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506 results for “crop data”

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Data from: Effects of carbon-based nanomaterials on seed germination, biomass accumulation and salt stress response of bioenergy crops

Bioenergy crops are an attractive option for use in energy production. A good plant candidate for bioenergy applications should produce a high amount of biomass and resist harsh environmental conditions. Carbon-based nanomaterials (CBNs) have been described as promising seed germination and plant growth regulators. In this paper, we tested the impact of two CBNs: graphene and multi-walled carbon nanotubes (CNTs) on germination and biomass production of two major bioenergy crops (sorghum and switchgrass). The application of graphene and CNTs increased the germination rate of switchgrass seeds and led to an early germination of sorghum seeds. The exposure of switchgrass to graphene (200 mg/l) resulted in a 28% increase of total biomass produced compared to untreated plants. We tested the impact of CBNs on bioenergy crops under salt stress conditions and discovered that CBNs can significantly reduce symptoms of salt stress imposed by the addition of NaCl into the growth medium. Using an ion selective electrode, we demonstrated that the concentration of Na+ ions in NaCl solution can be significantly decreased by the addition of CNTs to the salt solution. Our data confirmed the potential of CBNs as plant growth regulators for non-food crops and demonstrated the role of CBNs in the protection of plants against salt stress by desalination of saline growth medium.

opencc-zeroDec 2017View details →

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Data from: Slowing them down will make them lose: a role for attine ant crop fungus in defending pupae against infections?

Fungus-growing ants (Attini) have evolved an obligate dependency upon a basidiomycete fungus that they cultivate as their food. Less well known is that the crop fungus is also used by many attine species to cover their eggs, larvae and pupae. The adaptive functional significance of this brood covering is poorly understood. One hypothesis to account for this behaviour is that it is part of the pathogen protection portfolio when many thousands of sister workers live in close proximity and larvae and pupae are not protected by cells, as in bees and wasps, and are immobile. We performed behavioural observations on brood covering in the leaf-cutting ant Acromyrmex echinatior and we experimentally manipulated mycelial cover on pupae and exposed them to the entomopathogenic fungus Metarhizium brunneum to test for a role in pathogen resistance. Our results show that active mycelial brood covering by workers is a behaviourally plastic trait that varies temporally, and across life stages and castes. The presence of a fungal cover on the pupae reduced the rate at which conidia appeared and the percentage of pupal surface that produced pathogen spores, compared to pupae that had fungal cover experimentally removed or naturally had no mycelial cover. Infected pupae with mycelium had higher survival rates than infected pupae without the cover, although this depended upon the time at which adult sister workers were allowed to interact with pupae. Finally, workers employed higher rates of metapleural gland grooming to infected pupae without mycelium than to infected pupae with mycelium. Our results imply that mycelial brood covering may play a significant role in suppressing the growth and subsequent spread of disease, thus adding a novel layer of protection to their defence portfolio.

opencc-zeroDec 2015View details →

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Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images"

Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images".    image_prediction_pt1 and _pt2 have the same content as image_prediction.zip but split in two parts for faster downloading with Google Colab (to avoid time out)   Contact Ricardo Dalagnol ricds@hotmail.com

opencc-by-4.0Sep 2021View details →

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MAgPIE model input data sets: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections

These MAgPIE input data sets include harmonized crop yield projections from several crop models (9 crop models and 5 climate models). Additionally, regional, validation, and calibration data sets are also reported.

opencc-by-4.0Oct 2022View details →

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Spatial data of Moroccan crop pollinators

The data was collected in the framework of the IKI-FAP project.  This data was explored in the research of Sentil et al., 2024. The objective of this research was to assess the impact of the Farming with Alternative Pollinators (FAP) approach on pollinator diversity and abundance. FAP field is 300 m² (30m*10m). 25 % of the FAP field area (the surrounding area) is occupied by Marketable Habitat Enhancement Plants (MHEP) and 75 % of the field area (the central zone) is occupied by the main crop. To test the impact of the FAP appraoch on pollinators, we compared FAP fields with control fields (the main crop occupies 100 % of the field area).  The impact of the FAP approach on pollinator abundance and richness was assessed in four Moroccan agro-ecosystems (Settat, Kenitra, Errachidia and Sefrou), during two consecutive years (2018 and 2019) and using six main crops (faba bean, eggplant, pumpkin, zucchini, tomato and apple), resulting in 27 crop trials. Each crop trial represents one main crop (faba bean, eggplant, zucchini, pumpkin, apple or tomato) planted in one year (2018 or 2019) and in one region (Settat, Kenitra, Errachidia or Sefrou).  For each crop trial 8 fields were selected when possible : 5 FAP fields (FAP1, FAP2, FAP3, FAP4 and FAP5) and 3 control fields (C1, C2 and C3). The sampling in the main crop consisted of walking alongside two 28 m transects (T1 and T2) and the sampling in the 25 zone ( the main crop in control field and MHEP in FAP field) consisted of walking alongside an 80 m transect (T3). Four insect samplings were conducted in each FAP and control field: one before the blooming of the main crop (S1), two during the blooming of the main crop (S2 and S3) and one after the blooming of the main crop (S4).  For further details please see Sentil et al., 2024. References: Sentil, A, Lhomme, P, Reverte, S, El Abdouni, I, Hamroud, L, Ihsane, O, Bencharki, Y, Rollin, O, Rasmont, P, Chrif, M, Michez, D, Ssymank, A, Christmann, S. (2024). The pollinator conservation approach “ Farming with Alternative Pollinators ” : Success and drivers. Agriculture , Ecosystems and Environment 369. https://doi.org/10.1016/j.agee.2024.109029

openMay 2024View details →

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Data for the paper 'A Novel Simulation Optimization Framework for Multi Scale Irrigation Water Distribution and Scheduling Considering Crop Growth Process' submitted to Water Resources Research, an AGU journal

This data set contains the data for the paper 'A Novel Simulation Optimization Framework for Multi Scale Irrigation Water Distribution and Scheduling Considering Crop Growth Process' submitted to Water Resources Research, an AGU journal. The settings and crop parameters are consist of maize(<a title="Ran, 2018 #74" href="#_ENREF_44">Ran et al., 2018</a>; <a title="Shirazi, 2021 #75" href="#_ENREF_48">Shirazi et al., 2021</a>), flower(<a title="Reyhaneh alsadat Mousavi Zadeh Mojarad, 2018 #83" href="#_ENREF_45">Reyhaneh alsadat Mousavi Zadeh Mojarad, 2018</a>; <a title="Karimi Avargani, 2023 #80" href="#_ENREF_24">Karimi Avargani et al., 2023</a>) and wheat(<a title="Iqbal, 2014 #76" href="#_ENREF_17">Iqbal et al., 2014</a>; <a title="Huang, 2022 #79" href="#_ENREF_15">Huang et al., 2022</a>; <a title="Lyu, 2022 #99" href="#_ENREF_38">Lyu et al., 2022</a>; <a title="Karimi Avargani, 2023 #80" href="#_ENREF_24">Karimi Avargani et al., 2023</a>), which used for initializing AquaCrop-OS .

openNov 2024View details →

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Data from: Pollinator body size mediates the scale at which land use drives crop pollination services

1. Ecosystem services to agriculture, such as pollination, rely on natural areas adjacent to farmland to support organisms that provide services. Native insect pollinators depend on natural or semi-natural land surrounding farms for nesting and alternative foraging resources. Despite interest in conserving pollinators through habitat restoration, the scale at which land use affects pollinators and thus crop pollination services is not well understood. 2. We measured abundance of native, wild bee pollinators and the pollination services they provided to highbush blueberry Vaccinium corymbosum L. crops at 16 sites that varied in the proportion of surrounding agricultural land cover at both the field scale (300 m radius) and the landscape scale (1500 m radius). We designed our study such that agricultural land cover at the field scale was uncorrelated with agricultural cover at the landscape scale across sites. We used model selection to determine which spatial scale better predicted aggregate bee abundance, abundance of large versus small bees, and crop pollination services. 3. We found that, overall, bees responded more strongly to farm-scale than to landscape-scale land cover, but the scale at which land cover had the strongest effect varied by bee body size. Large bees showed a negative response to increasing agricultural cover at both scales, but were most strongly affected by the landscape scale. Small bees were negatively affected by agricultural land cover but only at the farm scale, while they had a small positive response to agricultural cover at the landscape scale. 4. Aggregate pollination services from native bees were more strongly influenced by local agricultural cover, due to the combined effects of both large and small bees responding at that scale. 5. Synthesis and applications. Bee abundance and pollination services were strongly determined by farm-scale agricultural cover, suggesting that farm-scale set-asides may provide significant benefits to pollination services. Further, we found that pollinators respond differently to land use depending on body size, but all groups of bees benefit from decreasing agricultural cover at the farm scale. Therefore, small-scale modifications to habitat can have significant impacts on both pollinator abundance and pollination services to crop plants.

opencc-zeroDec 2012View details →

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Data From: Diversifying bioenergy crops increases yield and yield stability by reducing weed abundance

Relationships between species diversity, productivity, temporal stability of productivity, and plant invasion have been well documented in grasslands, and these relationships could translate to improved agricultural sustainability. However, few studies have explored these relationships in agricultural contexts where fertility and weeds are managed. Using seven years of biomass yield and species composition data from twelve species mixture treatments varying in native species diversity, we found that species richness increased yield and inter-annual yield stability by reducing weed abundance. Stability was driven by yield as opposed to temporal variability of yield. Nitrogen fertilization increased yield, but at the expense of yield stability. We show how relationships between diversity, species asynchrony, invasion, productivity and stability observed in natural grasslands can extend into managed agricultural systems. Increasing bioenergy crop diversity can improve farmer economics via increased yield, reduced yield variability, and reduced inputs for weed control, thus promoting perennial vegetation on agricultural lands.

opencc-zeroOct 2021View details →

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Boston Trustees urban garden crop and tree data

<div> This dataset includes field data collected from gardens plots in 38 urban gardens across the city of Boston in 8 neighborhoods: Dorchester, East Boston, Fenway, Jamaica Plain, Mattapan, Mission Hill, Roxbury, and South End across 3 seasons: spring, summer, and fall (June 3-7, 2021; August 25-26, 2021; October 23-24, 2021). Neighborhood borders were defined by census tract block group designations available from the city <a href="https://www.zotero.org/google-docs/?iT7ffU">(Department of Innovation and Technology 2021)</a>. Census data was taken from American Community Survey -1 year estimates for 2021 available from the <a href="https://www.census.gov/data/developers/data-sets/acs-1year.html">U.S. Census Bureau</a>.  For each garden, all plots were hand mapped on site by researchers, enumerated, and then randomly selected using a number generator for further census. In each plot, every individual crop plant was counted and identified to species and cultivar if known or marked by gardeners. In addition, the number and species of each tree in the garden was surveyed.  We identified each crop species (no ornamental, non-edible plants were surveyed other than communal garden trees) and classified each plant species as annual or perennial according to whether they could survive across multiple growing seasons in the temperate region and are practiced as perennial crops, ie. gardeners do not typically remove the entire plant at the end of growing season. Biennial and woody plants were all classified as perennial. We classified all crops according to USDA classifications as vegetable, fruit, grain, culinary, medicinal, and combinations of these classifications.  We also include an R notebook with code used to generate figures and run analyses supporting the work entitled, "Rooting in place: Trees and perennials reflect permanence in urban gardens and communities" by the authors listed. A knitted html for R Markdown file is also included.   </div>

opencc-by-4.0Dec 2023View details →

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CN-P (version 1): a crop-specific, 1 km-resolution phosphorus rate data product in China over 2004–2016

CN-P harmonizes provincial and county-level phosphorus and component fertilizer statistics and crop distribution data to generate 1km gridded maps of phosphorus rate for rice, wheat and maize in the years of 2004-2016.

opencc-by-4.0Dec 2022View details →

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Kümmerer et al.; Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass; open data

The file contains data on cover crop biomass and canopy heights determined by ruler method, high-, and low-resolution imaging via unmanned aerial vehicle and structure-from-motion approach from an experimental field site in Triesdorf, Germany. The article to this data set is published in the journal MDPI Remote Sensing: Kümmerer, R.; Noack, P.O.; Bauer, B. Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass. Remote Sens. 2023, 15, 1520. https://doi.org/10.3390/rs15061520

openMar 2023View details →

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Data on global crop diversity and crop suitability maps

Data on current and predicted future global crop diversity and individual suitability maps for the twelve most important crops. Crop diversity: The total number of crops with suitability score ≥0.6 (crop diversity) calculated as the mean over periods 2008-2019; and projections for 2050-2061 (under RCP4.5 and RCP8.5). Important crops: For the twelve most economically important crops, defined as those with the highest global production value in 2022, the mean suitability calculated over periods 2008-2019 and projections for 2050-2061 (under RCP4.5 and RCP8.5).

openOct 2023View details →

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