Grasslands cover around 25% of the global ice-free land surface, they are used predominantly for forage and livestock production and are considered to contribute significantly to soil carbon (C) sequestration. Recent investigations into using 'nature-based solutions' to limit warming to <2 °C suggest up to 25% of GHG mitigation might be achieved through changes to grassland management. In this study we evaluate pasture management interventions at the Rothamsted Research North Wyke Farm Platform, under commercial farming conditions, over two years and consider their impacts on net CO2 exchange. We investigate if our permanent pasture system (PP) is, in the short-term, a net sink for CO2 and whether reseeding this with deep-rooting, high-sugar grass (HS) or a mix of high-sugar grass and clover (HSC) might increase the net removal of atmospheric CO2. In general CO2 fluxes were less variable in 2018 than in 2017 while overall we found that net CO2 fluxes for the PP treatment changed from a sink in 2017 (−5.40 t CO2 ha−1 y−1) to a source in 2018 (6.17 t CO2 ha−1 y−1), resulting in an overall small source of 0.76 t CO2 ha−1 over the two years for this treatment. HS showed a similar trend, changing from a net sink in 2017 (−4.82 t CO2 ha−1 y−1) to a net source in 2018 (3.91 t CO2 ha−1 y−1) whilst the HSC field was a net source in both years (3.92 and 4.10 t CO2 ha−1 y−1, respectively). These results suggested that pasture type has an influence in the atmospheric CO2 balance and our regression modelling supported this conclusion, with pasture type and time of the year (and their interaction) being significant factors in predicting fluxes.

Water quality impairment by elevated sediment loss is a pervasive problem for global water resources. Sediment management targets identify exceedance or the sediment loss 'gap' requiring mitigation. In the UK, palaeolimnological reconstruction of sediment loss during the 100-150 years pre-dating the post-World War II intensification of agriculture, has identified management targets (0.20-0.35 t ha(-1) yr(-1)) representing 'modern background sediment delivery to rivers'. To assess exceedance on land for grazing ruminant farming, an integrated approach combined new mechanistic evidence from a heavily-instrumented experimental farm platform and a scaling out framework of modelled commercial grazing ruminant farms in similar environmental settings. Monitoring (2012-2016) on the instrumented farm platform returned sediment loss ranges of 0.11-0.14 t ha(-1) yr(-1) and 0.21-0.25 t ha(-1) yr(-1) on permanent pasture, compared with between 0.19-0.23 t ha(-1) yr(-1) and 0.43-0.50 t ha(-1) yr(-1) and 0.10-0.13 t ha(-1) yr(-1) and 0.25-0.30 t ha(-1) yr(-1) on pasture with scheduled plough and reseeds. Excess sediment loss existed on all three farm platform treatments but was more extensive on the two treatments with scheduled plough and reseeds. Excessive sediment loss from land used by grazing ruminant farming more strategically across England, was estimated to be up to >0.2 t ha(-1) yr(-1). Modelled scenarios of alternative farming futures, based on either increased uptake of interventions typically recommended by visual farm audits, or interventions selected using new mechanistic understanding for sediment loss from the instrumented farm platform, returned minimum sediment loss reductions. On the farm platform these were 2.1 % (up to 0.007 t ha(-1) yr(-1)) and 5.1 % (up to 0.018 t ha(-1) yr(-1)). More strategically, these were up to 2.8 % (0.014 t ha(-1) yr(-1)) and 4.1 % (0.023 t ha(-1) yr(-1)). Conventional on-farm measures will therefore not fully mitigate the sediment loss gap, meaning that more severe land cover change is required.

This study compares data statistically that were collected from both long-term drained and undrained plots to test hypotheses concerning the effect of drainage on plant community, soil total nitrogen (TN), soil total carbon (TC) and stable isotopic (delta N-15, delta C-13) contents in a permanent grassland. In addition, the effects of soil depth, topography (elevation, slope, aspect and compound topographic index (CTI)) and spatial autocorrelation were taken into account. Data were collected in 2010 at Rowden Moor, North Wyke, Devon, UK, where, for the plots of this study, subsurface drainage was introduced in 1987. The results of a set of six linear mixed models showed that: (i) plant community did not depend on drainage, but on elevation and spatial effects, (ii) both TN and TC not only depended on drainage, but also topography and sample depth, (iii) the TC to TN ratio did not depend on drainage, but on elevation, CTI and sample depth only, (iv) delta N-15 values did not depend on drainage, but on topography and sample depth and (v) delta C-13 values depended on drainage together with topography and sample depth. Thus, drainage represented a significant effect for only TN, TC and delta C-13. Furthermore, changes in soil physicochemical conditions, following the introduction of drainage in the clay soil 24 years previously, induced a shift in the plant community from a Lolium perenne L. dominated grassland with numerous patches of Juncus species, towards one with Lolium perenne and Trifolium repens L.

The UK’s North Wyke Farm Platform (NWFP) for sustainable grassland farming is set up as a large agriculture modelling system of 15 hydrologically-isolated catchments, where in each catchment, water chemistry, precipitation and soil moisture data are continuously monitored. This spatio-temporal data are then interrogated with respect to climatic timings and changes in crop, livestock and farm management, across the NWFP. Complementary data sets are also found via spatial field surveys, remote sensing and greenhouse gas studies. This study focuses on one such field survey, consisting of soils data at 495 sites. We spatially explore this data using a geographically weighted principal components analysis, where we provide a novel adaptation of the technique to deal with the distinctly partitioned nature of the data, which is collected across 20 fields, spread over the 15 catchments.