Palmer Amaranth (<i>Amaranthus palmeri</i>): A Review

Ward, Sarah; Webster, Theodore M.; Steckel, Larry

doi:10.1614/wt-d-12-00113.1

Cited by 292 publications

(379 citation statements)

References 109 publications

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“…However, apparently this is not the case for glyphosate-resistant Palmer amaranth. A previously published work suggests the existence of different resistance mechanisms among A. palmeri biotypes (Ward et al, 2013). In laboratory experiments, Culpepper et al (2006) found no differences in glyphosate absorption and translocation between resistant and susceptible biotypes, but shikimate accumulation was not detected in tissues of y = a/(1+(x/b) c ) ou y = P mín + a/(1+(x/b) c ); DAA -days after application; * significant at 1% probability level.…”

Section: Results E Discussionmentioning

confidence: 94%

DETECTION OF GLYPHOSATE-RESISTANT PALMER AMARANTH (Amaranthus palmeri) IN AGRICULTURAL AREAS OF MATO GROSSO, BRAZIL

Carvalho

Netto²,

Nicolai

et al. 2015

Planta daninha

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Section: Results E Discussionmentioning

confidence: 94%

DETECTION OF GLYPHOSATE-RESISTANT PALMER AMARANTH (Amaranthus palmeri) IN AGRICULTURAL AREAS OF MATO GROSSO, BRAZIL

Carvalho

Netto²,

Nicolai

et al. 2015

Planta daninha

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“…Palmer amaranth, redroot pigweed, and velvetleaf infestations occur in soybean fields throughout the eastern U.S. [2] [3] [4]. The weeds produce numerous seeds and establish large populations in soybean fields, thus reducing yields.…”

Section: Introductionmentioning

confidence: 99%

Using Vegetation Indices as Input into Random Forest for Soybean and Weed Classification

Fletcher¹

2016

AJPS

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Weed management is a major component of a soybean (Glycine max L.) production system; thus, managers need tools to help them distinguish soybean from weeds. Vegetation indices derived from light reflectance properties of plants have shown promise as tools to enhance differences among plants. The objective of this study was to evaluate normalized difference vegetation indices derived from multispectral leaf reflectance data as input into random forest machine learner to differentiate soybean and three broad leaf weeds: Palmer amaranth (Amaranthus palmeri L.), redroot pigweed (A. retroflexus L.), and velvetleaf (Abutilon theophrasti Medik). Leaf reflectance measurements were acquired from plants grown in two separate greenhouse experiments conducted in 2014. Twelve normalized difference vegetation indices were derived from the reflectance measurements, including advanced, green, greenred, green-blue, and normalized difference vegetation indices, shortwave infrared water stress indices, normalized difference pigment and red edge indices, and structure insensitive pigment index. Using the twelve vegetation indices as input variables, the conditional inference version of random forest (cforest) readily distinguished soybean and velvetleaf from the two pigweeds (Palmer amaranth and redroot pigweed) and from each other with classification accuracies ranging from 93.3% to 100%. The greatest errors were observed between the two pigweed classes, with classification accuracies ranging from 70% to 93.3%. Results suggest combining them into one class to increase classification accuracy. Vegetation indices results were equivalent to or slightly better than results obtained with sixteen multispectral bands used as input data into cforest. This research further supports using vegetation indices and machine learning algorithms such as cforest as decision support tools for weed identification.

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“…is highly competitive at the beginning of the cropping season, and a critical weed-free period of 9 weeks after planting is required to avoid yield loss (Keeley and Thullen, 1991). However, weeds like A. palmeri can be detrimental even at later stages of crop growth, as substantial seed production is possible even under competitive environments (Ward et al, 2013). A. palmeri can significantly shade cotton and prevent it from reaching its full yield potential (Morgan et al, 2001).…”

Section: Weed Flora and Their Competitiveness In Cottonmentioning

confidence: 99%

Weed management in cotton (Gossypium hirsutum L.) through weed-crop competition: A review

Manalil

Coast

Werth³

et al. 2017

Crop Protection

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a b s t r a c tThe agriculture sector is embracing energy efficient conservation systems and technological innovations to meet the ever increasing demand for food, fibre, and fuel in tune with the rapidly increasing human population. The genetic modification of plants is one of the technological innovations that is adopted rapidly across the world. In cotton, many major producing countries have adopted herbicide-tolerant genetically modified crops. Over-reliance on herbicides for weed management in both genetically modified and conventional systems has led to the rapid evolution of herbicide-resistant weeds. Poor weed management can cause up to 90% yield loss in cotton. Undoubtedly, integration of non-chemical methods and diversifying weed control options would ensure the sustainability of available weed management options, including herbicides. Increasing crop competitiveness is one of the approaches that could be integrated with the current weed management systems. Choosing cultivars with early vigour, use of narrow row planting, orienting crop rows with regard to sunlight, and adjusting planting density are some of the approaches that could enhance the competitiveness of crops over weeds. Review of the available literature on cotton indicates weed suppressive benefits by enhancing crop competitiveness through increasing planting density and narrow row spacing. Early canopy closure in narrow row spaced systems would suppress many problem weeds. In addition, herbicide efficacy may be increased due to competition offered by a dense crop stand, which may reduce herbicide selection pressure on weeds. However, the use of narrow row spacing is still in an infant stage in many cottongrowing countries and the success may depend on the environment, soil type, and resource availability. This review analyses and reports the potential benefits of increasing crop competition as a weed management option and also highlights research to be undertaken to ensure the adoption of different strategies on a much wider scale.

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Palmer Amaranth (Amaranthus palmeri): A Review

Cited by 292 publications

References 109 publications

DETECTION OF GLYPHOSATE-RESISTANT PALMER AMARANTH (Amaranthus palmeri) IN AGRICULTURAL AREAS OF MATO GROSSO, BRAZIL

DETECTION OF GLYPHOSATE-RESISTANT PALMER AMARANTH (Amaranthus palmeri) IN AGRICULTURAL AREAS OF MATO GROSSO, BRAZIL

Using Vegetation Indices as Input into Random Forest for Soybean and Weed Classification

Weed management in cotton (Gossypium hirsutum L.) through weed-crop competition: A review

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