Effect of planter and tractor wheels on row and inter-row weed populations

Tardif-Paradis, Corinne; Simard, Marie‐Josée; Leroux, Gilles; Panneton, B.; Nurse, Robert E.; Vanasse, Anne

doi:10.1016/j.cropro.2015.01.026

Cited by 6 publications

(7 citation statements)

References 40 publications

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“…It seems that johnsongrass seeds disperse naturally over short distances (< 5 m) from the parent plants [44]. Although the response of seed germination to soil compaction had already been described [15,16], our analyses provides quantitative evidence of this effect. Soil compaction affects seeds' dormancy and germination in several ways [12][13][14].…”

Section: Aggregation Analysismentioning

confidence: 56%

“…Aggregate size = 9.04 − 4.89 1 − exp − ln(1000-seed-weight) 1.41 (15) as shown in Figure 3. Other factors are likely to be management, total abundance and underlying variation in the soil, as well as seed bank, which reflect the history of the field.…”

Section: Aggregation Analysismentioning

confidence: 99%

“…During planting, one part of the seeding unit (the opening disc) digs a furrow for the seed and simultaneously exposes buried weed seeds, while the other part (the press wheel) compacts the soil all within a space of tens of centimeters. The disturbance, caused either by the turnover of the soil or compaction, or both, stimulates the weeds to emerge because of the short exposure of seeds to light and changes in the ratios of soil gases and in water content [14,15,[39][40][41]. Compaction can also limit the available oxygen in soil, and competition from crop plants in the crop rows can reduce the density of weeds [42,43].…”

Section: Aggregation Analysismentioning

confidence: 99%

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Spatial Analysis of Digital Imagery of Weeds in a Maize Crop

Martín

Milne

Webster

et al. 2018

IJGI

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Abstract:Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m 2 ) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm-18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches.

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Section: Aggregation Analysismentioning

confidence: 56%

Section: Aggregation Analysismentioning

confidence: 99%

Section: Aggregation Analysismentioning

confidence: 99%

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Spatial Analysis of Digital Imagery of Weeds in a Maize Crop

Martín

Milne

Webster

et al. 2018

IJGI

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“…The fact that the authors had trained the algorithm on a dataset from an earlier season indicates that specific training may not be required each time a new field is reached. The authors of [32] showed that weed density was always higher within rows than on inter-row segments unless the latter had been tracked by tractor wheels. This indicated that soil disturbance by the planter and tractor wheels increased weed seed germination and subsequent seedling emergence.…”

Section: Discussionmentioning

confidence: 99%

Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops

Jørgensen

Midtiby

Jensen

et al. 2016

Sensors

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The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.

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“…Plowing and seedbed preparation can displace weed seed 2 to 15 m from initial position in one season (Marshall and Brain 1999). Furrow irrigation or wheel tracks can be more infested than undisturbed areas (Longchamps et al 2012;Norsworthy et al 2014;Tardif-Paradis et al 2015). Most of these anthropomorphic phenomena generate weed patches that are elongated (anisothropy) in the direction of cultivation, as seen in maps presented in Longchamps et al (2013) and in Norsworthy et al (2014).…”

mentioning

confidence: 99%

Spatial Pattern of Weeds Based on Multispecies Infestation Maps Created by Imagery

et al. 2016

Self Cite

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Weeds are often spatially aggregated in maize fields, and the level of aggregation varies across and within fields. Several annual weed species are present in maize fields before postemergence herbicide application, and herbicides applied will control several species at a time. The goal of this study was to assess the spatial distribution of multispecies weed infestation in maize fields. Ground-based imagery was used to map weed infestations in rain-fed maize fields. Image segmentation was used to extract weed cover information from geocoded images, and an expert-based threshold of 0.102% weed cover was used to generate maps of weed presence/absence. From 19 site-years, 13 (68%) demonstrated a random spatial distribution, whereas six site-years demonstrated an aggregated spatial pattern of either monocotyledons, dicotyledons, or both groups. The results of this study indicated that monocotyledonous and dicotyledonous weed groups were not spatially segregated, but discriminating these weed groups slightly increased the chances of detecting an aggregated pattern. It was concluded that weeds were not always spatially aggregated in maize fields. These findings emphasize the need for techniques allowing the assessment of weed aggregation prior to conducting site-specific weed management.

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Effect of planter and tractor wheels on row and inter-row weed populations

Cited by 6 publications

References 40 publications

Spatial Analysis of Digital Imagery of Weeds in a Maize Crop

Spatial Analysis of Digital Imagery of Weeds in a Maize Crop

Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops

Spatial Pattern of Weeds Based on Multispecies Infestation Maps Created by Imagery

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