Lint Yield Compensatory Response to Main Stem Node Removal in Upland Cotton (<i>Gossypium hirsutum</i>)

Yang, Seung-Hwan; Kaggwa, Ruth J.; Andrade-Sánchez, Pedro; Zarnstorff, Mark; Wang, G.

doi:10.1111/jac.12142

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…Furthermore, Smith and Varvil (1981) also observed that when the same severity of damage occurred in older plants, decreased recoverability also occurred which contradicts the findings of this research. Additionally, research conducted by Yang, Kaggwa, Andrade‐Sanchez, Zarnstorff, and Wang (2016) found that yield loss due to main stem node removal was compensated by increased boll number on vegetative branches, thus resulting in no difference in yield response to main stem node removal. Results from this research indicate no yield penalty may be observed if mainstem node removal is low late in the season, but that yield penalties will be likely as even slight damage (two node removal) occurs early in the season (four‐leaf stage).…”

Section: Resultsmentioning

confidence: 99%

Effect of physical damage to early‐ and late‐maturing cotton cultivars

et al. 2020

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Determining the effects of timing and intensity of physical damage in cotton (Gossypium hirsutum L.) grown in the Mid-South could improve management decisions and insurance adjustments after weather-or wildlife-related crop damage has occurred. Research was conducted to quantify the effects of time of damage and intensity of damage on early-and late-maturing cotton varieties in the Mid-South. Cultivar selection affected plant height at first bloom, first bloom + 4 wk, and harvest (p ≤ .0008), and number of nodes at first bloom + 4 wk and harvest (p ≤ .0372). The interaction of intensity of damage by time of damage had an effect on plant height and number of nodes at pinhead square, first bloom, first bloom + 4 wk, and at harvest (p ≤ .0001), where reductions in plant height ranged from 20 to 57% and the number of nodes were reduced by 12-64% as damage increased when compared to the untreated check. Damage intensity by time of damage interaction affected lint turnout, lint yield, micronaire, fiber length, and fiber strength (p ≤ .0391). Reductions in lint yield (11-70%), lint turnout (2-6%), and micronaire (3-8%) were observed. Increases of 3 and 2% in fiber length and fiber strength, respectively, were also observed at eight node removal at pinhead square. Overall, as the intensity of damage increased, lint yield declined regardless of growth stage, therefore it should be expected to have yield loss where damage has occurred in cotton.

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Section: Resultsmentioning

confidence: 99%

Effect of physical damage to early‐ and late‐maturing cotton cultivars

et al. 2020

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“…In this study, the yield reduction at different damage stages was the premise of developing yield prediction model at different damage stages. This may be correlated with cotton recoverability following hail damage, which has been proven to decrease with increasing damage levels and damage to older plants (Longer & Oosterhuis, 1999; Smith & Varvil, 1981; Yang et al., 2016). At the early growth stage of cotton, defoliation changes the canopy structure of the plant, increases light interception of bottom leaves and the carbon dioxide absorption rate of remaining leaves, promoting the rapid increase of net photosynthetic rate and transportation of photosynthetic products to vegetative organs (Lu et al., 2019; Prins & Verkaar, 1992).…”

Section: Discussionmentioning

confidence: 99%

Prediction of cotton yield reduction after hail damage using a UAV‐based digital camera

et al. 2021

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To elucidate different performances of cotton (Gossypium hirsutum L.) yield reduction prediction model at multiple damage stages under different hailstorm damage levels, six times (I-VI, no repeated damage) of hail damage simulation treatments with four damage levels (0, 30, 60, and 90%) for modeling and natural hailstorm tracking experiment for model verification were conducted in 2017-2019. Eleven image parameters derived from an unmanned aerial vehicle-based digital camera were analyzed. In addition, four regression methods (traditional regression analysis, partial least squares regression (PLSR), support vector regression, and back-propagation neural network) were employed to develop a prediction model of cotton yield reduction. The results indicate that cotton yield prediction model at each damage stage performed better than a general model developed for all damage stages. Considering the bloom stage (75 d after sowing) as a boundary, three PLSR models based on multiple image parameters were developed for pre-bloom hail damage. For post-bloom hail damage, a general quadratic polynomial model based on relative canopy cover performed the best among all regression models. In conclusion, for pre-bloom hail damage, the yield prediction model for each damage stage should be developed, whereas for post-bloom hail damage, a general yield prediction model should be developed. The results of this work might serve as a guide for farmers and agricultural insurance companies to estimate hailstorm damages quickly and accurately. INTRODUCTIONHailstorm, as natural damage, negatively impacts crop growth and ultimately crop yield (Jalali, 2013;Smith & Varvil, 1981;G. Wang et al., 2011). After hail damage, rapid and accurate yield prediction is greatly significant to formulate compensation standards and manage field recovery. However, due to the effectiveness of satellite image acquisition and low spectral

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“…As a result, hand‐harvesting in the United States has become a research tool, providing researchers and agronomists with a view into the subtle interactions between cotton plants in time and space. Each fruit on the plant represents an individual flowering date, so the distribution of fruit on the plant represents the plant's response to its environment (Hawkins & Peacock, 1973), including weather and water availability (Bauer, Foulk, Gamble, & Sadler, 2009; Bednarz & Nichols, 2005; Bednarz, Nichols, & Brown, 2006; Jenkins, McCarty, & Parrott, 1990; Snowden, Ritchie, Cave, Keeling, & Rajan, 2013), fertility (Bondada, Oosterhuis, Norman, & Baker, 1996; Boquet, Moser, & Breitenbeck, 1993), planting density (Bednarz et al., 2006; Sadras, 1997), weed and insect pressure (Bednarz & Roberts, 2001; Mills, Bednarz, Ritchie, & Whitaker, 2008; Sadras, 1995; Wilson, Sadras, Heimoana, & Gibb, 2003), and short‐term catastrophic events, such as hailstorms or off‐target chemical sprays (Byrd et al., 2016; Yang, Kaggwa, Andrade‐Sanchez, Zarnstorff, & Wang, 2016), all with respect to time.…”

Section: Introductionmentioning

confidence: 99%

Cotton boll distribution: A review

et al. 2021

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Boll distribution provides an important assessment of the response and adaptation of upland cotton (Gossypium hirsutum L.) to its environment and is strongly linked to yield and fiber quality and consequently to economic value. Several studies have measured boll distribution through end-of-season field or box mapping to study the spatiotemporal characteristics of bolls at both the plant and field levels, to quantify the effects of stress on cotton maturity and fiber quality, and to estimate yield as a function of the relative partitioning of resources to competing sinks. This paper discusses the environmental factors affecting boll production and distribution, reviews methods of measuring and analyzing boll distribution, and discusses the comparative advantages and drawbacks of these methods. With the advent of advanced imaging technologies, this paper also discusses the potential transition from traditional methods to machine-based boll distribution measurements, the challenges associated with these technologies, and how continuous improvements in the baseline technologies being used in the different methods will translate to improved measurement capacities. Overall, major strides can still be made in the area of boll distribution measurement by combining the positive elements in the different studies described in this review to circumvent each of their respective limitations.

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Lint Yield Compensatory Response to Main Stem Node Removal in Upland Cotton (Gossypium hirsutum)

Cited by 10 publications

References 17 publications

Effect of physical damage to early‐ and late‐maturing cotton cultivars

Effect of physical damage to early‐ and late‐maturing cotton cultivars

Prediction of cotton yield reduction after hail damage using a UAV‐based digital camera

Cotton boll distribution: A review

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