References of "Dumont, Benjamin"
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See detailHow does STICS crop model simulate crop growth and productivity under shade/shaded conditions
Artru, Sidonie ULiege; Dumont, Benjamin ULiege; Ruget, Francois et al

in Field Crops Research (2018)

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See detailUpscaling winter wheat above-ground biomass measurements using multispectral imagery and 3D data from unmanned aerial vehicle
Michez, Adrien ULiege; Bauwens, Sébastien ULiege; Heinesch, Bernard ULiege et al

Poster (2017, October 20)

Field measurements in the ICOS program are spatially limited whereas the monitored gas fluxes may have a large footprint. Aerial remote sensing has the advantage to monitor large areas. The main goal of ... [more ▼]

Field measurements in the ICOS program are spatially limited whereas the monitored gas fluxes may have a large footprint. Aerial remote sensing has the advantage to monitor large areas. The main goal of our research was to test the potential of unmanned aerial vehicles (UAV) to upscale parameters monitored through the ICOS program. In this study, we specifically focus on above-ground biomass (AGB) monitoring in a winter wheat crop. We used a octocopter drone (X frame type) to acquire a time series over the crop growing season (8 flights from the 14th of February 2017 to the 7th of July 2017) of multispectral imagery covering the ICOS candidate station of Lonzée (Wallonia, Belgium) and the surrounding field crop areas (ca. 0.25 km² per flight). The multispectral camera provides spectral information on the green (550 +/- 50 nm), red (660 +/- 50 nm), near infrared (735 +/- 50 nm) and red-edge (790 +/- 10 nm) wavelengths bands. The UAV also brought an off-the-shelf high resolution (20 Mpx) RGB camera to derive accurate 3D data. We performed a photogrammetric 3D reconstruction of the acquired imagery for every flight survey. The images provided by the RGB sensor (Sony RX100) were used to produce a high spatial resolution Digital Surface Model (0.05 m) and the images acquired by the multispectral sensor were used to derive reflectance maps (0.1 m) in the four wavelengths bands. The four reflectance layers were combined to produce two straight-forward vegetation indices (Normalized Difference Vegetation Index and Green NDVI). The photogrammetric DSM’s were combined to a LiDAR Digital Terrain Model (public database, survey in winter 2013) to produce Crop Height Models (CHM) of the study area. We used multiple linear regressions modelling in order to predict the AGB of the field crop monitored by the ICOS station of Lonzée with UAV imagery. AGB=a+b*GNDVI+c*NDVI+d *CHM The field crop data were provided by the ICOS program and by field research conducted in experimental field crops close to the flux tower. The field sampling consisted in destructive samples of the crop which were weighted after drying. For each field sample, an associated area was computed based on the outdistance sowing and the number of sampled crops in order to compute an AGB per area unit (t / Ha). Each AGB field estimation was associated to the closest flight date to build a multi date model presenting good performances (r² = 0.85, RMSE = 2.3 t/Ha). We used the same modelling approach to adjust a single date model to derive a predicted AGB map for the 7th of July. The performance of the single date model is lower but still highlights the biomass variation within the crop (r² = 0.71, RMSE = 1.9 t/Ha). The predicted AGB map displays a high spatial heterogeneity with some spatial patterns. Locally low AGB values are found along two old pedestrian whereas higher AGB values can be associated to areas which were sprayed twice (in-between two tractor tracks). Our results highlight the potential of UAV multispectral imagery to monitor the AGB variation within the footprint of the flux tower and highlight the need for repeated field sampling with a precise geolocation to improve the matching between the flight and the field surveys. [less ▲]

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See detailVariétés - 1. Froment d'hiver
Meza Morales, Walter ULiege; Dumont, Benjamin ULiege; Jacquemin, Guillaume et al

in Watillon, Bernard; Bodson, Bernard (Eds.) Livre Blanc Céréales (2017, September)

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See detailUsing crop modelling to determine the meteorological conditions to be implemented in an Ecotron facility - Prerequisites to improve the experimental design?
Dumont, Benjamin ULiege; Leemans, Vincent ULiege; Garré, Sarah ULiege et al

Poster (2017, May 23)

An Ecotron is a facility where ecosystems are confined in experimental chambers, allowing the simultaneous control of environmental conditions and the on-line monitoring of processes. Under the threats of ... [more ▼]

An Ecotron is a facility where ecosystems are confined in experimental chambers, allowing the simultaneous control of environmental conditions and the on-line monitoring of processes. Under the threats of climate change and the pressure of a world growing population, such facilities will be of major importance to study the relations between climate change and agro-ecosystems.As it can quickly become time- and money-consuming, conducting experiments in an Ecotron will force researchers to cautiously select the climate of interest to be generated. They will thus need reliable tools to help them support the decision making process.Here, we present an innovative methodology, supported by the use of crop model, to assist researchers in finding the climatic conditions under which crop services will be impacted.The meteorological datasets among which the choice can be done were generated by the ALARO-0 model (RMI, Belgium) for current and future climatic conditions. Runs were conducted for the historical period 1981-2010, and for two time frames - 2041-70 and 2071-2100 - under two emissions scenarios - RCP 4.5 and 8.5.A crop model (STICS, INRA, FR) was run over the entire database. Crop model outputs were synthesized for the main crop phenological phases, i.e. the juvenile, vegetative and reproductive phases. A particular emphasis was put on agronomical outputs (biomass and grain yield) and crop growth stresses (deficit and excess of water, thermal and nutrient stresses).Using these outputs as selection criteria, a novel multi-criteria approach was designed to retro-select the specific climatic conditions allowing to reach certain outcomes (e.g. yield target) while simultaneously exhibiting given thresholds of stresses for any considered crop stages. [less ▲]

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See detailProbabilistic assessment of adaptation options from an ensemble of crop models: a case study in the Mediterranean"
Ferrise, R.; Ruiz-Ramos, M.; Rodríguez, A. et al

in Book of Abstracts - MACSUR2017 Scientific Conference (2017, May)

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See detailEffect of changing size and composition of a crop model ensemble on impact and adaptation response surfaces
Rodríguez, A.; Ferrise, R.; Ruiz-Ramos, M. et al

in Book of Abstracts - MACSUR2017 Scientific Conference (2017, May)

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See detailLa terre ferme
Favre, Juliette; Beckers, Yves ULiege; Bindelle, Jérôme ULiege et al

Article for general public (2017)

Drones, GPS, robots, QR codes et autres lampes LED gagnent du terrain dans les fermes belges. Tandis que certains agriculteurs se réjouissent de cette vague technologique et voient déjà pointer une ... [more ▼]

Drones, GPS, robots, QR codes et autres lampes LED gagnent du terrain dans les fermes belges. Tandis que certains agriculteurs se réjouissent de cette vague technologique et voient déjà pointer une troisième révolution agricole, d’autres craignent de se transformer en de simples « presse-boutons ». Smartphone dans une main, joystick dans l’autre… Les agriculteurs touchent-ils encore seulement la terre ? [less ▲]

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See detailAgriculture de Précision, Modèle de culture et Phytotechnie
Dumont, Benjamin ULiege

Conference (2017, March 10)

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See detail9. Nutrition azotée de l’épeautre en Ardenne et en région limoneuse
Escarnot, Emmanuelle; Meza Morales, Walter ULiege; Crémer, S. et al

in Bodson, Bernard; Watillon, Bernard (Eds.) Livre Blanc Céréales (2017, February 22)

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See detail10. 1. Impact de la gestion des résidus de cultures sur la fertilité des sols et la production agricole
Hiel, Marie-Pierre ULiege; Barbieux, Sophie ULiege; Pierreux, Jérome ULiege et al

in Bodson, Bernard; Watillon, Bernard (Eds.) Livre Blanc Céréales (2017, February 22)

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See detailClassifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R. et al

in Agricultural Systems (2017)

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models ... [more ▼]

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities. [less ▲]

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See detailAdaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment
Ruiz-Ramos, M.; Ferrise, R.; Rodríguez, A. et al

in Agricultural Systems (2017)

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See detailEvaluating the impact of soil conservation measures on soil organic carbon at the farm scale
Pezzuolo, Andrea; Dumont, Benjamin ULiege; Sartori, Luigi et al

in Computers & Electronics in Agriculture (2017), 135

No-tillage (NT) is considered the least invasive conservation agriculture technique and has shown to be the effective in increasing soil C stocks, and reducing losses compared to others tillage systems ... [more ▼]

No-tillage (NT) is considered the least invasive conservation agriculture technique and has shown to be the effective in increasing soil C stocks, and reducing losses compared to others tillage systems. In Italy, the Veneto Region was the first to establish a subsidies scheme aimed at promoting the adoption of NT practices. This program encourages farmers to perform direct seeding, alternate autumn and winter crops and maintain soil cover throughout the year by leaving crop residues or sowing cover crops. The goals of this study were to: (i) compare the CO2 emission and soil C sequestration patterns of agricultural soils under CT and NT management practices in the Veneto region and (ii) analyse the potential mid-term benefits (2010–2025) of NT management in terms of soil organic C dynamics and CO2 balance. Agronomic data and soil organic carbon levels were measured from 2010 to 2014 in eight farms in the Veneto region that had adopted CT and NT techniques. Field measurements were used to calibrate first and then validate the SALUS model to compare the mid-term impact of CT and NT practices using climate projections. SOC carbon pools in the model were initialized using the procedure described in Basso et al. (2011c). This is the first study to employ a model using such an extensive dataset at the farm level to assess the CT and NT strategies within this region. Results of this research will assist farmers and policy makers in the region to define the tillage systems most suited to improve soil C stocks and thereby minimize CO2 emissions from agricultural soils. Overall, simulations indicated that SOC stocks can decrease under both CT and NT regimes, however SOC oxidation rates were substantially lower under NT. Critically, the greatest reduction in CO2 emission was observed when NT was adopted in soil with high levels of SOM. This highlights the benefits of NT adoption in terms of soil fertility preservation and CO2 emissions mitigation. [less ▲]

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See detailThe International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations.
Martre, P.; Reynolds, M. P.; Asseng, S. et al

in Open Data Journal for Agricultural Research (2017), 3

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact ... [more ▼]

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. [less ▲]

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See detail2. Variétés - 1. Froment d'hiver
Meza Morales, Walter ULiege; Eylenbosch, Damien ULiege; Jacquemin, Guillaume et al

in Bodson, Bernard; Watillon, Bernard (Eds.) Livre Blanc Céréales (2016, September 08)

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See detailChanges in crop yields, soil organic carbon and soil nitrogen content under climate change and variable management practices"
Dumont, Benjamin ULiege; Basso, Bruno; Shcherbak, Iurii et al

Conference (2016, September)

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See detailDetection of nitrogen stress on winter wheat by multispectral machine vision
Marlier, Guillaume ULiege; Gritten, Fanny; Leemans, Vincent ULiege et al

Conference (2016, August 01)

Hand-held sensors (SPAD meter, N-Tester, …) used for detecting the leaves nitrogen concentration (Nc) present several drawbacks. The nitrogen concentration is gained by an indirect way through the ... [more ▼]

Hand-held sensors (SPAD meter, N-Tester, …) used for detecting the leaves nitrogen concentration (Nc) 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. Hence, the objective of the paper is to analyse the potential of multispectral imaging for detecting nitrogen concentration. The tests were carried out on parcels submitted to nitrogen inputs varying from 0 to 180 kg N.ha-1. 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 1280×1024 pixels covering an area of approximately 0.25 m² 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 the Partial Least Square (PLS) method and by four filters selected by the Best Subset Selection (BSS) method. In comparison with the Kjeldahl method, the estimation of Nc by means of the Hydro N-Tester, the PLS method and the BSS method (filters 600-80, 950-100, 650-40 and 450-80 nm) gave determination coefficients equal to 0.53, 0.63, and 0.62, respectively. 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. [less ▲]

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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 ULiege; Eylenbosch, Damien ULiege; 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 ULiege; Gritten, Fanny; Fraipont, Guillaume ULiege 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 detailAccounting for the legacy of soil and crop management when assessing climate change impact on crop production"
Basso, Bruno; Dumont, Benjamin ULiege; Shcherbak, Iurii et al

Conference (2016, June)

Detailed reference viewed: 62 (2 ULiège)