Publications

<p>The main aim of this global review and systematic analysis was to investigate the impacts of previous land use system, climate zone and forest type and age on soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP), in the different soil layers (0-20, 20-60 and 60-100 cm), following afforestation. We collected 85 publications on SOC, TN and TP stock changes, covering different countries and climate zones. The data were classified into groups depending on these investigated parameters and analyzed using R version 3.6.1. We found that afforestation significantly increased SOC and TN stocks in the 0-20 and 20-60 soil layers, with values of 45% and 44% for SOC, 30% and 22% for TN, respectively, but had no impact on TP stock. Previous land use systems had the largest influence on SOC, TN and TP stocks, with greater accumulations on barren land compared to cropland and grassland. Climate zone influenced SOC, TN and TP stocks, with significant accumulations in the moist than in the dry climate zone. Afforestation with broadleaf deciduous and broadleaf evergreen forests led to greater SOC, TN and TP accumulations in each soil layer throughout the investigated profile (0-100 cm), compared to coniferous forests. Afforestation for <20 years had significantly increased SOC and TN stocks only at the soil surface (0-20 cm) whilst afforestation for ≥ 20 years had significantly accumulated them up to 100 cm soil depth. TP stock did not change with the forest age, suggesting that it may become a limiting factor for carbon sequestration under the older-age forest. Following afforestation, the change of soil bulk density had inverse relationships with SOC or TN stocks changes but had no effect on TP stock change.</p>

Abstract Forests are recognized as the largest natural source of nitrous oxide (N2O) emissions on land, with deforestation drastically reducing the cover and biodiversity of native forests worldwide. Yet, how losses in forest biodiversity affect soil N2O fluxes remains poorly understood. Here, we combined a global tree diversity-forest soil N2O dataset, including 201 paired comparable observations from global forests, with a three years field survey of in-situ fluxes data gathered from a long-term plant diversity field experiment. Our analyses reveal that tree diversity has a significant negative effect on soil N2O emissions, primarily driven by a decrease in N2O production associated with denitrification. More specifically, we showed that reductions in N2O emissions with tree diversity can be attributed to a decrease in the availability of soil inorganic nitrogen. Predictive modeling further shows that compared to single tree species, forests with two tree species can reduce global N2O emissions by 10.39%, while those with 24 tree species achieve the maximum mitigation effect, reducing emissions by 56.30%. Taken together, our work highlights the contribution of tree diversity for mitigating N2O emissions, highlighting the importance of accounting for biodiversity when reforesting old forests, and while supporting new afforestation processes.

Abstract The agriculture, forestry and other land use ( AFOLU ) sector is responsible for approximately 25% of anthropogenic GHG emissions mainly from deforestation and agricultural emissions from livestock, soil and nutrient management. Mitigation from the sector is thus extremely important in meeting emission reduction targets. The sector offers a variety of cost‐competitive mitigation options with most analyses indicating a decline in emissions largely due to decreasing deforestation rates. Sustainability criteria are needed to guide development and implementation of AFOLU mitigation measures with particular focus on multifunctional systems that allow the delivery of multiple services from land. It is striking that almost all of the positive and negative impacts, opportunities and barriers are context specific, precluding generic statements about which AFOLU mitigation measures have the greatest promise at a global scale. This finding underlines the importance of considering each mitigation strategy on a case‐by‐case basis, systemic effects when implementing mitigation options on the national scale, and suggests that policies need to be flexible enough to allow such assessments. National and international agricultural and forest (climate) policies have the potential to alter the opportunity costs of specific land uses in ways that increase opportunities or barriers for attaining climate change mitigation goals. Policies governing practices in agriculture and in forest conservation and management need to account for both effective mitigation and adaptation and can help to orient practices in agriculture and in forestry towards global sharing of innovative technologies for the efficient use of land resources. Different policy instruments, especially economic incentives and regulatory approaches, are currently being applied however, for its successful implementation it is critical to understand how land‐use decisions are made and how new social, political and economic forces in the future will influence this process.

Abstract Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations ( SD ) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield‐scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed.

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Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socio-economic scenarios (SSP1–3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by the model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritize future protection of biodiverse and carbon-rich areas. This article is part of the theme issue ‘Climate change and ecosystems: threats, opportunities and solutions’.

<title>Abstract</title> Regenerative Agriculture (RA) claims to build soil organic carbon (SOC) and increase crop yields through simultaneous adoption of a suite of management practices which restore soil health. However, this claim is largely unevidenced as few studies of fully integrated regenerative systems are currently available. As a first step to addressing this knowledge gap, we here examine three practices now being promoted as part of RA: reducing tillage intensity, cover cropping and including a grass-based phase in arable rotations (ley-arable rotations). Our Bayesian meta-analysis of 195 paired SOC and crop yield observations from a systematic review of published studies finds statistically significant increases in SOC concentration for reduced tillage intensity (0.06 g C.100g^-1) and ley-arable rotations (0.05 g C.100^-1g per year of ley) compared to conventional practice over an average study duration of 15 years, but no effect of cover crops. None of these practices reduce yield during cropping years, although we find no evidence of a win-win between increasing SOC and enhanced agricultural productivity following adoption. While future work should evaluate the net greenhouse gas emission implications of each practice and investigate the potential for synergistic effects if RA practices are adopted in combination, our results give land managers and policy makers confidence to further adopt these practices without loss of crop yield.

This database contains the average crop residue, manure nitrogen, manure organic carbon, net greenhouse gas emissions, and cropland area for 17 cropping systems at prefecture level during the period 2014-2018. In addition, it contained the changes of net greenhouse gas emissions caused by the optimization at prefecture level and province level. We also shared the key code for optimizing cropping systems at prefecture level and provided the all data. Users can run it the Matlab platform.