field experiment converting extensive pastures to intensive pastures and sugarcane plantations in Southeastern Brazil. Nested within experimental plots were 4,000-L mesocosms simulating ponds and puddles commonly found in productive landscapes. Mesocosms were monitored for basic physico-chemical parameters, nutrients, pesticides, phytoplankton standing crop, and the spontaneously colonizing biodiversity.</li> <li>3. Despite severe environmental change, the stability of sugarcane communities was no different from that of extensive and intensive pastures. This occurred because the local extinction of a sensitive top dragonfly predator following the application of vinasse and insecticide was compensated by colonization of a suite of more tolerant invertebrate mesopredators such as beetles and bugs. Community stability tended to increase with biomass asynchrony and species richness, evidencing a portfolio effect of biodiversity. Unfortunately, the species richness necessary to stabilize biomass production is unlikely to be available in many sugarcane fields and several other row crops.</li> <li> <em>Synthesis and applications.</em> Ponds and puddles could be effective centers of irradiation of ecosystem service provisioning in agricultural fields in terms of pest control; nutrient accumulation, cycling, and export back to fields; and habitat and stepping stones for freshwater biodiversity. However, the impoverished biodiversity that results from a combination of harsh local conditions and spatial isolation renders pond communities inherently unstable. Given the unlikely, immediate reduction in agrochemical use in much of the intensively managed crop area, a combination of large, protected source wetlands at the margin of fields and small constructed or naturally forming ponds and puddles in plantations could contribute to sustainable intensification.</li> </ol>
Data from: Long-term agricultural management does not alter the evolution of a soybean-rhizobium mutualism
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Data from: Soil carbon change in intensive agriculture after 25 years of conservation management
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Data from: Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity
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Impact of soil inoculation on crop residue breakdown and carbon and nitrogen cycling in organically and conventionally managed agricultural soils
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Data from: Quantifying trade-offs between butterfly abundance and movement in the management of agricultural set-aside strips
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The influence of inherent soil factors and agricultural management on soil organic matter
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Community reorganization stabilizes freshwater ecosystems in intensively managed agricultural fields
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Selection of a diversionary field and other habitats by large grazing birds in a landscape managed for agriculture and wetland biodiversity
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Agricultural management legacy effects on switchgrass growth and soil carbon gains
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Data from: Nitrogen content of herbarium specimens from arable fields and mesic meadows reflect the intensifying agricultural management during the 20th century
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Data from: Prevalence and abundance of bees visiting major conventionally-managed agricultural crops in Brazil
This study brings together data from a series of local pollinator surveys undertaken in major conventionally-managed agricultural crops in Brazil to determine the presence and abundance of bees visiting flowers within crops compared with adjacent off-crop habitats. Surveys were undertaken within crops and in adjacent off-crop areas using broadly the same methodology in flowering soybean, drybean, maize, citrus, coffee, rice, and cotton and in sugarcane immediately harvest. The bee species present were assessed twice per day at three times during crop flowering (post-harvest for sugar-cane). Pan traps were used to collect species present within the crop and off-crop twice per day. Drybeans, citrus, and coffee flowers displayed a consistent medium to high abundance of honey bees based on effort-adjusted transect and spot counts. In contrast, fields with flowering soybeans and cotton showed in-crop lower abundance and rice, corn, and harvested sugar cane showed consistently low numbers or no honey bees. In almost all cases honey bees were the most prevalent and abundant flower visitors within the studied crops. Where non-Apis bees were observed they often belong to the Meliponini (primarily Trigona) or the genera Bombus or Xylocopa. The off-crop assessments of species presence using pan traps showed a far wider diversity of species with greater proportional representation of solitary bee species. Overall, whilst honey bees accounted for the majority of observed bees visiting flowers in the agricultural fields, the off-crop habitat contained a greater abundance and diversity of non-Apis bee populations.
Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
<p><span>The potent greenhouse gas nitrous oxide (N</span><sub><span>2</span></sub><span>O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ~60% of the agricultural flux. Empirical models of N</span><sub><span>2</span></sub><span>O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited </span><span><span>geographic coverage;</span></span><span> process-based biogeochemical models are rarely able to predict daily to monthly emissions with > 20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured </span><span><span>soil</span></span><span> parameters. We used sub-daily N</span><sub><span>2</span></sub><span>O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper U.S. Midwest (~3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (~1100 daily fluxes), to train an ML model that explained 65-89% of daily flux variance with very few input variables –soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and N fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. </span><span><span>This represents a 2-3 times improvement over conventional process-based models and with substantially fewer input requirements.</span></span><span> This coupled approach </span><span><span>offers promise</span></span><span> for better predictions of agricultural N</span><sub><span>2</span></sub><span>O emissions and thus more precise global models and more effective </span><span><span>agricultural mitigation interventions.</span></span></p>
Data from: Flower resource and land management drives hoverfly communities and bee abundance in semi-natural and agricultural grasslands
1. Pollination is a key ecosystem service, and appropriate management, particularly in agricultural systems, is essential to maintain a diversity of pollinator guilds. However, management recommendations frequently focus on maintaining plant communities, with the assumption that associated invertebrate populations will be sustained. 2. We tested whether plant community, flower resources and soil moisture would influence hoverfly (Syrphidae) abundance and species richness in floristically-rich semi-natural and floristically-impoverished agricultural grassland communities in Wales (U.K.), and compared these to two Hymenoptera genera, Bombus and Lasioglossum. Interactions between environmental variables were tested using generalised linear modelling, and hoverfly community composition examined using canonical correspondence analysis. 3. There was no difference in hoverfly abundance, species richness, or bee abundance, between grassland types. There was a positive association between hoverfly abundance, species richness and flower abundance in unimproved grasslands. However, this was not evident in agriculturally improved grassland, possibly reflecting intrinsically low flower resource in these habitats, or the presence of plant species with low or relatively inaccessible nectar resources. There was no association between soil moisture content and hoverfly abundance or species richness. 4. Hoverfly community composition was influenced by agricultural improvement and the amount of flower resource. Hoverfly species with semi-aquatic larvae were associated with both semi-natural and agricultural wet grasslands, possibly because of localised larval habitat. Despite the absence of differences in hoverfly abundance and species-richness, distinct hoverfly communities are associated with marshy grasslands, agriculturally improved marshy grasslands and unimproved dry grasslands, but not with improved dry grasslands. 5. Grassland plant community cannot be used as a proxy for pollinator community. Management of grasslands should aim to maximise the pollinator feeding resource, as well as maintain plant communities. Retaining waterlogged ground may enhance the number of hoverflies with semi-aquatic larvae.
Data from: Habitat restoration promotes pollinator persistence and colonization in intensively managed agriculture
Widespread evidence of pollinator declines has led to policies supporting habitat restoration including in agricultural landscapes. Yet, little is yet known about the effectiveness of these restoration techniques for promoting stable populations and communities of pollinators, especially in intensively managed agricultural landscapes. Introducing floral resources, such as flowering hedgerows, to enhance intensively cultivated agricultural landscapes is known to increase the abundances of native insect pollinators in and around restored areas. Whether this is a result of local short-term concentration at flowers or indicative of true increases in the persistence and species richness of these communities remains unclear. It is also unknown whether this practice supports species of conservation concern (e.g., those with more specialized dietary requirements). Analyzing occupancies of native bees and syrphid flies from 330 surveys across 15 sites over eight years, we found that hedgerow restoration promotes rates of between-season persistence and colonization as compared with unrestored field edges. Enhanced persistence and colonization, in turn, led to the formation of more species-rich communities. We also find that hedgerows benefit floral resource specialists more than generalists, emphasizing the value of this restoration technique for conservation in agricultural landscapes.
Data from: Arthropod abundance is most strongly driven by crop and semi-natural habitat type rather than management in an intensive agricultural landscape in the Netherlands
<p>The dataset supporting the publication "Arthropod abundance is most strongly driven by crop and semi-natural habitat type rather than management in an intensive agricultural landscape in the Netherlands" is provided. Methods of data collection can be found in the respective publication.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Soil and environmental data for "Interacting management effects on soil microbial alpha and beta diversity in Swiss agricultural grassland"
<p>This data shows the soil, environmental, and management data of 86 grassland sites that were sampled within the Canton of Solothurn, Switzerland. This data was used in the manuscript by F.J. Richter, R. Feola Conz, A. Lüscher, N. Buchmann, K.H. Valentin and M. Hartmann (2024): Interacting management effects on soil microbial alpha and beta diversity in Swiss agricultural grassland, which is published in the Journal of Applied Soil Ecology. </p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</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)
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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.