Robust spatial estimates of biomass carbon on farms.

The drive for farm businesses to move towards net zero greenhouse gas emissions means that there is a need to develop robust methods to quantify the amount of biomass carbon (C) on farms. Direct measurements can be destructive and time-consuming and some prediction methods provide no assessment of uncertainty. This study describes the development, validation, and use of an integrated spatial approach, including the use of lidar data, and Bayesian Belief Networks (BBNs) to quantify total biomass carbon stocks (Ctotal) of i) land cover and ii) landscape features such as hedges and lone trees for five case study sites in lowland England. The results demonstrated that it was possible to develop and use a remote integrated approach to estimate biomass carbon at a farm scale. The highest achievable prediction accuracy was attained from models using the variables AGBC, BGBC, DOMC, age, height, species and land cover, derived from measured information and from literature review. The two BBN models successfully predicted the test values of the total biomass carbon with propagated error rates of 6.7 % and 4.3 % for the land cover and landscape features respectively. These error rates were lower than in other studies indicating that the seven predictors are strong determinants of biomass carbon. The lidar data also enabled the spatial presentation and calculation of the variable C stocks along the length of hedges and within woodlands.

Translating and applying a simulation model to enhance understanding of grassland management.

Each new generation of grassland managers could benefit from an improved understanding of how modification of nitrogen application and harvest dates in response to different weather and soil conditions will affect grass yields and quality. The purpose of this study was to develop a freely available grass yield simulation model, validated for England and Wales, and to examine its strengths and weaknesses as a teaching tool for improving grass management. The model, called LINGRA-N-Plus, was implemented in a Microsoft Excel spreadsheet and iteratively evaluated by students and practitioners (farmers, consultants, and researchers) in a series of workshops across the UK over 2 years. The iterative feedback led to the addition of new algorithms, an improved user interface, and the development of a teaching guide. The students and practitioners identified the ease of use and the capacity to understand, visualize and evaluate how decisions, such as variation of cutting intervals, affect grass yields as strengths of the model. We propose that an effective teaching tool must achieve an appropriate balance between being sufficiently detailed to demonstrate the major relationships (e.g., the effect of nitrogen on grass yields) whilst not becoming so complex that the relationships become incomprehensible. We observed that improving the user-interface allowed us to extend the scope of the model without reducing the level of comprehension. The students appeared to be interested in the explanatory nature of the model whilst the practitioners were more interested in the application of a validated model to enhance their decision making.

Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments.

Improved farm management of soil organic carbon (SOC) is critical if national governments and agricultural businesses are to achieve net-zero targets. There are opportunities for farmers to secure financial benefits from carbon trading, but field measurements to establish SOC baselines for each part of a farm can be prohibitively expensive. Hence there is a potential role for spatial modelling approaches that have the resolution, accuracy, and estimates to uncertainty to estimate the carbon levels currently stored in the soil. This study uses three spatial modelling approaches to estimate SOC stocks, which are compared with measured data to a 10 cm depth and then used to determine carbon payments. The three approaches used either fine- (100 m × 100 m) or field-scale input soil data to produce either fine- or field-scale outputs across nine geographically dispersed farms. Each spatial model accurately predicted SOC stocks (range: 26.7-44.8 t ha-1) for the five case study farms where the measured SOC was lowest (range: 31.6-48.3 t ha-1). However, across the four case study farms with the highest measured SOC (range: 56.5-67.5 t ha-1), both models underestimated the SOC with the coarse input model predicting lower values (range: 39.8-48.2 t ha-1) than those using fine inputs (range: 43.5-59.2 t ha-1). Hence the use of the spatial models to establish a baseline, from which to derive payments for additional carbon sequestration, favoured farms with already high SOC levels, with that benefit greatest with the use of the coarse input data. Developing a national approach for SOC sequestration payments to farmers is possible but the economic impacts on individual businesses will depend on the approach and the accounting method.

Managing declining yields from ageing tea plantations.

Strong growth in the demand for tea requires further increases in the productivity of plantations. Declining or stagnant yields are commonly observed in older plantations. Possible controlling factors for yield decline are reviewed including ageing of plants, chronic disease and sub-optimal soil conditions such as excess soil acidity and low soil organic matter. Management options for addressing these factors are evaluated, including replanting. A systematic approach to decision-making about replanting is presented. Practice for replanting is reviewed and it is concluded that evidence to support a general case for replanting is limited, unless based on the introduction of more productive clones and/or better plant spacing.

Predicting the impacts of bioenergy production on farmland birds.

Meeting European renewable energy production targets is expected to cause significant changes in land use patterns. With an EU target of obtaining 20% of energy consumption from renewable sources by 2020, national and local policy makers need guidance on the impact of potential delivery strategies on ecosystem goods and services to ensure the targets are met in a sustainable manner. Within agroecosystems, models are available to explore consequences of such policy decisions for food, fuel and fibre production but few can describe the effect on biodiversity. This paper describes the integration and application of a farmland bird population model within a geographical information system (GIS) to explore the consequences of land use changes arising from different strategies to meet renewable energy production targets. Within a 16,000 ha arable dominated case study area in England, the population growth rates of 19 farmland bird species were predicted under baseline land cover, a scenario maximising wheat production for bioethanol, and a scenario focused on mix of bioenergy sources. Both scenarios delivered renewable energy production targets for the region (>12 kWh per person per day) but, despite differences in resultant landscape composition, the response of the farmland bird community as a whole to each scenario was small and broadly similar. However, this similarity in overall response masked significant intra- and inter-specific variations across the study area and between scenarios suggesting contrasting mechanisms of impact and highlighting the need for context dependent, species-level assessment of land use change impacts. This framework provides one of the first systematic attempts to spatially model the effect of policy driven land use change on the population dynamics of a suite of farmland birds. The GIS framework also facilitates its integration with other ecosystem service models to explore wider synergies and trade offs arising from national or local policy interventions.