Stakeholder-driven transformative adaptation is needed for climate-smart nutrition security in sub-Saharan Africa.

Improving nutrition security in sub-Saharan Africa under increasing climate risks and population growth requires a strong and contextualized evidence base. Yet, to date, few studies have assessed climate-smart agriculture and nutrition security simultaneously. Here we use an integrated assessment framework (iFEED) to explore stakeholder-driven scenarios of food system transformation towards climate-smart nutrition security in Malawi, South Africa, Tanzania and Zambia. iFEED translates climate-food-emissions modelling into policy-relevant information using model output implication statements. Results show that diversifying agricultural production towards more micronutrient-rich foods is necessary to achieve an adequate population-level nutrient supply by mid-century. Agricultural areas must expand unless unprecedented rapid yield improvements are achieved. While these transformations are challenging to accomplish and often associated with increased greenhouse gas emissions, the alternative for a nutrition-secure future is to rely increasingly on imports, which would outsource emissions and be economically and politically challenging given the large import increases required.

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.

Food and nutrition security under global trade: a relation-driven agent-based global trade model.

This paper addresses the highly relevant and timely issues of global trade and food security by developing an empirically grounded, relation-driven agent-based global trade model. Contrary to most price-driven trade models in the literature, the relation-driven agent-based global trade model focuses on the role of relational factors such as trust, familiarity, trade history and conflicts in countries' trade behaviour. Moreover, the global trade model is linked to a comprehensive nutrition formula to investigate the impact of trade on food and nutrition security, including macro and micronutrients. Preliminary results show that global trade improves the food and nutrition security of countries in Africa, Asia and Latin America. Trade also promotes a healthier and more balanced diet, as countries have access to an increased variety of food. The effect of trade in enhancing nutrition security, with an adequate supply of macro and micronutrients, is universal across nutrients and countries. As researchers call for a holistic and multifactorial approach to food security and climate change (Hammond and Dubé 2012 Proc. Natl Acad. Sci. USA 109, 12 356-12 363. (doi:10.1073/pnas.0913003109)), the paper is one of the first to develop an integrated framework that consists of socio-economic, geopolitical, nutrition, environmental and agri-food systems to tackle these global challenges. Given the ongoing events of Brexit, the US-China trade war and the global COVID-19 pandemic, the paper will provide valuable insights on the role of trade in improving the food and nutrition security across countries.

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.

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).

Metrics of biomass, live-weight gain and nitrogen loss of ryegrass sheep pasture in the 21st century.

This study argues that several metrics are necessary to build up a picture of yield gain and nitrogen losses for ryegrass sheep pastures. Metrics of resource use efficiency, nitrous oxide emission factor, leached and emitted nitrogen per unit product are used to encompass yield gain and losses relating to nitrogen. These metrics are calculated from field system simulations using the DAYCENT model, validated from field sensor measurements and observations relating to crop yield, fertilizer applied, ammonium in soil and nitrate in soil and water, nitrous oxide and soil moisture. Three ryegrass pastures with traditional management for sheep grazing and silage are studied. As expected, the metrics between long-term ryegrass swards in this study are not very dissimilar. Slight differences between simulations of different field systems likely result from varying soil bulk density, as revealed by a sensitivity analysis applied to DAYCENT. The field with the highest resource use efficiency was also the field with the lowest leached inorganic nitrogen per unit product, and vice versa. Field system simulation using climate projections indicates an increase in nitrogen loss to water and air, with a corresponding increase in biomass. If we simulate both nitrogen loss by leaching and by gaseous emission, we obtain a fuller picture. Under climate projections, the field with the lowest determined nitrous oxide emissions factor, had a relatively high leached nitrogen per product amongst the three fields. When management differences were investigated, the amount of nitrous oxide per unit biomass was found to be significantly higher for an annual management of grazing only, than a silage harvest plus grazing, likely relating to the increased period of livestock on pasture. This work emphasizes how several metrics validated by auto-sampled data provide a measure of nitrogen loss, efficiency and best management practise.

Ecosystem services in different agro-climatic zones in eastern India: impact of land use and land cover change.

Land use and land cover (LULC) change have considerable influence on ecosystem services. Assessing change in ecosystem services due to LULC change at different spatial and temporal scales will help to identify suitable management practices for sustaining ecosystem productivity and maintaining the ecological balance. The objective of this study was to investigate variations in ecosystem services in response to LULC change over 27 years in four agro-climatic zones (ACZ) of eastern India using satellite imagery for the year 1989, 1996, 2005, 2011 (Landsat TM) and 2016 (Landsat 8 OLI). The satellite images were classified into six LULC classes, agriculture land, forest, waterbody, wasteland, built-up, and mining area. During the study period (1989 to 2016), forest cover reduced by 5.2%, 13.7%, and 3.6% in Sambalpur, Keonjhar, and Kandhamal districts of Odisha, respectively. In Balasore, agricultural land reduced by 17.2% due to its conversion to built-up land. The value of ecosystem services per unit area followed the order of waterbodies > agricultural land > forests. A different set of indicators, e.g., by explicitly including diversity, could change the rank between these land uses, so the temporal trends within a land use are more important than the absolute values. Total ecosystem services increased by US$ 1296.4 × 105 (50.74%), US$ 1100.7 × 105 (98.52%), US$ 1867 × 105 (61.64%), and US$ 1242.6 × 105 (46.13%) for Sambalpur, Balasore, Kandhamal, and Keonjhar, respectively.

Global projections of future cropland expansion to 2050 and direct impacts on biodiversity and carbon storage.

Cropland expansion threatens biodiversity by driving habitat loss and impacts carbon storage through loss of biomass and soil carbon (C). There is a growing concern land-use change (LUC) to cropland will result in a loss of ecosystem function and various ecosystem services essential for human health and well-being. This paper examines projections of future cropland expansion from an integrated assessment model IMAGE 3.0 under a "business as usual" scenario and the direct impact on both biodiversity and C storage. By focusing on biodiversity hotspots and Alliance for Zero Extinction (AZE) sites, loss of habitat as well as potential impacts on endangered and critically endangered species are explored. With regards to C storage, the impact on both soil and vegetation standing C stocks are examined. We show that if projected trends are realized, there are likely to be severe consequences for these resources. Substantial loss of habitat in biodiversity hotspots such as Indo-Burma, and the Philippians is expected as well as 50% of species in AZE sites losing part of their last remaining habitat. An estimated 13.7% of vegetation standing C stocks and 4.6% of soil C stocks are also projected to be lost in areas affected with Brazil and Mexico being identified as priorities in terms of both biodiversity and C losses from cropland expansion. Changes in policy to regulate projected cropland expansion, and increased measures to protect natural resources, are highly likely to be required to prevent these biodiversity and C losses in the future.

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

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ±â€¯74 g C m-2 yr-1 (animal density reduction) and -81 ±â€¯74 g C m-2 yr-1 (N and animal density reduction), against the baseline of -30.5 ±â€¯69.5 g C m-2 yr-1 (LSU [livestock units] ≥ 0.76 ha-1 yr-1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ±â€¯0.22 (baseline) to 0.1 ±â€¯0.05 g N m-2 yr-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ±â€¯8.1 t C LSU-1 yr-1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

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 (N2 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 N2 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 N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 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 N2 O emissions. Yield-scaled N2 O emissions (N2 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 N2 O emissions at field scale is discussed.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes.

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Systems approaches in global change and biogeochemistry research.

Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.

Bundle sheath leakiness and light limitation during C4 leaf and canopy CO2 uptake.

Perennial species with the C(4) pathway hold promise for biomass-based energy sources. We have explored the extent that CO(2) uptake of such species may be limited by light in a temperate climate. One energetic cost of the C(4) pathway is the leakiness () of bundle sheath tissues, whereby a variable proportion of the CO(2), concentrated in bundle sheath cells, retrodiffuses back to the mesophyll. In this study, we scale from leaf to canopy level of a Miscanthus crop (Miscanthus x giganteus hybrid) under field conditions and model the likely limitations to CO(2) fixation. At the leaf level, measurements of photosynthesis coupled to online carbon isotope discrimination showed that leaves within a 3.3-m canopy (leaf area index = 8.3) show a progressive increase in both carbon isotope discrimination and as light decreases. A similar increase was observed at the ecosystem scale when we used eddy covariance net ecosystem CO(2) fluxes, together with isotopic profiles, to partition photosynthetic and respiratory isotopic flux densities (isofluxes) and derive canopy carbon isotope discrimination as an integrated proxy for at the canopy level. Modeled values of canopy CO(2) fixation using leaf-level measurements of suggest that around 32% of potential photosynthetic carbon gain is lost due to light limitation, whereas using determined independently from isofluxes at the canopy level the reduction in canopy CO(2) uptake is estimated at 14%. Based on these results, we identify as an important limitation to CO(2) uptake of crops with the C(4) pathway.