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farming

84 record(s)

 

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  • This dataset contains yield data for wheat, oilseed rape and field beans grown in fields under different agri-environment practices. The fields were located at the Hillesden Estate in Buckinghamshire, UK, where a randomised block experiment had been implemented to examine the effects of converting differing proportions of arable land to wildlife habitat. The fields were planted with wheat (Triticum aestivum L.) followed by break crops of either oilseed rape (Brassica napus L.) or field beans (Vicia faba L.). Three treatments were applied at random: a control ("business as usual"), Entry Level Stewardship (ELS) treatment and ELS Extra treatment. The ELS treatment involved removing 1% of land to create wildlife habitats. The ELS Extra had a greater proportion of land removed (6%) and additional wildlife habitats included. The total yield of each crop was measured at the time of harvesting using a yield meter attached to the combine harvester. From these values, yield per hectare and the ratio of crop yield to regional average yield were calculated. Full details about this dataset can be found at https://doi.org/10.5285/e54069b6-71a9-4b36-837f-a5e3ee65b4de

  • This dataset comprises 259 smallholder agricultural field surveys collected from twenty-six villages across three Districts in Mozambique, Africa. Surveys were conducted in ten fields in each of six villages in Mabalane District, Gaza Province, ten villages in Marrupa District, Niassa Province, and ten villages in Gurue District, Zambezia Province. Data were collected in Mabalane between May-Sep 2014, Marrupa between May-Aug 2015, and Gurue between Sep-Dec 2015. Fields were selected based on their age, location, and status as an active field at the time of the survey (i.e. no fallow fields were sampled). Structured interviews using questionnaires were conducted with each farmer to obtain information about current management practices (e.g. use of inputs, tilling, fire and residue management), age of the field, crops planted, crop yields, fallow cycles, floods, erosion and other problems such as crop pests and wild animals. The survey also includes qualitative observations about the fields at the time of the interview, including standing live trees and cropping systems. This dataset was collected as part of the Ecosystem Services for Poverty Alleviation (ESPA) funded ACES project , which aims to understand how changing land use impacts on ecosystem services and human wellbeing of the rural poor in Mozambique. Full details about this dataset can be found at https://doi.org/10.5285/78c5dcee-61c1-44be-9c47-8e9e2d03cb63

  • This dataset consists of butterfly and bumblebee counts, winter bird counts, number of flowering units, and seed mass data, along with categories of soil type and quality, and temperature data. Data were collected from arable farms under the English Entry Level agri-environment Scheme (ELS) for two options: Nectar Flower Mixture option (NFM) and Wild Bird Seed Mixture (WBM). Surveys were carried out in 2007 and repeated in 2008. All data were collected using standardised protocols: butterfly and bumblebee counts were collected from transects in the NFM options during summer; flowering units were counted within quadrats along the same transects in summer; bird counts were made in winter within the whole WBM areas; seed resource was calculated for the WBM areas from seeds collected in quadrats along transects. The dataset also contains results from farmer interviews. The interviews were designed to explore farmer attitudes towards, and history of, environmental management and their perceptions and understanding of the management requirements. Three measures of farmer attitude were then calculcated from their responses: experience (4-point scale), concerns (5-point scale) and motivation (3-point scale). All data were collected as part of the FarmCAT project, the principal aim of which was to develop a holistic understanding of the social and ecological factors which lead to the successful delivery of agri-environmental schemes. This project was funded as part of the ESRC Rural Economy and Land Use (RELU) programme. Full details about this dataset can be found at https://doi.org/10.5285/d774f98f-030d-45bb-8042-7729573a13b2

  • The data consist of soil physicochemical and biological data for three soil depths (0-15, 15-30 and 30-60 cm) from a three-cut silage plot trial located at three grassland sites within the UK collected between April 2016 and October 2016. The sites were Rothamsted Research at North Wyke in Devon, Bangor University at Henfaes Research Station in North Wales, and Easter Bush in Scotland. At each site measurements were taken from sixteen plots, organised within a randomised complete block design: four (control) plots did not receive fertilizer, four plots received urea only, four plots received urea and urea-inhibitors, and four plots received ammonium-nitrate (Nitram). Fertiliser was applied three times and three cuts were performed. All parameters were measured following fertiliser application. Samples were taken before fertilizer additions at peak growth and before the last silage cut. Soil physical parameters were: aggregate size distribution, aggregate stability, texture (sand/silt/clay) and soil moisture. Soil chemical parameters were: soil nitrate and ammonium, dissolved organic carbon and nitrogen, amino acids and peptides, soil organic matter content as loss-on-ignition, pH, sodium, potassium, calcium, magnesium, permanganate oxdisable carbon, citric acid extractable phosphorous, Olsen-P and total carbon, nitrogen and phosphorus. Soil biological measures were: microbial biomass, carbon and nitrogen. Microbial community composition and nitrogen genes were measured on the same soil samples and are presented in a separate dataset (https://doi.org/10.5285/59f81d41-a789-4c5c-8ab8-36baa7ac2c55) Measurements were undertaken by members of staff from the Centre of Ecology & Hydrology (Bangor, Edinburgh, Lancaster, Wallingford), Bangor University, School of Environment, Natural Resources & Geography and Rothamsted Research, Sustainable Agricultural Sciences, North Wyke. Data was collected for the Newton Fund project "UK-China Virtual Joint Centre for Improved Nitrogen Agronomy". Funded by Biotechnology and Biological Sciences Research Council (BBSRC) and NERC - Ref BB/N013468/1 Full details about this dataset can be found at https://doi.org/10.5285/7a87dde4-b54e-49b0-8751-1d59e8aebb90

  • The dataset contains greenhouse gas fluxes (N2O, CO2 and CH4) following artificial and real sheep urine applied to organic soils within the Carneddau mountain range (556 m a.s.l.) in Snowdonia National Park, North Wales, UK. The study was conducted across two contrasting seasons (summer and autumn). Soil greenhouse gas emission data was collected using a combination of automated chambers and manually sampled chambers, with gas samples analysed via gas chromatography. Supporting data include characterisation of the soil properties at each site, meteorological data, soil moisture and soil chemistry on a time-series following treatment application. The data were used to calculate sheep urine patch N2O-N emission factors, to improve estimates of greenhouse gas emissions from sheep urine deposited to extensively grazed montane agroecosystems. Full details about this dataset can be found at https://doi.org/10.5285/01811fce-1e0f-43be-8649-336b5c51d6cf

  • The data consist of nitrogen gene data, soil biodiversity indices and microbial community composition for three soil depths (0-15, 15-30 and 30-60 cm) from a winter wheat field experiment located in the United Kingdom and collected between April 2017 and August 2017. The sites were Rothamsted Research at North Wyke in Devon and Bangor University at Henfaes Research Station in North Wales. At each site measurements were taken from 15 plots, organised within a randomised complete block design where 5 plots did not receive fertilizers (controls), 5 plots received food-based digestate, and 5 plots received acidified food based digestate a nitrification inhibitor. Soil samples were taken within two weeks of digestate application and shortly before winter wheat harvest. Soil chemical parameters were: soil nitrate, ammonium, dissolved organic carbon and nitrogen, amino acids and peptides, soil organic matter content as loss-on-ignition, pH, sodium, potassium, calcium, magnesium, permanganate oxdisable carbon citric acid extractable phosphorous, Olsen-P and total carbon, nitrogen and phosphorus. Soil biological measure were: microbial biomass carbon and nitrogen. Soil samples were taken by members of staff from Centre of Ecology & Hydrology (Bangor), Bangor University, School of Environment, Natural Resources & Geography Sustainable Agricultural Sciences, and Rothamsted Research North Wyke. Measurements were carried out Rothamsted Research Harpenden and the Centre of Ecology & Hydrology (Wallingford). Soil physico-chemical parameters were measured on the same soil samples and are presented in a related dataset. https://catalogue.ceh.ac.uk/id/90df9dfa-a0c8-4ead-a13d-0a0a13cda7ab Data was collected for the Newton Fund project “UK-China Virtual Joint Centre for Improved Nitrogen Agronomy”. Funded by Biotechnology and Biological Sciences Research Council (BBSRC) and NERC - Ref BB/N013468/1 Full details about this dataset can be found at https://doi.org/10.5285/391c0294-07f1-4856-b592-428bd44055ca

  • [THIS DATASET HAS BEEN WITHDRAWN]. Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum) for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for yield loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop yield losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project (NEC06476). Full details about this dataset can be found at https://doi.org/10.5285/181a7dd5-0fd4-482a-afce-0fa6875b5fb3

  • The data were collected during household surveys designed to investigate the strategies adopted by households to cope with environmental shocks linked to the 2015-2016 El Nino event in Tanzania, and the role played by Wildlife Management Areas (WMAs) - community-based natural resource management - in enabling or constraining these responses. The surveys referred to the period from 2014 to 2016 and were carried out with both heads of households and the wives of heads of household. Those carried out with household heads included sections on: basic household demographics; overall trends in wellbeing; experience of severe livelihood shocks; ownership and use of land and livestock; collection of bushmeat; changes in access to natural resources; composition of environmental income and livelihood portfolios; strategies for coping with environmental shocks; direct income and benefits from WMAs where they are present; conflict; and human casualties linked to wildlife. Those carried out with wives included sections on: basic household demographics; overall trends in wellbeing; use of produce from livestock, farms and gardens; income generating activities and remittances; food security; strategies for coping with environmental shocks; participation in WMAs where they are present; access to natural resources; conflict and safety; receipts of external aid and scholarships; and ability to perform ceremonies. https://reshare.ukdataservice.ac.uk/853955/

  • The dataset describes the results of a laboratory analysis investigating the presence of various infectious agents in goats, cattle, pigs, dogs and sheep from Mambwe District, Eastern Province, Zambia. Blood samples were collected in June, July and August 2013 and stored on Whatman FTA (Flinders Technology Associates) cards. Laboratory analysis was conducted using polymerase chain reactions (PCR) for African trypanosomes and tick-borne infections. In addition, serum was tested for Brucella using the Rose Bengal test. Cattle and dogs were tested for African trypanosomes, tick-borne infections and Brucella. Goats and sheep were tested for African trypanosomes and Brucella. Pigs were tested for African trypanosomes only. The objective was to evaluate the health status of domestic animals in the Mambwe District. This work was conducted alongside a human wellbeing questionnaire survey. The research was part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project no NE/J000701/1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Full details about this dataset can be found at https://doi.org/10.5285/f81ede76-a1d4-4367-aa8c-de087350457e

  • The number and type of natural enemies of crop pests found in winter-sown oilseed rape fields (Brassica napus L.) in relation to local plant diversity (in crop and field margin) and landscape characteristics. Natural enemies and pests were collected using two methods (suction sampling and pitfall traps). Local plant diversity was assessed using quadrats in field margins and in cropped area. The presence of hedges was also recorded. Landscape characteristics include the amount of mass flowering crops, arable land, presence of patches of different grassland types (intensive, restored and species rich) and the amount of grasslands and other semi natural habitat with up to a 3km radius of the collection points. These data were collected as part of Wessex BESS project, funded by the NERC Biodiversity and Ecosystem Service Sustainability research program. This dataset can be used in conjunction with other Wessex BESS WP4 datasets. Full details about this dataset can be found at https://doi.org/10.5285/6e2be4d6-a681-4ae5-8abf-0c3fc150365d