Prévision de la production nationale d’arachide au Sénégal à partir du modèle agrométéorologique AMS et du NDVI

As many subsaharian countries, the agriculture of Senegal is widely dependent on the climate. This agriculture takes up 60% of active population and contributes at 20% of the GDP. It’s dominated by many crops industries, whose groundnut industry. The aim of this study is to find a forecasting model of the national production of groundnut at the third decade of September and October. This model is based on the yield forecasting at departmental scale with the outputs of the agrometeorological model AgroMetShell, NDVI data and other meteorological data. This study which is one first approach in groundnut’s yield forecasting, shows the relation between yield and the explanatory variables at the third decade of October provide a best forecast of the yield of groundnut at national scale (R² = 0.55 and RMSE = 28 kg/ha).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)

Multispectral remote sensing as a tool to support organic crop certification: assessment of the discrimination level between organic and conventional maize

The annual certification of organic agriculture products includes an in situ inspection of the fields declared organic. This inspection is more difficult, time-consuming, and costly for large farms or in production regions located in remote areas. The global objective of this research is to assess how spatial remote sensing may support the organic crop certification process by developing a method that would enable certification bodies to target for priority in situ control crop fields declared as organic but that would show on satellite imagery an appearance closer to conventional fields. For this purpose, the ability of multispectral satellite images to discriminate between organic and conventional maize fields was assessed through the use of a set of four satellite images of different spatial and spectral resolutions acquired at different crop growth stages over a large number of maize fields (32) that are part of an operational farm in Germany. In support of this main objective, a set of in situ measurements (leaf hyperspectral reflectance, chlorophyll, and nitrogen content and dry matter percentage, crop canopy cover, height, wet biomass and dry matter percentage, soil chemical composition) was conducted to characterize the nature of the biochemical and biophysical differences between organic and conventional maize fields. The results of this research showed that highly significant biochemical and biophysical differences between a large number of organic and conventional maize fields may exist at identified crop growth stages and that these differences may be sufficiently pronounced to enable the complete discrimination between crop management modes using satellite images issued from quite common multispectral satellite sensors through the use of spectral or spatial heterogeneity indices. These results are very encouraging and suggest, for the first time, that satellite images could effectively support the organic maize certification process

Wheat yield forecasting at regional scale based on the senescence phase of the green area

Depuis de nombreuses années, les différents enjeux économiques, géostratégiques et humanitaires liés aux prévisions de la production agricole à l’échelle nationale ou régionale, ont fait du suivi agricole une priorité dans divers programmes de recherches. Pour ce faire, de nombreux modèles agrométéorologiques existent, avec différents niveaux de complexité et d’empirisme. De nos jours, les avancées scientifiques et technologiques devraient permettre une meilleure exploitation des informations sur les variables biophysiques extraites de la télédétection en condition opérationnelle de prévisions de rendements et productions agricoles. Pourtant, l’intégration des facteurs biotiques et abiotiques en condition de production, plus particulièrement en phase de sénescence, reste très peu exploitée. Cette recherche porte sur la mise en place d’une approche d’estimation du rendement du blé d’hiver (Triticum aestivum L.) basée sur sa phase de sénescence et utile dans un contexte opérationnel de prévisions des rendements. Dans un premier temps, la phase de sénescence du blé est analysée et deux fonctions d’ajustement - les fonctions Gompertz modifiée et logistique – sont choisies pour sa modélisation. L’élaboration de modèles basés sur les paramètres caractéristiques de cette phase de sénescence (i.e. valeur maximale du GAI – green area index-, durée de la surface verte et taux de décroissance) est ensuite effectuée pour l’estimation du rendement du blé. Cette approche, la Senescence-based approach for yield estimates (SenAFY), est testée tout d’abord à l’échelle parcellaire par utilisation des valeurs de GAI dérivées de photographies hémisphériques. L’appréciation de la faisabilité et des performances de la SenAFY à cette échelle spatiale ont permis, dans un second temps, sa transposition à une échelle régionale en utilisant des profils temporaux de GAI extraites des images MODIS. Les modèles élaborés à travers cette approche permettent d’estimer de manière satisfaisante les rendements de blé aux deux échelles spatiales considérées. Et plus particulièrement, à l’échelle régionale, l’application de la SenAFY dans l’optique d’une prévision des rendements a fourni des résultats intéressants, montrant ainsi l’exploitabilité de la SenAFY dans d’autres contextes. Cette recherche constitue ainsi une piste ouverte dans la valorisation de récentes techniques de traitement d’images satellitaires de moyenne résolution et l’exploitation d’informations issues de la phase de sénescence du blé en conditions opérationnelles.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

Wheat yield and PAI decreasing shape curve

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

Importance of a Well-distributed Frequency of Measurements in the Senescence Monitoring of Winter Wheat and Yield Estimates

Theoretical frequencies of green area index (GAI) measurements were assessed in order to bring out the optimum frequencies for the monitoring of the senescence of winter wheat as well as the relationships between metrics which could be derived and the final grain yield. Several profiles of GAI decreasing curves were elaborated based on field measurements. Two functions, usually employed in green leaf area decreasing curves fitting (i.e., modified Gompertz and logistic functions) were then used to characterize the senescence phase and to calculate their metrics. These analyses showed that the two curve fitting functions satisfactorily described the senescence phase on frequencies of four to six GAI measurements, well distributed throughout a period of 30-35 days. The regression-based modeling showed that those involving metrics from logistic function (i.e., maximum value of GAI, green area duration and senescent rate) were more suitable than that of the modified Gompertz function for wheat yield estimates. Such results could be useful for studies at larger scales (involving remote sensing airplane or satellite data) and focused on the senescence in terms of optimum number of measurements and frequencies for developing models for yield estimates

Effects of regional climate change on brown rust disease in winter wheat

Projected climate changes will affect wheat crop production both in the main processes of plant growth and development but also in the occurrences and severities of plant diseases. We assessed the potential infection periods of wheat leaf rust (WLR) at two climatologically differentsites in Luxembourg. A threshold-based model, taking hourlyvalues of air temperatures, relative humidity and precipitation during night-time into account, was used for calculating favourable WLR infection days during three periods throughout the cropping season. Field experiments were conducted during the 2003–2013 period at the selected sites. Projected climate data, from a multi model ensemble of regional climate models (spatial resolution 25 km) as well as an additional projection with a higher spatial resolution of 1.3 km, were used for investigating the potential WLR infection periods for two future time spans. Results showed that the infections of WLR were satisfactorily simulated during the development of wheat at both sites for the 2003–2013 period. The probabilities of WLR detection were close to 1 and the critical success index ranged from 0.80 to 0.94 (perfect score = 1 for both). Moreover, the highest proportions of favourable days of WLR infection were simulated during spring and summer at both sites. Regional climate projections showed an increase in temperatures by 1.6 K for 2041–2050 and by 3.7 K for 2091–2100 compared to the reference period1991–2000. Positivetrends infavourableWLR infection conditions occur at both sites more conducive than in the reference period due to projected climatic conditions

Wheat Yield Estimates at NUTS-3 level using MODIS data: an approach based on the decreasing curves of green area index temporal profiles

peer reviewedWheat 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

Global Monitoring for Food Security (GMFS). Service operations report SENEGAL 2007.

GMFS (Global Monitoring for Food Security) is a GMES Service Element (GSE) project, part of the ESA contribution to the EU / ESA GMES (Global Monitoring for Environment and Security) Programme. GMFS aims to establish an earth observation based operational service for crop monitoring to support food security decision makers and EU policy objectives. The aim of this document, as expressed in the statement of work is to: - Evaluate the operations planned versus the services agreed - Review the external infrastructure - Review issues and experiences with relation to adopted standards The aim of the document is to give a complete report of all activities executed by the service partners to deliver agricultural monitoring services to the Centre de Suivi Ecologique (CSE) in Senegal for the 2007 growing season

Images 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 ?

peer reviewedLe blé d’hiver constitue une culture largement cultivée au Grand-duché de Luxembourg. Une estimation précoce du rendement à l’échelle des régions à travers un système opérationnel serait un atout vu son importance économique. Le développement actuel de modèles pour la prévision des rendements dans les systèmes opérationnels classiques ne traduit cependant pas véritablement la prise en compte de la diminution de la surface verte utile et de sa relation avec des processus biotiques et abiotiques incriminés en situation de production. L’article présente une approche d’estimation du rendement final du blé d’hiver à partir de la cinétique de dégradation de sa surface verte utile suite à l’analyse de photographies hémisphériques. De plus cette approche peut être facilement applicable à l’échelle des régions agricoles. Les modèles issus de cette approche montrent que le rendement final en grains peut être estimé de manière intéressante : avec des coefficients de détermination (R²) allant de 0.73 à 0.86 et des erreurs quadratiques moyennes (RMSE) variant entre 0.43 et 0.56 t/ha. De par sa simplicité et le nombre réduit de variables explicatives du rendement final considérées, cette étude constitue un premier pas dans l’estimation du rendement du blé à échelle globale. Des études sont en cours pour l’application d’une telle approche avec des profils de surface verte utile issus des images satellitaires.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

Profitability of using warning system for foliar disease of wheat in the Grand-Duchy of Luxembourg

Although small grain cereals (i.e. winter wheat) are routinely protected with two or three foliar treatments in the Grand-Duchy of Luxembourg (GDL), environmental concerns and changes in the cost-benefit ratio are likely to increase the demand for more accurate identification of spraying needs. A Vol. 103 (Supplement 2), No. 6, 2013 S2.39 warning system assessing in real time the risk of progression of fungal diseases on winter wheat (Triticum aestivum L.) was tested in the GDL over the 2009-2012 period in four-replicated field experiments located in three representative villages of the different agro-climatological zones. The fungicide treatments recommended by the warning system during this period have ensured economic profitability equivalent to or even better than double and triple treatments. In 2010 and 2011, weather conditions impeded fungal infections of wheat and no warning was issued, reducing fungicide use. The study also highlighted that multiple fungicide applications were not better than a single application. In 2009 and 2012, although the weather conditions were very favourable for fungal wheat diseases, the single recommended fungicide application resulted in an additional yield of 30% compared to untreated plots. This study shows the importance of the positioning of fungicide treatment in such a warning system and in strategies aiming at reducing the spread fungicide molecules in the environment.Sentinell