Martin Notaro
email: martin.notaro@cirad.fr

Christian Gary

Jean-François Le Coq

Aurélie Metay

Bruno Rapidel

agricultural systems. Evidence from coffee-based agroforestry systems

Keywords: Ecosystem services, agroforestry systems, agroecological transition, technical levers

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

INTRODUCTION

Agroforestry systems (AFS) are generally considered to provide more ecosystem services (ES) than monocrop systems [START_REF] De Beenhouwer | A global meta-analysis of the biodiversity and ecosystem service benefits of coffee and cacao agroforestry[END_REF][START_REF] Santos | Can agroforestry systems enhance biodiversity and ecosystem service provision in agricultural landscapes? A meta-analysis for the Brazilian Atlantic Forest[END_REF]. Potentially harboring high floristic biodiversity [START_REF] Deheuvels | Vegetation structure and productivity in cocoa-based agroforestry systems in Talamanca, Costa Rica[END_REF][START_REF] Valencia | The role of coffee agroforestry in the conservation of tree diversity and community composition of native forests in a Biosphere Reserve[END_REF], AFS provide permanent shelter due to the perennial nature of the trees they contain, which can in turn accommodate highly diverse wildlife capable of regulating pests and diseases [START_REF] Cardinale | Biodiversity loss and its impact on humanity[END_REF][START_REF] Ratnadass | Plant species diversity for sustainable management of crop pests and diseases in agroecosystems: a review[END_REF].

Furthermore, this diversity and density of associated trees can potentially sequester high levels of carbon biomass [START_REF] Cerda | Effects of shade, altitude and management on multiple ecosystem services in coffee agroecosystems[END_REF][START_REF] Saj | Carbon storage and density dynamics of associated trees in three contrasting Theobroma cacao agroforests of Central Cameroon[END_REF][START_REF] Somarriba | Carbon stocks and cocoa yields in agroforestry systems of Central America[END_REF], both above and belowground [START_REF] Albrecht | Carbon sequestration in tropical agroforestry systems[END_REF][START_REF] Montagnini | Carbon sequestration: An underexploited environmental benefit of agroforestry systems[END_REF][START_REF] Niether | Below-and aboveground production in cocoa monocultures and agroforestry systems[END_REF], and potentially increase soil fertility through litter deposition and nitrogen fixation [START_REF] Durand-Bessart | Analysis of interactions amongst shade trees, coffee foliar diseases and coffee yield in multistrata agroforestry systems[END_REF][START_REF] Saj | Litterfall seasonal dynamics and leaf-litter turnover in cocoa agroforests established on past forest lands or savannah[END_REF][START_REF] Sauvadet | Shade trees have higher impact on soil nutrient availability and food web in organic than conventional coffee agroforestry[END_REF][START_REF] Souza | Protective shade, tree diversity and soil properties in coffee agroforestry systems in the Atlantic Rainforest biome[END_REF], while limiting soil erosion [START_REF] Meylan | Combining a typology and a conceptual model of cropping system to explore the diversity of relationships between ecosystem services: The case of erosion control in coffee-based agroforestry systems in Costa Rica[END_REF]. It can also enhance economic resilience by diversifying sources of income through other productions in farm plots [START_REF] Cerda | Contribution of cocoa agroforestry systems to family income and domestic consumption: looking toward intensification[END_REF][START_REF] Schroth | Farmer strategies for tree crop diversification in the humid tropics. A review[END_REF][START_REF] Van Asten | Agronomic and economic benefits of coffee-banana intercropping in Uganda's smallholder farming systems[END_REF], and ecological resilience by maintaining a suitable climate for the cropping system despite global warming [START_REF] Souza | Protective shade, tree diversity and soil properties in coffee agroforestry systems in the Atlantic Rainforest biome[END_REF].

The artificialization of agrosystems since the so-called industrial revolution [START_REF] Salembier | Genealogy of design reasoning in agronomy: Lessons for supporting the design of agricultural systems[END_REF], has gradually deteriorated the environmental footprint of these ecosystems. As a result, the ecological support and regulation functions of agro-ecosystems have been greatly reduced [START_REF] Maraux | From Artificialization to the Ecologization of Cropping Systems[END_REF]. For about three decades, many research and development projects were led to find solutions to greening and redesigning agriculture without negatively impacting productivity and profitability. These solutions rely on agroecological principles and elements that can be articulated in different ways to form diverse transition pathways [START_REF] Wezel | Agroecological principles and elements and their implications for transitioning to sustainable food systems. A review[END_REF]. They may consist in optimizing a set of ES (i.e. provisioning, as well as regulation and support services) provided by cropping systems [START_REF] Duru | Carbon stock in agroforestry coffee plantations with different shade trees in Villa Rica, Peru[END_REF][START_REF] Robertson | Reconciling agricultural productivity and environmental integrity: a grand challenge for agriculture[END_REF]. But the methods used in the field to identify the management practices that pave the way for optimization, i.e technical levers, remain to be worked on.

Depending on their nature, ES can be related in various ways to their ecosystems and with other ES and related services. One service can contain [START_REF] Bennett | Understanding relationships among multiple ecosystem services[END_REF] or, on the contrary, contribute to the provision of another service [START_REF] Kandziora | Interactions of ecosystem properties, ecosystem integrity and ecosystem service indicators-A theoretical matrix exercise[END_REF]. Several authors [START_REF] Lescourret | A social-ecological approach to managing multiple agro-ecosystem services[END_REF][START_REF] Rapidel | Analysis of ecosystem services trade-offs to design agroecosystems with perennial crops[END_REF] have highlighted the importance of gaining insight into the determinants of each targeted ES, how these services are produced and how they are related to each other in order to be able to meet farmers' expectations when designing cropping systems. Situations of trade-offs, independence or synergies can be observed between ES [START_REF] Kearney | Evaluating ecosystem service trade-offs and synergies from slash-and-mulch agroforestry systems in El Salvador[END_REF][START_REF] Tschora | Co-benefits and trade-offs of agroforestry for climate change mitigation and other sustainability goals in West Africa[END_REF]. Essentially, the aim is to find out how it is possible to limit trade-offs and foster synergies among ES. Cropping system design and management practices consequently influence different services, e.g. crop selection in a cropping system (including mixed crops) can influence biogeochemical cycles, pest regulation and soil fertility [START_REF] Gaba | Multiple cropping systems as drivers for providing multiple ecosystem services: from concepts to design[END_REF].

We assume that this approach for analyzing the ES provided by cropping systems would help to identify management strategies that are effective in providing a higher combined level of ES than those currently provided. Within these services, and the relationships among services, we assume that there are synergies that could be used to increase their overall provision, i.e. potential cropping system modifications that would be harmless or even good for agricultural production (provisioning services), while enhancing the provision of other services (e.g. environmental services). Finally, we hypothesize that greater knowledge on how combinations of cropping practices affect relations between ES could help make better decisions.

We selected four services, three of environmental interest, water quality, carbon sequestration and tree biodiversity conservation, and one of private economic interest, coffee production. These services were chosen because they are essential: "water quality" to the quality of life of people who directly use and drink river water [START_REF] Elfikrie | Occurrence of pesticides in surface water, pesticides removal efficiency in drinking water treatment plant and potential health risk to consumers in Tengi River Basin, Malaysia[END_REF]; "carbon sequestration" to limit global warming [START_REF] Albrecht | Carbon sequestration in tropical agroforestry systems[END_REF]; "tree biodiversity conservation" to maintain a diverse wildlife community [START_REF] Bhagwat | Agroforestry: a refuge for tropical biodiversity?[END_REF] and, more broadly, for the sustainability of ecosystems, particularly cropping systems [START_REF] Balima | Agricultural land use reduces plant biodiversity and carbon storage in tropical West African savanna ecosystems: Implications for sustainability[END_REF]; "coffee production" because it is the main cash crop providing income for farm households. This paper aims to provide insight into how these four ES are produced in coffee AFS and how these services relate to each other. We assume that the determinants of the provision of ecosystem services and the relationships between them have to be understood to ensure this joint provision.

We expect that a thorough understanding of the determinants of ES and their relationships is an essential step towards identifying sustainable practices for establishing agroecological intensification pathways in coffee agroforestry systems. In the first phase, we quantified the four ES and identified their determinants. We then explored possible agroecological intensification pathways providing a balanced provision of all services.

MATERIAL AND METHODS

Site description

Our study was carried out in Nicaragua's Matagalpa department, where coffee production accounts for 28% of national coffee production, with an average production of 777 kg.ha -1 .yr -1 (FAO, 2015). We analyzed coffee AFS plots located east of La Dalia (13° 08′ 00″ N and 85° 44′ 00″ W), in the vicinity of the Peñas Blancas Massif and scattered over an area of approximately 200 km². All plots were situated between 700 and 1,100 meters above sea level (m.a.s.l.), under a subtropical humid climate, with mean annual precipitation of around 1,400 mm.year -1 , a mild five-month dry season (December-April) and an average temperature of around 21°C.

Coffee plot network and experimental design

The survey of ES quantification was implemented in two phases (Table 1). In the first step, a sample of 82 coffee growers was selected using the snowball method [START_REF] Thompson | On sampling and experiments[END_REF] without prioritizing any particular type of farm (area, production diversity, etc.). In this sample of 82 farmers, we collected data on coffee production and agrochemical product use in the previous year (2013). There was no mechanical tillage for soil management, nor intercropping with soil cover plants. To our knowledge, the practice of pruning coffee trees was homogeneous in the study area. We estimated biodiversity and carbon sequestration on one 1,000 m² (20 m x 50 m) experimental coffee plot that was assumed to be representative of on-farm coffee plots.

As coffee productivity at the plot scale is usually not recorded by farmers and is highly variable, we decided to conduct a second survey to estimate this service and its relationships to coffee plantation structure and management. Because measuring coffee yields is relatively time-consuming, the farm sample was reduced to 27 farms from the initial 82 farmers. The selection was based on the strong interest shown in the study by the 27 selected producers. In this subsample, we estimated coffee productivity by counting beans on coffee trees located in three 100 m 2 squares within the initial 1,000 m 2 observation plot (Figure 1). Other management and state variables of AFS plots that could influence coffee yield were ascertained by questioning farmers or measured, as described below (Section 2.4.).

Research approach: from the quantification of service to the formulation of promising management approaches

The work carried out sought to highlight the technical levers that can be mobilized to foster a high level of service provision at the cropping system scale, following a methodology similar to that developed by [START_REF] Bhattarai | Combining farm typology and yield gap analysis to identify major variables limiting yields in the highland coffee systems of Llano Bonito, Costa Rica[END_REF] and [START_REF] Cerda | Coffee agroforestry systems capable of reducing disease-induced yield and economic losses while providing multiple ecosystem services[END_REF]. Technical levers refer to management practices that substantially increase the provision of a given service. First, we measured the provision of the four services studied, investigated the relationships among them, and characterized their determinants. Then we identified the situations providing a good level of the four services and highlighted the explanatory factors and possible levers to reach these successful situations.

To assess the determinants of coffee yield, a conceptual model was built according to the method proposed by [START_REF] Lamanda | A protocol for the conceptualization of an agrosystem to guide data acquisition and analysis and expert knowledge integration[END_REF] with AFS state and management variables (detailed in the next section). Then we tested the hypothetic correlations of the model that led to a statistical model showing the influence of some environmental and management variables on coffee yield.

Data collection and assessment

2.4.a. Assessment of ecosystem services α) Water quality

During the first stage of the survey, we asked farmers about the agrochemical products they applied to coffee plants and the doses they used for each application. Two interviews were carried out, the first in April-May 2014 and the other in October 2014 and specifically focused on the agrochemical products used during the previous year (2013) in all coffee plots and during the last crop season (2014) for the experimental plot.

A large number of water quality indicators based on agrochemical product use already exist and have been reviewed [START_REF] Bockstaller | Comparison of methods to assess the sustainability of agricultural systems. A review[END_REF]. However, most of them are designed to show the global impact of agrochemical product use on the environment, whereas in our study we focus on showing the potential harm for human health when active ingredients of agrochemical products end up in river water. We developed a simplified, rapid indicator of agrochemical product use that takes into account the features of disintegration, diffusion, dangerousness of the active ingredient contained in the agrochemical product to humans [START_REF] Lewis | An international database for pesticide risk assessments and management[END_REF], as well as the dose of each agrochemical product used.

The five features used for the calculation of the agrochemical product pressure index on humans Each of these features is expressed by a score ranging from 1 to 3 or 1 to 4, with the maximum values indicating a high risk of water contamination. These properties were adapted from the Pesticide Properties DataBase [START_REF] Lewis | An international database for pesticide risk assessments and management[END_REF].

The PPIH for agrochemical product j (PPIH j) and at the farm level (PPIH x) were calculated as follows: As PPIH corresponds to a nuisance or disservice, water quality index (WQ) of farm x was assessed as the opposite of the PPIHx value (Eq. 3).

= ∑ τ ½ sol + Ks + χ + τ ½ water * Xn * [i]) !"# (

WQ x = -PPIH x

(3)

The higher the WQx (so, closer to 0) of pest control practices, the less harmful they were to the environment.

β) Tree biodiversity conservation

We identified and counted all plant species higher than coffee trees present in the 1,000 m 2 observation plot with diameters at chest height of more than 10 cm. AFS biodiversity was assessed in each plot using the Shannon index (Sh) to highlight the abundance and the richness of diversity [START_REF] Shannon | A mathematical theory of communication[END_REF]. Sh indicates both the diversity of tree species and the distribution evenness of tree individuals into these species in the experimental plot and is calculated as follows:

<ℎ = -? @A (ln @A) !"# (4)
pi is the proportion of species i n: Number of species in the experimental plot

γ) Carbon sequestration

Using the tree inventory established as previously described, and adding the total height measured with a laser range finder, we estimated tree aboveground carbon sequestration using the following allometric equation from [START_REF] Chave | Tree allometry and improved estimation of carbon stocks and balance in tropical forests[END_REF]:

BCD + = E (FG,IJJK/7(L!MNO+ P Q+))
(5) AGBj (g): Aboveground biomass of tree j ρi (g.cm -3 ): Wood density at 12% water content, specific to each species i, taken from published data (ICRAF, 2016;[START_REF] Zanne | Global Wood Density Database[END_REF] DBH j (cm): Diameter at chest height of tree j h j (m): Height of tree j

Carbon sequestration in aboveground biomass (Seq C) was then expressed in Mg C.ha -1 using a constant C/biomass ratio of 0.5 [START_REF] Dixon | Carbon pools and flux of global forest ecosystems[END_REF]:

<ER S = ∑ BCD + * 0.5 * 10 FX * +"# (6)
m: Number of ligneous species other than coffee tree in the experimental plot

The factor 10 -5 corresponds to the conversion of g to Mg while taking into account the conversion factor of 1000m² to hectare.

We did not find it necessary to perform these measurements on coffee trees given the negligible value of C sequestered by coffee trees found in the literature, ranging from 3 to 19% of the C contained in the total aboveground biomass [START_REF] Dossa | Above-and belowground biomass, nutrient and carbon stocks contrasting an open-grown and a shaded coffee plantation[END_REF]Ehrenbergerová et al., 2016;[START_REF] Hergoualc'h | Changes in carbon stock and greenhouse gas balance in a coffee (Coffea arabica) monoculture versus an agroforestry system with Inga densiflora, in Costa Rica[END_REF].

δ) Coffee yield

Average coffee yield at the farm level was estimated from an interview with coffee growers in 2013.

In the second phase, we estimated coffee yield in the plots through a tailored equation not described here but similar to the one used by [START_REF] Meylan | Evaluating the effect of shade trees on provision of ecosystem services in intensively managed coffee plantations[END_REF], comprising the average density of coffee trees per hectare, the average fruit load per coffee tree and the average weight of dried beans. As this estimation is labor intensive, it was only applied to the subsample (27 plots).

2.4.b. Assessment of management and state AFS variables to identify determinants of coffee yield

The following variables were investigated in-depth to better explore coffee yield determinants.

ε) State variables

Four AFS state variables for characterizing the state of systems at a given moment, as defined by [START_REF] Lamanda | A protocol for the conceptualization of an agrosystem to guide data acquisition and analysis and expert knowledge integration[END_REF], were considered in the 100 m 2 squares in the subsample of 27 farmers' plots (Figure 1):

• Shade cover from AFS associated trees (%) was measured with a concave spherical densitometer [START_REF] Lemmon | A Spherical Densiometer For Estimating Forest Overstory Density[END_REF] in the center of each square in four directions. The percentage of transmitted radiation to the coffee canopy was roughly estimated by the complement of shade cover to 100%.

• Weed pressure (%) was es\mated using a 1 m² square frame divided into a grid of 100 identical 1 dm²-square cells. A mark of 1 was counted every time a cell included at least one weed. The percentage of cells with 1 mark yielded the weed pressure. This procedure was replicated at five locations in each 100 m 2 square at two dates (July and October 2014). The average of all the measurements taken for the two dates was used in the statistical analyses.

• Disease incidence (%) was evaluated for each plant selected for yield es\ma\on (10 per square), during two periods (July and October 2014), by observing each leaf of the plant and noting if it was infested or not (1/0) by the two most prevalent coffee diseases: coffee leaf rust (caused by Hemiliea vastatrix) that broke out in Central America in 2012-2013 [START_REF] Avelino | The coffee rust crises in Colombia and Central America (2008-2013): impacts, plausible causes and proposed solutions[END_REF], and American leaf spot (ALS caused by Mycena citricolor), which can severely affect heavily shaded coffee plantations.

The disease incidence calculation was then calculated as the sum of the incidence of the two diseases:

YAZE[ZE A\]A^E\]E(%) =
`*abc de fbghbi geebjkbl am c`ikK `*abc de fbghbi geebjkbl am nop qdkgf `*abc de fbghbi edc kQb jdeebb rfg k (7) • Soil chemical proper\es were analyzed by mixing four soil samples extracted in the upper 20 cm with an auger in each square. The resulting composite samples (one per 100 m 2 -square) were sent for analysis to the soil laboratory at Nicaragua's National Agriculture University (UNA). Organic matter (OM, %), total nitrogen in soil (N tot, ‰) and pH were analyzed.

ζ) Management variables

Agricultural practices were documented at the farm scale for 2013 and at the plot scale for 2014.

Through an interview, technical data for analyzing input efficiency (fertilizer and agrochemical product names and application doses) and labor efficiency (labor time and cost for pruning, weeding and fertilizing) were obtained.

Statistical analyses

All analyzes were run using the 'stats' and 'ade4' (Dray, 2007) R statistical packages (R Core Team, 2016) with R studio 3.3.1 (Rstudio Team, 2016). In order to sharpen the initial conceptual model built for coffee yield by maintaining only the significant relations, generalized linear models (GLM) were used to estimate the relationships: 1) between coffee yield and all the state variables, and 2) between those variables and the reported agricultural practices. Indeed, the different practices carried out by farmers lead to changes in the state of the systems, which in turn impact yields [START_REF] Doré | A diagnostic method for assessing regional variations in crop yield[END_REF][START_REF] Sebillotte | Systeme de culture, un concept operatoire pour les agronomes, Les systemes de culture[END_REF]. Without using statistical method and analysis software, we performed by our own discernment a clustering based on the variability of provision of the four ES to form two groups, one of which provided a higher overall level of supply of the four ES than the other. We selected for the group of situations providing high levels of services, situations showing high levels of provision for at least two of the four services studied. This differentiation then helped us to identify promising AFS, in terms of state variables and technical management, that provided the most balanced sets of ES.

RESULTS

The 27 coffee cropping systems that have been described in detail in agronomic terms included coffee trees with an average age of 13 years (±11.5), planted at an average density of 3,362 coffee trees ha -1 (±878) for an average production of 1,127 kg.ha -1 .yr -1 (±1,240), generating an average turnover of US$ 1,547 ha -1 yr -1 . All cropping systems were managed in agroforestry (AFS) with varying densities of species association: on average, 170 banana plants.ha -1 (±160), 108 Fabaceae trees.ha -1 (±96), 48 trees.ha -1 for timber production (±55) and 35 fruit trees.ha -1 (±51). The plants with annual production, i.e. banana and fruit trees, generated a turnover equivalent to US$ 136 ha -1 , which was 9% of the total turnover of the AFS. The products of these food crops were for 81% self-consumption by the farms' household.

The technical management of these AFS took an average of 43 days per hectare per year. Time requirements of practices were, in decreasing order: an average of 10 (±6.4) days of manual weeding with a machete, 7.7 (±6.8) days of sucker removal, 7.4 (±5.3) days of pruning shade trees), 6.6 (±4.1) days of phytosanitary treatments, 6.1 (±6.3) days of fertilization and 5.2 (±4.0) days of pruning coffee trees. Agrochemicals had an average total cost of US$ 135 ha -1 .yr -1 (±101), of which 70% is represented by fertilizers. Including the harvesting task, labor costs on average US$ 465 ha -1 (±228).

The net income, without taking into account post-harvest activities which represent a considerable charge, was US$ 1,083 ha -1 (±660).

The variability of the four services studied was high: for water quality index (WQ), the minimum was -88 and the maximum 0 (average of -18). For the Shannon index (Sh), the minimum was 0.22 and the maximum 2.63 (average of 1. 41). For carbon sequestration (seqC), the minimum was 1 t.ha -1 .yr -1 and the maximum 190 t.ha -1 (average 36t.ha -1 ). Finally, for coffee yield, the minimum was 233 kg.ha -1 .yr -1 and the maximum s 3,236 kg.ha -1 (average 1,127 kg.ha -1 .yr -1 ).

ES quantification and determinants

Water quality

What mainly explained the PPIHx were the properties of the active molecules that were used to calculate the PPIH of agrochemical product j, including the dangerousness for mammals. However, it also clearly depended on the dose of agrochemical products used and the number of treatments applied. The more the WQ decreased from class ([-10; 0]) to class ([-90; -50]), the more it was the dose that was involved in the value of the indicator rather than the PPIHj (Figure 2). There was a significant positive correlation between the number of treatments and WQ (R² = 0.80). Mann-Whitney statistical tests had shown significant differences (p-value < 0.05) between the number of treatments and the agrochemical product dose at the PPIHx between the most polluting situations ([-90; -50] and [-50; -40]) and the least polluting ([-10; 0]). There was no clear link between WQ and the different types of agrochemical products: insecticides, fungicides and herbicides (data not shown).

Tree biodiversity conservation

111 species were identified in total in the 82 experimental plots. The Shannon index was particularly dependent on species richness (R² = 0.76, p-value < 10 -16 ), but not on the density of associated trees (R² = 0.03, p-value = 0.08) (Figure 3 A and B). The more species present in the same proportion, the higher the Shannon index is. In the situations we studied, the higher the species richness, the more similar the proportions of species were, given the strong correlation between species richness and Sh (Figure 3).

According to farmers, associated trees have four different functions in the plots: fruit production, N fixation, timber production and firewood production, with each species having one major function.

The plants most common in coffee AFS according to their function were fruit species (Musa spp., Mangifera indica, Persea americana, Citrus sinensis, Theobroma cacao and Psidium guajava) and N-

Carbon sequestration 351

The highest seqC was found in plots with low and intermediate tree densities (Figure 4A). However, 352 there was no negative statistical correlation between carbon sequestration in aboveground biomass 353 and the density of associated trees (R² = 0.01, p-value = 0.3). Figures 4B and4C confirm that trees 354 with relatively thin trunks, although in high number, store little carbon. The presence of a few trees 355 with a high DBH is sufficient for a good provision of seqC. Therefore, DBH is the essential 356 determinant of the provision of carbon sequestration. 357

Coffee yield

The data from the first stage of the survey, recorded on a declaratory basis, indicated a mean yield of 1,380 kg.ha -1 .yr -1 , i.e. much higher than the value for the whole department (788 kg.ha -1 .yr -1 ) reported by Nicaragua's Ministry of Agriculture in 2013. In 51% of the plots the coffee variety grown was Catimor, while Caturra was grown in 17% and a mixture of the two in 23%. Other varieties were grown in the remaining 9% of plots. No significant difference in coffee yield was found between these different varieties. The mean yield measured in the plots for 2014 was 1,127 kg.ha -1 , which was not significantly different from the survey's 2013 yield.

To select the explanatory state variables on yield, we then statistically tested causal relationships based on our measurements via GLM, and produced a simple model, where only the significant relationships are reported (Figure 5). We did not find any direct influence of agricultural practices on coffee yield. Those practices had direct relationships with the state variables that in turn had effects on coffee yield (Figure 5). The number of shade trees from the Fabaecae family had a contradictory indirect relationship with coffee yield by increasing soil N content, which was positively related to yield, but also by decreasing pH, which had a negative effect on yield and also on coffee disease prevention. Some expected relationships did not appear in the analysis (Figure 5): no practices related to herbicides and pest and

Clustering among ecosystem services in coffee plantations

Through clustering analysis, we identified a group of farmers who achieved the provision of a better set of ES (Table 3). The analysis was conducted using only values of the four services as determining variables for the differentiation of two groups, with the first having higher provision of the four ES studied and another group with fluctuating ES values. We also extended the comparisons of the two groups formed according to the state variables, as possible sources of these differences in ES, and to management practices, to derive practical conclusions for the management of coffee agroforestry systems.

Clustering identified a group of 12 coffee growers who succeeded in providing a better set of ES (High-ES Type) in comparison to the other 15 farmers (Low-ES Type), but only coffee yield was significantly different between both types. Good ES provision situations were generally found in winwin situations when two services were compared, in the red areas at the top right of the graphs in Figure 6. The highest set of ES provision was achieved through a lower incidence of diseases and weeds, less shade cover and higher pH and total soil N (Table 3). Interestingly, farmers' practices in this group showed a higher diversity of tree species, but lower Fabaceae tree density. Surprisingly, there was no significant difference in labor per hectare between these two groups. Labor productivity was significantly higher for the High-ES Type with a production of 57.8 kg coffee.day -1 versus 30.5 kg coffee.day -1 for the Low-ES Type. No significant difference was observed in fertilization, disease and weed management between the two groups. No significant linear correlation was found between the four ES (figure 6), which means that there 447 were no trade-offs nor synergies between these services. This result suggests that there are no 448 barrier to jointly increasing service provision. The design of more agroecological systems (with high 449 service provision) then consists of understanding the determinants of these services and promoting upper right zones of each scatterplot where the provision of each of the two services exceeds their average provision at the scale of the study.

Possible benchmarks for agroecological intensification pathways

The management practices and the values of the state variables of the 12 coffee plantations in the High-ES Type could be used as benchmarks:

-Regarding the practices: (i) The number of tree species differed significantly between the two groups (9.5 and 5.9 on average), which translated into Sh differences, and which could also had an influence on coffee production. (ii) The density of nitrogen-fixing trees was much higher for the Low-ES Type, i.e. almost two-fold higher than for the High-ES Type.

-Regarding the state variables: (i) For Low-ES Type, soil pH was low (5.83), as was soil N concentration (0.23 mg.L -1 ), and significantly different from the High-ES Type (6.23 and 0.31 mg.L -1 , respectively). The link between Fabaceae trees, soil nitrogen content, and acidification is well known [START_REF] Moura | Contribution of legume tree residues and macrofauna to the improvement of abiotic soil properties in the eastern Amazon[END_REF]. So, there seemed to be a balance about Fabaceae density to increase soil nitrogen content while not decreasing pH, i.e. approximately 73 trees per hectare, in line with the current practices of the High-ES Type. (ii) Weed pressure and transmitted radiation differed between the two groups (23% versus 30.7% of weeds on the ground and 45.8% versus 50.6% of shade) and these factors were related to coffee yield, as observed in the statistical model (Figure 5). (iii) A significant difference existed in disease pressure (13% vs 31%), with fungi having a much higher effect on Low-ES Type.

DISCUSSION

Coffee AFS with varying levels of ES supply

We found a large range of delivery of the services we assessed in this small and relatively homogeneous region. With the exception of coffee yield, the average provision of these services was often much lower than in natural forests but much higher than in monocrop systems [START_REF] Santos | Can agroforestry systems enhance biodiversity and ecosystem service provision in agricultural landscapes? A meta-analysis for the Brazilian Atlantic Forest[END_REF].

Carbon sequestration in aboveground biomass (seqC) of shade trees ranged over two orders of magnitude, with a mean of 36.6 t.ha -1 , which was slightly above the 28.3 t.ha -1 estimated in the San Ramon coffee plantation [START_REF] Goodall | Shade tree diversity, carbon sequestration, and epiphyte presence in coffee agroecosystems: A decade of smallholder management in San Ramón, Nicaragua[END_REF], close to the study area, but better than the 13.9 t.ha -1 in neighboring Costa Rica [START_REF] Hergoualc'h | Changes in carbon stock and greenhouse gas balance in a coffee (Coffea arabica) monoculture versus an agroforestry system with Inga densiflora, in Costa Rica[END_REF], and far behind mature forests, even secondary forests, at 99.1 t.ha -1 [START_REF] Aryal | Carbon stocks and changes in tropical secondary forests of southern Mexico[END_REF]. This sequestered C is obviously in constant evolution, depending on the growth of the trees, the pedoclimatic conditions, and the use of the wood/timber. However, in the region, we can expect its value to increase or be sustained. On one hand, companies dedicated to timber exploitation do not log the trees in the area. On the other hand, wood and timber from the AFS are generally used sparingly by farmers for firewood (often pruning waste) or for houses building. Furthermore, associated trees help to buffer microclimate conditions that decrease soil CO2 efflux and mitigate global warming [START_REF] Gomes | Trees modify the dynamics of soil CO2 efflux in coffee agroforestry systems[END_REF].

Among the multitude of indicators for measuring diversity, we have chosen the Shannon indicator because it is simple to calculate and summarizes complex information. Moreover, it is very widespread and allows many comparisons with other situations. Shade tree diversity (Sh) ranged over more than one order of magnitude in plots, but it was rather high throughout the whole sample, higher than that reported in cacao-based AFS in a neighbouring region [START_REF] Cerda | Contribution of cocoa agroforestry systems to family income and domestic consumption: looking toward intensification[END_REF] but lower than that reported for coffee AFS established in the remnants of primary forests in a biosphere reserve in Chiapas, Mexico [START_REF] Valencia | The role of coffee agroforestry in the conservation of tree diversity and community composition of native forests in a Biosphere Reserve[END_REF]. The proximity of coffee plots to the Peñas Blancas natural reserve, a possible source for diversified tree species, could partly explain this high biodiversity.

Water quality, as assessed by the quantity and nature of agrochemical products used in plantations, was also relatively variable, although the indicator we developed only gave relative estimates and not absolute measurements. It is therefore difficult to compare these results with other studies.

Nevertheless, data collected for 2013 and 2014 show that farmers use very few pesticides (data not shown), i.e. about half as much as the quantities reported by [START_REF] Meylan | Evaluating the effect of shade trees on provision of ecosystem services in intensively managed coffee plantations[END_REF] in Costa Rica. Coffee plot productivity is not routinely measured by farmers, although it is the the studied service in which they have the most interest, as harvesting is usually done by hired hands who move between plots in different rounds. Coffee yields ranged from high values (more than 3 t.ha -1 .yr -1 of green coffee) to much lower values (below 400 kg.ha -1 .yr -1 ), and the average (1,127 kg.ha -1 .yr -1 ) was far lower than what has been found in Costa Rica [START_REF] Meylan | Combining a typology and a conceptual model of cropping system to explore the diversity of relationships between ecosystem services: The case of erosion control in coffee-based agroforestry systems in Costa Rica[END_REF] where around 400 kg of N per hectare was applied. Disease management, and the recent outbreak of coffee leaf rust [START_REF] Avelino | The coffee rust crises in Colombia and Central America (2008-2013): impacts, plausible causes and proposed solutions[END_REF], is probably another reason for this high variation. In addition, this variability could be the result of varietal adaptation of coffee trees in certain plots, where more rust-tolerant varieties such as Catimor have replaced more susceptible varieties such as Caturra [START_REF] Libert Amico | Learning from social-ecological crisis for legal resilience building: multi-scale dynamics in the coffee rust epidemic[END_REF]. Once transformed into an economic service relevant for the household, this production service also varies significantly, with an income from the sale of coffee of US$ 1,561 ha -1 .yr -1 for High-ES Type compared to only US$ 889 ha -1 .yr -1 for Low-ES Type. As other productions from species associated with coffee trees in AFS occupy only 9% of the total turnover (data not shown), with little variation among AFS, we did not consider these other productions in our studied services.

Analyzing ES determinants to identify promising technical levers

The intrinsic source of variation in the delivery of services was estimated for four ES that we could rapidly estimate. Similarly to [START_REF] Cerda | Effects of shade, altitude and management on multiple ecosystem services in coffee agroecosystems[END_REF], we found that there are no clear trade-offs between the different services, at least for the services we have studied. This means it could be possible to observe and design systems with a higher level of multiple service provision compared to current systems. Carbon sequestration depends on the presence and size of big trees much more than on the total number of trees (Figure 4), as demonstrated in forests and agroforestry systems worldwide [START_REF] Schroth | Contribution of agroforests to landscape carbon storage[END_REF]. Biodiversity depends on species richness more than on tree density (Figure 3). Finally, pesticide water contamination was mainly linked with the doses of the products used rather than the intrinsic physico-chemical properties of active ingredients (Figure 2). To prevent active molecules from leaching out and ending up in surface water, [START_REF] Pavlidis | Environmental Benefits and Control of Pollution to Surface Water and Groundwater by Agroforestry Systems: a Review[END_REF] have shown that the roots of associated trees, particularly certain species, in AFS have the ability to retain these pollutants. Coffee yield variation was related to factors that could be controlled by farmers, particularly shade tree selection and management (Figure 5). Indeed, plant composition and its management have a direct impact on soil quality (nutrient recycling, structure, moisture, competition and facilitation) as well as the photosynthetic active radiation (PAR) received by coffee trees, which are essential parameters for their physiological functioning and productivity. Furthermore, other AFS state variables significantly influenced coffee yield in ways that were in line with current knowledge, i.e. weeds and diseases limited coffee yield.

The causal relationship between AFS management, state variables describing the system environment and productivity were assessed through a conceptual model in which relationships were tested statistically. The idea that productivity is not directly related to practices, but rather includes their effects on the plant environment, is not new by any means as it is one of the cornerstones of modern agronomy [START_REF] Sebillote | Agronomie et agriculture. Essai d'analyse des tâches de l'agronome[END_REF]. Conceptual modeling, as proposed by [START_REF] Lamanda | A protocol for the conceptualization of an agrosystem to guide data acquisition and analysis and expert knowledge integration[END_REF] and applied here, is a convincing approach to screen for statistically significant relationships, such as those we found in this study.

The objective of improving the sustainability and performance of coffee AFS is to increase the provision of all services jointly. To meet this challenge, it is necessary to identify the most effective technical levers of ES determinants to use in practice.

Possible technical levers to improve the sustainability of coffee AFS

Quantifying ES and analyzing their relationships can be a first step in the implementation of sustainable land-use systems [START_REF] Tsonkova | Ecological benefits provided by alley cropping systems for production of woody biomass in the temperate region: A review[END_REF], adopting an approach resembling agroecological engineering [START_REF] Dalsgaard | Towards quantification of ecological sustainability in farming systems analysis[END_REF], as has already been done for cacao AFS with the Pareto frontier algorithm method [START_REF] Andreotti | Exploring management strategies to enhance the provision of ecosystem services in complex smallholder agroforestry systems[END_REF]. Technical choices affect the nature and quantity of ES in an ecosystem [START_REF] Rodriguez | Trade-offs across space, time, and ecosystem services[END_REF]. Interventions can have positive (e.g. rehabilitation of degraded areas) or negative effects (e.g. converting biodiversity rich areas into cropland) on ES [START_REF] Kovács | Understanding the links between ecosystem service trade-offs and conflicts in protected areas[END_REF]. For example, replacing agrochemical products with others that are potentially less dangerous, therefore different in their chemical composition but with similar objectives (reduction of weedy grasses and fungal pressure, etc.), could also be an effective solution to improve WQ [START_REF] Steingrímsdóttir | A screening framework for pesticide substitution in agriculture[END_REF].

Several studies have demonstrated that solutions to provide ES are determined largely by the number of species and the functional diversity of the species in the cropping system [START_REF] De Beenhouwer | A global meta-analysis of the biodiversity and ecosystem service benefits of coffee and cacao agroforestry[END_REF][START_REF] Malézieux | Mixing plant species in cropping systems: concepts, tools and models. A review[END_REF][START_REF] Storkey | Engineering a plant community to deliver multiple ecosystem services[END_REF][START_REF] Tschora | Co-benefits and trade-offs of agroforestry for climate change mitigation and other sustainability goals in West Africa[END_REF]. For example, to enhance WQ, besides reducing the doses of agrochemical products used and the number of treatments, consideration could be given to the selection of certain plant species that foster the regulation of some coffee pests [START_REF] Bagny Beilhe | Pest-regulating networks of the coffee berry borer (Hypothenemus hampei) in agroforestry systems[END_REF][START_REF] Nesper | Shade tree diversity enhances coffee production and quality in agroforestry systems in the Western Ghats[END_REF]. In addition to tree species selection, which is in fact a technical choice, management practices such as particular spatial arrangement for the selected tree species help to reduce the dispersal of spores of some coffee plant diseases [START_REF] Gagliardi | Contribution of shade trees to wind dynamics and pathogen dispersal on the edge of coffee agroforestry systems: A functional traits approach[END_REF]. Furthermore, to achieve good levels of carbon sequestration and coffee production, tree selection is also a decisive point. Indeed, the shading of coffee by associated trees, often correlated to their density, has a negative effect on the productivity of coffee trees past a threshold [START_REF] Durand-Bessart | Analysis of interactions amongst shade trees, coffee foliar diseases and coffee yield in multistrata agroforestry systems[END_REF][START_REF] Sarmiento-Soler | Effect of cropping system, shade cover and altitudinal gradient on coffee yield components at Mt. Elgon, Uganda[END_REF]. However, according to our findings, it is mainly trees with a high growth capacity (both height and width) that are responsible for high carbon sequestration. To jointly increase ES, it therefore seems interesting (i) to select various associated species that are useful in the biological control of coffee pests and diseases and whose roots have the capacity to limit the leaching of pollutants, including trees with a high carbon storage capacity, (ii) to plant them at densities that do not provide too much shade, and (iii) to manage them in a way adapted to an effective functioning of the AFS (pruning, spacing between plants, etc.) whether these objectives can be jointly met at acceptable management cost remains mostly unknonw.

A method that would benefit from scaling up from the cropping system to the farm level

We have proposed technical levers to improve the provision of four ES but their identification comes from analyses at the plot scale only. While this choice allowed us to obtain accurate relationships between services, which was our goal, the scale is too small to address two issues. While this may be relevant for seqC, i.e. it is an essentially additive service that can be summed up plot by plot, it is probably not very relevant for the delivery of other services such as WQ provision.

Indeed, pollution may be caused by other processes, such as contamination with organic waste from coffee milling at the farm scale, sediments at the watershed scale, etc. Upscaling the analysis of ES from the plot to the scale that is relevant to ES delivery can be much more challenging for some ES than for others.

The second issue of scale is related to the leeway to change the way coffee plantations are managed. Some elements can be changed at the plot scale because they have few consequences at the farm level, such as the substitution of an agrochemical product with another of similar efficacy but lower pollution potential. Decisions on most practices, however, are taken at the farm scale, where other trade-offs are also considered, e.g. between the best way to allocate labor, funding, etc. For example, shade tree species have different effects on coffee plants, but also require different degrees of labor input for their management. Such issues need to be considered when proposing new cropping systems. This study should be extended to encompass the farm scale and the decisions taken at this scale.

CONCLUSION

We proposed and tested an integrated method to assess the multiple ES provision of cropping systems and explored the management options in coffee AFS to increase them. Technical levers for improving the provision of ES and therefore the sustainability of coffee-based agroforestry systems, can be readily determined via the analysis of the ES provided. The application of our method enabled us to come up with some recommendations for cropping practices, in particular that a thoughtful choice of associated trees selected according to their expected functions is a key point when designing AFS that provide high levels of ES. However, other dimensions of intensification pathways have to be included in the analysis, particularly at the farming system scale. For an effective agroecological intensification of coffee systems, participatory approaches for the selection of innovative and acceptable practices, including farmers and their knowledge, should be developed. This work should make it possible to identify species to associate with coffee trees over time and space, to (1) improve coffee yields, and (2) provide other environmental services (e.g. regulation of diseases and pests, nutrient recycling, carbon sequestration, anti-erosion protection). Public policies could be designed to finance these participatory activities, which would be implemented by agricultural development and technical institutes. Furthermore, payment for ecosystem services policies could be strengthened. Finally, research could study the pros and cons of the different novelties that have emerged from participatory approaches in experimental stations under controlled conditions and investigate the underlying biogeochemical processes.

(

  potential exclusively based on the agrochemical product's chemical properties -τ ½ water = Half-life in water -Xn = Harmfulness for mammals: lethal ingested dose for 50% of the rodent population

  of the active ingredient in the agrochemical product n: The last active ingredient constituting the agrochemical product m: The last agrochemical product used by the farmer D: Dose (L.ha -1 or kg.ha -1 )

Figure 1 .

 1 Figure 1. Sampling scheme in coffee plots for ES, management and environmental variables measurement.

Figure 2 .

 2 Figure 2. Proportions in the water quality index (WQ) of the dose component (in black) and the 'dangerousness' component' of the pesticide itself (in gray) in the agrochemical product pressure index on humans for farm x (PPIHx) in 2013. The label corresponds to the number of treatments carried out over the year. The classes have a range of 10 except for the first [-90, -50] which groups three agrochemical product use cases (only one grower had a WQ of -88 and the next two had a WQ between -60 and -50).

Figure 3 .

 3 Figure 3. A) Shannon index as a function of the specific richness of the experimental plots. B) Shannon index as a function of the total number of associated trees in the experimental plots.s

Figure 4 .CFigure 5 .

 45 Figure 4. A) Total carbon sequestration depending on the associated tree density. B) Mean seqC per tree depending on tree DBH ranks from less than 0.3m to more than 0.9m. C) Mean number of trees depending on tree DBH ranks from less than 0.3m to more than 0.9. Error bars correspond to standard

Farmers

  ' selection of shade trees and their plantation density had marked impacts on the state variables (soil pH, total soil N and transmitted radiation). Fabaceae trees were especially positively correlated to total soil N and negatively to soil pH. Shade tree density obviously positively affected shade cover, thus decreasing radiation transmitted to coffee bushes. The density of coffee plants showed only a limited effect: it inhibited weed development and favored the spread of disease. At the same level of statistical significance, labor spent on pruning shade trees was positively related to light transmitted to coffee plants.

  any significant effect on their corresponding environmental variables, i.e. weeds and diseases.

Figure 6 .

 6 Figure 6. Relationships between ecosystem services in the sub-sample of 27 farmers. The red dots are the situations with the best ecosystem service provision (n = 12). Horizontal and vertical dotted lines represent

Table 1 .

 1 Services and variables collected, the way they have been collected and the type of results expected from those services (n indicates the sample size). The first stage was carried out in May and June,

the second in July and August 2014. Year of data concerned Ecosystem service or other variables Collection method Object of the study Expected results

	2013 (n = 82, 1 st phase)	Water quality Carbon sequestration	Declaratory survey Measurement	Coffee AFS 1,000 m² experimental	Characterization quality, carbon sequestration, of water biodiversity services and their determinants
		Biodiversity		coffee plot
		Coffee productivity			Characterization	of	coffee
	2014 (n = 27, 2 nd phase)	Environmental variables Water quality	Measurement Declaratory	1,000 m² experimental coffee plot	productivity determinants Possible correlations between and its the 4 ES taken in pairs, and with
		Management	survey		management
		variables

Table 2 .

 2 Table 2). 347 Most frequent plant species in coffee-based agroforestry systems, relative abundance and main

	348
	349	function.
		Species name	Occurrence	Total number	Density	Main expected
			on 82 plots	of individuals	when	function
			(%)	in the sample	species is
					present
					(ha -1 )
		Musa spp.	83	1,705	239	Fruit production
		Inga oerstediana	72	277	47	N fixation
		Inga punctata	54	322	73	N fixation
		Cordia alliodora	45	154	42	Timber production
		Mangifera indica L.	35	66	23	Fruit production
		Erythrina poeppigiana	32	153	59	N fixation
		Theobroma cacao	30	168	67	Fruit production
		Persea americana	29	72	30	Fruit production
		Lonchocaprus macrophyllus	23	57	30	Firewood production
		Trichilia sp.	21	32	19	Firewood production
		Brosimum alicastrum	18	25	17	Timber production
		Terminalia amazonia	17	18	13	Timber production
		Citrus sinensis	15	27	23	Fruit production
		Cedrela odorata	15	14	13	Timber production
		Guazuma ulmifolia	15	14	12	Firewood production
		Nectandra reticulata	13	18	16	Firewood production
		Juglans olanchana	12	32	32	Timber production
		Erythrina fusca	11	55	61	N fixation
		Cordia collococca	11	12	13	Timber production
		Psidium guajava	11	15	17	Fruit production
	350

  plots for two Types of AFS derived from the clustering analysis of ES. Standard deviations are indicated; for each 443 variable, t-test significant differences between the two groups are indicated: p-value < 0.05 '***', between 0.05

	444
	445	and 0.15 '*'
					High-ES type	Low-ES type
			Comparison element	Unit	(n = 12)	(n = 15)	T-test
					Mean (±SD)	Mean (±SD)
			Coffee yield	kg.ha -1 .yr -1	1,445 ±887	855 ±619	***
	ES		WQ (index) Seq C	-t.ha -1	-39.5 ±13.6 41 ±39.4	-41.2 ±7.2 24.8 ±18.8
			Sh (index)	-	1.49 ±0.60	1.16 ±0.50
			Diseases	% affected leaves	13 ±23	31 ±34	*
	State		Shade	% plot covered by tree shade	45.8 ±15.2	50.6 ±12.4	*
	variables of agroforestry	Weeds	% ground plot covered	23 ±14.9	30.7 ±16.6	*
	systems	pH	-	6.07 ±0.48	5.83 ±0.24	***
			N soil	mg/L of N in the soil	0.28 ±0.06	0.23 ±0.09	***
			Organic matter	% soil	4.65 ±0.69	4.56 ±0.73
			Nitrogen fertilization	kg N.ha -1	24.7 ±31.1	21.5 ±30.1
			Total volume of fungicides	L.ha -1 .yr -1	0.9 ±1.2	0.8 ±0.9
			Total volume of herbicides	L.ha -1 .yr -1	2.0 ±1.4	1.5 ±1.1
	Management	Total number of coffee protection treatments	nb.yr -1	3.4 ±1.9	2.7 ±1.6
	practices and	Pruning time of shade trees	days.ha -1	4.3 ±3.6	5.1 ±3.6
	shade tree	Number of tree species	nb.1000 m -²	9.5 ±6.1	5.9 ±2.7	*
	choices		Number of fruit trees	nb.ha -1	228 ±195	158 ±177
			Number of firewood/timber trees nb.ha -1	72 ±38	43 ±69
			Number of Fabaceae trees	nb.ha -1	73 ±80	143 ±98	***
			Coffee age	years	12.1 ±11.5	12.9 ±12.4
			Coffee plantation density	nb.ha -1	3,535 ±878	3,213 ±911
			Management workload	day.ha -1	25 ±10.1	28 ±9
	446

Acknowledgments

This work is part of the Scientific Partnership Platform on Agroforestry Systems with Perennial Crops (PCP AFS-CP) coordinated by CIRAD and CATIE. It was funded by the CIRAD-IRD 'SAFSE' project and the BID Fontagro 'Caf'Adapt' project. We would like to thank the field assistants, Jose Inocente Cerda Madriz, Sabas Urbina López and Jlenner Antonio Rizo Loza for support in data collection. We gratefully acknowledge the scientific support provided by Philippe Tixier (CIRAD/CATIE), Sergio Vílchez (CATIE) and Jacques Avelino (CIRAD/CATIE/IICA). We would like to thank Andrew Lewer for language edition.


Aurélien Dinh
email: aurelien.dinh@aphp.fr

Luc Jaulmes

Agnès Dechartres

Clara Duran

Hélène Mascitti

Xavier Lescure

Youri Yordanov

Patrick Jourdain

Time to resolution of respiratory and systemic COVID-19 symptoms in community setting

Keywords: COVID-19, symptom duration, dyspnea, sequalae, community setting, telemedicine

Objectives

During the Coronavirus disease 2019 , symptom course among outpatients is not well known. Our aim was to assess the time to resolution of respiratory and systemic symptoms and their associated factors.

Methods

Cohort study including adult outpatients, managed with Covidom, a telesurveillance solution, with RT-PCR confirmed diagnosis, from March 9 th 2020 until February 23 rd 2021.

Follow-up was 30 days after symptom onset.

Results

Among the 9,667 patients included, mean age was 43.2±14.0 years, and 67.5% were female (n=6,522). Median body mass index (BMI) (IQR) was 25.0 kg/m² (22.1-28.8). Main comorbidities were: hypertension (12.9%; n=1247), asthma (11.0%; n=1063), and diabetes mellitus (5.5%; n=527). The most frequent symptom during follow-up was dyspnea (65.1%; n=6,296), followed by tachypnea (49.9%; n=4,821), shivers (45.6%; n=4,410), and fever (36.7%; n=3,550). Median time to resolution of systemic and respiratory symptoms were 3 days (95% CI: 2-4) and 7 days (95% CI: 6-8), respectively. Ultimately, 17.2% (95% CI: 15.7%-18.8%) still presented respiratory symptoms at day 30. Longer time to respiratory symptom resolution was associated with older age, increased BMI, chronic obstructive pulmonary disease (COPD), coronary artery disease, asthma, and heart failure. Regarding systemic symptoms, coronary artery disease, asthma, age above 40 years, and elevated BMI were associated with longer time to resolution. 4 Conclusions Time to symptom resolution among outpatients with COVID-19 seemed shorter for systemic than respiratory symptoms. Prolonged respiratory symptoms were common at day 30. Risk factors associated with later resolution included age, cardiovascular and pulmonary diseases.

Introduction

The clinical manifestations in COVID-19 patients range from asymptomatic to severe forms requiring critical care, and the duration of symptoms can vary widely [START_REF] Guan | Clinical Characteristics of Coronavirus Disease 2019 in China[END_REF][START_REF] Tenforde | Symptom Duration and Risk Factors for Delayed Return to Usual Health Among Outpatients with COVID-19 in a Multistate Health Care Systems Network -United States, March-June 2020[END_REF]. Most studies described the clinical course of hospitalized severe cases [START_REF] Docherty | Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study[END_REF][START_REF] Zhou | Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study[END_REF][START_REF] Mizrahi | Longitudinal symptom dynamics of COVID-19 infection[END_REF]. However, most patients present with milder forms of COVID-19 [START_REF] Stokes | Coronavirus Disease 2019 Case Surveillance -United States, January 22-May 30, 2020[END_REF], and are therefore managed as outpatients. Data on this population is scarce, but describing the symptom course of outpatients with COVID-19 and factors associated with symptom duration may help provide adequate follow-up [START_REF] Tenforde | Symptom Duration and Risk Factors for Delayed Return to Usual Health Among Outpatients with COVID-19 in a Multistate Health Care Systems Network -United States, March-June 2020[END_REF][START_REF] Pullen | 12 Symptoms of COVID-19 Outpatients in the United States[END_REF][START_REF] Lapostolle | Clinical features of 1487 COVID-19 patients with outpatient management in the Greater Paris: the COVIDcall study[END_REF][START_REF] Kim | Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19[END_REF].

Our study aimed to evaluate the time to resolution of COVID-19 respiratory and systemic symptoms, and factors associated with a longer duration in a large cohort of outpatients.

Material and methods

This study included adult (≥ 18 years) patients, initially managed as outpatients, from March 9 th 2020 to February 23 rd 2021 by Covidom, a telesurveillance solution for home monitoring of patients with COVID-19 in the greater Paris area [START_REF] Yordanov | Covidom, a Telesurveillance Solution for Home Monitoring Patients With COVID-19[END_REF]. Covidom outpatients were registered by a physician after consulting for COVID-related symptoms and agreeing to the monitoring. We included patients with a RT-PCR confirmed diagnosis, having answered at least one monitoring questionnaire, and who provided medical background at inclusion. Available data for each patient included age, gender, date of first symptoms, weight, height, and comorbidities.

When included in Covidom, the patients completed, on a daily basis, one or two selfadministered questionnaires on symptoms until 30 days after symptoms onset. Self-reported data were: respiratory rate (tachypnea defined as >20 breaths per minute), heart rate (tachycardia defined as >100 beats per minute), temperature (fever and hypothermia defined by temperature >38.5°C and <35.5°C, respectively), dyspnea (on a 1-to-5 modified Borg scale [START_REF] Muza | Comparison of Scales Used to Quantitate the Sense of Effort to Breathe in Patients with Chronic Obstructive Pulmonary Disease[END_REF], slight dyspnea defined by a rating ≥2, and moderate dyspnea ≥3), oxygen saturation (desaturation defined by <95%), dizziness, and shivers.

Two main groups of symptoms were defined:

-Systemic symptoms: fever or hypothermia, dizziness, tachycardia, shivers.

-Respiratory symptoms: dyspnea, tachypnea, low oxygen saturation.

Kaplan-Meier estimators were used to evaluate time to resolution of symptoms. Patients were considered at risk from the date of first symptom onset (declared at inclusion) until the last occurrence of each symptom in daily questionnaires. Regarding symptom groups, patients were considered at risk until the last day of any symptom occurrence of the group.

Patients were censored at the end of their follow-up (30 days or earlier in case of premature ending). Factors independently associated with longer time to resolution of systemic and respiratory symptoms were evaluated separately on complete cases using multivariate Cox models and inversed hazard ratios as the event of interest was initially resolution of symptoms. Alpha risk was set at 5%. Patients provided electronic consent for the Covidom telesurveillance program and were informed of the use of their anonymized data for research. This study was approved by the scientific and ethical committee of APHP (IRB00011591). Mean age was 43.2 years old (SD 14.0) and 67.5% were female (n=6,522). Median body mass index (BMI) (IQR) was 25.0 kg/m² (22.1-28.8). Main comorbidities were hypertension (12.9%; n=1247), asthma (11.0%; n=1,063), and diabetes mellitus (5.5%; n=527). Other comorbidities were reported by less than 1.5% of patients. Weight (and BMI) was missing for 121 patients, and gender for 18 patients.

Results

Among

The last answer to a daily questionnaire occurred at a median time of 28 days after symptom onset (IQR 17-30 days), with a median Covidom monitoring duraZon of 19 days (IQR 11-24 days). There were 65.1% of patients (n=7,805) missing at least one daily monitoring questionnaire, and the median number of skipped questionnaires per patient was 2 (IQR 0-5).

The most common symptom was dyspnea in 65.1% of patients (n=6,296), with 2,802 patients (29.0%) rating their dyspnea as moderate or higher. Tachypnea, shivers, and fever were reported by 49.9% (n=4,821), 45.6% (n=4,410), and 36.7% (n=3,550) of patients, respectively. A total of 2,590 (26.8%) patients reported tachycardia, 1,197 (12.4%) reported dizziness, and 582 (25.1%) of 2,319 patients with an oximeter reported desaturation at least once.

Considering time to symptom resolution (Figure 1), the longest symptoms were desaturation with a median (95% Confidence Interval (CI)) of 10 days [START_REF] Lapostolle | Clinical features of 1487 COVID-19 patients with outpatient management in the Greater Paris: the COVIDcall study[END_REF][START_REF] Kim | Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19[END_REF][START_REF] Yordanov | Covidom, a Telesurveillance Solution for Home Monitoring Patients With COVID-19[END_REF][START_REF] Muza | Comparison of Scales Used to Quantitate the Sense of Effort to Breathe in Patients with Chronic Obstructive Pulmonary Disease[END_REF][START_REF] Lancet | Facing up to long COVID[END_REF], and slight dyspnea with 9 days (7-10). Median time to resolution of moderate dyspnea was 4 days (2-5). Symptoms with shorter time to resolution included shivers, tachypnea and tachycardia, with median durations (95% CI) respectively of 1 day (1-2), 2 days (1-2) and 2 days (1-2). The probability of slight dyspnea lasting until 30 days after first symptoms onset was 18.3% (95% CI 16.6%-20.0%), while the systemic symptom with highest probability at day 30 was dizziness with 1.1% (0.5%-2.2%). S2 in Supplementary appendix.

Lastly, the number and proportion of patients per monitoring question, answer, and day are described in Figure S1 in Supplementary appendix.

Discussion

Our study provides important inputs regarding time to resolution of respiratory and systemic symptoms over one month in ambulatory patients, which can help direct care [START_REF] Mizrahi | Longitudinal symptom dynamics of COVID-19 infection[END_REF][START_REF] Lancet | Facing up to long COVID[END_REF][START_REF] Davido | Post-COVID-19 chronic symptoms: a postinfectious entity?[END_REF][START_REF] Nehme | COVID-19 Symptoms: Longitudinal Evolution and Persistence in Outpatient Settings[END_REF].

Median time to resolution of symptoms was up to 10 days with wide variability, and 18.3% of slight dizziness symptoms still present at end of follow-up. Systemic symptoms were slightly less frequent than respiratory symptoms, and had shorter time to resolution. Factors associated with prolonged symptoms included age over 30, elevated BMI, and cardiovascular and pulmonary diseases.

The strength of our study includes the size of the cohort and the standardized questionnaire during the entire 30-day follow-up. Several studies showed, among hospitalized patients, that fever, cough, dyspnea, and gastrointestinal symptoms were the main clinical manifestations of COVID-19 [15]. In our cohort that included only non-hospitalized patients, those symptoms had a lower prevalence.

Our results corroborate that COVID-19 may cause prolonged symptoms, even among outpatients with mild disease [START_REF] Tenforde | Symptom Duration and Risk Factors for Delayed Return to Usual Health Among Outpatients with COVID-19 in a Multistate Health Care Systems Network -United States, March-June 2020[END_REF], or in patients with or without few comorbidities.

Limits

Our study relies on patients' self-reporting, resulting in possible recall bias. Furthermore, we focused on general and respiratory symptoms and did not evaluate the duration of other symptoms like smell and taste alteration. Secondarily, non-respondents might have differed from respondents regarding their clinical course. Finally, our models do not take into account each symptom's onset delay, only the date until which each symptom lasts, starting from the onset of the first symptom. This describes the course of symptoms relative to the illness globally, but different factors may affect symptom onset delay and symptom duration.

Conclusion

Ann Intern Med 2021;174:723-5. https://doi.org/10.7326/M20-5926.

[15] Jiang F, Deng L, Zhang L, Cai Y, Cheung CW, Xia Z. Review of the Clinical Characteristics of Coronavirus Disease 2019 (COVID-19). J Gen Intern Med 2020;35:1545-9. https://doi.org/10.1007/s11606-020-05762-w.

  Age above 30 years (hazard ratios (HR) 1.27 [95%CI 1.16-1.38]), 40 years (1.50 [1.37-1.65]), 50 years (1.68 [1.53-1.85]), and 60 years (1.64[1.45-1.84]), as well as increased BMI (both obesity and overweight, 1.13 [1.15-1.21] and 1.18 [1.09-1.28], respectively), chronic obstructive pulmonary disease (COPD) (2.00 [1.41-2.83]), coronary artery disease (1.55 [1.14-2.11]), asthma (1.43 [1.30-1.58]), heart failure (1.42 [1.08-1.86]), and hypertension (1.12 [1.01-1.23]) were associated with longer time to resolution of respiratory symptoms (Figure 2). No variable appeared associated with a shorter time to resolution. For systemic symptoms, coronary artery disease (1.53 [1.18-1.98]), as well as age above 40 years (1.12 [1.02-1.22], 1.16 [1.06-1.26] and 1.20 [1.08-1.34] respecZvely for ages >40, >50 and >60) and increased BMI (1.09 [1.03-1.16] for BMI >25kg/m², 1.10 [1.03-1.18] for BMI >30kg/m²) were associated with longer time to resolution (Figure 2). Male sex was associated with shorter time to resolution (0.92 [0.87-0.97]). Complete analysis results are presented in Table

Figure 1 :

 1 Figure 1: Time to resolution of respiratory and systemic symptoms (in days) modeled using

Figure 2 :

 2 Figure 2: Risk factors associated with longer time to resolution of clinical symptoms

  the 62,993 patients included in the Covidom cohort as outpatients, 15,086 (23.9%) had a COVID-19 RT-PCR confirmed diagnosis, 14,965 (23.8%) were adult patients, 11,984 (19.0%) answered at least one monitoring questionnaire, and 9,667 (15.3%) provided medical background at inclusion and thus were included in this study (patients characteristics presented in Table S1 in Supplementary appendix).

Funding

This study received a funding by the Programme Hospitalier de Recherche Clinique 2020 of the French Ministry of Health, by a research fund by APHP-Fondation de France. The Covidom platform received a funding from EIT Health specific Covid-19 fund.

Time to symptom resolution in outpatients with COVID-19 varies widely with shorter systemic symptoms and longer respiratory symptoms. Prolonged respiratory symptoms, especially dyspnea, were common at day 30. Factors associated with later resolution were age over 30, elevated BMI, cardiovascular and pulmonary diseases.

Transparency declaration

The authors declare that they have no conflicts of interest. All authors have full control of all primary data and they agree to allow review of their data upon request.

Authors' contributions

ADi, LJ, ADe, XL, and YY conceptualized the study. Adi, LJ, ADe, CD, HM, and YY collected, analysed and interpreted the data. LH conducted the statistical analysis. Adi, LJ, Ade, CD, and YY wrote the first draft of the manuscript. All authors revised the manuscript and approved the final version. ADi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

AP-HP / Universities / Inserm COVID-19 research collaboration members

Writing committee: Aurélien Dinh 1,2 , Luc Jaulmes 3 , Agnès Dechartres 4 , Clara Duran 1 , Hélène Mascitti 1 , Xavier Lescure 2,5 , Youri Yordanov 6 , Patrick Jourdain 2,7