References of "Destain, Marie-France"
     in
Bookmark and Share    
Full Text
Peer Reviewed
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 ▲]

Detailed reference viewed: 34 (5 ULg)
Peer Reviewed
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)

Detailed reference viewed: 24 (10 ULg)
Full Text
Peer Reviewed
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 ▲]

Detailed reference viewed: 28 (3 ULg)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 20 (2 ULg)
Peer Reviewed
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: 21 (3 ULg)
Full Text
Peer Reviewed
See detailPredicting biomass and grain protein content using Bayesian methods
Mansouri, Majdi ULg; Destain, Marie-France ULg

in Stochastic Environmental Research & Risk Assessment (2015)

This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback–Leibler divergence. The performances of IPF are ... [more ▼]

This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback–Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. [less ▲]

Detailed reference viewed: 11 (3 ULg)
Full Text
Peer Reviewed
See detailToward a tool aimed to quantify soil compaction risks at a regional scale: application to Wallonia (Belgium)
D'Or, Dimitri; Destain, Marie-France ULg

in Soil & Tillage Research (2014), 144

The spatial analysis of the soil compaction risk has been developed at the regional level and applied to Wallonia (Belgium). The methodology is based on the estimation of the probability of exceeding the ... [more ▼]

The spatial analysis of the soil compaction risk has been developed at the regional level and applied to Wallonia (Belgium). The methodology is based on the estimation of the probability of exceeding the preconsolidation stress due to the application of loads on the soil. Preconsolidation stresses (Pc) are computed from the pedotransfer functions of Horn and Fleige (2003) at pF 1.8 and 2.5 and classified into 6 categories ranging from very low Pc (< 30 kPa) to extremely high Pc (> 150 kPa). The computation requires the knowledge of pedological (texture, organic content), mechanical (bulk density, cohesion, internal friction angle), and hydraulic variables (water content available, non-available water content, air capacity, saturated hydraulic conductivity). These variables are obtained from databases like HYPRES or AARDEWERK or from pedotransfer functions. The computation of Pc takes into account the spatial structure of the data: in some cases, data are abundant (e.g. texture data) and spatial variability is taken into account through geostatistical methods. In other cases, the data is sparse but uncertainty information can be extracted from the knowledge of the statistical distribution. Maps of the most probable Pc class are produced. Uncertainty is computed as the classification error probability. Implementation of these methods in Wallonia showed that Pc values higher than 120 kPa are reached either on 64 % of the territory at pF 2.5 or on 55 % at pF 1.8. A higher uncertainty was found at pF 2.5 than at pF 1.8. Uncertainty was also found higher for clay and clayed loess than for other textural classes present in Wallonia. The risk of compaction is defined as the probability that Pc is exceeded by the stress created by a load applied to the soil at a depth of 40 cm, the loads being similar to those induced by agricultural or forestry tires. It appeared that subsoil compaction risks exist mainly in loamy forest soils with small coarse fragments supporting loads similar to that existing on logging machines. In the zones where the uncertainty is low, the developed tool could be used as a basis for providing policy measures in order to promote soil-friendly farming and forest practices. [less ▲]

Detailed reference viewed: 50 (22 ULg)
Full Text
Peer Reviewed
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 (2014)

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 ▲]

Detailed reference viewed: 45 (18 ULg)
Full Text
See detailSoil compaction resulting from different soil tillage systems
Destain, Marie-France ULg; Roisin, Christian; Mercatoris, Benoît ULg

in ASABE - CSBE/ASABE Joint Meeting Presentation (2014, July)

The effects of long-term use (8 years) of two different tillage systems were assessed on a Luvisol, under temperate climate (Belgium). The tillage treatments were (i) conventional tillage (CT) with ... [more ▼]

The effects of long-term use (8 years) of two different tillage systems were assessed on a Luvisol, under temperate climate (Belgium). The tillage treatments were (i) conventional tillage (CT) with moldboard ploughing to 27 cm depth and (ii) reduced tillage (RT) with a spring tine cultivator to 10 cm depth. The measurements included bulk density (BD) and precompression stress (Pc) chosen as indicators of mechanical strength, and the pore size distribution (PSD) measured by mercury intrusion porosimetry (MIP). The tillage systems, the depth and their interaction had a significant effect on BD, Pc and PSD. In CT, in the topsoil, the soil strength was low and the total porosity n was about 50 %. In the subsoil, n decreased to 43 %. The PSD of CT was uni-modal in topsoil and subsoil in the MIP measurement range. The mean value of the mode rmax diminished from the topsoil toward the subsoil (from 2.5 microns to 1.9 microns). In RT, in the topsoil, the soil strength was higher than CT. BD did not vary much according to the depth. The total porosity n of RT was comprised between 40-45 % in the soil profile. The PSD was uni-modal and rmax increased from topsoil (around 2 microns) to subsoil (> 3 microns). This suggested the agglomeration of fine particles under the long-term action of mechanical loads, climatic agents, biological organisms or clay minerals acting as cementing agents. These phenomena could be at the origin of the increase of Pc with the depth without significant modification of BD. Such high values of Pc could be responsible of negative effects on root-growth leading to a more superficial root lateral development. [less ▲]

Detailed reference viewed: 69 (15 ULg)
Full Text
Peer Reviewed
See detailRisk assessment of soil compaction in Walloon Region
D'Or, Dimitri; Destain, Marie-France ULg

in Proceedings of geoENV2014 (2014, July)

It is well established that soil compaction affects the growth and functioning of roots and disrupts the activity of microfauna and soil microorganisms, resulting in a loss of yields. Agriculture and ... [more ▼]

It is well established that soil compaction affects the growth and functioning of roots and disrupts the activity of microfauna and soil microorganisms, resulting in a loss of yields. Agriculture and forestry using increasingly heavy machines, the risk of soil compaction is increasing accordingly. Chosen as indicator of the susceptibility of soils to compaction, the precompression stress (Pc) is calculated using the pedotransfer functions (PTFs) proposed by Horn and Fleige (2003). These PTFs involve eight parameters linked to the hydraulic and mechanical behaviour of soils: organic matter content, bulk density, air capacity, available and non-plant available water capacity, saturated hydraulic conductivity, cohesion and angle of internal friction. The challenge consists in producing Pc maps at the regional scale for Wallonia. Those maps should also be accompanied by estimation uncertainty map. Finally, the results should be exploited to produce compaction risk maps according to various frequent scenarios. In this paper, a methodology is proposed, combining geostatistics and Monte Carlo simulations, to achieve these goals. [less ▲]

Detailed reference viewed: 39 (8 ULg)
Peer Reviewed
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)

Detailed reference viewed: 26 (9 ULg)
Peer Reviewed
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)

Detailed reference viewed: 24 (4 ULg)
Full Text
Peer Reviewed
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 ▲]

Detailed reference viewed: 56 (16 ULg)
Full Text
Peer Reviewed
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 ▲]

Detailed reference viewed: 82 (30 ULg)
Peer Reviewed
See detailPredicting Winter Wheat Biomass And Grain Protein Content
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Proceedings of the 12th International Conference on Precision Agriculture (2014)

Detailed reference viewed: 13 (1 ULg)
Full Text
Peer Reviewed
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), 15(3)

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 ▲]

Detailed reference viewed: 85 (37 ULg)
Full Text
Peer Reviewed
See detailNitrogen fertilisation recommendations : could they be improved using stochastically generated climates in conjunction with crop models ?
Dumont, Benjamin ULg; Basso, Bruno; Meza Morales, Walter ULg et al

in Proceedings of the 12th ICPA (2014)

Accurate determination of optimal Nitrogen (N) recommendations which ensure maximization of farmer's revenue while minimizing the environmental constraint is maybe among the major challenges in ... [more ▼]

Accurate determination of optimal Nitrogen (N) recommendations which ensure maximization of farmer's revenue while minimizing the environmental constraint is maybe among the major challenges in agriculture. Crop models have the potential to deal with such aspects and could thus be used to develop decision support systems. However unknown future weather conditions remains the key point of accurate yield forecast. This paper presents the results of a preliminary study that aims to supply the unknown future with stochastically generated climatic conditions. Coupling the methodology with appropriate decision rules led to a generic decision support system able to guide the N management practices. [less ▲]

Detailed reference viewed: 31 (2 ULg)