Tackling unintended consequences of grazing livestock farming: Multi-scale assessment of co-benefits and trade-offs for water pollution mitigation scenarios.

A farm-to-landscape scale modelling framework combining regulating services and life cycle assessment mid-point impacts for air and water was used to explore the co-benefits and trade-offs of alternative management futures for grazing livestock farms. Two intervention scenarios were compared: one using on-farm interventions typically recommended following visual farm audits (visually-based) and the other using mechanistical understanding of nutrient and sediment losses to water (mechanistically-based). At farm scale, reductions in business-as-usual emissions to water of total phosphorus (TP) and sediment, using both the visually-based and mechanistically-based scenarios, were <5%. These limited impacts highlighted the important role of land drains and the lack of relevant on-farm measures in current recommended advisory lists for the soil types in question. The predicted impacts of both scenarios on free draining soils were significantly higher; TP reductions of ∼9% (visually-based) and ∼20% (mechanistically-based) compared with corresponding respective estimates of >20% and >35% for sediment. Key co-benefits at farm scale included reductions in nitrous oxide emissions and improvements in physical soil quality, whereas an increase in ammonia emissions was the principal trade-off. At landscape scale, simulated reductions in business-as-usual losses were <3% for both pollutants for both scenarios. The visually-based and mechanistically-based scenarios narrowed the gaps between current and modern background sediment loads by 6% and 11%, respectively. The latter scenario also improved the reduction of GWP100 relative to business-as-usual by 4%, in comparison to 1% for the former. However, with the predicted increase of ammonia emissions, both eutrophication potential and acidification potential increased (e.g., by 7% and 14% for the mechanistically-based scenario). The discrepancy of on-farm intervention efficacy across spatial scales generated by non-agricultural water pollutant sources is a key challenge for addressing water quality problems at landscape scale.

Modelling field scale spatial variation in water run-off, soil moisture, N2O emissions and herbage biomass of a grazed pasture using the SPACSYS model.

In this study, we evaluated the ability of the SPACSYS model to simulate water run-off, soil moisture, N2O fluxes and grass growth using data generated from a field of the North Wyke Farm Platform. The field-scale model is adapted via a linked and grid-based approach (grid-to-grid) to account for not only temporal dynamics but also the within-field spatial variation in these key ecosystem indicators. Spatial variability in nutrient and water presence at the field-scale is a key source of uncertainty when quantifying nutrient cycling and water movement in an agricultural system. Results demonstrated that the new spatially distributed version of SPACSYS provided a worthy improvement in accuracy over the standard (single-point) version for biomass productivity. No difference in model prediction performance was observed for water run-off, reflecting the closed-system nature of this variable. Similarly, no difference in model prediction performance was found for N2O fluxes, but here the N2O predictions were noticeably poor in both cases. Further developmental work, informed by this study's findings, is proposed to improve model predictions for N2O. Soil moisture results with the spatially distributed version appeared promising but this promise could not be objectively verified.

Assessment of soil water, carbon and nitrogen cycling in reseeded grassland on the North Wyke Farm Platform using a process-based model.

The North Wyke Farm Platform (NWFP) generates large volumes of temporally-indexed data that provides a valuable test-bed for agricultural mathematical models in temperate grasslands. In our study, we used the primary datasets generated from the NWFP (https://nwfp.rothamsted.ac.uk/) to validate the SPACSYS model in terms of the dynamics of water loss and forage dry matter yield estimated through cutting. The SPACSYS model is capable of simulating soil water, carbon (C) and nitrogen (N) balance in the soil-plant-atmosphere system. The validated model was then used to simulate the responses of soil water, C and N to reseeding grass cultivars with either high sugar (Lolium perenne L. cv. AberMagic) or deep rooting (Festulolium cv. Prior) traits. Simulation results demonstrated that the SPACSYS model could predict reliably soil water, C and N cycling in reseeded grassland. Compared to AberMagic, the Prior grass could fix more C in the second year following reseeding, whereas less C was lost through soil respiration in the first transition year. In comparison to the grass cultivar of the permanent pasture that existed before reseeding, both grasses reduced N losses through runoff and contributed to reducing water loss, especially Prior in relation to the latter. The SPACSYS model could predict these differences as supported by the rich dataset from the NWFP, providing a tool for future predictions on less characterized pasture.