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

The data contains urination metrics including frequency, volume, chemical composition, estimated urine patch N loading rates and metabolomics profile of individual urine events from sheep (Welsh Mountain ewe) grazing a semi-improved upland pasture and a lowland improved pasture located in North Wales, UK. Urine collection studies were run in the spring, summer and autumn of 2016 for the semi-improved site and in autumn of 2016 on the lowland improved pasture. Sheep were housed in urine collection pens and while in the pens, each individual urine event was collected and stored separately. The study was conducted as a wider part of the NERC funded Uplands-N2O project (Grant No: NE/M015351/1). The frequency, volume and chemical composition of individual urine events has implications for nitrogen losses from the grazed pasture ecosystem, including emissions of the powerful greenhouse gas, nitrous oxide, and nitrate leaching. Full details about this dataset can be found at https://doi.org/10.5285/385ec5ab-0c47-46fc-b5df-008ca024296f

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 data contains the emissions of ammonia (NH3) from agricultural sources in the South Asia region, for the year 2015. Agriculture is represented by five sub-sectors: crop residue burning (CRB), crop residues left in fields (CRR), livestock management (MNM), livestock grazing and manure applications (GRM), application of synthetic fertilisers (SFA). Data are bottom-up calculations using activity data and emission factors, using methods outlined in the EDGARv6.1 methodology, the IPCC 2006 Guidelines, the IPCC 2019 Guidelines Refinement and in the EMEP/EEA air pollutant emission inventory guidebooks 2019 and 2023. Full details about this dataset can be found at https://doi.org/10.5285/e0114a4f-32c2-41d9-9c2a-c46f365d4c30

This dataset details information collected from smallholder oil palm farms in Sabah, Malaysian Borneo. Including: management practices, oil palm fruit yield, understorey vegetation, and soil chemical properties (SOC, total N, total P and available P). We collected data between August to November 2019 from 40 smallholdings (defined as farms < 50 ha) across six governance areas in Sabah. We used responses from face-to-face questionnaires to collect information about their management practices, including Best Management Practices (BMPs), and reported Fresh Fruit Bunch (FFB) yields. We also carried out field surveys on these farms to quantify vegetation cover and soil chemical properties. All smallholder farms had mature fruiting trees i.e. > 8 years since planting. The project received ethical approval from the Biology Ethics Committee, University of York (Ref. SGA201906), and permission from the Sabah Biodiversity Council (Ref. JKM/MBS.1000-2/2 JLD.8), Danum Valley Management Committee (Ref. YS/DVMC/2019/27), and South East Asia Rainforest Research Partnership (project number 18033) for permission to conduct our research in Sabah, Malaysia. This work was funded by the NERC iCASE studentship (NE/R007624/1) and Proforest. Full details about this dataset can be found at https://doi.org/10.5285/38487932-b32a-4b15-9fda-ea812c463466

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

Data on resilience of wheat yields in England, derived from the annual Defra Cereals and Oilseeds production survey of commercial farms. The data presented here are summarised over a ten-year time-series (2008-2017) at 10km x10km grid cell (hectad) resolution. The data give the mean yield, relative yield, yield stability and resistance to an extreme event (the poor weather of 2012), for all hectads with at least one sampled farm holding in each year of the time-series (i.e. the minimum data required to calculate the resilience metrics). These metrics were calculated to explore the impact of landscape structure on yield resilience. The data also give the number of samples per year per hectad, so that sampling biases can be explored and filtering applied. No hectads are included that contain data from <9 holdings across the time series (the minimum level required by Defra to maintain anonymity is <5). The data were created under the ASSIST (Achieving Sustainable Agricultural Systems) project by staff at the UK Centre for Ecology & Hydrology to enable exploration of the impacts of agriculture on the environment and vice versa, enabling farmers and policymakers to implement better, more sustainable agricultural practices. Full details about this dataset can be found at https://doi.org/10.5285/7dbcee0c-00ca-4fb2-93cf-90f2a5ca37ea

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