References of "Dumont, Benjamin"
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See detailSystematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions
Dumont, Benjamin ULg; Basso, Bruno; Leemans, Vincent ULg et al

in Precision Agriculture (2015), 16(4), 361-384

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is ... [more ▼]

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60 60 60 kgN ha-1), yield distribution was very highly significantly non normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60%) of achieving yields that were superior to the mean (10.5 t ha-1) of the distribution. [less ▲]

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See detailThe crucial role of soil when modelling the impact of climate change on crop production
Basso, Bruno; Dumont, Benjamin ULg; Shcherbak, Iurii et al

in Shirmohammadi, Adel; Bosch, David; Muñoz-Carpena, Rafa (Eds.) Proceedings of the 1st ASABE Climate Change Symposium - Adaptation and Mitigation (2015, May 03)

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See detailA comparison of within-season yield prediction algorithms based on crop model behaviour analysis
Dumont, Benjamin ULg; Basso, Bruno; Leemans, Vincent ULg et al

in Agricultural and Forest Meteorology (2015), 204

The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an ... [more ▼]

The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield (Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. [less ▲]

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See detailAdapting Nitrogen management to the increasing climatic uncertainty
Dumont, Benjamin ULg; Basso, Bruno; Bodson, Bernard ULg et al

in Shirmohammadi, Adel; Bosch, David; Muñoz-Carpena, Rafa (Eds.) Proceedings of the 1st ASABE Climate Change Symposium - Adaptation and Mitigation (2015, May)

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See detailClimatic risk assessment to improve nitrogen fertilisation recommendations : A strategic crop model-based approach
Dumont, Benjamin ULg; Basso, Bruno; Bodson, Bernard ULg et al

in European Journal of Agronomy (2015), 65(10-17),

Within the context of nitrogen (N) management, since 1950, with the rapid intensification of agriculture, farmers have often applied much larger fertiliser quantities than what was required to reach the ... [more ▼]

Within the context of nitrogen (N) management, since 1950, with the rapid intensification of agriculture, farmers have often applied much larger fertiliser quantities than what was required to reach the yield potential. However, to prevent pollution of surface and groundwater induced by nitrates, The European Community launched The European Nitrates Directive 91/6/76/EEC. In 2002, in Wallonia (Belgium), the Nitrates Directive has been transposed under the Sustainable Nitrogen Management in Agriculture Program (PGDA), with the aim of maintaining productivity and revenue for the country’s farmers, while reducing the environmental impact of excessive N application. A feasible approach for addressing climatic uncertainty lies in the use of crop models such as the one commonly known as STICS (simulateur multidisciplinaire pour les cultures standard). These models allow the impact on crops of the interaction between cropping systems and climatic records to be assessed. Comprehensive historical climatic records are rare, however, and therefore the yield distribution values obtained using such an approach can be discontinuous. In order to obtain better and more detailed yield distribution information, the use of a high number of stochastically generated climate time series was proposed, relying on the LARS-Weather Generator. The study focused on the interactions between varying N practices and climatic conditions. Historically and currently, Belgian farmers apply 180 kg N ha−1, split into three equal fractions applied at the tillering, stem elongation and flag-leaf stages. This study analysed the effectiveness of this treatment in detail, comparing it to similar practices where only the N rates applied at the flag-leaf stage were modified. Three types of farmer decision-making were analysed. The first related to the choice of N strategy for maximising yield, the second to obtaining the highest net revenue, and the third to reduce the environmental impact of potential N leaching, which carries the likelihood of taxation if inappropriate N rates are applied. The results showed reduced discontinuity in the yield distribution values thus obtained. In general, the modulation of N levels to accord with current farmer practices showed considerable asymmetry. In other words, these practices maximised the probability of achieving yields that were at least superior to the mean of the distribution values, thus reducing risk for the farmers. The practice based on applying the highest amounts (60–60–100 kg N ha−1) produced the best yield distribution results. When simple economical criteria were computed, the 60–60–80 kg N ha−1 protocol was found to be optimal for 80–90% of the time. There were no statistical differences, however, between this practice and Belgian farmers’ current practice. When the taxation linked to a high level of potentially leachable N remaining in the soil after harvest was considered, this methodology clearly showed that, in 3 years out of 4, 30 kg N ha−1 could systematically be saved in comparison with the usual practice. [less ▲]

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See detailOptimisation of the Nitrogen fertilisation in the context of climate change
Dumont, Benjamin ULg; Basso, Bruno; Bodson, Bernard ULg et al

in Soussana, Jean-Francois (Ed.) Proceedings of the Climate Smart Agriculture 2015 conference (2015, March)

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See detailThe AgMIP Coordinated Climate-Crop Modeling Project (C3MP) : Methods and Protocols
McDermid, S.; Ruane, A.; Hudson, N. et al

in Rosenzweig, Cynthia; Hillel, Daniel (Eds.) Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) - Integrated Crop and Economic Assessments (2015)

Climate effects on agriculture are of increasing concern in both the scientific and policy communities because of the growing population and the greater uncertainty in the weather during growing seasons ... [more ▼]

Climate effects on agriculture are of increasing concern in both the scientific and policy communities because of the growing population and the greater uncertainty in the weather during growing seasons. Changes in production are directly linked to variations in temperature and precipitation during the growing season and often to the offseason changes in weather because of soil water storage to replenish the soil profile. This is not an isolated problem but one of worldwide interest because each country has concerns about their food security. The Agricultural Model Intercomparison and Improvement Project (AgMIP) was developed to evaluate agricultural models and intercompare their ability to predict climate impacts. In sub-Saharan Africa and South Asia, South America and East Asia, AgMIP regional research teams (RRTs) are conducting integrated assessments to improve understanding of agricultural impacts of climate change (including biophysical and economic impacts) at national and regional scales. Other AgMIP initiatives include global gridded modeling, data and information technology (IT) tool development, simulation of crop pests and diseases, site-based crop-climate sensitivity studies, and aggregation and scaling. [less ▲]

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See detailWheat yield sensitivity to climate change across a European transect for a large ensemble of crop models
Pirttioja, N.; Carter, Timothy; Fronzek, S. et al

in Soussana, Jean-Francois (Ed.) Proceedings of the Climate Smart Agriculture 2015 conference (2015, March)

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See detailThe AgMIP Soil and Crop Rotation Initiative
Basso, Bruno; Dumont, Benjamin ULg; Shcherbak, Iurii et al

Conference (2015, February 25)

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

in Watillon, Bernard; Bodson, Bernard (Eds.) Livre Blanc Céréales (2015, February 25)

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See detailAnalysis of spray retention on a 3D black-grass plant model as a function of spray nozzle and formulation using a process-driven approach
Massinon, Mathieu ULg; Ouled Taleb Salah, Sofiene ULg; Dumont, Benjamin ULg et al

Poster (2014, August 13)

The efficiency of spray application of foliar plant protection products can be variable because of the different amount of spray solution intercepted and retained by leaves. On one hand, the spray ... [more ▼]

The efficiency of spray application of foliar plant protection products can be variable because of the different amount of spray solution intercepted and retained by leaves. On one hand, the spray interception by plants is affected by nozzle kind, size and operating pressure as well as by the plant architecture. On the other hand, the spray retention is affected by application parameters resulting from droplet size and velocity as well as spray mixture physicochemical properties. In this paper, spray retention is tackled with a physical approach at the droplet scale. The methodology deals with high-speed imaging to characterize droplet impacts; adhesion, rebound or shatter on small excised leaf areas and the spray granulometry. The 3D reconstruction of a black-grass plant involves a structured light technique. The overall spray retention was determined by using an interception algorithm combined with a process-driven retention approach as a function of the spray nozzle and formulation used. The interception model allowed determining the spray retention by a single plant and discriminating application parameters by explaining the variability resulting from various droplet size distributions intercepted by single plant. Such a model can be used to increase the understanding of interactions between spray techniques and plant architectures. [less ▲]

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See detailThe performance of mobile devices' inertial measurement unit for the detection of cattle's behaviors on pasture
Andriamandroso, Andriamasinoro ULg; Dumont, Benjamin ULg; Lebeau, Frédéric ULg et al

Conference (2014, July 21)

Over the past decade, the Precision Livestock Farming (PLF) concept has taken a considerable place in the development of accurate methods for a better management of farm animals, as Precision Agriculture ... [more ▼]

Over the past decade, the Precision Livestock Farming (PLF) concept has taken a considerable place in the development of accurate methods for a better management of farm animals, as Precision Agriculture has done for crop production. Mass consumption mobile devices have nowadays the possibility to record accurately user movements with their Inertial Measurement Unit (IMU). We used iPhone 4S to detect accurately cattle behaviors such as grazing and ruminating with the aim of performing a precision grazing management on the near future. Results showed accuracies ranging between 84% and 100% when detecting these two major behaviors by analyzing recorded raw signals in the time-domain. Ongoing research tries to link these behaviors to different pasture characteristics and performs a refined signal processing analysis for a better monitoring of some possible behavioral changes. [less ▲]

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See detailEvaluation of process-driven spray retention model on ear-ly growth stage barley
Massinon, Mathieu ULg; Ouled Taleb Salah, Sofiene ULg; De Cock, Nicolas ULg et al

Conference (2014, July)

The efficiency of spray application of foliar plant protection products with hydraulic nozzles on vertically oriented and hydrophobic plants at early growth stages can be very low. The spray retention by ... [more ▼]

The efficiency of spray application of foliar plant protection products with hydraulic nozzles on vertically oriented and hydrophobic plants at early growth stages can be very low. The spray retention by crop leaves is affected by application parameters resulting from nozzle kind, size and operating pressure as well as spray mixture physicochemical properties. When optimizing the spray application, such targets are often used to perform retention trials for comparative purpose, i.e. indoor grown monocotyledonous at two leaves stage. A typical arrangement consists in spraying few plants sufficiently spaced underneath the nozzle to avoid interference due to secondary droplets from impacts on other plants. However, retention trials turn out to ineffective for significantly discriminating between application methods and mixtures due to the high variability between trials resulting from the different droplets retained by each plant. An alternative to retention trials is to tackle spray retention with a physical approach at the droplet scale. Such tests are often performed using high speed imaging with high magnification optics to characterize droplet impacts; adhesion, rebound or shatter on small excised leaf areas and neglect, however, the overall plant architecture. The aim of this paper is to evaluate a droplet interception model connecting actual spray retention with process-driven retention models. In this study, barley plants (BBCH11) were sprayed with 2 formulations using the same nozzle. The actual spray retention was assessed by dosing a fluorescent tracer added to the sprayed mixture. The plants were placed linearly below the center of a single moving nozzle during sprayings. Each plant was reconstructed in 3D afterwards using a structured light 3D scanner and used as input for the model. A virtual nozzle was built on the base of droplet size distributions measured with high speed shadow imaging by performing an adjustment of the distribution by the method of moments. A ran-dom droplet distribution was allocated for each spraying of a barley plant. Droplet velocities were given to droplets on the basis of the droplet velocity – diameter correlation by resolving the droplet transport equations for different droplet sizes. Initial droplet positions were ran-domly given. The interception model is based on a mathematical formalism for the intercep-tion between triangles of the 3D plant and droplet directions. If the droplet impacts a leaf, the amount actually retained by the leaf was computed on the basis of the droplet impact energy and impact behavior from experiments with high speed shadow imaging. In conclusion, the interception model allowed determining the spray retention by plants and discriminating ap-plication parameters by explaining the variability resulting from various droplet size distribu-tions intercepted by single plant. [less ▲]

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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 detailExamining wheat yield sensitivity to temperature and precipitation changes for a large ensemble of crop models using impact response surfaces"
Pirttioja, N.; Fronzek, S.; Bindi, Marco et al

in Rotter, Reimund; Ewert, Frank (Eds.) Modelling climate change impacts on crop production for food security - Abstract book (2014, February)

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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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