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Selection for Drought Tolerance in Sorghum Using Desiccants to Simulate Post-anthesis Drought Stress

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Selection for drought tolerance in sorghum using desiccants to simulate post-anthesis drought stress

ABSTRACT

Late season drought, is a major limiting factor for sorghum production worldwide. Field screening for post-anthesis drought tolerance is, particularly, challenging as the stress is unpredictable and often confounded with diseases and stalk lodging. This study assessed the effectiveness of desiccants in simulating post-anthesis drought stress in sorghum, for potential usage in selection for drought resistance. First, the efficacies of three desiccants KI, NaClO3 and KClO3, were tested. The desiccants were each applied at three different concentrations: 0.4%w/v, 0.6%w/v and 1%w/v, on three genotypes: P89001 P898012 and TX7078. Mean grain yield and seed weight of the three genotypes differed significantly (p < 0.05) under each desiccant. Genotype P898012, known to be tolerant to post-anthesis drought, had the highest mean yield and seed weight after treatment with the desiccants. A known post-anthesis drought susceptible genotype, TX7078, had the lowest yield. The effect of the three desiccants were significantly different, with NaClO3 having the most severe effect on yield while KI had the least effect. Better genotype differentiations were observed at 0.4%w/v for NaClO3 and KClO3, while KI was best both at 0.6%w/v and 1%w/v. The second experiment assessed the sensitivity to KI (0.6%w/v) of 18 diverse sorghum genotypes. Differences among the genotypes were highly significant (p < 0.001). Known drought tolerant genotypes: P898012, SRN39, B35 and P9405 had lower but non-significant yield reduction than the susceptible ones: TX7078, P721N, P721Q and K159. A high stress tolerance index and low stress susceptibility index were observed for the known drought resistant genotypes as opposed to their susceptible counterparts. The same genotypes with low yield loss under desiccation stress had high and significant remobilization of stem dry matter, soluble sugars and starch. Grain yield under stress was significantly correlated with remobilized stem dry matter (r = 0.84***, sugar (r = 0.91***) and starch (r = 0.75**).  Results from this study, suggested that the desiccants simulated the effect of drought stress. This technique could be used in sorghum to rapidly screen for post-anthesis drought tolerance.

Key words: sorghum (Sorghum bicolor (L.) Moench); Drought; simulation; screening; remobilization; post-anthesis

 

 

  1. Introduction

Drought stress remains a major constraint on crop production in arid and semi-arid climates. Sorghum is widely grown in the more arid areas of the world because it is a relatively drought tolerant crop (Ejeta and Knoll, 2007; Reddy, et al 2008; Ramazanzadeh and Asgharipour, 2011), although sorghum responds favorably to irrigation and adequate rainfall. Drought stress in sorghum has been shown to reduce yield significantly when it occurs both at pre- and post-anthesis growth stages (Rosenow and Clark 1995; Rosenow et al., 1996; Tuinstra et al., 1997; Blum 2004). Post-anthesis drought stress, in particular, is associated with diseases like charcoal rot and Fusarium stalk rot which cause significant lodging, reduced seed size and total yield loss (Rosenow and Clark 1995; Tesso et al., 2004). Breeding efforts to address this challenge has in the past led to development of early maturing sorghum genotypes that employ drought escape mechanism, as well as the pre- and post-athesis drought resistant lines (Ejeta, 1986; Ejeta and Rosenow1993; Leslie, 2008; Reddy et al., 2008; Wani et al., 2009).

Physiological yield reduction under post-anthesis drought stress is mainly a result of disruption in current photosynthesis (Bdukli et al., 2007). Current photosynthesis is very important in providing assimilates needed during grain filling (Blum, 1998). Consequently, it has been recognized that, delayed leaf senescence, a trait commonly known as ‘stay green’, is associated with tolerance to post-anthesis drought in sorghum, and several quantitative trait loci (QTLs) anchoring this trait have been mapped (Tuinstra et al., 1996, 1997, 1998; Crasta et al., 1999; Subudhi et al., 2000; Tao et al., 2000; Xu et al., 2000; Hash et al., 2003; Harris et al., 2007).  ‘Stay green’ trait is also highly and positively correlated with resistance to charcoal rot and reduced lodging in sorghum, and as such, indirect selection for charcoal rot resistance by using ‘stay green’ has been effective (Duncan, et al ., 1981; Rosenow, 1977).

Despite the importance of staygreen in selection for post-anthesis drought tolerance, field evaluation of  the ‘stay green’ trait can be difficult and unreliable where drought is not optimally expressed, especially, due to timing, intensity of moisture stress, and large environmental interactions (Subudhi et al., 2000). An alternative approach to improving post-anthesis drought tolerance is to select for efficient stored reserve remobilization (Blum, 1997). When current photosynthates are disrupted due to moisture stress, plants attempt to remobilize stored reserves from other parts, especially from the stems and leaf sheath, for grain filling. Stored reserve remobilization is thus an important indicator of drought adaptation (Blum, 1997; Blum, 1998). Previous studies have shown that stem remobilization ability is under genetic control, making it possible to improve grain filling capacity using stem reserves (Blum, 1997; Blum, 1998; Beheshti and Behboodi, 2010). High yielding genotypes tend to have low stem storage reserves and therefore, suffer greater grain yield loss under post-anthesis drought compared to other potentially lower yielding cultivars (Blum, 1998; Hossain et al., 1990). Incorporating the reserve remobilization trait into elite varieties could therefore improve yield performance under post-anthesis drought stress.

Selection for improved stem reserves that support high grain filling under drought stress can be done by subjecting the materials to actual stress conditions in the field. However it has been strongly argued that standard level of drought stress is very difficult to achieve during grain filling in diverse genetic materials (Blum, 1998). This difficulty is evident firstly in the technique of imposing stress on a large breeding population in the field. The most popular approaches to imposing drought stress under field conditions often involves carefully managed experiments, requiring precise irrigation control and costly rain-out shelters for maintaining stress treatments. Secondly, trait expressions in drought stress environments often exhibit low genetic variation and heritability (Singh et al, 1995; Bidinger et al, 1994; Singh et al 2000). Dry environments are highly variable due to the unpredictable and highly variable seasonal rainfall, and heritability tends to be low under such variable conditions (Dabholkar, 2006), which is the likely reason for slow genetic improvement of yields in dry areas as compared to favorable environments or where irrigation is available (Eid, 2009).

The use of chemical desiccants allows for selection in favorable environments, making it possible to exploit better genetic variation and improved selection efficiency. The application of chemical desiccants or senescence inducing agents has been proposed as a means for inhibiting current photosynthesis and thus revealing the capacity for grain filling by stem reserves (Blum, 1998; Beheshti and Behboodi, 2010). This desiccation treatment is not meant to simulate drought stress per se, but the effect of stress by inhibiting current assimilates. Haley and Quick (1993) reported the use of NaClO3, applied at 2%w/v to achieve early generation selection of winter wheat for post-anthesis drought tolerance. Significant kernel weight reduction was reported among wheat varieties under NaClO3 desiccation stress, and this response was correlated with reactions to late season drought (Cseuz, et al., 2002). Dogan et al. (2012) created post-anthesis drought stress in triticale using 4% KClO3 and identified lines with a high capacity for stem reserves remobilization to grain filling. Herrett et al. (1962) reported that KI was one of the best desiccants for field use due its low toxicity and efficacy to induce senescence. In sorghum, Beheshti, (2010) observed an increase in dry matter remobilization when plants were stressed with KI. Limited information exists in literature on the elaborate assessment of how well chemical desiccants can mimic the effect of drought stress in sorghum. We therefore, utilized standard sorghum genotypes, most of which had known reactions to post-anthesis drought, to assess the effectiveness of desiccants in inducing post-anthesis stress, and hence, the potential application in screening and/or phenotyping for post-flowering drought tolerance.

  1. Materials and Methods

The study was conducted at the Agronomy Center for Research and Education (ACRE), Purdue University, West Lafayette, Indiana during the summer of 2012 and 2013. Two separate experiments were designed as presented below.

  1. Dosage experiment

This factorial experiment was aimed at testing the efficacy of three chemical desiccants to mimic the effect of post-anthesis drought stress in sorghum. It was composed of 27 treatment combinations (i.e. three genotypes × three desiccants × three doses), arranged in a randomized complete block design, replicated three times in 2012 and two replicates in 2013. The three genotypes used were P89001, primarily tolerant to pre-flowering drought but susceptible to post-flowering drought, P898012, tolerant to post flowering drought, and TX7078 which is susceptible to post flowering drought.  The same three genotypes were also included in the trial as checks and were not sprayed with the desiccants. The desiccants and the doses used included potassium iodide (KI), sodium chlorate (NaClO3), and potassium chlorate (KClO3), all applied at the following doses: (0.4%w/v), (0.6%w/v), and (1%w/v). These desiccants have been to screen wheat and barley lines against post-flowering drought (Nicolas and Turner, 1993; Blum, 1998; Cseuz, et al., 2002; Bduakli, et al., 2007), and in sorghum for plant growth analysis under impaired photosynthesis (Ramazanzadeh and Asgharipour, 2011). Desiccants were sprayed using a seven liter hand held pressure pump sprayer. During spraying, effort was made to ensure that the leaf canopy was completely washed. Care was also taken by covering the rows next the one being sprayed to ensure that each plot was treated with the right desiccant (Figure 1). Due to differences in genotype maturity, the application of desiccants was scheduled accordingly, 10 to 14 days after anthesis for each genotype. Data were collected on total grain weight (g) and 100 seed weight (g).

  1.  Genotype sensitivity experiment

The second experiment was designed to test the sensitivity to desiccation stress of 18 sorghum genotypes, some of which had known reactions to drought (Table 1). This trial had control and KI sprayed plots planted side-by-side, in a randomized complete block design. The control and sprayed plots were replicated 2 times in both 2012 and 2013. The plots were sprayed with KI at 0.6% w/v, ten to fourteen days after 50% flowering. This is the stage when grain growth enters the linear phase of development (Blum, 1998). In 2012, five stems per genotype were sampled from the sprayed plot before spraying and after harvest while in 2013, all samples were taken after harvest from both the control and the sprayed plots. Stems were cut down to five internodes, excluding the first two nodes from the base of the plant. Stems were dried at a 140oF and bulk dry weights of the five samples were taken, and samples stored for sugar analysis. Data were also taken on total grain weight (g) and 100 seed weight (g).

To quantify the amount of stem reserves that were remobilized for grain filling under leaf desiccation stress, sugar and starch analysis were conducted. The dried stem samples from control and stressed plots of the 18 genotypes were ground in to fine powder.  About 0.03 g of the powder from each genotype was used for sugar and starch extractions following the standard protocols developed by Smith and Dale, (1981). Sugar and starch were then quantified using Anthron and Trinder assay procedure of Koehler, (1952) and Trinder, (1969), respectively.

Yield loss was quantified as Yc – Ys; where Yc and Ys are yield under control and sprayed plots respectively. Amount of stem dry matter remobilized (DMREMOB), amount of sugar remobilized (SUGREMOB) and the amount of starch remobilized (STREBOM) were obtained by computing the difference between the stressed and non-stressed plants (Beheshti and Behboodi, 2010; Gauthami, et al 2013).

  1. Statistical analysis

Analysis of variance (ANOVA) was performed for the factorial experiment using ‘R’ statistical software, version 3.0.3 (2014-03-06), to test genotype behaviors under the three chemical desiccants and doses. The model used was as follows:

Y = grand mean + replication + genotypes + chemical + dose + (genotypes × chemical) + (genotypes × dose) + (chemical × dose) + (genotype × chemical × dose) + error

Pairwise mean comparison was done using Fisher’s protected least significant difference (LSD) at 5% probability level, and this was used to test how well the chemical desiccants were able to distinguish between the genotypes.

For the sensitivity experiment, differences between stressed and control plots were computed from the raw data for yield, seed weight, stem biomass, sugar and starch. The differences were then subjected to ANOA and each genotypic mean and standard deviations were obtained. A paired T-test was then used to test the significance of each genotypic as well as the overall mean difference, as follows: t =

dSTDV/(n), where; ‘d’ is the mean difference between control, ‘STDV’ is the standard deviation, and ‘n’ equals  number of replications in case of individual genotypic mean testing, but equals the total number of genotypes for overall mean difference.  The post-anthesis drought tolerance index was estimated from the performance of the stressed plots (S) relative to its respective non-stressed plots (C) and calculated as percent according to Blum et al. (1983): Calculation of stress tolerance index (STI) % = [(Ys / Yc) x 100], where Yc = yield under control and Ys  = yield under potassium iodide (KI) treatment. Stress susceptibility index (SSI) was also computed both for grain yield and 100 seed weight according to Fischer and Maurer, (1978) as; SSI = [1 – (Yc) / (Ys)] / SI, where; Yc and Ys are yield under control and sprayed plots, respectively, SI is the ‘Stress Intensity’ measured as SI = [1 – (Ӯc) / (Ӯs)]; Ӯc and Ӯs are the mean yield under control and sprayed plots, respectively.

  1. Results
    1. Efficacy of desiccants

When the three chemical desiccants were applied at three different rates, genotypes differed significantly (p < 0.001) for total grain weight and 100 seed weight. Genotype P898012 had the highest mean grain yield and 100 seed weight while TX7078 had the least measure of both traits (Table 2). The effect of the desiccants was noticeably and significantly different from each other, and consistent in both 2012 and 2013 evaluations (Table 2). Sodium chlorate was repeatedly the most severe, causing the most reduction in yield in both years, while potassium iodide had a milder effect that resulted in higher grain weight and 100 seed weight (Table 2). The effect of the three doses; 0.4%w/v, 0.6%w/v and 1%w/v were not significant, though numerically a 1%w/v dose registered the most severe effect on yield while 0.4%w/v had the least effect (Fig 2). It was observed that a milder desiccant KI could best be applied at 0.6%w/v or 1%w/v rate while NaClO3 and KClO3 would work well at 0.4% %w/v rate. Overall, each chemical desiccant was able to distinctively separate between the genotypes, suggesting their potential suitability in screening post-anthesis stress in sorghum.

  1. Genotype response to KI desiccation stress

When potassium iodide was applied at 0.6%w/v concentration to a more diverse set of sorghum lines, significant differences were observed among the genotypes for grain yield and 100 seed weight. Yield reduction due to KI spray was computed for each genotype and a T-test used to evaluate the magnitude of yield loss associated with desiccation stress. Genotypes were found to vary significantly in the magnitude of yield loss, with genotypes P898012, SRN39, B35 and P9405 having low and non-significant reduction in grain yield and 100 seed weight; while significant yield losses were observed for genotypes TX7078, P721N, P721Q and K1597 (Table 3). Results were consistent with drought response status of the genotypes included in this study, that is, genotypes that are known to be tolerant to post-anthesis drought showed low and non-significant yield reduction compared to those that are known to be susceptible. These observations suggested that the genotypes with less yield reduction are better adapted to stresses that limit photosynthesis. The overall mean difference between stress and control plots for grain yield and seed weight were highly significant (p <0.001) in both 2012 and 2013, with yield under control almost doubling that under KI spray (Table 3). This demonstrated that leaf desiccation is capable of inducing a high enough stress to mimic drought stress effect.

Stress tolerance levels of the genotypes were then assessed based on two drought stress indices: grain yield and seed weight stress tolerance index (STI), and grain yield and seed weight stress susceptibility index (SSI). Genotypes differed significantly for both indices. Consistent with observations under yield loss analysis, genotypes P898012, SRN39, B35 and P9405 showed high STI for both yield and seed weight, while TX7078, P721N, P721Q and K1597 had low STI (Figure 3). The same genotypes that had high STI’s were observed to have very low stress susceptibility indices, and the reverse being true for genotypes that showed low STI (Figure 3).

The role of stored stem reserves in grain filling during stress was then assessed. Significant differences were noticed among genotypes for DMREMOB, SUGREMOB and STREMOB. Genotypes SRN39, P9401, P898012, B35 and AG2102 displayed high and significant values for remobilized reserves, implying that they were efficient in utilizing stem reserves to fill the grain when photosynthesis was interrupted (Table 4). Genotypes P89001, SQR, P721N, P721Q, P851171 and TX7078 on the other had low and non-significant values and would be considered poorly adapted to stress that limit photosynthesis during the grain filling stage (Table 4).

Correlations among measured parameters including stress response indices have been presented in Table 5. Variation in grain yield (Ys) and seed weight (SWTs) measured under desiccation stress were significantly (p < 0.01) and positively correlated with the amount of DMREMOB, SUGREMOB and STCHREMOB. In turn, variation in the amount of DMREMOB, SUGREMOB and STCHREMOB were all positively correlated with STI but negatively associated with SSI. In addition, less reduction in grain yield and seed weight were associated with increase in stress tolerance levels of the genotypes. It was further found that variation in the magnitude yield and seed weight losses were positively associated with SSI. These observations suggest that genotypes with high efficiency in stem reserves remobilization tend to perform well under post-anthesis stress, and that the drought response indices are effective in screening for  drought tolerance in sorghum.

  1. Discussion and conclusion

Our analysis revealed that the three chemical desiccants were effective in imposing post-anthesis stress in sorghum.  This was evident by the fact that the three genotypes used could clearly be distinguished by their yield performance under these desiccants. The known drought resistant genotype, P898012, consistently had higher yield than the other two susceptible ones (P89001 and TX7078). Blum et al. (1983a, b) proposed the idea of using contact chemical desiccant as a means of separating genotypes based on their ability to support grain filling from storage carbohydrates in the absence of photosynthesis. Successful usage of Sodium chlorate, and potassium chlorate as desiccation stress inducers have been reported in wheat and barley (Cseuz, et al., 2002; Bduakli, et al., 2007).  In sorghum, KI has only been used to assess physiological plant growth response to desiccation stress (Ramazanzadeh and Asgharipour, 2011), but the study did not specifically elucidate the effectiveness of the desiccant in simulating drought stress.

The range in desiccant doses used in the current study appeared to be small such that the differences in their effects on genotype grain yields were not statistically significant.  However, numerically, it was observed that KI being mild could generate better genotype separation if applied at 0.6%w/v or 1% w/v. Effects of NaClO3 and KClO3 tended to be more severe, and a lower dose of 0.4% w/v would be appropriate. Previous studies revealed variation in desiccant application rates and these differences were crop specific. In wheat, Cseuz, et al., (2002) applied 2%w/v NaClO3 solution to evaluate the translocation ability of the stem reserves. In barley, Bduakli, et al., (2007) obtained successful results by applying 4% w/v of KClO3 solution, while a 0.5%w/v KI have was used in wheat (Kordenaeejand, 2008).

In our analysis, it was evident that desiccation stress was able to mimic drought stress after anthesis, given that most of the known drought resistant genotypes included in the study tended to show better yield performance under stress compared to the susceptible ones. Previous studies in wheat found that the rate of reduction in grain weight by chemical desiccation and the rate of reduction by actual drought stress was strong and highly correlated (Blum et al. 1993b, Nicolas and Turner 1993).  We also found that less yield loss and seed weight loss under stress were associated with high stem remobilization capacity. This observation is consistent with physiological responses under terminal drought; a rapid decline of photosynthesis after anthesis limits the contribution of current assimilates to the grain (Johnson et al. 1981). Flag leaf photosynthesis alone cannot support both respiration and grain growth under terminal stress (Rawson et al. 1983). Therefore, a substantial amount of the carbohydrates used during grain filling must come from reserves assimilated before anthesis (Gent, 1994). The capacity of stem reserve utilization for grain filling, when the photosynthetic source is completely inhibited by stress, can be assessed by destroying the photosynthetic source at the onset of grain filling.

The present study concluded that the technique for evaluating chemical desiccation tolerance in relation to drought stress tolerance could be integrated into the progeny selection phase of sorghum drought breeding programs. This would allow effective field selection for tolerance to post-flowering drought, especially in environments where late season drought stress is highly variable and not optimally expressed, to allow a reliable evaluation based on the ‘stay green’ trait.

Acknowledgement

We thank the Department of Agronomy, Purdue University for availing space and facilities for conducting this experiment. The authors also extend heartfelt thanks to Suzanne, the lab technician for her help during the sugar assays and quantification.  In a special way, we appreciate Terry Lemming for his tireless effort in organizing planting materials and field agronomic management. To Dr. Patrick Rich, your effort in editing and shaping grammatical issues in this paper is kindly acknowledged. Finally, we are grateful to Graduate students Dr. Ejeta’s lab who helped during desiccant applications and experimental management and to post-doctoral students, Daniel Gobena and Adedayo Adeyanju for giving useful insights that uplifted the quality of this article.

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Rosenow, D.T., 1977. Breeding for Lodging Resistance in Sorghum. In: Loden, H.D. and Wilkinson, D., Eds., Proceedings of the 32nd Annual Corn and Sorghum Industry Research Conference, American Seed Trade Association, Washington DC, 171-185.

Reddy, B.V., Ashok, Kumar, A., Sanjana, Reddy, P., 2008. Genetic improvement of sorghum in the semi-arid tropics. Genetic improvement of sorghum in the semi-arid tropics, 105-123.

Tables and Figures

 

Table1 List of genotypes used in the study and their reactions to post-anthesis drought stress

Genotype Origin post-anthesis drought reaction Reference
AG2102 USA Unknown
B35 Ethiopia Tolerant (Premachandra et a.,l 1994; Tuinstra, et al., 1996)
HD1 Sudan Susceptible* (Ejeta 1986; INTSORMIL, 1990)
K1597 USA Susceptible
P721N USA Susceptible (Monyo et al.,  1992; Wood, and  Goldsbrough, 1997)
P721Q USA Susceptible
P851171 USA Unknown
P89001 USA Susceptible* (Ejeta and Rosenow1993).
P898012 USA Tolerant (Leslie, 2008; Wani et al., 2009
P9401 USA Tolerant
P9405 USA Unknown
P954035 USA Tolerant (Monyo et al,  1992; Wood, and  Goldsbrough, 1997)
P954063 USA Unknown
SQR n/a Unknown
SRN39 n/a Tolerant (INTSORMIL, 1991; Leslie, 2008)
TX623B USA Susceptible* (Rosenow et al 1983; INTSORMIL, 1994)
TX7078 USA Susceptible* (Premachandra, et al 1994; Tuinstra, et al 1996)
XG3103 n/a Unknown

*Resistant to pre-anthesis drought

 

 

 

 

 

 

 

 

 

 

Table 2 Mean squares and mean comparisons for the effect of three chemical desiccants applied at three different concentrations on three sorghum genotypes (2012-2013).

2012 2013
Yield (g/plant) Seed weight(g) Yield (g/plant) Seed weight (g)
Source of variation D.F Mean squares D.F Mean squares
Replication 2             2.75 0.07 1         9.39        0.03
Genotypes (G) 2       3250.20*** 7.76*** 1 253.52***        0.43**
Chemical (Chem) 2         229.24*** 0.79*** 2 46.94** 1.21***
Dose 2            51.68ns 0.04ns 2 117.28***         0.05ns
G × Chem 4            18.49* 0.24*** 2       29.27*         0.39**
G ×  Dose 4            42.62ns  0.02ns 2       21.98ns         0.02ns
Chem ×  Dose 4            45.42ns  0.05ns 4       17.85ns         0.02ns
G × Chem×  Dose 8            35.86ns  0.01ns 4       51.45**          0.01ns
Error 52            19.72  0.03 17         9.81          0.04
    Mean comparisons of main factors
Genotype
P89001 17.08 b 1.75 b
P898012 22.76 a 2.41 a 16.29 a 1.54 a
TX7078 9.33 c 1.34 c 10.98 b 1.32 b
Chemical
KClO3 16.96 b 1.85 b 13.10 b 1.36 b
KI 19.97 a 2.00 a 15.83 a 1.78 a
NaClO3 13.23 c 1.65 c 11.99 b 1.16 c
Dose
0.4%w/v 17.62a 1.87 a 17.22 a 1.49 a
0.6%w/v 16.65a 1.84 a 11.46 b 1.44 a
1%w/v 14.89b 1.79 a 12.23 b 1.37 a
Mean 19.5 1.83 13.64 1.43
LSD (genotypes) 2.43 0.09 2.2 0.14
LSD (chemical) 2.43 0.09 2.7 0.14
LSD (dose) 2.43 0.09 2.7 0.14
CV (%) 22.77 8.75 23 14.1

“–“missing data due to low plant stand. Means sharing the same later are not significantly different.

 

 

 

Table 3 Influence of stress due to potassium iodide foliar application on grain yield and 100 seed weight loss (2012-2013).

100 seed weight (g) Grain Yield (g/plant)
————————2012—————– ————————2013—————— ————————2012——————- ————————2013——————
Genotype KI spray Control aSWR KI spray Control SWR KI spray Control bGYR KI spray Control GYR
AG2102 1.94 2.69    0.745ns 20.00 43.78 23.78*
B35 1.79 2.79 1.00ns 1.90 2.20 0.31ns 19.67 34.37 14.70ns 18.75 20.96 2.21ns
HD1 2.03 3.15 1.12+ 1.94 2.77 0.83* 25.00 48.38 23.38ns 15.00 20.01 5.01ns
K1597 1.50 2.82 1.32* 10.50 44.27 33.77*
P721N 1.40 2.62 1.22* 1.40 2.71 1.31* 8.10 33.60 25.50* 11.14 23.49 12.35*
P721Q 1.35 2.44 1.085+ 1.30 2.04 0.74+ 7.50 27.46 19.96** 12.00 18.01 6.01+
P851171 1.47 2.75 1.28* 1.50 2.78 1.28* 9.00 39.05 30.04* 13.50 23.51 10.00*
P89001 1.55 2.68 1.13* 11.50 44.96 33.46+
P898012 2.25 2.37 0.12ns 2.82 2.96 0.14ns 30.00 32.36 2.36ns 24.50 24.57 0.06ns
P9401 1.91 2.97 1.07* 2.00 2.25 0.25ns 13.45 26.36 12.91* 24.27 24.73 0.46ns
P9405 2.45 3.00 0.55ns 33.50 49.50 16.00*
P954035 1.60 2.26 0.66ns 1.80 2.34 0.54ns 17.50 35.74 18.24ns 16.73 21.64 4.91ns
P954063 1.62 2.87 1.25ns 1.70 3.02 1.32* 18.89 48.90 30.02* 14.59 23.97 9.38*
SQR 1.65 2.71  1.06** 1.55 2.76 1.21* 16.16 35.58 19.42ns 14.00 21.02 7.02*
SRN39 2.68 3.38 0.70ns 3.00 3.20 0.20ns 34.00 49.99 15.99ns 25.00 27.28 2.27ns
TX623B 1.89 2.62 0.73ns 2.05 2.53 0.48ns 16.64 35.56 18.92ns 23.00 26.30 3.30ns
TX7078 1.33 2.60 1.28* 7.12 33.46 26.34**
XG3103 1.51 2.74 1.23ns 1.00 2.27 1.27* 11.10 32.35 21.25* 10.46 23.43 12.98*
Mean 1.77 2.75    0.97*** 1.84 2.60     0.76*** 17.20 38.65      21.45*** 17.15     22.99       5.43***
SEM 0.07 0.10        0.14 0.10 0.13 0.14 1.22   1.79 2.55 0.70 0.96        1.1
LSD 0.30 0.41        0.44 0.41 0.54 0.42 5.15   7.57 7.87 2.97 4.03 3.43
CV(%) 7.89 7.14      20.56   10.52 9.86 25.33 14.18   9.28      16.84 8.20 8.32      26.91

a100 seed weight reduction due to KI stress computed as seed weight under control – seed weight under KI spray. b grain yield reduction due to KI stress computed as total grain weight under control – total grain weight under KI spray. “–“missing data due to low plant stand. *Significant yield reduction at p < 0.05, ** Significant yield reduction at p < 0.01, ***Significant yield reduction at p < 0.001, ns non-significant yield reduction at p < 0.05, + significant at p < 0.1.

Significance of each genotypic and overall yield and seed weight reduction is tested using a paired Tt-test, computed as t = d/(STDV/(√n)), n =2 for individual mean testing,  n=18 or 13 for overall mean difference in 2012 and 2013 respectively. Each genotypic value represents the mean of two replications.

Table 4. Differences in the remobilization of stem dry matter, soluble sugars and starch from stems for grain filling when photosynthesis was disrupted by foliar application of KI.

Year 2012:                      
Stem dry matter(g/plant) Sugar(mg/g) Starch(mg/g)
Genotype Control Stress DMREMOBa Control Stress SUGREMOBb Control Stress STCHREMOBc
AG2102 20.95 4.45 16.50* 32.41 17.6 14.77* 1.78 0.37 1.41*
B35 33.56 17.73 15.83* 33.76 21.3 12.51+ 1.76 0.31 1.46*
HD1 28.85 15.35 13.50+ 35.42 23.5 11.90+ 1.05 0.42 0.63+
K1597 28.00 25.50 2.50ns 30.13 22.1 8.00ns 0.76 0.54 0.22ns
P721N 12.43 7.46 4.98ns 31.86 21.8 10.04ns 1.23 0.93 0.30ns
P721Q 13.45 8.81 4.63ns 30.75 21.3 9.50ns 1.32 0.93 0.39ns
P851171 25.65 23.80 1.85ns 33.30 29.8 3.50ns 0.74 0.33 0.41ns
P89001 24.30 20.25 4.05ns 31.77 22.6 9.15ns 1.02 0.54 0.48ns
P898012 43.39 25.78 17.61* 40.95 22.5 18.50* 2.05 0.45 1.60*
P9401 34.44 13.75 20.69* 35.71 20.3 15.44* 1.17 0.57 0.60+
P9405 55.43 27.07 28.36* 33.20 21.3 11.91+ 1.58 0.39 1.20*
P954035 19.88 8.63 11.25ns 32.53 21.6 10.94+ 1.88 0.57 1.30*
P954063 22.55 10.26 12.30+ 35.53 24.2 11.29+ 1.28 0.93 0.35ns
SQR 20.30 17.30 3.00ns 28.46 20.5 7.91ns 1.85 0.70 1.15*
SRN39 48.10 16.70 31.39* 41.09 21.6 19.49* 2.06 0.52 1.53*
TX623B 22.75 5.75 17.00* 35.09 21.6 13.50+ 0.96 0.53 0.43ns
TX7078 10.46 5.17 5.29ns 32.09 29.5 2.59ns 1.05 0.69 0.36ns
XG3103 15.12 7.66 7.46ns 32.18 20.6 11.63+ 1.12 0.60 0.52ns
Mean 26.64 14.52 12.12*** 33.68 22.4    11.25*** 1.37 0.57    0.80***
SEM 2.83 1.88    2.71 1.15 0.6 1.19 0.15 0.08        0.15
LSD 8.71 5.79    8.34 3.56 1.8 3.65 0.47 0.25        0.47
CV(%) 15.00 18.30  31.56 4.85 3.6 14.89 15.83 20.26      27.21
  

Year 2013:

                     
B35 28.39 9.36 19.03* 22.36 4.37 18.00+ 0.99 0.29       0.70*
HDI 26.77 14.45 12.32ns 9.46 4.94 4.52ns 0.68 0.18 0.50ns
P721N 19.29 14.75 4.54ns 10.26 6.21 4.06ns 0.66 0.48 0.19ns
P721Q 21.70 18.73 2.96ns 12.55 8.25 4.30ns 0.78 0.42 0.37ns
P851171 39.94 18.70 21.25* 18.25 6.05 12.20ns 0.81 0.37 0.44ns
P8998012 34.75 10.25 24.50* 32.25 1.20 31.05* 1.70 0.21 1.50*
P9401 47.48 23.75 23.73* 23.25 3.25 20.00* 1.05 0.40 0.65*
P954035 26.50 9.00 17.50+ 19.22 5.09 14.13ns 1.18 0.40 0.78*
P954063 33.69 18.81 14.88ns 12.44 6.46 5.98ns 0.59 0.30   0.29ns
SQR 36.28 20.75 15.53ns 14.46 9.30 5.16ns 0.66 0.23   0.43ns
SRN39 55.29 29.66 25.63* 31.25 2.25 29.00* 1.22 0.32  0.90*
TX623B 40.10 22.05 18.05+ 18.25 2.75 15.50ns 0.80 0.20  0.60+
TX7078 15.79 10.66 5.13ns 16.75 14.60 2.15ns 0.96 0.49   0.48ns
Mean 32.77 16.99 15.77*** 18.52 5.75 12.77*** 0.93 0.33      0.60***
SEM 1.22 0.73   1.67 3.18 1.01   3.60 0.08 0.08         0.11
LSD 3.75 2.24   5.13 9.79 3.12 11.08 0.24 0.26         0.34
CV(%) 5.26 6.05 14.94 24.25 24.89 39.82 12.10 36.37       25.69

aAmount of dry matter remobilized; Amount of soluble sugars remobilized; Amount of starch remobilized. *Significant remobilization at p < 0.05, ** significant remobilization at p < 0.01, ***Significant remobilization at p < 0.001, ns non-significant remobilization at p < 0.05, +significant at p < 0.1.Significance of each genotypic and overall remobilized assimilate is tested using a paired Tt-test, computed as t = d/(STDV/(√n)), n =2 for individual mean testing,  n=18 or 13 for overall mean difference in 2012 and 2013 respectively. Each genotypic value represents the mean of two replications.

 

Table 5 Pearson correlations among indices and parameters used to measure genotype response to desiccation stress

Seed weight loss Yield loss aSTISeed bSTIYield cSSI Seed dSSI Yield eDMREMOB fSUGREMOB #STCHREMOB Ys
Year 2012:
Yield loss 0.78***
aSeed STI -0.97*** -0.77***
bYield STI -0.87*** -0.85***  0.94***
cSeed SSI 0.97*** 0.77*** -1.00*** -0.94***
dYield SSI 0.87*** 0.85*** -0.94*** -1.00*** 0.934***
eDMREMOB       -0.66** -0.62** 0.77*** 0.78*** -0.773*** -0.78***
fSUGREMOB       -0.69*** -0.68** 0.77*** 0.80*** -0.77*** -0.79***    0.79***
#STCHREMOB      -0.78*** -0.69*** 0.79*** 0.82*** -0.79*** -0.82***    0.61**     0.61**
Ys      -0.75*** -0.57** 0.86*** 0.89*** -0.86*** -0.89*** 0.85*** 0.71*** 0.75***
SWTs      -0.72*** -0.59**  0.851*** 0.85*** -0.85*** -0.85*** 0.92*** 0.78***      0.67** 0.95***
  

Year 2013:

Yield loss  0.94***
a STISeed -0.97*** -0.96***
bSTI Yield -0.94*** -0.99*** 0.98***
c SSISeed 0.97*** 0.96*** -1.00*** -0.98***
dSSI Yield 0.94*** 0.99*** -0.98*** -1.00*** 0.98***
eDMREMOB       -0.62*  -0.70**   0.74** 0.77**  -0.74**  -0.77**
fSUGREMOB       -0.84*** -0.79*** 0.89***   0.84*** -0.89*** -0.84*** 0.85***
#STCHREMOB       -0.78**  -0.73**  0.78** 0.75**  -0.78**  -0.75**     0.67** 0.87***
Ys       -0.87*** -0.88*** 0.93***   0.93*** -0.93*** -0.93*** 0.84*** 0.91***      0.75**
SWTs       -0.77**  -0.78** 0.87***   0.83*** -0.87*** -0.83*** 0.79*** 0.90*** 0.79*** 0.89***

stress tolerance index measured based on seed weight; stress tolerance index measured based on grain yield; stress susceptibility index measured based on seed weight

stress susceptibility index measured based on grain yield; eAmount of dry matter remobilized; Amount of soluble sugars remobilized; #Amount of starch remobilized;

YS = grain yield measured under KI stress; SWTs = Seed weight measured under KI stress; ***significant at p < 0.001; **significant at p < 0.01; ***significant at p < 0

 

 

Figure 1. Application of desiccants using hand-held pressure pump and the effects of desiccation on the sorghum leaves, five days after spraying. Nearby rows were covered to prevent drift and mixing of the different treatment combinations used.

 

Figure 2. Magnitude of mean differences within each main factor: Genotype, chemical desiccant and dose or application rate. In year 2013, data for one genotype was excluded due poor plant stand.

Figure 3. Response of genotypes to desiccation stress as determined by drought tolerance and susceptibility indices for grain yield and seed weight (2012-2013).

In this figure, STI for grain yield and 100 seed weight was computed as STI = [Ys/Ycl]*100, while SSI was computed as; SSI = [1 – (Yc) / (Ys)] / SI (Fischer and Maurer, 1978); Ys and Yc are yield/seed weight measured under stress and control respectively, SI is the susceptibility index. Each genotypic value represents the mean of two replications.



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