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Vol. 12(1), pp. 25-33, January-March 2020
DOI: 10.5897/JPBCS2019.0844
Article Number: 91062BC62915
ISSN 2006-9758
Copyright ©2020
Author(s) retain the copyright of this article
http://www.academicjournals.org/JPBCS
Journal of Plant Breeding and Crop
Science
Full Length Research Paper
Effect of low temperature stress on field performance of
highland sorghum (Sorghum bicolor (L.) Moench) at
flowering stages
Amandin Rutayisire1,3*, Alice Mukayiranga1,3, Jean Claude Habineza1, Millicent Avosa1,
Richard Edema1 and Geofrey Lubadde2
1Department of Agricultural Production, Makerere Regional Center for Crop Improvement (MaCCRI),
Makerere University, P. O. Box 7062, Kampala, Uganda.
2Faculty of Agriculture and Animal Science, Busitema University, P. O. Box 236, Tororo, Uganda.
3Rwanda Agriculture and Animal Resources Board, P. O. Box 5016, Kigali, Rwanda.
Received 17 September, 2019; Accepted 7 January, 2020
Sorghum is a C4 grass native in the semi-arid environments of the African sub-Saharan and
consequently chilling stress can affect the performance of the crop, especially at the reproductive
stages. Moreover, a significant delay of flowering and maturity was observed when sorghum grows
under low temperatures regions, and consequently farmers in highland areas of Uganda face yield
penalties. Forty genotypes were evaluated in 2017B and 2018A seasons under non-stress (Kabanyolo)
and cold stress (Kachwekano and Zombo) field conditions. Data were recorded on: Days to 50%
flowering, days to physiological maturity, culm height, panicle length, panicle weight, kernel weight per
panicle, and thousand grain weight. Mean comparison of most agronomic traits recorded indicated high
significant differences for season-by-genotype, location-by-genotypes, and the three-way interaction
(GxLxS). This indicates that cold stress significantly affects yield components. Significant positive
correlation was obtained between days to 50% flowering, days to maturity, and culm height, which
suggested that simultaneous improvement of these traits is possible. Some genotypes (IESV 91003LT,
IESV 91105LT and IS 29376) were best ranked in normal environment but poorly performed in cold
environments, which indicates lack of adaptation in highland. BM6, Cytanobe, IESV 91018, IESV 91609,
IS 25563 showed generally good performance and stability in all locations. Therefore, these genotypes
can be used as parental lines for further breeding process.
Key words: Sorghum, cold stress, flowering, maturity, yield component.
INTRODUCTION
Sorghum (Sorghum bicolor L. Moench) is among the
most important food and animal feed grain crop wordwide
and can be considered as the best bioenergy source in
this era of global climate change (Reddy et al., 2008),
owing to various merits in terms of tolerance to abiotic
stresses (Tari et al., 2013). As a C4 grass native in the
As a C4 grass native in the semi-arid environments of the
African Sub-Saharan regions, the crop is well adapted to
hot and dry conditions. However, its gradual introduction
into regions characterized by low temperatures has led to
*Corresponding author. E-mail: rutamandin@gmail.com Tel: +250788435472.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
26 J. Plant Breed. Crop Sci.
the evolution of adapted cold tolerant sorghum (Maulana
and Tesso, 2013). Although some progress has been
made, numerous abiotic stresses, including cold stress,
continue to present challenges in most sorghum
producing areas.
Cold stress is a major determinant in the rate of plant
growth and development, as well as distribution of plant
genotypes in various regions of the planet (Sharma and
Solanke and Sharma, 2008; Ramankutty et al., 2008;
Yadav, 2009). Sorghum genotypes differ in their growth
and development at the threshold temperature of around
15°C (Singh, 1985; Maiti, 1996). Therefore, a genotype
can mature faster in non-stress environment, while in
cold environment maturity is delayed. This is because
gene expression patterns responsible for growth and
development are altered under cold environment, and
therefore protein stability is compromised which impairs
stem and leaf growth (Rymen et al., 2007; Janmohhamadi
et al., 2015). Chilling stress was found to cause a
significant decline in key cellular functions and
photosynthetic activity (Allen and Ort, 2001; Rapacz et
al., 2008).
The effect of cold stress on plant growth rate and days
to flowering varies among sorghum genotypes (Maulana
and Tesso, 2013). However, earliness was found to be
affected by both genetic background, environmental
conditions, or the interaction of both. Recent studies
reported six maturity genes and 40 QTL with small
additive effects on flowering time (Rooney and Aydin,
1999; Mace et al., 2013). Hence, development of early
maturing sorghum genotypes is a paramount goal for
numerous breeding programs due to the fact that harvest
can be done before the new season of cold and rainy
weather resumes, thereby allowing famers to increase
productivity and as well reduce yield penalties.
In Uganda, sorghum is grown in almost all agricultural
regions, including the higher altitude regions that cover
25% of the arable land with high population density
compared to the national density (Kasozi et al., 2005). To
avoid the effects of seasonal cold temperatures, farmers
in highland regions of Uganda plant sorghum 4 to 6
weeks before the beginning of the cold period that usually
start from February to July. However, farmers are still
using unimproved varieties with a longer maturity period
of about eight months. Therefore, farmers would benefit
from having genotypes with reduced maturity period for
production twice per annum in order to alleviate issues of
malnutrition and food insecurity. The objective of this
study was to evaluate the effect of cold temperatures on
plant development, flowering, maturity and yield
components of sorghum genotypes in order to identify
genetic sources of early maturity under cold stress.
MATERIALS AND METHODS
Genetic plant materials
The forty highland sorghum genotypes used in this research study
were acquired from International Crops Research Institute for Semi-
Arid Tropics (ICRISAT, Nairobi – Kenya), including various breeding
lines, released varieties and local landraces. The names, origins
and characteristics of sorghum genotype used in the present study
are given in Table 1.
Experimental design
Sorghum genotypes were evaluated during two consecutive
seasons (2017B and 2018A) at three locations: Kachwekano field
farm (1° 15'S, 29° 57'E, 2,200 m.a.s.l.), located in the highland of
South Western region of Uganda, is characterized by a bi–modal
rainy season with an annual average rainfall of 1,300 mm, and has
a sandy clay loam soil; Zombo (2° 40'S, 30° 54'E; 1,705 m.a.s.l.)
situated in the northern region of the country, has heavy clay loam
soil with an annual temperature and cooler temperature amplitude;
while Kabanyolo (0° 32’N, 32°37’E, 1,240 m.a.s.l) is mid-altitude
region characterized by relative optimum temperature ranges (19 -
28°C) for sorghum growth (Table 2).
A 4 × 10 alpha lattice design was used for this experiment with
three replications. Plots of 3 m by 2.25 m were laid with spacing of
30 cm within row, and 75 cm between rows. Seeds were planted at
2 cm depth and agronomic practices were applied when necessary.
Insecticides (Cypermethrin) were also applied regularly in order to
control stem borer and shoot flies.
Data collection and statistical analysis
Data collection included: days to 50% flowering, days to
physiological maturity, culm height, panicle length, number of
leaves, panicle weight, kernel weight per panicle and thousand
kernel weight. Days to 50% flowering were determined as the mean
number of days from planting to half-bloom stage. Days to maturity
was measured as the average number of days from planting to
when the grains on the lower one-third section of the panicles have
reached physiological maturity (formed black layer called aleuron).
Culm height was recorded as the length of the plant from the
ground to the beginning of the panicle, while the panicle length was
measured as the length from the beginning to the tip of the panicle.
After completion of physiological maturity, panicles were detached,
dried and kernel threshed to measuring yield components. Panicle
weight was measured as the weight of panicles from individual
plants. Kernel weight per panicle was determined as the mean
weight of kernels threshed from the individual panicle. A thousand
kernel weight was measured and determined from each panicle.
A Restricted Maximum Likelihood (ReML) analysis was used to
generate analysis of variance (ANOVA) for single site analysis,
using Genstat 18th edition (VSN International, England). For
multiple interactions (Genotype x Location x Season), data were
analyzed as Randomized Complete Block Design (RCBD) in which
replications, locations and seasons were considered as random
and genotypes as fixed effects. Means were separated by Fisher’s
protected least significance difference at 5% probability level.
Pearson correlation was used to determine relationship among
traits recorded in this experiment.
RESULTS
Growth and phenological parameters
The pooled analysis of variance including genotype,
locations and seasons and their interactions, is presented
in Table 3a and b. Genotypes, locations and seasons
Rutayisire et al. 27
Table 1. List of sorghum accessions, origins and characteristics used the study.
Accession name
Origin
Status
Subspecies
Seed color
ABALESHYA
Rwanda
Fixed line
Caudatum
Red
AMASUGI
Rwanda
Fixed line
Durra
Red
BM 16
Uganda
Fixed line
Caudatum
Red
BM 21
Uganda
Fixed line
Bicolor-caudatum
Red
BM 27
Kenya
Fixed line
Caudatum
Red
BM 29
Kenya
Fixed line
Bicolor-caudatum
Red
BM 6
Kenya
Fixed line
Caudatum
Red
CYTANOBE
Uganda
Fixed line
Bicolor
Red
NYUNDO
Rwanda
Fixed line
Caudatum
Red
E 1291
Kenya
Fixed line
Bicolor-caudatum
Red
IESV 90015 LT
Kenya
Breeding line
Bicolor
Red
IESV 90042 LT
Kenya
Breeding line
Caudatum
Red
IESV 91003 LT
Kenya
Breeding line
Caudatum
White
IESV 91018 LT
Kenya
Breeding line
Kafir
Red
IESV 91054 LT
Kenya
Breeding line
Kafir
Red
IESV 91069 LT
Kenya
Breeding line
Caudatum
Red
IESV 91071 LT
Kenya
Breeding line
Bicolor
Red
IESV 91073 LT
Kenya
Breeding line
Bicolor-caudatum
Red
IESV 91075 LT
Kenya
Breeding line
Caudatum
Red
IESV 91105 LT
Kenya
Breeding line
Caudatum
White
IKINYARUKA
Rwanda
Fixed line
Caudatum
Red
IS 11141
Kenya
Breeding line
Bicolor
Red
IS 11612
Kenya
Breeding line
Bicolor
Red
IS 11721
Kenya
Breeding line
Caudatum
Red
IS 11838
Kenya
Breeding line
Bicolor-caudatum
Red
IS 25546
Kenya
Breeding line
Caudatum
Red
IS 25547
Kenya
Breeding line
Caudatum
Red
IS 25557
Kenya
Breeding line
Bicolor-caudatum
Red
IS 25558
Kenya
Breeding line
Bicolor
Red
IS 25561
Kenya
Breeding line
Caudatum
Red
IS 25562
Kenya
Breeding line
Durra
Red
IS 25563
Kenya
Breeding line
Kafir
Red
IS 29415
Kenya
Breeding line
Kafir
Red
IS 25545
Kenya
Breeding line
Bicolor
Red
MB 30
Kenya
Fixed line
Caudatum
Bright orange
N 12
Uganda
Fixed line
Bicolor-caudatum
Red
N 2
Uganda
Fixed line
Bicolor
Red
NDAMOGA
Uganda
Fixed line
Caudatum
Red
S 87
Kenya
Breeding line
Caudatum
Red
IS 11442
Kenya
Breeding line
Bicolor
Red
significantly affected culm height and panicle length.
Although, the average reduction of culm height was about
12 cm and 17 cm at Zombo and Kachwekano compared
to the optimal growth condition of Kabanyolo,
respectively, there was marked variation among sorghum
lines. Results showed that late maturing genotypes such
as IS 11442, IS 25545, IS 11612 and IS 11721, recorded
the tallest culm height (> 250 cm) across environments
(Appendix 2). Generally, culm height was greatly affected
by cold temperatures, since the non-stress environment
of Kabanyolo (Mean: 180.9 cm; range: 100.8 to 323 cm)
recorded the higher average compared to Zombo (Mean:
168.5 cm; range: 89.6 to 295.7 cm) and Kachwekano
(Mean: 163.3 cm; range: 85.3 to 281.3 cm) (Table 4).
Days to 50% flowering and maturity period were both
affected by cold stress, as indicated by the significant
interaction of genotype x location x season (Table 3a). As
expected, Kachwekano had the longest days to 50%
28 J. Plant Breed. Crop Sci.
Table 2. Data on climatic conditions of the field experiments at three locations.
Location
Season
Trial date (sowing-
harvest)
Mean
temp. (°C)
Mean max
temp. (°C)
Mean min.
temp. (°C)
Precipitatio
ns (mm)
Kachwekano
2017B
August-April
16.4
23.1
11.6
543
2018A
February-October
15.3
21.9
10.8
466
Zombo
2017B
August-February
18.4
25.7
14.4
572
2018A
February-September
19.1
25.5
13.8
508
Kabanyolo
2017B
August-January
22.4
29.2
16.6
612
2018A
February-July
21.7
28.7
15.3
487
flowering (Mean: 138.5 days; range: 117.8 to 167.3 days)
followed by Zombo (Mean: 113.2 days; range: 88.6 to
153.5 days) while the non-stress environment of
Kabanyolo recorded the shortest days to flowering
(Mean: 75.8 days; range: 58.2 to 108.3 days). A
significant genotype x season interaction indicated that
flowering took slightly longer in the second season as
compared to the first season. A similar trend was
observed in days to physiological maturity, since
additional 56 days at Zombo, and 73 days at
Kachwekano were required to complete this stage, as
compared to the non-cold stress environment of
Kabanyolo. Generally, genotypes IESV 90015 LT, IESV
90042 LT and IESV 91003 LT flowered earlier than
others across environments, while AMASUGI, IS 255545
and IS 11442 matured later (Appendix 2). Moreover,
sorghum lines such as IESV 91054 LT, and IS 29415
failed to reach their reproductive stages due to their cold
susceptibility under Kachwekano and Zombo
environment.
Yield components
As expected, highly significant differences on all yield
components evaluated in this study were observed and
the genotypes and locations contributed significantly as
sources of variation (Table 3a and b). Except at
Kabanyolo, the season 2018A recorded relatively inferior
yield components values because of extended periods of
lower temperatures that occurred from March to August
2018 at Kachwekano and Zombo (Table 4). Overall, the
cold weather of Kachwekano reduced 3 to 31.6% across
sorghum genotypes, as compared to Kabanyolo. Except
IESV 91105 LT that ranked first in the non-cold stress
environment (Mean panicle: 144.8 g) and failed to reach
maturity in the cold environments of Kachwekano and
Zombo, results showed that AMASUGI, BM 6,
CYTANOBE and IESV 91018 LT expressed higher
panicle weight across environments, however, variation
among other sorghum lines were marked.
Thousand kernel weight (TKW) and kernel weight per
panicle averaged 25.2 and 73.2 g, respectively, at
Kabanyolo, while it decreased at Zombo (TKW: 23.3 g;
Kernel weight: 63.2 g) and Kachwekano (TKW: 22.6 g;
Kernel weight: 60.8 g). As expected, highest kernel
weight per panicle was recorded at MUARIK (IESV
91105 LT: 121.5 g), while the maximum at Zombo and
KAZARDI was 102.3 g for IESV 91105 LT and 82.6 g for
BM 6, respectively. Although there was marked variation
in sorghum lines across locations and seasons
(significant genotype x location x season), IESV 91105
LT recorded the highest kernel weight per panicle (Mean:
121.5 g), while the maximum at Zombo and Kachwekano
was 102.3 g for IESV 91105 LT and 82.6 g for BM 6,
respectively. Although there was marked variation in
sorghum lines across locations and seasons (significant
genotype x location x season), IESV 91003 LT and IESV
91105 LT expressed a higher TKW but were partially
tolerant to cold, since they were unable to survive the
weather conditions of Kachwekano in the season B.
However, three sorghum genotypes recorded the lowest
TKW, less than 17 g, at Kachwekano (BM21, IESV 91071
LT, IS 25561), Zombo were (BM16, IS 11721, IS 29376),
while AMASUGI, IS 11612, and IS 11721 were ranked as
the last at Kabanyolo.
Relationship among observed traits in the field trials
At Kachwekano, days to flowering was positive and
highly significantly correlated to days to maturity (r =
0.95), culm height (r = 0.63), but negatively significant
correlated to thousand kernel weight (r = -0.57) and
slightly correlated to panicle weight (r = -0.29) (Table 5).
Days to maturity was also highly significant correlated
with culm height (r = 0.65) and thousand kernel weight (r
= -0.54), and slightly correlated with panicle weight (r = -
0.29) but non-significant with panicle length (r = 0.12) and
kernel weight (r = -0.29). Moreover, panicle weight was
also highly correlated to kernel weight (r = 0.96).
A similar trend was observed at both Kabanyolo and
Rutayisire et al. 29
Table 3. (a) Mean squares of recorded traits and their interactions across all locations and seasons; (b) Mean squares of the evaluated sorghum traits and their interactions partitioned into
2017B and 2018A seasons.
Source of variation
d.f.
Days to 50% Flowering
Days to Maturity
Culm Height (cm)
Panicle Length (cm)
Panicle Weight (g)
Kernel Weight (g)
T KW (g)
Location (L)
2
239,321.2***
332,489.2***
23,827***
698.2***
14,481.9***
12,140***
354.8***
Season (S)
1
6,076.9***
10,351.5***
14.3ns
27.7*
9.3ns
190.8ns
203.8***
L x S
2
765.8***
525.6***
511.5ns
2.3ns
624.4**
424.4*
81.2***
L x S/Rep
12
21.41
19.42
138.14
4.4
76.64
66.06
3.43
Genotype (G)
39
1,758.96***
1,779.43***
55,845.01***
343.23***
3,305.36***
2,240.98***
267.51***
G x L
76
140.19***
225.41***
463.90*
14.10ns
143.27*
132.16*
13.89*
G x S
39
29.02ns
51.71ns
488.84*
10.32ns
96.51ns
102.00ns
24.94***
G x L x S
72
49.62***
51.30***
285.45***
12.21***
90.82***
87.07***
9.42***
Pooled error
452
12.48
7.54
77.57
3.98
24.66
17.55
2.53
Source
d.f.
Days to 50% Flowering
Days to Maturity
Culm Height (cm)
Panicle Length (cm)
Panicle Weight (g)
Kernel weight (g)
TKW (g)
2017 (Season B)
Location (L)
2
120,707.82***
175,535.33***
9,071.24***
293.07***
5,925.23***
6,295.18***
62.08*
Rep/L
6
11.25
13.7
239.88
5.6
82.07
54.57
6.88
Genotype (G)
39
1,166.68***
997.11***
27,198.13***
161.52***
2,000.21***
1,383.22***
249.16***
G x L
73
122.46***
147.25***
308.35***
14.69***
144.59***
120.97***
9.70***
Error
224
11.92
5.76
59.74
3.23
26.28
18.07
2.63
s.e.d.
2.82
1.96
6.31
1.47
4.18
3.47
1.32
CV (%)
3.34
1.65
4.52
6.05
6.29
6.52
7.09
2018 (Season A)
Location (L)
2
154,288.36***
210,705.73***
1,6140.15***
377.703***
11,287.35***
8,392.96***
652.59***
Rep/L
6
31.75
19.22
101.9
3.7
142.17
117.24
1.968
Genotype (G)
49
1,170.87***
1,052.39***
29,552.40***
161.97***
2,498.08***
1,762.67***
343.93***
G x L
63
134.52***
186.87***
393.36***
10.47***
85.79***
75.75***
17.63***
Error
224
11.39
8.76
77.39
4.593
25.91
19.66
2.71
s.e.d.
2.75
2.41
7.18
1.75
4.15
3.62
1.34
CV (%)
3.1
1.94
5.14
7.27
6.18
6.64
6.73
*, **, *** Significant at P 0.05, P 0.01, and P 0.001, respectively; TKW= Thousand Kernel weight.
Zombo, where days to flowering was significantly
correlated to days to maturity (r = 0.90 and r =
0.93, respectively), culm height (Kabanyolo: r =
0.53; Zombo: r = 0.56) and thousand kernel
weight (Kabanyolo: r = -0.46, Zombo: r = -0.37).
Days to maturity was significantly correlated to
culm height (Kabanyolo: r = 0.53, Zombo: r =
0.56). Panicle length was only significant correlated
to culm height (Zombo: r = 0.28; Kabanyolo: r=
0.36) and thousand kernel weight (Kabanyolo: r =
-0.35; Zombo: r = -0.31).
Panicle weight was highly significant correlated
30 J. Plant Breed. Crop Sci.
Table 4. Descriptive statistics of phenological parameters and yield related traits at three locations.
Phenological parameter
Statistics
Kabanyolo
Zombo
Kachwekano
2017B
2018A
2017B
2018A
2017B
2018A
Days to 50% flowering
Min
55.33
61.00
86.33
91.00
112.67
122.95
Max
112.00
104.67
145.33
161.67
162.33
172.33
Mean
74.85
76.78
109.94
116.49
134.11
143.06
SD
10.93
8.51
11.56
13.51
10.55
9.91
Days to maturity
Min
93.67
101.00
129.67
135.33
158.67
166.83
Max
151.00
141.67
190.67
218.00
208.33
219.33
Mean
109.29
113.46
154.06
163.28
180.13
189.49
SD
10.78
8.20
12.82
15.47
10.12
9.94
Culm height (cm)
Min
105.67
101.00
86.00
93.33
84.67
84.32
Max
317.00
329.00
297.00
294.33
278.00
284.67
Mean
192.28
195.67
180.23
180.07
175.83
173.44
SD
56.29
61.32
55.26
56.55
53.89
56.31
Panicle length (cm)
Min
22.57
21.00
20.17
17.63
19.07
16.67
Max
45.93
46.33
42.80
38.60
43.83
41.17
Mean
31.95
31.78
29.38
28.85
28.98
28.50
SD
4.80
5.34
4.90
4.51
4.56
4.23
Panicle weight (g)
Min
54.40
58.23
47.37
48.70
50.10
44.53
Max
137.80
151.93
123.33
107.00
101.57
96.64
Mean
84.87
88.29
76.72
75.31
72.78
70.08
SD
16.58
17.23
15.08
13.13
12.74
12.21
Kernel weight (g)
Min
46.07
48.90
33.77
39.83
40.10
34.07
Max
113.70
129.40
102.83
90.20
83.20
80.02
Mean
69.43
71.99
58.24
60.81
58.52
56.48
SD
13.43
14.74
13.28
11.29
10.19
10.42
Thousand kernel weight (g)
Min
15.59
16.38
14.53
16.34
16.11
16.13
Max
38.87
40.77
37.29
38.72
35.64
34.57
Mean
21.81
24.09
20.88
21.83
20.59
20.55
SD
4.26
4.58
4.35
4.70
4.01
4.00
to kernel weight (Zombo: r = 0.96; Kabanyolo: r= 0.97),
and thousand kernel weight (Zombo: r = 0.53; Kabanyolo:
r = 0.54), but non-significant to panicle length (Table 5).
Moreover, kernel weight per panicle was significantly
correlated to thousand kernel weight at Kabanyolo (r =
0.54) and Zombo (r = 0.55).
DISCUSSION
Effect of cold stress to flowering time
Being a C4 plant native in the tropical regions, sorghum
is sensitive to temperature below 15°C at all growth and
developmental stages (Solanke and Sharma, 2008).
Delays in both flowering time and days to physiological
maturity are the most frequent phenomena found in cool
weather environments (Kapanigowda et al., 2013),
especially in the African highland regions. In the present
study, we noted that sorghum grown under cool weather
(Kachwekano and Zombo) delayed significantly to reach
days to 50% flowering and physiological maturity, even
for the cold tolerant lines. This is because cold stress
acts on key cellular functions, metabolism and
photosynthetic activity (Rymen et al., 2007; Zhu et al.,
2007; Liu et al., 2019). Therefore, plants responded by
slowing the growth rate during the vegetative stage,
except for susceptible sorghum lines that died at early
developmental stages.
Flowering time and physiological maturity are
Rutayisire et al. 31
Table 5. Phenotypic correlation among observed traits for 40 sorghum lines across locations.
Trait
Location
Days to flowering
Days to maturity
Culm height
Panicle length
Panicle weight
Kernel weight
Days to maturity
Kachwekano
0.95***
Zombo
0.93***
Kabanyolo
0.90***
Culm height
Kachwekano
0.63***
0.65***
Zombo
0.64***
0.55***
Kabanyolo
0.70***
0.53***
Panicle length
Kachwekano
0.19ns
0.12ns
0.24ns
Zombo
0.06ns
0.05ns
0.28*
Kabanyolo
0.27ns
0.28
0.36*
Panicle weight
Kachwekano
-0.29*
-0.29*
-0.20ns
-0.14ns
Zombo
0.10ns
0.11ns
-0.17ns
-0.21ns
Kabanyolo
0.09ns
0.12ns
-0.15ns
-0.19ns
Kernel weight
Kachwekano
-0.25ns
-0.26ns
-0.17ns
-0.15ns
0.96***
Zombo
0.04ns
0.05ns
-0.19ns
-0.21ns
0.96***
Kabanyolo
0.05ns
0.09ns
-0.13ns
-0.21ns
0.97***
Thousand kernel
weight
Kachwekano
-0.57***
-0.54***
-0.52***
-0.14ns
0.30*
0.28*
Zombo
-0.37**
-0.28*
-0.54***
-0.31*
0.53***
0.55***
Kabanyolo
-0.46***
-0.32*
-0.61***
-0.35*
0.54***
0.54***
ns=non-significant, *, **, *** Significant at 0.05, 0.01 and 0.001 levels, respectively.
characteristics controlled by the genetic make-up of the
plant and other environmental factors, especially
temperatures (Andres and Coupland, 2012). Therefore,
different sorghum genotypes can show variable
responses under different temperature regime. Since the
beginning of 20th century, maturity has been an important
trait and one of the main focus for sorghum breeding
programs (Quinby et al., 1974). This focus is because
knowledge about genetic mechanism that regulate
flowering and environmental factors that affect this trait
(Murphy et al., 2011), especially temperatures that are
responsible for the plasticity in different environments
(Marais et al., 2013), could play an important role in the
optimization of sorghum production in the highland
regions.
Kabanyolo (non-stress environment) was the best
environment to identify early maturing sorghum lines,
since both cold tolerant and susceptible genotypes were
able to reach the final maturity stages. IESV 91003 LT,
IESV 91054 LT, and IESV 91105 LT showed early
maturity attributes, but showed partial tolerance to cold
stress at Kachwekano. This indicates that those sorghum
lines could possess recessive alleles for genes
responsible for maturity, since they reduce days to
flowering (Wang et al., 2015). However, this hypothesis
needs to be tested through molecular and genetic
analyses.
Generally, sorghum lines delayed by 37 and 63 days,
at Zombo and Kachwekano, respectively, compared to
non-stress weather conditions of Kabanyolo. Towards the
end of the raining period, temperatures rose and cold
stress was relieved, thus plants were able to reach their
final plant height and complete maturity stage. Although
all growth and phenological parameters decreased in all
sorghum lines, cold-sensitive sorghum lines were
seriously affected compared to tolerant variants.
Moreover, physiological maturity was also affected since
the grain filling period was longer in both cold
environments, whereby the delay caused by this abiotic
stress averaged 47 days at Zombo, and 73 days at
Kachwekano.
Effects of cold stress on yield components
In tropical native plants like sorghum and maize, low
temperature stress cause significant reduction in
photosynthetic activity and biomass accumulation, which
are the main sources of grain yield (Tari et al., 2013;
Fiedler et al., 2014; Ortiz et al., 2017). In fact, cold stress
negatively affects chlorophyll function, and consequently
photosynthetic activities are significantly decreased (Allen
32 J. Plant Breed. Crop Sci.
and Ort, 2001; Tari et al., 2013). Furthermore,
reproductive organs of plants grown cool environments
can be seriously damaged and consequently cause
reduction in the yield and yield components, when cold
stress coincides with the grain filling period (Pereira da
Cruz et al., 2006; Maulana and Tesso, 2013). In case
where cold temperatures coincide with male and female
organs formation, it may cause irreversible damages
such as reduction anthesis rate, failed fertilization,
reduced grain filling, which lead to the insufficient grain
number per panicle and consequently low grain yield
(Clarke and Siddique, 2004; Thakur et al., 2010; Maulana
and Tesso, 2013).
The comparison between the non-stress (Kabanyolo)
and cold stressed environments (Kachwekano and
Zombo) indicated that yield related components were
reduced for all evaluated genotypes, although some
marked differences were identified whereby some
genotypes could only yield a half of their actual
performance as compared to Kabanyolo. However, cold
tolerance is mainly determined by the levels of
expression of cold tolerant responsive genes in the line
per se (Janmohammadi et al., 2015). Cold tolerant
genotypes have developed adaptation strategies to
withstand cold stress through cold acclimation, whereby
plants adjust to cold tolerance by exposing them to low
but non-freezing temperatures (Thomashow, 1999,
Chinnusamy et al., 2007). Genetic variability exists in
sorghum adapted to high altitude areas of Africa,
including the Eastern-African highland regions, which are
considered as an important source of cold tolerant
sorghum gene pool (Balota et al., 2010).
Conclusion
Low temperatures that coincide with vegetative period
can affect various metabolic pathways, slowing growth
rate and reduce photosynthetic activities. Consequently,
susceptible plants would not survive, while tolerant
genotypes with taller plant height can reach flowering and
physiological maturity later. In the present study,
sorghum genotypes with shorter stature coupled with
tolerance to coldness were best ranked as early maturing
in the cold environments of the highlands regions of
Uganda, and thus can be used as parental lines for future
breeding research based on line per se performance.
Therefore, sorghum breeders need to constantly improve
the genetic materials as far as flowering and grain filling
period are concerned, as well as other agronomic traits
based on farmer’s preferences, since this strategy would
result in reducing yield penalty and contribute to
enhancement of food security.
CONFLICT OF INTERESTS
The authors have not declared any conflict of interests.
ACKNOWLEDGEMENTS
The authors are grateful to Makerere University for
providing education and technical expertise, and
ICRISAT (Kenya) for providing sorghum lines used in this
research study. This research was funded by the Alliance
for Green Revolution in Africa (AGRA).
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