Influence of intra‐row cruciferous surrogate weed growth on crop yield in organic spring cereals

Melander, Bo; McCollough, Margaret R.

doi:10.1111/wre.12452

Cited by 7 publications

(10 citation statements)

References 27 publications

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“…Intra-row weeds remain a problem when inter-row hoeing, especially tallgrowing cruciferous species that can reduce crop yields markedly, as shown in Table 1 (Melander and McCollough, 2020). Mixed intra-row weed populations with a greater proportion of weed species short in stature may not be as competitive as seen in a Danish study with inter-row hoeing, performed in 11 weedy fields with organic spring cereals.…”

Section: Inter-row Cultivationmentioning

confidence: 99%

“…White mustard (Sinapis alba) was used to simulate cruciferous intra-row weed growth typical for Raphanus raphanistrum, Sinapis arvensis, and Brassica rapa. Intra-row surrogate weeds Sinapis alba (plants m −2 ) are defined as those plants growing in the uncultivated area 2.5 cm to either side of the crop row's center (Melander and McCollough, 2020) also means that the intensity of cultivation increases, and severe crop injuries become more likely since the crop is also treated. The most obvious advantage of stacking tools is that weed problems requiring several intense passes with a single tool might be controlled in one pass when employing the stacking concept.…”

Section: Stacking Tools For Intra-row Cultivationmentioning

confidence: 99%

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Advances in mechanical weed control technologies

Melander¹,

McCollough²

2022

Burleigh Dodds Series in Agricultural Science

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Mechanical weed control can be grouped into three categories: full-width cultivators, inter-row cultivators and intra-row cultivators. This chapter will highlight the most recent and relevant advances within each category. The focus will be on novel inventions and developments of mechanical devices, designs, and the weed problems they are meant to solve. Moreover, automation technologies that assist weeding operations are becoming increasingly important and will be given special attention.

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Section: Inter-row Cultivationmentioning

confidence: 99%

Section: Stacking Tools For Intra-row Cultivationmentioning

confidence: 99%

Advances in mechanical weed control technologies

Melander¹,

McCollough²

2022

Burleigh Dodds Series in Agricultural Science

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“…Excluding EXP 2019 WMT tineharrow RS 15 ambient weed biomass results, within the range of crop densities tested (CD 200 to CD 500 ), intrarow surrogate and ambient weed biomass were reduced by 49% to 82% and 53 to 86%, respectively (Figure 3). The effect that increasing seeding rate has on suppressing intrarow weeds is an important result, one that improves our understanding of how to limit potential yield and crop quality losses resulting from remaining competitive intrarow weeds in hoed cereals (Melander and McCollough 2020). Percent reduction of intrarow weed biomass was slightly higher for ambient weeds compared with surrogate weeds, comprising differences of 4.5% and 4.3% at CD 300 in EXP 2019 and EXP 2020 , respectively.…”

Section: Intrarow Weed Biomassmentioning

confidence: 99%

“…Depending on the competitive abilities of weed species present, remaining intrarow weeds can compete intensely with the crop due to their close proximity. In hoed spring barley (Hordeum vulgare L.), Melander and McCollough (2020) reported that an intrarow surrogate weed, white mustard (Sinapis alba L.), reduced crop yields by 4% to 7 %, 14% to 26%, and 21% to 40% when densities of 5, 25, and 50 plants m −2 were recorded, respectively. Therefore, it is necessary to investigate further how intrarow weed management can be improved to optimize the hoed cereal system.…”

Section: Introductionmentioning

confidence: 99%

“…In northern Europe, the standard interrow spacings for growing small-grain cereals is 12.5 cm. The practice of widening interrow spacings to distances ranging from 15 to 30 cm to accommodate interrow hoeing while maintaining seeding rate constitutes a recent topic of research (Gerhards et al 2020;Kolb et al 2010Machleb et al 2018;McCollough et al 2020a;Melander and McCollough 2020;Melander et al 2003Melander et al , 2018. Wide row sowing and hoeing effectively reduce weed biomass compared with standard cropping practices (Melander et al 2018) and may improve yields, depending upon the severity of weed pressure (Kolb et al 2010;Rasmussen 2004).…”

Section: Introductionmentioning

confidence: 99%

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Improving upon the interrow hoed cereal system: the effects of crop density and row spacing on intrarow weeds and crop parameters in spring barley

McCollough

Melander

2022

Weed Sci

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Automated guidance systems have advanced precise inter-row hoeing in narrowly spaced cereals. Compared to other direct mechanical strategies, hoeing provides superior weed control and improved yields. However, weeds in the uncultivated intra-row zone may survive and compete intensely with the crop, causing yield loss. Therefore, improved intra-row weed management strategies in hoed cereals must be investigated. In spring barley (Hordeum vulgare L.), the effect of crop density was assessed at four levels (200, 300, 400, and 500 plants m−2); inter-row spacing at two levels (15 and 20 cm), relevant to the abilities of current automated equipment to hoe between narrowly spaced rows, and weed management treatment at three levels (no additional controls, herbicide, and pre-emergence tine harrowing). All treatments received inter-row hoeing, and a surrogate weed (Sinapis alba L.) was sown and monitored throughout experiments. The manipulation of crop density was a reliable method for suppressing the growth of intra-row weeds. As barley density increased from target 200 to 500 plants m−2, percent reduction in intra-row surrogate and ambient weed biomass (g m−2) increased from 49 to 82% and 53 to 99%, respectively. Increasing crop density caused a decrease in grain bulk density (kg hL−1) both years, and grain protein (%) and thousand kernel weight (g) in one year; whether these changes constitute a loss in grain quality depends upon end-use. While row spacing had no effect on intra-row weeds, crop yields were 7 to 8% less at 20 cm compared to 15 cm, incentivizing narrow row sowing. Barley yields were unaffected by increasing crop density, and the effect of pre-emergence tine harrowing was inconsistent. In one year, harrowing reduced surrogate and ambient weed biomass and increased barley yield; however, in another year, ambient weed biomass increased, and harrowing did not affect yield or surrogate weed biomass.

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Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

Zhuang

Bagavathiannan

et al. 2021

Pest Management Science

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BACKGROUND In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat. RESULTS The object detection neural networks, including CenterNet, Faster R‐CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels. CONCLUSION CenterNet, Faster R‐CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.

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Influence of intra‐row cruciferous surrogate weed growth on crop yield in organic spring cereals

Cited by 7 publications

References 27 publications

Advances in mechanical weed control technologies

Advances in mechanical weed control technologies

Improving upon the interrow hoed cereal system: the effects of crop density and row spacing on intrarow weeds and crop parameters in spring barley

Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

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