Nitrogen deposition and climate drive plant nitrogen uptake in terrestrial ecosystems

doi:10.21203/rs.3.rs-3462045/v1

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Nitrogen deposition and climate drive plant nitrogen uptake in terrestrial ecosystems

https://doi.org/10.21203/rs.3.rs-3462045/v1

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The role of plants in sequestering atmospheric carbon dioxide is a critical component in mitigating the adverse effects of climate change. A key aspect of this role involves plant nitrogen (N) uptake (Nup) and N use efficiency (NUE), as these factors directly influence the capacity of plants to capture and store carbon. However, the contribution of climatic changes and N inputs remains inadequately understood, introducing significant uncertainties into climate change projections. Here, we used on-the-ground observations across 159 locations to calculate Nup and NUE and identify the main drivers of these processes in natural ecosystems. We found that Nup is primarily driven by abiotic factors, showing an increase with N deposition from anthropogenic activities such as agriculture and combustion, as well as increases in temperature and precipitation. NUE is primarily influenced by biotic factors, showing an increase with the presence of symbiotic ectomycorrhizal fungi and a decrease with microbial nitrogen stocks, likely due to microbial competition for N, and soil pH. In opposition to the classic paradigm in ecology, total soil N stocks were not found to be an important driver of Nup or NUE. A comparison with TRENDY land surface models revealed a potential Nup overestimation by land system models of around 100 Tg N yr-1 in the tropics and tripling the standard deviation on boreal latitudes. Our results underscore the importance of anthropogenic impacts, climate, and microbes as the main drivers of Nup and NUE.

Biological sciences/Ecology/Biogeochemistry

Biological sciences/Ecology/Climate-change ecology

Earth and environmental sciences/Biogeochemistry

Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Environmental health

Biological sciences/Ecology/Ecological modelling

Climate change and land use intensification are threatening the capacity of plants to sequester carbon by altering their biogeochemical cycles. Nup and NUE are fundamental processes supporting N cycling, biodiversity, ecosystem productivity, food security, human health, and sequestering carbon in plant biomass and soils¹. Hence, comprehensive quantification of Nup and NUE is crucial to understanding and quantifying human impacts on terrestrial ecosystems. Climate, biomass production, and Nup are strongly intertwined, where hotter and wetter environments have the capacity to grow more and therefore absorb more N^2,3. Traditionally, total soil N stocks have been used as a proxy of N availability or plant Nup^4,5. However, the quantitative responses of Nup and NUE to N inputs (e.g., anthropogenic N deposition) and soil microorganisms (e.g., soil microbes stocks and mycorrhizal associations) remain largely undescribed worldwide. N deposition is the primary source of anthropogenic N in natural ecosystems and a key driver of global change^6,7. Its impacts on terrestrial ecosystems are multifaceted, including a decrease in biodiversity^8,9, an increase in N leaching an elevated risk of eutrophication^10,11, and soil acidification¹² at a different scale. It has also been detected as the most important environmental driver in managed European forests¹³. On the other side, soil microorganisms are the regulators of organic matter decomposition and nutrient cycling^14,15. These processes, controlled by bacteria and fungi, drive N mineralization and N fixing, which are responsible for introducing and recycling N into the system^16,17,18. Therefore, comprehensive quantification of plant Nup and NUE worldwide needs to account for climate, human impact, and soil microorganisms as the main characters.

N regulates the capacity of ecosystems to store C^19,20,21,22 and response to climate change drivers^23,24,25,26 being the C-N assembly relevant for land surface models (LSM). Eight of the LSM of the TRENDY ensemble²⁷ include representations of the N cycle and plant N uptake. Nonetheless, its parameterization of N cycling is poorly constrained by observations^28,29,30. As a consequence, when models are assembled, the result leads to accumulated uncertainty^31,32 and therefore divergent predictions of the land sink^28,33,34. Furthermore, when accounting for N interactions, LSM do generally not consider the direct effects of microorganisms’ missing out on the role of soil bacteria or mycorrhizae on plant nutrient obtention. Thus, including global calculations of plant Nup and NUE based on empirical data as well as accounting for climate, N deposition, and soil biomass interactions would potentially refine the N accountability in LSM.

Here, we gathered information from 159 plots worldwide that describe woodlands and grasslands across different biomes to calculate plot-based plant Nup and plant NUE using exclusively empirical field data. Our analyses combine N concentration and net primary productivity (NPP) data in different aboveground and belowground plant tissues (i.e., leaves, roots, stem). We used linear models to identify the drivers of Nup and NUE, including N deposition, soil microbes, and climatic factors. We then upscaled those results using the machine-learning models to quantify yearly plant Nup and plant NUE at a global scale in natural terrestrial ecosystems (woodlands and grasslands) and compared these results with simulations from LSM. We hypothesize that the accumulation of N on land due to N deposition over the last decades may have a significant impact on Nup, and soil microorganisms to have a substantial role on NUE. Furthermore, we do not expect a total match of our worldwide predictions since TRENDY simulation outputs may lack empirical validation, especially on N components.

Nitrogen uptake and nitrogen use efficiency

Our findings indicate that N deposition and climate are fundamental factors explaining plant Nup on a global scale (Fig. 1). Further analysis revealed a positive relationship between Nup and accumulated N deposition, mean annual temperature (MAT), and mean annual precipitation (MAP). Thus, regions that are warm and wet, and also experience higher levels of N deposition, exhibit the highest rates of Nup. Our empirical results did not show important relationships between plant Nup and soil microbial interactions nor soil physico-chemical variables including soil N stocks (Fig. 1a). We further tested the univariate relation between Nup and total soil N stocks with no significant relation among them (Fig. S1a).

In contrast, when describing NUE our model selection analysis identified soil biotic and abiotic factors as NUE drivers with little direct control by climatic factors (Fig. 2). Specifically, our results described a negative relation between AM % and NUE but a positive relation between soil pH, soil microbial N stocks and NUE. Thus, when plant species are more colonized by arbuscular mycorrhizae (in contraposition to ectomycorrhizas), are less efficient in its N use to build biomass. In contraposition, basic pH and abundant soil microbial stocks facilitate higher NUE rates. Even though soil variables appear to be important for NUE, soil N stocks remain unrelated to NUE in the model and when tested individually (Fig. S1b).

Global maps of Nup and NUE

Using a machine-learning model, we upscaled Nup and NUE to a global scale. Our XGBoost model not only accounts for non-linear relationships but also automatically handles spatial autocorrelation and collinearity, thereby enhancing predictive power. For methodological consistency, the XGBoost model was trained using the same nine variables as the linear model, identifying temperature, precipitation, and N deposition as the most critical factors for describing Nup (Fig. S2). The variables' importance aligned with those in the linear model, albeit in a slightly different order. Partial dependence plots further corroborated these relationships, showing consistent correlation signs with those observed in the linear models (Fig. S3). The upscaled Nup map showed a total yearly Nup of 842.215 ± 236.11 Tg of N, with a mean coefficient of variation of 26.77% (Fig. S4). The lowest Nup values were on boreal latitudes and mountain ranges such as the Rocky Mountains in the USA, the Andes in South America, the different mountain ridges in Europe, and the Himalayan plateau in Asia. The higher rates of Nup are predicted in temperate latitudes in Europe, the eastern United States, Southeast Asia, East Australia, most of South America and central Africa, with the most intense spot around Congo, where there is the most N deposition, temperature and precipitation combined (Fig. 3a). Therefore, Nup map shows an NPP influence, driven by temperature and precipitation, but added to an N deposition distribution that shades the strictly latitudinal distribution of Nup.

The machine-learning models describing NUE showed the importance of microbial N stocks, altitude, precipitation, soil pH, and AM% as NUE drivers (Fig. S5). These results align with the variable importance shown in the linear models, with the addition of precipitation and altitude. The variables´ relation showed similar general trends as in the linear model (Fig. S6). The average predictions for NUE at a global scale were 110.262 units of C per unit of N with a mean coefficient of variation of 17.89% (Fig. S4). The map distribution showed general low NUE around the Equator, and progressively increasing towards the poles. Nonetheless, some heterogeneous parches alternating high and low NUE can be found between 50 and 60 degrees latitude north (Fig. 3b).

Global-scale Nup comparison with TRENDY models

We further seek to compare our estimates for the total yearly Nup upscaled from field observations with the mean of the Nup provided by the eight models included in TRENDY. When comparing TRENDY Nup with our Nup upscaled projections, we found clear geospatial pattern differences. TRENDY models produce higher Nup in the tropical regions, reaching differences of around 100 kg N ha^− 1 yr^− 1 in those areas (Fig. 4a) representing more than 100% of the Nup estimated by field observations (Fig. 4b). Other areas like the north and northeast of North America, Southeast Asia, and north of Eurasia also appear to have higher Nup values in TRENDY models than in field observations. In boreal latitudes, the TRENDY models deviation for Nup could even reach 300% of overestimation. On the other hand, areas where the upscaled approach projects higher values than the TRENDY models, are the austral latitudes, the Middle Eastern regions, the Somali peninsula, and the Rocky Mountains (Fig. 4). Overall, TRENDY models estimate higher values of Nup, by 16.61 kg N ha^− 1 yr^− 1, meaning the 48.54% of the variability. When aggregating the total year Nup, LPX-Bern and CLM5.0 were the models that predicted overall values exceding our range of confidence, assuming a significantly larger Nup (Fig. S7).

Nup global drivers and implications

Linear models and machine learning models are consistent when determining N deposition, temperature, and precipitation as global drivers of Nup. Hotter and wetter environments increase biological activity, leading to more biomass production and therefore more N demand. An increase in N demand with enough N availability is associated with an increase in Nup. The accumulation of N deposition throughout time originating from anthropogenic sources has been increasing the N availability in some areas, generally close to industrial or agroforestry pools. Hence, in a global change context where CO₂ fertilization and temperature increase have generated a greening effect³⁵, areas with higher N deposition were able to better supply this increasing N demand. Thus, according to our results, anthropogenic N supply has become a Nup driver as important as climate.

These results are concerning since our data emphasize the far-reaching influence of human-induced nitrogen deposition in shaping global Nup patterns. Some regions such as Europe, the Eastern USA, and the tropics have decreased their N deposition during the last four decades³⁶. Nonetheless, these efforts do not translate yet on low N deposition effects in natural woodlands and grasslands. This sustained entrance of anthropogenic N has been associated with a fertilization effect, enhancing the land C sink by 0.72 Pg C yr^− 1 during the 2010s³⁷. Nonetheless, this N fertilization effect showed evidence of saturation in forests and grasslands^38,39, where the biomass production and therefore the C sink increase slowed down. Consequently, this input of N not being captured by biomass will enhance the N leaching associated with eutrophication, acidification, loss of biodiversity, and N₂O emissions^40,41,8 exacerbating environmental problems.

NUE global drivers and implications

Our results indicate soil biotic and abiotic factors drive NUE in natural ecosystems. The main divergence between linear models and machine learning models is the importance of altitude and precipitation, which showed explicit relevancy only in machine learning models. We attribute these differences to the nature of the models, where machine-learning models accommodate correlations without modifying their variable importance. Thus, the important variables in the linear model could also have embedded important latitudinal gradients and therefore altitudinal or precipitation gradients. In that regard, we do not consider environmental variables such as precipitation to be totally detached from NUE relations, since they are somewhat drivers of important biotic variables such as AM %, soil pH, and microbial N stocks.

The response of NUE has been postulated as a method to assess N saturation in plant communities⁴². A negative relation between N addition and NUE and lower NUE levels would indicate N saturation⁴³. In our study, tropical areas are shown to have the lowest NUE, being the less N limited and matching with previous global upscaling studies using different approaches^44,45. According to the soil age hypothesis⁴⁶, N accumulates in ecosystems through time due to biological processes. Thus, newer formation areas, such as high elevation or lower pH areas are the ones showing higher values of NUE and where N is expected to be more limiting. Our results only show a modest effect of N saturation due to N deposition, so further studies are needed to better assess where and under what circumstances areas are N saturated due to N deposition.

Biological activity, such as the type of mycorrhizal associations and soil microbes N stocks has shown a strong impact in the terrestrial N cycle. Arbuscular mycorrhizal associations are the most abundant in the tropics⁴⁷ and are theorized to be more efficient in nutrient capture and more abundant in areas with fast N cycling⁴⁸. Our models show that AM associations have lower NUE, possibly driven by the abundance of N and the high efficiency of AM associations in N obtention. On the other side, N obtention was more efficient in areas with high soil microbes stocks. We hypothesize a potential competition effect between soil microbes and plants for N, but further studies are needed to corroborate this relation. Thus, given the importance of biological activity in fixing and transforming N, it is reasonable that total soil N stocks would not be a good indicator of N availability and plant N uptake.

Divergences between Nup map and TRENDY

TRENDY model ensemble projects substantially higher Nup values than the empirical upscaling. These differences were especially relevant in the tropics in absolute terms and in boreal latitudes in % of deviation. This mismatch could be associated with an overestimation of terrestrial C sink capacity and a misinterpretation of the role of vegetation in N cycling. A possible explanation of this phenomenon would lay on the overestimation of biomass production by LSM when not accounting for growth-limiting factors such as phosphorus, drought, or overall biotic competition. Alternatively, overestimation when accounting for N concentration in tissues could also lead to Nup overestimation, which would necessarily reflect in overall lower NUE values. In our calculations, we embraced the variability of N concentration and net primary productivity among tissues and leaf resorption to generate a truthfully Nup and NUE values. Nonetheless, we acknowledge that calculations based on empirical data can still have biases associated with sampling and the upscaling process. Still, we believe that calibrating and cross-checking models built over mathematical assumptions with field measurements is necessary to better root models to reality.

We showed that accumulated N deposition and climatic variables are the main global-scale factor describing Nup, where regions that are warm and wet and also experience higher levels of N deposition, exhibit the highest rates of Nup. This result highlights the far-reaching influence of human-induced nitrogen deposition in shaping the global Nup pattern. Interestingly, NUE was shown to be driven by soil biotic and abiotic factors, emphasizing the importance of soil microorganisms and pH as regulators of the N cycle. We further revealed that total soil N stocks are not a Nup nor NUE driver. Our upscaling showed large spatial-explicit differences with TRENDY Nup values, where TRENDY projects higher absolute values around the tropics and higher deviation values in boreal latitudes. This mismatch in the spatial correlation between empirical data and land system models could substantially affect model accuracy and future predictions of the C sink, where the tropical capacity to store C might have been overestimated. Our results provide insights to understand better the C – N interactions, N cycling, and absorption in terrestrial ecosystems and highlight that N deposition largely impacts plant Nup worldwide.

Data gathering

We gathered 159 (Table S1) field plot data in natural conditions, including dominant species and vegetation type (grassland, coniferous or broadleaved), foliar and root N concentration, foliar and root biomass production, and stem biomass production in the case of woody plants on the same location and time. We also collected, if available, field values of litter biomass production, litter N concentration, stem N concentration, soil pH, soil C %, soil N %, soil texture, soil moisture, mean annual precipitation, mean annual air temperature, and altitude. We included woody and grassland natural environments (Fig. S8), including representation from most biomes (Fig. S9). Each data point covered by the analysis has been collected from 1984 to 2022. If stem N was missing, happening in 25% of the data entries, we gap-filled it with the mean value of its vegetation type (coniferous = 0.33 or broadleaved = 0.52%). If litter biomass or foliar biomass production was missing, 52% of the time, we assumed it to be the same amount of green leaf biomass production or vice versa. If litter N concentration was missing, we calculated the net Nup using the predicted value from a linear model created with net Nup in the base of gross Nup, in 33% of the entries. This model had an r2 of 0.88, a p-value < 2.2e-16, and a correlation of 0.72 between gross and net Nup. For the environmental data, we gap-filled the missing values with mean annual air temperature and mean annual precipitation from WorldClim2⁴⁹, as well as soil pH, soil C, and soil N, soil moisture, soil bulk density, and soil texture from soilGrids⁵⁰. All soil data being collected or gap filled for the topsoil layer (0–15 cm). We also identified the potential mycorrhizal association from the dominant species based on Soudzilovskaia et al. 2020⁵¹, and categorized it into 0, 50, or 100 arbuscular mycorrhizal (AM) percentages. When dominant species were not provided, we extracted the AM% based on the AM map of Soudzilovskaia et al. 2019⁴⁷. Moreover, we extracted the microbial N stock from Xu et al. 2013⁵². We calculated and obtained the accumulated oxidized N deposition from Yang and Tian 2022⁵³ from 1901 to 2022 by georeferencing each field plot. Oxidized and reduced N deposition are correlated and are thought to have similar ecological effects^54,55. Oxidized forms generally come from combustion reactions while reduced forms generally come from agricultural practices. We decided to use the oxidized form because it is the most equally distributed at a global scale.

Nitrogen uptake calculation

We calculated the increase in annual N stock for each tissue (leaves, stem, roots, and litter) by multiplying the biomass increase by its N concentration. We obtained the gross annual Nup by aggregating tissue’s Nup (roots, leaves, and stem if woody). To account for the N that has been reabsorbed before senescence, we subtracted the litter N stock from the green leaves N stock. We subtracted the reabsorbed N from the gross Nup to obtain the final net Nup value as follows:

$$Nup = (NPPleaves * Nleaves + NPPstem * Nstem + NPProots * Nroots) – \left(NPPlitter * Nlitter\right)$$

Nup = Plant nitrogen uptake (kg N/ha/yr)

NPP = Net primary production (kg N/ha/yr)

N = Nitrogen (% of dry weight)

Nitrogen use efficiency calculation

We calculated the nitrogen use efficiency (NUE) by calculating the total amount of biomass produced in leaves, stems, and root tissue divided by the amount of nitrogen in each tissue. It will give the amount of biomass produced by a unit of nitrogen.

NUE = (NPP_leaves + NPP_stem + NPP_roots)/(Nstock_leaves + Nstock_stem + Nstock_roots)

NUE = Nitrogen use efficiency (kg C / kg N)

NPP = Net primary production or biomass increase (kg N/ha/yr)

Nstock = Nitrogen stock or amount of nitrogen calculated by NPP * N %

Linear statistical analysis

From the available variables collected, we selected the less correlated ones using the cor function in R to deal with multicollinearity. The less correlated variables selected were mean annual air temperature, mean annual precipitation, altitude, arbuscular mycorrhizae percentage, microbial N stock, soil N stock, soil pH, accumulated oxidized N deposition from 1901 to 2022, and woodiness. The greatest correlation among variables was 0.52 between mean annual temperature and AM presence (Table S2). Generalized linear models were created using Nup and NUE as dependent variables and the family was set up as Gamma with an inverse link to fulfill the residuals normality requirements. We performed a model selection using the dredge function in the MuMIn R package⁵⁶ and chose the best linear model based on its lowest AIC. We calculated the variable importance using the function sw on the MuMIN R package⁵⁶. Figures were created using the R package ggplot2⁵⁷.

Nitrogen uptake and nitrogen use efficiency upscaling

To upscale Nup and NUE to global grasslands and woody vegetation, we trained extreme gradient boosting (XGBoost) models splitting the database into train, test, and validation using a ratio of 70:20:10, respectively. Extreme gradient boosting is a machine learning algorithm that builds ensemble decision trees, applying regularization and pruning techniques to improve performance and prevent overfitting⁵⁸. We trained an XGBoost model using the R package xgboost⁵⁹, forcing an early stop based on minimum root mean squared error to avoid overfitting and setting up the objective as a gamma regression. We optimized the parameters based on performance at a maximum depth of 6, minimum child weight of 1, and eta of 0.3. We considered the same independent variables included in the linear model without interactions. We repeated this process 20 times with random database separation to stabilize the variability due to randomness in subset splitting. We extracted the variable importance of each model using the function xgb.plot.importance on the xgboost R package⁵⁹, calculated the mean of the values among the 20 different training sets, and displayed it using ggplot. We calculated partial dependence plots using the function partial in purrr R package⁶⁰ to explore the non-linear relations on the models. To calculate the model performance, we calculated the mean squared error of the test set and the r squared of the predicted vs observed in the validation subset, considering the validation set as completely independent.

To predict the values at a global scale, we used the spatial explicit mean annual precipitation, mean annual temperature, and altitude variables provided by WorldClim2⁴⁹; the microbial N stocks by Xu et al. 2013⁵²; the oxidized accumulated N deposition from 1909 to 2022 calculated from Yang and Tian 2022⁵³ and soil N stocks and soil pH provided by soilGrids 2.0⁵⁰ at 15 cm depth. We reclassified the European Space Agency Land Cover (ESA-LC) map (Table S3) and we downscaled its resolution to 2 km using the raster R package⁶¹. We upscaled each of the 20 Nup and NUE models using the trained XGBoost models and their prediction per pixel at 2 km resolution and calculated the mean to obtain the final maps. We parallelized the process using the parallel function and spaDES.tools R package⁶² to accelerate the upscaling. We masked areas not considered woodlands or grasslands in natural conditions according to the European Space Agency cover map⁶⁰ (Table S3), and then, we obtained a map of the yearly Nup, Nup standard deviation, and annual NUE. We obtained the final number of yearly Nup by summing all the pixels available.

Nitrogen uptake comparison with TRENDY models ensemble

We obtained the available Nitrogen uptake of Vegetation (fNup) variable associated with all the available models in TRENDY v7 S3^27,63. The models containing fNup are ORCHIDEE, LPX-Bern, LPJ-GUESS, JULES, JSBACH, DLEM, CLM5.0, and Cable-POP, and the S3 experiment in the simulation considering the adaptation of CO₂, land use, N deposition, and climate from 1850 representing current environmental conditions. We calculated the yearly mean Nup from 1984 to 2022 for each model. Then, we calculated the difference between each TRENDY model and our Nup estimations. We also calculated the latitudinal mean of the difference to achieve a latitudinal profile and calculated the overall mean.

Contributions

H.V. and C.T. conceived the project; C.T. got the funding and supervised the work; H.V., C.M., A.K., J.C., and D.T. collected and compiled the data; H.V. curated and analyzed the data, created the visuals and wrote the first draft; H.V., C.T., M.D.B., M.F.M., M.L., D.G., contributed with substantial ideas and feedback on the manuscript; all authors revised, edited, and agreed on the final manuscript.

Acknowledgments

We acknowledge the members of the Terrer Lab for providing scientific consulting as well as mental and emotional support during the investigation. We acknowledge the Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University for making possible this collaboration with D.G. J.C. was supported by the National Agency of Agricultural Research of the Czech Republic (Project No. QK22020008) and the Ministry of Agriculture (CR), institutional support MZE-RO0123, and he thanks FGMRI technician staff for help with field and lab works. M.F.M was supported by the European Research Council project ERC-StG-2022-101076740 STOIKOS and a Ramón y Cajal fellowship (RYC2021-031511-I) funded by the Spanish Ministry of Science and Innovation, the NextGenerationEU program of the European Union, the Spanish plan of recovery, transformation and resilience, and the Spanish Research Agency.

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Nitrogen deposition and climate drive plant nitrogen uptake in terrestrial ecosystems

Status:

Version 1

Abstract

Figures

MAIN

Nitrogen uptake and nitrogen use efficiency

Global maps of Nup and NUE

Global-scale Nup comparison with TRENDY models

Nup global drivers and implications

NUE global drivers and implications

Divergences between Nup map and TRENDY

Conclusion

METHODOLOGY

Data gathering

Nitrogen uptake calculation

Nitrogen use efficiency calculation

NUE = (NPP_leaves + NPP_stem + NPP_roots)/(Nstock_leaves + Nstock_stem + Nstock_roots)

Linear statistical analysis

Nitrogen uptake and nitrogen use efficiency upscaling

Nitrogen uptake comparison with TRENDY models ensemble

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Nitrogen deposition and climate drive plant nitrogen uptake in terrestrial ecosystems

Status:

Version 1

Abstract

Figures

MAIN

Nitrogen uptake and nitrogen use efficiency

Global maps of Nup and NUE

Global-scale Nup comparison with TRENDY models

Nup global drivers and implications

NUE global drivers and implications

Divergences between Nup map and TRENDY

Conclusion

METHODOLOGY

Data gathering

Nitrogen uptake calculation

Nitrogen use efficiency calculation

NUE = (NPPleaves + NPPstem + NPProots)/(Nstockleaves + Nstockstem + Nstockroots)

Linear statistical analysis

Nitrogen uptake and nitrogen use efficiency upscaling

Nitrogen uptake comparison with TRENDY models ensemble

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

NUE = (NPP_leaves + NPP_stem + NPP_roots)/(Nstock_leaves + Nstock_stem + Nstock_roots)