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129 results for “Crop yield”
Precision Agriculture Yield Monitoring in Row Crop Agriculture at the Kellogg Biological Station, Hickory Corners, MI (1996 to 2013)
Dataset Abstract The LTER annual crops (corn, soy and wheat), treatments 1-4, are harvested annually using a combine equipped with a GPS and precision agriculture software to allow detailed yield measurements with coincident GPS latitude and longitude data.. original data source http://lter.kbs.msu.edu/datasets/40
Forest cover and proximity decrease herbivory and increase crop yield via enhanced natural enemies in soybean fields
<p><span>Non-crop habitats are essential for sustaining biodiversity of beneficial arthropods in agricultural landscapes, which can increase ecosystem services provision and crop yield. However, their effects on specific crop systems are less clear, such as soybean in South America, where the responses of pests and natural enemies to landscape structure have only recently been studied. </span></p> <p><span>Here, we analyzed how native forest fragments at local and landscape scales influenced arthropod communities, herbivory, and yield in soybean fields in central Argentina. To do this, we selected soybean fields located in agricultural landscapes with varying proportions of forest cover. At two distances (10 and 100m) from a focal forest fragment, we sampled natural enemy and herbivore arthropods, and measured soybean herbivory and yield. We focused on herbivore diversity, abundance of key soybean pests in the region (caterpillars and stink bugs), and their generalist and specialist natural enemies.</span></p> <p><span>Higher abundance of predators, lower herbivory rates, and increased yield were found near forests, while overall forest cover in the landscape was positively related with parasitoid and stink bug abundance, soybean yield, and negatively with herbivory. Moreover, yield was positively linked to richness and abundance of generalist and specialist enemies and independent of herbivory according to piecewise Structural Equation Models. </span></p> <p><span><i>Synthesis and applications. </i>Our results show positive effects of native forests on biodiversity and yield in soybean crops, highlighting the need for conservation of forest fragments in agricultural landscapes. Moreover, the relation between natural enemies and crop yield suggests that Chaco forests support a diverse and abundant community of natural enemies that can provide sustained levels of ecosystem services and result in positive effects for farmers.</span></p>
Weed dataset - Crop diversity Experiment - Crop-weed relationships are context-dependent and cannot fully explain the positive effects of intercropping on yield
<p>This shows the weed mass per species and crop yield per species of each plot described in the experiment. </p>
Sustainable landscape, soil and crop management practices enhance biodiversity and yield in conventional cereal systems
<p>1. Input-driven, modern agriculture is commonly associated with large-scale threats to biodiversity, the disruption of ecosystem services and long-term risks to food security and human health. A switch to more sustainable yet highly productive farming practices seems unavoidable. However, an integrative evaluation of targeted management schemes at field and landscape scales is currently lacking. Furthermore, the often-disproportionate influence of soil conditions and agrochemicals on yields may mask the benefits of biodiversity-driven ecosystem services.</p> <p>2. Here, we used a real-world ecosystem approach to identify sustainable management practices for enhanced functional biodiversity and yield on 28 temperate wheat fields. Using path analysis, we assessed direct and indirect links between soil, crop and landscape management with natural enemies and pests, as well as follow-on effects on yield quantity and quality. A paired-field design with a crossed insecticide-fertilizer experiment allowed us to control for the relative influence of soil characteristics and agrochemical inputs.</p> <p>3. We demonstrate that biodiversity-enhancing management options such as reduced tillage, crop rotation diversity and small field size can enhance natural enemies without relying on agrochemical inputs. Similarly, we show that in this system controlling pests and weeds by agrochemical means is less relevant than expected for final crop productivity.</p> <p>4. Synthesis and applications: Our study highlights soil, crop and landscape management practices that can enhance beneficial biodiversity while reducing agrochemical usage and negative environmental impacts of conventional agriculture. The diversification of cropping systems and conservation tillage are practical measures most farmers can implement without productivity losses. Combining local measures with improved landscape management may also strengthen the sustainability and resilience of cropping systems in light of future global change.</p>
Data from: Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems
Knowledge of production-system performance is largely based on observations at the experimental plot scale. Although yield gaps between plot-scale and field-scale research are widely acknowledged, their extent and persistence have not been experimentally examined in a systematic manner. At a site in southwest Michigan, we conducted a 6-y experiment to test the accuracy with which plot-scale crop-yield results can inform field-scale conclusions. We compared conventional versus alternative, that is, reduced-input and biologically based–organic, management practices for a corn–soybean–wheat rotation in a randomized complete block-design experiment, using 27 commercial-size agricultural fields. Nearby plot-scale experiments (0.02-ha to 1.0-ha plots) provided a comparison of plot versus field performance. We found that plot-scale yields well matched field-scale yields for conventional management but not for alternative systems. For all three crops, at the plot scale, reduced-input and conventional managements produced similar yields; at the field scale, reduced-input yields were lower than conventional. For soybeans at the plot scale, biological and conventional managements produced similar yields; at the field scale, biological yielded less than conventional. For corn, biological management produced lower yields than conventional in both plot- and field-scale experiments. Wheat yields appeared to be less affected by the experimental scale than corn and soybean. Conventional management was more resilient to field-scale challenges than alternative practices, which were more dependent on timely management interventions; in particular, mechanical weed control. Results underscore the need for much wider adoption of field-scale experimentation when assessing new technologies and production-system performance, especially as related to closing yield gaps in organic farming and in low-resourced systems typical of much of the developing world.
Combined Biostimulant Applications of Trichoderma spp. with Fatty Acid Mixtures Improve Biocontrol Activity, Horticultural Crop Yield and Nutritional Quality
<p>Raw Data of "Combined Biostimulant Applications of Trichoderma spp. with Fatty Acid Mixtures Improve Biocontrol Activity, Horticultural Crop Yield and Nutritional Quality".</p>
Data supporting "Crop yield after 5 decades of contrasting residue management"
<p>Data supporting the pubblication "Crop yield after 5 decades of contrasting residue management" by Piccoli et al. (2020). Nutr Cycl Agroecosyst (2020) 117:231–241. https://doi.org/10.1007/s10705-020-10067-9(0123456789().,-volV() 0123458697().,-volV)</p>
Positive associations of soil organic matter and crop yields across a regional network of working farms
<p>The amount of soil organic matter (SOM) is considered a key indicator of soil properties associated with higher fertility. Despite the ubiquity of assumptions surrounding SOM's contributions to soil functioning, we lack quantitative relationships between SOM and yield outcomes on working farms. We quantified the relationship between SOM and yields of corn (<i>Zea mays </i>L.) and silage for a dataset of 170 fields arrayed across 49 farms in a network of growers based in Wisconsin and Minnesota, USA. As SOM concentrations increase, so do yields, though gains start to level off around 4% SOM. When examining the relationship between yield and soil health indicators representative of biologically active carbon pools, we found that mineralizable carbon (min-C) has a stronger relationship with yield than permanganate oxidizable C (POXC). Mineral fertilizer, manure, and SOM had relationships of similar magnitude with yield, highlighting that SOM in combination with exogenous inputs likely plays an important role in driving agricultural productivity in this region. An SOM by crop rotation interaction indicated that the impact of SOM on crop yields varied depending on rotation (continuous corn versus corn in rotation). That is, continuous corn had lower yields than corn in rotation despite higher SOM concentrations. Our findings provide insight into the relationship between indicators of soil health, farm management, and crop yields for a set of working farms and lend support to the goals of soil health initiatives that rest on building SOM in agricultural soils to improve agricultural outcomes.</p>
Pollinators, pests and yield – multiple trade-offs from insecticide use in a mass-flowering crop
<p>Multiple trade-offs are likely to occur between pesticide use, pollinators and yield (via crop flowers) in pollinator-dependent, mass-flowering crops (MFCs), causing potential conflict between conservation and agronomic goals. To date, no studies have looked at both outcomes within the same system, meaning 'win-win solutions' for pollinators and yield can only be inferred.</p> <p>Here, we outline a new framework to explore these trade-offs, using red clover (<em>Trifolium pratense</em>) grown for seed production as an example. Specifically, we address how the insecticide thiacloprid affects densities of seed-eating weevils (<em>Protapion</em> spp.), pollination rates, yield, floral resources and colony dynamics of the key pollinator, <em>Bombus terrestris</em>.</p> <p>Thiacloprid did not affect the amount of nectar provided by, or pollinator visitation to, red clover flowers but did reduce weevil density, correlating to increased yield and gross profit. Colonies of <em>B. terrestris</em> significantly increased their weight and reproductive output in landscapes with (compared to without) red clover, regardless of insecticide use.</p> <p><em>Synthesis and Applications</em>: <span>We propose a holistic, conceptual framework to explore trade-offs between pollinators, pesticides and yield that we believe to be essential for achieving conservation and agronomic goals. This framework </span><span>applies to all insecticide-treated mass-flowering crops (MFCs) and can be adapted to include other ecological processes. Trialling the framework in our study system, we found that our focal insecticide, thiacloprid, improved red clover seed yield with no detected effects on its key pollinator <em>Bombus terrestris</em> and that the presence of red clover in the landscape can benefit pollinator populations.</span></p>
Data and metadata of crop yield and quality derived from H2020 Diverfarming project
<p>Data and metadata of crop yield and quality from different cases studies and long terms from WP3 "Crop production and quality", derived from H2020 Diverfarming project. This workpackage provides robust and sound data about how diversified cropping systems with low-input practices and efficient use of resources have positive effects on crop production and quality, and so, farm yields and economic revenues for farmers is increased. This can increase trading productivity with high quality outputs, so improving the competitiveness of European agriculture in the global market. http://www.diverfarming.eu.</p>
Crop performance, aerial, and satellite data from multistate maize yield trials
<p>Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening hundreds and thousands of varieties, and policymakers in decisions contributing to the overall improvement of agriculture and food production systems. Effective, generalizable approaches to track plant growth and predict yield at the individual plot level require large matched datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional and field scales. Despite their lower spatial resolution, satellite platforms exhibit multiple logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments integrated with the ground truth measurements are not yet publicly available. Here we generated, described, and evaluated a set of more than 20,000 plot-level images of over 80 hybrid maize (Zea mays) varieties grown in six locations across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth measurements of crop yield. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing data collected from drones for both within-environment and cross-environment yield prediction. Large, multimodal, multi-environment, genetically diverse training datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important new addition to the tool of farmers, plant geneticists, crop breeders, and policymakers.</p>
A crop yield change emulator for use in GCAM and similar models: Persephone v1.0
<p>This is an archive of the raw data and analysis source code for the paper "A crop yield change emulator for use in GCAM and similar models: Persephone v1.0". The archive contains:</p> <ul> <li><strong>data.zip:</strong> All source code for analysis, input data for analysis, and results of analysis</li> <li><strong>persephone.proj : </strong>R project for ease of reproducing analysis</li> </ul>
Soil properties and crop yield in fruit orchards under Mediterranean conditions in terms of intercropping, tillage and fertilizer type
<p>This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil and crop yield in fruit orchards under Mediterranean conditions by a further meta-analysis of the data. </p> <p>These data correspond to the open-access article "The impact of intercropping, tillage and fertilizer type on soil and crop yield in fruit orchards under Mediterranean conditions: A meta-analysis of field studies" published in Agricultural Systems. (<a href="https://doi.org/10.1016/j.agsy.2019.102736">https://doi.org/10.1016/j.agsy.2019.102736</a>), funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. Raúl Zornoza acknowledges the financial support from the Spanish Ministry of Science, Innovation and Universities through the “Ramón y Cajal” Program [RYC-2015-18758].. </p> <p> </p>
Forecasted crop yield anomalies for NUTS3 level regions in the Pannonian Basin
<p>The datasets contain crop yield anomaly forecasts on NUTS3 level for the Pannonian Basin 2002-2016. The files contain the crop yield anomaly forecasts calculated one and two months before the harvest. Further information about the methodology can be found here:</p> <p>https://www.sciencedirect.com/science/article/pii/S0168192323002873</p>
Annual crop yields of the CS1 Study Case
<p>Database on main crop and secondary crop yields of the CS1 Study case for the period 2019-2021 </p>
Data of crop yield derived from H2020 Diverfarming project
<p>Crop yield data of a mandarin crop (main crop) and different secondary crops (alley crops) cultivated in different cropping systems (monocrop and diversification) with different treatments (control irrigation and regulated deficit irrigation)</p>
Dataset for yield and carbon sequestration of main tree crops in Indonesia
<p>Biophysical productivity of production systems involving oil palm, rubber, cacao, coffee and coconut on mineral soil using the WaNuLCAS model developed by ICRAF. The WaNuLCAS model (van Noordwijk and Lusiana 1999; van Noordwijk et al., 2011) is a generic tree-crop growth model for a wide range of agroforestry systems that considers both aboveground (light) and belowground (soil, water, and nutrients). Yield and carbon sequestration calculation of the corresponding production systems in peatland were developed using a hybrid approach through adjusting results for mineral soil production system with productivity gaps identified through literature review. Finally, land suitability and statistical data analyses were conducted to estimate productivity of sago and pineapple.</p> <p>The dataset cover current practices and potential management improvements of tree crops cultivation that can increase the benefits to people’s livelihood while contributing to climate change mitigation and biodiversity. Results from this study, in combination with other tree-growth and forest regrowth related information, will provide a comprehensive overview on ecological and economic impact of restoration interventions ranging from improved management to ecological restoration. This dataset was created as a part of the RESTORE+ project. The details are described in a separate document (see <a href="https://doi.org/10.5281/zenodo.7937135">https://doi.org/10.5281/zenodo.7937135</a>).</p>
Data from: Cover cropping history affects cotton boll distribution, lint yields, and fiber quality
<p>This is digital research data corresponding to a published manuscript, Cover cropping history affects cotton boll distribution, lint yields, and fiber quality, in Crop Science, Vol. 63 p. 1209–1220. </p> <p>There has been limited introduction of new cover crop species into cotton (<em>Gossypium</em> <em>hirsutum</em> L.) production within the last 30 years. Mounting evidence shows that traditional cover cropping species may be detrimental to cotton production, either by depleting soil fertility with crop removal, immobilizing minerals from high carbon residue, or excessive quantity of residue remaining at planting. The objective of this study was to determine the effects of growing a novel cover crop species, carinata (<em>Brassica</em> <em>carinata</em> A. Braun), as a winter annual cover crop for cotton rotation in the southeastern Coastal Plain. Over a 2-year period, carinata, winter wheat (<em>Triticum</em> <em>aestivum</em> L.), and fallow covers were maintained over winter months, then rotated into cotton. Each year, seedcotton and lint yields were collected, along with subsamples for ginning and subsequent fiber quality analyses. Additionally, end-of-season plant mapping was conducted on plants from 1-m of row per plot to determine cover crop effects on boll formation, retention, and distribution, as well as canopy architecture.</p>
Data supplementing Lichtenberg et al. (2023) Differential effects of soil conservation practices on arthropods and crop yields. Journal of Applied Entomology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. Differential effects of soil conservation practices on arthropods and crop yields. J. Appl. Entomol. 147: 931-940. <a href="https://doi.org/10.1111/jen.13188">https://doi.org/10.1111/jen.13188</a></p> <p>Please cite the above article if you use any of the included data or code.</p> <p>Files are described in README.md.</p>
Data for article Agricultural input shocks affect crop yields more in the high-yielding areas of the world
<p>This repository contains the agricultural input data of 12 crops used in the article analysis. The agricultural inputs used are (unit in parentheses):</p> <ul> <li>nitrogen (kg/ha)</li> <li>phosphorus (kg/ha)</li> <li>potassium (kg/ha)</li> <li>machinery (1000 metric horsepower cv)</li> <li>herbicides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>fungicides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>insecticides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>other pesticides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>soil organic carbon (t/ha)</li> <li>soil phosphorus (mg/kg(</li> <li>soil nitrogen (t/ha)</li> <li>non-mineral nitrogen (kg/ha)</li> <li>non-mineral phosphorus (kg/ha)</li> <li>irrigation (% of area under irrigation)</li> <li>agricultural workers (persons)</li> </ul> <p><br> This repository also includes the analysis results of yields after agricultural input shocks. This data is in<br> rasters named e.g. wheat_phosphorus_shock_75.tif, meaning that this file contains the yield data (t/ha) for wheat after a 75% shock in phosphorus input. Rasters are provided for the following:</p> <ul> <li>yields after 25%, 50% or 75% nitrogen shock</li> <li>yields after 25%, 50% or 75% phosphorus shock</li> <li>yields after 25%, 50% or 75% potassium shock</li> <li>yields after 25%, 50% or 75% machinery shock</li> <li>yields after 25%, 50% or 75% pesticide shock</li> <li>yields after 25%, 50% or 75% shock in all fertilizers (fertilizer shock)</li> <li>yields after 25%, 50% or 75% shock in all inputs</li> </ul> <p>In addition, the observed crop yield (t/ha) used to construct the model is provided. Other yield rasters are:</p> <ul> <li>modelled baseline yield (t/ha), used to calculate the yield changes in shock scenarios</li> <li>modelled zero input yield (t/ha), calculated as a scenario with zero fertilizers, machinery and pesticide inputs</li> </ul> <p>Please see the article for references on the datasets.</p> <p> </p>
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International Brain Laboratory public data
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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.