References of "Kouadio, Amani Louis"
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See detailCinétique de décroissance de la surface verte et estimation du rendement du blé d’hiver
Kouadio, Amani Louis ULg; Djaby, Bakary ULg; Grégory, Duveiller et al

in Biotechnologie, Agronomie, Société et Environnement = Biotechnology, Agronomy, Society and Environment [=BASE] (2012), 16(2), 179-191

A large number of agrometeorological models for crop yield assessment are available with various levels of complexity and empiricism. However, the current development of models for wheat yield forecasting ... [more ▼]

A large number of agrometeorological models for crop yield assessment are available with various levels of complexity and empiricism. However, the current development of models for wheat yield forecasting does not always reflect the inclusion of the loss of valuable green area and its relation to biotic and abiotic processes in production situation. In this study the senescence phase of winter wheat (Triticum aestivum L.) is monitored through the GAI (Green Area Index), calculated from digital hemispherical photography taken over plots in Belgium, Grand-Duchy of Luxembourg and France. Two curve-fitting functions (modified Gompertz and modified logistic) are used to describe the senescence phase. Metrics derived from these functions and characterizing this phase (i.e. the maximum value of GAI, the senescence rate and the time taken to reach either 37% or 50% of the green surface in the senescent phase) are related to final grain yields. The regression-based models calculated with these metrics showed that final yield could be estimated with a coefficient of determination of 0,83 and a RMSE of 0,48 t.ha-1. Such simple models may be considered as a first yield estimates that may be performed in order to provide a better integrated yield assessment in operational systems. Indeed, estimation of cereal-crop production, particularly wheat, is considered as a priority in most crop research programs due to the relevance of food grain to world agricultural production. [less ▲]

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See detailPrévisions des rendements du blé d’hiver à échelle régionale par modélisation de la courbe de chute de l’indice foliaire
Kouadio, Amani Louis ULg

Doctoral thesis (2012)

Estimation of cereal-crop production is considered as a priority in most crop research programs due to the relevance of food grain to world agricultural production. A large number of agrometeorological ... [more ▼]

Estimation of cereal-crop production is considered as a priority in most crop research programs due to the relevance of food grain to world agricultural production. A large number of agrometeorological models for crop yield assessment are available with various levels of complexity and empiricism. The current development of models for wheat yield forecasts, however, does not always reflect the inclusion of the loss of valuable green area and its relation to biotic and abiotic processes in production situation. At the field level, the close correlation between green leaf area during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. Our study sought to explore an approach for winter wheat (Triticum aestivum L.) yield forecasts at a regional scale, based on metrics derived from the senescence phase of the green area index (GAI). The senescence phase of winter wheat was analyzed and its modelling was achieved through two curve-fitting functions (modified Gompertz and logistic function). Metrics derived from these functions and characterizing this phase (i.e. the maximum value of GAI, the senescence rate and the time taken to reach either 37% or 50% of the remaining green surface in the senescent phase) were related to grain yields. The Senescence-based Approach For Yield estimates (SenAFY) was established and first tested at plot scale based on GAI values derived from digital hemispherical photograph. Then, it was applied at a regional scale using GAI temporal profiles retrieved from MODIS data. This second part of our study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the winter wheat. The regression-based models derived from the SenAFY provide interesting yield estimates at these two spatial scales. At regional scale, especially, the use of the SenAFY in order to forecast wheat yield gave satisfactory results. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems. [less ▲]

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See detailMaladies du blé d'hiver au Luxembourg Les interactions entre climat, sol et phytotechnie montrent l'importance primordiale du facteur climat
El Jarroudi, Moussa ULg; Giraud, Frédéric; Delfosse, Philippe et al

in Phytoma : La Défense des Végétaux (2012), 650

Malgré sa petite taille, le GDL est caractérisé par deux régions très contrastées en ce qui concerne les maladies cryptogamiques du blé d’hiver. Une analyse en composante principale a permis d’analyser la ... [more ▼]

Malgré sa petite taille, le GDL est caractérisé par deux régions très contrastées en ce qui concerne les maladies cryptogamiques du blé d’hiver. Une analyse en composante principale a permis d’analyser la distribution des maladies cryptogamiques entre le Gutland et l’Oseling. La distribution des maladies cryptogamiques est significativement différente (P < 0.001) entre le Gutland et l’Oesling. Entre 2003 et 2009, la septoriose et la rouille brune sont des maladies qui caractérisent le Gutland avec respectivement comme pourcentage 51 et 17%. Dans l’Oesling, la sévérité de ces maladies était très faible et n’atteignait même pas 1%. A l’opposé, l’Oesling est caractérisée par l’installation de l’Oïdium surtout en 2003 et 2009 avec respectivement 15 et 40% de sévérité alors cette maladie ne dépassait pas 1% de sévérité en Gutland. Parallèlement à ces maladies, d’autres pathogènes fongiques sont observées uniquement en Gutland et les maladies qu’ils causent sont influencées par la phytotechnie. Il s’agit de l’helminthosporiose et de la rouille jaune avec comme caractéristique le contournement du gène de résistance Yr 17+. La variation dans l’expression des maladies cryptogamiques entre le Gutland et l’Oesling est surtout due aux différences marquées des conditions climatiques entre les deux régions mais aussi aux pratiques agricoles en vigueur (fumure azotée, choix variétal, semis avec labour ou sans labour….). [less ▲]

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See detailEstimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data
Kouadio, Amani Louis ULg; Duveiller, Gregory; Djaby, Bakary ULg et al

in International Journal of Applied Earth Observation and Geoinformation (2012), 18

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation ... [more ▼]

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence date and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha−1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems. [less ▲]

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See detailImages hémisphériques et leur analyse pour prévoir le rendement du blé d’hiver. Comment la phase de décroissance de la surface verte de la plante nous renseigne-t-elle sur le rendement final ?
Kouadio, Amani Louis ULg; Djaby, Bakary ULg; Giraud, Frédéric et al

in Phytoma : La Défense des Végétaux (2011), 648

The prediction of cereal-crop yield is considered as a priority in most crop research programmes due to the relevance of food grain to feeding the world population. Today, a large number of ... [more ▼]

The prediction of cereal-crop yield is considered as a priority in most crop research programmes due to the relevance of food grain to feeding the world population. Today, a large number of agrometeorological models for crop yield assessment are available with various levels of complexity and empiricism. But, currently the development of wheat yield forecasting models in conventional operational systems do not reflect the loss of active green leaf area and its relation to biotic and abiotic processes implicated in the crop production situation. In 2009 a large field campaign in the Grand-Duchy of Luxembourg was realized to assess the validity of leaf-green-area approach to further improve yield prediction. Hemispherical photography were taken on winter wheat fields during the crop cycle, preferentially from inflorescence emergence to maturity. The variable of interest, the Green Area Index (GAI), was retrieved after image analyses using the CAN-EYE software. The regression-based models calculated with metrics derived from the decreasing curves of GAI showed that final yield could be better estimated with satisfactory precision: range of the coefficient of determination (R²) varies from 0.73 to 0.86 and RMSE (root mean square error) is varying between 0.43 and 0.56 t/ha. The validation of such approach at the scale of an agricultural zone or region is currently under progress, by using green area index temporal profiles and information on the phenology of winter wheat. Such simple models may be considered as a first step towards yield estimation that may be completed by other agrometeorological models in order to provide a better integrated yield assessment. [less ▲]

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See detailNew method for establishing a network of operational warning of Septoria leaf blotch disease in winter wheat
El Jarroudi, Moussa ULg; Giraud, Frédéric; Delfosse, Philippe et al

in Phytopathology (2011), 101

A mechanistic model, PROCULTURE, based on commonly available meteorological data and assessing in real time the risk of progression of septoria leaf blotch disease on winter wheat has been developed in ... [more ▼]

A mechanistic model, PROCULTURE, based on commonly available meteorological data and assessing in real time the risk of progression of septoria leaf blotch disease on winter wheat has been developed in Belgium and the Grand-Duchy of Luxemburg (GDL) to limit fungicide use. However, the reliability of meteorological stations used for the warning system varies according to the distance to the fields. A weather analysis based on the Fourier transform highlighted a great difference in the intraday variation between two sites in the GDL (Everlange and Reuland). The correlation between these two sites is very high for the hourly temperature (R = 0.96), and for the hourly relative humidity (RH) (R = 0.86), (P < 0.05). However, the intraday variation (<11 hours) highlights contrasts for a given meteorological parameter. Hence, the correlation between temperature or RH decreased respectively from 0.96 to 0.43 and from 0.86 to 0.30. The comparison between infection conditions given by PROCULTURE using the Fourier transform, shows: (i) a positive but weak correlation between temperature at Reuland and Everlange (R = 0.64), (ii) a good correlation between RH for these two sites (R = 0.86), and (iii) a contrasted difference for rain (R = 0.27), (P < 0.05). This Fourier transform based method enables to take into account the RH and temperature variation related to topography levels in the warning system and to understand and explain the variation in disease expression between a plateau and a valley bottom or between North and South slopes. [less ▲]

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See detailRegional-based typology of the main fungal diseases affecting winter wheat in the Grand-Duchy of Luxembourg
El Jarroudi, Moussa ULg; Giraud, Frédéric; Delfosse, Philippe et al

in Phytopathology (2011), 101

Despite its small territory size, the Grand-Duchy of Luxembourg (GDL) has several microclimates that result in a variability of disease severity between the South (Gutland) and the North (Oesling ... [more ▼]

Despite its small territory size, the Grand-Duchy of Luxembourg (GDL) has several microclimates that result in a variability of disease severity between the South (Gutland) and the North (Oesling). Septoria leaf blotch disease of wheat is an important disease in the GDL. Over 2003–2009, the severity was strong in Gutland (51% on average over the last two upper leaves at the late milk growth stage) and low in the Oesling (16% for the same leaves). For the years 2006, 2008 and 2009, the disease severity was less than 6% in the Oesling while it exceeded 40% in the Gutland. The second fungal disease that has become economically important is the wheat leaf rust. Over the same period, the Gutland and the Oesling showed consistently the highest and lowest disease severity respectively. In 2003 and 2007, the Gutland showed the highest disease severity with 66% and 57% respectively, whereas the lowest severity (<1%) was observed in the Oesling. Another important disease is wheat powdery mildew. The 2003 and 2009 cropping seasons showed the highest disease severity with 15% and 40%, respectively, in the Oesling whereas less than 1% severity was registered in the Gutland. Fusarium head blight was also present in the eastern part of the Gutland showing the highest prevalence and severity in 2007 and 2008 (8.5% and 8.3% respectively). These prevalence and severity percentages were significantly higher compared to the Oesling (% prevalence % severity, p = 0.049 and p = 0.012, respectively, Tukey’s test). [less ▲]

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See detailWheat Yield Estimates at NUTS-3 level using MODIS data: an approach based on the decreasing curves of green area index temporal profiles
Kouadio, Amani Louis ULg; Duveiller, Gregory; Djaby, Bakary ULg et al

in Cawkwell, Fiona (Ed.) Proceedings of RSPSoc2010 Annual Conference. 1st-3rd September 2010, Cork, Ireland (Nottingham: RSPSoc) (2010, September)

Wheat is the most widely-grown food crop in the world and the most important cereal crop traded on international markets. An early prediction of its yield prior to harvest at regional, national and ... [more ▼]

Wheat is the most widely-grown food crop in the world and the most important cereal crop traded on international markets. An early prediction of its yield prior to harvest at regional, national and international scales can play a crucial role in global markets, policy and decision making. Many models for yield forecasting are available with varying levels of complexity and empiricism. The use of remote sensing technology for monitoring crop condition and predicting crop yields at regional scales have been studied extensively during these last decades. Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for yield and production forecasting. At field level, studies on crop breeding showed that a close correlation exists between green leaf area during maturation and grain yield in wheat. Thus, the onset and rate of senescence appeared as important factors for determining grain yield of this crop. The aim of this research is to explore a simplified approach for wheat yield forecasting at the European NUTS-3 administrative level, based on metrics derived from the senescence phase of green area index (GAI) estimated from remote sensing data. This study takes advantage of considerable recent improvements in sensor technology and data availability through the opportunity of applying medium/coarse spatial resolution data for deriving crop specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is constituted by a high cover fraction of winter wheat. This approach is tested on 2 crop growing season over a 300 by 300 km study site comprising Belgium and northern France within the framework of the GLOBAM (GLObal Agricultural Monitoring systems by integration of earth observation and modelling techniques) project. The validation of such an approach will involve the comparison with official wheat yield data at NUTS-3 level. [less ▲]

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See detailModelling plant diseases impact with the Belgium Crop Growth Monitoring System.
El Jarroudi, Moussa ULg; Kouadio, Amani Louis ULg; Martin, Bertrand ULg et al

in Wery, Jacque; Shili-Touzi, I.; Perrin, A. (Eds.) PROCEEDING OF AGRO2010 the XIth ESA Congress (2010, September)

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See detailWheat yield and PAI decreasing shape curve
Kouadio, Amani Louis ULg; Tychon, Bernard ULg

Conference (2010, January)

Estimation of cereal-crop production is considered as a priority in most crop research programmes due to the relevance of food grain to world agricultural production. Today, a large number of ... [more ▼]

Estimation of cereal-crop production is considered as a priority in most crop research programmes due to the relevance of food grain to world agricultural production. Today, a large number of agrometeorological models for crop yield assessment are available with various levels of complexity and empiricism. A preliminary study was performed with simulated data of wheat yield and LAI derived from the WOFOST/CGMS agrometeorological model. The main hypothesis underlying this study is that it’s possible to improve wheat yield estimates from metrics stretched from LAI decreasing curves. This preliminary study showed that wheat yield can be estimated by metrics stretched from simulated LAI curve-fitting done by a modified Gompertz function [G =A*exp (-exp(-k(t-m)))] and a logistic function [G = A / 1+exp(-k(t-m)); where G is the green LAI (gLAI), A the initial percentage of LAI, m the position of the inflexion point in the decreasing part of the LAI curve and k the relative senescence rate. In 2009 a large field campaign in the Grand-Duchy of Luxembourg and France was done to check the validity of such approach with field data. Hemispheric images were taken on 18 winter wheat fields during the crop cycle, preferentially from inflorescence emergence to maturity. The variable of interest, green PAI (Plant Area Index), was retrieved after analyses of images by the CAN-EYE software (v. 6.1). Data used as input to establish the model of wheat yield estimate are the value of observed PAImax, and metrics k and m, stretched from observed PAI curves fitted by Gompertz and logistic functions. The model obtained by multilinear regression with these variables reveals that wheat yield can be estimated, at the scale of the plot, with a r² ≈ 0.70 and a RMSE = 0.87 t/ha (RRMSE = 9%). The validation of such approach at the scale of an agricultural zone or region will be performed in the next step of our study, by using remote sensing data (air temperature, PAI or LAI) and phenology data as input. Such simple models may be considered as a first yield estimates that may be completed, if justified, by other agrometeorological models in order to provide a better integrated yield assessment. [less ▲]

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See detailExploring the potential of crop specific green area index time series to improve yield estimation at regional scale
Duveiller, Gregory; de Wit, Allard; Kouadio, Amani Louis ULg et al

in Sobrino, J. A. (Ed.) Proceedings of the 3rd International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS'III) (2010)

Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the ... [more ▼]

Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the growing season can potentially improve crop growth modelling and yield estimation. However, such approaches have proven difficult to apply on coarse resolution satellite data due to the fragmented land cover in many parts of the World. Advances in operational crop mapping will sooner or later allow the production of crop maps relatively early in the crop growth season, thereby providing an opportunity to sample pixels from medium/coarse spatial resolution data with relatively high cover fraction of a particular crop type to derive crop specific GAI time series. This research explores how to use such time series derived from MODIS to produce indicators of crop yield using two approaches over part of Belgium. The first method consists in looking at metrics of the decreasing part of the GAI curves when senescence occurs. Such metrics, like the position of the inflexion point, have been shown to be significantly correlated to yield. The second approach is to optimize the WOFOST model used in the European Crop Growth Monitoring System (CGMS) based on the GAI time series. Results show that, although the optimized model shows considerably better performance than the model running on the default parameter, the model is sometimes outperformed by the simpler metric approach. In all cases, indicators including remote sensing information provide better estimates that the average yield of previous years. [less ▲]

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See detailPrévision de la production nationale d’arachide au Sénégal à partir du modèle agrométéorologique AMS et du NDVI
Kouadio, Amani Louis ULg

Master's dissertation (2007)

Au Sénégal, à l’instar de la plupart des pays subsahariens, l’agriculture est largement tributaire des conditions climatiques. L’agriculture paysanne occupe 60% de la population active et contribue pour ... [more ▼]

Au Sénégal, à l’instar de la plupart des pays subsahariens, l’agriculture est largement tributaire des conditions climatiques. L’agriculture paysanne occupe 60% de la population active et contribue pour 20% au PIB. Elle est dominée par plusieurs filières dont la filière arachide. L’objectif de cette étude est de trouver un modèle de prévision de la production nationale d’arachide à la troisième décade des mois de septembre et d’octobre. Ce modèle est basé sur la prévision du rendement de la culture au niveau départemental à partir des sorties du modèle agrométéorologique AgroMetShell, des données NDVI et de données météorologiques. Cette étude qui constitue une première approche dans la prévision du rendement de l’arachide, montre que la relation trouvée entre le rendement à l’échelle départementale à la troisième décade d’octobre et les variables explicatives fournit une bonne prévision du rendement de l’arachide à l’échelle nationale, avec un R² = 0.55 et une erreur de prédiction faible (RMSE = 28 kg/ha). [less ▲]

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