Publications

1. As the development of nature-based solutions (NbS) increases globally, it is important to ensure that projects meet the objective of benefiting biodiversity, alongside tackling societal challenges. However, most NbS projects do not directly monitor ecological outcomes, and those that do often focus on a limited set of metrics. It is therefore challenging to assess whether projects fulfil the aim of benefiting biodiversity. 2. We aimed to identify the most informative and feasible ecological metrics, both above- and below-ground, for monitoring ecological outcomes of NbS. We conducted a structured non-systematic literature review to identify possible biodiversity and soil health metrics, then developed a scoring system to rank these based on their informativeness and feasibility for monitoring. 3. Metrics were categorised into compositional, structural, and functional aspects of biodiversity, and biological, physical, and chemical aspects of soil health. We grouped biodiversity and soil health metrics into Tier 1 (the most informative and feasible metrics), Tier 2 (informative metrics with some limitations in scope or feasibility), and Future metrics (highly informative metrics which are currently less feasible to monitor). Tier 1 metrics collectively address multiple aspects of biodiversity and soil health and are currently the highest priority for NbS projects to assess. For biodiversity nine Tier 1, six Tier 2, and 15 Future metrics were identified, and for soil health 11 Tier 1, six Tier 2, and five Future metrics. 4. We identified existing standardised methodologies for monitoring the proposed metrics, noting that for many metrics standardised methodologies are not available and threshold or reference values for each metric are missing. 5. Solution: Our study provides practitioners with a framework for selecting optimum metrics for assessing above- and below-ground ecological outcomes of NbS relevant to the place in which they are being implemented. A definition of each metric and standardised methodologies for collecting data are summarised, providing information to develop an ecological monitoring protocol for an NbS project. The information on each metric has been made freely available as a searchable database in an interactive online interface geared towards UK practitioners, but with wider applicability.

Abstract. Soil organic carbon (SOC), one of the largest terrestrial carbon (C) stocks on Earth, has been depleted by anthropogenic land cover change and agricultural management. However, the latter has so far not been well represented in global C stock assessments. While SOC models often simulate detailed biochemical processes that lead to the accumulation and decay of SOC, the management decisions driving these biophysical processes are still little investigated at the global scale. Here we develop a spatially explicit data set for agricultural management on cropland, considering crop production levels, residue returning rates, manure application, and the adoption of irrigation and tillage practices. We combine it with a reduced-complexity model based on the Intergovernmental Panel on Climate Change (IPCC) tier 2 method to create a half-degree resolution data set of SOC stocks and SOC stock changes for the first 30 cm of mineral soils. We estimate that, due to arable farming, soils have lost around 34.6 GtC relative to a counterfactual hypothetical natural state in 1975. Within the period 1975–2010, this SOC debt continued to expand by 5 GtC (0.14 GtC yr−1) to around 39.6 GtC. However, accounting for historical management led to 2.1 GtC fewer (0.06 GtC yr−1) emissions than under the assumption of constant management. We also find that management decisions have influenced the historical SOC trajectory most strongly by residue returning, indicating that SOC enhancement by biomass retention may be a promising negative emissions technique. The reduced-complexity SOC model may allow us to simulate management-induced SOC enhancement – also within computationally demanding integrated (land use) assessment modeling.

Abstract Northern peatlands constitute a significant source of atmospheric methane ( CH 4 ). However, management of undisturbed peatlands, as well as the restoration of disturbed peatlands, will alter the exchange of CH 4 with the atmosphere. The aim of this systematic review and meta‐analysis was to collate and analyze published studies to improve our understanding of the factors that control CH 4 emissions and the impacts of management on the gas flux from northern (latitude 40° to 70°N) peatlands. The analysis includes a total of 87 studies reporting measurements of CH 4 emissions taken at 186 sites covering different countries, peatland types, and management systems. Results show that CH 4 emissions from natural northern peatlands are highly variable with a 95% CI of 7.6–15.7 g C m −2 year −1 for the mean and 3.3–6.3 g C m −2 year −1 for the median. The overall annual average (mean ± SD ) is 12 ± 21 g C m −2 year −1 with the highest emissions from fen ecosystems. Methane emissions from natural peatlands are mainly controlled by water table ( WT ) depth, plant community composition, and soil pH . Although mean annual air temperature is not a good predictor of CH 4 emissions by itself, the interaction between temperature, plant community cover, WT depth, and soil pH is important. According to short‐term forecasts of climate change, these complex interactions will be the main determinant of CH 4 emissions from northern peatlands. Drainage significantly ( p < .05) reduces CH 4 emissions to the atmosphere, on average by 84%. Restoration of drained peatlands by rewetting or vegetation/rewetting increases CH 4 emissions on average by 46% compared to the original premanagement CH 4 fluxes. However, to fully evaluate the net effect of management practice on the greenhouse gas balance from high latitude peatlands, both net ecosystem exchange ( NEE ) and carbon exports need to be considered.

Funding Information: This research is funded by the European Union, under the Horizon 2020 project “Developing SUstainable PERmanent Grassland systems and policies (Super-G)”, grant no. 774124. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European UnionEU or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Vanwalleghem and Milazzo also acknowledge additional financial support from the Spanish Ministry of Science and Innovation, the Spanish State Research Agency, and through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (Ref. CEX2019–000968-M). Funding Information: This research is funded by the European Union , under the Horizon 2020 project “Developing SUstainable PERmanent Grassland systems and policies (Super-G)”, grant no. 774124 . Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European UnionEU or the European Research Council . Neither the European Union nor the granting authority can be held responsible for them. Vanwalleghem and Milazzo also acknowledge additional financial support from the Spanish Ministry of Science and Innovation , the Spanish State Research Agency , and through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (Ref. CEX2019–000968-M ). Publisher Copyright: © 2023

<title>Abstract</title> Spatially explicit cropland greenhouse gas (GHG) emissions and management data are crucial for developing effective climate change mitigation strategies and promoting sustainable agricultural practices. This study presents high-resolution (5 arc-minute) global maps of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) emissions from agricultural activities across 46 crops circa 2020, referred to as emissions from croplands. Emission sources include synthetic nitrogen fertilizer use, manure application, crop residue incorporation, in-field residue burning, rice cultivation, and drained peatlands for cropping. Our findings indicate that global GHG emissions from croplands reached 2.7 Gt CO₂e in 2020, with drained peatlands (35%), rice paddies (35%), and synthetic N fertilizer (22%) as the primary contributors. The four major crop contributors-rice, maize, oil palm, and wheat-accounted for about 60% of total emissions. These results provide critical insights for effectively targeting GHG mitigation efforts and identifying key regions and crops within high-emission countries, enabling more precise and impactful climate action.

Projections of global changes in water scarcity with the current extent of maize, rice, wheat, vegetables, pulses and fruit production commodities were combined to identify the potential country level vulnerabilities of cropland land to water scarcity in 2050. The data relate to an analysis of the impact changes in water availability will have on maize, rice, wheat, vegetables, pulses and fruit production commodities availability in 2050.

Abstract Measurement, monitoring, and prediction of soil organic carbon (SOC) are fundamental to supporting climate change mitigation efforts and promoting sustainable agricultural management practices. This review discusses recent advances in methodologies and technologies for SOC quantification, including remote sensing (RS), proximal soil sensing (PSS), artificial intelligence (AI) for SOC modelling (in particular, machine learning (ML) and deep learning (DL)), biogeochemical modelling, and data fusion. Integrating data from RS, PSS, and other sensors usually leads to good SOC predictions, provided it is supported by careful calibration, validation across diverse pedo‐climatic and land management, and the use of data processing and modelling frameworks. We also found that the accuracy of AI‐driven SOC prediction improves when RS covariates are included. Although DL often outperforms classical ML, there is no single best AI algorithm. By incorporating simulated outputs from biogeochemical model as additional training data for AI, causal relationships in SOC turnover can be incorporated into empirical modelling, while maintaining predictive accuracy. In conclusion, SOC prediction can be enhanced through 1) integrating sensing technologies, 2) applying AI, notably DL, 3) addressing biogeochemical model limitations (assumptions, parameterization, structure), 4) expanding SOC data availability, 5) improving mathematical representation of microbial influences on SOC, and 6) strengthening interdisciplinary cooperation between soil scientists and model developers.