# A new in-field indicator to assess the impact of land management on soil carbon dynamics

Alexis Thoumazeau, Tiphaine Chevallier, Victor Baron, Nancy Rakotondrazafy, Phantip Panklang, Raphaël Marichal, Mark Kibblewhite, David Sebag, Florent Tivet, Cécile Bessou, et al.

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## To cite this version:

Alexis Thoumazeau, Tiphaine Chevallier, Victor Baron, Nancy Rakotondrazafy, Phantip Panklang, et al.. A new in-field indicator to assess the impact of land management on soil carbon dynamics. Geoderma, 2020, 375, pp.114496. 10.1016/j.geoderma.2020.114496. hal-02891616

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Distributed under a Creative Commons Attribution 4.0 International License
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A new in-field indicator to assess the impact of land management on
soil carbon dynamics

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Alexis Thoumazeau<sup>1,2,3*</sup>, Tiphaine Chevallier<sup>3</sup>, Victor Baron<sup>1,2</sup>, Nancy Rakotondrazafy<sup>3</sup>, Phantip

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Panklang<sup>4</sup>, Raphaël Marichal<sup>1,2</sup>, Mark Kibblewhite<sup>5</sup>, David Sebag<sup>6,7,#</sup>, Florent Tivet<sup>8</sup>, Cécile Bessou<sup>1,2</sup>,

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Frédéric Gay³, Alain Brauman³.

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¹CIRAD, UPR Systèmes de Pérennes, F-34398 Montpellier, France

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²Systèmes de pérennes, Univ Montpellier, CIRAD, F-34398 Montpellier, France

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³Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, Montpellier SupAgro, F-34398 Montpellier, France

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⁴Land Development Department, 10900 Bangkok, Thailand

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⁵Cranfield University, Bedford MK43 0AL, United Kingdom

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⁶Normandie Univ, UNIROUEN, UNICAEN, CNRS, M2C, Rouen, France

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⁷Institute of Earth Surface Dynamics, Geopolis, University of Lausanne, Lausanne, Switzerland

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⁸AIDA, Univ Montpellier, CIRAD, F-34398 Montpellier, France

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*Corresponding author:

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Thoumazeau Alexis

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CIRAD - UPR Systèmes de pérennes

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TA B-34/02

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F-34398 Montpellier Cedex 5

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+33 (0)4 67 61 65 73

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alexis.thoumazeau@cirad.fr

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#Current address:

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IFPEN, Geosciences Dept, Rueil-Malmaison, France
24 **Abstract**

The assessment of the impacts of land-use and management on soil organic carbon (SOC) dynamics is a major environmental concern, as the soil carbon cycle underpins key ecosystem services. However, assessments based on short-term SOC dynamics face methodological and experimental difficulties.

Hurisso et al. (2016) proposed a method to assess SOC dynamics by coupling two methods: Permanganate Oxidizable Carbon (POXC) and Basal Soil Respiration (BSR). This method has been used in laboratory on dried and re-wetted soil samples from temperate regions mainly. In our study, we adapted this method to the field and proposed a cost-effective in-field indicator combining the POXC and *in situ* Basal Soil Respiration (SituResp® method).

We tested the indicator at four study sites (n=169 points) within various tropical land-use and management contexts based on rubber, soybean and oil palm cropping systems respectively in Thailand, Cambodia and Indonesia. The results demonstrated the relevance, sensitivity and robustness of the POXC-SituResp® indicator to characterize the impact of a gradient of disturbance on SOC dynamics. The results also highlighted the potential of conservation agriculture (no-tillage and crops residues) and compost amendments to accumulate SOC. Rock-Eval analysis showed that POXC-SituResp® indicator is negatively linked to excess of potentially mineralizable labile carbon.

Carbon pools targeted by the POXC were specified by Rock-Eval pyrolysis measurements to be a rather thermal resistant pool of SOC.

Our study confirms that the integrated indicator based on POXC and BSR assess a relative carbon stabilization of SOC pools. This indicator can be measured in the field by a rapid and cost-effective method.

Key words: In-field indicator; Soil carbon dynamics; Land management; Tropical conditions; POXC; SituResp®
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# 1. Introduction

The management of soil organic carbon (SOC) is a key global challenge as it is implicated in the delivery of soil-supported ecosystem services and goods, such as climate change adaptation, mitigation and food provision (Lal, 2004). Growing awareness of the importance of managing SOC is reflected in initiatives such as the 4p1000 initiative (Soussana et al. 2019, http://4p1000.org/).

However, a better understanding of how human activities affect SOC requires the development and application of improved tools for exploring the impacts of different land uses and managements of varying intensities on SOC dynamics.

Commonly, SOC dynamics are evaluated by observing how total SOC levels are affected by land use and management, integrating spatial and temporal dynamics. This approach is useful when estimating SOC stocks within the carbon cycle and their contribution to maintaining global flows of ecosystems services and goods such as Soussana et al. (2019). Repeated observations within SOC monitoring programs provide data to support modelling of the potential of soil to store additional carbon and the evaluation of options to meet this objective (Chen et al., 2018; Fujisaki et al., 2018a).

However, these approaches have limitations when assessing SOC dynamics at the field scale.

Uncertainties in estimation of total SOC arise from the high temporal and spatial variability in its rate of change, so that the minimum time period to confirm a change in SOC levels may be too long to allow observations of short-term changes in SOC levels. Dignac et al., (2017) underlined this issue, estimating that changes in total SOC levels are challenging to detect over periods of less than 10 years in temperate regions. Less data is available on short-term SOC dynamics (Doane et al., 2003; Fiedler et al., 2016) in comparison to that from studies of longer-term changes in SOC levels, although the former is strongly linked to soil functioning (Wiesmeier et al., 2019) and gives a better understanding of global soil conditions (Bongiorno et al., 2019). Total SOC level is an important indicator of soil status but does not easily provide information about biological activity in soil - which is the main driver of SOC dynamics and its influence on soil functions. On the contrary, assessments of SOC dynamics should provide information about the soil biotic activity and, as a result, about the
SOC trajectories (increase or decline). This requires measurement of functional processes – for example, soil respiration is a basic indicator of SOC dynamics and biological activity that is responsive to short-term changes (Creamer et al., 2014).

To assess SOC trajectories, a variety of fractions of SOC have been proposed as being more sensitive to changes in land management than total SOC, with short half-lives and particular good availabilities to soil biota (Haynes, 2005; Soucémarianadin et al., 2018a). The estimation of such carbon pools is usually based on laboratory measurements made on dried and re-wetted soil samples of, for example, Particulate Organic Carbon (POC), basal soil respiration (BSR) or Microbial Biomass Carbon (MBC) (Six et al., 1999; van Wesmael et al., 2019; Wardle, 1992). Methods based on isotopes and enzymatic activity have also widely been used to study SOC dynamics (Balesdent et al., 1988; Sinsabaugh et al., 2008). However, a common disadvantage with all of these methods is that they are not easily adapted for use in the field, often require specialist expertise and equipment and this limits the number of samples that can be measured. Better access to in-field methods offers larger data sets and a more accurate picture of the relationships between soil processes and SOC dynamics (Vogel et al., 2018; Wiesmeier et al., 2019).

In summary, there is a need to develop methods that provide relevant, interpretable and prompt information about *in situ* SOC dynamics within changing land use and management scenarios, as far as possible without the need to directly access laboratory resources. Ideally, these methods should be accessible to non-experts and support decision-makers, as the main actors in SOC management (Bruun et al., 2017; Minasny et al., 2017).

To answer this need, Hurisso et al. (2016) have proposed a procedure to characterize SOC dynamics, as affected by land management. The approach consists of the combination of two methods based on carbon and biological analysis, namely i) Permanganate OXidizable Carbon (POXC) and ii) Basal Soil Respiration (BSR). These two methods combine two important properties of SOC dynamics, namely: i) the quality of the substrate (accessible for soil biota) as estimated from POXC and ii) soil biological activity as estimated by BSR. According to Culman et al. (2012), POXC represents a
"relatively processed or stabilized pool of active soil carbon" that is closely related to small and heavy particulate organic carbon fractions. BSR is defined as the steady rate of CO₂ emissions arising from microbial decomposition of soil organic matter in a root-free soil (Creamer et al., 2014; Kuzyakov, 2006). POXC and BSR have been used widely to assess soil carbon dynamics and have proved to be especially sensitive to changes in land management (Bongiorno et al., 2019; Stone et al., 2016). By combining these two complementary methods, a dynamic and functional view of soil carbon cycle is revealed through the lenses of estimates of available carbon (POXC) and CO₂ outputs (BSR). Similarly to other indices of soil activity such as the microbial metabolic quotient, the POXC and BSR combination indicates the efficiency of soil organisms to mineralize SOC pools represented by POXC fractions. The framework proposed by Hurisso et al. (2016) can support comparative assessments in a given pedo-climatic zone by computing a linear regression of POXC and BSR values without a pre-requisite for linear model significance (cf. CA Organic Tomato site, Hurisso et al., 2016). Interpretation is based on an analysis of the residual values from this linear regression for different land scenarios. The residuals are then used as an indicator to assess the short-term mineralizability of SOC: with negative residuals (statistically different to 0 value) indicating a trajectory with mineralization of SOC, whereas the opposite is indicated by positive residuals (Hurisso et al., 2016, Supplementary reading 1). Residuals values are thus an indication of the dynamic of SOC pools characterized by the POXC that tends to be more or less mineralized. While the indicator has proved to be sensitive to land management in the USA context (Hurisso et al., 2016), and in a specific case study in Kenya (Sprunger et al., 2019), its efficacy in other biogeographical regions needs to be evaluated. Moreover, an adaptation of the methods for use in the field is desirable, which is made more straightforward by the recent development of the SituResp® method for in-field estimation of BSR (Thoumazeau et al., 2017) and by the availability of an existing method for in-field estimation of POXC (Weil et al., 2003). Moreover, the indicator developed by Hurisso et al. (2016) has not been validated adequately by comparison with other more conventional methods for assessing SOC dynamics. To explore how the in-field indicator is related to soil carbon dynamics, we chose the Rock-
Eval pyrolysis method which is based on the thermal analysis of SOC (Disnar et al., 2003). Rock-Eval pyrolysis has been identified as one of the most promising methods for assessing SOC dynamics (Barré et al., 2016). Recent literature indicates that the stability of soil organic matter to thermal analysis provides a relevant indication of its biogeochemical stability (Cécillon et al., 2018; Plante et al., 2011; Soucémarianadin et al., 2018b). Rock-Eval pyrolysis has also been used recently to answer questions about the stabilization of organic matter during composting (Albrecht et al., 2015), the relative impact of biological inputs and geological substrate on properties of soil organic matter (Matteodo et al., 2018), and the biogenic control of soil engineers on soil organic matter dynamics (Schomburg et al., 2018).

This study evaluates a new in-field indicator of SOC dynamics based on combining POXC and SituResp® measurements (namely POXC-SituResp®), following the framework proposed by Hurisso et al. (2016). The objective of the study is to test the consistency of this new indicator and to inform its further application. The new indicator is applied to tropical soils, for which SOC turnover is higher than in temperate soils (Six et al., 2002), and the effects on indicator values of different pedo-climatic and agricultural activities are investigated. A characterization via Rock-Eval pyrolysis of the carbon pools targeted by this field indicator is provided and discussed.

# 2. Material and Methods

## 2.1 Study sites

The four study sites were located in three Asian countries providing a total of 169 sampling points.

### 2.1.1 Study sites in Thailand

Two sites were studied in Thailand. The sites were located in Chachoengsao (latitude 13°34' N, 101°27' E) and Khon Kaen (latitude 16°44' N, longitude 103°07' E) provinces. The mean annual precipitation was 1328mm and 1148mm, and the mean annual temperature was 28.1° and 27.2° respectively for Chachoengsao and Khon Kaen. Further information on the pedo-climatic conditions of the sites may be found in Thoumazeau et al. (2019b).
Soils were Ultisols in Chachoengsao (Kabin Buri series) and Alfisols in Khon Kaen (Yang Talat series), with clay content ranging between 9 to 21 % clay in the 0-10 cm soil layer depending on the site. Information on soil properties are presented in Table 1. In the two sites, we studied gradients of disturbance based on rubber plantation (RP) (*H. brasiliensis*) stand ages. The gradients were characterized by an increasing carbon input (litter) and reducing soil physical disturbance (tillage). Three age categories of rubber tree plantations were selected, namely "Y" (RP<10y) ; "M" (10y<RP<20y) ; "O" (RP>20y). Intensive cash crop systems and/or forest references were also studied. The most intensive soil management in terms of tillage and application of herbicide and fertilizers was in the intensive cash-crops cropping systems named "A" (cassava/*M. esculenta* or sugar cane/*S. officinarum*) (Peerawat et al., 2018). Near pristine or partially-degraded natural forest soils were sampled as a reference for undisturbed systems ("F"). In both sites, the land use preceding rubber plantation was intensive cash cropping.

2.1.2 Study site in Cambodia

The study site was at the Bos Khnor experimental station (latitude 12°12'30" N, longitude 105°19'07"E, elevation 118m) with activities implemented by the Department of Agricultural Land Resources Management (GDA/DALRM). The mean annual precipitation is 1577mm (2009-2017) mainly occurring during the six months of the rainy season. The mean annual maximum and minimum temperatures are 32°C and 23°C respectively. The soil is classified as an Oxisol, with 69.8% clay, 28.9% silt and 1.3% sand in the 0-40 cm soil layer. Further description of the study site may be found in Hok et al. (2015). The experimental design was set up in 2009, i.e. 8 years before soil samplings. It was designed to study the impact of soil tillage and cover crops on soil fertility (Hok et al., 2018; Pheap et al., 2019). It includes: (i) conventional plough-based tillage (CT) with soybean (*Glycine mac (L) Merr.*) cropped in annual succession with sesame (*Sesamum indicum*), (ii) a direct seeding mulch-based cropping system (CA) in which soybean is cropped as a monoculture (CA1), (iii) bi-annual rotation of soybean and maize under a direct seeding mulch-based cropping system (CA2a and CA2b). CA2a and CA2b differ by a rotation time difference. A diversity of cover crops were used
176 under CA1, CA2a and CA2b. The biomass carbon inputs of plant residues was : CT (2.19 MgC ha⁻¹ yr⁻¹);
177 CA1 (7.32 MgC ha⁻¹ yr⁻¹), CA2a (7.09 MgC ha⁻¹ yr⁻¹); CA2b (7.59 MgC ha⁻¹ yr⁻¹) (see Hok et al. 2018).
178 Soil properties are presented in Table 1.

179 **2.1.3 Study site in Indonesia**

180 The study site was located in an industrial oil palm (E. guineensis) plantation in Riau province on
181 Sumatra Island. The region has a humid equatorial climate with approximately 2600 mm of rainfall
182 and a mean annual temperature of 27°C (Carron et al., 2015). The palm oil plantation was planted in
183 1993 on an Inceptisol with sandy loam texture. Further information on soil properties of the site are
detailed in Table 1. The experimental design has two treatments: plots with, and plots without,
184 empty fruit bunch (EFB) compost amendment (25 kg DM tree⁻¹ year⁻¹). The plot without compost
185 received a standard application of mineral fertilizer. The plots with EFB compost received 25% of the
186 standard mineral fertilizer application. The compost and the mineral fertilizer were spread in a
circular zone at 0-80 cm around the trees (Carron et al., 2015). This first circular zone was studied as
187 "circle". In order to take into account the spatial heterogeneity in soil conditions, two other zones
188 were studied: the "windrow zone", where the palm fronds are placed after tree pruning during the
189 harvest (approximately 18 fronds tree⁻¹ y⁻¹, Carron et al., 2015) and the "harvesting path" zone, that
190 is the footpath for plantation workers. The three studied zones and their spatial positions are
191 illustrated in Supplementary reading 2.
192

194 **2.2 Sampling scheme**

195 At each of the study sites, the plots selected were either from an experimental site with a completely
196 randomized design (Cambodia, Indonesia) or from farmer plots (Thailand). A nested sampling design
197 was applied at each site, consisting of three replicates within three blocks giving 9 sampling points
198 for each land management treatment or zone (Table 1). In Thailand, for Chachoengsao site, an
199 additional block with 9 sampling points was integrated in the analysis for "M" category. POXC and
200 SituResp® measurements were made in the field on soil samples at 0-10 cm depth with a soil
201 sampling cylinder.
202 **2.3 Measurements handled**

203 **2.3.1 POXC**

204 Soil samples were directly sieved to < 2mm for POXC measurements and about 20 g of the sieved soil
205 was dried in the field for 5 min with a portable air-drier. 2.5 g of the dried soil were added to 20 mL
206 of 0.02 mol L⁻¹ KMnO₄ solution and hand shaken for 2 min, following the protocol detailed by Weil et
207 al., (2003) with an adaptation for the sites in Thailand with low C content (see protocol of
208 Thoumazeau et al., 2019b).

209 **2.3.2 SituResp®**

210 The protocol described in Thoumazeau et al. (2017) was followed for SituResp® measurements.
211 Briefly, fresh soil samples were sieved to < 5 mm and 100 g of the sieved soil was incubated for 24 h
212 in a jar containing a spectrophotometer cuvette with a gel with a CO₂ trap (NaHCO₃) and a pH
213 indicator (Cresol Red). The gel spectral absorbance was read in the field before (T₀) and after the
214 incubation process (T₂₄) using a portable spectrophotometer (SpectroVis, Vernier Software and
215 Technology, USA) and the difference (T₀-T₂₄) in absorbance was used as an indicator of soil microbial
216 activity. To avoid potential co-effects of soil moisture, light conditions and temperature variation, all
217 measurements were implemented within a short period (7 consecutive days or less; of n=12/days
218 approximately), always following the same procedure for soil sampling and incubation. During
219 incubation, the jars were placed inside cardboard boxes in a room, to minimize light effects.

220 **2.3.3 Rock-Eval pyrolysis measurements**

221 We analyzed a set of soil samples analyzed for POXC-SituResp® indicator by Rock-Eval pyrolysis
222 developed by the Institut Français du Pétrole – Energies Nouvelles. Eighty-one samples from
223 Chachoengsao (Thailand) and Bos Khnor (Cambodia) were analyzed in the University of Lausanne
224 using a Rock-Eval 6 pyrolyser (Vinci Technologies). The method is applied in geoscience investigations
225 using a stepwise pyrolysis of organic matter under an artificial air supply N₂/O₂ 80/20 and
226 combustion of residual carbon. The released gases are quantified using a flame ionisation detector
227 (FID) for hydrocarbon compounds and infrared detectors (IR) for CO and CO₂. Qualitative and
quantitative parameters are calculated by integrating the amounts of hydrocarbons, CO, and CO2 produced during thermal cracking of organic matter, between defined temperature limits (Behar et al., 2001; Lafargue et al., 1998). This technique has been recommended for characterizing soil organic matter (Derenne and Quénéa, 2015; Disnar et al., 2003). In this study, soil organic matter dynamics was analyzed through the combination of two indices (I and R) calculated from five areas of the hydrocarbon thermograms, according to predefined temperature thresholds (Disnar et al., 2003; Sebag et al., 2016, 2006). By construction, the R-index is related to the most thermally refractory fraction of organic matter, while the I-index is related to the most thermally labile one (see Sebag et al. (2016) for details). Previous studies showed both indices are highly correlated along a constant line ("humic trend" in Sebag et al., 2016) reflecting a direct relation between mineralization of labile organic fractions and SOC stabilization processes in undisturbed soil profiles. Raw data are presented in Supplementary readings 3 & 4. In this study we focused on the delta-I as the distance (or residuals) from the "humic trend" (delta-I = I<sub>measured</sub> – I<sub>model</sub>), which is considered as representing stabilization processes in undisturbed soil profiles. A positive delta-I indicates an excess of labile carbon potentially mineralizable, compared to the undisturbed reference.

## 2.4 Statistical analysis

Statistical analysis was performed with R software version 3.6.1 (R Development Core Team, 2008). The analytical framework proposed by Hurisso et al. (2016) was followed. The regression between POXC and SituResp® was computed in each study site using a linear model, fitted with the *lm*() function. In our study, the significance of the linear model was regarded as a pre-requisite to calculate the residual values. Indeed, in the case of non-significant relationship between the two variables, the approach of combining the two variables did not provide benefit to a separate classical univariate analysis of each variable (see additional study sites in Supplementary reading 5). In each case study, the residuals were calculated as the distance to the linear model. The sum of residuals of all sampling points for each study site was equal to 0. The average of the resulting residuals were subjected to univariate analysis. In Indonesia, treatments with and without compost were separated
in the analyses to avoid mixed effects within the various zones tested. ANOVA based on a linear-mixed effects model (package 'lme4', Bates et al., 2015. Nested design: land management as fixed factor; plot replicates as random factors) was used to compare the residuals mean, after validating preliminary assumptions such as homoscedasticity (Levene's test) and normality (Shapiro test). Finally, Tukey post-hoc tests (package 'car', Fox and Weisberg, 2019) were used to test the differences between residual means of land management categories. In addition, the differences against the 0 value were checked, by computing a simple t test on plot's average (n=3). The relationship between the in-field indicator (POXC-SituResp®) and Rock-Eval delta-1 index was assessed using p-values and R² for a linear model.

## 3. Results

### 3.1 Impact of land use on soil C dynamics (Thailand sites)

In Thailand, the fitted linear relationship between POXC and SituResp® always had a positive slope. This relationship was significant for both sites ($R^2_{adj}$ respectively equal to 0.33 and 0.30, p<0.01, Figure 1). The trends were similar in the two sites with a relative accumulation of SOC pools along the gradient of disturbance. Significant difference between land use was highlighted in both sites. The forest (F) or rubber plantation >20yr (O) were significantly different to A land uses respectively in Chachoengsao and Khon Kaen (Figure 1a, 1b). In both sites, the least disturbed land use (F or O) were significantly different to 0 (Figure 1a, 1b).

### 3.2 Impact of conservation agriculture practices on soil C dynamics (Cambodia site)

The relationship between the POXC and SituResp® variables was significant (p<0.05) for all soils from the different cropping systems in Cambodia (Figure 2). The conventional tillage treatment with low carbon input (CT) contrasts with the three other treatments with no tillage and higher carbon inputs (CA1, CA2a and CA2b), indicating SOC pools more susceptible to mineralization in the former group and to accumulation in the latter groups (Figure 2).
## 3.3 Impact of spatial heterogeneity induced by agricultural practices on soil C dynamics
(Indonesia site)

A significant positive relationship was found between the POXC and SituResp® results for both treatments (i.e. with and without compost amendment) in oil palm plantations at the study site in Indonesia ($R^2_{adj}$ respectively equal to 0.50 and 0.36) (Figure 3). For the treatment without compost, no significant differences were observed between the windrow and circle zones, however the results for the harvesting path zone indicated more mineralizable C, relatively to the two other zones (Figure 3a). For the treatment with compost, the residuals for the harvesting path and circle zones differed significantly, while the windrow zone presents as a transition zone between the two others (Figure 3b).

## 3.4 Linkage between POXC-SituResp® in-field indicator and Rock-Eval indices

To better specify the carbon dynamics linked to the POXC-SituResp® in-field indicator, a regression between the POXC-SituResp® and the delta-1 index from Rock Eval was made (Figure 4). The POXC-SituResp® in-field indicator results are strongly, and negatively, related to the delta-1 ($p$-value ≤ 0.01, Figure 4), although this relationship is stronger for the Bos Khnor samples than for those from Chachoengsao ($R^2$=0.75 and 0.69 respectively).

# 4. Discussion

## 4.1 Response of the indicator to soil disturbance

The in-field indicator combining POXC and SituResp® was sensitive to soil disturbance associated with different tropical land uses and agricultural practices of varying intensity. The results were consistent with expected SOC dynamics. SOC tends to be on an increasing trajectory when land use and management favor SOC inputs and limit tillage (Fujisaki et al., 2018b; Six et al., 2004). These results corroborate the efficacy of the framework and methods proposed by Hurisso et al. (2016).

The results for soils from Thailand indicated that the main differences in SOC dynamics occurred gradually along the gradient of disturbance. This is coincident with the rubber perennial cropping system canopy closure, resulting litter release (Blagodatsky et al., 2016) and consequent changes in the level of C inputs to soil. This result is consistent with the results of Peerawat et al. (2018) and
305 Puttaso et al. (2015), who observed similar changes of different carbon pools (SOC, microbial
306 biomass) along rubber plantations stand age preceded by intensive cash crops. It has also been
307 suggested that SOC in the topsoil may be stabilized by aggregation processes occurring during rubber
308 plantation evolution with minimum or no tillage (Plante and McGill, 2002; Six et al., 2004;
309 Thoumazeau et al., 2019a). In addition to being sensitive, the results were also robust. Indeed, in
310 Thailand where similar treatments were tested in various study sites, conclusions could be drawn
311 after aggregating the site-specific conclusions.

312 In Cambodia, the results for soils under conservation agriculture cropping systems (CA) were
313 significantly different from the soils under conventional plough-based tillage (CT). They indicate that
314 CA enhances SOC accumulation relative to CT, with the soils under CT having significantly different
315 and negative residuals indicative of higher potential SOC mineralization. These results are consistent
316 with others in the literature highlighting the potential for carbon accumulation and stabilization of
317 the soil upper layer under CA (Lal, 2015; Powlson et al., 2016), mainly due to increased C inputs to
318 surface horizons and reduced soil disturbance (Virto et al., 2012).

319 Differences between means of the POXC-SituResp® residuals were observed between zones in the
320 Indonesian palm plantation, with and without compost. This result is in line with the comparative
321 studies of Carron et al. (2015). The harvesting path zone always tends towards SOC mineralization
322 relatively to other zones, which may be linked to intensive soil compaction and low C inputs in this
323 zone (Carron et al., 2015). When compost was added to the circle zone, this had a direct effect on
324 SOC dynamics, accumulating SOC pool (Peltre et al., 2017). Also, the differences in POXC observed
325 between the three zones (circle > harvesting path > windrow, Figure 3) are globally counterbalanced
326 by augmented soil BSR, as illustrated by the strong positive relationship between the two indicators
327 for treatment without compost. Therefore the systems may also be differentiated in terms of global
328 functioning with more POXC and higher BSR.

329 In summary, a strong link has been demonstrated between the results of the proposed indicator and
330 the C input and levels of physical disturbance to the soil system. Despite the low proportion of SOC
accounted in POXC and BSR fractions (approximately < 5%), the results indicate the relevance and sensitivity of the POXC-SituResp® indicator to assess the impact of varying land management, as a proxy of soil carbon dynamics.

## 4.2 Mineralization and Accumulation: what do we measure?

The POXC-SituResp® indicator is aimed at assessing the SOC fractions that are involved in short-term SOC dynamics. It is possible that the indicator is more useful to describe short term dynamics (around 5-10 years) and the decay of the SOC that supports soil fertility and production services, than it is to assess potential C sequestration (Janzen, 2006; Wood et al., 2016). There is a need to better understand the precise nature of the SOC that this indicator assesses, especially in terms of turnover and residence time. For instance, very little is known about the chemical composition and seasonal dynamics of POXC, despite its sensitivity to land management and relevance to assessing soil quality (Bongiorno et al., 2019). The results from the Rock-Eval analysis indicate that POXC is associated with a potentially less labile pool of organic matter (i.e. the A2 fraction with decomposition ranges between 280 and 340°C; supplementary reading 6). This result supports both the hypothesis of Culman et al. (2012) that POXC reflects a more processed and stabilized pool of labile SOC and the observation made by Bongiorno et al. (2019) indicating that POXC reaction targets labile but also affects more recalcitrant forms of SOC.

According to Sebag et al., 2016, the delta-l index is directly related to the soil labile carbon excess compared to the undisturbed reference. We observed a strong negative relationship between values of Rock-Eval delta-l and the POXC-SituResp® indicator for the two study sites. This indicates a decreasing proportion of labile organic fractions compared to the undisturbed reference, with increasing POXC-SituResp® residual values. For Bos Khnor, a more processed carbon under CA cropping systems compared to CT was also observed in Brazil by Tivet et al. (2013). In the two cases study, the relative excess of labile organic fractions (delta-l) supports a higher level of mineralization (POXC-SituResp®) in the most highly disturbed agrosystems (cassava for Chachoengsao, conventional tillage system for Bos Khnor) which can be explained by the lack of SOC protection due to tillage and
soil engineer depletion (Lavelle and Martin, 1992; Peerawat et al., 2018). These results support the efficacy of the framework proposed by Hurisso et al. (2016) to assess SOC stabilization, as affected by land management in tropical contexts.

## 4.3 Toward improvements of the POXC-SituResp® indicator

Previously, the indicator was applied within given pedo-climatic conditions (Hurisso et al., 2016). To date, even though the robustness of the indicator was checked with two case studies in Thailand, an analysis of absolute residuals values is not possible. Therefore, it is impossible to indicate the absolute mineralization or accumulation state of a soil with the method. Linear regression analysis may make it possible to understand and even take into consideration eventual site-specificities (i.e. pedo-climatic context). Such analyses would require a large and consistent set of data obtained in different contexts.

In this study, unlike that of Hurisso et al. (2016), we did not analyze those results without significant relationships between POXC and SituResp®, which was the case for two additional sites sampled in Thailand (Buriram and Surat Thani site, Supplementary reading 5). However, possible explanations need to be considered for this lack of observed significant relationships. A question arises about the required number of individual samples and treatments necessary to build a well-founded baseline. Our experience showed that consistent results can be obtained with a minimum of 27 sampling points and at least 9 points per treatments with contrasted management (e.g. case of Riau, Indonesia site; Table 1). Finally, a practical consideration may restrict take-up of the in-field indicator, as it relies on the readings of both POXC and SituResp® with a portable spectrophotometer, for which a smartphone may be a viable reading tool (Moonrungsee et al., 2015).

## 4.4 Why should we further apply this in-field indicator?

Apart from the scientific relevance of the indicator and especially its capacity for an integrated assessment of soil biota activity (Vogel et al., 2018, Thoumazeau et al., 2019a), its in-field application provide a consistent, rapid and affordable assessment of short-term SOC dynamics. These characteristics match the requirements for an indicator that is straightforward to apply and
applicable in various conditions (Wiesmeier et al., 2019). Its relatively low cost should allow a greater
number of samples to be measured than would be feasible using more conventional methods; this is
important because of the spatial and temporal heterogeneity of soil and its SOC content. Finally,
there are currently very few in-field indicators available to support programs to increase soil C stocks
(Minasny et al., 2017) and the indicator offers opportunities for strengthening communication with
land managers and informing participatory assessments directly in the field.

## 5. Conclusion

This study proposes an in-field indicator to assess the impact of land management on short-term SOC turnover. It combines the POXC and SituResp® measurements in the field to provide an indicator of SOC dynamics. Application of the indicator to a range of tropical soils with varying land management demonstrates the relevance, sensitivity and robustness of the indicator, which was corroborated with Rock-Eval measurements. It offers a practical and efficient means to compare the potential of different land managements to relatively mineralize or accumulate SOC pool. As such, it meets a widespread need for an effective but low cost indicator that can be used to support the promotion of agricultural practices that conserve SOC.

## Acknowledgments

Alexis Thoumazeau was financially supported by the French National Research Agency (ANR) in the framework of the Heveadapt project (ANR-14-CE03-0012) and the French Center of Agricultural Research for Development (CIRAD). In Thailand, it was part of the project "Long term impact of rubber plantation on soil biodiversity" from the National Research Council of Thailand (NRCT) which provided logistical and administrative support. The authors would like to thank the LMI LUSES (Dynamic of Land Use Changes and Soil Ecosystem Services), the HRPP (Hevea Research Platform in Partnership), the French Institute for Natural Rubber (IFC), the companies SIPH, SOCFIN and MICHELIN within the program "HeveaBiodiv", and the JEAI ECCO-RUBBER for their technical and financial support. In Cambodia, the measurements were undertaken under the projects FIRST and EISOFUN with the support of the ACTAE project (Towards Agroecological Transition in South-East
Asia), CANSEA Research Platform, French Agency for Development (AFD), the National Council for
Sustainable Development, and the Cambodia Climate Change Alliance – Phase 2 (European
Union/Sweden/UNDP). Additional supports were provided to conduct the field experiments by the
support from the American people through support by the United States Agency for International
Development Feed the Future Innovation Labs for Collaborative Research on Sustainable
Intensification (Cooperative Agreement No. AID-OAA-L-14-00006, Kansas State University), Centre of
Excellence on Sustainable Agricultural Intensification and Nutrition. In Indonesia, the experiment was
implemented by PT-Smart company and the study was under the LCA&Indicator project financed by
PT-Smart and CIRAD. Rock-Eval analysis were financed by the Labex Cemeb of Montpellier through
the "Equipe de Recherche Junior" program. The authors are especially grateful to the farmers, to all
the people who helped in the field in the different experiments, to all people involved in long-term
field experiment maintenance, to Julie Jouis for soil samples preparation before Rock-Eval
measurements and to Rémi Cardinael for interesting discussions on the method. We thank the staff
at the University of Lausanne (Switzerland) for completing the Rock-Eval pyrolysis, and for their
technical and scientific supports; we are particularly grateful to Thierry Adatte (Institute of Earth
Sciences) and Eric Verrecchia (Institute of Earth Surface Dynamics). We thank Alyssa Fischer
(University of Neuchâtel) for the R script used in drawing the S2 thermograms. Finally, we would like
to warmly thank the two anonymous reviewers who provided useful comments to improve the
quality of the paper.
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<figure><img src="image_3.png" /><figcaption>**Figure 1:** Linear regression and average residuals from linear regression models for the five land uses tested in the two experimental sites in Thailand: Chachoengsao and Khonkaen. A: Intensive cash crop; Y: Young rubber plantation (<10 yr); M: Medium age rubber plantation (10<yr<20); O: Old rubber plantation (>20yr); F: Forest. For diagrams, plot average are represented (n=3 ± SE) and letters represent relative differences between treatments after Tukey post-hoc test (p=0.05) "*" represent difference against the 0 (p=0.05) "." represent difference against the 0 (p=0.1) with significant positive residuals meaning relative soil organic carbon stabilization and significant negative residuals meaning relative soil organic carbon mineralization.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 2:** Linear regression and average residuals from linear regression models for the four agricultural cropping systems tested in Bos Khnor experimental site in Cambodia. Data also presented in Pheap et al. (2019). CT: conventional tillage system in which soybean is cropped in annual succession with sesame; CA1: direct seeding mulch-based cropping system in which soybean is cropped as a monoculture; CA2a and CA2b: bi-annual rotation of soybean and maize under direct seeding mulch-based cropping system. Cover crops are used both under CA1 and CA2a and CA2b. For diagrams, plot average are represented (n=3 ± SE) and letters represent relative differences between treatments after Tukey post-hoc test (p=0.05) "*" represent difference against the 0 (p=0.05) "." represent difference against the 0 (p=0.1) with significant positive residuals meaning relative soil organic carbon stabilization and significant negative residuals meaning relative soil organic carbon mineralization.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 3:** Linear regressions and average residuals from linear regression models for the three zones and compost agricultural practices tested in oil palm plantation of Riau province, Sumatra in Indonesia. Harvesting Path, Windrow and Circles are the three zones sampled to test spatial heterogeneity. For diagrams, plot average are represented (n=3 ± SE) and letters represent relative differences between treatments after Tukey post-hoc test (p=0.05) "*" represent difference against the 0 (p=0.05) "." represent difference against the 0 (p=0.1) with significant positive residuals meaning relative soil organic carbon stabilization and significant negative residuals meaning relative soil organic carbon mineralization.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 4:** Regression between Rock-Eval indicators (delta-I) and POXC-SituResp® indicators for two sites tested. The equation, R² and p-value of each model is presented, based on plot's average values. **Figure 4a** presents the results of Chachoengsao (n=10). One block was removed for the analysis of Chachoengsao, as it was not sensitive to the treatment due to specific block effect. **Figure 4b** presents the results of Bos Khnor (n=12). For legends, see **figure 1** and **figure 2**.</figcaption></figure>
<table><thead><tr><th rowspan="3">Country</th><th colspan="12">Thailand</th><th colspan="3">Cambodia</th><th colspan="3">Indonesia</th></tr><tr><th colspan="4">Chachoengsao</th><th colspan="4">KhonKaen</th><th colspan="4">Bos Khnor</th><th colspan="3">Riau</th></tr><tr><th colspan="4">Land Use - Agricultural practice</th><th colspan="4">Agriculture</th><th colspan="4">Agriculture</th><th colspan="3">Compost</th></tr><tr><th>Site</th><th>A</th><th>M</th><th>O</th><th>F</th><th>A</th><th>Y</th><th>M</th><th>O</th><th>CT</th><th>CA1</th><th>CA2a</th><th>CA2b</th><th>Circle</th><th>HP</th><th>Windr.</th><th>Circle</th><th>HP</th><th>Windr.</th></tr></thead><tbody><tr><td>n=</td><td>9</td><td>18</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>9</td><td>7**</td><td>9</td></tr><tr><td>SOC (g.kg<sup>-1</sup>)</td><td>8.9 (±0.9)</td><td>10.28 (±2.1)</td><td>12.63 (±0.9)</td><td>13.2 (±2.5)</td><td>2.4 (±0.1)</td><td>2.1 (±0.5)</td><td>3.3 (±0.5)</td><td>5.7 (±0.7)</td><td>21.3 (±0.7)</td><td>22.6 (±0.6)</td><td>23.7 (±0.6)</td><td>23.6 (±0.4)</td><td>42.2 (±18.5)</td><td>24.6 (±7.2)</td><td>35.3 (±2.2)</td><td>52.9 (±10.0)</td><td>28.3 (±13.9)</td><td>44.6 (±16.5)</td></tr><tr><td>BD (g.cm<sup>-3</sup>)</td><td>1.44 (±0.05)</td><td>1.49 (±0.04)</td><td>1.38 (±0.05)</td><td>1.29 (±0.21)</td><td>1.58 (±0.04)</td><td>1.55 (±0.03)</td><td>1.51 (±0.05)</td><td>1.45 (±0.03)</td><td>1.27 (±0.03)</td><td>1.22 (±0.02)</td><td>1.18 (±0.02)</td><td>1.19 (±0.03)</td><td>0.90 (±0.17)</td><td>1.42 (±0.12)</td><td>0.97 (±0.22)</td><td>0.88 (±0.19)</td><td>1.17 (±0.17)</td><td>0.94 (±0.19)</td></tr><tr><td>pH<sub>water</sub></td><td>5.0 (±0.4)</td><td>4.75 (±0.3)</td><td>4.79 (±0.4)</td><td>5.35 (±0.4)</td><td>5.2 (±0.2)</td><td>4.9 (±0.1)</td><td>5.19 (±0.3)</td><td>5.4 (±0.4)</td><td>6.0 (±0.07)</td><td>6.0 (±0.10)</td><td>6.0 (±0.12)</td><td>5.9 (±0.06)</td><td>4.17 (±0.62)</td><td>4.06 (±0.27)</td><td>4.13 (±0.63)</td><td>4.8 (±0.68)</td><td>4.67 (±0.28)</td><td>4.75 (±0.63)</td></tr></tbody></table>

**Table 1:** Soil properties of the six sites in Thailand, Cambodia and Indonesia focusing on the impact of various land uses or agricultural practices on soil carbon dynamics (mean + sd). For Thailand: A: Intensive cash crop; Y: Young rubber plantation (<10 yr); M: Medium age rubber plantation (10<yr<20); O: Old rubber plantation (>20yr); F: Forest. For Cambodia: CT: conventional tillage system in which soybean is cropped in annual succession with sesame; CA1: direct seeding mulch-based cropping system in which soybean is cropped as a monoculture; CA2a and CA2b: bi-annual rotation of soybean and maize under DMC management. For Indonesia: Harvesting Path (HP), Windrow (Windr.) and Circle are the three zones sampled to test spatial heterogeneity (Supplementary reading 2). **=one sample could not be analysed because of field experimental problem and one outlier (based on POXC-SituResp® residuals) was removed for the analysis.
Agrosystems to compare

Practice A

<figure><img src="image_7.png" /></figure>

Practice B

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

POxC-SituResp measurement
**in the field**

<figure><img src="image_10.png" /></figure>

SituResp

POXC

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

Results analysis

< 0

Relative
C Mineralization

> 0

Relative
C Accumulation

<figure><img src="image_13.png" /></figure>

Practice A

Practice B