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farming

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  • This dataset includes values of 15 traits (total dry mass; root length to shoot length ratio; leaf mass fraction; root mass fraction; shoot mass fraction; leaf thickness; leaf force to punch; leaf area to shoot area ratio; leaf concentrations of N, P, K, Ca and Mg; leaf N: P concentration ratio; specific maximum root length) measured in February 2020 on 394 seedlings of 15 woody plant species growing in logged in the Ulu Segama Forest Reserve or unlogged forest in the Danum Valley Conservation Area, Malaysia. The purpose of this data collection was to determine whether the expression of plant functional traits differed between tree seedlings recruited into logged and unlogged forests. This information is important for understanding the drivers of variation in seedling growth and survival in response to logging disturbance, and to uncover the mechanisms giving rise to differentiation in tree seedling composition in response to logging. These data were collected as part of NERC project “Seeing the fruit for the trees in Borneo: responding to an unpredictable community-level fruiting event” (NE/T006560/1). Full details about this dataset can be found at https://doi.org/10.5285/e738e8af-554a-4940-bb56-267c7377d74d

  • 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 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 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

  • 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 contains over 4000 faecally-contaminated environmental samples collected over 2 years across 53 dairy farms in England. The samples were analysed for E. coli resistance to amoxicillin, streptomycin, cefalexin, tetracycline and ciprofloxacin and detection of resistant strains is presented in the dataset as a binary result, along with mechanisms of resistance to third generation cephalosporins where relevant. In addition there is comprehensive farm management data including antibiotic usage data. Full details about this dataset can be found at https://doi.org/10.5285/c9bc537a-d1c5-43a0-b146-42c25d4e8160

  • 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

  • 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 contains information on soil physico-chemical characteristics and palm nutrient concentrations collected in 2019 across twenty-five smallholder oil palm farms in Perak, Malaysia. Leaf and rachis were sampled from 3 palms within each plot. Soils were sampled to 30cm depth in the palm circle of the same 3 palms and the adjacent inter-row area. These data were collected to assess the soil condition and nutritional status of oil palms across smallholder farms. This information was used to advise on best agronomic practice. The work was supported by the Natural Environment Research Council (Grant No. 355 NE/R000131/1). Full details about this dataset can be found at https://doi.org/10.5285/4d3813b6-714b-403a-aeeb-e2fa518a1520

  • This dataset consists of landscape and agricultural management archetypes (1 km resolution) at three levels, defined by different opportunities for adaptation. Tier 1 archetypes quantify broad differences in soil, land cover and population across Great Britain, which cannot be readily influenced by the actions of land managers; Tier 2 archetypes capture more nuanced variations within farmland-dominated landscapes of Great Britain, over which land managers may have some degree of influence. Tier 3 archetypes are built at national levels for England and Wales and focus on socioeconomic and agro-ecological characteristics within farmland-dominated landscapes, characterising differences in farm management. The unavailability of several input variables for agricultural management prevented the generation of Tier 3 archetypes for Scotland. The archetypes were derived by data-driven machine learning. The three tiers of archetypes were analysed separately and not as a nested structure (i.e. a single Tier 3 archetype can occur in more than one Tier 2 archetype), predominantly to ensure that archetype definitions were easily interpreted across tiers. Full details about this dataset can be found at https://doi.org/10.5285/3b44375a-cbe6-468c-9395-41471054d0f3