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Leaf area index as an indicator of ecosystem services and management practices: An application for coffee agroforestry

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Abstract

Scalable indicators are promising to assess ecosystem services. In a large (660 ha) coffee agroforestry farm, we calibrated the relationship between the Normalized Difference Vegetation Index (NDVI), calculated on a High Resolution (HR) satellite image and ground-truth LAI, providing a 2-layer (shade trees and coffee) LAI calibration with LAI 2000 and a new technique based on the cumulative distribution of LAI along transects. The effective and apparent clumping of coffee leaves were computed (0.76 and 0.89, respectively). We also calibrated the relationship between the derived HR-LAI farm map and NDVI from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to re-construct LAI time-series (2001–2011).

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... Agroforestry systems (AFS) are probably the most complex perennial agroecosystems (Mal� ezieux et al., 2009), because they have the most heterogeneous vertical and/or horizontal canopies, and these affect all ecosystem fluxes Vezy et al., 2018). Yet, AFS have the potential to enhance ecosystem services (Jose, 2009;Lin, 2010;Taugourdeau et al., 2014) such as carbon sequestration (Jose and Bardhan, 2012;Oelbermann et al., 2004), and to mitigate climate pressure on crops (Lin, 2007). ...
... Van Oijen et al. (2010)b, andlater Rahn et al. (2018), who further developed the CAF2007 model, proposed a calibration based on an extensive screening of the literature and a sensitivity analysis, but no model evaluation against field data at this stage. Alternatively, we propose a multiple-objective strategy of evaluation in this study, relying on a large range of state and flux variables measured at the same time by the end of the crop rotation, including eddy-covariance fluxes, coffee and shade tree biomass measured at organ scale, necromass, yield, NPP, water balance and energy balance, and finally farm registers to describe management during a complete rotation (Charbonnier et al., , 2017Defrenet, 2016;G� omez-Delgado et al., 2011;Taugourdeau et al., 2014;Vezy et al., 2018). ...
... monitored continuously since 2009 and located on the Aquiares coffee farm. This research site has been intensively studied and described in detail, notably for hydrology and eddy-covariance by G� omez-Delgado et al. (2011); LAI by Taugourdeau et al. (2014); light budget by ; belowground biomass and NPP by Defrenet, 2016, ecosystem biomass, NPP andLUE by Charbonnier et al. (2017); and energy balance, water balance and surface temperature by Vezy et al. (2018). ...
Article
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.
... At the plot level and for various ecosystems, LAI can be considered an important parameter to analyze coffee ecosystem services (Taugourdeau et al., 2014). An increase of LAI due to shade-grown coffee interfere with the microclimate (Barradas & Fanjul, 1986;Ong et al., 2000), evapotranspiration (Padovan et al., 2018), hydrological behavior (Gómez-Delgado et al., 2011), erosion control (Ataroff & Monasterio, 1997), biomass and growth (Rodríguez-López et al., 2014) production (Alves et al., 2016) and net primary productivities (Defrenet et al., 2016;Charbonnier et al., 2017). ...
... The study of Coffee LAI, a parameter derived from MODIS sensor (Moderate Resolution Imaging Spectroradiometer) realized by Taugourdeau et al. (2014) in the central Caribbean region, reports that the seasonal variation of the LAI ranged from 2.4 to 4.4. The authors indicate that, for perennial crops such as coffee, LAI may vary seasonally due to abiotic factors such as drought, shade, temperatures, and biological factors, such as diseases and overproduction, or even by pruning or fertilization. ...
... Also sometimes some periods in a year, the locally measured coffee shrub experience higher soil evaporation (E) than leaf area transpiration (T) and Kc serves the critical purpose of representing averaged E and T process (Pereira et al., 2015b). In this work, the negative correlation between the two biophysical variables analyzed (Figure 7) may be acceptable (or reasonable) since the estimated Kc values by Allen et al. (1998); Doorenbos and Pruitt (1977); Flumignan et al. (2011);and Sato et al. (2007); and the LAI values by Taugourdeau et al. (2014); Pereira et al. (2011) were consistent with the results. ...
Article
Full-text available
Robust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area.
... Agroforestry systems (AFS) are probably the most complex perennial agroecosystems (Malézieux et al., 2009), because they have the most heterogeneous vertical and/or horizontal canopies, and these affect all ecosystem fluxes Vezy et al., 2018). Yet, AFS have the potential to enhance ecosystem services (Jose, 2009;Lin, 2010;Taugourdeau et al., 2014) such as carbon sequestration (Jose and Bardhan, 2012;Oelbermann et al., 2004), and to mitigate climate pressure on crops (Lin, 2007). ...
... Van Oijen et al. (2010b), and later Rahn et al. (2018), who further developed the CAF2007 model, proposed a Bayesian calibration based on an extensive screening of the literature and a sensitivity analysis, but no model evaluation against field data at this stage. Alternatively, we propose a multipleobjective strategy of evaluation in this study, relying on a large range of state and flux variables measured at the same time by the end of the crop rotation, including eddy-covariance fluxes, coffee and shade tree biomass measured at organ scale, necromass, yield, NPP, water balance and energy balance, and finally farm registers to describe management during a complete rotation Charbonnier et al., 2017;Defrenet et al., 2016;Gómez-Delgado et al., 2011;Taugourdeau et al., 2014;Vezy et al., 2018). ...
... year of the simulation (Figure 4). The leaves of E. poeppigiana shed naturally between January and February in Aquiares and then recover rapidly until May (Taugourdeau et al., 2014). The observed phenology was matched by the model, with a simulated range and dynamic of LAI values close to the observations made in the same plot averaged over the whole measurement period (Taugourdeau et al., 2014). ...
Preprint
Full-text available
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. We compared the model outputs with a highly comprehensive database on a coffee agroforestry farm in Costa Rica. The fluxes simulated by the model were close to the measurements over a 5-year period (RMSE= 1.60 gC m-2 d-1 for gross primary productivity; 0.63 mm d-1 for actual evapo-transpiration, 1.34 MJ m-2 d-1 for sensible heat flux and 1.88 MJ m-2 d-1 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 preprint is archived on ZENODO (https://zenodo.org/record/3246268). DynACof model website: https://vezy.github.io/DynACof
... The agronomic yield cycles among trainings are about 4-5 years in high-density and 6-9 in low-density plantations in monoculture (Androcioli-Filho, 2002;Gokavi et al., 2019). Whatever the cultivation system, coffee tree production and yield cycles are regulated by diverse systems of trainings and renovations (Taugourdeau et al., 2014), which have essential roles to maintain enough yield throughout the tree lifespan, to permit that berries remain easily accessible from the ground for harvest and to permit the entry of sunlight and air in low layers, thereby minimizing the pest/disease incidence and increasing whole-canopy photosynthesis (Boorah et al., 2016). ...
... Leaves of orthotropic axes respect an opposite-decussate phyllotaxy, and the plagiotropic axes exhibit decussate-phyllotaxy, as at orthotropic axes, but both internode torsion and petiole angle reorientation, result in dorsiventral phyllotaxy (Dengler, 1999). Every year, carbon investments in foliage expansion precedes investments in berry formation, expansion, and maturation (Taugourdeau et al., 2014;Rakocevic et al., 2020). ...
Article
In horticulture, different planting designs can be used to optimize the plant production. In the coffee tree, we assumed that different planting densities and spatial patterns may impact the growth of all branching order axes, which in turn would impact the berry production. The aim of this study was to quantify and compare axes growth, leaf area, berry distribution and yield in four production years, depending on four planting designs. Experimental data were collected in the 1st, 2nd, 6th and 7th production years (PY), on Coffea arabica trees planted in two high densities (6000 or 10,000 plants ha − 1) and planting patterns (square or rectangular). Coffee architecture was described from metamer to axis, layer, and plant scales. Metamer number per axis, axes number and cumulated length, together with berry distribution, were compared along 40 cm-thick layers of plant vertical profile, depending on the planting design. In the 1st and 2nd PY, plants were formed by two layers, with axes of 4th order appearing in layer 1. In the 2nd PY, the 3rd order axes which had a crucial role in total berry production , were about 3-4 folds more numerous than 2nd order axes in layer 1. In the 6th and 7th PY, the plants included five layers and five branching orders. The leaf area index, 2nd-4th order axes length, berry distribution and yield were shown to depend on both planting design and PY. Both planting patterns under 10,000 plants ha-1 and rectangular planting pattern under 6000 plants ha − 1 could be recommended for high production up to 7th PY, while the square planting pattern under 6000 plants ha − 1 must be pruned after 6th PY.
... Success in the combined provision of goods and services by agroforestry systems depends on delicate equilibria between the plant species involved, which can oscillate between competition and facilitation depending on the species involved, their management, or the environmental conditions (Jose 2009;De Beenhouwer et al. 2013;Taugourdeau et al. 2014). No combination of crop and tree species exists that can be used everywhere. ...
... Numerous processes are closely interrelated, so it is difficult to parameterize one process without having previously parameterized other connected processes. Measurements on diverse processes in coffee agroforestry systems have been carried out in experiments and in commercial plantations for some years now (van Oijen et al. 2010a;Haggar et al. 2011;Charbonnier et al. 2013;Meylan et al. 2013;Taugourdeau et al. 2014;Gagliardi et al. 2015;Padovan et al. 2015;Villatoro-Sánchez et al. 2015;Defrenet et al. 2016). This parameterization, necessary as it is to use a model with reasonable confidence, cannot be done everywhere. ...
Article
Full-text available
Coffee is often grown in production systems associated with shade trees that provide different ecosystem services. Management, weather and soil conditions are spatially variable production factors. CAF2007 is a dynamic model for coffee agroforestry systems that takes these factors as inputs and simulates the processes underlying berry production at the field scale. There remain, however, uncertainties about process rates that need to be reduced through calibration. Bayesian statistics using Markov chain Monte Carlo algorithms is increasingly used for calibration of parameter-rich models. However, very few studies have employed multi-site calibration, which aims to reduce parameter uncertainties using data from multiple sites simultaneously. The main objectives of this study were to calibrate the coffee agroforestry model using data gathered in long-term experiments in Costa Rica and Nicaragua, and to test the calibrated model against independent data from commercial coffee-growing farms. Two sub-models were improved: calculation of flowering date and the modelling of biennial production patterns. The modified model, referred to as CAF2014, can be downloaded at https://doi.org/10.5281/zenodo.3608877. Calibration improved model performance (higher R2, lower RMSE) for Turrialba (Costa Rica) and Masatepe (Nicaragua), including when all experiments were pooled together. Multi-site and single-site Bayesian calibration led to similar RMSE. Validation on new data from coffee-growing farms revealed that both calibration methods improved simulation of yield and its bienniality. The thus improved model was used to test the effect of N fertilizer and shade in different locations on coffee yield.
... For example, the mapping of AFS has been mainly done using high resolution imagery such as Quickbird or WorldView 2 using visual interpretation (Bégué et al. 2015), classification algorithms based on textural features (Gomez et al. 2010;Lelong et al. 2014), or a combination of different remote sensing products from Landsat, MODIS or IKONOS (Zomer et al. 2007). Mapping of canopy structure, such as leaf area index or above-ground biomass in tropical AFS however requires multispectral information, such as provided by MODIS and Sentinel-2 (Taugourdeau et al. 2014;Karlson et al. 2020). While the aforementioned studies are distributed around the tropics, there is no study to our knowledge about remote sensing of AFS in the Andean region. ...
... While CC, LAI and AGB have been successfully estimated in AFS around the world using multispectral sensors (e.g. Hansen et al. 2013;Dube and Mutanga 2015;Korhonen et al. 2017) and high resolution images (see Taugourdeau et al. 2014), to our knowledge this is the first study doing so in the Andean region. Even more, AFS in Colombia are frequently classified as forests in land-use classifications and hence no quantification or monitoring of their extent exists. ...
Article
Full-text available
In the Colombian Andes, agroforestry is a traditional form of agriculture, characterized by a heterogeneous and often diversified composition of trees and crops. This form of land use provides important ecosystem services, such as carbon sequestration, reduction of soil erosion and the maintenance of biodiversity by providing a structural complex habitat. Satellite remote sensing is widely used for studying land use patterns and forest cover, however the discrimination between agroforestry systems and forests is still a challenge, especially in heterogeneous landscapes and in rough terrain. Here, we aim to advance the remote sensing of agroforestry systems using field measurements of vegetation structure in combination with Sentinel-2 images. We use spectral and textural variables derived from Sentinel-2 imagery to predict above ground biomass (AGB), leaf area index (LAI) and canopy closure (CC). The relationship between predicted and observed values obtained from Random Forest regression models showed good fits: for AGB with an R2 = 0.92 and relative RMSE = 34%; for LAI with an R2 = 0.91 and relative RMSE = 19%; and for CC an R2 = 0.89 and relative RMSE = 9%. This allowed us to map these important ecosystem variables at landscape scale and establish empirical thresholds, with which a discrimination of agroforestry systems from forests was possible with an accuracy of 94%. Our results suggest that the relationship between vegetation structure and the spectral information obtained by Sentinel-2 can contribute to the detection and characterization of agroforestry systems and thus help quantifying the ecosystem services and biodiversity conservation potential provided by this type of tropical agriculture.
... The agronomic yield cycles among trainings are about 4-5 years in high-density and 6-9 in low-density plantations in monoculture (Androcioli-Filho, 2002;Gokavi et al., 2019). Whatever the cultivation system, coffee tree production and yield cycles are regulated by diverse systems of trainings and renovations (Taugourdeau et al., 2014), which have essential roles to maintain enough yield throughout the tree lifespan, to permit that berries remain easily accessible from the ground for harvest and to permit the entry of sunlight and air in low layers, thereby minimizing the pest/disease incidence and increasing whole-canopy photosynthesis (Boorah et al., 2016). ...
... Leaves of orthotropic axes respect an opposite-decussate phyllotaxy, and the plagiotropic axes exhibit decussate-phyllotaxy, as at orthotropic axes, but both internode torsion and petiole angle reorientation, result in dorsiventral phyllotaxy (Dengler, 1999). Every year, carbon investments in foliage expansion precedes investments in berry formation, expansion, and maturation (Taugourdeau et al., 2014;Rakocevic et al., 2020). ...
Preprint
In horticulture, different planting designs can be used to optimize the plant production. In the coffee tree, we assumed that different planting densities and spatial patterns may impact the growth of all branching order axes, which in turn would impact the berry production. The aim of this study was to quantify and compare axes growth, leaf area, berry distribution and yield in four production years, depending on four planting designs. Experimental data were collected in the 1st, 2nd, 6th and 7th production years (PY), on Coffea arabica trees planted in two high densities (6000 or 10,000 plants ha −1) and planting patterns (square or rectangular). Coffee architecture was described from metamer to axis, layer, and plant scales. Metamer number per axis, axes number and cumu-lated length, together with berry distribution, were compared along 40 cm-thick layers of plant vertical profile, depending on the planting design. In the 1st and 2nd PY, plants were formed by two layers, with axes of 4th order appearing in layer 1. In the 2nd PY, the 3rd order axes which have a crucial role in total berry production, were about 3-4 folds more numerous than 2nd order axes in layer 1. In the 6th and 7th PY, the plants included five layers and five branching orders. The leaf area index, 2nd-4th order axes length, berry distribution and yield were shown to depend on both planting design and PY. Both planting patterns under 10,000 plants ha-1 and rectangular planting pattern under 6000 plants ha −1 could be recommended for high production up to 7th PY, while the square planting pattern under 6000 plants ha −1 must be pruned after 6th PY.
... Phenological information supports crop productivity and crop management (Sakamoto et al., 2005;Couto Junior et al., 2013). Vegetation indices (VIs) are sensitive to phenological changes and have been used to correlate with agricultural productivity (Bolton & Friedl, 2013;Kogan et al., 2013;Fu et al., 2014), to estimate attributes such as Foliar Area Index that can be related to agricultural yield (Rembold et al., 2013;Taugourdeau et al., 2014;Jiang et al., 2014;Li et al., 2017;Liaqat et al., 2017) or to be incorporated into modeling (Padilla et al., 2012;Meroni et al., 2013;Kowalik et al., 2014). The incorporation of remote sensing data to the modeling improves the estimation of the agricultural yield, since a multispectral evaluation of the cultures state within a certain area can be obtained favoring the study of the relations of the plant with the environment (Delécolle et al., 1992;Rudorff & Batista, 1990). ...
... As reinforced by Rezende et al. (2014) and Taugourdeau et al. (2014), because it is a complex analysis target, the research on indirect methods of measuring coffee agronomic parameters is scarce. In this context, the objective of this study was to evaluate the relationship between yield of coffee crops and vegetation indexes with and without topographic correction derived from the OLI / Landsat-8 sensor for the 2013/2014 and 2014/2015 crops. ...
Article
Full-text available
The reflectance values of a coffee crop are influenced by several factors such as planting direction, crop spacing, time of the year, plant age and topography which reduces the accuracy of the estimates derived from remote sensing data. In this context were evaluated the relationships between coffee productivity and values of NDVI, SAVI and NDWI vegetation indexes with and without topographic reflectance correction for different coffee phenological phases for the crop years 2013/2014 (low productivity) and 2014/2015 (high productivity). The evaluations were made through the standard deviation of vegetation indices (VIs), linear relationship between the cosine factor and the VIs and between VIs and coffee productivity. The best phenological phases of coffee to determine productivity from spectral indexes were the stages of dormancy and flowering. The results indicated that the NDVI was the best index to estimate the productivity of coffee trees with coefficient of determination (R2) that ranged from 0.58 to 0.90. There was an increase in R2 between productivity and NDVI with topographic correction in the dormancy phase in the year of low productivity; between productivity and NDVI with topographic correction in the flowering phase in the year of high productivity; and between productivity and SAVI and NDWI with topographic corrections in the flowering phase in the year of high productivity.
... 1,2 To guarantee the investment made in the coffee crop implantation and production stages, the monitoring of coffee trees must be done continuously to make decisions regarding preventive or corrective interventions in a timely manner. [3][4][5][6] A dynamic way of monitoring the development of coffee crop was using spectral signatures acquired through orbital sensor systems, which responded differently according to the crop characteristics in the field, either by varying the phenological phases or by varying the vegetative vigor due to the occurrence of pests, diseases, and weeds. 7 By associating data from orbital sensor systems with remote sensing processing techniques, it was possible to obtain information on spectral responses of coffee trees, in order to follow changes in the landscape matrix during the monitoring period. ...
... 17,20,21 The application of modeling techniques based on time series of coffee plant vegetation index can be a method to reduce the confusion of classifying algorithms of agricultural crops, assisting in the decision making of protocols for preventive or corrective management. 6,22 Based on the assumption that a standard spectral-temporal signature can collaborate with the detection of coffee pest organisms, based on the coffee crop predicted and observed signatures, this work aimed to characterize the spectral dynamics of the vegetative development of coffee fields under different irrigation systems and evaluate the use of EVI to distinguish signatures and evaluate the accuracy of the prediction obtained from the temporal modeling of this index. ...
Article
The coffee crop spectral behavior identification throughout its cycle can contribute to its development monitoring under pest incidence. We aim to identify coffee development through time signatures of enhanced vegetation index (EVI), as well as to evaluate the use of seasonal autoregressive integrated moving average (SARIMA) models to identify coffee trees spectrum-time patterns under different irrigation management and design future scenarios. Three coffee fields were selected under different irrigation systems, whose EVI data of 8 years were obtained from the moderate resolution image spectroradiometer sensor. Each coffee crop model was subjected to residual autocorrelation test and classified according to information criteria, while its accuracy was assessed by means of prediction error measures and agreement index. The estimated and observed EVI values were similar, even for the predicted year. However, in agricultural years during which coffee diseases occurred, the crops showed vegetative vigor below the expected.We concluded that SARIMA models enabled the establishment of a reliable spectral signature expected for coffee crop, which could help with crop management defining, regardless of the irrigation system adopted. Based on the evaluation of divergence between expected and observed spectral signatures, early signs of coffee underdevelopment were detected, which could reduce economic loss risks on its commercial chain.
... Errors in NDVI values could largely be due to the mixture of information available on soil, other vegetation, and different plant intervals. Furthermore, the NDVI values can change depending on the phenological characteristics, the climatic factors and the management (Taugourdeau et al. 2014). As a result of the lack of an appropriate monitoring method, many studies for coffee leaf rust have focused on the development of disease-resistant varieties and pest control. ...
... The horizontal axis of each graph is NDVI values in the farm, Here, we should stress that the individual use of NDVI and σ NDVI is unfavourable for the damage discrimination. NDVI as the damaged indicator is insufficient since the values have a yearly change; the NDVI values can change depending on the phenological characteristics, the climatic factors and the management of coffee plants (Taugourdeau et al. 2014). Besides, the use of σ NDVI alone is inadequate for monitoring, because a distribution arises in the NDVI values depending on management after felling coffee plants to prevent infection spread, as indicated by aeronautical photos of damaged farms. ...
... Errors in NDVI values could largely be due to the mixture of information available on soil, other vegetation, and different plant intervals. Furthermore, the NDVI values can change depending on the phenological characteristics, the climatic factors and the management (Taugourdeau et al. 2014). As a result of the lack of an appropriate monitoring method, many studies for coffee leaf rust have focused on the development of disease-resistant varieties and pest control. ...
... The horizontal axis of each graph is NDVI values in the farm, Here, we should stress that the individual use of NDVI and σ NDVI is unfavourable for the damage discrimination. NDVI as the damaged indicator is insufficient since the values have a yearly change; the NDVI values can change depending on the phenological characteristics, the climatic factors and the management of coffee plants (Taugourdeau et al. 2014). Besides, the use of σ NDVI alone is inadequate for monitoring, because a distribution arises in the NDVI values depending on management after felling coffee plants to prevent infection spread, as indicated by aeronautical photos of damaged farms. ...
Article
Full-text available
Coffee leaf rust is for the coffee industry potentially one of the causes of a sustainability crisis. Currently, on-site disease detection is the only effective method to fell coffee trees for prevention of the infection. However, accurate infection detection over wide areas is difficult when conducted by ground surveys. Here, we examine the application of a remote sensing method. The Normalized Difference Vegetation Index (NDVI) values of coffee farms were computed using satellite images and compared with the results of the ground truth. We found that the standard deviation of the NDVI value (σNDVI) in damaged farms increases as the average NDVI value decreases. This fact implies that the disease progresses in-homogeneously inside a damaged area. In the present analysis, up to 94.1% of the damaged farms were discriminated by combining the NDVI and σNDVI thresholds when 75.0% of the damaged farms had NDVI values under 0.732 and σNDVI over 0.044. Our monitoring method enabled us to take early-stage countermeasures against the infection, and it could be applied to other vegetation diseases.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % (Taugourdeau et al., 2014). ...
Chapter
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Résumé. Huit ans de travaux de recherche sur les services écosystémiques dans une grande ferme caféière du Costa Rica (observatoire collaboratif Coffee-Flux, en système agrofo-restier 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 capa-cité 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 mala-dies 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 Agroforesterie et services écosystémiques en zone tropicale 38 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'om-brage, 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 (N 2) 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 séquestration de carbone, améliorent le microclimat et atténuent substan-tiellement les effets des changements climatiques. Dans notre étude de cas, aucun effet négatif sur le rendement n'a été enregistré. Abstract. Eight years of studying coffee ecophysiology and monitoring ecosystem services (ES) in a large coffee farm in Costa Rica revealed several practical recommendations for farmers and policy makers. The cropping system studied within our collaborative observatory (Coffee-Flux) corresponds to a coffee-based agroforestry system (AFS) under the shade of large trees of Erythrina poeppigiana (16 % of canopy cover). A lot of ES and disservices depend on local soil properties (here Andisols), especially erosion/infiltration, water/carbon and nutrient storage capacity. Therefore, for ES assessment, the type of soil is crucial. An adequate density of shade trees (rather low here) reduced the severity of leaf diseases with the prospect of reducing pesticide-fungicide use. A simple inventory of the basal area at collar of the coffee plants allowed estimating the belowground biomass and the average age of the plantation, to judge of its market value and to decide when to replace it. Coffee farms are probably much closer to C neutrality than predicted by the current C-Neutral protocol, which only considers shade trees. More comprehensive assessments, including trees, coffee, litter, soil, and roots in the C balance of the AFS are proposed. Shade trees offer many ES if they are adequately managed in the local context. As compared to full sun conditions, shade trees may (i) reduce laminar erosion by a factor of 2, (ii) increase N2 fixation and the % of N recycled into the system, thus reducing fertilizer requirements, (iii) reduce the severity of leaf diseases, (iv) increase C sequestration, (v) improve the microclimate, and (vi) substantially reduce the effects of climate change. In our case study, no negative effect on coffee yield was found.
... Eleven measurements were made for each treatment throughout the seasons. The total leaf area for each plant and measurement was determined using Eq. 1 [33,38]: ...
Article
Rapid and reliable measurements of leaf area index (LAI) of soybeans are important for modelling biophysical processes, energy and water flux and management of weeds in the plant communities. AccuPAR LP 80 and central leaflet width method were used in computing green LAI (gLAI) of two varieties of row spaced rainfed soybeans: TGX 1448 2E and TGX 1440 1E for two seasons. There were five levels of soil fertility which generated 2 by 5 factorial experiments that were arranged in a randomised complete block design. The gLAI estimated during the initial, development, reproductive and maturity of the crop was compared using regression analysis. The gLAI obtained by the methods was significantly correlated (0.77 ≤ r² ≤ 0.99, p < 0.0001; standard error of estimate, 0.05 ≤ SEE ≤ 0.67) in the two seasons. Pooled over the seasons and dataset, the two methods were linearly correlated (r² = 0.89, p < 0.0001; SEE = 0.53). Degree of agreement ranged from 0.96 to 1.00 in the seasons. Regression coefficients ranged from 0.50 to 1.21, while the intercepts were between − 0.15 and 0.78 which indicates deviation from 1:1 line. Mean biased error ranged from − 1.74 to 0.19. AccuPAR LP 80 underestimated LAI from flowering or LAI ≥ 1.11 m² m⁻² and overestimated it during initial and late seasons. Considering the overall performance of the sensor and the rapid measurements, the sensor gave reliable and useful LAI for row spaced rainfed soybeans.
... The leaf area index (LAI) is an important variable used to estimate water, carbon and energy flows. This index is relevant in studies related to the knowledge of phenomena at different scales, such as for the leaf to canopy scale and the calculation of the extinction coefficient of photosynthetically active radiation (kPAR), providing important information for the parameterization of physiological basis models (Sasaki, Imanishi, Ioki, Morimoto, & Kitada, 2008;Taugourdeau et al., 2014). ...
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The leaf area index (LAI) is relevant in studies of phenomena at different scales, such as for the leaf to canopy scale and the calculation of the extinction coefficient of photosynthetically active radiation (kPAR), providing input for the parameterization of physiological basis models. The objective of this work was to verify the variation of the LAI and the coffee kPAR subjected to different drip irrigation levels (130, 100, 70, and 40%) and to compare the data obtained from radiation bar linear sensors (SunScan) in the plants that received full irrigation with the values found by other LAI estimation methodologies. The study was conducted in Piracicaba, São Paulo State, Brazil, using the species Coffea arabica cv. Red Catuaí IAC 144; a drip irrigation system was adopted, with the irrigation controlled by tensiometry. The mean LAI values were higher in the L130 (irrigation level of 130%) and L100 (irrigation level of 100%) treatments than those with deficit irrigation depths. The mean kPAR values were lower for the L130 and L100 treatments than the values found in the deficit irrigation depth treatments. When comparing SunScan to other methodologies, the mean error (ME) and absolute mean error (AME) were high.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % (Taugourdeau et al., 2014). ...
... It is used for the estimation of vegetation parameters (Kerr and Ostrovsky 2003;Pettorelli et al. 2005;Huete et al. 2008;Elhag 2014), forest coverage and its structural application (Demarez et al. 2008;Jensen et al. 2008), crop assessment, monitoring, land surface process simulation, global change studies and yield estimation (Xiao et al. 2016a). Apart from this, long-term temporal LAI is very much important for the study of climate modeling and other problems (Xiao et al. 2016b), global atmosphere/biosphere interactions (Tang et al. 2014), ecosystem services and management practices (Taugourdeau et al. 2014) and so on. ...
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Abstract Vegetation status of Sali River basin has been evaluated in this study applying the Landsat 8 data. Here, NDVI, EVI, GI, LAI, PVI, SI, BI and NDMI have been used to assess vegetation status (VS). Indices have been classifed into fve categories following natural breaks classifcation method. Apart from BI, all the indices represented higher value in forest cover area. Weights for all the themes and sub-themes were assigned following multicriteria decision analysis with consistency ratio of 0.0685, and weighted overlay analysis technique had been employed for the assessment of the vegetation status. Very low, low and moderate VS was found mainly over the water body, urban and agricultural area, which is covering more than half of the basin. The rest of the area is covered with high and very high VS, representing fragmented and dense Sal forest and covering 15.81% and 22.88% basin area, respectively. Accuracy assessment and thorough feld verifcation were done with 90.43% classifcation accuracy. Our result is quite similar to land use land cover map of Bhuvan, ISRO. So, keeping in the view of health of the river basin and vegetation, this area needs urgent attention to control the degradation of vegetation in a scientifc way. Keywords Multi-criteria decision analysis (MCDA) · Leaf Area Index · Normalized Diference Moisture Index · Greenness Index · Perpendicular Vegetation Index · Weighted overlay analysis
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
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... In the Chiapas Sierra Madre, financial investments in coffee plantations tend to be minimal, fluctuating with the international price of this commodity. Falls in coffee prices lead to reduced profitability and thus limited investment in coffee ecosystem health (Taugourdeau et al. 2014), which in turn may increase plantation vulnerability to pests and diseases. ...
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A recent coffee leaf rust epidemic has generated a severe fall in Coffea arabica production throughout Mexico and Central America. This paper analyzes the social–ecological crisis presented by the Hemileia vastatrix outbreak, with a focus on how global, regional and national dynamics interact with local processes in the Chiapas Sierra Madre of south-eastern Mexico, a biodiversity hotspot with a tradition of smallholder, shade-grown coffee production. We explore the hypothesis that the current coffee rust epidemic is an expression of global environmental change, with implications for legal frameworks and international efforts towards risk management and climate change adaptation. Addressing debates on legal resilience building, we illustrate how mismatches of scale between social–ecological phenomena and legal and institutional arrangements may generate pathological solutions for small-scale coffee producers and shade-grown coffee ecosystems. Thereafter, using the analytical lens of modularity, the paper sheds light on landscape stewardship to reduce the risks of non-resilient characteristics such as isolation, on the one hand, and on the other, over-connectedness of habitat patches in the landscape of importance for ecosystem functions at larger scales. The interdisciplinary framework leads to recognizing the role of institutions and legal arrangements which are not limited to national boundaries in proposing solutions to this social–ecological crisis. We find that matching scales of law with agroforestry systems can be done through a variety of legal and policy instruments to contribute to resilience building. This matching of scales is vital to safeguarding biodiversity’s global benefits and the right of small-scale coffee farmers to a healthy and sustainable environment.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
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Régulation des bioagresseurs des cultures dans les systèmes agroforestiers tropicaux, revue des approches bagny beiLhe L., aLLinne c., aveLino J., babin r., brévauLt t., gidoin c., ngo bieng m.a., motisi n., soti v. et ten hooPen g.m. Résumé. Au sein des systèmes agroforestiers tropicaux, de nombreuses interactions se déroulent dans et entre les environnements biotiques et abiotiques. Elles favorisent une régulation naturelle des bioagresseurs des cultures de ces systèmes. Afin d'exploiter au mieux cette régulation naturelle et de limiter les pertes de production, il est primordial de bien comprendre ces interactions. Ce chapitre présente une synthèse d'études de ces mécanismes de régulation, à partir de données empiriques sur des maladies et ravageurs dans des systèmes agroforestiers à base de caféiers, de cacaoyers et de mil au Cameroun, au Costa Rica, au Kenya et au Sénégal. En fonction des caractéristiques biologiques des bioagresseurs et de l'environnement dans lequel ils se développent, des approches multi échelles, de l'arbre au paysage, adaptées aux modèles étudiés ont été utilisées pour évaluer les stratégies de régulation ascendante par les ressources « bottom-up » et descendante par les ennemis naturels « top-down ». Les approches développées ont permis d'évaluer l'effet de la composition et de l'organisation spatiale de la biodiversité associée au sein des systèmes agroforestiers sur les bioagresseurs, l'effet de l'ombrage sur le développement des bioagresseurs et l'effet de la biodiversité végétale associée aux échelles parcelle et paysage sur les communautés d'ennemis naturels et leur efficacité à réguler les bioagres-seurs. Des approches expérimentales et intégratives, d'écologie des communautés et du paysage fondées notamment sur l'étude des traits fonctionnels se sont avérées nécessaires pour estimer au mieux les services de régulation. Abstract. Tropical agroforestry systems are home to complex interactions between and within the biotic and abiotic environments, which govern natural regulation processes of pests and diseases of agricultural crops. In order to optimally exploit these control mechanisms , thereby limiting production losses, it is necessary to improve our understanding Agroforesterie et services écosystémiques en zone tropicale 230 of these interactions within agro-ecosystems. This chapter presents an overview of several studies that looked at naturally occurring control mechanisms in tropical agroforestry systems. This synthesis has been elaborated based on empirical data from studies on the regulation of pests and diseases in coffee, cacao, and millet-based agroforestry systems in Cameroon, Costa Rica, Kenya and Senegal. Based on the biological characteristics of the pest and/or disease and the environment in which they develop, scale dependent approaches, from tree to landscape, appropriate to the models being studied, have been used to evaluate both "bottom-up" and "top-down" control mechanisms. The developed approaches allowed to evaluate: the effects of the composition and spatial organization of associated plant diversity on the regulation of pests and diseases; the effects of shade on the development of pests and diseases and the effects of associated plant biodiversity at plot and landscape level on communities of natural enemies and their efficacy in controlling pests and diseases. Experimental and integrative approaches from population and landscape ecology, taking into account functional traits, are necessary tools to understand regulation services.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
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Respectueux de l'environnement et garantissant une sécurité alimentaire soutenue par la diversification des productions et des revenus qu'ils procurent, les systèmes agroforestiers apparaissent comme un modèle prometteur d'agriculture durable dans les pays du Sud les plus vulnérables aux changements globaux. Cependant, ces systèmes agroforestiers ne peuvent être optimisés qu'à condition de mieux comprendre et de mieux maîtriser les facteurs de leurs productions. L'ouvrage présente un ensemble de connaissances récentes sur les mécanismes biophysiques et socio-économiques qui sous-tendent le fonctionnement et la dynamique des systèmes agroforestiers. Il concerne, d'une part les systèmes agroforestiers à base de cultures pérennes, telles que cacaoyers et caféiers, de régions tropicales humides en Amérique du Sud, en Afrique de l'Est et du Centre, d'autre part les parcs arborés et arbustifs à base de cultures vivrières, principalement de céréales, de la région semi-aride subsaharienne d'Afrique de l'Ouest. Il synthétise les dernières avancées acquises grâce à plusieurs projets associant le Cirad, l'IRD et leurs partenaires du Sud qui ont été conduits entre 2012 et 2016 dans ces régions. L'ensemble de ces projets s'articulent autour des dynamiques des systèmes agroforestiers et des compromis entre les services de production et les autres services socio-écosystémiques que ces systèmes fournissent.
... Leaf area index (LAI) is extensively applied to observe and monitor ecosystem functions (e.g., vegetation growth, and physiological activity) [1][2][3]. Due to the control of LAI over primary production (e.g., photosynthesis), transpiration, evapotranspiration, energy exchange as well as other physiological characteristics pertinent to the wide range of ecosystem processes, the accurate prediction of LAI has been a concern for a broad spectrum of studies [4][5][6][7][8][9]. Moreover, LAI has lately been suggested as being one of the essential biodiversity variables (EBVs) that are suitable for satellite monitoring, among many other variables [10,11]. ...
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Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R 2 CV = 0.33, RMSE CV = 1.21 m 2 m −2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R 2 CV = 0.58, RMSE CV = 0.83 m 2 m −2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R 2 CV = 0.81, RMSE CV = 0.63 m 2 m −2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
... Based on farmers' statements and field evidence, our results confirmed that coffee rust epidemics had economic drivers, that determined crop and disease management, as previously proposed (Avelino et al., 2015), and did not only depend on meteorological aspects and host plant characteristics. Crop management was probably sub-optimum over that period, because management is normally adjusted each year to adapt on-farm investment to the economic context (Taugourdeau et al., 2014), and the coffee crop was not profitable in 2012. International prices dropped sharply (by 55% between September 2011 and December 2013) below the production costs, which reached high levels never seen before at the same time (Avelino et al., 2015). ...
... The northwest and northeastern parts of the basin which is high altitude areas and considered as the water towers of the basin seem to be most affected in the LAI values variation. LAI is affected by natural factors (interaction between vegetative and reproductive components, climate) and human factors (pruning of trees, farming, deforestation), while it has been observed that LAI affects partitioning between green water (evapotranspiration) and blue water (infiltration, aquifer recharge, stream flow) making it an important indicator of the ecosystem function and status (Taugourdeau et al. 2014). From the data below, it is evident that there is deforestation along the slopes of Mount Elgon and along the Cherangany ranges which fall along the northwest and northeast of the basin. ...
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Water resources face risks due to water use stress and water scarcity. Collective and integrated actions by different institutions and stakeholders are needed to reduce future water risks. This paper aimed to assess the potential for a water stewardship partnership in River Nzoia Basin to reduce future water risks facing the ecosystem, agriculture, and other sectors by quantifying water risks and mapping stakeholders for a water stewardship partnership in the basin. Water risks were quantified using indicators from remote sensing platforms and secondary sources. Stakeholder mapping was conducted using stakeholder analysis, while stakeholders’ views were collected using questionnaires. The results showed that there is a high fluctuation in the vegetation cover and primary productivity in the basin pointing to a degradation and deforestation. It was also noted that there is an increase in the frequency and severity of drought and high evapotranspiration rates in some parts of the basin due to the low vegetation cover. Combining the results indicated an increase in water risk between 2000 and 2014 in different parts of the basin at a different magnitude of risks. The conducted interviews found that the basin lacked a stewardship program. However, there was a potential for a successful stewardship partnership among stakeholders as most of the stakeholders showed their ability to play a role in the stewardship program. The paper showed a need to form a water stewardship program at the basin to tackle drought, deforestation, and land degradation. The proposed water stewardship program should be built on commitment, transparency, and inclusivity.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
Article
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Les impacts des karités (Vitellaria paradoxa) sur les contenus du sol en eau, en nutriments et en carbone, ainsi que sur la production associée de maïs pluvial ont été évalués dans un parc agroforestier au Nord-Est du Bénin. Pour ce faire, les résultats des mesures sous houpier et hors houppier ont été comparés. Le rendement de la culture s'est révélé inférieur sous le houppier, malgré des conditions de fertilité et d'humidité du sol qui restaient favorables. Notre hypothèse est que cet effet négatif serait causé par la limitation par le houppier du rayonnement incident sur la culture. De plus, on a observé une contribution significative des arbres à l'enrichissement de la matière organique du sol sur l'ensemble du parc agroforestier. Cet effet positif de la présence des arbres pourrait se traduire par un impact bénéfique sur le rendement du maïs. Pour le vérifier, les rendements obtenus dans les conditions de cette étude devront être comparés aux résultats obtenus dans une situation témoin (sans arbres), toutes choses égales par ailleurs.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
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Notre objectif est d'évaluer la contribution des arbustes de Guiera senegalensis aux flux d'eau et de carbone dans un parc agroforestier à petit mil (Pennisetum glaucum L.R. Br.) et dans une jachère. Le site expérimental est situé au sud-ouest du Niger, sur un bassin-versant sahélien de 2 km2. À l'échelle de l'arbuste de Guiera senegalensis, le taux de transpiration foliaire a été déduit de la conductance stomatique et du déficit de pression de vapeur. La dynamique de la biomasse aérienne et souterraine des arbustes a été suivie en saison des pluies, en saison sèche froide et en saison sèche chaude. À l'échelle de la parcelle, les bilans d'eau et d'assimilation du carbone ont été estimés par une modélisation de type "transferts surface-végétation-atmosphère". Le modèle est paramétré à partir de mesures automatiques de terrain selon la méthode des corrélations turbulentes. Le taux de transpiration foliaire et la biomasse aérienne de Guiera ont augmenté dans le parc, de la saison des pluies à la saison sèche chaude, alors qu'ils ont diminué dans la jachère. Les résultats du modèle montrent une activité de la jachère centrée sur la saison des pluies, mais décalée vers le début de la saison sèche pour le parc. À l'échelle de la parcelle, le modèle est capable de bien simuler l'évapotranspiration et l'assimilation de carbone au regard de la période de la croissance active des arbustes dans les deux types de couvert, tout en assurant un haut degré de cohérence avec le contenu en eau du sol.
... High yielding crop varieties with a significant level of tolerance towards stresses induced by disease, pests, climate change, water shortage and reduced soil fertility have been recognized as crucial needs to feed the burgeoning population [1][2][3]. In this regard, focus has been towards increasing crop yield through timely and precision management of crop inputs [4,5]. However, majorly existing techniques to manage crop stressors have been time-consuming, labor-intensive and most importantly destructive [6,7]. ...
Article
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The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman's rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs > 0.60, coefficient of determination (R²): 0.50–0.56, P < 0.05). Ten other VIs significantly accounted for crop stress at early and late stages. Overall, identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments.
... UAVs or other very high resolution (VHR) remote sensing systems could be used to simplify scaling-up from field measurements and conventional remote sensing systems (Taugourdeau et al., 2014). ...
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
Chapter
Here we show that spatial tree structure within cacao agroforests influences pest and disease attack intensity of cacao trees. At the plot scale, regular or random spatial organizations of forest trees reduced pest and disease intensity of frosty pod rot in Costa Rica, and mirids in Cameroon. At the individual scale, the number of neighboring cacao trees (at 3.7 m) and neighboring fruit trees (at 4.3 m) negatively influence the individual intensity of frosty pod rot in Costa Rica. Our results reveal the importance of spatial structure in the description of tropical agroforests and in understanding of the mechanisms influencing the agroecological regulation of pest and diseases within these complex systems. Optimization of spatial tree structure when managing tropical agroforests could be an interesting lever for agroecological control of pests and diseases.
... Several studies indicated that shade trees on the farmland protect crops from extremely high temperatures (Lin, 2007;Ricci et al., 2013), frosts and hails (Alvarenga et al., 2004), strong winds (Pezzopane et al., 2011) and diversifies income (Chengappa et al., 2017;Jezeer et al., 2017). Shade coffee production systems reduce the incoming solar radiation (Lopez-Bravo et al., 2012), buffer and mitigate the coffee plants from microclimate variability (Gomes et al., 2016) and at optimal level enhance resource capture, such as light (Taugourdeau et al., 2014). These improve the resilience and adaptation of coffee farming systems to climate change and variability and reduce the coffee plants physiological stress (Coltri et al., 2019). ...
Article
Recent climate change models predict that coffee production and the livelihood of millions of farmers will be hardly affected by climate change. Climate changes pronounced in increasing temperature and rainfall variability will reduce the bio-climatic suitable areas, growth and yield of coffee and will induce the occurrence of pests and diseases. Understanding the extent of the climate-driven impact on coffee production and farmers’ adaptation strategies is vital in sustaining coffee productivity. In the form of in-depth analysis, this review begins by contextualizing climate change and coffee production and gives insight into the impact of climate change on coffee suitability areas, growth, yield and the incidence of pests and diseases. It further examines the adaptation strategies pursued by farmers to reduce the impacts of climate change. Site-specific adaptation strategies implemented by farmers to minimize detrimental effects of climate change include (i) selecting appropriate shade tree species and their optimal management, (ii) farmers training, (iii) soil fertility maintenance and protection and (iv) pests and diseases management. Moreover, improving farmers’ access to weather, fair market and technology will enhance their adaptive capacity to climate change. Finally, designing adaptation policies and building the existing practices help the small farmers to pursue climate-resilient coffee production.
... Like defoliation, the lowest level of the process of leaf emergence was defined by the interaction between fruit load (P14, Table 1) and fruit phenology (P15 to P17, Table 1) to account for the influence of the source-sink relationship on leaf dynamics (Avelino et al., 1993;Vezy et al., 2020). Fruit load competes with vegetative growth (DaMatta et al., 2007), and possibly to a greater extent during the final stages of fruit development (Taugourdeau et al., 2014). We next aggregated the input attribute rainfall (P3 , Table 1) as a limiting factor for leaf emergence, followed by the input attribute nutrition (P7 , Table 1) as a stimulating factor for leaf emergence. ...
Article
CONTEXT Coffee leaf rust (CLR) epidemics on Coffea arabica have led to severe socio-economic crises in Latin America starting in 2008. Until now, the scattered nature of scientific and empirical knowledge of the highly complex CLR-coffee pathosystem has been an obstacle to the development of CLR forecasting models. OBJECTIVE To help prevent new severe epidemics, we built ExpeRoya, a qualitative model, based on a review of the scientific literature and expert opinion, to forecast the risk of a monthly increase in the incidence of CLR at plot and landscape levels. METHODS We adopted the IPSIM (Injury Profile SIMulator) framework, a qualitative and aggregative modeling approach that describes the effects of the cropping system and the plot environment on injuries, thereby making it possible to incorporate scattered knowledge on the system and all its complexity in a simplified way. Involving experts makes this approach powerful and robust because it builds on empirical knowledge based on a very large number of field observations. We argue that broad expert knowledge provides more accurate information on the manifold interactions in the system than existing quantitative models can. The structure of ExpeRoya was discussed with coffee sector experts in 19 workshops and validated in an online survey with 17 CLR experts. RESULTS AND CONCLUSIONS ExpeRoya successfully integrates in a simple way 229 multiple interactions that exist within the CLR-coffee pathosystem based on only 12 input variables easily acquired in the field: one incidence monitoring variable; two meteorological variables (temperature and rainfall), four crop management variables (management of shade cover, fungicide application, nutrition and pruning of coffee trees) and five coffee tree characteristics (dates of flowering, beginning and end of harvest, fruit load and cultivar genetic resistance). Coffee institutes in Honduras and Nicaragua now use ExpeRoya, hosted by the platform Pergamino (https://www.redpergamino.net/app-experoya), to assist them in preparing their monthly CLR warning bulletins for growers. ExpeRoya is an improved forecasting model of CLR by fully incorporating the main biophysical factors affecting CLR at the plot and landscape levels. SIGNIFICANCE ExpeRoya is both a framework and a proof of concept that improves both forecasting and the comprehensive modeling of CLR. ExpeRoya is a powerful yet user-friendly model designed for all actors of the coffee sector, particularly smallholder farmers and extension agents. ExpeRoya is adaptable: users can modify the model according to advances in knowledge and/or their own expertise of the system. ExpeRoya can help prevent future socio-economic crises.
... The expectation of the life of a coffee tree in their natural habitat is up to 100 years, while in plantations it is about 30-40 years (Gokavi et al., 2019). In plantations, tree production and yield cycles are regulated by diverse systems of training and renovations (Taugourdeau et al., 2014;Rakocevic et al., 2021a). In Brazil, about 300,000 ha are planted every year for orchard renovation or for the new area's plantation, which corresponds to 13% of the areas under coffee crops (CONAB, 2021). ...
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Increases in water use efficiency (WUE) and the reduction of negative impacts of high temperatures associated with high solar radiation are being achieved with the application of fine particle film of calcined and purified kaolin (KF) on the leaves and fruits of various plant species. KF was applied on young Coffea arabica and Coffea canephora plants before their transition from nursery to full sunlight during autumn and summer. The effects of KF were evaluated through the responses of leaf temperature (Tleaf), net CO2 assimilation rate (A), stomatal conductance (gs), transpiration (E), WUE, crop water stress index (CWSI), index of relative stomatal conductance (Ig), initial fluorescence (F0), and photosynthetic index (PI) in the first 2–3 weeks after the plant transitions to the full sun. All measurements were performed at midday. In Coffea plants, KF decreased the Tleaf up to 6.7�C/5.6�C and reduced the CWSI. The plants that were not protected with KF showed lower A, gs, E, and Ig than those protected with KF. C. canephora plants protected with KF achieved higher WUE compared with those not protected by 11.23% in autumn and 95.58% in summer. In both Coffea sp., KF application reduced F0, indicating reduced physical dissociation of the PSII reaction centers from the lightharvesting system, which was supported with increased PI. The use of KF can be recommended as a management strategy in the transition of Coffea seedlings from the nursery shade to the full sunlight, to protect leaves against the excessive solar radiation and high temperatures, especially in C. canephora during the summer.
... Leaves having lower SLA are usually thicker or denser in stomata. Variation in both leaf thickness and stomatal density are responsible for variation in SLA and this is modified by the local environment (Wilson et al. 1999, Taugourdeau et al. 2014. Higher SLA at lower elevations could imply a reduced drought stress, and an increased leaf water content of the coffee plant. ...
Article
Coffee is an important crop in the global south. However, ongoing changes in the climate system reinforce the need to quantify coffee plants' ecological and eco-physiological traits to assure coffee production in the future. One way to assess how environmental changes affect coffee performance is via leaf traits, most notably leaf carbon and nitrogen concentrations (to reflect the nutrient status), leaf stable carbon isotope composition (δ¹³C) to determine intrinsic water use efficiency (WUEi), and specific leaf area (SLA) to describe carbon gain relative to water loss within a plant canopy as these traits are related to yields. Therefore, we sampled coffee plants growing at contrasting elevations using a space-for-time substitution approach for warming and superimposed a canopy cover gradient to assess whether increasing canopy cover could modulate responses to temperature. Three coffee shrubs were sampled in each of 59 coffee farms in southwest Ethiopia across elevations of 1500–2160 m a.s.l. and along canopy cover gradients from open to deep shade. Soil nutrient concentrations, light availability, soil temperature and moisture were quantified for each coffee shrub. Elevation and shade tree canopy cover significantly and interactively affected WUEi. Elevation was found to be the driving factor for microclimate and soil factors which indirectly influenced both SLA and WUEi. Both of these coffee leaf traits are moderately governed by soil temperature whereas leaf N and C:N are mainly controlled by soil temperature and soil chemical variables. As elevation increased, WUEi kept increasing at light (< 35%) to intermediate shade levels (35–65%), and the values decreased at dense shade levels (65–100%) at high elevations, suggesting that coffee plants growing at high elevations with light shade can assimilate more CO2 with minimum evaporative water loss. SLA declined with elevation. Leaf N and leaf C:N responded negatively and positively to shade canopy cover, respectively. In sum, elevation and canopy cover interactively determined microclimate and coffee leaf traits. Our findings are useful to adjust the intensity of shade, along with other tree-level management tools, to modulate climate-change effects on coffee at the farm level.
... The canopy structure of crops directly affected radiation interception and conversion efficiency and thus reasonable and efficient canopy structure was the basis of high crop production [53,54] . This study showed that the irrigation level and shading cultivation mode notably affected leaf area index (LAI), which was consistent crop canopy structure not only was influenced by its own genetic characteristics and physiological and biochemical processes, but also by the constraints of cultivation measures and environmental conditions [53,55] . DI 1 and S 1 intercept more light radiation by increasing LAI, which was conducive to enhancing photosynthesis and promoting the growth of Arabica coffee. ...
... Other tools than the UAV can be used to assess the woody and herbaceous phytomass. For instance, very-high-resolution-spatial (VHRS) images from satellites have been used to assess tree density (Brandt et al., 2020), and so has the tree-leaf area index (Taugourdeau et al., 2014). However, VHRS remote sensing tools have a lower spatial resolution than onboard UAV sensors. ...
Article
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The phytomass of herbaceous and woody plants is the main source of feed for pastoral livestock in the Sahelian savanna. The assessment of the available feedstock plays a key role in national livestock policies and generally requires many field measurements of both herbaceous and woody plants. In this study, we tested the possibility of using a red-green-blue (RGB) unmanned aerial vehicle (UAV) to evaluate the phytomass of both woody and herbaceous species. We thus mapped 38 one hectare plots with a Dji Spark UAV in Northern Senegal. The herbaceous phytomass was measured on the ground. For the woody communities, we evaluated the leaf phytomass using dendrometric parameters combined with allometric equations. We performed partial-least square regressions between UAV-based three-dimension and color indices and phytomass. Results showed a Q² (cross validation results for each response variable) of 0.57 for woody phytomass, 0.68 for herbaceous dry mass, and 0.76 for their fresh mass. This study confirmed the relevance of using low-cost RGB UAV to assess savanna phytomass.
... The following variables were evaluated monthly during the 6-month experiment period: stem height was measured from ground to apex of each plant with a flexometer; stem thickness (diameter) was measured at 1 cm from the ground surface with a digital Vernier; and leaves were counted and measured (length (L) and width (W)) with a flexometer. Leaf area (A) was calculated according to Taugourdeau et al. (2014) using the following formula: ...
Article
Rhizosphere processes are critical for nutrient cycling, maintaining soil quality and sustaining plant growth and productivity. However, our understanding of the interplay between plant roots and other soil ecosystem engineers such as earthworms is still limited. Our objective was to determine the influence of Pontoscolex corethrurus, a common endogeic earthworm, on the bulk and rhizosphere soil chemical properties and on the growth of two economically important coffee species. A six-month mesocosm experiment was implemented to grow Coffea arabica and C. canephora with and without earthworms. We measured plant growth variables (height, stem thickness, number of leaves, leaf area, biomass and chemical composition) and soil physico-chemical parameters in both the rhizosphere and bulk soils. We found that soil properties significantly differed among soil zones (bulk vs. rhizosphere). Contents of total P, H, Ca, C/N ratio and CEC were consistently larger in the bulk soil than in the rhizosphere, while C, N and Mg contents were highest in the rhizosphere soil. Although the presence of earthworms had little effect on plant growth, their influence on plant and soil nutrient contents was stronger and highly dependent on coffee species. In C. arabica, earthworms reduced the depletion of Na and Ca in the rhizosphere, but promoted the accumulation of available P and possibly accelerated plant N uptake in that of C. canephora. These differences may be explained by the indirect effect of earthworms on nutrient dynamics, likely mediated by induced shifts in the rhizosphere microbial community. Our findings contribute to the understanding of nutrient dynamics in the rhizosphere of two coffee species, revealing a complex influence of earthworms in rhizosphere processes, and call for a further understanding of the microbe-mediated impact of earthworms in the rhizosphere of C. arabica and C. canephora.
... At present, simulation models based on physical phenomena are available to simulate flows involved in the major coffee growth mechanisms of photosynthesis, respiration and transpiration (van Oijen et al., 2010a;Rodríguez et al., 2011;Charbonnier et al., 2013;Vezy et al., 2018Vezy et al., , 2020, and even coffee canopy temperatures under shade trees (Vezy et al., 2018). However, these process models are based on physical phenomena whose descriptors, such as the global radiation extinction coefficient of the trees and tree leaf area index (Taugourdeau et al., 2014), are difficult to measure. Some studies developed simple equations to forecast minimum night crop temperatures, with a view to predicting frost events (Georg, 1978;Lhomme and Guilioni, 2004), but these models were still using complex parameters that were difficult to measure. ...
Article
In Central America, coffee is mainly grown in agroforestry systems. This practice modifies the microclimate, which, in turn, influences coffee growth and development. However, modeling these microclimate modifications is a challenge when trying to predict the development of a disease in the understory crop, based on variables usually monitored in weather stations exposed to full sunlight. Furthermore, critical variables for plant disease development, such as leaf wetness duration and leaf temperatures, are generally not measured by weather stations. In our study, we sought to build models explaining daily minimum and maximum coffee leaf temperatures, daily coffee leaf wetness duration, and minimum and maximum air temperatures in agroforestry systems with a single shade tree species, which are common in Central America, and which were characterized by shade tree height, canopy openness and light gap distribution. The modeled variables were mainly explained by one or more meteorological variables provided by reference weather stations exposed to full sunlight. The presence of shade trees resulted in a buffer effect, reducing daily maximum air and leaf temperatures, and increasing daily minimum air and leaf temperatures. Moreover, except for the daily minimum air temperature under shade, shade tree characteristics affected these microclimatic variables. Indeed, the buffer effect on the daily maximum air temperature increased with shade trees 7 m tall or over, whereas for extreme leaf temperatures, this effect seemed to be further intensified by a dense and homogeneous canopy. The tallest shade trees also tended to provide conditions that reduced coffee leaf wetness duration. The coffee leaf stratum affected the daily maximum leaf temperature, with a top layer intercepting radiation for the lower strata, but had no effect on the daily minimum leaf temperature, detected at night. The models developed were simple equations allowing interpretation of shade tree height, the effects of canopy characteristics on the microclimate and were therefore useful for designing and managing agroforestry system. The more accurate models could be incorporated into an early warning system for coffee pests and diseases in the region.
... Due to the need to protect coffee production, different monitoring techniques have been applied for years, to protect all coffee grower's energy, time and money investments. In order to make large-scale monitoring feasible, the use of remote sensing has grown in recent years (Chemura et al., 2017b;Taugourdeau et al., 2014). ...
Article
Using spectral information, obtained from orbital sensor systems, combined with spectral indices, it's possible to characterize coffee trees conditions with their intrinsic characteristics, which can improve the imaging accuracy and optimize the crop monitoring tasks. However, within center pivot shape, a circular planting orientation is a feature that can difficult to determine coffee yield through remote sensing, resulting in inaccurate inferences about its spectral signature and its yield correlation. The objective of this study was to evaluate the correlation of different phenological stages spectral responses of coffee, under distinct apparent brightness conditions, with the yield sampled in field. The study was carried out on a center pivot irrigated area, located in Presidente Olegário municipality, Minas Gerais, Brazil. Yield data was obtained of 114 georeferenced sample points. Landsat-8 data, obtained between 05/25/2015 and 06/28/2016, were submitted to statistical analysis of Correlation and Multiple Linear Regression in conjunction with yield data. It was observed that the positional arrangement and the planting orientation significantly interfered in the yield estimation. A brightness difference was observed within the collateral area sectors, which raised the coffee plants spectral complexity. This demanded a conditional approach for data analyses and interpretation processes. Based on results, an evidence of good explanatory and predictive relationship with the coffee yield was found for Landsat-8 images when divergent brightness subsets were evaluated separately, raising the forecast accuracy from spectral data. It was concluded that the coffee phenological stages with the highest yield predictive potential are the “dormant bud” and “lead bead” stages.
... As an indicator of overall productivity, green coffee yields averaged 1351 kg ha −1 year −1 (SD = 347 kg N ha −1 year −1 ) between 1994 and 2013. The coffee plants' LAI-an indicator of vegetative vigour-varied seasonally from 2.4 to 4.4 m leaf 2 m soil −2 between 2001 and 2011 (Taugourdeau et al. 2014). ...
Article
In coffee, fruit production on a given shoot drops after some years of high yield, triggering pruning to induce re-sprouting. The timing of pruning is a crucial farmer’s decision affecting yield and labour. A reason explaining fruit production drop could be the exhaustion of resources, particularly the non-structural carbohydrates (NSC). To test such hypothesis in a Coffea arabica agroforestry system, we measured the concentrations of NSC, carbon (C) and nitrogen (N) in leaves, stems, and stumps of the coffee plants, 2 and 5 years after pruning. We also compared shaded vs. full sun plants. For that purpose, both analytical reference and visible and near infrared reflectance spectroscopy (VNIRS) methods were used. As expected, concentrations of biochemical variables linked to photosynthesis activity (N, glucose, fructose, sucrose) decreased from leaves to stems, and then to stumps. In contrast, variables linked more closely to plant structure and reserves (total C, C:N ratio, starch concentration) were higher in long-lifespan organs like stumps. Shading had little effect on most measured parameters, contrary to expectations. Concentrations of N, glucose, and fructose were higher in 2-year-old organs. Conversely, starch concentration in perennial stumps was three times higher 5 years after pruning than 2 years after pruning, despite high fruit production. Therefore, the drop in fruit production occurring after 5–6 years was not due to a lack of NSC on plant scale. Starch accumulation in perennial organs concurrently to other sinks, such as fruit growth, could be considered as a ‘survival’ strategy, which may be a relic of the behavior of wild coffee (tropical shade-tolerant plant). This study confirmed that VNIRS is a promisingly rapid and cost-effective option for starch monitoring (coefficient of determination for validation, R2val = 0.91), whereas predictions were less accurate for soluble sugars, probably due to their too similar spectral signature.
... Understanding the complex mechanisms with which environmental variables and human agents drive LAI and its variation in forested landscapes is essential to develop suitable management interventions aimed at forest functions and services (Nakamura et al., 2017;Taugourdeau et al., 2014). However, previous studies either work at large spatial scales hampering mechanistic understanding (e.g. ...
Article
The Atlantic Forest, a global biodiversity hotspot, has changed dramatically due to land use pressures causing deforestation, degradation, and forest fragmentation. A major challenge is to understand and potentially mitigate the consequences of these changes, for the capacity of forests to deliver essential environmental services to rural areas. Here, we focus on unraveling the mechanisms underpinning spatial variation in forest leaf area index. Forest leaf area index can be used as an environmental indicator that controls key forest functions underlying environmental services and is also expected to respond to land use change. Specifically, we use Structural Equation Modelling to determine the direct and indirect pathways that link environmental drivers to canopy leaf area index (LAI) variation across forest types in the Atlantic Forest in Southern Brazil. We sampled 240 sample units (each 4000 m²), from a systematic and permanent forest inventory set which covers the State of Santa Catarina in a 10 km × 10 km grid, using hemispherical photographs. Environmental variables were extracted for each sample unit, including climatic and topographic data as well as indicators of anthropogenic pressure. Our results showed that forest types differed in their leaf area index (but not all of them) and that forest canopies show complex responses to environmental drivers, encompassing direct and indirect pathways. A major pathway was the positive effect of ‘Distance to city’ on the ‘Percentage of cropland in the matrix’. This led to a decline in the distance of the sample unit to the forest edge, indirectly reducing LAI, presumably because of elevated tree mortality at the forest edge. ‘Terrain steepness’ and ‘Rainfall in the driest month’ independently affected the ‘Percentage of cropland in the matrix’ and the ‘Distance to forest edge’. Halting forest fragmentation and increasing fragment size by landscape planning will mitigate these anthropogenic LAI declines. This can be achieved with a combination of legal and market mechanisms, like enforcement of the Brazilian Forest Act regulation on buffer zones around water bodies and steep slopes, landscape planning, and payment for environmental services to compensate the farmers for maintaining forest cover on otherwise productive land.
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % (Taugourdeau et al., 2014). ...
... This Lisbon UHI-LCC model was later compared to the measured UHI intensities of Andrade (2003). LCR is the landscape's ability to regulate thermal comfort due to its form, configuration of structures, patches, and species (Susca et al., 2011;De Carvalho and Szlafsztein, 2019;Kuang et al., 2017;Kuang et al., 2015;Marando et al., 2019;Taugourdeau et al., 2014). ...
Article
Urban land covers affect the thermal characteristics of the city, such as the urban heat island (UHI) effect, potentially increasing energy demand to maintain comfortable indoor and outdoor temperatures. As the land patterns change, the capacity of the landscape to regulate the UHI can change. The aim of this paper is to explore how simulating land cover changes (LCC) may affect UHI using an ecosystem service matrix approach. A LCC model, illustrated in the case study of Lisbon, Portugal, was implemented to estimate the UHI effects over time starting from the modelling of land cover changes associated with the supply of local climate regulation service. Our results show that the capacity of urban landscape to mitigate the UHI effect has decreased since 1990, and will continue to decrease slightly until 2022 although more smoothly than between 1990 and 2000. This is because no substantial land cover changes have occurred after 2000 that required the transition between highest to lowest ecosystem service supplier landscapes. The proposed modelling approach may be refined and used to aiding the decision making process for urban planners in the placement of built structures and green spaces that have the capacity to regulate local climate.
... UAVs or other very high resolution (VHR) remote sensing systems could be used to simplify scaling-up from field measurements and conventional remote sensing systems (Taugourdeau et al., 2014). ...
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Herbaceous aboveground biomass (HAB) is a key indicator of grassland vegetation and indirect estimation tools, such as remote sensing imagery, increase the potential for covering larger areas in a timely and cost-efficient way. Structure from Motion (SfM) is an image analysis process that can create a variety of 3D spatial models as well as 2D orthomosaics from a set of images. Computed from Unmanned Aerial Vehicle (UAV) and ground camera measurements, the SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study. Both UAV and ground camera recordings were used at three different scales: temporal, landscape, and national (across Senegal). All images were processed using PIX4D software (pho-togrammetry software) and were used to extract vegetation indices and heights. A random forest algorithm was used to estimate the HAB and the average estimation errors were around 150 g m − ² for fresh mass (20% relative error) and 60 g m − ² for dry mass (around 25% error). A comparison between different datasets revealed that the estimates based on camera data were slightly more accurate than those from UAV data. It was also found that combining datasets across scales for the same type of tool (UAV or camera) could be a useful option for monitoring HAB in Sahelian rangelands or in other grassy ecosystems. K E Y W O R D S 3D model, herbaceous aboveground biomass, savannah ecosystem, Senegal, Unmanned Aerial Vehicle, vegetation index
... L'indice foliaire a aussi été utilisé dans la modélisation des services hydrologiques. Lorsqu'il est doublé de 3,8 à 7,6, l'évapotranspiration est augmentée de 60 %, le ruissellement superficiel diminue de 1 %, le débit de la rivière se réduit de 17 % et la quantité d'eau qui traverse l'aquifère est réduite de 20 % ( Taugourdeau et al., 2014). ...
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Eight years of studying coffee ecophysiology and monitoring ecosystem services (ES) in a large coffee farm in Costa Rica revealed several practical recommendations for farmers and policy makers. The cropping system studied within our collaborative observatory (Coffee-Flux) corresponds to a coffee-based agroforestry system (AFS) under the shade of large trees of Erythrina poeppigiana (16% of canopy cover). A lot of ES and disservices depend on local soil properties (here Andisols), especially erosion/infiltration, water/carbon and nutrient storage capacity. Therefore, for ES assessment, the type of soil is crucial. An adequate density of shade trees (rather low here) reduced the severity of leaf diseases with the prospect of reducing pesticide-fungicide use. A simple inventory of the basal area at collar of the coffee plants allowed estimating the belowground biomass and the average age of the plantation, to judge of its market value and to decide when to replace it. Coffee farms are probably much closer to C neutrality than predicted by the current C-Neutral protocol, which only considers shade trees. More comprehensive assessments, including trees, coffee, litter, soil, and roots in the C balance of the AFS are proposed. Shade trees offer many ES if they are adequately managed in the local context. As compared to full sun conditions, shade trees may (i) reduce laminar erosion by a factor of 2, (ii) increase N2 fixation and the % of N recycled into the system, thus reducing fertilizer requirements, (iii) reduce the severity of leaf diseases, (iv) increase C sequestration, (v) improve the microclimate, and (vi) substantially reduce the effects of climate change. In our case study, no negative effect on coffee yield was found
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Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency's (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R 2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R 2 :~0.60.
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The Atlantic Forest, a global biodiversity hotspot, has changed dramatically due to land 12 use pressures causing deforestation, degradation, and forest fragmentation. A major challenge is 13 to understand and potentially mitigate the consequences of these changes, for the capacity of 14 forests to deliver essential environmental services to rural areas. Here, we focus on unraveling the 15 mechanisms underpinning spatial variation in forest leaf area index. Forest leaf area index can be 16 used as an environmental indicator that controls key forest functions underlying environmental 17 services and is also expected to respond to land use change. Specifically, we use Structural 18 Equation Modelling to determine the direct and indirect pathways that link environmental drivers 19 to canopy leaf area index (LAI) variation across forest types in the Atlantic Forest in Southern 20 Brazil. We sampled 240 sample units (each 4,000 m²), from a systematic and permanent forest 21 inventory set which covers the State of Santa Catarina in a 10 km x 10 km grid, using hemispherical 22 photographs. Environmental variables were extracted for each sample unit, including climatic and 23 topographic data as well as indicators of anthropogenic pressure. Our results showed that forest 24 types differed in their leaf area index (but not all of them) and that forest canopies show complex 25 responses to environmental drivers, encompassing direct and indirect pathways. A major pathway 26 was the positive effect of 'Distance to city' on the 'Percentage of cropland in the matrix'. This led 27 to a decline in the distance of the sample unit to the forest edge, indirectly reducing LAI, 28 presumably because of elevated tree mortality at the forest edge. 'Terrain steepness' and 'Rainfall 29 in the driest month' independently affected the 'Percentage of cropland in the matrix' and the 30 'Distance to forest edge'. Halting forest fragmentation and increasing fragment size by landscape 31 planning will mitigate these anthropogenic LAI declines. This can be achieved with a combination 32 of legal and market mechanisms, like enforcement of the Brazilian Forest Act regulation on buffer 33 zones around water bodies and steep slopes, landscape planning, and payment for environmental 34 services to compensate the farmers for maintaining forest cover on otherwise productive land. 35 Keywords: leaf area index; structural equation model; hemispherical photograph; national forest 36 inventory 37 38
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Thesis
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Chapter
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The arabic coffee (Coffea arabica L.) takes two years to complete the entire phenological cycle of the frutification, unlike most of the other crops, that complete the reproductive cycle in one year. Six different phenological phases, taking a total of two years, are proposed, starting in September of each year. The phases are: 1 st phase: vegetative, with seven months, September to March, with long days; 2 nd phase: also vegetative, April to August, with short days, when occurs the transformation of the vegetative buds of the knots formed in the 1 st phase to reproductive buds. At the end of this phase, July and August, the plants enter in relative dormancy with formation of one or two small pair of leaves, that usually do not flourish. The maturation of the reproductive buds comes after the accumulation of about 350 mm of potential evapotranspiration (ETp), starting by the beginning of April; 3 rd phase: flowering and grain expansion, September to December. Usually the flowering happens about 8 to 15 days after the increase of the water potential inside the floral buds caused by rain or irrigation; 4 th phase: grain formation, January to March; 5 th phase: grain maturation, when about 700 mm of ETp accumulates since the main flowering; 6 th phase: senescence and death of the non-primary productive branches, in July and August.
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Seasonal changes in vegetative growth, leaf gas exchanges, carbon isotope discrimination (Δ) and carbohydrate status were monitored in de-fruited coffee trees (Coffea arabica L.) grown in the field, from October 1998 through September 1999, in Viçosa (20°45′S, 42°15′W, 650 m a.s.l.), southeastern Brazil. Of the total growth over the 12-month study period, 78% occurred in the warm, rainy season (October–March), and 22% during the cool, dry season (April–September). Throughout the active growth period, the rate of net carbon assimilation (A) averaged 8.6 μmol m−2 s−1, against 3.4 μmol m−2 s−1 during the period of reduced growth. In the active period, growth, unlike A or Δ, was strongly negatively correlated with air temperature. In contrast, growth and A were both correlated positively, and Δ correlated negatively, with air temperature during the reduced growth period. However, the depressions of A and growth might have simply run in parallel, without any causal relationship. Changes in A appeared to be largely due to stomatal limitations in the active growing season, with non-stomatal ones prevailing in the slow growth period. Foliar carbohydrates seemed not to have contributed appreciably to changes in growth rates and photosynthesis.
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The ecophysiological constraints on the production of the arabica and robusta coffee under shading or full sunlight are reviewed. These two species, which account for almost all the world’s production, were originally considered shade-obligatory, although unshaded plantations may out-yield shaded ones. As a rule, the benefits of shading increase as the environment becomes less favorable for coffee cultivation. Biennial production and branch die-back, which are strongly decreased under shading, are discussed. The relationships between gas exchange performance and key environmental factors are emphasized. Ecophysiological aspects of high density plantings are also examined.
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The electromagnetic radiation (EMR) signals collected by satellites in the solar spectrum are modified by scattering and absorption by gases and aerosols while traveling through the atmosphere from the Earth's surface to the sensor. When and how to correct the atmospheric effects depend on the remote sensing and atmospheric data available, the information desired, and the analytical methods used to extract the information. In many applications involving classification and change detection, atmospheric correction is unnecessary as long as the training data and the data to be classified are in the same relative scale. In other circumstances, corrections are mandatory to put multitemporal data on the same radiometric scale in order to monitor terrestrial surfaces over time. A multitemporal dataset consisting of seven Landsat 5 Thematic Mapper (TM) images from 1988 to 1996 of the Pearl River Delta, Guangdong Province, China was used to compare seven absolute and one relative atmospheric correction algorithms with uncorrected raw data. Based on classification and change detection results, all corrections improved the data analysis. The best overall results are achieved using a new method which adds the effect of Rayleigh scattering to conventional dark object subtraction. Though this method may not lead to accurate surface reflectance, it best minimizes the difference in reflectances within a land cover class through time as measured with the Jeffries–Matusita distance. Contrary to expectations, the more complicated algorithms do not necessarily lead to improved performance of classification and change detection. Simple dark object subtraction, with or without the Rayleigh atmosphere correction, or relative atmospheric correction are recommended for classification and change detection applications.
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A primary objective of the Earth Observing System (EOS) is to develop and validate algorithms to estimate leaf area index (L), fraction of absorbed photosynthetically active radiation (fAPAR), and net primary production (NPP) from remotely sensed products. These three products are important because they relate to or are components of the metabolism of the biosphere and can be determined for terrestrial ecosystems from satellite-borne sensors. The importance of these products in the EOS program necessitates the need to use standard methods to obtain accurate ground truth estimates of L, fAPAR, and NPP that are correlated to satellite-derived estimates. The objective of this article is to review direct and indirect methods used to estimate L, fAPAR, and NPP in terrestrial ecosystems. Direct estimates of L, biomass, and NPP can be obtained by harvesting individual plants, developing allometric equations, and applying these equations to all individuals in the stand. Using non-site-specific allometric equations to estimate L and foliage production can cause large errors because carbon allocation to foliage is influenced by numerous environmental and ecological factors. All of the optical instruments that indirectly estimate L actually estimate “effective” leaf area index (LE) and underestimate L when foliage in the canopy is nonrandomly distributed (i.e., clumped). We discuss several methods, ranging from simple to complex in terms of data needs, that can be used to correct estimates of L when foliage is clumped. Direct estimates of above-ground and below-ground net primary production (NPPA and NPPB, respectively) are laborious, expensive and can only be carried out for small plots, yet there is a great need to obtain global estimates of NPP. Process models, driven by remotely sensed input parameters, are useful tools to examine the influence of global change on the metabolism of terrestrial ecosystems, but an incomplete understanding of carbon allocation continues to hamper development of more accurate NPP models. We summarize carbon allocation patterns for major terrestrial biomes and discuss emerging allocation patterns that can be incorporated into global NPP models. One common process model, light use efficiency or epsilon model, uses remotely sensed fAPAR, light use efficiency (LUE) and carbon allocation coefficients, and other meteorological data to estimates NPP. Such models require reliable estimates of LUE. We summarize the literature and provide LUE coefficients for the major biomes, being careful to correct for inconsistencies in radiation, dry matter and carbon allocation units.