This dataset contains the results of a farmers’ survey in the Ping Catchment in Thailand. The aim of this survey was to identify the specific socioeconomic impacts that historical droughts in the Ping catchment have had for agricultural communities, and identify factors affecting adaptation decisions, as well as analyse the communications with and amongst farmers at the local scale in the Ping catchment during drought. Villages in the Ping catchment with a history of drought were selected to represent typical agricultural production typologies. In total, 176 questionnaires were completed with a close to even distribution of respondents coming from the provinces of Chiang Mai (n=41), Lamphun (n=45), Kamphaeng Phet (n=45) and Tak (n=45). Full details about this dataset can be found at https://doi.org/10.5285/155e1867-bc9d-44f0-9f85-0f682964f720

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

These datasets were used for a study investigating the prevalence of diurnal variability of soil nitrous oxide (N¬2O) emissions. The datasets contain 286 diurnal N¬2O flux datasets and 160 diurnal soil temperature datasets, which were extracted from 46 published journal articles that were selected from a literature search and passed through a set of eligibility criteria. The datasets also include processed diurnal N¬2O flux data, which were used to classify the diurnal N¬2O pattern of the datasets. Data of non-diurnal factors from the literature including soil pH, bulk density, soil texture, season of measurement, soil water-filled pore space, irrigation and grazing are also included in the datasets. Full details about this dataset can be found at https://doi.org/10.5285/94e37080-4383-4f6e-b14a-04ac2ac79bf0

Gridded land use map of Peninsular Malaysia with a resolution of approximate 25 meters for the year 2018. The map includes nine different classes: 1) non-paddy agriculture, 2) paddy fields, 3) rural residential, 4) urban residential, 5) commercial/institutional, 6) industrial/infrastructure, 7) roads, 8) urban and 9) others. The land use map was created as part of the project “Malaysia - Flood Impact Across Scales”. The project is funded under the Newton-Ungku Omar Fund ‘Understanding of the Impacts of Hydrometeorological Hazards in South East Asia’ call. The grant was jointly awarded by the Natural Environment Research Council and the MYPAIR Scheme under the Ministry of Higher Education of Malaysia. Full details about this dataset can be found at https://doi.org/10.5285/36df244e-11c8-44bc-aa9b-79427123c42c

This data set consist of a single file which contains a set of optimised global surface fluxes of methane (CH4), produced through variational inverse methods using the TOMCAT chemical transport model, and the INVICAT inverse transport model. These surface fluxes are produced as monthly mean values on the (approximately) 5.6-degree horizontal model grid. The associated uncertainty for the flux from each grid cell is also included. The fluxes and uncertainties are global and cover the period Jan 2010 - Dec 2018. The emissions from fossil fuels are labelled FF_FLUX, whilst the uncertainties are labelled FF_ERROR. The emissions from natural, agricultural and biomass burning sources are labelled NAT_FLUX, whilst the uncertainties are labelled NAT_ERROR. These two sectors (fossil fuel and non-fossil fuel) are solved for separately in the inversion. Flux and uncertainty units are kg(CH4)/m2/s, and time units are days since January 1st 2010. These emissions show improved performance relative to independent observations when included in the TOMCAT model. Further details about the data can be found in Wilson et al. (2020) in the documentation section.

[This dataset is embargoed until June 30, 2024]. This dataset contains 2-hourly observations of biogenic fluxes of carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) measured from a winter wheat crop grown on a mineral soil in the UK treated with different fertilisers. The treatments were: i) inorganic fertiliser; ii) pig slurry and inorganic fertiliser; and iii) pig slurry treated with plasma-induction and inorganic fertiliser. Fluxes of biogenic greenhouse gases (GHGs) were measured over an 83-day period (20/03/2022-13/06/2022) within the winter wheat growing season using automated GHG flux chambers. Full details about this dataset can be found at https://doi.org/10.5285/4ed0023e-da9b-45a8-86de-3a371cc7dcc1

The dataset contains model output from an agricultural land use model at kilometre scale resolution over Great Britain (GB) for four different climate and policy scenarios. Specifically, arable area is modelled for with or without a climate tipping point (standard (medium emissions scenario SRES-A1B) climate change vs Atlantic Meridional Overturning Circulation (AMOC) collapse) and with or without widespread irrigation use for farmers from 2000 to 2089. Full details about this dataset can be found at https://doi.org/10.5285/e1c1dbcf-2f37-429b-af19-a730f98600f6

[This dataset is embargoed until January 1, 2024]. The data set shows the modelled change of soil organic carbon under different managements in agriculture for different climate scenarios globally. Dataset includes the change to a business as usual scenario for different soil managements for each decade from 2030 to 2100. The work was supported by the Natural Environment Research Council (NE/P019455/1) Full details about this dataset can be found at https://doi.org/10.5285/fbb03aba-ad1c-438a-a3b3-2a99bc1baaf8

Data are presented on earthworm abundance with distance from hedgerows was recorded for arable fields and pasture leys at farms at Little Langton, Hutton Wandesley, Overton and Whenby, Yorkshire. Sampling was carried out 12 to 26th May 2016. Pits were excavated and soil hand sorted for earthworms. Mustard solution was then poured into the pit and any emerging earthworms collected. All earthworms were preserved in ethanol for identification using the Sims and Gerard Field studies key. At each pit the following measurements were also taken: soil moisture, soil temperature, soil bulk density. The samples were taken to determine the influence of leys on soil quality by Miranda Prendergast-Miller and colleagues as part of the SoilBioHedge project (Grant Reference NE/M017095/1) funded by the NERC Soil Security Programme. (Grant Reference NE/M017044/1). Full details about this dataset can be found at https://doi.org/10.5285/a5638d26-a8be-4409-ac51-42904069d919

This dataset includes the transcript of discussion group activities on Human Wildlife conflict, conducted with ten rural communities in Marrupa District, Niassa (Northern Mozambique). It also comprises the results of semi-structured interviews conducted individually in three of the ten selected communities. The ten villages were selected from a forest cover gradient running from villages with a higher forest cover to those within degraded forest areas and consequently low cover. The villages had similar infrastructure, soils, rainfall, and vegetation types. The dataset contains information on the occurrence of conflict with both vertebrate and invertebrate wild species, mitigation strategies, conflict seasonality and trends, but also its impact on agricultural production and livestock rearing. The discussion groups were conducted with six to ten people and the presence of the leader of each village, between May and July 2015. Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Full details about this dataset can be found at https://doi.org/10.5285/7bd2e230-c219-4017-9914-b5cfd83a4eae