Gross primary productivity (GPP; ecosystem-level photosynthesis) represents the largest terrestrial carbon flux and is highly sensitive to temperature. Despite global warming, the trends and controlling factors of optimum temperature (Topt) and maximum rates of GPP (GPPmax) remain uncertain. We investigated the drivers of Topt and GPPmax trends during 2000-2019 using global observations of ground-based eddy covariance and satellite-based sun-induced chlorophyll fluorescence. Although GPPmax increased worldwide, Topt increased only in tropical and temperate regions but remained unchanged globally and in arid and cold regions. Thermal acclimation via shifting Topt was constrained by atmospheric and soil dryness, explaining less than 20% of the global GPPmax rise. In contrast, GPPmax trends were more strongly driven by stomatal regulation improving water-use efficiency and by enhanced canopy development under dryness constraints. These findings challenge the expectation that thermal acclimation is central to projecting GPP under warming and highlight dynamic physiological-structural shifts that sustain terrestrial carbon uptake.

Abstract Peatlands are the largest terrestrial stores of organic carbon, but drainage has turned them into substantial sources of CO 2 . While water level is widely recognized as the primary control of CO 2 emissions from peatlands, effective future management requires understanding its interaction with rising temperatures under a warming climate. Using 276 site-years of annual CO 2 flux observations across temperate and boreal peatlands, we apply explainable machine-learning to disentangle the combined effects of water table depths and temperature on ecosystem CO 2 exchange at annual scales. Across peatlands spanning diverse land-cover—including natural fens and bogs, croplands, grasslands, and extraction sites—CO 2 emissions exhibit a non-linear response to water table depth. Emissions decline when water tables are raised above 60-75 cm depth. Optimal mitigation requires water tables of 20 cm or higher. We find that deep water tables interact with temperatures to increase emissions at temperate sites. On a subset of 113 site-years of daily CO 2 flux data, we show that at warm temperatures, higher water tables suppress, whereas deeper water tables enhance temperature-driven CO 2 emissions from peatlands. We demonstrate that hydrology regulates the temperature sensitivity of peatland carbon release, revealing a key control on carbon–climate feedbacks under future warming.

The lack of energy balance closure in Eddy-Covariance (EC) measurements is a well-known, still unresolved challenge in micrometeorology, with energy balance closure (EBC) rates typically ranging between 60% and 80%. While numerous hypotheses have been proposed to explain this imbalance, the relative contributions of neglected energy storage terms, data quality and flux processing options remain insufficiently disentangled. Using standardized ICOS and NEON datasets, we show that a significant portion of the observed energy imbalance can be attributed to overlooked or inconsistently handled energy components and turbulent flux quality control. Using data drawn from 84 sites, we show that comprehensive energy accounting-including soil heat flux, storage terms (soil, air, biomass), photosynthetic energy demand, and strict quality filtering of turbulent fluxes-improved EBC by 16% on average, with site-specific gains up to 40%. However, we also identify a persistent residual imbalance that is unlikely to be resolved through methodological refinements or additional measurements alone, pointing to fundamental physical processes that are not accounted for in the standard measurement and processing. We argue that this unresolved imbalance should be explicitly acknowledged and bounded, rather than implicitly absorbed into correction schemes, and we outline practical guidance for diagnosing and interpreting EBC in standardized flux networks. This perspective evaluates methodological advances and residual uncertainties, providing an actionable framework for the appropriate use of EC energy fluxes in carbon, water, and climate research.

Accurate estimation of Gross Primary Productivity (GPP) for European winter wheat is critical for assessing regional food security and understanding land-atmosphere carbon exchange. Light Use Efficiency (LUE) models are widely applied in natural ecosystems, but their performance in dynamic agricultural landscapes, particularly for key crops like winter wheat, remains underexplored. To bridge this gap, we developed IB-WSE-LUE (INRAE-BORDEAUX-water stress enhanced-light use efficiency), a novel GPP model specifically tailored for winter wheat. This model leverages high-resolution Sentinel-2 satellite data and comprehensively integrates key environmental stress factors, enabling GPP simulation at an unprecedented 10-meter spatial resolution. We compared IB-WSE-LUE against thirteen established GPP models, using both tower-based meteorological data and the ERA5 reanalysis dataset (the latter ensuring broader applicability across large scales without reliance on extensive in-situ measurements). Validation demonstrated IB-WSE-LUE's superior performance, achieving average R2 improvements of 11.9% (with tower data) and 8.8% (with ERA5 dataset) in daily GPP simulations for European winter wheat. Furthermore, IB-WSE-LUE more accurately captured spatial, seasonal, and interannual GPP variations and significantly reduced the common underestimation at high GPP levels observed in other models. Its robust performance extended to drought and high-temperature conditions, demonstrating that water stress exerts a stronger influence on winter wheat GPP than temperature stress, a feature accurately captured by our model. This study provides a robust, high-resolution, and spatially transferable framework for accurately monitoring and predicting winter wheat GPP across large agricultural regions, offering key insights for food security assessments and improved agricultural land management in a changing climate.

Hydraulic activation of stomata (HAS), driven by hygroscopic salt films on leaf surfaces, can create an additional pathway for water loss beyond conventional stomatal vapour diffusion, yet this process remains largely overlooked in studies of plant-atmosphere interactions. We conducted the first field-based assessment of HAS-related responses following foliar interception of hygroscopic urea ammonium nitrate (UAN) fertilizer, using eddy covariance (EC) data from five cropland sites (2005-2021). During the 10-day period following UAN application, vegetation conductance (Gveg) increased significantly across barley, winter wheat and maize, while significant increases in the marginal water cost of carbon gain (λ) and stomatal sensitivity (G1) were observed in barley and winter wheat. This pattern could not be attributed to changes in canopy microclimatic conditions or short-term crop growth, and was not observed with alternative liquid mineral fertilizers. Gross primary productivity (GPP) and inferred transpiration (T) also increased following UAN application, with generally greater responses in T than in GPP in most crop-site combinations. The results suggest that UAN-induced HAS alters crop carbon-water exchange at the ecosystem scale. Neglecting HAS in current models may bias predictions of ecosystem water use in fertilized cereals, with broader implications under widespread hygroscopic aerosol deposition.

No-tillage and biochar amendment are widely regarded as effective strategies to mitigate greenhouse gas (GHG) emissions. However, the combined effects of no-tillage, biochar and nitrogen (N) fertilizer application on rice yield and GHG emissions remain scarcely investigated. We conducted a two-year field experiment in central Cote d'Ivoire to assess how biochar and N-fertilizer application under no-tillage influences methane (CH4) and nitrous oxide (N2O) emissions, global warming potential (GWP), GHG intensity (GHGI), and rainfed lowland rice yield. The experiment included three N-fertilizer rates (0, 60, and 120 kg N ha-1) combined with two biochar rates (3, and 6 t ha-1) under no-tillage, alongside two additional treatments; N-only fertilizer (120 kg N ha-1) under no-tillage, and under manual tillage (conventional practice). No-tillage combined with biochar and N-fertilizer increased N2O emissions (9-96 %), but reduced CH4 emissions (13-21 %) and increased rice yields (4-10 %) compared to conventional practice. This trade-off led to significant reductions in GWP (10-20 %) and GHGI (15-18) by no-tillage combined with biochar and N-fertilizer relative to conventional practice. Co-application of 6 t ha-1 biochar with 60 kg N ha-1 under no-tillage produced the highest partial N productivity. Conversely, applying N-fertilizer alone under no-tillage resulted in 43 % higher CH4 emission than conventional practice. Across treatments, CH4 emissions contributed 95 % of the total GWP, and soil moisture emerged as the main driver of CH4 fluxes. These findings suggest that applying biochar and N-fertilizer under no-tillage represents a promising pathway to enhance rainfed lowland rice yield and reduce GHG emissions.

Primary sources of ammonia (NH3) emissions originate from agriculture, impacting the environment, climate, and human health, thereby concomitantly reducing fertilizer nitrogen use efficiency. Accurate measurements under field conditions are required to provide a basis process understanding and for recommendations to policymakers and farmers. However, uncertainties remain regarding the accuracy and reliability of different low-cost NH3 measurement methods and new application of the eddy covariance method for emissions from low-intensity sources, such as synthetic fertilizers.In this study we focused on the quantification of NH3 concentrations and fluxes determined by a quantum cascade laser spectrometer (QCL) with passivated inlet line within an eddy covariance setup and the comparison to low-cost passive diffusion samplers (ALPHA sampler) used for emission estimations with the integrated horizontal flux (IHF) method. Measurements were carried out in Central Germany during the vegetation periods in 2021 - 2023 in a winter wheat crop field, which received three urea fertilizer applications (to a total of 170 kg N ha-1) per year. The atmospheric NH3 concentrations measured by the QCL and the ALPHA samplers were compared. Then, the cumulative losses of NH3 over the measurement periods (March - July) in the different years (2021 - 2023) were calculated and compared between the two approaches (QCL-eddy covariance and ALPHA-IHF).The NH3 concentration measurements showed that the ALPHA samplers generally yielded lower concentration values compared to the time-integrated QCL values. While the relative mismatch decreased with higher concentrations (>20 ppb), significant deviations were observed in the lower concentration regime. When ALPHA concentrations were corrected for measurement height to precisely align with QCL sampling height, a systematic underestimation was found. Reasons for the differences are currently under investigation and may be explained by the vertical and horizontal sampler separation from the main eddy flux tower and possibly due to varying environmental conditions. First results of NH3 eddy covariance fluxes (kg N ha-1 period-1) showed clear diurnal courses and emission peaks around noon on the days after each urea application throughout all years. High-frequency losses using a co-spectral method in the process of eddy flux calculation were estimated to be in the range of 25 to 30 %.The performance of both methods (ALPHA-IHF and QCL-eddy covariance) in estimating NH3 losses from field-scale fertilizer applications is discussed, along with the sensitivity of input concentrations on NH3 emission estimates.Our study is a step towards better comparability and integration of different NH3 measurement techniques and is expected to provide useful tools for robust estimation of NH3 emission factors for synthetic fertilizer applications.

Anthropogenic climate change and rising atmospheric COQ concentrations have the potential to alter ecosystem functioning and land-atmosphere interactions in African drylands. Recent studies suggest that arid ecosystems may respond more positively to these drivers-particularly in terms of primary productivity and water-use efficiency-than previously assumed. We applied a paired-site eddy covariance approach to quantify carbon, water, and energy fluxes across two contrasting dryland vegetation types-the Savanna and Nama-Karoo biomes-located 4 km apart within the Benfontein Nature Reserve, South Africa. Over a 33-month period, distinct diurnal and seasonal differences in the phase and magnitude of carbon fluxes were observed between sites. Cumulative net ecosystem exchange (NEE) was -567 g C m-2 y-1 at the Savanna site (mean NEE: -189 g C m-2 y-1) and -160 g C m-2 y-1 at the Nama-Karoo site (mean NEE: -53 g C m-2 y-1), indicating higher carbon sequestration in the Savanna. Soil moisture strongly modulated the relationship between nighttime respiration and soil temperature, with reduced microbial temperature sensitivity (lower Q10) once moisture exceeded intermediate levels. In addition, intermediate soil moisture conditions were associated with higher carbon uptake at both sites. Overall, ecosystem water-use efficiency was higher at the Nama-Karoo site but more variable at the Savanna site, potentially due to post-wildfire tree mortality. Sensible and latent heat fluxes followed expected seasonal trends related to radiation and moisture availability. Energy balance closure was greater at the Savanna site (96 %) compared to the Nama-Karoo (80 %), with respective energy balance ratios of 0.93 and 0.88.

Biochar and nitrogen (N) fertilizer application have been shown to increase rice yield in conventional rice production system. However, studies evaluating the responses of rice yield and farmers' incomes to biochar and N fertilizer application under no-tillage are rare. A two-year field study was conducted to assess the agronomic and economic effects of integrating biochar and N fertilizer to no-till rainfed lowland rice production system in Cote d'Ivoire. The treatments were factorial combination of two biochar rates (3 and 6 t ha(-)(1)) and three N fertilizer rates (0, 60 and 120 kg N ha(-)(1)) under no-tillage (T0), and a control (i.e. the farmers' practice of manual tillage with hoe and N fertilizer at 120 kg N ha(-)(1)). The results indicated that biochar and N fertilizer application under no-tillage significantly (p < 0.05) increased rice yield and labour productivity by 6 - 9% and 2 - 45%, respectively, over the control. The highest partial productivity of N (113%) was observed under no-tillage with biochar at 6 t ha(-)(1) and N at 60 kg N ha(-)(1), which reduced the production cost by 22% and increased the benefit by 224% relative to the control. Additionally, the soil properties improved significantly (p < 0.05) under biochar and N fertilizer application without tillage.

Drained peatlands under intensive agricultural land use are hotspots of greenhouse gas (GHG) emissions. While management intensity and soil water status have been identified as major controlling factors, only few studies focussed on temporal dynamics and the contribution of nitrous oxide (N2O) and methane (CH4) to full annual GHG balances, mainly due to the lack of continuous observations in high temporal resolution. We present four years of parallel eddy-covariance (EC) and chamber GHG measurements at an intensively managed grassland site on bog peat soil. The site (DE-Okd) is part of the Integrated Carbon Observation System (ICOS) and represents common agricultural practice in Northwest Germany.Average N2O fluxes measured by EC were consistently higher than those obtained by chambers. Following a grassland renewal, vegetation development in chamber frames was found to be more favourable than the average growth on the entire field that is seen by the EC tower. Poor grass development was identified by vegetation indices from remote sensing data and probably led to nitrogen surplus in the soil as observed by high ammonium and nitrate concentrations in drainage ditches. These conditions likely favoured both high N2O emissions and simultaneously high rates of nitrogen leaching. While N2O emissions made up considerable fractions of full annual GHG balances (~5 to 31%), the contribution of CH4 was negligible with hardly any significant fluxes detected by chambers and both seasonally varying emissions and uptake measured by EC cancelling out to non-significant shares to the overall budget.N2O and CH4 emissions were strongly influenced by biometeorological factors and land management. Highest N2O peaks were observed two days after fertilizer application coinciding with about one week after grass cutting and highlighting a well-chosen chamber sampling scheme after management events. Further, N2O emissions were elevated during daytime under medium soil moisture and high soil temperature regimes, while CH4 emissions were strongly correlated with soil moisture dropping to nearly zero exchange under dry conditions.Based on chamber measurements, the overall GHG balance of the site including harvest and carbon input through organic fertilization was in the range of 20 to 25 t CO2-equivalents ha-1 yr-1 in the period from 2020 to 2023 with generally higher emissions in dryer years. Replacing chamber N2O and CH4 by EC data for the full budget, individual annual values increased between 0.8 and 10.1 t CO2-equivalents ha-1 yr-1.We conclude that the combination of EC and chamber measurements helped identifying temporal dynamics of GHG exchange for a better understanding of ecosystem functioning and quantifying method-based uncertainties. Conventionally managed grassland on drained peat soils with high fertilizer input and 4 to 5 grass cuts per year proved to be a significant net GHG emission source. Accounting for footprint heterogeneity – for example through adequate positioning of chamber frames – is of utmost importance for robust determination of total GHG balances at site-level scale.

AbstractSemiarid South African ecosystems are managed for livestock production with different practices and intensities. Many studies have found grazing to be an important driver of vegetation change; however, its impacts on carbon fluxes remain poorly studied. Unsustainable management over the past 200 years has led to an increase of degraded areas and a reduction in species diversity, but destocking trends in the past three decades may be facilitating a recovery of net primary productivity and vegetation cover in some areas. This chapter provides a brief historical overview on livestock management practices and their likely impact on carbon exchange in the Nama-Karoo Biome. We present a case study based on five years of eddy covariance measurements, in which effects of past and current livestock grazing on CO2 exchange were studied. Two sites with different livestock management but similar climatic conditions formed the basis for this preliminary effort to improve the understanding of carbon exchange and its drivers under contrasting management regimes. The case study revealed that net CO2 exchange is near-neutral over an annual scale, with precipitation distribution emerging as the main controlling factor of subannual variance. Although CO2 release at the lenient grazing site was slightly higher than at the experimental grazing site, longer time series are likely needed in such variable ecosystems to make a pronouncement regarding long-term net fluxes. Given their vast extent, livestock rangelands may have an important effect on regional carbon balance.

AbstractIn this chapter, we highlight the importance and value of key Environmental Research Infrastructures, and how these can act as anchor points for long-term environmental observations and facilitate interdisciplinary environmental research. We briefly summarize the development of these efforts in South and southern Africa over the last three decades and from this perspective discuss how their successful maintenance and further implementation may turn such RIs into important anchor points for long-term environmental scientific work in support of environmental sustainability, national commitments under selected international policy discussions, and societal well-being. The fundamental role of Environmental Research Infrastructures is multifold and includes the provision of data that enable reporting and policy development, the provision of validation sites in the development of new observational sensors, measurement techniques and models, and the provision facilities for training of scientists and technicians. Humanity currently faces a number of global crises, including the impact of changes in the climate, resulting in droughts, floods, fires, storms, and other extreme events. These crises are significantly stressing and transforming the lives and livelihoods of the vast majority of humanity. The societal response to these events is dependent on the availability of scientific knowledge and its effective transfer to governance structures, industry, and the broader society. In order to effectively address these challenges, large amounts of long-term social-ecological data are required across a broad range of intersecting disciplines that are available for analysis by the scientific community. Research Infrastructures have the ability to act as anchor points in the provision and utilization of this data, and the development of indigenous capacity to develop the observations and technical skills.

This paper focuses on the meticulous selection of optimal remote sensing and climate datasets for Gross Primary Productivity (GPP) estimation in African rangelands. Utilizing Eddy Covariance Flux Tower data, we refine data selection and employ a Light Use Efficiency (LUE) model, with Sentinel 2 for photosynthetically active vegetation quantification, MODIS for Photosynthetically Active Radiation (PARin), and ERA5 Land reanalysis for climatic variables. The Eddy Covariance-based LUE-GPP model is identified as superior compare to other LUE based GPP models and further enhanced through fine-tuning LUE max and climate scalars. Footprint analysis determines a 500m footprint size, aligning with literature recommendations. Comparative analyses with various LUE models reveal ECLUE's superiority. Statistical validations affirm key parameter selections, leading to a reliable LUE-based GPP model tailored for African rangelands. The proposed model contributes to accurate GPP assessment, essential for informed environmental stewardship in these critical ecosystems.

The authors have requested that this preprint be removed from Research Square.

AbstractThe sustainability of southern Africa’s natural and managed marine and terrestrial ecosystems is threatened by overuse, mismanagement, population pressures, degradation, and climate change. Counteracting unsustainable development requires a deep understanding of earth system processes and how these are affected by ongoing and anticipated global changes. This information must be translated into practical policy and management interventions. Climate models project that the rate of terrestrial warming in southern Africa is above the global terrestrial average. Moreover, most of the region will become drier. Already there is evidence that climate change is disrupting ecosystem functioning and the provision of ecosystem services. This is likely to continue in the foreseeable future, but impacts can be partly mitigated through urgent implementation of appropriate policy and management interventions to enhance resilience and sustainability of the ecosystems. The recommendations presented in the previous chapters are informed by a deepened scientific understanding of the relevant earth system processes, but also identify research and knowledge gaps. Ongoing disciplinary research remains critical, but needs to be complemented with cross-disciplinary and transdisciplinary research that can integrate across temporal and spatial scales to give a fuller understanding of not only individual components of the complex earth-system, but how they interact.

Mapping in-situ eddy covariance measurements of terrestrial land-atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented up-scaling framework, FLUXCOM-X. The X-BASE products comprise of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP) as well as evapotranspiration (ET) and, for the first time, a novel fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at -5.75 ± 0.33 Pg C ⋅ yr-1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site-years resulting in a wider coverage of bio-climatic conditions. However, X-BASE global net ecosystem exchange shows a very low inter-annual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C ⋅ yr-1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x10³ ± 0.9x10³ km3 globally for the years 2001–2020, but exceeds precipitation in many dry areas likely indicating overestimation in these regions. On average 57 % of evapotranspiration are estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous up-scaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy-covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Mapping in situ eddy covariance measurements of terrestrial land-atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05 degrees) and temporal (hourly) resolution. X-BASE estimates the global NEE at -5.75 +/- 0.33 Pg C yr(-1) for the period 2001-2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 +/- 2.1 Pg C yr(-1) for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x10(3) +/- 0.9x10(3) km(3) globally for the years 2001-2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Management practices that increase the surface albedo of cultivated land could mitigate climate change, with similar effectiveness to practices that reduce greenhouse gas emissions or favor natural CO 2 sequestration. Yet, the efficiency of such practices is barely quantified. In this study, we quantified the impacts of seven different management practices on the surface albedo of winter wheat fields (nitrogen fertilizer, herbicide, fungicide, sowing, harvest, tillage, and crop residues) by analyzing observed daily albedo dynamics from eight European flux-tower sites with interpretable machine learning. We found that management practices have significant influences on surface albedo dynamics compared with climate and soil conditions. The nitrogen fertilizer application has the largest effect among the seven practices as it increases surface albedo by 0.015 ± 0.004 during the first two months after application, corresponding to a radiative forcing of −4.39 ± 1.22 W m −2 . Herbicide induces a modest albedo decrease of 0.005 ± 0.002 over 150 d after application by killing weeds in the fallow period only, resulting in a magnitude of radiative forcing of 1.33 ± 1.06 W m −2 which is higher than radiative forcing of other practices in the same period. The substantial temporal evolution of the albedo impacts of management practices increases uncertainties in the estimated albedo-mediated climate impacts of management practices. Although these albedo effects are smaller than published estimates of the greenhouse gas-mediated biogeochemical practices, they are nevertheless significant and should thus be accounted for in climate impact assessments.

AbstractEcosystems in southern Africa are threatened by numerous global change forces, with climate change being a major threat to the region. Many climate change impacts and environmental-based mitigation and adaptation options remain poorly researched in this globally important biodiversity hotspot. This book is a collection of chapters covering research undertaken in southern Africa by the German Federal Ministry of Education and Research’s (BMBF) SPACES and SPACES II programs. SPACES II covered a wide range of global change-linked environmental issues ranging in scope from the impacts of ocean currents on global climate systems through to understanding how small-scale farmers may best adapt to the impacts of climate change. All the research has identified policy implications, and the book strives for a balance between presenting the detailed science underpinning the conclusions as well as providing clear and simple policy messages. To achieve this, many chapters in the book contextualize the issues through the provision of a mini-review and combine this with the latest science emulating out of the SPACES II program of research. The book therefore consolidated both past and the most current research findings in a way that will be of benefit to both academia and policy makers.

In Europe, the heterogeneous features of crop systems with majority of small to medium sized agricultural holdings, and diversity of crop rotations, require high-resolution information to estimate cropland Net Ecosystem Exchange (NEE) and its two main components of Gross Ecosystem Exchange (GEE) and the Ecosystem Respiration (RECO). In this context, this paper presents an assimilation of high-resolution Sentinel-2 indices with eddy covariance measurements at selected European cropland flux sites in a new modified version of Vegetation Photosynthesis Respiration Model (VPRM). VRPM is a data-driven model simulating CO2 fluxes previously applied using satellite-derived vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study proposes a modification of the VPRM by including an explicit soil moisture stress function to the GEE and changing the equation of RECO. It also compares the model results driven by S2 indices instead of MODIS. The parameters of the VPRM model are calibrated using eddy-covariance data. All possible parameters optimization scenarios include the use of the initial version vs. the proposed modified VPRM, S2, or MODIS vegetation indices, and finally the choice of calibrating a single set of parameters against observations from all crop types, a set of parameters per crop type, or one set of parameters per site. Then, we focus the analysis on the improvement of the model with distinct parameters for different crop types vs. parameters optimized without distinction of crop types. Our main findings are: (1) the superiority of S2 vegetation indices over MODIS for cropland CO2 fluxes simulations, leading to a root mean squared error (RMSE) for NEE of less than 3.5 μmolm-2s-1 with S2 compared to 5 μmolm-2s-1 with MODIS (2) better performances of the modified VPRM version leading to a significant improvement of RECO, and (3) better performances when the parameters are optimized per crop-type instead of for all crop types lumped together, with lower RMSE and Akaike information criterion (AIC), despite a larger number of parameters. Associated with the availability of crop-type land cover maps, the use of S2 data and crop-type modified VPRM parameterization presented in this study, provide a step forward for upscaling cropland carbon fluxes at European scale.