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506 results for “crop data”
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>
Insights into short and long-term crop-foraging strategies in a chacma baboon (Papio ursinus) from GPS and accelerometer data
<p>Crop-foraging by animals is a leading cause of human-wildlife 'conflict' globally, affecting farmers and resulting in the death of many animals in retaliation, including primates. Despite significant research into crop-foraging by primates, relatively little is understood about the behaviour and movements of primates in and around crop fields, largely due to the limitations of traditional observational methods. Crop-foraging by primates in large scale agriculture has also received little attention. We used GPS and accelerometer bio-loggers, along with environmental data, to gain an understanding of the spatial and temporal patterns of activity for a female in a crop-foraging baboon group in and around commercial farms in South Africa over one year. Crop fields were avoided for most of the year, suggesting that fields are perceived as a high-risk habitat. When field visits did occur, this was generally when plant primary productivity was low, suggesting that crops were a 'fallback food'. All recorded field visits were at or before 15:00. Activity was significantly higher in crop fields than in the landscape in general, evidence that crop-foraging is an energetically costly strategy and that fields are perceived as a risky habitat. In contrast, activity was significantly lower within 100m of the field edge than in the rest of the landscape, suggesting that baboons wait near the field edge to assess risks before crop-foraging. Together this understanding of the spatiotemporal dynamics of crop-foraging can help to inform crop protection strategies and reduce conflict between humans and baboons in South Africa.</p>
Raw data for "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" (2022)
<p>This dataset accompanies the publication "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" by Stratton et al. in the journal Agriculture, Ecosystems, and Environment (2022). <a href="https://doi.org/10.1016/j.agee.2022.107925">https://doi.org/10.1016/j.agee.2022.107925</a></p> <p>METHODS:</p> <p>We conducted our experiment between May 2018 and December 2019 on 14 farms in the eastern coastal highlands region of Santa Catarina, Brazil. The mean altitude of sites was 467 m (+/- 161 m). Eastern Santa Catarina has a subtropical climatic pattern, with mean annual rainfall ranging from 1,500-1,700 mm (Wrege et al., 2012). While 2018 had typical weather patterns for the region, 2019 was a dry year, particularly during the spring months (Appendix B, Table B.1). All farms were located in the Colonial Serrana Catarinense soil microregion, one of 16 designated microregions in the state of Santa Catarina (EMBRAPA, 2004). Primary soil types in our study site are associations of dystric Cambisols and haplic Acrisols (typic Dystrocryepts and typic Paleudults in the USDA Soil Taxonomy), which tend to be moderately to highly acidic, with limited soil nutrient availability and moisture retention (EMBRAPA, 2004; IUSS Working Group WRB, 2015; USDA, 2010). To support crop production, farmers in the region typically apply lime (calcium and magnesium carbonate) to agricultural fields to increase soil pH from <5.5 to 6 (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). Exact farm locations within the region are not given and farmer identities have been anonymized.</p> <p><em>Experimental design</em></p> <p>The fully factorial experiment had six treatments (Figure 2): (1) cover crop + pea-cucumber intercrop, (2) cover crop + pea monocrop, (3) cover crop + cucumber monocrop, (4) fallow + pea-cucumber intercrop, (5) fallow + pea monocrop, and (6) fallow + cucumber monocrop. Due to the timing of farm recruitment, only conventional and transitioning farms participated in the first year of cover cropping (2018); agroecological farms were added to the study during the vegetable intercropping period of 2018 and had their first round of cover cropping in 2019. The cover crop mixture treatment was designed to emulate traditional practices in the region, as well as to include functionally complementary legume and grass species: common vetch (<em>Vicia sativa </em>L.) and black oat (<em>Avena strigosa </em>Schreb). We also selected vegetables with distinct ecological functional traits, such that intercropping represented an increase in functional diversity relative to mono-cropped vegetables. Snow peas are N-fixing legumes with a vining, upright structure and a deep root system, whereas cucumbers are low-lying, non-legume cucurbits that provide groundcover and have a relatively shallow, extensive root system.</p> <p> </p> <p>Cover crop treatments consisted of two adjacent 50 m2 plots in each field, one of which was planted with the cover crop mixture; the other served as a weedy fallow control. In 2018, the cover crop mixture seeding rate was 72 kg/ha black oat and 36 kg/ha common vetch. Due to poor vetch performance in mixtures at this rate, we increased the vetch seeding rate to 60 kg/ha in 2019, maintaining the black oat rate from 2018. Cover crop seeds were inoculated with the Brazilian strain <em>Rhizobium etli </em>(SEMIA 384; source: FEPAGRO) at 4 g/kg vetch seed prior to planting. Cover crops were grown until peak flowering, and then cover crops (and weeds in the fallow) were incorporated into the soil by rototiller (<em>n</em> = 7 farms) or by hand hoeing (<em>n</em> = 7 farms), based on farms’ available machinery, between September 5-10 in 2018 and September 10-18 in 2019 (approximately one week following cover crop sampling on each farm).</p> <p> </p> <p>Vegetables were planted two weeks following cover crop and weed biomass incorporation within a period of 7-10 days across sites. Harvest dates were spaced such that crops were growing for approximately the same period across farms. The 50 m2 plots were each divided into three intercrop treatments with a ~1 m2 pathway between each treatment, for a total of 6 treatments randomly assigned to plots per 100 m2. We planted a climbing variety of snow peas (<em>Pisum sativum</em> subsp. <em>sativum</em> var. <em>macrocarpum</em>,<strong> </strong>“<em>Torta de flor roxa”</em>) and pickling cucumber (<em>Cucumis sativa </em>L. var. <em>Pepino HT </em>05) in intercrops and in their respective monocrops, using a replacement design (i.e., equivalent crop densities in all treatments). Snow pea seeds were inoculated with <em>Rhizobium leguminosarum</em> var. <em>viceae</em> (SEMIA 3007/BR 619, source: UFSC ENR/CCA) at a rate of 4 g/kg directly prior to planting. There were five rows of crops per treatment, with only the three middle rows harvested to limit edge effects. In-row spacing was 60 cm for cucumber and 20 cm for peas, with 60 cm between rows in both intercrops and monocrops. Cucumbers were grown as starts for 2.5 weeks before planting, and peas were planted from seed on the same planting date as cucumber starts.</p> <p> </p> <p>In the summer between January and May 2019 all fields were planted to a sunflower (<em>Helianthus annuus</em> L.) crop, which was incorporated into the soil during flowering approximately two weeks prior to cover crop planting in 2019. Because we sought to understand the effects of crop diversification given existing water and nutrient limitations on working farms, the experiment was entirely rainfed and legume N fixation was the sole external N source.</p> <p><em>Soil sampling and analysis</em></p> <p>Prior to the first cover cropping period, we collected a composite sample of 15-20 soil cores (2.5 cm diameter, 20 cm depth) on both the cover crop and fallow sides of each experimental field (<em>n</em> = 28) for analysis of baseline conditions (see Appendix B for full details). Briefly, soil was analyzed for pH, macro- and micronutrients, and soil organic matter (SOM) by the Santa Catarina State Agricultural Agency (EPAGRI) in Ituporanga, Santa Catarina, Brazil, using standard protocols (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). pH was measured with a glass electrode both with and without Sikora’s buffer, and buffered pH is used throughout this paper (Tecnal TEC-11 MP). Soil organic C and total soil N to 20 cm were determined by dry combustion on a Leco TruMac CN Analyzer (Leco Corporation, St. Joseph, Michigan, USA). We measured soil texture (% clay, sand, and silt) using a total dispersion method with sodium hexametaphosphate (Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), 1997). Bulk density was estimated from the mass of 10 fresh soil cores per treatment, with subsequent accounting for soil moisture.</p> <p> </p> <p>We measured C mineralization as a baseline indicator of soil microbial activity and biological soil fertility at the start of the experiment, and N mineralization as a response variable following the second year of cover crop treatments. Specifically, using the baseline soil sample, we conducted a short-term (24-hour) C mineralization assay to determine potentially mineralizable C (PMC), which measures the flux of CO2 following re-wetting of previously air-dried, sieved soil using a Li-Cor (Franzluebbers et al., 2000; Hurisso et al., 2016). To measure potentially mineralizable N (PMN), we conducted a two-week aerobic incubation using fresh soil collected at vegetable crop planting in the second year of the experiment (spring 2019), two weeks after cover crop and weed biomass incorporation (Drinkwater et al., 1996; Appendix B.2). PMN was calculated as the difference between extractable soil inorganic N (NH4+ and NO3-) at the start and end of the incubation. We used pre-incubation extractable inorganic N concentration (mg/kg) as a measure of soil inorganic N availability at vegetable crop planting.</p> <p> <em>Cover crop sampling and analysis</em></p> <p>Cover crop biomass sampling took place from August 28-September 2 in 2018 and September 4-10 in 2019. During peak flowering of both common vetch and black oat, we destructively harvested the aboveground biomass of cover crop mixtures and weedy fallows from two 0.5 x 0.5 m quadrats of each treatment per field. We took care to avoid treatment edges, cut plant material to the soil surface, and separated harvested plant material by species, grouping all weeds together. Aboveground biomass was dried in a forced-air oven at 60 °C for 48 hours. Following grinding in a Wiley mill to 2 mm, % N and C content was determined by dry combustion on an elemental analyzer (Leco, as above). Community-weighted means were calculated for total aboveground biomass C and N in cover crop species and weeds, to determine the overall C and N inputs to soil following incorporation of biomass on each farm. We measured biological N2 fixation in inoculated common vetch from the cover crop phase of the experiment in 2018 and 2019. Vetch N fixation was estimated using the 15N natural abundance method (Shearer and Kohl, 1986), which compares stable N isotope ratios in the legume and reference species (oat monocultures) (Appendix C).</p> <p> <em>Vegetable crop sampling and analysis</em></p> <p>To capture the full production period of both cucumber and pea crops, yield was measured in two harvests, which were approximately 14 days apart on each farm. Harvest dates ran from November 16-December 6 in 2018 and November 20-December 4 in 2019. We measured yield by weighing all harvestable fruit from three designated, representative row sections (6 plants on average per row) per crop type per treatment. Rows were sampled from the center of each treatment to reduce edge effects. We calculated yield as total crop production (g) per plant harvested in each row. Mean yield for each crop type was calculated as the average of the three harvested rows per treatment on a per-plant basis and was then aggregated to the plot and hectare level based on experimental planting densities. Total N harvested, or “N yield”, was calculated for all treatments by multiplying the % N in each vegetable crop by its yield (kg/ha) after accounting for crop water content. Using plot-level yield data, we subsequently calculated the relative yield total (Land Equivalent Ratio, LER) for intercrop treatments by farm using the standard equation (Vandermeer, 1989) (Table 1). As a relative measure of total crop production per area, when mean LER > 1, intercrops were considered to have “overyielded” compared to their component monocrops. We calculated the LER for N yield (LERN in kg N/ha) using the same formula.</p> <p> </p> <p>At the second vegetable harvest, we destructively sampled whole aboveground crop biomass, including residues and remaining fruits, from the designated experimental rows. Following the harvest, a minimum of six representative cucumbers per treatment (from different plants) per farm were washed in deionized water, air-dried, sliced, and the middle sections were combined into a homogenized, composite sample of ~100 g and then dried for one week at 60 ºC. All peas from each treatment’s subplot were washed in deionized water, air-dried, de-stemmed, chopped, and each homogenized sample (35-60 g fresh material) was subsequently dried at 60 ºC in a forced-air oven for 48 h to one week, until fully desiccated. Dried vegetable biomass residues were ground using a Wiley mill; vegetable crop samples were ground in a coffee grinder; and all vegetable samples were analyzed for % C and N on a LECO elemental analyzer.</p> <p><strong>See</strong><strong> supplemental material from Stratton et al. 2022 for further detailed information on methods.</strong></p>
Data from: No evidence of foliar disease impact on crop root functional strategies and soil microbial communities: What does this mean for organic coffee?
<p><span>Global climate change is increasing pest and pathogen pressures on plant communities, deteriorating optimal plant functioning. In plant communities, root functional trait expression and microbial communities are important indicators of plant functioning belowground, and, when confronted with pathogens aboveground, can simultaneously reflect plant defence strategies. Yet, while research is continuing to emerge on the response of root functional traits and microbial processes to pathogens aboveground, little work has investigated these interactions in tree-crops, or the role organic amendments play in moderating these relationships. The main objective of this study is to disentangle the dynamic effects of pathogens and amendments on root functional traits (i.e., specific root length and area, root diameter, root length density, root nitrogen, and root carbon to nitrogen ratio) and root endophytic fungal communities. As a model, we use <em>Coffea arabica </em>(coffee) variety Caturra along a gradient of Coffee Leaf Rust – a foliar disease prominent in coffee systems – under contrasting but widespread amendment regimes in biodiverse agroforestry systems. We found that root trait expression varies along established conservation and collaboration gradients, where fungal endophyte community composition varies significantly as a function of root traits. Belowground resource acquisition strategies do not change with foliar disease incidence, suggesting they may be decoupled. Rather, amendment regimes </span>differentially shape root trait expression and microbial communities<span>, where coffee plants under organic amendments, regardless of foliar disease incidence, expressed greater acquisitive traits and enhanced collaboration with symbiotic fungi. </span>This is an important first step in disentangling the dynamic inter-relationships between plant traits, endophytes, and pathogens, generating new questions on the role of amendments in sustainable pathogen management in biodiverse agroecosystems.</p> <p> </p>
Data from: Effective specialist or jack of all trades? Experimental evolution of a crop pest in fluctuating and stable environments
<p>Understanding pest evolution in agricultural systems is crucial for developing effective and innovative pest control strategies. Types of cultivation, such as crop monocultures versus polycultures or crop rotation, may act as a selective pressure on pests' capability to exploit the host's resources. In this study, we examined the herbivorous mite <em>Aceria tosichella</em> (commonly known as wheat curl mite), a widespread wheat pest, to understand how fluctuating versus stable environments influence its niche breadth and ability to utilize different host plant species. We subjected a wheat-bred mite population to replicated experimental evolution in a single-host environment (either wheat or barley), or in an alternation between these two plant species every three mite generations. Next, we tested the fitness of these evolving populations on wheat, barley, and on two other plant species not encountered during experimental evolution, namely rye and smooth brome. Our results revealed that the niche breadth of <em>A. tosichella</em> evolved in response to the level of environmental variability. The fluctuating environment expanded the niche breadth by increasing the mite's ability to utilize different plant species, including novel ones. Such an environment may thus promote flexible host-use generalist phenotypes. However, the niche expansion resulted in some costs expressed as reduced performances on both wheat and barley as compared to specialists. Stable host environments led to specialized phenotypes. The population that evolved in a constant environment consisting of barley increased its fitness on barley without the cost of utilizing wheat. However, the population evolving on wheat did not significantly increase its fitness on wheat, but decreased its performance on barley. Altogether, our results indicated that, depending on the degree of environmental heterogeneity, agricultural systems create different conditions that influence pests' niche breadth evolution, which may in turn affect the ability of pests to persist in such systems.</p>
Data from: Crop health is predicted by soil microbial diversity across phylogenetic scales
<p>Soils contain diverse living communities that provide key ecosystem functions in agroecosystems. In many systems, ecosystems functions are positively related to the taxonomic, phylogenetic, and functional diversity of the community. Despite calls to incorporate microbial diversity in measures of soil health, whether increased microbial diversity <em>per se</em> can predict increased crop health and productivity has rarely been documented. Here we used microbial communities from commercial potato fields varying in diversity and composition, and experimentally assessed their ability to promote crop yield under low or high nutrient conditions and to suppress a soil-borne pathogen. Across two independent sets of communities, we found that yields under low nutrient conditions were predicted by high initial microbial diversity measured at broad phylogenetic levels, consistent with greater niche complementarity among unrelated taxa leading to greater total resource use. However, disease suppression was inconsistently linked to diversity and explained as well or better by microbial composition rather than diversity <em>per se</em>. Ecosystem multifunctionality was predicted by high diversity at broad to intermediate phylogenetic scales. These results indicate that the diversity of microbial taxa may influence multiple soil functions; however, the mechanisms underlying the diversity-function relationships may vary.</p>
Data set of studies and indicators on the impacts of crop diversification through coffee agroforestry.
<p>The dataset contains information about 215 papers that were included in the review study. </p>
Data for "Mixed impacts of protected areas and a cash crop boom on human well-being in north-eastern Madagascar"
<p>Dataset (1), codebook (2), explanation of responses (3), and interview protocol (4) for journal article "Mixed impacts of protected areas and a cash crop boom on human well-being in north-eastern Madagascar"<em> People and Nature</em>. DOI: 10.1002/pan3.10377</p> <p>Please note the following.</p> <p>The following variables have been removed from the dataset to guarantee respondents’ anonymity:</p> <p> </p> <p>1.a.2 - Place (place of origin if not born in the village)</p> <p>1.d.1b – Function (function in the village if the respondent has one)</p> <p> </p> <p>The following variable have been removed from the dataset given the few responses collected:</p> <p>4.c – Place_in_forest (places in the forest important to the respondent)</p>
Raw Data for Publication "Earth observations reveal impacts of climate variability on maize cropping systems in Sub-Saharan Africa"
<p>Phenological metrics extracted for all agricultural fields used in the study. Data also includes the coordinates of the fields.</p>
HISLAND-SA: Annual and 1-km crop-specific gridded data in South America from 1950 to 2020
<p><span>We developed spatially explicit crop-specific maps (i.e., soybean, maize, wheat, and rice) at a 1 km </span><span>×</span><span> 1 km resolution and annual step in South America from 1950 to 2020 by integrating historical agricultural census data, model-based crop type data, and high-resolution remote sensing-based crop type data. </span><span>Compared with existing data, our reconstructed data have higher spatial and temporal resolution which can better capture the dynamics of crop type changes during the historical period. This newly developed data can be used to assess the impacts of agricultural expansion on greenhouse gas emissions, ecosystem services, biodiversity loss, and to guide the formulation of land management and conservation policies for sustainable agricultural development and ecological conservation.</span></p>
Data from: A functional diversity approach of crop sequences reveals that weed diversity and abundance show different responses to environmental variability
1. Combining several crop species and associated agricultural practices in a crop sequence has the potential to control weed abundance while promoting weed diversity in arable fields. However, how the variability of environmental conditions that arise from crop sequences affects weed diversity and abundance remains poorly understood, with most studies to-date simply opposing weed communities in monoculture and in crop rotation. Here, we describe crop sequences along gradients of disturbance and resource variability using a crop functional trait and associated agricultural practices. We tested the hypothesis that variability of disturbances reduces weed abundance while variability of resources promotes weed diversity. 2. We used functional Hill's numbers to compute crop sequence functional diversity based on sowing date, herbicide spectrum and crop height - these are the respective proxies of disturbance timings, disturbance types and light availability. Using a large-scale weed monitoring database, we assessed crop sequence diversity for 1045 crop sequences of five consecutive cropping seasons. We computed weed richness and abundance at pluri-annual (pool of weeds observed across five cropping seasons) and annual (pool of weeds observed during a winter cereal cropping season preceded by five cropping seasons) scales. We also accounted for herbicide and tillage intensities to test whether management intensity affects the response of weed diversity and abundance to crop sequence diversity. 3. At the pluri-annual scale, weed richness increased with the diversity of crop height and sowing date while weed abundance decreased with sowing date diversity. Annual weed richness decreased with sowing date diversity while annual weed abundance poorly relied on crop sequence diversity. 4. Synthesis and applications. This study establishes a scientific basis for designing crop sequences according to specific weed management goals. We show that farmers may enhance arable weed diversity on a pluri-annual scale by sequentially sowing crop species that differ in their competitive ability and sowing date. They may also achieve a better control of weed abundance by increasing the diversity of crop sowing dates across the crop sequence.
Data from: Biochar from "Kon Tiki" flame curtain and other kilns: effects of nutrient enrichment and kiln type on crop yield and soil chemistry
Biochar application to soils has been investigated as a means of improving soil fertility and mitigating climate change through soil carbon sequestration. In the present work, the invasive shrub "Eupatorium adenophorum" was utilized as a sustainable feedstock for making biochar under different pyrolysis conditions in Nepal. Biochar was produced using several different types of kilns; four sub types of flame curtain kilns (deep-cone metal kiln, steel shielded soil pit, conical soil pit and steel small cone), brick-made traditional kiln, traditional earth-mound kiln and top lift up draft (TLUD). The resultant biochars showed consistent pH (9.1 ± 0.3), cation exchange capacities (133 ± 37 cmolc kg-1), organic carbon contents (73.9 ± 6.4 %) and surface areas (35 to 215 m2/g) for all kiln types. A pot trial with maize was carried out to investigate the effect on maize biomass production of the biochars made with various kilns, applied at 1% and 4% dosages. Biochars were either pretreated with hot or cold mineral nutrient enrichment (mixing with a nutrient solution before or after cooling down, respectively), or added separately from the same nutrient dosages to the soil. Significantly higher CEC (P< 0.05), lower Al/Ca ratios (P< 0.05), and high OC% (P<0.001) were observed for both dosages of biochar as compared to non-amended control soils. Importantly, the study showed that biochar made by flame curtain kilns resulted in the same agronomic effect as biochar made by the other kilns (P > 0.05). At a dosage of 1% biochar, the hot nutrient-enriched biochar led to significant increases of 153% in above ground biomass production compared to cold nutrient-enriched biochar and 209% compared to biochar added separately from the nutrients. Liquid nutrient enhancement of biochar thus improved fertilizer effectiveness compared to separate application of biochar and fertilizer.
Data from: Effects of landscape complexity on pollinators are moderated by pollinators' association with mass-flowering crops
Conserving and restoring semi-natural habitat, i.e. enhancing landscape complexity, is one of the main strategies to mitigate pollinator decline in agricultural landscapes. However, we still have limited understanding of how landscape complexity shapes pollinator communities in both crop and non-crop habitat, and whether pollinator responses to landscape complexity vary with their association with mass-flowering crops. Here, we surveyed pollinator communities on mass-flowering leek crops and in nearby semi-natural habitat in landscapes of varying complexity. Surveys were done before and during crop bloom and distinguished between pollinators that visit the crop frequently (dominant), occasionally (opportunistic) or not at all (non-crop). Forty-four percent of the species in the wider landscape were also observed on leek flowers. Crop pollinator richness increased with local pollinator community size and increasing landscape complexity, but relationships were stronger for opportunistic than for dominant crop pollinators. Relationships between pollinator richness in semi-natural habitats and landscape complexity differed between groups with the most pronounced positive effects on non-crop pollinators. Our results indicate that while dominant crop pollinators are core components of crop pollinator communities in all agricultural landscapes, opportunistic crop pollinators largely determine species-richness responses and complex landscapes are local hotspots for both biodiversity conservation and potential ecosystem service-provision.
Data from: Patterns of domestication in the Ethiopian oil-seed crop Noug (Guizotia abyssinica)
Noug (Guizotia abyssinica) is a semi-domesticated oil-seed crop, which is primarily cultivated in Ethiopia. Unlike its closest crop relative, sunflower, noug has small seeds, small flowering heads, many branches, many flowering heads, indeterminate flowering, and it shatters in the field. Here we conducted common garden studies and microsatellite analyses of genetic variation to test whether high levels of crop-wild gene flow and/or unfavorable phenotypic correlations have hindered noug domestication. With the exception of one population, analyses of microsatellite variation failed to detect substantial recent admixture between noug and its wild progenitor. Likewise, only very weak correlations were found between seed mass and the number or size of flowering heads. Thus, noug's 'atypical' domestication syndrome does not seem to be a consequence of recent introgression or unfavorable phenotypic correlations. Nonetheless, our data do reveal evidence of local adaptation of noug cultivars to different precipitation regimes, as well as high levels of phenotypic plasticity, which may permit reasonable yields under diverse environmental conditions. Why noug has not been fully domesticated remains a mystery, but perhaps early farmers selected for resilience to episodic drought or untended environments rather than larger seeds. Domestication may also have been slowed by noug's outcrossing mating system.
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>
Crop mapping data
<p>Images of different crops in the study area </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>
Data for: Metagenomics show high spatiotemporal virus diversity and ecological compartmentalisation: virus infections of melon, Cucumis melo, crops and adjacent wild communities
<p>Emergence of viral diseases results from novel transmission dynamics between wild and crop plant communities. The bias of studies towards pathogenic viruses of crops has distracted from knowledge of non-antagonistic symbioses in wild plants. Here we implemented a high throughput approach to compare the viromes of melon (<em>Cucumis melo</em>)<em>, </em>and wild plants of crop (Crop) and adjacent boundaries (Edge). Each of the 41-plant species examined was infected by at least one virus. The interactions of 104 virus operational taxonomic units (OTUs) with these hosts occurred largely within ecological compartments of either Crop or Edge, Edge having traits of a reservoir community. The positive correlation of virus and plant richness at each site, the tendency for increased specialist host use through seasons, and specialist host use by OTUs observed only in Melon, characterised local-scale patterns of infection. In this study of systematically sampled viromes of crop and adjacent wild communities most hosts showed no disease symptoms, suggesting non-antagonistic symbioses are common. The coexistence of viruses within species-rich ecological compartments of agro-systems might promote the evolution of a diversity of virus strategies for survival and transmission. These communities, including those suspected as reservoirs, are subject to sporadic changes in assemblages, and so too are the conditions that favour the emergence of disease.</p>
Data from: Genomic signatures of artificial selection during early domestication of a wood crop
<p>To determine how a century of artificial selection has changed the genome of <i>E. grandis</i>, we generated SNP genotypes for 1080 individuals from three advanced South African breeding programmes using the EUChip60K chip, and investigated population structure and genome-wide differentiation patterns relative to wild progenitors.</p>
Data for: Perennial biomass cropping and use: Shaping the policy ecosystem in European countries
<p><span>Demand for sustainably produced biomass is expected to increase with the need to provide renewable commodities, improve resource security, and reduce greenhouse gas emissions in line with COP26 commitments. Studies have demonstrated additional environmental benefits of using perennial biomass crops (PBCs), when produced appropriately, as a feedstock for the growing bioeconomy, including utilisation for bioenergy (with or without carbon capture and storage). PBCs can potentially contribute to Common Agricultural Policy (CAP) (2023–27) objectives provided they are carefully integrated into farming systems and landscapes. Despite significant R&D investment over decades in herbaceous and coppiced woody PBCs, deployment has largely stagnated due to social, economic and policy uncertainties. This paper identifies the challenges in creating policies that are acceptable to all actors. Development will need to be informed by measurement, reporting and verification (MRV) of greenhouse gas emissions reductions and other environmental, economic and social metrics. It discusses interlinked issues that must be considered in the expansion of PBC production: i) available land; ii) yield potential; iii) integration into farming systems; iv) research and development (R&D) requirements; v) utilisation options; and vi) market systems and the socioeconomic environment. It makes policy recommendations that would enable greater PBC deployment: 1) incentivise farmers and land managers through specific policy measures, including carbon pricing, to allocate their less productive and less profitable land for uses which deliver demonstrable greenhouse gas reductions; 2) enable GHG mitigation markets to develop and offer secure contracts for commercial developers of verifiable low carbon bioenergy and bio-products; 3) support innovation in biomass utilisation value chains; and 4) continue long-term, strategic R&D and education for positive environmental, economic and social sustainability impacts.</span></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.