Optimizing Irrigation and Plant Density for Improved Cotton Yield and Fiber Quality

Feng, Lu; Mathis, Garrett; Ritchie, Glen L.; Han, Yinchun; Li, Yabing; Wang, Guoping; Zhi, Xiaoyu; Bednarz, Craig W.

doi:10.2134/agronj13.0503

Cited by 35 publications

(43 citation statements)

References 26 publications

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“…This was due to variations in growing season temperature and rainfall, which affected fiber maturation. Several studies reported a decrease in fiber micronaire as irrigation rate increased (Bauer and Frederick, 2005; Basal et al, 2009; Dagdelen et al, 2009; Feng et al, 2014).…”

Section: Results and Dissucionmentioning

confidence: 99%

Yield, Quality, and Spectral Reflectance Responses of Cotton under Subsurface Drip Irrigation

Attia¹,

Rajan

Ritchie

et al. 2015

Agronomy Journal

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In the semiarid Texas Rolling Plains, the growth and yield of cotton (Gossypium hirsutum L.) is driven by the amount of water available to the crop through irrigation and precipitation. A fi eld study was conducted in 2012 and 2013 at Chillicothe, TX, to investigate the growth, yield, water use effi ciency (WUE), and spectral refl ectance responses of cotton. A split-split plot design with three replications was used with irrigation as the main plot (three irrigation levels and dryland), tillage (no tillage and conventional tillage) as the subplot, and cultivars (PHY499, DP1044, PHY375, and FM9170) as the sub-subplot treatments. Plant height, lint yield, WUE, and fi ber quality were signifi cantly aff ected by irrigation and irrigation × cultivar interaction. Increasing irrigation level resulted in a linear increase in lint yield and WUE. Th e irrigation × cultivar interaction showed that the 90% evapotranspiration (ET) replacement treatment with PHY375 produced the greatest lint yield and WUE. Tillage did not signifi cantly aff ect lint yield, WUE, or fi ber quality. Increasing irrigation resulted in a linear increase in fi ber length and strength, and a linear decrease in fi ber micronaire. Two vegetation indices, normalized diff erence vegetation index (NDVI) and normalized diff erence water index (NDWI) were calculated using spectral refl ectance measurements in this study. Th e NDWI increased steadily for all irrigation treatments during the measurement period. However, NDVI exhibited little change during the peak growth period between 79 and 89 d aft er planting. We suggest further investigation on using NDWI for irrigation management for cotton in the Texas Rolling Plains.

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Section: Results and Dissucionmentioning

confidence: 99%

Yield, Quality, and Spectral Reflectance Responses of Cotton under Subsurface Drip Irrigation

Attia¹,

Rajan

Ritchie

et al. 2015

Agronomy Journal

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“…Higher crop yield with increased plant density has been reported in many field crops, including cowpea (Vigna unguiculata L.) (El Naim and Jabereldar 2010), cotton (Gossypium hirsutum L.) (Feng et al 2014), maize (Zea mays) (Amanullah et al 2009;Antonietta et al 2014), rapeseed (Brassica napus) (Wang et al 2015), and winter wheat (Triticum aestivium) (Dai et al 2014). In these crops, seed yield is usually associated with plant morphological and physiological traits, such as plant density.…”

Section: Discussionmentioning

confidence: 99%

“…More available water postflower may increase seed yield but may decrease the weight of individual seeds (Feng et al 2014). In the present study, precipitation varied among growing seasons (1 May-31 August) from an average of 213 ± 2.7 mm (dry) to 308 ± 2.8 mm (normal) to 418 ± 1.9 mm (wet).…”

Section: Maturity and Plant Densitymentioning

confidence: 99%

Canola seed yield and phenological responses to plant density

et al. 2016

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Optimal plant density is required to improve plant phenological traits and maximize seed yield in field crops. In this study, we determined the effect of plant density on duration of flowering, post-flowering phase, and seed yield of canola in diverse environments. The field study was conducted at 16 site-years across the major canola growing area of western Canada from 2010 to 2012. The cultivar InVigor® 5440, a glufosinate-resistant hybrid, was grown at five plant densities (20, 40, 60, 80, and 100 plants m −2 ) in a randomized complete block design with four replicates. Canola seed yield had a linear relationship with plant density at 8 of the 16 site-years, a quadratic relationship at 4 site-years, and there was no correlation between the two variables in the remaining 4 site-years. At site-years with low to medium productivity, canola seed yield increased by 10.2 to 14.7 kg ha −1 for every additional plant per square metre. Averaged across the 16 diverse environments, canola plants spent an average of 22% of their life cycle flowering and another 27% of the time filling seed post-flowering. Canola seed yield had a negative association with duration of flowering and a positive association with the days post-flowering but was not associated with number of days to maturity. The post-flowering period was 12.7, 14.7, and 12.6 d (or 55, 68, and 58%) longer in high-yield experiments than in low-yield experiments in 2010, 2011, and 2012, respectively. We conclude that optimization of plant density for canola seed yield varies with environment and that a longer post-flowering period is critical for increasing canola yield in western Canada.Key words: flowering duration, reproductive phase, diverse environments, seeding rates, pod to flower ratio.Résumé : Les plantes doivent avoir une densité optimale pour qu'il y ait amélioration de leurs caractères phéno-logiques et pour que le rendement grainier atteigne son maximum. Dans le cadre de cette étude, les auteurs ont déterminé les effets de la densité du peuplement sur la durée de la floraison, la phase post-floraison et le rendement grainier du canola dans diverses conditions. L'étude sur le terrain, qui portait sur 16 sites-années dans la principale zone de culture du canola de l'Ouest canadien, a duré de 2010 à 2012. Le cultivar InVigor® 5440, hybride résistant au glufosinate, a été cultivé à cinq densités (20, 40, 60, 80 et 100 plants m-2 ) dans le cadre d'une étude en blocs aléatoires complets avec quatre répétitions. Le rendement grainier du canola présentait une relation linéaire avec la densité de peuplement à 8 sur 16 sites-années et une relation quadratique à 4 sites-années, mais aucune corrélation n'associait ces deux variables aux quatre autres sites-années. Aux sites-années se caractérisant par une productivité faible à moyenne, le rendement grainier du canola a augmenté de 10,2 à 14,7 kg ha -1 pour chaque plant supplémentaire par mètre carré. Lorsqu'on calcule la moyenne pour les 16 environnements, on constate que les plants de canola consacrent...

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“…Cotton is a crop whose perennial nature results in a flexible growth habit, with a relatively flexible relationship between in-field plant density and yield [1]. This flexibility complicates stand assessments and replant decisions [3][4][5][6][7].…”

Section: Of 14mentioning

confidence: 99%

“…Variation from the desired populations and distribution of plants within a field can reduce yield as compared to a more uniform planting [1][2][3][4]. Understanding the resource demands of the crop (radiation, water, and nutrition), and planting in a manner that provides for those needs, is an essential part of modern agriculture.…”

Section: Introductionmentioning

confidence: 99%

BLOB-Based AOMs: A Method for the Extraction of Crop Data from Aerial Images of Cotton

et al. 2020

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The use of aerial imagery in agriculture is increasing. Improvements in unmanned aerial systems (UASs) and the hardware and software used to analyze imagery are presenting new options for agricultural studies. One of the challenges associated with improving crop performance under water deficit conditions is the increased variability in the growth and development inherent in low water settings. The nature of plant growth and development under water deficits makes it difficult to monitor the response to environmental changes. Small field and plot-level experiments are often variable enough that averages of seasonal crop characteristics may be of limited value to the researcher. This variability leads to a desire to resolve fields on finer temporal and spatial scales. While UAS imagery provides an ability to monitor the crop on a useful temporal scale, the spatial scale is still difficult to resolve. In this study, an automated computer software framework was developed to facilitate resolving field and plot-level crop imagery to finer spatial resolutions. The method uses a Binary Large Object (BLOB)-based algorithm to automate the generation of areas of measurement (AOMs) as a tool for crop analysis. The use of the BLOB-based system is demonstrated in the analysis of plots of cotton grown in Lubbock, Texas, during the summer of 2018. The method allowed the creation and analysis of 1133 AOMs from the plots and the extraction of agronomic data that described plant growth and development.

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Optimizing Irrigation and Plant Density for Improved Cotton Yield and Fiber Quality

Cited by 35 publications

References 26 publications

Yield, Quality, and Spectral Reflectance Responses of Cotton under Subsurface Drip Irrigation

Yield, Quality, and Spectral Reflectance Responses of Cotton under Subsurface Drip Irrigation

Canola seed yield and phenological responses to plant density

BLOB-Based AOMs: A Method for the Extraction of Crop Data from Aerial Images of Cotton

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