Cairistiona F.E. Topp

Simulating the impact of global warming on milk and forage production in Scotland: 1. The effects on dry-matter yield of grass and grass-white clover swards

Abstract The purpose of the study was to assess the effect that global warming and changes in atmospheric CO2 concentration would have on grassland production within Scotland. This required the development of a mathematical model of herbage production that was responsive to climatic factors and changes in CO2 levels. A model of pure grass and grass-white clover swards is described, and this has been used to assess the effects that the predicted increases in temperature, rainfall and CO2 might have on grass and white clover production. It is projected that global warming will increase the length of the growing season by between 12 and 37 days for every 1°C rise in annual mean daily temperature. The indications are that global warming will have little effect on annual production of grass, either from pure grass or grass-white clover swards. On the other hand, white clover as a percentage of total herbage production is estimated to increase from 32% to 46% for a 2°C temperature rise. Nevertheless, increasing concentrations of CO2 is predicted to increase the yields of grass and white clover under both current climatic conditions and the global warming scenario.

Control of foliar diseases in barley: towards an integrated approach

Barley is one of the world’s most important crops providing food and related products for millions of people. Diseases continue to pose a serious threat to barley production, despite the use of fungicides and resistant varieties, highlighting the impact of fungicide resistance and the breakdown of host plant resistance on the efficacy of control measures. This paper reviews progress towards an integrated approach for disease management in barley in which new methods may be combined with existing measures to improve the efficacy of control in the long-term. Advances have been made in genetic mapping of resistance (R) genes and in identifying novel sources of genes in wild barley populations and land races. Marker assisted selection techniques are being used to pyramid R genes to increase the durability of resistance. Elicitors to induce host resistance used in combination with fungicides can provide effective disease control in the field and could delay the evolution of fungicide insensitivity. Traits that may contribute to disease tolerance and escape have been identified and the extent of genetic variation within barley germplasm is being determined. Tools are being developed to integrate the above methods via an assessment of the risk of economic injury occurring from disease to guide decisions on the requirement for fungicide treatment. Barriers exist to the adoption of integrated management approaches from growers and end-users further down the supply chain (e.g. acceptance of variety mixtures) and policy incentives from government may be required for it to be taken up in practice.

Selection of cereals for weed suppression in organic agriculture: a method based on cultivar sensitivity to weed growth

Cereal cultivars conferring a high degree of crop competitive ability, especially against aggressive weeds, are highly beneficial in organic farming as well as other farming systems that aim to limit the use of herbicides. In this study, thirteen winter wheat cultivars, plus one spring wheat and one winter oat were assessed for their competitive ability at key growth stages, across three seasons. The natural population of weed species was allowed develop without agronomic intervention. Weed suppression ability for each cultivar (Svar) was calculated as the difference between weed growth in plots for each cultivar and the maximal weed growth (Wmax) from adjacent uncropped areas. The sensitivity of Svar in response to changes in weed growth (SvarW) was derived from the linear regression coefficient of Svar plotted against Wmax. There was significant variation in Svar between cultivars and strong evidence for cultivars to vary in SvarW. Amongst groups of cultivars with similar levels of Svar some could be defined as being of higher or lower sensitivity to changes in weed growth. Some cultivars also had relatively good Svar at high levels of weed growth. The use of both weed suppression ability and sensitivity across different levels of weed growth or weed populations has considerable potential for selecting new cultivars suitable for organic agriculture. Ideally new cultivars will be selected on the basis of high Svar and/or low SvarW. This analysis provides the means to measure sensitivity of cultivar performance across a range of favourable and unfavourable conditions.

Simulating the impact of global warming on milk and forage production in Scotland: 2. The effects on milk yields and grazing management of dairy herds

Abstract The potential impact of global warming and the enhanced atmospheric CO 2 concentration on grassland management on dairy farms within the UK requires assessment. This has led to the development of a mathematical model of the grazing dairy cow. The model, that embraces grass and grass-white clover swards, has been used to assess the effects that the projected increases in temperature and rainfall under global warming and the increased levels of CO 2 might have on milk production and on silage conservation for a typical dairy farm. The results suggest that the impact on milk production for grass-based systems will vary depending on the locality. On the other hand, for herds grazed on grass-white clover swards milk output might increase regardless of site, when the concentration of CO 2 is enhanced. As regards silage production from grass-white clover swards, under global warming and at current levels of CO 2 there is an apparent tendency to increase the percentage of total silage yield obtained from the first cut, although this does not occur for grass swards. At the same time, there are also indications that global warming will increase the percentage of clover in the herbage cut for conservation.

Review and analysis of global agricultural N2O emissions relevant to the UK

As part of a UK government funded research project to update the UK N2O inventory methodology, a systematic review of published nitrous oxide (N2O) emission factors was carried out of non-UK research, for future comparison and synthesis with the UK measurement based evidence base. The aim of the study is to assess how the UK IPCC default emission factor for N2O emissions derived from synthetic or organic fertiliser inputs (EF1) compares to international values reported in published literature. The availability of data for comparing and/or refining the UK IPCC default value and the possibility of analysing sufficient auxiliary data to propose a Tier 2 EF1 reporting strategy is evaluated. The review demonstrated a lack of consistency in reporting error bounds for fertiliser-derived EFs and N2O flux data with 8% and 44% of publications reporting EF and N2O flux error bounds respectively. There was also poor description of environmental (climate and soil) and experimental design auxiliary data. This is likely to be due to differences in study objectives, however potential improvements to soil parameter reporting are proposed. The review demonstrates that emission factors for agricultural-derived N2O emissions ranged -0.34% to 37% showing high variation compared to the UK Tier 1 IPCC EF1 default values of 1.25% (IPCC 1996) and 1% (IPPC 2006). However, the majority (83%) of EFs reported for UK-relevant soils fell within the UK IPCC EF1 uncertainty range of 0.03% to 3%. Residual maximum likelihood (REML) analysis of the data collated in the review showed that the type and rate of fertiliser N applied and soil type were significant factors influencing EFs reported. Country of emission, the length of the measurement period, the number of splits, the crop type, pH and SOC did not have a significant impact on N2O emissions. A subset of publications where sufficient data was reported for meta-analysis to be conducted was identified. Meta-analysis of effect sizes of 41 treatments demonstrated that the application of fertiliser has a significant effect on N2O emissions in comparison to control plots and that emission factors were significantly different to zero. However no significant relationships between the quantity of fertiliser applied and the effect size of the amount of N2O emitted from fertilised plots compared to control plots were found. Annual addition of fertiliser of 35 to 557 kg N/ha gave a mean increase in emissions of 2.02 ± 0.28 g N2O/ha/day compared to control treatments (p<0.01). Emission factors were significantly different from zero, with a mean emission factor estimated directly from the meta analysis of 0.17 ± 0.02%. This is lower than the IPCC 2006 Tier 1 EF1 value of 1% but falling within the uncertainty bound for the IPCC 2006 Tier 1 EF1 (0.03% to 3%). As only a small number of papers were viable for meta analysis to be conducted due to lack of reporting of the key controlling factors, the estimates of EF in this paper cannot include the true variability under conditions similar to the UK. Review-derived EFs of 0.34% to 37% and mean EF from meta-analysis of 0.17 ± 0.02% highlight variability in reporting EFs depending on the method applied and sample size. A protocol of systematic reporting of N2O emissions and key auxiliary parameters in publications across disciplines is proposed. If adopted this would strengthen the community to inform IPCC Tier 2 reporting development and reduce the uncertainty surrounding reported UK N2O emissions.

Assessing the sensitivity of modelled estimates of N2O emissions and yield to input uncertainty at a UK cropland experimental site using the DailyDayCent model

Biogeochemical models such as DailyDayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers of N mineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations.

The challenge of modelling nitrogen management at the field scale: simulation and sensitivity analysis of N2O fluxes across nine experimental sites using DailyDayCent

The United Kingdom currently reports nitrous oxide emissions from agriculture using the IPCC default Tier 1 methodology. However Tier 1 estimates have a large degree of uncertainty as they do not account for spatial variations in emissions. Therefore biogeochemical models such as DailyDayCent (DDC) are increasingly being used to provide a spatially disaggregated assessment of annual emissions. Prior to use, an assessment of the ability of the model to predict annual emissions should be undertaken, coupled with an analysis of how model inputs influence model outputs, and whether the modelled estimates are more robust that those derived from the Tier 1 methodology. The aims of the study were (a) to evaluate if the DailyDayCent model can accurately estimate annual N2O emissions across nine different experimental sites, (b) to examine its sensitivity to different soil and climate inputs across a number of experimental sites and (c) to examine the influence of uncertainty in the measured inputs on modelled N2O emissions. DailyDayCent performed well across the range of cropland and grassland sites, particularly for fertilized fields indicating that it is robust for UK conditions. The sensitivity of the model varied across the sites and also between fertilizer/manure treatments. Overall our results showed that there was a stronger correlation between the sensitivity of N2O emissions to changes in soil pH and clay content than the remaining input parameters used in this study. The lower the initial site values for soil pH and clay content, the more sensitive DDC was to changes from their initial value. When we compared modelled estimates with Tier 1 estimates for each site, we found that DailyDayCent provided a more accurate representation of the rate of annual emissions.

Modelling Common Agricultural Policy–Water Framework Directive interactions and cost-effectiveness of measures to reduce nitrogen pollution

Selecting cost-effective measures to regulate agricultural water pollution to conform to the Water Framework Directive presents multiple challenges. A bio-economic modelling approach is presented that has been used to explore the water quality and economic effects of the 2003 Common Agricultural Policy Reform and to assess the cost-effectiveness of input quotas and emission standards against nitrate leaching, in a representative case study catchment in Scotland. The approach combines a biophysical model (NDICEA) with a mathematical programming model (FSSIM-MP). The results indicate only small changes due to the Reform, with the main changes in farmers decision making and the associated economic and water quality indicators depending on crop price changes, and suggest the use of target fertilisation in relation to crop and soil requirements, as opposed to measures targeting farm total or average nitrogen use.

A Comparative Nitrogen Balance and Productivity Analysis of Legume and Non-legume Supported Cropping Systems: The Potential Role of Biological Nitrogen Fixation.

The potential of biological nitrogen fixation (BNF) to provide sufficient N for production has encouraged re-appraisal of cropping systems that deploy legumes. It has been argued that legume-derived N can maintain productivity as an alternative to the application of mineral fertilizer, although few studies have systematically evaluated the effect of optimizing the balance between legumes and non N-fixing crops to optimize production. In addition, the shortage, or even absence in some regions, of measurements of BNF in crops and forages severely limits the ability to design and evaluate new legume–based agroecosystems. To provide an indication of the magnitude of BNF in European agriculture, a soil-surface N-balance approach was applied to historical data from 8 experimental cropping systems that compared legume and non-legume crop types (e.g., grains, forages and intercrops) across pedoclimatic regions of Europe. Mean BNF for different legume types ranged from 32 to 115 kg ha−1 annually. Output in terms of total biomass (grain, forage, etc.) was 30% greater in non-legumes, which used N to produce dry matter more efficiently than legumes, whereas output of N was greater from legumes. When examined over the crop sequence, the contribution of BNF to the N-balance increased to reach a maximum when the legume fraction was around 0.5 (legume crops were present in half the years). BNF was lower when the legume fraction increased to 0.6–0.8, not because of any feature of the legume, but because the cropping systems in this range were dominated by mixtures of legume and non-legume forages to which inorganic N as fertilizer was normally applied. Forage (e.g., grass and clover), as opposed to grain crops in this range maintained high outputs of biomass and N. In conclusion, BNF through grain and forage legumes has the potential to generate major benefit in terms of reducing or dispensing with the need for mineral N without loss of total output.

Key challenges and priorities for modelling European grasslands under climate change.

Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.