Spectral bands range and spatial resolution of Sentinel-2A MSI and Landsat 8 OLI sensors simulated in this study.

Spectral bands range and spatial resolution of Sentinel-2A MSI and Landsat 8 OLI sensors simulated in this study.

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We applied an empirical modelling approach for gross primary productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that best represent GPP changes between the annual peak of growth...

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... x λ2 , where the weights are given by the spectral response. The list of S2 and L8 bands used in this study is shown in Table 1. ...

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... Characteristics of Sentinel-2A bands, adapted fromCerasoli et al. (2018) andVanino et al. (2018) ...
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Soil is one of the most important factors for agricultural production. In tropical regions, soil variability is considerable, with the most diverse combinations of physical and chemical characteristics, an influence factor in crop growth and productivity. In this research, the main objective was to identify how soil characteristics and parent material can influence sugarcane development over time using remote sensing. An area located in Sao Paulo, Brazil, of 182 ha (one point per ha with soil analysis), with high variability in the parent material and soil types, was selected. Images from the Sentinel2-MSI satellite were used to describe the spectral behavior of sugarcane over a period of one year. The NDRE (normalized difference red-edge index) was calculated for each image and then the leaf area index (LAI) was obtained from it. Maps of soil classes, soil properties at two depths (0–0.20 and 0.80–1.0 m), and parent material classes were related to sugarcane LAI variability over time. Production environment zones, which is a classification based on soil characteristics to support sugarcane development, were also obtained and related to LAI variability. Spectral signatures of the crop presented different behaviors through the season, soil types and soil attributes provided useful responses for this variability. At the beginning of the season, the surface and subsurface soil properties (texture and fertility) impacted differently on crop development. On the other hand, soil classes and parent material influenced LAI in all production environments studied. The results indicated that the soil types and their properties at different depths have a significant impact on sugarcane development. Furthermore, RS was able to monitor the plant evolution and be related to soil types which may assist in plant management. The results can bring light on how better sugarcane management can be conducted using remote sensing data and soils variability.
... Numerous studies have shown that N deposition could promote grassland ANPP, and highlighted the importance of N and P synergistic co-limitation in grasslands (Borer et al., 2014;Fay et al., 2015;Grace et al., 2016;Zhao et al., 2019;Wang et al., 2020). N and P fertilizers have received considerable attention in grassland restoration (Fay et al., 2015;Cerasoli et al., 2018;Wang et al., 2018Wang et al., , 2020. However, to date, previous researches have focused primarily on natural grasslands (Borer et al., 2014;Fay et al., 2015;Grace et al., 2016;Zhao et al., 2019). ...
... Commonly, nutrient availability limits the primary productivity of grasslands, and fertilization can increase soil quality and nutrient bioavailability, effectively alleviating nutrient restriction and consequently yielding positive effects on plant community productivity of grassland ecosystems (Bracken et al., 2015;Fay et al., 2015;Shen, 2016a, 2017;Wang et al., 2020). In general, compared with the aggregate responses to N and P addition individually, the responses of grassland primary productivity to N and P addition simultaneously are greater (Harpole et al., 2011;Fay et al., 2015;Cerasoli et al., 2018;Wang et al., 2020). Such previous findings indicate the synergistic colimitation of N and P on grassland productivity and highlight the critical role of P for primary production. ...
... Meanwhile, another study also reported that P addition alone did not affect AGB and BGB in a continuous 4-year fertilization experiment in an alpine grassland on the QTP . However, the results of previous studies and our study were inconsistent with numerous research findings that grassland primary productivity was co-limited by N and P (Grace et al., 2016;Cerasoli et al., 2018;Wang et al., 2020). The following potential reasons might be responsible for the observations. ...
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Climate, land-use changes, and nitrogen (N) deposition strongly impact plant primary productivity, particularly in alpine grassland ecosystems. In this study, the differential responses of plant community primary productivity to N and phosphorus (P) nutrient application were investigated in the natural (NG) and “Grain for Green” restored (RG) alpine grasslands by a continuous 3-year experiment in the Qinghai Lake Basin. N addition only significantly promoted plant aboveground biomass (AGB) by 42% and had no significant effect on belowground biomass (BGB) and total biomass (TB) in NG. In comparison with NG, N addition elevated AGB and BGB concurrently in RG by 138% and 24%, respectively, which further significantly increased TB by 41% in RG. Meanwhile, N addition significantly decreased BGB and the AGB ratio (R/S) both in NG and RG. Compared with N addition, P addition did not perform an evident effect on plant biomass parameters. Additionally, AGB was merely negatively influenced by growing season temperatures (GST) under the N addition treatment in NG. AGB was negatively associated with GST but positively related to growing season precipitation (GSP) in RG. By contrast, changes in the R/S ratio in RG were positively correlated with GST and negatively related to GSP. In sum, the results revealed that plant community biomass exhibited convergent (AGB and R/S) and divergent (BGB and TB) responses to N addition between NG and RG. In addition, the outcomes suggested that climate warming would enhance plant biomass allocation to belowground under ongoing N deposition, and indicated the significance of precipitation for plant growth and AGB accumulation in this restored alpine grassland ecosystem.
... Thus, the authors believe that a low amount of sample individuals is not a major concern when the signal is quite constant. However, for grasses, which are definitely more impacted by water, applying the method as described in the paper implies a priori knowledge of the study site [50]. ...
... In this work, the time series used to derive GCC was very small compared to other studies [5,10] while still yielding useful results that are in agreement with other phenological observations in similar environments [26,50]. Hence, it goes in favor of the concept of using digital photography as a means to track phenology, enlarging the range of ecosystems where the method has shown success. ...
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Monitoring vegetation is extremely relevant in the context of climate change, and digital repeat photography is a method that has gained momentum due to a low cost–benefit ratio. This work aims to demonstrate the possibility of using digital cameras instead of field spectroradiometers (FS) to track understory vegetation phenology in Mediterranean cork oak woodlands. A commercial camera was used to take monthly photographs that were processed with the Phenopix package to extract green chromatic coordinates (GCC). GCC showed good agreement with the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) obtained with FS data. The herbaceous layer displayed a very good fit between GCC and NDVI (coefficient of determination, represented by r2 = 0.89). On the contrary, the GCC of shrubs (Cistus salviifolius and Ulex airensis) showed a better fit with NDWI (r2 = 0.78 and 0.55, respectively) than with NDVI (r2 = 0.60 and 0.30). Models show that grouping shrub species together improves the predictive results obtained with ulex but not with cistus. Concerning the relationship with climatic factors, all vegetation types showed a response to rainfall and temperature. Grasses and cistus showed similar responses to meteorological drivers, particularly mean maximum temperature (r = −0.66 and −0.63, respectively). The use of digital repeat photography to track vegetation phenology was found to be very suitable for understory vegetation with the exception of one shrub species. Thus, this method proves to have the potential to monitor a wide spectrum of understory vegetation at a much lower cost than FS.
... Recent developments in remote multispectral imagery and vegetation structure mapping have improved our ability to estimate plant productivity (Cerasoli et al., 2018;Fischer et al., 2019). Multispectral vegetation indices (VIs) are a collection of ratios and transformations of light reflectance intensities detected in certain spectral bands. ...
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Premise: Measuring plant productivity is critical to understanding complex community interactions. Many traditional methods for estimating productivity, such as direct measurements of biomass and cover, are resource intensive, and remote sensing techniques are emerging as viable alternatives. Methods: We explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity. We apply these various productivity measures to trace the evolution of plant productivity and the traits underlying it. Results: The correlation between remote sensing data and direct measurements of productivity varies depending on vegetation diversity, but the volume of vegetation estimated from drone-based photogrammetry is among the best predictors of biomass and cover regardless of community composition. The commonly used normalized difference vegetation index (NDVI) is a less accurate predictor of biomass and cover than other equally accessible vegetation indices. We found that the traits most strongly correlated with productivity have lower phylogenetic signal, reflecting the fact that high productivity is convergent across the phylogeny of prairie species. This history of trait convergence connects phylogenetic diversity to plant community assembly and succession. Discussion: Our study demonstrates (1) the importance of considering phylogenetic diversity when setting management goals in a threatened North American grassland ecosystem and (2) the utility of remote sensing as a complement to ground measurements of grassland productivity for both applied and fundamental questions.
... N and P are the primary soil fertility factors restricting plant growth in grassland ecosystems, and chemical fertiliser are widely used to support vegetation restoration (Cerasoli et al., 2018). Our meta-analysis found generally positive responses of investigated plant characteristics to nutrient addition, thus validating our second hypothesis that NP combined application can restore the plant biomass of alpine degraded D. Wang, et al. ...
Article
Climate warming and human disturbance are supposed to have severely affected the alpine grasslands on the Qinghai-Tibetan Plateau (QTP), a region where the extremely harsh and fragile ecological environment has attracted great attention because of its sensitivity to global change. However, there is still no unified understanding of the degree and magnitude of grassland degradation and the effectiveness of nutrient addition in this vast landscape, since most previous studies have focused on short-term observations at a single site. Here, we conducted a meta-analysis of 145 published studies on degraded alpine grassland along with 90 published studies, which concerning nutrient addition (nitrogen [N], phosphorus [P], and combined N and P [NP]) to quantitatively assess the responses of plant and soil characteristics to land degradation and restoration. Our results revealed that the response ratio (RR) of above-ground biomass (AGB), below-ground biomass (BGB), soil organic carbon (SOC), and soil total N (TN) decreased significantly (−47.23 %, −43.45 %, −32.35 %, and −37.97 %, respectively) in degraded grassland compared with non-degraded grassland. The RR of AGB was correlated with mean annual precipitation (MAP), while the RR of BGB was correlated with the interaction of MAP and mean annual temperature (MAT). Severely degraded grassland required additional nutrients to aid recovery. NP addition to severely degraded sites increased plant AGB (+32.44 %), TN (+10.99 %), soil total P (+32.25 %), and soil moisture (+9.21 %), but significantly decreased species richness (−45.46 %), diversity (−30.40 % for Shannon−Wiener index) and soil pH (−3.91 %). N addition increased the RR of AGB and grass biomass significantly by 28.77 % and 36.49 %, but had no significant effect on sedge and forb biomass. MAP influenced the RR of AGB, TN, TP under NP addition, the RR of BGB and the AGB of different function groups were significantly affected by MAT. We evidenced that the QTP has endured severe vegetation and soil degradation, which cannot be completely mitigated by supplementary fertilisation. Fertilisation could yield positive effects on plant performance and soil quality, but negative effects on biodiversity. Climate warming and associated precipitation change may regulate the effects of fertiliser on plant biomass and soil nutrients.
... Such approaches require additional research matching Sentinel-2 imagery to N concentration in a wider range of forest vegetation types and tree species to determine the relationship to N recycling and other senescence parameters. Studies using hyperspectral data have indicated the improved performance of simulated Sentinel-2 bands in comparison to Worldview-2, RapidEye and Landsat-8 datasets for estimation of nitrogen and its effect on yields [89,90]. However, a review of the literature points to the lack of usage of Sentinel-2 satellite data, which need to be explored further for their efficacy in estimating nitrogen concentration. ...
... The phenology (i.e., timing of SOS, peak and EOS) estimated from the combined Landsat-8 and Sentinel-2 data could also explain around 92% of the variability observed in gross primary productivity (GPP) estimates from flux towers. Cerasoli et al. (2018) [89] used Landsat-8 and Sentinel-2-based vegetation indices (VIs) and bands to assess the combination that could best describe grassland phenology and GPP from annual peak to senescence. This study revealed that, although many of the bands in the two sensors are similar, Sentinel-2 performed better (with an improvement of over 10% for predicting GPP) than Landsat-8 in VI-based models due to the presence of additional red-edge bands. ...
... The phenology (i.e., timing of SOS, peak and EOS) estimated from the combined Landsat-8 and Sentinel-2 data could also explain around 92% of the variability observed in gross primary productivity (GPP) estimates from flux towers. Cerasoli et al. (2018) [89] used Landsat-8 and Sentinel-2-based vegetation indices (VIs) and bands to assess the combination that could best describe grassland phenology and GPP from annual peak to senescence. This study revealed that, although many of the bands in the two sensors are similar, Sentinel-2 performed better (with an improvement of over 10% for predicting GPP) than Landsat-8 in VI-based models due to the presence of additional red-edge bands. ...
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Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.
... The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are the most widely used VIs in VI-based models [38]. In addition to the NDVI and EVI, numerous studies have utilized vegetation red-edge reflectance VIs to improve GPP estimation [39,40]. The Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI), originally derived from the MERIS onboard the Envisat satellite of the European Space Agency (ESA), is one of the red-edge VIs [41]. ...
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Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPPOLCI-FAPAR and GPPOTCI) were then compared with flux tower GPP (GPPEC) for each site. Furthermore, the GPPOLCI-FAPAR and GPP derived from the MODIS FAPAR (GPPMODIS-FAPAR) were compared. Results showed that the performance of GPPOLCI-FAPAR was varied in different sites, with the highest R2 of 0.76 and lowest R2 of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPPEC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPPOTCI and GPPEC from strong to nonsignificant. Moreover, GPPOLCI-FAPAR (R2 = 0.55) performed better than GPPMODIS-FAPAR (R2 = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.
... A produção animal extensiva neste sistema baseia-se no consumo de pastagens permanentes de sequeiro (Efe Serrano, 2006). Estas pastagens são ecossistemas biodiversos que ocupam cerca de 22% da área da União Europeia (Cerasoli et al., 2018) e que se desenvolvem a partir de Setembro-Outubro, após as primeiras chuvas, atravessam um período de fraco crescimento durante os meses de Dezembro a Fevereiro (devido à baixas temperaturas), a que se sucede o pico de produção primaveril (entre Abril e Maio), devido à conjugação favorável da humidade no solo com a subida da temperatura do ar (Efe Serrano, 2006). A partir de Junho, a escassez de precipitação, a consequente descida dos teores de água no solo e a subida acentuada das temperaturas leva à senescência das plantas e o fechar do ciclo, com uma quebra abrupta da produtividade. ...
... A disponibilidade de técnicas e tecnologias eficientes na monitorização de parâmetros relacionados com a qualidade da pastagem é essencial , especialmente num cenário de incerteza climática (Cerasoli et al., 2018). A avaliação convencional dos teores de humidade no solo exige trabalho exaustivo de recolha de amostras de solo e posterior análise laboratorial. ...
... Por outro lado, a monitorização da qualidade da pastagem tem merecido um grande desenvolvimento na última década (Moeckel et al., 2017;Nawar et al., 2017), nomeadamente, através de sensores ópticos como o "OptRx ® " (Serrano et al., 2018) Rouse et al., 1973), fortemente correlacionado com os teores de clorofila e, portanto, com o vigor vegetativo das plantas (Chai et al., 2015;Gebremedhin et al., 2019). Vários autores têm utilizado este índice para monitorizar o desenvolvimento qualitativo das culturas (Cerasoli et al. 2018;Gebremedhin et al., 2019;McEntee et al., 2019). No entanto, esta abordagem exige também deslocações frequentes ao campo para captar a evolução do estado vegetativo da pastagem (Cerasoli et al., 2018) e, consequentemente, da sua qualidade. ...
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The purpose of this study was to evaluate the potential of using satellite images for monitoring the quality of pastures and soil surface moisture in the montado ecosystem. The experiment was carried out between 2016 and 2018 in an experimental field located at Herdade da Mitra, at Valverde (Évora). Twelve 10m x 10m plots were selected and associated to “Sentinel-2” pixels. During Spring, monthly pasture samples were taken from these pixels, and Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated. Between January 2017 and June 2018 monthly values of soil surface moisture (0-0.20m) were recorded. The results indicate significant correlations between: (i) NDVI and NDWI; (ii) between both indexes and the pasture quality parameters (crude protein, CP; Neutral Detergent Fiber, NDF; moisture, HP; and Pasture Quality Degradation Index, PQDI); (iii) between NDWI and soil moisture content (HS). This information confirms the interest in using satellite images for management of pastures and animal stocking, especially in terms of monitoring the needs for supplementary animal feed in the critical period between spring and summer.
... Chapter 4 different management practices. We stratified the analysis according to the MODIS land cover product (MCD12Q1) i.e., grasslands, croplands, and croplands/natural vegetation mosaic (Friedl et al., 2010) and used multiple linear regressions, which have been often selected to analyze the relationship between GPP and covariates (Cerasoli et al., 2018;Chen et al., 2015;Solberg et al., 2009). In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. ...
Thesis
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Food production is one of the main drivers of land competition. Increasing the agricultural production of a certain area usually entails the application of fertilizers that in high quantities can produce water pollution, loss of biodiversity, and greenhouse gases emission and deposition. In Switzerland, manure input is the main cause of nutrient surplus in agroecosystems. Another source of nutrients is atmospheric deposition, which also contributes to nitrogen surplus to a lesser extent. Nitrogen surplus has been monitored using farm statistics but there is a lack of assessments of spatial and temporal patterns of nitrogen surplus. Remote sensing can contribute substantially to the monitoring and assessment of nitrogen surplus in agroecosystems, e.g., providing land cover and land use datasets to allocate farm statistics, monitoring grassland use intensity to control high manure inputs, and evaluating the impact of nitrogen deposition on carbon fixation response. Results can be integrated as part of modelling frameworks or used as ancillary information by decision-makers. The use of models brings along two challenges: first, the reliability of model outputs depends on the quality of model inputs; second, the integration of results in multidimensional frameworks may remain difficult because the same phenomenon can be characterised and analysed differently by diverse scientific disciplines. This thesis is motivated by three research questions aiming at investigating the impact of remote sensing datasets on the land allocation output, proposing a method to assess grassland use intensity following an ecological approach, and studying the role of nitrogen deposition and climatic factors in explaining carbon fixation responses. The research findings revealed that the spatial resolution, classification accuracy, and segmentation process impacted on the allocation of farm statistics. Three ecological indicators of grassland use intensity i.e., mowing frequency, grazing intensity, and fertilization inputs were assessed and further integration of results helped define areas prone to nutrients surplus. Nitrogen deposition was the variable that mostly explained carbon fixation response in grasslands, croplands, and croplands/natural vegetation mosaic. Finally, the main findings and contributions are discussed and future research lines proposed.
... The objectives of this study are (i) to analyze the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on C fixation response in alpine grasslands, and (ii) to compare the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis according to the MODIS land cover product (MCD12Q1) i.e., grasslands, croplands, and croplands/natural vegetation mosaic [35] and used multiple linear regressions, which have been often selected to analyze the relationship between GPP and covariates [36][37][38]. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. ...
Article
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Climate, soil type, and management practices have been reported as primary limiting factors of gross primary production (GPP). However, the extent to which these factors predict GPP response varies according to scales and land cover classes. Nitrogen (N) deposition has been highlighted as an important driver of primary production in N-limited ecosystems that also have an impact on biodiversity in alpine grasslands. However, the effect of N deposition on GPP response in alpine grasslands hasn’t been studied much at a large scale. These remote areas are characterized by complex topography and extensive management practices with high species richness. Remotely sensed GPP products, weather datasets, and available N deposition maps bring along the opportunity of analyzing how those factors predict GPP in alpine grasslands and compare these results with those obtained in other land cover classes with intensive and mixed management practices. This study aims at (i) analyzing the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on carbon (C) fixation response in alpine grasslands and (ii) comparing the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis using three land cover classes: Grasslands, croplands, and croplands/natural vegetation mosaic and built multiple linear regression models. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. In alpine grasslands, explanatory variables explained up to 80% of the GPP response. However, the explanatory performance of the covariates decreased to maximums of 47% in croplands and 19% in croplands/natural vegetation mosaic. Further information will improve our understanding of how N deposition affects GPP response in ecosystems with high and mixed intensity of use management practices, and high species richness. Nevertheless, this study helps to characterize large patterns of GPP response in regions affected by local climatic conditions and different land management patterns. Finally, we highlight the importance of including N deposition in C budget models, while accounting for N dynamics.