Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress

Hazratkulova, S.; Sharma, R. C.; Alikulov, Safar; Islomov, Sarvar; Yuldashev, Tulkin; Ziyaev, Zafar; Khalikulov, Z.; Ziyadullaev, Zokhid; Turok, J.

doi:10.1111/pbr.12003

Cited by 34 publications

(23 citation statements)

References 17 publications

(43 reference statements)

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“…The predictive value of NDVI for grain yield and other traits can be improved via some modifications in NDVI calculations, such as degree of NDVI reduction after anthesis (Hazratkulova et al, 2012), NDVI ratio before and after anthesis, and cumulative NDVI after anthesis (Li et al, 2011). There is value in relating spectral reflectance parameters to canopy and air temperature to reduce the effect of weather and differences related to maturity variation between genotypes (Kimball et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments

Morgounov¹,

Gummadov²,

Belen³

et al. 2014

Turk J Agric For

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The normalized difference vegetation index (NDVI) is gaining popularity as a complementary selection tool, even though it requires an instrument not readily available in the developing world. We evaluated several parameters (originating from the analysis of digital photos using BreedPix software) as potential selection criteria in 23 winter wheat yield trials grown over 4 years at 2 sites. NDVI and digital photos were taken at key development stages from stem elongation to maturity. The correlations between digital photo parameters a and u and grain yield, as well as correlations between NDVI and grain yield within individual trials, varied depending on crop stage, moisture availability, and germplasm composition. NDVI, photo-a, and photo-u parameters had equal power in distinguishing high-and low-yielding genotypes in the trials and were significantly associated with yield in approximately 50% of all observations. The association of vegetative indices with grain yield can be improved by evaluating germplasm with a similar maturity range. An important challenge is in utilizing these tools in unreplicated small plots, including head rows where selection efficiency is low.

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Section: Discussionmentioning

confidence: 99%

Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments

Morgounov¹,

Gummadov²,

Belen³

et al. 2014

Turk J Agric For

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show abstract

“…This analysis aimed to provide baseline information on the relationship between key environmental parameters and the reflectance of the bands/VIs. The bands and VIs were chosen within the ones currently used in other crops to characterize heat stress [31,[73][74][75][76]. The examination was carried out on the entire trial area using the Sentinel-2 time series available over the two growing seasons, 2016-2017 and 2017-2018 (31 images).…”

Section: Analysis Of Remote Sensing Datamentioning

confidence: 99%

Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards

et al. 2019

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Heatwaves are common in many viticultural regions of Australia. We evaluated the potential of satellite-based remote sensing to detect the effects of high temperatures on grapevines in a South Australian vineyard over the 2016–2017 and 2017–2018 seasons. The study involved: (i) comparing the normalized difference vegetation index (NDVI) from medium- and high-resolution satellite images; (ii) determining correlations between environmental conditions and vegetation indices (Vis); and (iii) identifying VIs that best indicate heatwave effects. Pearson’s correlation and Bland–Altman testing showed a significant agreement between the NDVI of high- and medium-resolution imagery (R = 0.74, estimated difference −0.093). The band and the VI most sensitive to changes in environmental conditions were 705 nm and enhanced vegetation index (EVI), both of which correlated with relative humidity (R = 0.65 and R = 0.62, respectively). Conversely, SWIR (short wave infrared, 1610 nm) exhibited a negative correlation with growing degree days (R = −0.64). The analysis of heat stress showed that green and red edge bands—the chlorophyll absorption ratio index (CARI) and transformed chlorophyll absorption ratio index (TCARI)—were negatively correlated with thermal environmental parameters such as air and soil temperature and growing degree days (GDDs). The red and red edge bands—the soil-adjusted vegetation index (SAVI) and CARI2—were correlated with relative humidity. To the best of our knowledge, this is the first study demonstrating the effectiveness of using medium-resolution imagery for the detection of heat stress on grapevines in irrigated vineyards.

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“…However, there are also contradictory reports regarding the relationship between NDVI measured at different growth stages and yield (Hazratkulova et al 2012). An association between NDVI and yield during the grain-filling stage in wheat has been reported by some researchers (Hazratkulova et al 2012;Freeman et al 2003), while other studies showed associations of yield and NDVI at the booting, heading and grain-filling stages (Babar et al 2006b). Identification of common chromosome regions shared by NDVI and biomass and yield should be helpful in understanding the genetic basis of the relationship between these traits, and should allow breeders to utilize the trait as an indirect selection criterion for grain yield improvement.…”

Section: Introductionmentioning

confidence: 99%

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Shi

Azam

et al. 2017

Euphytica

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Wheat (Triticum aestivum L.) yield is directly proportional to physio-morphological traits. A high-density genetic map consisting of 2575 markers was used for mapping QTL controlling stay-green and agronomic traits in wheat grown under four diverse water regimes. A total of 108 additive QTL were identified in target traits. Among them, 28 QTL for chlorophyll content (CC) were detected on 11 chromosomes, 43 for normalized difference vegetation index (NDVI) on all chromosomes except 5B, 5D, and 7D, five for spikes per plant (NSP) on different chromosomes, nine for plant height (PH) on four chromosomes, and 23 for thousand-kernel weight (TKW) on 11 chromosomes. Considering all traits, the phenotypic variation explained (PVE) ranged from 3.61 to 41.62%. A major QTL, QNDVI.cgb-5A.7, for NDVI with a maximum PVE of 20.21%, was located on chromosome 5A. A stable and major PH QTL was observed on chromosome 4D with a PVE close to 40%. Most distances between QTL and corresponding flanking markers were less than 1 cM, and approximately one-third of the QTL coincided with markers. Each of 16 QTL clusters on 10 chromosomes controlled more than one trait and therefore could be regarded as pleiotropic regions in response to different water regimes. Forty-one epistatic QTL were identified for all traits having PVE of 6.00 to 25.07%. Validated QTL closely linked to flanking markers will be beneficial for marker-assisted selection in improving drought-tolerance in wheat.

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Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress

Cited by 34 publications

References 17 publications

Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments

Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments

Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

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