Phenotypic variation of cassava root traits and their responses to drought

Abstract

Premise of the Study
The key to increased cassava production is balancing the trade‐off between marketable roots and traits that drive nutrient and water uptake. However, only a small number of protocols have been developed for cassava roots. Here, we introduce a set of new variables and methods to phenotype cassava roots and enhance breeding pipelines.

Methods
Different cassava genotypes were planted in pot and field conditions under well‐watered and drought treatments. We developed cassava shovelomics and used digital imaging of root traits (DIRT) to evaluate geometrical root traits in addition to common traits (e.g., length, number).

Results
Cassava shovelomics and DIRT were successfully implemented to extract root phenotypes, and a large phenotypic variation for root traits was observed. Significant correlations were found among root traits measured manually and by DIRT. Drought significantly decreased shoot dry weight, total root number, and root length by 84%, 30%, and 25%, respectively. High adventitious root number was associated with increased shoot dry weight (r = 0.44) under drought.

Discussion
Our methods allow for high‐throughput cassava root phenotyping, which makes a breeding program targeting root traits feasible. We suggest that root number is a breeding target for improved cassava production under drought.

Full text

1 of 14
Achieving higher edible yields over the next decade is crucial to
address the challenge of sustaining a growing human population
that faces the consequences of ongoing climate change (Godfray
etal., 2010). Current projections suggest that edible yield from all
annual crops has to double by 2050 to sustain the growing human
population (Jaggard etal., 2010; Tilman etal., 2011). In particular,
the rapid expansion of edaphic and drought stresses (Evans, 1998;
Santos- Medellín et al., 2017) poses an acute challenge for plant
scientists and breeders to develop crops with higher yields (Rötter
etal., 2015). Severe drought stress is identied as a yield- limiting
factor for 47% of the global population, primarily aecting Asia and
Africa (Organisation for Economic Co- operation and Development,
2008). Genetic improvement of crop root architecture has been
identied as a target (de Dorlodot etal., 2007) to develop more pro-
ductive and stress- tolerant crops (Hall and Richards, 2013).
Cassava (Manihot esculenta Crantz) is a tropical root crop that
can be produced eciently on a small scale, without the need for
mechanization or purchased fertilizer inputs (Fasinmirin and
Reichert, 2011). As such, genetic improvement of cassava is iden-
tied as a target for ensuring food security (Sheeld etal., 2006)
and reducing poverty (Leunufna and Evans, 2014). e economic
impact of cassava inuences developing countries like ailand,
which is the world’s largest exporter of dried cassava root (Hillocks
and resh, 2001). Despite cassava’s enormous capability to toler-
ate drought conditions, the increasing severity of drought events
challenges further improvements of cassava root yields (Bakayoko
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Applications in Plant Sciences is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America. This is an open access article under
theterms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is
properly cited.
INVITED SPECIAL ARTICLE
For the Special Issue: Methods in Belowground Botany
Phenotypic variation of cassava root traits and their
responses to drought
Jitrana Kengkanna1, Phissinee Jakaew1, Suwaluk Amawan2, Natalie Busener3, Alexander Bucksch4,5,6 , and Patompong Saengwilai1,7
APPLICATION ARTICLE
Manuscript received 7 October 2018; revision accepted 25 January
2019.
1 Department of Biology,Faculty of Science,Mahidol University,
Rama VI Road, Bangkok 10400, ailand
2 Rayong Field Crops Research Center,Huai Pong, Muang Rayong,
Rayong 21150, ailand
3 Department of Genetics,University of Georgia, 120 West Green
Street, Athens, Georgia 30602, USA
4 Department of Plant Biology,University of Georgia, 120 Carlton
Street, Athens, Georgia 30602, USA
5 Warnell School of Forestry and Natural Resources,University of
Georgia, 180 East Green Street, Athens, Georgia 30602, USA
6 Institute of Bioinformatics,University of Georgia, 120 West
Green Street, Athens, Georgia 30602, USA
7 Author for correspondence: patompong.sae@mahidol.edu
Citation: Kengkanna, J., P. Jakaew, S. Amawan, N. Busener,
A.Bucksch, and P. Saeng wilai. 2019. Phenotypic variation
of cassava root traits and their responses to drought.
Applications in Plant Sciences 7(4): e1238.
doi:10.1002/aps3.1238
PREMISE OF THE STUDY: The key to increased cassava production is balancing the trade- o
between marketable roots and traits that drive nutrient and water uptake. However, only a
small number of protocols have been developed for cassava roots. Here, we introduce a set of
new variables and methods to phenotype cassava roots and enhance breeding pipelines.
METHODS: Dierent cassava genotypes were planted in pot and eld conditions under
well- watered and drought treatments. We developed cassava shovelomics and used digital
imaging of root traits (DIRT) to evaluate geometrical root traits in addition to common traits
(e.g., length, number).
RESULTS: Cassava shovelomics and DIRT were successfully implemented to extract root
phenotypes, and a large phenotypic variation for root traits was observed. Signicant
correlations were found among root traits measured manually and by DIRT. Drought
signicantly decreased shoot dry weight, total root number, and root length by 84%, 30%,
and 25%, respectively. High adventitious root number was associated with increased shoot
dry weight (r = 0.44) under drought.
DISCUSSION: Our methods allow for high- throughput cassava root phenotyping, which makes
a breeding program targeting root traits feasible. We suggest that root number is a breeding
target for improved cassava production under drought.
KEY WORDS cassava; digital imaging of root traits (DIRT); drought; root; shovelomics.

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etal., 2009). Several studies have shown an increased susceptibility
of cassava to drought during the rst three months aer planting
(El- Sharkawy, 2007). Drought conditions were reported to consis-
tently reduce root yields by about 32% compared with well- watered
crops (Connor etal., 1981), and prolonged water stress lasting ve
months further increases yield loss to over 60% (Oliveira et al.,
1982). Consequently, there is a pressing demand for phenotyping
solutions that help breeders develop cassava genotypes that provide
sucient yield under drought conditions.
Improving root systems has been shown to be a successful strat-
egy to enhance water and nutrient uptake in important agronomic
crops (Lynch, 1995, 2013; Borch etal., 1999; Postma and Lynch, 2012;
York etal., 2013; Paez- Garcia etal., 2015). Root traits contributing
to deep root systems such as low crown root number (Saengwilai
et al., 2014b), high root cortical aerenchyma (Saengwilai et al.,
2014a), and reduced lateral root branching (Zhan etal., 2015) have
been demonstrated to enhance water and nitrate uptake, whereas
traits promoting shallow root systems such as increased adventi-
tious root number and lateral root development (Burridge etal.,
2016) have been shown to improve uptake of immobile nutrients
such as phosphorus in dicots (Burridge etal., 2016). Despite the
benets of improving root traits associated with nutrient and water
uptake, breeding programs oen consider the development of root
traits as infeasible for many crops because of a twofold knowledge
gap: (1) What are the important traits for a given crop? and (2) How
can belowground traits be phenotyped?
Less is known about the development of cassava roots com-
pared to other crops, because cassava is typically propagated from
woody stem cuttings in agricultural production systems. In the case
of stem cuttings, adventitious roots emerge at the basal region of
the stem cut and develop the shoot system through axillary bud
growth (Medina etal., 2007). Adventitious root formation can then
be divided into two types: (1) basal roots formed at the cut stem
ends that developed from the cambium and (2) nodal roots initiated
in deeper tissues surrounded by xylem and pith at the axillary bud
(Chaweewan and Taylor, 2015). Later in development, root bulking
occurs when photosynthates produced in the developing canopy
are diverted and translocated from the shoot to the adventitious
roots. As a result, adventitious roots transform into specialized stor-
age root organs to store starch (Alves, 2002). A noteworthy strong
morphological variation can be observed in storage roots, which
poses challenges and opportunities in the phenotypic description
of the cassava root system (Chaweewan and Taylor, 2015). However,
only a small number of protocols that record a limited number of
cassava root traits have been developed so far (Adjebeng- Danquah
etal., 2016a, b). In particular, geometric root traits such as root area
and width have been neglected in current protocols (Okogbenin
etal., 2013). Hence, a better understanding of morphological varia-
tion in cassava roots will expand opportunities to improve breeding
programs and agronomic management.
In recent years, a number of automated root phenotyping
approaches for cereals and annual crops such as maize, barley,
wheat, rice, and legume have been developed (Lobet etal., 2013). A
broad range of manual and 2D/3D- imaging phenotyping platforms
allow for the measurements of root traits from plant seedlings to
mature plants in the eld. A few examples of these methods include
rhizoponics (Mathieu etal., 2015), X- ray computed tomography
and magnetic resonance imaging (Metzner etal., 2015), GLO- Roots
(Rellán- Álvarez et al., 2015), rhizoslides (Le Marié et al., 2014),
minirhizotrons (Iversen et al., 2012), and shovelomics (Trachsel
etal., 2011). Root architectural traits such as root angle and crown
root number can be quickly evaluated with shovelomics. erefore,
shovelomics has been widely adopted for physiological and genetic
studies of a wide variety of crops (Atkinson etal., 2019). Coupled
with phenotyping platforms, several soware packages have been
developed to process thousands of root architectural and ana-
tomical images. Digital imaging of root traits (DIRT) is an online
platform for plant phenotyping that researchers can freely use to
analyze digital pictures of plant roots (Das etal., 2015). DIRT has
high throughput and can compute more than 70 phenotypic traits
of brous monocot and dicot roots (Bucksch etal., 2014). Moreover,
it is an open- source phenomics platform that allows collaboration
and sharing of root phenomics data with other researchers world-
wide. Currently, DIRT has been used to phenotype root traits of
many plant species including maize (Bray and Topp, 2018), cowpea
(Burridge etal., 2017), and common bean (Burridge etal., 2016),
but it has never been applied to tuber and root crops.
Our phenotyping approach uses both manual evaluation and
DIRT image analysis to observe the phenotypic variation of cas-
sava root traits of three- , 10- , and 12- month- old cassava from the
ai germplasm. A set of geometric root traits that relate to plant
performance under drought was quantied by DIRT. We identied
specic cassava root traits that correlate with increased water use
eciency. Hence, our results pave the way for new cassava varieties
bred for high marketable yields under drought.
METHODS
Plant materials and growth conditions
Experiments were carried out in pot growth systems and under
eld conditions. e pot system experiment included ve cassava
genotypes: Rayong 5 (R5), Rayong 9 (R9), Rayong 11 (R11), Huay
Bong 60 (HB60), and Kasetsart 50 (KU50). ese genotypes were
chosen to encompass the dierences in root lengths (long roots: R9,
HB60, and R11; short roots: KU50 and R5). All experiments were
planted using 20- cm stem cuttings obtained from the Rayong Field
Crops Research Center (Rayong, ailand). In the eld, seven com-
mon ai genotypes (R5, R9, R11, Rayong 86- 13 [R86- 13], HB60,
Huay Bong 80 [HB80], and KU50) were investigated for phenotypic
variation of root traits. R5, R9, and R11 were further selected for a
drought eld trial because they produced a high number and weight
of storage roots. Moreover, they are recommended by the ailand
Department of Agriculture as drought- tolerant candidates (http://
at.doa.go.th/cassava/variety_cas.php#).
Growth conditions
e pot experiment was conducted at Mahidol University
(Salaya campus) in Nakhon Pathom, ailand (13°47′40.2″N,
100°19′26.7″E), from November 2016 to January 2017. e stem
cuttings were placed vertically in the soil such that two- thirds of the
stem was below the soil line. We used 24- cm- tall white plastic pots
with a diameter of 25 cm at the top and 17 cm at the bottom. Each
pot contained 5 kg of organic growth media containing rain tree
leaf soil and bamboo soil (1:1 volume ratio). Water- holding capac-
ity was measured and processed as described by Noggle and Wynd
(1941), and it was calculated as the percentage from the ratio of
mass of the water in saturated soil to the mass of the saturated soil.

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e soil in each pot was saturated with water and drained for a day
to evaluate percentage of soil water content that was determined by
gravimetric measurement (Evett, 2008). We found that the water-
holding capacity in the pot system was 52.48% and the soil water
content was 83%. We planted ve biological replicates under well-
watered and drought conditions. e plants were placed outside
for 30 days and were watered every other day with 1 L of water.
Twenty- four days aer planting (DAP), 20 g of fertilizer contain-
ing 16% each of nitrogen, phosphorus, and potassium (16- 16- 16)
per pot was applied. According to Adu etal. (2018), cassava has the
highest relative growth rate during the rst 30 DAP and the growth
rate is subsequently decreased until nearly constant aer 30 to 60
DAP; therefore, drought treatment was applied at 30 DAP to allow
time for plants to acclimate to the system and yield information rel-
evant to developmental processes. All plants were transferred into
a growth shelter made of transparent plastic sheets to protect the
plants from rainwater. To simulate drought conditions, half of the
plants received no water from 30 to 90 DAP, aer which they were
harvested and phenotyped. e total amount of water that the plants
received in the well- watered and drought treatments were approx-
imately 45 L and 15 L, respectively. Soil water content decreased to
46.95% and 28.36% at seven and 14 days aer drought treatment,
respectively. At this time, the plants showed signicant reduction in
height and number of leaves under drought in all genotypes.
Two eld experiments were conducted at the Rayong Field
Crops Research Center, Mueng, Rayong, ailand (12°44′01″N,
101°08′02″E). For the rst trial, the seven genotypes were grown
in loamy sand soil from April 2015 to February 2016 under rain-
fed conditions. e eld capacity was approximately 14%. e
total amount of rainfall during this season was 1517.9 mm, with
drought periods from April to May. We planted a randomized com-
plete block design with four replications. In the second trial, three
genotypes (R5, R9, and R11) were planted under well- watered and
drought treatments between April 2016 to April 2017. e total
amount of rainfall during this experiment was 1714.3 mm. Each
plot had 700 plants, and seven replicates were harvested in this trial.
e distance between rows and between plants was 1 m. e plants
were watered once per week in the rst two months. We followed
the fertilization regime recommended by the ailand Department
of Agriculture. is regime applies 87.5 kg·ha−1 of urea, 54.81 kg·ha−1
of diammonium phosphate (18- 46- 0), and 271.56 kg·ha−1 of potas-
sium chloride (0- 0- 60). Aer two months of growth, well- watered
plants received irrigated water every other day, while the drought
treatment plants were rainfed and did not receive additional water.
Shoot and root traits were quantied at 10 months (rst trial) and
12 months (second trial) aer planting.
Data collection
Manual evaluation was carried out using a modied shovelom-
ics approach (Trachsel et al., 2011). Modications to the original
shovelomics protocol include the use of a handheld brush to clean
sand and soil particles from the root system instead of ringing with
water. Overall, we measured 19 cassava traits including shoot and
root traits for the pot experiment and 12 traits for the eld exper-
iments using a newly developed phenotyping protocol. e trait
measurements are specialized for perennial plants with large stor-
age root morphologies and shown in Table1. e number of basal
roots and nodal roots were quantied by counting manually. e
basal root length was measured using a standard ruler. e shape
of the storage root system was characterized by measuring the
widest part of a representative root as storage root girth and the
longest individual root length as storage root length (Fig.1A). In
addition, we counted the number of storage roots, measured the
largest extension of the whole root system as the root system width,
and evaluated root angle by using a shovelomics board (Trachsel
etal., 2011) for both pot and eld experiments (Fig.1B). e im-
aging station was set up to capture pictures of the root system for
DIRT (Fig.2). Two photos of top and side view were used for root
trait analysis. Our imaging setup consists of large black cloth, a
digital camera (Nikon D5300; Nikon, Tokyo, Japan) with tripod, a
white circle two inches in diameter, and tags to record genotype and
treatment in the captured image. Overall, we took two images per
root system: top view (Fig.2B) and side view (Fig.2C). e images
were uploaded to the DIRT website for automatic trait extraction
(http://dirt.cyverse.org/). We used the following DIRT traits to
validate and interpret cassava root morphology: median width
(WIDTH_MED) and maximum width (WIDTH_MAX) of the root
system, width derived from a medial axis transformation (SKL_
WIDTH), rooting angle, root system area (AREA), stem diameter
(DIA_STEM), rooting depth (SKL_DEPTH), percentage of total
accumulated width at depth levels ranging from 10–90% (D10- 90),
and the rate at which root system width accumulates at depth levels
ranging from 10–90% (DS10- 90) (Table1).
Statistical analysis
R version 3.2.1 (R Core Team, 2014) was used to perform ANOVA
analyses with the agricolae package (Mendiburu, 2010). We
assumed genotypes and treatments as independent variables to
compare trait variation among ve cassava genotypes in well-
watered and drought treatments. Measured root and shoot traits
were assumed to be dependent variables in our analysis. One- way
ANOVA was used to compare the traits of all seven genotypes
grown in the eld. e protected least signicant dierence (LSD)
post hoc test (α = 0.05) was used as a multiple comparison test.
Pearson correlation, by the PerformanceAnalytics package, was
used to determine relationships between cassava traits of the seven
genotypes grown in the eld under rainfed conditions and the ve
genotypes grown in pots under drought conditions. In addition, to
perform the validation of DIRT traits with manual measurements,
we used Python 2.7.13 (Oliphant, 2007) with the scikit-learn 0.19.1
(Pedregosa etal., 2011) package to perform the RANSAC regres-
sion as well as the SciPy package (Jones etal., 2001) to compute
standard linear regressions. e regression plots were generated
with the matplotlib library, and the normalized mean value com-
parisons were plotted with plotly.
RESULTS
Root traits show strong phenotypic variation among Thai
cassava genotypes
Manual root phenotyping revealed considerable variation for root
traits among dierent cassava genotypes. At three months in the
pot system, phenotypic variation ranged from 1.6- fold in stem
diameter to 40- fold in nodal root number under the well- watered
treatment (Table2). At 10 months in the eld, the range of varia-
tion was substantial: 2.72- fold in root system width, 3.57- fold in

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TABLE1. Overview of variables measured by cassava shovelomics and DIRT of cassava shoot and root traits. Trait names are listed with description and physiological
interpretation.
Traits Organ System Description
Physiological
interpretation Reference
Shovelomics measurements
Plant height (cm) Shoot Pot/field Measure from the base of a stem to the
highest point of a shoot
Light interception level and
growth
Mathan et al., 2016
No. of branches Shoot Pot Count number of branches Light interception level and
growth
Mathan et al., 2016
No. of leaves Shoot Pot Count number of leaves Photosynthetic activity and
growth
Mathan et al., 2016
Stem diameter (cm) Shoot Pot/field Measure a stem diameter at 2 cm above the
root
Xylem water potential and
growth
Genard, 2001
Shoot dry weight (g) Shoot Pot Weigh dry shoots after being dried in an
oven at 70°C for 48 h with a balance
Biomass
Shoot weight (kg) Shoot Field Weigh fresh shoots with a balance Biomass
Average root angle
(degree)
Root Pot/field Measure left and right angle with a
shovelomics scoreboard
Root nutrient foraging Trachsel et al., 2013
Root system width (cm) Root Pot/field Measure the width of a root system with a
ruler
Root nutrient foraging Subere et al., 2009
Root length (cm) Root Pot Measure the length of basal roots with a ruler Root nutrient foraging Lynch, 2013
No. of basal roots Root Pot Count number of basal roots Soil exploration volume
No. of nodal root Root Pot Count number of nodal roots Soil exploration volume
No. of adventitious roots Root Pot/field Total number of basal and nodal roots Soil exploration volume Alves, 2002
No. of storage roots Root Pot/field Count number of storage roots Soil exploration volume and
yield
Alves, 2002
Total no. of roots Root Pot Total number of basal, nodal, and storage
roots
Soil exploration volume Alves, 2002
Storage root girth (G) (cm) Root Pot/field Measure the width of a storage root using a
vernier caliper
Photosynthate
accumulation pattern
Adjebeng- Danquah
et al., 2016a
Storage root length (L)
(cm)
Root Pot/field Measure the length of a storage root with a
ruler
Photosynthate
accumulation pattern
Adjebeng- Danquah
et al., 2016a
Ratio L : G Root Pot Ratio between L and G Photosynthate
accumulation pattern
Adjebeng- Danquah
et al., 2016a
Storage root
circumference (cm)
Root Pot/field Measure circumference of storage root with a
tape measure
Photosynthate
accumulation pattern
Storage root weight (g) Root Field Weigh storage roots with a balance Yield
Root dry weight (g) Root Pot Weigh dry roots after being dried in an oven
at 70°C for 48 h with a balance
Biomass
Plant dry weight (g) Shoot/root Pot Weigh dry shoot, stem, and root with a
balance
Biomass
Plant weight (kg) Shoot/root Field Weigh fresh shoot, stem, and root with a
balance
Biomass
DIRT measurements
DIA_STEM Shoot Pot/field Stem diameter derived from the medial axis Xylem water potential Genard, 2001
WIDTH_MED Root Pot/field Median width of root system measured
horizontally
Root nutrient foraging Subere et al., 2009
WIDTH_MAX Root Pot/field Maximum width of root system measured
horizontally
Root nutrient foraging Subere et al., 2009
SKL_WIDTH Root Pot/field Skeleton width calculated from the medial
axis
Root nutrient foraging Subere et al., 2009
Root angle Root Field Average root angle of left and right angles of
root system
Root nutrient foraging Trachsel et al., 2013
AREA Root Pot/field Projected root area Soil exploration volume and
yield
SKL_DEPTH Root Pot/field Rooting depth skeleton calculated from the
root- tip path (RTP) skeleton
Root nutrient foraging Lynch, 2013
D 10-90 Root Field Accumulated width over the depth at x%
of the central path length. The change in
width accumulation denotes a change of
the root top angle.
Root nutrient foraging Bucksch et al., 2014
DS 10-90 Root Field Slope of the graph of central path length
vs. accumulated width at x% of the
accumulated width
Root nutrient foraging Bucksch et al., 2014

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FIGURE1. Cassava root traits assessed manually using the excavated root crowns of plants at three months (A) and 10 months (B) after planting.

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storage root number, fourfold in root angle, and 7.68- fold in storage
root weight (Table3). Among genotypes, R5 had the highest aver-
age storage root weight and plant weight as well as the highest root
angle. Correlation analysis revealed signicant positive correlations
between root system width and other traits, including plant weight
(r = 0.58, P < 0.01), storage root weight (r = 0.61, P < 0.001), and
storage root number (r = 0.35, P < 0.05), whereas root angle signi-
cantly correlated with plant weight (r = 0.33, P < 0.05) and storage
root weight (r = 0.36, P < 0.05) (Table4). In addition, storage root
number was signicantly correlated with plant weight (r = 0.56, P <
0.01) and storage root weight (r = 0.44, P < 0.05).
We observed signicant genotypic variation in several root
traits using the imaging setup and DIRT for image analysis.
Overall, image- based analysis could distinguish all genotypes by
at least four traits. e best discrimination of genotypes could be
found in width measurements (WIDTH_MED, WIDTH_MAX,
SKL_WIDTH), stem diameters (DIA_STEM), root area (AREA),
and root width accumulation at depth levels (D, DS values, and
SKL_DEPTH) as shown in Figures3 and 4. We performed a regres-
sion analysis to compare DIRT with our manual phenotyping pro-
tocol. e regression analysis used standard linear regression and
an advanced RANSAC regression to account for outliers in both
phenotyping protocols. All root width traits yielded very high cor-
relations, with a coecient of determination greater than 0.7 and
P values less than 0.01 for the RANSAC regression (Fig.5A–C).
e linear regression of the medial axis- derived width resulted
in a coecient of determination of 0.58 and a P value less than
0.01. High correlations for linear regression (r2 > 0.5, P < 0.01)
and RANSAC regression (r2 > 0.85, P < 0.01) were achieved for the
root weight and the measured root area in DIRT (Fig.5D). Root
weight represents the traditionally most important root trait, as it
reects the amount of yield harvested from the root system. Root
area could achieve highly signicant correlations with root weight
(P < 0.01) from the top and side view of the
images and explain up to 60% of the varia-
tion present between manual and automatic
measurement (Fig. 5D, E). e correlation
between root angle measured by the manual
protocol and by DIRT was not signicantly
correlated (Fig.5F) because of dierences in
the measurement procedure.
Eects of drought on shoot and root traits
We found that the progression of drought
signicantly reduced plant height and leaf
number as early as 14 days aer starting the
drought treatment. At harvest, drought sig-
nicantly reduced average shoot dry weight
by a factor of six. Furthermore, well- watered
cassava resulted in two times greater plant
height, leaf number, and nodal root number
compared to drought conditions. Drought
reduced root system width, total root num-
ber, and adventitious root length by 26%,
30%, and 25%, respectively (Table 2). ere
was no storage root formation under drought
except for genotypes R5, HB60, and KU50.
erefore, storage root number, storage root
girth, and storage root circumference were
reduced by 96%, 82%, and 82%, respectively. Storage root length to
girth ratio under drought increased by a factor of 2.6 compared to
well- watered treatments.
Of the dierent genotypes, R5, HB60, and KU50 increased
adventitious root number by 20%, 10%, and 50%, respectively,
whereas R9 and R11 decreased adventitious root number by 50%
and 67%, respectively, under drought (Table2). Only KU50 had
greater total root number under drought conditions compared to
well- watered treatment. R11 had the lowest shoot dry weight with
shoot dry weight reduced 92.7% under drought conditions, fol-
lowed by R9, HB60, KU50, and R5, for which shoot dry weight was
decreased by 85%, 79.9%, 79%, and 78%, respectively (Table2). In
addition, we found that the adventitious root number was posi-
tively correlated with shoot dry weight (r = 0.44, P < 0.05), root
dry weight (r = 0.70, P < 0.001), and plant dry weight (r = 0.37, P
< 0.05).
In the eld, drought signicantly reduced average storage root
number by 21%. Genotypes responded to drought dierently. R9 sig-
nicantly decreased height by 12%, whereas R11 and R5 increased
height under drought by 14% and 11%, respectively (Fig.6A). R9
and R11 maintained shoot weight, whereas R5 increased shoot
weight by 36% under drought (Fig. 6C). Although dierences
among root traits such as root system width and depth could not
be captured by manual measurements (Fig.6I, L), DIRT success-
fully distinguished the dierences among genotypes (Fig.7). In the
well- watered treatment, R5 had the lowest SKL_DEPTH compared
to R11 and R9 (Fig.7E). However, SKL_DEPTH of R5 was signi-
cantly increased under drought, which suggests that the root sys-
tem became deeper compared to well- watered treatments. Among
genotypes, R9 had the highest root system width (WIDTH_MAX)
and root depth (SKL_DEPTH) in all conditions (Fig.7A, E), as well
as the highest storage root and adventitious root number under
drought (Figs.6F, 7H).
FIGURE2. The setup for capturing cassava root images used in this study. (A) Imaging setup
consists of a large black cloth, a camera (Nikon D5300; Nikon, Tokyo, Japan) with a tripod, a
2- in- diameter circle scale, and a label. Images were captured from top view (B) and side view (C).

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TABLE2. Phenotypic variation of traits among ve cassava genotypes under two treatments: well- watered (W ) and drought condition (D) using the pot system.a
Traits Treatment
Genotypes
Average RangeR5 R9 R11 HB60 KU50
Height (cm) W 77.5 ± 0.78a 72.2 ± 1.31a 73.6 ± 0.88a 73.2 ± 0.81a 78.8 ± 0.73a 75.0 ± 0.02* 1.7
D 55.4 ± 0.85a 24.7 ± 0.38c 24.4 ± 0.56c 40.6 ± 0.58b 39.13 ± 1.11b 36.8 ± 0.06 3.8
No. of branches W 2 ± 0.00a 2.8 ± 0.11a 3.2 ± 0.08a 1.8 ± 0.08a 1.4 ± 0.05a 2.25 ± 0.02 1.8
D 2 ± 0.12a 3.6 ± 0.17a 2.8 ± 0.08a 3 ± 0.12a 2.75 ± 0.21a 2.83 ± 0.03 5.0
No. of leaves W 79.5 ± 0.78a 75 ± 1.30a 76.8 ± 0.89a 75 ± 0.83a 80.2 ± 0.70a 77.2 ± 0.18* 1.6
D 57.4 ± 0.73a 28.3 ± 0.42c 27.2 ± 0.53c 43.6 ± 0.48b 41.88 ± 1.04b 39.6 ± 0.27 3.1
Average root angle
(degree)
W 32 ± 2.06ab 40.8 ± 1.64a 23 ± 0.84b 31.4 ± 1.32ab 25.6 ± 0.56ab 30.5 ± 0.27 5.8
D 29.6 ± 0.93ab 42.67 ± 1.00a 26.5 ± 2.62ab 18.6 ± 0.81b 30 ± 1.53ab 28.5 ± 0.30 7.3
Root system width
(cm)
W 15.88 ± 0.15a 15.16 ± 0.34a 16.72 ± 0.37a 15.1 ± 0.24a 16.08 ± 0.24a 15.8 ± 0.05* 2.0
D 11.92 ± 0.3ab 11 ± 0.18ab 5.95 ± 0.06b 13.46 ± 0.57a 12.5 ± 0.51ab 11.7± 0.09 3.7
Adventitious root
length (cm)
W 23.78 ± 0.29a 26.66 ± 1.20a 23 ± 0.68a 26.26 ± 0.81a 29.94 ± 0.78a 26.0 ± 0.17* 2.9
D 19.04 ± 0.51a 19.25 ± 1.03a 20.17 ± 1.41a 19.58 ± 0.20a 20.03 ± 0.97a 19.6 ± 0.15 7.5
No. of basal roots W 16 ± 0.42de 22.4 ± 1.19cd 28.8 ± 0.91bc 26.8 ± 1.1bcd 30.6 ± 0.09bc 25.3 ± 0.19 3.1
D 27.8 ± 0.64b 10.2 ± 0.93c 9.4 ± 1.31c 38 ± 1.25ab 46.75 ± 0.61a 25.6 ± 0.36 54
No. of nodal roots W 13.75 ± 0.6bc 20.2 ± 0.91ab 25.4 ± 0.52a 12.2 ± 0.58bc 3.6 ± 0.20d 15.1 ± 0.23* 40.0
D 8 ± 0.16abc 11 ± 0.42a 8.6 ± 0.31ab 4.8 ± 0.26bc 4.5 ± 0.26c 7.5 ± 0.08 18
No. of adventitious
roots
W 29.8 ± 0.8cde 42.6 ± 0.75ab 54.2 ± 0.98a 39 ± 1.69bc 34.2 ± 0.27cd 40.4 ± 0.26 3.9
D 35.8 ± 0.55ab 21.2 ± 1.16bc 18 ± 1.54c 42.8 ± 1.28a 51.25 ± 0.66a 33.1 ± 0.34 14.8
No. of storage roots W 14.5 ± 0.22a 6.2 ± 0.28b 1.4 ± 0.09c 7.8 ± 0.30b 7 ± 0.21b 7.00 ± 0.10* 16.0
D 0.6 ± 0.09a 0.00 ± 0.00a 0.00 ± 0.00a 0.2 ± 0.05a 0.5 ± 0.07a 0.25 ± 0.01 2
Total no. of roots W 44.3 ± 0.9abc 48.8 ± 0.7abc 55.6 ± 0.90a 46.8 ± 1.6abc 41.2 ± 0.50bc 47.5 ± 0.21* 2.8
D 36.4 ± 0.53a 21.2 ± 1.16b 18 ± 1.54b 43 ± 1.31a 51.75 ± 0.70a 33.3 ± 0.34 15
Storage root girth (G)
(cm)
W 2.18 ± 0.03a 2.68 ± 0.08a 0.94 ± 0.07b 2.47 ± 0.03a 2.57 ± 0.03a 2.22 ± 0.02* 11
D 0.43 ± 0.01a — — 0.4 ± 0.00a 0.365 ± 0.01a 0.4 ± 0.001 1.7
Storage root length
(L) (cm)
W 17.05 ± 0.81a 13.53 ± 0.69a 16.20 ± 0.59a 11.60 ± 0.58a 15.61 ± 0.65a 14.6 ± 0.13 4.4
D 10.1 ± 0.113a — — 11.1 ± 0.00a 10.75 ± 0.13a 10.6 ± 0.02 1.2
Ratio L : G W 7.92 ± 0.41b 4.98 ± 0.19b 32.06 ± 2.64a 4.89 ± 0.29b 5.97 ± 0.21b 10.4 ± 0.30 23.5
D 24.44 ± 0.83a — — 27.75 ± 0.00a 30.04 ± 0.58a 27.3 ± 0.11* 1.8
Storage root
circumference (cm)
W 7.05 ± 0.06a 8.3 ± 0.20a 2.8 ± 0.24b 8.14 ± 0.10a 8.34 ± 0.06a 6.92 ± 0.05* 12.5
D 1.3 ± 0.06a — — 1.4 ± 0.00a 1.05 ± 0.08a 1.22 ± 0.01 2.8
Stem diameter (cm) W 1.943 ± 0.01d 2.788 ± 0.01a 2.3 ± 0.01bc 2.364 ± 0.02b 2.1 ± 0.02bcd 2.32 ± 0.01 1.6
D 2.306 ± 0.02a 2.068 ± 0.02a 2.056 ± 0.04a 2.32 ± 0.02a 2.25 ± 0.04a 2.2 ± 0.01 1.6
Shoot dry weight (g) W 38.25 ± 0.5bc 41.96 ± 1.01b 52.78 ± 0.73a 32.27 ± 1.44c 36.82 ± 0.5bc 40.5 ± 0.23* 4.6
D 8.324 ± 0.22a 6.3 ± 0.17ab 3.872 ± 0.13b 6.488 ± 0.19a 7.725 ± 0.33a 6.49 ± 0.05 5.2
Root dry weight (g) W 14.35 ± 1.46a 10.12 ± 2.04a 11.58 ± 2.33a 9.251 ± 2.06a 8.499 ± 2.44a 10.61 ± 0.1* 8.8
D 0.8764 ± 0.16a 0.2325 ± 0.10b 0.2459 ± 0.12b 0.7326 ± 0.10a 0.851 ± 0.16a 0.5767 ± 0.1 107
Plant dry weight (g) W 80.27 ± 3.55ab 75.73 ± 5.76ab 88.22 ± 5.80a 68.26 ± 7.71b 72.72 ± 5.19ab 76.9 ± 1.2* 2.4
D 27.14 ± 2.29a 19.85 ± 1.02b 18.44 ± 2.33b 20.97 ± 3.00ab 23.24 ± 3.63ab 21.87 ± 2.8 2.53
aDifferent letters denote significant differences among genotypes in each treatment (P < 0.05), while an asterisk (*) denotes significant difference between treatments of the average value
of traits (P < 0.05).
TABLE3. Summar y of phenotypic variation of root and shoot traits among seven cassava genotypes grown in the eld. The plants were harvested at 10 months after
planting.a
Genotypes
Root traits Shoot traits
No. of storage
roots
Storage root
weight (kg)Root angle (degree)
Root system
width (cm)Plant height (cm)Plant weight (kg)
R5 17.75 ± 1.11a 7.09 ± 0.56a 27.50 ± 4.79a 54.25 ± 6.74a 241.25 ± 13.28ab 10.24 ± 0.45a
R9 18.50 ± 2.33a 3.29 ± 0.26b 21.25 ± 4.27abc 55.25 ± 5.53a 232.75 ± 4.42b 5.33 ± 0.55b
R11 12.50 ± 2.06bc 2.56 ± 0.57bc 25.00 ± 2.89ab 53.00 ± 2.48a 180.25 ± 5.51c 4.52 ± 0.80b
R86- 13 19.75 ± 2.56a 2.43 ± 0.40bc 25.00 ± 6.45ab 45.00 ± 7.86a 250.75 ± 7.45ab 5.30 ± 0.92b
KU50 10.25 ± 1.18c 3.50 ± 0.87b 15.00 ± 2.89bc 53.50 ± 5.42a 254.00 ± 18.00ab 4.88 ± 0.85b
HB60 9.25 ± 0.63c 1.36 ± 0.19c 11.25 ± 1.25c 44.00 ± 4.14a 272.50 ± 17.97a 2.58 ± 0.23c
HB80 16.50 ± 1.71ab 2.11 ± 0.24bc 12.50 ± 2.50c 45.50 ± 4.99a 273.25 ± 5.45a 3.88 ± 0.33bc
Mean 15.21 ± 0.96 3.19 ± 0.37 19.64 ± 1.76 50.07 ± 2.05 243.53 ± 6.86 5.24 ± 0.48
Range 3.57 7.68 4.00 2.72 1.88 5.30
aDifferent letters denote significant difference at P < 0.05.

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DISCUSSION
We developed cassava root phenotyping protocols combining man-
ual measurements and automatic DIRT measurements. Several root
characteristics were identied and characterized (Table 1). ese
traits potentially inuence cassava development under drought,
volume of soil exploration, photosynthate accumulation patterns,
and root yield. Phenotyping agronomically relevant traits of cassava
is challenging because of its complicated root system and large size.
However, cassava root shovelomics may be less time- consuming
compared to other species because most cassava cultivation is in
sandy soils (USDA, NRCS, 2019) where the root system can easily
be cleaned o using a paintbrush.
We demonstrated that DIRT applied to cassava can distin-
guish all genotypes, including several manually accessible traits
such as width and depth as well as inaccessible traits such as D
and DS values. Such shape descriptors give insight into depth
development of the root systems; these traits had not previously
been investigated in cassava and will provide insight into develop-
mental traits that impact plant performance. Furthermore, DIRT
allows for relatively high- throughput measurements of the whole
root structure. Signicant correlations were found between data
obtained from manual measurements and DIRT, such as those of
root system width and root weight. is is important because root
system width and root weight are critical denominators of root
yield. Hence, our results suggest that the platform can facilitate
genetic analysis with genome- wide association studies (GWAS)
and quantitative trait loci mapping (QTL) and is feasible for large-
scale selection in breeding programs. Most importantly, our study
can be replicated by researchers around the world because of the
free availability of DIRT.
Cassava expresses substantial genotypic variation in root traits.
On average, the ranges of traits observed in our study are consis-
tent with those reported by others. Our germplasm had longer
TABLE4. Correlations among cassava traits in 10- month- old cassava grown in the eld.a
Trait
Shoot trait Root trait
Plant height (cm)Plant weight (kg)
Storage root weight
(kg)No. of storage roots
Root angle
(degree)
Plant weight (kg) 0.032
Storage root weight (kg) −0.075 0.95***
No. of storage roots 0.055 0.56** 0.44*
Root angle (degree) −0.25 0.33* 0.36* 0.13
Root system width (cm) −0.13 0.58** 0.61*** 0.35* 0.18
aSignificant difference at ***P < 0.001, **P < 0.01, and *P < 0.05.
FIGURE3. Genotypic discrimination of DIRT traits. Points represent average trait values. Error bars depict the standard error of the mean, and dotted
lines guide the reader visually between averages per genotype. Traits were made comparable by calculating the Z- score of each trait.

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adventitious root length at three months aer transplanting than
28 cassava varieties in a pot system reported by Subere etal. (2009).
is indicates opportunities for cassava breeders to use ai germ-
plasm as donors for increased adventitious root length in cassava
breeding programs.
Drought signicantly suppressed cassava shoot and root growth.
In the pot system, only three cassava genotypes (R5, HB60, and
KU50) formed small storage roots under drought. ese storage
roots were long and thin and had high storage root length : girth ra-
tio (Table2). Among dierent genotypes, KU50 increased total root
number by 25% and enhanced the number of adventitious basal
roots by more than 50%. Moreover, KU50 had the highest shoot dry
weight among all genotypes under drought. In contrast, R11, which
had the greatest total root number in well- watered conditions, had
a substantially reduced number of adventitious roots (by a factor of
3) and had the lowest shoot dry weight among cassava genotypes
under drought. Maintaining adventitious roots is related to enhanc-
ing growth and yield performance, as well as enabling cassava to
revive quickly aer rewatering (Subere etal., 2009). is is also evi-
denced by a positive correlation between adventitious root number
and biomass, including shoot dry weight (r = 0.44, P < 0.05), root
dry weight (r = 0.70, P < 0.001), and plant dry weight (r = 0.37,
P < 0.05), in the pot system and by the high storage root number
found in genotype R9 when grown in the eld under drought con-
ditions. In other crop species such as maize, however, an increase in
adventitious root number means less carbon and energy are allo-
cated to produce deeper rooting and sustain shoot growth and yield
(Saengwilai etal., 2014b). Dierent adaptive strategies such as shoot
growth arrest, early stomata closure, and shedding of old leaves
have been shown to be utilized by cassava (Zhao etal., 2017). ese
strategies could possibly balance the metabolic costs of increased
adventitious root production, resulting in an overall improvement
of plant growth under drought.
Several lines of evidence suggest that increased root growth
angle is key for drought adaptation. For example, variation in sev-
eral root traits of rice, including root growth angle, were linked
to rice crops grown in ooded elds and in elds without ood-
ing treatment (Saengwilai et al., 2018). Similarly, the DEEPER
ROOTING 1 (DRO1) gene was shown to increase root growth angle
in rice to compensate for drought by increasing rooting depth (Uga
etal., 2013). In maize, increased root growth angle was shown to be
an adaptive strategy for acquisition of deep soil resources, particu-
larly nitrate and water in poor soil and drought conditions (Lynch,
2013). In cassava, the physiological utility of root growth angle has
FIGURE4. DIRT trait comparison for R5, R9, R11, HB60, and KU50. The plants were harvested at three months after planting in the pot system under
well- watered conditions. (A) WIDTH_MAX, (B) WIDTH_MED, (C) SKL_WIDTH, (D) DIA_STEM, (E) AREA, and (F) SKL_DEPTH. Data were made comparable
through normalization of mean trait values (Z- score). Error bars represent the standard error of the mean for each treatment and genotype category
that corresponds to a particular trait.

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FIGURE5. Regression analysis of cassava shovelomics and DIRT traits between the root system width and medial axis- derived width (SKL_WIDTH)
(A), the maximum width (WIDTH_MAX) (B), and the median width (WIDTH_MED) (C). Signicant correlations were found between root weight and
DIRT traits including AREA of side view (D) and top view (E) images. No correlation was found for root angle (F) when compared between a manual
measurement and DIRT analysis.

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not yet been shown. Our results showed that cassava had a very
shallow root system compared to other crop species. e benet of
steep root growth angle for drought was not conclusive because the
range of variation of root angle in this study was very small. It is also
important to note that capturing root growth angle in mature cas-
sava plants is challenging. In our study, root angle is the only trait
that showed no correlation when compared using manual measure-
ment and DIRT analysis. e DIRT analysis revealed shortcomings
in the execution of the imaging protocol and diculties in accessing
the angle manually because of the complex arrangement of storage
roots. An angle variation of about 10–15 degrees was introduced
through root placement, because the radial arrangement of the
storage roots allows the root to fall over on the stem. As a result,
the projection of the 3D root structure onto the image plane dis-
torts the angle measured in the image. For the manual measure-
ment, it was dicult to identify a representative rooting angle
because there is signicant variation of present angles per storage
root. Consequently, hardly any correlation could be found between
manual and DIRT measurement of root angle in this study. Further
improvements, such as using a clamp to hold the root system and
repositioning the camera to capture images from a side angle, may
help to alleviate these problems.
In order to further improve highly drought- tolerant cassava
genotypes, it is essential to develop a cassava root phenotyping
protocol and platform and to provide more information about
phenotypic variation of root systems among ai cassava geno-
types. In our experiment, adventitious root traits are suggested as
a new breeding target to enhance water- use eciency. Moreover,
molecular plant breeders could benet from the phenotyping plat-
form to obtain trait measurements applicable for GWAS and QTL
analysis to facilitate marker- assisted selection (Vogel, 2014). In
moving forward, data sharing and recombination are important,
because collecting eld data sets that use the potential of high-
throughput and high- resolution phenotyping with many repe-
titions per genotype is laborious and expensive due to the initial
manual excavation process. Free accessibility of collected data sets
is key to tap the potential of large data sets that provide a plethora
of information that can only be revealed in community eorts. As
a result, breeding projects for water and nutrient eciency that do
not require expensive eld research facilities are enabled and can
be targeted to the needs of smallholder farmers who face the con-
straints of low soil fertility and drought. erefore, our phenotyp-
ing protocol can be useful for collecting data for low- cost breeding
and genetic research.
FIGURE6. Shoot and root traits of three cassava varieties grown in the eld under well- watered and drought conditions at 12 months after planting.
(A) Height, (B) plant weight, (C) shoot weight, (D) root weight, (E) stem diameter, (F) tuber number, (G) root angle, (H) adventitious root number, (I) root
system width, (J) circumference, (K) storage root girth, (L) storage root length.

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ACKNOWLEDGMENTS
is research was supported by the Science Achievement
Scholar ship of ailand (SAST), Mahidol University, and Rayong
Field Crops Research Center. is work used the Extreme Science
and Engineering Discovery Environment (National Science
Foundation grant no. ACI- 1548562) resource Stampede2 at the
Texas Advanced Computing Center (TACC) through allocation
BIO160088 in combination with computing resources at the Georgia
Advanced Computing Resource Center (GACRC). We thank Dr.
Tom Stewart and William LaVoy for revising the manuscript.
DATA ACCESSIBILITY
e images and data that support the ndings of this study are
openly available on CyVerse Data Commons (as Saengwilai_
Cassava_2019; https://doi.org/10.25739/ej8x-3b24).
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