The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands

Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector

Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N2O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N2O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process-based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low-ambition nitrogen regulation policies, EF would increase from 1.18%-1.22% in 2010 to 1.27%-1.34% by 2050, representing a relative increase of 4.4%-11.4% and exceeding the IPCC tier-1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%-0.35% (relative increase of 11.9%-17%). In contrast, high-ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying-wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N2O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N2O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio-economic assessments and policy-making efforts.

Plastic film mulching application improves potato yields, reduces ammonia emissions, but boosts the greenhouse gas emissions in China

With global population growth and climate change, food security and global warming have emerged as two major challenges to agricultural development. Plastic film mulching (PM) has long been used to improve yields in rain-fed agricultural systems, but few studies have focused on soil gas emissions from mulched rainfed potatoes on a long-term and regional scale. This study integrated field data with the Denitrification-Decomposition (DNDC) model to evaluate the impacts of PM on potato yields, greenhouse gas (GHG) and ammonia (NH3) emissions in rainfed agricultural systems in China. We found that PM increased potato yield by 39.7 % (1505 kg ha-1), carbon dioxide (CO2) emissions by 15.4 % (123 kg CO2 eq ha-1), nitrous oxide (N2O) emissions by 47.8 % (1016 kg CO2 eq ha-1), and global warming potential (GWP) by 38.9 % (1030 kg CO2 eq ha-1), while NH3 volatilization decreased by 33.9 % (8.4 kg NH3 ha-1), and methane (CH4) emissions were little changed compared to CK. Specifically, the yield after PM significantly increased in South China (SC), North China (NC), and Northwest China (NWC), with increases of 66.1 % (2429 kg ha-1), 44.1 % (1173 kg ha-1), and 43.6 % (956 kg ha-1) compared to CK, respectively. The increase in GWP and greenhouse gas emission intensity (GHGI) under PM was more pronounced in the Northeast China (NEC) and NWC regions, with respective increases of 57.1 % and 60.2 % in GWP, 16.9 % and 10.3 % in GHGI. While in the Middle and Lower reaches of the Yangtze River (MLYR) and SC, PM decreased GHGI with 10.2 % and 31.1 %, respectively. PM significantly reduced NH3 emissions in all regions and these reductions were most significant in Southwest China (SWC), SCand MLYR, which were 41 %, 38.0 %, and 38.0 % lower than CK, respectively. In addition, climatic and edaphic variables were the main contributors to GHG and NH3 emissions. In conclusion, it is appropriate to promote the use of PM in the MLYR and SC regions, because of the ability to increase yields while reducing environmental impacts (lower GHGI and NH3 emissions). The findings provide a theoretical basis for sustainable agricultural production of PM potatoes.

Impacts of reduced synthetic fertiliser use under current and future climates: Exploration using integrated agroecosystem modelling in the upper River Taw observatory, UK

The intensification of farming and increased nitrogen fertiliser use, to satisfy the growing population demand, contributed to the extant climate change crisis. Use of synthetic fertilisers in agriculture is a significant source of anthropogenic Greenhouse Gas (GHG) emissions, especially potent nitrous oxide (N2O). To achieve the ambitious policy target for net zero by 2050 in the UK, it is crucial to understand the impacts of potential reductions in fertiliser use on multiple ecosystem services, including crop production, GHG emissions and soil organic carbon (SOC) storge. A novel integrated modelling approach using three established agroecosystem models (SPACSYS, CSM and RothC) was implemented to evaluate the associated impacts of fertiliser reduction (10%, 30% and 50%) under current and projected climate scenarios (RCP2.6, RCP4.5 and RCP8.5) in a study catchment in Southwest England. 48 unique combinations of soil types, climate conditions and fertiliser inputs were evaluated for five major arable crops plus improved grassland. With a 30% reduction in fertiliser inputs, the estimated yield loss under current climate ranged between 11% and 30% for arable crops compared with a 20-24% and 6-22% reduction in N2O and methane emissions, respectively. Biomass was reduced by 10-25% aboveground and by <12% for the root system. Relative to the baseline scenario, soil type dependent reductions in SOC sequestration rates are predicted under future climate with reductions in fertiliser inputs. Losses in SOC were more than doubled under the RCP4.5 scenario. The emissions from energy use, including embedded emissions from fertiliser manufacture, was a significant source (14-48%) for all arable crops and the associated GWP20.

Feasibility of mitigation measures for agricultural greenhouse gas emissions in the UK. A systematic review

The UK Government has set an ambitious target of achieving a national "net-zero" greenhouse gas economy by 2050. Agriculture is arguably placed at the heart of achieving net zero, as it plays a unique role as both a producer of GHG emissions and a sector that has the capacity via land use to capture carbon (C) when managed appropriately, thus reducing the concentration of carbon dioxide (CO2) in the atmosphere. Agriculture's importance, particularly in a UK-specific perspective, which is also applicable to many other temperate climate nations globally, is that the majority of land use nationwide is allocated to farming. Here, we present a systematic review based on peer-reviewed literature and relevant "grey" reports to address the question "how can the agricultural sector in the UK reduce, or offset, its direct agricultural emissions at the farm level?" We considered the implications of mitigation measures in terms of food security and import reliance, energy, environmental degradation, and value for money. We identified 52 relevant studies covering major foods produced and consumed in the UK. Our findings indicate that many mitigation measures can indeed contribute to net zero through GHG emissions reduction, offsetting, and bioenergy production, pending their uptake by farmers. While the environmental impacts of mitigation measures were covered well within the reviewed literature, corresponding implications regarding energy, food security, and farmer attitudes towards adoption received scant attention. We also provide an open-access, informative, and comprehensive dataset for agri-environment stakeholders and policymakers to identify the most promising mitigation measures. This research is of critical value to researchers, land managers, and policymakers as an interim guideline resource while more quantitative evidence becomes available through the ongoing lab-, field-, and farm-scale trials which will improve the reliability of agricultural sustainability modelling in the future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13593-023-00938-0.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Deceleration of Cropland-N2O Emissions in China and Future Mitigation Potentials

Agricultural soils are the largest anthropogenic emission source of nitrous oxide (N2O). National agricultural policies have been implemented to increase crop yield and reduce nitrogen (N) losses to the environment. However, it is difficult to effectively quantify crop-specific and regional N2O mitigation priorities driven by policies, due to lack of long-term, high-resolution crop-specific activity data, and oversimplified models. Here, we quantify the spatiotemporal changes and key drivers of crop-specific cropland-N2O emissions from China between 1980 and 2017, and future N2O mitigation potentials, using a linear mixed-effect model and survey-based data set of agricultural management measures. Cropland-N2O emissions from China tripled from 102.5 to 315.0 Gg N yr-1 between 1980 and 2017, and decelerated since 1998 mainly driven by country-wide deceleration and decrease in N rate and the changes in sowing structure. About 63% of N2O emissions could be reduced in 2050, primarily in the North China Plain and Northeast China Plain; 83% of which is from the production of maize (33%), vegetables (27%), and fruits (23%). The deceleration of N2O emissions highlights that policy interventions and agronomy practices (i.e., optimizing N rate and sowing structure) are potential pathways for further ambitious N2O mitigation in China and other developing countries.

Climate impact from agricultural management practices in the Canadian Prairies: Carbon equivalence due to albedo change

It is generally accepted that land use and land management practices impact climate change through sequestration of carbon in soils, but modulation of surface energy budget can also be important. Using Landsat data to characterize cropland albedos in Canada's three prairie soil zones, this study estimates the atmospheric carbon equivalent drawdown of albedo radiative forcing for three management practices: 1) moving from conventional tillage to no-till, 2) eliminating summer fallow in crop rotations, and 3) growing crops with higher albedos. In a 50-year time horizon, conversion from conventional tillage to no-till results in a total equivalent atmospheric CO₂ (CO₂-eq) drawdown of 1.0-1.5 kg m^-2, and conversion from summer fallow to crops results in CO₂-eq drawdown of 1.1-2.4 kg m^-2. Conversion of summer fallow to crops results in different magnitudes of CO₂-eq drawdown depending on specific crops. Lentils, peas, and canola have relatively higher albedo than that of spring wheat and flax; hence, a larger magnitude of CO₂-eq drawdown results when they replace summer fallow in the rotation. For the management changes from 1990 to 2019 for the whole Canadian Prairies, albedo changes induced a CO₂-eq drawdown of about 179.3 ± 20.9 Tg due to increased area of no-till, and 101.6 ± 9.5 Tg due to reduced area under fallow. The study shows that the magnitudes of CO₂-eq drawdown due to albedo change are comparable to that due to soil carbon sequestration. Therefore, it is important to account for cropland albedo changes in assessing the potential of agricultural management practices to mitigate climate change.

Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

The productivity of permanent temperate cut grasslands is mainly driven by weather, soil characteristics, botanical composition and management. To adapt management to climate change, adjusting the cutting dates to reflect earlier onset of growth and expansion of the vegetation period is particularly important. Simulations of cut grassland productivity under climate change scenarios demands management settings to be dynamically derived from actual plant development rather than using static values derived from current management operations. This is even more important in the alpine region, where the predicted temperature increase is twice as high as compared to the global or Northern Hemispheric average. For this purpose, we developed a dynamic management module that provides timing of cutting and manuring events when running the biogeochemical model LandscapeDNDC. We derived the dynamic management rules from long-term harvest measurements and monitoring data collected at pre-alpine grassland sites located in S-Germany and belonging to the TERENO monitoring network. We applied the management module for simulations of two grassland sites covering the period 2011-2100 and driven by scenarios that reflect the two representative concentration pathways (RCP) 4.5 and 8.5 and evaluated yield developments of different management regimes. The management module was able to represent timing of current management operations in high agreement with several years of field observations (r² > 0.88). Even more, the shift of the first cutting dates scaled to a +1 °C temperature increase simulated with the climate change scenarios (-9.1 to -17.1 days) compared well to the shift recorded by the German Weather Service (DWD) in the study area from 1991-2016 (-9.4 to -14.0 days). In total, the shift in cutting dates and expansion of the growing season resulted in 1-2 additional cuts per year until 2100. Thereby, climate change increased yields of up to 6 % and 15 % in the RCP 4.5 and 8.5 scenarios with highest increases mainly found for dynamically adapted grassland management going along with increasing fertilization rates. In contrast, no or only minor yield increases were associated with simulations restricted to fertilization rates of 170 kg N ha^-1 yr^-1 as required by national legislations. Our study also shows that yields significantly decreased in drought years, when soil moisture is limiting plant growth but due to comparable high precipitation and water holding capacity of soils, this was observed mainly in the RCP 8.5 scenario in the last decades of the century.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.

Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects

The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental footprint. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how these lands can be better assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on modelling the influences of grazing of soil carbon (SC) and greenhouse gas emissions to identify current knowledge gaps and research priorities. We revisit three widely-used process-based models: DeNitrification DeComposition (DNDC), DayCent, and the Pasture Simulation model (PaSim) and two watershed models: The Soil & Water Assessment Tool (SWAT) and Variable Infiltration Capacity Model (VIC), which are widely used to simulate C, nutrient and water cycles. We review their structures and ability as process-based models in representing key feedbacks among grazing management, SOM decomposition and hydrological processes in grazing lands. Then we review some significant advances in the use of models combining biogeochemical and hydrological processes. Finally, we examine challenges of incorporating spatial heterogeneity and temporal variability into modelling C and nutrient cycling in grazing lands and discuss their weakness and strengths. We also highlight key research direction for improving the knowledge base and code structure in modelling C and nutrient cycling in grazing lands, which are essential to conserve grazing lands and maintain their ecosystem goods and services.

Global Research Alliance N2 O chamber methodology guidelines: Summary of modeling approaches

Measurements of nitrous oxide (N₂ O) emissions from agriculture are essential for understanding the complex soil-crop-climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N₂ O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process-based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high-level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models, common modeling techniques such as calibration and validation, assessing model fit, sensitivity analysis, and uncertainty assessment. We also review examples of N₂ O modeling for different purposes and describe three commonly used process-based N₂ O models (APSIM, DayCent, and DNDC).

Weakened growth of cropland-N2 O emissions in China associated with nationwide policy interventions

China has experienced rapid agricultural development over recent decades, accompanied by increased fertilizer consumption in croplands; yet, the trend and drivers of the associated nitrous oxide (N₂ O) emissions remain uncertain. The primary sources of this uncertainty are the coarse spatial variation of activity data and the incomplete model representation of N₂ O emissions in response to agricultural management. Here, we provide new data-driven estimates of cropland-N₂ O emissions across China in 1990-2014, compiled using a global cropland-N₂ O flux observation dataset, nationwide survey-based reconstruction of N-fertilization and irrigation, and an updated nonlinear model. In addition, we have evaluated the drivers behind changing cropland-N₂ O patterns using an index decomposition analysis approach. We find that China's annual cropland-N₂ O emissions increased on average by 11.2 Gg N/year² (p < .001) from 1990 to 2003, after which emissions plateaued until 2014 (2.8 Gg N/year² , p = .02), consistent with the output from an ensemble of process-based terrestrial biosphere models. The slowdown of the increase in cropland-N₂ O emissions after 2003 was pervasive across two thirds of China's sowing areas. This change was mainly driven by the nationwide reduction in N-fertilizer applied per area, partially due to the prevalence of nationwide technological adoptions. This reduction has almost offset the N₂ O emissions induced by policy-driven expansion of sowing areas, particularly in the Northeast Plain and the lower Yangtze River Basin. Our results underline the importance of high-resolution activity data and adoption of nonlinear model of N₂ O emission for capturing cropland-N₂ O emission changes. Improving the representation of policy interventions is also recommended for future projections.

Assessing the provision of carbon-related ecosystem services across a range of temperate grassland systems in western Canada

Reliable data on the provision of ecosystem services (ES) is essential to the design and implementation of policies that incorporate ES into grassland conservation and restoration. We developed and applied an innovative approach for regional parameterization, and calibration of the CENTURY ecosystem model. We quantified spatiotemporal variation of soil organic carbon stock (SOC) and aboveground plant biomass production (AGB) and examined their responses to the recent climate change across a diverse range of native grassland systems in Alberta, western Canada. The simultaneous integration of SOC and AGB into calibration and analysis accounted for most of the spatiotemporal variability in the SOC and AGB measurements and resulted in reduced simulation uncertainty across nine grassland regions. These findings suggest the importance of the systematic parameterization and calibration for the reliable assessment of carbon-related ES across a wide geographic area with heterogeneous ecological conditions. Simulation results showed a pronounced variation in the spatial distribution of SOC and AGB and their associated uncertainty across grassland regions. Under baseline grazing intensity regime, an overall negative effect of recent climatic changes on the SOC, and a less consistent effect on the AGB were found. While, an overall positive or slightly negative impact of recent climate change on the SOC and AGB was found under a proposed 10% lower grazing intensity regime. These heterogeneities in the magnitude and direction of climate change effects under different grazing regimes suggest needs for a range of climate change adaptation strategies to maintain carbon-related ES in Alberta's grasslands. The modeling framework developed in this study can be used to improve the spatially explicit assessment of carbon-related ES in other geographically vast grassland areas and examine the effectiveness of alternative management scenarios to ensure the long-term provision of carbon-related ES in grassland systems.

Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phenotyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.