Evaluation of drought indices to identify tolerant genotypes in common bean bush (Phaseolus vulgaris L.)

Journal of Integrative Agriculture 2020, 19(1): 99–107 Available online at www.sciencedirect.com

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RESEARCH ARTICLE

Evaluation of drought indices to identify tolerant genotypes in common bean bush (Phaseolus vulgaris L.) Alefsi David SÁNCHEZ-REINOSO, Gustavo Adolfo LIGARRETO-MORENO, Hermann RESTREPO-DÍAZ Departamento de Agronomía, Universidad Nacional de Colombia, Bogotá D.C. 111321, Colombia

Abstract Drought is one of the major abiotic stresses often causing negative impacts on bean crops in the Andean region in Colombia. An experiment under the greenhouse conditions was carried out to assess the effect of a prolonged drought period (15 days) at two different phenological stages (vegetative or reproductive) on grain yield and yield components of five bush bean cultivars (ICA-Cerinza, Bachue, NUA35, Bianca, and Bacatá). Nine tolerance indices including stress susceptibility index (SSI), tolerance (TOL), mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), yield stability index (YSI), yield index (YI), Harmonic mean (HM), and drought sensitivity index (DSI) were calculated based on grain yield under non-stressed (YP) and drought (YS) conditions. Based on the different drought indices, genotypes ICA-Cerinza and NUA35 had the best performance under drought conditions in both studied phases, which reflected in a reduction of grain yield ~≤40%. The biplot analysis also showed a clear superiority of these two genotypes at both phenological stages. Results also showed that SSI was more effective to identify genotypes less affected by drought. The above results allowed us to conclude that ICA-Cerinza and NUA35 may be considered for agricultural areas where long periods of water deficit are expected and can be used in breeding programs for drought tolerance. Keywords: yield components, selection indices, correlation, biplot analysis

covering 50% of the world production. Also, the per capita

1. Introduction Phaseolus vulgaris L. is the most important legume for human consumption because of its high content of protein and minerals (Beebe 2012; Beebe et al. 2013). Latin America is the world region with the highest bean production,

consumption of beans in Latin American countries ranges from 12–18 kg per year (Broughton et al. 2003; Beebe et al. 2013). In Colombia, P. vulgaris production reached 62.974 tons during the first semester of 2015 (Fenalce 2016). In general, biotic and abiotic stresses cause a decrease in crop yield, being the water deficit is the most limiting factor in the world agricultural production (Naghavi et al. 2013). Also, crops have been subjected to inclement weather conditions exacerbated by climate change in recent years

Received 5 September, 2018 Accepted 24 December, 2018 Correspondence Hermann RESTREPO-DÍAZ, E-mail: [email protected]

(Lobell and Gourdji 2012). Monasterio et al. (2011) stated

© 2020 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62620-1

deficit. In addition, the common bean production is exposed

that climate change has increased areas affected by low water availability, encouraging more episodes of water to regular water-deficit periods in highland Mexico, on the Pacific coast of Central America, in northeast of Brazil, and

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Alefsi David SÁNCHEZ-REINOSO et al. Journal of Integrative Agriculture 2020, 19(1): 99–107

in eastern and southern Africa from Ethiopia to South Africa (Beebe et al. 2013). In South America, the common bean crops are cultivated in the Andeans area where this region can be affected by water-deficit periods (Comunidad Andina 2009; Omae et al. 2012). In Colombia, it has been reported that there is an area of ~59 000 km2 exposed to drought conditions. Most of this area is located in the Andean region, where the common bean is commonly cultivated (Comunidad Andina 2009). Climate variation phenomena such as El Niño/Southern Oscillation (ENSO) also cause reductions in rainfall and increased temperatures (Ruiz and Pabon 2013). On the other hand, volumetric water content of soil can be reduced by 10% under drought conditions affecting different crops (Meyer et al. 2014). The knowledge of plant responses to drought stress has been of great importance for the selection of genotypes that are tolerant to adverse environmental conditions (Farshadfar et al. 2013). Yield under water-deficit conditions has also been one of the most important tools for the identification of desirable genotypes in regions affected by low water availability (Nouri et al. 2011). The selection of droughttolerance genotypes is not easy, due to the occurrence of strong interactions between genotypes and environment conditions, as well as, the lack of the knowledge in the function and role of the different mechanisms of tolerance (Naghavi et al. 2013). Currently, many researchers have worked on different selection methods of genotypes with drought tolerance through the use of tolerance and/or susceptibility indices (Farshadfar et al. 2013). Fernandez (1992) suggests that the stress tolerance index (STI) is defined as a useful marker to determine the potential of tolerance and yield under stress of the evaluated genotypes. Also, other alternative indices useful for the identification of tolerant genotypes to water deficit have been proposed, such as drought susceptibility index (DSI) (Fischer and Maurer 1978), stress tolerance (TOL), and mean productivity (MP) (Rosielle and Hamblin 1981) and geometric mean productivity (GMP) (Fernandez 1992). In this regard, indices such as STI, SSI and TOL have been widely used to characterize genotypes resistant to water deficit in maize (Naghavi et al. 2013), sorghum (Menezes et al. 2014), wheat (El-Rawy and Hassan 2014), and sunflower (Gholinezhad et al. 2014). However, drought tolerance indices have not been widely used in order to gather information about phenotyping traits of Andean common beans in Colombia (Perez-Vega et al. 2011; Polania et al. 2012). Consequently, the knowledge of different methods or tools for phenotyping is important to recognize the main characteristics for genotype selection processes and for practical considerations such as field and water management (Beebe et al. 2013). Therefore, the objective of this work was to study different drought indices

in five common bean bush genotypes (P. vulgaris L.) under a condition of water deficit at two different phenological stages (vegetative or reproductive).

2. Materials and methods 2.1. Plant materials and growth conditions Seeds of five common bush bean cultivars were used in this study. The genotypes studied were the following: i) ICA (Instituto Colombiano Agropecuario)-Cerinza and -Bachue, which are two materials that have been planted for 20 years in Colombia, ii) NUA35, which is a genotype that has been commercialized for seven years since its release and iii) Bianca and Bacatá, which are two cultivars that have been recently released (less than two years). Phenology was the same for all cultivars. Seeds were planted in a greenhouse at the Faculty of Agricultural Sciences of the Universidad Nacional de Colombia located in Bogota, Colombia (4°35´56´´N and 74°04´51´´E) from September 2015 to January 2016 in plots of 2.1 m2 (three rows of one linear meter long). The plant spacing was 16 cm×70 cm between plants and between rows, respectively (20 seeds per plot=85 000 plants ha–1). The soil characteristics in the greenhouse were: i) soil texture: sandy loam soil (26% sand, 42% silt, and 32% clay); ii) chemical characteristics: total nitrogen 0.36%; Ca, 10.6 meq 100 g–1; K, 0.98 meq 100 g–1; Mg, 1.75 meq 100 g–1; Na, 0.24 meq 100 g–1; Cu, 1.67 mg kg–1; Fe, 310 mg kg–1; Mn, 3.21 mg kg–1; Zn, 15.5 mg kg–1; B, 0.48 mg kg–1 and P>116 mg kg–1; iii) pH 5.4; and iv) effective cation exchange capacity (ECEC) 13.8 meq 100 g–1. Growth conditions during the experiment were the following: average temperature ±27°C, relative humidity 60 and 80% and a natural photoperiod of 12 h. After seed emergency, plants were fertilized with two edaphic fertilizers: i) 4 g/plant (350 kg ha–1) of a compound fertilizer 15-15-15 grade (TRIAN 15® Yara, Colombia) as a source of nitrogen, phosphorous, and potassium; ii) and 2 g/plant (170 kg ha–1) of microelements (granulated Agrimins, Colinagro®, Colombia) respectively, divided into two applications (Edaphic fertilizers were applied at 15 and 40 days after seed emergency (DAE)). Finally, the composition of Agrimins was: total nitrogen 8% (urea-N 7% and ammonium-N 1%), assimilable phosphorus (P2O5) 5.0%, calcium (CaO) 18%, magnesium (MgO) 6%, total sulphur 1.6%, copper 0.75%, boro 1%, molybdenum 0.005%, and zinc 2.5%. Finally, the irrigation volume used was 24 L wk–1 m–2 throughout the experiment.

2.2. Treatments Plants from each genotype were separated into three different groups to establish treatments. The first group

Alefsi David SÁNCHEZ-REINOSO et al. Journal of Integrative Agriculture 2020, 19(1): 99–107

plants were subjected to a water-deficit period at beginning of vegetative phase (the third and fourth fully expanded leaves). This vegetative phase was reached approximately at 40 DAE. As for the second group, the water-deficit period was developed during the reproductive stage (when plants reached 30 to 40% full blooming) at approximately 50 DAE. The third group was control plants (without waterdeficit during the experiment). Before and after water-deficit period, plants were irrigated early in the morning (07:00 to 09:00). The amount of water was the same for all plants. At initial phenological stages (up to 13–14 BBCH (Bayer, BASF, Ciba-Geigy, and Hoechst) (Feller et al. 1995)), plants were irrigated with 6 mm per week. From 15 to 55 BBCH, 12 mm were supplied per week. Finally, from 56 to 89 BBCH, 18 mm were applied per week. All plants were irrigated using a watering can. The water-deficit period was established by the total suspension of irrigation water for 15 days. This period of water deficit was selected based on previously developed studies (eg., SánchezReinoso et al. 2018). Finally, volumetric soil water content (VSWC) was constantly monitored by a humidity probe (Kelway HB-2 Soil Acidity and Moisture Tester, Kel Instruments Co., Inc. NJ, USA) at a depth of 20 cm during the water-deficit period and all cultivars were irrigated at the same time.

2.3. Yield parameters Plants per experimental plot were harvested in order to get yield components. Grain yield was estimated in each treatment at the end of the experiment (155 DAE) (approximately 14% moisture). The following yield components were recorded: number of grains per pod (GrPod), number of pods per plant, number of grains per plant (GrP), yield per plant, and yield per hectare.

101

TOL=YP–YS (2) MP=

YP+YS (3) 2

GMP= YS×YP  YP×YS  STI= (YP)2 YSI=

(4) (5)

YS YP (6)

YS

(7) YP 2YSYP (8) HM= YS+YP YI=

DSI=

YS–YP  YP

(9)

where YP is the mean yield of the genotype under non-stress conditions, YS is the mean yield of the genotype under stress conditions, YP is the mean yield of all genotypes under nonstress conditions, and YS is the mean yield of all genotypes under stress conditions.

2.5. Experimental design and data analysis A factorial analysis in randomized blocks was used. Each treatment was repeated four times. When significant differences were obtained by the ANOVA, a comparative Tukey test (P≤0.05) was carried out. Data were analyzed using the Program Statistix v 9.0 (analytical software, Tallahassee, FL, US). Additionally, a principal component analysis was carried out using the InfoStat 2016 Program (Universidad Nacional de Cordoba, Argentina).

3. Results and discussion

2.4. Drought indices

3.1. Soil moisture

Drought indices were calculated using the equations proposed by Fischer and Maurer (1978) for stress susceptibility index (SSI), Rosielle and Hamblin (1981) for tolerance (TOL), mean productivity (MP), and geometric mean productivity (GMP); Fernandez (1992) for stress tolerance index (STI); Bouslama and Schapaugh (1984) for yield stability index (YSI); Gavuzzi et al. (1997) for yield index (YI), Farshadfar et al. (2013) for harmonic mean (HM) and Farshadfar and Javadinia (2011) for drought susceptibility index (DSI): YS 1–( ) YP SSI= YS (1) 1–( ) YP

Fig. 1 summarizes the VSWC during the different treatments. In this regard, VSWC has been considered a reliable measurement to relate the effects of water deficit on water status, plant growth, yield and/or physiological responses in plants (Jones 2007). In control plant, VSWC was always around 90% during the trial. At 55 DAE, a lower VSWC (55%) was observed in the water-deficit treatment during vegetative stage. Similar values were also obtained in reproductive stage treatments at 65 DAE. The results obtained allow us to infer that plants under water-deficit treatments were subjected to different irrigation volumes through the experiment. In this sense, Abrisqueta et al. (2012) also pointed out that the VSWC has been a useful marker to associated plant water status with reductions or

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Control

Vegetative stage

Reproductive stage

different morphological, physiological, and phenological

Volumetric soil water content (%)

120 100

In general, grain yield is influenced by a complex of characteristics which in turn are affected by soil moisture

Vegetative

(El-Rawy and Hassan 2014). In this study, cultivars

Reproductive

under control conditions had a grain yield ~1 450 kg. In addition, Cerinza, Bachue, and NUA35 plants showed a

80

slight decrease on the grain yield (1 139.83, 901.04, and 877.67 kg, respectively) under water-deficit conditions

60

during vegetative stage. Meanwhile, Bacatá plants were the

40

WD 0

RW

most affected, with a reduction of about 80% (281.27 kg).

RW

Similar trends were obtained in all genotypes under water-

WD

5 10 15 20 25 Time of water deficit treatments (d)

30

Fig. 1 Effect of water stress in two different phenological stages on soil moisture. Arrows indicate when 15 days water-deficit (WD) periods start, then, plants were re-watered (RW). The bars represent standard error.

deficit conditions in the reproductive stage, where NUA35, Cerinza, and Bachue plants had the highest yield (1 074.88, 943.13, and 780.62 kg, respectively) and the lowest yields were found in Bacatá and Bianca genotypes (472.78 and 626.50 kg, respectively). In this regard, Darkwa et al. (2016) also observed reductions in grain yield ≥40% in tolerant and susceptible genotypes under water-deficit conditions. It has been widely documented that a poor grain yield under

changes in the irrigation volume.

water-deficit conditions is associated with a negative impact on yield components (Ramirez-Vallejo and Kelly 1998;

3.2. Yield components Table 1 shows yield components in each genotype at the end of experiment. Significant differences were found between interaction phenological stage×cultivar on grains per plant, 100-grain weight (WGr, g), plant yield (PY, g), and yield per hectare (HY, kg). On the other hand, differences (P≤0.001) were only obtained on GrPod in the genotype factor, whereas pods per plant were only observed in the phenological stage factor (P≤0.01). The GrP was negatively affected by the water deficit in both two phenological stages in almost all genotypes (~26.5 grains in control plants vs. ~15.85 in stressed plants). However, ‘Bacatá’ plants had the greatest reduction in grains/plant (~25.50 vs. ~7.00 grains, respectively) under water-deficit conditions during vegetative stage. Similarly, ‘Bacatá’ plants showed the lowest grain production when the different genotypes were under waterdeficit conditions in the reproductive stage (~12.33 grains per plant). Regarding WGr, Bianca showed the highest value among the genotypes under control conditions (72.03 g), whereas Cerinza showed the lowest (59.87 g). However, Cerinza plants under water deficit during vegetative stage did not show differences compared to control plants. In addition, Bacatá- and Bianca-stressed plants had the lowest WGr in both phenological stages studied (46.54 and 50.77 g, respectively) at the end of experiment. On the other hand, NUA35 plants did not vary significantly on WGr when plants were influenced by water deficit during neither vegetative or reproductive stage.

Kilic et al. 2010). This statement is confirmed in Fig. 2 through Biplot analysis. In this context, the Biplot analysis showed a positive correlation between yield components WGr, PY, HY, and GrPod due to acute angles between the corresponding vectors. Similarly, Kaya et al. (2002) indicated that genotypes with higher PCA1 and lower PCA2 values present high grain yields (stable genotypes), while genotypes with low PCA1 and high PCA2 values obtained low yields (unstable genotypes). Additionally, the first axis (PCA1) can be identified as potential yield and stress tolerant, while the second component may be identified as a component susceptible to stress with low grain yield in a stressful environment (Khodarahmpour et al. 2011). Consequently, PCA1 and PCA2 accounted for 87.6 and 6.5%, of the variation with different attributes in the genotypes studied, respectively. Based on the above, the biplot diagram showed that ICA-Cerinza and NUA35 plants exposed to a water deficit during the vegetative (Cz-S1) and reproductive stages (NUA-S2), respectively; showed the second-best yields after controls genotypes, being tolerant genotypes in those respective stages. Furthermore, it was observed susceptible to stress in Bacatá (Ba-S1 and BaS2) and Bianca (BI-S1 and B1-S2) plants exposed to water deficit during vegetative or reproductive growth stage. The PCA has also allowed to identify tolerant genotypes to water deficit in common bean (Darkwa et al. 2016), wheat (Akcura et al. 2001), maize (Naghavi et al. 2013), sorghum (Menezes et al. 2014), and sunflower (Gholinezhad et al. 2014).

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Table 1 Effect of water stress in two different phenological stages on the yield parameters of five genotypes of bush beans No. of grains per No. of pods per plant plant 

Treatment Stage of stress Control Vegetative Reproductive Significance Genotypes Cerinza Bachue NUA35 Bacatá Bianca Significance Interaction Control×Cerinza Control×Bachue Control×NUA35 Control×Bacatá Control×Bianca Vegetative×Cerinza Vegetative×Bachue Vegetative×NUA35 Vegetative×Bacatá Vegetative×Bianca Reproductive×Cerinza Reproductive×Bachue Reproductive×NUA35 Reproductive×Bacatá Reproductive×Bianca Significance CV (%)

26.50 a 15.20 b 16.50 b

7.30 a 4.55 b 4.80 b

***

***

22.83 a 20.67 a 21.14 a 14.94 c 17.42 b

6.33 a 5.75 a 6.08 a 4.25 b 5.33 ab

***

30.00 a 26.25 ab 26.00 ab 25.50 ab 24.75 bc 19.25 d 18.25 de 17.75 de 7.00 g 13.75 ef 19.25 d 17.50 de 19.67 cd 12.33 f 13.75 ef ***

10.27

***

8.00 7.00 7.50 6.75 7.25 5.50 5.25 5.00 2.25 4.75 5.50 5.00 5.75 3.75 4.00 NS 18.90

No. of grains per pod  3.80 a 3.35 b 3.45 ab *

Yield (g/plant)

Yield (kg ha–1)

16.80 a 9.04 b 9.17 b

1 427.75 a 768.80 b 779.58 b

***

100-grain weight (g) 64.25 a 55.80 b 54.81 b

***

3.67 3.58 3.58 3.50 3.33 NS

13.97 a 12.18 bc 13.12 a 8.40 d 10.68 c

3.75 4.00 3.75 4.00 3.50 3.75 3.25 3.50 3.25 3.00 3.50 3.50 3.50 3.25 3.50 NS 13.88

17.41 a 16.75 ab 16.40 ab 16.32 ab 17.10 a 13.41 bc 10.60 cde 10.33 cde 3.31 g 7.58 ef 11.10 cd 9.18 de 12.65 c 5.56 fg 7.37 ef

***

***

1 187.66 a 1 035.16 bc 1 115.53 ab 713.86 d 908.00 c

62.34 a 57.23 ab 61.67 a 52.34 b 57.85 ab

***

***

*

1 480.04 a 1 423.81 ab 1 394.05 ab 1 387.53 ab 1 453.31 a 1 139.82 bc 901.04 cde 877.67 cde 281.27 g 644.20 ef 943.13 cd 780.62 de 1 074.88 cde 472.78 fg 626.50 ef

65.87 abc 63.80 abcd 63.64 abcd 63.94 abcd 72.03 a 69.69 ab 55.97 abcd 56.67 abcd 47.17 cd 49.49 cd 59.48 abcd 51.92 bcd 64.72 abc 45.90 d 52.05 bcd

***

11.57

*

11.64

12.65

Values within one column followed by different letters are significantly different at P≤0.05 according to Tukey’s test. NS, no significance (P≤0.05). *, ** and ***, significance at P≤0.05, P≤0.01, and P≤0.001, respectively.

5.00 GrPod

PC2 (6.5%)

2.50

0

–2.50

–5.00 –5.00

–2.50

0 PC1 (87.6%)

2.50

5.00

Fig. 2 Biplot principal component analysis (PCA) of different plant yield variables in five bush bean genotypes under water stress conditions in two different phenological stages. GrPod, number of grains per pod; pod, pods per plant; WGr, 100-grain weight; PY, plant yield; HY, yield per hectare; GrP, number of grains per plant. C, control. S1, water stress at vegetative stage; S2, water stress at reproductive stage. Cz, Cerinza; Bch, Bachue; NUA, NUA35; Ba, Bacatá; Bl, Bianca.

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3.3. Comparison of cultivars based on resistance/ tolerance indices Plants cope with water-deficit stress by a mixture of strategies that vary with genotype (Chaves et al. 2002). In this regard, several criteria have been proposed to select genotypes based on their behavior in an environment under conditions with or without stress (Naghavi et al. 2013). The use of drought indices such as STI, MP, GMP, YI, HM, and YSI has been widely studied to screen genotypes to drought conditions (Akcura et al. 2011; Naghavi et al. 2013; Gholinezhad et al. 2014, Darkwa et al. 2016). Therefore, grain yield was recorded to calculate susceptibility and tolerance indices such as SSI, TOL, SDI, YS, YP, MP, HM, GMP, YSI, YI, and STI in all genotypes used (Table 2). As a selection criterion, it has been stated that tolerant genotypes show the lowest values in the SSI, TOL, and SDI; meanwhile; YS, YP, MP, HM, GMP, YSI, YI, and STI values should be higher (Naghavi et al. 2013; Gholinezhad et al. 2014; Darkwa et al. 2016). Based on the STI, MP, GMP, YI, HM, YSI indices, and grain yield in two conditions (stress vs. control), it can be inferred that Cerinza, Bachue, and NUA35 genotypes were tolerant to water deficit at the vegetative stage. Bianca genotype was considered semi-tolerant to water deficit at the vegetative stage as it showed intermediate values in the various indices as shown in Table 2. On the other hand, NUA35 and Cerinza were identified as tolerant materials to water-deficit stress during the reproductive stage according to STI, MP, GMP, YI, HM, YSI, and the grain yield obtained, while Bachue and Bianca genotypes were categorized as semi-tolerant materials, and Bacatá was considered as a susceptible cultivar. In this context, Fernandez et al. (1992) quoted that STI can be used to identify high yield genotypes under normal conditions and under water deficit. Similarly, Shirani-Rad

and Abbasian (2011) reported that genotypes that have high SSI are susceptible to water-deficit conditions. In addition, Fernandez et al. (1992) also mentioned that the MP may be related as a parameter selection to water-deficit conditions. The indices above mentioned allow us to suggest that ICACerinza and NUA35 genotypes were relatively tolerant genotypes to water-deficit condition.

3.4. Correlation analysis Correlation coefficients between YP, YS, and other drought tolerance indices were calculated (Table 3). In other words, the correlation analysis between drought indices and grain yield has been a good criterion to determine which indices can be used in the selection of genotypes under water deficit (Farshadfar et al. 2013). In this regard, an appropriate index should be positively correlated with grain yield (Mitra 2001). In the present study, grain yield under water-deficit condition (YS) was highly significantly and positively correlated with MP, GMP, YSI, YI, STI, and HM at either reproductive or vegetative stages. In turn, a highly negative correlation was observed between YS -SSI and -TOL. Positive correlations between YS and MP, GMP, YSI, YI, STI were also observed in wheat (Akcura et al. 2011; Farshadfar et al. 2013). Furthermore, there was no correlation between YP and any drought indice. In this aspect, Ehdaie and Shakiva (1996) did not also find any correlation between YP and drought indices. According to Fernandez (1992), genotypes can be divided into four groups based on yield behavior under water deficit: 1) genotypes with high yield under both stressful and non-stressful condition (group A); 2) genotypes with high yields under non-stressful condition (group B) or 3) stress conditions (group C) and 4) genotypes with low yield under both, drought and non-stressed conditions. Consequently, MP and SSI have been suggested as appropriate indices

Table 2 Tolerance and susceptibility index five genotypes of bush beans under conditions of drought stress1) Genotype YS YP SSI TOL MP GMP Tolerance and susceptibility index to drought stress at vegetative stage Cerinza 1.14 1.48 0.50 0.34 1.31 1.30 Bachue 0.90 1.42 0.80 0.52 1.16 1.13 NUA35 0.88 1.39 0.80 0.52 1.14 1.11 Bacatá 0.28 1.39 1.73 1.11 0.83 0.62 Bianca 0.64 1.45 1.21 0.81 1.05 0.97 Tolerance and susceptibility index to drought stress at reproductive stage Cerinza 0.94 1.48 0.80 0.54 1.21 1.18 Bachue 0.78 1.42 1.00 0.64 1.10 1.05 NUA35 1.07 1.39 0.50 0.32 1.23 1.22 Bacatá 0.47 1.39 1.45 0.91 0.93 0.81 Bianca 0.63 1.45 1.25 0.83 1.04 0.95 1)

STI

YSI

YI

HM

0.83 0.63 0.60 0.19 0.46

0.77 0.63 0.63 0.20 0.44

1.48 1.17 1.14 0.37 0.84

1.29 1.10 1.08 0.47 0.89

DSI   1.30 1.13 1.11 0.62 0.97

0.68 0.55 0.74 0.32 0.45

0.64 0.55 0.77 0.34 0.43

1.21 1.00 1.38 0.61 0.80

1.15 1.01 1.21 0.71 0.88

0.36 0.45 0.23 0.66 0.57

YS, mean yield of the genotype under stress conditions; YP, mean yield of the genotype under non-stress conditions; SSI, stress susceptibility index; TOL, tolerance; MP, mean productivity; GMP, geometric mean productivity; STI, stress tolerance index; YSI, yield stability index; YI, yield index; HM, harmonic mean; DSI, drought susceptibility index. For SSI, TOL, and SI, lower values are desirable while for YS, YP, MP, HM, GMP, YSI, YI, and STI, higher values are desirable.

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Table 3 Correlation coefficient between the different levels of tolerance and susceptibility to water deficit in five genotypes of bush beans1)   YP YS TOL MP GMP SSI STI YSI YI HM   YP YS TOL MP GMP SSI STI YSI YI HM

YS

TOL

0.59 NS

–0.51 NS –0.99***

 

YS 0.15 NS

 

 

TOL 0.026 NS –0.98*

 

Tolerance and susceptibility index to drought stress at vegetative stage MP GMP SSI STI YSI YI 0.65 NS 0.99*** –0.98*

 

0.61 NS 0.99*** –0.99** 0.99***  

–0.53 NS –0.99*** 0.99*** –0.99** –0.99***  

0.64 NS 0.99*** –0.99** 0.99*** 0.99*** –0.99***

0.53 NS 0.99*** –0.99*** 0.99** 0.99*** –1.00*** 0.99***

0.59 NS 1.00*** –0.99*** 0.99*** 0.99*** –0.99*** 0.99*** 0.99***

HM

DSI

0.58 NS 0.99*** –0.99** 0.99*** 0.99*** –0.99*** 0.99*** 0.99*** 0.99***

0.61 NS 0.99 *** –0.99** 0.99*** 1.00*** –0.99*** 0.99*** 0.99*** 0.99*** 0.99***

Tolerance and susceptibility index to drought stress at reproductive stage MP GMP SSI STI YSI YI HM 0.31 NS 0.27 NS –0.52 NS 0.23 NS 0.06 NS 0.14 NS 0.25 NS 0.99** 0.99*** –0.99*** 0.99*** 0.99*** 0.99*** 0.99*** –0.95* 0.99*** –0.97** –0.99*** –0.99*** –0.96* –0.94* –0.97* 0.99*** 0.97** 0.99** 0.99*** 0.99*** *** *** ** ** –0.97 0.99 0.98 0.99 0.99***       –0.98* –0.99*** –0.99*** –0.98** 0.99*** 0.99*** 0.99** 0.98** 0.99*** 0.99***

DSI 0.61 NS 0.83 NS –0.73 NS 0.90* 0.89* –0.77 NS 0.87 NS 0.78 NS 0.82 NS 0.89*

1)

YS, mean yield of the genotype under stress conditions; TOL, tolerance; MP, mean productivity; GMP, geometric mean productivity; SSI, stress susceptibility index; STI, stress tolerance index; YSI, yield stability index; YI, yield index; HM, harmonic mean; DSI, drought susceptibility index; YP, mean yield of the genotype under non-stress conditions. NS, no significance (P≤0.05). *, **, and ***, significance at P≤0.05, P≤0.01, and P≤0.001.

to characterize the response of genotypes according to the intensity of water deficit (Akcura et al. 2011; Farshadfar et al. 2013). MP is an index that correlates yield in stressful and non-stressful conditions. Also, it has been reported that MP is able to differentiate genotypes belonging to group A from the other groups (Fernandez 1992; Farshadfar et al. 2013). Furthermore, MP is not an appropriate indicator when stress conditions are severe. In this case, SSI would be more useful to discriminate resistant genotypes (Akcura et al. 2011) as in our case where only high yields were obtained under control conditions and no significances were observed between YP correlations and other indices studied.

4. Conclusion In this study, water-deficit stress (15 days) caused significant effects on grain yield and yield components in all genotypes studied. Also, drought indices such as YS, SSI, MP, and SDI helped to know the differential response between genotypes. Overall, our results suggest that phenological stage, GrP, WGr, and low SSI (SSI values≤1) could be useful indices to characterize common bean genotypes to water deficit. In addition, ICA-Cerinza and NUA35 genotypes shown to be less susceptible to water-deficit conditions, since those

genotypes had low SSI values and their yield components did not show drops over 40%. Finally, the biplot analysis also confirms that ICA-Cerinza and NUA35 are two cultivars that can be considered to be grown in agricultural areas where long water-deficit periods are expected and could be considered in breeding programs to improve drought tolerance.

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