# Analysis of interactions amongst shade trees, coffee foliar diseases and coffee yield in multistrata agroforestry systems

Clémentine Durand-Bessart, Philippe Tixier, Alcide Quinteros, Federico Andreotti, Bruno Rapidel, Camille Tauvel, Clementine Allinne

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## To cite this version:

Clémentine Durand-Bessart, Philippe Tixier, Alcide Quinteros, Federico Andreotti, Bruno Rapidel, et al.. Analysis of interactions amongst shade trees, coffee foliar diseases and coffee yield in multistrata agroforestry systems. Crop Protection, 2020, 133, pp.105137. 10.1016/j.cropro.2020.105137. hal-02573763

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Submitted on 26 Nov 2021

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1 **Analysis of interactions amongst shade trees, coffee foliar diseases and coffee yield in multistrata agroforestry systems**

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5 Clémentine Durand-Bessart<sup>a<fnref n="1" /></sup>, Philippe Tixier<sup>bᶜ</sup>, Alcide Quinteros<sup>d</sup>, Federico Andreotti<sup>e</sup>

6 **Andreotti<sup>e</sup>, Bruno Rapidel<sup>a</sup>, Camille Tauvel<sup>a</sup>, Clémentine Allinne<sup>de</sup>**

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8 a CIRAD, UMR SYSTEM, Université de Montpellier, CIHEAM-IAMM, CIRAD, INRA,
9 Montpellier SupAgro, Montpellier, France

10 b GECO, Université de Montpellier, CIRAD, Montpellier, France

11 c CIRAD, UPR GECO, F-34398 Montpellier, France

12 d CIRAD, UMR System, Turrialba, Costa Rica

13 e CATIE, Agriculture, Livestock and Agroforestry Program, Turrialba, Costa Rica

14 f Laboratory of Geo-information Science and Remote Sensing, Wageningen University &
15 Research, 6708 PB Wageningen, The Netherlands

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17 **Corresponding author**

18 Clémentine Durand-Bessart, Biogéosciences UMR CNRS/uB 6282, Université de
19 Bourgogne, 6 bd Gabriel, 21000 Dijon, France; + 33 (0)3 80 39 63 56; clementine.durand-bessart@u-bourgogne.fr

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22 **Abstract**

<footnote n="1">¹ Present address: Biogéosciences UMR CNRS/uB 6282, Université de Bourgogne, 6 bd Gabriel, 21000 Dijon, France; + 33 (0)3 80 39 63 56; clementine.durand-bessart@u-bourgogne.fr</footnote>
In complex coffee-based agroforestry systems, quantifying the impact of shade trees on coffee disease regulation and coffee yield is crucial for improving these systems and designing more sustainable ones. To this end, we analyzed interactions amongst shade trees, coffee plants (cv. Catimor), the coffee foliar disease complex and soil characteristics. We studied systems characterized by 40 variables measured in 60 plots located on three farms (monitored for 2 years) in Nicaragua. These variables characterized six system components grouped in six statistical blocks: shade trees (shade percentage and species abundance), soil characteristics (fertility), foliar diseases, coffee plant characteristics (age and size), coffee growth and yield. We used partial least square path modelling (PLS-PM), i.e. a structural equation modelling approach used to understand and quantify interactions between the six blocks. Shade trees (mostly the associated shade percentage) had direct positive effects on foliar disease severity and incidence and soil quality, while having negative effects on coffee growth and yield. Soil characteristics (carbon, nitrogen, litter index, water infiltration potential) were negatively correlated with foliar diseases. An excessive shade percentage then had an indirect negative effect on coffee growth and yield due to the increased prevalence of foliar diseases. Finding the optimal shade cover can help reduce foliar diseases and enhance coffee berry production. The 'dose effect' of shade cover must also be considered because excessive shade, as well as lack of shade, have negative impacts on coffee growth and yield. Overall, effective shade management requires an analysis of trade-offs between soil quality, disease regulation and yield gains. In conclusion, PLS-PM turned out to be a good tool for studying agroecosystem networks and enabled us to put forward some foliar disease management and coffee yield enhancement guidelines.

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46 **Keywords**

Central America; *Coffea arabica*; disease regulation; ecological processes; trade-off; tree-shade impact; structural equation modelling; plant diversity

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# 1. INTRODUCTION

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Pests and diseases reduce coffee yields in Central American coffee-based agroforestry systems by 15-30% (Cerda et al., 2015). Sustainable management of these diseases based on agroecological processes, e.g. biological regulation optimization, is thus a key lever to increase coffee yield while maintaining the environmental sustainability of the cropping system. Trees associated with coffee plots support biological regulation to a major extent through direct and indirect processes (Ratnadass et al. 2012). Direct regulation effects that reduce diseases involve different processes, including: 1) dilution of host density, 2) reduction of soil diseases by favoring beneficial microorganisms, 3) allelopathic effects, 4) reservoir of natural enemies, and 5) creation of microclimates unfavourable for the diseases (Ratnadass et al. 2012). Shade trees may have indirect beneficial effects on coffee plants, mostly by enhancing coffee nutrition (Sauvadet et al. 2018). In complex agroecosystems with high spatially heterogeneous plant diversity associated with coffee plants, unravelling the direct and indirect effects of shade trees on all coffee crop systems is a great challenge.

Nicaragua is eighth largest C. arabica producing country in the world, with production reaching 2.54 million kg in the 2017-2018 cycle. Coffee cropping has a huge socioeconomic impact in this country, where 44 thousand coffee producers cultivate a total area of 1.5 million ha. Most of the farms grow coffee under agroforestry systems, and 97% of them are less than 14 ha. Nicaraguan coffee-based agroforestry systems are known to be particularly complex with a remarkable diversity of shade trees (Haggar et al., 2015). This diversity includes species that produce goods for local markets, native forest species that are grown mainly for timber, along with service tree species — mostly Fabaceae — that are planted to provide shade while improving soil fertility and crop system sustainability (Barradas and Fanjul, 1986; Vaast et al., 2005). The coffee rust outbreak that occurred in 2013–2014 led farmers to replace the rust-sensitive cv. Caturra plants in their coffee plantations with rust-resistant cv. Catimor plants (Libert Amico et al., 2019). However, Catimor cultivars are
particularly sensitive to the American leaf spot (ALS) disease caused by *Mycena citricolor* (Sequeira 1958; Staver et al., 2001; Allinne et al., 2016). Other diseases like brown-eye spot (*Cercospora coffeicola* Berk. & Cooke), anthracnose (*Colletotrichum* sp.) and coffee thread blight (*Corticium kolegora*) also affect cv. Catimor coffee plants (Waller et al., 2007). These foliar diseases have negative effects on coffee growth and production, and interact with each other as a disease complex depending on the coffee crop status and the microclimatic conditions. A major way to improve disease management is to integrate the role of shade trees on these foliar diseases (Avelino et al., 2018; Allinne et al., 2019). Indeed, ALS and coffee thread blight are favored by high humidity when shade levels are elevated, unlike brown-eye spot and anthracnose that tend to affect sun-grown coffee plants (Staver et al., 2001; Muller et al., 2008; Bedimo et al., 2012). Most Nicaraguan farmers do not have sufficient financial resources to manage pests and diseases using pesticides (Bro et al., 2019). Understanding the relations within disease complexes that affect coffee plants and the diversity of shade trees is crucial and requires an overview of the entire pathosystem.

In these complex agroforestry systems, shade trees do not just provide shade to the crop system, they also have directly impact coffee growth and yield by increasing the leaf surface and coffee quality (Vaast et al., 2005; Charbonnier et al., 2017). Conversely, shade trees and coffee plants can compete for light and nutrients, especially under high shade tree density conditions (Charbonnier et al., 2013). However, shade trees may also markedly alter the soil characteristics, and in some cases improve soil fertility (Sauvadet et al., 2018). This is well-known by farmers who often plant nitrogen-fixing trees (e.g. *Inga* spp.) to favor nitrogen fixation (Cerdán et al., 2012). These interactions between shade trees and agroecosystem processes are also driven by farmers through their pruning practices whereby the canopy is opened and dead branches are left on the ground (Cerdán et al., 2012). This litter addition around coffee plants may enhance soil fertility and promote the activity of beneficial soil microorganisms (Sauvadet et al., 2018). Coffee plant resistance against foliar diseases is dependent on these soil characteristics. Indeed, coffee plants growing in more fertile soil
have higher regeneration properties and growth, which are key physiological resistance factors (Ratnadass et al., 2012). Soil fertility also influences the quality and abundance of coffee beans produced (Barel and Jacquet, 1994; Lin, 2010).

New tools are needed to study this network of interactions between different agroecosystem components overall. We used a structural equation modelling (SEM) approach called partial least square path modelling (PLS-PM) to gain further insight into the direct and indirect effects of shade trees on coffee foliar diseases and coffee yield in Nicaraguan coffee-based agroforestry systems.

## 2. MATERIAL AND METHODS

### 4.2. STUDY SITES

The study took place in the Matagalpa region (Nicaragua's main coffee production area) near the village of El Tuma-La Dalia. We studied three small coffee farms from May 2016 to February 2018 under conventional, low-input and organic disease management conditions. The farms were chosen for their high shade tree diversity, with marked variability in the proportion of shade, i.e. 49-85% (Table 1). These farms only grew non-certified *Coffea arabica* (Rubiaceae) cv. Catimor plants. This genetic material was rust resistant (no evidence of rust affection was observed during the experiment) but sensitive to American leaf spot caused by *Mycena citricolor* (Allinne et al., 2016; Libert Amico et al, 2019). The farms were located between 13°02'67.7"N and 13°08'75.6"N and between 85°61'42.7"W and 85°71'48.3"W, within a similar elevation range (650-850 m a.s.l.). The mean annual temperature was 23°C, with annual precipitation ranging from 2,000 to 2,600 mm. The rainy season in this region is between May and December (Amores Contreras, 2015).

Table 1. Description of the three farms where the study was conducted in the area around El Tuma-La Dalia, Nicaragua.

Farm
<table><thead><tr><th>Variables</th><th>Unit</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>Community</td><td></td><td>Yale 3</td><td>Hilipo 2</td><td>Aguas Amarillas</td></tr><tr><td>Farm description</td><td>GPS location (N, W)</td><td>13.08756,- 85.61427</td><td>13.03735,- 8571483</td><td>13.02677,- 85.67999</td></tr><tr><td></td><td>Elevation (m)</td><td>750-800</td><td>850</td><td>650 - 700</td></tr><tr><td></td><td>Area (ha)</td><td>5</td><td>3</td><td>3</td></tr><tr><td>Meteorological data</td><td>Average temperature [min-max] (°C)</td><td>22.3 [19.2-29]</td><td>21.9 [19.2-28.4]</td><td>23.9 [21.1-28.7]</td></tr><tr><td></td><td>Cumulative rainfull (mm)</td><td>2600</td><td>2341</td><td>2132</td></tr><tr><td>Coffee plot description</td><td>Average coffee age (year)</td><td>9</td><td>6</td><td>6</td></tr><tr><td></td><td>Coffee density (plants/ha)</td><td>8882</td><td>8620</td><td>8679</td></tr><tr><td></td><td>Average shade cover [min-max] (%)</td><td>72 [62-77]</td><td>65 [49-76]</td><td>80 [71-85]</td></tr><tr><td></td><td>Average shade tree density (tree/ha)</td><td>360</td><td>350</td><td>487</td></tr><tr><td></td><td>Average shade species richness</td><td>30</td><td>18</td><td>37</td></tr><tr><td>Coffee crop management</td><td>Weeds</td><td>/ manual (3 x)</td><td>manual (3x)</td><td>chemical: glyphosate+ paraquat</td></tr><tr><td></td><td>Diseases</td><td>/ copper (Bordeaux mixture) (1x)</td><td>copper (Bordeaux mixture) (1x)</td><td>carbendazim (2x)</td></tr><tr><td></td><td>Pests</td><td>/ /</td><td>/ biofertilizer (foliar)</td><td>cipermetrina</td></tr><tr><td></td><td>Fertilization</td><td>/ /</td><td></td><td>NPK (20-5-20)</td></tr></tbody></table>

## 4.3. AGROFORESTRY SYSTEM CHARACTERIZATION

For each farm, we selected 20 circular plots (14 m dia.), centered on a shade tree and sorted in four different situations, with five replicates each. The first three situations were based around three common tree species spread on the farms, while the last situation was made around a random tree, from another tree species. The common species were: 1) *Cordia alliodora* (Ruiz & Pav.) Oken, laurel (Boraginaceae) a native forest species; 2) *Inga oerstediana* Benth, guaba roja (Leguminosae) service plant species; 3) *Musa* spp. Jussieu, guineo (Musaceae).

The distance between each plot was maximized. Inside each plot, the analysis included: (1) four coffee plants selected randomly within 5 m of the central tree, and (2) all shade trees taller than the coffee plants.
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### 4.3.1. SHADE TREE CHARACTERIZATION

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All shade trees within the sampled area were identified according to their species and family.

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Their characteristics (height (m), circumference (cm), leaf size) and their host status for ALS, brown-eye spot and anthracnose were recorded (Boshier et al., 2009; Cerdán et al., 2012).

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For all species, we also specified their main usage, and classified them in one or more categories: wood, timber, fruit, shade, N-fixation, native and wild (Pineda, 2006; Boshier et al., 2009; Román et al., 2012; Amores Contreras, 2015; Caceido, 2016).

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We combined the variables describing each shade tree by performing a multiple factor analysis (MFA) to cluster the shade-tree species within homogeneous groups based on the previously described variables. MFA was performed with R software using the MFA function from the FactoMineR package (Pagès, 2013).

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We took hemispherical pictures to characterize the shade percentage at four different times: November 2016, February 2017, June 2017 and September 2017. Hemispherical pictures were taken above all selected coffee plants with a Nikon Coolpix 4500 equipped with a fisheye converter (FC-E8 0.21x). These pictures were analyzed using Gap Light Analyzer (GPA-V2) software to assess the shade percentage above each coffee tree (Frazer et al., 1999). The annual mean shade percentage was used for the analysis.

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### 4.3.2. COFFEE TREE CHARACTERIZATION

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The coffee plant variables were measured from May 2017 to February 2018, describing:

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* Inherent coffee characteristics not affected by the local environment, such as age (years) and circumference (cm).

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* Coffee vegetative growth, described by the total number of nodes per branch, the number of new nodes per branch, the number of leaves per branch and the average leaf area. We measured these variables on three branches (one at the bottom, one in

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170 the middle and one at the top of each selected coffee tree). All measurements were
171 obtained at four times, representing a complete annual coffee physiological
172 development cycle: beginning of the rainy season (May 2017), beginning of the
173 harvest period (September 2017), peak of the harvest (December 2017) and post-
174 harvest (February 2018). All physiological variables were integrated over a time
175 course by determining the area under the disease progress curve (AUDPC), as is
176 frequently done for diseases (Simko and Piepho, 2011).

177 • Coffee yield was described by the number of fruiting branches per plant and the
178 number of fruiting nodes per plant as proxies of the accessible yield. The number of
179 dead branches per coffee plant after harvest is a proxy of primary yield loss (Cerda et
180 al., 2017).

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182 **4.3.3. COFFEE FOLIAR DISEASE CHARACTERIZATION**

183 The measured foliar disease complex encompassed American leaf spot (ALS; *Mycena
citricolor*), brown-eye spot (*Cercospora caffeicola* Berk. & Cooke), anthracnose
184 (*Colletotrichum* sp.) and coffee thread blight (*Corticium kolegora*). We measured the severity
185 (i.e. the percentage of diseased leaves) and the incidence (i.e. the percentage of leaf area
186 affected by diseases) of these diseases on three branches of selected coffee plants. As ALS
187 is a major foliar disease, we decided to treat it separately from other diseases to gain clear
188 insight into the relationship between the agrosystem and the ALS incidence and severity. We
189 separately integrated ALS variation patterns and those of other diseases by calculating the
190 relative AUDPC based on the measurements obtained at four dates (May 2017, September
191 2017, December 2017, February 2018), which represented a complete disease development
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195 **4.3.4. SOIL CHARACTERIZATION**
Measurements for characterizing the soil in each coffee plot were obtained at the beginning of the 2017 rainy season (between June and August). According to the protocol described by Thoumazeau et al. (2019a, 2019b) and adapted by Andreotti (2018). The measured soil characteristics included:

* The soil chemical composition: organic carbon (g/kg), pH, nitrogen percentage (N), iron (Fe), potassium (K), magnesium (Mg), and phosphorus (P) in ppm. Soil organic carbon and nitrogen are key soil components and are both indicators of soil fertility.

* The litter index accounted for the litter quantity and quality, which highly influences soil fertility, the nutrient cycle while being the main carbon source for soil organisms (Sauvadet et al., 2016).

* The Beerkan test was applied to measure the soil infiltration potential and generate information on the water infiltration potential (Lassabatere et al., 2006).

* The cation exchange capacity (CEC), which expresses the capacity of a soil to retain nutrients, was used as a soil fertility indicator (Chapman, 1965).

All three farms had the same soil physical characteristics, including a loamy sandy texture.

## 4.4. STATISTICAL ANALYSIS

Structural equation modelling (SEM) is particularly appropriate for gainign insight into interactions between shade trees, soil, diseases and coffee plants. SEM analyses are able to explain relationships between observed variables by clustering them as latent variables representative of common concepts. Structural equation modelling analyses can be used to understand complex systems (Hoyle, 2012; Vinzi and Trinchera, 2013) and was successfully applied to analyse ecological regulations in agroforestry systems in banana and cocoa plantation settings (Poeydebat et al. 2017; Oliveira et al., 2018). This type of analysis is divided into two main types: the SEM-ML method based on the maximum likelihood (ML) and the partial least square path modelling (PLS-PM) method based on simple regressions to explain the latent variables (Vinzi and Trinchera, 2013). The PLS-PM method was
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specifically chosen for its flexibility to manipulate datasets with numerous variables, and its
capacity to represent clearly complex interaction systems.

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PLS-PM is a blend of two models: a measurement model and a structural model (Fig. 1). The measurement model defines the relationships between observed variables and latent variables inside blocks, with each block being represented by a latent variable and built with observed variables (Fig. 1). The structural model investigates relationships between latent variables using a linear regression approach. We performed this network analysis with R software using the plspm function from the plspm package (Sanchez et al., 2013).

<figure><img src="image_3.png" /></figure>

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Figure 1. Scheme of the PLS-PM. Description of the measurement model (inside each block) and the structural model (black arrows between blocks). Exogenous latent variables are just explanatory, while endogenous are explanatory and explained (by other latent variable, either exogenous or endogenous). Block 1 and 3 are explicative, while block 2 is formative.

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4.5. MEASUREMENT MODEL BUILDING

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Our measurement model contained six blocks including latent variables corresponding to the
measurement domains presented earlier, i.e. inherent coffee characteristics (hereafter simply
called 'coffee characteristics'), shade trees, soil characteristics, foliar diseases, coffee growth and coffee yield (Table 2). We built three blocks related to coffee plants because we assumed that these blocks represent a specific aspect of coffee plants, and influence each other. Each block was composed of a latent variable and its related observed variables (Table 2). According to the method described by Sanchez (2013), the coffee characteristics, shade trees, soil characteristics, foliar diseases and coffee growth blocks were reflective because the variables observed inside each block were well correlated. Indeed, for each reflective block, observed variables have to move in the same direction, and when a variable increases or decreases, the others change in the same way (Sanchez, 2013). We verified this condition by examining the unidimensionality of these blocks with Dillon-Goldstein's rho. A block is unidimensional when its rho value is higher than 0.7 (Sanchez, 2013).

Inversely, the coffee yield block was formative because the numbers of fruiting nodes and fruiting branches were not closely correlated with the number of dead branches. As formative blocks do not require highly correlated observed variables, the block unidimensionality is not calculated.

Table 2. Description of blocks represented by their latent variable and observed variables. ALS is American leaf spot of coffee; organic C is soil organic carbon; N is the nitrogen percentage; iron (Fe), potassium (K), magnesium (Mg), and phosphorus (P) are in ppm; CEC is the cation exchange capacity.

<table><thead><tr><td>Latent variable</td><td>Related observed variables</td></tr></thead><tbody><tr><td>Coffee characteristics</td><td>Age, size (circumference)</td></tr><tr><td>Shade trees</td><td>Shade (%), abundance of services trees, fruit trees and timber trees</td></tr><tr><td>Soil characteristics</td><td>Litter index, Beerkan test, organic C, N, pH, Mg, Fe, K, CEC</td></tr><tr><td>Diseases</td><td>ALS severity, ALS incidence, other disease severity, other disease incidence</td></tr><tr><td>Coffee growth</td><td>Number of nodes, number of new nodes, number of leaves, leaf size, coffee height</td></tr><tr><td>Coffee yield</td><td>Number of fruiting branches, number of fruiting nodes, number of dead branches</td></tr></tbody></table>
We only kept observed variables that were correlated with the latent variable with a regression coefficient higher than 0.5 (Sanchez, 2013). Inside each block, the regression coefficient value explained how the observed variables influenced the latent variable. Higher coefficients indicated a higher influence on the block.

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## 4.6. STRUCTURAL MODEL BUILDING

The relationship between blocks was defined in the structural model according to previous studies (Fig. 2) (Allinne et al., 2016; Cerda et al., 2017). The latent variables shade trees and coffee characteristics were not explained by the other blocks and therefore were exogenous, while the latent variables soil, diseases, coffee growth and coffee yield were endogenous, because they were explained by the other blocks (Fig. 2).

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<figure><img src="image_4.png" /></figure>

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Figure 2. Pathways between the latent variables. Coffee characteristics and shade trees are exogenous (i.e. only explanatory) (white), while soil characteristics, diseases, coffee growth and coffee yield are endogenous (grey).

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In order to validate our block, the PLS-PM model was used to calculate the R² coefficient of each exogenous block that expressed the explained variability for each block. Other latent variables better explained the block when the R² coefficients were high. The model
281 parameters were thus adjusted to have R² coefficients higher than 0.2, which is a moderately
282 low value (Sanchez, 2013).

283 The regression coefficient between blocks clarified the relationship between the block, i.e.
284 either positive or negative.

285 Finally, the goodness-of-fit test was used to evaluate the model robustness (Sanchez, 2013).

286 All statistical analyses were performed with the R 3.5.1 package (Team R Core, 2018) and
287 with an alpha level of 0.05.

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# 3. RESULTS

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## 4.1. CLUSTERING OF SHADE TREES

291 The MFA led to three groups of shade tree, i.e. timber, service and fruit trees. The timber
group was composed mostly of native and forest species intended for wood production. The
292 service group had shade-tree species, mostly N-fixing species, that were planted to improve
293 the soil quality and shade percentage, though some of these species were hosts of some
294 coffee foliar diseases like ALS. The fruit group was represented mostly by species producing
295 secondary fruit products sold in local markets or consumed locally. These groups were used
296 in the PLS-PM model to build the ‘shade trees’ block.
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## 4.2. PLS-PM MODEL OUTPUT

300 The measurement model and structural model results shed light on the complex network of
301 interactions between shade trees, coffee plants, foliar diseases and soil characteristics.
302 While the measurement model showed the block compositions, the structural model revealed
303 the strength of the relation between them. The overall PLS-PM had a goodness-of-fit of
304 0.4971.

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## 4.3. RELATIONSHIPS BETWEEN THE OBSERVED AND ASSOCIATED LATENT VARIABLES (WITHIN BLOCK)
308 The measurement model findings showed that all reflective blocks ('coffee characteristics',
309 'shade trees', 'soil characteristics', 'diseases' and 'coffee growth') had a Dillon-Goldstein's
310 rho higher than 0.7 (Fig. 3). The correlation coefficients between the observed and
311 associated latent variables were higher than 0.5, except for the 'other-disease severity'
312 variable in the diseases block (Fig. 3). Although the 'other-disease severity' variable had a
313 coefficient of 0.41, we decided to keep it in the model because it provided a better
314 representation of the pest complex in the system (Fig. 3).

315 The 'coffee characteristics' latent variable was well-explained by its two observed variables,
316 i.e. age (0.96) and circumference (0.91) (Fig. 3). From the 'shade trees' block, only the
317 observed variables of the mean shade percentage (M. shade, 0.94) and service group (Serv,
318 0.64) had a significant impact. The 'soil characteristics' latent variable was explained only by
319 the Beerkan test (0.83), organic carbon quantity (0.6) and nitrogen percentage (0.62), as well
320 as the litter index (0.68). The 'soil characteristics' parameter thus represented the soil fertility.

321 The 'diseases' block was mainly explained by the observed variables related to ALS severity
322 (0.94) and ALS incidence (0.94). The remaining observed variables that explained the 'coffee
323 growth' block were the height (0.6), number of nodes (0.87), number of new nodes (0.9) and
324 number of leaves (0.55). Inside the 'coffee yield' block, the number of fruiting branches per
325 tree was more significant (0.87), it was correlated with the number of fruiting nodes (0.76) per
326 branch and with the number of dead branches (0.56).

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<figure><img src="image_5.png" /><figcaption>Figure 3. Results of the measurement model representing the six blocks with their latent variables (ovals) and observed variables (rectangles), and the correlation coefficient between each latent variable and the observed variables. Exogenous blocks are shown in white and endogenous blocks in grey. Reflective blocks are represented by arrows going from the latent to the observed variables, while the direction is reversed for formative blocks. The Dillon-Goldstein's rho values are shown above each block. Circ is the circumference of coffee plants; Age is age of coffee plants; Serv is the abundance of service trees; M. shade is the mean shade percentage; C is soil organic carbon; Lit. Index is the litter index; Beerkan is the Beerkan test results; N is soil nitrogen percentage; Inc. ALS is the ALS incidence; Sev. ALS is the ALS severity; Sev. Other is the severity of the other foliar diseases; Leaf nb is the number of leaves; Height is the height of coffee plants; New nod is the number of new nodes; Nodes nb is the number of nodes; Dead br is the number of dead branches, Fruit. Nod is the number of fruiting nodes; Fruit. Br is the number of fruiting branches.</figcaption></figure>

4.4. RELATIONSHIPS BETWEEN BLOCKS

All endogenous blocks had an R² coefficient higher than 0.2, the diseases and 'coffee yield' latent endogenous variables had an R² coefficient of between 0.2 and 0.5, the 'soil characteristics' and 'coffee growth' latent endogenous variables had an R² higher than 0.5 (Fig. 4).

The 'soil characteristics' block was positively correlated with the 'shade trees' block (0.14), the 'coffee characteristic' block (0.74) and the 'coffee growth' block (0.19), but negatively
349 correlated with 'coffee yield' (-0.08) and 'diseases' (-0.1). 'Diseases' was positively correlated
350 with 'coffee characteristics' (0.33) and 'shade trees' (0.49).

351 The 'coffee growth' and 'coffee yield' blocks were negatively correlated with 'diseases' (-0.02; -0.12) and 'shade trees' (-0.25; -0.37).

353 The 'coffee characteristics' and 'coffee growth' had a positive correlation (0.54). The 'coffee
354 yield' block was negatively correlated with the 'coffee characteristics' and 'coffee growth'
355 blocks (-0.36; -0.06).

<figure><img src="image_6.png" /></figure>

356
357 Figure 4. Results of the structural model representing the network of interactions between blocks, as
358 shown by significant paths. Each arrow represents shade with its regression coefficient: blue arrows
359 represent shade with a positive coefficient and red arrows with a negative coefficient. Endogenous
360 blocks (grey) are also represented by their R² coefficient, with the coefficient being null for exogenous
361 blocks (white).
362

363 4. DISCUSSION

364 4.1. ANALYSIS OF DIRECT EFFECTS AMONG THE SYSTEM COMPONENTS

365 The soil characteristics block (soil fertility proxy) was positively correlated with the shade tree
366 block. This correlation meant that soil fertility was higher in plots with a larger shade
367 percentage and with a greater number of N-fixing trees, confirming the findings of the recent
368 study of Sauvadet et al. (2018). Indeed, the fertility under shade trees was increased by the
369 N-fixation capacity of the service trees as well as by the shade tree pruning practices (Beer
370 et al., 1997). The increased litter quality and quantity restored soil organic carbon and
371 promoted the development of vital soil microorganisms like bacterial-feeding nematodes
372 (Sauvadet et al., 2018). Most N-fixing tree species lose their leaves during the dry season;
373 those leaves are fast decomposing materials that represent a source of C and nutrients for
374 the soil (Tapia-Coral et al., 2005). Litter restitution thus improves and maintains the soil
375 activity and fertility during this season, which is crucial for coffee production (Wintgens,
376 2004).

377 The diseases block was positively correlated with shade trees, thus highlighting their
378 importance in disease management (Avelino et al., 2018). The model confirmed that ALS —
379 a major component of the block — was favored by high shade which induces high humidity
380 (Avelino et al., 2007). In addition, some species of the service tree group, especially *Inga*
381 spp. were ALS hosts (Granados Montero, 2015) and could be a significant source of
382 inoculum (Staver et al., 2001).

383 The negative correlation between shade trees and coffee growth and coffee yield suggested
384 that reducing the canopy openness limits the light available for coffee growth (DaMatta,
385 2004). Shade trees were generating 49-85% shade (73% on average), which was much
386 above the shade percentage found in most conventional coffee plantations. Although the
387 effect of the shade percentage on coffee growth is still quite controversial, higher growth
388 rates (up to the 40% threshold) are usually observed under shade (Charbonnier et al. 2017).
389 In our case, all plots were equal or above this threshold and a negative effect of shade on
390 coffee growth and yield was therefore expected.

391 The negative correlation between the 'soil characteristics' block and the 'diseases' block,
392 indicated that coffee plants growing in more fertile soils are less affected by foliar diseases.
393 Soil fertility could have induced a physiological resistance, as demonstrated with coffee rust
394 (Toniutti et al 2017).
395

396

The positive relation observed between coffee characteristics (bigger and older coffee plants) and soil characteristics (fertility proxy) was probably related to the fact that soil quality was higher in older plantations. This could be explained by the acceleration of carbon cycle dynamics due to the increased organic matter input in the soil system, notably from litter fall linked to increased biotic activity, as demonstrated in rubber plantations (Thoumazeau et al, 2019b). Moreover, old coffee plants were more pruned in the previous year and pruning residue was left on the ground, thus increasing the soil organic matter (Gomez-Munoz et al. 2016). Pruning practices on older coffee plants could also explain why, despite the fact that they were growing on more fertile soils and had the better growth, they had the lowest berry production. Indeed, freshly pruned coffee plants first distribute their resources to promote growth (Charbonnier et al. 2017). The positive correlation between the coffee plant age and the disease incidence and severity illustrated that, besides their better growth and resistance related to high fertility, older coffee plants were more sensitive to foliar diseases.

397

398

399

400

401

402

403

404

405

406

407

408

409

410

As expected, foliar-diseases had a negative effect on coffee growth and yield. Foliar diseases reduced the leaf area available for photosynthesis process and did not allow plants to recover and sprout leaves or new nodes (Waller et al., 2007). Higher disease incidence and severity reduced fruiting production — this was the combined result of decreased photosynthesis and reduced redistribution of resources from leaves to fruits (Cerda et al., 2017).

411

412

413

414

415

416

417

## 4.2. ANALYSIS OF INDIRECT EFFECTS OF THE SYSTEM COMPONENTS ON DISEASES, COFFEE GROWTH AND YIELD

418

419

Here we review the indirect and antagonistic effects highlighted by the PLS-PM model. First, a close relationship was noted between coffee characteristics, coffee growth and soil blocks.

420

421

As discussed previously, the plots with higher fertility were associated with older coffee
422

plants, which had higher growth due to the pruning practices. However, for the same reason,
these coffee plants had the lowest berry production.

423

424

Although shade cover had antagonistic effects on the leaf diseases studied, with a high level
of ALS but a low level of brown-eye spot (Avelino et al, 2018), we found that the shade trees
favored foliar diseases overall. By increasing the soil fertility, they increased the coffee
resistance, thereby reducing the disease incidence and severity. Moreover, the negative
relationship between high shade cover and coffee growth and yield was direct but also
indirect via the foliar diseases fostered by the shade trees. Conversely, high shade cover
was also indirectly and positively related to high soil fertility, which increases the growth of
coffee plants and reinforces their resistance (Toniutti et al. 2017).

425

426

427

428

429

430

431

432

Finally, coffee production resulted from a set of factors derived from the direct and indirect
effects of all components. All effects within the agroecosystem must be taken into account to
achieve balanced foliar disease management. It is now essential to quantify the trade-off
between shade trees, soil characteristics, diseases, coffee growth and yield in order to
improve overall agroecosystem management, and above all coffee production.

433

434

435

436

437

438

4.3. PLS-PM TO UNDERPIN FUTURE INITIATIVES AND PROSPECTS

439

The PLS-PM findings had a goodness-of-fit of about 0.5, which is an average value. We
noted that all of the ‘soil characteristics’ and ‘coffee growth’ blocks were better explained
than others. Inversely, the ‘diseases’ and ‘coffee yield’ blocks were less well explained than
other blocks.

440

441

442

443

It would be interesting to integrate the herbaceous layer in the analysis so as to gain insight
into the ‘diseases’ and ‘coffee yield’ blocks. Recent studies showed that this herbaceous
layer also has an impact on the ALS incidence and severity (Granados Montero, 2015). The
extent of the incidence and severity of the herbaceous layer would directly affect the
incidence and severity of diseases on the coffee plants and indirectly the coffee yield via a

444

445

446

447
448 direct impact on dead branches. Adding the secondary loss, i.e. dead branches, in 2016
449 directly affected the number of available berry producing branches in 2017.

450 In future studies, it would also be interesting to integrate coffee growth from previous years to
take the biannual resource allocation of coffee plants into account. Another improvement
451 would be to integrate temperature fluctuations and precipitation patterns, which have a
452 marked impact on coffee tree growth and production (Charbonnier et al., 2017), as well as on
453 the degree of ALS incidence and severity (Avelino et al., 2007), but that will require larger
454 datasets.
455

456

457

## 5. CONCLUSION

458 PLS-PM enabled us to study the network of interactions occurring within the agroecosystem,
including antagonistic effects of shade trees. First, shade trees had a negative effect on
459 coffee growth and yield and increased the foliar diseases incidence and severity, and
460 secondly, they increased soil fertility which in turn decreased the disease prevalence and
461 increased coffee growth. This holistic approach regarding the role of trees in the ecosystem
462 highlighted the need to consider the shade percentage quantitatively (an excess or lack of
463 shade negatively impacted coffee growth and yield). It will be essential to assess the trade-
464 offs between shade management, soil quality, disease regulation and yield gain when
465 designing cropping systems that optimize shade cover.
466

467

468

## CONFLICTS OF INTEREST

469 The authors declare that they have no conflicts of interest.

470

471

## ACKNOWLEDGEMENTS
472 This research was financially supported by the STRADIV project (grant n° 1504-003) funded
473 by Agropolis Fondation. Special thanks to Marvin Rivera, Mariano Suazo and Adolfo Cajina,
474 the three farmers who kindly welcomed and guided us in their coffee plantations over the 2-
475 year study period. We are grateful to David Manlay for English revision. We thank the editors
476 and the anonymous reviewers for the comments we received that helped improving the
477 manuscript.

478

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