Oil palm is the most rapidly expanding tropical perennial crop. Its
cultivation raises environmental concerns, notably related to the use of
nitrogen (N) fertilisers and the associated pollution and greenhouse gas
emissions. While numerous and diverse models exist to estimate N losses from
agriculture, very few are currently available for tropical perennial crops.
Moreover, there is a lack of critical analysis of their performance in the
specific context of tropical perennial cropping systems. We assessed the
capacity of 11 models and 29 sub-models to estimate N losses in a typical oil
palm plantation over a 25-year growth cycle, through leaching and runoff, and
emissions of NH
Oil palm is the most rapidly expanding tropical perennial crop. The area of
land under oil palm, currently amounting to approximately 19 Mha, has been
rising at 660 000 ha yr
While a number of models exist to estimate N losses from agricultural fields, they mostly pertain to temperate climate conditions and annual crops. N losses under perennial tropical crops are expected to follow specific dynamics, given, for instance, the higher ranges of temperature and rainfall experienced in these climatic zones, and the high amount of crop residues recycled over the growth cycle. However, few models are available for tropical crops, and even fewer for perennial tropical crops (Cannavo et al., 2008). Such models, in particular mechanistic ones, were primarily developed for research purposes, in order to simulate crop growth as affected by biogeochemical processes, and to gain insight into the underlying processes. Nowadays, models are also widely used to estimate the emission of pollutants for the purpose of environmental assessment, aiming either at more accurate estimates of mean emissions, or at evaluation of the impact of certain management practices on emissions. Different types of models are used, ranging from highly complex process-based models to more simple operational models such as empirical regressions. Despite some consensus and recommendations regarding best practices for the modelling of field emissions, notably within the framework of life cycle assessment (e.g. IPCC, 2006; EC ILCD, 2011), there has not been any comprehensive review and comparison of potentially useful models for environmental assessment. Moreover, various publications pinpointed the need for models that are better adapted to tropical crops in the estimation of field emissions (Basset-Mens et al., 2010; Bessou et al., 2013; Cerutti et al., 2013; Richards et al., 2016). To improve field emissions modelling in oil palm plantations, we need to determine the potential applicability and pitfalls of state-of-the art models regarding N cycling and losses in these systems.
Most environmental impact assessment methods, such as life cycle assessment, consider perennial systems to behave similarly to annual ones. Following this assumption, the inventory data on the farming system are generally based on one productive year only, corresponding to the time the study was carried out or the year for which data were available (Bessou et al., 2013; Cerutti et al., 2013). However, models of annual cropping systems do not account for differences in N cycling that occur during the growth cycle of perennial crops such as oil palm. Some key parameters in these dynamics, such as the length of the crop cycle, the immature and mature stages, and inter-annual yield variations, are thus not accounted for. This also applies to other long-term eco-physiological processes, such as the delay between inflorescence meristem initiation and fruit bunch harvest. To improve the reliability and representativeness of the environmental impacts of oil palm, we thus need to better account for the spatio-temporal variability of both the agricultural practices and the eco-physiological responses of the plant stand throughout the perennial crop cycle (Bessou et al., 2013). Since most of these impacts hinge on N management and losses, modelling the N budget of palm plantations is a key area for improvement and is the focus of this work.
Here, we assess the capacity of existing models to estimate N losses in oil palm plantations, while accounting for the peculiarities of oil palm plantations related to the N dynamics over the course of the growth cycle. We start with a review of models that could be used for oil palm, and we detail how they were selected, calibrated, and run with relevant input data for a particular case study. Outputs from the models were subsequently compared to each other and to previously reported field measurements. Key model parameters were identified using a Morris sensitivity analysis (Morris, 1991). Finally, we discuss the relevance of existing models and the remaining challenges to adequately predict N fluxes in oil palm plantations.
Among existing models, we first selected those that appeared most comprehensive and relevant. We then also selected partial models, in order to cover the diversity of current modelling approaches as much as possible, and to explore potential complementarities between them. By “partial models” we mean models that simulate only one or a few N losses.
The selection criteria were (i) the possibility of estimating most of the N losses of the palm system; (ii) the applicability to the peculiarities of the oil palm system; and additionally, for partial models, (iii) those most widely used in environmental assessments, e.g. EMEP (2013). In total, we selected 11 comprehensive plus 5 partial models.
We compared models at two levels. At the first level the aim was to compare the 11 comprehensive models, to obtain an overview of their abilities to estimate the various N fluxes constituting the complete N budget of the plantations. The second level involved the partial models and aimed at better understanding the factors governing the variability of each type of N loss. Most of the 11 comprehensive models were actually a compilation of sub-models. We hence included these sub-models in the second-level comparison, in addition to the 5 partial models originally selected. In total, 29 partial models, hereafter referred to as sub-models, were compared at this second level.
Following the typology defined by Passioura (1996), three of the
comprehensive models were classified as mechanistic, dynamic models (WANULCAS
from van Noordwijk et al., 2004; SNOOP from de Barros, 2012; APSIM from Huth
et al., 2014). The others were simpler static models mainly based on
empirical relationships (Mosier et al., 1998; NUTMON from Roy, 2005; IPCC,
2006, from Eggleston et al., 2006; Banabas, 2007; Schmidt, 2007; Brockmann et
al., 2014; Meier et al., 2014; Ecoinvent V3 from Nemecek et al., 2014) The models are hereafter referred to by their name or their first author in order to ease reading of both the text and figures.
The mechanistic models were built or adapted explicitly for oil palm. The other models were developed or are mainly used for environmental assessment. Among the latter, some were explicitly built for oil palm or proposed parameters adaptable to oil palm (Banabas, Schmidt, Ecoinvent V3), some involved parameters potentially adaptable to perennial crops (NUTMON, Brockmann, Meier-2014), while others were designed to be used in a wide range of situations, without specific geographical or crop-related features (Mosier and IPCC-2006, which are often used in Life Cycle Inventories).
Most of the models distinguished between mineral and organic fertiliser inputs, some included symbiotic N fixation, and a few considered atmospheric deposition and non-symbiotic N fixation (Table 1). All models required parameters related to soil, climate, and oil palm physiology, except for two of them (Mosier and IPCC-2006), which only required N input rates. Management parameters were mainly related to fertiliser application, i.e. the amount and type applied, and the date of application. The splitting of application was considered in APSIM, SNOOP, and WANULCAS, and the placement of the fertiliser was only taken into account in WANULCAS.
Main input/output variables and processes modelled in the 11 comprehensive models.
All models considered the main internal fluxes of N, either modelling them or using them as input data. The most common fluxes were transfer from palms to soil, via the mineralisation of N, in the residues left by the palms of the previous cycle and pruned fronds, followed by oil palm uptake and root turnover. The least considered fluxes were cycling of N through the other oil palm residues such as male inflorescences and frond bases, and uptake and recycling by legumes (accounted for by only five models).
Finally, the main losses modelled were leaching (all models), N
Each of the 29 sub-models modelled N losses from the soil–plant system via
one of the following three types of pathways: loss via leaching and runoff
(8 sub-models); loss by emission of NH
For the first pathway (leaching and runoff), eight sub-models were tested.
Leaching concerned inorganic N losses (NO
For the second pathway (the volatilisation of NH
For the third pathway (gaseous losses of N
Oil palm plantations are usually established for a growth cycle of approximately 25 years. Palms are planted as seedlings and the plantation is considered immature until about 5 years of age, when the palm canopy closes and the plantation is considered mature. Harvesting of fresh fruit bunches starts after about 2–3 years. The models were run over the whole growth cycle, including changes in management inputs and output yields between immature and mature phases. We considered replanting after a previous oil palm growth cycle. Potential impacts of land use change on initial conditions were hence not considered. However, when possible, the initial decomposing biomass due to felling of previous palms was included in the models.
In order to compare the models, we kept calibration parameters and input variables consistent across models as much as possible. However, all models did not need the same type of parameters and input data. In particular, for some static models, input variables were initially fixed and could be considered as calibrated parameters based on expert knowledge. For instance, NUTMON and Ecoinvent V3 needed the oil palm uptake rate as an input value, but Schmidt and APSIM used their own calculations for uptake.
We considered a 1 ha plantation located in the Sumatra region of Riau,
Indonesia. For climate during this period, the dataset contained daily rain,
2407 mm yr
Regarding management input variables, we used a set of values representing a
standard average industrial plantation (Pardon et al., 2016). These values
were consistent and based on a comprehensive review of available
measurements. For oil palm the main peculiarities were the yield (25 t of
fresh fruit bunches ha
For model comparison, we calculated the annual estimated losses, considering
the relative contributions of leaching, runoff, and erosion; NH
For the sub-model comparisons, we compared the three groups of
sub-models separately: (1) leaching, runoff, erosion; (2) NH
We compared the magnitude of the losses estimated by the various sub-models, and when possible, we also identified the contribution of the various N input sources to the losses estimated, i.e. the influence of mineral and organic fertiliser inputs, biological N fixation, plant residues, and atmospheric depositions.
Estimates of N losses by 11 models.
Sensitivity analysis investigates how the uncertainty of a model output can be apportioned to different sources of uncertainty in the model inputs (Saltelli et al., 2008). Sensitivity analysis aims at ranking sources of uncertainty according to their influence on the model outputs, which helps to identify inputs that should be better scrutinised in order to reduce the uncertainty in model outputs.
We conducted a Morris sensitivity analysis (Morris, 1991) for the three groups of sub-models in order to identify the input variables that have the most effect on the magnitude of the losses. We used RStudio software to code and run the models (R Development Core Team, 2010), and the “morris” function from the “sensitivity” package version 1.11.1. Process-based models were not included in the sensitivity analysis as the source code of SNOOP was not accessible and APSIM and WANULCAS were not directly programmable without adapting the model structure to run the sensitivity analysis, which fell beyond the scope of this study.
Each model used
The Morris sensitivity analysis technique belongs to the class of
“one-at-a-time” sampling designs. For each model, we carried out
400
Then, following Morris's method, we calculated two sensitivity indices for
each variable
Temporal patterns of N losses along the growth cycle for four approaches selected to illustrate the variability of the results. Most of the
models simulated maximum losses near the beginning of the cycle. The timing
of the peak depended on the model, occurring between the first and the fourth
year. The magnitude of the peak was very variable, up to
738 kg N ha
Estimations of total losses of N were very variable, ranging from
21 to 39 kg N ha
According to the models, the leaching and runoff pathway was the most
important of the three, with an average loss of
61 kg N ha
According to the models, N losses varied substantially along the growth
cycle. On average, 31 % of the losses occurred during the immature
period, which represents 12 % of the cycle duration (Fig. 1b). Most of
the models simulated maximum losses near the beginning of the cycle. The
magnitude of this peak was very variable, up to
738 kg N ha
For this pathway, eight sub-models were tested (Fig. 3), which were all
sub-models integrated in the comprehensive models. There were no stand-alone
models focusing on this pathway. Banabas, Schmidt, and Meier-2014 models
were not included in this comparison because they did not use specific
sub-models but calculated leaching, runoff, and erosion as the surplus of the
N budget. The average loss estimate of the eight sub-models was
59 kg N ha
Comparison of annual average losses through leaching and runoff,
estimated by eight sub-models. The average loss estimate was
59 kg N ha
All eight sub-models considered leaching. Five models considered runoff, but this
flux was very low, i.e. < 0.06 kg N ha
Comparison of measured and modelled N losses in oil palm
plantations. The range of modelled values for leaching and runoff was wider
than the one of measured values of leaching, runoff, and erosion. Modelled
NH
Without accounting for N inputs via empty fruit bunches application,
atmospheric deposition, and biological N fixation, the average annual losses
were estimated at 26 kg N ha
In terms of temporal patterns (Fig. SM1 in the Supplement), APSIM estimated
peak losses through leaching and runoff of up to 251 kg N ha
In terms of spatial patterns, WANULCAS calculated that, of the
135 kg N ha
For this pathway, nine sub-models were tested (Fig. 5). In this comparison, two
sub-models were partial models not used in the 11 comprehensive models (EMEP-2013 and Bouwman-2002a). Two sub-models were used by several comprehensive
models: Asman (1992) was used by Ecoinvent V3 and Meier-2014, and Agrammon
was used by Ecoinvent V3 and Brockmann. Modelled estimates averaged
10.0 kg N ha
Comparison of annual average losses through NH
Whenever possible, we differentiated the influence of mineral fertiliser,
empty fruit bunches, and leaves on the emissions. The average emissions from
mineral fertiliser were estimated at 9.2 kg N ha
In terms of temporal patterns, only the sub-models considering emissions from empty fruit bunches presented a peak which occurred over the first 2 years.
For this pathway, 12 sub-models were tested (Fig. 6). Three of these
sub-models were partial models not used in the 11 comprehensive models
(Crutzen, EMEP-2013, and Shcherbak). Four sub-models
were used in several comprehensive models: Nemecek-2007 was used in Ecoinvent
V3 and Brockmann; and IPCC-2006 was used in Schmidt, Ecoinvent V3,
Meier-2014 and Brockmann. The average estimate of combined
N
Comparison of annual average losses through N
Comparison of annual average losses through N
For N
Comparison of annual average losses through NO
Morris's sensitivity indices for five sub-models calculating leaching
and runoff losses. Clay content, rooting depth, and oil palm N uptake had high
interaction indices, and they had the most important mean indices with
IPCC (2006) emission factor. Sub-models tested: IPCC-2006, Mosier, Smaling,
de Willigen, and SQCB-NO3. Indices lower than
50 kg N ha
In terms of temporal patterns (Fig. SM2), the sub-models that included
mineral fertiliser inputs only did not show any peak of emissions over the
crop cycle, e.g. in Bouwman et al. (2002b), whereas the ones taking into
account at least one other N input, such as felled palms, empty fruit
bunches, and biological N fixation, showed a peak during the immature period,
e.g. in Crutzen and APSIM. In field measurements, higher levels of losses
through N
For the leaching and runoff pathway, five out of eight sub-models were tested
(Fig. 9). None of these sub-models took erosion into account. We therefore
did not test the influence of slope. On average for the five sub-models, the
most influential input variables were clay content, rooting depth, oil palm N
uptake, and the IPCC emission factor, resulting in values of
For NH
Morris's sensitivity indices for sub-models calculating NH
For N
Morris's sensitivity indices for sub-models calculating N
Across the three pathways, i.e. 19 sub-models, the five most influential
variables were related to leaching and runoff losses (Fig. 12). These
variables, which had
Average Morris indices for 31 variables of the 19 sub-models. The
five variables with the highest influence (
The model comparison revealed large variations between models in the estimation of N losses from oil palm plantations. This variability was apparent a priori in the structures of the models, which were process-based or regression-based, had a yearly or daily time-step, and were more or less comprehensive in terms of processes accounted for. We may assume that other models exist, which we could not access or calibrate, but those tested very likely provide a representative sample of modelling possibilities for simulating the N budget of oil palm plantations. Some models were clearly operated beyond their validity domains, especially regression-based models for leaching. As this study did not aim to validate the robustness of the models, we did not filter out any of them as the overall set of model outputs helped highlight key fluxes and uncertainties. Further modelling work across contrasting plantation situations might be worthwhile to further test the validity of the models. In particular, nutrient, water, or disease stresses, or the impact of the previous land use, may critically influence the overall crop development and associated N budget.
The variability in model type or structure resulted in a large range of model
outputs for the oil palm case simulated. There was an approximate 7-fold
difference between the lowest and the highest overall N loss estimates. In
order to investigate the plausibility of these estimates, we used a simple
budget approach. Assuming that soil N content remained constant over the
cycle, N inputs would equal N exported in fresh fruit bunches plus the
increase in N stock in palms plus N lost. The assumption of constant soil N
appears reasonable because soil N dynamics are closely related to soil C
dynamics, and soil C stocks in plantations on mineral soil have been shown to
be fairly constant over the cycle, especially when oil palm does not replace
forest (Smith et al., 2012; Frazão et al., 2013; Khasanah et al., 2015).
In our scenario based on measured values (Pardon et al., 2016), average N
inputs, N exports, and N stored in palms were 156, 60, and
22 kg N ha
Based on this plausible estimate of 74 kg N ha
Underestimates may be due to simulated leaching losses being too low. This
was particularly clear for SQCB-NO3 and NUTMON, which used regressions not
adapted to the high N uptake rates of oil palm, resulting in negative
leaching losses in some instances. However, IPCC-2006, Mosier, and SQCB-NO3
estimated leaching losses within the of 3.5–55.8 kg N ha
Overestimates of losses were primarily related to leaching losses, which were very high for both WANULCAS and SNOOP. This could result from interactions developing between modules in process-based models. For instance, the zoning of the palm plantation might have interacted with N inputs in WANULCAS, as the mineral N input from fertiliser was applied close to the palm trunks where water infiltration is likely to be higher due to stemflow. Another potentially important interaction involves N immobilisation and mineralisation in soil. Indeed, in WANULCAS, the mineralisation of residues and empty fruit bunches caused high losses through leaching in the first years of the crop cycle, while in APSIM, the immobilisation of N dominated the dynamics over several years and leaching losses were delayed and reduced to a large extent. However, more work is necessary to better understand how the structure of the models can lead to overestimate leaching.
Lastly, the models that came up with a plausible estimate of overall N
losses, i.e. close to 74 kg N ha
Some notable patterns differentiated process-based vs. regression-based models, and more comprehensive vs. less comprehensive models. The process-based models tended to predict higher overall losses and appeared to overestimate leaching losses. The less comprehensive models either seemed to underestimate overall losses, or tended to overestimate leaching losses, which counterbalanced missing fluxes in the N budget. Regarding leaching losses, the process-based models produced similar estimates to those that deduced these losses from the total balance.
Process-based models have the advantage of being able to simulate the impact of management practices, such as the timing, splitting, and placement of fertilisers. They also take into account other processes related to the N cycle, such as carbon cycling, plant growth, and water cycling. However such models need more data, e.g. related to soil characteristics. Furthermore, the interactions between modules may generate unexpected behaviours, e.g. for simulating leaching, and they are generally not easily handled by non-experts. On the other hand, simple models, such as IPCC-2006 and Mosier, have the potential to provide plausible results if some N fluxes were supplemented, without requiring a lot of data. However they cannot take into account peculiarities of oil palm or the effects of management practices. One way forward is the development of simple models, such as agro-ecological indicators based on the Indigo© concept (Girardin et al., 1999). These indicators are designed to be easy to use, while incorporating some specificities of crop systems such as management practices.
We identified two important challenges for better modelling the N cycle in oil palm plantations: (1) to model most of the N inputs and losses while accounting for the whole cycle, and (2) to model particular processes more accurately by accounting for the peculiarities of the oil palm system (Table 2).
Synthesis of the challenges identified in modelling the N cycle in oil palm plantations. BNF: biological N fixation.
Given the changes in N dynamics, management practices, and N losses through the growth cycle of oil palm, it is important for models to be built in a way that accounts for this whole cycle. In particular, the immature phase is an important period to consider, as about a third of the N losses occurred during this phase according to the models. Measurements in the field have also shown losses to peak during this phase (Pardon et al., 2016), which involves large inputs of N from the felled palms, the spreading of empty fruit bunches, and biological N fixation. This results in complex N dynamics on the understorey crop, litter, and soil components of the ecosystem. Regarding N inputs, it seems important to also account for biological N fixation and atmospheric deposition since their contributions to the N budget were not negligible, besides fertiliser applications. Internal fluxes, such as the decomposition of felled palms and residues of oil palm and groundcover, are among the largest fluxes in the oil palm system, and their influence on N dynamics is substantial (Pardon et al., 2016). In the case of a new planting, the impacts of land use change and land clearing might also need to be further investigated to better quantify the input fluxes due to decomposition as well as the influence of transitional imbalance state of the agroecosystem on N loss pathways.
For N losses, further model development is also needed to close the N budget.
First, it would be worthwhile to model erosion without requiring detailed
input data, while accounting for changes in erosion risk through the crop
cycle and the effects of erosion control practices on N dynamics. Erosion was
not modelled independently of other losses in most of the reviewed models.
Further, NH
Finally, losses should not be calculated jointly if the objective is to assess the environmental impacts of the plantation and to identify those practices most likely to reduce N losses and impacts. Indeed, different N fluxes may lead to different N pollution risks. N losses through erosion, runoff, or leaching do not end up in the same environmental compartments, e.g. surface water vs. groundwater. They hence do not contribute in the same way to potential environmental impacts such as eutrophication. For the purpose of environmental assessment, models should hence be as comprehensive and detailed as possible. Regarding these criteria, the Schmidt model appeared the most comprehensive and detailed one, as it distinguishes between six N fluxes. However, this model could be improved by separately modelling losses through erosion, runoff, and leaching, i.e. calculating a total of eight N fluxes.
The second challenge is to improve the modelling of some of the key N cycling processes, while accounting for the peculiarities of the oil palm system. Regarding internal fluxes, a better representation of the interaction between legumes and soil N dynamics is an important challenge, as the actual role of legumes during the immature period is complex and not fully understood yet. Indeed, legumes have the capacity to regulate their N provision, by fostering N fixation or N uptake, depending on soil nitrate content (Pipai, 2014; Giller and Fairhurst, 2003). They may contribute to the reduction of N losses through immobilisation or to their increase through N fixation and release.
Reducing the uncertainty in the modelling of leaching is an important
challenge, as about 80 % of the total losses came from leaching,
according to the models, and results were very variable across models. Models
should be better adapted to the oil palm systems, as some regression models
clearly appeared out of their validity domain. Further research on leaching
prediction should focus on the effects of soil clay content, oil palm rooting
depth and oil palm N uptake, since they emerged as the most influential
variables according to the sensitivity analysis. The
In order to take into account the influence of management practices on
internal fluxes and losses, it would be necessary to use a daily step
approach, to account for the timing or splitting of N fertiliser
applications. Modelling approaches that incorporate spatial heterogeneity, as
in WANULCAS, should be favoured, to assess the effect of fertiliser or empty
fruit bunch placements. For gaseous losses, emission factors could be adapted
to the oil palm system, as all of them, i.e. for NH
The main levers that managers can use to reduce N losses involve the level of inputs, including fertiliser management, but also the handling of the immature phase. To manage fertiliser inputs, managers need to know the economic response, which is the main driver of practices, and the environmental response, to type, rate, timing, and placement. They may decide on the optimum fertiliser management practices based on these two dimensions. Models that include both N losses and fresh fruit bunch production in relation to management scenarios can provide the information needed to evaluate both responses.
The model comparison showed the importance of the immature phase with respect to N losses, and suggested field research lines and modelling approaches to improve our understanding of loss processes and their estimation. There are also direct implications of our results for crop management during this phase. Light, water, and N are not fully used by the young palms, as their canopies and root systems do not cover the available ground in the field. Thus, in the current systems, the combination of high input rates with sub-optimal resource capture capacity of the growing oil palms in the immature period results in high losses and negative environmental impacts. There are two possible approaches for reducing those. One is to reduce the inputs: for instance, it might be better to plant a non-legume cover crop and to manage N supply to the palms only with fertilisers. An alternative approach would be to grow another crop during this phase, which would use the surplus N and either export it in product or take it up in biomass so that it would decompose later. For instance, for fast-growing trees like balsa, trunks could be harvested after 5 years and exported, whilst leaving some branches, leaves, and roots to decompose on the soil.
There are also re-planting systems that make it possible to combine old and young palm trees in the same plantation block. The advantage can be both economic and agro-ecological as the immature phase actually becomes productive thanks to the remaining old palm trees and the nutrient cycling potentially more competitive. However, there is still limited data available to quantify and model the potential competition and adapt fertiliser management. Moreover, potential reduction in N losses should not come at the cost of increased use of herbicides, which may be used to kill the old palm trees without damaging the newly planted ones.
From the environmental point of view, it is also important to consider fertiliser management and N losses within a wider system and value chain. First, fertilisers encompass residues from the mill, whose environmental costs and benefits to the plantation should be considered from a whole life cycle perspective. This would include the production of waste, transport, or avoided impact through the substitution of synthetic fertilisers, etc. This can be done using life cycle assessments. Second, the carbon balance, i.e. the balance of carbon sequestration and release, is closely coupled to the N balance. Thus, models that include both cycles are warranted to fully evaluate the environmental impacts of oil palm production.
N losses are a major concern when assessing the environmental impacts of
oil palm cultivation, and management practice targeted at reducing N losses
and costs is critical to this industry. Modelling N losses is crucial because
it is the only feasible way to predict the type and magnitude of losses, and
thus to assess how improved management practices might reduce losses. Our
study showed that there were considerable differences between existing
models, in terms of model structure, comprehensiveness, and outputs. The
models that generate N loss estimates closest to reality were the most
comprehensive ones, and also took into account the main oil palm
peculiarities, irrespective of their calculation time step. However, in order
to be useful for managers, a precise modelling of the impact of management
practices on all forms of N losses seems to require the use of a daily time
step or the modelling of spatial heterogeneity within palm plantations. The
main challenges are to better understand and model losses through leaching,
and to account for most of the N inputs and outputs. Leaching is the main
loss pathway and is likely to be high during the young phase when inputs are
high due to decomposition of felled palms and N fixation by legumes. Field
data are still needed to better understand temporal and spatial variability
of other losses as well, such as N
Research data may be made available upon request to the corresponding author.
The authors would like to thank the French National Research Agency (ANR) for
its support within the frame of the SPOP project (