Keyword

Farming

103 record(s)
 
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  • 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 data were created as part of the NIMFRU project and consists of 21 flood matrices. These have been completed by community members from the project target communities of Anyangabella, Agule and Kaikamosing which are all found in the Katakwi district. Five of the matrices were completed by local district officers. The data were collected in December 2020. These data were collected to understand how communities resilience had changed as a result of the NIMFRU project. Full details about this dataset can be found at https://doi.org/10.5285/463b2bcc-731a-42af-ba69-1662aa21f1bf

  • This dataset is a product of the raw HEA (household economy approach) data that were collected in sixteen communities in the Katakwi district, and the raw IHM (individual household method) data that was collected with 42 households in the community of Anyangabella, and 51 households in the community of Kaikamosing. These data were collected in December 2020 and shows the crop calendars of the Katakwi district. These data consist of quantitative information relating to crop and fishing production timelines throughout a typical agricultural year. The data were collected to support the analysis of vulnerability levels of different to further support livelihood impact modelling, and the development of targeted policies to support resilience at household and community level. The data collection team comprised of local, Ugandan partners. All data were collected in the local language and translated into English. Full details about this dataset can be found at https://doi.org/10.5285/d91bd655-ad51-42c1-a8d0-91923246244b

  • [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

  • [This nonGeographicDataset is embargoed until March 14, 2024]. The data report plant growth parameters measured on six week old rice plants grown under two contrasting nitrogen treatments. The purpose is to conduct genome wide association mapping of nitrogen response and uses 229 cultivars of the Bengal and Assam Aus Panel. Traits are plant height, shoot dry weight, tiller number and two measures from a Dualex hand-held meter, the nitrogen balance index and the chlorophyl index. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/7ac2ba22-6915-43ca-93cc-d75b9c458b51

  • 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 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

  • This dataset represents a cohort of heifers followed from birth to 18 months or first pregnancy on 37 farms in the South West of England. Faecally-contaminated environmental samples were collected over 2 years and the samples analysed for E. coli resistance to amoxicillin, cefalexin and tetracycline with detection of resistant strains presented in the dataset as a binary result. Farm-level antibiotic usage data is also given. Full details about this dataset can be found at https://doi.org/10.5285/7c3ad803-fbd4-45c3-826b-fa04c902ded8

  • Data are presented from an ozone exposure experiment performed on four African crops. The crops (Beans, Cowpeas, Amaranth and Sorghum) were exposed to three different levels of ozone and two heat treatments in the UK CEH Bangor solardomes. The experiment ran from May 2018 to September 2018. The crop plants were grown from seed, in pots in solardomes. The aim of the experiment was to investigate the impact of ozone exposure on the crop yield and plant health. The dataset comprises of manually collected data on plant physiology, biomass and yield. In addition the automatically logged data of ozone concentration and meteorological variables in the solardomes are presented. Plant physiology data is stomatal conductance of individual leaves, measured on an ad-hoc basis. The dataset includes the associated data measured by the equipment (relative humidity, leaf temperature, photosynthetically active radiation – a small number of photosynthetically active radiation measurements are missing due to faulty readings). Soil moisture of the pots was always measured at the same time, and chlorophyll content of the measured leaf was usually, but not always, determined at the same time. Yield of beans and cowpeas was determined for each plant. For Amaranth, only the seed head weight was determined. Sorghum did not reach yield, therefore, total biomass at harvest is given as an alternative. Total biomass was not determined for those plants of other crop types that did reach yield. The ozone and meteorological dataset is complete, but with some gap-filling for short periods when the computer was not logging data The work was carried out as part of the NERC funded SUNRISE project (NE/R000131/1). Full details about this dataset can be found at https://doi.org/10.5285/f7da626c-f39c-474f-b2e7-8638ab26d166

  • [This dataset is embargoed until August 1, 2024]. This dataset includes results from biodiversity, social and environmental surveys of 46 oil palm smallholders and farms in Riau, Indonesia. Biodiversity data includes: pitfall trap data on arthropod abundance and higher-level order identification, sticky trap data on flying invertebrate abundance (identified to higher-level order), transect data on assassin bugs, Nephila spp. spiders and butterflies (identified to species), counts of insects visiting oil palm inflorescences if any open (identified to Elaeidobius kamerunicus and higher-level orders for other groups) and data on meal worm removal from each plot. Environmental data includes: soil temperature readings recorded over 24 hours, information on size of plot, crop type and cover, GPS location, vegetation cover, vegetation height, canopy density, epiphyte cover, soil pH, soil moisture, leaf litter depth, horizon depths, palm herbivory and palm health. Social data includes information (all anonymised) on: plot area, number of palms, sociodemographic data, plantation management practices applied, knowledge and value assigned to wildlife, and yield. Data were collected from November 2021 to June 2022. Full details about this dataset can be found at https://doi.org/10.5285/b61a12a2-d091-41af-b451-a14de4f4a3c3