References of "Dumont, Benjamin"
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See detailPredicting Grain Protein Content of Winter Wheat
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in ESANN 2014 Proceedings (2014, April 24)

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See detailIncertitudes liées à la modélisation agro-environnementale en vue de développer des outils d'aide à la décision
Dumont, Benjamin ULg

Doctoral thesis (2014)

The European Nitrates Directive 91/6/76/EEC aims to ensure water quality by preventing pollution of surface and groundwater induced by nitrates originating from agricultural sources and by promoting ... [more ▼]

The European Nitrates Directive 91/6/76/EEC aims to ensure water quality by preventing pollution of surface and groundwater induced by nitrates originating from agricultural sources and by promoting agronomical good practices. While the implementation of this Directive seems effective, it appears however that the use of nitrogen has still increased by 6% over the last four years in 27 European countries. Furthermore, agricultural sources would be still at the origin of 50% of the total amount of nitrogen discharged into surface waters (http://ec.europa.eu/environment/water/water-nitrates/index_en.html). In Wallonia (Belgium), the Nitrates Directive has been transposed under the Sustainable Nitrogen Management in Agriculture Program (PGDA). Launched in 2002, it involves different sets of actions, like rules definitions concerning fertilizers application, specific and appropriate crop management in vulnerable areas, the control of potentially leachable nitrogen (APL) levels in soils, etc. This is the global context in which lies the present thesis. The main aim is to optimise the nitrogen fertiliser practices to ensure that the needs of a winter wheat culture (Triticum aestivum L.) could be met while reducing the environmental pressure. It relies on the use of crop models, which describe the growth and the development of a culture interacting with its environment, namely the soil and the atmosphere. The major difficulty while working with crop models and model-based decision support tools lies in the fact that different sources of uncertainties have an impact on the modelled phenomena. Indeed, crop models are constituted by a consequent number of differential non-linear equations, involving a lot of parameters which need to be determined as accurately as possible in order to match as close as possible observed sequences of measurements. The first source of uncertainty is thus constituted by the parameters definition. Once the model has been correctly and robustly calibrated it can be used to perform predictions. However, in an agronomical context, the time-delay between sowing and harvest is consequent. As the end-season yield is often the expected output, the uncertainty linked to the non-knowledge of the future implies for the modeller to refer to different hypothesis concerning upcoming climatic scenarios. Finally, moving from models to decision systems dealing with N management involves a last source of uncertainty. Indeed the main problem is that the impact of a given practice is delayed in time from its realisation. In addition to the uncertainty linked to climatic projections themselves, it is highly important to consider the interactions between the practices and the climate. Furthermore, in a decision-making process, it could be highly relevant to know the uncertainty's estimation that could be tolerated on the decision.. Therefore, the present thesis aims to study these different sources of uncertainty in order to design an efficient decision support system. It is divided into five parts. In the first part, a Bayesian sampling algorithm, known as DREAM (DiffeRential Evolution Adaptative Metropolis) will be presented. It was successfully coupled with the STICS soil-crop model used in this study. The a posteriori probability density function of many parameters was sampled in order to improve the simulations of the growth of a winter wheat culture (Triticum aestivum L.). The DREAM algorithm offers different advantages in comparison to usual methods. Among these, it is possible to study i) the most probable a posteriori parameters distributions, ii) the parameters correlations, and iii) the uncertainties impacted on model outputs. Furthermore, a new version of the likelihood function was proposed, making an explicit use of the coefficient of variation. Results showed that it allowed the noise existing on measurements to be considered, but also the heteroscedasticity phenomenon usually encountered in biological growth processes. In parallel, assimilation data is another way to improve models simulations. These techniques allow considering measurements performed in real-time (e.g. remote measures of LAI or soil water content) in order to correct and adjust the possible drift of model simulations. In particular, a recently developed algorithm, known as variational filter, was evaluated. Its superiority, both in term of state variables simulations improvement and parameter resampling, was demonstrated. The third part of the research focuses on the real-time end season yield prediction. It involves building climate matrix ensembles, combining different time ranges of projected mean data and real measured weather originating from the historical records. As the crop growing season progresses, the effects of real monitored data plays a greater role and the prediction reliability increases. Our results demonstrated that a reliable predictive delay of 3-4 weeks before harvest could be obtained. Finally, using real-time data acquired with a micrometeorological station enabled to (i) predict, daily, potential yield at the local level, (ii) detect stress occurrence, and (iii) quantify yield losses (or gains). Being based on projected seasonal norms, this methodology is in opposition to another technique that consists to offer a panel of solution for what concerns the future. Such probabilistic technique relies on the use of stochastic weather generator (LARS-WG in this case). However, in the fourth part of this thesis, on the basis of the convergence in law theorem, it was demonstrated that in 90% of the climatic situations, both approaches were equivalent, exhibiting RRMSE and normalised deviation criteria inferior to 10%. Furthermore the two approaches offered similar predictive delay-time. The main difference between techniques lies in the finality. The first allows to quickly simulate the remaining yield potential, while the second aims to quantify the uncertainty level associated to the predictions. In the fifth and last part of this thesis, in order to quantify the uncertainty level associated to different modalities of N applications, the STICS model answers were studied under stochastic climatic realisations. It was demonstrated that, if no N was applied, under our temperate climatic conditions, the yield distribution could be considered as normal. However, with increasing N practices, the asymmetry level was found itself increasing. As soon as N was applied, not only were the yields higher, but also was the probability to achieve yields that were at least superior to the mean of the distribution. This undoubtedly reduced the risk for the farmer to achieve low yields levels. To summary all the researches conducted in this thesis, a N strategic decision support system was developed. In a general way, for what concerns the Hesbaye Region, the superiority of three fractions N protocols was demonstrated. In addition, the three rates fertilisation management based on the systematic applications of 60 kgN.ha-1 at tillering and stem extension stages and offering the possibility to adapt the flag-leaf fraction in real-time appeared as an optimal strategy. Within this tool, the uncertainty associated to climatic variability could be finely characterised, and the risk encountered by the farmer was quantified for different investigated practices. But far more important, it was demonstrated that N management could be optimised in real-time. In a general way, the research should be pursued by studying more fundamentally and systematically a wide range of different agro-environmental situations. In particular, it would be interesting to study of the Genotype × Environment × Cultural practices interactions to ensure food security in a climatic changing world. [less ▲]

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See detail4. La fumure azotée
Meza Morales, Walter ULg; Monfort, Bruno; Dumont, Benjamin ULg et al

in Bodson, Bernard; Destain, Jean-Pierre (Eds.) Livre Blanc céréales (2014, February 26)

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See detailBayesian methods for predicting and modelling winter wheat biomass
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

Poster (2014, February)

The objectives of this paper are threefold. The first objective is to propose to use an Improved Particle Filtering (IPF) based on minimizing Kullback-Leibler divergence for crop models' predictions. The ... [more ▼]

The objectives of this paper are threefold. The first objective is to propose to use an Improved Particle Filtering (IPF) based on minimizing Kullback-Leibler divergence for crop models' predictions. The performances of the proposed technique are compared with those of the conventional Particle Filtering (PF) for improving nonlinear crop model predictions. The main novelty of this task is to develop a Bayesian algorithm for nonlinear and non-Gaussian state and parameter estimation with better proposal distribution. The second objective is to investigate the effects of practical challenges on the performances of state estimation algorithms PF and IPF. Such practical challenges include (i) the effect of measurement noise on the estimation performances and (ii) the number of states and parameters to be estimated. The third objective is to use the state estimation techniques PF and IPF for updating prediction of nonlinear crop model in order to predict winter wheat biomass. PF and IPF are applied at a dynamic crop model with the aim to predict a state variable, namely the winter wheat biomass, and to estimate several model parameters. Furthermore, the effect of measurement noise (e.g., different signal-to-noise ratios) on the performances of PF and IPF is investigated. The results of the comparative studies show that the IPF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. [less ▲]

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See detailParameter identification of the STICS crop model, using an accelerated formal MCMC approach
Dumont, Benjamin ULg; Leemans, Vincent ULg; Mansouri, Majdi ULg et al

in Environmental Modelling & Software (2014), 52

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The ... [more ▼]

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling [less ▲]

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See detailBayesian methods for predicting LAI and soil water content
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Leemans, Vincent ULg et al

in Precision Agriculture (2014), 15(2), 184-201

LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was ... [more ▼]

LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: Extended Kalman Filtering (EKF), Particle Filtering (PF), and Variational Filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error (RMSE) of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation. [less ▲]

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See detailAssessing the potential of an algorithm based on mean climatic data to predict wheat yield
Dumont, Benjamin ULg; Leemans, Vincent ULg; Ferrandis, Salvador et al

in Precision Agriculture (2014)

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in ... [more ▼]

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. [less ▲]

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See detailA comparison of within-season yield prediction methodologies
Dumont, Benjamin ULg; Basso, Bruno; Bodson, Bernard ULg et al

Conference (2013, November)

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See detailPrediction of non-linear time-variant dynamic crop model using bayesian methods
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in John Stafford (Ed.) Precision agriculture '13 (2013, July)

This work addresses the problem of predicting a non-linear time-variant leaf area index and soil moisture model (LSM) using state estimation. These techniques include the extended Kalman filter (EKF ... [more ▼]

This work addresses the problem of predicting a non-linear time-variant leaf area index and soil moisture model (LSM) using state estimation. These techniques include the extended Kalman filter (EKF), particle filter (PF) and the more recently developed technique, variational filter (VF). In the comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the layer 1, HUR1 and the volumetric water content of the layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error with respect to the noise-free data. The results show that VF provides a significant improvement over EKF and PF. [less ▲]

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See detailYield variability linked to climate uncertainty and nitrogen fertilisation
Dumont, Benjamin ULg; Basso, Bruno; Leemans, Vincent ULg et al

in Stafford, John V. (Ed.) Precision agriculture '13 (2013, July)

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilisation). In combination with a weather ... [more ▼]

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilisation). In combination with a weather generator, we built up a general methodology that allows studying the yield variability linked to climate uncertainty, in order to assess the best N practice. Our study highlighted that, applying the Belgian farmer current N practice (60 60 60 kgN.ha-1), the yield distribution was found to be very asymmetric with a skewness of -1.02 and a difference of 5% between the mean (10.5 t.ha-1) and the median (11.05 t.ha-1) of the distribution. Which implied that, under such practice, the probability for farmers to achieve decent yields, in comparison of the mean of the distribution, was the highest. [less ▲]

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See detailA Site-Specific Grain Yield Response Surface : Computing the Identity Card of a Crop Under Different Nitrogen Management Scenarios
Dumont, Benjamin ULg; Basso, Bruno; Leemans, Vincent ULg et al

in The acts of the EFITA2013 congress (2013, June)

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilization). In combination with a weather ... [more ▼]

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilization). In combination with a weather generator, we propose a general methodology that allows studying the yield variability linked to climate uncertainty, in order to assess the best practices in applying fertilizers. Our study highlights that, using the usual practice of Belgian farmers, namely applying three doses of 60kgN/ha, the yield’s distribution presents the highest degree of asymmetry. This implies the highest probability to achieve yields superior to the mean. The computed return time of expected yield shows that 9 years out of 10, a grain yield of 7.26 tons.ha-1 could at least be achieved. [less ▲]

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See detail4. La fumure azotée
Meza Morales, Walter; Monfort, Bruno; Dumont, Benjamin ULg et al

in Bodson, Bernard; Destain, Jean-Pierre (Eds.) Livre Blanc - Céréales (2013, February 27)

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See detailModeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Masegosa, Antoçnio; Villacorta, Pablo; Cruz-Corona, Carlos (Eds.) et al Exploring Innovative and Successful Applications of Soft Computing (2013)

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to ... [more ▼]

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model (input data), to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this work, we address the problem of modeling and prediction of leaf area index and soil moisture (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and the more recently developed technique variational Bayesian filter (VF). The objective of this work is to extend the state and parameter estimation techniques (i.e., EKF, UKF, PF and VF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The randomness of the expectation and the covariance of the state/parameter vector are assumed here to further capture the uncertainty of the state distribution. One practical choice of these distributions can be a Gaussian distribution for the expectation and a multi-dimensional Wishart distribution for the covariance matrix. The assumption of random mean and random covariance of the state leads to a probability distribution covering a wide range of tail behaviors, which allows discrete jumps in the state variables. [less ▲]

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See detailModeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Masegosa, Antoçnio; Villacorta, Pablo; Cruz-Corona, Carlos (Eds.) et al Exploring Innovative and Successful Applications of Soft Computing (2013)

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to ... [more ▼]

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model (input data), to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this work, we address the problem of modeling and prediction of leaf area index and soil moisture (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and the more recently developed technique variational Bayesian filter (VF). The objective of this work is to extend the state and parameter estimation techniques (i.e., EKF, UKF, PF and VF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The randomness of the expectation and the covariance of the state/parameter vector are assumed here to further capture the uncertainty of the state distribution. One practical choice of these distributions can be a Gaussian distribution for the expectation and a multi-dimensional Wishart distribution for the covariance matrix. The assumption of random mean and random covariance of the state leads to a probability distribution covering a wide range of tail behaviors, which allows discrete jumps in the state variables. [less ▲]

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See detailModeling and Prediction of Nonlinear Environmental System Using Bayesian Methods
Mansouri, Majdi; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Computers & Electronics in Agriculture (2013), 92

An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination ... [more ▼]

An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. This work addresses the problem of monitoring and modeling a leaf area index and soil moisture model (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), the particle filter (PF), and the more recently developed technique variational filter (VF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the leaf-area index LAI , the volumetric water content of the soil layer 1, HUR1 and the volumetric water content of the soil layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of number of estimated model parameters on the accuracy and convergence of these techniques are also assessed. The results of both comparative studies show that the PF provides a higher accuracy than the EKF, which is due to the limited ability of the EKF to handle highly nonlinear processes. The results also show that the VF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VF can still provide both convergence as well as accuracy related advantages over other estimation methods. [less ▲]

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See detailVers un système de prédiction du rendement en temps réel
Dumont, Benjamin ULg; Ferrandis Vallterra, Salvador ULg; Leemans, Vincent ULg et al

Poster (2012, October 16)

L'objectif de cette recherche est le développement d'un outil capable de prédire les rendements d'une culture de blé en temps réel, au fur et à mesure que la saison avance. Pour atteindre cet objectif ... [more ▼]

L'objectif de cette recherche est le développement d'un outil capable de prédire les rendements d'une culture de blé en temps réel, au fur et à mesure que la saison avance. Pour atteindre cet objectif, nous avons développé une méthodologie qui repose sur l'adjonction des éléments suivants : (i) le modèle de culture STICS (INRA, France), (ii) un réseau de capteurs sans fil pour le monitoring des variables agro-environnementales (éKo system, The Crossbow technologies, USA) et (iii) une base de données météorologiques. [less ▲]

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See detailAssessing the potential of an algorithm based on mean climatic data to predict wheat yield.
Dumont, Benjamin ULg; Leemans, Vincent ULg; Ferrandis Vallterra, Salvador ULg et al

in Proceedings of the 11th International Conference on Precision Agriculture (2012, July)

Real-time non-invasive determination of crop biomass and yield prediction are maybe among the major challenges in agriculture. But unknown future weather remains the key point of accurate yield forecast ... [more ▼]

Real-time non-invasive determination of crop biomass and yield prediction are maybe among the major challenges in agriculture. But unknown future weather remains the key point of accurate yield forecast. This paper presents the results of a preliminary study that aims to supply the unknown future by daily mean climatic data. The results show that under the Belgian weather, this approach is relevant. Furthermore, the developed methodology appears to be a powerful diagnosis tool of the remaining yield potential under ongoing weather. [less ▲]

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