North Wyke Farm Platform Research Articles

Wind Speed Prediction Using Deep Recurrent Neural Networks and Farm Platform Features for One-Hour-Ahead Forecast

This paper proposes a deep recurrent neural network (DRNN) approach to model the one-hour-ahead wind speed forecasting by using various meteorological sensory data from the North Wyke farm platform (NWFP). To refine model input, mutual information analysis is applied to eliminate irrelevant sensory data. The DRNN architecture employs three recurrent layers Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and simple Recurrent Neural Network (RNN) to capture temporal relationships. The proposed networks are tested using real-life, one-year data from the NWFP. The results showed a strong correlation between the actual and predicted wind speed for LSTM, GRU, and RNN layers-based DRNN, however, simple RNN slightly outperformed the other two recurrent layers. The distribution of the network errors over the year is also analyzed. Although the observed meteorological data between the years was from different distributions, the proposed network generalized well even though these data were altered due to global warming.

Comparison of direct and indirect soil organic carbon prediction at farm field scale

To advance sustainable and resilient agricultural management policies, especially during land use changes, it is imperative to monitor, report, and verify soil organic carbon (SOC) content rigorously to inform its stock. However, conventional methods often entail challenging, time-consuming, and costly direct soil measurements. Integrating data from long-term experiments (LTEs) with freely available remote sensing (RS) techniques presents exciting prospects for assessing SOC temporal and spatial change. The objective of this study was to develop a low-cost, field-based statistical model that could be used as a decision-making aid to understand the temporal and spatial variation of SOC content in temperate farmland under different land use and management. A ten-year dataset from the North Wyke Farm Platform, a 20-field, LTE system established in southwestern England in 2010, was used as a case study in conjunction with an RS dataset. Linear, additive and mixed regression models were compared for predicting SOC content based upon combinations of environmental variables that are freely accessible (termed open) and those that require direct measurement or farmer questionnaires (termed closed). These included an RS-derived Ecosystem Services Provision Index (ESPI), topography (slope, aspect), weather (temperature, precipitation), soil (soil units, total nitrogen [TN], pH), and field management practices. Additive models (specifically Generalised Additive Models (GAMs)) were found to be the most effective at predicting space-time SOC variability. When the combined open and closed factors (excluding TN) were considered, significant predictors of SOC were: management related to ploughing being the most important predictor, soil unit (class), aspect, and temperature (GAM fit with a normalised RMSE = 9.1%, equivalent to 0.4% of SOC content). The relative strength of the best-fitting GAM with open data only, which included ESPI, aspect, and slope (normalised RMSE = 13.0%, equivalent to 0.6% of SOC content), suggested that this more practical and cost-effective model enables sufficiently accurate prediction of SOC.

Are single global warming potential impact assessments adequate for carbon footprints of agri-food systems?

The vast majority of agri-food climate-based sustainability analyses use global warming potential (GWP100) as an impact assessment, usually in isolation; however, in recent years, discussions have criticised the ‘across-the-board’ application of GWP100 in Life Cycle Assessments (LCAs), particularly of food systems which generate large amounts of methane (CH4) and considered whether reporting additional and/or alternative metrics may be more applicable to certain circumstances or research questions (e.g. Global Temperature Change Potential (GTP)). This paper reports a largescale sensitivity analysis using a pasture-based beef production system (a high producer of CH4 emissions) as an exemplar to compare various climatatic impact assessments: CO2-equivalents using GWP100 and GTP100, and ‘CO2-warming-equivalents’ using ‘GWP Star’, or GWP*. The inventory for this system was compiled using data from the UK Research and Innovation National Capability, the North Wyke Farm Platform, in Devon, SW England. LCAs can have an important bearing on: (i) policymakers’ decisions; (ii) farmer management decisions; (iii) consumers’ purchasing habits; and (iv) wider perceptions of whether certain activities can be considered ‘sustainable’ or not; it is, therefore, the responsibility of LCA practitioners and scientists to ensure that subjective decisions are tested as robustly as possible through appropriate sensitivity and uncertainty analyses. We demonstrate herein that the choice of climate impact assessment has dramatic effects on interpretation, with GWP100 and GTP100 producing substantially different results due to their different treatments of CH4 in the context of carbon dioxide (CO2) equivalents. Given its dynamic nature and previously proven strong correspondence with climate models, out of the three assessments covered, GWP* provides the most complete coverage of the temporal evolution of temperature change for different greenhouse gas emissions. We extend previous discussions on the limitations of static emission metrics and encourage LCA practitioners to consider due care and attention where additional information or dynamic approaches may prove superior, scientifically speaking, particularly in cases of decision support.

A commentary on key methodological developments related to nutritional life cycle assessment (nLCA) generated throughout a 6-year strategic scientific programme.

Rothamsted Research (RRes) is the world's oldest agricultural research centre, notable for the development of the first synthetic fertilizer (superphosphate) and long-term farming experiments (LTEs) spanning over 170 years. In 2015, RRes recruited several life cycle assessment (LCA) experts and began adopting the method to utilize high resolution agronomical data covering livestock (primarily ruminants), grassland/forage productivity and quality, and arable systems established on its North Wyke Farm Platform (NWFP) and the LTEs. The NWFP is a UK 'National Bioscience Research Infrastructure' (NBRI) developed for informing and testing systems science utilising high-resolution data to determine whether it is possible to produce nutritious food sustainably. Thanks largely to the multidisciplinary knowledge at RRes, and its collaborators, its LCA Team has been at the forefront of methodological advances during a 6-year Institute Strategic Programme (ISP) 'Soil-to-Nutrition' (S2N). While S2N investigated the co-benefits and trade-offs of new mechanistic understanding of efficient nutrient use across scales from pot to landscape, this commentary specifically synthesizes progress in incorporating human nutrition in the context of environmental footprinting, known as 'nutritional LCA' (nLCA). We conclude our commentary with a brief discussion on future pathways of exploration and methodological developments covering various activities along entire agri-food supply-chains.

Performance and enteric methane emissions from housed beef cattle fed silage produced on pastures with different forage profiles.

Methane (CH4) produced by ruminants is a significant source of greenhouse gases from agriculture in the United Kingdom (UK), accounting for approximately 50% of the emissions in this sector. Ration modification is linked to changes in rumen fermentation and can be an effective means of CH4 abatement. In temperate climate countries, forage silage represents a major feed component for cattle during the housing period. The objective of this study was, therefore, to compare enteric CH4 emission from cattle offered silage produced from different types of grassland. Beef cattle, steers (n=89) and heifers (n=88) with average liveweight (LW) of 328±57.1kg were evaluated during two housing seasons (2016-2017 and 2017-2018) from November to April, at the Rothamsted Research North Wyke Farm Platform (UK). The treatments corresponded to three diet types, comprising silage harvested from three different pastures: MRG, monoculture of perennial ryegrass (PRG, Lolium perenne L.cv. AberMagic), bred to express the high-sugar phenotype; RG-WC, a mixed sward comprised of the same perennial ryegrass cultivar with white clover (Trifolium repens L.) with a target clover proportion of 30% as land cover; and permanent pasture (PP) dominated by PRG and a small number of non-introduced species. MRG and PP received 160-200kgN/ha/year. Cattle were weighed every 30days, and the enteric CH4 emission was determined using GreenFeed automated systems. No significant differences in enteric CH4 emission per head or per kg LW were observed between treatments. However, emission expressed per average daily gain (ADG) in LW was greater (P<0.001) for MRG compared with RG-WC and PP, at 270, 248 and 235g CH4/kg ADG, respectively. This related to a lower ADG (P=0.041) for the animals fed MRG silage compared with RG-WC and PP which were similar, with respective values of 0.67, 0.71 and 0.74kg/day. The forages compared in this study showed little or no potential to reduce enteric CH4 emission when fed as silage to growing beef cattle during the winter housing period.

Response of soil health indicators to dung, urine and mineral fertilizer application in temperate pastures

Healthy soils are key to sustainability and food security. In temperate grasslands, not many studies have focused on soil health comparisons between contrasting pasture systems under different management strategies and treatment applications (e.g. manures and inorganic fertilisers). The aim of this study was to assess the responses of soil health indicators to dung, urine and inorganic N fertiliser in three temperate swards: permanent pasture not ploughed for at least 20 years (PP), high sugar ryegrass with white clover targeted at 30% coverage reseeded in 2013 (WC), and high sugar ryegrass reseeded in 2014 (HG). This study was conducted on the North Wyke Farm Platform (UK) from April 2017 to October 2017. Soil health indicators including soil organic carbon (SOC, measured by loss of ignition and elemental analyser), dissolved organic carbon (DOC), total nitrogen (TN), C:N ratio, soil C and N bulk isotopes, pH, bulk density (BD), aggregate stability, ergosterol concentration (as a proxy for fungi biomass), and earthworms (abundance, mass and density) were measured and analysed before and after application of dung and N fertilizer, urine and N fertiliser, and only N fertiliser. The highest SOC, TN, DOC, ergosterol concentration and earthworms as well as the lowest BD were found in PP, likely due to the lack of ploughing. Differences among treatments were observed due to the application of dung, resulting in an improvement in chemical indicators of soil health after 50 days of its application. Ergosterol concentration was significantly higher before treatment applications than at the end of the experiment. No changes were detected in BD and aggregate stability after treatment applications. We conclude that not enough time had passed for the soil to recover after the ploughing and reseeding of the permanent pasture, independently of the sward composition (HG or WC). Our results highlight the strong influence of the soil management legacy in temperate pasture and the positive effects of dung application on soil health over the short term. In addition, we point out the relevance of using standardised methods to report soil health indicators and some methodological limitations.

CO2 fluxes from three different temperate grazed pastures using Eddy covariance measurements

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.

Simulating grazing beef and sheep systems

CONTEXTRuminant livestock make an important contribution to global food security by converting feed that is unsuitable for human consumption into high value food protein, demand for which is currently increasing at an unprecedented rate because of increasing global population and income levels. Factors affecting production efficiency, product quality, and consumer acceptability, such as animal fertility, health and welfare, will ultimately define the sustainability of ruminant production systems. These more complex systems can be developed and analysed by using models that can predict system responses to environment and management. OBJECTIVEWe present a framework that dynamically models, using a process-based and mechanistic approach, animal and grass growth, nutrient cycling and water redistribution in a soil profile taking into account the effects of animal genotype, climate, feed quality and quantity on livestock production, greenhouse gas emissions, water use and quality, and nutrient cycling in a grazing system. METHODSA component to estimate ruminant animal growth was developed and integrated with the existing components of the SPACSYS model. Intake of herbage and/or concentrates and partitioning of the energy and protein contained in consumed herbage and/or concentrates were simulated in the component. Simulated animal growth was validated using liveweight data from over 200 finishing beef cattle and 900 lambs collected from the North Wyke Farm Platform (NWFP) in southwest England, UK, between 2011 and 2018. Annual nitrous oxide (N2O), ammonia, methane and carbon dioxide emissions from individual fields were simulated based on previous validated parameters. RESULTS AND CONCLUSIONSA series of statistical indicators demonstrated that the model could simulate liveweight gain of beef cattle and lamb. Simulated nitrogen (N) cycling estimated N input of 190 to 260 kg ha−1, of which 37–61% was removed from the fields either as silage or animal intake, 15–26% was lost through surface runoff or lateral drainage and 1.14% was emitted to the atmosphere as N2O. About 13% of the manure N applied to the NWFP and excreta N deposited at grazing was lost via ammonia volatilisation. SIGNIFICANCEThe extended model has the potential to investigate the responses of the system on and consequences of a range of agronomic management and grazing strategies. However, modelling of multi-species swards needs to be validated including the dynamics of individual species in the swards, preferential selection by grazing animals and the impact on animal growth and nutrient flows.

The Distribution of Soil Micro-Nutrients and the Effects on Herbage Micro-Nutrient Uptake and Yield in Three Different Pasture Systems

Pasture micro-nutrient concentrations are often deficient for herbage productivity and the health of livestock. The aim of this study was to investigate soil and herbage micro-nutrient content and the effects on yield on the three pasture systems of the North Wyke Farm Platform (NWFP): high-sugar grass + legume mix minus nitrogen (N) fertilizer (blue/HSG + L); permanent pasture plus N fertilizer (green/P + N); high-sugar grass plus N fertilizer (red/HSG + N). The locations with high soil total micro-nutrient concentrations had a greater slope and higher soil organic matter (SOM) content. Herbage micro-nutrient concentrations were often greater at the locations with high soil total micro-nutrient concentrations. The concentration and uptake of nearly all micro-nutrients was greatest in the herbage of the green/P + N system, which had the highest SOM content, whereas they were often lowest in the red/HSG + N system, which had the lowest SOM and the highest yield, indicating biomass dilution of micro-nutrients in the herbage. At the locations with high soil micro-nutrient concentrations, yield was higher than at locations with low micro-nutrient concentrations, and was equal across the three pasture systems, regardless of fertilizer N treatment. Variation in micro-nutrient uptake/yield in the blue grass–legume system was predominantly explained by the soil molybdenum (Mo) concentration, possibly relating to the requirement for Mo in biological nitrogen fixation. There was, therefore, a trade-off in ploughing and re-seeding for higher yield, with the maintenance of SOM being important for herbage micro-nutrient content.

Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems

The use of key performance indicators (KPIs) to assist on-farm decision making has long been seen as a promising strategy to improve operational efficiency of agriculture. The potential benefit of KPIs, however, is heavily dependent on the economic relevance of the metrics used, and an overabundance of ambiguously defined KPIs in the livestock industry has disincentivised many farmers to collect information beyond a minimum requirement. Using high-resolution sheep production data from the North Wyke Farm Platform, a system-scale grazing trial in southwest United Kingdom, this paper proposes a novel framework to quantify the information values of industry recommended KPIs, with the ultimate aim of compiling a list of variables to measure and not to measure. The results demonstrated a substantial financial benefit associated with a careful selection of metrics, with top-ranked variables exhibiting up to 3.5 times the information value of those randomly chosen. When individual metrics were used in isolation, ewe weight at lambing had the greatest ability to predict the subsequent lamb value at slaughter, surpassing all mid-season measures representing the lamb’s own performance. When information from multiple metrics was combined to inform on-farm decisions, the peak benefit was observed under four metrics, with inclusion of variables beyond this point shown to be detrimental to farm profitability regardless of the combination selected. The framework developed herein is readily extendable to other livestock species, and with minimal modifications to arable and mixed agriculture as well.

Nutritional value of suckler beef from temperate pasture systems

The role of beef in human diets has been questioned over the last few decades, due largely to its typically high mass-based carbon footprint. However, recent advancements in sustainability literature challenge this paradigm based on the new theory that climate impacts of food commodities should be measured relative to their overall nutritional value rather than their nominal mass. This shift has opened a new opportunity for the global beef industry, and especially for pasture-based systems that can avoid food-feed competition for land and other resources, as beef is a nutritionally dense food. Nonetheless, the sector’s true capability to supply a wide range of nutrients for humans, consistently across multiple systems under multiple weather patterns, has not been well-documented. Using whole-system datasets from the North Wyke Farm Platform in the South West of England, we investigated the nutritional value of beef produced from the three most common pasture systems in temperate regions: permanent pasture (PP), grass and white clover (GWC) and a short-term monoculture grass ley (MG). Beef produced from these three pasture systems was analysed for key nutrients (fatty acids, minerals and vitamin E) over three production cycles (2015–2017) to determine potential differences between systems. Fatty acid, mineral and vitamin E profiles of the pasture and silage fed to each group were also assessed, with subtle differences between pastures reported. For beef, subtle differences were also observed between systems, with GWC having higher omega-6 polyunsaturated fatty acid (PUFA) concentrations than PP and MG. However, the overall nutritional quality of beef was found to be largely comparable across all systems, suggesting that temperate pasture-based beef can be classified as a single commodity in future sustainability assessments, regardless of specific sward types. A 100 g serving of temperate pasture-based beef was found to be a high source (>20% recommended daily intake: RDI) of protein, monounsaturated fatty acids, saturated fatty acids, vitamins – B2, B3, B12 and minerals – Fe, P, Zn; a good source (10–19% RDI) of vitamin – B6 and mineral – K; and a complementary source (5–9% RDI) of omega-3 PUFA, vitamin – B9 and minerals – Cu, Mg, Se. The nutritional value of a food item should be used in defining its environmental cost (e.g. carbon footprint) to make fair comparisons across different food groups (e.g. protein sources). Here, we showed that pasture-based beef had a nutrient indexed carbon footprint of between 0.19 and 0.23 Kg CO2-eq/1% RDI of key nutrients.

Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data

Grasslands, natural and managed, cover a large part of the Earth’s surface and play an important role in the global carbon (C) cycle. Human management strongly affects grassland C budgets through grass cutting and removal, varied grazing intensities, and organic matter additions. Thus managed grassland C cycles are highly heterogeneous and challenging to quantify. In this study, we combine a process-based model of the grassland C cycle, validated against field data on C fluxes and pools, with satellite-derived data (Proba-V and Sentinel-2) on leaf area index (LAI) in order to quantify field-scale grassland productivity and C dynamics under climatic and management conditions typical of northwest Europe. Input data on the weekly vegetation canopy anomaly (estimated from Proba-V LAI) and meteorology are used to drive the grassland C model (DALEC-Grass) that is integrated into a Bayesian model-data fusion (MDF) framework. The novelty of the MDF algorithm is that it infers weekly livestock grazing and grass cutting events based on expected canopy growth estimated by the model, and constrained by LAI observations (estimated from Sentinel-2). The MDF approach also resolves observational, parametric, and input uncertainties on C cycling estimates. We analysed four years (2015–2018) of C dynamics at three variably-managed fields of the Rothamsted Research North Wyke Farm Platform (UK). Compared against independent field data, the MDF was able to (i) identify 87.5% of the harvest events that occurred, (ii) accurately predict the annual yields in 83% of the identified harvest years and (iii) reproduce the observed grazing intensity in each field (r=0.8, overlap = 90%). We demonstrate that the fusion of process modelling with earth observations is an effective method for monitoring biomass removals and quantifying management impacts on field-scale C balance, without the need for frequent and laborious ground measurements. This approach can support the delivery of more robust national greenhouse gas (GHG) accounting that takes account of grassland vegetation management.

Does cattle and sheep grazing under best management significantly elevate sediment losses? Evidence from the North Wyke Farm Platform, UK

PurposeIntensive livestock grazing has been associated with an increased risk of soil erosion and concomitant negative impacts on the ecological status of watercourses. Whilst various mitigation options are promoted for reducing livestock impacts, there is a paucity of data on the relationship between stocking rates and quantified sediment losses. This evidence gap means there is uncertainty regarding the cost–benefit of policy preferred best management.MethodsSediment yields from 15 hydrologically isolated field scale catchments on a heavily instrumented ruminant livestock farm in the south west UK were investigated over ~ 26 months spread across 6 years. Sediment yields were compared to cattle and sheep stocking rates on long-term, winter (November–April), and monthly timescales. The impacts of livestock on soil vegetation cover and bulk density were also examined. Cattle were tracked using GPS collars to determine how grazing related to soil damage.ResultsNo observable impact of livestock stocking rates of 0.15–1.00 UK livestock units (LU) ha−1 for sheep, and 0–0.77 LU ha−1 for cattle on sediment yields was observed at any of the three timescales. Cattle preferentially spent time close to specific fences where soils were visually damaged. However, there was no indication that livestock have a significant effect on soil bulk density on a field scale. Livestock were housed indoors during winters when most rainfall occurs, and best management practices were used which when combined with low erodibility clayey soils likely limited sediment losses.ConclusionA combination of clayey soils and soil trampling in only a small proportion of the field areas lead to little impact from grazing livestock. Within similar landscapes with best practice livestock grazing management, additional targeted measures to reduce erosion are unlikely to yield a significant cost-benefit.

Using a lamb's early-life liveweight as a predictor of carcass quality

The commercial value of lamb carcasses is primarily determined by their weight and quality, with the latter commonly quantified according to muscle coverage and fat depth. The ability to predict these quality scores early in the season could be of substantial value to sheep producers, as this would enable tailored flock management strategies for different groups of animals. Existing methods of carcass quality prediction, however, require either expensive equipment or information immediately before slaughter, leaving them unsuitable as a decision support tool for small to medium-scale enterprises. Using seven-year high-resolution data from the North Wyke Farm Platform, a system-scale grazing trial in Devon, UK, this paper investigates the feasibility of using a lamb's early-life liveweight to predict the carcass quality realised when the animal reaches the target weight. The results of multinomial regression models showed that lambs which were heavier at weaning, at 13 weeks of age, were significantly more likely to have leaner and more muscular carcasses. An economic analysis confirmed that these animals produced significantly more valuable carcasses at slaughter, even after accounting for seasonal variation in lamb price that often favours early finishers. As the majority of heavier-weaned lambs leave the flock before lighter-weaned lambs, an increase in the average weaning weight could also lead to greater pasture availability for ewes in the latter stage of the current season, and thus an enhanced ewe condition and fertility for the next season. All information combined, therefore, a stronger focus on ewes' nutrition before and during lactation was identified as a key to increase system-wide profitability.

Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform.

Peak flow events can lead to flooding which can have negative impacts on human life and ecosystem services. Therefore, accurate forecasting of such peak flows is important. Physically-based process models are commonly used to simulate water flow, but they often under-predict peak events (i.e., are conditionally biased), undermining their suitability for use in flood forecasting. In this research, we explored methods to increase the accuracy of peak flow simulations from a process-based model by combining the model’s output with: a) a semi-parametric conditional extreme model and b) an extreme learning machine model. The proposed 3-model hybrid approach was evaluated using fine temporal resolution water flow data from a sub-catchment of the North Wyke Farm Platform, a grassland research station in south-west England, United Kingdom. The hybrid model was assessed objectively against its simpler constituent models using a jackknife evaluation procedure with several error and agreement indices. The proposed hybrid approach was better able to capture the dynamics of the flow process and, thereby, increase prediction accuracy of the peak flow events.

Elucidating three-way interactions between soil, pasture and animals that regulate nitrous oxide emissions from temperate grazing systems

Pasture-based livestock farming contributes considerably to global emissions of nitrous oxide (N2O), a powerful greenhouse gas approximately 265 times more potent than carbon dioxide. Traditionally, the estimation of N2O emissions from grasslands is carried out by means of plot-scale experiments, where externally sourced animal excreta are applied to soils to simulate grazing conditions. This approach, however, fails to account for the impact of different sward types on the composition of excreta and thus the functionality of soil microbiomes, creating unrealistic situations that are seldom observed under commercial agriculture. Using three farming systems under contrasting pasture management strategies at the North Wyke Farm Platform, an instrumented ruminant grazing trial in Devon, UK, this study measured N2O emissions from soils treated with cattle urine and dung collected within each system as well as standard synthetic urine shared across all systems, and compared these values against those from two forms of controls with and without inorganic nitrogen fertiliser applications. Soil microbial activity was regularly monitored through gene abundance to evaluate interactions between sward types, soil amendments, soil microbiomes and, ultimately, N2O production. Across all systems, N2O emissions attributable to cattle urine and standard synthetic urine were found to be inconsistent with one another due to discrepancy in nitrogen content. Despite previous findings that grasses with elevated levels of water-soluble carbohydrates tend to generate lower levels of N2O, the soil under high sugar grass monoculture in this study recorded higher emissions when receiving excreta from cattle fed the same grass. Combined together, our results demonstrate the importance of evaluating environmental impacts of agriculture at a system scale, so that the feedback mechanisms linking soil, pasture, animals and microbiomes are appropriately considered.

Field-based determination of controls on runoff and fine sediment generation from lowland grazing livestock fields

Compared with arable land, there is a paucity of field-based measurements of erosion rates and controls for lowland temperate grassland supporting ruminant agriculture. Despite this evidence gap, reducing diffuse fine sediment pollution from intensively farmed grassland has been recognised as essential for improving compliance with water quality targets. Improved information on erosion rates and controls within intensively managed lowland grazing livestock systems are prerequisites for informing best management practices for soil and water resource conservation.Accordingly, this study assembled such information using the North Wyke farm platform in south west England where flow, suspended sediment concentration, rainfall and soil moisture are monitored quasi-continuously in 15 hydrologically-isolated (1.54–11.12 ha) catchments. This region of the UK is representative of temperate lowland ruminant grazing landscapes with semi permeable soil drainage.Catchment area was the major control on both water and sediment flux. When normalised to catchment area, sediment yields were controlled by the erodibility of the catchment's soils. Ploughing for re-seeding of grass swards was the major factor that affected this. Whilst total rainfall had a small effect on sediment yields, slope and the damage of soils by livestock had no significant effects. This finding may be due to the overriding effects of ploughing and re-seeding of some fields during the study period.Detachment by impacting raindrops mobilised sediment particles across the entire field with diffuse saturation-excess overland flow responsible for their transport. The majority of erosion occurred during the rising limbs of storm events when there is an abundance of easily detached soil particles. Given that erosion and sediment transport are driven mechanistically by processes affecting the entire field areas, a reduction in sediment yield through the implementation of highly spatially-targeted in-field management such as that for feeder ring use, troughs, poached tracks or gateways would likely be very challenging. Instead, stocking density and grazing regime management, as well as carefully planned ploughing and re-seeding will be more beneficial for erosion control.

Impact of transition from permanent pasture to new swards on the nitrogen use efficiency, nitrogen and carbon budgets of beef and sheep production

There is currently much debate around the environmental implications of ruminant farming and a need for robust data on nitrogen (N) and carbon (C) fluxes from beef and sheep grazing systems. Here we use data collected from the North Wyke Farm Platform along with the SPACSYS model to examine the N and C budgets and the N use efficiency (NUE) of grassland swards at different stages of establishment. We assessed the transition from permanent pasture (PP) to a high-sugar grass (HSG), and a mixed sward of HSG with white clover (HSGC), identifying data specifically for the reseed (RS) years and the first year following RS (HSG-T and HSGC-T). Dominant fluxes for the N budget were N offtake as cut herbage and via livestock grazing, chemical-N fertiliser and N leaching at 88–280, 15–177, and 36–92 kg N ha−1 a−1, respectively. Net primary productivity, soil respiration and C offtake as cut herbage and via livestock grazing at 1.9–15.9, 1.74–12.5, and 0.34–11.7 t C ha−1 a−1, respectively, were the major C fluxes. No significant differences were found between the productivity of any of the swards apart from in the RS year of establishment. However, NUE of the livestock production system was significantly greater for the HSGC and HSGC-T swards at 32 and 42% compared to all other swards, associated with the low chemical-N fertiliser inputs to these clover-containing swards. Our findings demonstrate opportunities for improving NUE in grazing systems, but also the importance of setting realistic NUE targets for these systems to provide achievable goals for land-managers.

Livestock Performance for Sheep and Cattle Grazing Lowland Permanent Pasture: Benchmarking Potential of Forage-Based Systems

Here we describe the livestock performance and baseline productivity over a two-year period, following the establishment of the infrastructure on the North Wyke Farm Platform across its three farmlets (small farms). Lowland permanent pastures were continuously stocked with yearling beef cattle and ewes and their twin lambs for two years in three farmlets. The cattle came into the farmlets as suckler-reared weaned calves at 195 ± 32.6 days old weighing 309 ± 45.0 kg, were housed indoors for 170 days then turned out to graze weighing 391 ± 54.2 kg for 177 days. Therefore, it is suggested for predominantly grass-based systems with minimal supplementary feeding that target live weight gains should be 0.5 kg/day in the first winter, 0.9 kg/day for summer grazing and 0.8 kg/day for cattle housed and finished on silage in a second winter. The sheep performance suggested that lambs weaned at 100 days and weighing 35 kg should finish at 200 days weighing 44 to 45 kg live weight with a killing out percentage of 44%. Good levels of livestock production are possible with grass and forage-based systems using little or no additional supplementary concentrate feeds.

Roles of instrumented farm-scale trials in trade-off assessments of pasture-based ruminant production systems

For livestock production systems to play a positive role in global food security, the balance between their benefits and disbenefits to society must be appropriately managed. Based on the evidence provided by field-scale randomised controlled trials around the world, this debate has traditionally centred on the concept of economic-environmental trade-offs, of which existence is theoretically assured when resource allocation is perfect on the farm. Recent research conducted on commercial farms indicates, however, that the economic-environmental nexus is not nearly as straightforward in the real world, with environmental performances of enterprises often positively correlated with their economic profitability. Using high-resolution primary data from the North Wyke Farm Platform, an intensively instrumented farm-scale ruminant research facility located in southwest United Kingdom, this paper proposes a novel, information-driven approach to carry out comprehensive assessments of economic-environmental trade-offs inherent within pasture-based cattle and sheep production systems. The results of a data-mining exercise suggest that a potentially systematic interaction exists between ‘soil health’, ecological surroundings and livestock grazing, whereby a higher level of soil organic carbon (SOC) stock is associated with a better animal performance and less nutrient losses into watercourses, and a higher stocking density with greater botanical diversity and elevated SOC. We contend that a combination of farming system-wide trials and environmental instrumentation provides an ideal setting for enrolling scientifically sound and biologically informative metrics for agricultural sustainability, through which agricultural producers could obtain guidance to manage soils, water, pasture and livestock in an economically and environmentally acceptable manner. Priority areas for future farm-scale research to ensure long-term sustainability are also discussed.