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High-throughput phenotyping platforms (HTPPs) provide novel opportunities to more effectively dissect the genetic basis of drought-adaptive traits. This genome-wide association study (GWAS) compares the results obtained with two Unmanned Aerial Vehicles (UAVs) and a ground-based platform used to measure Normalized Difference Vegetation Index (NDVI) in a panel of 248 elite durum wheat (Triticum turgidum L. ssp. durum Desf.) accessions at different growth stages and water regimes. Our results suggest increased ability of aerial over ground-based platforms to detect quantitative trait loci (QTL) for NDVI, particularly under terminal drought stress, with 22 and 16 single QTLs detected, respectively, and accounting for 89.6 vs. 64.7% phenotypic variance based on multiple QTL models. Additionally, the durum panel was investigated for leaf chlorophyll content (SPAD), leaf rolling and dry biomass under terminal drought stress. In total, 46 significant QTLs affected NDVI across platforms, 22 of which showed concomitant effects on leaf greenness, 2 on leaf rolling and 10 on biomass. Among 9 QTL hotspots on chromosomes 1A, 1B, 2B, 4B, 5B, 6B, and 7B that influenced NDVI and other drought-adaptive traits, 8 showed per se effects unrelated to phenology.
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ORIGINAL RESEARCH
published: 26 June 2018
doi: 10.3389/fpls.2018.00893
Frontiers in Plant Science | www.frontiersin.org 1June 2018 | Volume 9 | Article 893
Edited by:
Roberto Papa,
Università Politecnica delle Marche,
Italy
Reviewed by:
Pasquale De Vita,
Consiglio per la Ricerca in Agricoltura
e l’Analisi dell’Economia Agraria
(CREA), Italy
Frédéric Marsolais,
Agriculture and Agri-Food Canada
(AAFC), Canada
*Correspondence:
Marco Maccaferri
marco.maccaferri@unibo.it
Specialty section:
This article was submitted to
Plant Breeding,
a section of the journal
Frontiers in Plant Science
Received: 10 January 2018
Accepted: 07 June 2018
Published: 26 June 2018
Citation:
Condorelli GE, Maccaferri M,
Newcomb M, Andrade-Sanchez P,
White JW, French AN, Sciara G,
Ward R and Tuberosa R (2018)
Comparative Aerial and Ground
Based High Throughput Phenotyping
for the Genetic Dissection of NDVI as
a Proxy for Drought Adaptive Traits in
Durum Wheat. Front. Plant Sci. 9:893.
doi: 10.3389/fpls.2018.00893
Comparative Aerial and Ground
Based High Throughput Phenotyping
for the Genetic Dissection of NDVI as
a Proxy for Drought Adaptive Traits in
Durum Wheat
Giuseppe E. Condorelli 1, Marco Maccaferri 1
*, Maria Newcomb 2,
Pedro Andrade-Sanchez 2, Jeffrey W. White3, Andrew N. French 3, Giuseppe Sciara 1,
Rick Ward 2and Roberto Tuberosa 1
1Department of Agricultural Sciences, University of Bologna, Bologna, Italy, 2Maricopa Agricultural Center, University of
Arizona, Tucson, AZ, United States, 3US Arid Land Agricultural Research Center, USDA-ARS, Maricopa, AZ, United States
High-throughput phenotyping platforms (HTPPs) provide novel opportunities to more
effectively dissect the genetic basis of drought-adaptive traits. This genome-wide
association study (GWAS) compares the results obtained with two Unmanned Aerial
Vehicles (UAVs) and a ground-based platform used to measure Normalized Difference
Vegetation Index (NDVI) in a panel of 248 elite durum wheat (Triticum turgidum L. ssp.
durum Desf.) accessions at different growth stages and water regimes. Our results
suggest increased ability of aerial over ground-based platforms to detect quantitative trait
loci (QTL) for NDVI, particularly under terminal drought stress, with 22 and 16 single QTLs
detected, respectively, and accounting for 89.6 vs. 64.7% phenotypic variance based on
multiple QTL models. Additionally, the durum panel was investigated for leaf chlorophyll
content (SPAD), leaf rolling and dry biomass under terminal drought stress. In total,
46 significant QTLs affected NDVI across platforms, 22 of which showed concomitant
effects on leaf greenness, 2 on leaf rolling and 10 on biomass. Among 9 QTL hotspots
on chromosomes 1A, 1B, 2B, 4B, 5B, 6B, and 7B that influenced NDVI and other
drought-adaptive traits, 8 showed per se effects unrelated to phenology.
Keywords: Triticum turgidum L. subsp. durum, durum wheat, drought, high-throughput phenotyping, UAV, NDVI,
GWAS, QTL
INTRODUCTION
Global warming and the increasing frequency and severity of drought events unequivocally
underline the urgency to select crops able to sustain growth in rainfed conditions, particularly
when grown in Mediterranean countries, where climatic change is expected to exacerbate yield
uncertainty (Ortiz et al., 2008; Kelley et al., 2015; Kyratzis et al., 2017). The selection of
drought-resistant cultivars increasingly relies on the use of yield-related proxies selected either
directly (Reynolds and Tuberosa, 2008) or via marker-assisted selection once the quantitative
trait loci (QTLs) underpinning the variability of the relevant trait are identified (Langridge and
Reynolds, 2015; Maccaferri et al., 2016; Mason et al., 2018).
Condorelli et al. GWAS for NDVI in Wheat
The recent progress in high-throughput phenotyping
platforms (HTTPs) based primarily on the use of ground-
based and/or Unmanned Aerial Vehicles (UAVs) provides
unprecedented opportunities to accurately measure proxy traits
in hundreds of plots (Pauli et al., 2016; Duan et al., 2017; Shakoor
et al., 2017; Shi et al., 2017; Trapp et al., 2017), as required
in experiments to identify QTLs. In this respect, increasing
attention is being devoted to the use of ground-based and aerial
HTPPs that allow for such high-throughput phenotyping levels
(Araus and Cairns, 2014; Zaman-Allah et al., 2015; Kefauver
et al., 2017; Madec et al., 2017). A potential limitation of
ground-based phenotyping platforms is the considerably longer
time required to complete the measurements as compared to
UAV-based remote sensing which allows phenotyping over a
larger area in less time, an important prerequisite to minimize
the effects due to daily fluctuations in environmental conditions,
inevitable in large-scale experiments (Tuberosa, 2012). However,
a potential advantage of ground-based platforms is the increased
data resolution as result of shorter distances between sensors and
plant targets. Empirical data are needed to compare benefits of
the different platforms for different experimental objectives.
Because water shortage affects vegetative state and cover,
drought-stress monitoring can be based on the use of vegetation
indices (VIs). Normalized Difference Vegetation Index (NDVI)
was found to be an effective indicator of vegetation response
to drought based on the relationships between NDVI and a
meteorologically based drought index (Ji and Peters, 2003).
NDVI is based on the difference between the maximum
absorption of radiation in the Red spectral region (from 620 to
690 nm) as result of chlorophyll pigments and the maximum
reflectance in near infrared (NIR, from 760 to 900 nm) light
as result of the leaf cellular structure (Tucker, 1979). Healthy
and living canopies absorb most of the Red light by the
photosynthetic pigments, while the NIR light is mostly reflected
due to light scattering in leaf internal structure and canopy
architecture. Therefore, NDVI-value, computed as (NIR –
Red)/(NIR +Red), integrates biomass (or leaf area) and leaf
chlorophyll content (Lukina et al., 1999), hence providing a
proxy for grain yield (Labus et al., 2010). In wheat, NDVI has
been shown to be associated with drought-adaptive traits as
well as grain yield under stressed conditions (Bort et al., 2005;
Marti et al., 2007; Reynolds et al., 2007; Lobos et al., 2014;
Bowman et al., 2015; Tattaris et al., 2016; Yousfi et al., 2016),
which ultimately allows for the identification of the relevant
QTL governing the adaptive response to drought. In this case,
it is important to account for the effects of the single QTLs
on flowering time, a trait well known to influence drought
adaptation (Tuberosa, 2012). A number of key genes (PPD-A1,
PPD-B1,FT-7A-indel, Rht-B1b, and VRN-A1) affect flowering
time and, consequently, NDVI and other drought-adaptive traits
(Milner et al., 2016). Therefore, their effects should be accounted
for when interpreting the results of QTL analyses, particularly
when aiming at identifying loci that affect drought resistance on a
per se basis, i.e., irrespectively of indirect effects due to differences
in flowering time.
Although remote sensing based on the utilization of
UAVs equipped with either conventional or hyperspectral
and multispectral cameras is being increasingly adopted as
an alternative to portable cameras and spectroradiometers to
measure NDVI in wheat (Haghighattalab et al., 2016; Holman
et al., 2016; Yang et al., 2016; Kyratzis et al., 2017) no study
has yet compared the QTL results of a genome-wide association
study (GWAS) for NDVI measured with both aerial- and ground-
based phenotyping platforms in crops under both well-watered
and water-deficit conditions of increasing severity. To our best
knowledge, this study is the first to report on the use of UAV-
based NDVI remote sensing for GWAS analysis in crops and
to compare the results with those obtained via a ground-based
HTPP. Importantly, GWAS of NDVI and other drought-adaptive
traits allowed us to identify a number of QTL hotspots with
per se effects that provide suitable targets for enhancing drought
tolerance via marker-assisted selection.
MATERIALS AND METHODS
Plant Material and Field Management
The field trial was conducted at Maricopa Agricultural Center
(33.070N, 111.974W, elevation 360 m) on a Casa Grande
soil (fine-loamy, mixed, superactive, hyperthermic Typic
Natrargids) (Supplementary Figure 1). The plant material
included 248 accessions of durum wheat from the association
mapping population UNIBO-Durum Panel (hereafter referred
to as “Durum Panel”) assembled at the University of Bologna
(UNIBO), representing a large portion of the genetic diversity
present in the most important improved durum wheat gene
pools.
The Durum Panel includes Mediterranean-adapted
accessions selected and released from breeding programs
in Italy, the International Maize and Wheat Improvement
Center (CIMMYT), the International Center for Agricultural
Research in the Dry Areas (ICARDA), the National Institute
for Agricultural Research (INRA, France) and the Institute of
Agrifood Research and Technology (IRTA, Spain). The Durum
Panel also includes accessions released by public breeding
programs in the Northern Great Plains of the USA and Canada
(North Dakota, Montana, Saskatchewan and Alberta), private
French breeders and Australian breeding programs, as well as
representative accessions from the Pacific Southwest of the US,
commonly referred to as “Desert-Durum R
” (Supplementary
Table 1).
The 248 accessions were planted on 20 December 2016
according to a Randomized Complete Block Design (RCBD) with
two replicates and border plots (cv. Orita). Each accession was
evaluated in two-row plots (3.5 m long, 0.76m apart) with a final
density of 22 plants/m2. Before planting, nitrogen at 112 kg ha1
and phosphorus (P205) at 56 kg ha1were incorporated into
the soil and 28 days after sowing, irrigation was managed by a
pressurized drip system using lines buried 10 cm deep. Drip
irrigation was stopped on 16 March 2017 and from that date the
accessions were subjected to a progressive drought stress until
3–4 April 2017 when plants were harvested to measure biomass.
Soil moisture data were collected for monitoring the water
stress conditions using time domain reflectometry (TDR) probes
(rod length: 15 cm) on 22 and 23 March 2017. TDR probes
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Condorelli et al. GWAS for NDVI in Wheat
worked in 8 equidistant field ranges by inserting the rods into
the soil and within a few seconds the moisture value is presented
on a display unit.
Plants were harvested on 105 days after planting (DAP)
to allow for planting the next phenotyping experiment and
therefore biomass data indicate the status at a point in time rather
than direct estimates of final yields.
Disease and insect pest pressure were negligible throughout
the crop.
Leaf Water Status
Relative Water Content (RWC) was measured in flag leaves
collected from the two replicates for the cultivars “Gallareta,
“Karim,” “Mexicali 75,” and “Svevo.” Flag leaves were sampled
on 24 March 2017 (DAP: 94), 27 March 2017 (DAP: 97) and 31
March 2017 (DAP: 101) placed in glass containers within a cooler
and transported immediately to the laboratory to minimize water
loss due to evaporation. Samples were weighed as fresh weight
(FW) and then submerged in distilled water. After rehydration
for 24 h at 4C in the dark, the turgid leaves were rapidly blotted
to remove surface water and weighed to obtain turgid weight
(TW). Finally, the leaves were oven-dried at 60C for 24 h and
then the dry weight (DW) was obtained. RWC-values were
computed as follows: [(FW – DW)/(TW – DW)] ×100 (Barrs,
1968).
NDVI Measurements
NDVI was measured on progressive days after planting using
two UAV-based and one tractor-based platforms and related
phenology of each accession was evaluated on the basis of the
Zadoks scale (Supplementary Table 2).
UAV-based NDVI was extracted from georeferenced
orthomosaic GeoTIFFs generated from imagery captured from
autopiloted flights of either a MicaSense RedEdge multi-spectral
camera (MicaSense, Seattle, WA) carried on a hexacopter, or
a Parrot Sequoia (Parrot, Paris, France) multi-spectral camera
carried on an eBee (SenseFly, Lausanne) fixed wing aircraft.
Table 1 compares features of the two multispectral cameras in
terms of band centers and bandwidths.
Flights were conducted at 40–42 m above ground level,
resulting in ground sampling distances of 3 cm/pixel for the
RedEdge, and 4.4 cm/pixel for the Sequoia. Mission planning was
done with UgCS (UgCS, Riga) for the RedEdge camera, and either
eMotion 3 (senseFly, Lausanne) or Atlas Flight (MicaSense,
Seattle, WA) for the Sequoia camera. All flights were planned for
80% image overlap along flight corridors. Both the Sequoia and
RedEdge cameras use global shutters.
Pix4DMapperPro desktop software (Pix4D SA, Switzerland,
http://pix4d.com) was used to generate orthomosaics for each
camera band. Six to eight ground control points (GCP)
geolocated with Real Time Kinematic (RTK) survey precision
were used to georeference the orthomosaics. Camera images
were calibrated using manufactured supplied reflectance panels
that were imaged at the beginning of each flight. The Pix4D
processing options were essentially the same as those of Pix4D’s
“Ag Multispectral” template version 4.1.10, except that GeoTIFF
tiles were merged to create the NDVI orthomosaic.
TABLE 1 | Properties of Sequoia, RedEdge, and GreenSeeker Normalized
Difference Vegetation Index (NDVI) sensors and including type of recorded
spectral band, bandcenter, and bandwidth.
Sensor Spectral
band
Band center
(nm)
Band width
(nm)
UAV-SequoiaaGreen 550 40
Red 660 40
Red Edge 735 10
NIR 790 40
Blue 475 20
Tractor-GreenSeekerbRed 660 25
NIR 770 25
UAV-RedEdgecGreen 560 20
Red 668 10
Red Edge 717 10
NIR 840 40
ahttps://www.micasense.com/parrotsequoia/
bhttps://agriculture.trimble.com/precision- ag/products/greenseeker/
chttps://www.micasense.com/
Plot-level NDVI means from UAV’s were created in QGIS
software version 2.18.3 (QGIS, US, http://www.qgis.org). Shape
files containing annotated single plot polygons were generated
with an R (r-project.org) script. Shape files with GCPs as
features (points) were also employed based on RTK survey grade
measuring devices. For all flights, the GeoTIFF with the NDVI
orthomosaic from Pix4D was combined with the plot polygon
and GCP shape files in a single QGIS project. Confirmation of
proper geolocations of the Pix4D orthomosaics was achieved
by visually confirming alignment of the visible GCPs with
the corresponding points in the feature shape file. NDVI plot
means were generated using the Zonal Statistics function in
QGIS.
The tractor-based system was similar to that described by
Andrade-Sanchez et al. (2013) but carried five GreenSeeker
spectral sensors and RT200 communication module (Trimble,
Inc., Sunnyvale, CA) mounted in a frame at the front of the
vehicle. These active sensors are equipped with their own source
of modulated white light, which is directed toward the top of
the crop canopy with the platform in motion at an average
speed of 0.84 m s1. A portion of the sensor-generated light
reflects off the crop and is measured by Red and Near Infrared
(NIR) wide-band filters located in the sensor head. The height
position of the sensors was set to 1.32 m above ground in
every event. Since the approximate view angle of this sensor
model is 28, the field-of-view (FOV) of each sensor was 50-
cm at the soil surface. The ground platform was retrofitted
with an ultra-precise RTK Global Navigation Satellite System
(GNSS) receiver, AgGPS332 (Trimble, Inc., Sunnyvale, CA) to
generate positioning data via “GGA” National Marine Electronics
Association (NMEA) messages. The data acquisition system used
in the tractor platform was a CR3000 micro-logger (Campbell
Scientific, Logan, UT) programmed to record the NDVI output
of all five spectral sensors plus latitude and longitude coordinates
at a rate of 5 Hz. The combination of data sampling frequency
Frontiers in Plant Science | www.frontiersin.org 3June 2018 | Volume 9 | Article 893
Condorelli et al. GWAS for NDVI in Wheat
and platform speed of operation produced an average of 20 NDVI
data points for each plot.
The lme4 package (r-project) and custom R scripts were used
to conduct a spatial adjustment analysis of the raw NDVI plot
data from aerial- and ground-based platforms using a mixed
procedure including row and column random effects and a
moving mean of variable size for optimizing spatial adjustment.
Repeatability values and Pearson’s correlation rcoefficients
among growth stages were also calculated in R.
Phenology Score (Zadoks System), Leaf
Chlorophyll Content (SPAD), Leaf Rolling,
and Dry Biomass Evaluation
Phenology of each accession was evaluated on the basis of the
Zadoks scale (Zadoks et al., 1974) (Supplementary Table 2).
Flag leaf “greenness” on 101 DAP was assessed based on
Soil-Plant Analysis Development (SPAD) estimates obtained
with a non-destructive chlorophyll meter SPAD-502Plus
(Konica Minolta Sensing, Inc., Japan) as an indicator of leaf
photosynthetic activity, chlorophyll content and nitrogen (N)
status. The hand-held SPAD meter operates by an illuminating
system that emits Red (650 nm) and infrared (940 nm) light
transmitted through a leaf to a receptor.
Leaf rolling (LR) was visually estimated on 99 DAP with a
score from 0 (no leaf rolling) to 9 (severely rolled).
At the end of the field trial, plants within the entire two-
row plots were cut with mechanical harvester (Carter mfg
equipment) while subsamples of 2–3 plants were collected to
evaluate moisture content in order to estimate dry biomass on
3–4 April 2017. Dry weight of the harvested plot assumed plot
dimensions of 1.5 m width and 3.5 m length and was adjusted
to 0% moisture. Plant moisture content (%) at harvest was
estimated from a subsample of biomass either placed directly in
a drying oven or stored temporarily in an uncooled greenhouse
that reached a diurnal high temperature of 60C before being
transferred to an oven at 60C for final drying.
SNP Genotyping, Population Structure, and
GWAS Model
For each accession, genomic DNA was extracted using
NucleoSpin R
8/96 Plant II Core Kit from Macherey Nagel
and sent for SNP genotyping to TraitGenetics (http://www.
traitgenetics.com/en/).
The Illumina iSelect 90K wheat SNP assay (Wang et al.,
2014) was used and genotype calls were obtained as described
in Maccaferri et al. (2015b). The tetraploid-consensus-2015
reported in Maccaferri et al. (2015a) was used to assign
polymorphisms to chromosomes and map positions.
Linkage disequilibrium (LD) among markers was calculated
in HaploView 4.2 software (Barrett et al., 2005), for each
chromosome of A and B genomes and only SNPs with known
position and with a minor allele frequency >0.05 were
considered. LD decay pattern as a function of consensus genetic
distances was inspected considering squared allele frequency
correlation (r2) estimates obtained for all pairwise comparisons
among intra-chromosomal SNPs. Curve fit and distance at which
LD decays below r20.3 were used to define the confidence
intervals of QTLs detected in this study as already reported for
the same germplasm by Liu et al. (2017) using a custom script in
R following the methodology described in Rexroad and Vallejo
(2009) and in Maccaferri et al. (2015a).
Population structure was assessed in STRUCTURE software
2.3.4 (Pritchard et al., 2000) using a reduced subset of 2,382
markers pruned for r2=0.5 using the corresponding tagger
function in Haploview 4.2 (Barrett et al., 2005).
The model-based quantitative assessment of subpopulation
memberships of the accessions was carried out in STRUCTURE
using inferences based on molecular SNP data only.
STRUCTURE model included admixture and correlated allele
frequencies among subpopulations. Numbers of hypothetical
subpopulations ranging from k=2 to 10 were assessed using
50,000 burn-in iterations followed by 100,000 recorded Markov-
Chain iterations. To estimate the sampling variance (robustness)
of population structure inference, five independent runs were
carried out for each k.
The rate of change in the logarithm of the probability
of likelihood [LnP(D)] value between successive k-values (1k
statistics, Evanno et al., 2005) together with the inspection
of the rate of variation (decline) in number of accessions
clearly attributed to subpopulations (no. of accessions with Q
membership’s coefficient 0.5 and 0.7) and meaningful
grouping based on pedigree and accessions’ passport data were
used to predict the optimal number of subpopulations. Finally,
to determinate the level of differentiation among subpopulations,
we considered the Fixation Index (Fst) among all possible
population pairwise combinations.
A maximum and optimal number of eight subpopulations
with accession memberships consistent with the known pedigree
and passport data was chosen for subsequent analysis and GWAS
results interpretation based upon the integrated analysis of (i) the
derivation of the variance of the maximum likelihood estimation
of the model plotted vs. increasing k(1k, Evanno et al., 2005) and
(ii) analysis of pre-existing pedigree and passport information on
the accessions included in the panel which provides an estimation
of parentage among accessions. A kinship matrix of genetic
relationships among individual accessions of the durum panel
was calculated with all non-redundant SNP markers (7,723) using
the Haploview 4.2 tagger function set to r2=1.0. Kinship based
on Identity-by-State (IBS) among accessions was calculated in
TASSEL (Trait Analysis by aSSociation, Evolution and Linkage)
5.2.37.
Subsequently, 17,721 SNP markers with minor allele
frequency (MAF) >0.05, imputed with LinkImpute (LDkNNi)
(Money et al., 2015) in TASSEL, were used in a GWAS of
NDVI, leaf chlorophyll content, leaf rolling and phenology
scores (Zadoks system) on 87 and 100 DAP. Marker-trait
association (GWAS) analysis was implemented in the software
package TASSEL 5.2.37 with a Mixed Linear Model (MLM; Yu
et al., 2006; Bradbury et al., 2007) which included either the
Kinship matrix (MLM-K) alone or STRUCTURE subpopulation
membership estimates plus Kinship plus (MLM-Q+K) as
random effect. Following Zhang et al. (2010), MLM was specified
as follows: y =Xβ+Zu +e, where y is the phenotype value,
Frontiers in Plant Science | www.frontiersin.org 4June 2018 | Volume 9 | Article 893
Condorelli et al. GWAS for NDVI in Wheat
βis the fixed effect due to marker and u is a vector of random
effects not accounted for by the markers; X and Z are incidence
matrices that related y to βand u while e is the unobserved
vector of random residual. Based on GWAS Q-Q (quantile-
quantile) plot results (Supplementary Figure 2), the MLM-K was
considered as the optimal model to control the P-value inflation
associated to population structure while the MLM-Q+K model
was noticed to lead to overcorrections. Thus, all GWAS analyses
were subsequently carried out based on the MLM-K model.
In addition, the allelic state of loci relevant for phenology
(PPD-A1,PPD-B1,FT-7A-indel, Rht-B1b, and VRN-A1) was
included as covariate in MLM analysis (Yu et al., 2006; Price
et al., 2010). These genes are associated with the most important
agronomic traits influencing NDVI and other drought-adaptive
traits. GWAS p-values and R2effects were extracted and QTL
selection criteria was carried-out based on standard conditions
of significance: “highly significant” refers to P<0.0001 and
“significant” refers to P<0.001. The average genetic distance
at which LD decayed below r2of 0.3, a threshold frequently
adopted in GWAS (Berger et al., 2013; Maccaferri et al., 2015a;
Liu et al., 2017), was used to select the QTL confidence Interval
(cM) in the association analysis in this study. By setting LD
r2=0.3, the corresponding inter-marker genetic distance was
3.0 cM as reported by Liu et al. (2017). Therefore, the confidence
interval of ±3.0 cM based on map positions of QTL tag-SNPs
was chosen. The proportion of variance for phenotypic traits
explained by selected SNPs was calculated with Minitab1R
18.
RESULTS
Population Structure and LD Decay of the
Elite Durum Panel
Out of the 17,721 polymorphic SNPs (minimum allele frequency
0.05) suitable for GWAS analysis, a representative reduced
set of 2,382 SNPs obtained after pruning for LD at r2=0.50
threshold was used to investigate the population structure of the
elite durum panel of 248 elite accessions. STRUCTURE analysis
indicated a strong population genetic structure, as reported in
previous analyses of this durum wheat germplasm, using SSR,
DArT, and SNP markers (Maccaferri et al., 2011; Letta et al.,
2013; Liu et al., 2017). The number of optimal ksubpopulations
ranged from five to eight. With k=8, 155 accessions (62.5%) were
clearly grouped into one of the eight main gene pools (Figure 1)
at a Qmembership coefficient 0.5, while the remaining 93 were
considered as admixed.
Subgroup S1 corresponded to native Mediterranean and
North African germplasm. Subgroup S2 included germplasm
specifically bred for dryland areas at ICARDA (Syria) from
the early 1970s. Subgroup S3 included Spanish and Moroccan
cultivars from early 1970s, and CIMMYT and ICARDA
selections for temperate areas. Subgroup S4 mostly included
ICARDA high-yielding lines/cultivars for temperate areas and
contemporary (1970s) Italian accessions obtained from cv. Creso,
an important Italian founder also related to CIMMYT materials.
1Minitab Statistical Software release 9. Minitab Inc., 3081 Enterprise Drive, State
College, PA 16801-3008.
Subgroup S5 included accessions derived from widely adapted
(photoperiod insensitive) CIMMYT germplasm released in the
late 1970s to early 1980s. Subgroup S6 included accessions
from the mid-1970s breeding program in Italy (Valnova group)
while subgroup S7 included accessions from the high-yielding
CIMMYT germplasm released in the late 1980s to early 1990s
(founders Altar84 and Gallareta).Finally, subgroup S8 included
40 accessions from North Dakota (USA), Canada, France and
Australia (Supplementary Table 3).
The division into eight subpopulations was supported by
pairwise comparisons among and within subgroups based on the
Fixation Index (Fst) which provides a measure of subpopulation
diversity (Supplementary Table 4) and by Neighbor Joining tree
(Saitou and Nei, 1987;Figure 1). High genetic diversity was
detected between the old Italian cultivars (S1) and the French,
North American, Canadian and Australian cultivars (S8), while
a considerable admixture among subgroups characterized the
ICARDA, CIMMYT, and Italian groups. As a further note, only a
relatively small portion of the molecular variation was accounted
for by the origin of the accessions, as expected based on the high
exchange rate of germplasm among breeding programs.
Quantitative Trait Variation in Relation to
Population Genetic Structure
Multiple linear regression was performed to estimate the
impact of genetic population structure on the phenotypic traits
(Supplementary Table 5). The R2-values ranged from 0.02 to 0.11
for NDVI-UAV-Sequoia scores and from 0.08 to 0.09 for NDVI-
tractor-GreenSeeker scores. R2for SPAD was higher (R2=0.17),
reflecting the selection for high flag leaf chlorophyll content
in more recent germplasm groups such as S7, while R2-values
for leaf rolling and dry biomass were equal to 0.09 and 0.08,
respectively. Figure 2 shows violin-plot distributions in relation
to the eight subpopulations.
Although multiple regression showed a limited relationship
between population structure and NDVI, violin plots and
median values based on the eight subgroups evidenced trends
for increased NDVI and, even more pronounced, for SPAD
from the oldest subgroups (S1-S2-S3) to the most recently
improved groups S5-S6-S7. Notably, subgroup S8 showed the
widest within-group variation for NDVI and SPAD-values, as
expected based on the concomitant presence within the same
genetically highly homogeneous group of conventional plant
height accessions from the Northern Plains of the US and Canada
and semidwarf (RhtB1b) accessions from France and Austria.
NDVI From UAV-Sequoia, UAV-Rededge,
and Ground-Based Greenseeker Sensors
NDVI measurements from the aerial platforms included data
from the Sequoia-sensor on four DAP associated with differing
growing stages (GS), and from the RedEdge sensor on two DAP,
the first of which coincided with the last measurement with
the Sequoia. Phenotypic distributions approximated normality
for both traits (Figure 3). Repeatability (h2) values for NDVI
were mostly high for both UAV-Sequoia (from 0.77 on 55
DAP to 0.89 on 83 DAP) and UAV-RedEdge (from 0.80 on
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Condorelli et al. GWAS for NDVI in Wheat
FIGURE 1 | Bar Plot (A) and Neighbor Joining Tree (B) using STRUCTURE 2.3.4. for the eight durum wheat subpopulations (S1-S8) sorted by Q and relative genetic
distance.
FIGURE 2 | Violin-plot distributions for the eight durum wheat subpopulations (S1-S8) related to NDVI-UAV-Sequoia at 91 DAP (A), NDVI-tractor-GreenSeeker at 94
DAP (B), leaf chlorophyll content (SPAD) at 101 DAP (C), leaf rolling at 99 DAP (D), and dry biomass (ton/ha) at 105 DAP (E).
91 DAP to 0.89 on 98 DAP) and medium-high for ground-
based GreenSeeker (from 0.61 on 58 DAP to 0.67.5 on 94
DAP).
NDVI-UAV-Sequoia mean values progressively increased
during the time interval from 13 February (55 DAP) (NDVI
from 0.40 to 0.63) to 21 March (91 DAP) (NDVI from 0.84
to 0.91). NDVI reached the highest mean value (0.87) at 21
March (91 DAP), the last measurement. NDVI-UAV-RedEdge
measurements averaged 0.82 at 21 March (91 DAP) (comparable
to NDVI-UAV-Sequoia) while at 29 March (98 DAP) the mean
value decreased to 0.77. Summary statistics are reported in
Table 2.
The NDVI data collected with the GreenSeeker showed
distributions with lower mean values compared to the UAV-
derived data and, most importantly, reached the plateau already
at 6 March (76 DAP) (Figure 3). Similarly to NDVI-UAV-
Sequoia, the mean values progressively increased from 16
February to 24 March (55 DAP to 91 DAP). NDVI on 58 DAP
averaged 0.36 and on 76 DAP reached 0.64, considered as the
plateau for this platform (Figure 4). Table 3 reports Pearson’s
correlation coefficients among NDVI consecutive measurements,
separately for UAV-Sequoia and tractor-GreenSeeker sensors.
Correlations reached medium to high values only for
measurements taken at consecutive DAP, and were lower for
non-consecutive DAP. Table 4 shows the correlations between
UAV-Sequoia and tractor-GreenSeeker at comparable DAP. The
correlations were all highly significant and ranged from 0.42
to 0.61 (P<0.01), with the latter observed for the two
measurements taken on 91 and 94 DAP.
Leaf Chlorophyll Content (SPAD), Leaf
Rolling (LR), Soil Moisture, RWC, and Dry
Biomass
Leaf chlorophyll content (SPAD) and leaf rolling (LR) as assessed
under terminal drought stress conditions showed a normal
distribution (Supplementary Figure 3). SPAD measurements
ranged from 35.3 to 53.7 with an average of 46.0 while leaf rolling
had an average of 4.45. Repeatability values were equal to 0.88
for SPAD, 0.40 for LR and 0.64 for dry biomass (Table 2). RWC
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Condorelli et al. GWAS for NDVI in Wheat
FIGURE 3 | Normal distribution curves and Pearson correlation coefficients for NDVI data from tractor-GreenSeeker (A) and UAV-Sequoia (B) at four different times.
***P<0.0001, *0.001<P<0.01.
results show that the cessation of irrigation on March 16 resulted
in progressively lower leaf RWC for the four tested varieties
(Supplementary Figure 4). In addition, soil moisture data on
volumetric basis ranged from 7.1 to 13.8% indicating high levels
of drought stress.
Dry biomass showed a normal distribution with an average
of 2.61 ton ha1. A positive correlation was observed between
dry biomass and NDVI from aerial and tractor platforms
with Pearson correlation coefficients ranging from 0.32 (91
and 94 DAP) to 0.53 (83 and 84 DAP) (Supplementary
Figure 5).
Effect of Phenology-Relevant Loci on NDVI
Association testswere performed to investigate the effect of
known phenology-relevant loci on the target traits (records of
phenological stage and NDVI repeated measurements) (Table 5).
PPD-A1 had the strongest effect on phenology score, followed
by FT-7A and PPD-B1. The photoperiod sensitive allele PPD-
A1-452 (Bentley et al., 2011), against all photoperiod-sensitive
alleles had the strongest effects on phenology-score and NDVI
measurements with Log P-values equal to 9.69 and 12.16 for the
two phenological scores and Log P-values ranging from 2.46 to
7.52 for ground-based NDVI. The photoperiod-insensitive allele
PPD-A1-380 showed only a mildly significant effect compared to
the insensitive allele PPD-A1-452. Also FT-7A showed significant
effects on phenological scores and on both UAV- and ground-
based NDVI on 91, 94, and 98 DAP. PPD-B1 showed mild effects
on phenology scores only, while VRN-A1 had no effect on any of
the drought-adaptive traits. In addition, Rht-B1b had a significant
effect on dry biomass with Log P-value equal to 3.14. The
phenology-relevant loci did not affect the manually scored SPAD.
Based on these results, the loci relevant for phenology/plant
development were used as covariates in GWAS analysis for NDVI
traits.
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Condorelli et al. GWAS for NDVI in Wheat
TABLE 2 | Summary statistics for Normalized Difference Vegetation Index (NDVI),
leaf chlorophyll content (SPAD), phenology score (PHENO-score 1 and
PHENO-score 2), leaf rolling (LR), and dry biomass on different days after planting
(DAP) in a panel of 248 durum wheat elite advanced lines and cultivars from
worldwide.
Trait DAP Range Mean St. dev. h2(%)
NDVI-UAV
(Sequoia)
55 0.40–0.63 0.54 0.012 77.2
77 0.66–0.81 0.74 0.029 83.9
83 0.64–0.79 0.71 0.026 88.5
91 0.84–0.91 0.87 0.032 87.3
NDVI-tractor
(GreenSeeker)
58 0.30–0.42 0.36 0.025 61.1
76 0.54–0.70 0.64 0.022 66.3
84 0.63–0.75 0.69 0.028 66.9
94 0.58–0.73 0.66 0.023 67.5
NDVI-UAV
(RedEdge)
91 0.78–0.87 0.82 0.016 80.0
98 0.64–0.84 0.77 0.029 88.6
Leaf cholorophyll
content (SPAD)
101 35.3–53.65 45.9 3.04 87.5
PHENO-score 1 87 37.00–51.50 43.06 3.99 66.2
PHENO-score 2 100 37.00–75.00 59.49 10.3 69.3
Leaf rolling (LR) 99 1.00–8.00 4.45 1.44 40.4
Dry biomass
(ton/ha)
105 1.9–3.7 2.6 0.29 63.5
GWAS for NDVI, Dry Biomass, Leaf
Chlorophyll Content (SPAD), Leaf Rolling,
and Phenology Score
A total of 55 single NDVI QTLs were detected for the
UAV-Sequoia platform on 55, 77, 83 and 91 DAP (detailed
results reported in Supplementary Table 6), while for the
similar DAP (58, 76, 84, and 94) the tractor-mounted platform
identified 41 QTLs, about 25% fewer than with the UAV
platform (Supplementary Tables 6, 7). In total, 28 QTLs were
identified exclusively with the UAV-Sequoia platform while 15
QTLs were uniquely detected the tractor-mounted platform.
When overlapping QTLs across platforms and GSs were
considered as single identities, a total of 46 unique NDVI QTLs
were identified (Supplementary Table 12). MLM-Q+K analysis
detected 17 out of 46 unique NDVI QTLs on chromosomes
1A, 1B, 2B, 4A, 4B, 5A, 6A, 6B and 7A (Supplementary
Table 13).
As to NDVI-UAV-Sequoia, the global R2of multiple QTL
models ranged from 24.2% on 77 DAP (6 QTLs) to 89.6% on 91
DAP (22 QTLs), as shown in Supplementary Table 6. For NDVI-
tractor-GreenSeeker at the same growth stages, the global R2of
multiple QTL models ranged from 15.1% on 76 DAP (11 QTLs)
to 64.7% on 94 DAP (16 QTLs). Notably, 19 of the 46 unique
NDVI QTLs were consistently detected by both Sequoia-UAV
and tractor-mounted platforms (41.30%, Supplementary Table
8). A common feature of both platforms was that the number
of detectable NDVI QTLs and the global R2of multiple QTL
models sharply increased from 55–77 DAP (14 QTLs for UAV-
Sequoia and 9 QTLs for tractor-GreenSeeker) to 76–94 DAP (41
QTLs for UAV-Sequoia and 32 QTLs for tractor-GreenSeeker),
in coincidence with and/or after anthesis. Twelve QTLs (52%
of all 23 QTLs) were detected by both platforms for 55–77
DAP and 41 of 73 NDVI QTLs (56%) were detected at 76–94
DAP.
As expected from the medium to low correlation value,
NDVI QTLs were detected at each of the four DAP herein
considered. Specific QTLs were found particularly on 76–77
DAP and 91–94 DAP. Table 6 reports the QTLs, commonly
detected over at least two of the following inter-related
traits: NDVI-UAV-Sequoia, NDVI-tractor-GreenSeeker, leaf
chlorophyll content (SPAD), and dry biomass. A strong per
se QTL influencing all eight NDVI measurements, SPAD and
dry biomass was identified on chromosome 2B (QNDVI.ubo-
2B.1), positioned at 5.9 cM on the tetraploid consensus map
of Maccaferri et al. (2015a).R2-values for this QTL were
5.38% for NDVI-UAV-Sequoia (91 DAP), 6.29% for SPAD,
and 5.67% for dry biomass. Importantly, the confidence
interval of this QTL did not overlap with that of PPD-B1
(mapped at 51.3 cM on chromosome 2B of the consensus
map) and can thus be considered as a valuable constitutive
per se QTL affecting NDVI from the vegetative stage under
well-watered conditions up to late-milk grain filling under
water-deficit conditions. Additional QTLs consistently detected
for NDVI, SPAD and dry biomass mapped on chromosomes
4A and 4B (QNDVI.ubo-4A.2 and QNDVI.ubo-4B.1), with
the latter closely mapping to the well-known RhtB1b locus.
At least nine additional QTLs on chromosomes 1A, 2B,
3A, 4B, 5B, 6B, 7A, and 7B (QNDVI.ubo-1A.1, QNDVI.ubo-
2B.1, QNDVI.ubo-2B.4, QNDVI.ubo-3A.1, QNDVI.ubo-4B.1,
QNDVI.ubo-5B.4, QNDVI.ubo-6B.6, QNDVI.ubo-7A.4, and
QNDVI.ubo-7B.1) affected NDVI concomitantly with both
SPAD and dry biomass (chr. 1A, 2B, 4B, 5B, 6B, and 7B) or
dry biomass only (chr. 2B, 3A, and 7A), suggesting that these
QTLs affected biomass accumulation during the fast-growing
stage or during the remobilization/translocation phases. In all
cases, eight out of nine QTLs had no effects on phenology,
hence suggesting per se effects on NDVI unrelated to growth
stage.
Additionally, QTLs showed concurrent effects on NDVI
(Table 7), and SPAD as well. However, for these QTLs no
significant effects were detected on dry biomass, suggesting
a prevalence of effects on chlorophyll content and/or
senescence at the grain-filling stage without an appreciable
impact on total biomass. Examples of these NDVI QTLs
are QNDVI.ubo-1B.3,-2A.1,-2A.2,-3A.2,-3B.1, -3B.3,-3B.4,-
3B.5, -4A.1,-4A.2,-4B.2, -5B.1,-5B.3,-7A.2, and -7B.4. Several
QTLs affected only a single NDVI measurement and were
therefore considered of marginal interest. UAV-RedEdge
platform on 91 and 98 DAP identified 45 single QTLs for
NDVI (Supplementary Table 9). A major per se NDVI locus
(QNDVI.ubo-6B.5), not detectable by SPAD, was detected on
91 DAP (R2=8.43%) and on 98 DAP (R2=6.71%). However,
this QTL was then ascertained to be coincident with a QTL
for visual leaf rolling. The UAV-based platforms identified
13 common NDVI QTLs out of the 22 that were detected
with at least one of the UAV-based platforms (Supplementary
Table 9).
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Condorelli et al. GWAS for NDVI in Wheat
FIGURE 4 | Histograms for NDVI-UAV-Sequoia (A), NDVI-tractor-GreenSeeker (B), and NDVI-UAV-RedEdge (C) at different days after planting (DAP).
TABLE 3 | Broad-sense heritability and Pearson correlation coefficient for NDVI by
UAV-Sequoia and tractor-GreenSeeker platforms on different days after planting
(DAP).
NDVI-UAV-Sequoia h2(%) 55 77 83 91
55 77.2 1 – –
77 83.2 0.747*** 1
83 88.5 0.562*** 0.859*** 1
91 87.3 0.291*** 0.555*** 0.782*** 1
NDVI-Tractor-GreenSeeker h2(%) 58 76 84 94
58 61.1 1 – –
76 66.3 0.661*** 1
84 66.9 0.535*** 0.869*** 1
94 67.5 0.142* 0.415*** 0.590*** 1
***P<0.001, *0.01<P<0.05.
Most of the NDVI-QTLs were detected from 76 to 94
DAP, with 12 out of 19 QTLs common to UAV- and tractor-
mounted platforms, in contrast to only 3 QTLs detected on
55–58 DAP. As reported in Table 6,R2of multiple QTL
models for common NDVI QTLs showed a himasongher
percentage of explained variance (PEV) for UAV-Sequoia than
for NDVI-tractor-GreenSeeker. PEV was 45.0% on 55 DAP
for UAV-Sequoia and considerably lower (15.4%) on 58 DAP
for tractor-GreenSeeker. UAV-Sequoia and tractor-GreenSeeker
showed a PEV of 59.5% (77 DAP) and 42.1% (76 DAP),
TABLE 4 | Pearson correlation coefficient for NDVI between UAV-Sequoia and
tractor-GreenSeeker platforms on different days after planting (DAP).
NDVI Tractor-GreenSeeker
DAP 58 76 84 94
UAV-Sequoia 55 0.506*** 0.469*** 0.384*** 0.189**
77 0.467*** 0.507*** 0.376*** 0.243***
83 0.357*** 0.435*** 0.423*** 0.383***
91 0.105*** 0.219** 0.397*** 0.614***
***P<0.001, **0.001<P<0.01.
respectively, while PEV was 89.6% (91 DAP) and 64.7% (94
DAP), respectively. In addition, PEV was equal to 73.9 and 91.8%
for NDVI-UAV-RedEdge on 91 and 98 DAP, respectively.
A total of 39 significant QTLs were detected for SPAD,
particularly on chromosomes 1A (R2=9.7%), 3B (R2=6.8%),
5A (R2=10.3%), 5B (R2=8.0%), and 7A (R2=9.3%). Out of
the 39 SPAD-QTLs, a total of 22 loci (56%) overlapped between
SPAD and NDVI. Among these 22 loci, 19 were not related to
phenology. Selected SNPs associated to SPAD showed a very high
global R2of 97.2% (Table 6), most likely overestimated due to
residual population structure effects not accounted for.
Leaf rolling (LR) was associated to nine significant QTLs with
one with the largest effect on chromosome 3A (R2=6.34%),
while selected SNPs associated to LR showed a global R2of
36.0% (Supplementary Table 11). Co-localization was observed
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Condorelli et al. GWAS for NDVI in Wheat
TABLE 5 | Significance and associated effect for major loci known to affect phenology and plant height PPD-A1-452 (sensitivity vs. insensitivity), PPD-A1-380/290
(insensitivity alleles), PPD-B1 (copy number variation polymorphism), Rht-B1 (RhtB1b semi-dwarfism allele) and FT-7A (indel in promoter region) on phenology score
(17.3.2017 and 30.3.2017), NDVI and dry biomass on different days after planting (DAP).
Traits DAP PPD-A1-452 PPD-A1-380/290 PPD-B1 Rht-B1 FT-7A
–Log PEffect –Log PEffect –Log PEffect –Log PEffect –Log PEffect
NDVI-UAV-Sequoia 91 6.40a9ab3.45 9a
NDVI-tractor-GreenSeeker 76 2.99 20a
84 2.46 9a 2.79 10a
94 7.52 9a 4.11 1a
NDVI-UAV-RedEdge 91 3.08 7a
98 6.11 21a 3.31 1a
PHENO-score1 87 12.16 8.65 2.76 2.05 6.15 2.71
PHENO-score2 100 9.69 3.79 2.12 3.57 2.56 8.65 6.78 7.46
Dry biomass 105 3.14 0.24
aGWAS significance P <0.0001 (corresponding to Bonferroni P 0.05 multiple test significance threshold) correspond to a bold underlined font, 0.0001 <P<0.001 to a bold font and
0.001 <P<0.01 to a regular font; bEffect: a =E03.
for LR and NDVI at QNDVI.ubo-5B.2 and QNDVI.ubo-6B.5.
In particular, the first locus was related to NDVI measured
with both UAV- and ground-mounted platforms under drought
stress. QNDVI.ubo-6B.5 was even more interesting as it was
strongly associated to NDVI signals from all three platforms and
to LR but not SPAD nor biomass. In addition, LR co-mapped
with leaf chlorophyll content (SPAD) measured on 101 DAP on
chromosome 2B.
GWAS for dry biomass identified 19 significant QTLs
(Supplementary Table 11) with the strongest effects shown by
those on chromosomes 2B (R2=6.3%), 4B (R2=6.5%), 6A
(R2=7.2%), and 7B (R2=6.4%). Nine of these QTLs were linked
to NDVI (47% of UAV-detected QTLs) with QNDVI.ubo-5A.3
(R2=5.8%) and QNDVI.ubo-5B.4 (R2=5.7%) only detected
with UAV-based platforms. Selected SNPs associated to dry
biomass QTLs accounted for 64% of the phenotypic variance.
The full QTL list for NDVI, dry biomass, leaf chlorophyll
content (SPAD), LR and phenology scores is available in
Supplementary Tables 6, 7, 11. For comparative analysis
of our results with previously published work, all QTLs
identified in this study were positioned on the tetraploid-
consensus map assembled by Maccaferri et al. (2015a)
and are reported in Figure 5, including also NDVI QTLs
gathered from the literature, mainly identified in the
hexaploid wheat germplasm with hand-held portable
instruments such as the classic GreenSeeker. Notably, 23
of the 46 NDVI QTLs did not overlap with growth stage
QTLs, hence suggesting a prevalence of effects on a per se
basis.
Based on the results reported herein, eight QTL hotspots
for NDVI and/or chlorophyll content (SPAD), leaf rolling
(LR) and biomass unrelated to phenology were detected on
chromosomes 1A (QNDVI.ubo-1A.1), 1B (QNDVI.ubo-1B.3),
2B (QNDVI.ubo-2B.1), 4B (QNDVI.ubo-4B.1), 5B (QNDVI.ubo-
5B.1), 6B (QNDVI.ubo-6B.5 and QNDVI.ubo-6B.6), and 7B
(QNDVI.ubo-7B.1).
DISCUSSION
NDVI Measurements by UAV- and
Ground-Based Platforms
To our best knowledge, this study is the first to report on the use
of UAV-based NDVI remote sensing for GWAS analysis in crops
and to compare the results to those obtained using a ground-
based platform. We compared two UAV- and one ground-based
platforms to search for NDVI QTLs in a field trial first conducted
under well-watered conditions until flowering, then followed by
2 weeks of progressively increasing water-deficit conditions that
decreased leaf relative water content (RWC) to 53%. The rapid
decrease in RWC after stopping irrigation was consequent to
the high evaporative demand typical of the environment where
the field trial was conducted. During the time interval from
16 to 31 March when irrigation was terminated and plants
experienced an increasing water-deficit stress, the average mean
daily and average maximum temperatures were 20.9 and 29.7C,
respectively while the average reference daily evapotranspiration
using the standardized Penman-Monteith method was 5.41 mm.
It is well known that NDVI devices/platforms show different
sensitivity features and, consequently, differ in their capacities
to discriminate genotypes, specifically depending on the crop
developmental stage and/or agronomic management (Marti
et al., 2007; Cabrera-Bosquet et al., 2011; Christopher et al.,
2016). Sensitivity of commonly used ground-based sensors
such as GreenSeeker is maximum at early growth stages and
then at the grain-filling/senescence stage while the sensitivity
of UAV-based sensors, particularly for GWAS-QTL analysis,
has not been assessed. Based on the known relationships of
NDVI (as an integrative measure) with chlorophyll content
and total plant/canopy biomass, a time-series of consecutive
NDVI measurements were cross-referenced with flag leaf relative
chlorophyll content (SPAD), leaf rolling and dry biomass data in
order to identify the growth stage when NDVI and its relevant
QTLs were most informative.
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Condorelli et al. GWAS for NDVI in Wheat
TABLE 6 | NDVI GWAS-QTLs for UAV-Sequoia (DAP: 55, 77, 83, and 91) and Tractor-GreenSeeker platforms (DAP: 58, 76, 84, and 94), leaf chlorophyll content (SPAD)
(101 DAP) and dry biomass (105 DAP), commonly detected for at least two traits. QTL significance, tagging-marker R2-value and co-localization with previously known
NDVI QTLs are reported.
QTL Marker Position NDVI UAV-Sequoia NDVI Tractor-GreenSeeker SPAD Dry biomass NDVI QTL from
literature
cM155 77 83 91 58 76 84 94 101 105 QTL2
QNDVI.ubo-1A.1 IWB72019 59.7 4.934.6 4.74 3.92 5.03 5.13 e
QNDVI.ubo-1B.2 IWA8557 25.4 6.56 a,d
QNDVI.ubo-1B.3 IWA6917 67.6 5.99 6.99 6.99 7.07
QNDVI.ubo-2A.3 IWB8175 107.0 7.21 3.90
QNDVI.ubo-2B.1 IWB47560 5.9 3.41 4.53 4.82 5.38 5.35 2.69 5.24 5.37 6.29 5.67
QNDVI.ubo-2B.4 wPt-2929 170.6 5.35 6.08 6.3
QNDVI.ubo-3A.1 IWA5039 64.3 4.95 2.91 4.21 e,f
QNDVI.ubo-3B.1 IWB6062 2.4 4.84 6.84 e
QNDVI.ubo-3B.3 IWB8435 41.3 5.37 3.95 4.84
QNDVI.ubo-3B.4 IWB24050 147.2 4.86 8.45
QNDVI.ubo-3B.5 IWB22805 204.5 4.2 4.6 6.79 b
QNDVI.ubo-4A.1 IWB73476 22.2 7.83 4.63 b,f,g
QNDVI.ubo-4A.2 IWB60692 167.6 4.73 6.59 5.38 5.35 5.23 5.23 3.13
QNDVI.ubo-4B.1 IWB70795 2.8 8.01 5.02 4.48 4.79 5.25 5.11 4.34 b
QNDVI.ubo-4B.2 IWB56078 32.9 3.00 7.56 b,d
QNDVI.ubo-4B.3 IWB72120 92.9 5.98 4.15 6.63
QNDVI.ubo-5A.3 IWA3583 112.1 4.23 5.76
QNDVI.ubo-5B.1 IWB73979 14.7 5.89 5.00 5.72 b,d,e
QNDVI.ubo-5B.2 IWB59038 48.9 4.75 c,d
QNDVI.ubo-5B.3 IWB54773 93.9 4.79 f
QNDVI.ubo-5B.4 wPt-0498 109 5.35 3.2 5.67
QNDVI.ubo-6B.6 IWB45581 155.1 3.14 4.59 3.24 4.08 4.75 2.9
QNDVI.ubo-7A.2 IWB44791 59.8 2.61 4.21 5.78 e
QNDVI.ubo-7A.3 IWB58341 131.3 4.90 7.18 4.32 4.63
QNDVI.ubo-7A.4 IWB28063 181.8 3.30 4.37 5.73 6.91 4.61
Global QTL model (R2, %) - 45.0 24.2 59.5 89.6 15.4 15.1 42.1 64.7 97.2 64.0
The full list of GWAS-QTLs is reported in Supplementary Table 12. 1Chromosomes of QTL regions based on the tetraploid wheat consensus map (Maccaferri et al., 2015a); 2a: (Shi
et al., 2017); b: (Pinto et al., 2016); c: (Sukumaran et al., 2015); d: (Gao et al., 2015); e: (Li et al., 2014); f: (Bennett et al., 2012); g: (Pinto et al., 2010); 3Tagging-marker R2-values are
reported. GWAS significance P <0.0001 (corresponding to Bonferroni P 0.05 multiple test significance threshold) correspond to a bold underlined font, 0.0001 <P<0.001 to a bold
font and 0.001 <P<0.01 to a regular font.
When compared to the two UAV-based platforms, NDVI-
values collected with the ground-based platform plateaued earlier
from 76 to 84 DAP, indicating its lower capacity to monitor
plant biomass accumulation and leaf greenness during the
reproductive stage of the wheat growth cycle. Additionally, UAV-
mounted platforms allowed us to measure hundreds of plots
in very short time, hence minimizing the confounding effects
due to time-related environmental variation, which inevitably
affect the results of studies conducted with ground-based
platforms (Haghighattalab et al., 2016). Whether differences
between the ground-based platform and UAV-based platforms
are due to the means of locomotion or the nature of the
sensors employed, they could not be assessed with these
data.
NDVI has long been recognized for its ability to estimate crop
biomass and grain yield (Lewis et al., 1998; Araus et al., 2001;
Chuvieco Salinero, 2002) and this correlation becomes stronger
when estimated with UAV platforms (Kyratzis et al., 2015). In our
study, the two UAV-based platforms showed a markedly higher
repeatability for NDVI measurements as compared to those
collected with the ground-based platform. High repeatability,
hence heritability, is critical to effectively identify and eventually
clone QTLs (Tuberosa, 2012). Therefore, from a methodological
perspective on the use of the aerial vs. ground-based HTPPs
to detect QTL for NDVI, our results show the increased ability
of the former, particularly under terminal drought stress, as
shown by the considerably higher number of QTLs and overall
R2-values detected with the UAV-based platforms. Accordingly,
a recent study conducted in barley grown under 10 different
nitrogen treatments has also shown an increased sensitivity of
aerial vs. ground-based platforms to measure NDVI using RGB
(conventional digital cameras), multispectral and thermal aerial
imagery in combination with a matching suite of ground sensors
(Kefauver et al., 2017). The relative benefits and comparison
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Condorelli et al. GWAS for NDVI in Wheat
TABLE 7 | Highly-significant GWAS-QTLs for NDVI (P<0.0001) from UAV-RedEdge (DAP: 91 and 98), UAV-Sequoia (DAP: 55, 77, 83, and 91) and tractor-GreenSeeker
(DAP: 58, 76, 84, and 94).
Platform DAP QTL Marker Chr. Position (cM)1CI (cM) Alleles Effect –Log P R2(%)
UAV-Sequoia 55 QNDVI.ubo-4B.1 IWB70795 4B 7.95 4.95-10.95 A/G39.9c24.89 8.01
77 QNDVI.ubo-4B.1 IWB70795 4B 16 13-19 A/G 6.2a 4.15 5.02
83 QNDVI.ubo-7A.3 IWB58341 7A 124.1 121.1-127.1 A/G 1.5b 4.57 7.18
91 QNDVI.ubo-1B.3 IWA6917 1B 58.5 55.5-61.5 A/G 5.9a 4.90 6.99
QNDVI.ubo-2A.1 IWB34575 2A 46.6 43.6-49.6 A/G 0.09 4.43 7.21
UAV-RedEdge 91 QNDVI.ubo-1B.3 IWB31673 1B 59.1 56.1-62.1 C/T 1.2b 4.07 5.89
QNDVI.ubo-6B.5 IWB71546 6B 94.8 91.8-97.8 A/G 1.5b 5.53 8.43
QNDVI.ubo-7A.4 IWB28063 7A 181.8 178.8-184.8 A/G 0.01 4.62 6.83
98 QNDVI.ubo-1B.2 IWB8612 1B 43.6 40.6–46.6 G/T 0.02 4.98 6.61
QNDVI.ubo-3B.3 IWB1757 3B 32 29–35 A/C 2.1b 4.89 6.47
QNDVI.ubo-6B.5 IWB71546 6B 94.8 91.8–97.8 A/G 2.3b 5.05 6.71
Tractor 58 QNDVI.ubo-7A.4 IWA8393 7A 183.2 180.2–186.2 C/T 0.03 4.11 6.91
76 QNDVI.ubo-2B.1 IWB47560 2B 5.9 2.9–8.9 C/T 0.02 4.45 2.69
84 QNDVI.ubo-2B.1 IWB47560 2B 5.9 2.9–8.9 C/T 0.01 4.29 5.24
94 QNDVI.ubo-4A.2 wPt-3449 4A 161.5 158.5–164.5 A/T 0.01 5.35 4.25
QTL regions were defined based on a confidence interval of ±3.0 cM from the map positions of the QTL tagging-SNPs. 1Chromosomes of QTL regions based on the tetraploid wheat
consensus map (Maccaferri et al., 2015a); 2Allele effect: a =E+01, b =E+02, and c =E04; 3The estimate of the effect is referred to the allele highlighted in bold.
of UAV- and ground-based platforms remain to be empirically
evaluated for other phenotypic variables beyond NDVI.
Notably, the NDVI measurements from UAV-RedEdge on 91
DAP showed a decrease in NDVI average values under water
shortage, most likely consequent to the cumulative effects of
senescence and drought stress severity. As reported by Peters
et al. (2002), NDVI can indicate vegetation response to water
stress and could be used as a proxy to evaluate drought effects
(Kyratzis et al., 2015; Liu et al., 2016).
GWAS Analysis for NDVI and Other
Drought-Adaptive Traits
It is known that spectrometers to measure NDVI and other
vegetative indexes show different sensitivities. Consequently,
sensors/platforms are also characterized by different capacity to
discriminate among genotypes, depending on the developmental
stage and/or agronomic management. In wheat, sensitivity
of commonly used ground-based active sensors such as
GreenSeeker is maximum at early growth stages while
progressively decreasing approaching heading/anthesis (canopy
closure) and then increasing again with the onset of the
grain-filling/senescence phase (Marti et al., 2007; Christopher
et al., 2016). While several GWAS studies have reported on
the dissection of genetic inheritance of NDVI data collected
with traditional ground-based sensors (as detailed below), no
specific study has so far addressed the effectiveness of UAV-based
sensors in providing NDVI scores suitable for QTL discovery.
In our study, the UAV-based (Sequoia) NDVI data allowed for
the identification of a considerably larger number (58%) of
NDVI QTLs as compared to the ground-based platform (42%).
Moreover, the use of the UAV-based platforms allowed us to
increase the level of QTL significance and repeatability across
growth stages. As expected, grain-filling stages appeared the most
valuable for detecting NDVI-related genetic differences among
genotypes (61.2% of which were identified at the grain-filling
stage) in response to the progressive onset of senescence and
drought stress related to the water-shortage treatment. Along
this line, breeding strategies for enhancing drought tolerance
are increasingly adopting remote-sensing of NDVI and other
spectral technologies (Monneveux et al., 2012; Araus and Cairns,
2014; Ramya et al., 2016; Trapp et al., 2017).
Two main loci identified on chromosomes 2A (R2from 6.40
to 7.21%) and 6B (R2from 6.13 to 8.43%) were associated to
NDVI QTLs as per UAV-based (Sequoia and RedEdge sensors)
data during the water-stressed treatment (Supplementary Table
10). Based on the known relationships of NDVI (as integrative
measure) with chlorophyll content and total plant biomass,
NDVI measurements were cross-referenced to leaf chlorophyll
content (SPAD) and dry biomass accumulation data. Among the
39 significant loci mapped for SPAD, 22 (56%) overlapped with
NDVI QTLs from aerial and ground-based platforms.
QTL Hotspots for NDVI and Other
Drought-Related Proxy Traits
The major loci known to influence photoperiod, vernalization,
flowering time, and plant height (Milner et al., 2016) significantly
affected phenology score, NDVI, leaf rolling and dry biomass.
In particular, PPD-A1 and FT-7A influenced phenology score,
UAV- and ground-based NDVI, especially under water-deficit
stress. The strong effect on phenology score and adaptation
of PPD-A1 allelic variants is well documented (Snape et al.,
2001) while the effects of variants at PPD-B1 (copy number
variation) and at FT-7A have been less explored. PPD-A1 (452-
bp allele) influenced UAV-based NDVI on 91 and 98 DAP as
well as ground-based NDVI on 84 and 94 DAP, while FT-7A
influenced UAV-based NDVI on 91 and 98 DAP as well as
NDVI-tractor-GreenSeeker on 94 DAP (Table 5). Accounting
for the effects (as covariates) of these major loci in the GWAS
Frontiers in Plant Science | www.frontiersin.org 12 June 2018 | Volume 9 | Article 893
Condorelli et al. GWAS for NDVI in Wheat
FIGURE 5 | Chromosome position on the durum consensus map (Maccaferri et al., 2015a) of (i) QTLs identified in this study, (ii) previously mapped NDVI QTLs and (iii)
main genes for phenology. NDVI QTLs significant for UAV-Sequoia are highlighted with a dark-green vertical bar, NDVI QTLs significant for UAV-RedEdge with a
light-green bar, NDVI QTLs significant for tractor-GreenSeeker with a green-bar. QTLs highlighted with a yellow bar were significant for dry biomass, QTLs with a blue
bar are significant for SPAD, QTLs with a red bar were significant for leaf rolling (LR) and QTLs indicated with an orange bar (shown directly on the chromosomes) are
significant for phenology score. Black vertical bars indicate NDVI QTLs identified from the literature. Horizontal gray-dotted lines indicate the QTL peak positions.
mixed model allowed us to markedly increase the power of QTL
detection while providing more accurate estimates of their effects
and identifying QTLs influencing drought-adaptive traits on a
per se basis. After covariance analysis based on the molecular
genotypes of accessions at the major PPD,VRN, and FT loci, six
QTLs were still found to influence both NDVI and phenology
score (Zadoks system). This notwithstanding, our study also
highlighted the presence of eight hotspot QTLs affecting NDVI
and/or chlorophyll content (SPAD), leaf rolling (LR), biomass
and/or visual response to water shortage independently from
phenology, further supported by co-location with NDVI QTLs
reported in bread wheat.
The leaves of many important cereal crops (maize, rice,
sorghum, and wheat) show a tendency to roll up into a cylinder in
response to drought conditions and then unroll when leaf water
balance improves (Sirault et al., 2015). Apart from mutant genetic
stocks showing a constitutively high leaf rolling (LR), this trait
in cultivated wheat germplasm is associated with leaf water loss
and thus provides a proxy of drought stress over a certain degree
of relative water loss. In this regard, the negative relationship
observed between NDVI and LR, particularly evident with
subgroup S1 (ancient Italian accessions) which showed the lowest
NDVI (98 DAP) and the highest LR-values, suggests that modern
durum wheat varieties for Mediterranean countries have been
selected for both enhanced chlorophyll content and improved
drought-response.
Comparative Analysis With Other QTL
Studies in Wheat
Although recent studies have identified significant NDVI QTLs
in cereals (Pinto et al., 2010, 2016; Bennett et al., 2012; Li et al.,
2014; Gao et al., 2015; Sukumaran et al., 2015; Shi et al., 2017;
Figure 5), none of these studies deployed UAV-mounted cameras
to collect multi-spectral images.
Among the five NDVI QTLs detected by Pinto et al. (2010)
in elite Seri/Babax recombinant inbred lines (RILs) at vegetative
and grain-filling stages, two overlapped with our QTLs on
chromosomes 1B and 4A for ground-based NDVI. More recently,
Pinto et al. (2016) detected the major QTL for NDVI at the
vegetative stage in Seri/Babax wheat mapping population on
Frontiers in Plant Science | www.frontiersin.org 13 June 2018 | Volume 9 | Article 893
Condorelli et al. GWAS for NDVI in Wheat
chromosome 1B and other NDVI loci on chromosomes 1A, 2A,
4A, 5B, and 7A, all of which overlapped with each locus of this
study, except for the QTL on chromosome 2A.
In particular, the NDVI QTL on chromosome 1B is of great
interest, since it showed the highest LOD score and percentage
explained variance (PEV) in Seri/Babax (Pinto et al., 2016) and it
has been consistently detected across three NDVI phenotyping
methods in our experiment, within a coincident confidence
interval of <10 cM. Most notably, this QTL did not affect
phenology. Therefore, this chromosome region represents an
important hotspot for NDVI and leaf greenness and is a good
candidate for marker-assisted selection as well as positional
cloning (Salvi and Tuberosa, 2015). Studying the inheritance
of this region in tetraploid wheat and eventually cloning the
underlining functional polymorphism would also be a good
complement toward the dissection of drought-adaptive traits in
hexaploid wheat, in view of the simplified genetics of tetraploid
wheats and, particularly, the recent assembly of a high-quality
assembly in emmer wheat (T. turgidum ssp. dicoccum Schrank;
Avni et al., 2017), the tetraploid progenitor of both durum and
bread wheat.
Bennett et al. (2012) identified four significant loci for NDVI
in RAC875/Kukri doubled-haploid population under heat and
drought treatments and two of those overlapped with our QTLs
for UAV-based NDVI on chromosomes 2B and 5B. Gao et al.
(2015) reported NDVI QTLs in the Chinese Wheat Cross Zhou
8425b/Chinese Spring at anthesis and at 10 days post-anthesis,
eight of which co-mapped with NDVI QTLs detected in this
study and, in particular, a strong overlapping was identified on
chromosomes 3B and 5B. Additionally, Li et al. (2014) detected
NDVI QTLs in bread wheat (Jingdong 8/Aikang 5) overlapping
with QTLs on chromosomes 1A, 3A, 3B, 5B, and 7A also
identified in the present work. Additionally, according to the
markers shared with the tetraploid consensus map (Maccaferri
et al., 2015a), two significant NDVI QTLs were identified on
chromosomes 1B and 5B at 13 and 7 cM, respectively, from
the QTLs previously identified by Sukumaran et al. (2015) for
NDVI at vegetative and grain-filling stages using GreenSeeker
portable sensors on spring hexaploid wheat lines. According
to Kyratzis et al. (2017), there is a close association between
NDVI and leaf/canopy greenness in durum wheat. Moreover,
SPAD provides an estimation of grain yield (Islam et al., 2014;
Monostori et al., 2016) and grain protein concentration (Le
Bail et al., 2005). As reported by Kyratzis et al. (2015), NDVI
represents also a proxy for biomass and the efficient application
of this technology in large breeding programs has become the
next challenge. We identified 19 significant GWAS QTLs for dry
biomass, nine of which (47.3%) co-mapped with NDVI from
both aerial and ground-based platforms, hence confirming the
usefulness of this vegetation index for predicting final wheat
biomass (Marti et al., 2007; Pantazi et al., 2016). Notably, nine
of these loci on chromosomes 1B, 2B, 3B, 4B, 6A, 6B, and 7B
overlapped with QTLs for field thousand grain weight (TGW)
and/or grain yield (GY) from data published in Maccaferri
et al. (2011) and reanalyzed for GWAS based on the same SNP
platform (Maccaferri et al., 2016) considered herein. In addition
QNDVI.ubo.5A.3 and QNDVI.ubo.5B.4 were linked to both dry
biomass and NDVI captured only from aerial platforms with a
R2of 5.76 and 5.67%, respectively (Table 6). Among the four
main QTLs mapped for LR on chromosomes 1B, 3A, 3B, and
6B, the last one overlapped with the LR QTL reported by Peleg
et al. (2009) in durum wheat ×wild emmer RIL evaluated under
drought stress.
In summary, eight QTL hotspots for NDVI and/or chlorophyll
content (SPAD), leaf rolling (LR) and biomass unrelated to
phenology were detected on chromosomes 1A (QNDVI.ubo-
1A.1), 1B (QNDVI.ubo-1B.3), 2B (QNDVI.ubo-2B.1), 4B
(QNDVI.ubo-4B.1), 5B (QNDVI.ubo-5B.1), 6B (QNDVI.ubo-6B.5
and QNDVI.ubo-6B.6) and 7B (QNDVI.ubo-7B.1). Notably,
QNDVI.ubo-2B.1,QNDVI.ubo-4B.1 and QNDVI.ubo-6B.6
overlapped with QTLs for TGW and/or GY (Maccaferri et al.,
2016).
CONCLUSIONS
This study compared NDVI field phenotyping based on the
emerging UAV-based platforms vs. the standard ground-based
methods targeting an elite durum wheat collection suitable for
GWAS analysis and representative of global durum breeding.
The results reported herein demonstrated the great potential
and effectiveness of both fixed-wing and multi-rotor UAV-
based platforms to gather rapid, precise, and detailed NDVI
measurements, which in turn considerably improved trait
repeatability estimates, QTL identification and considerably
increasing the portion of phenotypic variation accounted for by
the multiple-QTL models. NDVI phenotypes and NDVI QTLs
were cross-referenced by parallel leaf greenness (SPAD) and
final biomass evaluation. The durum panel proved informative
for the identification of QTLs for NDVI, SPAD, LR, and
biomass. Strong effect NDVI QTLs were consistently detected
across phenotyping platforms, with concomitant QTL effects
on SPAD, LR and/or biomass. One major per se NDVI QTL
detected on chromosome 1B (QNDVI.ubo-1B.4) across the three
NDVI phenotyping platforms and for SPAD co-mapped in
a 10-cM interval with a major NDVI QTL described in the
CIMMYT spring hexaploid wheat germplasm. Therefore, this
QTL is worth considering for further characterization as well
as positional cloning. Moreover, three additional per se NDVI
QTLs were detected across measurements, consistently expressed
from the end of fast-growth stage on 91 DAP (QNDVI.ubo-
2B.1,QNDVI.ubo-4A.2 and QNDVI.ubo-4B.1) in addition to
several specific NDVI QTLs were also detected, particularly for
the grain-filling drought-stressed stages. Importantly, our results
demonstrate that UAV-based platforms allow phenotypic data to
be collected in high-throughput and with precision capable of
discerning genetic differences to facilitate the detection of QTLs
for drought-adaptive traits.
AUTHOR CONTRIBUTIONS
MN and PA-S provided overall coordination of the field
experiments including manual, ground, and aerial phenotyping.
JW, MN, RW, MM, and RT designed the experiment. GC, JW,
Frontiers in Plant Science | www.frontiersin.org 14 June 2018 | Volume 9 | Article 893
Condorelli et al. GWAS for NDVI in Wheat
and MN conducted the field and laboratory measurements. RW
and AF managed the UAV workflow including photogrammetry
and zonal statistics. PA-S and JW managed ground-based
data workflows. GC and MM conducted genotyping experiments.
GC and MM analyzed the data, interpreted the results, and
wrote the manuscript, under the supervision of RT and with
contributions from all the other authors.
ACKNOWLEDGMENTS
This study is the result of the collaborative project among the
Department of Agricultural Sciences of University of Bologna
(Italy), Maricopa Agricultural Center (MAC) of University of
Arizona (USA) and US Arid-Land Agricultural Research Center
of USDA ARS (USA). USDA is an equal opportunity provider
and employer. Mention of trade names or commercial products
in this publication is solely for the purpose of providing
specific information and does not imply recommendation or
endorsement by the U.S. Department of Agriculture.
Technical assistance was provided by John T. Heun and Sara
J. Harders. UAV flights were conducted by Mark Yori (Phoenix
Drone Services).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.
00893/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2018 Condorelli, Maccaferri, Newcomb, Andrade-Sanchez, White,
French, Sciara, Ward and Tuberosa. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is permitted, provided the original
author(s) and the copyright owner are credited and that the original publication
in this journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these terms.
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Supplementary resources (18)

... Finally, the level of differentiation among subpopulations was measured using the Fixation Index (Fst) among all possible population pairwise combinations [59]. Subsequently, 17,721 SNPs with MAF > 0.05, imputed with LinkImpute (LDkNNi) [60,61], were used for GWAS-Mixed Linear Model [MLM; [62,63] in TASSEL. MLM was specified as follows: y = Xβ + Zu + e [64], where y is the phenotype value, β is the fixed effect due to the marker, and u is a vector of random effects not accounted for by the markers; X and Z are incidence matrices that related y to β and u, while e is the unobserved vector of random residual. ...
... Notably, none of the eight major QTL hotspots evidenced by GWAS analysis overlapped with the osmoregulation gene locus described by Reference [85] in bread wheat. DR_QTL_cluster_1#, DR_QTL_cluster_2#, and DR_QTL_cluster_5# overlapped with Normalized Difference Vegetation Index (NDVI) loci identified in 2017 on the same Durum Panel under similar drought conditions using Unmanned Aerial Vehicles (UAV-Sequoia and UAV-Red-Edge), as well as ground-based platforms [60]. Additionally, DR_QTL_cluster_3# and DR_QTL_cluster_5# overlapped with chlorophyll content (SPAD) loci under drought described in Reference [60]. ...
... DR_QTL_cluster_1#, DR_QTL_cluster_2#, and DR_QTL_cluster_5# overlapped with Normalized Difference Vegetation Index (NDVI) loci identified in 2017 on the same Durum Panel under similar drought conditions using Unmanned Aerial Vehicles (UAV-Sequoia and UAV-Red-Edge), as well as ground-based platforms [60]. Additionally, DR_QTL_cluster_3# and DR_QTL_cluster_5# overlapped with chlorophyll content (SPAD) loci under drought described in Reference [60]. Both NDVI and SPAD have long been recognized for their ability to estimate crop biomass and predict grain yield [90][91][92][93][94]. DR_QTL_cluster_2# overlapped with grain yield, thousand-kernel weight, and NDVI loci previously reported in a durum wheat elite population tested in contrasting thermo-pluviometric conditions [76]. ...
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... Although the potential of these high-throughput phenotyping technologies have already been and being continuously demonstrated for various applications in wheat (Crain et al. 2018;Sandhu et al. 2021d), somehow these technologies have not been fully explored for GWA studies in wheat. Only a few papers have been published so far which utilized phenotypic data recorded via high-throughput phenotyping platforms for dissecting the different complex traits such as normalized difference vegetation index (NDVI) (Condorelli et al. 2018), lodging , and transpiration efficiency (Gehan and Kellogg 2017) in wheat. For the first time in wheat, a study reported the increased ability of aerial platforms, viz. ...
... For the first time in wheat, a study reported the increased ability of aerial platforms, viz. UAVs over ground-based phenotyping platforms to identify the QTLs by GWAS for NDVI under terminal drought stress conditions (Condorelli et al. 2018). Recently in 2019, one more study provided a proof-of-concept application of UAS-based phenotyping of a complex phenological trait, i.e. (Jansen et al. 2009) 3. ...
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... Field phenotyping platforms can be generally split into two categories the ground-based and the aerial-based platforms. Ground-based platforms can generate higher resolution data since they can capture images at a nearer range relative to the plants [84,85]. Aerial-based platforms can be quicker in capturing and measuring traits of a larger royalsocietypublishing.org/journal/rsob Open Biol. ...
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... Combining high-throughput phenotypic data with QTL mapping and a genome-wide association study (GWAS), several agronomic traits in wheat were efficiently investigated. Condorelli et al. [19] used UAV multispectral remote sensing and manual measurement to obtain the normalized difference vegetation index (NDVI) for 248 durum wheat lines, performed GWAS analysis on the population, and found that the number of QTLs detected with UAV-based NDVI increased by 25% compared to the QTLs detected with manual measurement, with 46 QTLs overlapping for the two phenotypic data sources. The individual loci explained 2.69-8.43% of the phenotypic variance. ...
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... As they are sensitive to the level of photosynthetic activity, the biomass production refers to live mass residing above ground and able to implement the photosynthesis process. Additionally, for wheat plants, they showed a high correlation with the aboveground plant biomass and biomass vigor (see, for example, [66][67][68]). In addition, the NDVI acquired in the wheat flowering stage also had good correlation with the final yield [69]. ...
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... (Chivasa et al., 2021), (T. Xie et al., 2021), (Tenreiro et al., 2021), (Hassan et al., 2019), (De Rango et al., 2019), (Paredes et al., 2017), (Chea et al., 2019), ( Haboudane et al., 2004), (Marcial-Pablo et al., 2019),(Marino and Alvino, 2018),(Tsouros et al., 2019),( Condorelli et al., 2018),,( Gitelson et al., 2003),(Wu, 2014),(Scher et al., 2020),(Imran et al., 2020), (Q.Xie et al., 2018),( Avola et al., 2019),, (L.Zeng et al., 2020) 06 NDRE NDRE = (NIR -Red Edge) (NIR + Red Edge)It is a more accurate predictor of plant health or vigor for mid-to late-season crops with high chlorophyll levels in their leaves.,(Hassan et al., 2019),(Tsouros et al., 2019),(Boiarskii, 2019),(Eitel et al., 2011),) 07 NGRDI NDVI = (Green -Red) (Green + Red) ...
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Recent advancements in the application of unmanned aerial vehicles (UAVs) based remote sensing (RS) in precision agricultural practices have been critical in enhancing crop health and management. UAV-based RS and advanced computational algorithms including Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), are progressively being applied to make predictions, solve decisions to optimize the production and operation processes in many farming industries such as sugarcane. UAVs with various advanced sensors, including RGB, multispectral, hyperspectral, LIDAR, and thermal cameras, have been used for crop RS applications as they can provide new approaches and research opportunities in precision sugarcane production. This review focuses on the use of UAVs in the sugarcane industry for pest and disease management, yield estimation, phenotypic measurement, soil moisture assessment, and nutritional status evaluation to improve the productivity and environmental sustainability. The goals of this review were to: (1) assemble information on the application of UAVs in the sugarcane industry; and (2) discuss their benefits and limitations in a variety of applications in UAV-based sugarcane cultivation. A literature review was conducted utilizing three bibliographic databases, including Google Scholar, Scopus, Web of Science, and 179 research articles that are relevant to UAV applications in sugarcane and other general information about UAV and sensors collected from the databases mentioned earlier. The study concluded that UAV-based crop RS can be an effective method for sugarcane monitoring and management to improve yield and quality and significantly benefits on social, economic, and environmental aspects. However, UAV-based RS should also consider some of the challenges in sugar industries include technological adaptations, high initial cost, inclement weather, communication failures, policy, and regulations.
... High-throughput phenotyping through a range of spectral reflectance indices has been widely used in cereal crops for the screening of desirable plant traits adapted to abiotic and biotic stresses, including drought (Condorelli et al., 2018;Gupta et al., 2012;Kim et al., 2020), nutrition (Tan et al., 2020), frost (Nuttall et al., 2019), salinity (Beisel et al., 2018), heat (Ullah et al., 2019), herbicide, weed infestation (Huang et al., 2018), insect pest infestation (Bhattarai et al., 2019) and disease incidence (Su et al., 2018). Such indices have been used to screen germplasm for genetic variability, thereby increasing breeding efficiencies in field crops (Li et al., 2014). ...
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The aims of this study were to (i) test ground and aerial-based remote sensing vegetation indices (VIs) for trait-based breeding line selection, (ii) improve our understanding of the association between measured plant traits and readings derived from active and passive sensors and (iii) establish an optimal time for growth assessments in relation to field pea vigour and seed yield. Multispectral sensors were deployed with the handheld Crop Circle (CC) and a sensor mounted on an unmanned aerial vehicle (UAV) to collect data from field trials conducted between 2017 and 2020 at Beulah and Horsham in Victoria and Yenda, Wagga Wagga and Ardlethan in New South Wales in Australia. The result showed that normalised difference vegetation index (NDVI) derived from an aerial-based passive sensor (UAV) was strongly and significantly correlated to NDVI derived from a ground-based active sensor (CC) at both Beulah (R² = 0.85; n = 1165; p < 0.001) and Horsham (R² = 0.77; n = 210; p < 0.001). Both methods showed similar NDVI trends in pea genotype rankings. Based on the three seasons of field trial data, NDVI derived from both the CC and UAV sensors were linearly related to biomass production during pre-canopy closure growth. In water limiting environments, seed yield was positively correlated to NDVI measures. Measures calculated from the area under the NDVI curve throughout the growth season, and an additive main effect and multiplicative interaction model (AMMI) identified varieties with high vigour scores (high NDVI). Overall, a high vigour score was correlated to seed yield in lower yielding environments. From these results it appeared that higher vigour helps achieve higher yields in drier environments, however it was correlated with lower yields in better environments.
... Although the leaf-level and canopylevel devices can estimate pigment content non-destructively, it is still hard to implement them for large-scale measurements in field breeding practices. Recently, the Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) with multispectral sensors has become an easy operational base for high-throughput field phenotyping (HFP) in large-scale field studies (Yang et al., 2017), which can be used to discover dynamic novel traits invisible to the human eye, such as leaf area index (LAI), N accumulation in the canopy, drought adaptive traits, and yield estimation (Kyratzis et al., 2017;Condorelli et al., 2018;Zheng et al., 2018;Blancon et al., 2019;Duan et al., 2019a,b;Prey et al., 2020). ...
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