Cotton Maturity Determination through Vertical Mapping

Schaefer, Curtis R.; Nichols, Bob; Collins, Guy; Whitaker, Jared R.; Bednarz, Craig W.; Main, Chris L.; Ritchie, Glen L.

doi:10.2135/cropsci2016.03.0168

Cited by 13 publications

(14 citation statements)

References 21 publications

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“…One of the first reports on the cotton pattern of flowering was published by McClelland (1916), who observed vertical and horizontal flowering intervals of 3 and 6 d on sympodial branches, respectively, resulting in a peculiar spirally distributed sequence. Schaefer et al (2017) observed that the chronological order previously described is still a general pattern. Schaefer et al (2017) observed that the chronological order previously described is still a general pattern.…”

Section: Shading and Nitrogen Effects On Cotton Earliness Assessed Bymentioning

confidence: 67%

“…This spirally outward and upward chronological order of flowering is currently a reference system used in phenological scales (Oosterhuis, 1999;Marur and Ruano, 2001). Schaefer et al (2017) observed that the chronological order previously described is still a general pattern.…”

Section: Shading and Nitrogen Effects On Cotton Earliness Assessed Bymentioning

confidence: 67%

“…The application of the two-parameter logistic regression estimates the median PP (x 0 ) as an additional index to evaluate the earliness of crop maturity. Schaefer et al (2017) also suggested that the fruiting site at which 50% of the crop production is accumulated provides an accurate estimate of the relative maturity of the plant. The two-parameter logistic regression successfully fitted to A c values as a function of PP indicates the yield distribution throughout the PP scale as an approximately normal pattern, where x 0 is the theoretically most productive position within the plant.…”

Section: Discussionmentioning

confidence: 99%

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Shading and Nitrogen Effects on Cotton Earliness Assessed by Boll Yield Distribution

2019

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Shading and N fertilization affect fruit distribution in cotton (Gossypium hirsutum L.), but there is no detailed information on earliness of crop maturity according to phenological development. The aim of this work was to evaluate the effect of shading at early flowering and N top‐dressing rates on relative cotton earliness using plant mapping. Field experiments were conducted in Itapeva (16 d of shading; and 0, 60, 120, and 180 kg N ha−1) and Chapadão do Sul (17 d of shading; 0, 80, and 160 kg N ha−1; and early‐ and full‐season cultivars), Brazil. Seed cotton yield was grouped by phenological position (PP) according to the standard phenological scale, for interpolation calculation at each 10% increment in accumulated harvestable yield (Ac), weighted average PP (PPwa) determination, and logistic‐regression analysis. Crop maturity earliness was predicted based on the reduction in PPwa and in PP. Shading increased PP up to 10 and 30% of Ac due to a decrease of 33 and 40% in the number of bolls on early fruiting sites in Itapeva and Chapadão do Sul, respectively, but did not affect PPwa. Increases in PP up to high Ac percentages and in PPwa values were observed at the two higher N rates in both experiments, mainly due to lower and higher boll number at earlier and later fruiting sites, respectively. Short‐term shading during early flowering of cotton changed yield distribution by decreasing boll number on early fruiting sites, but did not affect the earliness of crop maturity. Earliness was decreased by high N rates due to higher cumulative seed cotton yield at later fruiting sites.

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Section: Shading and Nitrogen Effects On Cotton Earliness Assessed Bymentioning

confidence: 67%

Section: Shading and Nitrogen Effects On Cotton Earliness Assessed Bymentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

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Shading and Nitrogen Effects on Cotton Earliness Assessed by Boll Yield Distribution

2019

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“…Often node‐by‐node boll fraction, which is the fraction of plants with a boll at a given fruiting site, is used to quantify the number of plants within a population that would be expected to have fruit at an individual fruiting site. Because node‐by‐node analysis can be limited by small individual node‐to‐node differences between cultivars (Schaefer et al., 2017), some researchers have suggested instead using the accumulated fraction of total bolls by mainstem node to estimate yield accumulation over time (Bednarz & Nichols, 2005; Ritchie et al., 2009; Whitaker, Ritchie, Bednarz, & Mills, 2008). Presenting boll accumulation fraction by mainstem node, with a box and whisker plot, is one way to visualize how a cultivar matures and to estimate yield (Schaefer et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Plant mapping is a dynamic way to understand the growth and development of cotton. However, the contradistinction between fruiting habits and fruit distributions between plants creates a significant challenge to mapping (Schaefer et al., 2017). The process can be very arduous and labor‐intensive.…”

Section: Introductionmentioning

confidence: 99%

Cotton boll distribution and yield estimation using three‐dimensional point cloud data

et al. 2020

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Node-by-node boll mapping has been used to determine the effects of several environmental and management strategies, including irrigation rate and cultivar, on cotton (Gossypium hirsutum L.) boll distribution. The advent of consumer 3D digital imaging may make rapid node-by-node mapping of greater acreage possible in the future. Effects of irrigation rate and cultivar on boll distribution were determined from line plots of node-specific boll distribution and boll accumulation estimate data and from vertical box and whisker plots of boll fraction using data collected by a 3D sensor system to rapidly detect open bolls before harvest over three seasons. Differences were observed between the dryland (2018 only), low irrigation, and high irrigation rates in terms of boll fraction and boll accumulation for each cultivar. The low irrigation tended to produce bolls more toward the bottom of the plant, while the high irrigation produced bolls towards the top of the plant. Yield correlation between sensor obtained and manually counted measurements was strong, with r 2 values as high as 0.87. Results obtained indicated that differences among irrigation rate and cultivar were identifiable using the sensor system during the 3 yr research study. Similar systems may allow rapid, broad-scale identification of boll distribution and yield in breeding and physiology research in the future, leading to improved identification of elite cultivars and improved management practices in cotton. Abbreviations: RGB, red-green-blue; UAV, unmanned aerial vehicle This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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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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Cotton Maturity Determination through Vertical Mapping

Cited by 13 publications

References 21 publications

Shading and Nitrogen Effects on Cotton Earliness Assessed by Boll Yield Distribution

Shading and Nitrogen Effects on Cotton Earliness Assessed by Boll Yield Distribution

Cotton boll distribution and yield estimation using three‐dimensional point cloud data

Cotton boll distribution: A review

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