Is plant biomass input driving soil organic matter formation processes in grassland soil under contrasting management?

Grassland management practices vary in stocking rates and plant removal strategies (grazing versus mowing). They influence organic matter (OM) inputs, which were postulated as main controls of soil organic carbon (SOC) sequestration and might therefore control SOC stabilization. The aim of this study was to test this hypothesis by investigating the impacts of grassland harvesting regimes on parameters related to soil microbial functioning and soil organic matter (SOM) formation processes. We used a thirteen-year experiment in Central France under contrasting management (unmanaged, grazing with two intensities, mowing, bare fallow) to establish a carbon input gradient based on biomass leftovers after harvest. We investigated microbial biomass, basal respiration and enzyme activities as indicators of microbial functioning, and amino sugar content and composition as indicator of persistent SOM formation and origin through necromass accumulation. Responses of these parameters to carbon input along the gradient were contrasting and in most cases unrelated. Only the microbial C/N ratio and amino sugar contents showed a linear response indicating that they are influenced by plant-derived OM input. Other parameters were most probably more influenced by root activity, presence of herbivores, and/or physicochemical changes following management activities impacting soil microbial functioning. Grassland harvesting strategies influence SOC sequestration not only by changing carbon input quantity, but also through their effects on belowground processes possibly related to changing carbon input types and physiochemical soil properties.

Functional trait trade-offs define plant population stability across different biomes.

Ecological theory posits that temporal stability patterns in plant populations are associated with differences in species' ecological strategies. However, empirical evidence is lacking about which traits, or trade-offs, underlie species stability, especially across different biomes. We compiled a worldwide collection of long-term permanent vegetation records (greater than 7000 plots from 78 datasets) from a large range of habitats which we combined with existing trait databases. We tested whether the observed inter-annual variability in species abundance (coefficient of variation) was related to multiple individual traits. We found that populations with greater leaf dry matter content and seed mass were more stable over time. Despite the variability explained by these traits being low, their effect was consistent across different datasets. Other traits played a significant, albeit weaker, role in species stability, and the inclusion of multi-variate axes or phylogeny did not substantially modify nor improve predictions. These results provide empirical evidence and highlight the relevance of specific ecological trade-offs, i.e. in different resource-use and dispersal strategies, for plant populations stability across multiple biomes. Further research is, however, necessary to integrate and evaluate the role of other specific traits, often not available in databases, and intraspecific trait variability in modulating species stability.

Eighteen years of upland grassland carbon flux data: reference datasets, processing, and gap-filling procedure.

Plant-atmosphere exchange fluxes of CO2 measured with the Eddy covariance method are used extensively for the assessment of ecosystem carbon budgets worldwide. The present paper describes eddy flux measurements for a managed upland grassland in Central France studied over two decades (2003-2021). We present the site meteorological data for this measurement period, and we describe the pre-processing and post-processing approaches used to overcome issues of data gaps, commonly associated with long-term EC datasets. Recent progress in eddy flux technology and machine learning now paves the way to produce robust long-term datasets, based on normalised data processing techniques, but such reference datasets remain rare for grasslands. Here, we combined two gap-filling techniques, Marginal Distribution Sampling (short gaps) and Random Forest (long gaps), to complete two reference flux datasets at the half-hour and daily-scales respectively. The resulting datasets are valuable for assessing the response of grassland ecosystems to (past) climate change, but also for model evaluation and validation with respect to future global change research with the carbon-cycle community.

Variation in methane uptake by grassland soils in the context of climate change - A review of effects and mechanisms.

Grassland soils are climate-dependent ecosystems that have a significant greenhouse gas mitigating function through their ability to store large amounts of carbon (C). However, what is often not recognized is that they can also exhibit a high methane (CH4) uptake capacity that could be influenced by future increases in atmospheric carbon dioxide (CO2) concentration and variations in temperature and water availability. While there is a wealth of information on C sequestration in grasslands there is less consensus on how climate change impacts on CH4 uptake or the underlying mechanisms involved. To address this, we assessed existing knowledge on the impact of climate change components on CH4 uptake by grassland soils. Increases in precipitation associated with soils with a high background soil moisture content generally resulted in a reduction in CH4 uptake or even net emissions, while the effect was opposite in soils with a relatively low background moisture content. Initially wet grasslands subject to the combined effects of warming and water deficits may absorb more CH4, mainly due to increased gas diffusivity. However, in the longer-term heat and drought stress may reduce the activity of methanotrophs when the mean soil moisture content is below the optimum for their survival. Enhanced plant productivity and growth under elevated CO2, increased soil moisture and changed nutrient concentrations, can differentially affect methanotrophic activity, which is often reduced by increasing N deposition. Our estimations showed that CH4 uptake in grassland soils can change from -57.7 % to +6.1 % by increased precipitation, from -37.3 % to +85.3 % by elevated temperatures, from +0.87 % to +92.4 % by decreased precipitation, and from -66.7 % to +27.3 % by elevated CO2. In conclusion, the analysis suggests that grasslands under the influence of warming and drought may absorb even more CH4, mainly because of reduced soil water contents and increased gas diffusivity.

Provision of metadata of European agricultural long-term experiments through BonaRes and EJP SOIL collaboration.

Agricultural Long-Term Experiments (LTEs) are crucial agricultural research infrastructures for monitoring the long term effects of management and environment on crop production and soil resources. We have compiled the meta-information of 616 LTEs from 30 different countries across Europe with a duration of typically 20 years, including clustered information of the European LTEs in different categories (management operations, land use, duration, status, etc.). It consists of the updated version of the dataset published by Grosse et al., (2020) but is extended by further LTE metadata, categories and research themes. Each set of metadata consists of up to 49 different attributes (categorical or numeric). Collected attributes were analyzed according to several research themes, including fertilization, crop rotation and tillage treatments. The collection of individual metadata was enlarged by the recent agreement between the BonaRes (www.bonares.de) and EJP SOIL (www.ejpsoil.eu) groups into the most comprehensive dataset in Europe, providing access to LTE and other, shorter running experiments. This dataset centralized past and existing information usually dispersed across several national actors. As such, it provides an extensive database that can be used by decision-makers, scientists, LTE owners and the public. The dataset can be updated in the future to foster networking and information exchange continuously.

Estimating the carbon storage potential and greenhouse gas emissions of French arable cropland using high-resolution modeling.

Many studies have assessed the potential of agricultural practices to sequester carbon (C). A comprehensive evaluation of impacts of agricultural practices requires not only considering C storage but also direct and indirect emissions of greenhouse gases (GHG) and their side effects (e.g., on the water cycle or agricultural production). We used a high-resolution modeling approach with the Simulateur mulTIdisciplinaire pour les Cultures Standard soil-crop model to quantify soil organic C (SOC) storage potential, GHG balance, biomass production and nitrogen- and water-related impacts for all arable land in France for current cropping systems (baseline scenario) and three mitigation scenarios: (i) spatial and temporal expansion of cover crops, (ii) spatial insertion and temporal extension of temporary grasslands (two sub-scenarios) and (iii) improved recycling of organic resources as fertilizer. In the baseline scenario, SOC decreased slightly over 30 years in crop-only rotations but increased significantly in crop/temporary grassland rotations. Results highlighted a strong trade-off between the storage rate per unit area (kg C ha-1  year-1 ) of mitigation scenarios and the areas to which they could be applied. As a result, while the most promising scenario at the field scale was the insertion of temporary grassland (+466 kg C ha-1  year-1 stored to a depth of 0.3 m compared to the baseline, on 0.68 Mha), at the national scale, it was by far the expansion of cover crops (+131 kg C ha-1  year-1 , on 17.62 Mha). Side effects on crop production, water irrigation and nitrogen emissions varied greatly depending on the scenario and production situation. At the national scale, combining the three mitigation scenarios could mitigate GHG emissions of current cropping systems by 54% (-11.2 from the current 20.5 Mt CO2 e year-1 ), but the remaining emissions would still lie far from the objective of C-neutral agriculture.

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems.

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH4) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N2O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH4 and/or N2O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.

Synchrony matters more than species richness in plant community stability at a global scale.

The stability of ecological communities is critical for the stable provisioning of ecosystem services, such as food and forage production, carbon sequestration, and soil fertility. Greater biodiversity is expected to enhance stability across years by decreasing synchrony among species, but the drivers of stability in nature remain poorly resolved. Our analysis of time series from 79 datasets across the world showed that stability was associated more strongly with the degree of synchrony among dominant species than with species richness. The relatively weak influence of species richness is consistent with theory predicting that the effect of richness on stability weakens when synchrony is higher than expected under random fluctuations, which was the case in most communities. Land management, nutrient addition, and climate change treatments had relatively weak and varying effects on stability, modifying how species richness, synchrony, and stability interact. Our results demonstrate the prevalence of biotic drivers on ecosystem stability, with the potential for environmental drivers to alter the intricate relationship among richness, synchrony, and stability.

How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal.

There is growing international interest in better managing soils to increase soil organic carbon (SOC) content to contribute to climate change mitigation, to enhance resilience to climate change and to underpin food security, through initiatives such as international '4p1000' initiative and the FAO's Global assessment of SOC sequestration potential (GSOCseq) programme. Since SOC content of soils cannot be easily measured, a key barrier to implementing programmes to increase SOC at large scale, is the need for credible and reliable measurement/monitoring, reporting and verification (MRV) platforms, both for national reporting and for emissions trading. Without such platforms, investments could be considered risky. In this paper, we review methods and challenges of measuring SOC change directly in soils, before examining some recent novel developments that show promise for quantifying SOC. We describe how repeat soil surveys are used to estimate changes in SOC over time, and how long-term experiments and space-for-time substitution sites can serve as sources of knowledge and can be used to test models, and as potential benchmark sites in global frameworks to estimate SOC change. We briefly consider models that can be used to simulate and project change in SOC and examine the MRV platforms for SOC change already in use in various countries/regions. In the final section, we bring together the various components described in this review, to describe a new vision for a global framework for MRV of SOC change, to support national and international initiatives seeking to effect change in the way we manage our soils.

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.

Greenhouse gas fluxes over managed grasslands in Central Europe.

Central European grasslands are characterized by a wide range of different management practices in close geographical proximity. Site-specific management strategies strongly affect the biosphere-atmosphere exchange of the three greenhouse gases (GHG) carbon dioxide (CO2 ), nitrous oxide (N2 O), and methane (CH4 ). The evaluation of environmental impacts at site level is challenging, because most in situ measurements focus on the quantification of CO2 exchange, while long-term N2 O and CH4 flux measurements at ecosystem scale remain scarce. Here, we synthesized ecosystem CO2 , N2 O, and CH4 fluxes from 14 managed grassland sites, quantified by eddy covariance or chamber techniques. We found that grasslands were on average a CO2 sink (-1,783 to -91 g CO2  m-2  year-1 ), but a N2 O source (18-638 g CO2 -eq. m-2  year-1 ), and either a CH4 sink or source (-9 to 488 g CO2 -eq. m-2  year-1 ). The net GHG balance (NGB) of nine sites where measurements of all three GHGs were available was found between -2,761 and -58 g CO2 -eq. m-2  year-1 , with N2 O and CH4 emissions offsetting concurrent CO2 uptake by on average 21 ± 6% across sites. The only positive NGB was found for one site during a restoration year with ploughing. The predictive power of soil parameters for N2 O and CH4 fluxes was generally low and varied considerably within years. However, after site-specific data normalization, we identified environmental conditions that indicated enhanced GHG source/sink activity ("sweet spots") and gave a good prediction of normalized overall fluxes across sites. The application of animal slurry to grasslands increased N2 O and CH4 emissions. The N2 O-N emission factor across sites was 1.8 ± 0.5%, but varied considerably at site level among the years (0.1%-8.6%). Although grassland management led to increased N2 O and CH4 emissions, the CO2 sink strength was generally the most dominant component of the annual GHG budget.

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.

Future productivity and phenology changes in European grasslands for different warming levels: implications for grassland management and carbon balance.

BACKGROUND: Europe has warmed more than the global average (land and ocean) since pre-industrial times, and is also projected to continue to warm faster than the global average in the twenty-first century. According to the climate models ensemble projections for various climate scenarios, annual mean temperature of Europe for 2071-2100 is predicted to be 1-5.5 °C higher than that for 1971-2000. Climate change and elevated CO2 concentration are anticipated to affect grassland management and livestock production in Europe. However, there has been little work done to quantify the European-wide response of grassland to future climate change. Here we applied ORCHIDEE-GM v2.2, a grid-based model for managed grassland, over European grassland to estimate the impacts of future global change. RESULTS: Increases in grassland productivity are simulated in response to future global change, which are mainly attributed to the simulated fertilization effect of rising CO2. The results show significant phenology shifts, in particular an earlier winter-spring onset of grass growth over Europe. A longer growing season is projected over southern and southeastern Europe. In other regions, summer drought causes an earlier end to the growing season, overall reducing growing season length. Future global change allows an increase of management intensity with higher than current potential annual grass forage yield, grazing capacity and livestock density, and a shift in seasonal grazing capacity. We found a continual grassland soil carbon sink in Mediterranean, Alpine, North eastern, South eastern and Eastern regions under specific warming level (SWL) of 1.5 and 2 °C relative to pre-industrial climate. However, this carbon sink is found to saturate, and gradually turn to a carbon source at warming level reaching 3.5 °C. CONCLUSIONS: This study provides a European-wide assessment of the future changes in productivity and phenology of grassland, and their consequences for the management intensity and the carbon balance. The simulated productivity increase in response to future global change enables an intensification of grassland management over Europe. However, the simulated increase in the interannual variability of grassland productivity over some regions may reduce the farmers' ability to take advantage of the increased long-term mean productivity in the face of more frequent, and more severe drops of productivity in the future.

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.

Continuous soil carbon storage of old permanent pastures in Amazonia.

Amazonian forests continuously accumulate carbon (C) in biomass and in soil, representing a carbon sink of 0.42-0.65 GtC yr-1 . In recent decades, more than 15% of Amazonian forests have been converted into pastures, resulting in net C emissions (~200 tC ha-1 ) due to biomass burning and litter mineralization in the first years after deforestation. However, little is known about the capacity of tropical pastures to restore a C sink. Our study shows in French Amazonia that the C storage observed in native forest can be partly restored in old (≥24 year) tropical pastures managed with a low stocking rate (±1 LSU ha-1 ) and without the use of fire since their establishment. A unique combination of a large chronosequence study and eddy covariance measurements showed that pastures stored between -1.27 ± 0.37 and -5.31 ± 2.08 tC ha-1  yr-1 while the nearby native forest stored -3.31 ± 0.44 tC ha-1  yr-1 . This carbon is mainly sequestered in the humus of deep soil layers (20-100 cm), whereas no C storage was observed in the 0- to 20-cm layer. C storage in C4 tropical pasture is associated with the installation and development of C3 species, which increase either the input of N to the ecosystem or the C:N ratio of soil organic matter. Efforts to curb deforestation remain an obvious priority to preserve forest C stocks and biodiversity. However, our results show that if sustainable management is applied in tropical pastures coming from deforestation (avoiding fires and overgrazing, using a grazing rotation plan and a mixture of C3 and C4 species), they can ensure a continuous C storage, thereby adding to the current C sink of Amazonian forests.

Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits.

The aim of this study was to systematically analyze the potential and limitations of using plant functional trait observations from global databases versus in situ data to improve our understanding of vegetation impacts on ecosystem functional properties (EFPs). Using ecosystem photosynthetic capacity as an example, we first provide an objective approach to derive robust EFP estimates from gross primary productivity (GPP) obtained from eddy covariance flux measurements. Second, we investigate the impact of synchronizing EFPs and plant functional traits in time and space to evaluate their relationships, and the extent to which we can benefit from global plant trait databases to explain the variability of ecosystem photosynthetic capacity. Finally, we identify a set of plant functional traits controlling ecosystem photosynthetic capacity at selected sites. Suitable estimates of the ecosystem photosynthetic capacity can be derived from light response curve of GPP responding to radiation (photosynthetically active radiation or absorbed photosynthetically active radiation). Although the effect of climate is minimized in these calculations, the estimates indicate substantial interannual variation of the photosynthetic capacity, even after removing site-years with confounding factors like disturbance such as fire events. The relationships between foliar nitrogen concentration and ecosystem photosynthetic capacity are tighter when both of the measurements are synchronized in space and time. When using multiple plant traits simultaneously as predictors for ecosystem photosynthetic capacity variation, the combination of leaf carbon to nitrogen ratio with leaf phosphorus content explains the variance of ecosystem photosynthetic capacity best (adjusted R2 = 0.55). Overall, this study provides an objective approach to identify links between leaf level traits and canopy level processes and highlights the relevance of the dynamic nature of ecosystems. Synchronizing measurements of eddy covariance fluxes and plant traits in time and space is shown to be highly relevant to better understand the importance of intra- and interspecific trait variation on ecosystem functioning.

Modeled Changes in Potential Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe over 1961-2010.

About 25% of European livestock intake is based on permanent and sown grasslands. To fulfill rising demand for animal products, an intensification of livestock production may lead to an increased consumption of crop and compound feeds. In order to preserve an economically and environmentally sustainable agriculture, a more forage based livestock alimentation may be an advantage. However, besides management, grassland productivity is highly vulnerable to climate (i.e., temperature, precipitation, CO2 concentration), and spatial information about European grassland productivity in response to climate change is scarce. The process-based vegetation model ORCHIDEE-GM, containing an explicit representation of grassland management (i.e., herbage mowing and grazing), is used here to estimate changes in potential productivity and potential grass-fed ruminant livestock density across European grasslands over the period 1961-2010. Here "potential grass-fed ruminant livestock density" denotes the maximum density of livestock that can be supported by grassland productivity in each 25 km × 25 km grid cell. In reality, livestock density could be higher than potential (e.g., if additional feed is supplied to animals) or lower (e.g., in response to economic factors, pedo-climatic and biotic conditions ignored by the model, or policy decisions that can for instance reduce livestock numbers). When compared to agricultural statistics (Eurostat and FAOstat), ORCHIDEE-GM gave a good reproduction of the regional gradients of annual grassland productivity and ruminant livestock density. The model however tends to systematically overestimate the absolute values of productivity in most regions, suggesting that most grid cells remain below their potential grassland productivity due to possible nutrient and biotic limitations on plant growth. When ORCHIDEE-GM was run for the period 1961-2010 with variable climate and rising CO2, an increase of potential annual production (over 3%) per decade was found: 97% of this increase was attributed to the rise in CO2, -3% to climate trends and 15% to trends in nitrogen fertilization and deposition. When compared with statistical data, ORCHIDEE-GM captures well the observed phase of climate-driven interannual variability in grassland production well, whereas the magnitude of the interannual variability in modeled productivity is larger than the statistical data. Regional grass-fed livestock numbers can be reproduced by ORCHIDEE-GM based on its simple assumptions and parameterization about productivity being the only limiting factor to define the sustainable number of animals per unit area. Causes for regional model-data misfits are discussed, including uncertainties in farming practices (e.g., nitrogen fertilizer application, and mowing and grazing intensity) and in ruminant diet composition, as well as uncertainties in the statistical data and in model parameter values.

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence.

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.