Publications

Using an Agent-Based Model with a two-scale decision making process incorporating economic, geographic, social and political subsystems, we projected the rate and proportion of land use change in England and Wales from the year 2000 to the year 2050. These projections were used to assess the impact of proposals to improve arable yields, change diet and reduce consumer waste under two contrasting political ideologies, protectionist or free trade, on the proportions and rate of change of agricultural land use. The model does not calculate what is possible to achieve but what is probable, given the simulated processes, which are based on landowner satisficing behaviour and government political ideologies. Our main finding is that protectionist policies produced the least change. We also found that arable crop yield improvements, dietary change and waste reduction promoted pasture over arable use because these drivers reduced arable income on marginal arable land. The proportion of land in private and farm-forestry, a land use proposed as a greenhouse gas (GHG) mitigation measure, reduced, when private and farm-forestry was not a protected use and when there were no incentives to plant or maintain trees.

Abstract. Remote sensing and inverse modelling studies indicate that the tropics emit more CH4 and N2O than predicted by bottom-up emissions inventories, suggesting that terrestrial sources are stronger or more numerous than previously thought. Tropical uplands are a potentially large and important source of CH4 and N2O often overlooked by past empirical and modelling studies. To address this knowledge gap, we investigated spatial, temporal and environmental trends in soil CH4 and N2O fluxes across a long elevation gradient (600–3700 m a.s.l.) in the Kosñipata Valley, in the southern Peruvian Andes, that experiences seasonal fluctuations in rainfall. The aim of this work was to produce preliminary estimates of soil CH4 and N2O fluxes from representative habitats within this region, and to identify the proximate controls on soil CH4 and N2O dynamics. Area-weighted flux calculations indicated that ecosystems across this altitudinal gradient were both atmospheric sources and sinks of CH4 on an annual basis. Montane grasslands (3200–3700 m a.s.l.) were strong atmospheric sources, emitting 56.94 ± 7.81 kg CH4-C ha−1 yr−1. Upper montane forest (2200–3200 m a.s.l.) and lower montane forest (1200–2200 m a.s.l.) were net atmospheric sinks (−2.99 ± 0.29 and −2.34 ± 0.29 kg CH4-C ha−1 yr−1, respectively); while premontane forests (600–1200 m a.s.l.) fluctuated between source or sink depending on the season (wet season: 1.86 ± 1.50 kg CH4-C ha−1 yr−1; dry season: −1.17 ± 0.40 kg CH4-C ha−1 yr−1). Analysis of spatial, temporal and environmental trends in soil CH4 flux across the study site suggest that soil redox was a dominant control on net soil CH4 flux. Soil CH4 emissions were greatest from habitats, landforms and during times of year when soils were suboxic, and soil CH4 efflux was inversely correlated with soil O2 concentration (Spearman's ρ = −0.45, P < 0.0001) and positively correlated with water-filled pore space (Spearman's ρ = 0.63, P <0.0001). Ecosystems across the region were net atmospheric N2O sources. Soil N2O fluxes declined with increasing elevation; area-weighted flux calculations indicated that N2O emissions from premontane forest, lower montane forest, upper montane forest and montane grasslands averaged 2.23 ± 1.31, 1.68 ± 0.44, 0.44 ± 0.47 and 0.15 ± 1.10 kg N2O-N ha−1 yr−1, respectively. Soil N2O fluxes from premontane and lower montane forests exceeded prior model predictions for the region. Comprehensive investigation of field and laboratory data collected in this study suggest that soil N2O fluxes from this region were primarily driven by denitrification; that nitrate (NO3−) availability was the principal constraint on soil N2O fluxes; and that soil moisture and water-filled porosity played a secondary role in modulating N2O emissions. Any current and future changes in N management or anthropogenic N deposition may cause shifts in net soil N2O fluxes from these tropical montane ecosystems, further enhancing this emission source.

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Approximately a third of all annual greenhouse gas emissions globally are directly or indirectly associated with the food system, and over a half of these are linked to livestock production. In temperate oceanic regions, such as the UK, most meat and dairy is produced in extensive systems based on pasture. There is much interest in the extent to which such grassland may be able to sequester and store more carbon to partially or completely mitigate other greenhouse gas emissions in the system. However, answering this question is difficult due to context-specificity and a complex and sometimes inconsistent evidence base. This paper describes a project that set out to summarize the natural science evidence base relevant to grassland management, grazing livestock and soil carbon storage potential in as policy-neutral terms as possible. It is based on expert appraisal of a systematically assembled evidence base, followed by a wide stakeholders engagement. A series of evidence statements (in the appendix of this paper) are listed and categorized according to the nature of the underlying information, and an annotated bibliography is provided in the electronic supplementary material.

The development of climate mitigation services partly depends on our ability to simulate, with confidence, agricultural production and greenhouse gas (GHG) emissions so as to understand the effectiveness of the mitigation approach on both gas emissions and food production. The Soil C-N Group of the Global Research Alliance (GRA) on GHG has initiated an international model benchmarking and inter-comparison that will assess GHG balance and soil C sequestration of arable crops and grasslands as affected by agricultural practices. The inter-comparison arises from collaborations between GRA, AgMIP and four FACCE-JPI projects to lead to the largest exercise in this domain. An initial stock take has been conducted, resulting in the selection of datasets from five grasslands and five crop sites worldwide. A total of 28 models used in 11countries for the prediction of GHG emissions in crop and grassland systems are contributing, ranging from process-oriented models to simpler models. The study has been set up with five successive steps that gradually release information to the modeling groups ranging from fully-blind application of the models to complete availability of the experimental measurements. Model simulations are compared to experimental measurements for crop yield and grassland dry-matter production, N<sub>2</sub>O emissions, soil C stocks and net CO<sub>2</sub> exchanges. The precision and accuracy of the predictions are evaluated at each step of the inter-comparison with statistical methods, facilitating quantification of projection uncertainties. Results from the first step on N<sub>2</sub>O emissions with no prior information show variability between model predictions for any site and that model error tends to be conserved across sites. Moreover, the frequency distribution of N<sub>2</sub>O emissions already provides an understanding of model functioning in terms of N<sub>2</sub>O peak prediction. Further steps will allow for improved site-specific prediction and, as a final step, will expose the measured GHG emissions for model improvement.

There is increasing interest in the use of nonfood second-generation lignocellulosic feedstocks and a move away from food crops for bioenergy applications, but questions still remain on sustainability. Empirical data are needed to quantify the GHG balance of land-use transition to lignocellulosic bioenergy cropping systems, to inform lifecycle analyses and aid model validation. The aim of this project ‘Ecosystem Land Use Modeling and Soil Carbon GHG Flux Trial’ is to produce a framework for predicting the sustainability of bioenergy deployment across the UK. This GB£4m consortium project is commissioned and funded by the Energy Technologies Institute, UK.

Analysis of an integrated waste-to-protein system for a sustainable protein future to ameliorate the rising global hunger pandemic.

Just and fair transitions to low-carbon and nature-positive ways of living need to occur fast enough to limit and reverse the climate and nature crises, but not so fast that the public is left behind. We propose the concept of “Regenerative Good Growth” (RGG) to replace the language and practice of extractive, bad GDP growth. RGG centres on the services provided by five renewable capitals: natural, social, human, cultural, and sustainable physical. The term “growth” tends to divide rather than unite, and so here we seek language and storylines that appeal to a newly emergent climate-concerned majority. Creative forms of public engagement that lead to response diversity will be essential to fostering action: when people feel coerced into adopting single options at pace, there is a danger of backlash or climate authoritarianism. Policy centred around storytelling can help create diverse public responses and institutional frameworks. The practises underpinning RGG have already created business opportunities, while delivering sharp falls in unit costs. Fast transitions and social tipping points are emerging in the agricultural, energy, and city sectors. Though further risks will emerge related to rebound effects and lack of decoupling of material consumption from GDP, RGG will help cut the externalities of economies.