Quantifying gross vs. net agricultural land use change in Great Britain using the Integrated Administration and Control System.

Land use change has impacts upon many natural processes, and is one of the key measures of anthropogenic disturbance on ecosystems. Agricultural land covers 70% of Great Britain's (GB) land surface and annually undergoes disturbance and change through farming practices such as crop rotation, ploughing and the planting and subsequent logging of forestry. It is important to quantify how much of GB's agricultural land undergoes such changes and what those changes are at an annual temporal resolution. Integrated Administration and Control System (IACS) data give annual snapshots of agricultural land use at the field level, allowing for high resolution spatiotemporal land use change studies at the national scale. Crucially, not only do the data allow for simple net change studies (total area change of a land use, in a specific areal unit) but also for gross change calculations (summation of all changes to and from a land use), meaning that both gains and losses to and from each land use category can be defined. In this study we analysed IACS data for GB from 2005 to 2013, and quantified gross change for over 90% of the agricultural area in GB for the first time. It was found that gross change totalled 63,500 km2 in GB compared to 20,600 km2 of net change, i.e. the real year-on-year change is, on average, three times larger than net change. This detailed information on nature of land use change allows for increased accuracy in modelling the impact of land use change on ecosystem processes and is directly applicable across EU member states, where collection of such survey data is a requirement. The modelled carbon flux associated with gross land use change was at times >100 Gg C y-1 larger than that based on net land use change for some land use transitions.

The influence of residential and workday population mobility on exposure to air pollution in the UK.

Traditional approaches of quantifying population-level exposure to air pollution assume that concentrations of air pollutants at the residential address of the study population are representative for overall exposure. This introduces potential bias in the quantification of human health effects. Our study combines new UK Census data comprising information on workday population densities, with high spatio-temporal resolution air pollution concentration fields from the WRF-EMEP4UK atmospheric chemistry transport model, to derive more realistic estimates of population exposure to NO2, PM2.5 and O3. We explicitly allocated workday exposures for weekdays between 8:00 am and 6:00 pm. Our analyses covered all of the UK at 1 km spatial resolution. Taking workday location into account had the most pronounced impact on potential exposure to NO2, with an estimated 0.3 µg m-3 (equivalent to 2%) increase in population-weighted annual exposure to NO2 across the whole UK population. Population-weighted exposure to PM2.5 and O3 increased and decreased by 0.3%, respectively, reflecting the different atmospheric processes contributing to the spatio-temporal distributions of these pollutants. We also illustrate how our modelling approach can be utilised to quantify individual-level exposure variations due to modelled time-activity patterns for a number of virtual individuals living and working in different locations in three example cities. Changes in annual-mean estimates of NO2 exposure for these individuals were considerably higher than for the total UK population average when including their workday location. Conducting model-based evaluations as described here may contribute to improving representativeness in studies that use small, portable, automatic sensors to estimate personal exposure to air pollution.

Impact of two centuries of intensive agriculture on soil carbon, nitrogen and phosphorus cycling in the UK.

This paper describes an agricultural model (Roth-CNP) that estimates carbon (C), nitrogen (N) and phosphorus (P) pools, pool changes, their balance and the nutrient fluxes exported from arable and grassland systems in the UK during 1800-2010. The Roth-CNP model was developed as part of an Integrated Model (IM) to simulate C, N and P cycling for the whole of UK, by loosely coupling terrestrial, hydrological and hydro-chemical models. The model was calibrated and tested using long term experiment (LTE) data from Broadbalk (1843) and Park Grass (1856) at Rothamsted. We estimated C, N and P balance and their fluxes exported from arable and grassland systems on a 5km×5km grid across the whole of UK by using the area of arable of crops and livestock numbers in each grid and their management. The model estimated crop and grass yields, soil organic carbon (SOC) stocks and nutrient fluxes in the form of NH4-N, NO3-N and PO4-P. The simulated crop yields were compared to that reported by national agricultural statistics for the historical to the current period. Overall, arable land in the UK have lost SOC by -0.18, -0.25 and -0.08MgCha-1y-1 whereas land under improved grassland SOC stock has increased by 0.20, 0.47 and 0.24MgCha-1y-1 during 1800-1950, 1950-1970 and 1970-2010 simulated in this study. Simulated N loss (by leaching, runoff, soil erosion and denitrification) increased both under arable (-15, -18 and -53kgNha-1y-1) and grass (-18, -22 and -36kgNha-1y-1) during different time periods. Simulated P surplus increased from 2.6, 10.8 and 18.1kgPha-1y-1 under arable and 2.8, 11.3 and 3.6kgPha-1y-1 under grass lands 1800-1950, 1950-1970 and 1970-2010.