A framework to type crop management strategies within a production situation to improve the comprehension of weed communities

Yvoz, Séverin; Petit, Sandrine; Biju‐Duval, Luc; Cordeau, Stéphane

doi:10.1016/j.eja.2020.126009

Cited by 27 publications

(15 citation statements)

References 53 publications

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“…The direct effects of farming intensity and crop rotation on bacterial richness were positive, but weaker and not very accurate in 2016. Farming practices were heterogeneous across the Fénay landscape, despite similar production situations across the whole area [97]. They changed progressively but significantly between 2011 and 2016, in agreement with observations reported at the European Union level [98].…”

Section: Plos Onesupporting

confidence: 84%

Soil microbial communities in the face of changing farming practices: A case study in an agricultural landscape in France

et al. 2021

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According to biogeography studies, the abundance and richness of soil microorganisms vary across multiple spatial scales according to soil properties and farming practices. However, soil microorganisms also exhibit poorly understood temporal variations. This study aimed at better understanding how soil microbial communities respond to changes in farming practices at a landscape scale over time. A regular grid of 269 sites was set up across a 1,200 ha farming landscape, and soil samples were characterized for their molecular microbial biomass and bacterial richness at two dates (2011 and 2016). A mapping approach highlighted that spatial microbial patterns were stable over time, while abundance and richness levels were modified. The drivers of these changes were investigated though a PLS-PM (partial least square path-modeling) approach. Soil properties were stable over time, but farming practices changed. Molecular microbial biomass was mainly driven by soil resources, whereas bacterial richness depended on both farming practices and ecological parameters. Previous-crop and management effects and a temporal dependence of the microbial community on the historical farming management were also highlighted.

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Section: Plos Onesupporting

confidence: 84%

Soil microbial communities in the face of changing farming practices: A case study in an agricultural landscape in France

et al. 2021

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“…Farm-field surveys monitor cultural practices and real-life weed floras in a large range of contrasting situations, at a regional (Petit et al, 2016;Yvoz et al, 2020) or even national scale (Rydberg and Milberg, 2000;Fried et al, 2008). They allow assessing the environmental, agronomic and ecological drivers of the in-field weed flora, and notably the relative contribution of agronomic and environmental factors (Schumacher, 1987;Fried et al, 2008;Seifert et al, 2015), landscape context and/or of farming systems (Gabriel et al, 2005;Geiger et al, 2010;Petit et al, 2016) on weed infestation levels and/or weed community composition.…”

Section: Farm-field Surveysmentioning

confidence: 99%

“…This section deals with interaction between herbicide use and other farming practices ( Figure 1C) Herbicide use intensity depends not only on initial weed infestation and the farmer's weeding strategy (Figure 2) but also on other practices (Beltran et al, 2013;Colbach and Cordeau, 2018), particularly in Integrated Weed Management (Swanton and Weise, 1991). The intensity decreases if fields are tilled, weeded mechanically and/or grown with diversified rotations (Yvoz et al, 2020), depending not only on the frequency but also the timing of non-chemical disturbances ( Table 3).…”

Section: How Far Farmers Adapt Their Herbicide Strategies To the Othementioning

confidence: 99%

“…The herbicide strategy used by a farmer in terms of commercial products, rates and timings of application depends on the other practices applied in the field as well as on the weed flora perceived by the farmer, his risk strategy and his production situation (Yvoz et al, 2020). Any attempt to assess the impact of herbicide use intensity on weed flora and yield loss must account for these interactions to avoid confusing effects (Quinio et al, 2017).…”

Section: How Far Farmers Adapt Their Herbicide Strategies To the Othementioning

confidence: 99%

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The Pitfalls of Relating Weeds, Herbicide Use, and Crop Yield: Don't Fall Into the Trap! A Critical Review

et al. 2020

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The growing recognition of the environmental and health issues associated to pesticide use requires to investigate how to manage weeds with less or no herbicides in arable farming while maintaining crop productivity. The questions of weed harmfulness, herbicide efficacy, the effects of herbicide use on crop yields, and the effect of reducing herbicides on crop production have been addressed over the years but results and interpretations often appear contradictory. In this paper, we critically analyze studies that have focused on the herbicide use, weeds and crop yield nexus. We identified many inconsistencies in the published results and demonstrate that these often stem from differences in the methodologies used and in the choice of the conceptual model that links the three items. Our main findings are: (1) although our review confirms that herbicide reduction increases weed infestation if not compensated by other cultural techniques, there are many shortcomings in the different methods used to assess the impact of weeds on crop production; (2) Reducing herbicide use rarely results in increased crop yield loss due to weeds if farmers compensate low herbicide use by other efficient cultural practices; (3) There is a need for comprehensive studies describing the effect of cropping systems on crop production that explicitly include weeds and disentangle the impact of herbicides from the effect of other practices on weeds and on crop production. We propose a framework that presents all the links and feed-backs that must be considered when analyzing the herbicide-weed-crop yield nexus. We then provide a number of methodological recommendations for future studies. We conclude that, since weeds are causing yield loss, reduced herbicide use and maintained crop productivity necessarily requires a redesign of cropping systems. These new systems should include both agronomic and biodiversity-based levers acting in concert to deliver sustainable weed management.

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“…The crop management category involves versatile aspects that originated from the combination of farming techniques in the direction of managing the biological, chemical and physical crop environment with the aim of reaching both quantitative and qualitative targets [ 52 ]. Using advanced approaches to manage crops, such as yield prediction, disease detection, weed detection, crop recognition, and crop quality, contributes to the increase of productivity and, consequently, the financial income.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

384

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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A framework to type crop management strategies within a production situation to improve the comprehension of weed communities

Cited by 27 publications

References 53 publications

Soil microbial communities in the face of changing farming practices: A case study in an agricultural landscape in France

Soil microbial communities in the face of changing farming practices: A case study in an agricultural landscape in France

The Pitfalls of Relating Weeds, Herbicide Use, and Crop Yield: Don't Fall Into the Trap! A Critical Review

Machine Learning in Agriculture: A Comprehensive Updated Review

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