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1,425 results for “Agriculture”
Quantification of losses in agriculture production in eastern Ukraine due to the Russia-Ukraine war
<h3>This repository contains the necessary data and code used to support major results and findings of the research on "Quantification of losses in agriculture production in eastern Ukraine due to the Russia-Ukraine war". It encompasses:</h3> <h3>1. The generated 10-m crop type map of Ukraine in 2020<br>2. Validation sample sets for crop type and planting condition<br>3. Python and JavaScript code for reproducing main figures and tables in the manuscript, including sample-based area estimation, economic loss calculation, NDVI difference computation, and regression analysis</h3> <h3>For more information, please refer to our accompanying paper:</h3> <p>Bin Chen, Ying Tu, Jiafu An, Shengbiao Wu, Chen Lin, and Peng Gong. "Quantification of losses in agriculture production in eastern Ukraine due to the Russia-Ukraine war". Communications Earth & Environment (2024).</p>
Data from: Longevity of hymenopteran parasitoids in natural vs. agricultural habitats and implications for biological control
<p>Agricultural habitats are frequently disturbed, and disturbances could have large effects on species in upper trophic levels such as hymenopteran parasitoids that are important for biological control. A strategy for conservation biological control is to provide a diversified agricultural landscape which increases the availability of resources such as sugar required by parasitoid biological control agents. Here, we ask whether parasitoids occurring in agriculture benefit from sugar resources more or less than parasitoids occurring in natural habitats surrounding agricultural fields. We collected parasitoids from agricultural alfalfa fields, field margins and natural prairies, and in the lab we randomly divided them into two treatments: half were given a constant supply of a sugar source to test their residual lifespan, and half were given neither sugar nor water to test their hardiness. Collected individuals were monitored daily and their day of death recorded. Parasitoids receiving a sugar source lived substantially longer than those without. Parasitoids collected in prairies lived longer than those from alfalfa fields in both the residual lifespan and hardiness treatments, with parasitoids from field margins being intermediate between them. Furthermore, the benefits of a sugar source to increase longevity was lower for parasitoids collected in agriculture than in natural habitats. This suggests that, even though parasitoid biological control agents benefit from sugar resources, their short lifespans make the benefit of sugar resources small compared to parasitoids that occur in natural habitats and have longer lifespans, and are adapted to consistent sugar sources.</p>
Testing novel climate accounting identifies animal agriculture as the leading emissions sector
<p>Data and spreadsheet supporting the paper <span>"Testing novel climate accounting identifies animal agriculture as the leading emissions sector" G Wedderburn-Bisshop, 2024</span></p>
Urban Agriculture and Farmers Well-Being In Dar Es Salaam And Greater Lomé
<p>Primary data collected on Urban Agriculture and Farmers Well-Being In Dar Es Salaam And Greater Lomé.</p>
Data from: Influence of agricultural intensification on pollinator pesticide exposure, food acquisition and diversity
<p>Pollinators are essential for maintaining sustainable crop production, while the decline of pollinators is a widespread concern. Agricultural intensification is one of the primary drivers of the decline of insect pollinators. Agricultural intensification usually involves a decreasing of non-crop semi-natural habitat and an increasing of pesticide exposure for pollinators. However, causal links between agricultural intensification, increased pesticide exposure, and reduced pollinator's food sources and pollinator diversity remain underexplored.</p> <p>We assessed pollinator diversity across a landscape gradient where the proportion of rice ranged from 11% to 85% in South China. We placed honeybee (<em>Apis mellifera</em>) and mason bee (<em>Osmia excavata</em>) in these landscapes and investigated pesticide exposure in honeybee foragers and pollen, and in mason bee pollen and nesting materials. We also assessed the acquisition of food by mason bees.</p> <p>We found a higher frequency of pesticide detection in honeybee foragers and honeybee pollen samples in areas with a higher proportion of rice fields. There was a strong positive relationship between mason bee food acquisition and the proportion of semi-natural habitats, while no significant effects of pesticide exposure on pollinator diversity were found in addition to the effect of semi-natural habitat.</p> <p><em>Synthesis and applications</em>: Our results suggest that pollinator communities could be at an increased risk of pesticide exposure due to intensified agriculture, while the negative impact on pollinator diversity mainly results from the loss of habitat and/or reduced food sources. This study highlights the importance of conserving semi-natural habitat to mitigate the causes of decline in pollinator diversity. We also recommend long-term, multi-year studies to further understand the mechanisms behind the loss of pollinators in farming landscapes.</p>
Advanced IoT Agriculture 2024
<h3>Source Data with Authors</h3> <p>In the master's thesis research conducted by student Mohammed Ismail Lifta (2023-2024) at the Department of Computer Science, College of Computer Science and Mathematics- Tikrit University,Iraq.Data were collected from the Agriculture Lab on plants that grow in a IoT greenhouse and Traditional greenhouse .The study was supervised by Professor (Assistant) Wisam Dawood Abdullah, administrator of Cisco Networking Academy / Tikrit University.</p> <h3>Dataset Description</h3> <p>The dataset "Advanced_IoT_Dataset.csv" consists of 30,000 entries and 14 columns. Below are the detailed descriptions of each column:</p> <p>Random: An identifier for each record, likely indicating a random sample or batch (object type). Average of chlorophyll in the plant (ACHP): The average chlorophyll content in the plant (float type). Plant height rate (PHR): The rate of plant height growth (float type). Average wet weight of the growth vegetative (AWWGV): The average wet weight of vegetative growth (float type). Average leaf area of the plant (ALAP): The average leaf area of the plant (float type). Average number of plant leaves (ANPL): The average number of leaves per plant (float type). Average root diameter (ARD): The average diameter of the plant's roots (float type). Average dry weight of the root (ADWR): The average dry weight of the plant's roots (float type). Percentage of dry matter for vegetative growth (PDMVG): The percentage of dry matter in vegetative growth (float type). Average root length (ARL): The average length of the plant's roots (float type). Average wet weight of the root (AWWR): The average wet weight of the plant's roots (float type). Average dry weight of vegetative plants (ADWV): The average dry weight of vegetative parts of the plant (float type). Percentage of dry matter for root growth (PDMRG): The percentage of dry matter in root growth (float type). Class: The class or category to which the plant record belongs (object type).</p> <h3>More detailed description of the columns in the dataset:</h3> <p>Random: A categorical identifier for each record. This column appears to have values like R1, R2, and R3, which could represent different random samples.</p> <p>Average of chlorophyll in the plant (ACHP): This column contains float values representing the average chlorophyll content in the plant. Chlorophyll is vital for photosynthesis, and its measurement can indicate the health and efficiency of the plant in converting light energy into chemical energy.</p> <p>Plant height rate (PHR): This column contains float values representing the rate of growth in the height of the plant. This metric is essential for understanding the vertical growth dynamics of the plant over time.</p> <p>Average wet weight of the growth vegetative (AWWGV): This column contains float values representing the average wet weight of the vegetative parts of the plant. Wet weight can be an indicator of the water content and overall biomass of the plant's vegetative growth.</p> <p>Average leaf area of the plant (ALAP): This column contains float values representing the average leaf area of the plant. Leaf area is a critical factor in photosynthesis, as it determines the surface area available for light absorption.</p> <p>Average number of plant leaves (ANPL): This column contains float values representing the average number of leaves per plant. The number of leaves can correlate with the plant's ability to perform photosynthesis and its overall health.</p> <p>Average root diameter (ARD): This column contains float values representing the average diameter of the plant's roots. Root diameter can affect the plant's ability to absorb water and nutrients from the soil.</p> <p>Average dry weight of the root (ADWR): This column contains float values representing the average dry weight of the plant's roots. Dry weight is a measure of the plant's biomass after removing water content and is an indicator of the root's structural and storage capacity.</p> <p>Percentage of dry matter for vegetative growth (PDMVG): This column contains float values representing the percentage of dry matter in the vegetative parts of the plant. This metric indicates the proportion of the plant's biomass that is not water, which can be crucial for understanding its structural and nutritional status.</p> <p>Average root length (ARL): This column contains float values representing the average length of the plant's roots. Root length can influence the plant's ability to explore and absorb nutrients and water from the soil.</p> <p>Average wet weight of the root (AWWR): This column contains float values representing the average wet weight of the plant's roots. Wet weight includes the water content in the roots, indicating their overall biomass and water retention capacity.</p> <p>Average dry weight of vegetative plants (ADWV): This column contains float values representing the average dry weight of the vegetative parts of the plant. This measure reflects the structural biomass of the plant without water content.</p> <p>Percentage of dry matter for root growth (PDMRG): This column contains float values representing the percentage of dry matter in the plant's roots. This metric shows the proportion of the root biomass that is not water, important for assessing root health and function.</p> <p>Class: A categorical column indicating the class or category to which the plant record belongs. This could represent different groups or conditions under which the plants were studied or classified.</p> <p>The dataset provides comprehensive information about various plant metrics related to both vegetative and root growth, along with classification labels that could be used for analysis or machine learning purposes.</p> <h3>How to Use</h3> <p>This data can be used for environmental research and studies. Proper attribution must be given when using this data in any publication.No Change the dataset.</p> <h3>Contact</h3> <p>For more information or inquiries, please contact the principal researcher: Professor ( Assistant) Wisam Dawood Abdullah (Email: wisamdawood@tu.edu.iq).</p>
Code&Data_'Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion'
<p>File "R_Scripts_biodiversity_indicators.zip" includes codes for calculating the biodiversity impact on the terrestrial vertebrates of the Cerrado biome using the countryside Species Area Relationship (cSAR), the Species Threat Abatement and Restoration (STAR) metric and the Species Habitat Index (SHI).</p> <p>File "results_biodiversity_indicators.zip" includes the result tables of the calculations done with the above mentioned codes. </p>
Fig. 3 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig. 3. Mean numbers of more important invertebrate taxa longer than 10 mm in four types of habitat
Fig. 2 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig. 2. Comparison of invertebrate biomass 4–10 mm in length in four types of habitat
Fig.4 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig.4. Comparison of invertebrate biomass longer than 10 mm in four types of habitat
List of agricultural insect pests affecting crops worldwide
<p>This list of agricultural pests is part of the supplementary material of the article entitled "<strong>Pest suppression by bats and management strategies to favour it: a global review</strong>", publised in the journal Biological Reviews in 2023.</p> <p>We compiled a comprehensive list of agricultural insect pests occurring in temperate and tropical regions. Since no more recent public documents or published lists were available at the time of the study, we extracted the main agricultural insect pests cited in Hill (<a href="https://onlinelibrary.wiley.com/doi/full/10.1111/brv.12967#brv12967-bib-0083">1983</a>, <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/brv.12967#brv12967-bib-0084">1987</a>). Note that species might be considered pests in certain regions while not in others, meaning that this comprehensive list will need careful review by entomologists and local or regional experts for use in agricultural management.</p> <p>We assembled a first list of 1236 insect pest species or genera extracted from Hill (<a href="https://onlinelibrary.wiley.com/doi/full/10.1111/brv.12967#brv12967-bib-0084">1987</a>, <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/brv.12967#brv12967-bib-0083">1983</a>). We then conducted a second literature search (LS2) in the ISI <em>Web of Science</em> using the R package <em>wosr</em>. We searched for any indexed document containing the following terms in the topic field: ‘pest species name’ AND ‘bat*’. After the first check of the articles found, we added 562 new pest species to the first list, which were not included in Hill (<a href="https://onlinelibrary.wiley.com/doi/full/10.1111/brv.12967#brv12967-bib-0084">1987</a>, <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/brv.12967#brv12967-bib-0083">1983</a>) but were studied in the papers found. The updated list consisting of 1798 insect pest species then was used to perform the same literature search with the R package <em>wosr</em>. </p>
Soil C saturation in tropical agricultural systems: soil fertility, climate, texture and N controls on SOM fractions
<p><span>Nature-based solutions for C sequestration in tropical croplands are paramount strategies in a changing climate. Soybean-maize-forage intercropped systems associated with soil acidity alleviation and nitrogen (N) fertilization effectively accumulate carbon (C) in weathered soils. However, the soil saturation status and the controls of C stabilization up to 40 cm soil depth into particulate (POM) and mineral-associated organic matter (MAOM) fractions are poorly understood in tropical croplands. This study tested the hypothesis of C saturation in the MAOM fraction by comparing field and published data focused on Brazilian tropical soils. We assessed the N fertilization effect on C stabilization in POM and MAOM pools, and climate, soil texture, and chemical attributes controls on POM and MAOM formation in tropical croplands using a machine-learning Random Forest (RF) modeling approach. Our findings do not support C saturation in the MAOM regardless of contrasting soil contents of silt plus clay and depths. C in the MAOM and POM fractions were not affected by N fertilizer. However, legume inclusion in the system resulted in higher total soil C and lower soil C:N ratio compared with N fertilization, which indicates that C dynamics differ whether synthetic or organic-N forms are applied to tropical croplands. Our RF model showed robust predictive performance for the MAOM but poorer for the POM fraction. Total organic carbon (TOC), total N, silt plus clay, phosphorus (P) and soil depth, zinc (Zn), cation exchange capacity, and TOC covariates were the most important variables for predicting MAOM and POM fractions, respectively. Our findings show, besides the well-known effect of calcium, that other nutrients such as P, Zn, manganese and copper play a key role in either MAOM or POM formation. Our work provides key insights on C saturation status in tropical croplands and land-management C sequestering potential, and N, climate, soil chemical attributes and texture controls on C distribution into MAOM and POM fractions up to 40 cm soil depth in tropical conservation agricultural systems. We advocate for upscaling C saturation concept nationally and further research investigating the role of climate and micronutrients contribution on MAOM and POM formation in broad scenarios and N fertilizer responsiveness for C stabilization in tropical croplands.</span></p>
Agricultural flood resistance enhanced after returning farmlands to lakes
<ul> <li>The Code text files are the Java script used to map three types of rice and flood in 1998 and 2020.</li> <li>Data_maps and samples_GEE.txt records the path to get all the maps and samples stored in the assets of GEE, containing 9 layers.</li> <li>README.txt details the elements of each data layer of maps and samples in GEE.</li> </ul>
Enhancing the Agricultural Efficiency of Welded Mesh Solutions
<p>Modern agriculture is critically dependent on productivity and efficiency. Farmers are perpetually in pursuit of innovative methods to optimize operations and augment productivity. One solution that is gaining popularity is welded mesh, a flexible material that offers numerous benefits in a variety of agricultural applications.</p> <h1>Agricultural Landscapes Welded mesh undergoes transformation.</h1> <p>Contemporary farming methods are revolutionized by the adaptability and efficacy of <a href="https://www.dukesmetal.com/meshes/" target="_blank" rel="noopener"><strong>welded mesh</strong></a> solutions in a variety of applications. Welded mesh offers unparalleled durability and adaptability, making it suitable for a wide range of applications, including perimeter fencing, animal enclosures, and agricultural protection. It is an excellent choice for ensuring sustainable farming practices and enhancing operational efficiency due to its adaptability to satisfy a variety of agricultural needs.</p> <p>The utilization of welded mesh in agriculture has significantly altered the methods of infrastructure development employed by farmers. The modular design of the farm enables it to rapidly adjust to evolving requirements due to its scalability and ease of installation. Whether used as trellises for vertical farming or to establish secure boundaries for cattle, welded mesh solutions provide a robust foundation that supports agricultural productivity and encourages optimal land use.</p> <h2>The Advantages of Utilizing Welded Mesh Systems</h2> <p>Farmers can derive numerous advantages from the implementation of welded mesh systems in order to optimize agricultural productivity. In addition to its structural strength and endurance, welded mesh enhances farm security, reduces labour expenses, and promotes environmentally friendly agricultural practices. These systems promote sustainable agriculture by optimizing agricultural yield through efficient land management and protection, while simultaneously reducing resource waste.</p> <p>Welded mesh systems provide benefits that extend beyond structural integrity for the purpose of enhancing farm management methods. By establishing secure boundaries and confinement areas, farmers mitigate the risks of wildlife encroachment and illicit entry. This proactive approach not only safeguards invaluable assets but also cultivates an environment that is conducive to sustainable agricultural practices. Moreover, the modular design of welded mesh systems enables the flexible application of these systems to a variety of agricultural applications, including modest family farms and substantial commercial operations.</p> <h2>An Innovative Producer of Perforated Metal That is Revolutionizing Agricultural Methods</h2> <p>Perforated metal, which is manufactured by industry leaders, is a critical factor in the evolution of agricultural techniques. It is a critical instrument for the construction of efficient fencing and enclosures due to its versatility and durability. Farmers may be guaranteed of low maintenance costs and long-term dependability due to its resistance to external factors.</p> <p>Perforated metal panels are meticulously crafted by specialized producers to withstand the severe conditions of agricultural settings. They provide robust barriers that effectively protect cattle and produce from external threats. The high-strength composition of these materials may provide farmers with the assurance of longevity and significant cost reductions in comparison to traditional fencing materials.</p> <h2>Benefits of Selecting a Reliable Steel Wire Supplier</h2> <p><a href="https://www.dukesmetal.com/product/wire-rope-and-cable/" target="_blank" rel="noopener"><strong>Steel wire supplier</strong></a> play a substantial role in the agricultural industry by providing the premium materials necessary for the production of welded mesh. Farmers can be confident that their products meet the highest quality standards due to their expertise in procurement and processing. These vendors enable the improvement of agricultural efficacy by offering a wide range of products, including intricate mesh designs for crop protection and durable fencing wires.</p> <p>It is imperative that producers seeking welded mesh solutions choose a reliable supplier of steel wire. These sources offer a wide range of wire varieties, including those that are flexible, corrosion-resistant, and strong, to accommodate specific agricultural requirements. Farmers are able to install durable fencing and enclosure systems that can withstand the test of time and environmental challenges due to their collaboration with perforated metal producers.</p>
Fig. 1 in Circadian activity patterns of the Red fox (Vulpes vulpes) and the Stone marten (Martes foina) in agricultural landscape of Northwestern Bulgaria during autumn-winter period
Fig. 1. Location of the protected area "Zlatiyata" in Northwestern Bulgaria.
Dataset - Biogas composition from agricultural sources and organic fraction of municipal solid waste
<p>Data in this file compiled by H. Madi, A. Calbry-Muzyka, F. Rüsch-Pfund, M. Gandiglio, and S. Biollaz as Supplementary Information for "Biogas composition from agricultural sources and organic fraction of municipal solid waste", 2021. Table format in this file adapted from D. Papadias et al. at Argonne National Labs, www.cse.anl.gov/FCs_on_biogas/Impurities%20-%20LFG.xls and www.cse.anl.gov/FCs_on_biogas/Impurities%20-%20WWTP.xls (see also doi.org/10.1016/j.energy.2012.06.031).</p>
Data output for: Climate change stimulated agricultural innovation and exchange across Asia, in review.
<p>The GitHub repository for this project does not contain the output<br> generated by the script—3.2 GB of compressed data. All output data is<br> available as this Zenodo archive.</p> <p>The `vignettes/` directory contains all data generated by the<br> `guedesbocinsky2018.Rmd` RMarkdown vignette:</p> <p> - `data/raw_data` contains data downloaded from web sources for this<br> analysis<br> - `data/derived_data/` contains tables of the raw site chronometric<br> data without locational information, and the modeled chronometric<br> probability and niche information for each site.<br> - `data/derived_data/models/` contains R data objects describing the<br> Kriging interpolation models across the study area<br> - `data/derived_data/recons/` contains NetCDF format raster bricks of<br> the model output (i.e., the reconstructed crop niches)<br> - `figures/` contains all figures output by the script, including<br> videos of how each crop niche changes over time<br> - `figures/site_densities/` contains figures of the estimated<br> chronometric probability density for each site in our database<br> - `submission/` contains all of the figures, tables, movies, and<br> supplemental datasets included with d’Alpoim Guedes and Bocinsky<br> (2018)</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset3
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wu Ji, Hua Ming, Zhang Guang, Li Wentao, Shuang Chendong, Li Aimin. (2017). Preparation of Permanent Magnetic Resin Crosslinking by Diallyl Itaconate and Its Adsorptive and Anti-fouling Behaviors for Humic Acid Removal. <em>Scientific Report. </em> <a href="https://doi.org/10.1038/s41598-017-17360-8">https://doi.org/10.1038/s41598-017-17360-8</a></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: Subset2
<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>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset1
<p>This dataset contains the data underlying the following publication: Li Wen-Tao, Cao Meng-Jie, Young Tessora, Ruffino Barbara, Dodd Michael, Li Ai-Min, Korshin Gregory. (2017).</p> <p>Application of UV absorbance and fluorescence indicators to assess the formation of biodegradable dissolved organic carbon and bromate during ozonation. Water Research 2017, 111, 154-162. <a href="http://dx.doi.org/10.1016/j.watres.2017.01.009">http://dx.doi.org/10.1016/j.watres.2017.01.009</a>.</p>
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International Brain Laboratory public data
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OpenNeuro
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