8 Agro-physiological Responses of 10 West Africa Sorghum Varieties to Early Water Deficit Assessed by UAV and Ground Phenotyping

Boubacar Gano, Cheikh Anta Diop University of Dakar, Senegal & ISRA/CERAAS, Senegal
Joseph Dembele, Cheikh Anta DIOP University of Dakar, Senegal & ISRA/CERAAS, Senegal
Bassirou Sine, ISRA/CERAAS, Senegal
Diaga Diouf, Cheikh Anta DIOP University of Dakar, Senegal
Alain Audebert, CIRAD, France & ISRA/CERAAS, Senegal



Sorghum is a staple food for many in the Sahel. However, it often faces early-stage water deficit resulting in production decrease. Research is focusing on developing early drought tolerant varieties. This study assessed the effects of early drought stress on 10 elite varieties of West African sorghum collection tested over 2 years (2018-2019) in Bambey (Senegal). Water stress was applied by withholding irrigation 25 days after sowing for one month, followed by optimal irrigation until maturity. Soil moisture and agro-physio-morphological traits were monitored. Results showed highly significant effects of early drought stress on sorghum plants growth. The combined analysis of variance revealed highly significant differences (P ≤ 0.01) between varieties in the different environments for most traits studied. Under water deficit, the genotypic adaptation was linked to the capacity of varieties to increase the dead leaves weight and the roots length density and to reduce photosynthesis rate, stomata conductance, and leaf transpiration. The analysis of spectral indices across water treatments revealed significant variation. However, the differential responses between varieties remained the same. Fadda (V1), Nieleni (V2), Soumba (V8) and 621B (V9) showed promising behavior under drought stress and could be suitable for further use in West Africa.

Keywords: early drought tolerance; genetic variability; root adaptation; sorghum


Plant adaptation to water deficit is one of the key factors that will determine the severity of climate change on food production, because in the next decade, water availability will be greatly affected by climate change (Molden, 2007). Currently, agriculture uses 75% of the world’s total water consumption and that is likely to increase (Molden, 2007). Food production increases at only 1.2% for the four key global crops—maize, rice, wheat, and soybean—and future demands will cause it to double (Ray et al., 2013). This is also valid for sorghum (Sorghum bicolor (L.) Moench), which is a staple food for millions of people in arid and semiarid tropical regions (Agrama et al., 2003). Its production in Africa has been estimated at 29.83 million tons from 30.54 million hectares of land (FAOSTAT, 2019).

However, the production remains unstable despite cultural practices farmers implemented to adapt and cope with the growing needs and population rise (Chaléard, 2010; Sanchez, 2002). Indeed, the latter has caused strong pressure on the arable land leading to soil depletion.

Actions to promote food security in the Sudano-Sahelian zones include the promotion of local cereal production through the identification of high-performance varieties and the breeding of new varieties. In Senegal, sorghum is one of the most important local cereals grown in the various agro-ecological zones of the country. However, climate change has increased the risk of rainfall shortage during July—the beginning of the growing season (Salack et al., 2012). This could be the cause of the low production (291.171 tons in 2019) (DAPSA, 2019). Early-stage water deficit is experimented with in many research studies by withholding water supply at the beginning of the crop growth cycle, about 3 weeks after germination, for 3 to 4 weeks (Debieu et al., 2018; Qazi et al., 2014; Zegada-Lizarazu & Monti, 2013). This causes a decrease in leaf appearance, transpiration, and photosynthesis through leaf senescence (Craufurd & Peacock, 1993; Dwivedi et al., 2008; Tari et al., 2013). The combined effects of drought and rewatering are still not well-known, especially regarding sorghum. The ability to recover from early water deficit during rewatering can explain in some cases the yield difference between varieties at harvest (Zegada-Lizarazu et al., 2013). The drought recovery index (DRI) represents rewatering-induced recovery of the growth traits related to biomass, height, etc., and would be suitable for investigating drought tolerance. According to Perrier et al., (2017), recovery capacity is an important trait for future phenotyping, genetic and breeding studies while its process and genotypic variability are poorly understood. Late maturity genotypes appeared to be more tolerant to early drought because the stress occurs before the panicle initiation phase, when plants have more ability to recover after rewatering. However, these genotypes no longer suit the farming systems of semiarid regions because of the shortened rainy season. Thus, the solution would be to identify improved short-cycle varieties capable of adapting to early season water stress. This requires a characterization of sorghum behavior under early season water stress and the identification of relevant selection criteria to facilitate the decision on the choice of varieties for cultivation and breeding programs.

Root response is of prime importance to crop productivity under drought stress. This is because the root size, architecture, and distribution determine the ability of plants to access and take up the water for proper physiological functioning of shoots (Henry, 2013; Taiwo et al., 2020). Sorghum’s ability to adapt to water shortage is also due to its root system, which can extend into the deep soil layers. The investigation of this trait through the measurement of root length density (RLD) trait revealed useful (Masi & Maranville, 1998). Researchers have been working to identify specific root traits targeted for plant improvement under drought and limited nutrients conditions (Comas et al., 2013; Girma et al., 2020; Lynch et al., 2014). Recently, greater focus was given to root system architecture, especially the RLD distribution in the soil, which is a key factor for water and nutrient uptake (Chopart et al., 2008; Gregory, 2006). However, field assessment of RLD is not obvious. Among other methods, the trench profile method for mapping root intersection in a soil profile was identified as more efficient and feasible to provide information on roots distribution (Böhm, 1976; Chopart et al., 2008; Tardieu, 1988).

Another method investigated in this study is the use of vegetation indices (VIs) calculated from Unmanned Aerial Vehicle (UAV) multispectral images to phenotype sorghum varieties under drought. Previous findings have promoted VIs related to normalized difference vegetation index (NDVI) as an important multi spectral index to track the agro-physiological dynamics of key traits such as biomass, leaf area index (LAI), yield, etc. (Foster et al., 2017; Magney et al., 2016; Samborski et al., 2015). However, breeders are not aware and have less understanding about the application of the different reflectance bands ratio on monitoring crops development and adaptation. Higher level of NDVI is associated with faster growth rate, higher biomass accumulation during the vegetative stage, and a longer grain filling period by delaying leaf senescence during the ripening phase thereby increasing yield (Babar et al., 2006).

The objectives of this study were to investigate the effects of early water deficit in sorghum to determine the main adaptation mechanisms and simultaneously highlight an interesting method and criteria for the agro-physiological characterization of sorghum in water deficit conditions.

Material and Methods

1. Plant Material

The plant material used in this study consisted of 10 elite varieties of sorghum from Senegal, Mali, and Nigeria (West Africa) (see Table 1). They differ in terms of phenology (from 90 days to 128 days cycle duration), plant architecture (120 cm to 450 cm height), response to inputs (hybrid vs. open pollinated varieties caudatum or guinea), and yield (2 t/ha to 4.5 t/ha) (Dembele et al., 2020). These varieties are widely cultivated by the farmers because of their adaptability and agronomic characteristics (Dembele et al., 2021; Gano et al., 2021; Ndiaye et al., 2018; Ndiaye et al., 2019).
Table 1
Characteristics of the Varieties Studied
Variety Code Type Cycle (days) Height (cm) Potential yield (t/ha) Panicle form Photoperiod-sensitivity Isohyet (mm) Origin
Fadda V1 Guinea (hybrid) 128 200-300 4.5 noncompact mean 700-1000 Mali
NIELENI V2 Guinea (hybrid) 115 300 4 semicompact low 700-800 Mali
IS15401 V3 Guinea-Caudatum 115 400-450 2 semicompact high 900-1200 Mali
PABLO V4 Guinea (hybrid) 125 400 4 noncompact mean 700-1000 Mali
CSM63E V5 Guinea 90 400 2 noncompact low 600-1000 Mali
SK5912 V6 Caudatum 170 200 2.5-3.5 semicompact high 700-900 Nigeria
GRINKAN V7 Caudatum 90 120 4 semicompact non 500-800 Mali
SOUMBA V8 Caudatum 115 250 2.5 semicompact low 600-1000 Mali
621B V9 Caudatum 105 175 2.5-3 semicompact non 600-900 Senegal
F2-20 V10 Caudatum 110 210 3-5.3 semicompact low 600-900 Senegal

2. Methods

2.1 Trial Conditions

Trials were conducted at Bambey (14°42’N; 16°28’W) (Senegal) in the Centre National de la Recherche Agronomique (CNRA) on a sandy soil (sand = 94.2%, silt = 3.5%, clay = 2.3%) with a previous cowpea crop. Three trials were carried out between 2018 and 2019 during the dry seasons, which allowed control of the water supply by irrigation. The experimental design is a randomized complete block design, with irrigation as the main factor, and varieties randomized in three replications in each main block. The two water treatments (well-watered [ww] and drought stress [ds]) were placed 10 m apart to avoid involuntary irrigation. A sprinkler method of water supply was provided twice weekly with 25 mm per irrigation (50 mm per week) until physiological maturity. The amount of irrigated water was calculated to cover the weekly average evapotranspiration of sorghum that varied between 25 mm to 37 mm (see Figure 1D). One irrigation was applied before and after sowing to promote good seedling emergence. Fertilizers were applied after sowing and at 21 days after sowing (DAS) as NPK and urea respectively following standard recommendation (i.e., 150 kg ha-1 of NPK [15-15-15] and 100 kg ha-1 of urea). Water stress was applied by withholding irrigation in the drought stress environment for 1 month from the 25th DAS.

2.2 Weather Conditions

Figure 1 shows the climate conditions (vapor pressure deficit [VPD], solar radiation, relative humidity, and temperature) during both experiments. There was a high evaporative demand during these dry seasons as shown by the VPD data that reached 5 KPa during the day. The dry season is characterized by high temperature (above 30°c) and high solar radiation (800 w.m²) during the day. These climate conditions are representative of the dry season, allowing control over water supply.

Figure 1

4 graphs to demonstrate Climate Conditions During the Experiments in Bambey in 2018 and 2019. Graph A shows bell curves of solar radiation and VPD with X axis as Hours of day. Graph B shows lines of Temperature and Relative Humidity with X axis as Hours of day. Graph C shows plot lines of VPmin and VPmax with X axis as Months. Graph D shows lot line of potential evaporation (mm) with X axis as Weeks.
Figure 1 – Climate Conditions During the Experiments in Bambey in 2018 and 2019.

Notes. (A) Daily averages of vapor pressure deficit (VPD) and solar radiation; (B) Temperature and relative humidity; (C) Minimum and maximum VPD; (D) Potential evapotranspiration. Although climate conditions were measured in both years, only representative annual graphics were presented. (A) and (B) were recorded during 2018 experiment and (C) and (D) during the 2019 experiment.

2.3 Monitoring Water Stress

Water stress in the field was monitored by measuring the volumetric soil moisture (Diviner 2000, Sentek Pty Ltd) once a week during the irrigated period and twice a week during the stress period to assess the fraction of transpirable soil water (FTSW) (Debieu et al., 2018; Sinclair & Ludlow, 1986). Diviner probe tubes of 1.6 m length were used to record water stock at every 10 cm of depth. In this work, a total of 1.6 m depth water stock was considered to assess the FTSW as this depth represents the sorghum’s root activity zone. Soil moisture measurements allowed us to follow the level of stress down to FTSW = 0.3 at the end of the stress period. The average of the three most contrasted varieties was used to evaluate the general trend. We also monitored the predawn leaf water potential once a week using the pressure chamber (PMS Instrument Co., Corvallis, Oregon, USA) according to the protocol of Peyrano et al. (1997). The plant’s water potential was measured before sunrise, when there is a balance between plant and soil for water potential.

2.4 Assessment of Agro-physiological Traits

Plants’ agro-physiological traits—such as number of leaves appeared (NLA) and plant height after stress (PHS)—were measured on three tagged plants per plot. The photosynthesis rate (Pn), leaf temperature (Tleaf), transpiration (Tr), and stomata conductance (C) were measured on the last ligulate leaf using the CI340 handheld photosynthesis system (CID Bio-science, USA). Biomass production was evaluated by estimating plant dry weight (DWP) and dead leaves weight (DLW) using six plants per plot at seven different dates (before the stress, at the end of the stress, and after recovery [2 weeks after rewatering]). At physiological maturity, grain yield, plant height (PHT) and straw dry biomass (SDW) were measured. The specific leaf area (SLA) was calculated as the ratio between plant leaf area and biomass.

Moreover, other morphological traits were measured weekly during drone flights on three randomly tagged plants per plot, from crop emergence (25 DAS) to flowering-maturity stage (89 DAS). Seven sampling weeks’ data were used for LAI and biomass. LAI was measured using the SunScan septometer (Delta-T Devices, Cambridge, England). Nondestructive measurements (LAI) were performed just before UAV flights. Plants were sampled, dried outdoors for 2 weeks, and oven-dried for 3 days before biomass measurements were taken using ad-venturer pro precision balance (OHAUS Corporation, Pine Brook, New Jersey, NJ, USA).

To better study the recovery performance and classify varieties, we introduce a new parameter that we have named the DRI. The DRI represents the relative recovery of the growth traits (NLA, PHS, DWP, Pn) after a freely defined time of drought stress followed by rewatering. This approach was inspired by the drought factor index (DFI) used by Strauss et al. (2006) for the detection of dark chilling tolerance in soybean genotypes and by Oukarroum et al., (2007) for probing the response of barley cultivars under drought stress conditions. DRI was calculated by applying the formula:

DRI= log A + 2 log B (1)

in which A is the relative trait measured at the end of the drought period and B is the relative trait measured 2 weeks after rewatering. The relative trait is calculated as trait drought over trait control. The principle of the DRI is that recovery efficiencies should play an important role on the production capacity of some genotypes. Varieties with DRI near to zero have good recovery and varieties with DRI around -1 have bad recovery index (Oukarroum et al., 2007).

2.5 Roots Phenotyping

Root measurements were done on two tagged plants per plot. We used the described methods, to count the number of adventitious roots and estimate RLD from intersections between the roots and the face of a soil trench profile (Chopart et al., 2008; Dusserre et al., 2009; Faye et al., 2019). The trench profiles were dug perpendicular to the rows of seedlings and at two distances (20 cm then 10 cm) from the plant stem. Iron grids of 60 cm length by 30 cm width, in relation to the spacing between rows and between plants, were used to count root impacts. Square meshes of 10 cm side length were made inside the grids to facilitate the measurement of the number of impacts. At the end of the stress period, the plants were dug up for additional measurements on the tilling tray, such as the number of adventitious roots.

2.6 UAV Data Capture and Image Analyses

Time series flights were done at the altitude of 25 m and a constant speed of 2.2 m.s-1 with the hexacopter drone (FeHexaCopterV2, MikroKopter Company, Moormerland, Germany). Seven UAV weekly flight data, from emergence to maturity, were recorded. Nine grey colored ground control points (GCPs) were uniformly distributed over the entire field area with fixed position for all the flights throughout the experiment. These were surveyed using Precis BX305 Real Time Kinematics (RTK) GNSS unit (Tersus GNSS Inc., Shanghai, China). The GCPs were made of painted PVC disks of 60 cm diameter where the central disk of 40 cm diameter was 20% gray level and the outer disk 60% gray level color. These gray levels were selected to avoid saturation and allow automatic target detection on the images. The UAV system, equipped with six motors, can perform user-defined waypoint flights with a differential global navigation satellite system (GNSS) receiver. The UAV support software (Mikrokopter tools, Mikrokopter Company, Moormerland, Germany) that implements the flight plan also monitors the flight and records information such as drone position. The flight was performed with an RGB ILCE-6000 digital camera (Sony Corporation, New York, NY, USA) with a 6000×4000-pixel sensor equipped with a lens of 60 mm focal length. To minimize the blurring effect and noise in the images, the camera was set on speed priority (1/1250 sec) and auto ISO mode. Another flight was performed with an Airphen multispectral camera (hi-phen, Avignon, France, https://www.hiphen-plant.com/) equipped with a lens of 8 mm focal length and acquiring 1280×960-pixel images. The Airphen consists of six individual cameras equipped with filters centered on 450 nm, 530 nm, 560 nm, 675 nm, 730 nm, and 850 nm, with a spectral resolution of 10 nm. For each camera (RGB and MS), the flight lasted about 15 minutes with a break of approximately 10 minutes in between to prepare the second flight. The cameras captured images at 1-second intervals and recorded them in JPG and Tiff format on the SD memory card. The drone did round trips spaced by 4 m that allowed a side and forward overlapping fraction of 0.75. To reduce the effects of ambient light conditions, such as plant shadow that can greatly affect spectral measures especially between rows at maturity stages, the flights were limited to clear and cloudless days between 10:00 a.m. and 12:00 a.m. UTC.

An automatic image-processing pipeline was designed to generate radiometrically calibrated and geometrically corrected multiband orthoimages using Agisoft PhotoScan digital photogrametric software version 1.4.0 (Agisoft LLC, St. Petersburg, Russia, https://www.agisoft.com/downloads/installer) (see Part 2 Chapter 1, Mbaye et al. in this book). Radiometric calibration included automatic correction of vignetting effects (Iqbal et al., 2018). Real reflectances were computed using a reference target positioned to the ground during UAV flights. This target was previously spectrally characterized in controlled conditions. Geometric correction involved firstly, multiband coregistration to modify and adjust the images’ coordinate system to decrease geometric distortions and make pixels in different pictures coincide with the corresponding map-grid points. The coregistration process was based upon the internal GNSS from raw image metadata. Orthorectification was then performed using GCPs to increase the accuracy of the generated orthoimage. As Agisoft Photoscan manages multilayer images, we used the 450 nm band for tie point searching. For a better plots segmentation, we uploaded the RGB orthoimage in QGIS (Geographical Information SYSTEMS, version 3.10.0, QGIS Development Team, open source 2019, https://www.qgis.org/fr/site/forusers/download.html) and designed the plots boundaries. The created shapefile with the GNSS coordinates of each plot was exported as spatial vector data. The extraction of the average values of the varieties’ vegetation index in each plot was performed according to the GNSS coordinates of plots, extracted on QGIS and MS orthoimage. The computation was performed using R software (version 3.6.0) libraries (sf, raster, rgdal, RSToolbox and uavRst) (R Core Team, 2020). Four vegetation indices (NDVI, CTVI, MSAVI2 and SR) were used to estimate LAI and biomass during the dry seasons of 2018 and 2019. These vegetation indices are single values computed by grid calculation.

They are invariant to the difference in illumination conditions, slope, seasons, etc. They represent a quick way to distinguish green leaves from other objects and to estimate the relative biomass present on the image, therefore, distinguishing stressed vegetation from nonstressed (Li et al., 2018; Shi et al., 2016; Steven et al., 2015; Zhang et al., 2017).

2.7 Statistical Analyses

The raw data were analyzed using R software version 3.6.0 (www.r-project.org). An analysis of variance was performed for each environment (ww and ds) to verify statistical differences between varieties. Subsequently, a combined analysis of variance was performed to test the effects of water stress and years on varieties. The homogeneity between residual variances was tested using Bartlett’s test (Bartlett, 1937). RLD was modeled on the basis of measurements of root intersections density (RID) on a vertical perpendicular plane within a sorghum row because this method is most commonly used for studying roots in a soil profile. Relationships between RLD and RID were evaluated taking the slope, the standard error of the slope (SE), the intercept, and the regression (r²) into account. Ordinary least squares linear regression models were applied. Regression models were developed to predict LAI and biomass using vegetation indices. The performance of regression models in estimating LAI and biomass were evaluated by calculating the root mean squared error (RMSE), the coefficient of determination (r2) and p-values at the probability level of 0.05.


1. Water Stress

During the irrigation period, FTSW and predawn leaf water potential showed a very low variation and revolved around 0.7 and -1.5 bars, respectively (see Figure 2). However, when the plots in the ds treatment were let to dry down, FTSW and predawn leaf water potential decreased progressively and reached 0.3 and -5 bars respectively, showing an effective drought stress experience that occurred between 35 DAS and 55 DAS. Thereafter, after resuming irrigation, these parameters increased again and stabilized around the initial values with a slight drop (0.6 and -2 bars for FTSW and predawn leaf water potential, respectively).

Figure 2

6 graphs showing Monitoring of Water Stress Parameters in Well-watered (ww) and Drought Stress (ds) Treatments. Graph A shows 2 plots lines demonstrating soil water stock percentage with ww (black) & ds (white) in 2018. Graph B shows 2 plots lines demonstrating soil water stock percentage with ww (black) & ds (white) in 2019. Graph C shows 2 plots lines with whiskers demonstrating fraction of transpirable with ww (black) & ds (white) in 2018. Graph D shows 2 plots lines with whiskers demonstrating fraction of transpirable soil water with ww (black) & ds (white) in 2019. Graph D shows 2 plots lines demonstrating fraction of transpirable soil water with ww (black) & ds (white) in 2019. Graph E shows 2 plots lines with whiskers demonstrating Predawn leaf water potential with ww (black) & ds (white) in 2018. Graph F shows 2 plots lines with whiskers demonstrating Predawn leaf water potential with ww (black) & ds (white) in 2019.
Figure 2 – Monitoring of Water Stress Parameters in Well-watered (ww) and Drought Stress (ds) Treatments

Notes. (A) Evolution of soil moisture stock (%) in 2018 and (B) in 2019; (C) Fraction of transpirable soil water in 2018 and (D) in 2019; (E) Predawn leaf water potential (bar) in 2018 and (F) in 2019.

2. Effects of Early Water Deficit on Growth, Recovery, and Yield

Table 2 presents the effects of water deficit on agro-physiological traits assessed in the experiments. The results showed highly significant differences (P ≤ 0.01) between the different environments for all the characters under study. The early water deficit led to a reduction of leaf appearance (NLA) (-9.18% in 2018 and -6.75% in 2019); PHS (-16.37% and -48.99%); Pn (-12.45% and -27.75%); C (-18.37% and -35.32%); and Tr (-26.37% and -25.92%). After maturity and harvest, we observed a decrease in yield (-22.78% and – 28.15%), SDW (-18.25% and -27.79%) and PHT (-15.01% and -23.23%). However, we noticed an increase in the DLW (+43.29% and +15.10%) and Tleaf (+1.29% and +7.64%).

Table 2
Average Performance and Statistical Parameters of Some Agro-, Physio- and Morphological Traits of Sorghum Genotypes Under Well-watered (ww) and Drought Stress (ds) Conditions of 2018 and 2019 Field Trials
Year 2018 Year 2019
Traits Mean ww Mean ds ΔWS Signif. Mean ww Mean ds ΔWS Signif.
NLA 15.33a 13.92b -9.18 *** 15.30a 14.26b -6.75 ***
PHS 143.89a 120.33b -16.4 *** 130.55a 66.59b -49 ***
DLW 13.65b 19.56a 43.29 *** 14.22b  16.37a 15.1 ***
PHT 174.83a 148.58b -15 *** 165.75a 127.24b -23.2 ***
SDW 453.38a 370.61b -18.3 *** 427.35a 308.60b -27.8 ***
Yield 3271.85a 2526.21b -22.8 *** 2419.84a 1738.64b -28.2 ***
Pn 39.32a 34.43b -12.5 *** 41.38a 29.90b -27.8 ***
C 184.94a 150.97b -18.4 *** 179.14a 115.88b -35.3 ***
Tr 7.43a 5.47b -26.4 *** 7.04a 5.22b -25.9 ***
Tleaf 39.02b 39.53a 1.29 ** 39.16b 42.15a 7.64 ***

NLA: number of leaves appeared; PHS: plant height after stress (cm); DLW: dead leaves weight (g); PHT: plant height (cm); SDW: Straw dry weight (g); Yield: grain yield (kg/ha); Pn: photosynthesis rate; C: stomata conductance; Tr: leaf transpiration; Tleaf: leaf temperature; Signif: significance at p:s 0.001 (***) and p:s 0.01 (**); ΔWS: delta water stress (%). For a given trait, numbers followed by the same letters are not significantly different between water treatments.

Figure 3 represents the monitoring of C, SLA per plant, plant height, NLA on the main stem. DWP, and Pn of varieties under both conditions (ww and ds). The results showed that the number of leaves on the main stem, plant height, and dry weight per plant gradually increased in a similar way between varieties, but the advent of water stress induced a drop. These results indicated that water stress causes significant reduction in biomass production and shoots growth in sorghum. The SLA of the plant, which reflects the thickness of the leaves, the C, and the (Pn) initially increased to reach their maximum at the 30th DAS, before gradually decreasing until maturity. In the water-stressed environment, the drop in SLA, C, and Pn was greater than in the nonstressed conditions. After the end of the stress period, these traits rebounded but without recovering to the SLA, C, and Pn values of the nonstressed environment. In terms of height and biomass, the varieties have all lost pace despite the high plasticity found in sorghum.

Figure 3

6 graphs showing Evolution of Agro-physiological Traits of Sorghum Varieties Under Well-watered (22) and Drought Stress (ds) Conditions. Graph A shows 2 plots lines with whiskers demonstrating dry weight per plant (g) with ww (black) & ds (white) in 2018. Graph B shows 2 plots lines with whiskers demonstrating SLA per plan (cm2. g-1) with ww (black) & ds (white) in 2019. Graph C shows 2 plots lines with whiskers demonstrating plants height (cm) with ww (black) & ds (white) in 2018. Graph D shows 2 plots lines with whiskers demonstrating stomata conductance (mmol.m-2.s-1) with ww (black) & ds (white) in 2019. Graph E shows 2 plots lines demonstrating number of leaves on main stam with ww (black) & ds (white) in 2018. Graph F shows 2 plots lines with whiskers demonstrating photosynthesis rate (umol.m-2.s-1_ with ww (black) & ds (white) in 2019.
Figure 3 – Evolution of Agro-physiological Traits of Sorghum Varieties Under Well-watered (22) and Drought Stress (ds) Conditions

Note. (A) Dry weight per plant, (B) Specific leaf area (SLA) per plant, (C) Plant height, (D) Stomata conductance, (E) Number of leaves, (F) Photosynthesis rate

Sorghum varieties that exhibited the smallest values of DRI had more problems recovering. We noted that varieties had good recovery in the NLA and Pn with a DRI of -0.11 and -0.04 respectively. However, varieties’ recovery on PHS and the DWP was more difficult with a DRI of -0.62 and -0.65 respectively (see Table 3). The results indicated a best recovery on plant height and dry weight for the variety V3 and V1 respectively; varieties V4, V5, and V6 revealed the worst recovery on the same traits.

Table 3
Drought Recovery Index of Sorghum Varieties on Some Growth Trait
Drought Recovery Index (DRI)
Varieties NLA PHS DWP Pn
V1 -0.04 -0.54 -0.32 -0.22
V2 -0.1 -0.43 -0.6 -0.24
V3 -0.12 -0.33 -0.75 -0.08
V4 -0.13 -0.81 -0.98 0.07
V5 -0.1 -0.68 -0.64 0.35
V6 -0.09 -0.8 -0.97 -0.16
V7 -0.13 -0.55 -0.93 -0.05
V8 -0.11 -0.77 -0.57 0.12
V9 -0.12 -0.62 -0.44 -0.18
V10 -0.12 -0.69 -0.32 0
MEAN -0.11 -0.62 -0.65 -0.04

NLA: number of leaves appeared; PHS: plant height after stress; DWP: dry weight per plant; Pn: photosynthesis rate

3. Adaptation Responses to Early Water Deficit

The combined analysis of variance revealed highly significant differences (P ≤ 0.05) between varieties; environment; and the interactions between variety (V), environment (E), and year (Y), (V*E, V*Y, E*Y and V*E*Y) (see Table 4). Variety V7 showed the smallest number of leaves and PHS; V6 and V3 recorded respectively the highest values for these traits. Varieties V8 and V4 had the highest DLW in both years; V1, V6, and V9 recorded the smallest values. At harvest, variety V5 exhibited the highest plant height and SDW but the lowest grain yield. V7 and V9 exhibited the lowest plant height and SDW respectively. V1 and V10 recorded the highest grain yield under ww conditions in 2019 and 2018 respectively; V1 and V9 recorded the highest one under ds conditions in 2019 and 2018 respectively.

Table 4a
Performance of 10 Sorghum Varieties Under Two Water Treatments (Well-watered and Drought Stress) for Agro-morphological Traits Measured During 2018 and 2019 Field Trials
    NLA   PHS   DLW  
  V ww ds ww ds ww ds
Year 2018 V1 14.6cd 13.5bc 133.7bc 111.7bcd 5.7e 13.9cd
V2 15.3bc 13.6bc 125.7bc 101.6bcd 16.8ab 28.7a
V3 15.5abc 13.2bc 204.3a 179.3a 12.6bcd 25.8a
V4 15.5abc 13.8bc 200.6a 137.5b 10.7cde 17.3bc
V5 15.0bc 13.5bc 186.3a 193.5a 6.0e 16.6bc
V6 16.6a 15.9a 108.5cd 91.2cd 7.8de 9.9de
V7 13.5d 12.4c 92.5d 86.2cd 14.2bc 19.2b
V8 16.0ab 14.4ab 147.5b 111.8bcd 22.4a 26.9a
V9 15.7abc 14.9ab 98.4d 74.0d 18.3ab 8.6e
V10 15.1bc 13.8bc 141.0b 116.1bc 21.4a 28.3a
Grand mean 15.3a 13.9b 143.8a 120.3b 13.6b 19.5a
V *** *** *** *** *** ***
E *** *** ***
V×E ns ** ***
Year 2019 V1 15.7ab 13.7a 120.4a 77.5a 13.7d 15.1e
V2 16.5a 14.2a 140.6a 83.5a 14.9ab 16abcd
V3 15.0ab 13.8a 163.1a 103.1a 11.9f 15.7de
V4 15.2ab 14.3a 107.3a 38.3a 15.3a 16.1cd
V5 15.5ab 14.3a 154.6a 68.5a 14.8ab 16.3bcd
V6 16.0a 15.3a 107.1a 60.8a 12.6e 16.1cd
V7 13.5b 14.1a 106.5a 55.3a 14.5bc 16abcd
V8 15.8ab 14.4a 124.5a 49.0a 15.1ab 17.2a
V9 15.1ab 14.4a 99.4a 53.5a 15.0ab 17.1ab
V10 14.3ab 13.7a 149.4a 66.3a 14.0cd 16.7abc
Grand mean 15.3a 14.2b 130.5a 66.5b 14.2b 16.3a
V * ns ns ns *** ***
E *** *** ***
V×E ns ns ***
Y ns *** ***
V×Y * *** ***
E×Y ns *** ***
V×E×Y ns ** *
Table 4b
    PHT   SDW   YIELD  
  V ww ds ww ds ww ds
Year 2018 V1 172.5cd 148.9cd 427.6bc 358.7abc 4183.4abc 3182.3b
V2 162.8d 140.4cde 470.2abc 349.3abc 3715.9d 3359.3b
V3 191.2bc 193.6ab 498.7ab 409.0abc 2001.5e 1817.6de
V4 205.6b 161.3bc 530.0ab 449.8ab 1606.4ef 1540.3e
V5 235.3a 215.6a 603.2a 461.4a 1473.1f 1168f
V6 159.8d 133.6cde 408.0bc 424.4ab 3926abcd 2598.3c
V7 127.2e 108.9e 315.0c 294.5bc 3812.1cd 2006.9d
V8 168.1d 126.0de 496.8ab 339.9abc 3922bcd 3132.8b
V9 152.2d 117.1de 321.5c 259.3c 4246.0ab 3851.7a
V10 169.3cd 140.0cde 451.0abc 359.3abc 4349.3a 2643.02c
Grand mean 174.8a 148.5b 453.3a 370.6b 3271.85a 2526.21b
V *** *** *** ** *** ***
E *** *** ***
V×E * Ns ***
Year 2019 V1 173.2cd 127abcd 494.2bc 377.3abc 3876.8a 2424a
V2 149.4de 120.1bcd 404.5bcd 259.0bc 2127.7cd 2070.3abc
V3 216.7ab 170.4abc 329.6cd 323.0abc 1886.4d 1752.5bc
V4 204.6bc 183.2ab 791.2a 399.4ab 1233.9e 1124.5de
V5 250.8a 198.2a 564.5b 428.4a 771.2f 817.4e
V6 137.9ef 88.4d 413.2bcd 274.0abc 3429.2ab 1582bcd
V7 109.2f 93.4d 380.8bcd 246.5c 3166.7b 2027.6abc
V8 143def 106.1cd 330.4cd 267.8abc 2514.8c 2087.6ab
V9 131.4ef 101.8cd 303.7d 272.0abc 2105.5cd 2037abc
V10 140def 102.6cd 382.6bcd 278.3abc 3085.8b 1462.9cd
Grand mean 165.7a 127.2b 427.3a 308.6b 2419.84a 1738.64b
V *** *** *** ** *** ***
E *** *** ***
V×E * ** ***
Y *** *** ***
V×Y *** ** ***
E×Y ns ** *
V×E×Y ns ns ***

ww: well-watered; ds: drought stress; NLA: number of leaves appeared; PHS: plant height after stress; DLW: dead leaves weight; PHT: plant height at harvest; SDW: Straw dry weight; Yield: grain yield; V: variety; E: environment; Y: year; *** significant at p = 0.001; ** significant at p = 0.01; * significant at p = 0.05; ns: not significant. The means with the same letters are not significantly different. The bold values indicate the highest and lowest value measured.

Physiological traits like photosynthesis rate, stomata conductance, leaf transpiration, and leaf temperature revealed a wide range of genetic variability among the varieties under both ww and ds conditions (see Table 5). The effects of variety, environment, and their interaction (V× E) were highly significant in both years. Under ww conditions, variety V1 recorded the highest photosynthesis rate, stomata conductance, and leaf transpiration; V5, V9, and V2 recorded respectively the lowest values for the same traits in 2018. However, the occurrence of drought stress induced various responses in these varieties. Variety V10 recorded the lowest photosynthesis rate in 2018 but the highest rate in 2019, showing variation in the behavior of this variety from year to year. Overall, a decrease of photosynthesis rate, stomata conductance, and leaf transpiration—and an increase of leaf temperature—were the physiological responses of the studied varieties to early water deficit (see Table 5).

Table 5
Performance of 10 Sorghum Varieties Under Two Water Treatments (well-watered and drought stress) for Physiological Traits Measured During 2018 and 2019 Field Trials
Pn C Tr Tleaf
V ww ds ww ds ww ds ww ds
Year 2018 V1 43.14a 31.92abc 213.06a 119.35cd 9.34a 5.06ab 36.34cd 40.70ab
V2 41.57ab 33.29abc 189.14ab 101.85d 7.00b 4.00b 34.80d 39.12abc
V3 39.88ab 39.66a 179.04ab 169.84ab 7.12b 6.42a 40.30ab 39.83abc
V4 38.99ab 38.61a 169.08b 140.07abcd 7.38b 5.58ab 40.77a 39.77abc
V5 35.64b 26.05bc 169.11b 128.31bcd 7.13b 4.45ab 39.87ab 39.08bc
V6 42.17ab 39.83a 202.09ab 181.85a 7.56ab 6.16a 39.32ab 40.94a
V7 40.69ab 37.81a 194.62ab 181.45a 6.39b 5.89a 40.40ab 39.54abc
V8 38.16ab 38.02a 182.93ab 183.21a 7.68ab 6.36a 40.83a 38.51c
V9 36.05ab 35.74ab 166.88b 166.72ab 7.50ab 6.53a 39.34ab 39.02bc
V10 37.70ab 22.96c 186.72ab 147.17abc 7.22b 4.61ab 37.83bc 38.75c
Grand mean 39.32a 34.43b 184.94a 150.97b 7.43a 5.47b 39.02b 39.53a
V * *** ** *** ** *** *** **
E *** *** *** **
V×E *** *** *** ***
Year 2019 V1 47.03a 22.66c 193.03a 78.41c 7.28ab 4.00b 38.75b 42.86ab
V2 42.37abc 29.28abc 189.03a 108.90bc 7.25ab 5.17ab 39.98a 42.72ab
V3 41.56abc 35.05ab 191.45a 140.03ab 6.70abc 5.73ab 40.44a 41.68bc
V4 37.76bc 34.51ab 137.08b 135.79ab 6.12c 5.40ab 38.30bc 43.11a
V5 41.08abc 27.06abc 167.77ab 96.88bc 7.24ab 4.96ab 39.94a 40.73c
V6 38.79abc 26.58abc 165.52ab 94.97bc 6.89abc 5.23ab 40.50a 42.12ab
V7 43.72ab 35.59ab 194.77a 177.36a 7.65a 6.18a 37.36c 42.34ab
V8 34.15c 27.81abc 164.44ab 98.39bc 6.56bc 4.78ab 40.00a 40.62c
V9 43.24ab 25.57bc 190.44a 93.53bc 7.14abc 4.64ab 37.63bc 43.27a
V10 44.10ab 36.41a 197.92a 134.17ab 7.61a 6.11a 38.67b 42.06ab
Grand mean 41.38a 29.90b 179.14a 115.88b 7.04a 5.22b 39.16b 42.15a
V ** *** *** *** *** * *** ***
E *** *** *** ***
V×E *** *** ** ***
Both years Y ns *** ** ***
V×Y *** *** *** *
E×Y *** *** ns ***
V×E×Y *** *** ** **

ww: well-watered; ds: drought stress; Pn: photosynthesis rate; C: stomata conductance; Tr: leaf transpiration; Tleaf: leaf temperature; SDW: Straw dry weight; V: variety; E: environment; Y: year; *** significant at p = 0.001; ** significant at p = 0.01; * significant at p = 0.05; ns: not significant. The means with the same letters are not significantly different. The bold values indicate the highest and lowest value measured.

The RLD was estimated by RID along the soil profiles using the geometrical model for both ww and ds treatments at the end of the stress. Our results showed a strong and significant effect of water deficit on the number of total roots (NTR) and RLD profiles (see Table 6).

Varieties V1 and V8 exhibited the highest NTR in ww and ds conditions respectively, while V2 and V10 exhibited the lowest in ww and ds conditions respectively. Among varieties, V4 exhibited the lowest RLD (0 cm – 120 cm) in both the ww and ds environment. Under drought stress, V1 and V8 recorded the strongest RLD in the shallow horizon (0 cm – 50 cm) and deep horizon (60 cm – 120 cm) respectively (see Table 6). From the data presented in Table 6, the global trend of the varieties’ root system’s responses to early drought stress is highlighted on Figure 4. Drought stress induced significant reduction of RLD in the 0 cm – 50 cm soil horizon; it increased in the 60 cm –120 cm deep soil layers.

Table 6
Average Performance of Sorghum Varieties for Root Traits Under Well-watered and Drought Stress Conditions
NTR RLD [0-120cm] RLD [0-50cm] RLD [60-120cm]
V ww ds ww ds ww ds ww ds
V1 63.00a 28.66ab 1922.80a 1687.83g 3457.35c 2764.00a 800.02c 1578.33c
V2 32.00f 22.33cd 1760.68bc 2041.48c 3286.61e 2698.90b 654.07f 1571.89c
V3 40.33c 26.00bc 1751.29bc 1769.92f 3358.45d 2435.41f 648.08f 1294.57e
V4 50.33b 31.66a 1467.24e 1574.29h 2519.95h 2236.74g 715.29e 1090.53f
V5 62.66a 28.66ab 1781.89bc 1945.37e 3245.57e 2641.44c 719.73e 1448.18d
V6 40.00cd 28.66ab 1739.46bc 1993.00d 3564.50b 2520.00e 873.33a 1421.00d
V7 36.00e 25.33bc 1827.72b 2190.90b 3311.3de 2598.55d 772.75cd 1807.34b
V8 42.00c 30.66a 1991.88a 2453.95a 3659.27a 2704.23b 800.89c 2266.67a
V9 37.00de 25.00bcd 1625.93d 1687.83g 2738.30g 2271.24g 831.38b 1271.11e
V10 37.00de 21.33d 1696.45cd 1757.95f 3121.66f 2428.02f 763.37d 1279.32e
Grand mean 44.03a 26.83b 1758.61b 1897.27a 3214.64a 2522.88b 757.89b 1502.8a
V *** *** *** *** *** *** *** ***
E *** *** *** ***
V×E *** *** *** ***

ww: well-watered; ds: drought stress; NTR: number of total roots; RLD: root length density (m m−2); [0–120 cm], [0–50 cm] and [60–120 cm] represent the depth considered; V: variety; E: environment; *** significant at p = 0.001. Means with the same letters are not significantly different. The bold values indicate the highest and lowest value measured.

Figure 4

2 graphs showing Root Length Density (RLD) of Sorghum Varieties Under Well-watered (ww) and Drought Stress (ds) Conditions at the End of the Stress Period. Graph A is bar chart with whiskers with ww (3 blue bars) & ds (3 yellow bars) showing growth of RLD (mm-3) across X axis of Depth (cm). Graph B is 2 plot lines with whiskers with RLD_ww (blue) & RLD_ds (yellow) showing Depth (cm) across X axis of RLD (mm-3).
Figure 4 – Root Length Density (RLD) of Sorghum Varieties Under Well-watered (ww) and Drought Stress (ds) Conditions at the End of the Stress Period

Notes. (A) RLD distribution at [0-50], [60-120] and [0-120] depth horizons (B) Impact of water deficit on RLD profile. Data are means +/- standard error. Significant differences are indicated by different letters.

4. Relationship Between Vegetation Indices and Sorghum’s Growth Traits

Results presented in Table 7 show the relationship between LAI, biomass, and vegetation indices. Nonlinear and linear regression models were fitted using the 2018 field data set (n = 390, calibration data). Regression analysis revealed a good relationship between LAI or biomass with NDVI, CTVI, GNDVI, MSAVI2, and SR. To assess the performance of vegetation indices to estimate LAI, we compared the coefficients of determination (r2) of the relationships between NDVI, CTVI, MSAVI2, SR, and LAI that were respectively of 0.83, 0.82, 0.76 and 0.77 with highly significant p values. However, the r2 for biomass estimation using the same indices were comparatively lower than those for LAI (0.6, 0.6, 0.57, and 0.47).

Table 7
Regression of Sorghum LAI and Biomass on Vegetation Indices (n = 390)
Vegetation indices Regression models r P-value
NDVI LAI=0.3732*e2.9648*NDVI 0.91 0.83 <0.001
CTVI LAI=0.0069*e5.5322*CTVI 0.9 0.82 <0.001
MSAVI2 LAI=0.3392*e2.7498*MSAVI2 0.9 0.82 <0.001
SR LAI=0.4438*SR+0.0126 0.87 0.77 <0.001
NDVI Biomass = 3.1153*e3.7021*NDVI 0.77 0.6 <0.001
CTVI Biomass = 0.0206*e6.9446*CTVI 0.77 0.6 <0.001
MSAVI2 Biomass = 2.7357*e3.4587*GNDVI 0.77 0.6 <0.001
SR Biomass = 6.0128*SR + 0.8947 0.68 0.47 <0.001

NDVI, CTVI, MSAVI2 and SR: vegetation indices; r: coefficient of correlation; r²: coefficient determination; LAI: leaf area index.

Figure 5 shows the NDVI plots with the corresponding LAI. The LAI values varied from 0.3 m2 m−2 to 5.7 m2 m−2 per plant across varieties, treatments, and developmental stages during the calibration trial in 2018. A saturation of the different vegetation indices was observed above LAI values higher than 4 m2 m−2 per plant. To test the variance of calibration models, an ordinary least squares linear regression between calculated and measured LAI was done, and it revealed an r2 value of 0.8 for LAI (see Figure 5). The ANOVA revealed highly significant effect of varieties, environment, and the interaction (V*E) (p<0.05) on both calculated and measured LAI and biomass with almost the same values (see Tables 8 and 9).

Figure 5

2 scatter plot line graphs demonstrating Calibration of LAI prediction model from NDVI vegetation index. Graph A shows a curved line gradually increasing in LAI across NDVI X axis. Graph B shows a linear line increasing in measured LAI across calculated LAI X axis.
Figure 5 – Calibration of LAI prediction model from NDVI vegetation index

Notes. (A) Relationship between normalized difference vegetation index (NDVI) and leaf area index (LAI); (B) Measured LAI vs. corresponding LAI values predicted using empirical equation in Figure 5A. The dashed red line in the graph is the 1:1 line.

Table 8
ANOVA of Leaf Area Index Measured (LAIm) and Calculated from Vegetation Indices (LAIc) and Average Performance of the Varieties Under Well-watered and Drought Stress
V ww ds ww ds ww ds
V1 1.5 0.62 1.53 0.62 1.9 0.6
V2 1.56 0.61 1.59 0.61 1.9 0.8
V3 2.09 0.7 2.12 0.72 1.57 0.9
V4 1.6 0.58 1.63 0.57 1.4 0.45
V5 1.95 0.56 1.97 0.56 2.23 0.6
V6 1.39 0.7 1.43 0.7 1.63 0.57
V7 2.01 0.53 2.03 0.52 1.9 0.47
V8 1.93 0.68 1.96 0.69 2.63 0.8
V9 1.78 0.64 1.81 0.65 1.53 0.67
V10 1.74 0.48 1.77 0.47 1.57 0.53
Mean 1.75 0.61 1.78 0.61 1.83 0.64
V * * **
E *** *** ***
V*E * * *

V: varieties; E: environment; ww: well-watered; ds: drought stress; CTVI and NDVI: vegetation indices; Significance codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

Table 9
ANOVA of the Biomass Per Plant Measured (BPPm) and Calculated from Vegetation Indices (BPPc) and Average Performance of the Varieties Under Well-watered and Drought Stress
V ww ds ww ds ww ds
V1 17.62 5.53 21.12 9.69 18.3 15.7
V2 18.27 5.38 22.08 9.54 22.55 10.79
V3 25.85 6.65 27.56 10.5 19.11 12.36
V4 18.73 4.96 22.38 9.01 17.38 7.9
V5 23.94 4.73 26.1 8.93 29.82 11.84
V6 15.77 6.62 20.49 10.49 23.87 11.39
V7 24.47 4.35 28.25 8.57 35.72 8
V8 23.59 6.32 26.84 10.36 27.32 9.21
V9 21.52 5.76 24.51 9.87 22.49 18.88
V10 21 3.75 24.55 7.91 18.28 5.86
Mean 21.08 5.42 24.39 9.5 23.48 11.31
V * * ***
E *** *** ***
V*E * ** ***

V: varieties; E: environment; ww: well-watered; ds: drought stress; MSAVI2 and SR: vegetation indices; Significance codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

5. Assessment of the Impact of Water Treatment on UAV Derived Traits

The combined analysis of variance revealed highly significant effect (P ≤ 0.05) of the environment (E), variety (V), and their interaction (V*E) on all the vegetation indices under study (see Table 10). In ww conditions, the varieties recorded on average 0.6, 1.08, 0.76, 7.37 for NDVI, CTVI, MSAVI2, and SR respectively; the stressed plants showed lower values (i.e., 0.37, 0.90, 0.44, and 3.01). In ds conditions, the variety V3 exhibited the highest indices (0.59, 0.94, 0.66 and 4.02 for NDVI, CTVI, MSAVI2, and SR); V10 had the lowest (0.20, 0.83, 0.25, and 1.74).

Table 10
Average Performance and ANOVA of the Vegetation Indices of the Varieties Under Well- watered and Drought Stress
V ww ds ww ds ww ds ww ds
V1 0.63 0.38 1.06 0.92 0.71 0.46 6.83 3
V2 0.68 0.37 1.08 0.91 0.76 0.44 6.77 3
V3 0.73 0.59 1.11 0.94 0.81 0.66 7.97 4.02
V4 0.7 0.33 1.09 0.89 0.77 0.4 7.53 2.71
V5 0.71 0.39 1.1 0.94 0.79 0.47 7.91 2.85
V6 0.62 0.36 1.05 0.89 0.7 0.42 5.69 3.24
V7 0.74 0.28 1.11 0.88 0.81 0.35 8.59 2.2
V8 0.74 0.45 1.11 0.94 0.81 0.53 8.17 4.46
V9 0.7 0.4 1.09 0.94 0.78 0.49 6.99 2.92
V10 0.67 0.2 1.08 0.83 0.75 0.25 7.33 1.74
Mean 0.69 0.37 1.08 0.9 0.76 0.44 7.37 3.01
V ** ** *** *
E *** *** *** ***
V*E * * * *

V: varieties; E: environment; NDVI, CTVI, MSAVI2 and SR: vegetation indices (VIs); ww: well-watered; ds: drought stress; Significant codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

Results presented in Figure 6 showed the general trend of the evolution of VIs during plants growth in both ww and ds conditions. The vegetation indices of nonstressed plants gradually increased to reach their maximum at the 60th DAS (flowering time) before dropping slightly. In the stressed plots, the vegetation indices initially recorded a slight increase but then water deficit induced a reduction that was followed by a progressive increase after rewatering.

However, the VIs values of stressed plants did not recover values of the nonstressed plants. The results highlighted in this study testify the relevance of VIs to capture the differences induced by drought and that occurred from the 30th DAS to the 55th DAS. The post flowering decrease of the VIs observed in the ww treatment was due to the saturation of the VIs as plants have grown up enough in this environment.

Figure 6

4 floating bar charts with whiskers demonstrating Dynamics of Estimated Vegetation Indices Under Well-watered and Drought Stress Conditions. Chart 1 shows ds (pink) & ww (light green) increasing in NDVI across DAS X axis. Chart 2 shows ds (pink) & ww (light green) increasing in CTVI across DAS X axis. Chart 3 shows ds (pink) & ww (light green) increasing in MSAVI2 across DAS X axis. Chart 3 shows ds (pink) & ww (light green) increasing in SR across DAS X axis.
Figure 6 – Dynamics of Estimated Vegetation Indices Under Well-watered and Drought Stress Conditions

Note. Box plots represent the estimations for all the 10 sorghum varieties at a given stage. NDVI, CTVI, MSAVI2 and SR: vegetation indices; DAS: days after sowing; ds: drought stress; ww: well-watered.


1. The Adaptive Behavior of the Sorghum Varieties Studied

The stress characterization revealed very negative values of water potential (WP = -5 bars) at the end of the stress period. This can be explained by the intensity of the stress experienced by the plants (FTSW = 0.3). To counteract the water deficit that occurs in their tissues, plants implement water retention mechanisms with very high energy. Consequently, it is necessary to provide equivalent energy to extract this water from the tissues (Brou et al., 2007). The behavior of the sorghum varieties, which consists in maintaining a high level of hydration despite the stress pressure, reflects a dehydration tolerance mechanism, with a low water potential of the tissues (Levitt, 1985). This limits cell growth because of loss of turgidity.

Water-limiting conditions lead to altered cell elongation, mainly due to low water flow from the xylem to neighboring cells (Fahad et al., 2017; Nonami, 1998). The number of leaves and the size of each leaf were reduced in drought conditions because their expansion depends on the turgor pressure and the amount of water assimilated. This induced a decrease in plant biomass accompanied by a reduction in leaf appearance and plant growth. Regarding physiological traits, drought stress causes a decrease in the transpiration rate, photosynthetic activity, and stomata conductance (Dwivedi et al., 2008; Fracasso et al., 2016). This adaptation mechanism also was revealed in pearl millet (Kholova et al., 2009, 2010) and cowpea (Belko et al., 2012). Under water deficit, the diffusion of CO2 to the carboxylation sites is limited because of stomata closure and increased mesophyll resistance. This inhibits the transport of electrons, leading to an imbalance between the electron transport rate and

CO2 fixation rate (Verma et al., 2018). The photosynthetic performance is one of the parameters providing useful and quantitative information on plants condition and vitality (Banks, 2018; Oukarroum et al., 2007; Zegada-Lizarazu & Monti, 2013). Tingting et al. (2010) showed that the process of photosynthesis is sensitive to changing environmental conditions, and the way in which plants adapt to their environment is propitious to photosynthesis. The recovery of photosynthesis upon rewatering indicates that the PSII systems had recovered their ability to deal with the absorbed light and the accumulated energy. Hence, oxidative permanent damage may not have occurred at the early growth stages, or as suggested by Oukarroum et al. (2007), the maximum quantum yield of photochemistry was not affected by the drought. The drop of plant growth rate caused by the lack of water during a given period often leads to difficulties in covering the normal development in terms of height or biomass (Hud et al., 2016). This behavior is not always a disastrous consequence. It could be a means of adaptation that allows the plant to maintain its development but at a slower pace. This was the case of the variety V6, for instance, which produced well despite a low DRI in height and biomass. These are part of physiological functioning that confer drought tolerance to sorghum (Hadebe et al., 2017; Harris et al., 2007; Kapanigowda et al., 2013). The agro-physiological behavior of a plant depends on the genotype, the severity of the drought, and the time of occurrence (Chaves et al., 2002; Jaleel et al., 2008). Early water stress acts differently on sorghum varieties depending on the variety’s stage of development. It is well-known that when drought occurs at the vegetative stage before panicle exertion, plants recover better. This may explain why the agronomic performances of some long-duration varieties of the guinea and bicolor breed (V1, V4) were weakly affected by early cycle drought. Long-duration varieties were able to catch up and stabilize production despite a lack of water at the early stage. According to Araus et al. (1989), this phenomenon is due to stomata control, which is more effective in the young growth stages. This could have been the case of variety V1, which responded to water deficit by closing its stomata, thus allowing it to limit exchanges with the environment until water conditions become favorable and the growth could resume and compensate for the losses due to drought. Contrariwise, V4 showed the lowest DRI and a slight variation of stomata conductance and yield under drought stress. A hypothesis is that this variety recovered well after the 2 weeks allowed for recovery measurement. However, the drought adaptation conferred by the long cycle is not sufficient to consider such varieties appropriate for the future. Previous studies have shown that the duration of drought episodes at the beginning of the season is likely to increase with the worsening of global climate change impacts (Blanc, 2012; Vadez et al., 2012), therefore even long-duration varieties may be affected by early drought stress if the duration is long.

Roots play an important role as a support, but also provide the plant with the water and mineral elements it needs. Thus, their study represents a very effective means of characterizing drought adaptation. Some authors showed that the spatial distribution of root length density determines water and nutrient uptake (Intergovernmental Panel on Climate Change, 2014). In the present study, the varieties V1 and V8 turned out to be very interesting. They yielded well under drought, and their adaptations were mainly based on a high RLD (60 cm – 120 cm) (V8) and the increase of dead leaves (V8 and V1) contrarily to V4, which had the lowest RLD (60 cm – 120 cm) and a low grain yield. Additionally, some varieties have densified their root system to be able to exploit a larger surface area of soil and to increase the absorption of water and mineral nutrients (Comas et al., 2013). The high root density at depth allows them to reach moisture in the deeper soil layers and compensate for the lack of water supply.

Although the varieties V3, V4, and V5 were less productive, they could be of high interest for height or fodder breeding programs. Varieties V1, V2, V8, and V9 could be devoted for grain yield breeding. Phenotypic evaluation of germplasm can be useful for characterization, conservation, and maintenance of genetic resources (Naoura et al., 2019). This study revealed a large agro-morphological diversity of quantitative traits. Overall, the results showed that plant response to early drought was genotype dependent (Sinclair et al., 2018) and some varieties expressed a strong ability to reduce water loss by decreasing leaf transpiration rate through stomata closure and increasing the number of dead leaves. These were among the adaptation strategies used by the studied varieties to tolerate drought stress conditions in both seasons.

2. Monitoring Plant Growth by UAV Based Phenotyping

Recent advances in high throughput field phenotyping have boosted the power of physiological breeding (Araus & Cairns, 2014; Fahlgren et al., 2015; Hu et al., 2018; Reynolds & Langridge, 2016). Currently, UAV technology is an alternative to the manual collection of crop data, offering information on traits and factors affecting crop development and productivity with relatively shorter time and lower cost (Du & Noguchi, 2017; Yu et al., 2016). The moderate to strong relationships (see Table 7) found between the UAV-derived plant spectral traits and the leaf area index and the biomass indicate that UAVs could be useful for phenotyping West Africa sorghum genotypes (Gano et al., 2021). The NDVI is an indicator of the combined effects of chlorophyll concentration, canopy leaf area, and yield (Erdle et al., 2011). The estimation of the NDVI can be used as a reference index for the dynamic monitoring of the biomass changes during the growth season of sorghum. NDVI estimates are influenced by many factors, such as measurement time, sensors, and environmental conditions (Crusiol et al., 2017), and there is no one absolutely accurate measurement method for NDVI estimation. Improved precision would also contribute to further applications for field management (Foster et al., 2017). Results presented herein demonstrate the importance of using NDVI related vegetation index as indirect selection criteria by reporting genetic variation for VIs among varieties, the effect of water treatment on VIs and their interaction with varieties, and the relationships between VIs and LAI and biomass of sorghum. This attests the ability of VIs in estimating growth rate, biomass accumulation during the vegetative stage and yield set up (Babar et al., 2006).

3. Key Traits Involved in Drought Tolerance for Breeding

As a major challenge for agricultural production, drought tolerance is a prime target for molecular approaches to crop improvement. To obtain significant results, these approaches must be based on phenotyping protocols that are appropriate at all stages of plant development (Salekdeh et al., 2009). Because drought adaptation traits are complex and polygenic, the understanding of their physiological and genetic basis is still incomplete. This challenge comes at a time when plant biologists are witnessing an explosion in the availability of new high-throughput technologies and genomic information. However, the identification of preferred selection criteria remains unclear and still makes phenotyping laborious. According to Passioura (1977), the conceptual framework for drought phenotyping is based on the equation expressing the product yield of WU (quantity of water used), WUE (conversion of WU into dry biomass), and HI (the fraction of dry matter converted into grain). Therefore, it is important to design experiments to test these factors by distinguishing the impact of WU and WUE on production. In other words, when looking at productivity, it is important to identify the effects of growth stress that may affect assimilations transport. By considering these components of performance individually, it is possible to target traits more effectively in relation to environmental constraints. In the case of early-cycle water stress, there are several key phenotypic traits highlighted (see Table 11) to help target phenotyping. In addition, it would be useful to select high yielding genotypes that are stable across environmental conditions and years. To do so, it would be interesting to follow growth-related traits such as plant height and NLA, DWP, Tr and Pn because these traits are susceptible to environmental changes.

Table 11
Example of Traits Associated with Different Yield Factors Worth to Phenotype Under Conditions of Early-cycle Water Stress in Sorghum
Traits Stage Phenotyping technique
Interest for breeding
Root Length Density (RLD) Growth Count Yield
Number of adventitious roots Growth Count Yield
Soil water stock Growth Metric Yield
Biomass rate Growth Metric Biomass
Plant height rate Growth Metric Biomass
Dead leaves weight Growth Metric Yield
Photosynthesis rate Growth Metric Yield
Transpiration rate Growth Metric Yield
NDVI Growth Metric Biomass
Grain weight Harvest Metric Yield
Number of grains Harvest Count Yield
Stem dry weight Harvest Metric Biomass

Moreover, this study demonstrated successful and rapid assessment of NDVI related VIs (CTVI, MSAVI2 and SR) using a UAV platform that showed high accuracy in assessing variation in plant development. The accuracy of the UAV platform was validated by ground truth data, and it proved a significant advantage of UAV over the handheld data acquisition platform from the stem elongation stage to late grain filling stage, especially under water- limited conditions.

Drought-prone environments are diverse and the biotic and abiotic stresses that affect yield during drought periods are numerous (Richards et al., 2002). Therefore, our objective is not to propose unique criteria for drought stress phenotyping. Rather, we suggest that each experiment be conducted with a specific, realistic goal and with WUE and yield set up as reference traits (Venuprasad et al., 2007). Such reference traits will ensure the relevance of field results that are assessed and deposited in public databases for a standardized recording and reporting of drought-related phenotypic data.


This study is justified by the challenge that researchers have set to improve adaptation to early drought stress in cereals, particularly sorghum. We have shown the impact of water deficit on sorghum growth and development. The early stress is a major factor in the evolution of biomass, height, and leaf development. Even though it occurred early, its impact leads to yield instability. This study also highlighted plant adaptation mechanisms under early water deficit based on growth; photosynthesis and transpiration reduction; senescence increase; stomata closure; and roots length density increase. We also highlighted the ability of UAV platform to phenotype drought stress in West Africa sorghum varieties. Finally, we proposed key phenotyping traits that involve the different factors that govern production for a more efficient characterization of drought adaptation. Future areas of study could include phenotyping entire sorghum collections in different growing conditions during the year to better fix the adaptation mechanisms. Based on the predictions of precariousness linked to climate change, it would be more than necessary to select varieties that are able to adapt and stabilize performance independently of the season and year.


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Crop Adaptation and Improvement for Drought-Prone Environments Copyright © 2022 by Editors: Ndjido A. Kane, Daniel Foncéka, and Timothy J. Dalton is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, except where otherwise noted.

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