References of "Dumont, Benjamin"
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See detailApplication of NIR hyperspectral imaging combined to chemometrics to assess the impact of tillage on the root system development of a winter wheat crop
Fraipont, Guillaume ULg; Eylenbosch, Damien ULg; Baeten, Vincent et al

Poster (2016, July)

This poster presents de results of a study of the influence of tillage on the root development of a winter wheat crop. The originality of this research lies in the application of an innovative root ... [more ▼]

This poster presents de results of a study of the influence of tillage on the root development of a winter wheat crop. The originality of this research lies in the application of an innovative root quantification method based on the near infrared hyperspectral imaging. [less ▲]

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See detailQuantification of nitrogen stress on winter wheat by multispectral vision based on reflectance measurements and textural descriptors
Marlier, Guillaume ULg; Gritten, Fanny; Fraipont, Guillaume ULg et al

Conference (2016, June 27)

Hand-held sensors (SPAD meter, N-Tester, …) are used for detecting the leaves nitrogen concentration (Nc) on the basis of an optical detection of the chlorophyll concentration. These devices are active ... [more ▼]

Hand-held sensors (SPAD meter, N-Tester, …) are used for detecting the leaves nitrogen concentration (Nc) on the basis of an optical detection of the chlorophyll concentration. These devices are active sensors: an internal radiation source emits light and transmission through a leaf is measured in the red (650 nm) and in the near-infrared (920 nm) spectral regions. These devices present several drawbacks. The nitrogen concentration is gained by an indirect way through the chlorophyll concentration and the leaves have to be fixed in a defined position for the measurements. These drawbacks could be overcome by an imaging device that measures the canopy reflectance. In this context, the objective of the paper is to analyse the potential of different image parameters for estimating nitrogen concentration. The tests were carried out on parcels submitted to total nitrogen inputs of 180 kg N.ha-1 but with different fertilization modalities. Reference Nc measurements were obtained by the Kjeldahl method and a Hydro N-Tester (Yara). The developed imaging system comprised a CMos camera and a set of 22 interference filters ranging from 450 to 950 nm mounted on a wheel steered by a stepper motor. The image acquisition and the motor rotation were controlled by a program written in C++. The crop was imaged vertically at one meter height. The raw images presented 1280x1024 pixels covering an area of approximately 0.5x0.4 m and were recorded with a 12-bit luminance resolution. To deal with the natural irradiance variability of the scene, a white reference was used and the integration time was automatically adjusted for each image. The image treatment included the segmentation of Photosynthetically Active Leaves (PAL) by using Bayes theorem and the computation of the mean PAL reflectance after correction of background and illumination fluctuations. Nc was estimated on the basis of the 22 filters by Partial Least Square (PLS) method and by four filters selected by Best Subset Selection (BSS). In comparison with the Kjeldahl method, the estimation of Nc by the Hydro N-Tester, the PLS and the BSS (filters 600-80, 950-100, 650-40 and 450-80 nm) gave determination coefficient and standard error respectively equal to of 0.53, 0.29 %; 0.67, 0.21%; 0.56 and 0.25%. This indicated that the full multi-spectral approach gave significantly better Nc estimation than a portable device and suggested that a camera equipped with four filters would give similar results. In addition to these promising results, the Nc measurement were correlated to leaf area measurements and textural descriptors. For this purpose, image analysis based on Grey Level Co-occurrence Matrix (GLCM), Fourier transform and spatial autocorrelation were performed to characterize the nitrogen state of the crop. [less ▲]

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See detailCharacterization of the impact of tillage on the root development and distribution of crop residues of a winter wheat crop by the use of NIR hyperspectral imaging
Fraipont, Guillaume ULg; Eylenbosch, Damien ULg; Fernández Pierna, Juan Antonio et al

Poster (2016, May 20)

The objectives of the study were to show the impact of tillage and crop residues management on the distribution of crop residues and root system of a winter wheat crop in the 0-30 cm soil profile.This ... [more ▼]

The objectives of the study were to show the impact of tillage and crop residues management on the distribution of crop residues and root system of a winter wheat crop in the 0-30 cm soil profile.This poster shows the results obtained during one crop year (2011-2012). This study is allowed by a rapid and innovative method of root system quantification based on the near infrared hyperspectral Imaging. [less ▲]

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See detailAre crop models able to efficiently simulate crop growth under shade?
Artru, Sidonie ULg; Dumont, Benjamin ULg; Lassois, Ludivine ULg et al

in Gosme, Marie (Ed.) 3rd European Agroforestry conference 2016 - Book of abstracts (2016, May)

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See detailCharacterization of the impact of tillage and nitrogen fertilization on the root development of a winter wheat crop by use of NIR hyperspectral imaging combined to chemometrics
Fraipont, Guillaume ULg; Eylenbosch, Damien ULg; Fernández Pierna, Juan Antonio et al

Poster (2016, March 16)

The objective of this study is to characterize the impact of tillage management and nitrogen fertilization on the root system development of a winter wheat crop. To reach this objective, an innovative ... [more ▼]

The objective of this study is to characterize the impact of tillage management and nitrogen fertilization on the root system development of a winter wheat crop. To reach this objective, an innovative root quantification method that combines NIR hyperspectral imaging (NIR-HSI) and chemometric tools will be applied. [less ▲]

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See detailClassifying simulated wheat yield responses to changes in temperature and precipitation across a european transect
Fronzek, S.; Pirttioja, N.; Carter, T. R. et al

Conference (2016, March)

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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 (2016, February 24)

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See detailAssessing and modeling economic and environmental impact of wheat nitrogen management in Belgium
Dumont, Benjamin ULg; Basso, Bruno; Bodson, Bernard ULg et al

in Environmental Modelling & Software (2016), 79

Future progress in wheat yield will rely on identifying genotypes and management practices better adapted to the fluctuating environment. Nitrogen (N) fertilization is probably the most important practice ... [more ▼]

Future progress in wheat yield will rely on identifying genotypes and management practices better adapted to the fluctuating environment. Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic and environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 and 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers. [less ▲]

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See detailTradeoffs between Maize Silage Yield and Nitrate Leaching in a Mediterranean Nitrate- Vulnerable Zone under Current and Projected Climate Scenarios
Basso, Bruno; Giola, Pietro; Dumont, Benjamin ULg et al

in PLoS ONE (2016), 11(1): e0146360

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i ... [more ▼]

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term. [less ▲]

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See detailCrop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles
Maiorano, Andrea; Martre, Pierre; Asseng, Senthold et al

in Field Crops Research (2016), In press

o improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the ... [more ▼]

o improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. [less ▲]

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See detailEnvironmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone
Basso, Bruno; Dumont, Benjamin ULg; Cammarano, Davide et al

in Science of the Total Environment (2016), 545/546

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial ... [more ▼]

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005–2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha -1 applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha -1 applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching. [less ▲]

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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 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 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 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)

Detailed reference viewed: 55 (6 ULg)