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 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 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 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 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 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

[This dataset is embargoed until December 15, 2025]. This data set represents field-based monitoring of insect pollinator communities found within soya (Glycine max L. Merril) crops located along a latitudinal gradient ranging from -37.669486 to -24.495121 covering both Argentina and Brazil. Yield data was also collected from these same sites to elucidate the dependencies of this crop on insect pollination with a focus on managed and wild pollinators. Data was collected over multiple seasons between 2020 and 2022. Soybean is one of the most traded agricultural commodities and is of significant economic importance in South America. Full details about this dataset can be found at https://doi.org/10.5285/2bd21042-ebbc-4454-8ca3-96e18333ccd2

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 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

This dataset contains responses from an online choice experiment with associated socio-economic covariates on the topic of environmental land management schemes. Sample: 348 farmers based in the north of England in 2022. Full details about this dataset can be found at https://doi.org/10.5285/1409404f-564f-43c5-81dd-00339a674dc8