Local impacts of climate change on winter wheat in Great Britain

Under future CMIP5 climate change scenarios for 2050, an increase in wheat yield of about 10% is predicted in Great Britain (GB) as a result of the combined effect of CO2 fertilization and a shift in phenology. Compared to the present day, crops escape increases in the climate impacts of drought and heat stresses on grain yield by developing before these stresses can occur. In the future, yield losses from water stress over a growing season will remain about the same across Great Britain with losses reaching around 20% of potential yield, while losses from drought around flowering will decrease and account for about 9% of water limited yield. Yield losses from heat stress around flowering will remain negligible in the future. These conclusions are drawn from a modelling study based on the response of the Sirius wheat simulation model to local-scale 2050-climate scenarios derived from 19 Global Climate Models from the CMIP5 ensemble at 25 locations representing current or potential wheat-growing areas in GB. However, depending on susceptibility to water stress, substantial interannual yield variation between locations is predicted, in some cases suggesting low wheat yield stability. For this reason, local-scale studies should be performed to evaluate uncertainties in yield prediction related to future weather patterns

Communicating the uncertainty in estimated greenhouse gas emissions from agriculture

In an effort to mitigate anthropogenic effects on the global climate system, industrialised countries are required to quantify and report, for various economic sectors, the annual emissions of greenhouse gases from their several sources and the absorption of the same in different sinks. These estimates are uncertain, and this uncertainty must be communicated effectively, if government bodies, research scientists or members of the public are to draw sound conclusions. Our interest is in communicating the uncertainty in estimates of greenhouse gas emissions from agriculture to those who might directly use the results from the inventory. We tested six methods of communication. These were: a verbal scale using the IPCC calibrated phrases such as ‘likely’ and ‘very unlikely’; probabilities that emissions are within a defined range of values; confidence intervals for the expected value; histograms; box plots; and shaded arrays that depict the probability density of the uncertain quantity. In a formal trial we used these methods to communicate uncertainty about four specific inferences about greenhouse gas emissions in the UK. Sixty four individuals who use results from the greenhouse gas inventory professionally participated in the trial, and we tested how effectively the uncertainty about these inferences was communicated by means of a questionnaire. Our results showed differences in the efficacy of the methods of communication, and interactions with the nature of the target audience. We found that, although the verbal scale was thought to be a good method of communication it did not convey enough information and was open to misinterpretation. Shaded arrays were similarly criticised for being open to misinterpretation, but proved to give the best impression of uncertainty when participants were asked to interpret results from the greenhouse gas inventory. Box plots were most favoured by our participants largely because they were particularly favoured by those who worked in research or had a stronger mathematical background. We propose a combination of methods should be used to convey uncertainty in emissions and that this combination should be tailored to the professional grou

Obtaining more benefits from crop residues as soil amendments by application as chemically heterogeneous mixtures

Crop residues are valuable soil amendments in terms of the carbon and other nutrients they contain, but incorporation of residues does not always translate into increases in nutrient availability, soil organic matter (SOM), soil structure, and overall soil fertility. Studies have demonstrated accelerated decomposition rates of chemically heterogeneous litter mixtures, compared to the decomposition of individual litters, in forest and grassland systems. Mixing high C:N ratio with low C:N ratio amendments may result in greater carbon use efficiency and non-additive benefits in soil properties. We hypothesised that non-additive benefits would accrue from mixtures of low-quality (straw or woodchips) and high-quality (vegetable-waste compost) residues applied before lettuce planting in a full-factorial field experiment. Properties indicative of soil structure and nutrient cycling were used to assess benefits from residue mixtures, including soil respiration, aggregate stability, bulk density, SOM, available and potentially mineralisable N, available P, K and Mg, and crop yield. Soil organic matter and mineral nitrogen levels were significantly and non-additively greater in the straw-compost mixture compared to individual residues, which mitigated the N immobilisation occurring with straw-only applications. Addition of compost significantly increased soil available N, K and Mg levels. Together, these observations suggest that greater nutrient availability improved the ability of decomposer organisms to degrade straw in the straw-compost mixture. We demonstrate that mixtures of crop residues can influence soil properties non-additively. Thus, greater benefits may be achieved by removing, mixing, and re-applying crop residues, than by simply returning them to the soils in situ

Facilitating the elicitation of beliefs for use in Bayesian Belief modelling

Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE Hassall (2019

Model-based optimization of agricultural profitability and nutrient management: a practical approach for dealing with issues of scale

To manage agricultural landscapes more sustainably we must understand and quantify the synergies and trade-offs between environmental impact, production and other ecosystem services. Models play an important role in this type of analysis as generally it is infeasible to test multiple scenarios by experiment. These models can be linked with algorithms that optimise for multiple objectives by searching a space of allowable management interventions (the control variables). Optimisation of landscapes for multiple objectives can be computationally challenging, however, particularly if the scale of management is typically smaller (e.g. field-scale) than the scale at which the objective is quantified (landscape scale) resulting in a large number of control variables whose impacts do not necessarily scale linearly. In this paper, we explore some practical solutions to this problem through a case study. In our case study we link a relatively detailed, agricultural landscape model with a multiple-objective optimisation algorithm to determine solutions that both maximise on profitability and minimise greenhouse gas emissions in response to management. The optimisation algorithm combines a non-dominated sorting routine with differential evolution, whereby a “population” of 100 solutions evolve over time to a Pareto optimal front. We show the advantages of using a hierarchical approach to the optimisation, whereby it is applied to finer scale units first (i.e. fields), and then the solutions from each optimisation are combined in a second step to produce landscape-scale outcomes. We show that if there is no interaction between units then the solution derived using such an approach will be the same as the one obtained if the landscape is optimised in one step. However, if there is spatial interaction, or if there are constraints on the allowable sets of solutions then outcomes can be quite different. In these cases, other approaches to increase the efficiency of the optimisation may be more appropriate – such as initialising the control variables for half of the population of solutions with values expected to be near optimal. Our analysis shows the importance of aligning a policy or management recommendation with the appropriate scale

Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes

Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences on other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimisation algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model

Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the UK’s greenhouse gas inventory for agriculture

The UK’s greenhouse gas inventory for agriculture uses a model based on the IPCC Tier 1 and Tier 2 26 methods to estimate the emissions of methane and nitrous oxide from agriculture. The inventory 27 calculations are disaggregated at country level (England, Wales, Scotland and Northern Ireland). 28 Before now, no detailed assessment of the uncertainties in the estimates of emissions had been 29 done. We used Monte Carlo simulation to do such an analysis. We collated information on the 30 uncertainties of each of the model inputs. The uncertainties propagate through the model and result 31 in uncertainties in the estimated emissions. Using a sensitivity analysis, we found that in England and 32 Scotland the uncertainty in the emission factor for emissions from N inputs (EF1) affected 33 uncertainty the most, but that in Wales and Northern Ireland, the emission factor for N leaching and 34 runoff (EF5) had greater influence. We showed that if the uncertainty in any one of these emission 35 factors is reduced by 50%, the uncertainty in emissions of nitrous oxide reduces by 10%. The 36 uncertainty in the estimate for the emissions of methane emission factors for enteric fermentation 37 in cows and sheep most affected the uncertainty in methane emissions. When inventories are 38 disaggregated (as that for the UK is) correlation between separate instances of each emission factor 39 will affect the uncertainty in emissions. As more countries move towards inventory models with 40 disaggregation, it is important that the IPCC give firm guidance on this topic

Effect of different organic amendments on actual and achievable yields in a cereal-based cropping system

Soil fertility is at risk in intensive cropping systems when using an exclusive regime of inorganic fertilisers without returning sufficient organic matter to the soil. Our objective was to evaluate the long-term effects of commonly used organic amendments interacting with different rates of inorganic nitrogen fertiliser on crop yields of winter wheat. Yield data from winter wheat were collected for five seasons between 2013 and 2019 from a continuous field trial based at Rothamsted Research, SE England. Organic amendments (anaerobic digestate, compost, farmyard manure, and straw at a rate of 0 and 2.5 ton C per hectare) and five rates of inorganic nitrogen fertiliser (NH4NO3 at 0, 80, 150, 190, 220 kg N ha−1) were applied to winter wheat grown in an arable rotation. At the same inorganic N rate, grain yields for the different organic amendment treatments (excluding the straw treatment) were statistically similar but significantly greater than the unamended control treatment. The nitrogen rate required for optimum yields tended to be lower in plots receiving a combination of organic amendments and mineral fertiliser. Based on the observed and modelled response functions, organic amendments excluding straw increased maximum achievable yields compared to non-amended controls. The size of the effect varied between seasons and amendments (+4.6 to +19.0% of the control yield), increasing the mean maximum achievable yield by 8.8% across four seasons. We conclude that the application of organic amendments can increase the yield potential in winter wheat substantially over what is achievable with inorganic fertiliser only

The landscape model: a model for exploring trade-offs between agricultural production and the environment

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3–28.6%), grain N (RMSE 21.3–42.5%) and P (RMSE 20.2–29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE 3.1 − 13.8 %), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses

Implications and impacts of aligning regional agriculture with a healthy diet

One of the most intractable challenges currently facing agricultural systems is the need to produce sufficient food for all to enjoy a healthy balanced diet while minimising impacts to the environment. Balancing these competing goals is especially intractable because most food systems are not locally bounded. This study aims to investigate the likely impacts on production, profit and the environment that result from aligning food systems to a healthy diet, as defined by EAT-Lancet. For this, we consider two distinct areas of the UK, one in East Anglia and the other in South Wales. These two regions reflect different ecosystems and therefore differing specialisations in UK agriculture. We used the Rothamsted Landscape Model (a detailed agroecosystems process-based model) to predict soil carbon dynamics, nutrient flows and crop production for the dominant crops grown in these regions, and the IPCC inventory models to estimate emissions from six livestock systems. Two scenarios were considered, one in which the study regions had to meet healthy diet requirements independently of each other and another in which they could do so collectively. To map their production to healthy diets, both study areas require increases in the production of plant proteins and reductions in the production of red meat. While changes in production can feed more people a healthy diet compared to the business-as-usual state, the overall calories produced reduces dramatically. Emissions and leaching decrease under the healthy diet scenarios and pesticide impacts remain largely unchanged. We show that local infrastructure and environment have a bearing on how “localised” food systems can be without running into substantial constraints. Whilst isolation of the farming system to a regional level, as explored here, is unlikely to be practical, we nevertheless demonstrate that aligning agricultural production towards healthier diets can generate food systems with many associated benefits in terms of agroecosystems' health and resilience to shocks in the food supply chain