Vincent Droissart
email: vincent.droissart@ird.fr

Simon Verlynde

Brigitte Ramandimbisoa

Lalao Andriamahefarivo

Tariq Stévart

Diversity and distribution of Orchidaceae in one of the world's most threatened plant hotspots (Madagascar)

Keywords: biodiversity hotspot, Malagasy orchids, plant database, sampling gaps, tropical Flora

Introduction. In recent decades, Madagascar has become one of the most important plant hotspots in the world. The country's remaining forests and vegetation are disappearing at an alarming rate, while dozens of new species are discovered each year. Amongst the plant families that have long been studied, Orchidaceae appear as one of the most charismatic, diverse and of high conservation concern. Based on a reviewed, comprehensive herbarium dataset, we have compiled a curated checklist of all orchid species occurring in Madagascar. Based on this complete dataset, we then compiled sampling effort, species diversity distribution and some general statistics on their ecology and IUCN conservation status.

Introduction

Oceanic islands generally harbour a rich endemic flora and Madagascar, the fourth largest island in the world, does not deviate from this rule [START_REF] Whittaker | Island biogeography: ecology, evolution, and conservation[END_REF]. The recently-published Catalogue of the Vascular Plants of Madagascar [START_REF] Callmander | New combinations and other taxonomic changes for the forthcoming 'Flore des Mascareignes' Orchidaceae accounts[END_REF], Madagascar Catalogue 2022) confirms the presence of more than 11,500 plant species on the island, of which 80-85% are endemic. This high rate of endemism, combined with the strong human impact compared to mainland regions [START_REF] Kier | A global assessment of endemism and species richness across island and mainland regions[END_REF], has significant implications for the conservation of the global biodiversity including the highly-diversified Malagasy flora [START_REF] Antonelli | Madagascar's extraordinary biodiversity: Evolution, distribution, and use[END_REF].

Madagascar has experienced extensive deforestation in the last decade and 44% of the natural forest area has been lost in the period 1953-2014 [START_REF] Vieilledent | Combining global tree cover loss data with historical national forest cover maps to look at six decades of deforestation and forest fragmentation in Madagascar[END_REF]. Anthropogenic climate change will continue to threaten Madagascar's exceptional biodiversity. Due to the combination of high endemism and high deforestation rates, [START_REF] Humphreys | Global dataset shows geography and life form predict modern plant extinction and rediscovery[END_REF] identify Madagascar as one of the seven regions in the world with the highest rate of plant species extinction (i.e. 19 extinct species). These previous studies call for better identification of gaps in plant coverage to find rare or unknown species before they disappear.

To date, there have been few studies on the spatial distribution and diversity of plants in Madagascar (but see, for example, Palms: [START_REF] Rakotoarinivo | Comprehensive Red List assessment reveals exceptionally high extinction risk to Madagascar palms[END_REF]; Sarcolaenaceae: [START_REF] Soulebeau | Biodiversity Conservation and Phylogenetic Systematics: Preserving our evolutionary heritage in an extinction crisis[END_REF]; Pandanaceae: [START_REF] Callmander | Identification of priority areas for plant conservation in Madagascar using Red List criteria: rare and threatened Pandanaceae indicate sites in need of protection[END_REF]) and none focuses on Orchidaceae. In fact, Orchidaceae is the largest family of flowering plants in Madagascar (Madagascar Catalogue 2022) and is known worldwide as flagship for habitat preservation and biodiversity conservation. With approximately 1000 species described to date, orchids make up about 10% of Malagasy flora and up to 85% of them are currently considered endemic [START_REF] Hermans | Orchidaceae[END_REF]. The orchid flora of Madagascar has been studied by scientists and naturalists for about two centuries (see [START_REF] Hermans | Orchidaceae[END_REF], Hermans et al. (2007), [START_REF] Hillerman | An Introduction to the Cultivated Angraecoid Orchids of Madagascar[END_REF] for a complete history of Malagasy orchids), but several new species are still being discovered and described each year. Conversely, [START_REF] Roberts | Inferring the extinction of species known only from a single specimen[END_REF] have shown that 11 orchid species may be extinct in Madagascar, although the current IUCN Red List (IUCN 2022) has not assessed any extinct orchid species in the island. As Malagasy orchids exhibit a wide range of life forms (i.e. terrestrial, lithophytic or epiphytic) and distribution types (from microendemic to widespread species), we advocate they may be an appropriate test group for evaluating the island's current botanical sampling coverage.

In this study, we compiled a database of all herbarium records of Madagascar's largest plant family, the Orchidaceae, to: i) analyse sampling and diversity patterns of orchids in Madagascar, accounting for sampling biases and ii) provide scientists and conservationists with an updated checklist of orchid species collected in Madagascar along with summary statistics on their ecology and conservation. In particular, we addressed the following three important questions: i) how many orchid species have been collected in Madagascar to date and to what extent are these species well known and/or collected? ii) how many species remain to be potentially discovered and iii) what should future plant surveys focus on to increase the likelihood of finding rare or unknown orchid species?

Material and methods

Records database and checklist compilation

The main goal of this work is to provide scientists and conservationists with a state-of-theart dataset and key figures on the diversity and distribution of orchid species in Madagascar. To estimate the total number of orchid species in Madagascar, we compiled a global dataset using five public databases as main data sources: the Missouri Botanical Garden's Tropicos database (http://www.tropicos.org/, last excerpted 10 March 2021), the GBIF database (GBIF 2019), the MNHN's vascular plant database (https://science.mnhn.fr/ institution/mnhn/collection/p/item/search), the BM database (https://data.nhm.ac.uk/search) and the Kew Herbcat database (http://apps.kew.org/herbcat/). We also reviewed the literature to add new or missing records (Hermans and Cribb 2014, Hermans et al. 2021a, Hermans et al. 2021b, Hermans et al. 2021c, Hermans et al. 2021d, Hermans et al. 2020a, Hermans et al. 2020b, Hermans et al. 2020c, Hermans et al. 2017, Hermans et al. 2021e, Hermans 2020a, Hermans 2020b, Hermans and Cribb 2021). We then used recent literature (e.g. Chase et al. ( 2020 To avoid taxonomic conflicts, we restricted the identification of specimens to the species level, although some specimens were identified as infraspecific taxa (subspecies or varieties). In addition, because identification of Malagasy orchids can be difficult, we preferred not to use direct observations or photographs without herbarium vouchers (e.g. iNaturalist, https://www.inaturalist.org/ or Pl@ntnet, https://plantnet.org/) to avoid dubious or/and unverifiable data. All occurrences in our dataset are, therefore, supported by specimens deposited in public herbaria.

Richness estimates and sampling bias

Geographic coordinates of recent collections were recorded using a global positioning system or assigned retrospectively using a gazetteer of botanical collecting localities (e.g. http://legacy.tropicos.org/Project/Madagascar) for old specimens. One should note that locality data for some of the used databases (especially P and K) are sometimes incomplete and this will affect the results of spatial analysis. In order to minimise these biases, we avoided performing fine scale analyses and applied data resampling methods (see below).

Sampling intensity and species richness were calculated using fixed grid cell sizes of 0.5° x 0.5° and 1° x 1°, providing an appropriate balance between precision and detail. Rarefaction methods were used to calculate an unbiased estimate of species diversity per grid cell, Hurlbert's effective number of species (Ek) found in fixed-size k subsamples (Hardy 2010, Dauby and Hardy 2012). For comparison with raw species richness, we have calculated Ek for k = 10 (i.e. for grid cells in which at least 10 herbarium specimens were collected) and for k = 100. We do not considered Ek values when the number of specimens in a grid cell is equal to the number of species collected in the same grid cell. Richness estimates and sampling completeness were assessed with sample-based rarefaction curves using the R package iNEXT [START_REF] Hsieh | iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers)[END_REF]. We used the iNEXT package to calculate the seamless rarefaction (interpolation) and extrapolation (prediction) sampling curves and the associated 95% confidence intervals of individual-based abundance data. All maps were prepared with ArcMap 10.8.1 (ESRI 2020).

Ecology and IUCN conservation status

Life forms of orchid species were classified into three different growth form types (epiphyte, lithophyte and terrestrial) and were mainly taken from Govaerts et al. ( 2022) and supplemented by literature or ecological descriptions on specimen labels. Conservation status, when published, was taken from the IUCN Red List Portal (IUCN 2022).

Results

Malagasy orchid checklist and database

A total of 913 orchid species, representing 53 genera, are reported from Madagascar (Suppl. material 1). Of these, 759 species (83.1%) are endemic. We compiled a database of 13473 specimens identified to species (Suppl. material 2), representing 11093 unique herbarium records (i.e. excluding the duplicates collected from the same living individuals in shadehouse collections), of which 10300 are georeferenced (890 species).

In Madagascar, the first orchid specimens were collected in 1816 (three specimens were collected by Louis-Marie Aubert du Petit-Thouars) and about 60% of specimens were collected after 2000 (Fig. 1). Most specimens were collected by the Ambatovy/MBG teams (1305 unique herbarium specimens collected between 2010 and 2019) and by Henri Perrier de la Bâthie (1134 unique herbarium specimens collected between 1896 and 1953). Our database also shows that 214 species have not been collected for over 50 years and that 189 species are known only from type specimens (Suppl. material 1), which together represent about one third of the Malagasy orchid flora (297 species).

Sample coverage and species distribution

The current sample coverage for Malagasy orchid species diversity was strikingly high (98.3%), suggesting that the family is relatively well known in Madagascar (Fig. 2) . From the sample accumulation curves, we can estimate that a doubling of the current sampling effort will result in the discovery of a hundred species and that the total number of orchid species in Madagascar could be estimated between 986 and 1048 species.

The large majority of specimens and species known to date have been collected in the moist forest of eastern Madagascar (Fig. 3). In 2012, a large collection programme was designed yielding thousands of new specimens and allowing the discovery of a large number of new species that have yet to be described. The raw data (Fig. 3a,b,e,f) show that the greatest local species richness of Malagasy orchids is found in the Andasibe-Moramanga area. With more than 1000 records per square grid, this area also has the highest sampling intensity. Contrastingly, our analysis of bias-adjusted data shows that the centres of richness of orchids in Madagascar are instead located in the Tsaratanàna Strict Nature Reserve and the Ranomafana National Park (Fig. 3d,h). Temporal distribution of orchid's collections across Madagascar. Maps present the number of botanical collections made within 0.5° grid cells on different dates (1900,1950,2000,2020). The upper figure is based on dated herbarium records (12693 records), while lower maps are based on dated and georeferenced herbarium records (9824 records).

Ecology and conservation

Statistics on life forms (Fig. 4) show that 54.9% of the 913 orchid species recorded for Madagascar are strict epiphytes while 36.5% are terrestrial species, 2.6% are lithophytes and 6.0% share at least two life forms.

The main flowering season of orchids (Fig. 5) in Madagascar is between November and March and about 330 species may flower during these five months. These flowering peaks are congruent with the rainy season in different parts of Madagascar. Distribution of orchid records and species richness across Madagascar, using fixed grid cell sizes of 1° x 1° (a-d) and 0.5° x 0.5° (e-h); a and e, number of herbarium records (samples) collected per grid-cells; b and f, number of species collected per grid-cells; c and g, unbiased estimate of species diversity per grid cell, Hurlbert's effective number of species (Ek) found in grid-cells that contain at least 10 herbarium records (Ek = 10); d and h, unbiased estimate of species diversity per grid cell, Hurlbert's effective number of species (Ek) found in grid-cells that contain at least 100 herbarium records (Ek = 100). To date, the extinction risk of 226 Malagasy orchid species has been assessed using the IUCN Red List Categories and Criteria (Fig. 6). Of these fully assessed species, 84% are threatened with extinction: 55 species are classified as Critically Endangered (CR), 93 species as Endangered (EN) and 42 species as Vulnerable (VU).

Discussion and conclusions

Our database and checklist analyses, as well as recent literature (e.g. [START_REF] Hermans | Orchidaceae[END_REF]), indicates that Madagascar hosts about 1000 orchid species, confirming their status as the most diverse family in the country, surpassing the Rubiaceae (806 species) and the Fabaceae (603 species), according to the Madagascar Catalogue (2022). The high level of endemism (~ 83%) is consistent with that of the global Malagasy flora, recently estimated at 82% by [START_REF] Antonelli | Madagascar's extraordinary biodiversity: Evolution, distribution, and use[END_REF] and once again highlights the importance of this family for conservation of Madagascar's plant biodiversity.

Of particular significance for the diversity analysis presented here are the potential error margins in the identification of orchid specimens. Indeed, our dataset and subsequent analysis incorporated some herbarium specimen identification as they were originally recorded. Based on our expertise (but also Mackay-Smith and Roberts ( 2019)), it is important to note that such identifications can carry a substantial margin of error, with higher discrepancies observed for more intricate genera (e.g. Liparis Rich. or Polystachya Hook.). This highlights the pressing requirement for a thorough reassessment of several genera in Malagasy orchids. Such taxonomic revisions could also serve as a constructive approach to enhance the accuracy of the statistics presented in our study, as well as to help general knowledge and conservation of the rich Malagasy orchid flora.

Many orchid collections in Madagascar are concentrated near roads and within the established nature reserves and parks. Consequently, significant portions of the country, notably remote parts such as the eastern rainforests (e.g. the Ambatovaky Special Reserve or the Vondrozo Forest Corridor) and extensive regions of arid western forests remain largely unexplored for their orchid diversity. Our sampling bias-corrected analysis also indicated that future inventories should be conducted primarily in two of Madagascar's most famous protected areas, Tsaratanàna Strict Nature Reserve and Ranomafana National Park. These two conservation zones have historically been important areas for orchid sampling, but many of the collections are relatively old and may not reflect the current status of their orchid diversity.

There is still much to learn about the orchids of Madagascar and their conservation remains a neglected priority. Our study highlighted that a significant number of species have not been seen for a long time and may already be extinct, as more than 25% of the Malagasy orchids have not been observed in the last 50 years. Conversely, our dataset surprisingly shows that several species, predicted by Roberts and Jarić (2020) to be extinct have been collected more recently (i.e. Jumellea spathulata collected in 2003, Liparis longipetala collected in 2012 or Liparis parva collected in 1997; see Suppl. material 2). It also highlights the importance of continued monitoring and survey efforts to accurately assess the conservation status of Malagasy orchids.

Recent publications have highlighted Madagascar as a top global priority for orchid conservation (e.g. [START_REF] Zizka | Automated conservation assessment of the orchid family with deep learning[END_REF], [START_REF] Wraith | Orchid conservation and research: An analysis of gaps and priorities for globally Red Listed species[END_REF], [START_REF] Vitt | Global conservation prioritization for the Orchidaceae[END_REF]). However, to date, less than 25% of Malagasy orchids have been fully assessed by the IUCN, and the conservation status of most Malagasy orchids is currently unknown. To address this significant gap, we urge further research on the conservation status of orchids in Madagascar, as well as increased conservation efforts "on the ground" to protect these unique and threatened species. Efforts should also be made to engage local communities in these conservation efforts [START_REF] Birkinshaw | Supporting Target 4 of the Global Strategy for Plant Conservation by Integrating Ecological Restoration into the Missouri Botanical Garden's Conservation Program in Madagascar[END_REF]. Many Malagasy orchids are found in areas that are also important for agriculture and other livelihood activities and local people often have a deep understanding of these plants and their uses. By working with local communities, conservationists could better understand the ecological and cultural significance of orchids in Madagascar and develop more effective conservation strategies that offset human impacts. like to thank Ambatovy Minerals S.A. for their financial support. Finally, we thank J. Hermans and P.J. Cribb for comments on earlier versions of the manuscript.

  ), Chase et al. (2021)) or online databases (IPNI 2022, Madagascar Catalogue 2022, WCVP 2022) to standardise taxonomic names. Our dataset contains specimens from the following 22 herbaria (acronyms after Thiers 2018, continuously updated): AMES, B, BM, BRLU, CNARP, G, HEID, HUNT, K, MO, NM, NU, NY, OS, P, SZU, TAN, TEF, UPS, US, W, WAG.

  Figure 1.

Figure 2 .

 2 Figure 2. Occurrence-based accumulation curves and sample coverage for Malagasy orchid species diversity. The numbers below rarefaction curves indicate the observed species richness (left) and sample completeness (right). The shaded area represents the upper-lower 95% confidence interval.

  Figure 3.

Figure 4 .

 4 Figure 4. Venn diagram depicting life forms repartition amongst Malagasy orchids (913 species). Different growth form types (epiphyte, lithophyte and terrestrial) were mainly taken from Govaerts et al. (2022), supplemented by literature or ecological descriptions on specimen labels.

Figure 5 .

 5 Figure 5. Flowering patterns for orchids in Madagascar. The blue and orange bars indicate the number of samples and the number of species collected with flowers per month, respectively. Monthly precipitation data (lines) were collected between 1999 and 2019 (source: https://climatedata.org/).

Figure 6 .

 6 Figure 6. IUCN Red List Categories of the 226 Malagasy orchids assessed to date and published on the IUCN Red List Portal (https://www.iucnredlist.org/).

Diversity and distribution of Orchidaceae in one of the world's most threatened ...

Acknowledgements

The authors are grateful to the curators and staff at BRLU, K, P and TAN for making their collections available, hosting us during multiple stays and providing facilities. We are also thankful to the Direction générale des Forêts in Madagascar for delivering collecting permits and research authorisations. We express our warmest thanks to the Madagascar's Missouri Botanical Garden team for its indispensable help in this work, especially the field botanists. We express our gratitude to the National Geographic Society (Grant NGS-50970C-19, V. Droissart as PI) and the American Orchid Society (Grant C-2020-22, V. Droissart and T. Stévart as co-PI), both of which have supported recent fieldwork and conservation efforts (living orchid collection and seed bank) in Madagascar. We would also

Author contributions

VD conceived and designed the analysis; VD, SV, BR and TS collected the data; VD performed the analysis and designed the figures; VD wrote the paper with input from TS, SV, BR and LA. All authors gave approval for publication.

Conflicts of interest

The authors have declared that no competing interests exist. Disclaimer: This article is (co-)authored by any of the Editors-in-Chief, Managing Editors or their deputies in this journal. (Hermans et al. 2017, Hermans et al. 2020a, Hermans et al. 2020b, Hermans et al. 2020c, Hermans et al. 2021a, Hermans et al. 2021b, c, d, Hermans et al. 2021e). Given the rarity of several listed species, we have not provided detailed GPS coordinates and have rounded these to the nearest 0.1 degrees. Download file (1.07 MB)


Carbon farming, Result-based schemes and NIVA indicators

arable lands have a high potential for storing carbon. That is why at the COP21, the "4 per 1000" initiative was presented which aims at increasing the amount of carbon stored in soils on average by 0.4 % every year globally. It would allow maintaining global warming below 1.5°C at the end of the century but whether or not this target can be reached has since been the subject of much debate. Yet, soils rich in organic matter are more fertile, which increases food security, they decrease the need of chemical fertilizers, irrigation and they limit the risk of soil erosion.

Carbon farming represents all agricultural practices which allow to preserve and increase the soil organic carbon stocks. For France, a study1 conducted by INRAE in 2019 at the request of the French Ministry of Agriculture and Ademe (The French Agency for Ecological Transition) has identified the most promising practices to store carbon in the soils, it also has quantified their potential and their cost-effectiveness. Given their efficiency, their potential surface area of implementation and their moderate cost of implementation, cover crops present the best potential to store carbon in the arable soils.

Carbon market and carbon farming

Under the Kyoto Protocol, the European Union has established a mandatory "carbon market". It requires companies in highly carbon-emitting sectors to purchase "quotas" (entitlement to emit CO2) in order to offset their over emissions. In addition to this mandatory carbon market, there is a voluntary carbon market which allows other companies and individuals on a voluntary basis to offset their emissions by buying carbon credits. This voluntary market is supplied with carbon credits by companies or groups of individuals that have developed carbon storage projects. These projects are evaluated based on scientific based certified and auditable methods.

In the field of agriculture, such projects aiming to generate carbon credits already exist. These projects, generically called "carbon farming", allow farmers to get a financial support when they adopt certain agricultural practices that contribute to carbon storage in the biomass and/or in the soils. Several methods (e.g. based on in-situ soil sampling, soil or coupled plant/soil models) exist to quantify biomass and/or soil organic carbon (SOC) stock changes caused by such projects2 .

Carbon farming, Result-based schemes and NIVA indicators Certification and labels for carbon farming

Whatever the method used, it must certified. For this, several certification bodies exist (e.g. Verra, Goldstandard…). For instance, the VM0042 methodology from Verra aims at quantifying the greenhouse gas (GHG) emission reductions and the SOC stock changes resulting from changes in agricultural land management practices (e.g. carbon farming practices).

In France, the Low Carbon Label (Label Bas Carbone) was launched in 2019 by the French Ministry of the Environment that approve three methods to quantify GHG reductions and carbon storage for animal farming, arable crops and hedges/orchards. Once a project is accepted and certified, farmers can receive support from funders: local communities, companies, or associations can purchase the carbon credits generated by the project as part of a voluntary approach to offset their own CO2 emissions.

Other initiatives or labels exist in Europe: e.g. Healthy Soils for Healthy Food (SPAR/WWF), UK Woodland Carbon Code, MoorFutures.

European legislation on carbon farming

For now, the European Commission defines carbon farming as "a green business model" that rewards farmers for setting up carbon sequestration practices, coupled with significant biodiversity benefits. However, the details of such a scheme remain to be clarified, starting with a joint agreement to certify the storage capacity of the soil but also the rules in terms of additionality and the sources of financing (e.g. public through CAP subsidies, private through the voluntary carbon market).

The European Commission issued on the 30 th of November 2022 a "Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL establishing a Union certification framework for carbon removals". Given the climate situation, and the fact that the CAP is now performance-driven it would be relevant to base support measures on result rather than on actions. This part presents a discussion on this topic by taking the example of carbon farming.

Action based

An action based-scheme (farming practices) is relatively easy and cheap to monitor. We know which farming practices are likely to restore SOC (soil organic carbon). Policy makers can suggest these farming practices and monitor whether they are followed up. An action-based scheme can be based on indicators that Paying Agencies already use.

The drawback is that if we only look at farmers' activities, we have no evidence that organic carbon is stored in the soil. The Paying Agency pays but gets no guarantee that SOC has increased.

If a policy maker focuses on checking whether farmers do certain practices, we call that an action-based scheme.

The results of land management practices are not measured in an action-based scheme.

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009

Carbon farming, Result-based schemes and NIVA indicators  Promising farming practices

To monitor an action-based carbon farming scheme, one needs to decide on a set of actions or practices for land-owners, assuming that these will increase SOC. Interreg published indepth report on carbon farming practices in 20203 and a short report with the five most promising carbon farming methods in 20214 . The five most promising practices (indicators) are:

1. Use cover crops 2. Enrich crop rotations 3. Add compost and solid manure 4. Improve grassland

Introduce agroforestry

These carbon farming practices can be used as direct monitoring indicators. These fit in an action-based scheme, where farmers and land owners get CAP payments depending on whether they carry out these practices. It is not measured whether they actually managed to store carbon in the soil.

Result based

Setting up a result-based carbon farming scheme includes quantifying SOC stock changes in the soil.

A result-based carbon farming scheme based on insitu soil sampling has proven to be time consuming, costly and difficult to almost impossible to monitor (e.g. because of soil properties and biomass inputs spatial-temporal variability), especially if changes are small, which is the case when they are measured over a short period of time (i.e. 1-3 years)5 . Because of the costs and time involved, we do not think it is realistic to take physical soil samples to measure SOC on farms all over Europe. Note that methods based on proxy (e.g. on tractors)/aerial sensors (drones) or on remote sensing only allow to map superficial soil organic carbon content but do not allow to quantify SOC stocks (as organic matter is distributed along the soil profile with patterns that may differ greatly according to the pedoclimatic conditions/current and past practices).

Carbon farming, Result-based schemes and NIVA indicators

Other approaches to quantify SOC stock changes in the soil are based on soil or coupled plant/soil models. They are typically used for NDCs 2 or for the voluntary carbon market (e.g. French Label Bas Carbone). Such models often require detailed information on farming practices and recent plot/farm specific soil analysis as inputs for the models. A major drawback is that they often fail account for the spatial-temporal variability in crop/cover crop biomass development and of the resulting biomass inputs into the soil that impact SOC stock changes in the first order.  Ease of measuring SOC and degree of certainty.

The risk of non-delivery in a result-based scheme is fully concentrated at the land-owners' side. In an action-based scheme it is concentrated at the Paying Agency.

 Alternative Carbon Farming schemes

We concluded that a large scale result-based scheme is out of reach with the current methodologies (soil sampling, current models) and that an action-based scheme doesn't satisfy the need for evidence. Either we need new approaches for quantifying SOC stock

 Hybrid scheme

A hybrid scheme is a scheme where a farmer will get a part of the payment based on actions to restore carbon in the soil, and bonus payments based on result (a measured increase in carbon). In such a hybrid scheme, the farmer and policy body share the risk of non-delivery. In a result-based scheme, the farmer bears the full risk and in an action-based scheme the policy body does. In a hybrid scheme, the action-based and the result-based approach together form the full payment. So the land-owner commits to the desired practices, plus there is an incentive to do more and use creativity to store more carbon in the soil.

Note that the price of the ton of CO2 on the voluntary carbon market is currently too low to compensate for the price of implementation of the carbon farming practices 1 . It would therefore not be shocking to consider the possibility of additionality of carbon credit through the voluntary carbon market and subsidies that would be paid through the second pillar of the CAP for practices contributing to store carbon in arable soils. Ideally, the same monitoring tool should be used for both the payment based on action and results to ensure consistency in the hybrid scheme. So note that the TIER3 Carbon indicator tool developed in NIVA could answer this need.

 Convinced-based scheme

A convinced-based scheme would be a scheme based on the conviction (underlined by scientific research) that some actions increase the amount of organic carbon in the soil. Practices to restore carbon can be the same as in an action-based scheme, but this scheme uses a mathematical model to calculate changes in SOC (see for instance TIER 2 Carbon indicators tool here below). These calculations are based on land management practices in combination with external effects. Such model could use for instance satellite imagery and farm data.

 Agro-environmental indicators for alternative schemes?

The NIVA consortium is creating promising tools 7 to make IACS administration easier and to lower the administrative burden for the farmer and the Paying Agency (PA). Work done and achievement on carbon budget are presented in the following part. Those tools could be part of the tool package used in the future for a more result-driven scheme and performance-driven policy. For the time being, the more advanced developments concern the carbon indicators, with three methods (TIERs) of increasing complexity. Yet for some of them validations/methodological improvements are still needed for accurate estimation of the soil carbon budget and systematic large scale application.

Carbon indicators -Work done and achievements

The indicators are calculated based on the data available in Europe such as LPIS data, satellite data (Sentinel-2), pan-European meteorological data (ERA-5 from the ECWF) and data from the farmers (collected through FMIS -Farm Management Information System).

Tools to produce the carbon indicators have been developed at different levels (TIERs) of complexity. The TIER approach (T1, T2 and T3) offers increasing level of precision, but also increasing complexity. The indicators are calculated for an agricultural year and they can be calculated at different levels: parcel or pixel (i.e. 10m).

Carbon indicators are calculated for arable crops. Annual carbon budgets represent how much organic matter has been lost or gained by the soil during a cropping year (typically between 1 st October of year n and the end of September of year n+1). It is calculated by considering 1) the net annual CO2 flux between the plot and the atmosphere during the cropping year, 2) how much carbon has been exported at harvest and 3) how much carbon has been brought as organic amendments. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009

Carbon farming, Result-based schemes and NIVA indicators

Carbon Tier 1

The methodology for TIER 1 estimates the net annual CO2 fluxes between the parcels and the atmosphere: it takes into account the carbon dioxide emitted to atmosphere by the plants and the soil respiration (mineralisation of the soil organic matter) and the CO2 absorbed by the plants through photosynthesis.

Carbon Tier 1 can easily be implemented at plot or pixel levels; it only requires IACS data (information about the crop type and the plots contours) and NDVI (Normalised Difference Vegetation Index) time series calculated from Sentinel-2 satellite data or from other satellites providing data with high spatio-temporal resolutions. The indicator computation is based on an empirical linear relationship between the net annual CO2 flux and the number of days with active vegetation which is valid for most crop types in Europe (not rice). This relationship was established 8 based on measurements done at flux tower sites in Europe 9 . Using NDVI time series derived from Sentinel-2 we hypothesized that it is possible to determine the number days with active vegetation from remote sensing and therefore the net annual CO2 flux.

The methodology for TIER 1 estimates the net annual CO2 fluxes between the parcels and the atmosphere: it takes into account the carbon dioxide emitted to atmosphere by the plants and the soil respiration and the CO2 absorbed by the plants through photosynthesis.

Stands alone tools (at parcel 10 and pixel levels 11 ) are available at NIVA's Gitlab. A more advanced tool has been developed (integrating carbon TIER 1 algorithms in the IOTA2 software) to produce national level pixel scale indicators.

Carbon farming, Result-based schemes and NIVA indicators

Carbon Tier 1 at parcel level has been tested in various areas in Europe: in France, Denmark, the Netherlands and in Spain. The tool is operational for large areas, i.e. it was calculated or the entire country in the Netherlands. The results in the form of a map are easy to interpret, for example one can recognize parcels that fix the CO2 (in green on the figure below, left) and parcels that loose CO2 (in red).

Results can be also presented and analyzed in a form of charts, for example by grouping the results per crop type and analyzing the capacity of a particular crop type to fix the CO2 in a region.

Results of Carbon Tier 1 at pixel level allow analysis of the heterogeneity of the indicator inside of the agricultural parcel (example on figure below).

Carbon farming, Result-based schemes and NIVA indicators

Carbon Tier 2

This indicator allows computing annual carbon budgets at plot level, which represents how much carbon has been lost or gained by the soil over a cropping year. The tool 12 uses the results from the Carbon Tier 1 combined with the farmer's FMIS data (the type and on amount of organic amendments, harvest). As for Tier 1, Tier 2 is based on an empirical approach and it can be applied to most crop species. The main challenge to produce this TIER2 indicator is access to farmer's FMIS data.

CT2 is assessing the carbon budget of crops, taking into account not only the CO2 exchanges between the plot and the atmosphere but also the agricultural practices. The results on the figure below are presented only for parcels for which the FMIS data were provided allowing calculation of CT2. By comparing the results for Carbon Tier 1 and Tier 2, it can be observed that some of the plots that were close to CO2 neutrality (in yellow on the left, CT1) become red on the right (CT2), meaning that their soil lost carbon when we account for the carbon exported at harvest.

Carbon farming, Result-based schemes and NIVA indicators

The image below shows 10m resolution net CO2 flux map for straw cereals in South West France in 2018.

Combined with FMIS data in France results show (see below), some high spatial variability in the carbon budget inside and between the plots. Yellowish plots are winter wheat that loose carbon while greenish ones are maize crops preceded by cover crops. The high spatial variability is mainly caused by differences in biomass inputs in the soil.

The model also produces high resolution agronomical variables such as cover crop dry biomass (DM) and the associated uncertainties (respectively on the left and on the right).

Where to find the tools and results? Interpretation, validity Carbon Tier 1 is relatively easy to calculate, easy to interpret, but the results should be interpreted with caution: if the results highlight the effect of some management practices (impact the crop chosen, effect of cover crops) on the net annual CO2 fluxes it does not account for the effect of other management practices (e.g. organic amendments) and it does not provide information on the carbon budget itself (only on one of the 3 components of the carbon budget is addressed).

Apart from the intrinsic uncertainty associated to the empirical relationship used to calculate the indicator, the quality of the indicator depends on the number of satellite observations; for example cloud coverage increase the uncertainty on the estimate of the duration with active vegetation which impacts the results. For this purposes a note named "Quality Assessment of Carbon indicator Tier 1" can be found on the Zenodo platform (https://zenodo.org/record/7097103#.Y4oVvISZPIU) Also the method is currently being improved by accounting for climatic variables. Indeed even if green vegetation is observed in the field by satellites, it may not be active on a photosynthetic point of view if solar radiation or temperature are limiting the plants metabolism. For this reason, the current method tends to overestimate the net CO2 fixation of winter/cover crops. Next step will be to validate the improved methodology against ground truth (recent flux data concomitant with Sentinel 2 data).

Carbon Tier 2 is also rather easy to calculate but the main challenge is to access farmer's data through the FMIS (different formats) and to obtain their consent. This step requires conventions/agreements with farmer's or their representatives (e.g. agricultural councils, cooperatives). Also the process is costly and time consuming as the data quality/consistency must be checked. There are also privacy issues as the input data and the results cannot be shared openly or published. Note also that the uncertainty of the results combines the uncertainty of the TIER 1 approach with the uncertainty of the FMIS data. A dedicated validation procedure will have to be implemented to quantify the overall uncertainty of the approach. Tier 2 could be relevant for Convinced-based schemes.

Carbon Tier 3 is the most advanced approach but it requires significant IT infrastructure and addition of a new crop species/types in the modeling process requires a specific parametrisation/validation process which is time consuming. The main components of carbon budget calculation (biomass, yield, CO2 fluxes…) are validated against ground truth data and this approach offers assessment of uncertainties on the variables simulated. This modeling approach can be applied both to the voluntary carbon market for cropland and for the CAP (eco schemes).

  https://climate.ec.europa.eu/document/fad4a049-ff98-476f-b626-b46c6afdded3_en This regulation gives a large place to carbon farming and aims to guarantee the removals by proposing an EU certification framework based on four quality criteria (called QU.A.L.ITY): QUantification of carbon removal, Additionality of removals against basic requirements, Long-term storage (removals) and sustainabilITY as regard other environmental and climate objectives. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009 Carbon farming, Result-based schemes and NIVA indicators 2. CAP subsidies and carbon farming: action based versus result based schemes.



  A result-based payment scheme needs different monitoring indicators. According to COWI et al indicators should be 6 :  linked to a desired outcome at farm/plot scale;  consistently measurable using a simple technology;  sensitive to changes in agricultural-or land management within a reasonable time frame;  unlikely to be influenced by external factors beyond control of land manager. These requirements are rather qualitative, they differ per farm and seem harder to meet than for action-based indicators. These requirements also demonstrate that some factors are outside the control of the farmer or land manager.  Risk and certainty COWI et al. mention two critical factors that determine the feasibility of result-based scheme:  Risk of non-delivery due to external factors. Non-delivery means that no increase in SOC is measured, due to a dry season for example.

  Carbon farming, Result-based schemes and NIVA indicators changes at large scale meeting the requirements listed by COWI et al., or we need something in the middle between action-based and result-based schemes or that combines both approaches (see hybrid schemes here below). In the meantime, we decided in NIVA to develop a new generation of tools to produce agro-environmental indicators and some of them could be used for result based schemes (e.g. tools for carbon indicator TIER3).

  Some of the tools aim at producing agro-environmental indicators at large scale by combining IACS and remote sensing (Sentinel-2) data. This action was led by the French Paying Agency (ASP) and developments were based on research done at the French national research institute on Agronomy, Food and Environment (INRAe). Tools related to three indicators have been selected for development: 1) Soil Organic Cabon storage for arable This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009 Carbon farming, Result-based schemes and NIVA indicators lands (related to climate change issues), 2) risk of Nitrate leaching (for water quality tackling environmental care) and 3) preservation of Biodiversity.



  The tools are available on NIVA GitLab : https://gitlab.com/nivaeu/uc1b_tier1_co2 and https://gitlab.com/nivaeu/uc1b_indicators_tool  French and Danish anonymous results from agro-environmental tools at Tier 1 are available openly at Zenodo platform: https://zenodo.org/communities/niva4cap This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009 Carbon farming, Result-based schemes and NIVA indicators Opportunities and challenges (Discussion)

https://www.inrae.fr/actualites/stocker-4-1-000-carbone-sols-potentiel-france

Smith, P., Soussana, J.-F., Angers, et al. (2020) How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal, Global Change Biology, 26, 219-241, This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009

Paulsen, H.M., Inventory of techniques for carbon sequestration in agricultural soils. 2020: Interreg North Sea Region.

Interreg, Five promising measures to protect the climate by Carbon Farming. Short report. Interreg North Sea Reagion Carbon Farming. European Regional Development Fund. European Union. 2021

Smith, P., et al., How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Global Change Biology, 2020. 26(1): p. 219-241. If a policy maker focuses on measuring changes in SOC, we talk about a result-based scheme. In a result-based scheme, farming practices are not taken into account. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009

COWI, Ecologic Institute, and IEEP, Technical Guidance Handbook -setting up and implementing result-based carbon farming mechanisms in the EU. DG Climate Action, under Contract No. CLIMA/C.3/ETU/2018/007. COWI, Kongens Lyngby. 2021. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 842009

It is produced by the most advanced and complex tools 13,14 that we developed and calculations require intensive computing and large data storage capacities. Carbon Tier 3 is based on a modelling approach that has been tested and validated for straw cereals, maize, sunflower and cover crops. The method has been validated against in-situ data (flux tower measurements, data from the Regional Space Observatory 15 ). The model simulates CO2 fluxes (photosynthesis, plant and soil respiration), biomass and yield.

As an input, it requires IACS data (information on crops and plot contours), meteorological data, LAI (Leaf Area Index data derived from Sentinel 2 like satellites) to calibrate the model's phenology and photosynthesis capacity. As for Tier2, farmer's FMIS data on organic amendment and straw export is required to compute the carbon budget.

 Carbon Tier 3 tools

There are two tools to calculate Carbon Tier 3: 1) SAFY-CO2 at parcel level allows calculations on a few thousands of plot land.

2) AgriCarbon-EO 16 at pixel level that allows simulations at pixel scale over several Sentinel 2 tiles.

The tools were tested in France and in Spain and validated against in-situ measurements 17, 18 . The AgriCarbon-EO tool produces high resolution results of the carbon budget components as well as their uncertainties.

13 https://framagit.org/ahmad.albitar/safye_co2.git https://www.cesbio.cnrs.fr/agricarboneo/agricarbon-eo/ 15 https://www.cesbio.cnrs.fr/la-recherche/activites/observatoires/l-observatoire-spatial-regional-osr/ 16 https://www.cesbio.cnrs.fr/agricarboneo/ 17 Pique G., Fieuzal R., Al Bitar A., Veloso A., Tallec T., Brut A., Ferlicoq M., Zawilski B., Dejoux J.-F, Gibrin H., Ceschia E. 2020a. Estimation of daily CO2 fluxes and of the components of the carbon budget for winter wheat by the assimilation of Sentinel 2-like remote sensing data into a crop model. Geoderma, 376, 114428. 18 Pique G., Fieuzal R., Debaeke P., Al Bitar A., Tallec T, Ceschia E. 2020b. Combining high-resolution remote sensing products with a crop model to estimate carbon and water budget components: Application to sunflower. Remote Sensing, 12, 2967 Carbon Tier 3 approach can be applied both to the voluntary carbon market in agriculture and to the CAP. Also, it can be validated against in-situ data. Among the tools developed in the NIVA project for agri-environmental monitoring, the ones on carbon flux/budget are the most advanced. Three approaches (Tiers) of increasing complexity and precision have been developed. The tier 3 carbon indicator, which is based on an agro-pedo-meteorological model, makes it possible to quantify the carbon budget in the soil with good precision. This precision seems sufficient to base a carbon farming measure at least partly on the result, encouraging this way the performance. At first sight this indicator seems compatible with the carbon farming requirements presented in the new coming Commission's regulation proposal. Since the carbon indicator is developed at the scale of the pixel or of the agricultural plot, it informs the farmer about the performance of each of the plots but also on the intraparcellar variations resulting for instance from soil properties. Some farmer's activity data are needed to calculate the tier 2 and tier 3 indicators. These data being usually already part of those present in the FMIS, calculating carbon indicator does not lead to an additional burden for farmers. Moreover, the results obtained can also be the subject of benchmark between farmers and allow agronomic advices promoting performance (e.g. precision farming).


M Bosselet

C Sacerdote-Peronnet

R Sigala

J C Chiriac

Viala

J Andrieux

O Dezellus

F Bosselet

M Sacerdote-Peronnet

C Sigala

R Chiriac

J C Viala

Details on the

Keywords: intermetallics, stability, ternary phase diagram, experimental study

Introduction

Alloys of the Ag-Cu-Ti ternary system are often used for brazing ceramics to metals in the temperature range 800-900 °C [START_REF] Eustathopoulos | Wettability at High Temperature[END_REF]. Some alloy compositions containing 40 at% of copper (28 wt%Cu, binary eutectic alloy with a melting point at 780 °C) and a few percents of titanium are commercially available in the form of pre-alloyed powders, ribbons or plates. To develop high performance metal/ceramic brazed joints, more especially when the metal is a titanium base alloy [START_REF] Wu | Growth Mechanism of Compound at Interface of Ti-6Al-4V Joint Brazed with 72Ag-28Cu Filler Alloy[END_REF][START_REF] Chan | Microstructural Evolution of Brazing Ti-6Al-4V and TZM Using Silver-Based Braze Alloy[END_REF], a thorough knowledge of the phase diagram of the Ag-Cu-Ti system is needed. Thermodynamic data on the binary Cu-Ti system and the ternary Ag-Cu-Ti system are available from different sources [START_REF] Andrieux | Reactivity of the Ag-Cu28 Braze Alloy with Titanium[END_REF][START_REF] Schubert | On the Constitution of the Ti-Cu and Ti-Ag Systems[END_REF][START_REF] Eremenko | The System Ti-Ag-Cu at 700 °C[END_REF][START_REF] Eremenko | Polythermal.and Isothermal Sections of the System Ti-Cu-Ag. Part II[END_REF][START_REF] Eremenko | The Liquidus Surface of the System Ti-Cu-Ag[END_REF][START_REF] Kubaschewski | Ag-Cu-Ti[END_REF][START_REF] Prince | Handbook of Ternary Alloy Phase Diagrams[END_REF][START_REF] Arroyave | Thermodynamics and Kinetics of Ceramic/Metal Interfacial Interactions[END_REF][START_REF] Arroyave | Thermodynamic Assessment of the Ag-Cu-Ti System[END_REF][START_REF] Murray | The Cu-Ti system, Phase Diagrams of Binary Titanium Alloys[END_REF][START_REF] Hari Kumar | Thermodynamic Optimisation of the Cu-Ti System[END_REF] and assessments have recently been made [START_REF] Ansara | Cu-Ti (Copper-Titanium)[END_REF][START_REF]Ag-Cu-Ti (Silver-Copper-Titanium), Non-Ferrous Metal Ternary Systems. Selected Soldering and Brazing Systems: Phase Diagrams, Crystallographic and Thermo-dynamic Data[END_REF]. The 800 °C section reported in Fig. 1 and the partial projection drawn in Fig. 2 show the most probable phase equilibria in the Ag-Cu-Ti system at temperatures ranging from 780 to 860 °C. It is to note that the reaction scheme given in [START_REF]Ag-Cu-Ti (Silver-Copper-Titanium), Non-Ferrous Metal Ternary Systems. Selected Soldering and Brazing Systems: Phase Diagrams, Crystallographic and Thermo-dynamic Data[END_REF] is drawn in dotted line for that temperature range because of uncertainty on the stability of Ti 2 Cu 3 in the binary Cu-Ti and the ternary Ag-Cu-Ti systems. For Eremenko et al. who conducted extensive experimental investigations on both systems, Ti 2 Cu 3 is stable only at temperatures ranging from 805 to 890 ± 4 °C in the Cu-Ti binary system and from 803 to 890 °C in the ternary Ag-Cu-Ti system [START_REF] Eremenko | The System Ti-Ag-Cu at 700 °C[END_REF][START_REF] Eremenko | Polythermal.and Isothermal Sections of the System Ti-Cu-Ag. Part II[END_REF]. For other authors who achieved thermodynamic modelling, Ti 2 Cu 3 is stable at any temperature lower than 875 ± 10 °C [START_REF] Murray | The Cu-Ti system, Phase Diagrams of Binary Titanium Alloys[END_REF] or 885 °C [START_REF] Hari Kumar | Thermodynamic Optimisation of the Cu-Ti System[END_REF]. Enthalpies of formation and crystallization of the Ti-Cu compounds were experimentally determined [START_REF] Colinet | Enthalpies of Formation of Ti-Cu Intermetallic and Amorphous Phases[END_REF] but the values thus obtained do not remove the uncertainty on the stability of Ti 2 Cu 3 .

In the course of a recent investigation on the chemical reactivity near 800 °C of liquid Ag-Cu eutectic alloy with solid titanium [START_REF] Andrieux | Reactivity of the Ag-Cu28 Braze Alloy with Titanium[END_REF], the question arose why the intermetallic compound Ti 2 Cu 3 was systematically missing from the reaction layer sequence that was observed to develop at the liquid/solid interface [START_REF] Andrieux | Reactivity of the Ag-Cu28 Braze Alloy with Titanium[END_REF]. To provide a response to that simple but practical issue, more detailed information had to be acquired on the thermal stability and formation kinetics of the intermetallic compound Ti 2 Cu 3 . It is with this aim in view that isothermal diffusion experiments were undertaken in the Ag-Cu-Ti system between 700 and 860°C.

Experimental

For isothermal diffusion experiments, mixtures of commercial powders of silver (99.99 wt%, grain size d ∼ 100 µm, Goodfellow) copper (99 wt%, grain size d ∼ 50 µm, Goodfellow) and titanium (98.5 wt%, grain size 3 < d < 300 µm, Fluka) were ball-homogenized and coldpressed under 200 MPa into small rods (3 mm x6 mm x30 mm). The titanium powder was previously sifted two times so that the diameter of the biggest particles was lower than 100µm. Each rod was then placed in an alumina boat lined with yttria (STOPYT 62A, Morgan Wesgo) and heated in a silica reaction tube for up to 500 h under pure argon (3-5 10 4 Pa) in the presence of titanium powder as gas getter. It is to note that at 850 °C and above, heat treatments were realized under dynamic primary vacuum and the annealing time was reduced down to 50 min to avoid spreading of the Ag-Cu-Ti liquid on the alumina boat through the yttria liner. The horizontal furnace was regulated to better than ± 1 °C. The exact treatment temperature was controlled by putting the hot junction of a K type thermoelectric couple inside the alumina boat in place of the rods. At the end of the isothermal treatment, the reaction tube was pulled out of the furnace and allowed to cool in ambient air. Starting from a heating temperature in the range 790-860 °C, the cooling rate measured during the first 100 °C drop was faster than 10 °C.s -1 . Two samples were analysed by differential thermal analysis (TGA/SDTA 851, Mettler-Toledo) in Al 2 O 3 crucibles (sample weight: 1-100 mg) under 10 5 Pa argon. The other samples were characterized after heat treatment by X-ray diffraction (XRD), optical metallography (OM), scanning electron microscopy (SEM) and electron probe microanalysis (EPMA). The XRD spectra were recorded on grossly polished sections, using standard diffraction equipment (Panalytical MPD-Pro diffractometer equipped with a back monochromator and a X'celerator detector, Cu Kα radiation). OM and SEM observations were made on diamond polished sections. SEM observations and EPMA analyses were carried out using a Camebax apparatus (Cameca) equipped with an energy dispersive analyser. The accelerating voltage was of 10 kV and the beam current of 9 nA. After background subtraction, the counting rates obtained for Ag, Cu and Ti in at least eight different points were averaged and referred to the counting rates recorded under the same conditions on pure and freshly polished element standards. After corrections for atomic number, absorption and fluorescence, the atomic contents of Ag, Cu and Ti in the different phases deriving from the Cu-Ti binary system were obtained with accuracy better than ± 0.5 at%.

Results and discussion

1 Synthesis and annealing at 815 °C and above

All authors who have reported on the Ag-Cu-Ti system agree that in the temperature range 810-830 °C, Ti 2 Cu 3 is in equilibrium with an Ag-Cu-Ti liquid [START_REF] Eremenko | The System Ti-Ag-Cu at 700 °C[END_REF][START_REF] Eremenko | Polythermal.and Isothermal Sections of the System Ti-Cu-Ag. Part II[END_REF][START_REF] Eremenko | The Liquidus Surface of the System Ti-Cu-Ag[END_REF][START_REF] Kubaschewski | Ag-Cu-Ti[END_REF][START_REF] Prince | Handbook of Ternary Alloy Phase Diagrams[END_REF][START_REF] Arroyave | Thermodynamics and Kinetics of Ceramic/Metal Interfacial Interactions[END_REF][START_REF] Arroyave | Thermodynamic Assessment of the Ag-Cu-Ti System[END_REF]. Therefore, first attempts to synthesize the compound Ti 2 Cu 3 from the elements in the ternary Ag-Cu-Ti system were carried out in this temperature range. More precisely, three different powder mixtures with compositions F, F' and G (Fig. 1) were prepared and heated at 815 °C or 825 °C (Table 1). In full agreement with the literature data, all the treated samples contained as a major constituent Ti 2 Cu 3 (tetragonal, P4/nmm, a = 0.313 nm, c = 1.395 nm [START_REF] Schubert | On the Constitution of the Ti-Cu and Ti-Ag Systems[END_REF]). Ti 3 Cu 4 (tetragonal, I4/mmm, a = 0.313 nm, c = 1.994 nm) was still present in little amounts in sample F1 heated at 815 °C for 205 h but was no more detected in samples F2, F'1 and G1 heated at higher temperature (825 °C) and for longer durations (350 h or more). Ti 2 Cu 3 grown from the liquid during isothermal heating always appeared in the form globular crystals (Fig. 3). These were surrounded by a very thin layer of TiCu 4 crystals (orthorhombic, Pnma, a = 0.453 nm, b = 0.4342 nm, c = 1.293 nm) that were formed on cooling near 808 °C, as a product of the incomplete ternary transition reaction (U 9 in Fig. 2 In the experimental approach by Eremenko et al. [START_REF] Eremenko | Polythermal.and Isothermal Sections of the System Ti-Cu-Ag. Part II[END_REF], the invariant transformation ( 2) is reported to occur at 843 °C, whereas we find it at 852 ± 2 °C. The slight shift between these two values may have different origins, one of these being the purity of the samples. Indeed, isothermal diffusion needs use of fine powders that cannot be as pure as the massive ingots used by Eremenko's co-workers. An illustration of this purity problem is given in Fig. 3 and Fig. 5 with the presence in the solidified liquid of small well-facetted crystals that analyse as TiCu with extra aluminium and silicon for a total amount of 2-3 at% (Table 1, phase designated as TiCu(Al,Si)). Because of their too low abundance, these crystals could not be characterized by XRD. They might be either of the tetragonal TiCu type (P4/nmm, a = 0.3107 nm, c = 0.5919 nm [START_REF] Murray | The Cu-Ti system, Phase Diagrams of Binary Titanium Alloys[END_REF]) stabilized by impurities or of another crystal type like B2 cubic Cu 2 AlTi, as evoked for crystals with an approaching composition in a paper by He et al. [START_REF] He | Microstructure and Kinetics of Induction Brazing TiAlbased Intermetallics to Steel 35CrMo Using AgCuTi Filler Metal[END_REF]. It is to note that the weak reflection at 2θ = 39° in Fig. 4 might be a strong X-ray diffraction line coming from the small crystals of TiCu(Al,Si) and that Al and Si were not detected in the other phases constituting the samples.

2 Synthesis and annealing below 815 °C

A part of sample F1 that was first heated for 205 h at 815 °C was placed in an alumina boat along with an untreated cold-pressed mixture of Ag, Cu and Ti powders having the same composition. Both samples were heated at 790 °C for 330 h and characterized (Table 2, samples F3 and F4). In sample F3 first heated at 815 °C and annealed at 790 °C, Ti 2 Cu 3 was still the major constituent. The only change concerned the compound Ti 3 Cu 4 that disappeared upon annealing at 790 °C. The cold-pressed mixture directly heated at 790 °C (sample F4) also contained Ti 2 Cu 3 as major constituent but some Ti 3 Cu 4 was present, like in sample F1 before annealing at 790 °C. Stage I: the first process that develops during the rise in temperature of the cold-pressed powder mixture (Fig. 7a) is the solid state volume interdiffusion of atoms, more especially by Ag/Cu substitution. Such a solid state interdiffusion is known to modify the unit cell parameters of the two elements and a shift with enlargement of their XRD reflections is effectively observed in Fig. 6 after 10 and 11.5 min heating; Stage II: when the temperature attains 780 °C, which occurs between 10 and 11.5 min heating, a eutectic reaction proceeds between the grains of Ag and Cu giving a Ag-Cu liquid alloy. As soon as formed, the Ag-Cu eutectic alloy spreads at the surface of the titanium grains by reactive wetting. Comparison between Fig. 7a and 7b clearly shows consumption of copper and spreading of a silver rich phase over the titanium grains. As previously shown by XRD (Fig. 6, 11.5 min and 15 min) and confirmed by EPMA, it is essentially TiCu 4 that is produced in that fast rate process. Two simultaneous reactions can then be written:

Ag s + Cu s → L (3) L + Ti → TiCu 4 + Ag sol (4)
At the end of this second stage, most of solid elemental copper initially introduced has been first dissolved in the Ag-Cu liquid, L, and then converted into TiCu 4 at the Ti grains surface.

It is this process that has left the large pores visible in Fig. 7b. As to the nearly eutectic liquid phase, L, it has disappeared. Indeed, formation of TiCu 4 by reaction between the (Ag-Cu) liquid, L, and solid Ti according to reaction (4) implies the precipitation of solid Ag from the liquid. Arrived at this point, the Ti grains are surrounded with a TiCu 4 reaction layer and embedded in a white Ag base matrix with many small crystals of TiCu 4 dispersed in it (Fig. 7b). Of course, the real scenario might be more subtle. First, reactions may progress at different rates depending on the size of Ti grains and on the distance between them. Secondly, because of the detection limit inherent to the characterization techniques used, several minor reactions have not been considered such as for instance the possible formation of TiCu or Ti 2 Cu in the solid state at the interface between Ti and Ti 3 Cu 4 . It remains that although a bit simplistic, the proposed scenario describes the four main processes that successively develop in a Ag-Cu-Ti mixture isothermally heated at 790 °C before attainment of equilibrium. The most striking features are that (i) TiCu 4 is the first phase to form by interface reaction at 780 °C between solid Ti and a liquid Ag-Cu eutectic alloy and (ii) when Ti 2 Cu 3 and Ti 3 Cu 4 are likely to form from the elements, the former develops at a much slower rate than the latter. This explains why TiCu 4 and Ti 3 Cu 4 can be the major reaction products in a heated mixture whereas Ti 2 Cu 3 is actually the equilibrium phase for that mixture. The same explanation remains valid to justify that Ti 2 Cu 3 is "missing" from the reaction layer sequence at the interface of Ti/Ag-Cu couples brazed at 800 °C [START_REF] Andrieux | Reactivity of the Ag-Cu28 Braze Alloy with Titanium[END_REF].

Conclusion

Ti 2 Cu 3 has been synthesized by solid-liquid reaction from Ag, Cu and Ti powder mixtures after long time annealing at 790, 815 or 825 °C. No indication for decomposition of Ti 2 Cu 3 at 700 °C for 500 h was observed being in line with data described in references [START_REF] Arroyave | Thermodynamics and Kinetics of Ceramic/Metal Interfacial Interactions[END_REF][START_REF] Arroyave | Thermodynamic Assessment of the Ag-Cu-Ti System[END_REF][START_REF] Murray | The Cu-Ti system, Phase Diagrams of Binary Titanium Alloys[END_REF][START_REF] Hari Kumar | Thermodynamic Optimisation of the Cu-Ti System[END_REF][START_REF] Ansara | Cu-Ti (Copper-Titanium)[END_REF][START_REF]Ag-Cu-Ti (Silver-Copper-Titanium), Non-Ferrous Metal Ternary Systems. Selected Soldering and Brazing Systems: Phase Diagrams, Crystallographic and Thermo-dynamic Data[END_REF]. At high temperature, Ti 2 Cu 3 is stable in the Ag-Cu-Ti system up to at least 860 °C, temperature at which existence of the three-phased equilibrium Ti 2 Cu 3 -Ti 3 Cu 4 -L is confirmed. Occurrence of the invariant transformation:

Ti 2 Cu 3 + Ag s → Ti 3 Cu 4 + L (2)
is also confirmed at a temperature that might be slightly higher than 843 °C. From a kinetics standpoint, it has been shown by SDTA in the range 750-800 °C and by isothermal diffusion at 790 °C that TiCu 4 is the first phase to form when a transient Ag-Cu eutectic liquid spreads onto solid titanium between 780 and 790 °C. Then, Ti 3 Cu 4 is formed by reaction between TiCu 4 and unconverted titanium. Finally, Ti 2 Cu 3 slowly appears as a product of the reaction between TiCu 4 and Ti 3 Cu 4 . These features explain why Ti 2 Cu 3 is not characterized after reaction at 790 °C for a too short time (less than 60 hours) or after reaction for a long time (more than 500 h) at a too low temperature (700 °C). They also explain why Ti 2 Cu 3 can be missing from the reaction layer sequence at Ti/Ag-Cu interfaces after brazing near 800 °C.

1 )Fig. 1 .

 11 Fig.1. Ag-Cu-Ti isothermal section at 800 °C, according to experimental data and thermodynamic evaluations[START_REF] Eremenko | The System Ti-Ag-Cu at 700 °C[END_REF][START_REF] Eremenko | Polythermal.and Isothermal Sections of the System Ti-Cu-Ag. Part II[END_REF][START_REF] Eremenko | The Liquidus Surface of the System Ti-Cu-Ag[END_REF][START_REF] Kubaschewski | Ag-Cu-Ti[END_REF][START_REF] Prince | Handbook of Ternary Alloy Phase Diagrams[END_REF][START_REF] Arroyave | Thermodynamics and Kinetics of Ceramic/Metal Interfacial Interactions[END_REF][START_REF] Arroyave | Thermodynamic Assessment of the Ag-Cu-Ti System[END_REF][START_REF] Murray | The Cu-Ti system, Phase Diagrams of Binary Titanium Alloys[END_REF][START_REF] Hari Kumar | Thermodynamic Optimisation of the Cu-Ti System[END_REF][START_REF] Ansara | Cu-Ti (Copper-Titanium)[END_REF][START_REF]Ag-Cu-Ti (Silver-Copper-Titanium), Non-Ferrous Metal Ternary Systems. Selected Soldering and Brazing Systems: Phase Diagrams, Crystallographic and Thermo-dynamic Data[END_REF]. The tie lines joining Ag s to Ti 2 Cu 3 have been drawn in dotted lines since for Eremenko et al.[START_REF] Eremenko | The System Ti-Ag-Cu at 700 °C[END_REF][START_REF] Eremenko | Polythermal.and Isothermal Sections of the System Ti-Cu-Ag. Part II[END_REF][START_REF] Eremenko | The Liquidus Surface of the System Ti-Cu-Ag[END_REF], Ti 2 Cu 3 is not stable at 800 °C

2 )Fig. 2 .Fig. 3 .Fig. 4 .

 2234 Fig. 2. Partial liquidus projection between 860 and 780 °C of the Ag-Cu-Ti phase diagram, according to [7, 10, 15, 16] (transformations are indexed like in reference [16])

Fig. 6 .Fig. 7 .

 67 Fig. 6. Evolution of the XRD pattern of mixture F placed for increasing times in a tubular furnace held at the constant temperature of 790 °C

5 )Fig. 8 .

 58 Fig. 8. Thermal behaviour upon first cycle of two cold-pressed powder mixtures analysed by SDTA at 4 °C.min -1 : mixture R with composition Ag:Cu = 60:40 at% and mixture A with composition Ag:Cu:Ti = 43:28.5:28.5 at% (temperature versus time is drawn in the upper part of the graph)

Table I

 I

		Initial composition,	Reaction duration	Phases by XRD	Composition by
		at%			and	(decreasing	EPMA, at%
	Sample Ag	Cu	Ti	temperature	abundance)	Ag	Cu	Ti
	F1	20	50	30	205 h at 815 °C	Ag	84	16	-
						Ti 2 Cu 3	1.3	58.3	40.4
	F2	20	50	30	353 h at 825 °C	TiCu 4	2.1	75.6	22.3
						Ti 3 Cu 4 *	-	-	-
	F'1	20	51	29	353 h at 825 °C	TiCu(Al,Si)**	-	47	51
						Cu	4.9	91.2	3.9
	G1	5	61	34	424 h at 825 °C	Ti 2 Cu 3	1.5	58.4	40.1
						TiCu 4	1.6	76.2	22.2
						Ag	87	13	-
						TiCu(Al,Si)**	-	48.4	49.6

. Crystal nature and composition of the phases produced by reacting Ag-Cu-Ti mixtures at 815 or 825 °C (*):little amounts detected by XRD only in sample F1, not found by EPMA (**): small crystals containing Al and Si (∼2at%) not characterized by XRD but analysed by EPMA

Table 2

 2

	. Phases characterized in Ag-Cu-Ti mixtures after reaction or annealing at 790 and
	700 °C
		Initial composition,	Heat treatment	Phases by XRD	Composition by
		at%				(decreasing	EPMA, at%
	Sample Ag	Cu	Ti		abundance)	Ag	Cu	Ti
	F3	20	50	30	205 h at 815 °C	Ag	84.3	15.7	-
					+	Ti 2 Cu 3	1.5	58.7	39.9
					330 h at 790 °C	TiCu 4	1.3	76.2	22.5
						TiCu(Al,Si)*	-	47.5	49.3
	F4	20	50	30	330 h at 790 °C	Ag	85.1	14.9	-
						Ti 2 Cu 3	1.4	58.0	40.6
						TiCu 4	1.2	76.4	22.4
						Ti 3 Cu 4 **	-	-	-
	G2	5	61	34	424 h at 825 °C	Ti 2 Cu 3	0.9	59.3	39.8
					+	TiCu 4	1.2	76.6	22.2
					512 h at 700 °C	Ag	91.3	8.7	-
						TiCu(Al,Si)*	-	48	50
	G3	5	61	34	512 h at 700 °C	Ti 3 Cu 4	0.7	55.7	43.6
						TiCu 4	0.9	76.6	22.5
						Ag	90.9	9.1	-
	(*): small crystals containing Al and Si (∼2at%) not characterized by XRD but analysed by EPMA
	(**): still present in little amounts by XRD, not found by EPMA
	The same type of experiment was reproduced at 700 °C on samples with composition G. After
	512 h annealing at 700 °C, only small changes occurred in the sample previously treated at
	825 °C (

Table 2 ,

 2 samples G2): Ti 2 Cu 3 , TiCu 4 and Ag were still the major constituents. As to the cold -pressed mixture directly reacted in the solid state at 700 °C (Table2, sample G3), it only contained Ti 3 Cu 4 and TiCu 4 ; no trace of Ti 2 Cu 3 at all was found.

If there is no ambiguity from the foregoing results about the existence of Ti 2 Cu 3 at 790 °C, things are not so simple at 700 °C. Effectively, starting from the same initial composition, a mixture heat-treated for a long time at 700 °C contains either Ti 2 Cu 3 , TiCu 4 and Ag or Ti 3 Cu 4 , TiCu

[START_REF] Andrieux | Reactivity of the Ag-Cu28 Braze Alloy with Titanium[END_REF]

and Ag according as it has previously been heated at a higher temperature (790-850 °C) or not. As a matter of fact, equilibrium has not been reached in one of the two mixtures treated at 700 °C. Complementary experiments have then been carried out at 790 °C to acquire more detailed information on that question.

Acknowledgements. The authors gratefully acknowledge the staff of CTµ (Université Lyon 1, France) where SEM observations and EPMA analyses were performed.


Emilien Segret

Cécile Heraud

Yann Marchand

Jerome Roy

Claire Vigor

Battitte Sancho-Zubeldia

Camille Oger

Anaelle Durbec

Jean-Marie Galano

Thierry Durand

Geneviève Corraze

Yoann Cachelou

Frederic Cachelou

Sandrine Skiba-Cassy

Emilie Cardona
email: emilie.cardona@inrae.fr

THE DIETARY ARACHIDONIC ACID CONTENT IMPACTS THE FATTY ACID METABOLISM, ENZYMATIC AND NON-ENZYMATIC METABOLITES OF LIPIDS AND THE RESPONSE TO ACUTE STRESSOR IN RAINBOW TROUT FRY

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

The dietary arachidonic acid content impacts the fatty acid metabolism, enzymatic and non-enzymatic metabolites of lipids and the response to acute stressor in rainbow trout fry

Emilien Segret, Cécile Heraud, Yann Marchand, Jérôme Roy, Claire Vigor, Battitte Sancho-Zubeldia, Camille Oger, Anaëlle Durbec, Jean-marie Galano, Thierry Durand, et al.

To cite this version:

Emilien

Introduction

The importance of arachidonic acid (20:4n-6, ARA) in fish nutrition is receiving increasing attention. Indeed, this fatty acid (FA) plays a crucial role in growth, survival, stress resistance, and immunity in fish [START_REF] Bell | Arachidonic acid in aquaculture feeds: Current status and future opportunities[END_REF]. Freshwater fish are capable of synthesizing ARA from linoleic acid (LA, 18:2n-6), unlike marine species, which have a reduced ability. The endogenous capacity of freshwater fish to synthesize polyunsaturated fatty acids (PUFAs) and thus ARA differs between species. The ARA requirements of rainbow trout have not yet been determined. Therefore, the objective of the present study was to evaluate the consequences of variable dietary ARA intake on survival, growth but also PUFAs biosynthetic capacity and stress response in rainbow trout fry.

Materials & methods

For this purpose, rainbow trout fry were fed diets containing different proportions of ARA (i.e. 0.6, 1.1 or 2.5% of total FA) for 8 weeks and survival and growth were monitored during the whole duration of the trial. At the end of the experiment, fish were exposed to acute confinement (10 min at 10 kg/m3) to evaluate stress response. Whole fish were collected to investigate the effects of diets on (1) endogenous biosynthesis of PUFAs, (2) production of FA oxidation-derived compounds, and (3) stress response through serotonin (5-HT) and dopamine (DA) pathways by monitoring 5-HT/5-HIAA ratios (target/one metabolite of 5-HT) and L-DOPA/HVA ratios (direct precursor of DA/one metabolite of DA).

Results & discussion

The ARA diet level did not affect the growth, but the lowest level of ARA significantly increased mortality, although the percentage of mortality was very low (1.6% for the ARA0.6 diet compared to 0.7% for the other diets, p-value=0.01). This ARA0.6 diet also showed a significantly higher level of expression of fatty acid elongase 5 (elovl5). This enzyme converts, in particular, LA to ARA, suggesting that rainbow trout can synthesize ARA from LA when dietary supply of ARA is low.

Oxylipins are bioactive lipids generated by the oxidation of PUFAs. Those derived from ARA, such as prostaglandin D2 and E2, are considered to be pro-inflammatory and may, in excess, harmful to fish [START_REF] Bell | Biosynthesis of polyunsaturated fatty acids in aquatic ecosystems: General pathways and new directions[END_REF]omega-3 (ω3 or n-3. When fingerlings were fed the diet containing the highest level of ARA (2.5% of total FA), the production of oxylipins derived from ARA significantly increased while that of oxylipins derived from other PUFAs, such as eicosapentaenoic acid (20:5n-3, EPA), docosahexaenoic acid (22:6n-3, DHA), LA, and α-linolenic acid (ALA 18:3n-3) were not altered.

The serotoninergic and dopaminergic activities resulting from stress revealed a more effective response when the ARA content of the diet was 1.1% of the total FA. This was evidenced by higher 5-HT/5-HIAA and L-DOPA/HVA ratios with the ARA1.1 diet than with the other two diets. These results indicated that the ARA content of the diet can modulate the response of the fish to an acute confinement stress.

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  Segret, Cécile Heraud, Yann Marchand, Jérôme Roy, Claire Vigor, et al.. The dietary arachidonic acid content impacts the fatty acid metabolism, enzymatic and non-enzymatic metabolites of lipids and the response to acute stressor in rainbow trout fry. Aquaculture Europe 2023 -Balanced Diversity in Aquaculture Development, Sep 2023, Vienne, Austria. hal-04226070

Acknowledgements

This study is funded by the Nouvelle-Aquitaine Region (French local authorities).

Conclusion

In conclusion, a dietary intake of ARA corresponding to 1.1% of total FA seems to be the most appropriate to promote the robustness of rainbow trout fry. Indeed, with an ARA intake of only 0.6% of the total FA, survival decreases even though the capacity to synthesize ARA from its precursor LA increases. With an ARA level of 2.5% of the total FAs, the production of ARA-derived oxylipins rises, which could ultimately have negative effects on the health of the fish.