Wild Proso Millet (<i>Panicum miliaceum</i>) Interference in Dry Beans (<i>Phaseolus vulgaris</i>)

Wilson, R. G.

doi:10.1017/s0043174500076396

Cited by 35 publications

(27 citation statements)

References 9 publications

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“…Weed management is a very important economic component of dry bean production. Weed interference has been reported to reduce dry bean quality, interfere with harvesting efficiency, and reduce yield as much as 80% (Bassett and Munro 1985;Blackshaw 1991;Ogg and Rogers 1989;Parker and Fryer 1975;Wilson 1993;Zimdahl 1980 been recently withdrawn from the market. Imazethapyr is the only soil-applied broadleaf herbicide available to dry bean growers.…”

Section: Introductionmentioning

confidence: 99%

Responses of Various Market Classes of Dry Beans (Phaseolus vulgarisL.) to Linuron

2006

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Tolerance of eight market classes of dry beans (black, brown, cranberry, kidney, otebo, pinto, white, and yellow eye beans) to the PRE application of linuron at the rate of 2.25 and 4.50 kg ai/ha was studied at two locations in Ontario, Canada, in 2003 and 2004. The eight market classes differed in their response to linuron. Linuron PRE caused as much as 43, 20, 7, 17, 54, 36, 56, and 12% visual injury in black, brown, cranberry, kidney, otebo, pinto, white, and yellow eye beans, respectively. Linuron PRE at 2.25 kg/ha reduced plant height 38% in otebo beans and 31% in white beans. Linuron PRE at 4.50 kg/ha reduced plant height 24 to 56% in black, brown, otebo, pinto, and white beans. Shoot dry weight was reduced in otebo beans by 56% and in white beans, by 46% at the low rate. Shoot dry weight was decreased 26 to 92% in black, otebo, pinto, white, and yellow eye beans at the high rate. There were no differences in the shoot dry weight of the other market classes. Linuron PRE at the low rate reduced otebo bean yield 42% and at the high rate reduced yields by 56, 74, and 61% in black, otebo, and white beans, respectively. There was no effect on the yield of other market classes. Differences in dry bean market class tolerance to linuron exists and may be summarized for these cultivars as cranberry > kidney > brown > yellow eye > pinto > black > white > otebo. Additional research is needed to determine if cultivars within a dry bean market class differ in their response to linuron.

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Section: Introductionmentioning

confidence: 99%

Responses of Various Market Classes of Dry Beans (Phaseolus vulgarisL.) to Linuron

2006

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“…Traditionally, studies of crop yield response to weed density involved, at least implicitly, a presumption of the existence of a damage threshold referred to by Cousens (1985) as a "biological threshold weed density" (Radosevich and Holt, 1984); however, more recent perspectives challenge this notion with regard to weeds (Radosevich and Rousch, 1990;Radosevich et al, 1997). In many empirical studies conducted during recent years of the degree of crop yield loss due to competition, weed pests are regarded as competing for resources with agricultural crops, and crop yield response to weed density is frequently described by a rectangular hyperbolic model (Bell, 1995;Cardina et al, 1995;Cousens, 1985;Kwon et al, 1995;Limon-Ortega et al, 1998;Swinton et al, 1994;Wilson, 1993). As noted in Cousens (1985), use of the rectangular hyperbolic model precludes the existence of pest-tolerant response for weed pests.…”

Section: Pest-tolerant Response and Weed Pestsmentioning

confidence: 99%

Statistical analysis of pesttolerant crop response with reference to corn weed control

Morzuch

Moffitt

et al. 2000

Am J Alt Ag

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A statistical model of crop-weed competition is developed to permit, but not require, the existence of crop tolerance of weed pests. The popular rectangular hyperbolic model of crop-weed competition is shown to be a special case of the more comprehensive model. Application of the model to corn weed control in Massachusetts demonstrates its practicality and detects evidence of pest-tolerant crop response in this crop-weed system.

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“…(Norris et al. , 2001), and dry beans ( Phaseolus vulgaris L.) – Panicum miliaceum L. (Wilson, 1993). In an analysis of 162 data sets on weed seed production, Canner et al.…”

Section: Introductionmentioning

confidence: 99%

“…-Echinochloa crusgalli (L.) Beauv. (Norris et al, 2001), and dry beans (Phaseolus vulgaris L.) -Panicum miliaceum L. (Wilson, 1993). In an analysis of 162 data sets on weed seed production, Canner et al (2002) found that the relationship between weed density and fecundity varied by at least twofold and by as much as 24-fold between different growing conditions and management systems for a given crop-weed combination.…”

Section: Introductionmentioning

confidence: 99%

Assessing variability in fecundity of Amaranthus powellii using a simulation model

Brainard¹,

Bellinder

2004

Weed Research

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Summary Estimates of weed fecundity and its variability are critical for the development of population dynamic models and for evaluating the long‐term consequences of weed management practices. The purpose of this research was to estimate Amaranthus powellii fecundity across years, seasons and competitive environments using a mechanistic model. Existing models were modified to account for weed responses to shade, and to dynamically simulate seed production among subthreshold densities of A. powellii. The model was parameterized and tested using five sets of field data in which A. powellii was grown either alone or with transplanted broccoli. The model overestimated A. powellii height, but predicted both dry weight and fecundity well. Mean simulated fecundity for A. powellii ranged from 0 to 268 000 seeds per plant depending on year, crop maturity date, relative time of emergence and location. Year‐to‐year variability in simulated fecundity was large with coefficients of variation ranging from 77 to 128 under low and high competitive environments respectively. Our results suggest that estimates of weed fecundity based on 1 or 2 years of empirical data may result in significant errors in population dynamic models, and that the use of economic optimum thresholds based on weed density alone entails considerable risks.

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Wild Proso Millet (Panicum miliaceum) Interference in Dry Beans (Phaseolus vulgaris)

Cited by 35 publications

References 9 publications

Responses of Various Market Classes of Dry Beans (Phaseolus vulgarisL.) to Linuron

Responses of Various Market Classes of Dry Beans (Phaseolus vulgarisL.) to Linuron

Statistical analysis of pesttolerant crop response with reference to corn weed control

Assessing variability in fecundity of Amaranthus powellii using a simulation model

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