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149 results for “farmers”
Understanding farmers' reasons behind mitigation decisions is key in supporting their coexistence with wildlife
<p>1. Coexistence between wildlife and farmers can be challenging and can endanger the lives of both, prompting the provisioning of mitigation methods by governments and non-governmental organisations (NGOs). However, provision of materials, demonstration of the effectiveness of methods or willingness to uptake a method do not predict uptake of methods.</p> <p>2. We used Ethnographic Decision Models to understand how farmers' work through the decisions of uptake or non-uptake of methods to mitigate crop consumption by elephants, and how the government and NGOs can either enable or impede the ability of farmers to protect themselves and their crops.</p> <p>3. While farmers were motivated to use methods if they received or could afford to buy materials and they believed in the effectiveness of the methods, they still did not use them if they considered a method to be dangerous, or issues with elephants not to be severe enough, or when the supply of materials or income was not sufficient. Methods were not even considered by farmers if they lacked awareness or knowledge of the method. Government departments and NGOs enabled farmers to mitigate elephant crop consumption by providing opportunities for cash income, and providing materials and knowledge. Yet, there was disparity between the materials farmers received and methods they wished to adopt.</p> <p>4. One-off inputs of materials did not result in sustainable use of mitigation methods. We see an opportunity for governmental departments or NGOs to stimulate logistics (e.g. roads and retail) to increase availability of mitigation materials since this promoted farmer autonomy. We also highlight the importance of empowering farmers by facilitating within community sharing of mitigation ideas and increasing knowledge about the effectiveness of promising wildlife conscious farming, as despite promising farmer testimonies, only a few farmers used these techniques.</p>
Model for Smart-Agri app for farmers to enable optimum fertilizer application
<p>For the safe management of these fertilizers (i.e., safe application rates to land), a tool was needed to guide growers and processing plants. A parsimonious program was developed in MS Excel<sup>TM</sup> to determine the maximum legal application rate of the different types of DPS and DPS-derived STRUBIAS products based on the soil test P content, the legal limits for N, P and metal application, the dry solid content, and nutrient (N and P) and metal concentration of the DPS and DPS-derived STRUBIAS products. </p>
CONSOLE_WP3_Task3.2_Pan-EU survey of farmers and other rural landowners_PL_2022.10.20_v01
<p>Data collected from the Polish farmers' survey (DCE), properly operated through data management and assembled as a coherent and cohesive database in Rdata format to be used for econometric modeling.</p> <p> </p> <p>Data Set Title: CONSOLE_WP3_Task3.2_Pan-EU survey of farmers and other rural landowners_PL_2022.10.20_v01;<br> Data Set Author/s: Wąs Adam, SGGW, ORCID 0000-0001-8643-5985; <br> Data Set Contributor/s: Majewski Edward, SGGW, 0000-0003-0886-6645 <br> Malak-Rawlikowska Agata, SGGW,0000-0002-1484-0989; <br> Data Set Contact Person/s: Wąs Adam, SGGW, ORCID 0000-0001-8643-5985, adam_was@sggw.edu.pl;<br> Data Set License: this data set is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, https://creativecommons.org/licenses/by/4.0/;<br> Publication Year: 2022; <br> Project Info: CONSOLE CONtract SOLutions for Effective and lasting delivery of agri-environmental-climate public goods by EU agriculture and forestry, funded by European Union, Horizon 2020 Programme. Grant Agreement num. 817949; https://console-project.eu;</p>
Human-cheetah conflict subsistence and commercial farmers
<p>This data set (published in biodiversity and conservation) is a subset of data collected between 2012 and 2015 during a nationwide questionnaire based interview survey. For more information about the methodology see Van der Meer E (2016b) The cheetahs of Zimbabwe: distribution and population status 2015. CCPZ, Victoria Falls. <em>DOI</em>: 10.13140/RG.2.2.36719.84648 (also downloadable from cheetahzimbabwe.org)</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>
LAB4SUPPLY MARKET SURVEY - FARMERS- FIG- CREDA
<p>Results of the market survey for fig's producers</p>
Farmer Survey Dataset
<p>Farmer Survey Dataset containing some 15000 survey responses as part of the FarmFit programme of IDH. Dataset has been subset, cleaned and responses recoded to preserve anonymity </p> <p>These reponses were collected via the Akvo Foundation following our <a href="https://farmfitinsightshub.org/resources/farmer-survey-primary-data-collection-methodology">standardised methodology</a>, underpinned by our <a href="https://farmfitinsightshub.org/resources/farm-fit-learning-framework">Learning Framework</a> and based upon our <a href="https://farmfitinsightshub.org/resources/farmer-survey-question-library">Farmer Survey Question Library</a>. </p> <p>To be informed of our latest updates, insights and events, sign up to our <a href="https://farmfitinsightshub.org/newsletter">Smallholder-Inclusive Business Newsletter</a>. </p> <p>To read more of our Insights generated with this and other data, please visit the <a href="https://farmfitinsightshub.org/">FarmFit Insights Hub</a>. </p> <table> <tbody> <tr> <td><strong>variable </strong></td> <td><strong>question</strong></td> </tr> <tr> <td>country</td> <td>country where survey was conducted</td> </tr> <tr> <td>region</td> <td>region where survey was conducted</td> </tr> <tr> <td>education level</td> <td>What is the highest level of education that the farmer achieved?</td> </tr> <tr> <td>gender</td> <td>What is the gender of the farmer?</td> </tr> <tr> <td>age group</td> <td>What is the age of the farmer?</td> </tr> <tr> <td>household size</td> <td>How many people live in the household?</td> </tr> <tr> <td>farm size (acres)</td> <td>What is the total size of the farm?</td> </tr> <tr> <td>crop type</td> <td>Type of focus crop</td> </tr> <tr> <td>farming experience</td> <td>For how many years have you been farming on the current farm location?</td> </tr> <tr> <td>land tenure</td> <td>Do you own or rent the land you use for farming?</td> </tr> <tr> <td>fertiliser</td> <td>In this period (within the last 12 months), did you use fertiliser (including compost) to take care of the focus crop? </td> </tr> <tr> <td>certified seeds</td> <td>In this period (within the last 12 months), did you buy seeds for the focus crop? </td> </tr> <tr> <td>pest management</td> <td>In this period (within the last 12 months), did you use pesticides/fungicides/herbicides to take care of the focus crop? </td> </tr> <tr> <td>phone ownership</td> <td>Do you currently own a personal mobile phone?</td> </tr> <tr> <td>internet use</td> <td>Do you use internet?</td> </tr> <tr> <td>agricultural financing</td> <td>In the past 12 months, have you taken any loans? (e.g. from a local lender, microfinance bank, NGO, relative, cooperative)</td> </tr> <tr> <td>bank account</td> <td>Do you have a bank/Microfinance account?</td> </tr> <tr> <td>mobile account</td> <td>Do you have a mobile money account? (Farmer does not need to have credit or a loan, this question concerns mainly the account)</td> </tr> <tr> <td>losses-rain pattern</td> <td>Over the past 5 years, did you experience crop losses due to changing rain patterns (including floods) on this farm location?</td> </tr> <tr> <td>losses-drought</td> <td>Over the past 5 years, did you experience crop losses due to drought on this farm location?</td> </tr> <tr> <td>losses-heatwave</td> <td>Over the past 5 years, did you experience crop losses due to heatwaves on this farm location?</td> </tr> <tr> <td>losses-storms</td> <td>Over the past 5 years, did you experience crop losses due to storms (including cyclones, monsoons) on this farm location?</td> </tr> <tr> <td>losses-mudslides</td> <td>Over the past 5 years, did you experience crop losses due to mudslides on this farm location?</td> </tr> <tr> <td>farmer organization</td> <td>Are you member of a farmer organisation? (Cooperative, VSLA, SACCO, community group etc.)</td> </tr> <tr> <td>service provider awareness</td> <td>Have you heard of [service provider]? (if farmer receives input financing from [service provider] you can fill in yes without asking the question)</td> </tr> <tr> <td>agricultural continuity</td> <td>Do you intend to continue working in agriculture?</td> </tr> <tr> <td>land expansion</td> <td>Do you plan to increase the land you farm on in the future?</td> </tr> </tbody> </table>
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
Life-Cycle Inventory (LCI) For the comparative Life-Cycle Assessment of a restoration and renovation of a traditional Danish farmer house
<p>This document provides the Life-Cycle Inventory of the study comparing the environmental performance of the restoration (Scenario 1, also mentioned as "S1") or renovation (Scenario 2, "S2") of a traditional Danish farmer house. The LCI is used as input for the life-cycle impact assessment phase in the LCA where the inputs and outputs of elementary flows in the LCI are characterized as potential impacts on the environment. The LCI is based on primary data from the "Apprentices' House" combined with secondary data from materials specific environmental product declaration and the large LCI database ecoinvent v3.</p> <p> </p> <p>The file contains several spreadsheets organized as follows:</p> <p>Thicknesses,S1 - Reports the materials and their corresponding thicknesses used for walls, ceilings, floors and insulation in S1</p> <p>Thicknesses, S1b - Reports the materials and their corresponding thicknesses used for walls, ceilings, floors and insulation in S1b</p> <p>Thicknesses, S1c - Reports the materials and their corresponding thicknesses used for walls, ceilings, floors and insulation in S1c</p> <p>Thicknesses, S2 - Reports the materials and their corresponding thicknesses used for walls, ceilings, floors and insulation in S2</p> <p>Surfaces - Reports the area of all surfaces of the house (walls, ceilings, floors) for all scenarios. </p> <p>Bill of materials - Sums up the list and quantity of all the materials used in all scenarios, providing details as to the quantity kept from the actual house, as well as the new input and output of materials during the restoration/renovation</p> <p>Heat loss, S1 - Provides details for the calculation of heat loss through the building envelope of the house in Scenario 1</p> <p>Heat loss, S1b - Provides details for the calculation of heat loss through the building envelope of the house in Scenario 1b</p> <p>Heat loss, S1c - Provides details for the calculation of heat loss through the building envelope of the house in Scenario 1c</p> <p>Heat loss, S2 - Provides details for the calculation of heat loss through the building envelope of the house in Scenario 2</p> <p>Parameters - Describes the main parameters used in the LCI and their corresponding uncertainty</p> <p>LCI Materials - Provides the full breakdown of the processes created and used for the LCI modelling on OpenLCA</p> <p>LCI Building - Provides the full breakdown of the processes created and used for the LCI modelling on OpenLCA</p>
Evaluating livestock farmers' knowledge, beliefs, and management of arboviral diseases in Kenya
<p>Globally, arthropod-borne virus (arbovirus) infections continue to pose substantial threats to public health and economic development, especially in developing countries. In Kenya, although arboviral diseases (ADs) are largely endemic, little is known about the factors influencing livestock farmers' knowledge, beliefs, and management (KBM) of the three major ADs: Rift Valley fever (RVF), dengue fever and chikungunya fever. This study evaluates the drivers of livestock farmers' KBM of ADs from a sample of 629 respondents selected using a three-stage sampling procedure in Kenya's three hotspot counties of Baringo, Kwale, and Kilifi. </p>
Pesticide use and its consequences on the health of farmers in Mala
<p>Summary</p> <p>In Peru, the use of pesticides is very common in the agricultural sector to protect crop varieties, which in turn cause damage to health due to their toxic effects. The aim of this thesis was to</p> <p><strong>Objective: To </strong>assess the consequences of pesticides on the health of the population of Mala</p> <p>- Peru.</p> <p><strong>Methodology: </strong>Non-experimental, descriptive and cross-sectional study. A total of 315 inhabitants of the District of Mala between the ages of 18 and 50 years were evaluated. The method used was a survey and the data collection instrument was a questionnaire.</p> <p><strong>Results: </strong>The most frequently used pesticides by farmers were Chlopyrifos (17.5%), Dicrotophos (14.6%) and Carbofuran (14%). The most frequent main symptoms of pesticide use were nausea (17.5%) and salivation (10.2%). The exposure time ranged from 30 minutes (23.8%) to 2 hours (16.8%).</p> <p><strong>Conclusions: </strong>Malathion (43.5%) was found to be the most commonly used pesticide by farmers in Mala and the main pesticide was headache (55.9%).</p> <p><strong>Keywords: </strong>Chlopyrifos, Pesticides, Symptomatology, Mala, Malathion, Protective Equipment</p>
Adolescence is characterized by more sedentary behavior and less physical activity even among highly active forager-farmers
<p>Over 80% of adolescents worldwide are insufficiently active, posing massive public health and economic challenges. Declining physical activity (PA) and sex differences in PA consistently accompany transitions from childhood to adulthood in post-industrialized populations and are often attributed to psychosocial and environmental factors. An overarching evolutionary theoretical framework and data from pre-industrialized populations are lacking. This cross-sectional study tests hypotheses from life history theory, that adolescent PA is inversely related to age, but this association is mediated by Tanner stage, reflecting higher and sex-specific energetic demands for growth and reproductive maturation. Detailed measures of PA and pubertal maturation are assessed among Tsimane forager-farmers (age: 7–22 yrs.; 50% female, n=110). Most Tsimane sampled (71%) meet World Health Organization PA guidelines (≥60 minutes/day of moderate-to-vigorous PA). Like post-industrialized populations, sex differences and inverse age-activity associations were observed. Tanner stage significantly mediated age-activity associations. Adolescence presents difficulties to PA engagement that warrant further consideration in PA intervention approaches to improve public health.</p>
Online WIC Nutrition Education to Promote Farmers' Market Fruit and Vegetable Purchases and Consumption
ClinicalTrials.gov study NCT02565706. IPD Sharing: YES. Countries: 1. Publications: 5.
Impact of BC Farmers' Market Nutrition Coupon Program on Diet Quality and Psychosocial Well-being of Low-income Adults
ClinicalTrials.gov study NCT03952338. IPD Sharing: NO. Countries: 1. Publications: 2.
Repercussions of Patrilocal Residence on Mothers’ Social Support Networks Among Tsimane Forager-Farmers
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Who knows, who cares? Untangling ecological knowledge and nature connection among Amazonian colonist farmers
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Understanding farmers' reasons behind mitigation decisions is key in supporting their coexistence with wildlife
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Data from: Farmer preferences for conservation incentives that promote voluntary phosphorus abatement in agricultural watersheds
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West African cocoa farmer tree valuation data
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Adolescence is characterized by more sedentary behavior and less physical activity even among highly active forager-farmers
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.