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Theobroma cacao, a tropical cauliflorous fruit tree, typically produces flowers and fruits twice a year and exhibits alternate harvesting patterns over consecutive six-monthly seasons in regions with bi-modal rainfall distribution, such as Côte d'Ivoire. This study investigated seasonal variations in flowering and fruiting among trees in populations of mixed cacao genotypes. The intensities of crown and trunk flowering and pod production were monitored for eight consecutive six-monthly seasons on 114 adult cacao trees grown from seedlings. We investigated distributions of seasonal flowering and fruiting values, relationships between flowering and pod production, and the effects of seasonal cumulative rainfall. The patterns of seasonal flowering and fruiting series were analyzed using two descriptors: the first distinguishing between regular and variable patterns, and the second analyzing the structure of such variability, classifying it as either irregular or alternating. Despite being subjected to similar climate and agronomic management, individual trees exhibited highly variable flowering and fruiting behaviors within each season, as well as variable patterns of flowering and fruiting across seasons. Seasonal alternate fruiting on a population scale masked highly variable patterns among trees, and only 19 % of the trees exhibited marked alternate fruiting patterns. Variations in pod production on a tree scale were mainly related to variations in trunk flowering. Endogenous factors seemed to control seasonal variations in flowering and fruiting, even though exogenous factors, both climatic and agronomic, could structure flowering and fruiting patterns at the orchard scale.

To assess the extent to which trees in a semi-arid silvopastoral system (SPS) can offset the greenhouse-gas (GHG) emissions of the system's livestock, this study used two process-based models (STEP-GENDEC-N2O and DynACof) to simulate 9 years of agricultural activity and resulting emissions in a SPS that has been operating in sahelian Senegal. STEP-GENDEC-N2O simulated soil N2O and CO2 fluxes, plus growth of the herbaceous layer, while DynACof focused on the tree layer. Outputs from the models included simulated time series of vegetative growth, water fluxes, and emissions. This output was validated through the use of published data, and measurements that were made at the SPS. Overall, the outputs from STEP-GENDEC-N2O agreed well with validation data for water fluxes, soil N, soil C, herbaceous biomass, and N2O emissions. Good agreement was also found between the measured fluxes of the SPS ecosystem, and the simulated values that were generated by combining STEP-GENDEC-N2O's simulations (of the herbaceous layer's heterotrophic respiration, autotrophic respiration, and gross primary productivity (GPP)) with DynACof's simulations of the tree layer's autotrophic respiration and GPP. Among the insights gained from the simulations was that in this SPS's sandy soils, nitrification was the dominant process that leads to N2O emissions. Our results show that the trees, at their current density (81 ha−1) offset 18 % to 41 % of the GHG emissions from livestock. With further development, the model set-up can be used for estimating the GHG offset at other tree densities, and will be useful for guiding future policies regarding climate-change adaptation and mitigation in the management of the Sahel's SPSs.

The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment, and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems. In this study, we propose a set of simple and generic formalisms (i.e. the structure and mathematical representation necessary for designing a model) for simulating key interactions in bi-specific intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Soybean-Sunflower, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for grain yield). This is the first time a complete set of formalisms has been developed and published for simulating bi-specific intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing in silico

To assess the extent to which trees in a semi-arid silvopastoral system (SPS) can offset the greenhouse-gas (GHG) emissions of the system's livestock, this study used two process-based models (STEP-GENDEC-N2O and DynACof) to simulate 9 years of agricultural activity and resulting emissions in a SPS that has been operating in sahelian Senegal. STEP-GENDEC-N2O simulated soil N2O and CO2 fluxes, plus growth of the herbaceous layer, while DynACof focused on the tree layer. Outputs from the models included simulated time series of vegetative growth, water fluxes, and emissions. This output was validated through the use of published data, and measurements that were made at the SPS. Overall, the outputs from STEP-GENDEC-N2O agreed well with validation data for water fluxes, soil N, soil C, herbaceous biomass, and N2O emissions. Good agreement was also found between the measured fluxes of the SPS ecosystem, and the simulated values that were generated by combining STEP-GENDEC-N2O's simulations (of the herbaceous layer's heterotrophic respiration, autotrophic respiration, and gross primary productivity (GPP)) with DynACof's simulations of the tree layer's autotrophic respiration and GPP. Among the insights gained from the simulations was that in this SPS's sandy soils, nitrification was the dominant process that leads to N2O emissions. Our results show that the trees, at their current density (81 ha−1) offset 18 % to 41 % of the GHG emissions from livestock. With further development, the model set-up can be used for estimating the GHG offset at other tree densities, and will be useful for guiding future policies regarding climate-change adaptation and mitigation in the management of the Sahel's SPSs.

Functional–structural plant modelling approaches (FSPM) explore the relationships between the 3D structure and the physiological functioning of plants in relation to environmental conditions. In this study, we present a methodological approach that integrated architectural responses to planting design in an oil palm FSPM, and test the impact of planting design and architectural plasticity on physiological responses such as light interception and carbon assimilation. LiDAR-derived and direct measurements were performed on five planting designs to assess the phenotypic plasticity of architectural traits, and allowed evaluating the variations of the main parameters of an existing 3D plant model. Accordingly, we proposed a neighbourhood index (NI) as a simple explanatory variable of architectural plasticity, and used NI-based allometries to simulate architectural variations in 3D virtual plants. Light interception and carbon assimilation were then simulated on virtual plots reproducing the five studied designs. We found that the main traits affected by plant proximity were leaf dimensions, leaf weight and leaf erectness, whereas other structural traits like the frequency of leaflets along the rachis or biomechanical properties of leaves remained unchanged. Our simulation study highlighted model compliance to reproduce architectural plasticity and illustrated how architectural plasticity improved light interception via leaf area expansion, but how the competition for light imposed by the design can counterbalance this benefit in terms of carbon assimilation at stand scale. We conclude on the importance of planting patterns for plants with low architectural plasticity such as oil palm, and how in silico experiments can help in designing innovative planting patterns.

Promoting plant diversity through crop mixtures is a mainstay of the agroecological transition. Modelling this transition requires considering both plant–plant interactions and plants' interactions with abiotic and biotic environments. Modelling crop mixtures enables designing ways to use plant diversity to provide ecosystem services, as long as they include crop management as input. A single modelling approach is not sufficient, however, and complementarities between models may be critical to consider the multiple processes and system components involved at different and relevant spatial and temporal scales. In this article, we present different modelling solutions implemented in a variety of examples to upscale models from local interactions to ecosystem services. We highlight that modelling solutions (i.e. coupling, metamodelling, inverse or hybrid modelling) are built according to modelling objectives (e.g. understand the relative contributions of primary ecological processes to crop mixtures, quantify impacts of the environment and agricultural practices, assess the resulting ecosystem services) rather than to the scales of integration. Many outcomes of multispecies agroecosystems remain to be explored, both experimentally and through the heuristic use of modelling. Combining models to address plant diversity and predict ecosystem services at different scales remains rare but is critical to support the spatial and temporal prediction of the many systems that could be designed.

Functional–structural plant modelling approaches (FSPM) explore the relationships between the 3D structure and the physiological functioning of plants in relation to environmental conditions. In this study, we present a methodological approach that integrated architectural responses to planting design in an oil palm FSPM, and test the impact of planting design and architectural plasticity on physiological responses such as light interception and carbon assimilation. LiDAR-derived and direct measurements were performed on five planting designs to assess the phenotypic plasticity of architectural traits, and allowed evaluating the variations of the main parameters of an existing 3D plant model. Accordingly, we proposed a neighbourhood index (NI) as a simple explanatory variable of architectural plasticity, and used NI-based allometries to simulate architectural variations in 3D virtual plants. Light interception and carbon assimilation were then simulated on virtual plots reproducing the five studied designs. We found that the main traits affected by plant proximity were leaf dimensions, leaf weight and leaf erectness, whereas other structural traits like the frequency of leaflets along the rachis or biomechanical properties of leaves remained unchanged. Our simulation study highlighted model compliance to reproduce architectural plasticity and illustrated how architectural plasticity improved light interception via leaf area expansion, but how the competition for light imposed by the design can counterbalance this benefit in terms of carbon assimilation at stand scale. We conclude on the importance of planting patterns for plants with low architectural plasticity such as oil palm, and how in silico experiments can help in designing innovative planting patterns.

Les épidémies de rouille orangée du caféier (ROC) entraînent des crises socio-économiques qui menacent la durabilité des territoires de production de café en Amérique Centrale. Nous présentons le processus de co-construction d'un modèle de prévision de la ROC et sa mise en oeuvre lors d'ateliers participatifs de simulation interactive (SI) visant à renforcer les capacités des acteurs de la caféiculture pour faire face à la ROC. Basée sur un modèle de croissance du caféier et de la ROC, ce modèle fait évoluer un système caféier-ROC en fonction de la météorologie. Dans le contexte de l'agriculture familiale, les participants disposent de capacités réduites les obligeant à la parcimonie pour lutter contre la ROC. La SI a dû être virtualisée en raison de la pandémie de Covid-19, ce qui a nécessité des compromis sur les échanges entre participants mais les objectifs d'apprentissage ont été atteints. La virtualisation a permis une participation étendue, des économies substantielles, une meilleure gestion du temps, une prise en compte des opinions individuelles, le débriefing à chaud et la traçabilité des décisions. Plus contraignante, la virtualisation est aussi une opportunité pour améliorer nos pratiques de SI.

Ocho años de estudio de la ecofisiología del café, a través de experimentación y de modelación y el monitoreo de los servicios del ecosistema (SE) en una gran finca cafetalera en Costa Rica, revelaron varias recomendaciones prácticas para los agricultores y los formuladores de políticas. El sistema de cultivo estudiado dentro de nuestro observatorio colaborativo (Coffee-Flux), corresponde a un sistema agroforestal (SAF) a base de café bajo la sombra de grandes árboles de Erythrina poeppigiana (16% de la cubierta del dosel). Una gran cantidad de SE y limitantes dependen de las propiedades locales del suelo (en este caso Andisoles), especialmente de la erosión/infiltración, el agua/carbono y la capacidad de almacenamiento de nutrientes. Por lo tanto, para la evaluación de SE, el tipo de suelo es crucial. Una densidad adecuada de árboles de sombra (bastante baja aquí por la condición de libre crecimiento), redujo la severidad de las enfermedades de las hojas con la posibilidad de reducir el uso de plaguicidas y fungicidas. Un inventario simple del área basal en el collar de las plantas de café permitió estimar la biomasa subterránea y la edad promedio de la plantación, para juzgar su valor de mercado y decidir cuándo reemplazarla. Las fincas de café probablemente estén mucho más cerca de la neutralidad de C que lo indicado en el protocolo actual de C-neutralidad, que solo considera árboles de sombra, no los cafetos ni el suelo. Se proponen evaluaciones más completas, que ncluyen árboles, café, hojarasca, suelo y raíces en el balance C del SAF. Los árboles de sombra ofrecen muchos SE si se gestionan adecuadamente en el contexto local. En comparación con las condiciones a pleno sol, los árboles de sombra pueden (i) reducir la erosión laminar en un factor de 2; (ii) aumentar la fijación de N y el % de N reciclado en el sistema, reduciendo así los requisitos de fertilizantes; (iii) reducir la severidad de enfermedades de las hojas; (iv) aumentar el secuestro de C; (v) mejo

Les épidémies de rouille orangée du caféier (ROC) entraînent des crises socio-économiques qui menacent la durabilité des territoires de production de café en Amérique Centrale. Nous présentons le processus de co-construction d'un modèle de prévision de la ROC et sa mise en oeuvre lors d'ateliers participatifs de simulation interactive (SI) visant à renforcer les capacités des acteurs de la caféiculture pour faire face à la ROC. Basée sur un modèle de croissance du caféier et de la ROC, ce modèle fait évoluer un système caféier-ROC en fonction de la météorologie. Dans le contexte de l'agriculture familiale, les participants disposent de capacités réduites les obligeant à la parcimonie pour lutter contre la ROC. La SI a dû être virtualisée en raison de la pandémie de Covid-19, ce qui a nécessité des compromis sur les échanges entre participants mais les objectifs d'apprentissage ont été atteints. La virtualisation a permis une participation étendue, des économies substantielles, une meilleure gestion du temps, une prise en compte des opinions individuelles, le débriefing à chaud et la traçabilité des décisions. Plus contraignante, la virtualisation est aussi une opportunité pour améliorer nos pratiques de SI.

The mechanistic model GO+ describes the functioning and growth of managed forests based upon biophysical and biogeochemical processes. The biophysical and biogeochemical processes included are modelled using standard formulations of radiative transfer, convective heat exchange, evapotranspiration, photosynthesis, respiration, plant phenology, growth and mortality, biomass nutrient content, and soil carbon dynamics. The forest ecosystem is modelled as three layers, namely the tree overstorey, understorey and soil. The vegetation layers include stems, branches and foliage and are partitioned dynamically between sunlit and shaded fractions. The soil carbon submodel is an adaption of the Roth-C model to simulate the impact of forest operations. The model runs at an hourly time step. It represents a forest stand covering typically 1 ha and can be straightforwardly upscaled across gridded data at regional, country or continental levels. GO+ accounts for both the immediate and long-term impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes exhaustive and versatile descriptions of management operations (soil preparation, regeneration, vegetation control, selective thinning, clear-cutting, coppicing, etc.), thus permitting the effects of a wide variety of forest management strategies to be estimated: from close to nature to intensive. This paper examines the sensitivity of the model to its main parameters and estimates how errors in parameter values are propagated into the predicted values of its main output variables.The sensitivity analysis demonstrates an interaction between the sensitivity of variables, with the climate and soil hydraulic properties being dominant under dry conditions but the leaf biochemical properties being most influential with wet soil. The sensitivity profile of the model changes from short to long timescales due to the cumulative effects of the fluxes of carbon, energy and wa

Agro-silvo-pastoralism is a highly representative Land Use in Africa, often presented as a strategical option for ecological intensification of cropping systems towards food security and sovereignty. We set up a new long-term observatory (“Faidherbia-Flux”) to monitor and model microclimate, energy and C balance in Niakhar (central Senegal, rainfall ~ 500 mm), dominated by the multipurpose tree Faidherbia albida (12.5 m high; 7 tree ha-1; 5% canopy cover). Faidherbia is an attractive agroforestry tree species in order to partition fluxes, given that it is on leaf during the dry season (October-June) and defoliated during the wet season, just when crops take over. Pearl-millet and groundnut crops were conducted during the wet season, following annual rotation in a complex mixed mosaic of ca. 1 ha fields. Early 2018, we installed an eddy-covariance (EC) tower above the whole mosaic (EC1: 20 m high). A second EC system was displayed above the crop (EC2: 4.5 m if pearl-millet, 2.5 m if groundnut) in order to partition ecosystem EC fluxes between tree layer and crop+soil layers. Sap-flow was monitored from April 2019 onwards in 5 faidherbia trees (37 sensors). The ecosystem displayed moderate but significant daily CO2 and H2O fluxes during the dry season, when faidherbia (low canopy cover) was in leaf and the soil was evaporating. At the onset of the rainy season, the soil bursted a large amount of CO2. Just after the growth of pearl-millet in 2018, CO2 uptake by photosynthesis increased dramatically. However, this was largely compensated by high ecosystem respiration. Surprisingly in 2019, although the crop was turned to groundnut, the fluxes behaved pretty much the same as with pearl millet in 2018: comparing annual balances between 2018 and 2019 we obtained [454, 513] for rainfall (P: mm yr-1), [3500, 3486] for potential evapotranspiration (ETo: mm yr-1), [0.13, 0.15] for P/ETo, [470, 497] for actual evapotranspiration (E: mm yr-1), [2809, 2785] for net radiation (Rn

Functional-structural plant modelling approaches (FSPM) open the way for exploring the relationships between the 3D structure and the physiological functioning of plants in relation to environmental conditions. FSPMs can be particularly interesting when dealing with perennial crops like oil palm, for which research on innovative management practices requires long and expensive agronomic trials. The present study is part of the PalmStudio project, which aims at developing a FSPM for oil palm capable of conducting virtual experiments to test the relevance of innovative management practiceds and/or design ideotypes. We propose a methodological approach which integrates architectural responses to planting density in an existing oil palm FSPM (Perez et al. 2018a b). Combining standard field phenotyping with Lidar-based derived measurements, we manage to evaluate the phenotypic plasticity of the main parameters required for the calibration of the 3D plant model. LiDAR scans were processed using the PlantScan3D software (Boudon et al. 2014) to derive phenotypic traits of leaf geometry that were compared to labour-intensive measurements. Density-based allometries of leaf geometry and biomass are then derived from the observed variations in phenotypic traits and integrate into the FSPM. Our results illustrate the accuracy and the efficiency of Lidar-based phenotyping of leaf geometrical traits. In average, we find less than 3% of difference in leaf dimensions (i.e. rachis length) in comparison with traditional hand-made field measurements. The fast and efficient measurements of usually labour-intensive traits such as leaf curvature allowed estimating the plasticity of leaf geometry in response to density. We find that the main traits affected by density were leaf dimensions (up to 15% and 25% of increase in rachis length and petiole length respectively) and curvature (15% of increase in leaf erectness-related parameter), whereas other structural traits like the number of lea

The mechanistic model GO+ describes the functioning and growth of managed forests based upon biophysical and biogeochemical processes. The biophysical and biogeochemical processes included are modelled using standard formulations of radiative transfer, convective heat exchange, evapotranspiration, photosynthesis, respiration, plant phenology, growth and mortality, biomass nutrient content, and soil carbon dynamics. The forest ecosystem is modelled as three layers, namely the tree overstorey, understorey and soil. The vegetation layers include stems, branches and foliage and are partitioned dynamically between sunlit and shaded fractions. The soil carbon submodel is an adaption of the Roth-C model to simulate the impact of forest operations. The model runs at an hourly time step. It represents a forest stand covering typically 1 ha and can be straightforwardly upscaled across gridded data at regional, country or continental levels. GO+ accounts for both the immediate and long-term impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes exhaustive and versatile descriptions of management operations (soil preparation, regeneration, vegetation control, selective thinning, clear-cutting, coppicing, etc.), thus permitting the effects of a wide variety of forest management strategies to be estimated: from close to nature to intensive. This paper examines the sensitivity of the model to its main parameters and estimates how errors in parameter values are propagated into the predicted values of its main output variables.The sensitivity analysis demonstrates an interaction between the sensitivity of variables, with the climate and soil hydraulic properties being dominant under dry conditions but the leaf biochemical properties being most influential with wet soil. The sensitivity profile of the model changes from short to long timescales due to the cumulative effects of the fluxes of carbon, energy and wa

Functional-structural plant modelling approaches (FSPM) open the way for exploring the relationships between the 3D structure and the physiological functioning of plants in relation to environmental conditions. FSPMs can be particularly interesting when dealing with perennial crops like oil palm, for which research on innovative management practices requires long and expensive agronomic trials. The present study is part of the PalmStudio project, which aims at developing a FSPM for oil palm capable of conducting virtual experiments to test the relevance of innovative management practiceds and/or design ideotypes. We propose a methodological approach which integrates architectural responses to planting density in an existing oil palm FSPM (Perez et al. 2018a b). Combining standard field phenotyping with Lidar-based derived measurements, we manage to evaluate the phenotypic plasticity of the main parameters required for the calibration of the 3D plant model. LiDAR scans were processed using the PlantScan3D software (Boudon et al. 2014) to derive phenotypic traits of leaf geometry that were compared to labour-intensive measurements. Density-based allometries of leaf geometry and biomass are then derived from the observed variations in phenotypic traits and integrate into the FSPM. Our results illustrate the accuracy and the efficiency of Lidar-based phenotyping of leaf geometrical traits. In average, we find less than 3% of difference in leaf dimensions (i.e. rachis length) in comparison with traditional hand-made field measurements. The fast and efficient measurements of usually labour-intensive traits such as leaf curvature allowed estimating the plasticity of leaf geometry in response to density. We find that the main traits affected by density were leaf dimensions (up to 15% and 25% of increase in rachis length and petiole length respectively) and curvature (15% of increase in leaf erectness-related parameter), whereas other structural traits like the number of lea

Agro-silvo-pastoralism is a highly representative Land Use in Africa, often presented as a strategical option for ecological intensification of cropping systems towards food security and sovereignty. We set up a new long-term observatory (“Faidherbia-Flux”) to monitor and model microclimate, energy and C balance in Niakhar (central Senegal, rainfall ~ 500 mm), dominated by the multipurpose tree Faidherbia albida (12.5 m high; 7 tree ha-1; 5% canopy cover). Faidherbia is an attractive agroforestry tree species in order to partition fluxes, given that it is on leaf during the dry season (October-June) and defoliated during the wet season, just when crops take over. Pearl-millet and groundnut crops were conducted during the wet season, following annual rotation in a complex mixed mosaic of ca. 1 ha fields. Early 2018, we installed an eddy-covariance (EC) tower above the whole mosaic (EC1: 20 m high). A second EC system was displayed above the crop (EC2: 4.5 m if pearl-millet, 2.5 m if groundnut) in order to partition ecosystem EC fluxes between tree layer and crop+soil layers. Sap-flow was monitored from April 2019 onwards in 5 faidherbia trees (37 sensors). The ecosystem displayed moderate but significant daily CO2 and H2O fluxes during the dry season, when faidherbia (low canopy cover) was in leaf and the soil was evaporating. At the onset of the rainy season, the soil bursted a large amount of CO2. Just after the growth of pearl-millet in 2018, CO2 uptake by photosynthesis increased dramatically. However, this was largely compensated by high ecosystem respiration. Surprisingly in 2019, although the crop was turned to groundnut, the fluxes behaved pretty much the same as with pearl millet in 2018: comparing annual balances between 2018 and 2019 we obtained [454, 513] for rainfall (P: mm yr-1), [3500, 3486] for potential evapotranspiration (ETo: mm yr-1), [0.13, 0.15] for P/ETo, [470, 497] for actual evapotranspiration (E: mm yr-1), [2809, 2785] for net radiation (Rn

The DynACof model was designed to model coffee agroforestry systems and study the trade-offs to e.g. optimize the system facing climate changes. The model simulates net primary productivity (NPP), growth, yield, mortality, energy and water balance of coffee agroforestry systems according to shade tree species and management. Several plot-scale ecosystem services are simulated by the model, such as production, canopy cooling effect, or potential C sequestration. DynACof uses metamodels derived from a detailed 3D process-based model (MAESPA) to account for complex spatial effects, while running fast. It also includes a coffee flower bud and fruit cohort module to better distribute fruit carbon demand over the year, a key feature to obtain a realistic competition between sinks. The model was parameterized and evaluated using a highly comprehensive database on a coffee agroforestry experimental site in Costa Rica. The fluxes simulated by the model were close to the measurements over a 5-year period (nRMSE = 26.27 for gross primary productivity; 28.22 for actual evapo-transpiration, 53.91 for sensible heat flux and 15.26 for net radiation), and DynACof satisfactorily simulated the yield, NPP, mortality and carbon stock for each coffee organ type over a 35-year rotation.

The mechanistic model GO+ describes the functioning and growth of managed forests based upon biophysical and biogeochemical processes. The biophysical and biogeochemical processes included are modelled using standard formulations of radiative transfer, convective heat exchange, evapotranspiration, photosynthesis, respiration, plant phenology, growth and mortality, biomass nutrient content, and soil carbon dynamics. The forest ecosystem is modelled as three layers, namely the tree overstorey, understorey and soil. The vegetation layers include stems, branches and foliage and are partitioned dynamically between sunlit and shaded fractions. The soil carbon submodel is an adaption of the Roth-C model to simulate the impact of forest operations. The model runs at an hourly time step. It represents a forest stand covering typically 1 ha and can be straightforwardly upscaled across gridded data at regional, country or continental levels. GO+ accounts for both the immediate and long-term impacts of forest operations on energy, water and carbon exchanges within the soil-vegetation-atmosphere continuum. It includes exhaustive and versatile descriptions of management operations (soil preparation, regeneration, vegetation control, selective thinning, clear-cutting, coppicing, etc.), thus permitting the effects of a wide variety of forest management strategies to be estimated: from close to nature to intensive. This paper examines the sensitivity of the model to its main parameters and estimates how errors in parameter values are propagated into the predicted values of its main output variables. The sensitivity analysis demonstrates an interaction between the sensitivity of variables, with the climate and soil hydraulic properties being dominant under dry conditions but the leaf biochemical properties being most influential with wet soil. The sensitivity profile of the model changes from short to long timescales due to the cumulative effects of the fluxes of carbon, energy an

Agroforestry systems (AFS) are complex to model mainly due to the high spatial variability induced by the shade trees. Recently, the microclimate and lighf heterogeneity issue in AFS has been addressed using the 30 ecophysiological process-based model MAESPA (Charbonnier et al., 2013; Vezy et al., 2018). MAESPA surpassed the classical sun/shade dichotomy in AFS (Charbonnier et al., 2014) and provided continuous maps of e.g. available light, light-use~ fficiency and canopy temperature within Coffea Agroforestry Systems (GAS). A step further was to design a crop model for Coffea grown under agroforestry that would benefit from this continuum to estimate ecosystem services on the long term and under climate change scenarios. We designed DynA_Cof, a new process-based growth and yield model to compute plot-scale net and gross primary productivity, carbon allocation, growth, yield, energy, and water balance of GAS according to shade tree species and management, while accounting for fine-scale spatial effects using MAESPA metamodels (Figure 1). DynA_Cof satisfactorily simulated the daily plot-scale gross primary productivity (RMSE= 1.69 gc m-2 d-1 on 1562 days) and the energy and water balances (RMSE: AET = 0.63 mm d-1 , H= 1.27 MJ m-2 d-1, Rn= 1.98 MJ m-2 d-1) compared to measurements from an eddy-flux tower in Aquiares (Costa Rica) and also the NPP for above and below-ground organs, coffee bean yield and shade tree wood production compared to a comprehensive database from this site.

The mitigation of climate change by agro-sylvo-pastoral systems is complex to assess or model, owing to high spatial and temporal heterogeneities. We set a new long-term observatory up for the monitoring and modelling of microclimate, GHG and deep SOC in a semi-arid agro-sylvo-pastoral system (Niakhar, Sénégal), dominated by the multipurpose Faidherbia albida tree. Crops were mainly millet and peanut, under annual rotation. Transhumant livestock contributed largely to manure, SOM and soil fertility. Early 2018, we installed 3 eddy-covariance towers above (i) the whole mosaic, (ii) millet and (iii) peanut and monitored energy, CO2 balance and evapotranspiration for one full year. The mosaic ecosystem displayed low but significant CO2 and H2O fluxes during the dry season, owing to Faidherbia in leaf (Fig. 1). When rains resumed, the soil bursted a large amount of CO2. Just after the raising of millet, CO2 uptake by photosynthesis increased dramatically, then stabilized before harvest. However, this was compensated by large ecosystem respiration. The annual ecosystem CO2 balance was close to nil. This observatory is currently installing soil chambers for GHG fluxes, studying the horizontal variability of SOC by Vis-NIR and of deep soil roots and C using wells. Microclimate (land surface temperature, energy balance and gas exchanges) and light-use-efficiency will be mapped through 3D modelling (Charbonnier et al., 2017; Vezy et al., 2018). This observatory is open for collaboration.

The adaptation of semi-arid crops to climate changes is theoretically possible through agroforestry, provided that the trees exert little competition, or even increase the multifunctional LER (LER_M). We monitored microclimate, net primary productivity (NPP), CO2 and H2O fluxes in a semiarid agro-sylvo-pastoral system (Niakhar, Senegal), dominated by the multipurpose Faidherbia albida (FA) tree. Undercrops were mainly millet and peanut, under annual rotation. We scanned a 1.24 ha millet under FA plot with UAV photogrammetry in RGB, thermal infrared and multispectral bands. At harvest, we collected 12 subplots of 15 millet holes each, distributed either below the crown of FA, or at 2.5 x crown radius, or at 5 x crown radius. We separated all organs. The whole millet root system (0-200 cm) was sampled also in 2 m trenches, totalizing 4 millet holes, where all roots were sorted by layer. The whole plot harvest will allow extrapolating yield from subplots, through UAV images. Millet yield per unit ground area was about 3 times higher below FA, with still a positive influence at 2.5 x crown radius and less impacts of pests close to FA. In the trenches, we noted higher soil humidity and SOC close to the FA trunks. This observatory is open for collaboration.

Huit ans de travaux de recherche sur les services écosystémiques dans une grande ferme caféière du Costa Rica (observatoire collaboratif Coffee-Flux, en système agroforestier à base de caféiers sous de grands arbres d'Erythrina poeppigiana, surface projetée de couronne de l'ordre de 16 %) ont suggéré plusieurs applications pour les agriculteurs et les décideurs. Il est apparu que de nombreux services écosystémiques dépendaient des propriétés du sol (ici des Andisols), en particulier de l'érosion, de l'infiltration, de la capacité de stockage de l'eau et des éléments nutritifs. Nous confirmons qu'il est essentiel de lier les services hydrologiques et de conservation au type de sol en présence. Une densité adéquate d'arbres d'ombrage (plutôt faible ici) permet de réduire la sévérité des maladies foliaires avec, en perspective, une réduction de l'usage de pesticides-fongicides. Un simple inventaire de la surface basale au collet des caféiers permet d'estimer la biomasse souterraine et la moyenne d'âge d'une plantation de caféiers, ce qui permet d'évaluer sa valeur marchande ou de planifier son remplacement. Le protocole de calcul actuel pour la neutralité carbone des systèmes agroforestiers ne prend en compte que les arbres d'ombrage, pas la culture intercalaire. Dans la réalité, si on inclut les caféiers, on se rapproche très probablement de la neutralité. Des évaluations plus complètes, incluant les arbres, les caféiers, la litière, le sol et les racines dans le bilan en carbone du système agroforestier sont proposées. Les arbres d'ombrage offrent de nombreux servies écosystémiques s'ils sont gérés de manière adéquate dans le contexte local. Par rapport aux parcelles en plein soleil, nous montrons qu'ils réduisent l'érosion laminaire d'un facteur 2, augmentent la fixation de l'azote (N2) atmosphérique et le pourcentage d'azote recyclé dans le système, réduisant ainsi les besoins en engrais. Ils réduisent aussi la sévérité des maladies foliaires, augmentent la séquest

Agroforestry systems (AFS) are complex to model mainly due to the high spatial variability induced by the shade trees. Recently, the microclimate and lighf heterogeneity issue in AFS has been addressed using the 30 ecophysiological process-based model MAESPA (Charbonnier et al., 2013; Vezy et al., 2018). MAESPA surpassed the classical sun/shade dichotomy in AFS (Charbonnier et al., 2014) and provided continuous maps of e.g. available light, light-use~ fficiency and canopy temperature within Coffea Agroforestry Systems (GAS). A step further was to design a crop model for Coffea grown under agroforestry that would benefit from this continuum to estimate ecosystem services on the long term and under climate change scenarios. We designed DynA_Cof, a new process-based growth and yield model to compute plot-scale net and gross primary productivity, carbon allocation, growth, yield, energy, and water balance of GAS according to shade tree species and management, while accounting for fine-scale spatial effects using MAESPA metamodels (Figure 1). DynA_Cof satisfactorily simulated the daily plot-scale gross primary productivity (RMSE= 1.69 gc m-2 d-1 on 1562 days) and the energy and water balances (RMSE: AET = 0.63 mm d-1 , H= 1.27 MJ m-2 d-1, Rn= 1.98 MJ m-2 d-1) compared to measurements from an eddy-flux tower in Aquiares (Costa Rica) and also the NPP for above and below-ground organs, coffee bean yield and shade tree wood production compared to a comprehensive database from this site.

The adaptation of semi-arid crops to climate changes is theoretically possible through agroforestry, provided that the trees exert little competition, or even increase the multifunctional LER (LER_M). We monitored microclimate, net primary productivity (NPP), CO2 and H2O fluxes in a semiarid agro-sylvo-pastoral system (Niakhar, Senegal), dominated by the multipurpose Faidherbia albida (FA) tree. Undercrops were mainly millet and peanut, under annual rotation. We scanned a 1.24 ha millet under FA plot with UAV photogrammetry in RGB, thermal infrared and multispectral bands. At harvest, we collected 12 subplots of 15 millet holes each, distributed either below the crown of FA, or at 2.5 x crown radius, or at 5 x crown radius. We separated all organs. The whole millet root system (0-200 cm) was sampled also in 2 m trenches, totalizing 4 millet holes, where all roots were sorted by layer. The whole plot harvest will allow extrapolating yield from subplots, through UAV images. Millet yield per unit ground area was about 3 times higher below FA, with still a positive influence at 2.5 x crown radius and less impacts of pests close to FA. In the trenches, we noted higher soil humidity and SOC close to the FA trunks. This observatory is open for collaboration.

Huit ans de travaux de recherche sur les services écosystémiques dans une grande ferme caféière du Costa Rica (observatoire collaboratif Coffee-Flux, en système agroforestier à base de caféiers sous de grands arbres d'Erythrina poeppigiana, surface projetée de couronne de l'ordre de 16 %) ont suggéré plusieurs applications pour les agriculteurs et les décideurs. Il est apparu que de nombreux services écosystémiques dépendaient des propriétés du sol (ici des Andisols), en particulier de l'érosion, de l'infiltration, de la capacité de stockage de l'eau et des éléments nutritifs. Nous confirmons qu'il est essentiel de lier les services hydrologiques et de conservation au type de sol en présence. Une densité adéquate d'arbres d'ombrage (plutôt faible ici) permet de réduire la sévérité des maladies foliaires avec, en perspective, une réduction de l'usage de pesticides-fongicides. Un simple inventaire de la surface basale au collet des caféiers permet d'estimer la biomasse souterraine et la moyenne d'âge d'une plantation de caféiers, ce qui permet d'évaluer sa valeur marchande ou de planifier son remplacement. Le protocole de calcul actuel pour la neutralité carbone des systèmes agroforestiers ne prend en compte que les arbres d'ombrage, pas la culture intercalaire. Dans la réalité, si on inclut les caféiers, on se rapproche très probablement de la neutralité. Des évaluations plus complètes, incluant les arbres, les caféiers, la litière, le sol et les racines dans le bilan en carbone du système agroforestier sont proposées. Les arbres d'ombrage offrent de nombreux servies écosystémiques s'ils sont gérés de manière adéquate dans le contexte local. Par rapport aux parcelles en plein soleil, nous montrons qu'ils réduisent l'érosion laminaire d'un facteur 2, augmentent la fixation de l'azote (N2) atmosphérique et le pourcentage d'azote recyclé dans le système, réduisant ainsi les besoins en engrais. Ils réduisent aussi la sévérité des maladies foliaires, augmentent la séquest

The mitigation of climate change by agro-sylvo-pastoral systems is complex to assess or model, owing to high spatial and temporal heterogeneities. We set a new long-term observatory up for the monitoring and modelling of microclimate, GHG and deep SOC in a semi-arid agro-sylvo-pastoral system (Niakhar, Sénégal), dominated by the multipurpose Faidherbia albida tree. Crops were mainly millet and peanut, under annual rotation. Transhumant livestock contributed largely to manure, SOM and soil fertility. Early 2018, we installed 3 eddy-covariance towers above (i) the whole mosaic, (ii) millet and (iii) peanut and monitored energy, CO2 balance and evapotranspiration for one full year. The mosaic ecosystem displayed low but significant CO2 and H2O fluxes during the dry season, owing to Faidherbia in leaf (Fig. 1). When rains resumed, the soil bursted a large amount of CO2. Just after the raising of millet, CO2 uptake by photosynthesis increased dramatically, then stabilized before harvest. However, this was compensated by large ecosystem respiration. The annual ecosystem CO2 balance was close to nil. This observatory is currently installing soil chambers for GHG fluxes, studying the horizontal variability of SOC by Vis-NIR and of deep soil roots and C using wells. Microclimate (land surface temperature, energy balance and gas exchanges) and light-use-efficiency will be mapped through 3D modelling (Charbonnier et al., 2017; Vezy et al., 2018). This observatory is open for collaboration.

Les effets des changements climatiques sur les systèmes agronomiques sont encore très incertains. Par conséquent, il existe un besoin croissant d'informations pour mieux prédire les impacts futurs des changements climatiques sur les cultures pérennes et les forêts, ainsi que pour concevoir de nouvelles pratiques agricoles et sylvicoles pour faire face à ces changements (Brisson et al., 2010). Ces changements ont des effets combinés complexes sur les bilans d'énergie, hydriques et de carbone des écosystèmes, et peuvent donc affecter la production des agroécosystèmes (Way et al., 2015). Les modèles basés sur les processus (PBM) sont généralement bien adaptés pour relever ces défis. Ils appliquent notre compréhension des processus physiques et écophysiologiques fondamentaux pour simuler physiquement le système (Bohn et al., 2014). Ils peuvent être utilisés pour estimer les flux et les stocks d'énergie, d'eau et de carbone dans l'écosystème, en fonction des caractéristiques du climat, du sol et des plantes. La croissance du café et la production de fruits sont particulièrement sensibles aux températures élevées et à la disponibilité de l'eau, et des études antérieures prédisent souvent une perte conséquente de production ou une réduction des aires potentielle de culture. Néanmoins, l'ombrage fourni dans les systèmes agroforestiers pourrait atténuer les effets des changements climatiques selon différentes options de gestion. Ainsi, au cours de cette thèse, nous avons d'abord mis à jour un PBM 3D (MAESPA) pour tenir compte de la température et de la pression de vapeur dans la canopée, puis l'avons validé sur deux écosystèmes : une plantation d'Eucalyptus au Brésil et une plantation de Coffea arabica au Costa Rica. Nous avons ensuite utilisé MAESPA pour créer des métamodèles qui ont été intégrés à un nouveau modèle de croissance et de rendement développé pour évaluer la réponse du caféier au changement climatique et les solutions possibles offertes par la gestion agrofores

Les effets des changements climatiques sur les systèmes agronomiques sont encore très incertains. Par conséquent, il existe un besoin croissant d'informations pour mieux prédire les impacts futurs des changements climatiques sur les cultures pérennes et les forêts, ainsi que pour concevoir de nouvelles pratiques agricoles et sylvicoles pour faire face à ces changements (Brisson et al., 2010). Ces changements ont des effets combinés complexes sur les bilans d'énergie, hydriques et de carbone des écosystèmes, et peuvent donc affecter la production des agroécosystèmes (Way et al., 2015). Les modèles basés sur les processus (PBM) sont généralement bien adaptés pour relever ces défis. Ils appliquent notre compréhension des processus physiques et écophysiologiques fondamentaux pour simuler physiquement le système (Bohn et al., 2014). Ils peuvent être utilisés pour estimer les flux et les stocks d'énergie, d'eau et de carbone dans l'écosystème, en fonction des caractéristiques du climat, du sol et des plantes. La croissance du café et la production de fruits sont particulièrement sensibles aux températures élevées et à la disponibilité de l'eau, et des études antérieures prédisent souvent une perte conséquente de production ou une réduction des aires potentielle de culture. Néanmoins, l'ombrage fourni dans les systèmes agroforestiers pourrait atténuer les effets des changements climatiques selon différentes options de gestion. Ainsi, au cours de cette thèse, nous avons d'abord mis à jour un PBM 3D (MAESPA) pour tenir compte de la température et de la pression de vapeur dans la canopée, puis l'avons validé sur deux écosystèmes : une plantation d'Eucalyptus au Brésil et une plantation de Coffea arabica au Costa Rica. Nous avons ensuite utilisé MAESPA pour créer des métamodèles qui ont été intégrés à un nouveau modèle de croissance et de rendement développé pour évaluer la réponse du caféier au changement climatique et les solutions possibles offertes par la gestion agrofores