Marie-France Destain

Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions

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 (60-60-60xa0kgN 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 (60xa0%) of achieving yields that were superior to the mean (10.5xa0t ha−1) of the distribution.

Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium

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. The economic and environmental impact of Nitrogen fertilization was evaluated.A complete and generic methodology for tactical N optimization is proposed.Climatic conditions occurring between sowing and flag leaf stage greatly impacts N optimization.Environment?× management interactions have to be considered when optimizing N.Environmental consideration is a more limiting factor than expected revenues for N optimization.

Risk Assessment of Soil Compaction in the Walloon Region in Belgium

It is well known that soil compaction affects root growth and disrupts the activity of soil microfauna and microorganisms, resulting in yield loss. With the more intensive use of heavy machines in agriculture and forestry, the risk of soil compaction is increasing. In this study, precompression stress (Pc) was chosen as an indicator of the susceptibility of soils to compaction and was calculated using pedotransfer functions (PTFs). PTFs involve eight variables related to the hydraulic and mechanical behaviour of soils: organic matter content, bulk density, air capacity, available water capacity, non-plant available water capacity, saturated hydraulic conductivity, cohesion, and angle of internal friction. Combining these PTFs with geostatistics and Monte Carlo simulations, Pc maps were produced at the regional scale for Wallonia in Belgium, accompanied by uncertainty quantification maps. These maps were then used to produce compaction risk maps based on common scenarios. The results showed that the modal Pc map was coherent with the spatial distribution of the main variables, namely soil texture and organic matter content. The risk maps enabled areas with a compaction risk in both agricultural and forestry contexts to be identified. These maps could be useful in drawing up soil protection measures and policies.

Predicting biomass and grain protein content using Bayesian methods

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.

Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging

Precision agriculture can be considered as one of the solutions to optimize agricultural practice such as nitrogen fertilization. Nitrogen deficiency is a major limitation to crop production worldwide whereas excess leads to environmental pollution. In this context, some devices were developed as reflectance spot sensors for on-the-go applications to detect leaves nitrogen concentration deduced from chlorophyll concentration. However, such measurements suffer from interferences with the crop growth stage and the water content of plants. The aim of this contribution is to evaluate the nitrogen status in winter wheat by using multispectral imaging. The proposed system is composed of a CMOS camera and a set of filters ranged from 450 nm to 950 nm and mounted on a wheel which moves due to a stepper motor. To avoid the natural irradiance variability, a white reference is used to adjust the integration time. The segmentation of Photosynthetically Active Leaves is performed by using Bayes theorem to extract their mean reflectance. In order to introduce information related to the canopy architecture, i.e. the crop growth stage, textural attributes are also extracted from raw images at different wavelength ranges. Nc was estimated by partial least squares regression (R² = 0.94). The best attribute was homogeneity extracted from the gray level co-occurrence matrix (R² = 0.91). In order to select in limited number of filters, best subset selection was performed. Nc could be estimated by four filters (450 ± 40 nm, 500 ± 20 nm, 650 ± 40 nm, 800 ± 50 nm) (R² = 0.91).