# Conversion of degraded forests to oil palm plantations in the Peruvian Amazonia: Shifts in soil and ecosystem-level greenhouse gas fluxes

Kristell Anaïk Hergoualc'H, Mariela López Gonzales, Natalia Málaga, Christopher Martius

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Kristell Anaïk Hergoualc'H, Mariela López Gonzales, Natalia Málaga, Christopher Martius. Conversion of degraded forests to oil palm plantations in the Peruvian Amazonia: Shifts in soil and ecosystem-level greenhouse gas fluxes. Agriculture, Ecosystems and Environment, 2025, 386, pp.109603. 10.1016/j.agee.2025.109603. hal-05182938

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# Conversion of degraded forests to oil palm plantations in the Peruvian
Amazonia: Shifts in soil and ecosystem-level greenhouse gas fluxes

Kristell Hergoualc'h<sup>a,b,*</sup>, Mariela López Gonzales<sup>a</sup>, Natalia Málaga<sup>c</sup>, Christopher Martius<sup>d</sup>

## ARTICLE INFO

**Keywords:**
Agriculture
Fertilizer
Nitrous oxide
Carbon dioxide
Methane
Carbon stock
Tropical land use

## ABSTRACT

Oil palm (OP) expansion and associated forest clearance can significantly impact greenhouse gas (GHG) fluxes. We investigated carbon stocks and soil GHG ($N_2O, CO_2, CH_4$) fluxes in a degraded forest and an adjacent 17-year-old OP plantation in Peruvian Amazonia. The plantation comprised three nitrogen (N) fertilizer treatments: 0 (OPN0), 84 (OPN1), 168 (OPN2) kg N ha$^{-1}$ y$^{-1}$. Carbon stocks were inventoried across all pools. GHG and environmental parameters were monitored monthly for 11 months and (bi)daily when fertilizing the OP, with measurements taken both near and far from trees/palms. Ecosystem-scale $CO_2$ equivalent losses from the conversion were computed by balancing carbon stock losses against $N_2O$ emission changes. $N_2O$ emissions (kg N ha$^{-1}$ y$^{-1}$) in the forest (6.7 ± 1.2) where litterfall N inputs were large (213 kg N ha$^{-1}$ y$^{-1}$) were 11, 5, and 3 times the emissions in OPN0 (0.6 ± 0.2), OPN1 (1.4 ± 0.2), and OPN2 (2.3 ± 0.3). In the plantation, 1 % of the N fertilizer applied was released as $N_2O$. Across ecosystems, N inputs primarily controlled $N_2O$ emissions. Soil respiration ($Mg C ha^{-1} y^{-1}$) was 1.4 times higher in the forest (9.1 ± 0.6) than in the plantation (7.3 ± 1, 5.5 ± 0.5, 6.5 ± 0.3 in OPN0, OPN1, OPN2). The forest was a soil $CH_4$ (kg C ha$^{-1}$ y$^{-1}$) sink (-1.5 ± 0.3) while all OP treatments were sources (0.2 ± 0.3, 0.7 ± 0.5, 0.2 ± 0.4 in OPN0, OPN1, OPN2). Ecosystem carbon stock losses from forest-to-OP conversion were substantial (196.8 ± 44.0 Mg $CO_2$ ha$^{-1}$ y$^{-1}$) and partially offset (14–20 %) by decreased $N_2O$ emissions. Complementary studies for this transition are needed to improve global GHG assessments.

## 1. Introduction

Land-use change, particularly deforestation for agriculture, significantly alters carbon (C) storage and greenhouse gas (GHG) fluxes, contributing to climate change (Houghton and Nassikas, 2017). Amazonian tropical forests are of high ecological significance, offering vital ecosystem services at both local and global scales. Peru is the second-largest Amazonian forest country, with approximately 68 million hectares of Amazonian forests, covering about 53 % of its territory (Málaga et al., 2021). Between 2001 and 2020, Peru experienced a loss of around 2.6 million ha of these forests, averaging a net loss of 127, 500 ha y$^{-1}$, with an increasing trend over time (Móstiga et al., 2024).

The tremendous expansion of oil palm plantations over the past decades has been the source of many debates (Levang et al., 2008).

While the crop is remarkably efficient in producing oil and contributes to poverty alleviation and food security, its expansion has often come at the cost of carbon-rich biodiverse tropical forests, leading to severe environmental consequences (Skiba et al., 2020; Zhao et al., 2024). In South America, a third of the oil palm plantations inspected by Vijay et al. (2016) in 2013 had been forest areas in 1989; in Peru, half of them were previously forests. The exponential growth of oil palm plantations in the country from 15,000 ha in 2000 to around 100,000 ha in 2021 (FAO, 2023) has been driven by national and regional policies (Málaga et al., 2021) that failed to restrict the expansion of the crop to deforested or degraded lands. Currently, Ucayali (44 % of the planted area) and San Martin (42 %) are the primary regions for oil palm plantations, followed by Loreto (9 %) (Junpalma, 2021). In Ucayali, smallholders expanded oil palm plantations by 35,000 ha between 2010 and 2016, more than

<footnote><sup>a</sup> Center for International Forestry Research (CIFOR), Lima, Peru</footnote>

<footnote><sup>b</sup> Centre de coopération International en Recherche Agronomique pour le Développement (CIRAD), UMR Eco&Sols, Montpellier, France</footnote>

<footnote><sup>c</sup> Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, the Netherlands</footnote>

<footnote><sup>d</sup> Center for International Forestry Research (CIFOR), Bonn, Germany</footnote>

<footnote>* Correspondence to: Center for International Forestry Research (CIFOR) c/o Centro Internacional de la Papa (CIP), Av. La Molina 1895, La Molina, Apdo Postal 1558, Lima 15024, Peru.</footnote>

<footnote>E-mail address: k.hergoualch@cifor-icraf.org (K. Hergoualc'h).</footnote>
twice the industry's 14,000 ha. Smallholders mainly utilized degraded lands (56 %), whereas the industry cleared mostly old growth forests (70 %) (Glinskis and Gutiérrez-Vélez, 2019).

Despite the significant expansion of oil palm plantations in the Peruvian Amazon, the environmental impacts of converting forests to plantations remain largely unexplored. To date, only one study in Ucayali (Málaga et al., 2021) has examined C stock loss from this conversion, reporting a 55 % decrease in ecosystem C stock after the conversion of degraded forests to oil palm plantations by smallholders. However, there remains a notable gap in quantifying the associated GHG fluxes, both in Peru and across South America. More broadly, paired forest-oil palm plantation experiments that enable site-specific quantification of GHG flux change are extremely limited throughout the tropics (Swails et al., 2024). Given that environmental variables (e.g. soil moisture, soil nitrogen) play a critical role in regulating GHG dynamics, it is essential to monitor these variables concomitantly.

Nitrous oxide (N₂O) is a potent GHG primarily emitted by the agricultural sector (Jia et al., 2019). It is produced by soil microbes through nitrification (aerobic oxidation of ammonium to nitrate) and denitrification (anaerobic reduction of nitrate to N₂O and N₂) of the organic matter (OM) and of nitrogen (N) that is deposited or applied to soils (Butterbach-Bahl et al., 2013). The spatiotemporal variation of soil N₂O fluxes is essentially controlled by N availability and the soil aeration status, commonly expressed by the water-filled pore space (WFPS) (Firestone and Davidson, 1989). In forests, substantial N₂O emissions can result from high N inputs via litterfall. In oil palm plantations, the application of N fertilizer is expected to raise substantially background emissions from OM decomposition. In these systems typically planted in a triangular design (8–9 m between palms), palms are fertilized around their trunks at doses that vary according to the palm’s age following local technical guidelines. In Peru, N fertilizers are recommended at rates of 1–2 kg N palm⁻¹ y⁻¹, placed in a ring located at least 0.5 m from the trunk (Loli, 2012; Minagri, 2018). Based on the default IPCC emission factor for synthetic fertilizer use in wet climates (1.6 % of the N applied) (Hergoualc'h et al., 2019), these N amendment practices alone would lead to emissions of 2.5–5.5 kg N ha⁻¹ y⁻¹. A meta-analysis by van Lent et al. (2015) indicates that forest conversion to croplands and to N-fertilized systems across the tropics induces large N₂O emission increases. Nevertheless, N₂O emissions are among the most uncertain and least documented fluxes in oil palm production systems (Pardon et al., 2016), and it remains unclear how their magnitude may be affected by plantation replacement of forest, especially on mineral soil (Skiba et al., 2020).

Soil respiration is the second largest terrestrial-atmospheric C flux after gross photosynthesis (Hashimoto et al., 2023). It comprises both autotrophic respiration, driven by root activity, and heterotrophic respiration, which results from soil micro and macro-organisms feeding on OM (Rustad et al., 2000). The relative contribution of these processes varies seasonally and among ecosystems (Hanson et al., 2000; Hergoualc'h et al., 2017). Soil respiration rates are dependent on temperature and moisture conditions, substrate quantity and quality, and the dynamics of the vegetation and soil organisms, among other factors (Raich and Tufekciogul, 2000; Rustad et al., 2000). In oil palm plantations, fresh substrate available for heterotrophs including litter and root C inputs can be reduced by 65 % compared to forests (Guillaume et al., 2018). Additionally, the spatial distribution of this substrate differs substantially between ecosystems, as palm fronds in plantations are piled in lines that cover approximately 15 % of the plantation area (Guillaume et al., 2018). Although N fertilization can stimulate soil respiration, these effects are typically short-term (Comeau et al., 2016; Hergoualc'h et al., 2008). The limited number of paired forest-oil palm plantation studies (Aini et al., 2020; Hassler et al., 2015) does not allow drawing conclusions on the direction and magnitude of changes in soil respiration following conversion.

Methane (CH₄) is a strong GHG which atmospheric concentration increase is due to the rising imbalance between its production and

consumption (Tate, 2015). Globally, upland soils are the largest biotic sink for atmospheric CH₄ (Dutaur and Verchot, 2007). Methane is simultaneously consumed by methanotrophs in soil aerobic zones and produced by methanogens where anaerobic conditions prevail (Dutaur and Verchot, 2007). Methanotrophy and methanogenesis are controlled by a combination of interacting abiotic (e.g. temperature, moisture, nitrogen) and biotic (e.g. microbial and plant activity) factors, among which soil moisture accounts for most of the variability in CH₄ flux rates (Liu et al., 2019; Xu et al., 2016). Upland forests typically act as CH₄ sinks (Liu et al., 2019), but their conversion to any other land use generally reduces the strength of this CH₄ sink (McDaniel et al., 2019). Furthermore, CH₄ oxidation rates are generally depressed in soils where inorganic fertilizers have been applied (Tate, 2015). However, the specific land-use or management changes that dictates whether the system acts as a CH₄ source or sink remains not well understood (Tate, 2015). As for N₂O and soil respiration, empirical data from nearby forests and oil palm plantations remain very scarce, highlighting the need for further research in these ecosystems.

Tropical forests account for 69 % of the global terrestrial biomass (Mo et al., 2023). In the Americas, old-growth tropical rainforests have higher aboveground biomass (AGB) compared to moist, dry and montane forests (Rozendaal et al., 2022). Most of the C stock in these forests lies in their biomass (Berenguer et al., 2015), making them particularly vulnerable to human disturbance such as logging (Berenguer et al., 2014). For instance, in the Peruvian Amazon, degraded forests have been found to contain 40 % less AGB carbon than undisturbed forests (Málaga et al., 2021). Although oil palm plantations are commonly developed on degraded forestland across the tropics (Austin et al., 2017; Gutiérrez-Vélez et al., 2011; Kho and Jepsen, 2015), assessments of C stock changes associated with this land conversion remain sporadic; and studies examining ecosystem-scale GHG flux change studies are even rarer.

In this context, this study addresses critical knowledge gaps by investigating ecosystem C stocks and soil GHG (N₂O, CO₂, and CH₄) fluxes in a disturbed forest and an adjacent oil palm plantation in the Peruvian Amazonia. It aims to address the following research questions: (i) How do soil fluxes of GHG vary in space and time? (ii) How does N fertilization in the plantation impact the fluxes? (iii) How do fluxes compare between ecosystems? (iv) How do environmental parameters control the variation in fluxes? And (v) What is the ecosystem-scale CO₂ equivalent loss resulting from the conversion of degraded forest to oil palm plantation? By integrating C stock assessments with soil GHG flux and environmental parameter measurements, this research provides a comprehensive analysis of the environmental consequences of oil palm expansion in the Peruvian Amazon. The findings will contribute to new emissions estimates and inform land-use policies aimed at balancing agricultural development with climate change mitigation goals.

## 2. Materials and methods

### 2.1. Site description

The study was conducted in Nuevo San Pedro (km 47 of the Federico Basadre highway) about 20 km from Pucallpa city, in Campo Verde district, Ucayali region, Peru (Fig. 1a). Long-term records (2002–2011) from the “Ucayali Palms” weather station near the study site indicate steady average annual temperatures between 24 and 26°C. The average annual precipitation amounts to 2331 mm y⁻¹ and a pronounced dry season occurs between May and August (Gobierno Regional de Ucayali, 2016a). The area is located on a terrace at an altitude of 192 m above sea level. The soil is well-drained and is classified as Luvisol / Cambisol (Gobierno Regional de Ucayali, 2016b).

We selected a degraded forest (F) representative of the remaining forest in the area (Málaga et al., 2021) (08°30′42.0″ S; 74°56′14.0″ W) and a 17-year-old oil palm plantation (OP) (Elaeis guineensis) (08°30′51.4″ S; 74°55′47.5″ W), distant approximately 800 m from the
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Location of the study site in Nuevo San Pedro, Campo Verde district, Ucayali, Peru and of the plots in the degraded forest and the oil palm plantation (OP). In the OP, three nitrogen fertilizer treatments were studied (OPN0, OPN1, OPN2) (a). In the OP chambers (for soil N₂O and CH₄ flux measurement) and collars (for soil respiration measurement) (black circles) were placed close to palm (CT, within the fertilized ring, orange area) and far from palm (FT) (b). Source of images: Google Earth Pro. Images from May 2023.</figcaption></figure>

degraded forest (Fig. 1a). These two sites were part of the research undertaken by Málaga et al. (2021) (and named F4 and OP15Y in the corresponding study) who evaluated ecosystem C stock losses from degraded forest conversion to oil palm plantation using a chronosequential approach. The forest was dominated by *Hieronima* sp. 1 and *Moraceae* sp. for trees with a diameter at breast height (DBH) ≥ 30 cm, and by *Pseudolmedia* laevigata and *Fabaceae* sp. for trees with a DBH < 30 cm (Málaga et al., 2021). Its tree density, average DBH, and average height were 728 trees ha⁻¹, 23.4 cm, and 19.8 m, respectively. According to satellite imagery, the OP was developed on a formerly degraded forest (Málaga et al., 2021). Before being planted with palms, the land underwent a clearing fire and was cultivated with a nitrogen-fixing leguminous species (*Pueraria* sp.) as ground cover. The plantation was in its first rotation. The palms were 8.3 m equidistant in a triangular pattern, corresponding to a density of 168 palms ha⁻¹. The land belonged to a medium-sized holder who fertilized the palms irregularly at a rate of 1.5–3.5 kg palm⁻¹ y⁻¹ of rock phosphate (24 % P205 + 33 % CaO), 1.1 kg palm⁻¹ y⁻¹ of urea (46 % Nitrogen), and compounds of K₂O, MgO + B + S in different concentrations. The palms were rain-fed.

## 2.2. Sampling design

We established four experimental plots: One in the forest (F) and three in the oil palm plantation where a fertilization essay was conducted. Each of the OP plots corresponded to a nitrogen (N) treatment as follows: The OPN0 plot received no fertilizer, the OPN1 received a dose representative of local practices (0.5 kg N palm⁻¹ y⁻¹), and the OPN2 received the double of the N1 dose (1 kg N palm⁻¹ y⁻¹).

In each plot, we randomly selected three tree/palm individuals around which we conducted soil GHG flux measurements. In both ecosystems our experimental design made a distinction between the area close to the tree or palm (CT), and the area further from the tree or palm (FT). In the forest, the CT position comprised the area around a tree within a radius of about 50 cm from the trunk, while the FT position

encompassed the remaining of the plot. For plot-scale computations, we assumed both positions to represent an equal share of the plot (50 %). In the OP, the CT position was the area of fertilizer application, and the FT position was assumed to be representative of the rest of the plot. As per local recommendations for mature plantations (Minagri, 2018), the fertilization area was a 0.8 m wide ring located 2.5 m away from the base of the palm trunk (Fig. 1b). Based on the equilateral spacing between palms and the area of the fertilization ring, the CT position was estimated to represent 29 % of the plot area and the FT position 71 %.

The monitoring protocol included two complementary sampling approaches during the 11-month period (February-December 2019). The first consisted of monthly measurements in both ecosystems. The second consisted of an intensive sampling following N application in the plantation, when fluxes are expected to be stimulated (Aini et al., 2015, 2020). Intensive measurements began one day prior to fertilizer application, continued daily for eight days, then every two days until day 29 post-application. The fertilizer was applied in May when the season transitions from wet to dry. The granular fertilizer was evenly sprinkled by hand inside each chamber and collar located at the CT position in the OPN1 and OPN2 treatments, less than 30 minutes before sampling on the fertilization day. The urea application rate was calculated as the surface ratio between the chamber or collar and the CT multiplied by the rate (0.5 or 1 kg N palm⁻¹ y⁻¹). In OPN1 chambers and collars received 4.6 and 0.5 g N, respectively, while OPN2 chambers and collars received double these amounts.

This sampling strategy captured seasonal soil GHG flux variations by spanning both wet and dry seasons over 11 months. Among the three GHG, N₂O is known to be the most temporally variable in tropical upland systems (Kiese et al., 2003, Werner et al., 2006). While monthly sampling might miss short-term N₂O peaks triggered by events such as heavy rainfall, the post-fertilization intensive monitoring accounted for them. This protocol adheres to current methodological standards for GHG measurements in oil palm plantations (Skiba et al., 2020). Throughout both sampling regimes, we monitored soil GHG fluxes alongside environmental variables (air and soil temperature and soil moisture) and
measured monthly litterfall rates in both ecosystems.

## 2.3. Soil GHG flux measurement and computation

Soil N₂O and CH₄ fluxes were measured by the static chamber method (Clough et al., 2020) in opaque PVC chambers (25 cm height, 30 cm diameter) permanently installed 3–5 cm into the ground. All 24 chambers (4 plots × 3 replicates × 2 spatial positions) were deployed one month prior to starting the measurements. Before sampling, the chambers were manually fanned and then sealed with a vented lid equipped with a center port for gas sampling. Gas samples were collected from the enclosed chamber headspace immediately after closure and 10, 20 and 30 minutes hereafter. Samples were taken with a 20 ml syringe and transferred into in 10 ml pre-evacuated glass vials (Perkin Elmer). The vials were sealed with silicone to avoid leakage during transportation by air from Pucallpa to the laboratory in Lima. Within two weeks of collection samples were analyzed by gas chromatography (Clarus 580 Perkin Elmer) using an electronic capture detector for N₂O analysis and a flame ionization detector for CH₄ analysis. Gas fluxes were computed from the rate of change in N₂O or CH₄ concentration in the chamber headspace, determined by linear regression based on the four samples. Whenever one sample had leaked or if the accumulation curve became non-linear the regression was based on three points.

Soil respiration was measured using the dynamic closed chamber approach (Heinemeyer et al., 2011) using a portable infrared gas analyzer (EGM-4, PP systems, USA) connected to a soil respiration chamber (SRC-1, PP systems, USA). The chamber was placed on top of a PVC collar (10 cm height, 10 cm diameter) permanently installed in the ground at a 3–5 cm depth to ensure air-tight closure. All 24 collars were deployed one month before starting the measurements and were located at the side of the chambers used for non-CO₂ GHG flux monitoring. The concentration of CO₂ was recorded automatically every 4.8 seconds for about 180 seconds until reaching a constant flux rate. The CO₂ flux was calculated by the EGM from the linear increase over time of CO₂ concentration in the headspace.

For all three GHG average emission rates were computed for two distinct periods: The outside of fertilization period which lasted 259 days and the post-fertilization period which lasted 29 days. Cumulative annual fluxes were estimated by linear interpolation between measurement dates. Furthermore, for the plantation the emission factor for direct N₂O emission from nitrogen input to the soil was computed as the difference in annual emission rate between the OPN1 or OPN2 treatment and the OPN0 treatment divided with the annual N application rate (84 and 168 kg N ha⁻¹ y⁻¹ for OPN1 and OPN2, respectively).

## 2.4. Soil properties, environmental parameters and litterfall

Soil properties in the plots were measured at the end of the experiment. These included bulk density, carbon, nitrogen and phosphorous contents, particle density, pH, and exchangeable cations. Precipitation, air temperature, soil temperature, and soil moisture were monitored concomitantly with gas measurements. Litterfall was assessed monthly in both ecosystems. The methods for soil properties, environmental parameters, and litterfall measurements and their results are available in supporting information S1, S2, and S3, respectively.

## 2.5. Ecosystem C stocks and CO₂ equivalent loss from degraded forest conversion to oil palm plantation

Carbon stocks were measured in 2015 by Málaga et al. (2021) in the degraded forest (F4) and the oil palm plantation when it was 15-year-old (OP15Y). Briefly, the aboveground biomass (AGB) of trees/palms and standing dead wood (DW) (diameter at breast height (DBH) > 30 cm) was measured in a 1-ha plot. The AGB of small trees (5 < DBH < 30 cm), the necromass (fallen DW, litter, frond), the understory mass, and the

soil (top 30 cm) were monitored in three 16 x 36 m subplots. The nested plot design is presented in S4. The AGB of trees and standing DW was calculated using the allometric model by Chave et al. (2014). The AGB of palms was computed after the allometric equation by Khasanah et al. (2015). The belowground biomass (BGB) of standing live trees was computed using the 0.235 root-to-shoot ratio for tropical/subtropical moist forest below 125 Mg d.m. ha⁻¹ (Mokany et al., 2006). The palm BGB was estimated using the root-to-shoot ratio of 0.29 for middle-aged stands (Syahrinudin, 2005). The phytomass was expressed in C stock based on site-specific, pool-specific C contents. Soil C stocks were calculated on a volume basis from measurements of bulk density and C content.

Ecosystem-scale CO₂ equivalent loss was calculated as the sum of ecosystem C stock loss expressed in CO₂, and soil N₂O emission change expressed in CO₂ equivalent. We used for N₂O a global warming potential of 273 over a 20-year time horizon (Forster et al., 2021). We assumed soil N₂O emissions presented in this paper to be representative of rates two years earlier, when C stocks were assessed. The computation was processed on a 15-year basis to reflect the age of the plantation when its C stock was inventoried. Soil methane flux changes are by default included in soil C stock changes, nevertheless their values expressed in CO₂ equivalent over 15 years are presented in S5, for comparison purposes.

## 2.6. Statistical analysis

For statistical analysis, the InfoStat software was used (Di Rienzo et al., 2019), with a probability level of 5 % to test the significance of effects. The distribution of the variables is presented in S6. The normality of the distribution of residuals was evaluated using the Shapiro-Wilks test. The *t*-test or the non-parametric test of Wilcoxon was used to compare two means for variables whose residuals were normally and not normally distributed, respectively (e.g. difference between spatial positions, seasons). For multicomparison, ANOVA was performed on variables which residuals were normally distributed, and the non-parametric test of Kruskal-Wallis was used when residuals were not normally distributed. For both, the Fisher's Least Significant Difference test was used for post hoc comparison. Relationships between GHG fluxes and environmental variables or N inputs were examined through simple and multiple linear or non-linear regression analysis. The analyses were performed considering either monthly or annual fluxes, spatially stratified or at the plot-scale, combining both ecosystems or not. For instance, the response of annual soil N₂O emissions to N inputs considered plot-scale values in the forest (where litterfall is evenly distributed) and spatially stratified values in the plantation (where the fertilizer is placed close to palm).

The Gaussian error propagation method was used for propagating uncertainties of monthly averages and computing the standard error of annual rates. This method is suitable for step-by-step calculations intended to compute ecological quantities that are expressed as an analytical equation using addition, subtraction, multiplication and division, such as GHG fluxes (Lo, 2005). Annual values ± standard error which did not overlap were considered significantly different between themselves. Results are presented as mean or cumulative values ± standard error. 95 % Confidence intervals are also presented for GHG flux averages.

# 3. Results

## 3.1. Soil N₂O emissions

### 3.1.1. Monthly and seasonal trends

In the degraded forest, soil N₂O emissions were steady throughout the year except for the months of March and November when very large rates were recorded (Fig. 2a). Emissions in the wet season were higher than the dry season at the FT position (16.1 ± 6.9 versus -1.7 ± 0.9 g N
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Mean and standard error (n = 3) of soil N₂O (a), CO₂ (d), CH₄ (g) fluxes measured monthly in the degraded forest (F), measured monthly in the OPN0, OPN1 and OPN2 treatments of the oil palm plantation (OP) (b, e, h), and measured daily after the fertilization in the OP (c, f, i). The arrow in (b), (c), (e), (f), (h), (i) indicates the fertilization event. Fluxes were monitored close to tree / palm (CT) and further from tree / palm (FT). In the OP, the CT position coincides with the area of fertilizer application.</figcaption></figure>

ha⁻¹ d⁻¹, P = 0.005, W = 129, df = 31), with a similar but insignificant tendency at the CT position (37.2 ± 18.1 versus -0.3 ± 0.85 g N ha⁻¹ d⁻¹, P = 0.079, W = 157, df = 31). Average emission rates at both spatial positions were not significantly different from each other (Table 1). Monthly N₂O fluxes were positively related to CH₄ fluxes (Fig. 3a).

In the oil palm plantation, soil N₂O emissions outside of the fertilization period fluctuated only slightly except for a pronounced increase in March at CT position in the OPN2 treatment (Fig. 2b). Emissions did

not exhibit a wet-dry seasonal pattern (P > 0.26). Average rates were higher at the CT position than the FT position in the OPN1 treatment only (Table 1). All treatments emitted N₂O at similar average rates. Following the fertilization, emissions remained stable at the FT position in all treatments and at the CT position in the OPN0 treatment (Fig. 2c). In both fertilized treatments, rates at the CT position were stimulated by N application. Emissions reached their maximum 11 and 21 days after fertilizing the OPN1 and OPN2 treatments, respectively. While in the OPN0 treatment the average post-fertilization flux was higher at the FT

**Table 1**

Average and cumulative annual soil N₂O emissions in the degraded forest (F) and the oil palm plantation treatments (OPN0, OPN1, OPN2). In the plantation, average emissions are detailed per period: Outside of the fertilization period (259 days) and during the post fertilization period (29 days). Emission rates are presented per spatial position (CT: close to tree; FP: far from tree) and at the plot scale (i.e. 50 % and 50 % for CT and FT in the forest, 29 % and 71 % for CT and FT in the plantation). Standard errors are presented in parenthesis. 95 % Confidence intervals of averages are displayed in brackets.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Average emission rate (g N ha<sup>-1</sup> d<sup>-1</sup>)</th><th colspan="4">Annual emission rate (kg N ha<sup>-1</sup> y<sup>-1</sup>)</th></tr><tr><th>Outside fert.</th><th>Post fert.</th><th></th><th></th><th>Per spatial position</th><th></th><th>Plot-scale</th><th></th></tr></thead><tbody><tr><td rowspan="2">F</td><td>CT</td><td>23.5 (11.9)<br/>[0.3; 46.8]</td><td>β</td><td>-</td><td></td><td>9.4 (2.3)</td><td>b</td><td>γ</td><td>6.7 (1.2)</td><td>δ</td></tr><tr><td>FT</td><td>9.7 (4.6)<br/>[0.6; 18.7]</td><td></td><td>-</td><td></td><td>3.9 (0.6)</td><td>a</td><td>β</td><td></td><td></td></tr><tr><td rowspan="3">OPN0</td><td>CT</td><td>2.5 (0.9)<br/>[0.8; 4.2]</td><td>α</td><td>0.5 (0.7)<br/>[-0.8; -1.7]</td><td>a</td><td>α</td><td>0.7 (0.2)</td><td>α</td><td>0.6 (0.2)</td><td>α</td></tr><tr><td>FT</td><td>1.8 (1.0)<br/>[-0.2; 3.9]</td><td></td><td>2.4 (0.6)<br/>[1.2; 3.7]</td><td>b</td><td></td><td>0.5 (0.2)</td><td>α</td><td></td><td></td></tr><tr><td>OPN1</td><td>CT</td><td>5.8 (1.2)<br/>[3.4; 8.1]</td><td>b</td><td>αβ</td><td>A</td><td>28.8 (5.8) [17.4; 40.2]</td><td>b</td><td>β</td><td>B</td><td>β</td></tr><tr><td rowspan="3">OPN2</td><td>FT</td><td>2.3 (1.2) [0.0; 4.6]</td><td>a</td><td>1.6 (1.2)<br/>[-0.7; 3.9]</td><td>a</td><td></td><td>0.7 (0.3)</td><td>a</td><td>α</td><td></td></tr><tr><td>CT</td><td>13.3 (5.4) [2.7; 23.9]</td><td>αβ</td><td>39.9 (4.8) [30.6; 49.2]</td><td>b</td><td>β</td><td>6.0 (1.1)</td><td>b</td><td>γ</td><td>2.3 (0.3)</td><td>γ</td></tr><tr><td>FT</td><td>2.0 (0.6) [0.7; 3.2]</td><td></td><td>1.6 (0.7)<br/>[0.2; 3.1]</td><td>a</td><td></td><td>0.8 (0.1)</td><td>a</td><td>α</td><td></td><td></td></tr></tbody></table>

Letters (a, b) indicate a significant difference between spatial positions within a plot (F, OPN0, OPN1, OPN2) and a period. Symbols (α, β, γ, δ) indicate a significant difference between plots (F, OPN0, OPN1, OPN2) within a spatial position and a period. Letters (A, B) indicate a significant difference between the outside of fertilization and the post fertilization periods, within a spatial position of an N treatment in the plantation. No letters or symbols are presented in the absence of a significant difference. n = 33 for annual averages for each CF and FT position in the forest and in the plantation outside of the fertilization period, n = 57 for annual averages for each CF and FT position in the plantation during the post-fertilization period. Cumulative emission rates are based on 11 and 30 sampling days in the forest and plantation, respectively.
<figure><img src="image_4.png" /><figcaption>Fig. 3. Relationships between spatially stratified monthly soil N₂O and CH₄ fluxes in the forest (F) (a), plot-scale annual soil N₂O emissions and nitrogen (N) application in the oil palm plantation (OP) (b), annual soil N₂O emissions (plot-scale in the F, spatially stratified in the OP) and N inputs (c), spatially stratified monthly soil respiration and soil water-filled pore space (WFPS) in the OP (d), plot-scale monthly soil CH₄ fluxes and rainfall in the F (e), and spatially stratified monthly soil CH₄ fluxes and WFPS in the F (f). Dashed gray lines show the models. The models are presented with coefficients and their level of significance (* P < .05, ** P < .01, *** P < .001).</figcaption></figure>

position than the CT position (P = 0.012, W = 2835, df = 112), the trend was reversed in the OPN1 and OPN2 treatments (P < 0.0001) (Table 1). Post-fertilization emissions were similar among treatments at the FT position, while at the CT position, they were higher in the fertilized treatments than the OPN0 treatment (P < 0.0001). Average post-fertilization rates were approximately 5 and 3 times higher than rates outside of the fertilization period at the CT position in the OPN1 and OPN2 treatments, respectively (P ≤ 0.0001, W = 1031 and 967, df = 88).

Land use comparison outside of the fertilization period indicates higher emissions in the forest than the oil palm plantation at the CT position for the OPN0 treatment (P = 0.03, H = 8.7, df = 3).

### 3.1.2. Annual emissions and emission factor from N inputs

Annual emissions of N₂O were higher at the CT position than the FT position in both ecosystems, except in the OPN0 treatment (Table 1). Close to tree, annual rates were similar in the forest and OPN2, followed by OPN1 and OPN0. In the latter, the rates were, respectively, half and one tenth of the rate in OPN2. Far from tree, annual rates were higher in the forest than in the plantation, where no treatment effect was observed. Plot-scale annual emissions followed the order forest > OPN2 > OPN1 > OPN0. The rate in OPN2 was about two and four times the rate in OPN1 and OPN0, respectively.

In the plantation, annual N₂O emissions responded linearly to N application (Fig. 3b), with an emission factor for direct N₂O emission from N input to the soil of 1 %. Additionally, across ecosystems, annual N₂O fluxes increased linearly with rising nitrogen inputs from either

### Table 2

Average and cumulative annual soil respiration in the degraded forest (F) and the oil palm plantation treatments (OPN0, OPN1, OPN2). In the plantation, average emissions are detailed per period: Outside of the fertilization period (259 days) and during the post fertilization period (29 days). Emission rates are presented for each spatial position (CT: close to tree; FP: far from tree) and at the plot scale (i.e. 50 % and 50 % for CT and FT in the forest, 29 % and 71 % for CT and FT in the plantation). Standard errors are presented in parenthesis. 95 % Confidence intervals of averages are displayed in brackets.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="6">Average emission rate (kg C ha<sup>-1</sup> d<sup>-1</sup>)</th><th colspan="6">Annual emission rate (Mg C ha<sup>-1</sup> y<sup>-1</sup>)</th></tr><tr><th colspan="3">Outside fert.</th><th colspan="3">Post fert.</th><th colspan="3">Per spatial position</th><th colspan="3">Plot-scale</th></tr></thead><tbody><tr><td rowspan="2">F</td><td>CT</td><td>36.2 (4.1)</td><td>[28.1; 44.2]</td><td>b</td><td>γ</td><td>-</td><td>-</td><td>11.3 (1.1)</td><td>b</td><td>γ</td><td>9.1 (0.6)</td><td>γ</td></tr><tr><td>FT</td><td>23.8 (2.2)</td><td>[19.5; 28.0]</td><td>a</td><td>-</td><td>-</td><td>-</td><td>6.9 (0.4)</td><td>a</td><td>β</td><td>-</td><td>-</td></tr><tr><td rowspan="2">OPN0</td><td>CT</td><td>13.1 (2.1)</td><td>[9.5; 17.6]</td><td>α</td><td>A</td><td>16.7 (0.9)</td><td>a</td><td>α</td><td>B</td><td>5.2 (0.5)</td><td>a</td><td>α</td><td>7.3 (1.0)</td><td>β</td></tr><tr><td>FT</td><td>22.5 (6.1)</td><td>[10.5; 34.5]</td><td>β</td><td>B</td><td>20.0 (1.0)</td><td>b</td><td>β</td><td>A</td><td>8.1 (1.3)</td><td>b</td><td>β</td><td>-</td><td>-</td></tr><tr><td rowspan="2">OPN1</td><td>CT</td><td>17.0 (2.5)</td><td>[12.2; 21.8]</td><td>αβ</td><td>A</td><td>21.6 (1.4)</td><td>b</td><td>β</td><td>B</td><td>5.6 (0.4)</td><td>α</td><td>5.5 (0.5)</td><td>α</td><td>-</td></tr><tr><td>FT</td><td>17.6 (3.3)</td><td>[11.1; 24.0]</td><td>-</td><td>-</td><td>16.1 (1.0)</td><td>a</td><td>α</td><td>-</td><td>5.4 (0.7)</td><td>-</td><td>α</td><td>-</td><td>-</td></tr><tr><td rowspan="2">OPN2</td><td>CT</td><td>21.5 (2.6)</td><td>[16.4; 26.6]</td><td>β</td><td>A</td><td>29.7 (2.6)</td><td>b</td><td>γ</td><td>B</td><td>6.9 (0.4)</td><td>β</td><td>6.5 (0.3)</td><td>β</td><td>-</td></tr><tr><td>FT</td><td>20.2 (2.0)</td><td>[16.3; 24.2]</td><td>-</td><td>-</td><td>22.9 (2.6)</td><td>a</td><td>β</td><td>-</td><td>6.3 (0.4)</td><td>-</td><td>α</td><td>-</td><td>-</td></tr></tbody></table>

Letters (a, b) indicate a significant difference between spatial positions within a plot (F, OPN0, OPN1, OPN2) and a period. Symbols (α, β, γ, δ) indicate a significant difference between plots (F, OPN0, OPN1, OPN2) within a spatial position and a period. Letters (A, B) indicate a significant difference between the outside of fertilization and the post fertilization periods, within a spatial position of an N treatment in the plantation. No letters or symbols are presented in the absence of a significant difference. n = 33 for annual averages for each CF and FT position in the forest and in the plantation outside of the fertilization period, n = 57 for annual averages for each CF and FT position in the plantation during the post-fertilization period. Cumulative emission rates are based on 11 and 30 sampling days in the forest and plantation, respectively.
litterfall or applied fertilizer (Fig. 3c).

## 3.2. Soil respiration

### 3.2.1. Monthly and seasonal trends

In the degraded forest, soil respiration fluctuated throughout the year with no discernable pattern (Fig. 2d) and was similar in the dry season and the wet season (P > 0.39). Emissions were on average higher at the CT position than the FT position (P = 0.0143, W = 780, df = 49, Table 2).

In the oil palm plantation, soil respiration was relatively steady except for high rates in February and March in the OPN0 treatment at the FT position (Fig. 2e). There was no wet-dry seasonal trend in OPN0 and OPN1, while in the OPN2 treatment the soil respiration rate was higher in the wet season (24.3 ± 3.0 CT, 22.6 ± 2.3 FT) than the dry season (11.7 ± 2.6 CT, 12.4 ± 2.0 FT) (P = 0.02, W = 44, df = CT, P = 0.03, W = 45, df = 24). Monthly soil respiration increased linearly as the WFPS rose, though with a low explanatory power (19%, Fig. 3d). In all treatments, average rates were similar at both spatial positions (Table 2). Emissions were greater in the OPN2 treatment as compared to the OPN1 treatment, at the CT position (P < 0.0001, H = 29.5, df = 3). Following fertilizer application, soil respiration remained steady at the CT position of the unfertilized treatment and at the FT position of all treatments except for a high rate on day 11 in the OPN2 treatment (Fig. 2f). Conversely, emissions peaked from day 1 to day 3 at the CT position of the amended treatments, after which rates stabilized. In these treatments emissions cumulated over the four days following N application were higher than in OPN0 with an emission surplus resulting from urea mineralization (51–54%) and fertilized-induced respiration (46–49%) (Fig. 4). In the OPN0 treatment, soil respiration was on average lower at the CT position than at the FT position (P = 0.0161, W = 2853, df = 112), while this trend was reversed in the fertilized treatments (P < 0.0231). At the CT position, emission rates followed the order OPN2 > OPN1 > OPN0, with the rate in the OPN2 treatment reaching about two times the rate in the unfertilized treatment. Post-fertilization soil respiration rates at the CT position of the fertilized treatments were 1.3–1.4 times higher than rates outside of the fertilization period (P < 0.037).

Land use comparison outside of the fertilization period denotes a higher soil respiration in the forest than the oil palm plantation at the CT position (P < 0.0001, H = 29.5, df = 3) while at the FT position, there was no difference among land uses (P = 0.72, H = 1.6, df = 3).

### 3.2.2. Annual emissions

Annual CO₂ emissions were higher at the CT position than the FT

<figure><img src="image_5.png" /></figure>

**Fig. 4.** Partitioning of post-fertilization cumulated (day 0 - day 4) soil respiration at the close to palm (CT) position in the oil palm plantation treatments (OPN0, OPN1, OPN2). Background respiration in OPN1 and OPN2 was assumed to equal background respiration in OPN0. The carbon in the applied urea (36 and 72 kg C ha⁻¹ in OPN1 and OPN2) was assumed to have fully volatilized as CO₂ (Conde et al., 2005). The remaining emissions correspond to fertilizer-induced respiration. The percentages indicate the share of fertilizer-induced respiration and urea mineralization contributing to emission surplus resulting from fertilization.

position in the degraded forest (Table 2). An opposite trend occurred in the OPN0 treatment while no spatial effect was apparent in the fertilized treatments. Close to tree, annual soil respiration of the forest exceeded respiration in all plantation treatments. Far from tree, annual rates were higher in the forest as compared to the fertilized oil palm treatments. Plot-scale annual emissions followed the order forest > N0, N2 > N1.

## 3.3. Soil CH₄ fluxes

### 3.3.1. Monthly and seasonal trends

Methane fluxes in the degraded forest were predominantly net uptakes (Fig. 2g). Uptakes were larger in the dry season (-7.1 ± 2.7 and -8.4 ± 2.0 g C ha⁻¹ d⁻¹ CT and FT, respectively) than the wet season (-1.1 ± 2.6 and -2.2 ± 1.9 g C ha⁻¹ d⁻¹ CT and FT, respectively) (P < 0.012). The spatial position did not influence uptake averages (P = 0.89, W = 1116, df = 64, Table 3). Monthly variations in CH₄ fluxes were partially explained by changes in rainfall (r² = 0.50) and WFPS (r² = 0.23) (Fig. 3e, f).

In the oil palm plantation, CH₄ fluxes tended towards emissions during the first three months and alternated between emissions and uptakes depending on the treatment as from the start of the dry season in June (Fig. 2h). As in the forest, there was no spatial effect on the fluxes (P > 0.45). Besides, CH₄ emission rates of the treatments were all close to each other (P < 0.05). There was no noticeable fertilizer effect on the fluxes at the CT position in the fertilized treatments (Fig. 2i). Also, post-fertilization fluxes were not influenced by the spatial position (P < 0.05) or the treatment (P > 0.21). Finally, average post-fertilization fluxes were similar to rates outside of the fertilization period (P > 0.05).

Land use comparison outside the fertilization period points to CH₄ fluxes in the OPN0 and OPN1 treatments (emissions) significantly higher that fluxes in the forest (uptakes) at both spatial positions (P < 0.03).

### 3.3.2. Annual emissions

Annual CH₄ fluxes in the degraded forest were net uptakes while fluxes in the oil palm plantation were mostly emissions (Table 3). In the plantation, emissions were similar among treatments except for the OPN1 treatment where emissions were higher than in the OPN2 treatment at the CT position. Plot-scale fluxes were net uptakes in the forest, and emissions in the plantation, where rates did not differ between treatments.

## 3.4. Ecosystem C stocks and CO₂ equivalent losses from degraded forest conversion to oil palm plantation

Carbon stocks were mainly allocated in the biomass pool in both ecosystems (64 % and 56 % respectively in the forest and the plantation) (Table 4). The carbon stock decreased in all pools following forest to oil palm conversion, except for the soil where no change was discernible. The conversion led to substantial ecosystem-scale C stock losses of 53.7 ± 12.0 Mg C ha⁻¹ 15 y⁻¹ or 196.8 ± 44.0 Mg CO₂ ha⁻¹ 15 y⁻¹. Comparatively, the shift of CH₄ from a sink in the forest to a source in the plantation resulted in a marginal emission increase of around 0.2 Mg CO₂eq ha⁻¹ 15 y⁻¹ (S5). Given the higher soil N₂O emission rate in the forest as compared to the rate in the OP treatments, the conversion resulted in emission decreases between 38.9 ± 7.7 and 28.0 ± 7.9 Mg CO₂eq ha⁻¹ 15 y⁻¹. Ecosystem-scale CO₂eq losses (i.e. the balance between C stock loss and soil N₂O emission reduction) from the conversion were in the range 157.9 ± 44.7 – 168.8 ± 44.7 Mg CO₂eq ha⁻¹ 15 y⁻¹. These results indicate that the decrease in soil N₂O emission offsets ecosystem-scale C stock losses by 14–20 %.
<caption>**Table 3**</caption>

Average and cumulative annual soil CH₄ emissions in the degraded forest (F) and the oil palm plantation treatments (OPN0, OPN1, OPN2). In the plantation, average emissions are detailed per period: Outside of the fertilization period (259 days) and during the post fertilization period (29 days). Emission rates are presented for each spatial position (CT: close to tree; FP: far from tree) and at the plot scale (i.e. 50 % and 50 % for CT and FT in the forest, 29 % and 71 % for CT and FT in the plantation). Standard errors are presented in parenthesis. 95 % Confidence intervals of averages are displayed in brackets.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Average emission rate (g C ha<sup>-1</sup> d<sup>-1</sup>)</th><th colspan="2">Annual emission rate (kg C ha<sup>-1</sup> y<sup>-1</sup>)</th></tr><tr><th>Outside fert.</th><th>Post fert.</th><th>Per spatial position</th><th>Plot-scale</th></tr></thead><tbody><tr><td rowspan="2">F</td><td>CT</td><td>-3.3 (2.0) [-7.1; 0.6]</td><td>α</td><td>-</td><td>-1.5 (0.5)</td><td>α</td><td>-1.5 (0.3)</td><td>α</td></tr><tr><td>FT</td><td>-4.4 (1.5) [-7.3; 1.5]</td><td>α</td><td>-</td><td>-1.6 (0.3)</td><td>α</td><td>-1.6 (0.3)</td><td>α</td></tr><tr><td rowspan="2">OPN0</td><td>CT</td><td>2.3 (1.7) [-0.9; 5.6]</td><td>β</td><td>-2.0 (1.3) [-4.6; 0.6]</td><td>0.4 (0.3)</td><td>β γ</td><td>0.2 (0.3)</td><td>β</td></tr><tr><td>FT</td><td>1.6 (1.5) [-1.4; 4.5]</td><td>β</td><td>-0.3 (1.1) [-2.4; 1.8]</td><td>0.1 (0.3)</td><td>β</td><td></td><td></td></tr><tr><td rowspan="2">OPN1</td><td>CT</td><td>4.5 (2.4) [-0.3; 9.2]</td><td>β</td><td>0.2 (0.9) [-1.5; 1.9]</td><td>1.2 (0.6)</td><td>γ</td><td>0.7 (0.5)</td><td>β</td></tr><tr><td>FT</td><td>2.2 (2.3) [-2.4; 6.8]</td><td>β</td><td>-1.6 (1.1) [-3.7; 0.5]</td><td>0.5 (0.6)</td><td>β</td><td></td><td></td></tr><tr><td rowspan="2">OPN2</td><td>CT</td><td>0.7 (2.1) [-3.4; 4.9]</td><td>αβ</td><td>-0.7 (1.0) [-2.6; 1.2]</td><td>-0.1 (0.5)</td><td>β</td><td>0.2 (0.4)</td><td>β</td></tr><tr><td>FT</td><td>0.1 (1.7) [-3.2; 3.5]</td><td>αβ</td><td>-0.1 (1.2) [-2.3; 2.2]</td><td>0.2 (0.5)</td><td>β</td><td></td><td></td></tr></tbody></table>

Symbols (α, β) indicate a significant difference between plots (F, N0, N1, N2) within a spatial position and a period. There were no significant differences between spatial positions within a plot and a period or between the outside of fertilization and the post fertilization periods, within a spatial position of an N treatment in the plantation. No letters are presented in the absence of a significant difference. n = 33 for annual averages for each CF and FT position in the forest and in the plantation outside of the fertilization period, n = 57 for annual averages for each CF and FT position in the plantation during the post-fertilization period. Cumulative emission rates are based on 11 and 30 sampling days in the forest and plantation, respectively.

<caption>**Table 4**</caption>

Carbon stocks per pool and soil N₂O emissions in the degraded forest and the oil palm plantation treatments (OPN0, OPN1, OPN2). The difference (F – OP) denotes ecosystem C stock loss, soil N₂O emission decrease over 15 years, and their balance (Ecosystem-scale losses) resulting from forest to OP conversion. Standard errors are presented in parenthesis. AGB: Aboveground Biomass, BGB: Belowground Biomass.

<table><thead><tr><th>Carbon stocks</th><th>Forest (Mg C ha<sup>-1</sup>)</th><th>OP</th><th>F - OP (Mg CO<sub>2</sub> ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td>AGB</td><td>77.6 (n.a.)</td><td>41.8 (n.a.)</td><td></td></tr><tr><td>BGB</td><td>18.2 (n.a.)</td><td>12.1 (n.a.)</td><td></td></tr><tr><td>Understory</td><td>0.6 (0.1)</td><td>0.04 (0.02)</td><td></td></tr><tr><td>Dead wood</td><td>22.9 (7.1)</td><td>0.4 (7.1)</td><td></td></tr><tr><td>Fronds</td><td>n.a.</td><td>10.1 (1.2)</td><td></td></tr><tr><td>Litter</td><td>2.5 (0)</td><td>0.2 (0.1)</td><td></td></tr><tr><td>Soil (top 30 cm)</td><td>28.9 (1.5)</td><td>32.4 (6.3)</td><td></td></tr><tr><td>Ecosystem</td><td>150.7 (7.3)</td><td>97.1 (9.6)</td><td>196.8 (44.0)</td></tr><tr><td>Soil N₂O emissions</td><td>(Mg CO₂eq ha<sup>-1</sup> 15 y<sup>-1</sup>)</td><td></td><td></td></tr><tr><td>OPN0</td><td>42.8 (7.6)</td><td>3.9 (1.3)</td><td>38.9 (7.7)</td></tr><tr><td>OPN1</td><td>42.8 (7.6)</td><td>9.0 (1.6)</td><td>33.8 (7.8)</td></tr><tr><td>OPN2</td><td>42.8 (7.6)</td><td>14.8 (1.9)</td><td>28.0 (7.9)</td></tr><tr><td>Ecosystem-scale losses</td><td>(Mg CO₂eq ha<sup>-1</sup> 15 y<sup>-1</sup>)</td><td></td><td></td></tr><tr><td>OPN0</td><td></td><td></td><td>157.9 (44.7)</td></tr><tr><td>OPN1</td><td></td><td></td><td>163.0 (44.7)</td></tr><tr><td>OPN2</td><td></td><td></td><td>168.8 (44.7)</td></tr></tbody></table>

# 4. Discussion

## 4.1. Soil N₂O emissions

Annual N₂O emissions in the degraded forest fall within the upper range of values reported for the tropics (Figure S7a and review by van Lent et al. 2015). Similarly, litter deposition in the forest (12 Mg d.m. ha⁻¹ y⁻¹, S3) is important compared to pantropical rates (Vitousek, 1984) and those observed in tropical South American secondary forests (8 ± 3 Mg d.m. ha⁻¹ y⁻¹ (Chave et al., 2010). The relationship between N₂O emissions and N inputs (Fig. 3c) along with the global positive response of N₂O emissions to litterfall in temperate and tropical forests (Gao et al., 2018; Zhou et al., 2022) suggest that litterfall, by supplying organic N, was a major control of soil N₂O emissions in the degraded forest. While monthly N₂O emission and litterfall rates are not correlated, the higher N₂O emissions in the wet than the dry season may be explained by a lag between increased litterfall – occurring a month after the start of the dry season (Figure S3) – and its decomposition when rainfall raised during the wet season. Indeed, litter decomposition in tropical forests is known to be strongly regulated by rainfall patterns, among other factors (Powers et al., 2009). Further, litter entrance and

decomposition may also be at the origin of the relationship between soil N₂O and CH₄ fluxes (Fig. 3a). The mineralisation of litter organic N discharges ammonium into the soil (Krishna and Mohan, 2017) which stimulates N₂O emissions (Cheng et al., 2014) and has the potential to enhance CH₄ emissions through inhibition of CH₄ oxidation (Bodelier and Laanbroek, 2004).

Annual N₂O emissions in the oil palm plantation lie in the range of rates measured in Southeast Asia (Figure S7a). Fertilization at a rate typical of local practices (84 kg N ha⁻¹ y⁻¹, OPN1) and at twice that rate (168 kg N ha⁻¹ y⁻¹, OPN2), doubled and quadrupled the emissions of the unfertilized treatment, respectively. Notwithstanding, the response of N₂O emissions to N application was four and seven times less important than in seven-year (Sakata et al., 2015) and 13-year (Rahman et al., 2019) old plantations of Riau and Sumatra, Indonesia, where urea was applied at a similar dose to that brought to OPN2. Since soil physical and chemical properties in these plantations are comparable to those at our sites, the weaker N₂O response to fertilization may have stemmed from other site-specific factors. These include climatic conditions after fertilizer application, such as higher temperatures at our site causing NH₃ volatilisation, and/or higher precipitation inducing nitrate leaching. They could also relate to plant demand with e.g. a greater demand of the palms at our site due to irregular fertilization. Likewise, the less intense response of soil N₂O emissions to N application in our plantation is reflected in a 1 % emission factor (Fig. 3b), which is below the 1.6 % IPCC default for wet climate and synthetic fertilizer (Hergoualc'h et al., 2019).

The conversion of degraded forest to oil palm plantation substantially diminished soil N₂O emissions (Figure S7a) owing to reduced N inputs (from 213 kg N ha⁻¹ y⁻¹ to 84–168 kg N ha⁻¹ y⁻¹) (Fig. 3c). This result contrasts with findings in Sumatra, Indonesia where such a conversion had no impact on N₂O emissions (Aini et al., 2015; Hassler et al., 2017) despite a similar reduction in N inputs from about 180 kg N ha⁻¹ y⁻¹ to 30–90 kg N ha⁻¹ y⁻¹. Conversely, larger N₂O emissions after degraded forest conversion have been reported in industrial plantations of Indonesia and Malaysia, where N fertilizer application rates were presumably high but not explicitly detailed (Drewer et al., 2021; Hassler et al., 2017). It is worth noting that none of the current four studies evaluating the effect of forest or degraded forest conversion to oil palm plantations considered emissions from frond piles. Even though these piles cover only a small area (6–15 % of the plantation) (Chen et al., 2024; Manning et al., 2019), the soil underneath receive meaningful N inputs (58.9 ± 6.7 kg N ha⁻¹ y⁻¹, S3). While Chen et al. (2024) found a low contribution of frond piles to plantation-scale N₂O emissions, their experimental design leaves uncertainty regarding whether the mass of senesced fronds included in the measurement chamber accurately
represented N inputs.

## 4.2. Soil respiration

Contrary to N₂O emissions, soil respiration in the degraded forest ranks low among rates observed in the tropics (Figure S7b). Despite - as mentioned above – high litterfall rates, a low respiration may be related to slow organic matter decomposition by microorganisms and/or reduced root respiration. Forest soil respiration displayed no seasonal pattern; however, this result is based on a limited number of sampling months during the dry season owing to equipment failure.

In the plantation, soil respiration is aligned with rates measured in Southeast Asia (Figure S7b). It was higher during the wet than the dry season in the OPN2 treatment (CT position), in agreement with the positive relationship between soil WFPS and soil respiration (Fig. 3d) and the higher WFPS observed in the wet season (Figure S2). Similarly, Chen et al. (2024) reported greater respiration in wet than dry months in an oil palm plantation, more specifically in areas corresponding to the CT position and the frond pile. The effect of fertilizer application on soil respiration in the OPN1 and OPN2 treatments was short-lived, lasting only a few days (Fig. 2f), consistent with findings elsewhere in the tropics (Hergoualc'h et al., 2008; Khalil et al., 2007). Approximately half of soil respiration surplus (relative to OPN0) resulted from urea hydrolysis and volatilization (Fig. 4), while the remainder can be attributed to root respiration stimulation and the priming effect on organic matter decomposition (Kuzyakov et al., 2000). Given the brief duration of increased respiration rates after fertilizing (three days) and its spatial restriction to the CT area, annual soil respiration of the fertilized treatments was not substantially different from that of the unfertilized treatment (Table 2).

Our results show decreased soil respiration following degraded forest conversion to oil palm plantation (Table 2), in agreement with findings by Drewer et al. (2021) and Hassler et al. (2015). However, as in these studies, our experimental design did not account for frond piles, which may introduce some bias in land use comparison. Indeed, Chen et al. (2024) reported that frond piles contributed 30 % to plantation-scale soil respiration.

## 4.3. Soil CH₄ fluxes

In agreement with the literature acknowledging well-drained tropical forest soils as CH₄ sinks (Dutaur and Verchot, 2007; Le Mer and Roger, 2001), the forest even if degraded up took CH₄ (Table 3). Its annual soil CH₄ uptake is slightly higher than rates measured elsewhere in the tropics (Figure S7c). The primary controls of rainfall (Fig. 3e) and soil WFPS (Fig. 3f) over CH₄ fluxes explain the seasonal pattern of larger uptakes in the dry season than the wet season. Such seasonality has also been documented in a logged forest in Jambi, Indonesia (Hassler et al., 2015), and in secondary forests of the Brazilian Amazon (Verchot et al., 2000) and Costa Rica (Keller and Reiners, 1994).

In the oil palm plantation, the soil was an annual source of CH₄, aligning with overall findings in Southeast Asia (Figure S7c). However, some of these studies found that oil palm plantations acted as CH₄ sinks (Chen et al., 2024; Hassler et al., 2015). The discrepancy between studies may come from differences in climate, soil fauna and physicochemical properties of soils like moisture, oxygen availability, pH, texture and mineralogy among other factors (Le Mer and Roger, 2001). Fertilization practices may also influence soil CH₄ fluxes; for instance in Indonesia urea application has been shown to shift soils from CH₄ sources to sinks compared to unfertilized controls (Rahman et al., 2019).

Degraded forest conversion to oil palm plantation led to increased CH₄ emissions to the atmosphere (Table 3). While Aini et al. (2020) and Drewer et al. (2021) found no significant change in CH₄ fluxes, Hassler et al. (2015) observed increased uptakes following conversion. In the latter study, the soil WFPS decreased from 64 % in the degraded forest to 59 % in the plantation, whereas at our sites, the WFPS increased from

30 % to around 50 % (Table S1) following conversion. Given that both studies identified the WFPS as a key driver of CH₄ fluxes, these differing trends in WFPS likely explain the contrasting patterns in annual CH₄ flux changes. At our sites the WFPS increase was primarily driven by higher soil bulk density, which rose from approximately 1.0 g d.m. cm⁻³ in the forest to 1.4 in the plantation. In contrast, at the sites investigated by Hassler et al. (2015), the WFPS decrease was seemingly orchestrated by a reduction in soil moisture. As with other GHGs, accounting for CH₄ fluxes from frond piles appears to be crucial, given their significant contribution - 41 % of annual plantation-scale soil CH₄ uptake - reported by Chen et al. (2024).

## 4.4. Ecosystem C stocks and CO₂ equivalent losses from degraded forest conversion to oil palm plantation

The degraded forest and the plantation examined in this study are part of the sites sampled by Málaga et al. (2021). Our findings indicate that the conversion resulted in an ecosystem C stock loss of 196.8 ± 44.0 Mg CO₂ ha⁻¹ 15 y⁻¹ (Table 4), which is lower than the 229.9 ± 22.5 Mg CO₂ ha⁻¹ 30 y⁻¹ loss estimated by Málaga et al. (2021). This discrepancy likely arises from differences in methods - Málaga et al. (2021) accounted for the variability of degraded forests in the landscape by including four forest replicate sites and assessed the 30-year rotation average C stock of oil palm plantations using a chronosequential sampling approach. Additionally, their ecosystem C stock for oil palm plantations (78.2 ± 2.0 Mg C ha⁻¹) was smaller than the stock in the 15-year-old plantation (97.1 ± 9.6 Mg C ha⁻¹ Mg C ha⁻¹), since oil palm biomass typically increases over time, plateauing around 15 years after planting (Kho and Jepsen, 2015).

Our findings show that the reduction in soil N₂O emissions following degraded forest conversion to oil palm plantation offsets 14–20 % of the ecosystem C stock loss. However, to determine whether this offset is a consistent trend, further sampling of additional degraded forests and oil palm plantations of varying age is necessary. Furthermore, the ecosystem CO₂ equivalent losses computed here (157.9 ± 44.7–168.8 ± 44.7 Mg CO₂eq ha⁻¹ 15 y⁻¹) may be underestimated. According to the landowner, at least one fire occurred to clear the land prior to planting the palms. The absence of data on the amount of biomass burnt hinder a proper estimation of N₂O released from biomass burning. Nevertheless, based on IPCC generic methods and defaults (Ogle et al., 2019), additional emissions from fire would amount to 1.6, 3.2, 4.9, and 6.6 Mg CO₂eq ha⁻¹ 15 y⁻¹ – assuming fuel masses equivalent to 25 %, 50 %, 75 %, and 100 %, respectively, of the live biomass and dead wood in the degraded forest.

# 5. Conclusion

This study provides the first assessment of GHG fluxes and C stock changes following forest conversion to oil palm in the Peruvian Amazon, addressing a significant geographical data gap in land-use change research. Our findings reveal that conversion resulted in substantial ecosystem C stock losses (196.8 ± 44.0 Mg CO₂ ha⁻¹ over 15 years), primarily from biomass reduction. However, this conversion simultaneously decreased soil N₂O emissions, offsetting 14–20 % of C stock losses when accounting for their respective global warming potentials (273 for N₂O vs. 1 for CO₂).

The observed decrease in N₂O emissions following conversion contrasts with several Southeast Asian studies, highlighting the importance of region-specific assessments. This discrepancy likely stems from exceptionally high nitrogen inputs in our degraded forest site compared to the plantation. Concurrently, the conversion shifted soil CH₄ dynamics from a net sink to a net source, though with marginal impact on the overall GHG balance.

Our study examined only one site pair and disregarded emissions from frond piles – potential hotspots of soil respiration and CH₄ fluxes, limiting generalizability. Future research should expand sampling
across multiple sites throughout tropical regions to test whether the offsetting effect of reduced N₂O emissions following conversion is consistent across varying conditions. Moreover, addressing spatial variability within plantations, particularly in fertilized zones versus frond piles, will enhance the accuracy of GHG flux estimates.

From a policy perspective, our findings suggest that GHG mitigation strategies should consider the complex interplay between C stocks and non-CO₂ GHG. While N₂O emission reductions partially offset C losses, the net climate impact of conversion remains strongly negative. Therefore, preserving forest C stocks, even in degraded systems, represents an effective climate mitigation strategy, while optimizing nitrogen management in existing plantations could further improve their GHG balance.

## Funding

International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, and Nuclear Safety (BMU), Grant # 20_III_108; Norwegian Agency for Development Cooperation (NORAD), Grant # QZA-21/0124; CGIAR Fund INIT-32-Mitigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+), Alliance Biodiversity/CIAT, Grant # C22PAL159 M+(G199).

## CRediT authorship contribution statement

**Martius Christopher:** Writing – review & editing, Project administration, Funding acquisition. **Málaga Natalia:** Writing – original draft, Investigation, Formal analysis. **López Gonzales Mariela:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Hergoualc'h Kristell:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

## Acknowledgements

This research was part of the project "Transparent monitoring in practice: supporting post-Paris land use sector mitigation". The funding partners that have generously supported it include the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, and Nuclear Safety (BMU, Grant No. 20_III_108), the Norwegian Agency for Development Cooperation (Norad, Grant No. QZA-21/0124) and the CGIAR Fund INIT-32-Mitigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+), Alliance Biodiversity/CIAT (ABC, Grant No. C22PAL159 M+ (G199)). The authors are very grateful to Carlos Romaní for his excellent contribution and continued commitment to field data collection, and Max Isuiza for his support as a field assistant. The authors are particularly thankful to Max Gamarra, who provided access to his lands and facilities and strongly supported the development and implementation of the research. The authors thank the COCEPU oil palm association of Ucayali, for guiding the search of sites and providing technical information on local management practices. Finally, the authors express their sincere gratitude to the anonymous reviewers for their thoughtful comments, constructive criticism, and valuable suggestions, which have significantly improved the quality and clarity of this manuscript.

# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agee.2025.109603.

## Data availability

The data that support the findings of this study are available at https://doi.org/10.17528/CIFOR/DATA.00307.

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