Jean-Philippe Guillemin

Evaluation of cropping systems for management of herbicide-resistant populations of blackgrass (Alopecurus myosuroides Huds.).

Abstract Simplification of cropping systems often leads to an increase in weed populations which require an intensive use of herbicides to maintain populations at an acceptable level. Due to a heavy reliance on herbicides and a lack of cultural control measures, herbicide-resistant blackgrass (Alopecurus myosuroides Huds.) biotypes appeared recently in France. An experiment was conducted to evaluate the effects of different cropping systems on a population of herbicide-resistant blackgrass. Two crop rotations, one consisting exclusively of winter crops and another including spring crops, were assessed over a three-year period. Crop rotation was combined with different cultural practices (mouldboard plough, delayed sowing dates, reduced nitrogen fertiliser applications and effective herbicides on resistant blackgrass). Blackgrass densities decreased in all the cropping systems, but blackgrass control by herbicides was most effective when combined with non-chemical practices. The benefits of the different weed management systems are discussed in relation to their effect on blackgrass density and their cost to the farmer. In our conditions, the introduction of spring crops into the rotation gave the best results, both from an economical and weed management point of view.

Mapping and determinism of soil microbial community distribution across an agricultural landscape.

Despite the relevance of landscape, regarding the spatial patterning of microbial communities and the relative influence of environmental parameters versus human activities, few investigations have been conducted at this scale. Here, we used a systematic grid to characterize the distribution of soil microbial communities at 278 sites across a monitored agricultural landscape of 13 km². Molecular microbial biomass was estimated by soil DNA recovery and bacterial diversity by 16S rRNA gene pyrosequencing. Geostatistics provided the first maps of microbial community at this scale and revealed a heterogeneous but spatially structured distribution of microbial biomass and diversity with patches of several hundreds of meters. Variance partitioning revealed that both microbial abundance and bacterial diversity distribution were highly dependent of soil properties and land use (total variance explained ranged between 55% and 78%). Microbial biomass and bacterial richness distributions were mainly explained by soil pH and texture whereas bacterial evenness distribution was mainly related to land management. Bacterial diversity (richness, evenness, and Shannon index) was positively influenced by cropping intensity and especially by soil tillage, resulting in spots of low microbial diversity in soils under forest management. Spatial descriptors also explained a small but significant portion of the microbial distribution suggesting that landscape configuration also shapes microbial biomass and bacterial diversity.

Contrasting spatial patterns and ecological attributes of soil bacterial and archaeal taxa across a landscape.

Even though recent studies have clarified the influence and hierarchy of environmental filters on bacterial community structure, those constraining bacterial populations variations remain unclear. In consequence, our ability to understand to ecological attributes of soil bacteria and to predict microbial community response to environmental stress is therefore limited. Here, we characterized the bacterial community composition and the various bacterial taxonomic groups constituting the community across an agricultural landscape of 12 km2, by using a 215 × 215 m systematic grid representing 278 sites to precisely decipher their spatial distribution and drivers at this scale. The bacterial and Archaeal community composition was characterized by applying 16S rRNA gene pyrosequencing directly to soil DNA from samples. Geostatistics tools were used to reveal the heterogeneous distribution of bacterial composition at this scale. Soil physical parameters and land management explained a significant amount of variation, suggesting that environmental selection is the major process shaping bacterial composition. All taxa systematically displayed also a heterogeneous and particular distribution patterns. Different relative influences of soil characteristics, land use and space were observed, depending on the taxa, implying that selection and spatial processes might be differentially but not exclusively involved for each bacterial phylum. Soil pH was a major factor determining the distribution of most of the bacterial taxa and especially the most important factor explaining the spatial patterns of α‐Proteobacteria and Planctomycetes. Soil texture, organic carbon content and quality were more specific to a few number of taxa (e.g., β‐Proteobacteria and Chlorobi). Land management also influenced the distribution of bacterial taxa across the landscape and revealed different type of response to cropping intensity (positive, negative, neutral or hump‐backed relationships) according to phyla. Altogether, this study provided valuable clues about the ecological behavior of soil bacterial and archaeal taxa at an agricultural landscape scale and could be useful for developing sustainable strategies of land management.

Development of methods based on double Hough transform or Gabor filtering to discriminate between crop and weed in agronomic images

This paper presents two spatial methods to discriminate between crop and weeds. The application is related to agronomic image with perspective crop rows. The first method uses a double Hough Transform permitting a detection of crop rows and a classification between crop and weeds. The second method is based on Gabor filtering, a band pass filter. The parameters of this filter are detected from a Fast Fourier Transform of the image. For each method, a weed infestation rate is obtained. The two methods are compared and a discussion concludes about the abilities of these methods to detect the crop rows in agronomic images. Finally, we discuss this method regarding the capability of the spatial approach for classifying weeds from crop.

Biodiversity-based options for arable weed management. A review

In the context of a shift towards pesticide reduction in arable farming, weed management remains a challenging issue. Integrated Weed Management currently recommends agronomic practices for weed control, but it does not integrate the use of biodiversity-based options, enhancing the biological regulation of weeds. Here, wereview existing knowledge related to three potentially beneficial interactions, of crop–weed competition, weed seed granivory, and weed interactions with pathogenic fungi. Our main finding are the following : (1) promoting cropped plant–weed competition by manipulating cropped cover could greatly contribute to weed reduction ; (2) weed seed granivory by invertebrates can significantly lower weed emergence, although this effect can be highly variable because seed predation is embedded within complex multitrophic interactions that are to date not fully understood ; (3) a wide range of fungi are pathogenic to various stages of weed development, but strain efficacy in field trials does not often match that in controlled conditions. We present a framework that superimposes biodiversity-based options for weed biocontrol on a classical Integrated Weed Management system. We then describe the current state of knowledge on interactions between agronomic practices and the organisms at play and between the different biological components of the system. We argue that further advances in our understanding of biodiversity-based options and their performance for weed biocontrol will require farm-scale experimental trials.

Feasibility study for a catadioptric bi-spectral imaging system

In the context of sustainable agriculture, matching accurately herbicides and weeds is an important task. The site specific spraying requires a preliminary diagnostic depending on the plant species identification and localisation. In order to distinguish between weeds species or to discriminate between weeds and soil from their spectral properties, we investigate a spectral approach developing a catadioptric bi-spectral imaging system as a diagnostic tool. The aim of this project consists in the conception and feasibility of a vision system which captures a pair of images with a single camera by the use of two planar mirrors. Then fixing a filter on each mirror, two different spectral channels (e.g. Blue and Green) of the scene can be obtained. The optical modeling is explained to shot the same scene. A calibration based on the inverse pinhole model is required to be able to superpose the scene. The choice of interferential filters is discussed to extract agronomic information from the scene by the use of vegetation index.