Publications

Soil carbon changes are difficult to measure globally, and global models are poorly constrained. Here, we propose a framework to map annual changes in soil carbon and litter (SOCL) as the difference between the net land CO₂ flux from atmospheric inversions and satellite-based maps of biomass changes. We show that SOCL accumulated globally at a rate of about 0.34 ± 0.30 ( ± 1 sigma) billion tonnes of carbon per year (PgC yr^-¹) during 2011-2020. The largest SOCL sink is found in boreal regions (0.93 ± 0.45 PgC yr^-¹ in total) particularly in undisturbed peatlands and managed forests. The largest losses occur in the dry tropics (-0.50 ± 0.47 PgC yr^-¹) and correspond with agricultural expansion from land use change, cropland management and grazing. By contrast, forests in the wet tropics act as a net soil carbon sink (0.32 ± 0.35 PgC yr^-¹). Our findings highlight the large mitigation opportunities in the dry tropics to restore agricultural soil carbon.

Soil organic carbon sequestration (SOCseq) is considered the most attractive carbon capture technology to partially mitigate climate change. However, there is conflicting evidence regarding the potential of SOCseq. The additional storage potential on existing global cropland is missing. SOCseq is region-specific and conditioned by management but most global estimates use fixed accumulation rates or time frames. Here, we show how the SOC storage potential and its steady state varies globally depending on climate, land use and soil. Using 83,416 soil observations, we developed a quantile regression neural network that quantifies the SOC variation within soils with similar characteristics. This allows us to identify similar areas that present higher SOC with the difference representing an additional storage potential. The estimated additional SOC storage potential of 29 to 67 Pg C in the topsoil of global croplands equates to only 2 to 5 years of emissions offsetting and 32 % of agriculture's 92 Pg historical carbon debt estimate due to conversion from natural ecosystems. Since SOC is temperature-dependent, this potential is likely to reduce by 18 % by 2040 due to climate change.

Abstract Major sources of greenhouse gas ( GHG ) emissions from agricultural crop production are nitrous oxide ( N 2 O ) emissions resulting from the application of mineral and organic fertilizer, and carbon dioxide ( CO 2 ) emissions from soil carbon losses. Consequently, choice of fertilizer type, optimizing fertilizer application rates and timing, reducing microbial denitrification and improving soil carbon management are focus areas for mitigation. We have integrated separate models derived from global data on fertilizer‐induced soil N 2 O emissions, soil nitrification inhibitors, and the effects of tillage and soil inputs of soil C stocks into a single model to determine optimal mitigation options as a function of soil type, climate, and fertilization rates. After Monte Carlo sampling of input variables, we aggregated the outputs according to climate, soil and fertilizer factors to consider the benefits of several possible emissions mitigation strategies, and identified the most beneficial option for each factor class on a per‐hectare basis. The optimal mitigation for each soil‐climate‐region was then mapped to propose geographically specific optimal GHG mitigation strategies for crops with varying N requirements. The use of empirical models reduces the requirements for validation (as they are calibrated on globally or continentally observed phenomena). However, as they are relatively simple in structure, they may not be applicable for accurate site‐specific prediction of GHG emissions. The value of this modelling approach is for initial screening and ranking of potential agricultural mitigation options and to explore the potential impact of regional agricultural GHG abatement policies. Given the clear association between management practice and crop productivity, it is essential to incorporate characterization of the yield effect on a given crop before recommending any mitigation practice.

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R 2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R 2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.

Greenhouse gas (GHG) emissions from agriculture, including crop and livestock production, forestry and associated land use changes, are responsible for a significant fraction of anthropogenic emissions, up to 30% according to the Intergovernmental Panel on Climate Change (IPCC). Yet while emissions from fossil fuels are updated yearly and by multiple sources—including national-level statistics from the International Energy Agency (IEA)—no comparable efforts for reporting global statistics for agriculture, forestry and other land use (AFOLU) emissions exist: the latest complete assessment was the 2007 IPCC report, based on 2005 emission data. This gap is critical for several reasons. First, potentially large climate funding could be linked in coming decades to more precise estimates of emissions and mitigation potentials. For many developing countries, and especially the least developed ones, this requires improved assessments of AFOLU emissions. Second, growth in global emissions from fossil fuels has outpaced that from AFOLU during every decade of the period 1961–2010, so the relative contribution of the latter to total climate forcing has diminished over time, with a need for regular updates. We present results from a new GHG database developed at FAO, providing a complete and coherent time series of emission statistics over a reference period 1961–2010, at country level, based on FAOSTAT activity data and IPCC Tier 1 methodology. We discuss results at global and regional level, focusing on trends in the agriculture sector and net deforestation. Our results complement those available from the IPCC, extending trend analysis to a longer historical period and, critically, beyond 2005 to more recent years. In particular, from 2000 to 2010, we find that agricultural emissions increased by 1.1% annually, reaching 4.6 Gt CO2 yr−1 in 2010 (up to 5.4–5.8 Gt CO2 yr−1 with emissions from biomass burning and organic soils included). Over the same decade 2000–2010, the ratio of agriculture to fossil fuel emissions has decreased, from 17.2% to 13.7%, and the decrease is even greater for the ratio of net deforestation to fossil fuel emissions: from 19.1% to 10.1%. In fact, in the year 2000, emissions from agriculture have been consistently larger—about 1.2 Gt CO2 yr−1 in 2010—than those from net deforestation.

What is the purpose of Glastir Monitoring and Evaluation Programme? Glastir is the main scheme by which the Welsh Government pays for environmental goods and services whilst the Glastir Monitoring and Evaluation Programme (GMEP) evaluates the scheme’s success. Commissioning of the monitoring programme in parallel with the launch of the Glastir scheme provides fast feedback and means payments can be modified to increase effectiveness. The Glastir scheme is jointly funded by the Welsh Government (through the Rural Development Plan) and the EU. GMEP will also support a wide range of other national and international reporting requirements. What is the GMEP approach? GMEP collects evidence for the 6 intended outcomes from the Glastir scheme which are focussed on climate change, water and soil quality, biodiversity, landscape, access and historic environment, woodland creation and management. Activities include; a national rolling monitoring programme of 1km squares; new analysis of long term data from other schemes combining with GMEP data where possible; modelling to estimate future outcomes so that adjustments can be made to maximise impact of payments; surveys to assess wider socio-economic benefits; and development of novel technologies to increase detection and efficiency of future assessments. How has GMEP progressed in this 2nd year? 90 GMEP squares were surveyed in Year 2 to add to the 60 completed in Year 1 resulting in 50% of the 300 GMEP survey squares now being completed. Squares will be revisited on a 4 year cycle providing evidence of change in response to Glastir and other pressures such as changing economics of the farm business, climate change and air pollution. This first survey cycle collects the baseline against which future changes will be assessed. This is important as GMEP work this year has demonstrated land coming into the scheme is different in some respects to land outside the scheme. Therefore, future analysis to detect impact of Glastir will be made both against the national backdrop from land outside the scheme and this baseline data from land in scheme. A wide range of analyses of longterm data has been completed for all Glastir Outcomes with the exception of landscape quality and historic features condition for which limited data is available. This has involved combining data with 2013/14 GMEP data when methods allow. Overall analysis of long term data indicates one of stability but with little evidence of improvement with the exception of headwater quality, greenhouse gas emissions and woodland area for which there has been improvement over the last 20 years. Some headline statistics include: 51% of historic features in excellent or sound condition; two thirds of public rights of way fully open and accessible; improvement in hedgerow management with 85% surveyed cut in the last 3 years but < 1% recently planted; 91% of streams had some level of modification but 60% retained good ecological quality; no change topsoil carbon content over last 25 years. What is innovative? GMEP has developed various new metrics to allow for more streamlined reporting in the future. For example a new Priority Bird species Index for Wales which combines data from 35 species indicates at least half have stable or increasing populations. The new GMEP Visual Quality Landscape Index has been tested involving over 2600 respondents. Results have demonstrated its value as an objective and repeatable method for quantifying change in visual landscape quality. A new unified peat map for Wales has been developed which has been passed to Glastir Contract Managers to improve targeting of payments when negotiating Glastir contracts. An estimate of peat soil contribution to current greenhouse gas emissions due to human modification has been calculated. Models have allowed quantification of land area helping to mitigate rainfall runoff. We are using new molecular tools to explore the effects of Glastir on soil organisms and satellite technologies to quantify e.g. small woody features and landcover change. Finally we are using a community approach to develop a consensus on how to define and report change in High Nature Value Farmland which will be reported in the Year 3 GMEP report.

&amp;lt;p&amp;gt;The two most urgent and interlinked environmental challenges humanity faces are climate change and biodiversity loss. We are entering a pivotal decade for both the international biodiversity and climate change agendas with the sharpening of ambitious strategies and targets by the Convention on Biological Diversity and the United Nations Framework Convention on Climate Change. Within their respective Conventions, the two biodiversity and climate interlinked challenges have been addressed separately. There is evidence that conservation actions that halt, slow or reverse biodiversity loss can simultaneously slow anthropogenic mediated climate change significantly. This review highlights conservation actions which have the largest potential for mitigation of climate change. We note that conservation actions have mainly synergistic benefits and few antagonistic trade-offs with climate change mitigation. Specifically, we identify direct co-benefits in 14 out of the 21 action targets of the draft post-2020 global biodiversity framework of the Convention on Biological Diversity, notwithstanding the many indirect links that can as well support both biodiversity conservation and climate change mitigation. These relationships are context and scale-dependent; therefore, we showcase examples of local biodiversity conservation actions that can be incentivized, guided and prioritized by global objectives and targets. The close interlinkages between biodiversity, climate change mitigation, other nature&amp;rsquo;s contributions to people and good quality of life are seldom as integrated as they should be in management and policy. This review aims to re-emphasize timeously the vital relationships between biodiversity conservation actions and climate change mitigation, prior to major Conferences of Parties that are about to negotiate strategic frameworks and international goals for the decades to come.&amp;lt;/p&amp;gt;

Acknowledgements: The James Hutton Institute is supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS). This research was funded through both ClimatexChange and two strategic Research Programs (2011-16 and 2016-21). The authors acknowledge the assistance provided by other staff – Marie Castellazzi, Nikki Baggaley, Allan Lilly (The James Hutton Institute); Jo Smith (University of Aberdeen); Philip Taylor, Duncan Ray (Forest Research).