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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.

In the face of climate change, cropping systems need to achieve a high performance, providing food and feed and adapting to variable environmental conditions. Diversification of cropping systems can support ecosystem services and associated biodiversity, but there is little evidence on which temporal field arrangement affects the performance of crop yields (productivity and stability), partly due to a lack of long-term data and appropriate indicators. The objectives of this study were to quantify the effect of cropping system diversification on yield stability, environmental adaptability, and the probability of diversified systems to outperform less diverse cereal-based systems in Europe. Spring and winter cereal yields were analyzed from long-term field experiments from Sweden, Scotland, and France. We investigated diversification through (i) introduction of perennial leys, (ii) increasing the proportion of ley in the rotation, (iii) varying the order in which crops are positioned in the rotation, (iv) introduction of grain legumes, and (v) introduction of cover crops. The results showed that cereal crops within cropping systems incorporating perennial leys outperformed systems without leys in 60–94% of the comparisons with higher probabilities at low fertilizer intensities. The yield stability of oat did not differ, but mean yields were 33% higher, when grown directly after the ley compared to oat grown two years later in the crop sequence under similar management. Durum wheat grown in a cropping system with grain legumes had higher yields in lower-yielding environmental conditions compared to rotations without legumes. Diversification with cover crops did not significantly affect yield stability. We conclude that diverse cropping systems can increase cereal productivity and environmental adaptability and are more likely to outperform less diverse systems especially when introducing perennial forage legumes into arable systems. Effects of diversification on cereal yield stability were inconsistent indicating that higher productivity is achievable without reducing yield variability. These novel findings can support the design of more diverse and high-performing cropping systems.

A method is proposed which considers Integrated Pest Management (IPM) through several lenses, in order to obtain a more holistic view of the potential for IPM, and is described using a case study of Scottish spring barley. Long-term experimental field trial databases are used to determine which management methods are best suited to the system at hand. Stakeholder engagement provides insight into which of these methods are most likely to be taken up by farmers. Finally, a database of commercial practice allows an estimate of the potential for improving management patterns, based on current levels of IPM uptake across a wider sample of Scottish farmers. Together, these diverse sources of information give a more complete view of a complex system than any individual source could and allow the identification of IPM methods which are robust, practical, and not already in widespread use in this system. Bringing together these sources of information may be of particular value for policy and other decision makers, who need information about strategies which are both practical and likely to have a large positive impact. In the case of Scottish spring barley, there is good potential to reduce the need for fungicide use through the increased use of highly resistant barley varieties.

Crop residue incorporation is a common practice to increase or restore organic matter stocks in agricultural soils. However, this practice often increases emissions of the powerful greenhouse gas nitrous oxide (N

Agricultural injuries are a valuable social sustainability indicator. However, current methods use sector-scale production data, so are unable to assess the impact of changes in individual farming practices. Here, we developed a method that adopts a life cycle approach to quantify the number of serious injuries during agricultural production processes and assess the potential impact of changes in agricultural practices. The method disaggregates agricultural production into operations and estimates the contribution each operation makes to the frequency of different types of injuries. The method was tested using data collected by survey during an expert workshop in which sixteen participants were asked to estimate the parameters related to typical dairy cattle and pig farms. Parameter estimates for specific operations varied considerably between participants, so normalized values were used to disaggregate sector-scale statistics to production operations. The results were in general agreement with the results from other studies. Participants found it challenging to quantify the potential effect of new technologies. Provided suitable empirical statistical data are available, the method can be used to quantify the risk of injury associated with individual products and provide an ex-ante assessment of future developments in farming practices.

Funding Information: This modeling study was a joint effort of the Models4Pastures project within the framework of FACCE-JPI. Lutz Merbold and Kathrin Fuchs acknowledge funding received for the Swiss contribution to Models4Pastures (FACCE-JPI project, SNSF funded contract: 40FA40_154245/1) and for the Doc. Mobility fellowship (SNSF funded project: P1EZP2_172121). Lutz Merbold further acknowledges the support received for CGIAR Fund Council, Australia (ACIAR), Irish Aid, the European Union, the Netherlands, New Zealand, Switzerland, UK, USAID, and Thailand for funding to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as well as for the CGIAR Research Program on Livestock. The NZ contributors acknowledge funding from the New Zealand Government Ministry of Primary Industries to support the aims of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases and from AgResearch's Strategic Science Investment Fund (the Forages for Reduced Nitrate Leaching (FRNL) research program). The UK partners acknowledge funding by DEFRA and the RCUK projects: N-Circle (BB/N013484/1), UGRASS (NE/M016900/1), and GREENHOUSE (NE/K002589/1). R.M. Rees and C.F.E. Topp also received funding from the Scottish Government Strategic Research Programme. Lorenzo Brilli, Camilla Dibari, and Marco Bindi received funding from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF). The FR partners acknowledge funding from CN-MIP project funded by the French National Research Agency (ANR-13-JFAC-0001) and from ADEME (no. 12-60-C0023). Open access funding enabled and organized by Projekt DEAL Funding Information: This modeling study was a joint effort of the Models4Pastures project within the framework of FACCE‐JPI. Lutz Merbold and Kathrin Fuchs acknowledge funding received for the Swiss contribution to Models4Pastures (FACCE‐JPI project, SNSF funded contract: 40FA40_154245/1) and for the Doc. Mobility fellowship (SNSF funded project: P1EZP2_172121). Lutz Merbold further acknowledges the support received for CGIAR Fund Council, Australia (ACIAR), Irish Aid, the European Union, the Netherlands, New Zealand, Switzerland, UK, USAID, and Thailand for funding to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as well as for the CGIAR Research Program on Livestock. The NZ contributors acknowledge funding from the New Zealand Government Ministry of Primary Industries to support the aims of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases and from AgResearch's Strategic Science Investment Fund (the Forages for Reduced Nitrate Leaching (FRNL) research program). The UK partners acknowledge funding by DEFRA and the RCUK projects: N‐Circle (BB/N013484/1), UGRASS (NE/M016900/1), and GREENHOUSE (NE/K002589/1). R.M. Rees and C.F.E. Topp also received funding from the Scottish Government Strategic Research Programme. Lorenzo Brilli, Camilla Dibari, and Marco Bindi received funding from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF). The FR partners acknowledge funding from CN‐MIP project funded by the French National Research Agency (ANR‐13‐JFAC‐0001) and from ADEME (no. 12‐60‐C0023). Open access funding enabled and organized by Projekt DEAL Publisher Copyright: ©2020. The Authors. Open access funding enabled and organized by Projekt DEAL

Static chambers are often used for measuring nitrous oxide (N

The main aim of this paper was to calibrate and evaluate the DeNitrification-DeComposition (DNDC) model for estimating N2O emissions and crop productivity for a summer maize-winter wheat double cropping system with different N fertilizer rates in Hebei, China. The model’s performance was assessed before and after calibration and model sensitivity was investigated. The calibrated and validated DNDC performed effectively in estimating cumulative N2O emissions (coefficient of determination (1:1 relationship; r2) = 0.91; relative deviation (RD) = −13 to 16%) and grain yields for both crops (r2 = 0.91; RD = −21 to 7%) from all fertilized treatments, but poorly estimated daily N2O patterns. Observed and simulated results showed that optimal N fertilizer treatment decreased cumulative N2O flux, compared to conventional N fertilizer, without a significant impact on grain yields of the summer maize-winter wheat double cropping system. The high sensitivity of the DNDC model to rainfall, soil organic carbon and temperature resulted in significant overestimation of N2O peaks during the warm wet season. The model also satisfactorily estimated daily patterns/average soil temperature (o C; 0–5 cm depth) (r2 = 0.88 to 0.89; root mean square error (RMSE) = 4 °C; normalized RMSE (nRMSE) = 25% and index of agreement (d) = 0.89–0.97) but under-predicted water filled pore space (WFPS; %; 0–20 cm depth) (r2 = 0.3 to 0.4) and soil ammonium and nitrate (exchangeable NH4+ & NO3−; kg N ha−1; r2 = 0.97). With reference to the control treatment (no N fertilizer), DNDC was weak in simulating both N2O emissions and crop productivity. To be further improved for use under pedo-climatic conditions of the summer maize-winter wheat double cropping system we suggest future studies to identify and resolve the existing problems with the DNDC, especially with the control treatment.

Our call set out to enlarge the evidence base and methods for improving and evaluating grasslands in a changing environment as a sustainable ecosystem for all life [...]

This study examines the effectiveness of a model called LINGRA-N-Plus to simulate the interaction of climate, soil and management on the green leaf and total dry matter yields of ryegrass in England and Wales. The LINGRA-N-Plus model includes modifications of the LINGRA-N model such as temperature- and moisture-dependent soil nitrogen mineralization and differential partitioning to leaves and stems with thermal time from the last harvest. The resulting model was calibrated against the green leaf and total grass yields from a harvest interval x nitrogen application experiment described by Wilman et al. (1976). When the LINGRA-N-Plus model was validated against total grass yields from nitrogen experiments at ten sites described by Morrison et al. (1980), its modelling efficiency improved greatly compared to the original LINGRA-N. High predicted yields, at zero nitrogen application, were related to soils with a high initial nitrogen content. The lowest predicted yields occurred at sites with low rainfall and shallow rooting depth, mitigating the effect of drought at such sites increased yields by up to 4 t ha−1. The results highlight the usefulness of grass models, such as LINGRA-N-Plus, to explore the combined effects of climate, soil, and management, like nitrogen application, and harvest intervals on grass productivity.

SUMMARYThe current study investigated use of a controlled misting environment to simulate field conditions that have been implicated in high levels of the malting barley defect, grain skinning. More than 200 spring barley varieties were assessed to identify those varieties that were particularly resistant or susceptible to the defect. Relationships between skinning severity and the traits ear length, floret number, grain number and grain weight were examined among the varieties. In a panel of seven varieties chosen as treatment controls, misting was found to significantly increase skinning severity. The misting treatment had no effect on measured ear traits of these varieties. Among the 200 varieties grown under the misting treatment, there was a continuous spectrum of skinning severities, which were not correlated with ear length, floret number, grain number or grain weight. Using the misting treatment, differences in susceptibility to grain skinning could be determined among varieties. As the misting treatment did not affect measured ear traits, and no correlation was found between ear traits and skinning severity among varieties, the effect of misting on skinning severity must be mediated through other physiological characteristics.

The range of uncertainties inherent in climate models can only be portrayed by provision of multiple climate projections. Unfortunately, such provision poses a challenge to model-based impact studies, since driving the relevant impact models using weather data from large numbers of climate projections may not be computationally feasible. Hence, it is important to investigate how to draw sub-samples of climate projections in a manner that reduces the subsequent computational burden. We describe a stratification-based protocol for sub-sampling climate projections to drive crop models with strata based on changes in mean temperature and changes in relative mean rainfall. As an example of the protocol's utility, simulated weather for each selected climate projection was used to drive 3 contrasting process-based models of plant-environment interactions to predict yields of spring barley, managed grassland, and short-rotation coppice. Many of the questions about potential impact that we wish to answer are related to variation in predicted yields. Variance components analyses of predicted yields for each of 2 time periods (2040s and 2080s) indicated that, after allowing for variability between grid squares, between 16 and 61% of the remaining variance in annual yields was uncertainty due to climate projections, the corresponding range for mean yields over 9 yr being from 63 to 93%. We found that our stratification procedure enhanced the precision in the estimate of the variance component due to climate projection, enabling reductions of up to 20% in the number of climate projections required to achieve equivalent precision compared to simple random sampling.

A scoring rule is a device for evaluation of forecasts that are given in terms of the probability of an event. In this article we will restrict our attention to binary forecasts. We may think of a scoring rule as a penalty attached to a forecast after the event has been observed. Thus a relatively small penalty will accrue if a high probability forecast that an event will occur is followed by occurrence of the event. On the other hand, a relatively large penalty will accrue if this forecast is followed by non-occurrence of the event. Meteorologists have been foremost in developing scoring rules for the evaluation of probabilistic forecasts. Here we use a published meteorological data set to illustrate diagrammatically the Brier score and the divergence score, and their statistical decompositions, as examples of Bregman divergences. In writing this article, we have in mind environmental scientists and modellers for whom meteorological factors are important drivers of biological, physical and chemical processes of interest. In this context, we briefly draw attention to the potential for probabilistic forecasting of the within-season component of nitrous oxide emissions from agricultural soils.

The biogeochemical processes that lead to the production of N

Intensively managed grazed grasslands in temperate climates are globally important environments for the exchange of the greenhouse gases (GHGs) carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4). We assessed the N and C budget of a mostly grazed, occasionally cut, and fertilized grassland in SE Scotland by measuring or modelling all relevant imports and exports to the field as well as changes in soil C and N pools over time. The N budget was dominated by import from inorganic and organic fertilisers (21.9 g N m2 yr−1) and losses from leaching (5.3 g N m2 yr−1), N2 emissions and NOx and NH3 volatilisation (6.4 g N m2 yr−1). The efficiency of N use by animal products (meat and wool) averaged 11 %. On average over nine years (2002–2010) the balance of N fluxes suggested that 7.2 ± 4.6 g N m−2 y−1 (mean ± confidence interval at p > 0.95) were stored in the soil. The largest component of the C budget was the net ecosystem exchange of CO2 (NEE), at an average uptake rate of 218 ± 155 g C m−2 y−1 over the nine years. This sink strength was offset by carbon export from the field mainly as harvest (48.9 g C m2 yr−1) and leaching (16.4 g C m2 yr−1). The other export terms, CH4 emissions from the soil, manure applications and enteric fermentation were negligible and only contributed to 0.02–4.2 % of the total C losses. Only a small fraction of C was incorporated into the body of the grazing animals. Inclusion of these C losses in the budget resulted in a C sink strength of 163 ± 140 g C m−2 y−1. On the contrary, soil stock measurements taken in May 2004 and May 2011 indicated that the grassland sequestered N in the 0–60 cm soil layer at 4.51 ± 2.64 g N m−2 y−1 and lost C at a rate of 29.08 ± 38.19 g C m−2 y-1, respectively. Potential reasons for the discrepancy between these estimates are probably an underestimation of C and N losses, especially from leaching fluxes as well as from animal respiration. The average greenhouse gas (GHG) balance of the grassland was −366 ± 601 g CO2 eq m−2 y−1 and strongly affected by CH4 and N2O emissions. The GHG sink strength of the NEE was reduced by 54 % by CH4 and N2O emissions. Enteric fermentation from the ruminating sheep proved to be an important CH4 source, exceeding the contribution of N2O to the GHG budget in some years.

Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensification of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a priority. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are applied to develop 'smart' empirical modeling, may overcome the trade-off between complexity and usability. Developing the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems related to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks.