Phantip Panklang

Alexis Thoumazeau
email: alexis.thoumazeau@cirad.fr

Rawee Chiarawipa
email: rawee.c@psu.ac.th

Sayan Sdoodee

David Sebag

Frédéric Gay

Philippe Thaler

Alain Brauman
email: alain.brauman@ird.fr

Rubber, rubber and rubber: how 75 years of successive rubber plantations rotations affect topsoil quality

Keywords: Rubber plantations, Successive rotation, Long term, Soil quality

main drivers of declining topsoil quality

Introduction

Natural rubber (Hevea Brasiliensis) plays an important role in the world economy as it supplies about half the world demand for elastomer driven by industry (FAOSTAT, 2018). Southeast Asia (including parts of Southwest China) is the epicentre of rubber cultivation (Warren- [START_REF] Warren-Thomas | Increasing Demand for Natural Rubber Necessitates a Robust Sustainability Initiative to Mitigate Impacts on Tropical Biodiversity[END_REF] and accounts for nearly 80% of world production (FAOSTAT, 2018). This development originally occurred at the expense of natural forest and rubber is still considered as one of the main drivers of deforestation in Southeast Asia [START_REF] Fox | Expansion of rubber (Hevea brasiliensis) in Mainland Southeast Asia: what are the prospects for smallholders?[END_REF][START_REF] Warren-Thomas | Increasing Demand for Natural Rubber Necessitates a Robust Sustainability Initiative to Mitigate Impacts on Tropical Biodiversity[END_REF]. Thailand has the second largest area under rubber in the world, with more than 3.3 million ha of rubber plantations, and is the top rubber producing country (FAOSTAT, 2018), because the Thai rubber sector has been very efficiently organized with government incentives [START_REF] Delarue | La Thaïlande : premier exportateur de caoutchouc naturel grâce à ses agriculteurs familiaux[END_REF]. In Southern Thailand, the country's historical rubber producing area, the first plantations were established in the first half of the 20 th century [START_REF] Chambon | Can the cocoa cycle model explain the continuous growth of the rubber (Hevea brasiliensis) sector for more than a century in Thailand?[END_REF]. This area is characterized by optimal soil and climatic conditions for successive rubber plantations. This encouraged smallholders to cultivate up to three successive rotations of rubber monoculture on the same plots.

In terms of land use, this considerably relieves the pressure on natural forest and a recent survey by [START_REF] Chambon | Can the cocoa cycle model explain the continuous growth of the rubber (Hevea brasiliensis) sector for more than a century in Thailand?[END_REF] showed that less than 10% of the rubber expansion during the last 30 years in Thailand has taken place on forest land. However, repeated plantation of the same species on the same land may have detrimental effects on soil and calls the sustainability of the system into question.

The long-term effect of tree plantations on productivity was first documented in the late 1950s. For example, [START_REF] Thomas | Growth Problems of Pinus Radiata in South Australia[END_REF] observed a declining tree growth after the second successive rotation of Pinus Radiata. This declining trend after the second rotation was confirmed across several sites in Australia [START_REF] Woods | Second Rotation Decline in P. Radiata Plantations in South Australia has been Corrected[END_REF] and for other tree species such as Eucalyptus and Acacia [START_REF] Hardiyanto | Productivity of successive rotations of Acacia mangium plantations in Sumatra, Indonesia: impacts of harvest and inter-rotation site management[END_REF][START_REF] Huong | Sustainable Management of Acacia auriculiformis Plantations for Wood Production over Four Successive Rotations in South Vietnam[END_REF]. The decline in tree productivity was mainly linked to a decrease in soil quality. Indeed short rotations lead to a rapid degradation of soil organic matter, nutrient content, and soil biodiversity [START_REF] Selvaraj | Influence of long-term successive rotations and stand age of Chinese fir (Cunninghamia lanceolata) plantations on soil properties[END_REF][START_REF] Zhu | Effects of the successive planting of Eucalyptus urophylla on soil bacterial and fungal community structure, diversity, microbial biomass, and enzyme activity[END_REF]. The intensity of the depletion varies markedly depending on logging and replanting practices that may accelerate the loss of organic matter This article is protected by copyright. All rights reserved. and nutrient reserves [START_REF] Hardiyanto | Productivity of successive rotations of Acacia mangium plantations in Sumatra, Indonesia: impacts of harvest and inter-rotation site management[END_REF]. Logging using heavy machinery including windrowing and subsoiling cause severe and substantial damage to soil physical properties, leading to soil compaction and reducing hydraulic conductivity, particularly during wet conditions [START_REF] Ampoorter | Assessing the effects of initial soil characteristics, machine mass and traffic intensity on forest soil compaction[END_REF][START_REF] Hartmann | Resistance and resilience of the forest soil microbiome to loggingassociated compaction[END_REF]. Compaction can have detrimental effects in the long term, even 20 years after logging in Pinus Radiata plantations [START_REF] Murphy | Long-term impacts of forest harvesting related soil disturbance on log product yields and economic potential in a New Zealand forest[END_REF]). However, most such studies concerned tree plantations systems with short rotations (< 10 years) such as for Acacia and Eucalyptus [START_REF] Chen | Soil microbial community structure and function responses to successive planting of Eucalyptus[END_REF][START_REF] Hardiyanto | Productivity of successive rotations of Acacia mangium plantations in Sumatra, Indonesia: impacts of harvest and inter-rotation site management[END_REF][START_REF] Huong | Improving productivity and sustainability of successive rotations of Acacia auriculiformis plantations in South Vietnam[END_REF][START_REF] Xu | Effects of different rotation periods of Eucalyptus plantations on soil physiochemical properties, enzyme activities, microbial biomass and microbial community structure and diversity[END_REF][START_REF] Zhu | Effects of the successive planting of Eucalyptus urophylla on soil bacterial and fungal community structure, diversity, microbial biomass, and enzyme activity[END_REF]. Longer-term studies are scarce, particularly those that deal with trees with a longer lifespan such as rubber or oil palm. To our knowledge, this study is the first to explore the effect of successive rotations of rubber stands. In Thailand, the lifespan of a rubber plantation is around 25 years. This long period could be more favorable than shorter-rotation species for topsoil restoration following the initial disturbance caused by logging. Indeed, soil quality and soil biodiversity at 0-10 cm depth improves with age over one cycle of rubber plantation [START_REF] Peerawat | Rubber plantation ageing controls soil biodiversity after land conversion from cassava[END_REF]Thoumazeau et al., 2019a;[START_REF] Tondoh | Soil Health Changes Over a 25-Year Chronosequence From Forest to Plantations in Rubber Tree (Hevea brasiliensis) Landscapes in Southern Côte d'Ivoire: Do Earthworms Play a Role? Front[END_REF][START_REF] Zhou | Functional Diversity of Soil Microbial Communities in Different-age Rubber Plantations -A Case Study of Hainan Province, China[END_REF] especially due to the absence of physical perturbation, high litter input and improved soil moisture [START_REF] N'dri | Can litter production and litter decomposition improve soil properties in the rubber plantations of different ages in Côte d'Ivoire?[END_REF]. Therefore, plantations were sometimes considered close to a restored forest ecosystem [START_REF] Bremer | Does plantation forestry restore biodiversity or create green deserts? A synthesis of the effects of land-use transitions on plant species richness[END_REF]. However, this restoration process has only been assessed for one first rubber tree cycle, leaving the effect of successive rubber rotations on soil quality an open question.

The concept of soil quality and the way to assess it is still the subject of debate in the scientific community [START_REF] Bünemann | Soil quality -A critical review[END_REF] despite a certain consensus linked to the the definitions of soil quality given by [START_REF] Karlen | Soil quality: a concept, definition, and framework for evaluation[END_REF]. To assess soil quality in the present study, we focused on the following parameters (i) soil physico-chemical parameters, as they are particularly disturbed by tree logging (ii) soil organic carbon (SOC) content and thermal stability because they reflect the chemical, biological and physical status of the soils [START_REF] Karlen | Soil quality: a concept, definition, and framework for evaluation[END_REF], which could be affected by successive tree rotations. Topsoil (0-10cm) seems especially adapted to tackle land management effects, as it illustrate the interface between above ground inputs and deeper soil layer characteristics (Matteodo This article is protected by copyright. All rights reserved. et al., 2018). Topsoil also harbor the main part of soil biota that drive soil processes and is at the spotlight of soil quality assessments [START_REF] Bünemann | Soil quality -A critical review[END_REF].

The specific aim of the present study was to assess the impact of successive rubber rotations on topsoil quality. To evaluate the long-term impact and include the temporal dimension, we selected a 75-year chronosequence of rubber plantations including three full rotations, each with an average lifespan of about 25 years. We disentangled problems concerning land use and changes in land use in rubber plantations by highlighting the relative importance of forest-to-rubber conversion, rubber stand age and the long-term impacts of cultivation on soil quality. Our main hypothesis was that, following an initial disturbance caused by logging, topsoil quality would recover during each rubber cycle, and the extent of the recovery would determine the resilience of the system.

Materials and Methods

Site description and experimental design

The study site is located in Ban Nasan district, Surat Thani province, Thailand (8°54'-8°56'N; 99°24'-99°28'E). The site is characterized by a tropical monsoon climate with cumulative annual precipitation of 1,497 mm and an average temperature of 27 °C in 2015 (Thai Meteorological Department, 2015).

The soils are Inceptisols (Thung Wa serie, [START_REF] Vijarnsorn | Characteristics and classification of three granite-derived soils in peninsular Thailand[END_REF] with a sandy loam texture comprising around 11% clay, 12% silt and 77% sand in the 0-10 cm soil layer. This area is the historical region for rubber plantations in Thailand, where rubber has been cultivated for more than 80 years [START_REF] Chambon | Can the cocoa cycle model explain the continuous growth of the rubber (Hevea brasiliensis) sector for more than a century in Thailand?[END_REF]. This can be explained by favourable soil-climatic conditions and low pest pressure on rubber tree in the area. The experiment was implemented in smallholders' plots in a succession of three rubber plantation rotations (R1 for first rotation, R2 for second rotation, R3 for third rotation) using a space-for-time approach. One rubber rotation usually lasts around 25 years meaning that a total chronosequence of 75 years was studied. For each rotation, we investigated two different rubber stand ages with young (immature) rubber plantations 3-6 years old (y) and older (mature) rubber plantations 18-22 years old (o). A neighbouring natural forest (F) was studied as the baseline. All the rubber plots were originally cultivated after conversion from the This article is protected by copyright. All rights reserved.

undisturbed forest. The plot design was the same (6-7 m x 2-3 m) with around 550 trees/ha, and the same rubber clone was used throughout (RRIM 600). All the rubber plots were cultivated using standard practices. At immature stage, there were no intercrops and soil surface was covered with spontaneous vegetation controlled both chemically and mechanically. There was no tillage in the interrows. Understory vegetation was controlled and almost no weed was observed in mature plantations (see pictures in Supplementary material 1).

Soil sampling

Soil sampling was performed in September 2015, except for two measurements linked to nutrient cycling (Nav and AEMNO3, see part 2.3) that were performed in the same plots (but not in R1y) in 2017.

Soils were collected half way between the rows and inter-rows. For each type of land management, three plots representing three replications were studied. The study plots were located maximum 10 km apart and plot slope was generally low (Supplementary material 2). We focused on farmers plots and the design could not be perfectly randomized. We identified soil texture as a relevant proxy to highlight possible cofounded effect of soil inherent parameters with the land management tested. Soil texture was especially relevant for this study tackling changes of topsoil quality (0-10cm). Prior to analysis, the effects of soil inherent parameters (i.e. soil texture at 0-10cm) on the soil physicochemical properties was checked using RDA methodology (Supplementary material 3, R vegan package; [START_REF] Oksanen | vegan: Community Ecology Package[END_REF]. The results showed that soil texture did not significantly affect the soil properties studied, whereas land management did. This preliminary test proved that, despite a possible effect of soil texture due to possible differences within the study area, soil texture was not the driver of the observed differences in soil properties. Three internal replicates were sampled in each plot to account for spatial heterogeneity of the plot. We collected a total of 63 soil samples (7 treatments x 3 blocks x 3 internal-replicates).

Soil physico-chemical analysis

Fresh soil samples were taken in the 0-10 cm layer using a 7cm diameter cylinders, then weighed and dried at 105 °C for 24 h to measure soil moisture and bulk density (BD). For other soil analyses, soils This article is protected by copyright. All rights reserved.

were sampled with an auger in the 0-10 cm soil layer and most of the analyses were performed following [START_REF] Peerawat | Rubber plantation ageing controls soil biodiversity after land conversion from cassava[END_REF] at the soil laboratory of the Office of Science for Land Development at Land Development Department, Bangkok. Briefly, soil samples were air-dried and then sieved at 2 mm. Soil texture was determined by the Bouyoucos Hydrometer method adapted from Gee and Bauder (1986). The pH was determined in distilled water (1:1 soil-water ratio). Available phosphorus was determined using the Bray II method (Bray and Kurtz, 1945). K, Ca and Mg in soil solution were extracted with neutral 1 N ammonium acetate (Chapman, 1965) and analysed with a flame photometer (Sherwood model 420) for K and Ca and a Shimadzu AA 6200 atomic absorption spectrophotometer for Mg. Analysis of soil available nitrogen (Nav) and nitrate adsorbed on ion exchange membrane (AEMNO3) were analysed as described in Thoumazeau et al. (2019). Briefly, Nav consisted in extracting soil available nitrogen with a 1 M KCl solution and analysing NO3 -and NH4 + . AEMNO3 consisted in using an anion exchange membrane [START_REF] Qian | Practical applications of ion exchange resins in agricultural and environmental soil research[END_REF] to analyse NO3 - adsorbed on the membrane. The membranes were incubated for 19 days. For nutrient quantitative analysis in the Eco&Sols laboratory (Montpellier, France), we used a continuous flow analyser (SAN++, Skalar, Breda, The Netherlands). For soil organic carbon (SOC) and soil total nitrogen (Ntot), 2-mm subsamples of sieved bulk soils were ground (<150 mm) and analysed either using the Rock-Eval® method for SOC (see part 2.4) or the dry combustion method using an elemental CHN analyser for Ntot (Thermo Flash 2000, analysed at Eco&Sols laboratory in Montpelllier, France).

Rock-Eval® analyses

We analyzed the set of soil samples by Rock-Eval® thermal analysis. The 63 soil samples were analyzed in the University of Lausanne with Rock-Eval 6 device (Vinci Technologies). The method uses a ramped pyrolysis of organic matter under artificial air supply (N2) between 200 and 650°C , then a combustion of residual carbon in oxidative conditions between 300 and 850°C. The released gases are quantified using a flame ionisation detector (FID) for hydrocarbon compounds (HC) 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 This article is protected by copyright. All rights reserved.

temperature limits [START_REF] Behar | Rock-Eval 6 Technology: Performances and Developments[END_REF][START_REF] Feller | Selected pioneering works on humus in soils and sediments during the 20th century: A retrospective look from the International Humic Substances Society view[END_REF]Lafargue et al., 1998a). This technique has been recommended for the characterisation of soil organic matter [START_REF] Derenne | Analytical pyrolysis as a tool to probe soil organic matter[END_REF][START_REF] Disnar | Soil organic matter (SOM) characterization by Rock-Eval pyrolysis: scope and limitations[END_REF] and recently proved to be relevant in a similar context [START_REF] Thoumazeau | A new in-field indicator to assess the impact of land management on soil carbon dynamics[END_REF].

Thermograms are presented in Supplementary material 4. Soil organic carbon (SOC) content was calculated by including the amounts of C moities produced during thermal cracking and combustion of OC between defined temperature limits [START_REF] Behar | Rock-Eval 6 Technology: Performances and Developments[END_REF]Lafargue et al., 1998b). Soil organic carbon quality was assessed with the R-index calculated as the relative HC contribution of the most thermally resistant organic fraction (i.e. thermal cracking above 400°C; see [START_REF] Disnar | Soil organic matter (SOM) characterization by Rock-Eval pyrolysis: scope and limitations[END_REF][START_REF] Sebag | Dynamics of soil organic matter based on new Rock-Eval indices[END_REF][START_REF] Sebag | Monitoring organic matter dynamics in soil profiles by 'Rock-Eval pyrolysis': bulk characterization and quantification of degradation[END_REF]. The higher the R-index, the more organic fractions are thermally resistant. Recent application of the R-index proved its interest in agricultural management systems in tropical areas [START_REF] Malou | The Rock-Eval® signature of soil organic carbon in arenosols of the Senegalese This article is protected by copyright. All rights reserved. groundnut basin. How do agricultural practices matter? Agriculture[END_REF].

Calculations and statistical analysis

All statistical analysis were performed with R software version 3.6.1 (R Development Core Team, 2008).

First, univariate analysis was run on each of the soil physico-chemical parameters using a linear-mixed effects model (lme4 package, [START_REF] Bates | lme4: Linear Mixed-Effects Models using[END_REF]. Treatment was defined as fixed factor and plots/inner-replicates as random factors to take into account the nested design of the experiment.

After checking the normality of the model residuals (Shapiro test) and the homoscedasticity of residuals of variance (Levene test), ANOVAs were run using the car package (Fox and Weisberg, 2011).

Variables were sometimes log or square root transformed to validate our initial assumptions. In some cases, our assumptions regarding the response of the mixed model on normality and heteroscedasticity were not confirmed (i.e. for Mg, Ca, pH). In these cases, a standard linear model was run on plot average. All the tests were followed by post hoc mean comparisons using a Tukey's test with Bonferroni correction (Hothorn et al., 2008). Second, multivariate analysis was computed with a principal component analysis (PCA) including all physico-chemical variables (FactomineR package, [START_REF] Husson | FactoMineR: Multivariate Exploratory Data Analysis and Data Mining[END_REF] based on the mean of the replicates for three plots (Supplementary material 5). We used the missMDA package to estimate the data that were not available for Nav and This article is protected by copyright. All rights reserved.

AEMNO3 in R1y. Third, we calculated a soil quality index (SQI), using the methodology defined by [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF]. We selected appropriate and non-redundant variables [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF] that led to the removal of (i) pH (not sensitive to land management) and (ii) Nav and Ca that were strongly correlated with SOC and Ntot respectively (redundancy; R > 0.8). All variables results were normalized between 0 and 1 following a "the more the better" scoring functions, except bulk density for which a higher value was given to the lower values ("the less the better"). The SQI was calculated according to equations ( 1) and ( 2), with a weighting of the transformed variables using the PCA eigenvectors and the percentages of total variability explained by each principal component:

Wi =∑ λj x fj 𝑝𝑝 𝑗𝑗=1
Eq. ( 1)

SQI =∑ Si x Wi 𝑛𝑛 𝑖𝑖=1
Eq. ( 2)

Fij=relative percentage of total variability attributed to each PC; λj= sum of squared coordinates on each eigenvector; Si= Normalized indicator scores; Wi= weighted factors"

To assess the transition dynamics, we calculated two indexes of logging and the long-term effects from the data of SQI. For the three rubber rotations, the "logging effect" was calculated as the difference in soil quality between the land use before logging and that of new rubber plantation. The "long term effect" was calculated as the difference between the quality of the soil in the natural forest and the quality of the soil under the mature rubber tree plantation in the three rubber rotations. All combinations of differences between the two stages were included (n=9). After checking the normality of the model residuals and homoscedasticity of variance, ANOVAs and Tukey's post-hoc tests were computed on the data sets. Rock-Eval results were analysed with ANOVA or with a t-test to compare rubber plantations rotations with the reference forest.

Results

Impacts on individual soil parameters

Most of the soil parameter were sensitive to land management, except for pH (Table 1). A strong effect of deforestation was observed for SOC content, with a significant difference only between land uses (Forest vs Rubber). Thereafter, a continuous decrease was observed from forest to the third

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Impacts on soil quality

Modifications along the chronosequence

All soil parameters were combined in the PCA multivariate analysis shown in Figure 1. The two first axes of the PCA explained 76% of the total variability of the data set. The two dimensions were contrasted in terms of percentage of inertia, with Dim 1 explaining 56% and Dim 2 explaining only 20%

(Figure 1a). The first dimension highlighted both (i) the effect of forest deforestation (F to R1y-R1o) and (ii) the effect of long-term rubber rotations following a continuum of rotation 1 and 2 (R1y-R1o-R2y-R2o) distinguishable from rotation 3 (R3y-R3o). Most of the variables were positively correlated with the first dimension of the PCA (i.e., Ca, K, Mg, Ntot, Nav, TOC) highlighting the change in land use and the effect of rotation (Figure 1b). The second dimension mostly represented the effect of plantation age, with old (mature) rubber plantations (R1o, R2o, R3o) that were distinct from young (immature) plantations (R1y, R2y, R3y). Pav and AEMNO3 parameters mostly explained the second dimension with a low relative contribution to the overall PCA (Figure 1b).

Assessment of the transition using indexes

A significant decrease in soil quality linked to the shift from natural forest to rubber plantations is showed in Figure 2a. A second decrease can be observed between the two first rubber rotations and the third one, and the soil quality under the third rotation was much lower. These results are summarised in Figure 2b, in which high "logging effects" were calculated for the first and third rotation

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whereas a relatively limited effect of logging was observed between the first and second rubber rotations (Figure 2b). The long-term effect of rubber tree plantations on soil quality was highlighted in the third rotation (Figure 2c). No age effect of the plantation was observed on soil quality in the three rotations, and young immature plantations had a similar score to that of old mature plantations.

Impacts on soil carbon dynamics

As the effect of stand age was negligible compared to the effects of the other factors, Figure 3 focuses on deforestation and long-term effects by combining rubber stand ages. In our context with relatively low organic carbon contents, the impact of the conversion from a natural forest to rubber plantations on SOC was clear (Figure 3a). A decrease of about half the initial SOC content in the forest was observed in the first rubber plantation rotation. The long-term effect of rotation on SOC was not significant, despite a trend toward a decrease between the first rotation (R1) and the third rotation (R3) (p < 0.1).

The R-index did not differ significantly between F and R1-R2 (Figure 3b), implying that the marked decrease in SOC content between forest and rubber plantations was not related to a decrease in the quality of SOC. However, the R-index identified a significant change in the quality of SOC in the 0-10 cm soil layer over successive rubber rotations. A significant increase in the thermally resistant fraction of SOC was observed with successive rotations (R1<R2<R3, Figure 3b).

Discussion

Strengths and limits of the experiment

This study aimed to tackle a key challenge for perennial cropping system that is the effect of long-term successive cultivation on soil quality. Very few studies exist in the literature focusing on this long-term change. Our results could be fruitful for land planning decisions in historical production area of 25-30 years stand perennial cropping systems (e.g. rubber and oil palm). The experiment made it possible to consider local agricultural systems of repeated rubber cultivation for 75 years. To this end, we followed a space-for-time approach based on plot selection from surveys over farmer's plots. Those preliminary surveys aimed at i. understanding land management practices linked to rubber rotations, together with ii. limiting spatial variation between the plots. One of the key challenge for space-for-time experiments

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among farmer plots is the difficulty to have fully spatially randomized design. Thus, there is a need to verify and limit the effect of possible cofounded inherent factors that are independent to the land management targeted in space-for-time experiments. Our question focused on topsoil assessment and we chose the proxy of soil texture to characterize the soil inherent properties that may have influenced the results. In this specific case, our statistical analysis based on variance partition (Supplementary material 3) showed that topsoil assessment were only driven by changes of land management, and not by soil texture. This statistical analysis made it possible to discard an eventual cofounded effects of soil inherent properties on the topsoil property change observed. However, to have a full understanding of the soil functioning and linkages with tree productivity, it would have been interesting to implement analyses and observations at deeper layer (e.g. soil depth, soil type, bedrock type…).

Deforestation degrades different soil physico-chemical parameters simultaneously

In the soil-climatic context of this study, changing land use from a forest to rubber plantations had a negative effect on most of the soil quality variables measured (Table 1). This confirms the results of other studies focused on depletion of SOC [START_REF] Guillaume | Losses of soil carbon by converting tropical forest to plantations: erosion and decomposition estimated by δ13C[END_REF][START_REF] Straaten | Conversion of lowland tropical forests to tree cash crop plantations loses up to onehalf of stored soil organic carbon[END_REF], Ntot [START_REF] Allen | Soil Nitrogen-Cycling Responses to Conversion of Lowland Forests to Oil Palm and Rubber Plantations in Sumatra, Indonesia[END_REF] and an increase in soil BD [START_REF] Li | Soil Changes Induced by Rubber and Tea Plantation Establishment: Comparison with Tropical Rain Forest Soil in Xishuangbanna, SW China[END_REF] after deforestation. When analysed independently, other soil quality variables (Nav, K, Pav, Ca) were affected to a lower extend by forest conversion into rubber plantations. The results reinforce the site specificities of the effects of changes in land use associated to the fact that remaining natural forests in a land use change dynamic are often located on soils with low quality [START_REF] Veldkamp | Deforestation and reforestation impacts on soils in the tropics[END_REF]. In our case, fertilisation in rubber plantations may also have counterbalanced soil nutrient depletion linked to deforestation [START_REF] Chambon | WHAT DETERMINE SMALLHOLDERS' FERTILIZATION PRACTICES DURING THE MATURE PERIOD OF RUBBER PLANTATIONS IN THAILAND?[END_REF][START_REF] Vrignon-Brenas | Nutrient management of immature rubber plantations. A review[END_REF]. Interestingly, despite the clear drop in SOC content associated with deforestation, refractory carbon pools were not affected by this transition (Figure 3). In the 0-10 cm soil layer, the change in land use mostly affected labile carbon pools. This may be explained by a flush of labile organic matter mineralisation after deforestation [START_REF] Fujisaki | Soil organic carbon changes after deforestation and agrosystem establishment in Amazonia: An assessment by diachronic approach[END_REF]. Aggregating all variables through multivariate analysis identified a clear and significant gap between soil quality in This article is protected by copyright. All rights reserved.

natural forest and rubber plantations (Figure 2). This result is consistent with our previous results on soil quality in rubber tree plantations located in another soil-climatic context (Thoumazeau et al., 2019a). The present study confirmed that rubber tree plantations do not have the same soil characteristics as natural forests, even at the mature stage (R1o). Preserving forest soil physicochemical properties and the intensity of related ecosystem services (carbon sequestration, nutrient cycling) seems to be a key challenge that is not met with monospecific plantations.

Restoration during each rotation does not compensate for long-term negative effects

The expected recovery of soil quality during each rotation was not observed and was counterbalanced by deteriorating soil quality over time. This could firstly be explain by the relative variance explanation by each of the factor tested, with deforestation and rotation effects overtaking the effects of stand age. We observed that long-term successive rubber cultivation has a detrimental effect on soil quality and there was a significant drop in quality after the second consecutive rubber rotation (Figure 2). This decline after consecutive rotations has also been reported in other tropical perennial cropping systems with a shorter stand period [START_REF] Selvaraj | Influence of long-term successive rotations and stand age of Chinese fir (Cunninghamia lanceolata) plantations on soil properties[END_REF]. The degradation in soil quality was associated with a general decrease in SOC content over successive plantations, as also reported in other systems [START_REF] Zhang | Stability of soil organic carbon changes in successive rotations of Chinese ffir (Cunninghamia lanceolata (Lamb.) Hook) plantations[END_REF][START_REF] Zhang | Carbon stock changes in successive rotations of Chinese fir (Cunninghamia lanceolata (lamb) hook) plantations[END_REF]. Interestingly, we found an increase in soil carbon thermally resistant pools over successive rotations (Figure 3). This result is in line with that of [START_REF] Zhang | Stability of soil organic carbon changes in successive rotations of Chinese ffir (Cunninghamia lanceolata (Lamb.) Hook) plantations[END_REF] who also reported a decline in SOC quality in successive rotations of Chinese fir. In our study, this decline may be linked to disturbance of the topsoil layer (with a higher labile carbon content) caused by the heavy machinery used during clear cutting and land preparation. These disturbances increased the thermally resistant fraction of SOC originating from the subsoil [START_REF] Zhang | Stability of soil organic carbon changes in successive rotations of Chinese ffir (Cunninghamia lanceolata (Lamb.) Hook) plantations[END_REF], as illustrated with [START_REF] Sebag | Dynamics of soil organic matter based on new Rock-Eval indices[END_REF] soil references. Thus, successive rotations of plantation rubber result in homogenisation of SOC quality between the topsoil and the subsoil. Such soil degradation over time may have considerable impacts on soil nutrient dynamics and tree productivity. O'Hehir and Nambiar (2010) also reported this "second rotation decline" in tree biomass after two successive rotations. In addition to tree productivity, other soil ecosystem services are certainly affected including soil biodiversity (Xu et This article is protected by copyright. All rights reserved. We did no detect an effect of rubber tree age with multivariate analysis, which advocate the fact that deforestation and rotation effects overtaking the effects of stand age. Moreover the soil parameters selected are known to be sensitive to long-term changes rather than to rapid micro-climatic differences that could by caused by the difference in the age of the rubber trees. Integrating functional and dynamic indicators linked to soil biota diversity and activities that are more sensitive to rubber age structural changes (soil water content, temperature, litter input, etc.), may have increased the impacts of rubber stand age [START_REF] Liu | Drivers of soil bacterial community structure and diversity in tropical agroforestry systems[END_REF][START_REF] Peerawat | Rubber plantation ageing controls soil biodiversity after land conversion from cassava[END_REF]Thoumazeau et al., 2019a). It is noteworthy to note that our results only focused on the topsoil (0-10cm). This layer was relevant in our case as it is the part of the soil that is the most sensitive to land management changes, especially integrating both soil disturbance (replanting) and carbon input from litter fall that is key for soil restoration processes along rubber stand age [START_REF] Blagodatsky | Carbon balance of rubber (Hevea brasiliensis) plantations: A review of uncertainties at plot, landscape and production level[END_REF]Thoumazeau et al., 2019a). To better understand processes and SOC/nutrient stocks, we advocate to enlarge this study with deeper soil layer analyses.

Toward less disruptive replanting practices to limit soil degradation

Here we show that current long-term rubber production is not sustainable as far as soil quality is concerned. Successive rotations degraded soil quality and altered soil carbon dynamics. It is important to state that, in this context of forest conversion into rubber plantations, smallholders usually started to cut the forest in lands that were more fertile and appropriate for rubber production. Then they moved to the forest and less optimal condition for production with time. This trends especially leads to the fact that remaining natural forests are located on heavily weathered soils with low fertility [START_REF] Veldkamp | Deforestation and reforestation impacts on soils in the tropics[END_REF]. In our case, topsoil quality even follow the opposite trends, highlighting and eventual even stronger effect of long term management practices on soil properties. This can mainly

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be explained by intensive logging activities at the end of life of the rubber trees. In Eucalyptus plantations, Chen et al. ( 2013) also highlighted the need to limit soil disturbance during replanting.

Current rubber replanting practices are very disruptive for soils, with deep soil mechanical disturbance caused by extracting as much as possible of the rubber tree rooting system (see pictures in Supplementary material 6), which alters the soil physico-chemical properties. Those disturbance were less detrimental in preceding rotations (for R2 and R3), as previous traditional practices did not involve mechanical uprooting. In addition, in the context of traditional rubber production systems in Thailand, the entire rubber wood from logging activities is exported, mostly for the rubberwood industry [START_REF] Petsri | Greenhouse gas emissions and carbon stock changes in rubber tree plantations in Thailand from 1990 to 2004[END_REF]. Only some of the residues (leaves, branches and roots) representing about 25% of the total biomass [START_REF] Hytönen | Rubber Tree (Hevea brasiliensis) Biomass, Nutrient Content, and Heating Values in Southern Thailand[END_REF] are left in the plot. Wood biomass removal is known to be detrimental to soil quality [START_REF] Hardiyanto | Productivity of successive rotations of Acacia mangium plantations in Sumatra, Indonesia: impacts of harvest and inter-rotation site management[END_REF]Perron et al., 2021a).

According to [START_REF] Perron | Dynamics of biomass and nutrient accumulation in rubber (Hevea brasiliensis) plantations established on two soil types: Implications for nutrient management over the immature phase[END_REF], exporting biomass may lead to a critical lack of nutrients in rubber plantations. Fertilisation practices may overcome this loss for the second rotation, but may not be sufficient to restore overall soil characteristics. In previous rotations (for R2 and R3), all residues were mostly burnt as the plots were not easily accessible. Those logging practices changes and intensification with time reinforce the concerns on soil quality degradation. Under the current logging practices, soil quality of the next generations would be even more jeopardized (Perron et al., 2021a). [START_REF] Huong | Improving productivity and sustainability of successive rotations of Acacia auriculiformis plantations in South Vietnam[END_REF] identified an opportunity to increase and sustain production of tree plantations over at least three rotations through better management practices. For example, in Eucalyptus and This article is protected by copyright. All rights reserved.

constraints [START_REF] Hougni | The household economics of rubber intercropping during the immature period in Northeast Thailand[END_REF][START_REF] Langenberger | Rubber intercropping: a viable concept for the 21st century?[END_REF]. As a result, in mountainous regions, soil erosion during the mature phase of rubber plantations is huge [START_REF] Neyret | Higher runoff and soil detachment in rubber tree plantations compared to annual cultivation is mitigated by ground cover in steep mountainous Thailand[END_REF].

Conclusion

This study highlights the long-term impact of rubber plantations on topsoil quality. In addition to the effects of deforestation, we observed a detrimental effect of three successive rotations on soil properties. The soil system was still resilient after one rotation, but failed after the second rotation, which is equivalent to about 50 years of monoclonal plantation. To avoid the detrimental effect of the second rotation and to substantially increase rubber plantation sustainability, we suggest either reviewing replanting practices or changing the land use in landscapes that already undergone three rubber rotations, which is the case in historical rubber producing areas in Thailand. This article is protected by copyright. All rights reserved.

Figure 3: SOC and R-index from Rock-Eval® analysis. The higher the R-index, the more organic fractions are thermally resistant is. Boxplots were computed with all the data sets (n=9 for forest and n=18 for rubber plantations). F: Forest, R1: 1 st rotation including the two rubber plantation ages, R2: 2 nd rotation including the two ages of rubber plantations, R3: 3 rd rotation including the two rubber plantation ages. Asterisks below the boxplots indicate differences between the plantations and the forest ("***", p < 0.001, "**", p < 0.01, "*", p < 0.05); p-values above the boxplots indicate differences between rotations.

Coloured bands correspond to Rock-Eval references for organic layer (green) and top soil (brown) presented in [START_REF] Sebag | Dynamics of soil organic matter based on new Rock-Eval indices[END_REF].

(a) (b)

This article is protected by copyright. All rights reserved. Table 1: Univariate analysis of the impact of land management on soil physico-chemical properties. n=3 (average of plots) + SD. Different letters indicate significant differences between land management after ANOVA analysis of the nested design. F : Forest, R1y : 1 st rotation of 3-6yo rubber, R1o: 1 st rotation of 18-22yo rubber, R2y: 2 nd rotation of 3-6yo rubber, R2o: 2 nd rotation of 18-22yo rubber, R3y: 3 rd rotation of 3-6yo rubber, R3o = 3 rd rotation of 18-22yo rubber.

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  rotation for most of soil parameters. The decrease was particularly apparent for soil total nitrogen (Ntot) with a 3-fold lower concentration in the old plantations during the third rotation (R3o) than in Forest. Other nutrients (K, Ca, Mg) were also significantly depleted in the last rotation with 10-fold lower concentrations compared to the reference forest. The trend was inverted for BD, where higher values are observed in the third rubber rotation. When we compared young and old plantations, soil parameters were generally not sensitive to the age of the plantation. Only soil Pav content was significantly higher in young plantations in R1 and R2 rotations. A trend towards an increase in SOC, Nav and AEMNO3 was also observed in old plantations compared to in young ones.

  al., 2020;[START_REF] Zhu | Effects of the successive planting of Eucalyptus urophylla on soil bacterial and fungal community structure, diversity, microbial biomass, and enzyme activity[END_REF]. In our case study, the age of the rubber tree stand did only significantly affected few soil physico-chemical properties. It was especially interesting to highlight the Pav decrease over plantations age, that was also observed by[START_REF] Liu | Phosphorous fractions in soils of rubber-based agroforestry systems: Influence of season, management and stand age[END_REF];[START_REF] Wang | Conversion of rainforest into agroforestry and monoculture plantation in China: Consequences for soil phosphorus forms and microbial community[END_REF], and could be explained by fertilization practices, tree uptake needs evolution and micro-climatic plantation changes.

  Prince Ruprecht's larch,[START_REF] Zhang | Changes in soil quality after converting Pinus to Eucalyptus plantations in southern China[END_REF] and[START_REF] Zhao | Effects of Long-Term Successive Rotations, Clear-Cutting and Stand Age of Prince Rupprecht's larch (Larix principis-rupprechtii Mayr) on Soil Quality[END_REF] observed a long-term improvement in soil quality with reduced soil disturbance during logging, keeping understory coverage and litter retention. These general recommendations need to be tested under local agronomic constraints (e.g. pest pressures) and socio-economic bottlenecks (e.g. shortfall in timber sales) that have to be included in any changes made in smallholder plantations. For example, the use of legume cover-crops, which is widespread in industrial rubber plantations, is seldom used by Thai smallholders, mostly due to labour

Figure 1 :

 1 Figure 1: Principal component analysis of soil quality parameters. (a) presents individuals with average per plot, n=3 replicates tested. F: Forest, R1y: 1 st rotation of 3-6yo rubber, R1o: 1 st rotation of 18-22yo rubber, R2y: 2 nd rotation of 3-6yo rubber, R2o: 2 nd rotation of 18-22yo rubber, R3y: 3 rd rotation of 3-6yo rubber, R3o: 3 rd rotation of 18-22yo rubber. (b) presents individuals correlations, see table 1 for details. Bartlett's test of sphericity < 0.05. Kaiser-Meyer-Olkin Statistics = 0.65. PCA factor loadings are presented in supplementary material 5.

Figure 2 :

 2 Figure 2: (a) results of Soil Quality Index following the procedure described by[START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF]. For each type of land management, n=3 replicates tested ± SD. Letters indicate significant differences between types of land management according to Tukey's post-hoc test. F: Forest, R1y: 1 st rotation of 3-6yo rubber, R1o: 1 st rotation of 18-22yo rubber, R2y: 2 nd rotation of 3-6yo rubber, R2o: 2 nd rotation of 18-22yo rubber, R3y: 3 rd rotation of 3-6yo rubber, R3o: 3 rd rotation of 18-22yo rubber. (b) logging effect assessed through score value differences from (a), all combinations were used and n=9 ±SD. (c) long term effect assessed through differences in scores from (a), all combinations were used and n=9 ±SD.

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Acknowledgements

Phantip Panklang's PhD scholarship was funded by the Graduate school and Natural Rubber Innovation Research Institute, Prince of Songkla University (PSU), contract No. NAT590338S. We thank Department of National Park (DNP) of Thailand for permission to sample in the forest. This study was also supported by the LMI LUSES (Dynamic of Land Use Changes and Soil Ecosystem Services), the French National Research Agency (ANR) in the framework of the Heveadapt project (ANR-14-CE03-0012) and the HRPP platform, the French Institute for Natural Rubber (IFC), the companies SIPH, SOCFIN and MICHELIN in the framework of the programme "HeveaBiodiv". This work benefited from the support of Nancy Rakotondrazafy for nutrient analysis in the ECOTROP platform of UMR Eco&Sols, with the support of LabEx CeMEB and ANR "Investissements d'avenir" programme (ANR-10-LABX-04-01). We are also grateful to Thierry Adatte (Institute of Earth Sciences, University of Lausanne, Switzerland) for his technical and scientific supports in Rock-Eval® analysis, a trademark registered by IFP Energies Nouvelles. The authors also thank Raphaël Chaillé, Céline Venot and all the people involved in field measurements as well as the farmers who allowed us to sample their plots. Finally, we thank the two anonymous reviewers who provided very useful comments to improve the quality of the paper.

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Declaration of interests

☒ 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 paper.

☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

No potential conflict of interest are declared. All financial and partnership aspects are presented in the acknowledgments of the manuscript: Phantip Panklang's PhD scholarship was funded by the Graduate school and Natural Rubber Innovation Research Institute, Prince of Songkla University (PSU), contract No. NAT590338S. We thank Department of National Park (DNP) of Thailand for permission to sample in the forest. This study was also supported by the LMI LUSES (Dynamic of Land Use Changes and Soil Ecosystem Services), the French National Research Agency (ANR) in the framework of the Heveadapt project (ANR-14-CE03-0012) and the HRPP platform, the French Institute for Natural Rubber (IFC), the companies SIPH, SOCFIN and MICHELIN in the framework of the programme "HeveaBiodiv". This work benefited from the support of Nancy Rakotondrazafy for nutrient analysis in the ECOTROP platform of UMR Eco&Sols, with the support of LabEx CeMEB and ANR "Investissements d'avenir" programme (ANR-10-LABX-04-01). We are also grateful to Thierry Adatte (Institute of Earth Sciences, University of Lausanne, Switzerland) for his technical and scientific supports in Rock-Eval® analysis, a trademark registered by IFP Energies Nouvelles. The authors also thank Raphaël Chaillé, Céline Venot and all the people involved in field measurements as well as the farmers who allowed us to sample their plots. Finally, we thank the two anonymous reviewers who provided very useful comments to improve the quality of the paper.


Olivier Sènankpon

Olivier Sènankpon Dassou

Wouter Vanhove

Hervé Aholoukpè

CIRAD Xavier Bonneau

Albert Flori

Palmelit Sas

Bénoît Cochard

Brice Sinsin

Patrick Van Damme

Jean Ollivier

Oil Palm (Elaeis Guineensis Jacq.) Genetic Differences in Mineral Nutrition (Part III): Speci c Lea et Mineral Concentrations of High-Yielding Oil Palm Progenies and their Implications for Managing K And Mg Nutrition

Keywords:

Background and aims: In oil palm, similar fertilization treatments can result in lea et potassium and magnesium concentrations that vary signi cantly from one progeny to another. This hinders the development of standardized fertilizer recommendations for this crop, as they are usually calculated based on optimum lea et nutrient concentrations.

Methods: 4-high-yielding oil palm progenies with contrasting lea et K and Mg concentrations (C1, C2, and C3 of Deli x La Mé origin and C4 of Deli x Yangambi origin) were treated with combinations of 3-levels of KCl and MgSO 4 , in a completely randomized split-plot factorial design with 6-replicates, where progenies were a sub-factor.

Results: For a given level of KCl or MgSO 4 , different lea et K and Mg concentrations were found between progenies (p < 0.0001). Lea et K concentration and yield response to KCl applications revealed that the four oil palm progenies have different optimum lea et K concentrations. In our study period (5-8 YAP), progenies C1 and C3 showed their highest fresh fruit bunch (FFB) yields (13.62 and 16.54 t ha -1 year -1 , respectively) at K2, whereas progenies C2 and C4 showed their highest yields (14.62 and 12.39 t ha -1 year -1 , respectively) at K1.

Conclusion:

Our study highlighted speci c optimum lea et K and Mg concentrations for different oil palm progenies in a given environment. It paves the way for adopting K and Mg fertilizer application rates adapted to speci c requirements of each type of oil palm planting material.

Introduction

Oil palm (Elaeis guineensis Jacq.) (Fam. Arecaceae) is a very productive perennial oil crop when grown under suitable pedo-climatic conditions [START_REF] Tarmizi | Nutrient demands of Tenera oil palm planted on Inland soils of Malaysia[END_REF][START_REF] Nodichao | Développement du système racinaire chez le palmier à huile selon l'origine génétique et le régime hydro-potassique du sol[END_REF]; [START_REF] Aholoukpè | Effet de la gestion des feuilles d'élagage du palmier à huile sur le stock de carbone et les propriétés physico-chimiques du sol dans les palmeraies villageoises du Bénin[END_REF]. It is the high-yielding oil crop in the world (in t oil/ha) [START_REF] Corley | The oil palm[END_REF]. Fresh fruit bunch (FFB) production exceeds 30 t ha -1 year -1 in Southeast Asia, whereas in West Africa, production levels are often below 24 t ha -1 year -1 at full maturity [START_REF] Dufrène | Photosynthèse et productivité du palmier à huile en liaison avec les facteurs climatiques[END_REF]Probowo et al. 2002;[START_REF] Nouy | Oil palm under limiting water supply conditions[END_REF][START_REF] Cros | Differential response to water balance and bunch load generates diversity of bunch production pro les among oil palm[END_REF]). In commercial plantations, high-yielding oil palm genotypes can produce 8 t of palm oil ha -1 year -1 under optimum conditions: rainfall > 1800 mm year -1 well distributed with water de cit < 200mm year -1 , temperature between 25.0°C to 34.0°C, and annual radiation > 12 MJ m -2 d -1 , with a soil rich in organic matter [START_REF] Omont | Contribution à la production d'huile de palme au développement durable : problématique générale et controverses[END_REF][START_REF] Durand-Gasselin | Sélection du palmier à huile pour une huile de palme durable et responsabilité sociale[END_REF][START_REF] Pic | Rapport du Programme d'Intérêt Commun (PIC) entre INRAB -CIRAD et PalmElit : Bilan exercice 2010 -perspective[END_REF][START_REF] Corley | The oil palm[END_REF]. In Indonesia, palm oil yields improved from an average of 1.3 t ha -1 year -1 in 1960 to 5.4 t ha -1 year -1 in 2010, half of which can be attributed to the breeding of more productive genotypes and the other half to environmental conditions [START_REF] Davidson | Management for e ciency, cost effective and productive oil palm plantations[END_REF][START_REF] Cochard | Oil palm genetic improvement and sustainable development[END_REF][START_REF] Durand-Gasselin | Sélection du palmier à huile pour une huile de palme durable et responsabilité sociale[END_REF][START_REF] Pic | Rapport du Programme d'Intérêt Commun (PIC) entre INRAB -CIRAD et PalmElit : Bilan exercice 2010 -perspective[END_REF].

Soil fertility (i.e. the combination of soil texture, structure and mineral and organic matter) is a signi cant yield-determining factor in oil palm cultivation.

Consequently, it is essential to apply the right types and amounts of nutrients to achieve maximum fruit bunch and oil yields. Potassium (K) plays a role in physiological processes, such as stomatal opening and closure, along with CO 2 -assimilation by enabling the transformation of light into chemical energy during photosynthesis [START_REF] Yanbo | Effects of Potassium De ciency on Chloroplast Ultrastructure and Chlorophyll Fluorescence in Ine cient and E cient Genotypes of Rice[END_REF]. K is also the main driver of oil palm vegetative growth and fruit and oil production, due to its role in chlorophyll uorescence emission [START_REF] Yanbo | Effects of Potassium De ciency on Chloroplast Ultrastructure and Chlorophyll Fluorescence in Ine cient and E cient Genotypes of Rice[END_REF]. K is further involved in transporting sugar assimilates from photosynthetic tissues to other organs of the plant [START_REF] Lamade | Occurrence of potassium location in oil palm tissues with reserve sugars: consequences for oil palm K status determination[END_REF], such as in orescences and developing fruits. Applying K to oil palms, seeing its soil content, usually increases their lea et K concentrations [START_REF] Dubos | High soil calcium saturation limits use of leaf potassium diagnosis when kcl is applied in oil palm plantations[END_REF], number of functional fronds (i.e. non-senescent), number of in orescences and fruit bunch weight [START_REF] Kusnu | Effects of N, P, K and Mg fertilizer on the growth and yield of oil palm on Typic Paleudult[END_REF]. According to [START_REF] Corley | The oil palm[END_REF], K application increases overall oil palm dry matter production and oil yield. Oil palm lea et K concentration is a good yield indicator [START_REF] Goh | Fertiliser management in oil palm : Agronomic principles and eld practices[END_REF]. It is therefore used as a basis for the calculation of the amount of K fertilization required to maximize oil palm yield.

Under Nigerian environmental conditions, with 2066 mm of average rainfall year -1 [START_REF] Bonneau | Initial results of an oil palm planting density experiment in west Africa[END_REF], mature commercial oil palms that yield between 133 and 148 kg of FFB palm -1 year -1 have been found to require up to 2.5 kg KCl palm -1 year -1 [START_REF] Bonneau | Optimal fertilization for oil palm (Elaeis guineensis Jacq.) plantations: Conclusions from a Long-Term Fertiliser Trial in Nigeria[END_REF].

Magnesium also plays essential and speci c roles in oil palm metabolism. As the central element of the chlorophyll molecule, Mg enhances photosynthesis and is also involved in enzymatic reactions related to energy transfer [START_REF] Gerendás | The signi cance of magnesium for crop quality[END_REF]. However, there are indications that oil palm's lea et mineral concentrations can vary greatly from one progeny to another, suggesting that mineral requirements depend on the vegetal material (Jacquemard et Leaf analysis is a commonly used agronomic tool for assessing oil palm mineral requirements, hence for determining the right amounts of fertilizers to be applied [START_REF] Dubos | Precision agriculture in oil palm plantations : diagnostic tools for sustainable n and k nutrient supply[END_REF]. Factorial trials can provide useful data for developing fertilizer x yield response curves and for determining optimum mineral concentrations in oil palm lea ets that are linked to the best economic yields [START_REF] Webb | A conceptual framework for determining economically optimal fertiliser use in oil palm plantations with factorial fertiliser trials[END_REF]). To that end, fertilizer recommendations are made by comparing the actual nutrient concentrations in oil palm lea ets with optimum nutrient levels, as determined by fertilizer x yield response curves [START_REF] Dubos | Potassium uptake and storage in oil palm organs: the role of chlorine and the in uence of soil characteristics in the Magdalena valley, Colombia[END_REF]).

Environmental conditions affect lea et K and Mg dynamics in oil palm progenies [START_REF] Doğan | Effects of water stress on the uptake of nutrients by bean seedlings (Phaseolus vulgaris L.)[END_REF][START_REF] Da Silva | Drought Stress and Plant Nutrition[END_REF]. Studies in Indonesia and Nigeria have revealed signi cant variations in lea et potassium (K) and magnesium (Mg) concentrations for different oil palm progenies [START_REF] Ollivier | Hacia un diagnostic nutricional preciso para la palma de aceite, teniendo en cuenta el origen del material de siembra[END_REF][START_REF] Ollivier | Genetic variation in nutrient uptake and nutrient use e ciency of oil palm[END_REF][START_REF] Lee | Rachis nutrient concentrations of different oil palm genotypes as affected by irrigation and terrain[END_REF][START_REF] Dassou | Oil palm (Elaeis guineensis Jacq.) leaf K and Mg contents differ with progenies: implications and research needs[END_REF] producing similar oil yields and receiving equal amounts of fertilizers [START_REF] Ollivier | Genetic variation in nutrient uptake and nutrient use e ciency of oil palm[END_REF]. Such variation hampers the unequivocal conversion of lea et nutrient concentrations into standardized fertilizer recommendations for all commercially used oil palm genotypes. Consequently, there is a need for deeper knowledge of interactions between oil palm genetic materials (GM) and mineral nutrition (MN).

In this study, we assess how different oil palm progenies react when subjected to different combinations of potassium and magnesium fertilizer rates. We hypothesize that in a given environment, different oil palm progenies have different fertilizer x yield response curves and therefore different optimum lea et K and Mg concentrations. The results of this study should help to identify progenies that require less fertilizer to obtain a given oil palm bunch and oil production levels in the speci c agroclimatic environment of Nigeria (West Africa), which could reduce fertilizer costs in the Nigerian oil palm sector. Providing oil palm growers with more productive planting material that requires fewer fertilizers can increase the ecological and economic sustainability of global oil palm cultivation.

Materials And Methods

Experimental site characteristics

The trial was set up at Ologbo (Presco Plc oil palm plantation) near Benin City (N6.03652° E 5.55609° at 20 m a.s.l) in Nigeria. Average annual rainfall in the study area between 1996 and 2018 was 2,066 mm [START_REF] Bonneau | Initial results of an oil palm planting density experiment in west Africa[END_REF]). Rainfall distribution is monomodal, with a dry season from November to March, and a rainy season from April to October [START_REF] Bonneau | Initial results of an oil palm planting density experiment in west Africa[END_REF]. According to the latter author, it is highly unlikely that oil palms suffer from drought in this area, unless an exceptional dry period occurs. Unfortunately, the El Niño phenomenon resulted in more than ve months of unusual drought with only 338.7 mm total precipitation between November 2015 and March 2016. In the same period (November to March), between 1996 and 2018, average annual temperatures ranged from 25.0°C to 27.8°C, and average annual radiation was 13.78 MJ m -2 d -1 . The ferralsols (FOA nomenclature) have a at relief, are uniform, deep and very sandy on the surface, with a gradual increase in clay contents with depth. Soil fertility was low (Supplementary table ST1).

Plant material

Four different Tenera oil palm (Elaeis guineensis Jacq.) progenies (C1, C2 and C3 of Deli x La Mé origin, and C4 of Deli x Yangambi origin) were selected for this study (Supplementary table ST2). In an earlier experiment (oil palm genetic trial) in Indonesia [START_REF] Ollivier | Genetic variation in nutrient uptake and nutrient use e ciency of oil palm[END_REF], under a given fertilization regime, these progenies showed the most contrasting lea et K and Mg concentrations among a population of 116 progenies, with high and similar fresh fruit bunches (FFB) and oil yields. These progenies represent a wide genetic diversity of the E. guineensis species. Their contrasts in lea et K and Mg concentrations covered the extreme ranges of lea et concentrations in these minerals (PIC 2011).

Fertilizer treatments

During the 8-year trial period (2011-2019), the four oil palm progenies were treated with potassium chloride (KCl: 60% K 2 O) and kieserite (MgSO 4 : 27% MgO) fertilizers according to all possible combinations of three levels (0-1-2) of each fertilizer (Supplementary table ST3).

In the rst year after planting (YAP), fertilizers were applied as a basal and uniform top dressing. A basic application of urea (1000 g palm -1 ) and triple superphosphate (500 g palm -1 ) was applied in the second year. Additional combinations of KCl and MgSO 4 fertilizer rates were applied for seven years, by increasing their amounts each year in the main fertilizer plots, starting from year two (Supplementary table ST3).

Starting from 4 YAP, a xed fertilizer rate of 0, 1.5 and 3.0 kg palm -1 year -1 of KCl for K0, K1 and K2, respectively, and of 0, 0.75 and 1.5 kg palm -1 year -1 of kieserite for Mg0, Mg1 and Mg2, respectively, were applied. Over the eight years of fertilization, the cumulative KCl applied per palm amounted to 0, 9 and 18 kg palm -1 for K0, K1 and K2 respectively, whereas the cumulative MgSO 4 applied per palm amounted to 0, 3.7 and 7.4 kg palm -1 for Mg0, Mg1 and Mg2, respectively. Leaf analysis revealed a chlorine (Cl) de ciency in the K0 plots, which might have limited K uptake [START_REF] Dubos | Potassium uptake and storage in oil palm organs: the role of chlorine and the in uence of soil characteristics in the Magdalena valley, Colombia[END_REF]). Chlorine was applied as NaCl to all palms in the K0 plots until lea et Cl concentrations reached the required level of 0.5% of dry matter (DM), to avoid a chlorine de ciency, while Na concentrations remained very low (0.005% DM on average) with no visual toxicity effects in the eld. The progenies also displayed de cient lea et boron (B) concentrations in all plots, which were resolved by applying borax (B 2 O 3 ) at a rate of 100 g palm -1 in the rst 4 YAP (split into 10 g palm -1 in the rst year, 20 g palm -1 in the following two years and 50 g palm -1 in the fourth YAP), applied to all palms in the trial to prevent a boron de ciency.

Experimental design

The trial, consisted of a K 3 Mg 3 factorial split plot design (Supplementary g. SF1), which was set up in 2011 on 33 ha with palms planted 9 m apart in a staggered, equilateral triangle design corresponding to a planting density of 143 plants ha -1 (PIC, 2012). Measurements were carried out over 6 years (2013 to 2019). All combinations of three levels of potassium chloride (KCl: 60% K 2 O) and kieserite (MgSO 4 : 27% MgO) fertilizers were tested, resulting in a total of nine completely randomized fertilizer treatments. Each fertilizer treatment was applied to 4 subplots, each planted with one of the four oil palm progenies and in three rows of three palms. The experimental trial layout comprised six replicates (i.e. 36 subplots per replicate, 216 subplots in total and 1944 palms in the trial as a whole). The studied palms in each subplot were surrounded by border palms in all directions (45 border palms per fertilizer plot, i.e. a total of 2,430 border palms for all 54 fertilized plots). Another 350 border palms were planted along the sides of the road to protect the palms under study and their main border palms from attacks by rodents, as the trial is located near a forest.

Measured parameters 2.5.1. Fresh Fruit Bunch (FFB) and male in orescence production

Each month from 2013 ( rst year of FFB production) to 2019 (3 to 8 YAP), three harvesting rounds were organized to collect ripe bunches. Bunch number and bunch weight of each individual palm were recorded on each harvesting round. Three yield components were considered for each YAP: number of bunches per palm, average bunch weight per palm and total bunch weight per palm. Yield data were summed on a monthly and annual basis. Annual production in t ha -1 was obtained by multiplying the total production obtained for one year (in tons) by 135 palms, rather than the actual 143 palms per ha (planting density of industrial plantations), following Durand-Gasselin et al. (2006). The latter deduction of 8 palms per ha accounted for non-productive palms (dead and abnormal palms estimated to be 4 palms per hectare on the basis of a phytosanitary census, Durand-Gasselin et al. ( 2006)) and areas occupied by roads in plantations (also estimated to amount to 4 palms per hectare). The production is calculated on the basis of 135 palms per hectare to represent yield that could be achieved in an industrial plantation with the same treatments.

Male in orescences were counted on a monthly basis by painting the petiole of all fronds holding a male in orescence. Different paint colours were used for successive months to avoid mistakes in the in orescences counting. This allowed us to distinguish fronds bearing male from those bearing female in orescences even if the male in orescence had completed its growth cycle and was abscised or when the bunches resulting from the female in orescences had been harvested.

Bunch analysis: oil extraction rate (OER) and crude palm oil (CPO) production

Five years after planting, bunches were analysed following the CIRAD bunch analysis protocol (IRHO-CIRAD-IGK [START_REF] Da Silva | Drought Stress and Plant Nutrition[END_REF]1996) to assess the oil extraction rate (OER). For each progeny subplot, 40 bunches were randomly selected and analysed over two years at weekly intervals (resulting in 960 analysed bunches: 40 bunches x 4 progenies x 6 replicates). Progeny OER values were averaged over all 6 replicates. To obtain the industrial oil extraction rate (OER mill ), the laboratory oil extraction rate (OER Lab ) was multiplied by 0.855 to account for losses (as a result of ine cient extractors), as per Durand-Gasselin et al. (2006).

Total bunch weights per palm were subsequently multiplied by their respective OER to obtain their crude palm oil (CPO) production.

Optimum lea et K and Mg concentration levels

Progenies' lea et mineral (K and Mg) concentrations were assessed each year in December. From each of the nine central useful palm trees in each subplot, 3 lea ets were sampled on each side (left and right) at the middle of the leaf rachis of rank 17 [START_REF] Ollivier | Genetic variation in nutrient uptake and nutrient use e ciency of oil palm[END_REF]. Lea et samples were dried in an oven at 80°C for at least 48 hours (CIGE 2013). K, Mg and Ca concentrations were analyzed using the standard CIRAD laboratory protocol for plant tissue analysis (CIRAD, US 49, France), which uses inductive coupled plasma-optical emission spectrometry (Model: ICP-OES Agilent 720-ES) after double calcination extraction following [START_REF] Pinta | Méthode de référence pour la détermination des éléments minéraux dans les végétaux (Reference methods for the determination of mineral elements in plants)[END_REF], [START_REF] Doğan | Effects of water stress on the uptake of nutrients by bean seedlings (Phaseolus vulgaris L.)[END_REF] and Ciríaco da Silva et al. (2011). Optimum lea et mineral concentrations were determined using lea et mineral concentration and yield response curves. Optimum lea et mineral concentrations correspond to the maximum yield obtained in response to a fertilization level beyond which additional fertilizer application does not add extra yield [START_REF] Ochs | Stratégie de mise en oeuvre du contrôle nutritionnel des plantes pérennes : Gestion de la nutrition minérale et programmation des fumures[END_REF]. It is therefore possible that an optimum lea et mineral concentration is lower than the maximum lea et mineral concentration.

Statistical analysis

To examine genotype, fertilizer and genotype x fertilizer interaction effects, three-way ANOVAs were performed with the factors "genotype", "K" and "Mg". The split-plot nature of the design was taken into account by conducting the ANOVAs using a mixed effects model, with the sub-block effects (i.e. fertilizer x block interaction) as a random factor. Tukey's multiple comparison test was run to evaluate the signi cance of differences in parameter means between progenies and between K-Mg fertilization level combinations. Linearity of the response curves was tested with a quadratic contrast test. Statistical analyses were performed using software packages XLSAT, (Version 2018-7, www.xlstat.com) and R version R-3.6.0 of Windows, 32/64 bit.

Results

Effect of KCl and MgSO 4 fertilization on lea et K and Mg concentrations

Progenies displayed different lea et K concentrations in response to the applied KCl levels. Treatment K1 signi cantly (p < 0.0001) increased lea et K concentrations in the palms of progenies C1, C2 and C3 compared to treatment K0, whereas no signi cant differences were observed in lea et K concentrations between oil palms with treatment K2 compared to the palms with treatment K1 in the same progenies. Lea et K concentrations of progeny C4 signi cantly increased between treatments K0 and K1 and between treatments K1 and K2. With data pooled for all progenies, average lea et K concentrations signi cantly increased from 0.715-0.829% and 0.864% of DM for K0, K1 and K2, respectively (Fig. 1A).

Without KCl application (K0) over the same period (5 to 7 YAP), progeny C4 had a lea et K concentration (0.82%) that was 11%, 18% and 25% higher than in progenies C1, C2 and C3, respectively. Respective increases were 12%, 18% and 24%, for treatment K1 and 11%, 20% and 26% for treatment K2 (Fig. 1A). MgSO 4 applications (data pooled for all progenies) had a signi cant effect (p = 0.0002) on lea et Mg concentrations. Across all progenies, average lea et Mg concentrations in oil palms with treatment Mg2 (0.288% of DM) differed signi cantly (p = 0.0002) from those with treatment Mg1 (0.270% of DM) and those with treatment Mg0 (0.263% of DM), with the last two concentrations not signi cantly differing from one another (Fig. 1B).

With treatment Mg0, the palms of progeny C3 had a lea et Mg concentration (0.292%) that was 13%, 18% and 10% higher than that of the palms of progenies C1, C2 and C4, respectively. Respective increases were 12%, 18%, and 13%, for treatment Mg1 and 13%, 16%, and 10%, for treatment Mg2, respectively (Fig. 1B).

Average lea et Mg concentrations signi cantly (p = 0.002) decreased from 0.302-0.266% and 0.255 of DM, for treatments K0, K1 and K2, respectively.

However, in all progenies there were no signi cant differences in lea et Mg concentrations between treatments K1 and K2. Lea et Mg concentrations decreased in all progenies from K0 to K1, whereas there was no signi cant difference between treatments K1 and K2 for none of the progenies (Fig. 1C).

Increasing MgSO 4 levels had no signi cant effect on lea et K concentrations in the progenies. However, lea et K concentrations differed between progenies for each Mg level (Fig. 1D). Statistical analysis did not reveal any signi cant K*Mg interactions for lea et K or Mg concentrations.

In uence of KCl fertilization on oil palm progeny yield components

Progeny C1 produced the largest number of bunches per year (14.4 ± 6.0), which was not statistically different from those of progeny C2 (14.3 ± 5.1). The palms of progeny C4 produced the lowest annual bunch number (11.4 ± 2.5), which was signi cantly lower than that of progenies C1 and C2, whereas progeny C3 palms had an intermediate average annual bunch number (13.6 ± 5.3), which signi cantly differed from that of progenies C1, C2 and C4 (Table 1).

The number of male in orescences signi cantly (p < 0.0001) differed between progenies. Progeny C1 (Deli x La Mé origin) exhibited the lowest annual number of male in orescences (6.8 ± 4.3). Progenies C2 and C3 had intermediate male in orescence numbers (9.1 ± 4.6 and 9.5 ± 4.4), whereas progeny C4 produced the largest number of male in orescences (15.3 ± 3).

The "bunch number to emitted frond number" ratio was 0.40, 0.37, 0.36 and 0.31 in the palms of progenies C1, C2, C3 and C4, respectively. Progeny C3 palms had the highest average bunch weight (8.9 ± 2.8 kg bunch -1 ) over the 5 to 8 YAP period and an average weight of 10.7 and 11.9 kg bunch -1 at the age of 7 and 8 YAP (last two years of bunch harvesting), respectively (Table 1). On average, the palms of progenies C1 and C2 produced the lightest bunches, with bunch weights for progeny C1 that were signi cantly lower than those of progeny C2 (7.3 ± 1.8 and 7.7 ± 2.4 kg bunch -1 , respectively). Average bunch weight for progeny C4 palms at 8 YAP (11.8 kg bunch -1 ) was similar to that of progeny C3 at the same age. However, at 7 YAP, the palms of progeny C4 had an average bunch weight of 8.07 kg, which was signi cantly lower than that of progeny C3 at the same age.

Between 5 and 8 YAP, progeny C3 (all fertilizer treatments combined) produced 15.80 t of FFB ha -1 year -1 and 5.16 t of crude palm oil (CPO Lab ) ha -1 year -1 on average (Table 2), which was signi cantly higher than the FFB and CPO Lab of the palms of the other progenies, particularly those of progeny C4, which produced the lowest average yield (12.20 t of FFB ha -1 year -1 and 3.78 t of CPO Lab ) ha -1 year -1 . The highest bunch producing progenies (C3 > C2) were also the highest oil producers, with similar oil extraction rates (Table 2). The lowest oil extraction rate (OER Lab : 31.65%) was found for progeny C4, whereas progenies C2 and C3 had signi cantly higher extraction rates (32.46% and 32.74%, respectively). No KCl effect on the oil extraction rate was observed for any progeny (p = 0.19).

KCl treatments were associated with bunch weight increases for the palms of progenies C2 (+0.6 kg from K0 to K1; p = 0.017), C3 (+0.8 kg from K0 to K2; p = 0.0001) and C4 (+1.0 kg from K0 to K1; p < 0.0001) and bunch number increases for the palms of progeny C1 (+0.7 bunches year -1 from K0 to K2) with a signi cant linear contrast test result (F = 4.37; p = 0.038) over the 5 to 8 YAP period.

In none of the progenies did KCl applications have a signi cant effect on the average number of male in orescences produced per palm, except at 5, 6 and 7 YAP for progenies C4, C2 and C3, respectively (Table 1).

The linear contrast test on the average bunch weight for the KCl treatment of progeny C3 was signi cant (F = 19.7; p < 0.0001). The quadratic contrast test was signi cant (F = 10.0; p = 0.002) for progeny C4, but not for progeny C2 (F = 3.2; p = 0.074).

Yield responses to KCl applications

Over the 5 to 8 YAP period, we found signi cant (p < 0.0000) differences in bunch production between progenies. For all KCl levels applied, annual bunch production per palm could be ranked as C3 > C2 > C1 > C4 (Fig. 2), with a difference of 25 to 30 kg palm -1 year -1 between the highest (C3) and the lowest yielding progeny (C4).

In all progenies, bunch production was signi cantly lower for treatment K0 than for treatments K1 and K2. Treatment K1 signi cantly improved bunch yields for the palms of all progenies (p = 0.0007) compared to treatment K0. For progenies C2 (p = 0.037) and C4 (p = 0.015), there was a signi cant increase in bunch production only between treatments K0 and K1 (Fig. 2). For progenies C1 (p = 0.045) and C3 (p = 0.003), signi cant increases in bunch production gains were also observed between oil palms receiving treatments K1 and K2.

Progenies' optimum K levels

The quadratic contrast test results (F = 2.56; p = 0.111 and F = 3.11; p = 0.079, for C2 and C4, respectively) were not signi cant, indicating that K1 was the optimum level for these two progenies (Fig. 3). Lea et K concentrations of palms with the K1 treatment were 0.78% and 0.95% for progenies C2 and C4, respectively.

For progenies C1 and C3, maximum yield was only reached with treatment K2 (Fig. 3). The contrast test results showed KCl response linearity (F = 6.16; p = 0.014 and F = 11.8; p = 0.000 for C1 and C3 respectively), indicating that in our experiment, K2 was the optimum KCl treatment for progenies C1 and C3 (Fig. 3). Lea et K concentrations at K2 were 0.91% and 0.77% for progenies C1 and C3, respectively. MgSO 4 had no effect on the yield of any progeny, except for the average bunch weight of progeny C3 over the 5 to 8 YAP period (p = 0.049), with an increase of 0.35 kg bunch -1 between treatments Mg0 and Mg1.

Discussion

Oil palm progeny (optimum) lea et mineral concentrations

Leaf analysis remains the most frequently used tool for predicting oil palms' mineral requirements. It allows to tailor fertilizer applications to speci c oil palm genotypes when cultivated in a speci c environment [START_REF] Dubos | Precision agriculture in oil palm plantations : diagnostic tools for sustainable n and k nutrient supply[END_REF]).

We showed that oil palms of different progenies had speci c optimum lea et K levels in our experiment in the Nigerian growth conditions. The lea et Mg concentrations corresponding to optimum lea et K levels were found to be 0.227%, 0.224%, 0.264% and 0.236% of DM for progenies C1, C2, C3 and C4, respectively.

The fact that different progenies have different optimum lea et K concentrations speci c KCl fertilization rates is most likely linked to progeny-speci c lea et K allocation and/or progeny-speci c K uptake. The different progeny-speci c optimum KCl levels might imply that each progeny has its speci c K requirement.

The linearity KCl response showed by the contrast test results in our study (Fig. 3) for progeny C1 and C3 implies that the latter progenies may request a higher KCl amount than that supplied with K2.

The fact that oil palms of different progenies exhibit different yields for a given KCl application rate could also mean that these progenies may be more or less e cient in using the amounts of K taken up. These hypotheses should be further explored. However, after being equally fertilized, progenies have shown signi cant differences in lea et mineral concentrations, which could be related to the differential partitioning of the minerals in the palm tissues that implies that for the assessment of the more e cient material, the comparison may be done on the basis of the mineral uptake by the progenies at the whole palm tree level.

Since the FFB number of C2 and C3 palms is relatively high, they are also relatively large mineral sinks [START_REF] Legros | IOPC-Optimum use of resources: Challenges and opportunities for sustainable oil palm developpement[END_REF][START_REF] Lamade | Occurrence of potassium location in oil palm tissues with reserve sugars: consequences for oil palm K status determination[END_REF]). The resulting relatively high amounts of minerals stored in their FFB are ultimately exported from the plantation during harvesting. This might explain why their lea et K concentrations were low compared to those of the C1 and C4 palms, which were less productive. However, further research should con rm the latter hypothesis.

High FFB production is not necessarily linked to a high optimum lea et K concentration, as observed in the progenies that showed the highest bunch production (C3 & C2) while displaying the lowest optimum lea et K concentrations, whereas the progenies with the lowest bunch production (C1 & C4) showed the highest optimum lea et K concentrations. We can therefore hypothesize that lea et K concentration is related to different allocations of K in the different oil palm tissues.

The seemingly inverse relation between a progeny's high yield and low lea et K concentration does not mean that leaf analysis is an inaccurate tool for assessing nutrient de ciencies in oil palms, but does imply that there is room for ne-tuning [START_REF] Dubos | Precision agriculture in oil palm plantations : diagnostic tools for sustainable n and k nutrient supply[END_REF], and implies that genotypic differences in oil palm mineral use e ciency should be explored. Under equal K availability in the soil, contrasting lea et K concentrations are linked to speci c physiological mechanisms in oil palm progenies, such as photosynthesis and biochemical metabolism (soluble sugar and starch production), where minerals are used more or less e ciently ( ). The high bunch and oil production observed in the palms of progeny C3, which had the lowest lea et K concentration of all tested progenies, was probably the result of e cient K use, which might be linked to speci c physiological traits between progenies (such as functional potassium channels in oil palm tissues). These hypotheses warrant further research. In hybrid cotton seedlings (Gossypium hirsutum) as in many other crops such as rice, sweet potato, oil palm, Further studies are necessary to quantify mineral contents in different oil palm organs, in order to determine the true K and Mg uptake capacities and use e ciencies of the studied oil palm progenies.

Oil palm progeny yield components and nutrient uptake

We observed the highest bunch number in palms of Deli x La Mé origin and the lowest number in oil palms of Deli x Yangambi origin, which was also found by [START_REF] Cros | Differential response to water balance and bunch load generates diversity of bunch production pro les among oil palm[END_REF] in Benin and [START_REF] Nouy | Oil palm under limiting water supply conditions[END_REF] in Benin, Ivory Coast and Indonesia. In Indonesia, from 6 to 9 YAP, average bunch yields, produced by oil palms of the same progenies as in our study, amounted to 31.1, 29.6, 30.7 and 32.5 t ha -1 year -1 (230.2, 219.6, 227.5, 240.6 kg palm -1 year -1 ) for progenies C1, C2, C3 and C4, respectively, with no statistical differences between these yield gures (PIC 2012).

Despite that the oil palms in Indonesia were tested over a period that was just one year longer than in our experiment in Nigeria, we can safely assume that the higher bunch and oil production gures in Indonesia resulted from more favourable environmental conditions. Unlike in Indonesia, in Nigeria we found signi cant differences in bunch production between the progenies. This con rms the well-known fact that oil palm production is determined by physiological processes brought about by both genetic and environmental factors. The palms of progeny C3 that produced the largest bunches in Nigeria had the smallest bunch sizes in Indonesia. On the other hand, the palms of progeny C4 (Deli x Yangambi) had a signi cantly higher bunch weight than the palms of progenies C1 and C2 in both countries. Moreover, oil palms of Deli x Yangambi origin are generally not able to fully express their production potential in less favourable environments [START_REF] Nouy | Oil palm under limiting water supply conditions[END_REF]. The low bunch number per palm and the signi cantly higher male in orescence numbers observed in our experiment for progeny C4 of Deli x Yangambi origin (which is more susceptible to drought, [START_REF] Nouy | Oil palm under limiting water supply conditions[END_REF][START_REF] Legros | IOPC-Optimum use of resources: Challenges and opportunities for sustainable oil palm developpement[END_REF]), can therefore be linked to the relatively drier Nigerian environment compared to that of Indonesia and probably to differences in soil fertility. The drought period that occurred between 2015 and 2016 (El Niño phenomenon with more than 5 months of an inhabitual drought) was a factor that might have increased male in orescence production in Nigeria.

As observed in our experiment, the high oil production found in progeny C3 in Nigeria is linked to the high bunch number and weight of C3 progeny oil palms (higher than that of all the other Deli x La Mé materials (C1, C2), as well as that of progeny C4 oil palms, which are of Deli x Yangambi origin. As a result, these ndings suggest that C3 progeny oil palms are interesting oil palm genetic material for oil palm production in Nigeria and -more generally -in all West African zones. experimental soil, which was (as generally observed in ferralsols in tropical zones) even higher than the amount of exchangeable K (Supplementary table ST1), and need to be monitor to avoid excess Mg application in the eld. Also in a factorial trial in Sumatra (Indonesia), in which different combinations of N, P, K and Mg rates were applied, [START_REF] Kusnu | Effects of N, P, K and Mg fertilizer on the growth and yield of oil palm on Typic Paleudult[END_REF] only found a Mg effect on the bunch number in the third and fourth year of MgSO 4 applications, but did not nd any MgSO 4 effect on the other yield components. This was probably linked to a lower Mg exchange capacity (exchangeable Mg: 0.12 cmol kg -1 ) of their experimental soil compared to that of our experimental soil (exchangeable Mg: 0.37 cmol kg -1 between 0 and 20 cm).

Conclusion

The four oil palm progenies considered this study showed different yield potential. Our results showed that under Nigerian environmental conditions, oil palms of progeny C3 (Deli x La Mé) had a higher oil and bunch production than those of the other progenies with the lowest lea et K concentrations. C3 oil palms thus appear to be an excellent planting material for the West African environment.

Potassium proved to be the main mineral involved in oil palm development, as well as in palm oil production. Our results also revealed speci c optimum lea et K concentration levels depending on the genetic origins of the oil palms in the Nigerian environment.

Recent clone development with more productive oil palm material on a global scale for new oil palm plantations enables the establishment of plantations with speci c genotype, whereby oil palm growers will have tailor K fertilizer recommendations to the speci c lea et K concentrations of oil palm genotypes in a speci c environment.

This study is a rst step towards the selection of oil palm germplasm with improved K use e ciency. However, in order to con rm that palms of progeny C3 have the highest K use e ciency, the tested progenies' mineral (K and Mg) uptake capacities and use e ciencies need to be assessed on a whole plant level, rather than on the level of a single leaf.

Declarations

Statements & Declarations Upper-case letters (A-D) indicate differences (p < 0.0001) in yield parameter averages for all KCl treatments between progenies revealed by the Tukey test for each production year.

For each parameter, the last column presents the average production of the four years (5-8 YAP) ± standard deviation.

  al. 2010: Prabowo et al. 2012; Ollivier et al. 2017).

  Lee et al. 2011, Chwill 2001&2009, Jacquemard et al. 2010, Ollivier et al. 2013, Ollivier et al. 2017, Lamade et al. 2014, Legros et al. 2006 and Fabre et al. 2020

  Fuqiang et al. (2010) and Zhang et al. (2007), Rengel and Damon (2008), Liu et al. 2009, Melvin et al. 2002, Prabowo et al. 2012 found signi cant genotypic differences in leaf K concentrations and K use e ciency.

FFB: fresh fruit

  bunch, OER Lab : oil extraction rate in the laboratory, OER mill : oil extraction rate in the mill, CPO Lab : crude palm oil in the laboratory, CPO mill : crude palm oil in the mill. Production data are the averages of the last four years (5 -8) of observation. a, b, c and d indicate the differences in parameters between progenies revealed by the Tukey test.

Figures Figure 1

 1 Figures

  The lack of a KCl effect on the progenies' oil extraction rate (OER) implies that the latter parameter is in uenced by genetic factors and therefore is the result of oil palm breeding (Durand-Gasselin et al. 2000, Cochard et al. 2005).In progeny C3, the average bunch weight increased by 0.35 kg bunch -1 between treatments Mg0 and Mg1 over the 5 to 8 YAP period, suggesting that Mg fertilization contributes to oil palm productivity in this progeny. However, given the antagonistic effect of Mg and K applications, KCl applications may inhibit the effects of Mg application. Despite the lack of MgSO 4 effects on the evaluated parameters (apart from average bunch weight in progeny C3), the required Mg rates ought to depend on the balance between the optimum lea et K and Mg concentrations (Doğan and Akinci 2011; Ciríaco da Silva et al. 2011) of the planting material used. The lack of a MgSO 4 effect found in our study for most production parameters is frequently observed in oil palm fertilization experiments[START_REF] Caliman | Oil palm mineral nutrition[END_REF][START_REF] Tang | Proceedings of the PIPOC 2001 International Palm Oil Congress[END_REF] and Bonneau et al. 2018). It might be explained by the high amounts of exchangeable Mg in our

  Olivier Dassou and Hervé Aholoukpè are employed by INRAB, where Adolphe Adjadohoun is the General Director. Reinout Impens is employed by Presco. Jean Ollivier, Xavier Bonneau and Albert Flori are employed by CIRAD. Benoît Cochard is Scienti c Director at PalmElit, a subsidiary of CIRAD. All these companies have a collaboration partnership.We declare that they have no known competing nancial interests or personal relationships that could have appeared to in uence the work reported in this Lower-case letters (a-d) indicate differences (p < 0.01) revealed by the Tukey test in the yield parameters between the 3 different KCl treatments for each progeny and for each production year.

			Number of bunches per palm			Average bunch weight (Kg)			Number of male in
		K levels					Average					Average
	Progenies		5 years	6 years	7 years	8 years	5 -8	5 years	6 years	7 years	8 years	5 -8	5 years	6 years
							years					years
							old					old
		K0	21.5 a	7.55 a	15.4 a	11.33 b	14.0 ±	4.95 a	6.76 a	7.75 a	9.0 a	7.1 ±	0.85 a	11.6 a
							6.0 b					1.7 a
		K1	21.9 a	8.21 a	15.1 a	12.19	14.3 ±	5.02 a	6.92 a	8.04 a	9.4 a	7.3 ±	0.90 a	10.8 a
						ab	5.8 ab					1.9 a
	C1	K2	23.2 a	8.27 a	15.0 a	12.38 a	14.7 ±	5.01 a	6.98 a	8.16 a	9.5 a	7.4 ±	0.74 a	10.9 a
							6.3 a					1.9 a
			22.2 A	8.01 BC	15.2 A	12.0 B	14.4 ±	5.0 B	6.89 A	7.98 C	9.28 C	7.3 ±	0.83 C	11.1 D
							6.0 A					1.8 D
		K0	20.1 a	8.12 a	15.0 a	12.9 a	14.1 ±	4.88 a	6.06 b	8.41 a	9.9 a	7.3 ±	4.03 a	12.4 ab
							5.0 a					2.3 b
		K1	20.8 a	8.52 a	15.0 a	12.9 a	14.3 ±	5.11 a	6.70 a	9.04 a	10.6 a	7.9 ±	3.27 a	11.7 b
							5.1 a					2.4 a
	C2	K2	20.8 a	8.30 a	15.6 a	13.12 a	14.4 ±	5.05 a	6.57 ab	8.82 a	10.6 a	7.8 ±	3.93 a	12.6 a
							5.2 a					2.4 ab
			20.6 B	8.31 B	15.2 A	13.0 A	14.3 ±	5.01 B	6.45 B	8.76 B	10.4 B	7.7 ±	3.7 B	12.2 C
							5.1 A					2.4 C
		K0	20.4 a	7.57 a	12.9 a	13.46 a	13.6 ±	5.82 b	6.43 b	10.4 b	11.3 b	8.5 ±	3.79 a	15.0 a
							5.2 a					2.8 b
	C3	K1	20.7 a	7.88 a	12.8 a	13.16 a	5.2 a 13.64 ±	6.08 ab	6.84 b	10.6 ab	12.1 a	2.9 b 8.9 ±	4.14 a	14.5 a
		K2	24.4 a	7.49 a	12.7 a	13.76 a	14.59 ±	6.35 a	7.49 a	11.1 a	12.4 a	9.3 ±	4.35 a	14.8 a
							7.1 a					2.9 a
			20.5 B	7.65 C	12.8 B	13.5 A	13.6 ±	6.08 A	6.92 A	10.7 A	11.9 A	8.9 ±	4.1 B	14.8 B
							5.3 B					2.8 A
		K0	14.7 a	8.95 a	12.8 a	10.2 a	11.3 ±	5.6 b	5.73 b	7.6 b	10.6 b	7.4 ±	10.4 b	16.1 a
							2.6 a					2.3 b
	paper. C4	K1	14.2 a	9.46 a	12.7 a	9.89 ab	2.3 a 11.66 ±	6.11 a	6.68 a	8.31 a	12.3 a	2.8 a 8.4 ±	12.8 a	15.9 a
		K2	14.3 a	8.60 a	13.1 a	9.15 b	11.29 ±	6.35 a	6.47 a	8.31 a	12.7 a	8.5 ±	10.9 b	15.9 a
							2.8 a					3.0 a
			14.4 C	9.00 A	12.87 B	9.75 C	11.4 ±	6.02 A	6.29 B	8.07 C	11.8 A	8.1 ±	11.4 A	16.0 A
							2.5 C					2.7 A
			19.43	8.24	14.02	12.03	13.1 ±	5.53	6.64	8.9	10.8	8.0 ±	5.01	13.5
	Average						5.6					2.4
	P		<0.0001	<0.0001	<0.0001	<0.0001		<0.0001	<0.0001	<0.0001	<0.0001		<0.0001	<0.0001

Table 2

 2 Average crude palm oil production parameters of the four oil palm progenies.

	Progeny	FFB :5 -8 years	OER Lab	OER mill	CPO Lab :5 -8 years	CPO mill :5 -8 years
		(t ha -1 year -1 )	(%)	(%)	(t ha -1 year -1 )	(t ha -1 year -1 )
	C1	13.10 c	32.10 bc	27.45 bc	4.21 c	3.60 c
	C2	14.20 b	32.46 ab	27.75 ab	4.62 b	3.95 b
	C3	15.80 a	32.74 a	27.99 a	5.16 a	4.41 a
	C4	12.20 d	31.65 c	27.06 c	3.78 d	3.23 d

ACKNOWLEDGEMENTS

The authors sincerely thank the Islamic Development Bank (IsDB) for providing a PhD scholarship to the rst author, thanks to which the present research could be undertaken. We further thank INRAB, CIRAD, PRESCO and UGent for their technical and nancial support.

Average annual fresh fruit bunch yields (kg) per palm of the different oil palm progenies from 5 to 8 YAP (four consecutive harvesting years) according to their lea et K concentrations between 5 to 7 YAP y: fresh fruit bunch (FFB) production, x: lea et K concentrations for the different progenies (C1, C2, C3 and C4), K levels (K0, K1 and K2), E1, E2, E3 and E4: Equation of FFB production determination on the basis of each progeny lea et K concentrations.

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