Seedling stage drought screening of candidate cowpea (Vigna unguiculata (L) Walp.) genotypes

Abstract

Prolonged drought of cowpea at the seedling stage can affect survival and productivity. This study sought to evaluate cowpea genotypes for their resistance to seedling-stage drought stress. Fifteen cowpea genotypes were used in this study. The experiment was a factorial experiment arranged in a Completely Randomized Design with three replications. Drought stress was imposed for 4 weeks after the full expansion of the first trifoliate leaf. Soil Plant Analysis Development (SPAD) chlorophyll meter readings of unifoliate, first, and second trifoliate leaves of each genotype were measured on a weekly basis due to the predictive ability of chlorophyll content on drought tolerance. The data were subjected to ANOVA, and the means were separated at a 5% probability level. Genotypes were ranked according to their tolerance level based on their chlorophyll inflectance index (CII). The results revealed significant genotype by stress interaction effects. Seedling stage screening under water-stressed conditions in combination with measurements of leaf chlorophyll measurements proved to be an effective way for rapid screening of genotypes for drought tolerance. IT14K-1424-12, IT10K-837-1 and IT10K-303-1 had consistently high chlorophyll content under drought stress for unifoliate and trifoliate leaves. These genotypes also fell into one cluster. Based on CII with a threshold of 51% for the fourth week of drought stress of unifoliate and trifoliate leaves, three genotypes (IT14K-1424-12, IT10K-837-1 and IT10K-303-1) were found to have Type I drought-tolerant mechanism. These genotypes could be used in future breeding interventions or released as varieties.

Keywords:

• Cowpea
• unifoliate leaves
• trifoliate leaves
• water stressed
• chlorophyll
• chlorophyll inflectance index
• drought tolerance

1. Introduction

Cowpea (Vigna unguiculata L. Walp.) is a grain legume crop of global economic importance and it originated from Africa (Carvalho et al., 2019). It is grown for its grains and fodder due to its diverse morphology (Gonçalves et al., 2016; Ravelombola et al., 2020). According to Santos et al. (2020), dry cowpea grain has high protein levels (20.0–39.4%) and low-fat content (3.1–30.4%). Aside from being an excellent source of protein, cowpea contains all the essential amino acids, making it an important food component in several countries including Ghana (Freire Filho et al., 2012; Oliveira et al., 2016; Vasconcelos et al., 2010). Apart from being a good source of nutrients, cowpea contains dietary fibres, antioxidants, polyunsaturated fatty acids (PUFA) and polyphenols (da Silva et al., 2018; Nkomo et al., 2021). Cowpea also plays an important role in soil fertility improvement, it has the capability of fixing atmospheric nitrogen and suppressing weeds as a cover crop (Kebede & Bekeko, 2020).

The global production of cowpea is about 8.9 million metric tons cultivated in an area of around 15 million hectares (FAOSTAT, 2020). Cowpea cultivation is widely distributed throughout the tropics and subtropical regions, with the largest production in Africa coming from West Africa (Gnankambary et al., 2020).

Cowpea cultivation is challenged by various factors including abiotic stresses such as drought. Cowpea yield remains one of the lowest among all grain legumes in sub-Saharan Africa, due to several constraints, such as drought. According to Ibitoye (2015), drought causes grain and fodder yield reduction by 62% and 56%, respectively. It can occur at any stage of crop development. Early-season drought stress can affect crop establishment. Drought stress during flowering or pod-filling stages may also result in yield losses (Tumwesigye & Musiitwa, 2002). It is reported that climate change will intensify the frequency of drought and flood, particularly in several African countries, and it is projected that by 2050, water shortages are expected to affect 67% of the world’s population (Ceccarelli et al., 2004).

In Ghana, cowpea is cultivated across all ecological zones. However, most of the cowpea production occurs in the Savanna ecological zone of Ghana where drought is predominant due to erratic rainfall and heat stress accompanied by poor soil fertility (Fahad et al., 2017; Yahaya et al., 2019). Most farmers rely on rainfall for crop production. The rainfall pattern in northern Ghana is monomodal and spans from June to mid-October. As cowpea is a short-season crop, it is usually planted from mid-July to mid-August so that the harvesting period will coincide with the onset of the dry season. Due to climate change, consistently over the years, drought has been observed to affect plant stands of cowpea (Omomowo & Babalola, 2021). This drought takes place for a minimum of 2 weeks in August in most parts of northern Ghana. Efforts have therefore been made to screen cowpea genotypes for resistance to seedling-stage drought (Ajayi et al., 2018; Carvalho et al., 2019; Ravelombola et al., 2020). Only a few studies have exploited the measurement of physiological traits, such as chlorophyll content in assessing drought tolerance of cowpea in Ghana (Alidu et al., 2019). According to previous findings, Chlorophyll is responsible for converting light energy into chemical energy and the photosynthetic rate is directly affected by the chlorophyll content (Arjenaki et al., 2012). Determining drought tolerance by measuring the chlorophyll content is important because it gives an indication of the health of the plant (Monteoliva et al., 2021). Arunyanark et al. (2008) established a relationship among SPAD chlorophyll meter reading (SCMR), chlorophyll density and transpiration efficiency, and demonstrated that the Soil Plant Analysis Development (SPAD) chlorophyll meter could be used for rapid and cost-effective assessment of chlorophyll as proposed by Percival and Sheriffs (2002) in its use to rapidly and accurately detect and quantify drought tolerance.

Photosynthesis plays an important role in plant growth; however, this can be reduced in plants under stressed conditions due to the potential damage to the chloroplast (Munné-Bosch et al., 2001). Given the complexity of drought tolerance in cowpeas as a result of the various traits for drought tolerance evaluation (Verbree et al., 2015), it will be useful to exploit a rapid and accurate drought screening method such as seedling stage drought screening combined with measuring chlorophyll content of stressed plants. Mai-Kodomi et al. (1999) described two types of drought tolerance (Type I and Type II) of cowpea at the seedling growth stage. In Type I drought tolerance, both unifoliate and trifoliate leaves are fully green under drought conditions, whereas in Type II, genotypes have delayed senescence of their trifoliate leaves. However, there is no information on the drought tolerance mechanism of genotypes used in this study. The purpose of this study was to evaluate the drought-tolerance ability of advanced cowpea breeding lines, specifically, to determine the chlorophyll content of cowpea seedlings under moisture stressed conditions, explore the drought tolerance mechanisms in the cowpea genotypes and identify cowpea genotypes that have high photosynthetic abilities under drought stress.

2. Materials and methods

2.1. Experiment 1

An initial study was carried out between April and May 2021 at the screen house of the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute (CSIR-SARI), Nyankpala, Ghana. Using all the test genotypes (15) and two stress factors (water stressed and non-stressed), the experiment was a factorial experiment laid out in a completely randomized design (CRD) with three replications. The primary data collected were on the trifoliate leaves (TF) across five sampling occasions. The outcome and challenges of experiment 1 provided a basis for the experimental procedure and data collected in experiment 2.

2.2. Experiment 2

2.2.1. Location of study

The experiment was conducted at the screen house of the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute (CSIR-SARI), Nyankpala, Ghana, between April and May of 2021 and 2022. List of genotypes used in this study have been listed in Table 1. Environmental descriptions of the experimental site are presented in Table 2.

Table 1. List of selected cowpea genotypes used in this study obtained from CSIR-SARI

No.	Genotype	Source	Biological status
1	IT07K-299-6	IITA	Advanced Breeding Line
2	IT07K-303-1	IITA	Advanced Breeding Line
3	IT10K-817-3	IITA	Advanced Breeding Line
4	IT10K-837-1	IITA	Advanced Breeding Line
5	IT11K-61-82	IITA	Advanced Breeding Line
6	IT13K-1070-2	IITA	Advanced Breeding Line
7	IT14K-1424-12	IITA	Advanced Breeding Line
8	IT86D-610	IITA	Advanced Breeding Line
9	SARI-2-50-80	SARI	Advanced Breeding Line
10	SARI-3-11-100	SARI	Advanced Breeding Line
11	SARI-6-2-6	SARI	Advanced Breeding Line
12	SARVX-09-004	SARI	Advanced Breeding Line
13	Wangkae	SARI	Released Variety
14	Kirkhouse Benga	SARI	Released Variety
15	Padi-tuya	SARI	Released Variety

Table 2. Environmental description of experimental site during the experimental period (April to May)

Year	Month	Soil type	Temperature (℃)	Altitude (m)	Precipitation (mm)	Rainfall pattern	Humidity (%)
2021	April	Sandy loam	32.6	180	76.6	Monomodal	53
	May		30.2		84.0		59
2022	April	Sandy loam	29.9	180	173	Monomodal	72
	May		29.1		100.4		76

2.2.2. Source of experimental materials

A total of 15 cowpea genotypes were used in this study (Table 1). These materials have been identified to have high and stable yields from previous multilocational yield trials. Eight of these lines were obtained from the International Institute for Tropical Agriculture (IITA) while the remaining seven were obtained from CSIR-SARI.

2.2.3. Experimental design

The experiment was a factorial experiment laid out in a Completely Randomized Design (CRD). The two factors were the genotypes (15) and stress factor (water stressed and non-water stressed conditions). The treatment was a combination of the levels of the two factors.

2.2.4. Screenhouse evaluation for drought tolerance

The procedure for evaluating drought tolerance was described previously by Ravelombola et al. (2018). Plastic containers were filled up with sandy-loamy soil. Each plastic container was irrigated 2 days before sowing to obtain the required field capacity moisture level. A total of six holes were created in each container, and two seeds were sown per hole. The plants were later thinned to one plant/stand or per hole at emergence. Uniform and healthy plants were kept for evaluation.

A week after emergence, a blanket NPK 15-15-15 fertilizer was applied at a rate of 100 kg/ha, and pots were watered with 150 mL of tap water at 3-day intervals until the first trifoliate leaves expanded. Environmental impact was not minimized, but plants were placed to ensure uniformity of environmental impacts on the plants. At this time, irrigation was withdrawn for stress to be imposed, whereas watering proceeded for the control treatment. In an attempt to minimize environmental impact within the screen house, each drought stress container was placed next to a well-watered container.

2.2.5. Data collection

Data were taken on chlorophyll content of unifoliate, first trifoliate and second trifoliate leaves of water-stressed and non-water-stressed plants using SPAD-502 Plus (Spectrum Technologies, Inc., Plainfield, IL). Measurements were taken every week following the onset of drought. Measurements were made three times for each leaflet of the unifoliate or trifoliate plant and the mean kept for the final chlorophyll value.

The Chlorophyll Inflect Index (Gonzalez, 1996) parameters were approximated using the following formulas previously described: Chlorophyll absolute decrease (CD) = CC − CS; Chlorophyll Inflect Index (CII) = 100%* CD/(CC), where CC means the chlorophyll content for control and CS means the chlorophyll content for drought stress.

During the fourth week of drought stress, data was taken on plant greenness score using a previously described scale of 1–5 (1 = completely green plants, 2 = plants beginning to lose greenness, 3 = signs of chlorosis and necrosis become visible, 4 = severe chlorosis and necrosis and 5 = plants were completely dead (Ravelombola et al., 2018)). Data on plant greenness and drought stress were taken for each plant in a pot, and the average values were recorded.

The score value was changed into a percentage to reduce the error in the statistical analysis. The “Greenness”’ value was determined as 100% (5-Overall plant greenness score-1)/4. The Greenness Inflect Index (GII) parameter was employed using the following formulas to track changes in Greenness over weeks: Greenness absolute decrease (GD) = GC − GS; Greenness inflect index (GII) = 100%* GD/(GC), where GC stands for control and GS for drought stress.

The overall plant healthiness was determined in the fifth week of drought stress using a 1-to-5-point scale (1 = fully green, 2 = new trifoliate totally green with chlorotic unifoliate and trifoliate, 3 = chlorotic new trifoliate, 4 = severe necrosis on all leaves with a green developing tip, and 5 = dead plants). The score value was changed into a percentage to reduce the error in the statistical analysis. The “Healthiness”’ value was determined as 100% (5-Overall plant healthiness score-1)/4. The Healthiness Inflect Index (HII) parameter was employed using the following formulas to track changes in healthiness over weeks: Healthiness absolute decrease (HD) = HC − HS; healthiness inflect index (HII) = 100%* HD/(HC), where HC stands for control and HS for drought stress.

After data acquisition, plants were re-watered for 1 to 2 weeks and percentage of plants that recovered after the period of imposed drought was recorded as the recovery rate.

2.2.6. Data analysis

Percentage data were square root transformed before analysis.

The data collected were subjected to a one-way ANOVA using the Statistical Tool for Agricultural Science, version 2.0.1 (STAR, 2014). Means were separated using the Tukey at 5% probability level. Principal component analysis (PCA) and Cluster analysis was done using R version 4.0.3 (RStudio Team, 2021) to group and identify most tolerant genotypes

Pearson’s correlations test was also done to determine relations between the drought tolerance indices.

2.2.7. Canonical discriminant analysis and drought tolerance index

Drought tolerance indicators were standardized before final evaluation in order to fully assess drought tolerance. In this study, we used two methods to assess the drought tolerance of each genotype: 1) Principal component analysis (PCA) and Cluster analysis was done for the 15 genotypes based on context parameters; and 2) all breeding lines were scored by their orders in five drought-tolerance parameters one by one, according to the tolerance from small to large (the most tolerance line shall be scored as 1 and most susceptible as 15). We then added five scores for each line, respectively, to comprehensively evaluate drought tolerance.

The broad-sense heritability (H) of the measured traits was estimated using the formula:

$\mathrm{H}=\hspace{0.17em}\frac{{\sigma }_G^2}{\left[{\sigma }_G^2+({\sigma }_e^2/n_r\right)]}$

where ${\sigma }_G^2$ is the total genetic variance, ${\sigma }_e^2$ is the residual variance, and $n_r$ is the number of replications. The estimates for ${\sigma }_G^2$ and ${\sigma }_e^2$ were $\frac{\left[EMS\left(G\right)-Var\left(Residual\right)\right]}{n_b}$ and Var (Residual), respectively. EMS (G) and Var (Residual) were obtained from the ANOVA table.

3. Results

3.1. Experiment 1

Water stressed and non-water stressed plants of the different genotypes differed significantly in their chlorophyll content for trifoliate leaves for all the different weeks (p < 0.001) (Table 3). Except for week 1, genotypic effect on chlorophyll content was not significant (p < 0.001).

Table 3. ANOVA table for chlorophyll content in trifoliate leaves for week 1 to week 5 in experiment 1

Week of stress	Source	DF	Mean square
1	GENOTYPE	14	112.91***
	Stress.factor	1	424.67***
	GENOTYPE:Stress.factor	14	7.86^NS
	Error	60	26.76
	Total	89
2	GENOTYPE	14	77.16^NS
	Stress.factor	1	3900.62***
	GENOTYPE:Stress.factor	14	76.17^NS
	Error	60	57.30
	Total	89
3	GENOTYPE	14	129.84^NS
	Stress.factor	1	8734.00***
	GENOTYPE:Stress.factor	14	106.63^NS
	Error	60	72.29
	Total	89
4	GENOTYPE	14	90.13^NS
	Stress.factor	1	19196.08***
	GENOTYPE:Stress.factor	14	29.51^NS
	Error	60	70.23
	Total	89
5	GENOTYPE	14	61.86^NS
	Stress.factor	1	17056.90***
	GENOTYPE:Stress.factor	14	15.42^NS
	Error	60	45.10
	Total	89

^NSNot significant at 0.05 probability level; *, **, *** Significant at P < 0.05, P < 0.01 and P < 0.001, respectively

3.2. Experiment 2

Except for the second trifoliate (non-stressed) chlorophyll content which had a medium heritability score of 60.44%, all the other traits had a high broad-sense heritability score that ranged from 73.23% to 95.56%. The highest broad-sense heritability score was obtained from water-stressed plants for the second trifoliate chlorophyll content trait (Table 4).

Table 4. Broad-sense heritability of chlorophyll content, plant greenness and plant healthiness

Trait description	Treatment	Broad-sense heritability (%)
Unifoliate chlorophyll content	Non-stressed	88.00
	Water-stressed	94.92
First trifoliate chlorophyll content	Non-stressed	88.31
	Water-stressed	79.57
Second trifoliate chlorophyll content	Non-stressed	60.44
	Water-stressed	95.56
Plant greenness	Non-stressed	83.87
	Water-stressed	76.45
Plant healthiness	Non-stressed	88.97
	Water-stressed	73.23

There was a significant genotype by stress effect in unifoliate chlorophyll content during the second, third and fourth weeks of drought stress (Table 5). IT14K-1424-12 had the highest chlorophyll content of 36.93, which was not significantly different from IT10K-837-1, IT10K-303-1, IT10K-817-3, IT86D-610, Kirkhouse Benga and Wangkae during the third week of drought stress in unifoliate leaves. In week 4, however, the highest chlorophyll content was observed in IT14K-1424-12 (26.1), which was not significantly different from IT10K-837-1 and IT10K-303-1 (Table 6).

Table 5. ANOVA table of unifoliate leaf chlorophyll contents from week 1 to week 4

Week of stress	Source	DF	Mean square
1	GENOTYPE	14	122.667***
	Stress.factor	1	931.868***
	GENOTYPE:Stress.factor	14	24.1053^NS
	Error	60	18.8051
	Total	89
2	GENOTYPE	14	125.65***
	Stress.factor	1	1560***
	GENOTYPE:Stress.factor	14	34.0105**
	Error	60	11.5309
	Total	89
3	GENOTYPE	14	197.507***
	Stress.factor	1	6580.94***
	GENOTYPE:Stress.factor	14	150.648***
	Error	60	19.758
	Total	89
4	GENOTYPE	14	144.96***
	Stress.factor	1	14766.1***
	GENOTYPE:Stress.factor	14	113.977***
	Error	60	10.5266
	Total	89

^NSNot significant at 0.05 probability level; *, **, *** Significant at P < 0.05, P < 0.01 and P < 0.001, respectively. Significant (P < 0.05) interaction effect between the chlorophyll contents of unifoliate leaf of cowpea genotype in weeks 2, 3 and 4 of drought stress.

Table 6. Chlorophyll content of unifoliate leaves under water-stressed and non-water stressed condition

Genotype	Unifoliate
	Week 2		Week 3		Week 4
	Non-stressed	Water-stressed	Non-stressed	Water-stressed	Non-stressed	Water-stressed
IT07K-299-6	42.87 a-d	28.43 bc	42.67 abc	21.40 c-f	38.93 abc	12.77 bc
IT10K-303-1	45.47 abc	38.40 a	46.77 abc	36.27 ab	38.97 abc	19.00 ab
IT10K-817-3	34.70 de	31.00 abc	35.27 bcd	25.53 a-f	30.33 cd	12.37 bcd
IT10K-837-1	41.57 a-d	38.23 ab	40.47 a-d	33.53 abc	34.90 bc	20.27 ab
IT11K-61-82	27.07 e	22.53 c	27.77 d	17.87 ef	24.70 d	1.90 e
IT13K-1070-2	38.57 bcd	33.67 ab	38.63 a-d	17.80 ef	34.47 bc	12.87 bc
IT14K-1424-12	47.13 ab	37.97 ab	48.87 a	36.93 a	43.33 ab	26.10 a
IT86D-610	46.67 ab	36.17 ab	44.63 abc	29.20 a-e	41.93 ab	6.93 cde
SARI-2-50-80	49.10 a	30.63 abc	46.40 abc	4.17 g	44.73 a	1.03 e
SARI-3-11-100	44.33 a-d	29.17 abc	47.60 ab	23.83 b-f	39.00 abc	13.20 bc
SARI-6-2-6	36.47 cde	32.23 abc	34.03 cd	18.77 def	30.73 cd	8.23 cde
SARVX-09-004	41.87 a-d	33.23 ab	40.23 a-d	19.87 def	36.10 abc	3.33 de
Wangkae	35.03 de	31.13 abc	36.77 a-d	28.53 a-e	31.90 cd	8.47 cde
Kirkhouse Benga	38.47 bcd	32.73 ab	38.53 a-d	30.83 a-d	34.10 bc	14.53 bc
Padi-tuya	45.57 abc	34.43 ab	46.60 abc	14.17 fg	42.77 ab	1.63 e

Note: Lowercase letters represent mean separation. Values followed by the same letters in each column imply they are not significantly different.

Significant differences (p < 0.05) were observed in the first trifoliate and second trifoliate leaf chlorophyll contents of stressed and non-stressed cowpea genotypes (Tables 7 and 8). Genotypes were, however, more discriminated during the fourth week of drought stress. For the first trifoliates, during the fourth week of drought stress, IT10K-837-1 had the highest chlorophyll content of 30.43 which was not significantly different from IT14K-1424-12, IT10K-303-1, IT86D-610, SARI-3-11-100, IT10K-817-3 and IT13K-1070-2. For the second trifoliates, IT14K-1424-12 had a significant (p < 0.05) high chlorophyll content of 39.6 which is not significantly different from IT10K-303-1, IT10K-837-1, IT13K-1070-2, IT11K-61-82, IT86D-610 and Wangkae during the fourth week of drought stress (Table 9).

Table 7. ANOVA table for first trifoliate leaf chlorophyll contents from week 1 to week 4

Week	Source	DF	Mean square
1	GENOTYPE	14	76.3354***
	Stress.factor	1	912.662***
	GENOTYPE:Stress.factor	14	7.6132^NS
	Error	60	9.1158
	Total	89
2	GENOTYPE	14	77.8542***
	Stress.factor	1	2952.67***
	GENOTYPE:Stress.factor	14	21.9985**
	Error	60	7.8406
	Total	89
3	GENOTYPE	14	104.433***
	Stress.factor	1	6196.78***
	GENOTYPE:Stress.factor	14	40.2012**
	Error	60	13.5224
	Total	89
4	GENOTYPE	14	129.828***
	Stress.factor	1	12303.7***
	GENOTYPE:Stress.factor	14	89.379***
	Error	60	16.1634
	Total	89

^NSNot significant at 0.05 probability level; *, **, *** Significant at P < 0.05, P < 0.01 and P < 0.001, respectively. Significant (P < 0.05) interaction effect between the chlorophyll contents of first trifoliate leaf of cowpea genotype in weeks 2, 3 and 4 of drought stress.

Table 8. ANOVA table for second trifoliate leaf chlorophyll contents from week 1 to week 4

Week	Source	DF	Mean square
1	GENOTYPE	14	119.922***
	Stress.factor	1	1101.8***
	GENOTYPE:Stress.factor	14	16.3874^NS
	Error	60	17.9747
	Total	89
2	GENOTYPE	14	84.8835***
	Stress.factor	1	1403.83***
	GENOTYPE:Stress.factor	14	19.2738**
	Error	60	6.8748
	Total	89
3	GENOTYPE	14	124.361***
	Stress.factor	1	4261.47***
	GENOTYPE:Stress.factor	14	22.4294^NS
	Error	60	17.1348
	Total	89
4	GENOTYPE	14	199.1***
	Stress.factor	1	8617.16***
	GENOTYPE:Stress.factor	14	71.1035***
	Error	60	20.1546
	Total	89

^NSNot significant at 0.05 probability level; *, **, *** Significant at P < 0.05, P < 0.01 and P < 0.001, respectively. Significant (P < 0.05) interaction effect between the chlorophyll contents of second trifoliate leaf of cowpea genotype in weeks 2 and 4 of drought stress.

Table 9. Chlorophyll content of cowpea trifoliate leaves of water-stressed and non-water-stressed condition

	First trifoliate						Second trifoliate
	Week 2		Week 3		Week 4		Week 2		Week 4
Genotype	Non-stressed	Water Stressed	Non-stressed	Water Stressed	Non-stressed	Water Stressed	Non-stressed	Water Stressed	Non-stressed	Water Stressed
IT07K-299-6	45.20 abc	33.97 a-d	42.27 cde	28.07 ab	42.50 a-d	11.13 de	40.97 cd	38.55 abc	42.8 ab	25.7 bcd
IT10K-303-1	52.73 a	39.53 a	52.77 abc	37.33 a	52.33 a	25.57 abc	51.60 ab	42.90 ab	51.2 ab	35.37 ab
IT10K-817-3	43.40 bc	34.40 a-d	43.90 b-e	32.73 ab	37.73 b-e	20.53 a-d	44.23 bcd	39.50 abc	40.87 ab	26.03 bcd
IT10K-837-1	46.27 abc	37.20 abc	47.07 a-d	33.67 ab	42.70 a-d	30.43 a	48.53 abc	40.10 abc	44.93 ab	32.7 abc
IT11K-61-82	39.50 c	29.87 cd	40.23 de	26.93 ab	36.30 de	20.27 a-d	42.60 cd	32.93 cd	39.8 b	27.03 a-d
IT13K-1070-2	46.83 abc	36.40 a-d	46.80 a-d	29.13 ab	42.90 a-d	19.07 a-e	54.27 a	42.30 ab	53.03 a	32.35 abc
IT14K-1424-12	50.73 ab	38.17 ab	55.87 a	35.63 a	50.17 a	27.83 ab	45.87 bcd	43.20 a	48.83 ab	39.6 a
IT86D-610	50.90 ab	39.57 a	51.57 abc	33.50 ab	49.30 ab	24.03 abc	51.27 ab	38.60 abc	46.47 ab	26.8 a-d
SARI-2-50-80	46.00 abc	30.63 bcd	44.33 b-e	27.13 ab	46.63 a-d	8.17 e	39.80 d	35.60 bcd	47.37 ab	21 cd
SARI-3-11-100	50.10 ab	33.13 a-d	52.27 abc	26.80 ab	47.50 a-d	20.83 a-d	44.33 bcd	35.60 bcd	43.6 ab	21 cd
SARI-6-2-6	40.13 c	29.07 d	35.10 e	24.97 b	30.47 e	18.70 b-e	41.03 cd	29.97 d	39.6 b	5.65 e
SARVX-09-004	45.57 abc	35.43 a-d	45.27 a-e	30.77 ab	44.83 a-d	17.13 b-e	47.13 a-d	39.45 abc	44.43 ab	16.55 de
Wangkae	39.93 c	33.03 a-d	43.47 b-e	29.30 ab	36.87 de	17.33 b-e	42.70 cd	37.90 abc	40.07 b	26.8 a-d
Kirkhouse Benga	39.27 c	34.37 a-d	42.80 cde	28.27 ab	37.47 cde	19.43 a-e	41.20 cd	34.20 cd	39.03 b	17.7 de
Padi-tuya	52.20 a	32.17 a-d	53.77 ab	24.30 b	48.63 abc	15.10 cde	47.30 a-d	33.55 cd	46.5 ab	20.7 cd

Chlorophyll inflect index (CII) measures the chlorophyll content change of stressed plants compared to the same genotype of non-stressed plants. A higher value indicates a higher chlorophyll content loss.

Padi-tuya, SARI-2-50-80 and SARVX-09-004 had CII higher than 51% for unifoliate leaves during the third and fourth weeks of drought stress and during the fourth week of stress in the first and second trifoliate leaves. This means these genotypes lost their chlorophyll content more quickly under drought stress and are therefore susceptible to seedling stage drought stress (Table 10).

Table 10. Chlorophyll inflect index (CII) of unifoliate, first trifoliate and second trifoliate at third and fourth weeks of drought stress

Genotype	CII_W3U	CII_W4U	CII_W4FT	CII_W4ST
IT07K-299-6	7.04 (49.9)* abc	8.21 (67.39) bcd	8.61 (74.14) cd	6.27 (39.79) abc
IT10K-303-1	4.69 (22.44) ab	7.15 (51.29) ab	7.154 (51.31) a-d	5.45 (30.18) ab
IT10K-817-3	5.245 (27.63) ab	7.66 (59.13) abc	6.73 (45.43) abc	5.91 (36.08) abc
IT10K-837-1	3.97 (17.15) a	6.41 (41.77) a	5.23 (28.28) a	5.18 (27.02) ab
IT11K-61-82	5.78 (34.85) ab	9.59 (92.09) de	6.62 (44.04) abc	5.64 (31.94) abc
IT13K-1070-2	7.00 (52.99) abc	7.94 (63.06) bc	7.29 (54.24) a-d	6.18 (38.98) abc
IT14K-1424-12	4.91 (24.4) ab	6.24 (39.29) a	6.66 (44.55) abc	4.23 (18.47) a
IT86D-610	5.82 (34.39) ab	9.12 (83.33) cde	7.15 (51.28) a-d	6.29 (40.46) abc
SARI-2-50-80	9.54 (91.02) c	9.88 (97.53) e	9.07 (82.33) d	7.38 (54.69) bcd
SARI-3-11-100	6.98 (49.49) abc	8.16 (66.58) bcd	7.44 (55.66) bcd	7.10 (50.67) bcd
SARI-6-2-6	6.68 (45.23) abc	8.55 (73.18) b-e	6.22 (39.13) ab	9.24 (85.46) d
SARVX-09-004	7.13 (51.2) abc	9.53 (90.76) de	7.77 (60.95) bcd	7.87 (62.13) cd
Wangkae	4.71 (22.26) ab	8.57 (73.59) b-e	7.29 (53.64) a-d	5.68 (32.55) abc
Kirkhouse Benga	3.89 (19.38) a	7.58 (57.54) ab	6.89 (47.8) a-d	7.37 (54.36) bcd
Padi-tuya	8.28 (69.04) bc	9.79 (95.97) e	8.28 (68.84) bcd	7.35 (54.53) bcd
CV (%)	20	6	10.2	12.4

*Values in parentheses represent untransformed scores

IT14K-1424-12, IT10K-837-1 and IT10K-303-1 had consistently low CII of unifoliate leaves during the third and fourth weeks (less than 30% and 60% respectively) of drought stress and during the fourth week of stress in the first and second trifoliate leaves. Only these two genotypes (IT14K-1424-12 and IT10K-837-1) had CII less than 51% during the fourth week of drought stress in unifoliate leaves while the remaining genotypes had a chlorophyll inflect index higher than 51%. Based on this observation, these two genotypes can be described to have Type I drought tolerance. IT10K-817-3, IT11K-61-82, IT10K-1070-2, IT86D-610, Wangkae and Kirkhouse Benga had CII of trifoliate less than 51% but have CII_W4U be greater than 51%. These genotypes can be considered to have Type II drought tolerance mechanism (Table 11).

Table 11. Tolerance rating based on chlorophyll inflect index of unifoliate and trifoliate leaves

	CII_W4U (%)	CII_Trifoliate (Avg of CII_W4FT and CII_W4ST) (%)	Type of drought resistance	Mean_CII (%)	Tolerance rating
IT07K-299-6	67.39	56.965		62.1775	MT
IT10K-303-1	51.29	40.745	Type I	46.0175	T
IT10K-817-3	59.13	40.755	Type II	49.9425	T
IT10K-837-1	41.77	27.65	Type I	34.71	T
IT11K-61-82	92.09	37.99	Type II	65.04	MT
IT13K-1070-2	63.06	46.61	Type II	54.835	MT
IT14K-1424-12	39.29	31.51	Type I	35.4	T
IT86D-610	83.33	45.87	Type II	64.6	MT
Kirkhouse Benga	57.54	51.08	Type II	54.31	MT
Padi-tuya	95.97	61.685		78.8275	S
SARI-2-50-80	97.53	68.51		83.02	S
SARI-3-11-100	66.58	53.165		59.8725	MT
SARI-6-2-6	73.18	62.295		67.7375	S
SARVX-09-004	90.76	61.54		76.15	S
Wangkae	73.59	43.095	Type II	58.3425	MT

A dendrogram was used to generate two major clusters of accessions under drought stress at 50% similarity index (Figure 1). Cluster 1 consisted of IT10K-837-1, IT14K-1424-12 and IT10K-303-1. These genotypes have been identified to have high chlorophyll content under drought stress and have been identified to be tolerant to both Type I and II drought stress. Cluster 2 is made of two sub-clusters: 2a and 2b.

Figure 1. Dendrogram (UPGMA) based on the ranking of cowpea genotypes under drought stress.

Cluster 2a could be considered to have genotypes that were moderately tolerant to seedling stage drought stress, while cluster 2b could be considered to have genotypes that were susceptible to seedling-stage drought stress. Out of the four significant sampling parameters (third and fourth weeks of drought stress for unifoliate leaves and during the fourth week of stress in the first and second trifoliate leaves), the eight genotypes under cluster 2a had chlorophyll inflectance index greater than 50% in one to three of these parameters. These genotypes could be classified as being moderately tolerant to drought stress.

Cluster 2b consisted of four genotypes: Padi-tuya, SARI-3-11-100, SARI-2-50-80 and IT07K-299-6. These genotypes were characterized by a high (>50%) percentage chlorophyll inflectance index during all the significant sampling periods (third and fourth weeks of drought stress and during the fourth week of stress for the first and second trifoliate leaves).

IT14K-1424-12, IT10K-837-1, Kirkhouse Benga and IT10K-303-1 had a greenness index score of less than 35, indicating tolerance to drought. While SARI-6-2-6, IT07K-299-6, SARVX-09-004 and SARI-2-50-80 had greenness index greater than 60%, indicating susceptibility (Table 12).

Table 12. Plant greenness and plant healthiness of water-stressed and non-water-stressed plants

	Plant greenness at fourth week of drought stress
Genotype	Non-water stressed	Water stressed	Greenness index
IT07K-299-6	2.13 a-d	4.13 ab	8.27 (68.55) cd
IT10K-303-1	2.5 abc	3.31 abc	5.54 (31.44) ab
IT10K-817-3	2.13 a-d	3.33 abc	6.29 (40.5) abc
IT10K-837-1	2.57 abc	3 c	4.2 (18.06) a
IT11K-61-82	2.53 abc	3.53 abc	6.2 (40.49) abc
IT13K-1070-2	2.97 a	3.8 abc	5.7 (39.7) ab
IT14K-1424-12	2.7 abc	3.1 bc	4.12 (17.78) a
IT86D-610	1.37 d	3.2 abc	6.96 (49.08) bcd
Kirkhouse Benga	2.3 a-d	3.2 abc	5.57 (31.9) ab
Padi-tuya	1.93 a-d	3.23 abc	6.51 (42.41) bcd
SARI-2-50-80	1.87 a-d	4.17 ab	8.54 (73.4) d
SARI-3-11-100	1.33 d	3.13 bc	6.75 (47.22) bcd
SARI-6-2-6	2.87 ab	4.27 a	8.11 (66.11) cd
SARVX-09-004	1.7 cd	3.97 abc	8.28 (69.31) cd
Wangkae	1.77 bcd	3.2 abc	6.67 (44.62) bcd

Chlorophyll content of unifoliate leaves (W4U_s) on the one hand is positively and strongly correlated with chlorophyll content of first trifoliate leaves at 4 weeks of drought stress (W4FT_s) (p < 0.05) and chlorophyll content of second trifoliate leaves at 4 weeks of drought stress (W4ST_s) on the other. As the chlorophyll content of unifoliate leaves at 4 weeks of drought stress (W4U_s) increases, chlorophyll content of the first trifoliate (W4FT_s) and chlorophyll content of the second trifoliate leaves at 4 weeks of drought stress (W4ST_s) also increases. Plant healthiness inflect index (HII) on the one hand was highly and strongly correlated with chlorophyll inflect index of unifoliate leaves (CII_W4U), first trifoliate leaves (CIIW4FT) and second trifoliate leaves (CII_W4ST) on the other (p < 0.05). The plant healthiness inflect index had an inverse relationship with chlorophyll content (p < 0.05) (Table 13).

Table 13. Correlation of analysis of drought-related traits

	W4U_s	CII_W4U	W4FT_s	CII_W4FT	W4ST_s	CII_W4ST	W4PLT_Greenness_s	W4_GII	W5PLT.Healthiness_s
CII_W4U	−0.98***
W4FT_s	0.69**	−0.70**
CII_W4FT	−0.51	0.60*	−0.85***
W4ST_s	0.60*	−0.54*	0.52*	−0.2
CII_W4ST	−0.54*	0.5	−1	0.23	−0.95***
W4PLT_Greenness_s	−0.46	0.45	−0.69**	0.47	−0.52*	0.60*
W4_GII	−0.69**	0.70**	−0.82***	0.66**	−0.67**	0.68**	0.84***
W5PLT.Healthiness_s	−0.34	0.34	−0.52*	0.46	−0.3	0.41	0.80***	0.74**
W5_HII	−0.39	0.41	−0.55*	0.52*	−0.4	0.5	0.74**	0.81***	0.96***

***: p = 0.001; **: p = 0.01; *: p = 0.05, no asterisk: p > 0.05

W4U_s—Chlorophyll contents of unifoliate leaf at 4 weeks of drought stress

W4FT_s—Chlorophyll contents of first trifoliate leaf at 4 weeks of drought stress

W4ST_s—Chlorophyll contents of second trifoliate leaf at 4 weeks of drought stress

CII_W4U—Chlorophyll inflex index of unifoliate leaf at 4 weeks of drought stress

CII_W4FT—Chlorophyll inflex index of first trifoliate leaf at 4 weeks of drought stress

CII_W4ST—Chlorophyll inflex index of second trifoliate leaf at 4 weeks of drought stress

W4PLT_Greenness_s—Plant Greenness score of stressed plants at 4 weeks of drought stress

W5PLT.Healthiness_s—Plant healthiness score of stressed plants at 5 weeks of drought stress

W4_GII—Greenness inflex index at 4 weeks of drought stress

W5_HII—Healthiness inflex index at 5 weeks of drought stress

This indicates that any of these traits could be used in classifying genotypes as either tolerant or susceptible as ranking of correlated traits will be consistent with each other. By using these multiple traits, a dendrogram was constructed and this helped to group genotypes according to their tolerance level.

Genotype ranking across traits was used to identify the genotypes that were drought-tolerant and drought-susceptible based on multiple traits. Genotypes were ranked for all traits (Table 14) and genotypes that overlapped between highly correlated traits were chosen.

Table 14. Tolerance index of the cowpea genotypes

Genotype	CII_rank	Unifoliate_rank	FT_rank	ST_rank	PG_rank	PGII_rank	Tolerance_index
IT14K-1424-12	2	1	2	1	2	1	9
IT10K-837-1	1	3	3	3	1	2	13
IT10K-303-1	3	2	1	2	8	3	19
IT86D-610	9	5	4	5	4	11	38
Kirkhouse Benga	5	4	8	13	5	4	39
IT10K-817-3	4	8	5	6	9	7	39
IT13K-1070-2	8	7	6	4	11	5	41
Wangkae	7	6	10	7	6	9	45
SARI-3-11-100	11	9	9	12	3	10	54
IT11K-61-82	6	14	12	9	10	6	57
SARVX-09-004	13	10	7	10	12	14	66
IT07K-299-6	12	12	11	8	13	13	69
Padi-tuya	14	13	15	14	7	8	71
SARI-6-2-6	10	11	13	15	15	12	76
SARI-2-50-80	15	15	14	11	14	15	84

The score is the ordinal of drought- parameter among all breeding lines. The drought-tolerant index is the sum of six parameters.

4. Discussion

Drought stress has been reported to cause significant yield losses of crops globally (Cairns et al., 2013; Ravelombola et al., 2020). Cowpea production can be seriously impacted by seedling-stage drought (Ravelombola et al., 2020; Verbree et al., 2015), in addition drought at the reproductive and seed filling stages is yield limiting (Nunes et al., 2022). The use of drought-tolerant crop varieties is the most sustainable way of mitigating the impact of drought stress. Drought is a complex trait, and evaluating several genotypes for drought tolerance can be laborious and time-consuming given the number of indicators of drought resistance (Xiong et al., 2020). Research has established that genotypes which exhibit seedling drought tolerance are more resistant to drought under field conditions than those genotypes which exhibit susceptibility to seedling-stage drought stress (Isma’ila et al., 2016; Ravelombola et al., 2018; Singh & Matsui, 2002; Tomar et al., 2016). Seedling stage screening of cowpea genotypes and measuring the chlorophyll content using SPAD chlorophyll meter will help to rapidly screen several genotypes and detect and quantify their drought tolerance.

The findings from this study indicate that there was a differential response of cowpea genotypes to drought stress. This indicates that genetic variability of the traits exists for the genotypes. Cowpea leaf chlorophyll content reduced as the duration of drought stress increased. This corroborates the findings of other studies in which chlorophyll content decreased as the time of drought stress increased (Alidu et al., 2019; Khan et al., 2015; Pirzad et al., 2011). The variable response of cowpea leaf types to imposed drought suggests different mechanisms for drought tolerance in the leaf types. This is probably due to the genetic differences that exist in the photosynthetic apparatus of each leaf type and leaf types that had high chlorophyll content under drought stress have more robust photosynthetic apparatus, although other physiological and morphological characteristics control productivity of crops beyond chlorophyll content.

Based on the differences in chlorophyll contents of unifoliate and trifoliate leaves of the cowpea genotypes, genotypes were classified as either having Type I or Type II drought resistance. According to Mai-Kodomi et al. (1999) Type I drought-tolerant genotypes will have both unifoliate and trifoliate leaves fully green under drought stress, while Type II genotypes are able to delay senescence at the trifoliate leaf level. In this study, genotypes which had highest chlorophyll contents and lowest chlorophyll inflect index at both unifoliate and trifoliate leaves were considered to exhibit Type I drought tolerance, while genotypes that had high chlorophyll content and lowest chlorophyll inflect index only for trifoliate leaves were considered to exhibit Type II drought tolerance. IT14K-1424-12, IT10K-837-1 and IT10K-303-1 were the three genotypes showing Type I drought tolerance indicating they are good materials for cowpea improvement. These same set of genotypes were grouped together under the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and were the top ranking based on the drought tolerance rating. Five genotypes, namely, IT10K-817-3, IT11K-61-82, IT10K-1070-2, IT86D-610, Wangkae and Kirkhouse Benga, exhibited type II resistance mechanisms. These genotypes under the UPGMA clustering fell in between the two extremes and appeared to be in the same group based on their drought tolerance index. The number of genotypes identified to have Type II drought tolerance was higher than the number of genotypes exhibiting Type I drought tolerance, a similar observation was made by (Singh & Matsui, 2002).

Some genotypes could not be classified either as Type I or Type II. Among these were SARI-2-50-80 and Padi-tuya, which belonged to same cluster under UPGMA, SARVX-09-004 and SARI-6-2-6 also belonged to same cluster under UPGMA. These genotypes were found to lose a significant amount of both unifoliate and trifoliate leaf chlorophyll content and were also ranked among the lowest according to the tolerance index. High chlorophyll loss in these genotypes signifies that the chlorophyll-containing apparatus in their leaves may have been destroyed leading to a reduction in photosynthesis. This group of genotypes could be highly susceptible to the seedling-stage drought. Therefore, using a combination of UPGMA clustering, tolerance index and chlorophyll inflectance index of unifoliate and trifoliate leaves can help to distinguish between drought tolerant and drought-susceptible genotypes.

IT07K-299-6 and SARI-3-11-100 had a moderately low trifoliate chlorophyll content loss but high unifoliate chlorophyll content loss. These genotypes fall in the upper quartile of below average tolerance index values and under cluster 2 of the UPGMA tree. These genotypes could be deemed to be moderately tolerant to drought stress.

The chlorophyll content values obtained in this study for tolerant genotypes are a little higher than some of the values reported by Xiong et al. (2020). The unifoliate values observed were generally lower compared to values obtained for trifoliates. This same pattern was observed in a study by Xiong et al. (2020). This variation in unifoliate and trifoliate chlorophyll content could be attributed to the fact that trifoliate leaves emerged later than unifoliate leaves and therefore do not have much of their photosynthetic apparatus damaged.

Chlorophyll contents of leaf unifoliate and trifoliate leaves and their chlorophyll inflectance indices were found to have a strong association with Plant greenness/Greenness inflectance index. A plant greenness score of 5 means plants are completely dead. This indicates that reduced chlorophyll content is associated with reduced greenness of the leaf tissues. This is in concordance with studies that reported that chlorophyll fluorescence parameters are strongly correlated with whole-plant mortality under environmental stresses (Araus & Hogan, 1994; Baker & Rosenqvist, 2004; Hakam et al., 2000; Percival & Sheriffs, 2002; Valladares et al., 2005) and were reliable indicators of stresses (Baker & Rosenqvist, 2004). Genotypes that can maintain a high chlorophyll content under drought stress will be more tolerant to drought stress, and hence maintain their greenness.

Correlation between the following pairs of traits demonstrates that these traits could help in distinguishing drought-tolerant genotypes: unifoliate and trifoliate chlorophyll content, healthiness inflect index and chlorophyll inflect index, and chlorophyll inflect index and greenness index.

This study reveals that drought stress is involved in chlorophyll content loss. Drought stressed plants had necrotic wilted leaves (Figure 2), indicating a reduction in photosynthetic activity, and this will later translate into low yields.

Figure 2. Phenotyping for drought tolerance. A = Layout of drought experiment, B = IT10K-837-1 showing tolerance at 5 weeks of drought stress and C = SARI-6-2-6 and IT11K-61-82 showing drought symptoms at 5 weeks of stress.

5. Conclusion

Seedling stage screening under water-stressed conditions in combination with measurement of leaf chlorophyll measurement using SPAD Chlorophyll meter reading has proven to be an effective way to rapidly screen genotypes for tolerance to drought. Through a combination of UPGMA clustering, tolerance index and chlorophyll inflectance index of unifoliate and trifoliate leaves, tolerance genotypes have been identified. IT14K-1424-12, IT10K-837-1 and IT10K-303-1 were identified to be the most drought-tolerant cowpea genotypes and exhibited Type I drought tolerance mechanism. IT10K-817-3, IT11K-61-82, IT10K-1070-2, IT86D-610, Wangkae and Kirkhouse Benga exhibited Type II resistance mechanisms. SARI-2-50-80, Padi-tuya, SARI-6-2-6, IT07K-299-6 and SARVX-09-004 were susceptible to drought. The resistant genotypes can either be used as donors of drought-tolerant genes, while susceptible ones could be improved prior to their releases.

Authors’ contributions

Theophilus Kwabla Tengey: Conceptualization; Investigation; Supervision; Methodology; Data curation; Formal analysis; Writing – original draft. Raphael Adu Gyamfi and Edward Kofi Sallah: Supervision; Writing – review & editing. Issahaku Memunatu, Mariam Seidu and Abbas Francis Senyabor: Methodology. Dominic Ngagmayan Ndela, Emmanuel Israel Affram and Charles Naapoal: Analysis; Writing – review & editing. Ophelia Asirifi Amoako: Writing – review & editing. All authors contributed to manuscript revision, read, and approved the submitted version.

Acknowledgments

This work was supported, in part, by the Bill & Melinda Gates Foundation [OPP1198373] through IITA and ICRISAT on the project “Accelerated Varietal Improvement and Seed Delivery of Legumes and Cereals in Africa (AVISA).” The CSIR-Savanna Agricultural Research Institute (SARI) and the Department of Crop Science of the University for Development Studies are duly acknowledged for the partnership to ensure successful implementation of this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the Bill and Melinda Gates Foundation [OPP1198373]

Notes on contributors

Theophilus Kwabla Tengey

Theophilus Tengey is a Research Scientist (Plant Breeder) at the CSIR-Savanna Agricultural Research Institute (SARI) and leads the cowpea Improvement Programme of the Institute.

Raphael Adu Gyamfi

Dr. Raphael Adu-Gyamfi is an Associate Professor of Plant Science at the University for Development Studies (UDS), Nyankpala.

Edward Kofi Sallah

Mr. Edward Kofi Sallah is a lecturer (Plant Pathology) at the UDS.

Memunatu Issahaku

Issahaku Memunatu is a Technical Officer at the CSIR-SARI.

Dominic Ngagmayan Ndela

Dominic Ngagmayan Ndela holds an MPHIL degree in Crop Science from UDS.

Mariam Seidu

Mariam Seidu is a Technical Officer at the CSIR-SARI.

Abbas Francis Senyabor

Abbas Senyabor is a Technical officer with the CSIR-SARI.

Emmanuel Israel Affram

Emmanuel Israel Affram is Graduate Research Associate with the CSIR–SARI.

Ophelia Asirifi Amoako

Ophelia Asirifi Amoako is a Research Scientist at the CSIR-SARI with specialty in cropping systems.

Charles Naapoal

Charles Naapoal is a Research Assistant at the CSIR-SARI.