Lucie Drévillon

Mohamed Koubaa
email: m.koubaa@escom.fr

Jean-Marc Nicaud

Eugène Vorobiev

Cell disruption pre-treatments towards an effective recovery of oil from Yarrowia lipolytica oleaginous yeast

Keywords: Yarrowia lipolytica, oil recovery, high-pressure homogenization, ultrasound, pulsed electric fields, high voltage electrical discharges cyclopropane fatty acid DM: dry matter GC-FID: gas chromatography-flame ionization detector moderate pulsed electric fields assisted mechanical

The aim of this work was to evaluate the efficiency of different cell disruption techniques on oil extraction from Y. lipolytica oleaginous yeast. The techniques tested were mechanical expression (ME), moderate pulsed electric field assisted mechanical expression (MPEF-ME), high pulsed electric fields (HPEF), high voltage electrical discharges (HVED), ultrasound (US), and high-pressure homogenization (HPH). The impact of each cell disruption technique on fatty acid composition was investigated. Results show that the most efficient technique for Y. lipolytica disruption was HPH leading to the obtaining of 83.8±4.8% oil extraction yield, compared to only 19.8±0.5% for the control, without any pre-treatment. In terms of fatty acid composition, HVED induced significant changes in almost all the analyzed fatty acids, while US was the softest technique. HPH, ME and MPEF-ME induced changes in the content of some fatty acids.

Introduction

Single cell oils (SCO) could be defined as the edible oils that are synthesized by microorganisms and are similar in type and composition to those oils and fats produced by plants or animals [START_REF] Cohen | Single cell oils: Microbial and algal oils[END_REF]. Microbial SCO seems to be an interesting alternative to crude, plant, and fish oils, especially when considering the foreseeable depletion of crude oil and the need to reduce greenhouse gas emissions, along with overfishing and the high arable land surfaces required to cultivate oilseed plants [START_REF] Schulze | Microbial lipid production with oleaginous yeasts[END_REF]. In addition, the microorganisms used to produce oils are generally able to grow in a wide range of carbon substrates, which could be the waste and by-products (e.g. sugar beet molasses, hydrolysates of starch and cellulose, etc) generated in the food industry [START_REF] Li | Perspectives of microbial oils for biodiesel production[END_REF][START_REF] Ward | Omega-3/6 fatty acids: Alternative sources of production[END_REF]. Moreover, enhanced oil content could be reached with genetically modified organisms, especially when the genome is sequenced, such as for Yarrowia lipolytica [START_REF] Dujon | Genome evolution in yeasts[END_REF]. Thus, high oil producing strains of this yeast have been developed by overexpressing various modifying fatty acid enzymes such as desaturases, acyltransferase, hydroxylase, and cyclopropane synthase [START_REF] Papanikolaou | Yarrowia lipolytica: A model microorganism used for the production of tailor-made lipids[END_REF][START_REF] Ledesma-Amaro | Yarrowia lipolytica as a biotechnological chassis to produce usual and unusual fatty acids[END_REF]. Despite these advantages, microbial SCO are still facing high production costs compared to that obtained from plants for example, and therefore mainly destined to produce oil enrichment with health-promoted fatty acids such as very long chain polyunsaturated fatty acids (e.g., docosahexaenoic acid (DHA; 22:6, ω-3) and arachidonic acid (ARA; 20:4, ω-6)) [START_REF] Nichols | Long-chain omega-3 oils-an update on sustainable sources[END_REF][START_REF] Ochsenreither | Production strategies and applications of microbial single cell oils[END_REF]. One of the most energy and time-consuming steps in microbial SCO production is biomass recovery from the fermenter, and the oil extraction process [START_REF] Cohen | Single cell oils: Microbial and algal oils[END_REF]. In addition, one of the bottlenecks of the entire process is the need to operate at moderate temperatures in order to avoid fatty acid alteration, oxidation, and the rancidity of the oil. In this way, replacing the current disruption techniques of microorganisms for oil extraction is of paramount importance. Although some methods (e.g. bead milling, microwave, ultrasound, freezing or defrosting, high-pressure homogenization (HPH), and cold drying) have been introduced in the available literature for the extraction of oil from Y. lipolytica [START_REF] Meullemiestre | Microwave, ultrasound, thermal treatments, and bead milling as intensification techniques for extraction of lipids from oleaginous Yarrowia lipolytica yeast for a biojetfuel application[END_REF][START_REF] Drévillon | Lipid extraction from Yarrowia lipolytica biomass using high-pressure homogenization[END_REF], several other alternative techniques such as high pulsed electric fields (HPEF), high voltage electrical discharges (HVED), mechanical expression (ME), and moderate pulsed electric fields assisted mechanical expression (MPEF-ME) have not yet been investigated. PEF represents one of the most emerging non-thermal technologies characterized by its short treatment times and low processing temperatures [START_REF] Roselló-Soto | Emerging opportunities for the effective valorization of wastes and by-products generated during olive oil production process: Non-conventional methods for the recovery of high-added value compounds[END_REF]. Its mechanism of action consists of cell electroporation when exposed to a high electrical field, largely exceeding the transmembrane potential of the cell [START_REF] Barbosa-Cánovas | Preservation of foods with pulsed electric fields[END_REF][START_REF]Electrotechnologies for extraction from plant foods and biomaterials[END_REF]. HVED technology has been applied to the disintegration of plant cells [START_REF] Boussetta | Extraction of valuable biocompounds assisted by high voltage electrical discharges: A review[END_REF] and microorganisms [START_REF] Delsart | Impact of pulsed-electric field and high-voltage electrical discharges on red wine microbial stabilization and quality characteristics[END_REF][START_REF] Liu | Yeast cell disruption strategies for recovery of intracellular bio-active compounds-A review[END_REF][START_REF] Shynkaryk | Electrically-assisted extraction of bio-products using high pressure disruption of yeast cells (Saccharomyces cerevisiae)[END_REF]. The principle here is to produce an arc discharge from a needle electrode to one that is grounded, or between two needle electrodes. This arc discharge is associated with several phenomena such as bubble formation and cavitation, UV radiation, high-pressure shockwaves, and high liquid turbulence, which together induce cell disintegration [START_REF] Barba | Green alternative methods for the extraction of antioxidant bioactive compounds from winery wastes and by-products: A review[END_REF][START_REF] Boussetta | Pulsed electrical discharges: Principles and application to extraction of biocompounds[END_REF]. High-pressure homogenization has been widely investigated for microbial cell disruption [START_REF] Diels | High-pressure homogenization as a non-thermal technique for the inactivation of microorganisms[END_REF][START_REF] Franchi | High-pressure homogenization: a nonthermal process applied for inactivation of spoilage microorganisms in beer[END_REF][START_REF] Middelberg | Microbial cell disruption by high-pressure homogenization[END_REF]. Its mechanism of action consists of pumping a cell suspension and forcing it under pressure through a narrow gap, where it is accelerated (200 m.s -1 ) [START_REF] Floury | Analysis of a new type of high pressure homogeniser. A study of the flow pattern[END_REF], after which it undergoes shear stress, cavitation, turbulence, and friction [START_REF] Lacroix | Inactivation of pectin methylesterase and stabilization of opalescence in orange juice by dynamic high pressure[END_REF][START_REF] Tribst | Ultra-high pressure homogenization treatment combined with lysozyme for controlling Lactobacillus brevis contamination in model system[END_REF], leading to cell fragmentation. Ultrasound is another technology used for cell disruption. The principle is based on cavitational phenomena, through the formation of vapor bubbles and their implosion near the cell surfaces, thereby causing the cell permeabilization [START_REF] Chemat | Applications of ultrasound in food technology: Processing, preservation and extraction[END_REF][START_REF] Koubaa | Oilseed treatment by ultrasounds and microwaves to improve oil yield and quality: An overview[END_REF]. Hence, investigating the potential of the above-mentioned technologies for the disruption of Y. lipolytica cells is of great importance. In this way, this work is devoted to comparing the efficiency of ME, MPEF-ME, HPEF, HVED, HPH, US, and bead milling for oil recovery from Y. lipolytica. The impact of cell disruption technique on fatty acid composition was also investigated.

Materials and methods

Yarrowia lipolytica culture

Y. lipolytica strain JMY5578 (pox1-6Δ tgl4Δ p4UAS1pTEF-EcCFA pTEF-LRO1) overexpresses both the Escherichia coli cyclopropane synthase under the strong promoter p4UAS1-TEF [START_REF] Dulermo | Using a vector pool containing variable-strength promoters to optimize protein production in Yarrowia lipolytica[END_REF], and the Y. lipolytica LRO1 gene encoding a phospholipid diacylglycerol acyltransferase. This strain is able to accumulate cyclopropane fatty acids and was previously used in the study of Merlier and co-workers [START_REF] Merlier | A gas chromatography full scan high resolution Orbitrap mass spectrometry method for separation and characterization of 3-hydroxymethyl pyridine ester of fatty acids at low levels[END_REF]. The microbial biomass was produced by fed-batch fermentation in 5-L fermentors at the BIOGIS Center (Venette, France). The medium composition was as follows: yeast extract (15 g.L -1 ), dextrose (50 g.L -1 ), NH 4 Cl (12 g.L -1 ), and KH 2 PO 4 (3 g.L -1 ). Y. lipolytica was cultivated during 63 h under aerobic conditions (8 L of air.min -1 ), continuous agitation (1000 rpm), and at 301 K. The pH was automatically adjusted to 6 using a solution of NaOH (1 N).

Glucose (900 g.L -1 ) was added after 24 h culture, at a rate of 2 g.L -1 .h -1 . The culture broth was centrifuged at 3076 g (Sigma 3-16P, Fisher Bioblock Scientific) during 10 min, and the pellet of biomass was washed twice with water. This step was performed by adding one volume of water to the pellet followed by centrifugation for 40 min at 3076 g. The final biomass concentration was adjusted with water to get either 15% or 25% dry matter, depending on the pre-treatment being applied.

Oil quantification in Y. lipolytica 2.2.1. Oil extraction

The washed cell suspension was lyophilized using Alpha 1-4 LD Plus equipment (Martin Christ, Germany). A sample of 100 mg lyophilized cells was introduced in triplicate into screw tubes containing a ceramic bead in each one. The tubes were shaken vigorously using Precellys-24 homogenizer (Ozyme, France), for 3x30 s at 6000 counts per minute (cpm). After grinding, 1 mL of chloroform:methanol (2:1, v:v) mixture was added and the tubes were vortexed for sample solubilization. Another agitation step using Precellys-24 homogenizer, for 3x30 s at 6000 cpm, was performed and the mixtures were then centrifuged for 3 min at 6000 g. The supernatant was transferred to a new tube and the extraction was repeated under the same conditions. In order to remove the non-lipidic compounds, 2 mL of KCl (1 M):methanol solution (4:1, v:v, with 0.034% of MgCl 2 ) was added to the 2 mL collected after grinding and centrifugation, and the mixture was agitated vigorously for 2 min. All tubes were then left motionless until phase separation and the lower phase (containing lipids) was recovered and evaporated under a nitrogen flow at 323 K.

Fatty acid methyl esters preparation and analysis

The recovered oil was transesterified in order to generate fatty acid methyl esters (FAMEs). For quantification, 100 µL of an internal standard (C12:0, 5 mg.mL -1 ) was added. Afterward, 750 µL of methanolic HCl (4%) was added, and the mixture was heated for 1 h at 353 K. FAMEs were then extracted by adding 1.5 mL n-heptane, vortexing vigorously, and centrifuging for 2 min at 100 g. The upper phase containing n-heptane and FAMEs was recovered and transferred into a new tube. The FAMEs extraction step was repeated and n-heptane in the pooled supernatants was evaporated under a nitrogen flow. FAMEs were then dissolved in n-heptane (the volume used depending on the quantity of lipids) and analyzed using a Shimadzu gas chromatographyflame ionization detector (GC-FID) equipment as previously demonstrated [START_REF] Koubaa | Recovery of oil, erucic acid, and phenolic compounds from rapeseed and rocket seeds[END_REF], with slight modifications. The GC-FID instrument was equipped with a BPX-70 capillary column (30 m length, 0.25 mm diameter, and 0.25 mm film thickness) (Fisher Scientific, Illkirch, France). The GC oven temperature was set first at 393 K and held for 2 min, then increased to 523 K at 283 K.min -1 and held for 2 min. The H 2 carrier gas flow was set to 1.2 mL.min -1 and the injection volume was 1 µL with split 10. FAME standards were injected in order to identify the different eluted molecules.

Cell disruption pre-treatments 2.3.1. Yeast mechanical expression

Mechanical expression (ME) was performed using 85 g yeast suspension (25% DM) during 45 min and at 5.10 5 Pa (Figure 1). Filters (in polyvinylidene fluoride) with 0.2 µm pore size were used to retain the yeast cells. The water content in the cake was determined after pressurization using a Memmert oven at 373 K. Both filtrate and cake were kept at 277 K for oil quantification.

Moderate PEF assisted mechanical expression of yeasts

The effect of moderate pulsed electrical fields (MPEF) was studied during pressurization (Figure 1). The same conditions as those described in section 2.3.1 were applied with the difference that in addition to both filters, two electrodes were added and were connected to a high voltage pulse generator (5 kV, 1 kA) (Hazemeyer, Saint Quentin, France), providing monopolar pulses of near-rectangular shape. PEF pre-treatment was carried out after 30 min pressurization at a moderate electric field strength of E = 900 V.cm -1 , using 20 trains of n = 10 pulses, and a pulse duration of dt = 100 μs. The time of PEF pretreatment was thus 20 ms. Specific software, developed by "Service Electronique UTC" (Compiègne, France) was used with a data logger to measure the current and voltage during the pre-treatment.

Electroporation using high PEF

The impact of high PEF (HPEF) was also studied on yeast cells without pressurization. This experiment was performed by preparing 200 g of yeast cell suspension (15% DM) in a 1-L cylindrical batch treatment chamber (Figure 1). Two parallel stainless steel electrodes were used for electroporation, where the distance between them was fixed to 2 cm, which allows the delivery of ≈20 kV.cm -1 electrical field intensity. One train of 500 pulses was used for HPEF pre-treatment with pulse duration of 10 µs. The time of HPEF pre-treatment was thus equal to 5 ms. The shape of PEF pulses was rectangular

High voltage electrical discharges

Yeast cells were pre-treated using high voltage electrical discharges (HVED). A 1-L batch cylindrical chamber containing a stainless steel cylindrical needle (diameter = 10 mm) and a grounded plate electrode (diameter = 45 mm) was connected to a high-voltage pulse generator of 40 kV and 10 kA (Tomsk Polytechnic University, Russia), and was used for the pre-treatment (Figure 1). A cell suspension of 200 g (15% DM) was introduced in the treatment chamber and the distance between the electrodes was fixed to 1 cm, allowing the delivery of ≈40 kV.cm -1 electric field strength. For each treatment, a number of pulses of n = 500 was applied with a pulse duration of 10 μs. The pulses were delivered every 0.3 s (frequency 0.3 Hz), thereby making a total treatment time of 5 ms.

Ultrasound pre-treatments

A suspension of 300 g yeast cells (15% DM) was introduced in a double-jacketed reactor and pre-treated using ultrasounds (US). An US processor UP 400S (Hielscher GmbH, Germany) was connected to a titanium probe (H14 Hielscher GmbH, Germany), having a diameter of 14 mm and a maximum submerged length of 90 mm (total length = 100 mm) was used. The experiments were conducted in batch mode using the maximum operational US power of 400 W (100% amplitude), a frequency of 24 kHz, and a duty cycle of 50% (1 s sonication followed by a rest period of 1 s). The pre-treatment duration was 1 h and the temperature of the solution was monitored by Huber Unistat Tango (France) equipment. It was either maintained at 293 K using silicone oil injected in the jacket or without temperature control, thereby increased from 293 K to 348 K.

High-pressure homogenization (HPH)

HPH of yeast cells was performed using GEA Niro Soavi PandaPlus 2000 equipment. A cell suspension (600 g, 15% DM) was pumped and homogenized 20 times (20 passes), which is a sufficient treatment time allowing maximizing cell disruption. The temperature of the suspension was maintained at 298 K during the pre-treatment, whereas the pressure was manually adjusted to 1500.10 5 Pa, which was close to the highest pressure delivered by the system.

Oil extraction and analysis

After the pre-treatments, yeast suspensions were lyophilized. Liquid:solid extraction of oil was performed by mixing 10 g of lyophilized yeast cells with 100 mL n-hexane (ratio of 1:10). The mixture was agitated during 1 h at ambient temperature under a constant agitation of 700 rpm. The suspension was then centrifuged at 3373 g, and n-hexane was recovered and evaporated under vacuum using a rotary evaporation system (Heidolph VV where total oil content was determined as described in section 2.2 of the experimental section and verified using a Soxhlet instrument after 24 h extraction using n-hexane.

Optical microscopy

Optical microscopy pictures of yeast cells before and after HPH were obtained using an optical microscope BX60 (Olympus, France).

Statistical analysis

All experiments were performed in triplicate and presented in the graphs as the average value ± standard deviation (SD). The fatty acid analysis was performed on each extraction. One-way analysis of variance (ANOVA) was used to determine the significant differences using StatPlus V6 software for Macintosh systems.

Results and discussion

Oil quantification and fatty acid composition

Results for oil quantification in Y. lipolytica show that the JMY5578 strain contains 11±3% oil dry matter basis. This value is lower than that obtained in the wild type strain, which is ≈36% [START_REF] Beopoulos | Yarrowia lipolytica as a model for bio-oil production[END_REF], probably due to the accumulation of unusual fatty acids. Fatty acid composition determined using GC-FID is presented in table 1. The results obtained show that despite the lower oil content accumulated, compared to the wild type strain of Y. lipolytica, cyclopropane fatty acids (CFAs) were detected, consisting mainly of C17-CFA and C19-CFA. The contents in fatty acids were very close to that found recently by Merlier and co-workers [START_REF] Merlier | A gas chromatography full scan high resolution Orbitrap mass spectrometry method for separation and characterization of 3-hydroxymethyl pyridine ester of fatty acids at low levels[END_REF].

Cell disruption and oil extraction

Y. lipolytica cells were disrupted using different technologies including mechanical expression (ME), moderate PEF assisted mechanical expression (MPEF-ME), high pulsed electric fields (HPEF), high voltage electrical discharges (HVED), ultrasound (US), and high-pressure homogenization (HPH). Results presented in figure 2 show that the lowest extraction yields were obtained using ME and MPEF-ME, compared to the control (diffusion in n-hexane after lyophilization without any pre-treatment). This is probably due to the low pressure applied (5.10 5 Pa) to the cells, which was mainly used to dehydrate the sample, initially containing 25% DM, and was not sufficient to disrupt the cell membranes. In addition, no significant differences were observed between the oil yields obtained after ME and MPEF-ME. Therefore, moderate PEF was insufficient for the extraction of lipids from JMY5578. Increasing the electrical field strength up to 20 kV.cm -1 without mechanical expression led to some increase in the oil extraction yield from 19.8±0.5% (control) to 29.4±3% (HPEF). The damage occurring after exposing microbial cells to high electrical fields has been considered the main cause of cell inactivation for many decades [START_REF] Sale | Effects of high electric fields on microorganisms: I. Killing of bacteria and yeasts[END_REF], and was reported as the electromechanical instability of the cell membrane [START_REF] Coster | The mechanism of electrical breakdown in the membranes of Valonai utricularis[END_REF][START_REF] Jacob | Microbiological implications of electric field effects II. Inactivation of yeast cells and repair of their cell envelope[END_REF]. So far, the inactivation phenomenon is underlined by electroporation, which is a direct result of the electrical breakdown, and both lead to perforating the cytoplasmic membrane, which allows in this study further release of oil from Y. lipolytica, compared to the untreated material (control).

No significant difference was observed between HVED (31.7±6.5%) and HPEF (29.4±3%). HPEF and HVED could be very effective for the selective extraction of ions, sugars, colorants, and proteins from the electroporated cells [START_REF] Shynkaryk | Electrically-assisted extraction of bio-products using high pressure disruption of yeast cells (Saccharomyces cerevisiae)[END_REF][START_REF] Ganeva | High yield electroextraction of proteins from yeast by a flow process[END_REF]. However, the effectiveness of these treatments for lipid extraction from Y. lipolytica was not demonstrated through this work.

Some increase in oil recovery was also observed when applying US as pre-treatment. In fact, two experiments were performed: 1) US pre-treatment with the temperature maintained at 293 K, and 2) US pre-treatment up to the temperature elevation to 348 K (insert in figure 2). Results show that applying the US with temperature maintain at 293 K led to increasing the extraction yield from 19.8±0.5% (without pre-treatment) to 35.5±6.1%. These results show that the US has a positive impact on the cell disruption and thus leads to an increased oil extraction yield. This fact is mainly related to the acoustic cavitation and the formation of gas micro-bubbles in the microbial suspension [START_REF] Capelo-Martínez | Ultrasound in chemistry: analytical applications[END_REF]. The gas bubbles grow until reaching an unstable size then collapse violently.

Intense local energy is thereby released with important chemical (i.e., free radicals) and mechanical effects (i.e., micro-jets) leading to particle and cell destruction [START_REF] Chemat | Applications of ultrasound in food technology: Processing, preservation and extraction[END_REF][START_REF] Mason | Sonochemistry[END_REF].

However, despite the significant increase of oil yield compared to the untreated sample (control), the US as a cell disruption method remains insufficiently efficient to release all the oil content. This result was in agreement with that reported in previous works [START_REF] Halim | Microalgal cell disruption for biofuel development[END_REF][START_REF] Lee | Comparison of several methods for effective lipid extraction from microalgae[END_REF][START_REF] Wang | Combined enzymatic and mechanical cell disruption and lipid extraction of green alga Neochloris oleoabundans[END_REF][START_REF] Wang | Disruption of microalgal cells using high-frequency focused ultrasound[END_REF].

The

Impact of disruption method on the fatty acid composition

The fatty acid composition of the different oils obtained after cell disruption was analyzed using GC-FID. Results presented in table 2 show that the disruption method affects the fatty acid profile. Compared to control, without pre-treatment, ANOVA analysis showed that these changes differ from one cell disruption method to another. For instance, US treatment did not lead to any significant change (p>0.05) in fatty acid composition, whether applied at 293 K or up to 348 K. It should be noted that some previous works discussed the fatty acid composition following the application of US for oil extraction. Luque-García and Luque de Castro used a laboratory scale equipment of US-assisted Soxhlet extraction for oil recovery from oleaginous seeds (sunflower, rape, and soybean) and reported no changes in the fatty acid composition when applying US, compared to conventional extraction method (Soxhlet) [START_REF] Luque-García | Ultrasound-assisted Soxhlet extraction: an expeditive approach for solid sample treatment: Application to the extraction of total fat from oleaginous seeds[END_REF]. However, some other authors discussed the possible degradation of lipids by US [START_REF] Pingret | Degradation during application of ultrasound in food processing: A review[END_REF], which could most probably be related to the extraction parameters, oil, and matrix properties. In the current work, applying MPEF-ME only led to a significant difference in minor fatty acids (C20:0) (Table 2). Some significant changes (p<0.05) in fatty acid composition were reported following the application of ME and HPH. The review of the literature shows that the information required to explain such changes between the control and the samples pre-treated by ME, MPEF-ME, or HPH is scarce and this subject requires further investigations. The most destructive method applied to disrupt the cells was HVED.

Using this method, all the fatty acids, except C14:0, C18:0 and C18:2, showed significant changes (p<0.05) in their composition compared to the untreated sample. This result could be explained by the formation of free radicals and other reactive species in the extraction medium when applying HVED [START_REF] Boussetta | Extraction of valuable biocompounds assisted by high voltage electrical discharges: A review[END_REF][START_REF] Koubaa | Current and new insights in the sustainable and green recovery of nutritionally valuable compounds from Stevia rebaudiana Bertoni[END_REF], which thus may enhance the degradation of oil. Taken together, the results of oil extraction yield and fatty acid composition show that although some changes were noticed in fatty acid composition, HPH could be selected as the most efficient cell disruption method, allowing the recovery of the highest oil yield.

Optical microscopy

Microscopy pictures were taken before and after HPH treatment (1500.10 5 Pa, 20 passes) (Figure 3). Results show some intact cells and aggregated fragments before and after the HPH treatment. After 20 passes in the homogenizer (Figure 3.B) some aggregates were present in the suspension, and the number of intact cells was reduced compared to the untreated sample (Figure 3.A). The high shear stress applied to the cells leads to their fragmentation and thereby the release of their content. This result is supported by the additional oil extraction yields obtained after HPH treatment (Figure 2). The aggregation of the cells and their fragments could be explained by the modification of particles electrokinetic potential, which needs further investigation for deeper analysis.

Conclusion

The most effective technique in term of oil recovery yield from the oleaginous yeast Y. lipolytica was HPH, as compared to the ME, MPEF-ME, HPEF, HVED, and US. GC-FID analyses showed that the cell disruption methods may affect the fatty acid composition, with the highest degradation being observed using HVED, and the lowest using US. Although effective at laboratory scale, further analyses and process optimization of HPH should be performed, especially for evaluating the total oil cost, including all upstream and downstream processing steps.

  2011) and a water bath at 313 K (Heidolph OB 2001). Oil extraction yield was determined according to Equation (6) below. Oil yield (%) = 𝐸𝑥𝑡𝑟𝑎𝑐𝑡𝑒𝑑 𝑜𝑖𝑙 𝑚𝑎𝑠𝑠 (𝑔 𝑝𝑒𝑟 100𝑔 𝐷𝑀) 𝑇𝑜𝑡𝑎𝑙 𝑜𝑖𝑙 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 (𝑔 𝑝𝑒𝑟 100𝑔 𝐷𝑀) * 100 Equation (6)

  last disruption cell technology tested in this work was HPH. It is obvious from figure 2 that HPH was the most effective technique among the other tested technologies. It leads to obtaining 83.9±4.8% oil extraction yield, compared to only 19.8±0.5% for the control. This result concurs with previous works demonstrating the efficiency of HPH on the cell disruption and lipid extraction from microorganisms, such as the enhancement of lipid extraction from microalgae [43-45].

Figure captions:Figure 1 .

 1 Figure captions:

Figure 2 .

 2 Figure 2. Oil extraction yields obtained after cell disruption, lyophilization, and n-hexane

Figure 3 .

 3 Figure 3. Optical microscopy pictures of Y. lipolytica yeast. A. Non-treated cells,

Table 1 .

 1 Lipid contents and fatty acid compositions of the strain used in this work (JMY5578) and the wild type strain. CFA denotes cyclopropane fatty acid, and ND denotes not detected.

	Strain of Y. lipolytica		JMY 5578 (this study)	Wild type [36]
	% Lipid (g lipid /g biomass )		12	36
	Fatty acid	C14:0	0.3	ND
	composition (%)	C15:0	0.6	ND
		C16:0	21.4	11
		C17:0	1.7	ND
		C17:CFA	9.4	ND
		C18:0	6.6	1
		C18:1	15.7	28
		C18:2 trans	0.4	ND
		C18:2 cis	19.0	51
		C18:3	ND	1
		C19:CFA	22.5	ND
		C20:0	0.7	ND
		C24:0	1.8	ND

Table 2 .

 2 The fatty acid composition of oils extracted from Y. lipolytica after cell disruption using different technologies. ME; mechanical expression (5 bar, 45 min), PEF-ME; pulsed electric field (900 V/cm) assisted mechanical expression (5 bar, 45 min), PEF; pulsed electric fields (20 kV/cm), HVED; high voltage electrical discharges (40 kV/cm), US; ultrasound, HPH; high-pressure homogenization. MFA1 and MFA2 denote respectively modified fatty acids 1 and 2. The control represents the sample without any pre-treatment. SD denotes standard deviation.

		Control		ME		MPEF-ME	HPEF		HVED		US at 20 °C	US up to 75 °C	HPH
	Fatty acid	Average (%)	SD (%)	Average (%)	SD (%)	Average (%)	SD (%)	Average (%)	SD (%)	Average (%)	SD (%)	Average (%)	SD (%)	Average (%)	SD (%)	Average (%)	SD (%)
	C14:0	0.24	0.12	0.31	0.01	0.21	0.10	0.30	0.00	0.07	0.11	0.29	0.08	0.39	0.03	0.30	0.01
	C15:0	0.57	0.04	0.71*	0.02	0.64	0.05	0.68*	0.02	0.69*	0.01	0.58	0.06	0.53	0.05	0.55	0.06
	C16:0	21.34	0.08	21.88	0.41	20.51	0.46	22.17*	0.30	22.57*	0.25	21.36	0.79	22.39	0.73	19.95*	0.50
	C17:0	1.71	0.25	0.72*	0.01	1.54	0.08	1.15	0.16	2.24*	0.09	2.01	0.53	2.45	0.44	2.4*	0.05
	C17:CFA	9.43	0.33	9.96	0.28	9.29	0.24	10.62*	0.26	8.44*	0.22	9.21	0.52	9.38	0.63	8.84	0.15
	C18:0	6.54	0.06	7.39	0.52	6.30	0.67	6.54	0.37	6.91	0.27	6.41	1.12	6.42	0.65	6.66	0.36
	C18:1	15.66	0.98	12.02*	0.40	14.79	0.64	14.07	0.53	17.94*	0.62	16.85	1.70	18.18	1.73	21.24*	0.16
	C18:2 trans	0.29	0.14	0.38*	0.05	0.45	0.07	0.42	0.03	0.34	0.01	0.35	0.10	0.38	0.05	0.17	0.17
	C18:2 cis	19.05	0.06	16.94	1.00	19.39	1.23	16.09*	0.22	17.68*	0.02	18.35	1.26	17.56	0.74	17.73*	0.63
	C19:CFA	22.59	1.03	26.28*	0.23	23.96	0.51	24.99*	0.26	20.53*	0.18	21.91	1.70	19.52	1.40	19.83	1.42
	C20:0	0.74	0.05	1.17*	0.07	0.99*	0.06	0.71	0.06	0.62*	0.01	0.74	0.25	0.63	0.12	0.4*	0.03
	C24:0	1.80	0.02	2.24*	0.22	1.93	0.43	2.25*	0.09	2.18*	0.06	1.93	0.57	2.16	0.42	1.94	0.11

Acknowledgments

This work was performed, in partnership with the SAS PIVERT, within the frame of the French Institute for the Energy Transition (Institut pour la Transition Energétique (ITE) P.I.V.E.R.T. (www.institut-pivert.com) selected as an Investment for the Future ("Investissements d'Avenir"). This work was supported, as part of the Investments for the Future, by the French Government under the reference ANR-001-01. The authors thank as well the members of the laboratory « Biologie Intégrative du Métabolisme Lipidique, INRA, UMR1319, Jouy-en-Josas, France » for providing the JMY5578 strain used in this study.

Conflict of interest statement

The authors declare no conflict on interest


Sambo Pheap

Clara Lefèvre

Alexis Thoumazeau
email: alexis.thoumazeau@cirad.fr

Vira Leng

Stéphane Boulakia

Ra Koy

Lyda Hok

Pascal Lienhard

Alain Brauman
email: alain.brauman@ird.fr

Florent Tivet
email: florent.tivet@cirad.fr

Multi-functional assessment of soil health under Conservation Agriculture in Cambodia

Keywords: Soil biological functioning, Biofunctool®, annual cropping system, Soil Quality Index, in-field assessment, red Oxisol

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In agriculture, the soil system has historically been seen as a medium for food production, while it is also a source of further supporting, regulating and cultural services which are crucial for human well-being [START_REF] Adhikari | Linking soils to ecosystem services -A global review[END_REF]. However, this system is widely jeopardized and especially in Asia, where [START_REF] Montanarella | World's soils are under threat[END_REF] showed that soils are under pressure for commodities production (i.e., maize, cassava) generating soil erosion, soil organic carbon (SOC) depletion, and nutrient imbalance. These threats are confirmed in Cambodia where in 2008, 43% of the territory was estimated to be degraded due to intensification of agriculture after forest clearance, and that degradation affected almost 25 % of the population living on degraded areas [START_REF] Bai | Proxy global assessment of land degradation[END_REF]. Under this context, there is a need to promote sustainable agricultural management, to limit soil degradation and to improve soil functioning.

As a response, it is recognized that Conservation Agriculture (CA) may strengthen a large set of soil ecosystem services [START_REF] Palm | Conservation agriculture and ecosystem services: An overview[END_REF]. CA is based on three technical principles which are (i) minimum soil disturbance, (ii) retention of crop and cover crop residues and (iii) use of diversified cropping patterns (FAO, 2014;[START_REF] Séguy | Direct-seeded tropical soil systems with permanent soil cover: learning from Brazilian experience[END_REF]. CA was promoted for providing multiple beneficial effects on soil physical [START_REF] Indoria | Conservation agriculture -a panacea to improve soil physical health[END_REF][START_REF] Patra | Effect of conservation agriculture on stratification of soil organic matter under cereal-based cropping systems[END_REF], biological [START_REF] Lienhard | Soil microbial diversity and C turnover modified by tillage and cropping in Laos tropical grassland[END_REF][START_REF] Mathew | Impact of No-tillage and conventional tillage systems on soil microbial communities[END_REF][START_REF] Souza | Outstanding impact of soil tillage on the abundance of soil hydrolases revealed by a metagenomic approach[END_REF] and chemical properties [START_REF] Ivy | Medium-term effects of conservation agriculture on soil quality[END_REF][START_REF] Parihar | Long-term impact of conservation agriculture and diversified maize rotations on carbon pools and stocks, mineral nitrogen fractions and nitrous oxide fluxes in inceptisol of India[END_REF][START_REF] Ranaivoson | Agroecological functions of crop residues under conservation agriculture[END_REF]. Amongst soil chemical properties, soil organic carbon (SOC) was widely studied under CA, due to its potential to adapt farming systems to climate change and variability, and mitigate the effects of climate change through carbon sequestration [START_REF] Lal | Soil carbon management and climate change[END_REF]. Globally, these studies allowed to provide process-based analysis of the soil physical, biological and/or chemical compartments, as affected by CA practices. However, such analyses do not seem sufficient to propose consistent soil health assessments.

Soil health was defined by [START_REF] Kibblewhite | Soil health in agricultural systems[END_REF] as the capacity of a soil to produce a good quantity and quality food and fibre together with the delivery of other ecosystem services. However in most studies in the literature, all variables used to assess soil health are treated without proper consideration of their role along the soil health causal chain. Indeed, there is often no consideration of the cause/consequence relation between soil properties, soil functions and soil ecosystem services [START_REF] Ivy | Medium-term effects of conservation agriculture on soil quality[END_REF][START_REF] Lienhard | Soil microbial diversity and C turnover modified by tillage and cropping in Laos tropical grassland[END_REF][START_REF] Thierfelder | Effects of conservation agriculture on soil quality and productivity in contrasting agro-ecological environments of Zimbabwe[END_REF], while the realization of soil properties leads to the expression of soil functions which allows a soil to provide ecosystem services [START_REF] Kibblewhite | Soil health in agricultural systems[END_REF][START_REF] Su | A process-based framework for soil ecosystem services study and management[END_REF]. Also, many of the published studies do not, or weakly, consider the interactions between the different soil compartments (e.g. [START_REF] Das | Effects of tillage and biomass on soil quality and productivity of lowland rice cultivation by small scale farmers in North Eastern India[END_REF] , which should be at the heart of soil health assessments [START_REF] Karlen | Soil quality: A concept, definition, and framework for evaluation[END_REF][START_REF] Kibblewhite | Soil health in agricultural systems[END_REF][START_REF] Rinot | Soil health assessment: A critical review of current methodologies and a proposed new approach[END_REF]. These classical approaches are also usually based on the accumulation of distinct soil properties measurements (e.g. soil organic carbon, soil biodiversity…). The lack of consensus on this causal chain, the different interaction between soil properties and the addition of separated soil health indicators lead to different considerations of the components of soil systems which may lead to misleading conclusions on soil functioning assessment. To fill this gap, integrative approaches based on the results of soil physical-biological-chemical interactions should be implemented [START_REF] Kibblewhite | Soil health in agricultural systems[END_REF][START_REF] Vogel | A systemic approach for modeling soil functions[END_REF][START_REF] Rinot | Soil health assessment: A critical review of current methodologies and a proposed new approach[END_REF]. Finally, assessments should be easily transferable to smallholder farmers, development practitioners and land managers who are the first beneficiaries of soil health [START_REF] Idowu | Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods[END_REF], but most studies rely on laboratory tools, protocols, and complicated data-analysis interpretation which may limit the further dissemination of new practices [START_REF] Bünemann | Soil quality -A critical review[END_REF]. Thoumazeau et al. (2019a) proposed Biofunctool®, an operational framework to address soil health assessment and its quantification in a holistic manner, taking into consideration soil component interactions. Biofunctool® does not address nor evaluate soil mechanisms, but proposes a methodology to assess the impact of agricultural practices on soil health by studying three main soil functions identified by [START_REF] Kibblewhite | Soil health in agricultural systems[END_REF], namely (i) carbon transformation, (ii) nutrient cycling and (iii) structure maintenance. Biofunctool® consists in a core-set of 10 integrative, in-field, and low-tech indicators that may easily be transferred to land managers. In previous studies, Biofunctool® was applied and validated over a gradient of soil disturbance in Thailand, on rubber plantations (Thoumazeau et al., 2019b). In addition, part of the Biofunctool® set, two indicators (i.e. POXC and SituResp®) may provide further indications on soil carbon dynamics (mineralisation vs. stabilization, see [START_REF] Hurisso | Comparison of Permanganate-Oxidizable Carbon and Mineralizable Carbon for Assessment of Organic Matter Stabilization and Mineralization[END_REF] which have a particular interest under the current environmental context [START_REF] Lal | Soil carbon management and climate change[END_REF].

The aim of our study is to make an integrative and quantified assessment of several CAbased cropping systems on soil health. To do so, the Biofunctool® set of indicators was used to assess the impact of contrasted agricultural practices on soil health in a long-term experiment in Cambodia uplands. Biofunctool® indicators were grouped according to the three main soil functions and finally integrated into a Biofunctool® Index. The impact of CA vs. CT cropping system on soil functions, indexes and short-term SOC dynamics were analysed. The potential applications of the Biofunctool® set are finally discussed and further methodological perspectives are proposed.

2) Materials and methods

2.1) Site description and experimental design

The experiment is located at the Bos Khnor experimental station that belongs to the General Directorate of Agriculture (GDA), Bos Khnor Commune, Chamkar Leu District, Kampong Cham province, in Cambodia (latitude 12°12'30" N, longitude 105°19'07"E, 118m elevation, no slope). The history of the station was described in [START_REF] Hok | Short-term conservation agriculture and biomass-C input impacts on soil C dynamics in a savanna ecosystem in Cambodia[END_REF]. Soil is made of 68.2% clay, 30.3% silt and 1.5% sand at 0-10 cm depth, and is classified as a Ferralsol or Red Oxisol in the FAO and USDA soil classification, respectively. According to the Köppen classification, the climate at the experimental site is Aw with rainfall concentrated during the wet season (May to November). The mean annual precipitation (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) replicates with a plot dimension of 8 m × 37.5 m. The four treatments are (i) conventional tillage (CT) system in which soybean (Glycine mac (L) Merr.) is cropped in annual succession with sesame (Sesamum indicum), (ii) direct seeding mulch-based cropping system (CA) in which soybean is cropped as a monoculture (CA 1), and (iii) bi-annual rotation of soybean and maize (Zea Mays) under CA management (CA 2a and CA 2b). In 2017, due to inaccurate density of soybean, rice-bean (Vigna umbellata) was used (Table 1). The experiment was designed to compare the conventional tillage management represented by one cash crop per year (soybean) under plow-based management (CT), with cropping systems involving different cropping pattern and biomass-C inputs under no-tillage (CA 1, CA 2a and CA 2b).

2.2) Cropping systems management

2.2.1) Agroecology and rotations engineering

Soybean in conventional tillage (CT) represents the standard cropping system of smallholder farmers. Under CA 1, Stylosanthes guianensis (cv. CIAT 184) was used as cover crop and seeds are broadcasted 30 days before soybean (rice-bean in 2017) maturation. S. guianensis started growing at the end of the wet season, passed the dry season and was terminated at the end of June using a roller crimper with cutting discs. Soybean (rice-bean in 2017) was then sown mid-July. Under the bi-annual rotation (CA 2a and 2b), S. guianensis was associated with maize at sowing, and S. guianensis and Sorghum bicolor seeds were broadcasted 30 days before soybean maturation. All crops and cover crops used were grown under rainfed conditions. The cropping sequence is presented in Table 1. As a result of those cropping patterns, CA treatments receive annually from 2.5 to 3 times more biomass-C inputs (above-ground biomass resulting from main crops and cover crops) than CT (Table 1).

2.2.2) Agricultural practices

Tillage for CT treatment consists in disc ploughing to a 15-20 cm depth prior soybean cultivation. Soybean, rice-bean in 2017, and maize were sown with a no-till planter (Fitarelli or Vence Tudo). Under CA management, crop residues of the main crops and cover crops are left on the soil surface after the harvest. In case of poor development of the cover crops during the dry season, sorghum, pearl millet (Pennisetum typhoides) and/or sunn-hemp (Crotalaria juncea) are intercropped for 60 to 75 days with S. guianensis to increase the biomass-C inputs early in the wet season.

Since 2009, thermo phosphate (16% P2O5, 31% CaO and 16% MgO, 200 kg.ha -1 ) has been applied in March followed by potassium chloride (KCl, 0-0-60, 50 kg.ha -1 ) mid-May. From 2009 to 2013, inorganic fertilization was based on the use of urea (46-0-0, 50 kg.ha -1 at sowing on soybean and maize, and 100 kg.ha -1 for maize 25 days after sowing). N and P fertilization was modified in 2014. Since that, a basal fertilizer is applied at sowing on soybean (100 kg.ha -1 of 18-46-0) and maize (150 kg.ha -1 of 16-20-0) and a top-dressing with urea is added on maize (100 kg.ha -1 ). Fertiliser inputs are detailed in supplementary reading 1.

2.3) Soil sampling and analysis

2.3.1) Biofunctool® indicators

Biofunctool® consists in a core-set of ten functional indicators that directly assess three main soil functions: carbon transformation, nutrient cycling and soil structure maintenance (Thoumazeau et al., 2019a). However, due to on-field rodents destruction, two indicators of the core-set, based on in situ incubation could not be analysed i.e., bait lamina and anion exchange membranes. Also, even though earthworms were observed, cast could very hardly be differentiated to soil aggregates due to the soil type and colour. In order to avoid misleading measurements, cast indicator was not integrated in the set of measurements. Notwithstanding these experimental inconveniences, Biofunctool® was applied as i) the bait lamina is one of the less sensitive to land management and rather describe inter-plot spatial heterogeneity (Thoumazeau et al., 2019b), ii) nutrient cycling function was adapted by splitting the results of NH4 + and NO3 -from soil extraction, and iii) earthworm activity was also indirectly integrated with other indicators such as water infiltration and aggregate stability.

All in all, we analysed 8 indicators with (1) for the carbon transformation function:

Permanganate oxidizable carbon (POXC, [START_REF] Weil | Estimating active carbon for soil quality assessment: a simplified method for laboratory and field use[END_REF]) and basal soil respiration (SituResp®, [START_REF] Thoumazeau | SituResp®: A time-and cost-effective method to assess basal soil respiration in the field[END_REF]), (2) for the nutrient cycling function, available ammonium (NH4 + ) and nitrates (NO3 -) from soil extraction with 1M KCl, and (3) for the soil structure maintenance function: surface aggregates at 0-2 cm depth (AggSurf) and soil aggregates at 2-10 cm depth (AggSoil) [START_REF] Herrick | Field soil aggregate stability kit for soil quality and rangeland health evaluations[END_REF], water infiltration (Beerkan) [START_REF] Lassabatère | Beerkan Estimation of Soil Transfer Parameters through Infiltration Experiments-BEST[END_REF]) and VESS at 0-30 cm depth [START_REF] Guimarães | Improvements in the visual evaluation of soil structure[END_REF].

Soil samples were collected in November 2017 and the sampling was done at 0-10 cm depth (except for the VESS). Three sampling points (inner-replicates) were collected per plot representing 36 soil samples for the Biofunctool® analysis (except for the VESS indicator with only one replicate per plot collected).

2.3.2) Soil physico-chemical analysis

In addition to Biofunctool® indicators, further soil analysis were undertaken in the laboratory. Soil bulk density was sampled at each sampling points and measured by the core method [START_REF] Blake | Bulk density[END_REF]. Sub-samples of 2 mm sieved bulk soils were finely ground (<150 mm) and analysed for total C and N concentrations by dry combustion method (Van Moort and De Vries, 1970) using an elemental CHN analyser (Thermo Flash 2000). P concentrations were measured with the P Olsen method [START_REF] Olsen | Estimation of available phosphorus in soils by extraction with sodium bicarbonate[END_REF], and finally pH was measured in water, using a 1:3 ratio (Table 2). All the analysis were implemented in Eco&Sols laboratory, Montpellier, France.

2.4) Statistical analysis

Statistical analysis were computed using R software (R Development Core Team, 2008). Each Biofunctool® indicator was first studied separately using a linear-mixed effects model (package lme4, [START_REF] Bates | Fitting Linear Mixed-Effects Models using lme4 67[END_REF]). Treatment was defined as fixed factor and replicates (plots and inner-replicates) as random factors. After checking the normality of the model's residuals and homoscedasticity of variances' residuals, ANOVAs were run using the car package [START_REF] Fox | An {R} Companion to Applied Regression[END_REF]. This was followed by post-hoc mean comparisons, using Tukey with adjustment Bonferroni [START_REF] Hothorn | Simultaneous Inference in General Parametric Models[END_REF]. As the VESS was sampled at 0-30 cm, a weighted score average was calculated and based on 0-10 cm depth only.

After studying each indicator separately, they were computed within a Principal Component Analysis (PCA) (FactomineR package, [START_REF] Lê | FactoMineR : An R Package for Multivariate Analysis[END_REF]) based on the mean of their three replicates (except for NO3 -, see Figure 2). The last step of analysis consisted in calculating the Biofunctool® index, for which the methodology was defined by [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF] and Thoumazeau et al. (2019a). This indicators' aggregation followed by scorings have already been handled by many authors [START_REF] Armenise | Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment[END_REF][START_REF] Askari | Quantitative soil quality indexing of temperate arable management systems[END_REF][START_REF] Ngo-Mbogba | Assessing soil quality under different land cover types within shifting agriculture in South Cameroon[END_REF][START_REF] Yu | Selecting the minimum data set and quantitative soil quality indexing of alkaline soils under different land uses in northeastern China[END_REF]. In our case two adaptations to the original scoring methodology of [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF] and Thoumazeau et al. (2019a) were computed. First, concerning the scoring function of the VESS indicator, it was rather considered as an optimum at a score of 2.5 than following "the less the better" response curve. According to expert opinion, it was agreed that a highly friable soil corresponded to a disaggregated soil and thus could not be associated to an optimum. Then, in the study, a statistical PCA variable weighting was implemented to provide the same weight to each soil function. This allowed to study the absolute contribution of each function to the total index, rather than focusing only on relative evolution along the treatment studied. After calculation of the index, a variance analysis of the contribution of each soil function to the final score was run using one-way ANOVA.

The crossed-analysis of POXC and SituResp® was implemented following the conceptual framework proposed by [START_REF] Hurisso | Comparison of Permanganate-Oxidizable Carbon and Mineralizable Carbon for Assessment of Organic Matter Stabilization and Mineralization[END_REF], based on the distribution of residuals of the linear regression (lm) between POXC and SituResp®. Repartition of model's residuals between treatments was assessed using one-way ANOVA followed by a post-hoc mean comparison test, using HSD function (de Mendiburu, 2017). In addition, several t-test were performed to assess if the mean value of residuals per treatment was statistically different than 0. The "0 baseline" representing a state of equilibrium between mineralization (negative values) and short-term SOC stabilization (positive values).

3) Results

3.1) Impact of Conservation Agriculture on soil functioning

3.1.1) Impact of Conservation Agriculture on the three soil functions All the indicators were sensitive to the agricultural practices studied, except the visual structure evaluation (VESS) and nitrate concentration (NO3 -) (Table 3). The other six sensitive indicators showed significant higher values under CA compared to CT.

For carbon transformation indicators, POXC (C labile) and SituResp® (basal soil respiration) values were three times higher in CA 1 when compared with CT. The evaluation of C dynamics (stabilization vs. mineralisation), using regression residuals between values of POXC and SituResp®, showed a gradual increase from CT to CA 1 (monoculture) to bi-annual rotation (CA 2a and CA 2b). CT showed a significant C mineralization pattern (on average -292.72) (Figure 1; supplementary readings 2). By contrast, among CA treatments, soil under CA 2b exhibited a short-term SOC stabilization (p<0.05) when no significant impact was observed under CA 1 and CA 2a.

For the structure maintenance function, results showed that soil of CT treatment tended to be less structured (score of VESS = 1.3), significantly less stable (AggSurf and AggSoil) and with a significant lower infiltration rate of water (2 times) compared to CA treatments. Also, for each treatment, an opposite pattern of aggregate stability with soil depth was observed.

It increased with depth for CT treatment (score increasing of 0.8) while it decreased for CA (-0.7 score for CA 1 and CA 2a, -1.6 score for CA 2b).

For the nutrient cycling function, only NH4 + concentrations significantly differed between treatments. In CA treatments, NH4 + was almost two times higher than in the CT treatment (on average 15 mg.kg -1 vs 8.3 mg.kg -1 ).

3.1.2) Impact of Conservation Agriculture on global soil health

In order to gather all the functional parameters, a principal component analysis (PCA) was undergone (Figure 2). The first and second PCA axes represented 45.7% and 19.5% of the total variability respectively. The PCA differentiated CA and CT and the difference was mainly related to three indicators linked to the soil functions studied with POXC (C transformation), NH4 + (nutrient cycling), and AggSurf (soil structure). The Biofunctool® index values for CA treatments had average score of 0.64 (p<0.05), which is about twice the score of CT treatment (Figure 3). Carbon transformation function was the most contributing function explaining the differences in the index. Its contribution was more than two times higher in the CA treatments.

3.2) Impact of rotation on soil functioning

3.2.1) Impact of rotation on the three soil functions

The indicators of carbon transformation function, POXC (C labile) and SituResp® did not differ between the three CA treatments (Table 3). Only CA 2b showed a significant C stabilisation dynamic (p=0.049) (Figure 1). For the two other CA treatments, although the regression mean residuals were positive, they were not significant (p>0.05) (Figure 1).

For the nutrient cycling function, NO3 -concentrations appeared higher in CA 2a and CA 2b (Table 3). However, due to high data variability, these differences were not significant. No difference was noted in the NH4 + concentrations between the three CA treatments.

For the structure maintenance, no difference was found between the three CA treatments for VESS and aggregate stability, except for water infiltration which was almost two times higher under CA 1 than under CA 2b (p<0.05) (Table 3).

3.2.2) Impact of rotation on global soil health

The Biofunctool® index values were not significantly different for the three CA, but the contribution of each function to the index differed (Figure 3). The analysis of relative contributions to the Biofunctool® index show that CA 1 and CA 2b have a rather similar structure with (i) carbon transformation function contributing the most to the index (41-39%), (ii) soil structure maintenance (35-33%), and (iii) nutrient cycling function contributing the less (24-28%). Under CA 2a, functions were more evenly distributed, with relative contribution of the three functions reaching around one third of the final index.

4) Discussion

This study allowed to assess soil health under conservation agriculture, following a multifunctional approach, taking into account the interactions between the different soil compartments based on a consistent set of soil indicators previously selected. The integration of all indicators in a multivariate analysis enabled to understand how soil functions are impacted by contrasted agricultural practices under a tropical climate on a clayey Oxisol.

4.1) Impact of Conservation Agriculture on soil health

4.1.1) Impact of CA on carbon transformation function

The increase of POXC confirms the measurements of [START_REF] Hok | Enzymes and C pools as indicators of C build up in short-term conservation agriculture in a savanna ecosystem in Cambodia[END_REF] on the same experimental trial, who found more labile carbon under CA systems at 0-10 cm depth. On a similar experiment in Brazil, Sá et al. ( 2014) also observed on average two times more POXC under the no-till system up to 20 cm when compared with plough-based management. For soil respiration measurements, SituResp® values tended, or were significantly higher under CA, thus showing an higher soil microbial activity linked to the improvement of microorganisms habitat and the increase in soil labile carbon [START_REF] Balota | Long-term tillage and crop rotation effects on microbial biomass and C and N mineralization in a Brazilian Oxisol[END_REF][START_REF] Wang | Relationships of soil respiration to microbial biomass, substrate availability and clay content[END_REF]. When integrating POXC and SituResp®, CT and CA treatments follow a dynamic in line with the results of [START_REF] Hurisso | Comparison of Permanganate-Oxidizable Carbon and Mineralizable Carbon for Assessment of Organic Matter Stabilization and Mineralization[END_REF] i.e. CT tends to mineralize fresh organic carbon, when CA treatments tend to stabilize C inputs. Also, the results follow the trends of the current literature highlighting the potential for carbon accumulation under CA management systems [START_REF] Lal | Sequestering carbon and increasing productivity by conservation agriculture[END_REF][START_REF] Powlson | Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropical agro-ecosystems? Agriculture[END_REF], mainly due to increased carbon inputs on superficial horizons [START_REF] Fujisaki | Soil carbon stock changes in tropical croplands are mainly driven by carbon inputs: A synthesis[END_REF][START_REF] Virto | Carbon input differences as the main factor explaining the variability in soil organic C storage in no-tilled compared to inversion tilled agrosystems[END_REF].

4.1.2) Impact of CA on the nutrient cycling function

Soil total nitrogen is higher under CA systems than CT with significant difference when comparing CT with CA 2a and CA 2b (Table 2). This trend is also observed for available NH4 + and NO3 -. This result may be linked to the higher activities of decomposers and microbial communities under CA when compared with CT [START_REF] Baudoin | Direct seeding mulch-based cropping increases both the activity and the abundance of denitrifier communities in a tropical soil[END_REF]. [START_REF] Hok | Enzymes and C pools as indicators of C build up in short-term conservation agriculture in a savanna ecosystem in Cambodia[END_REF], on the same experiment, emphasized that the β-glucosidase and aryl-sulfatase activities are significantly higher under CA 2a and CA 2b when compared with CT at 0-5 cm depth. Indeed no till practices stimulate the microbial biomass, enzyme activities and denitrification processes especially in the upper soil surface [START_REF] Baudoin | Direct seeding mulch-based cropping increases both the activity and the abundance of denitrifier communities in a tropical soil[END_REF][START_REF] Bergstrom | Sensitivity of Soil Enzyme Activities to Conservation Practices[END_REF][START_REF] Rabary | Effects of living mulches or residue amendments on soil microbial properties in direct seeded cropping systems of Madagascar[END_REF]. The increase in NH4 + and in some extend NO3 -, may be related to several factors including (i) the increase N inputs through symbiotic N2-fixing pathways, (ii) diversity and amount of organic inputs that may enhance soil decomposers and microbial communities activities (iii) no tillage practices that affect microbiota activities linked to the N cycle (nitrifiers and denitrifiers) in the top soil [START_REF] Krauss | Tillage system affects fertilizer-induced nitrous oxide emissions[END_REF]. In a study conducted in middle-west region of Madagascar, [START_REF] Zemek | The contribution of Stylosanthes guianensis to the nitrogen cycle in a low input legume-rice rotation under conservation agriculture[END_REF] observed under low inputs ricebased cropping systems incorporating one-year long Stylosanthes guianensis fallow, a total N uptake of 225 kg.ha -1 , with 55% originating from the soil and 45% from the atmosphere.

4.1.3) Impact of CA on the structure maintenance function

The absence of tillage combined with presence of soil surface residues, living root systems of the cover crops during the dry season globally improved the structure maintenance function, which confirms the conclusions made on the improvement of soil structure under CA cropping systems [START_REF] Hobbs | The role of conservation agriculture in sustainable agriculture[END_REF][START_REF] Indoria | Conservation agriculture -a panacea to improve soil physical health[END_REF][START_REF] Tivet | Aggregate C depletion by plowing and its restoration by diverse biomass-C inputs under no-till in subtropical and tropical regions of Brazil[END_REF]. By contrast with the study conducted by [START_REF] Castioni | Soil physical quality response to sugarcane straw removal in Brazil: A multi-approach assessment[END_REF] on a Brazilian Oxisol (55% clay), VESS was less sensitive to detect soil structure changes compared with aggregate stability. Thoumazeau et al. (2019b) also found that VESS was not sensitive to land management based on a perturbation gradient with very contrasted land uses. In our system, the superficial layer (0-5 cm) seemed to be the main part affected by CA practices. This soil layer may not have been studied precisely enough with the VESS method to raise differences between systems. In order to answer this issue, methodological improvements should be proposed to refine the scoring methods and to readapt it to the studied system [START_REF] Emmet-Booth | GrassVESS: a modification of the visual evaluation of soil structure method for grasslands[END_REF]. Then, the better aggregate stability under CA is in agreement with several studies including those by [START_REF] Nascente | Soil Aggregation, Organic Carbon Concentration, and Soil Bulk Density As Affected by Cover Crop Species in a No-Tillage System[END_REF] and [START_REF] Ivy | Medium-term effects of conservation agriculture on soil quality[END_REF] under similar pedo-climatic conditions. Also, It is recognized that CA-based cropping systems lead to stratification of soil aggregates and C content [START_REF] Mrabet | Stratification of soil aggregation and organic matter under conservation tillage systems in Africa[END_REF][START_REF] Patra | Effect of conservation agriculture on stratification of soil organic matter under cereal-based cropping systems[END_REF][START_REF] Tivet | Aggregate C depletion by plowing and its restoration by diverse biomass-C inputs under no-till in subtropical and tropical regions of Brazil[END_REF], with the highest accumulation and aggregation on the surface layer. On the opposite, tillage disrupts top soil aggregates which leads to a uniform aggregates and carbon distribution. The stratification ratio, calculated as AggSurf/AggSoil [START_REF] Franzluebbers | Soil organic matter stratification ratio as an indicator of soil quality[END_REF] ranged from 0.76 under CT to 1.42 under CA 2b. This stratification under CA can be directly attributed to the absence of soil disturbance combined with higher biomass-C inputs from aboveground residues, root systems that strengthen soil aggregation in surface layers and contribute to build up a more stable aggregated soil matrix [START_REF] De Mendiburu | Driving factors of soil carbon accumulation in Oxisols in long-term no-till systems of South Brazil[END_REF][START_REF] Indoria | Conservation agriculture -a panacea to improve soil physical health[END_REF][START_REF] Six | A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics[END_REF]. Despite a high variability among CA results, soil water infiltration tends to increase under CA cropping systems. It is commonly recognized that water infiltration is improved under no-tillage and residue cover [START_REF] Powlson | Soil management in relation to sustainable agriculture and ecosystem services[END_REF].

4.1.4) Global impact of CA on the soil functions

The main difference observed in terms of soil functioning was observed between conventional tillage (CT) and CA systems (CA 1, 2a and 2b). The Biofunctool® index showed a better soil health under CA than under CT. Therefore, in addition to the positive impact on numerous individual soil properties [START_REF] Das | Effects of tillage and biomass on soil quality and productivity of lowland rice cultivation by small scale farmers in North Eastern India[END_REF][START_REF] Hobbs | The role of conservation agriculture in sustainable agriculture[END_REF][START_REF] Kumar | Crop Residues and Management Practices : Effects on Soil Quality , Soil Nitrogen Dynamics , Crop and Nitrogen Recovery[END_REF][START_REF] Thierfelder | Effects of conservation agriculture on soil quality and productivity in contrasting agro-ecological environments of Zimbabwe[END_REF], the multi-functional assessment of soil functions showed an improvement of soil health under CA.

4.2) Impact of rotation on soil health

4.2.1) Impact of rotation on the three soil functions Part of the carbon transformation function, POXC, although defined as a "soil fraction that is sensitive to management" [START_REF] Bongiorno | Sensitivity of labile carbon fractions to tillage and organic matter management and their potential as comprehensive soil quality indicators across pedoclimatic conditions in Europe[END_REF][START_REF] Culman | Permanganate Oxidizable Carbon Reflects a Processed Soil Fraction that is Sensitive to Management[END_REF], was not sensitive to the crop rotation under CA in our case. This confirms the results of [START_REF] Hok | Enzymes and C pools as indicators of C build up in short-term conservation agriculture in a savanna ecosystem in Cambodia[END_REF] who found similar POXC stocks in the three CA treatments in 2013 (2.5 Mg.ha -1 for CA 1, 2.5 Mg.ha -1 for CA 2a and 2.6 Mg.ha -1 for CA 2b). Similarly, basal soil respiration did not allow to distinguish CA treatments. Thus, even though these indicators (POXC and SituResp®) are recognized to be sensitive to land management, the rotation effect was not highlighted. This result emphasized that high biomass-C inputs under CA management, root systems deposition and rhizodeposites of crops and cover crops are predominant over the rotational pattern. Despite no difference among individual indicators, when crossing POXC and SituResp®, the CA treatments seemed to reach an equilibrium between short-term SOC mineralization and stabilization. Within the CA treatments, CA 2b was the only that showed a significant carbon stabilization. These information on carbon dynamics followed the observation of [START_REF] Hok | Short-term conservation agriculture and biomass-C input impacts on soil C dynamics in a savanna ecosystem in Cambodia[END_REF], who noted a higher increase in CA 2b SOC stock in 0-5 cm depth in comparison with the other treatments.

No difference could be assessed between the three CA treatments for the nutrient cycling function, despite nutrient cycling is usually significantly impacted by the nature of the organic inputs and the long-term diversity of the cropping systems [START_REF] Franchini | Rapid transformations of plant water-soluble organic compounds in relation to cation mobilization in an acid Oxisol[END_REF][START_REF] Pavinato | Effects of organic compounds produced by plants on soil nutrient availability[END_REF]. At the time of the soil sampling in November 2017, residues on the top soil of each cropping system were at different stages of decomposition and mineralization. Under CA 2b, maize was harvested almost 4 weeks before the soil sampling.

Therefore, the decomposition process had started and, in addition, a new mix of cover crops was sown (i.e., sorghum and sunnhemp) potentially leading to new rhizodeposites. By contrast, under CA 1 and CA 2a, rice-bean was still under vegetative development and the residues of former cover crops were already decomposed. Although this could not be highlighted by our measurements, the residues' diversity (i.e., crops and mix of cover crops) may have provided organic inputs with different characteristics that may have impacted microbial communities build-up and turnover rate, thus differentiating kinetics of decomposition processes and nutrients releases [START_REF] Giacomini | Dry matter, C/N ratio and nitrogen, phosphorus and potassium accumulation in mixed soil cover crops in Southern Brazil[END_REF]. Further investigation on nutrient dynamics, as affected by rotations, should further be handled to better understand the cycles. Also, the ion exchange membrane indicator could not be measured in this study and prevented us to conduct a complete assessment of the nutrient cycling function.

The Beerkan indicator differentiated CA 1 and CA 2b, and the rice-bean cultivated in both plots in 2017 (CA 1 and CA 2a) may have a positive impact on water infiltration. The other indicators of structure maintenance and nutrient cycling were hardly sensitive to the rotation, therefore showing the importance of no-tillage and biomass-C inputs rather than the rotation itself.

Eventually we noted an improvement of inter-connected functions like C transformation, soil structure maintenance, and nutrient cycling. This strengthens adaptive capacities under CA systems.

4.2.2) Global impact of rotation on soil health

Even if the Biofunctool® index did not differ significantly under CA, contrasted patterns were observed. Rotation seemed to induce differences in the balance between soil functions. The contributions of each function to the index were more balanced under CA 2a, with a higher contribution of nutrient cycling than the others. However, the absence of significant difference on this soil function in the index could be explained by an insufficient complexity of the bi-annual rotation when compared with CA 1 where globally similar cover crops were used. To answer this hypothesis, further studies with more complex cropping patterns should be implemented. Also, CA 2a and CA 2b showed different patterns despite being a biannual rotation since 2009. Similar differences among a bi-annual rotation under CA systems were observed on an alkaline soil in the Battambang province, Cambodia (Tivet, pers. com.).

The differences observed between CA 2a and CA 2b could be linked to the number of years of the cropping pattern that has a significant impact on soil functioning despite being a biannual rotation since 2009, and/or to a legacy effect from the past use of the plots.

4.3) Is Biofunctool® adapted to annual cropping system?

Until now, the only published data on Biofunctool® came from a study in Thailand, implemented over a contrasted gradient of disturbance in perennial cropping systems (rubber plantations) and sandy soils (65 to 77% of sand) (Thoumazeau et al., 2019a;2019b).

In our study, Biofunctool® was used on annual-based cropping systems in a very different soil context (highly clayey red Oxisol). Globally, the Biofunctool® index was sensitive to contrasted agricultural practices (perennial/annual, till/no-till) and the conclusions were consistent with other studies from the literature. This confirms the tools' robustness and the relevance. 20 However, the Biofunctool® index did not raise difference between the monoculture and rotations under CA systems. The impact of rotation on soil health is thus rather low emphasizing that the biomass-C inputs (above-ground and below-ground) from the cover crops are predominant over the rotational pattern. The indicators that could not be implemented (Ion exchange membrane, Lamina and Cast) may have provided important information on carbon transformation or nutrient cycling function among the index. In the future, and in such contexts, those Biofunctool® indicators should be modified, or better protected to avoid on-field destruction.

Conclusion

The study was a first application of Biofunctool® under contrasted annual cropping systems and showed to be well-adapted to an annual cropping system under tropical climate conditions. The multi-functional study of soil health globally raised large differences between tillage-based management and CA management on soil functioning. All tested indicators improved under CA compared with CT (except VESS that was less sensitive to soil management), with a soil quality index twice higher under CA. These conclusions globally confirm the trends in literature on the improvement of soil health under CA. Also, although biomass-C inputs and tillage were more important than the rotation pattern in the improvement of soil health, the presence of rotation in the management pattern seemed to induce a better balance between soil functions. Further, Biofunctool® supports awareness raising of development practitioners and policymakers on the negative impacts of current cultivation practices and demonstrates the positive impacts of alternatives cultivation methods such as CA on soil health. After the methodological development and the first applications in Thailand, Cambodia was another pilot country for Biofunctool® implementation. The Biofunctool® index was a good communication tool, easy to explain to a range of stakeholders (i.e., smallholder farmers, development practitioners, and policy-makers), and thus appears to be an accurate pathway to guide decision-making to improve cropping systems management, performances and efficiency.

  in the experimental site was 1577 mm. The mean annual minimum and maximum temperatures were 23 °C and 32 °C respectively on the period 2009-2014 (NCEP, 2016). The experiment was implemented in 2009 by the Conservation Agriculture Service Center (CASC), Department of Agricultural Land Resources Management (DALRM, GDA) under a partnership with the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD). A randomized block design experiment was established consisting in four treatments, three

Figure 1 .Figure 2 .

 12 Figure 1. Mean values of regression residuals per treatment (n=9). (CT) Monoculture of soybean under conventional tillage, (CA 1) Monoculture of soybean under permanent cover crops, (CA 2a) Bi-annual rotation soybean-maize under conservation agriculture, and (CA 2b) Inversed bi-annual rotation maize-soybean under conservation agriculture. Regression was made between values of POXC and SituResp® (see supplementary readings 2). Residuals mean values below zero represent a trend of mineralizable soil organic C, the above-zero values reflect a trend of short-term SOC stabilization. Vertical line represents the standard error per treatment. The p-values correspond to t-test analysis of divergence from 0 (α=0.05).

Acknowledgments

This research was made possible through the projects FIRST and EISOFUN with the support of the ACTAE project (Towards Agroecological Transition in South-East Asia), French Agency for Development (AFD), the National Council for Sustainable Development, and the Cambodia Climate Change Alliance -Phase 2 (European Union/Sweden/UNDP). Additional supports were provided to conduct the field experiments by the support from ACTAE and the American people through support by the United States Agency for International Development Feed the Future Innovation Labs for Collaborative Research on Sustainable Intensification (Cooperative Agreement No. AID-OAA-L-14-00006, Kansas State University), Centre of Excellence on Sustainable Agricultural Intensification and Nutrition. Experimental information was also supported by previous grants from the PAMPA, French Agency for Development, Ministry of Foreign affairs and French Global Environment Funds. We would like to thank Sovannara Chheong, Ouddom Chett, Thara Theng, all students from the Royal University of Agriculture and the Faculty of Agronomy for providing support to conduct the experiments and assist during the soil sampling in November 2017. We also thank Nancy Rakotondrazafy for the supports in Eco&Sols laboratory.

Conflict of interest

None. (Total Carbon) in order to gather all the hypothesis needed to run the ANOVA. Table 3. Biofunctool® indicators per treatment. (CT) Monoculture of soybean under conventional tillage, (CA 1) Monoculture of soybean under permanent cover crops, (CA 2a) Bi-annual rotation soybean-maize under conservation agriculture, and (CA 2b) Inversed bi-annual rotation maizesoybean under conservation agriculture. The analysis was made in 0-10 cm depth, except for AggSurf (0-2 cm) and AggSoil (2-10 cm). n=9 for each treatment except for VESS where n=3 per treatment (no inner-replicate). Due to the loss of one sample, n=8 instead for CT of NO3 -and NH4 + . Letters indicate significant differences according to Tuckey test.