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.

Can biomass supply meet the demands of bioenergy with carbon capture and storage (BECCS)?

To reach the reduced carbon emission targets proposed by the Paris agreement, one of the widely proposed decarbonizing strategies, referred to as negative emissions technologies (NETs), is the production and combustion of bioenergy crops in conjunction with carbon capture and storage (BECCS). However, concerns have been increasingly raised that relying on the potential of BECCS to achieve negative emissions could result in delayed reductions in gross CO2 emissions, with consequent high risk of overshooting global temperature targets. We focus on two particular issues: the carbon efficiency and payback time of bioenergy use in BECCS and the potential constraints on the supply of bioenergy. The simplistic vision of BECCS is that 1 tonne of CO2 captured in the growth of biomass equates to 1 tonne of CO2 sequestered geologically, but this cannot be the case as CO2 is emitted by variable amounts during the lifecycle from crop establishment to sequestration below ground in geological formations. The deployment of BECCS is ultimately reliant on the availability of sufficient, sustainably sourced, biomass. The two most important factors determining this supply are land availability and land productivity. The upper bounds of the area estimates required correspond to more than the world's harvested land for cereal production. To achieve these estimates of biomass availability requires the rapid evolution of a multitude of technological, social, political and economic factors. Here, we question whether, because of the limited sustainable supply of biomass, BECCS should continue to be considered the dominant NET in IPCC and other scenarios achieving the Paris targets, or should it be deemed no longer fit for purpose?

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.

Model comparison and quantification of nitrous oxide emission and mitigation potential from maize and wheat fields at a global scale.

Maize and wheat are major cereals that contribute two-thirds of the food energy intake globally. The two crops consume about 35% of the nitrogen (N) fertilizer used in agriculture and thereby contribute to fertilizer-induced nitrous oxide (N2O) emissions. Thus, estimation of spatially disaggregated N2O emissions from maize and wheat fields on a global scale could be useful for identifying emission and mitigation hotspots. It could also be needed for prioritizing mitigation options consistent with location-specific production and environmental goals. N2O emission from four models (CCAFS-MOT, IPCC Tier-I, IPCC Tier-II and Tropical N2O) using a standard gridded dataset from global maize and wheat fields were compared and their performance evaluated using measured N2O emission data points (777 globally distributed datapoints). The models were used to quantify spatially disaggregated N2O emission and mitigation potential from maize and wheat fields globally and the values were compared. Although the models differed in their performance of capturing the level of measured N2O emissions, they produced similar spatial patterns of annual N2O emissions from maize and wheat fields. Irrespective of the models, predicted N2O emissions per hectare were higher in some countries in East and South Asia, North America, and Western Europe, driven mainly by higher N application rates. The study indicated a substantial N2O abatement potential if application of excess N in the maize and wheat systems is reduced without compromising the yield of the crops through technological and crop management innovations. N2O mitigation potential is higher in those countries and regions where N application rates and current N2O emissions are already high. The estimated mitigation potentials are useful for hotspot countries to target fertilizer and crop management as one of the mitigation options in their Nationally Determined Contributions (NDCs) to the United Nations Framework Convention on Climate Change (UNFCCC).

Global high-resolution gridded dataset of N2O Emission and mitigation potential from maize and wheat fields.

This data article provides a high-resolution raw data on Nitrous Oxide (N2O) emission and its mitigation potential from global maize and wheat fields. The analytical results, discussion and conclusion thereof is presented in the related manuscript "Model Comparison and Quantification of Nitrous Oxide Emission and Mitigation Potential from Maize and Wheat Fields at a Global Scale" [1]. This raw dataset has a spatial resolution of 0.0833° × 0.0833°, and comprises pixel level baseline emissions estimated using four empirical N2O emission models (CCAFS-MOT, IPCC Tier-I, IPCC Tier-II and Tropical-N2O) and the model results were validated using experimental data extracted from the literature. Spatially explicit soil, climate and crop management data were obtained from various sources detailed in "Experimental Design, Materials and Methods" section below. N2O mitigation potential were then quantified under four scenarios of excess nitrogen reduction (i.e. 25%, 50%, 75% and 100% reduction of excess nitrogen). We believe that the dataset is a valuable source of information to assess N2O emissions and mitigation measures from maize and wheat fields and to make informed decision. Countries can use this dataset to determine emissions reduction targets in their nationally determined contributions (NDCs) from agricultural sector.

Measurement of N2O emissions over the whole year is necessary for estimating reliable emission factors.

Nitrous oxide emission factors (N2O-EF, percentage of N2O-N emissions arising from applied fertilizer N) for cropland emission inventories can vary with agricultural management, soil properties and climate conditions. Establishing a regionally-specific EF usually requires the measurement of a whole year of N2O emissions, whereas most studies measure N2O emissions only during the crop growing season, neglecting emissions during non-growing periods. However, the difference in N2O-EF (ΔEF) estimated using measurements over a whole year (EFwy) and those based on measurement only during the crop-growing season (EFgs) has received little attention. Here, we selected 21 studies including both the whole-year and growing-season N2O emissions under control and fertilizer treatments, to obtain 123 ΔEFs from various agroecosystems globally. Using these data, we conducted a meta-analysis of the ΔEFs by bootstrapping resampling to assess the magnitude of differences in response to management-related and environmental factors. The results revealed that, as expected, the EFwy was significantly greater than the EFgs for most crop types. Vegetables showed the largest ΔEF (0.19%) among all crops (0.07%), followed by paddy rice (0.11%). A higher ΔEF was also identified in areas with rainfall ≥600 mm yr-1, soil with organic carbon ≥1.3% and acidic soils. Moreover, fertilizer type, residue management, irrigation regime and duration of the non-growing season were other crucial factors controlling the magnitude of the ΔEFs. We also found that neglecting emissions from the non-growing season may underestimate the N2O-EF by 30% for paddy fields, almost three times that for non-vegetable upland crops. This study highlights the importance of the inclusion of the non-growing season in the measurements of N2O fluxes, the compilation of national inventories and the design of mitigation strategies.

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems - A Review And Modelling Of Emission Factors.

There has been much debate about the uncertainties associated with the estimation of direct and indirect agricultural nitrous oxide (N2O) emissions in developing countries and in particular from tropical regions. In this study, we report an up-to-date review of the information published in peer-review journals on direct N2O emissions from agricultural systems in tropical and sub-tropical regions. We statistically analyze net-N2O-N emissions to estimate tropic-specific annual N2O emission factors (N2O-EFs) using a Generalized Additive Mixed Model (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear continuous functions. Overall the mean N2O-EF was 1.2% for the tropics and sub-tropics, thus within the uncertainty range of IPCC-EF. On a regional basis, mean N2O-EFs were 1.4% for Africa, 1.1%, for Asia, 0.9% for Australia and 1.3% for Central &South America. Our annual N2O-EFs, estimated for a range of fertiliser rates using the available data, do not support recent studies hypothesising non-linear increase N2O-EFs as a function of applied N. Our findings highlight that in reporting annual N2O emissions and estimating N2O-EFs, particular attention should be paid in modelling the effect of study length on response of N2O.

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.