domain: sde sdv

text:
评估三个常见遮荫树种的影响:b Javanica (茄苳

、 C Camphora

和 Jacaranda

遮荫树和咖啡树的根系和土壤水分剖面 , 深度

。 为此

, 促进景观健康

, 以及云南南部咖啡行业的可持续发展

。 关键词:农林系统

, 阿拉伯咖啡

, 咖啡质量

, 生态系统服务

, 民族植物学知识

),Dimocarpus longan(龙 眼),Litchi chinensis(荔枝),Macadamia integrifolia(澳洲坚果)和 Mangifera indica (芒果)-因其被认同的经济潜力和防止咖啡树受到环境危害的保护能力而受到高度青睐。

Keywords: Agroforestry system, Coffea arabica, Coffee quality, Coffee yield, Ecosystem service, Ethnobotanical knowledge, Soil fertility

Alstonia scholaris(灯台叶),Bischofia javanica(茄苳),Cerasus cerasoides(樱 花),Cinnamomum camphora(番樟),Delonix regia(凤凰木

VI

highly favored for their perceived economic potential and the protection that they provided to coffee trees against environmental hazards. I also identified non-promoted species, such as Artocarpus heterophyllus and Diospyros kaki, with high potential to provide locally relevant ecosystem services in coffee farms. These results led to the upgrade of an online tool (www.shadetreeadvice.org) which allows extension services generating lists of recommended shade tree species tailored to the specific local ecological context of southern Yunnan and to individual farmers' needs. Lastly, this study pointed out knowledge gaps from coffee farmers regarding the impacts of shade trees on soil fertility, coffee yield, coffee quality and pests and diseases control.

In the second and third studies (Chapters IV and V), I set up a field experiment in Liushun Township, located in Pu'er Prefecture, to assess the impacts of three commonly found shade tree species -B. javanica, C. camphora and Jacaranda mimosifoliaon soil fertility, coffee yield, and coffee quality only four years after their introduction into intensively managed monoculture coffee farms under near-optimal growing conditions. For soil fertility (Chapter IV), I examined soil chemical parameters (total N, available P, exchangeable K, Ca and Mg, organic matter, pH), soil biological communities (nematodes and microbial communities) and soil enzyme activities (β-glucosidase, N-acetyl-glucosaminidase and acid phosphatase) in the top (0-20 cm) soil layer, as well as root systems of shade trees and coffee trees and soil water profiles to a depth of 1.2m. I demonstrated that all three shade tree species contributed to improving soil fertility. In particular, I measured higher soil chemical fertility (higher pH, OM, N, P and Ca concentrations); similar or higher soil enzyme activities throughout the year (all three measured enzymes); more abundant fungi communities throughout the year; and more abundant microbial communities during the dry season below shade trees than in open areas. Soil water profiles highlighted that annual rainfalls were sufficient to provide enough water resources for both coffee trees and shade trees.

On the other hand, root profiles pointed out fierce root competition between B. javanica and coffee trees in the 0-20 cm soil layer and between C. camphora and coffee trees below 20 cm depth. Overall, Chapter IV evidenced that shade trees rapidly contributed to preserving and/or restoring soil fertility and buffering seasonal variability in soil biological activity in intensively managed coffee farms.

In Chapter V, I assessed the above-ground impacts of these same shade trees on coffee yield and coffee quality. To do so, I estimated the yield of 309 coffee trees from their fruit load in November 2016, one month before harvest. In 2017, I recorded micro-climate data (air temperature and humidity every 30 minutes) for one year below the canopies of the three selected shade tree species and in open conditions. I simultaneously followed the coffee development cycle of 90 coffee trees located either below shade tree canopies, at the edge of shade tree canopies or Table 3-

LIST OF FIGURES

LIST OF TABLES

. Coffee growing in Yunnan Province

Coffee often ranks as the first commodity in the international agricultural trade market [START_REF] Damatta Fm | Ecophysiological constraints on the production of shaded and unshaded coffee: a review[END_REF]. Most of the production is shared between Arabica coffee (Coffea arabica) and Robusta coffee (Coffea cenaphora var. Robusta), the former accounting for 55% of the world production while the latter accounts for the remaining 45% (FAO). It is estimated that 25 million households rely on coffee production to sustain their livelihoods, underlying its importance in the global rural economy [START_REF] Pendergrast M | Uncommon Grounds: The History of Coffee and How it Transformed Our World[END_REF]. In China, coffee consumption and production have been growing at double digit rates since the mid-90s and, according to the International Coffee Organization (ICO), this trend shows no sign of slowing down.

Over 95% of the coffee grown in China is sourced from Yunnan Province [START_REF] Zhang | Arabica Coffee Production in the Yunnan Province of China[END_REF], a region traditionally devoted to tea cultivation. Its mild climate and mountainous landscape offers near optimal growing conditions for Arabica coffee. Indeed, Arabica coffee performs best in mountainous areas with annual average temperatures ranging from 18 to 22 °C [START_REF] Descroix | Environemental Factors Suitable for Coffee Cultivation[END_REF][START_REF] Zullo | Potential for growing Arabica coffee in the extreme south of Brazil in a warmer world[END_REF]) and acid soils with pH values ranging from 5.0 to 6.0 [START_REF] Descroix | Environemental Factors Suitable for Coffee Cultivation[END_REF][START_REF] Vaast | Effects of solution pH, temperature, nitrate/ammonium ratios, and inhibitors on ammonium and nitrate uptake by Arabica coffee in short-term solution culture[END_REF]. Arabica coffee requires 1200 to 2000mm of annual precipitation as well as a marked dry season coinciding with a cool season and lasting 2 to 4 months, which plays an important role in stimulating flowering [START_REF] Damatta Fm | Impacts of drought and temperature stress on coffee physiology and production: a review[END_REF][START_REF] Descroix | Environemental Factors Suitable for Coffee Cultivation[END_REF]. Members from the Jingpo cross border ethnic minority group and French missionaries first took advantage of these suitable growing conditions and introduced Arabica coffee in Yunnan Province at the end of the 19 th century, thereby contributing to some of the oldest coffee plantations in the area, such as those found in Baoshan and Dali (Chen 2017).

These coffee estates remained confined to a few thousand hectares during most of the 20 th century (Chen 2017; FAO). The introduction of the Catimor cultivar, a hybrid resistant to coffee leaf-rust disease (Hemileia vastatrix) contributed to the expansion of coffee farms in the 1980s [START_REF] Zhang | Arabica Coffee Production in the Yunnan Province of China[END_REF]. Indeed, H. vastatrix is one of the main coffee diseases worldwide, causing up to 20 to 25% losses in harvest during bad years [START_REF] Mccook S | Global rust belt: Hemileia vastatrix and the ecological integration of world coffee production since 1850[END_REF]. This fungal disease is also found in Yunnan Province. For this reason, around 90% of coffee cultivars cultivated in Yunnan are now derived from Catimor [START_REF] Zhang | Arabica Coffee Production in the Yunnan Province of China[END_REF]; this cultivar is resistant to H. vastatrix, and it produces high yield with a cup quality suitable to commodity coffee. It was introduced along with intensive farming practices similar to those promoted in Central America in the 1970s, relying on dense monoculture plantations (around 5000 trees.ha -1 ) and sustained with high mineral fertilizer inputs [START_REF] Haggar | Coffee agroecosystem performance under full sun, shade, conventional and organic management regimes in Central America[END_REF][START_REF] Mccook S | Global rust belt: Hemileia vastatrix and the ecological integration of world coffee production since 1850[END_REF]. Unfortunately, new races of H. vastatrix have recently been identified in China and detected in cultivated Catimor cultivar samples, highlighting a susceptibility of Catimor to these new rust races [START_REF] Zhang | Arabica Coffee Production in the Yunnan Province of China[END_REF].

The second factor explaining the boost in coffee production in Yunnan Province is the arrival of major agro industries in the 1980s and 1990s, following the economic reforms and the opening of Chinese economy [START_REF] Jia | Sustainable Champions: How International Companies are Changing the Face of Business in China[END_REF]. These companies offered an outlet for a commodity otherwise seldom consumed within the domestic market at the time. Following that, coffee estates underwent a rapid areal growth in locations traditionally dominated by tea production.

The challenges to coffee growing in Yunnan Province

The rapid expansion of coffee farming areas under intensive management practices has successfully established Yunnan Province as an important coffee growing center. With yield over 3 t.ha -1 .yr -1 recorded in Baoshan and Dehong Prefectures [START_REF] Liu X | Impacts of regulated deficit irrigation on yield, quality and water use efficiency of Arabica coffee under different shading levels in dry and hot regions of southwest China[END_REF][START_REF] Zhang | Arabica Coffee Production in the Yunnan Province of China[END_REF], coffee yield in Yunnan is indeed among the highest worldwide [START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF]. The coffee growing sector nonetheless faces several challenges that threaten its sustainability. First, despite the emergence of a select few specialty coffee producers [START_REF] Sun | China: Coffee Grows Quickly in a Tea Culture. In: Coffee A comprehensive guide to the bean, the beverage, and the industry[END_REF], Yunnan coffee remains characterized by low to medium quality beans sold as commodity coffee. Due to global and recurrent oversupplies of coffee, commodity coffee often fetches low prices (Pendergrast 1999). In spring 2018, Arabica coffee sold for approximately 1.2 USD.lb -1 on the commodity market, barely enabling smallholder farmers to cover their yearly production costs. Furthermore, the commodity market is prone to high price volatilityfetching as high as 3.0USD.lb -1 in April 2011, and as low as 1.1USD.lb -1 in October 2013and hence exposes coffee farmers to high financial vulnerability [START_REF] Gemech | Coffee price volatility in Ethiopia: effects of market reform programmes[END_REF].

In addition, due to high mineral fertilizer inputs, often exceeding 200 kg.N.ha -1 .yr -1 as frequently observed in smallholder farms in China [START_REF] Ju | Reducing China's fertilizer use by increasing farm size[END_REF], and low return of organic matter, intensively managed monoculture coffee farms are confronted to a continuous decline in soil fertility [START_REF] Cannavo | Low nitrogen use efficiency and high nitrate leaching in a highly fertilized Coffea arabica-Inga densiflora agroforestry system: a 15N labeled fertilizer study[END_REF]. As documented by Zhao et al. (2018a) in a chronosequence of coffee farms in Hainan Province, soils in intensively managed coffee farms suffer from erosion, acidification, depletion of their organic carbon content, and a reduction of their microbial activity. This decline in soil fertility in turn leads to a rising need for external inputs to replace the loss of soil regulated ecosystem services [START_REF] Sandhu Hs | Organic agriculture and ecosystem services[END_REF] and delay an otherwise inevitable decline in coffee yield.

Lastly, coffee species are known to be highly sensitive to climate change [START_REF] Bunn | A bitter cup: climate change profile of global production of Arabica and Robusta coffee[END_REF][START_REF] Craparo Acw | Coffea arabica yields decline in Tanzania due to climate change: Global implications[END_REF], and smallholder farmers particularly vulnerable to its consequences [START_REF] Baca | An Integrated Framework for Assessing Vulnerability to Climate Change and Developing Adaptation Strategies for Coffee Growing Families in Mesoamerica[END_REF][START_REF] Harvey Ca | Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar[END_REF][START_REF] Jf | The impact of climate change on smallholder and subsistence agriculture[END_REF]. In Yunnan Province, annual rainfalls have already declined since 2000, while droughts have increased in frequency [START_REF] Qiu | China drought highlights future climate threats[END_REF][START_REF] Zomer Rj | Projected impact of climate change on the effectiveness of the existing protected area network for biodiversity conservation within Yunnan Province, China[END_REF]. This observation is of particular importance considering that drought is the main environmental factor hindering coffee yield in most coffee growing areas [START_REF] Laderach | Predicted Impact of Climate Change on Coffee Supply Chains[END_REF].

The effects of droughts are especially pronounced when they occur during the coffee bean filling stage, with losses reaching up to 80% in extremely dry years [START_REF] Damatta Fm | Physiological and Agronomic Performance of the Coffee Crop in the Context of Climate Change and Global Warming: A Review[END_REF]. Furthermore, the changes in climate can already be observed through studying the average annual temperature, which has increased by 1.6C in the last 50 years [START_REF] Wong Mh-G | How will the Distribution and Size of Subalpine Abies Georgei Forest Respond to Climate Change? A Study in Northwest Yunnan, China[END_REF] and is projected to further increase by 1.6 to 4.8C by 20501.6 to 4.8C by (Zomer et al. 2015)). As shown in previous studies, an increase in temperature should result in a reduction in coffee growth [START_REF] Damatta Fm | Physiological and Agronomic Performance of the Coffee Crop in the Context of Climate Change and Global Warming: A Review[END_REF]; more frequent photoinhibition periods [START_REF] Batista Kd | Photosynthetic limitations in coffee plants are chiefly governed by diffusive factors[END_REF][START_REF] Chaves Arm | Seasonal changes in photoprotective mechanisms of leaves from shaded and unshaded field-grown coffee (Coffea arabica L.) trees[END_REF]; faster ripening of coffee cherries [START_REF] Pezzopane | Heat requeriments of Mundo Novo coffee for the flowering-harvest phenological stage[END_REF]; and an overall decline in coffee quality [START_REF] Tolessa | Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee[END_REF]. On top of these expected negative impacts on coffee yield and coffee quality, climate change will also increase the frequency of extreme temperature events [START_REF] Adachi | Basic performance of a new earth system model of the Meteorological Research Institute (MRI-ESM1)[END_REF]) and might favor pest outbreaks [START_REF] Kutywayo | The Impact of Climate Change on the Potential Distribution of Agricultural Pests: The Case of the Coffee White Stem Borer (Monochamus leuconotus P.) in Zimbabwe[END_REF].

The promotion of agroforestry practices to answer these challenges

In many countries, coffee is traditionally grown under agroforestry systems, whereby perennial trees are integrated into coffee fields. The World Agroforestry Center (ICRAF) defines agroforestry (AFS) as "a sustainable land management system which increases the overall yield of the land, combines the production of crops (including tree crops) and forest plants and/or animals simultaneously or sequentially, on the same unit of land, and applies management practices that are compatible with the cultural practices of the local population" (King and Chandler 1978). These more complex ecological systems are especially well-suited for coffee cultivation since coffee originated in the shaded high tropical forests of Ethiopia [START_REF] Zullo | Potential for growing Arabica coffee in the extreme south of Brazil in a warmer world[END_REF]. Their management requirements are also more complex than in the case of monoculture coffee systems. Nonetheless, these systems provide multiple socio-economic benefits and environmentally positive impacts, referred to as ecosystem services (ES) [START_REF] Cerdán Cr | Local knowledge of impacts of tree cover on ecosystem services in smallholder coffee production systems[END_REF].

Amongst others, agroforestry systems can simultaneously provide resources to local stakeholders such as food, fodder, or fuelwood [START_REF] Neupane Rp | Impact of agroforestry intervention on soil fertility and farm income under the subsistence farming system of the middle hills, Nepal. Agriculture[END_REF][START_REF] Rao Kpc | Adaptation to Climate Change through Sustainable Management and Development of Agroforestry Systems[END_REF][START_REF] Rice Ra | Agricultural intensification within agroforestry: The case of coffee and wood products[END_REF]. They are especially well-suited for smallholder farmers thanks to their high potential in decreasing vulnerability towards market fluctuations [START_REF] Haggar | Coffee agroecosystem performance under full sun, shade, conventional and organic management regimes in Central America[END_REF]. Agroforestry practices can additionally benefit Yunnan coffee farmers as an efficient mitigation strategy against the white stem borer (X. quadripes), which is the main pest in the area [START_REF] Waller | Stem-and branch-borers[END_REF].

Shade trees are also known to sustain higher soil fertility than conventional monoculture systems [START_REF] Barrios E | Soil biota, ecosystem services and land productivity[END_REF]. Deep rooted shade trees can access deep soil layers and recycle water and nutrients otherwise inaccessible to coffee plants [START_REF] Lehmann | Subsoil root activity in tree-based cropping systems[END_REF][START_REF] Padovan Mp | Root distribution and water use in coffee shaded with Tabebuia rosea Bertol. and Simarouba glauca DC. compared to full sun coffee in sub-optimal environmental conditions[END_REF]Tully et al. 2012). AFS advantages water infiltration over runoff [START_REF] Cannavo | Agroforestry associating coffee and Inga densiflora results in complementarity for water uptake and decreases deep drainage in Costa Rica[END_REF]) and reduces soil erosion [START_REF] Barrios E | Agroforestry and Soil Health: Linking Trees, Soil Biota, and Ecosystem Services[END_REF][START_REF] Blanco Sepúlveda | Soil erosion and erosion thresholds in an agroforestry system of coffee (Coffea arabica) and mixed shade trees (Inga spp and Musa spp) in Northern Nicaragua[END_REF]. Litter input from shade trees contributes to soil nutrient and organic matter [START_REF] Payán | Soil characteristics below Erythrina poeppigiana in organic and conventional Costa Rican coffee plantations[END_REF]Tully and Lawrence 2012).

Combined with trees below-ground activity, litter also contributes to larger and richer soil communities [START_REF] Bagyaraj Dj | Below ground microbial diversity as influenced by coffee agroforestry systems in the Western Ghats, India. Agriculture[END_REF][START_REF] Muleta | Distribution of arbuscular mycorrhizal fungi spores in soils of smallholder agroforestry and monocultural coffee systems in southwestern Ethiopia[END_REF]) and increases nutrient use efficiency [START_REF] Cannavo | Low nitrogen use efficiency and high nitrate leaching in a highly fertilized Coffea arabica-Inga densiflora agroforestry system: a 15N labeled fertilizer study[END_REF][START_REF] Nygren P | Symbiotic dinitrogen fixation by trees: an underestimated resource in agroforestry systems?[END_REF]. Nonetheless, these benefits are subject to the choice and management of relevant shade tree species, to ensure that they promote beneficial ecosystem services while circumscribing above-ground and below ground competition [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Haggar | Coffee agroecosystem performance under full sun, shade, conventional and organic management regimes in Central America[END_REF]. Furthermore, C. arabica lacks efficient mechanisms to regulate its fruit set in open conditions [START_REF] Cannell Mgr | Physiology of the Coffee Crop[END_REF]. Shade helps to balance fruit production and vegetative growth, attenuating the common phenomenon of biannual patterns in yield, when high crops are followed by a recovery period with low yield [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Damatta Fm | Ecophysiological constraints on the production of shaded and unshaded coffee: a review[END_REF], thereby reducing farmers' economic vulnerability. Shade, like altitude, also slows down the ripening of coffee beans and better synchronizes it with the ripening of coffee pulp, thereby improving the physical, chemical, and organoleptic qualities of coffee beans [START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF]. The increase in coffee quality can help farmers reaching specialty coffee standards. This can in turn lead to higher incomes and can lower their vulnerability to quick market fluctuations for commodity coffee. As a general rule, these benefits increase as growing conditions become less favorable to coffee agriculture [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Rahn E | Exploring adaptation strategies of coffee production to climate change using a process-based model[END_REF]. On the other hand, shade trees can be detrimental to coffee yield and quality under optimal conditions [START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF]. Similarly, highly competitive and poorly managed shade trees can reduce coffee yield and quality [START_REF] Steiman | Shade coffee in Hawai'i -Exploring some aspects of quality, growth, yield, and nutrition[END_REF][START_REF] Vaast | Biophysical Interactions Between Timber Trees and Arabica Coffee in Suboptimal Conditions of Central America[END_REF].

Furthermore, agroforestry systems are more efficient than monocultures in sequestrating carbon dioxide and thus in tackling climate change [START_REF] Ramachandran Nair | Agroforestry as a strategy for carbon sequestration[END_REF]). In addition, they are especially well-suited for smallholder farmers, thanks to their high potential in decreasing vulnerability to extreme climatic events [START_REF] Charles Rl | Agroforestry as Adaptation Strategy under Climate Change in Mwanga District, Kilimanjaro, Tanzania[END_REF][START_REF] Bb | Synergies between Agricultural Intensification and Climate Change Could Create Surprising Vulnerabilities for Crops[END_REF]. Shade tree canopies buffer extreme temperatures and can reduce leaf damages from heat waves and cold spells [START_REF] Bb | Agroforestry management as an adaptive strategy against potential microclimate extremes in coffee agriculture[END_REF][START_REF] Vaast | Coffee and Cocoa Production in Agroforestry-A Climate-Smart Agriculture Model[END_REF]. Shade trees also increase relative air humidity during daytime, decrease vapor pressure deficit and, given that below-ground competition for water is not too fierce, relieve hydric stress and improve the water status of coffee trees located under their canopies [START_REF] Cannavo | Agroforestry associating coffee and Inga densiflora results in complementarity for water uptake and decreases deep drainage in Costa Rica[END_REF][START_REF] Van Kanten R | Transpiration of Arabica Coffee and Associated Shade Tree Species in Sub-optimal, Low-altitude Conditions of Costa Rica[END_REF]. This can in turn increase coffee stomatal conductance and light use efficiency of this shade-adapted species [START_REF] Batista Kd | Photosynthetic limitations in coffee plants are chiefly governed by diffusive factors[END_REF][START_REF] Franck | Limitation of coffee leaf photosynthesis by stomatal conductance and light availability under different shade levels[END_REF].

In light of all these assets, agroforestry is receiving more and more attention from researchers and policy makers, and is getting wildly promoted amongst smallholder farmers [START_REF] Garrity Dp | Evergreen Agriculture: a robust approach to sustainable food security in Africa[END_REF]. In Pu'er and Xishuangbanna Prefectures in particular, local governments have adopted agroforestry as a strategy to answer the many challenges faced by the coffee sector, in particular the decrease in soil fertility and low coffee prices. In 2012, after a similar campaign for promoting the use of shade trees in tea estates, the government began to encourage the largescale production of "ecologically-friendly coffee" by distributing free shade tree seedlings to coffee farmers. The distribution of a set of both indigenous and exotic shade tree species, among which were included a few trees of economic importance such as the Macadamia nut tree (Macadamia integrifolia), illustrates the trade-off between biodiversity conservation and economic objectives. There has been a large-scale transition from monoculture coffee to shaded coffee cultivation in both Pu'er and the neighbouring prefecture of Xishuangbanna following this campaign.

Research Gaps

Preliminary investigations revealed that most coffee-agroforestry systems in Pu'er and Xishuangbanna Prefectures relied on young shade trees. These young trees were planted in the past five years following promotion activities by local governments. However, due to the recent expansion of coffee farms, and the more recent expansion of shaded coffee, no comprehensive description of these farming systems could be found in scientific literature except for studies focusing on cultivar selection [START_REF] Bai | Report on coffee varieties comparaison tests[END_REF][START_REF] Zhang | Arabica Coffee Production in the Yunnan Province of China[END_REF][START_REF] Zhang | Use of coffee varieties in Yunnan and Inovative application[END_REF]) and irrigation (Liu et al. 2018). Liu et al. (2017) calls for further research on coffee farming practices in Yunnan Province, based on experiences retrieved from other traditional coffee producing countries.

Contributing to research on coffee-agroforestry in Yunnan Province therefore requires a description of the existing farming practices and inventories of shade trees currently found in coffee estates.

Coffee-agroforestry systems have long been studied worldwide in traditional coffeeproducing countries [START_REF] Tscharntke | Multifunctional shade-tree management in tropical agroforestry landscapesa review[END_REF]. However, despite the general consensus regarding the beneficial impacts of trees among agricultural systems, more research is needed in order to understand agroforestry systems to an extent that would permit the development of comprehensive models [START_REF] Avelino | Effects of crop management patterns on coffee rust epidemics[END_REF][START_REF] Luedeling | Agroforestry systems in a changing climatechallenges in projecting future performance[END_REF]. In particular, the global impact of shade on coffee ecophysiology, coffee production and coffee quality is now well understood [START_REF] Damatta Fm | Ecophysiological constraints on the production of shaded and unshaded coffee: a review[END_REF][START_REF] Rahn E | Exploring adaptation strategies of coffee production to climate change using a process-based model[END_REF]), but research is still needed to untangle the impacts of specific shade tree species in specific growing conditions and under specific management practices. For instance, it is now broadly accepted that shade increases coffee quality, as does altitude, by delaying the ripening process of the berries, and that coffee quality decreases with fruit load [START_REF] Avelino | Effects of slope exposure, altitude and yield on coffee quality in two altitude terroirs of Costa Rica, Orosi and Santa María de Dota[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF]. Shade trees are also known to improve coffee growing conditions, especially in sub-optimal environments, with negligible negative impacts on coffee yield within a shade coverage range between 0 and 20-30% (DaMatta 2004). Nonetheless, the impacts of shade trees on coffee vary between coffee farms in optimal conditions [START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF]) and those in sub-optimal conditions [START_REF] Vaast | Biophysical Interactions Between Timber Trees and Arabica Coffee in Suboptimal Conditions of Central America[END_REF][START_REF] Van Kanten R | Transpiration of Arabica Coffee and Associated Shade Tree Species in Sub-optimal, Low-altitude Conditions of Costa Rica[END_REF]; alongside elevation gradients [START_REF] Avelino | Effects of slope exposure, altitude and yield on coffee quality in two altitude terroirs of Costa Rica, Orosi and Santa María de Dota[END_REF][START_REF] Bertrand B | Comparison of bean biochemical composition and beverage quality of Arabica hybrids involving Sudanese-Ethiopian origins with traditional varieties at various elevations in Central America[END_REF]; and in organic versus intensively managed farms [START_REF] Haggar | Coffee agroecosystem performance under full sun, shade, conventional and organic management regimes in Central America[END_REF][START_REF] Tully | Organically managed coffee agroforests have larger soil phosphorus but smaller soil nitrogen pools than conventionally managed agroforests[END_REF]). In addition, ecosystem services and disservices from agroforestry systems vary with the shade intensity provided by shade tree canopy [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Charbonnier | Competition for light in heterogeneous canopies: Application of MAESTRA to a coffee (Coffea arabica L.) agroforestry system[END_REF][START_REF] Jonsson | Contrasting effects of shade level and altitude on two important coffee pests[END_REF][START_REF] Klein A-M | Fruit set of highland coffee increases with the diversity of pollinating bees[END_REF], shade tree species and shade tree species mixes [START_REF] Bagyaraj Dj | Below ground microbial diversity as influenced by coffee agroforestry systems in the Western Ghats, India. Agriculture[END_REF][START_REF] Boreux | Interactive effects among ecosystem services and management practices on crop production: Pollination in coffee agroforestry systems[END_REF][START_REF] Nesper | Shade tree diversity enhances coffee production and quality in agroforestry systems in the Western Ghats[END_REF][START_REF] Romero-Alvarado Y | Coffee yields and soil nutrients under the shades of Inga sp. vs. multiple species in Chiapas, Mexico[END_REF]. Research is hence needed to understand the specific impacts of a set of shade tree species in intensively managed coffee systems in Yunnan Province, in order to design locally adapted coffee-agroforestry systems. This is particularly the case in Yunnan Province, where agroforestry systems have recently spread and where shade trees are still young, since most research to date has been carried out in mature systems where the impacts of shade trees are most visible [START_REF] Babbar | Nitrogen cycling in coffee agroecosystems: net N mineralization and nitrification in the presence and absence of shade trees[END_REF][START_REF] Jw | Soil microbial and nutrient properties in the rhizosphere of coffee under agroforestry management[END_REF]; Tully and Lawrence 2011).

Research question and hypotheses

The overall research question of this PhD thesis is the following: what are the impacts of young shade trees on soil fertility, coffee yield and coffee quality in emerging coffee-agroforestry systems in Southern Yunnan Province? Answering this question will contribute to the development of sustainable coffee farming practices in Yunnan Province. In order to answer that overall question, this PhD thesis will fulfill the specific objectives mentioned below. Each objective is formulated alongside its main hypothesis. Obj 1. Describing the existing coffee-agroforestry systems in southern Yunnan. H1 Coffee farming systems contain low shade tree biodiversity compared to forested areas, due to their recent conversion from coffee monocultures.

Obj 2. Assessing the potential of shade trees to benefit soil fertility in coffee farms. H1 There is already a positive impact of young shade trees on soil fertility, especially on soil communities and soil enzyme activities; Obj 3. Assessing the impact of shade trees on coffee production. H1 Young shade trees hinder coffee yield but increase coffee quality.

Thesis structure

This thesis contains six chapters including the current one.

Chapter 1 describes the overall background of the present research, the research gaps, the objectives and hypotheses of the thesis.

Chapter 2 describes the study sites.

Chapter 3 focuses on the description of existing coffee-agroforestry systems and on the ethnobotanical knowledge of coffee farmers regarding the provision of ecosystem services by shade trees.

Chapter 4 focuses on the impact of young shade trees on soil fertility.

Chapter 5 focuses on the impact of young shade trees on coffee yield and coffee quality.

Finally, chapter 6 discusses the overall findings of the previous chapters and provides recommendations for locally adapted coffee agroforestry systems.

CHAPTER 2 -Study Site

Study site 1: Pu'er and Xishuangbanna Prefectures

Fieldwork for the description of coffee farming systems and the assessment of local ecological knowledge of coffee farmers was conducted in Pu'er and Xishuangbanna Prefectures (22.80N -100.97E / 22.00N -100.78E), of Southern Yunnan, China. These prefectures are located between the Southeast Asian Peninsular and continental Asia [START_REF] Cao | Tree species diversity of tropical forest vegetation in Xishuangbanna, SW China[END_REF]. The climate is sub-tropical, with an annual average temperature of 19°C (Figure 2-2). Annual rainfall reaches 1400 mm, of which nearly 80% occurs during the rainy season from May to September (Figure 2-2). Mountains dominate the region, with elevations ranging from 340 to 3160 meters as it rises from mainland Southeast Asia to the Eastern Himalayas [START_REF] Zomer Rj | Environmental stratification to model climate change impacts on biodiversity and rubber production in Xishuangbanna, Yunnan, China[END_REF]. Most soils fall under Acrisols and Ferralsols types (FAO -Harmonized World Soil Database v1.2). Areas below 800 m elevation support tropical seasonal rain forests, tropical montane rain forests are found between 800 m and 1200 m high, and tropical montane evergreen broad-leaved forests are found at higher elevations [START_REF] Cao | Tree species diversity of tropical forest vegetation in Xishuangbanna, SW China[END_REF]Song et al. 2016b). This succession of ecosystems along the elevation gradient harbors an exceptionally high biodiversity [START_REF] Cao | Tree species diversity of tropical forest vegetation in Xishuangbanna, SW China[END_REF][START_REF] Zhang | Tropical forest vegetation of Xishuangbanna, SW China and its secondary changes, with special reference to some problems in local nature conservation[END_REF].

The landscape was traditionally cultivated with swidden-fallow practices [START_REF] Xu | Functional Links Between Biodiversity, Livelihoods, and Culture in a Hani Swidden Landscape in Southwest China[END_REF].

From 1965 onward, changes in land-use rights and market economy led to the conversion of swidden-fallow fields and secondary forests to perennial cash crops, such as tea and rubber plantations [START_REF] Xu | Functional Links Between Biodiversity, Livelihoods, and Culture in a Hani Swidden Landscape in Southwest China[END_REF]). In the early 1990's, economic liberalization and the establishment of international buyers in Yunnan Province provided market opportunities for coffee production.

Government-owned model farms began cultivating this new cash crop and distributing coffee seedlings to neighbouring farmers. Coffee farms spread between 900 and 1300 m elevation, where climate is most suitable for coffee growing. They replaced perennial fields and patches of secondary tropical montane rain forest. By the early 2000's, farms were privatized and coffee farming kept expanding, driven by smallholder farmers and large commercial companies.

Smallholder farmers typically manage 0.5 to 1.5 ha of coffee plantations. Coffee represents their main source of revenues in the farm. Most of them possess small post-harvest treatment stations for wet processing, a necessary condition to comply with the certification schemes required by large agri-businesses in recent years. From spring to fall, when coffee does not require heavy labour inputs, they complement their incomes with temporary jobs outside of the farm such as construction jobs, especially important in periods of low coffee prices. Coffee companies own long-term leases for lands ranging from ten to several hundred ha. Workers typically live on farm in villages built by companies and manage plots of 0.8 to 1.2 ha. By 2012, Pu'er and Xishuangbanna Prefectures had 95,000 ha under coffee cultivation (Pu'er Statistical Yearbook 2012), making them the main regional centres for coffee production in Yunnan according to governmental statistics (Figure 2 Until 2012, all plantations (bar some demonstration farms) had been established within the past 30 years. They relied on intensive management practices, with high planting densities of unshaded Catimor coffee (often 5000 plants ha -1 ) and high inputs of mineral fertilizer (often exceeding 200 kg N ha -1 yr -1 ). Farmlands were terraced, which reduced land erosion, increased water infiltration and eased manual management practices. Fungicides were seldom used, due to the leaf rust resistance of Catimor and to the absence of other major diseases. The white stem borer, Xylotrechus quadripes, is present, but cannot be effectively controlled by chemical inputs (Venkatesha and Dinesh 2012; [START_REF] Waller | Stem-and branch-borers[END_REF]). Preliminary interviews with governmental sources indicated that local authorities began free distribution of 1-year old and 2-year old shade tree seedlings in 2012, at a density of 60 to 120 trees.ha -1 .This lead farmers to converting their full sun coffee to shaded coffee systems. [START_REF] Egunyu | Social learning by whom? Assessing gendered opportunities for participation and social learning in collaborative forest governance[END_REF]. Therefore, with a concerted effort to involve farmers, an ethnobotanical approach has recently been developed to explore coffee farmers' LEK regarding shade tree species and which Ecosystem Services (ES) and Ecosystem Disservices (ED) they can provide in coffee farms [START_REF] Gram | Local tree knowledge can fast-track agroforestry recommendations for coffee smallholders along a climate gradient in Mount Elgon, Uganda[END_REF][START_REF] Lamond | Local knowledge of tree attributes underpins species selection on coffee farms[END_REF][START_REF] Van Der | Turning Local Knowledge on Agroforestry into an Online Decision-Support Tool for Tree Selection in Smallholders' Farms[END_REF].

Study site 2: Liushun Township

In this chapter, I first document coffee-agroforestry systems and their shade tree biodiversity at the farm and landscape levels, in view of the recent establishment of coffee as a commodity crop and the even more recent large-scale conversion from coffee monoculture towards shaded systems. Then, with ethnobotanical surveys based on van der Wolf et al. ( 2016), I document farmers' LEK regarding shade tree species and their provision of ES and ED in coffee landscapes, including on ES and ED related to soil fertility and coffee production. I hypothesize that the species promoted by the government are perceived favorably by farmers, especially by those most experienced in coffee-agroforestry practices. Second, I expect to find differences in farmers' LEK according to gender. Lastly, I hypothesize that coffee farmers from mountain ethnicities would rank indigenous tree species higher than would farmers from lowland ethnicities.

Materials and methods

Data collection

Fieldwork was conducted in Pu'er and Xishuangbanna Prefectures in 2016. A detail description of the study site is given in Chapter 2.1. In April 2016, inventories of shade tree species were conducted in 29 coffee farms (S1 Appendix). All the trees in coffee plantations with a diameter at breast height ≥ 5 cm were systematically identified to the lowest taxonomic level possible. Abundance was estimated through rapid appraisal (visual assessment) combined with farmers' saying. These farms were selected to include plantations representative of both young (≤ 15 year) and more mature trees (> 15 year) and both light shaded (≤ 20%) and medium shaded (> 20%) coffee agroforestry farms. Our sample also included 3 demonstration farms with high levels of tree diversity. All these farms were located along a latitudinal gradient (North 22.00 to North 23.26) in order to encompass the diversity of shade tree species likely to be found in the study area. Tree identification cards depicting tree characteristics along with their local names, were created for tree species that scored higher than 1% in a rank-abundance analysis and were seen in more than 20% of the coffee farms.

Between May and August 2016, coffee farmers were interviewed to document their local knowledge about shade tree species and their impacts on locally important ES & ED, following the methodology of van der [START_REF] Van Der | Turning Local Knowledge on Agroforestry into an Online Decision-Support Tool for Tree Selection in Smallholders' Farms[END_REF]. 122 farmers were selected from the list of 4Ccertified suppliers working with Nestlé Company. Furthermore, additional farms that I identified during our visits or referred to us by local authorities, which were not 4C-certified but nonetheless supported diverse and mature agroforestry systems, were also included in the sample. This way, [START_REF] Davis | Who Knows? On the Importance of Identifying "Experts" When Researching Local Ecological Knowledge[END_REF]. In total, 143 coffee farmers whose agroforestry systems had been established four or more years ago were interviewed. It was made clear to farmers that the results of this independent research would not impact their support either by the government or Nestlé.

Farmers with mature shade trees (> 15 year), medium shaded systems (> 20%) and high tree species richness (> 15) were expected to have richer first-hand agroforestry experience and were therefore included in priority in the sample to increase the quality of the LEK results as proposed by [START_REF] Davis | Who Knows? On the Importance of Identifying "Experts" When Researching Local Ecological Knowledge[END_REF]. I tried to have a gender-balanced sample of respondents, and to interview both farmers of ethnic minority groups traditionally inhabiting mountainous areas (Bulang, Hani, Lahu, Wa, Yi, and Zang) and Han and Dai farmers, who traditionally inhabit valleys [START_REF] Wu Z-L | Rubber cultivation and sustainable development in Xishuangbanna, China[END_REF]. Each farmer was asked to list the shade tree species present in their farm, aided by the tree identification cards. Any additional tree species that were named during this process were cross-checked with the results from previous inventories and given a new tree identification card. In total, 44 cards were produced. Interviewees were then asked to select up to 10 tree species present in their farms, with which they were the most familiar. Using the tree identification cards, tree species selected by interviewees were ranked for their performances for During the interviews, socio-economic information about the respondents was collected, including gender, ethnicity, original hometown and year of arrival in the village if respondents were not local. I also gathered comprehensive background information about their coffee farms and management practices.

Data analysis

Shade tree species inventories were analysed using the Vegan and Biodiversity R packages in R3.3.1 (Kindt and Coe 2005). The species accumulation curve was plotted with its confidence interval and the total richness of shade tree species was extrapolated using a first-order Jackknife formula (Kindt and Coe 2005). Rankings by respondents on their perceptions of the shade tree species performances were analysed using the BradleyTerry2 package in R2.2.11 [START_REF] Turner | Bradley-Terry Models in R: The BradleyTerry2 Package[END_REF]. Rankings were converted into pairwise comparisons to be fitted to the Bradley-Terry model. A separate analysis was conducted for each ES or ED. To take into account the small sample size, the model was fitted with a bias-reduced maximum likelihood. Species that were ranked less than 10 times by coffee farmers were excluded from the analysis, to ensure enough comparisons between tree species to yield statistically significant results [START_REF] Lamond | Local knowledge of tree attributes underpins species selection on coffee farms[END_REF]. Consequently, 28 tree species were included in the analysis. A score αi and quasi-standard error were calculated for each species i and each ES & ED. These scores reflect the likelihood that species i would perform better than species j for the considered attribute based on equation (1). These are comparative, rather than absolute values. For ease of comparison, scores were normalized between 0 and 1 before being uploaded to an online tool (http://www.shadetreeadvice.org) (van der [START_REF] Van Der | Turning Local Knowledge on Agroforestry into an Online Decision-Support Tool for Tree Selection in Smallholders' Farms[END_REF]. The quasi-standard errors give an indication of how many times a species was selected and an indication of consistency in the respondents' rankings. High quasi-standard errors indicate low LEK regarding a given species.

Scores were compared pairwise using a Wald test. Results were then presented and validated through focus group discussions with coffee farmers and extension agents.

Equation ( 1):

P(Species i performs better than Species j) = ∝ 𝑖 ∝ 𝑖 +∝ 𝑗 where ∝i is the score of species i

Exploration of local knowledge

The Bradley-Terry method has previously been used to analyze rankings of tree species [START_REF] Gram | Local tree knowledge can fast-track agroforestry recommendations for coffee smallholders along a climate gradient in Mount Elgon, Uganda[END_REF][START_REF] Lamond | Local knowledge of tree attributes underpins species selection on coffee farms[END_REF]. When comparing the perceptions of respondents from distinct groups, for instance based on gender or elevation, these studies conducted separate analysis for each group and compared the resulting scores. The present study is the first to incorporate predictor variables into applications of the Bradley-Terry method to LEK. This allows comparing perceptions of respondents from distinct groups through a single analysis, thus maintaining the initial sample size. Specifically, it allows analysis of interactions between a tree species characteristic and a socio-economic attribute of respondents' rankings. This analysis is used to answer the question: do farmers with this attribute rank tree species with this characteristic differently than do other farmers? Tree species were classified as being promoted or not by the local authorities and as indigenous species versus exotic ones. Respondents were assigned three qualitative attributes: 1) gender (M / F), 2) agroforestry system (low shade / medium shade) and 3) traditional location of ethnic groups (mountains / lowlands). The shade tree species richness on their farms was included as a quantitative attribute.

Interactions between respondent attributes and tree species attributes were incorporated into separate analyses to test the following three models:

 Model A: Farmers with high tree species richness and medium shaded systems (respondent attributes) rank promoted species (tree species attributes) more highly than farmers with low species richness and low shaded systems.

 Model B: Gender affects the rankings of promoted species.

 Model C: Farmers from mountain ethnicities give higher ranks to indigenous species than farmers from lowland ethnicities.

3.2.4. Suitability of government-promoted tree species

The regional impact of government promotion of shade tree species, through the distribution of free seedlings, was quantified by comparing scores of promoted species versus non-promoted species using a Student's T test for each ES or ED. Then, scenarios were simulated for three hypothetical coffee farmers, all located in the study area, but facing contrasting local constraints and hence adopting different strategies (Figure 34). These were: 1) a farmer whose land is located at high altitude and has a need for additional frost-protection by shade trees, 2) a farmer who is enhancing soil fertility through the addition of shade trees to reduce fertilizer inputs, and 3) a farmer whose goals are to cut input costs (fertilizers) and enhance income diversification with shade trees. Based on the results from the Bradley-Terry analysis and on the tree selection tool developed by van der Wolf et al. ( 2016), scores were attributed to 28 tree species for each scenario. In each scenario, species with the highest scores are those perceived to have the highest performance for the selected set of ecosystem services, and so would be the species recommended to farmers in comparable circumstances.

Results

Shade tree species inventories

During initial inventories in coffee farms, 162 shade tree species were encountered, of which two could not be identified, five were identified up to genus level and the other 155 were identified to species level. Across the 29 inventoried coffee farms, the Shannon index ranged from 0.00 to 3.42, with a mean value of 2.22. The effective number of species, calculated as the exponential of Shannon entropy (Jost 2007), ranged from 0 to 30.57 species, with a mean value of 9.21 species. The Simpson index varied from 0.00 to 0.96, with a mean value of 0.79. Further details are provided in S2 Appendix. First-order Jackknife formula was used to extrapolate the total richness of shade tree species in the study area. This led to an estimated value of 218 tree species (Figure 3-1), according to which 74 % of all shade tree species were encountered during our inventories. When two non-representative demonstration farms which each exhibited a high and non-representative level of tree species diversity were removed from the analysis, the extrapolated number was reduced to only 162 tree species.

Only 17 tree species accounted for more than half (51 %) of all the trees inventoried (Figure 3-2). 84 species were only encountered in one or two coffee farms. The nine governmentpromoted species (Table 3-1) represented 27 % of all the trees inventoried. The two most abundant non-promoted species were Mangifera indica and Schima wallichii. The latter is an early successional species that has spread through natural regeneration.

Main characteristics of tree species

A total of 42 tree species were selected and ranked by respondents. 28 of these species were ranked more than 10 times for each attribute and therefore included in the ranking analysis. Amongst these, 20 species were endemic to the study area. Nine species have been actively promoted by the local authorities through distribution of free seedlings, amongst which five are fruit tree species and four are species valued as ornamental trees for urban landscaping

Tree species rankings and pairwise comparisons

After the initial 30 interviews, the following ten ES & ED were assessed as the most locally important: 1) protection of coffee trees against high temperatures (ES), 2) soil moisture enhancement (ES), 3) protection of coffee trees against cold temperatures (ES), 4) suppression of weeds (ES), 5) negative impact of shade trees on average coffee yield (ED), 6) control of the white stem borer (WSB) (ES), 7) protection from soil erosion (ES), 8) root competition between shade trees and coffee trees (ED), 9) soil nutrient cycling enhancement from leaf litter and Nfixation (ES) and 10) additional economic benefits from the shade trees (ES) (Figure 3-3). After conducting the Bradley-Terry analysis on the 124 rankings, pairwise comparisons of species' scores showed that tree species were easiest to rank for economic benefits, weed control and protection from high temperatures (81 %, 76 % and 75 % of all pairs were significantly distinct (p ≤ 0.05)). Trees were hardest to rank for their impact on coffee yield, root competition and enhancement of nutrient cycling (59 %, 62 % and 66 % of all pairs were distinct). No respondents were able to rank tree species for ability to control WSB, so this ES was excluded from the analysis. Although coffee quality did not make it into the top ten ES and ED, it is interesting to note that no farmer was able to rank tree species against this important ES during the 30 first interviews. In their views, rainfalls during harvest seasons was the single most influential parameter impacting coffee quality.

Promoted tree species

Promoted species were perceived to perform significantly better than non-promoted species for all attributes except one ES (nutrient cycling enhancement), and two ED, (reduction in coffee yield and root competition (Table 3-2). Seven of the nine promoted species were ranked in the top eight of species favoured by coffee farmers (Figure 3-4). Artocarpus heterophyllus was the only highly favoured non-promoted species. Cerasus cerasoides (ranked 13 th ) and Delonix regia (ranked 16 th ) were the only two promoted shade tree species that were ranked lower than some non-promoted species.

Coffee farming practices and ranking

Overall, farmers gave similar rankings to trees regardless of the tree species richness and degree of shade in their farms (Table 3-3 -Model A). However, respondents whose farms supported higher tree species richness perceived lower root competition between promoted species and coffee plants (p ≤ 0.05). Respondents whose agroforestry systems had more shade thought that promoted species were beneficial to soil moisture (p ≤ 0.05), but had a negative impact on coffee yield (p ≤ 0.05). Promoted species scored high in the list of preferred species for all respondent groups. Farmers with lower species richness and lower shade intensity on their farms had an even stronger preference for promoted species (p ≤ 0.01).

Gender, ethnicity and tree species preferences

Women were more likely than men to perceive non-promoted species as more beneficial to nutrient cycling enhancement (p ≤ 0.1). Men were more likely to perceive promoted trees to bring higher economic income (p ≤ 0.1) and showed a significant preference for these species (p ≤ 0.05) (Table 3-3 -Model B). Men showed higher overall preferences than women for C. camphora and C. cerasoides while women ranked Litchi chinensis and Leucaena leucocephala higher than men did (Figure 345).

Autochthon farmers from mountain ethnicities expected greater economic benefits from indigenous species than did farmers from lowland ethnicities (Table 3-3 -Model C). Farmers from mountain ethnicities gave high rankings for economic benefits to the indigenous species Alstonia scholaris, Toona ciliata, Diospyros kaki, Michelia baillonii, Betula alnoides and S.

wallichii (Figure 3-6). However, they thought that indigenous species had higher negative impacts on coffee yield through below ground interactions.

Tailored list of recommended species

Promoted species outranked most of the non-promoted species for economic gain via cutting fertilizer cost and boosting incomes through diversification (Figure 3-4 Scenario 4).

However, in this scenario, the non-promoted A. heterophyllus and D. kaki were advised species while the promoted C. cerasoides and D. regia were among least recommended species. Although many respondents reckoned that timber from M. baillonii is in high demand, it was only ranked 25 th out of 28.

Among the short-list of 28 trees, tree species that gave the best financial returns were also the best in protecting coffee trees from frost (Figure 34). Strikingly, the top nine trees for frostprotection were also the nine overall most-preferred trees. These included seven trees promoted by the local authorities, plus A. heterophyllus and L. leucocephala. As explained by farmers, six of these promoted trees were evergreen species with dense canopies, which are likely to tolerate cold temperatures and buffer coffee plants underneath from cold spells.

Tree species promoted by local authorities were not necessarily the best choice for nutrient cycling, limiting root competition or sustaining coffee yield. For this set of ES and ED, only five promoted species were among the ten most recommended trees (Figure 3-4). The other five non-promoted species included indigenous species with low economic benefits but perceived as enhancing soil fertility, mostly through leaf litter, such as Melia azedarach, Ficus hispida and S. wallichii, and L. leucocephala, identified as a Nitrogen-fixing tree by farmers in Nestlé's demonstration farm.

Figure 3-4: Tool outputs displaying scores for 17 shade tree species out of 28 according to four scenarios: 1) overall preference, 2) a high altitude farm exposed to frost risks, 3) a farm with limited or no input of chemical fertilizers, and 4) a farm where trees are primarily planted and managed for income diversification. Grey boxes indicate promoted species; striped boxes indicate non-promoted species that score highly in a specific scenario. Figure 3-6: Scores and 95% confidence intervals for economic benefits from shade tree species broken down by groups of ethnicities. Grey boxes indicate timber species for which respondents from mountain ethnicities perceived more economic potential than respondents from lowland ethnicities did.

Discussion

3.4.1. Tree diversity, richness and density on coffee farms I expected to find low tree species diversity in coffee farms, due to the recent expansion of coffee areas and current adoption of coffee agroforestry practices in Yunnan Province. In fact, our tree species inventories showed that the study area supports an estimated 162 tree species. This unexpectedly high tree species diversity is likely to reflect the biodiversity of previous ecosystems in the study area. Coffee farms spread in areas used for swidden-fallow practices until the mid-20 th century [START_REF] Xu | Functional Links Between Biodiversity, Livelihoods, and Culture in a Hani Swidden Landscape in Southwest China[END_REF], or previously dominated by secondary to mature tropical montane rain forests [START_REF] Zhu | Species Composition, Physiognomy and Plant Diversity of the Tropical Montane Evergreen Broad-leaved Forest in Southern Yunnan[END_REF][START_REF] Zhu | Tropical rain forest fragmentation and its ecological and species diversity changes in southern Yunnan[END_REF]). Song et al. (2016b) recorded 156 tree species between 800 and 1400m elevation and [START_REF] Cao | Tree species diversity of tropical forest vegetation in Xishuangbanna, SW China[END_REF] noted that rank-abundant curves were characterized by long tails, indicating that the tree species diversity of tropical montane forests mostly depended on rare species. The dominance of S. wallichii and Castanopsis species (Song et al. 2016b;[START_REF] Zhu | Species Composition, Physiognomy and Plant Diversity of the Tropical Montane Evergreen Broad-leaved Forest in Southern Yunnan[END_REF] , also common in our tree inventories, further indicate similarities between shade trees found in coffee farms and the forest ecosystems they replaced. Lastly, diversity indices from these forest studies were also similar to those derived from our tree inventories.

Diversity indices from the present study can be compared to those derived from tree inventories conducted in similar intensive coffee systems, located between 800 and 1,250 m elevation in Costa Rica [START_REF] Häger | Effects of management and landscape composition on the diversity and structure of tree species assemblages in coffee agroforests[END_REF]. That study recorded only 104 shade tree species in conventional farms. On the other hand, there were 19 species on average per farm in Costa Rica compared to 15 species per farm in our study area.

Tree species were unevenly distributed across our study area, with 52% of species occurring in only one or two farms. [START_REF] Dawson Ik | What is the relevance of smallholders' agroforestry systems for conserving tropical tree species and genetic diversity in circa situm, in situ and ex situ settings? A review[END_REF] emphasized that the prospects of conservation for low density species in the agricultural landscape are poor because these species are particularly vulnerable to being wiped out by the decision-making process of a few farmers.

Low densities can also restrict pollination and reproduction. If maintenance of high coffee yield requires the thinning of shade trees, the least appreciated tree species will be the first to be felled [START_REF] Pinard | Are coffee agroforestry systems suitable for circa situm conservation of indigenous trees? A case study from Central Kenya[END_REF]). This is likely to reduce biodiversity by increasing the proportion of economically profitable non-native species [START_REF] Albertin | Farmers' Perspectives on the Role of Shade Trees in Coffee Production Systems: An Assessment from the Nicoya Peninsula, Costa Rica[END_REF].

Gaps in ethnobotanical knowledge of shade trees

Agroforestry practices in coffee systems have only recently been promoted and adopted on a wide-scale across Yunnan. Still, ethnobotanical knowledge on shade tree species and their impacts on ecosystem services in coffee farms is well-developed. 87% of respondents could rank tree species for at least one issue. Some level of LEK was present across all socio-economic groups, although the degree of LEK varied with agroforestry systems, gender and ethnicities.

Although coffee farmers know local tree species and their phenology, they still have limited experience of the impact of mature shade trees on coffee yield. They gave high ranks to several fruit tree species such as M. integrifolia, L. chinensis and Dimocarpus longan, which have dense canopies and hence have high potential to compete for light with coffee plants. Because most shade trees are young, such negative impacts may not yet have become apparent [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Damatta Fm | Ecophysiological constraints on the production of shaded and unshaded coffee: a review[END_REF], or farmers could be more concerned about other economic factors. Farmers with more first-hand experience in agroforestry systems did nonetheless perceive higher negative impacts of promoted species on coffee yield, than did farmers with seemingly less experience.

Farmers varied widely in their assessment of root competition and nutrient cycling. This would be expected because such complex below-ground factors are the most difficult to assess [START_REF] Graefe | Evaluating Farmers' Knowledge of Shade Trees in Different Cocoa Agro-Ecological Zones in Ghana[END_REF][START_REF] Lamond | Local knowledge of tree attributes underpins species selection on coffee farms[END_REF][START_REF] Dumont | Farmers in Côte d'Ivoire value integrating tree diversity in cocoa for the provision of ecosystem services[END_REF]. They also lacked knowledge regarding the white stem borer and coffee quality. As farmers gain greater experience in cultivating coffee, their LEK is likely to be progressively enriched [START_REF] Soto-Pinto L | The role of local knowledge in determining shade composition of multistrata coffee systems in Chiapas, Mexico[END_REF]).

Relevance of promoted tree species

Local stakeholders perceived the tree species promoted by the local authorities to provide the best protection against weather hazards and bring the greatest economic benefits. It appears that shade tree species were preferentially promoted when they have dense canopies and high economic returns, despite their negative impact on coffee yield. Apart from D. regia, which has only aesthetic value, all promoted species were perceived to have positive economic returns. Income can be derived from fruit production or selling saplings to the emerging market for tree landscaping in nearby fast-growing cities. Five year-old saplings are uprooted and sold for re-planting alongside the new roads and sidewalks of China's rapidly expanding cities. Our respondents reported that A.

scholaris is the "urban tree" in highest demand, followed by C. camphora and B. javanica. Income diversification is a prime motive for the selection of shade tree species by coffee farmers. Therefore, it is not surprising that farmers prefer those promoted species that produce fruit or saleable saplings.

The non-promoted fruit trees, A. heterophyllus and D. kaki, were thus also highly ranked.

Farmers appeared to be most interested in gaining short-term economic benefits. They did not favor valuable indigenous timber species (M. baillonii and T. ciliata) that require long-term investment. Regional policies that emphasize forest protection and hence prohibit felling trees [START_REF] Xu | China's ecological rehabilitation: Unprecedented efforts, dramatic impacts, and requisite policies[END_REF]) currently discourage growing timber crops. Although there are specific conditions under which permits are granted for timber harvest, these are seldom delivered, leaving farmers doubtful of their ability to harvest timber, a concern frequently mentioned during interviews. Such problems have been reported in Kodagu, India, where policies for the protection of indigenous species in coffee estates counter-intuitively lead to decreased planting of species that will be difficult to harvest and the replacement of indigenous trees with exotic shade trees (Garcia et al.

2010; Nath et al. 2016

). There is a need for incentive programs that support the use of indigenous timber trees and promote their planting and/or natural regeneration.

Governmental promotion of some tree species can also succeed in shaping the preferences of farmers. In Mexico, [START_REF] Valencia | The use of farmers' knowledge in coffee agroforestry management: implications for the conservation of tree biodiversity[END_REF] showed that there had been little scientific foundation for the promotion of Inga oerstediana in coffee agroforestry systems by NGOs and government agencies 30 years ago. Nonetheless, farmers still highly prefer this species. In the present study, because farmers are new to coffee-growing and agroforestry practices, promotion and dissemination of shade trees is especially likely to shape their perceptions and actions. Indeed, farmers with seemingly low experience in agroforestry systems ranked promoted species higher than farmers with rich first-hand experience in overall preference rankings. This further support the interpretation of a collective bias driven by promotion activities from local authorities. It is therefore important to keep in mind the limits of ethnobotanical approaches. Perceptions of trees can be biased or reflect partial views about provision of ES/ED by particular tree species (van der [START_REF] Van Der | Turning Local Knowledge on Agroforestry into an Online Decision-Support Tool for Tree Selection in Smallholders' Farms[END_REF]. Wherever possible, studies on LEK should thus be complemented and validated with on-farm studies of the actual interactions between shade trees and coffee trees.

Gender, ethnicity and tree species ranking

Impact of gender on tree preferences was noticeable, with men having a stronger preference for promoted species [START_REF] Egunyu | Social learning by whom? Assessing gendered opportunities for participation and social learning in collaborative forest governance[END_REF]. Women were the most likely to include N-fixing tree species in their list of favoured shade trees. No explanation could be found to substantiate either findings. Surprising gender differences in LEK have been found elsewhere; Ayantunde et al.

(2008) pointed out that women, who were responsible for cooking, could identify fewer firewood species than men. In our study, men and women had similar responsibilities in coffee farms; all participated in agricultural activities such as fertilizing, weeding, harvesting and processing crops.

Further investigation of how gender is affecting LEK will require complementary tools (e.g. ATK tool), to relate rankings with socio-economic attributes [START_REF] Lamond | Local knowledge of tree attributes underpins species selection on coffee farms[END_REF][START_REF] Su Y | Gendered Responses to Drought in Yunnan Province, China[END_REF].

I had expected that farmers from ethnicities traditionally settled in mountainous areas might exhibit preferences for some indigenous tree species commonly found at these altitudes that would not be valued in other areas [START_REF] Brandt R | Knowledge and valuation of Andean agroforestry species: the role of sex, age, and migration among members of a rural community in Bolivia[END_REF]. Indeed, indigenous timber trees were ranked higher by these farmers. Coffee farmers from mountain ethnicities thereby exhibited a 'hybrid type of knowledge' (Gómez-Baggethun et al. 2013), combining their traditional knowledge with both first-hand experience and outside sources, in our case promotion activities from the local authorities.

3.4.5. Tailored list of recommended species I produced three separate lists of tree species that I recommend for three different farmer priorities (Figure 3-4). Our lists were very similar to that of the government, when considering strategies to diversify income sources (our third scenario), and to lower the frost risk in highaltitude farms while maintaining high coffee yield (our first scenario). The government's list of promoted species thus appears to be well suited to coffee farmers whose priorities are primarily related to either economic benefits or protection from climatic hazards. Based on farmers' overall preferences, and these two simulated scenarios, I recommend future promotion of A. heterophyllus and L. leucocephala.

Simulations based on soil fertility enhancement (our second scenario) would lead to the recommendation of additional species such as M. azedarach, L. leucocephala, F. hispida and S.

wallichii. The promoted shade tree species boost profits at the expense of below-ground ecosystem services. On the other hand, the suggested additional species do not bring high economic benefits but are perceived to favor soil fertility. Therefore, there is no single solution for a top list of the best tree species for the entire study area. The list of shade-tree species promoted by the local authorities is a useful starting point, but it cannot address the need for complex agricultural strategies or trade-offs between economics and key ecosystem services. Rather, recommendations should be area-specific and farmer specific. The lists generated in the present study can be used as a starting point. Individual farmers should then modify them to take into account their individual LEK, their local conditions and their economic strategies.

Conclusion

This study on newly emerging coffee farming systems in Pu'er and Xishuangbanna

Prefectures revealed an unexpectedly high level of diversity amongst shade tree species, at both farm and landscape levels. At the landscape level, tree species richness in coffee farms was similar to that documented in tropical montane forests in southern Yunnan. This indicates that tree species diversity has persisted despite the spread of intensive coffee monoculture and the governmental promotion of a limited number of species. These governmentally-promoted tree species were valued by coffee farmers for their perceived high economic returns and protection against environmental hazards. Favorable perceptions of promoted trees also probably reflected a collective bias driven by promotion activities by governmental extension services. However, as trees grow, there will be increased competition between some shade trees and coffee trees for light, water and nutrients. Combining the existing LEK with further research on the actual interactions between mature shade trees and coffee trees is needed in order to refine locally adapted advice on shade tree management practices, including thinning and pruning.

Currently, there appears to be a hybrid ethnobotanical knowledge that mostly relies on traditional knowledge of tree species combined with fresh experience from newly-implemented coffee-agroforestry practices. Farmers of traditional ethnic groups from mountain areas or with richer first-hand experience in coffee-agroforestry practices differed from other farms in their perceptions of shade trees and their impacts on ES and ED; they preferred some indigenous and non-governmentally-promoted species. LEK still needs to be enriched by developing experience with mature shade trees. Furthermore, this study identifies knowledge gaps regarding the impact of shade trees on coffee yield, coffee quality, soil fertility and control of the white stem borer. These gaps should orientate future research works to complement the existing LEK.

This ethnobotanical approach results in the upgrading of an online tool (www.shadetreeadvice.org) which complements the top-down engineered program of the government, by allowing farmers and extension services producing lists of tree species tailored to the farmers' needs and the local ecological contexts of Yunnan Province. Further research will improve this decision support tool on tree selection and contribute to sustainable coffee management benefiting farmers' livelihood and landscape health in Yunnan Province.

Additional information on shade tree inventories in coffee farms

Diversity indices are detailed for the 3 model farms in Table 3-4. It can be seen that shade tree species in model farm 3 are slightly more diverse than in the 2 other model farms. Higher diversity could result from the larger area under coffee farming. However, these species were found not only in model farm 3 but also in other coffee farms, while many shade tree species found in model farms 1 and 2 were only encountered in these 2 farms.

There is a positive relationship between coffee farm area and tree species richness, as found from the respondent database (Figure 345678). Tree density was not estimated. Nonetheless, it is likely to be higher than 180 trees/ha, which would represent the density resulting from the average number of tree seedlings distributed by the government, plus trees originating from natural regeneration, as high levels of seedling survival were observed. Additionally, soil microbial communities are increasingly encompassed in the study of soil fertility [START_REF] Lehman Rm | Soil biology for resilient, healthy soil[END_REF]. They provide services amongst which soil formation, nutrient cycling through enzymatic actions and control of pathogens [START_REF] Buyer | Soil microbial communities under cacao agroforestry and cover crop systems in Peru[END_REF]. Through a feedback loop, microbial communities are in turn linked to plant communities which provide micro habitats and energy resources through litter [START_REF] Singh | Effect of different leaf litters on carbon, nitrogen and microbial activities of sodic soils[END_REF]) and root exudates (Schweinsberg-Mickan et al.

2012

). Coffee being a perennial crop, it is believed to harbor numerous beneficial species in its rhizosphere [START_REF] Mulaw | The Rhizosphere of Coffea Arabica in Its Native Highland Forests of Ethiopia Provides a Niche for a Distinguished Diversity of Trichoderma[END_REF] As stated above, the study of soil enzymes is also increasingly associated with soil fertility assessment. For instance, β-glucosidase, which catalyzes the hydrolysis of maltose and cellobiose The development of more comprehensive soil fertility indicators has confirmed that coffee-AFS sustain higher soil fertility than conventional monoculture systems [START_REF] Bagyaraj Dj | Below ground microbial diversity as influenced by coffee agroforestry systems in the Western Ghats, India. Agriculture[END_REF][START_REF] Barrios E | Agroforestry and Soil Health: Linking Trees, Soil Biota, and Ecosystem Services[END_REF][START_REF] Muleta | Distribution of arbuscular mycorrhizal fungi spores in soils of smallholder agroforestry and monocultural coffee systems in southwestern Ethiopia[END_REF]. These benefits of slow-growing shade trees on soil fertility become more visible as systems age [START_REF] Vallejo | Soil enzymatic activities and microbial biomass in an integrated agroforestry chronosequence compared to monoculture and a native forest of Colombia[END_REF]) and under low-input management practices [START_REF] Kl | Closing the Loop: Nutrient Balances in Organic and Conventional Coffee Agroforests[END_REF]. Researchers have thus mostly focused their work on longterm trends, and few studies have investigated the short-term impacts of shade trees on soil fertility rapidly after conversion of conventional agriculture systems into intensively managed AFS [START_REF] Hergoualc'h K | 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][START_REF] Youkhana Ah | Leucaena-KX2 mulch additions increase growth, yield and soil C and N in a managed full-sun coffee system in Hawaii[END_REF]. With the availability of cheap and highly reliable techniques to measure soil fertility parameters, I believe that potential short-term soil benefits are more detectable. In the present study, I therefore examine soil chemical parameters, soil communities and enzyme activities, as well as root systems and soil water profiles, to assess the impact of young shade trees on soil fertility, only four years after conversion from intensively managed monoculture Arabica coffee into intensively managed AFS in Pu'er Prefecture, located in southwest China. Our first working hypothesis was that fine scale spatial and temporal variability patterns would be detectable, with higher soil fertility within coffee rows than in the inter-rows and higher activity during the rainy season than the dry season. Second, I expected that soil communities and enzyme activities would already show significant positive impacts from the introduction of shade trees, but that soil chemistry would not have yet been significantly impacted.

Materials and Methods

Experimental design

Fieldwork was conducted in Liushun Township, in Pu'er Prefecture, from March 2017 to March 2018. A detailed description of the study site and of the coffee farming system is given in Chapter 2.2. Three tree species planted by the local government and commonly found on the mountainside were selected for the experiment: Bischofia javanica, Cinnamomum camphora and Jacaranda mimosifolia. These species were planted within coffee rows, with alternate tree species within a shade tree row. Bischofia javanica is a deciduous broadleaved tree species native to the study area. It is commonly found in areas with a distinct dry season, on deep loose soils and occasionally on limestone soils. In the study area, it reached 5 m high on average, 11 cm DBH and 10m 2 of ground canopy projection. It shed its leaves from December 2016 to March 2017 and from February 2018 to March 2018. Cinnamomum camphora is an evergreen broadleaved tree species with a dense canopy. This tree species has been widely cultivated in Southeastern China since ancient times and used for camphor production [START_REF] Kameyama | Environmental conditions for seed germination and seedling growth of Cinnamomum camphora (Lauraceae): the possibility of regeneration in an abandoned deciduous broad-leaved forest, eastern Japan[END_REF]. It is a light demanding tree that grows well on fertile well drained soils. In the study area, it reached 5 m high on average, 10 cm DBH and 6 m 2 of canopy projection. Jacaranda mimosifolia is a deciduous tree native to South America, often used as an ornamental tree in urban areas and gardens due to the light shade it provides and the spectacular sight of its flowers in full bloom. It grows best in highland areas with a mean temperature around 20°C and on well drained sandy loams. In the study area, it reached 7 m high on average, 11 cm DBH and 16 m 2 of canopy projection. It shed its leaves concomitantly to B. javanica.

Six replicates were selected for each tree species after an inventory of all trees present on the mountainside, a characterization of coffee trees located below and around their canopies and a set of preliminary soil chemical analysis. The selection was based on shade tree sizes, shapes, locations, coffee trees size and vigor and results from soil analysis, so as to reduce intra-species variability and secure a set of coffee trees and growing conditions as homogeneous as possible.

Each shade tree was at least 10 meters away from the next closest shade tree. 4 locations were designated around each shade tree; 1) within the coffee row and below the shade tree canopy; 2) in inter-row and below the shade tree canopy; 3) within the coffee row and outside of the shade tree canopy; 4) in inter-row and outside of the shade tree canopy. Samples taken in inter-rows were located 20 to 30 cm away from the edge of the coffee canopy. Samples taken outside of the shade tree canopy were located at least 2m away from the edge of the shade tree.

Soils samples were collected from the top 20 cm using a stainless steel auger, after removal of the litter horizon. Visible stones and roots were removed. Each location was sampled 3 times.

Soils sampled either in December 2017 or 2018 were air dried and sent for chemical analysis. Soils sampled in June-July 2017 and January 2018 were stored at 4°C until tested for a set of biological activity indicators including nematode abundance, microbial communities and soil enzyme activities.

Soil chemical properties

Soil chemistry was determined on air-dried samples. Soil pH was measured in 1:2.5 soilwater suspension. Soil organic matter (SOM) was measured following the Walkley-Black method [START_REF] Nelson | Methods of soil analysis Part 2 Chemical and microbiological properties[END_REF]. Soil total nitrogen concentration was determined using the semimicro Kjeldahl method [START_REF] Bremner | Methods of soil analysis Part 2 Chemical and microbiological properties[END_REF]. Available P was determined with Olsen extraction [START_REF] Olsen | Phosphorous. In: Methods of soil analysis Part 2 Chemical and microbiological properties[END_REF]. Exchangeable Potassium, Calcium and Magnesium were measured with titration after extraction with ammonium acetate [START_REF] Ciesielski | A comparison between three methods for the determination of cation exchange capacity and exchangeable cations in soils[END_REF].

Soil communities

Soil community indicators were measured in both the rainy and the dry seasons, except for earthworm abundance which was only measured during the rainy season (Fonte et al. 2010). Soil microbial communities were characterized using phospholipid fatty acid (PLFA) method. PLFA analysis was performed on 10 g field-moist sub-samples using the procedure of [START_REF] Bossio | Impacts of Carbon and Flooding on Soil Microbial Communities: Phospholipid Fatty Acid Profiles and Substrate Utilization Patterns[END_REF]. Selected PLFAs were used as molecular markers based on previous research [START_REF] Feng | Soil microbial communities under conventional-till and no-till continuous cotton systems[END_REF][START_REF] Frostegård | The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil[END_REF][START_REF] Mathew Rp | Impact of No-Tillage and Conventional Tillage Systems on Soil Microbial Communities[END_REF]). These included Gram-positive (Gram+) bacteria (i15:0, a15:0, i16:0, i17:0, a17:0), Gram-negative (Gram-) bacteria (cy17:0, cy19:0, 18:1ω7c), non-specified bacteria (14:0, 15:0, 16:0, 17:0, 18:0), arbuscular mycorrhiza fungi (AMF)

(16:1ω5c) and non-AMF fungi (18:1ω9c). Absolute amounts were expressed in nmol.g -1 dry soil based on the 19:0 internal standard (Zelles 1999). Bacterial sums were calculated using the Gram+, Gram-and non-specified bacteria markers. The fungi to bacteria ratios were calculated by dividing 18:1ω9c by the bacterial sums. Total microbial community was estimated using the sum of all PLFA markers.

Free living nematodes were extracted using Baermann's funnel method on 100 g subsamples immersed in water for 48 h (Van Bezooijen 2006). Nematodes were counted using a microscope. Nematode abundance was expressed as individuals per 100 g of equivalent dry soil.

Earthworms were collected by hand from pits (25 * 25 * 30 cm) excavated in August 2017 [START_REF] Sj | Earthworms, soil fertility and aggregate-associated soil organic matter dynamics in the Quesungual agroforestry system[END_REF]). The 30 cm deep horizons were inspected for burrows of deeper dwelling earthworms.

However, due to low mean earthworm occurrence after digging the first 10 pits (average of 0.9 earthworm per pit), this measurement was discontinued.

Soil enzyme activities

Activities of three soil enzymes, β-glucosidase (BG) involved in C cycling, N-acetylglucosaminidase (NAG) involved in C and N cycling, and acid phosphatase (ACP) involved in P cycling, were assayed in both the rainy and the dry seasons, following a method adapted from (Tabatabai 1994). All chemical reagents were bought from Suzhou Comin Biotechnology Company, with modified protocols for reducing sample sizes and solutions while maintaining the original soil to solutions ratios. Briefly, air-dried soil samples were ground and sieved through a 0.6 mm mesh. BG activity was assayed using duplicate 0.02 g samples, one incubated for 1h at 37°C with tuolene and p-nitrophenyl-β-D-glucopyranoside used as substrate, and one control to which substrate was only added to after the incubation. Upon termination of the incubation, soil solutions were filtered and analyzed colorimetrically at 400 nm. The absorbance value from the control sample was subtracted from that of the test sample to assay the release of p-nitrophenol.

Each soil sample was tested twice following this method and results were averaged. NAG activity was assayed using a similar protocol, incubated with p-nitrophenyl-N-acetyl-b-D-glucosaminidine used as substrate. ACP activity was assayed using 0.1g samples incubated with p-nitrophenyl phosphate used as substrate. All enzyme activities were expressed in μmol of p-nitrophenol released.g -1 of soil.day -1 .

Litter

Litter was captured in 0.25 m 2 (50 cm * 50 cm) meshed baskets and collected every six weeks from May 2017 to April 2018. Twenty four litter traps were installed on the mountainside; 75% of the traps under the canopy of B. javanica, C. camphora and J. mimosifolia and the remaining 25% in open areas, half of which below coffee rows and half in inter-rows. Trapped litter was dried at 80°C for 48 h in an oven and manually sorted in coffee leaf, shade tree leaf, woody and miscellaneous fractions before being weighed. Annual litter inputs from coffee and shade trees were extrapolated to the farm level using tree densities and areas of canopy projection to the ground.

In February 2018, three samples made of 4 th developed pairs of leaves were taken from coffee trees. nine samples were collected from shade trees. Samples were dried, ground and analyzed for chemical composition. Total N, P, K, Ca and Mg were determined using an atomic absorption spectrophotometer after tissue wet extraction with sulphuric and perchloric acid. Iron concentration was assessed using the wet ash method. Lignin was determined through the acid detergent lignin measurement method.

Data analysis

Soil chemical, biological and biochemical parameters were analyzed separately in R.3.4.4.

Outliers were identified as beyond two times the inter-quartile range from the first or third quartile and excluded from analysis. Shapiro's test was used to test for normality. Levene's test was used to test for homogeneity of variances. Whenever necessary, data were log-transformed to meet these two assumptions. Analysis of variance was used to detect significant effects of season, shade, location within coffee rows compared to inter-rows, and all the interactions between these factors.

Terraces were used as a random effect in the ANOVA, in order to take into account variability in management practices among the five smallholder farmers. In a first analysis, the three selected shade tree species were considered as three treatment levels and open coffee a fourth level. In a second analysis, the three shade tree species were pooled to increase sample size and reflect the general impact of shade trees as compared to open areas. Throughout the article, values are expressed as means ± standard error.

When significant effects were detected, they were investigated through Student t-tests on pairwise differences between treatment samples and control samples. Specifically, when investigating the seasonal impact, values from samples taken during the dry season were subtracted from values from the corresponding samples taken during the rainy season. When studying the impact of coffee trees and fertilizer inputs within coffee rows on soil fertility, values from samples in inter-rows were subtracted from values from the corresponding samples within coffee rows.

When studying the impact of shade trees, values from samples taken below shade tree canopies were subtracted from values from the corresponding samples taken in open areas.

Redundancy analysis (RDA) was performed to assess the linear relationships between soil communities and a set of explanatory variables encompassing soil water content, soil chemical composition and indicators of litter input (dry mass, N:lignin ratio). Most discriminating variables were selected through a forward procedure and tested for significance (1000 permutations) using the vegan package in R3.4.4 [START_REF] Oksanen | vegan: Community Ecology Package. R package version 2[END_REF]). Similarly, RDA was performed to assess the relationships between soil enzyme activities and a set of explanatory variables encompassing soil water content, soil chemical composition and soil communities.

Results

Litter

Planted at a density of 56 trees.ha -1 , 5 years old B. javanica would provide an estimated amount of 450 (±90) kg dry leaf.ha -1 .yr -1 . At the same density, C. camphora would provide an estimated amount of 100 (±20) kg.ha -1 .yr -1 and J. mimosifolia in the range of 140 (±10) kg.ha -1 .yr - 1 . In comparison, planted at a density of 5000 trees.ha -1 , coffee trees in AFS would provide an estimated amount of 4170 (±203) kg.ha -1 .yr -1 and monoculture coffee in the range of 4281 (±421) kg.ha -1 .yr -1 . Woody and miscellaneous fractions provided an estimated average of 1890 (±292) kg dry matter.ha -1 .yr -1 in all 4 coffee systems, most of which being aborted coffee cherries. Bischofia javanica leaf litter thus represented less than 10% of total leaf litter in shaded coffee farms. This amount fell below 3% in the case of C. camphora and J. mimosifolia. However, this overall low contribution of shade tree leaf litter hid important spatial differences. Within coffee rows and under their canopies, B. javanica, C. camphora and J. mimosifolia leaf litter respectively represented 68%, 49% and 23% of the total leaf litter falling in that location. In the corresponding inter-rows, these percentages increased to 93%, 71% and 55%. Spatial variability was also evidenced with an estimated amount of 553 (±78) kg.m -2 .yr -1 of coffee leaf litter falling within coffee rows in open areas, compared with an estimated amount of 334 (±19) kg.m -2 .yr -1 in the inter-rows.

Based on coffee leaf and shade tree leaf analysis (Table 4-1), leaf litter could return 99 to 107 kg.N.ha.yr-1, 5 to 6 kg.P.ha.yr-1 and 67 to 73 kg.K.ha.yr-1 (Figure 4-1). Nutrient return was consistently higher in B. javanica-coffee AFS than in the 3 other systems, in accordance with the additional input of leaf litter from this shade tree species, although differences are not statistically significant due to high variability in coffee leaf litter amounts.

Seasonal impact on soil fertility

Soil water content was 1.5 to 2.8% higher during the rainy season than the dry season, in all sample locations. Free living nematodes, total PLFA, bacterial communities, fungi and AMF were all significantly more abundant during the rainy season than during the dry season, both within coffee rows and in inter-rows, and in shaded and open areas (Table 4-3). Only fungi communities in shaded inter-rows were not statistically more abundant during the rainy season (2.4 ±0.2 nmol.g - 1 ) than during the dry season (1.9 ±0.2 nmol.g -1 ). Fungi to bacteria ratios remained similar in the dry and the rainy seasons. Gram+ to Gram-bacteria ratios also remained similar, except under shaded coffee rows where this ratio decreased in the dry season (-0.46 ±0.13). ACP activity rates were higher in the rainy season under coffee rows (66 ± 3 μmol.g -1 .d -1 ) than in the dry season (45 ± 6 μmol.g -1 .d -1 ), but did not show seasonal variations in inter-rows (Table 4-4 4-2). Under the combined effect of shade trees and coffee rows, soils also had significantly higher pH and available P than in inter-rows. There was no significant effect of coffee rows on total N and exchangeable K. Soil bulk density was similar in all sample locations, averaging 1.63 ±0.10 g.cm -3 .

Most significant effects of coffee rows on soil biology and biochemistry were detected during the rainy season (Table 4-3 &Table 4-4). These included significantly more abundant freeliving nematodes within coffee rows than in inter-rows. Similarly, fungi and AMF were more abundant and fungi:bacteria ratios were significantly higher under coffee rows than in inter-rows, in both shaded and unshaded areas. Soils under coffee rows also had increased C, N and P cycling rates compared to inter-rows, shown by significantly greater NAG (+5.6 ±2.1 μmol.kg - During the dry season, there was no marked difference in soil water content, abundance of free-living nematodes, bacterial communities, total microbial communities and P cycling rates between coffee rows and inter-rows. Fungi and AMF communities were more abundant within shaded coffee rows compared to shaded locations in inter-rows (+0.6 ±0.3 and +0.38 ±0.08 nmol.g - 1 ). There was no significant effect of coffee rows on NAG and BG activities under shade trees, but NAG and BG activities were lower in coffee rows than in inter-rows in unshaded locations (-7.2 ±3.0 μmol.kg -1 .h -1 for NAG and -9.9 ±4.3 μmol.kg -1 .h -1 for BG).

Impact of shade trees on soil fertility

As few significant differences between shade tree species were detected in the analysis (Table 4-6 to Table 45678), the rest of the article focuses on shade tree effects with shade tree species pooled. Most significant effects of shade trees on soil chemistry were detected within coffee rows (Table 4-2). These included higher pH (+0.50 ±0.14), SOM (+3.0 ±1.2 g.kg -1 ), total N (+0.36 ±0.08 g.kg -1 ), available P (+79 ±23 mg.kg -1 ) and exchangeable Ca (+0.90 ±0.44 cmol kg-1 ) under shaded coffee than under open coffee. There was no marked effect on exchangeable K and Mg. In interrows, shade trees only had a significant impact on soil pH which was lower than in open areas (-0.30 ±0.11).

During the rainy season, free living nematodes where more abundant under shade trees than in open areas, both within coffee rows (+1.6 ±1.1 ind.100g -1 ) and in inter-rows (+1.5 ±1.1

ind.100g -1 ) (Table 4-3). There was no significant impact of shade trees on soil microbial communities, except on the fungi:bacteria ratio that was higher in inter-rows in shaded areas than in inter-rows in open areas (+0.011 ±0.005). Shade trees had a positive impact on C and N cycling rates, as indicated by a higher BG activity under coffee (+5.2 ±1.8 μmol.kg -1 .h -1 ) and a higher NAG activity in inter-rows (+2.9 ±1.4 μmol.kg -1 .h -1 ) (Table 4-4). Shade trees had no marked effect on ACP activity.

Most significant effects of shade trees on soil biology were detected during the dry season (Table 4-3). These included more abundant bacterial communities, fungi and total microbial communities in shaded areas than in open areas, both within coffee rows and in inter-rows.

Bacterial communities showed similar compositions in shaded and in open areas, as indicated by similar Gram+ to Gram-ratios. Within coffee rows, AMF were more abundant in shaded areas than in open areas (+0.36 ±0.08 nmol.g -1 ). In inter-rows, fungi:bacteria ratios were higher in shaded

areas than in open areas (+0.014 ±0.006). Shade trees had no marked effect on enzyme activities (Table 4-4), except on ACP activity higher in shaded inter-rows than in open inter-rows (+17.3

±5.1 μmol.kg -1 .h -1 ). There was no marked effect of shade trees on soil water and abundance of free living nematodes.

Redundancy Analysis

Soil communities' composition was best explained by soil water, exchangeable Mg and the dry weight of litter input (Figure 4-2). Fungi:bacteria ratio was positively correlated with litter input and exchangeable Mg. Gram+:Gram-ratio was negatively correlated with litter input and exchangeable Mg. Nematode abundance was best correlated with soil water. In both the rainy and the dry season, AMF, fungi and soil water were discriminating variables selected in the RDA models that best explained soil enzyme activities (Figure 4-3). ACP cycling rate was positively correlated with AMF and fungi communities. NAG and BG were positively correlated with soil water content.

Discussion

Soil chemistry

Chemical fertilization is the most common management practice to influence soil fertility.

It is used to compensate for potential soil deficiencies in nutrients and replace depletions caused by plant growth, export of crops and lixiviation [START_REF] Kl | Closing the Loop: Nutrient Balances in Organic and Conventional Coffee Agroforests[END_REF]. The studied mountainside exhibits high nutrient concentrations (Table 4-2), which indicate high mineral fertilizers inputs, frequent in smallholder farming systems in China [START_REF] Ju | Reducing China's fertilizer use by increasing farm size[END_REF]. In a chronosequence of intensively managed monoculture coffee farms in Hainan Province, China, Zhao et al. (2018a) evidenced the long-term decrease in soil fertility through the combined loss of SOM and increase in soil acidity. In the present study, with current SOM values between 22.0 and 28.4 g.kg -1 and soil pH values between 4.5 and 4.9, SOM is already low and acidity high, although not unusual [START_REF] Banks | Effects of Synthetic Fertilizer on Coffee Yields and Ecosystem Services: Parasitoids and Soil Glomalin in a Costa Rican Coffee Agroecosystem[END_REF][START_REF] Amador | Contribution of nitrogen from litter and soil mineralization to shade and sun coffee (Coffea arabica L.) agroecosystems[END_REF]. These values are already significantly lower than those found in natural forests ecosystems and indicate soil degradation (Zhao et al. 2018b). A further decrease in SOM and pH would nonetheless hinder coffee growth and be detrimental to coffee production [START_REF] Snoeck | Fertilization[END_REF]Zhao et al. 2018a). Furthermore, a number of studies have highlighted the low recovery rate of mineral fertilizers in high input systems, resulting in economic losses and downstream pollutions. For instance, in Costa Rica, due to high water drainage, coffee farms receiving 250 kg.N.ha -1 .year -1 lost 33-55% of it through NO3-leaching alone [START_REF] Cannavo | Low nitrogen use efficiency and high nitrate leaching in a highly fertilized Coffea arabica-Inga densiflora agroforestry system: a 15N labeled fertilizer study[END_REF]). On the other hand, it is common practice in coffee farms worldwide to use coffee pulp, a by-product of post-harvest processing, as compost. In the study area, farmers solely relied on chemical fertilizer. A change in their management practices, to include these readily available organic fertilizers, would contribute to increasing soil chemical fertility.

Seasonal impact on soil fertility

As expected, soil biological activity was significantly higher during the rainy season than the dry season. The density of all measured soil communities increased. These findings are in agreement with previous studies highlighting the rapid seasonal response of soil communities to changes in soil water content and justifying their use as sensitive soil fertility indicators [START_REF] Buscardo | Spatiotemporal dynamics of soil bacterial communities as a function of Amazon forest phenology[END_REF][START_REF] Feng | Soil microbial communities under conventional-till and no-till continuous cotton systems[END_REF][START_REF] Finney Dm | Living cover crops have immediate impacts on soil microbial community structure and function[END_REF][START_REF] Prescott Ce | Tree species influence on microbial communities in litter and soil: Current knowledge and research needs[END_REF]. Seasonal changes in soil communities are also often linked to an increase in root growth and exudates during the rainy months, increasing the available carbon for microorganisms in the root rhizosphere [START_REF] Feng | Soil microbial communities under conventional-till and no-till continuous cotton systems[END_REF][START_REF] Geisseler | Long-term effects of mineral fertilizers on soil microorganisms -A review[END_REF][START_REF] Kong Ayy | Microbial community composition and carbon cycling within soil microenvironments of conventional, lowinput, and organic cropping systems[END_REF]). In the present study, density of soil communities 4.4.4. Impact of shade trees on soil fertility

Despite the marked effect of 20 years of intensive fertilization practices and abundant coffee leaf litter on soil fertility, I still observed a richer soil chemical fertility under shade trees than in open areas, only four years after the conversion from monoculture to agroforestry. pH, SOM, total N, available P and exchangeable Ca concentrations were all higher under shaded coffee than under open coffee. The positive impact of shade trees on chemical fertility in coffee farms has been widely evidenced in mature AFS and can be explained by shade trees' below-ground activity and litterfall [START_REF] Hairiah | Litter layer residence time in forest and coffee agroforestry systems in Sumberjaya, West Lampung[END_REF][START_REF] Huang | Differential seasonal effects of water addition and nitrogen fertilization on microbial biomass and diversity in a temperate desert[END_REF][START_REF] Amador | Contribution of nitrogen from litter and soil mineralization to shade and sun coffee (Coffea arabica L.) agroecosystems[END_REF][START_REF] Notaro Kda | Agroforestry systems, nutrients in litter and microbial activity in soils cultivated with coffee at high altitude[END_REF][START_REF] Payán | Soil characteristics below Erythrina poeppigiana in organic and conventional Costa Rican coffee plantations[END_REF]Tully and Lawrence 2012). In our study, shade tree leaf litter did contribute 23 to 68% of all leaf litter biomass under shaded coffee.

In addition, throughout the year and in all sample locations, soil communities and soil enzyme activities were always similar or higher under shade trees than in corresponding open areas.

This demonstrates that shade trees also had a positive impact on soil biological and biochemical fertility. This positive impact was most visible during the dry season, when microbial communities were all significantly more abundant under shade trees than in open areas (Table 4-3). In the present study, leaf litter biomass best explained the higher abundance of fungi communities under shade trees while bacterial communities were best correlated with soil water content (Figure 4-2). These results are in line with many studies showing that shade trees support higher and more diverse microbial communities than monoculture systems [START_REF] Bagyaraj Dj | Below ground microbial diversity as influenced by coffee agroforestry systems in the Western Ghats, India. Agriculture[END_REF][START_REF] Bainard Ld | Arbuscular mycorrhizal fungi in tree-based intercropping systems: A review of their abundance and diversity[END_REF][START_REF] Bainard Ld | Growth response of crops to soil microbial communities from conventional monocropping and tree-based intercropping systems[END_REF][START_REF] Barrios E | Agroforestry and Soil Health: Linking Trees, Soil Biota, and Ecosystem Services[END_REF][START_REF] Mortimer Pe | Alder trees enhance crop productivity and soil microbial biomass in tea plantations[END_REF]. Our study also highlights the buffering effect of shade trees on seasonal variability of soil communities.

Impacts of trees on soil fertility are usually not detected so rapidly [START_REF] Souza Hnd | Protective shade, tree diversity and soil properties in coffee agroforestry systems in the Atlantic Rainforest biome[END_REF][START_REF] Vallejo | Soil enzymatic activities and microbial biomass in an integrated agroforestry chronosequence compared to monoculture and a native forest of Colombia[END_REF]. For example, 6 to 9 years after the introduction of shade trees in an intensively managed monoculture coffee farm, (Hergoualc'h et al. 2012) did not notice any change in SOC. In Kenyan highlands, soils under 20 to 30 years of agroforestry practices tended to exhibit higher soil chemical and biological fertility, but differences with monoculture systems were not statistically significant [START_REF] Lagerlöf | Landuse intensification and agroforestry in the Kenyan highland: Impacts on soil microbial community composition and functional capacity[END_REF]. Therefore, the detection of clear positive impacts of young shade trees on soil fertility in an intensively managed system is one of the main results of this study [START_REF] Schaller | Species and site characteristics that permit the association of fast-growing trees with crops: the case of Eucalyptus deglupta as coffee shade in Costa Rica[END_REF]. These positive impacts are all the more important considering the long-term trend of soil chemical and biological degradation under intensive monoculture coffee (Zhao et al. 2018a). The present study hence highlights the fact that shade trees can not only be part of long-term solutions to produce more sustainable coffee, but can also have some immediate contributions to preservation and/or restoration of soil fertility. A reduction in mineral fertilizer inputs should complement this agro-ecological intensification and further contribute to preservation and/or restoration of soil fertility [START_REF] Tscharntke | Multifunctional shade-tree management in tropical agroforestry landscapesa review[END_REF]. Indeed, as shade trees grow, their root systems will increasingly reduce nutrient leakage [START_REF] Cannavo | Low nitrogen use efficiency and high nitrate leaching in a highly fertilized Coffea arabica-Inga densiflora agroforestry system: a 15N labeled fertilizer study[END_REF]Tully et al. 2012) and their leaf litter will be an increasingly important component of nutrient cycling in coffee AFS [START_REF] Kl | Closing the Loop: Nutrient Balances in Organic and Conventional Coffee Agroforests[END_REF]. I also expect that differences in impacts of shade tree species on soil fertility, which were not apparent in this study, will become more distinguishable as trees grow [START_REF] Bagyaraj Dj | Below ground microbial diversity as influenced by coffee agroforestry systems in the Western Ghats, India. Agriculture[END_REF][START_REF] Sb | Soil organic carbon under a linear simultaneous agroforestry system in Uganda[END_REF]. According to the local ecological knowledge of coffee farmers in southern Yunnan documented in Chapter IV, B. javanica should eventually outperform other shade tree species for preservation and/or restoration of soil fertility. According to that same chapter, Artocarpus heterophyllus, Melia azedarach and the N-fixing Leucaena leucocephala would also highly benefit soil fertility.

Conclusion

This study demonstrated a clear positive and rapid impact of shade trees on soil fertility in the 0-20 cm soil layer, only four years after their introduction into previously monoculture coffee farms. This improvement in soil fertility translated in higher soil chemical fertility below shaded coffee trees than below open coffee, more abundant fungi communities all year long and a buffering effect during the dry season on all soil microbial communities under shade tree canopies compared to open areas. These results are particularly important in light of the long-term soil degradation documented in intensively-managed monoculture coffee farms. They indicate that agroforestry practices can not only be part of long-term solutions for more sustainable coffee systems, but that shade trees can also quickly contribute to preserving and/or restoring soil fertility.

The introduction of shade trees in coffee systems should then be complemented with changes in management practices, notably through a reduction of the currently excessive fertilizer inputs. This would result in a comprehensive agro-ecological intensification supported by the provision of ecosystem services by shade trees, whether these are improvement in superficial soil chemical and biological fertility, reduced nutrient leakage in deep soil layers or enhanced nutrient cycles through abundant leaf litter. Altogether, this agro-ecological intensification would contribute to the production of more sustainable coffee in southern Yunnan.

Supporting information

Soil water profiles

Soil water content was measured twice towards the end of the dry season, in March 2017 and April 2018, and once after the peak of the rainy season, in September 2017. Soil was sampled with an auger from 0 to 120 cm depths at 20 cm intervals. Soil samples were weighed in the field then dried in an oven at 105°C for 48 h. A total of 36 soil water profiles were derived from the measurements; 9 under each of the 3 selected shade tree species and 9 in full sun. One of the terraces had a sandy 60-80 cm soil horizon that contrasted with the rest of the study site. Soil water content measurements were therefore stopped at 60 cm depth for this terrace. March and September 2017 was higher under B. Javanica, with 5 to 7% water content increase in the 0-60 cm soil horizon. There was no noticeable seasonal fluctuation below 60 cm depth.

Soil water content measured in January 2018 at the 0-20 cm soil depth showed intermediate values between those of the rainy and the dry seasons, averaging 23%. Seasonal fluctuations ranged from 1.5% in inter-rows to 2.8% under coffee rows. Shade trees had no significant impact on soil water content in the top 20 cm horizon.

Arabica coffee grows best in areas with 1400 to 2000 mm of annual rainfall, 1 to 3 dry months necessary to trigger flowering and a rainy season concomitant to fruit development [START_REF] Carr M | The water relations and irrigation requirements of coffee[END_REF][START_REF] Descroix | Environemental Factors Suitable for Coffee Cultivation[END_REF]. These conditions were met in the present study and explain the absence of hydric stress found in the present study. Indeed, soil water profiles (Figure 4-4) show that coffee trees and shade trees mostly used water from the 0-40 cm soil layer. All root systems extended deeper than 1m deep so coffee trees and shade trees also have access to deeper water pools in the event of severe droughts. Shade trees had no significant effect on soil water content.

These results are in line with studies highlighting a complementary use of water resources by coffee trees and shade trees under sufficient precipitations in Costa Rica [START_REF] Cannavo | Agroforestry associating coffee and Inga densiflora results in complementarity for water uptake and decreases deep drainage in Costa Rica[END_REF][START_REF] Bb | The role of agroforestry in reducing water loss through soil evaporation and crop transpiration in coffee agroecosystems[END_REF][START_REF] Van Kanten R | Transpiration of Arabica Coffee and Associated Shade Tree Species in Sub-optimal, Low-altitude Conditions of Costa Rica[END_REF]. In contrast, coffee yield in Baoshan Prefecture, Yunnan Province, was dependent to irrigation practices due to a severe lack of rainfall with only 755 mm.yr -1 (Liu et al. 2018).

Root system distribution

Vertical root distribution of coffee trees and shade trees was recorded using the root impact counting method [START_REF] Van Noordwijk | Trench Profile Techniques and Core Break Methods[END_REF]. 4 trenches were dug parallel to the terraces, 1 for each shade tree species planted within a coffee row and 1 for full sun coffee. Trenches were 2 m wide and 1.2 m deep, located about 30 cm from the coffee and shade tree stems. Root intersections with the vertical plane were recorded using a 10 cm * 10 cm grid. Roots were categorized into 2 diameter classes: fine roots (≤ 2 mm) and coarse roots (> 2 mm). Roots from coffee trees and shade trees were distinguished based on color and odor. Trenches were extended to about 15 cm from coffee stems to observe a second profile wall following the same methodology [START_REF] Van Noordwijk | Trench Profile Techniques and Core Break Methods[END_REF]. Vertical root distributions were converted as percentages of total root impacts per tree. of fine roots (≤ 2 mm) and 30% coarse roots (> 2 mm). In contrast, coffee roots were not recorded below 110 cm depth and exhibited proportionally more fine roots, with a 85:15 fine to coarse roots ratio. Jacaranda mimosifolia and B. Javanica roots were homogeneously distributed from 0 to 60 cm with approximately 10% of root impacts per 10 cm layer, and a decreasing root concentration in deeper soil layers. In contrast, C. camphora had a very low root density in the top 10 cm horizon and half of its roots concentrated between 20 and 50 cm depths.

Open coffee trees had 60% of their roots located in the top 40 cm soil layer, and a decreasing root density in deeper soil layers, down to 110 cm depth. Vertical root distribution was most similar to open coffee one in the case of coffee trees shaded by J. mimosifolia, but significantly altered for coffee trees shaded by B. javanica and C. camphora. In the first case, 86% of roots were concentrated in the 20 to 60 cm soil layer, with less than 7% in the top 20 cm layer. On the contrary under C. camphora, 57% of coffee roots were located in the top 10 cm soil layer and only 25% in the 10 to 40 cm layer.

With 75% of roots located in the 0-60 cm soil layer (Figure 4-5), coffee root systems in open conditions were similar to those documented in Costa Rica and Nicaragua [START_REF] Defrenet | Root biomass, turnover and net primary productivity of a coffee agroforestry system in Costa Rica: effects of soil depth, shade trees, distance to row and coffee age[END_REF][START_REF] Padovan Mp | Root distribution and water use in coffee shaded with Tabebuia rosea Bertol. and Simarouba glauca DC. compared to full sun coffee in sub-optimal environmental conditions[END_REF][START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF]. Extensive colonization of the fertile 0-60 cm soil layer enabled a good access of coffee plants to nutrient and water resources. In comparison, the root systems of shade trees and shaded coffee trees illustrated three distinct scenarios (Figure 45). The low level of disturbance of coffee roots below J. mimosifolia illustrated complementarity between this shade tree species and coffee trees. Bischofia javanica exhibited a root profile similar to that of J. mimosifolia but was significantly more competitive in the 0-20 cm soil layer, where it nearly excluded coffee roots. Still, the absence of noticeable water stress as well as high coffee yield under B. javanica indicate that coffee roots could take up sufficient water and nutrient resources in the 20-60 cm soil layer. Lastly, C. camphora exhibited the most competitive root system of all shade tree species. It confined more than half of coffee roots into the top 0-10 cm soil layer. This could be especially detrimental to coffee trees in case of an extended dry season [START_REF] Cannavo | Agroforestry associating coffee and Inga densiflora results in complementarity for water uptake and decreases deep drainage in Costa Rica[END_REF][START_REF] Padovan Mp | Root distribution and water use in coffee shaded with Tabebuia rosea Bertol. and Simarouba glauca DC. compared to full sun coffee in sub-optimal environmental conditions[END_REF]. Already, coffee yield was lower below C. camphora than below other shade tree species or in open conditions. CHAPTER 5 -Impact of shade trees on coffee yield and quality

Introduction

Coffea arabica is a shade-adapted species that originated in the high tropical forests of Ethiopia [START_REF] Batista Kd | Photosynthetic limitations in coffee plants are chiefly governed by diffusive factors[END_REF]. Its saturation irradiance threshold for photosynthesis is low, between 300 and 600 μmol photon.m -2 .s -1 [START_REF] Franck | Limitation of coffee leaf photosynthesis by stomatal conductance and light availability under different shade levels[END_REF], compared to a photosynthetic active radiation regularly above 2000 μmol photon.m -2 .s -1 in open conditions around middays [START_REF] Chaves Arm | Seasonal changes in photoprotective mechanisms of leaves from shaded and unshaded field-grown coffee (Coffea arabica L.) trees[END_REF]). Furthermore, coffee leaves in open conditions regularly experience photoinhibition around midday and in the afternoon from temperatures above 30°C and vapor pressure deficit (VPD) above 1.5kPa [START_REF] Franck | Limitation of coffee leaf photosynthesis by stomatal conductance and light availability under different shade levels[END_REF]. In these conditions, shade trees can create a favorable above-ground environment below their canopies, lowering temperatures and VPD [START_REF] Chaves Arm | Seasonal changes in photoprotective mechanisms of leaves from shaded and unshaded field-grown coffee (Coffea arabica L.) trees[END_REF][START_REF] Damatta Fm | Physiological and Agronomic Performance of the Coffee Crop in the Context of Climate Change and Global Warming: A Review[END_REF]) while maintaining a photosynthetic active radiation above saturation irradiance if their canopies is not too dense [START_REF] Charbonnier | Competition for light in heterogeneous canopies: Application of MAESTRA to a coffee (Coffea arabica L.) agroforestry system[END_REF][START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF]). In addition, coffee trees lack efficient mechanisms to regulate fruit set. In open conditions, large flowering events and high fruit sets result in high competition between fruits for water resources during the expansion stage [START_REF] Cannell Mgr | Factors affecting Arabica coffee bean size in Kenya[END_REF], and high competition for nutrients and carbohydrates during the bean filling stage and the maturation stage. Lack of resources in turn leads coffee trees to shed numerous fruits during the coffee cherry development cycle (Bote and Jan 2016). It also contributes to smaller and lighter coffee cherries at harvest, compared to cherries from shaded conditions. Furthermore, fruits act as priority sinks for carbohydrates and nutrients (Bote and Jan 2016). High fruit sets therefore hinder vegetative growth [START_REF] Cannell Mgr | Effects of Fruiting, Defoliation and Ring-Barking on the Accumulation and Distribution of Dry Matter in Branches of Coffea Arabica L. in Kenya[END_REF], lower the amount of newly produced fruiting nodes and flowers per node and hence hinder yield in the following year [START_REF] Cannell Mgr | Physiology of the Coffee Crop[END_REF]. This in turn leads to the well-documented biannual pattern in coffee production, when high yield alternate with low yield. All these reasons partly explains why coffee yield in shaded conditions can be similar to or higher than coffee yield in open conditions (Bote and Jan 2016; [START_REF] Vaast | Biophysical Interactions Between Timber Trees and Arabica Coffee in Suboptimal Conditions of Central America[END_REF]. Shade trees are also known to impact coffee quality. In sub-optimal conditions, shade and elevation similarly delay coffee ripening [START_REF] Geromel | Effects of shade on the development and sugar metabolism of coffee (Coffea arabica L.) fruits[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF], allowing for a better filling of the beans and higher physical, chemical and organoleptic attributes [START_REF] Bertrand B | Comparison of bean biochemical composition and beverage quality of Arabica hybrids involving Sudanese-Ethiopian origins with traditional varieties at various elevations in Central America[END_REF][START_REF] Bote Ad | Tree management and environmental conditions affect coffee (Coffea arabica L.) bean quality[END_REF][START_REF] Tolessa | Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee[END_REF]). However, shade can also become detrimental in optimal conditions or under high elevations [START_REF] Bosselmann As | The influence of shade trees on coffee quality in small holder coffee agroforestry systems in Southern Colombia[END_REF][START_REF] Rahn E | Exploring adaptation strategies of coffee production to climate change using a process-based model[END_REF].

In this chapter, I studied the impacts of young shade trees providing a gradient of shade intensity on micro-climate, coffee development cycle, coffee yield and coffee quality. Overall, I researched whether or not young shade trees provided ecosystem services similar to those provided by mature shade trees, albeit at a smaller scale. More specifically, our first hypothesis was that young shade trees delayed coffee phenophases, from flowering to cherry ripeness. Second, I expected young shade trees to decrease coffee flower set as well as fruit losses throughout the coffee cherry development cycle. Third, I hypothesized that young shade trees would hinder coffee yield but increase coffee quality.

Materials and methods

Experimental design

This chapter relies on the same experimental set up as that used in chapter 4. The study site is described in details in Chapter 2.2. Fieldwork was conducted from November 2016 to March 2018. The same six replicates of B. javanica, C. camphora and J. mimosifolia were used in this experiment, for a total of 18 shade trees. Between four and eight coffee trees were selected around each shade tree, for a total of 90 coffee trees. They were categorized as follow: 40 fully located below shade tree canopies (C1 = shaded coffee); 24 located below the edge of shade tree canopies or being the first or second coffee trees encountered outside of shade tree canopies (C2 = buffer coffee); and 26 located further away from shade tree canopies (C3 = open coffee).

Shade characterization

Leaf area index (LAI) was used as a proxy for shade intensity throughout the 2017-2018 fruit development cycle. To estimate LAI, three replicates were selected for each shade tree species. Their leaves were periodically counted (B. javanica and J. mimosifolia) or extrapolated from a sub-sample of branches (C. camphora) from February 2017 to March 2018. At each occurrence, 15 representative leaves were harvested, scanned, and their areas measured using the image analysis software ImageJ 1.50i. LAI was obtained by multiplying the number of leaves by their mean area, and dividing this number by the area of canopy projected to the ground. LAI was then averaged within shade tree species.

Micro-climate characterization

Air temperature and relative humidity were recorded every 30 minutes by four data loggers (ibuttons DS1923-F5#). The data loggers were mounted both below shade tree canopies (C1) and in open conditions (C3). In both cases (C1 and C3), data loggers were placed directly above coffee trees and protected from direct sunlight. Following a cold spell in December, damages on coffee leaves were evaluated through a visual assessment, ranking coffee trees from 0 (no visible damage) to 5 (all new leaves were damaged and/or aborted). Temperature data was combined with the dates of blossom peak and harvest peak to calculate growing degree days (DD) of coffee cherries for each coffee tree, using a base temperature of 10°C [START_REF] Pezzopane | Agrometeorological parameters for prediction of the maturation period of Arabica coffee cultivars[END_REF][START_REF] Rahn E | Exploring adaptation strategies of coffee production to climate change using a process-based model[END_REF]. Furthermore, monthly maximum temperatures (Tmax)

were calculated as the average of maximum daily temperatures for each month. Monthly minimum temperatures (Tmin) and monthly average temperatures (Tavg) were calculated in the same way. Monthly wind speed and sunshine hours were retrieved from the weather station in Pu'er city airport, through

Fruit development cycle

Coffee cherry development was studied throughout the growing season 2017-2018, from first flowers to final harvest, on each of the 90 selected coffee trees. To do so, the two stems with most productive branches were selected from each coffee tree before the first flowering event. Three to five productive branches were then selected on each of these stems: three branches if fewer than 20 productive branches were recorded, four branches if fewer than 25 productive branches were recorded, and five branches otherwise. These branches were selected following an ascending spiraling pattern in order to be representative of all heights and orientations. They were carefully tagged and used for all subsequent follow-up activities.

The number of flower buds, flowers, fruits (from pinheads to ripe cherries), and aborted fruits were periodically counted on each tagged branch. Counting activities were carried out in March (twice), April, and May to encompass all the main flowering events. Because both flowering events in March were modest, they were aggregated and analyzed together. Blossom peak was defined as the weighted average date of flowering, with weights corresponding to the percentage of blossom at each counting event. Two more counting activities occurred in June and September to follow-up fruit shedding during swelling and filling stages. A last counting was carried out in November 2017, one month before the start of harvest. Green coffee cherries on branches suffering from die-back and C. coffeanum were considered unable to reach a ripeness degree suitable for harvest, and were considered aborted in November. Data from May served as the reference point to correct indistinguishable flower buds from previous counting activities and aborted fruits in subsequent counting. Data was multiplied by the number of productive branches to extrapolate results to the coffee tree.

After beans with visual defects were manually removed, 52 samples of 100 g green beans were roasted to a light-medium roast. The following day, a panel of 11 professional cuppers assessed beverage quality after a round of calibration using a standard wet-processed Yunnan coffee. Cupping took place in 5 rounds of 9 samples and 1 round of 6 samples, using the Sustainable Coffee Institute cupping form. Three 200 mL infusions were prepared with 11 g of coffee per cup. In addition, two cups of the standard coffee used for calibration were added at each end of the cupping tables. Cuppers evaluated the following organoleptic attributes on a scale from 6 to 10 with 0.25 increments: fragrance, aroma, flavor, acidity, body, sweetness, and pleasant aftertaste. Three additional attributes were used to evaluate defects -fresh crop, off-flavor, and uniformity -and were each rated 10 points in the absence of noticeable defect. Samples with too light (coffee crust self-breaks) or too dark (oil on the beans and smoky flavor) a roast were noted and discarded. Results from 1 cupper were found inconsistent with results from the 10 others and thus excluded from analysis. Scores of individual attributes were averaged among cuppers and summed up to get a total score out of 100 points.

Data analysis

Parameters related to fruit development, coffee yield, and coffee quality were analyzed separately through analysis of variance (ANOVA). Data normality was tested with Shapiro's test, and homogeneity of variances with Levene's test. Data was log-transformed to meet these assumptions whenever necessary.

Terraces were considered as a random effect in the ANOVA model, to account for small-scale variability in soil fertility and fertilizer inputs. Significant groups were separated with Wilcoxon's test. All results were expressed as mean ± standard error.

A redundancy analysis (RDA) was performed to explain organoleptic quality parameters from a matrix of explanatory variables that included shade tree species, coffee category, coffee yield, percentage of aborted fruits, DD, ETc/ ET0, month of harvest peak, and physical quality indicators. The most discriminating variables were selected through a forward procedure and tested for significance with 1000 permutations, using the vegan package in R3.4.4 [START_REF] Oksanen | vegan: Community Ecology Package. R package version 2[END_REF]. temperatures recorded in open conditions reached 0°C twice (at roughly 6am and 8am); however, the temperatures under shade trees was always 0.5 to 1°C higher (Figure 5-2). The buffering effect from shade trees was confirmed in February through a visual assessment showing a clear and significant trend of increasing damage with decreasing shade (p < 10 -5 ). On the scale of damages ranging from 0 to 5, C1 coffee trees averaged 0.88 ±0.17, C2 coffee trees averaged 1.74 ±0.34, and C3 coffee trees averaged 2.91 ±0.37.

Results

5

Fruit growing period

Coffee quality

No significant differences in physical and organoleptic attributes were detected amongst shade treatments and coffee categories. On average, 68.6 ±0.9 cherries were collected per 100 g of coffee cherries harvested. Once processed, green beans weighted 643 ±2 g.L -1 , and 74 ±1% of bean weight was of size 16 or higher. All organoleptic attributes were rated between 7.2 and 7.6 out of 10, and no defects were detected.

Total scores of coffee cupping ranged from 80.6 to 82.5, averaging 81.6 ±0.1 out of 100.

Shade tree species, coffee category, coffee yield, percentage of aborted fruits, DD, ETc and ET0, cherry density, and green bean density did not significantly explain variability in cupping scores. Coffee organoleptic attributes were best explained by month of harvest and green bean size (Figure 567). These two variables explained 37% of variability in coffee cupping scores. Coffee samples from mid-harvest (January) were those most positively correlated with high scores in acidity, aftertaste, body, flavor, sweetness, and total scores. Coffee samples collected during the latest round of harvest (February), were those most negatively correlated with these attributes. This resulted in higher total cupping scores for coffee samples harvested in January than those harvested in February (Figure 5-8). Total cupping scores were also positively correlated with percentages of large green beans, mostly through higher scores in fragrance and aroma (Figure 5-7). However, there was no significant difference between the percentages of large beans found in coffee samples grouped by harvest date (Figure 5-8).

Discussion

Shade and micro-climate

J. mimosifolia had a high spreading canopy shade and provided a light shade intensity similar to that provided by Inga densiflora trees in Costa Rica, for which LAI were measured between 1.0 in the dry season and 1.3 in the wet season [START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF]). On the other hand, C. camphora had a low and compact canopy shape, and provided by far the densest shade intensity of all three tree species, with LAI slightly higher than that of tall Erythrina poepiggiana trees [START_REF] Charbonnier | Competition for light in heterogeneous canopies: Application of MAESTRA to a coffee (Coffea arabica L.) agroforestry system[END_REF]. For E. poepiggina, LAI was estimated around 5.2 and light interception under the canopy ranged from 40 to 75%. This range of light interception is often considered the upper limit of acceptable shade, all the more under near-optimal growing conditions such as those found in the study area [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF]. This was a first indication of the excessive competition for light from C. camphora, which resulted in lower coffee yields under its canopy.

Although young, shade trees impacted micro-climate in ways similar to those recorded in more mature agroforestry systems. Their impact was more noticeable in reducing diurnal fluctuations in air temperatures and buffering extreme temperatures than in reducing average air temperature (Lin 2007).

Buffering low temperatures was particularly important during the December cold spell, when shade trees maintained coffee trees just above freezing temperatures. This protective effect from shade trees was highlighted and confirmed through visual assessment in February, with fewer damages recorded below shade trees than in open areas.

Lastly, young shade trees significantly increased air relative humidity and decreased VPD under their canopies during daytime. This might have contributed to lowering photoinhibition [START_REF] Batista Kd | Photosynthetic limitations in coffee plants are chiefly governed by diffusive factors[END_REF] and maintaining coffee daily net photosynthesis despite lower irradiance intensities under shade trees. For instance, [START_REF] Franck | Limitation of coffee leaf photosynthesis by stomatal conductance and light availability under different shade levels[END_REF] showed that coffee daily net photosynthesis under 55% shade was similar to that measured in open conditions, and that low irradiance under 81% shade only contributed to a 20% decrease in photosynthesis. Similarly, [START_REF] Ronquim | Carbon gain in coffea arabica during clear and cloudy days in the wet season[END_REF] evidenced that on a clear days coffee trees only achieved around 1/3 rd of their potential daily photosynthesis, mostly due to low stomatal conductance under high VPD, while on cloudy days, VPD was on average 0.5kPa lower, and coffee trees achieved daily net photosynthesis close to their potential.

In line with our second hypothesis, flower set decreased with shade intensity. Coffee trees under the dense shade of C. camphora had only around half the flower set (7900 ±1790) of coffee trees in open conditions (14860 ±1580). A similar impact of mature shade trees has been noted in coffee agroforestry systems [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Damatta Fm | Ecophysiological constraints on the production of shaded and unshaded coffee: a review[END_REF], with some studies highlighting even more dramatic effects. For instance, [START_REF] Franck | Limitation of coffee leaf photosynthesis by stomatal conductance and light availability under different shade levels[END_REF] noted a decrease from 4620 flowers in full sun down to 605 under 81% shade, therefore an 87% decrease in flower set. In Kenya, coffee trees under 50% shade only had 21% of the initial flower set recorded in full sun, due to a decrease in the number of nodes and number of flowers per node [START_REF] Cannell Mgr | Crop physiological aspects of coffee bean yield-a review[END_REF][START_REF] Cannell Mgr | Physiology of the Coffee Crop[END_REF]. Calculated on absolute numbers instead of percentages, our study displayed an even higher reduction of flower set. This reflects the very intensive coffee practices in the present study site, with highly productive Catimor cultivars and large fertilizer inputs, as well as a highly productive year within a biannual cycle.

By the end of the growing cycle, only 25 to 41% of flower buds resulted in ripe cherries. Most fruits aborted between June and September, when they entered the expansion stage and became a priority sink for carbohydrates (Bote and Jan 2016; Cannell 1971). During that stage, cherries can represent up to 72% of all dry weight increment in coffee trees and up to 95% NPK uptake [START_REF] Cannell Mgr | Physiology of the Coffee Crop[END_REF]. A minimum 20 cm 2 leaf-to-fruit ratio is then needed to balance carbohydrate source and sink and sustain fruit expansion, otherwise coffee trees shed fruits and, in more extreme cases, suffer from die-back (Bote and Jan 2016; [START_REF] Liu X | Impacts of regulated deficit irrigation on yield, quality and water use efficiency of Arabica coffee under different shading levels in dry and hot regions of southwest China[END_REF][START_REF] Vaast | Biophysical Interactions Between Timber Trees and Arabica Coffee in Suboptimal Conditions of Central America[END_REF] and similar in open conditions and under light shade in the present study, it appears that the present coffee system behaves similarly to a system under near-optimal conditions [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Bosselmann As | The influence of shade trees on coffee quality in small holder coffee agroforestry systems in Southern Colombia[END_REF][START_REF] Rahn E | Exploring adaptation strategies of coffee production to climate change using a process-based model[END_REF]).

Coffee yields measured in 2017-2018 were similar to local average yields [START_REF] Zhang | Arabica Coffee Production in the Yunnan Province of China[END_REF]) , but significantly higher than those measured in most other studies worldwide. For instance, in Costa Rica, the average national yield reaches 1.7 to 2.0 t.ha -1 , and high yields under optimal conditions reach an approximate 3 t.ha -1 [START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF]) . These high yields reflect the near-optimal growing conditions and the intensive management practices, and explain the marked biannual pattern observed between 2016 and 2018, with years of low production alternating with years of high production [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Damatta Fm | Ecophysiological constraints on the production of shaded and unshaded coffee: a review[END_REF] . Only coffee trees below the dense shade of C. camphora had lower and constant yields over the two consecutive years, therefore indicating a balance between vegetative and reproductive growth.

Coffee quality

Very few differences were detected in physical and organoleptic attributes. Elevation and cultivars are commonly cited as the two most influential factors impacting coffee quality [START_REF] Bertrand B | Comparison of bean biochemical composition and beverage quality of Arabica hybrids involving Sudanese-Ethiopian origins with traditional varieties at various elevations in Central America[END_REF][START_REF] Guyot B | Effect of altitude and shading on Arabica coffees[END_REF], followed by fruit load [START_REF] Bote Ad | Tree management and environmental conditions affect coffee (Coffea arabica L.) bean quality[END_REF][START_REF] Cannell Mgr | Factors affecting Arabica coffee bean size in Kenya[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF], , soil and climatic factors related to a geographical origin [START_REF] Aguilar P | Sensory profiles of coffee in the Dominican Republic are linked to the terroirs[END_REF][START_REF] Avelino | Effects of slope exposure, altitude and yield on coffee quality in two altitude terroirs of Costa Rica, Orosi and Santa María de Dota[END_REF][START_REF] Avelino | Identifying terroir coffees in Honduras. Research and coffee growing[END_REF][START_REF] Laderach | Geographical Analyses to Explore Interactions between Inherent Coffee Quality and Production Environment[END_REF]) and shade [START_REF] Bosselmann As | The influence of shade trees on coffee quality in small holder coffee agroforestry systems in Southern Colombia[END_REF][START_REF] Tolessa | Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF]. In this study, elevation, cultivar, as well as soil and climatic factors were all similar; only shade intensity varied across treatments. This explained the relative homogeneity in quality of all coffee samples. Furthermore, [START_REF] Bertrand B | Comparison of bean biochemical composition and beverage quality of Arabica hybrids involving Sudanese-Ethiopian origins with traditional varieties at various elevations in Central America[END_REF] explained that new cultivars, such as Catimor, are more vigorous than traditional varieties, and therefore less impacted by environmental factors. Time of harvest was the most discriminating factor to explain cup quality scores, with the highest scores achieved by samples taken from December and January, corresponding to early and mid-harvest, in line with [START_REF] Tolessa | Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee[END_REF].

Here again, results tend to confirm that the present coffee system behaves similarly to a system under near-optimal conditions. Elevation and shade, which similarly impact coffee quality [START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF], only had a limited effect [START_REF] Avelino | Effects of slope exposure, altitude and yield on coffee quality in two altitude terroirs of Costa Rica, Orosi and Santa María de Dota[END_REF][START_REF] Steiman | Shade coffee in Hawai'i -Exploring some aspects of quality, growth, yield, and nutrition[END_REF][START_REF] Worku | Effect of altitude on biochemical composition and quality of green arabica coffee beans can be affected by shade and postharvest processing method[END_REF] . The present study hence shows that B. javanica and J. mimosifolia provided positive externalities only four years after their introduction-through buffering high temperatures in summer and cold spells in winter, and lowering coffee trees' stress under their canopies during the bean filling and maturation stages-without negatively impacting coffee yield and quality through light competition. In addition to these measured ecosystem services, shade trees have also been shown to provide a range of other services, such as contributing to income diversification [START_REF] Jassogne | Perceptions and outlook on intercropping coffee with banana as an opportunity for smallholder coffee farmers in Uganda[END_REF]) and preservation and/or restoration of soil fertility in coffee farms. These two shade tree species therefore appear as suitable options for the agro-ecological intensification of coffee farms. On the other hand, C. camphora provided positive externalities and maintained coffee quality, but also hindered coffee yield. This shade tree species should therefore be actively managed, in particular through pruning activities to lower shade intensity of its dense canopy and reduce its negative impact on coffee yield.

Conclusion

This study demonstrates that, only four years after their introduction in former monoculture coffee farms, young shade trees impacted coffee production in a way similar to that of mature shade trees, albeit over far smaller areas. Shade trees can therefore not only bring long-term benefits but also short-term ones during the transition period from monoculture to mature agroforestry systems. In the growing conditions found in Pu'er Prefecture, young shade trees buffered extreme climatic events, lowering coffee stress from high temperatures in summer (-3 to -6°C) and from cold spells in winter (+0.5 to +1°C). Fruit sets and subsequent losses during the bean filling and maturation stages decreased with shade intensity, while blossom peaks and harvest peaks shifted by two to three weeks. Furthermore, coffee trees located either at the edge of shade trees or below B. javanica and J. mimosifolia produced similar yields over two consecutive harvests as well as similar coffee quality to coffee trees in open conditions. Cinnamomum camphora, however, hindered coffee production with its low and compact canopy.

Young B. javanica and J. mimosifolia were shown to provide positive externalities without negative impacts on coffee yield and quality. In addition, shade trees are known to provide numerous ecosystem services, such as improving soil fertility, supporting higher biodiversity, and supplementing coffee incomes.

This study hence supports the use of these species for agro-ecological intensification of coffee systems. The present results also highlight the fact that management practices must be adapted to individual shade tree species in order to maximize their positive externalities and lower their negative impacts. In the case of shade trees with dense canopies resulting in excessive shade intensity for coffee production, such as C. camphora, pruning must become an essential component of coffee farming management practices. The introduction of shade trees should also be complemented with changes in fertilization practices, in order to better adapt fertilizer inputs to coffee needs and to take advantage of improved soil fertility under shade

CHAPTER 6 -Summary of the research

The present research focused on studying the ecosystem services provided by the most common shade tree species in coffee farms in southern Yunnan, only a few years after their introduction. In the first study (Chapter III), I carried out tree inventories to characterize shade tree biodiversity in coffee farms. I also documented farmers' local ecological knowledge in order to get a broad understanding of the ecosystem services and disservices provided by the 28 most common shade tree species in coffee farms. In the following section (Chapter IV), I set up a field experiment to study the specific impacts of three shade tree species -Bischofia javanica, Cinnamomum camphora and Jacaranda mimosifolia -on soil fertility, only four years after their introduction. Lastly, in Chapter V, I recorded the impacts of these same shade tree species on micro-climate, coffee yield and coffee quality.

Shade tree biodiversity

Shade tree inventories revealed an unexpectedly high level of diversity in the coffee farms of Pu'er and Xishuangbanna Prefectures. Diversity was high both at the farm level (average of 15 tree species per farm) and at the landscape level (with an estimated 162 tree species, similar to that documented in tropical mountain forests in southern Yunnan). This finding highlights the good prospects for biodiversity conservation in coffee-agroforestry systems. Still, a limited number of economically profitable tree species dominate the coffee landscape, and 84 of the 162 total tree species were only encountered in one or two coffee farms. Therefore, these species are particularly vulnerable to the decision-making process of a select few farmers.

Relevance of the shade tree species promoted by local governments

Nine shade tree species were promoted by the local governments of Pu'er and Xishuangbanna Prefectures, namely: Alstonia scholaris, B. javanica, Cerasus cerasoides, C. camphora, Delonix regia, Dimocarpus longan, Litchi chinensis, Macadamia integrifolia and Mangifera indica. These species were all highly favored by coffee farmers for their perceived economic potential, as well as the protection that they provided from environmental hazards. I also identified additional tree species valued by coffee farmers for the ecosystem services they provided, which could be added to the list of promoted species, such as Artocarpus heterophyllus and Diospyros kaki. Furthermore, I upgraded an online tool (www.shadetreeadvice.org) to complement the list of promoted shade tree species and provide tailored advice to farmers, based on their specific needs and their local ecological context. management practices to lower competition for light. Lastly, shade trees had no marked effect on the physical, chemical, or organoleptic quality of coffee. This last result reflects the fact that growing conditions in Pu'er Prefecture are already near-optimal and that the ecosystem services provided by shade trees thus only have a limited impact on coffee production.

Recommendations

Throughout this PhD thesis, I demonstrated that correctly selected and managed young shade trees provided more positive externalities than negative ones. In particular, they contribute to biodiversity conservation, to preservation and/or restoration of soil fertility, and to the buffering of extreme temperatures. Furthermore, provided that their canopies are not too dense, shaded coffee trees yield similarly to open coffee trees. The large-scale conversion program from monoculture coffee systems to coffee-agroforestry systems initiated by local governments in Pu'er and Xishuangbanna Prefectures therefore seem a relevant program to prepare the coffee sector to face upcoming challenges.

Nonetheless, based on the present results, I would add several recommendations to further increase the benefits of this program.

1) Additional shade tree species should be added to the list of promoted shade tree species, including A. heterophyllus and D. kaki;

2) The list of recommended shade tree species should be tailored to each farmer's needs and constraints in order to maximize the locally most important ecosystem services. This can be done using the online tool www.shadetreeadvice.org;

3) The conversion to agroforestry systems should be complemented by a decrease in fertilizer inputs, which are currently exceeding coffee needs and causing soil degradation and downstream pollution; 4) Emphasis should be put on the above-ground management of shade trees, in particular the pruning of shade trees with dense canopies timed with the coffee production cycle, so as to lower competition for light and ensure high yield in emerging coffee-agroforestry systems.

Future research

The many findings of this PhD research should be complemented with a long-term follow up of the provision of ecosystem services by shade trees and their impacts on coffee yield and quality. Indeed, the transition from monoculture coffee systems to agroforestry systems at such a large scale is unprecedented and provides a unique opportunity to study the impacts of shade trees at the landscape level at the point of introduction into coffee farms. Long term follow-up is also necessary to document the emerging competition between shade trees and coffee trees as shade trees grow, and adapt management practices accordingly until an equilibrium point is reached. Indeed, the present study of farmers' ethnobotanical knowledge revealed knowledge gaps regarding the impact of mature trees on soil fertility, coffee yield, coffee quality and pests and diseases control.

Further research should also be carried out to document the impacts of shade trees on the main

Figure 2 - 1 :Figure 3 - 1 :

 2131 Figure 2-1: Coffee growing areas by county in Yunnan Province in 2012 (retrieved from 2013 statistical yearbooks). Liushun Township is represented by the blue star. ................................... 9

Figure 3 Figure 5 - 1 :

 351 Figure 3-2: Rank-abundance curve from coffee farm inventories. The 9 most abundant species and the promoted species are represented on the curve. .............................................................18

1 :

 1 List of the 28 shade tree species ranked by coffee farmers and ecosystem services reported by farmers ....................................................................................................................19 Table 3-2: Mean scores of promoted versus non-promoted shade tree species for individual ES & ED and overall preference according to the Bradley Terry analysis. ..........................................25 Table 3-3: Interactions between coffee farmer attributes and their rankings of promoted and indigenous shade tree species by ecosystem services and disservices. .......................................25 Table 3-4: Biodiversity indices for three model farms ...............................................................35 Table 4-1: Leaf chemical composition of shade trees (BJ: B. javanica; CC: C. camphora; JM: J. mimosifolia) and coffee trees .....................................................................................................44 Table 4-2: Soil chemistry as affected by position with respect to coffee row or inter-row under shade of trees (3 species pooled) or sun (monoculture) ..............................................................45 Table 4-3: Soil communities as affected by position with respect to coffee row or inter-row under shade of trees (3 species pooled) or sun (monoculture) ..............................................................45 Table 4-4: Soil enzymatic activities as affected by position with respect to coffee row or interrow under shade of trees (3 species pooled) or sun (monoculture) and according to rainy and dry seasons .......................................................................................................................................46 Table 4-5: Coffee yield (kg.tree -1 ) under shade trees and in open conditions for 2 consecutive years (mean ± standard error). ....................................................................................................46 Table 4-6: Soil chemistry as affected by position with respect to coffee row or inter-row under shade trees (B. javanica, C. camphora, J. mimosifolia) or sun (monoculture)............................60 Table 4-7: Soil communities as affected by position with respect to coffee row or inter-row under shade trees (B. javanica, C. camphora, J. mimosifolia) or sun (monoculture)............................61 Table 4-8: Soil enzymatic activities as affected by position with respect to coffee row or interrow under shade trees (B. javanica, C. camphora, J. mimosifolia) or sun (monoculture) and according to rainy and dry seasons .............................................................................................62

  -1).

Figure 2 - 1 :

 21 Figure 2-1: Coffee growing areas by county in Yunnan Province in 2012 (retrieved from 2013 statistical yearbooks). Liushun Township is represented by the blue star.

Figure 2

 2 Figure 2-2: Average monthly temperatures and rainfall in Pu'er Prefecture, from 2002 to 2011

  each of the ten locally most important ES & ED, plus one additional rank for overall personal preference. Ties were allowed in the ranking. During this exercise, farmers were asked to comment on and explain their choices. This allowed checking how reliable their rankings were and further understanding the conceptions underlying LEK on shade trees. Locally important ES & ED were identified in a step-wise manner. A list of 28 ES & ED was derived from the literature. Through discussions with coffee workers and managers during coffee-farm inventories, I reduced this list to 24 locally relevant ES & ED. A final list was established after the completion of 30 interviews and rankings, with each respondent selecting what they considered to be the ten most relevant ES & ED. At the end of the initial set of 30 interviews, a final list was produced of the ten most frequently mentioned ES & ED.

Figure 3 - 1 :

 31 Figure 3-1: Species accumulation curves and 1st order Jackknife asymptotes with (dashed line) and without (dotted line) data from 2 demonstration farms. Grey areas represent the 95% confidence intervals.

Figure 3

 3 Figure 3-2: Rank-abundance curve from coffee farm inventories. The 9 most abundant species and the promoted species are represented on the curve.

Figure 3

 3 Figure 3-3: Locally relevant ecosystem services and disservices (ES & ED) after the 30 first interviews. Boxes represent the percentage of respondents for which each ES or ED was locally relevant. Grey boxes show the ES and black boxes show the ED selected for further ranking of shade tree species. The 2 striped boxes indicate ES that respondents thought were locally relevant, but for which they were unable to rank tree species.

Figure 3

 3 Figure 3-5: Scores and 95% confidence intervals for overall preferences of shade tree species broken down by gender. Grey areas indicate species ranked more highly by female respondents; striped boxes indicate those ranked more highly by male respondents.

FarmFigure 3

 3 Figure 3-8: Relationship between shade tree richness and coffee farm area based on the database of respondents (R 2 = 0.19, p-value < 10-6)

(

  C cycle), is the most common enzyme used as indicator of soil quality[START_REF] Mc | Different approaches to evaluating soil quality using biochemical properties[END_REF].This reaction takes place in the last stage of plant debris degradation to produce glucose, used as an energy source for soil microorganisms[START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF]. Its activity is impacted by soil pH and soil moisture content[START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF]. Furthermore, acid phosphatase is the main enzyme that intensify the solubilization of phosphate in soil with pH values ranging from 4 to 6 (Adetunji et al. 2017). These enzymes are key to agricultural systems since most P in soil is organically bound and therefore inaccessible to plants, and are thus good indicators of phosphorus availability to plants[START_REF] Nannipieri | Soil enzymology: classical and molecular approaches[END_REF]. N-Acetyl-β-D-glucosaminidase degrades chitin, a major structural component of cell walls amongst insects and fungi, which are important pools of C and N[START_REF] Parham | Detection, quantification and characterization of beta-glucosaminidase activity in soil[END_REF]. Its importance in C and N cycles has thus long been recognized, all the more since it has an optimum pH value of 5.5 while many other soil enzymes involved in N cycle function better in alkaline soils[START_REF] Parham | Detection, quantification and characterization of beta-glucosaminidase activity in soil[END_REF].

  ). Although not expected, C and N cycling rates were slower during the rainy season than during the dry season, as indicated by lower NAG and BG activities (rainy season: 26 to 37 μmol.g -1 .d -1 ; versus dry season: 38 to 71 μmol.g -1 .d -1 ).

Figure 4 - 1 :

 41 Figure 4-1: Annual NPK return from leaf litter extrapolated for 3 coffee agroforestry systems (BJ: B. javanica; CC: C. camphora and JM: J. mimosifolia) and monoculture (SUN) in comparison to average annual mineral fertilizer inputs and nutrient export through harvested coffee cherries. Error bars represent standard errors (replicates = 3).

Figure 4 - 2 :

 42 Figure 4-2: Redundancy analysis (RDA) of soil communities (dots) constrained by soil chemical properties and litter input indicators (arrows). Discriminating explanatory variables were selected with a forward procedure. Axes 1 and 2 represent 10.4% and 2.0% of the total variation. Crosses represent soil samples. Ellipses represent 95% confidence interval of the mean samples for Shaded Coffee (SC), Open Coffee (OC), Shaded Inter-row (SI) and Open Inter-row (OI)

Figure 4 - 4 :

 44 Figure 4-4: Soil water profiles dug under each of the 3 selected shade tree species and in open areas (replicates = 3)

Figure 4 - 5 :

 45 Figure 4-5: Root distribution of coffee trees and shade trees in three coffee-agroforestry systems (B. javanica, C. camphora and J. mimosifolia) and in coffee-monoculture (open coffee). Root counts are expressed in percentages

LocClim 1 .

 1 10 software. Monthly vapor pressure deficit (VPD) and monthly potential evapotranspiration of coffee trees (ET0 in open areas, ETc in shaded areas) were estimated according to Allen et al. (1998). In shaded areas, ETc was calculated for the coffee trees themselves, excluding the evapotranspiration of the shade trees in their direct vicinity.

  .3.1. Shade and micro-climate LAI of C. camphora was steady throughout the year, averaging 6.3 ±0.6 (Figure 5-1). LAI of B. javanica and J. mimosifolia increased with the development of new leaves in the spring, averaging 2.1 ±0.6 and 0.7 ±0.2 in April 2017, respectively. Their LAI peaked around September, reaching 3.1 ±0.3 and 2.4 ±0.1, respectively, then decreasing when the trees shed their leaves in February 2018. Throughout the year, canopies of C. camphora were denser than those of B. javanica, which in turn were denser than those of J. mimosifolia.

Figure 5 - 1 :

 51 Figure 5-1: Climatic data in 2017 in open areas averaged over half-month periods. Minimum, maximum and average temperatures (top) and rainfall (middle). Leaf area index of the 3 selected shade tree species (bottom) (mean ± se, n=3)

Figure 5 - 2 :

 52 Figure 5-2: Temperatures recorded under shade trees and in open areas during the cold spell, December 21 st 2017

Figure 5 - 4 :

 54 Figure 5-4: Coffee cherry development cycle in 2017 in three coffee-agroforestry systems (B. javanica, C. camphora and J. mimosifolia), at different distance from shade trees (C1: below shade tree canopy, C2: at the edge of shade tree canopy, C3: open coffee) (mean ± se, letters indicate significant groups)

Figure 5

 5 Figure 5-7: RDA for organoleptic quality of coffee beans constrained by shade (B. javanica, C. camphora, J. mimosifolia and open coffee), coffee category (C1 to C3), coffee yield, percentage of aborted fruits, DD, ETC and ET0, month of harvest peak and physical quality indicators. Axis 1 and 2 represent 35.6% and 1.5% of the total variation

  [START_REF] Cannell Mgr | Physiology of the Coffee Crop[END_REF]. For instance, coffee trees with LAI around 3 m 2 .m -2[START_REF] Charbonnier | Competition for light in heterogeneous canopies: Application of MAESTRA to a coffee (Coffea arabica L.) agroforestry system[END_REF]) , planted at a density of 5000 trees.ha -1 and adequately managed, could theoretically support the growth of 3000 cherries, similar to the final fruit loads recorded in November in the present study. Interestingly, in the present study, percentages of aborted fruits from die-back and C. coffeanum recorded in September were similar irrespective of fruit set and shade intensity (Figure5-5). This result highlights the absence of major water or nutrient deficits in the summer. This interpretation is further supported by the fact that water limitations during the expansion stage usually translates to smaller beans at harvest[START_REF] Cannell Mgr | Physiology of the Coffee Crop[END_REF][START_REF] Carr M | The water relations and irrigation requirements of coffee[END_REF] , while the percentages of large beans in the present study were similar irrespective of fruit set and shade intensity (74 ±1%). The impact of shade nonetheless became visible in November, in line with our second hypothesis, as coffee trees under light shade and in open conditions continued shedding fruit between September and November, while coffee trees under denser shade maintained fruit loads similar to those recorded in September (Figure5-5). This indicates that shade trees did provide positive externalities and lowered coffee trees' stress during the bean filling and maturation stages. Considering that temperatures started declining from September onward (Figure5-1), this stress was probably induced by high nutrients and carbohydrate

  Coffee areas reached 17200 ha in 2005 and 117000 ha in 2016 (+20% annually) according to government statistics (Yunnan Statistical Yearbook 2017). This rapid expansion has secured China a seat within the top 15 coffee growing countries (FAO). As a comparison, Chinese coffee production in 2014 exceeded that of Costa Rica and Kenya, two well-established coffee producing countries (FAO). This rapid growth is best illustrated in Pu'er Prefecture, where the local government recorded 42000 ha of coffee farms in 2012 (Pu'er Statistical Yearbook 2012). The second largest coffee production center is located in Dehong and Baoshan Prefectures, with over 25000 ha recorded in 2012 (Dehong and Baoshan Statistical Yearbooks 2012).

CHAPTER 3 -Tree inventories and ethnobotanical surveys in coffee- agroforestry systems 3.1. Introduction

  Xylotrechus quadripes and the two major diseases are fungal diseases, namely Hemileia vastatrix and Colletotrichum coffeanum. Due to the resistance of Catimor cultivars to H. vastatrix, the relatively low incidence of C. coffeanum and the difficulty to control X. quadripes population with insecticides (Venkatesha and Dinesh 2012; Waller et al. 2007), farmers seldom use chemical pesticides. Coffee harvest occurs during the dry season, from November to February.

	borer Scientific data regarding the impacts of shade trees in coffee farms in Yunnan Province
	is lacking to select a list of relevant shade tree species to promote locally (Liu et al. 2017). In this
	context, local experts should be identified (Davis and Wagner 2003; Mathez-Stiefel et al. 2012)
	and their ethnobotanical knowledge should be used to identify suitable tree species, to
	complement existing scientific data and to guide future research (Cámara-Leret et al. 2014; Gram
	et al. 2017; Liebig et al. 2016). In particular, studies investigating Local Ecological Knowledge
	(LEK) have confirmed that this ethnobotanical knowledge is highly reliable and advocated for its
	greater integration into policy recommendations and conservation programs (Coe et al. 2014;
	Cook et al. 2014; Malley et al. 2009). Furthermore, the comparison of farmers' possession of
	Studies focusing on the impact of young shade trees on soil fertility, micro-climate, coffee LEK according to gender, ethnicity and farming practices can help refining our understanding of
	yield and coffee quality were conducted in Liushun Township (N22.611 / E100.669), on a factors underpinning this ethnobotanical knowledge and improving policy recommendations
	mountainside representative of the coffee landscape found in Pu'er Prefecture; high density (Ayantunde et al. 2008; Cerdán et al. 2012; Yuan et al. 2014). In the present study, it can be
	Catimor cultivars (1 m * 2 m i.e. 5000 plants ha -1 ) planted on terraced lands, with one row of expected that farmers with mature coffee-agroforestry systems have a rich experience and can put
	coffee trees per terrace. 1337 mm of rainfall were recorded between March 2017 and March 2018, the list of tree species promoted by local authorities into perspective. Farmers from ethnicities
	with a dry season extending until the end of June 2017. The soils are Acrisol (FAO -Harmonized traditionally settled in mountainous areas might be better acquainted with indigenous tree species
	World Soil Database v1.2) with a top layer characterized by light clay texture. The mountainside from tropical montane forests, and therefore better able to point out indigenous species suitable
	was first converted to a coffee farm by a private company in 1997-1998. In the early 2000's, the for coffee-agroforestry systems (Brandt et al. 2013). Additionally the comparison of LEK
	land was divided into plots of 0.5 to 2 ha and redistributed to local farmers. In 2013, the local according to gender could reveal discrepancies in perceptions reflecting the division of
	government distributed and planted 2-year old shade tree seedlings within coffee rows, at a responsibilities at the farm level
	density of 56 trees.ha -1 (12 m * 15 m), as part of a large-scale campaign to convert monoculture
	coffee farms into AFS. On the selected mountainside, the majority of planted trees belonged to
	Alstonia scholaris, Bischofia javanica, Cinnamomum camphora, Jacaranda mimosifolia and
	Macadamia integrifolia. They were planted within coffee rows at a density of 56 trees.ha -1 (12 m
	* 15 m).
	The experimental design for these experiments was set up in a single mountainside, in 7
	coffee plots managed by 5 smallholder farmer families. Elevation ranged from 1000 to 1050
	meters above sea level and orientation varied between East and North. Lately, with low coffee
	prices, farmers only applied fertilizers twice a year; first application in June at the beginning of
	the rainy season and a second one in August or September before the ripening stage of coffee
	cherries. Fertilizers are spread right below coffee trees. Nitrogen inputs often exceeded 300 kg
	ha -1 year -1 . Weeding was carried out both manually and with herbicides. The major pest is trunk

Table 3 -

 3 1: List of the 28 shade tree species ranked by coffee farmers and ecosystem services reported by farmers

	Latin Name	Chinese Name	Promotion Status	Indigenous (I) / Exotic (E)	Ecosystem Services
	Albizia kalkora	山合欢		I	N-fixation
	Alstonia scholaris	灯台树	Promoted	I	Urban landscaping, Medicine
	Aporosa villosa	毛银柴		I	Fruit
	Aporosa yunnanensis	滇银柴		I	Fruit
	Artocarpus heterophyllus	波萝蜜		E	Fruit
	Betula alnoides	西南桦		I	Timber
	Bischofia javanica	重阳木	Promoted	E	Urban landscaping, timber
	Castanopsis calathiformis	枹丝锥		I	Firewood
	Cerasus cerasoides	云南樱桃	Promoted	I	Fruit, Ornamental
	Cinnamomum camphora	香樟	Promoted	I	Urban landscaping, Medicine
	Delonix regia	凤凰木	Promoted	E	Urban landscaping, Ornamental
	Dimocarpus longan	龙眼	Promoted	I	Fruit
	Diospyros kaki var. silvestris	野柿		I	Fruit
	Eurya groffii	岗柃		I	Shade
	Ficus hispida	对叶榕		I	Shade
	Leucaena leucocephala	银合欢		E	N-fixation
	Litchi chinensis	荔枝	Promoted	I	Fruit
	Litsea sp.	木姜子		I	Shade, Fruit
	Macadamia integrifolia	澳洲坚果	Promoted	E	Fruit
	Mallotus tetracoccus	四籽野桐		I	Shade
	Mangifera indica	芒果	Promoted	I	Fruit
	Melia azedarach	苦楝		I	Seeds
	Michelia baillonii	合果木		I	Timber
	Phyllanthus emblica	余甘子		I	Shade, Fruit
	Psidium guajava	番石榴		E	Fruit
	Schima wallichii	西南木荷		I	Timber
	Syzygium szemaoense	思茅蒲桃		I	Shade, Fruit
	Toona ciliata	红椿		I	Timber

Table 3 -

 3 2: Mean scores of promoted versus non-promoted shade tree species for individual ES & ED and overall preference according to the Bradley Terry analysis. Student T-test results highlight significant differences between groups for each ES & ED. Statistical significance is indicated by '***' < 0.001 / '**' < 0.01 / '*' < 0.05 / '.' < 0.1 / 'NS' Non Significant.

	Species	Heat Protection	Cold Protection	Erosion Control	Soil Moisture	Nutrient Enhancement	Root Competition	Coffee Yield	Weed Control	Economic Benefit	Overall Preferences
	Promoted	0.74	0.71	0.66	0.63	0.63	0.53	0.73	0.67	0.66	0.80
	Not Promoted	0.48	0.38	0.39	0.46	0.51	0.53	0.62	0.44	0.33	0.42
	Significance	**	****	*	.	NS	NS	NS	*	**	***

Table 3 -

 3

		Heat	Cold	Erosion	Soil	Nutrient	Root	Coffee	Weed	Economic	Overall
		Protection	Protection	Control	Moisture	Enhancement	Competition	Yield	Control	Benefit	Preferences
	Model A
	Promoted Spp. × Spp Richness ( †)						0.01 *				-0.01 **
	Promoted Spp. × Medium-Shade AFS				0.09 *			-0.13 *			-0.12 **
	Model B
	Promoted Spp. × Gender [M]	-0.07 *				0.10 .			0.11 **	0.06 .	0.11 *
	Model C
	Indigenous Spp. × Mountain Ethnicity					-0.13 *	-0.14 *	-0.19 **		0.12 ***

3: Interactions between coffee farmer attributes and their rankings of promoted and indigenous shade tree species by ecosystem services and disservices. Only significant results are shown. Statistical significance is indicated by '***' < 0.001 / '**' < 0.01 / '*' < 0.05 / '.' < 0.1. ( †) refers to quantitative variables.

Table 3 -

 3

4: Biodiversity indices for three model farms Model Farm Area (ha) Shannon Simpson Effective number

  . These include for instance auxin-producer Gram-positive bacteria, which in turn stimulate nutrient uptake by plants and root growth[START_REF] Tsavkelova Ea | Microbial producers of plant growth Stimulators and their practical use: A review[END_REF]. Fungi are equally central to nutrient cycles and nutrient uptake by plants ([START_REF] Barrios E | Agroforestry and Soil Health: Linking Trees, Soil Biota, and Ecosystem Services[END_REF]. They play an active role in C cycle and C transfer as well as in improved diffusion of nutrients otherwise limited to plants such as P, Zn and Cu[START_REF] Balakrishna | Influence of alley copping system on AM fungi, microbial biomass C and yield of finger millet, peanut and pigeon pea[END_REF]. Being both inside and outside the roots, and occurring in about 80% of plant species including coffee[START_REF] Vaast | Effects of VA-mycorrhizae and nitrogen sources on rhizosphere soil characteristics, growth and nutrient acquisition of coffee seedlings (Coffea arabica L.)[END_REF]

Arbuscular Mycorrhizal (AM) fungi are essential in the plant-soil interface

[START_REF] Bagyaraj Dj | Below ground microbial diversity as influenced by coffee agroforestry systems in the Western Ghats, India. Agriculture[END_REF]

.

Furthermore, they sometimes form tripartite alliances with Gram-positive bacteria which strengthen the mycorrhizal symbiosis

[START_REF] Francis I | The Gram-positive side of plant-microbe interactions[END_REF]

).

Table 4 -

 4 1: Leaf chemical composition of shade trees (BJ: B. javanica; CC: C. camphora; JM: J. mimosifolia) and coffee trees (mean ± standard error, replicates = 3)

		N (%)	P (%)	K (%)	Ca (%)	Mg (%)	Fe (mg/kg)	Lignin (%)
	BJ	2.54 (±0.51) 0.13 (±0.02) 1.63 (±0.65) 1.21 (±0.55) 0.28 (±0.14)	79.10 (±8.46)	16.22 (±4.80)
	CC	2.50 (±0.24) 0.12 (±0.02) 0.71 (±0.19) 3.59 (±0.30) 0.20 (±0.05)	91.55 (±28.27)	8.44 (±0.64)
	JM	2.32 (±0.34) 0.10 (±0.02) 0.60 (±0.07) 1.46 (±0.10) 0.19 (±0.06) 117.05 (±11.29)	8.60 (±1.68)
	Coffee	2.30 (±0.21) 0.13 (±0.02) 1.59 (±0.27) 2.08 (±0.21) 0.43 (±0.07) 121.29 (±11.43)	3.98 (±0.71)

Table 4 -

 4 2: Soil chemistry as affected by position with respect to coffee row or inter-row under shade of trees (3 species pooled) or sun (monoculture)(Mean ± Standard Error, replicates = 18)

	Row	Shade	pH (water 2.5:1)	SOM (g/kg)	N (g/kg)	P (mg/kg)	K (cmol /kg)	Ca (cmol /kg)	Mg (cmol /kg)
	Coffee row	Pooled Trees 4.9 (±0.1)	28.4 (±1.1) 1.6 (±0.0) 110 (±20) 0.61 (±0.09) 3.43 (±0.33) 0.49 (±0.06)
	Coffee row	Sun	4.5 (±0.1)	25.8 (±1.2) 1.3 (±0.1) 36 (±10)	0.48 (±0.05) 2.36 (±0.43) 0.45 (±0.06)
	Inter-row	Pooled Trees 4.5 (±0.1)	24.2 (±1.2) 1.6 (±0.1) 34 (±7)	0.68 (±0.07) 1.30 (±0.25) 0.32 (±0.04)
	Inter-row	Sun	4.6 (±0.1)	22.0 (±0.9) 1.4 (±0.0) 27 (±8)	0.61 (±0.06) 1.00 (±0.12) 0.33 (±0.04)
	p-values
	Row		0.03 *	<0. 001 *** 0.87	0.001 **	0.26	<0. 001 ***	0.007 **
	Shade		0.32	0.02*	0.003**	0.03*	0.36	0.02 *	0.58
	Row * Shade		0.002 **	0.82	0.33	0.03*	0.46	0.19	0.66

Table 4 -

 4

	Season	Row	Shade	Nematodes	All PLFA	Bacteria	Gram+:Gram-	Fungi	AMF	Fungi:Bacteria
				ln(ind.100g -1 )	nmol.g -1	nmol.g -1	ratio	nmol.g -1	nmol.g -1	ratio
	Rainy	Coffee row	Pooled Trees	5.3 (±0.1)	53.4 (±3.6)	38.3 (±2.5)	3.0 (±0.1)	3.5 (±0.3)	1.4 (±0.2)	0.091 (±0.003)
	Rainy	Coffee row	Sun	5.0 (±0.1)	49.1 (±4.4)	35.4 (±3.1)	2.9 (±0.1)	3.0 (±0.3)	1.3 (±0.2)	0.085 (±0.004)
	Rainy	Inter-row	Pooled Trees	4.7 (±0.1)	41.1 (±1.9)	30.2 (±1.4)	3.1 (±0.1)	2.4 (±0.2)	0.9 (±0.1)	0.074 (±0.004)
	Rainy	Inter-row	Sun	4.3 (±0.1)	46.8 (±6.4)	34.5 (±4.6)	3.0 (±0.2)	1.8 (±0.2)	1.0 (±0.1)	0.062 (±0.004)
	Dry	Coffee row	Pooled Trees	2.8 (±0.3)	34.5 (±2.6)	26.0 (±2.0)	2.6 (±0.1)	2.5 (±0.3)	0.8 (±0.1)	0.091 (±0.004)
	Dry	Coffee row	Sun	3.0 (±0.3)	21.1 (±1.9)	16.0 (±1.4)	2.7 (±0.1)	1.4 (±0.2)	0.5 (±0.0)	0.088 (±0.004)
	Dry	Inter-row	Pooled Trees	2.4 (±0.3)	29.3 (±2.1)	22.5 (±1.7)	2.8 (±0.1)	1.9 (±0.2)	0.5 (±0.0)	0.083 (±0.005)
	Dry	Inter-row	Sun	2.8 (±0.3)	21.9 (±1.9)	16.6 (±1.4)	3.0 (±0.1)	1.2 (±0.1)	0.5 (±0.1)	0.074 (±0.005)
	p-values
	Season			<0. 001 ***	<0. 001 ***	<0. 001 ***	0.001 **	<0. 001 ***	<0. 001 ***	0.4 *
	Row			0.002 **	0.01 *	0.04 *	0.06 .	<0. 001 ***	<0. 001 ***	<0. 001 ***
	Shade			0.98	0.11	0.06 .	0.36	<0. 001 ***	0.43	0.004 **
	Season : Row		0.12	0.28	0.34	0.43	0.04 *	0.13	0.09 .
	Season : Shade		0.02 *	0.02 *	0.01*	0.12	0.25	0.20	0.63
	Row : Shade		0.91	0.11	0.12	0.55	0.74	0.16	0.33
	Season : Row : Shade		0.59	0.66	0.60	0.95	0.33	0.54	0.99

3: Soil communities as affected by position with respect to coffee row or inter-row under shade of trees (3 species pooled) or sun (monoculture)

Table 4 -

 4 4: Soil enzymatic activities as affected by position with respect to coffee row or inter-row under shade of trees (3 species pooled) or sun (monoculture) and according to rainy and dry seasons (Mean ± Standard Error, replicates = 18) Impact of coffee rows on soil fertility SOM, exchangeable Ca and exchangeable Mg were significantly higher within coffee rows than in inter-rows, irrespective of the presence of shade trees (Table

	Season	Row	Shade	NAG	BG	ACP
				μmol.g -1 .d -1	μmol.g -1 .d -1	μmol.g -1 .d -1
	Rainy	Coffee row	Pooled Trees	32.4 (±2.0)	37.3 (±2.8)	65.8 (±3.1)
	Rainy	Coffee row	Sun	32.2 (±1.5)	35.5 (±3.1)	66.6 (±3.5)
	Rainy	Inter-row	Pooled Trees	27.5 (±1.5)	36.7 (±1.9)	52.6 (±3.4)
	Rainy	Inter-row	Sun	26.0 (±1.5)	35.7 (±3.4)	44.4 (±3.6)
	Dry	Coffee row	Pooled Trees	42.1 (±2.2)	65.6 (±5.5)	46.0 (±2.5)
	Dry	Coffee row	Sun	37.6 (±3.5)	62.7 (±7.9)	44.3 (±6.4)
	Dry	Inter-row	Pooled Trees	42.1 (±2.4)	70.5 (±7.7)	49.0 (±5.2)
	Dry	Inter-row	Sun	43.6 (±3.1)	69.5 (±7.8)	35.2 (±3.1)
	p-values
	Season			<0. 001 ***	<0. 001 ***	<0. 001 ***
	Row			0.17	0.64	<0. 001 ***
	Shade			0.13	0.09 .	0.05 *
	Season : Row		0.002 **	0.22	0.007 **
	Season : Shade		0.58	0.96	0.49
	Row : Shade		0.50	0.74	0.07 .
	Season : Row : Shade		0.21	0.96	0.77
	Table 4-5: Coffee yield (kg.tree -1 ) under shade trees and in open conditions for 2 consecutive years (mean ± standard error). Letters represent Wilcoxon significant
	groups within a year.
			B. javanica

Table 4 -

 4 8: Soil enzymatic activities as affected by position with respect to coffee row or inter-row under shade trees (B. javanica, C. camphora, J. mimosifolia) or sun (monoculture) and according to rainy and dry seasons(Mean ± Standard Error, replicates = 6)

	Season	Row	Shade	NAG	BG	ACP
				μmol.g -1 .d -1	μmol.g -1 .d -1	μmol.g -1 .d -1
	Rainy	Coffee row	B. javanica	32.8 (±4.7)	32.5 (±1.9)	65.1 (±6.4)
	Rainy	Coffee row	C. camphora	35.7 (±2.4)	44.6 (±6.1)	60.8 (±4.2)
	Rainy	Coffee row	J. mimosifolia	28.0 (±2.0)	33.3 (±3.0)	72.6 (±4.8)
	Rainy	Coffee row	Pooled Trees	32.4 (±2.0)	37.3 (±2.8)	65.8 (±3.1)
	Rainy	Coffee row	Sun	32.2 (±1.5)	35.5 (±3.1)	66.6 (±3.5)
	Rainy	Inter-row	B. javanica	28.2 (±0.7)	36.9 (±3.0)	40.2 (±2.0)
	Rainy	Inter-row	C. camphora	31.2 (±2.1)	43.5 (±1.5)	59.8 (±4.9)
	Rainy	Inter-row	J. mimosifolia	22.2 (±3.0)	29.7 (±2.4)	59.0 (±6.8)
	Rainy	Inter-row	Pooled Trees	27.5 (±1.5)	36.7 (±1.9)	52.6 (±3.4)
	Rainy	Inter-row	Sun	26.0 (±1.5)	35.7 (±3.4)	44.4 (±3.6)
	Dry	Coffee row	B. javanica	42.4 (±4.2)	66.2 (±6.2)	43.2 (±1.0)
	Dry	Coffee row	C. camphora	46.9 (±3.3)	77.1 (±11.3)	41.5 (±2.6)
	Dry	Coffee row	J. mimosifolia	35.8 (±3.0)	51.1 (±8.0)	53.3 (±6.1)
	Dry	Coffee row	Pooled Trees	42.1 (±2.2)	65.6 (±5.5)	46.0 (±2.5)
	Dry	Coffee row	Sun	37.6 (±3.5)	62.7 (±7.9)	44.3 (±6.4)
	Dry	Inter-row	B. javanica	40.5 (±0.7)	78.6 (±3.4)	63.7 (±7.3)
	Dry	Inter-row	C. camphora	44.4 (±4.9)	87.6 (±13.9)	31.8 (±4.0)
	Dry	Inter-row	J. mimosifolia	40.7 (±4.5)	43.6 (±6.0)	51.6 (±8.9)
	Dry	Inter-row	Pooled Trees	42.1 (±2.4)	70.5 (±7.7)	49.0 (±5.2)
	Dry	Inter-row	Sun	43.6 (±3.1)	69.5 (±7.8)	35.2 (±3.1)

  In August, the hottest month of the rainy season during 2017, Tavg reached 24.9°C in open conditions, whereas Tavg under shade ranged from 23.1 to 24.3°C. In January, the coldest month of the dry season during 2017-2018, Tavg reached 14.5°C in open conditions. The same month, Tavg reached 15.3°C under J. mimosifolia, 14.7°C under B. javanica, and 14.0°C under C. camphora. Tmax was always higher in open conditions than under shade trees, except in December 2017 and January 2018. In June, at the end of the dry period, Tmax peaked at 39.1°C in open conditions. During the same month, Tmax reached 36.0°C under J. mimosifolia, 33.4°C under B. javanica, and 32.9°C under C. camphora. In August, Tmax reached 37.8°C in open conditions, 31.4°C under J. mimosifolia and B. javanica, and 29.1°C under C. camphora. Tmin was similar throughout the year in open conditions and under C. camphora, and was on average 0.5°C lower than Tmin under B. javanica and J. mimosifolia. In December, Tmin reached 9.8°C in open conditions, compared to 9.9°C under C. camphora, 10.3°C under B. javanica, and 10.6°C under J. mimosifolia. During the sole frost event of the recorded growing period (the night of December 20 th to 21 st ),

). In comparison, yearly average temperatures below B. javanica and C. camphora averaged 20.8 and 20.1°C, respectively.

  Coffee trees at the edge of B. javanica, and those in open conditions, were the first to bloom, with average blossom peaks between April 13 th and April 15 th (±2 days). Coffee trees below C. camphora were the last to bloom, doing so on April 25 th on average (±2 days). All other coffee trees bloomed on average between April 18 th and April 21 st . Furthermore, shade delayed the average date of harvest peak. Coffee trees in open conditions (C3) reached harvest peak around January 13 th (±6 days); those at the edge of shade trees (C2) reached harvest peak around January 27 th (±5 days); and coffee trees below shade trees (C1) reached harvest peak around January 31 st (±4 days). Overall, there was a trend of increasing DAF with shade and ripening stages. During this time, relative losses were markedly higher with decreasing shade. Coffee trees below shade trees (C1) shed 7 ±1%, coffee trees at the edge of shade trees (C2) shed 10 ±1%, and coffee trees in open conditions (C3) shed 17 ±2% of their cherries. As a result, fruit loads were all similar at the beginning of the harvest (2910 to 4290), despite higher fruit sets in open conditions and under light

	shade intensity (Figure 5-4). Only coffee trees below C. camphora had significantly fewer cherries (2,422
	±412).

  pests and diseases of the study areas, namely Hemileia vastatrix, Colletotrichum coffeanum and Xylotrechus quadripes. Shade trees are already known to lower damages from X. quadripes but their impacts on H. vastatrix are still subject to debate. Furthermore, climate change might further exacerbate pest and disease outbreaks. Research is thus needed to provide locally adapted management practices that will complement and support the current ecological intensification of coffee farms in southern Yunnan Province.  Consultant o Jun-Aug 2013, with Planète Urgence, Indonesia Assessment of the opportunity for certification of a mangrove reforestation project in Borneo Island under a carbon standard o Nov 2012, with Gold Standard, Germany Member of the advisory panel for the carbon standard for community-based forestry projects  Research assistant o Feb-Mar 2011, at Montpellier University, France and India Territorial diagnosis and prospective of the coffee sector in Kodagu District o Nov-Dec 2010, at ENGREF, France and Germany Potential for wood energy in European forests o Sep 2010, at ENGREF Guyana, France Field inventories in mangroves o Apr-Aug 2010, at CIFOR & University of Kisangani, Democratic Republic of Congo Modeling of rattan's ecology

	Language skills
	French: native speaker
	English: working proficiency
	Mandarin Chinese: conversational proficiency
	Spanish: basic proficiency

trees. Altogether, the introduction of shade trees and changes in management practices would contribute to the production of more sustainable coffee in Pu'er Prefecture.

ACKNOWLEDGEMENTS

I wish to express my deepest gratitude to all family members, friends, colleagues and senior scientists who actively supported me with their personal and academic encouragements, and helped me go through this doctoral degree. I also wish to thank the institutions which made this research possible.

First of all, I am very thankful to my esteemed supervisor, Prof. 许建初 (Xu Jianchu), for the trust he put in me right from the start when he accepted me as a PhD student, providing guidance, vision and leadership. Thank you for offering me this wonderful opportunity. My warmest gratitude also goes to my mentor and co-supervisor, Dr. Philippe Vaast, for the time and energy he invested in this project. Thank you for accompanying me in this journey. I could only complete this thesis and become a researcher thanks to both of your support. I extend my appreciation to all colleagues, fellow students and friends at KIB: Dr. Anne

升级,该工具通过扩展服务生成推荐遮荫树种的列表,这些遮荫树种是根据云南南部特 II 定的当地生态环境和农民个人的需求定制的mimosifolia(蓝花楹),主要针对树种对土壤肥力,咖啡产量,还有咖啡质量三个方面, 时间是仅在引入集约管理的单一咖啡农场 4 年后。对于土壤肥力(第四章),主要检测了表 层(在旱季, 遮荫树下的微生物群落比开阔地区丰富。土壤水分剖面显示,每年的降雨足以为咖啡树 和遮荫树提供足够的水资源。另一方面,根侧轮廓显示,0-20cm 土层的蓝花楹与咖啡树 之间的根系竞争非常激烈,20 cm 以下的番樟和咖啡树之间的根系竞争非常激烈。总的来 说,第四章证明了在集中管理的咖啡农场中,遮荫树迅速有助于保持和/或恢复土壤肥力 和缓冲土壤生物活动的季节性变化。 在第五章中录了三个选定遮荫树种的树冠下,以及开放条件下一年的微气候数据(气温和湿度,30 分 钟/次)。同时跟踪了 90 研究显示,年轻的遮荫树行成的微气候可增加咖啡在遮荫下的产量。夏季相比开放区域显 示出气压不足(-0.5 --0.9 kPa)和最高温度较低(3 到 6°C), 在冬季相比开放区则显示 出更高的最低温度(+ 在开阔区域和阴凉条件下,咖啡产量相似(2016 年为 2.8 公斤/棵,2017 年为 4.5 公斤/棵)。只有 C.camphora (番樟)下方的咖啡树的产量明 显低于其他咖啡树(2016 年为 2.8 公斤/棵,2017 年为 4.5 公斤/棵)。最后,树荫对咖 啡品质没有明显影响。总体而言,第五章表明,树冠较低的遮荫树 B. javanica(茄冬) III 和 J. mimosifolia(兰花楹)在树冠下提供了良好的微气候,对咖啡产量和品质没有负 面影响。另一方面,树冠浓密的遮荫树-C.camphora(番樟)-

3.6. Supporting information 3.6.1. Methodological steps Figure 3-7: Methodological steps for inventorying shade trees in coffee farms and documenting the associated ethnobotanical knowledge below the three shade tree species were not significantly different (Table 4567). In addition, there was no evidence of changes in soil community compositions, as indicated by steady fungi:bacteria and Gram+:Gram-ratios. These results might reflect high levels of disturbances and low microbial diversity in intensively managed landscapes [START_REF] Bowles Tm | Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape[END_REF].

Nutrient cycling also underwent significant seasonal variations. The increase in ACP activity during the rainy season was best explained by the increase in fungi communities (Figure 4-3). This result is in line with other studies linking P-cycling rates to fungi communities and soil water content [START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF]. It could also be associated with N and P fertilizer inputs [START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF][START_REF] Geisseler | Long-term effects of mineral fertilizers on soil microorganisms -A review[END_REF][START_REF] Olander Lp | Regulation of soil phosphatase and chitinase activityby N and P availability[END_REF][START_REF] Zhang | Fatty-Acid Profiles and Enzyme Activities in Soil Particle-Size Fractions under Long-Term Fertilization[END_REF]).

On the other hand, the lower NAG and BG activities during the rainy season than during the dry season was unexpected. In the majority of existing studies, higher C and N cycling rates follow an increase in soil water content [START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF], N fertilization [START_REF] Geisseler | Long-term effects of mineral fertilizers on soil microorganisms -A review[END_REF][START_REF] Sy | Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: A meta-analysis[END_REF][START_REF] Mbuthia Lw | Long term tillage, cover crop, and fertilization effects on microbial community structure, activity: Implications for soil quality[END_REF][START_REF] Zhang | Fatty-Acid Profiles and Enzyme Activities in Soil Particle-Size Fractions under Long-Term Fertilization[END_REF]) and an increase in C availability from sources such as root exudates [START_REF] Bowles Tm | Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape[END_REF][START_REF] Prescott Ce | Tree species influence on microbial communities in litter and soil: Current knowledge and research needs[END_REF][START_REF] Zhao | Changes in soil microbial community, enzyme activities and organic matter fractions under long-term straw return in north-central China[END_REF]. I suspect this result to be the consequence of an atypical 50mm rainfall event that took place 8 days before sample collection in January. The re-wetting of dry soils could explain a short-lived C and N mineralization pulse, with NAG and BG activities higher than those measured in the rainy season [START_REF] Borken | Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils[END_REF][START_REF] Mikha Mm | Carbon and nitrogen mineralization as affected by drying and wetting cycles[END_REF]. Overall, higher soil biological activity and Pcycling rate during the rainy season confirm our first hypothesis of seasonal variations in soil fertility, albeit with uncertainties regarding seasonal effects on C and N mineralization rates 4.4.3. Impact of coffee rows on soil fertility Soils under coffee rows exhibited significantly higher chemical, biological and biochemical fertility than soils in inter-rows (Table 4-2 to Table 4-4). Coffee leaf litter was 66% more abundant below coffee trees than in inter-rows, which partly explained this result. In addition, higher soil fertility below coffee trees highlights 20 years of high mineral fertilizer inputs placed below coffee trees. Higher concentrations of available P, exchangeable Ca and exchangeable Mg presumably reflected excessive fertilization practices (Zhao et al. 2018a;[START_REF] Zhao | Changes in soil microbial community, enzyme activities and organic matter fractions under long-term straw return in north-central China[END_REF].

Considering N and K fertilizer inputs well in excess of crop demands (Figure 4-1) (Tully and Lawrence 2011), we also expected total N and exchangeable K concentrations higher in coffee rows than in inter-rows. However, we did not detect significant differences with N and K soil concentration. On top of the direct effect of fertilizer inputs on soil chemistry, long-term fertilization also resulted in increasing root turnover, root exudate and crop residues, which all contribute to SOM and soil chemical fertility [START_REF] Defrenet | Root biomass, turnover and net primary productivity of a coffee agroforestry system in Costa Rica: effects of soil depth, shade trees, distance to row and coffee age[END_REF][START_REF] Geisseler | Long-term effects of mineral fertilizers on soil microorganisms -A review[END_REF] and support the growth of soil communities [START_REF] Finney Dm | Living cover crops have immediate impacts on soil microbial community structure and function[END_REF][START_REF] Geisseler | Long-term effects of mineral fertilizers on soil microorganisms -A review[END_REF][START_REF] Mbuthia Lw | Long term tillage, cover crop, and fertilization effects on microbial community structure, activity: Implications for soil quality[END_REF]. This was specifically evidenced in coffee farms, with [START_REF] Defrenet | Root biomass, turnover and net primary productivity of a coffee agroforestry system in Costa Rica: effects of soil depth, shade trees, distance to row and coffee age[END_REF] recording 125% higher coffee fine root biomass and 140% higher fine root net primary productivity in the 0-30cm soil layer within coffee rows than in inter-rows. This increase in coffee below-ground activity supports the present results of more abundant free-living nematodes, bacteria and fungi in coffee rows than in inter-rows (Table 4-3). Fertilization and organic matter input are also known to contribute to higher NAG activity [START_REF] Bowles Tm | Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape[END_REF][START_REF] Mbuthia Lw | Long term tillage, cover crop, and fertilization effects on microbial community structure, activity: Implications for soil quality[END_REF][START_REF] Zhang | Fatty-Acid Profiles and Enzyme Activities in Soil Particle-Size Fractions under Long-Term Fertilization[END_REF]) and ACP activity [START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF], especially when fertilizers are applied on soils with low SOM [START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF]). In addition, high dependency of coffee plants to vesicular arbuscular mycorrhizal fungi [START_REF] Andrade | Arbuscular mycorrhizal association in coffee[END_REF][START_REF] De Beenhouwer | Changing soil characteristics alter the arbuscular mycorrhizal fungi communities of Arabica coffee (Coffea arabica) in Ethiopia across a management intensity gradient[END_REF][START_REF] Vaast | Effects of vesicular-arbuscular mycorrhizal inoculation at different soil P availabilities on growth and nutrient uptake of in vitro propagated coffee (Coffea arabica L.) plants[END_REF]) could explain that AMF communities were more abundant in coffee rows than in inter-rows, in turn supporting ACP activity [START_REF] Adetunji At | The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: a review[END_REF]. Non-AMF fungi were also more abundant in coffee rows than in inter-rows. According to the redundancy analysis, litter weight explained this difference better than litter quality did. This predominant effect of amount over quality has been seen, especially in farming systems where N is not as limiting a factor [START_REF] Huang Z | Soil microbial biomass, community composition and soil nitrogen cycling in relation to tree species in subtropical China[END_REF], although it cannot be generalized (Brennan and Acosta-Martinez 2017; Prescott and Grayston 2013).

Nonetheless, fungi:bacteria ratios in coffee rows remained low and highlighted a state of high soil disturbance, especially due to intensive mineral fertilization practices [START_REF] Bowles Tm | Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape[END_REF][START_REF] Brennan Eb | Cover cropping frequency is the main driver of soil microbial changes during six years of organic vegetable production[END_REF].

Higher soil fertility in coffee rows than in inter-rows was best visible during the rainy season. Most biological differences evened out during the dry season, reflecting an overall decrease in plant and soil activity. Only NAG and BG activities were higher in unshaded inter-rows than below open coffee trees in the dry season. I suspect this to result from the atypical 50 mm rainfall event leading to an increased C and N mineralization pulse in unshaded inter-rows [START_REF] Borken | Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils[END_REF][START_REF] Mikha Mm | Carbon and nitrogen mineralization as affected by drying and wetting cycles[END_REF]. In contrast, I suspect that shade trees and coffee trees intercepted part of the rainfall [START_REF] Cannavo | Agroforestry associating coffee and Inga densiflora results in complementarity for water uptake and decreases deep drainage in Costa Rica[END_REF][START_REF] Van Kanten R | Transpiration of Arabica Coffee and Associated Shade Tree Species in Sub-optimal, Low-altitude Conditions of Costa Rica[END_REF]. This interpretation is further evidenced by the correlation between soil water content and NAG and BG activities in the redundancy analysis for winter enzyme activities (Figure 4-3). 4.6.3. Results of soil fertility analysis by tree species

Coffee yield

In the studied area, coffee harvest occurs during the dry season, from November to February, with a peak in December and January. Yields were recorded over 2 consecutive harvests -in 2016-2017 and 2017-2018 -to encompass the well-documented biannual cycle of coffee production (DaMatta 2004). In November 2016, coffee yield was estimated from fruit load two weeks prior to harvest. A sample of 309 coffee trees was selected and categorized as C1 (44%), C2 (35%), and C3 (21%). The number of productive branches was recorded and coffee cherries counted on 20% of them. Fruit load was then extrapolated to coffee trees.

In year two (2017-2018), fruit loads from the 90 monitored coffee trees were derived from counting activities that occurred in November 2017. Fully ripe coffee cherries (exhibiting dark red pericarps) were then harvested in three harvests, from mid-December to mid-February, and weighed in the field. When a coffee tree was at peak ripeness, with more than 40% of its cherries being ripe, then all cherries were harvested at once. Days after flowering (DAF) was estimated based on blossom peak and harvest peak, After cherries were weighed in the field, the weight of green cherries was multiplied by a 1.22 factor to account for the lower density of the yet-to-ripen cherries. This 1.22 factor was derived from field measurements in February 2018. A linear relationship between fruit loads and measured yields was then established through regression analysis for the 2017-2018 crop. This equation was then used to estimate yields for the 2016-2017 crop.

Coffee quality

At harvest peak, samples of fully ripe coffee cherries were set aside and used to assess coffee quality through physical and organoleptic attributes. First, the average weight of the coffee cherries was determined in the field from a subsample of 100 cherries. Due to small sample size from individual coffee trees, samples from coffee trees located in the same category (C1 to C3) and under the same shade tree replicate were aggregated for subsequent quality assessment. Samples were then wet-processed on the day they were harvested. Specifically, samples were depulped with a small mechanical drum pulper, kept separated in fine mesh bags and put to fermentation in a bucket of water until mucilage could easily detach from the endocarp (12 to 18 hr). Samples were then washed manually, left to dry in the sun until humidity content reached around 11%, and then stored appropriately. In April, parchment was hulled, green beans were weighed and tested for density, and the percentage of beans over sieve size 16 (16/64 in) was measured.

sinks from coffee cherries (Bote and Jan 2016; Cannell 1971) , or from low leaf-to-fruit ratios in open conditions [START_REF] Beer | Shade management in coffee and cacao plantations[END_REF][START_REF] Damatta Fm | Ecophysiological constraints on the production of shaded and unshaded coffee: a review[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF] , rather than caused by high temperatures [START_REF] Damatta Fm | Physiological and Agronomic Performance of the Coffee Crop in the Context of Climate Change and Global Warming: A Review[END_REF]. This result also indicates that farmers should adjust their second round of fertilizer inputs to better meet the needs of coffee trees.

Fruit growing period and coffee yield

In accordance with our first hypothesis, shade delayed both blossom peak and harvest peak. Since both these phenophases were similarly delayed by 2 to 3 weeks, there was no marked effect on DAF, approximating 280 days under all shade regimes. In contrast, most other studies found that shade delayed ripening by 4 to 6 weeks [START_REF] Tolessa | Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee[END_REF]Vaast al. 2008) , with a marked effect on DAF [START_REF] Geromel | Effects of shade on the development and sugar metabolism of coffee (Coffea arabica L.) fruits[END_REF].

DD and total ETc were most impacted by C. camphora, with values respectively 12 and 10% lower than those recorded in open conditions. This indicates that the canopy of C. camphora is too dense, and is therefore detrimental to coffee production. This interpretation is further supported by the fact that coffee yields were lower under C. camphora (2760 ±380 g.tree -1 ) than under all other shade regimes (4650 to 5710 g.tree -1 ). Still, detrimental impacts from C. camphora might diminish with time, as trees grow in height, extend their canopy widths, and are pruned to decrease shade intensity [START_REF] Vaast | Biophysical Interactions Between Timber Trees and Arabica Coffee in Suboptimal Conditions of Central America[END_REF]).

Shade had no visible impact on degree days, yield, or alternate production pattern, with the exception of coffee trees below C. camphora. DD under the light shade of young B. javanica and J.

mimosifolia were within 5% of values recorded in open conditions and yields were not statistically different from those of open conditions. These results are mostly in line with studies on fruit set manipulation. In Costa Rica and in Brazil, reducing initial fruit set by 25% did not impact coffee production cumulated over 2 years, while reducing fruit set by 50% only reduced yield by 25 to 28% [START_REF] Damatta Fm | In field-grown coffee trees source-sink manipulation alters photosynthetic rates, independently of carbon metabolism, via alterations in stomatal function[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF]. In Ethiopia, coffee trees with 25% and 50% fruit sets yielded slightly less than control trees in the first year but over-yielded control trees over 2 cumulated years (Bote and Jan 2016) . Similarly, coffee trees under B. javanica and J. mimosifolia had approximately 25% lower fruit sets but similar yields to coffee trees in open conditions, while an approximate 50% reduction in fruit set under C. camphora trees resulted in a 32% yield decrease over the two cumulated crop cycles, 2016-2017 and 2017-2018. These results also support the conclusion that Arabica coffee is a shade-adapted species [START_REF] Franck | Limitation of coffee leaf photosynthesis by stomatal conductance and light availability under different shade levels[END_REF]. . Furthermore, considering that coffee yields are often higher in full sun than under shade in optimal growing conditions [START_REF] Siles | Effects of Inga densiflora on the microclimate of coffee (Coffea arabica L.) and overall biomass under optimal growing conditions in Costa Rica[END_REF][START_REF] Vaast | Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions[END_REF] ; lower in full sun than under shade in sub-optimal conditions

Impact of young shade trees on soil fertility

Chapter IV demonstrated that B. javanica, C. camphora and J. mimosifolia all had a positive impact on soil fertility in the 0-20cm soil layer only four years after their introduction. This positive impact was attributed to leaf litter and tree below-ground activity. In addition, soil water profiles showed that rainfalls were sufficient to meet both coffee trees and shade trees needs for water resources.

Higher soil chemical fertility (higher pH, SOM, total N, available P and exchangeable Ca concentrations) was measured below shaded coffee than below open coffee. Furthermore, soil biological communities (fungi, bacteria, and nematodes) and soil enzyme activities (BG, NAG and ACP) were always similar or higher under shade trees than in open areas. The positive impact of shade trees on soil biology was particularly visible during the dry season, highlighting a buffering effect of shade trees on seasonal variations in soil biological communities. This chapter thus evidenced that young shade trees can quickly contribute to preserving and/or restoring soil fertility, which appears particularly important considering the documented trend of soil degradation in intensively managed coffee farms.

Impacts of young shade trees on micro-climate

Chapter V shows that B. javanica, C. camphora and J. mimosifolia all buffered extreme temperatures under their canopies: maximum temperatures decreased by 3 to 6°C in summer, and minimum temperatures increased by 0.5 to 1°C in winter. This proved especially important to protect coffee trees from leaf damage when a cold spell hit the study area in December 2017. Young shade trees hence appear suitable to decrease the vulnerability of coffee farmers to extreme climatic events, which will keep increasing in frequency due to climate change.

Impacts of young shade trees on coffee yield and coffee quality

Fruit set was positively correlated with light intensity. On a more personal note, my warmest thanks go to all my family members, in particular my parents and sisters, and to all my dear friends scattered around the globe, who provided the moral and emotional support and encouragements much needed during this long journey. I also wish to extend my thanks to the many authors and journalists who unlighted the long days in buses and evenings in villages with their poetic vision, with particular thoughts for Antoine de Saint Exupery, Emile Zola and Rudyard Kippling.

Last but not least, I would like to express my deepest and most sincere gratitude to all coffee farmers who warmly welcomed me, shared their knowledge of coffee agronomy and opened up their farms to my experimental designs. I hope that the results from this research will find their way back to them and contribute to their livelihood.

CURRICULUM VITAE

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