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The quality of wheat grain can impact the farmer’s income and also affect human health. The interaction between the planting time and genetic information indicated a significant effect. Determining correct planting time that can exploit the result of interaction between genotypes and atmosphere that enhance the production and grain quality. The main objective of this study was to choose the more suitable planting dates that maximize the wheat production and quality of wheat grain in cotton-wheat cropping system at different locations. Four wheat cultivars were planted at November 10th ,20th, December 1st, and December 10th at three different cottonwheat system growing districts, i.e., Bahawalpur, Khanewal, and Multan during both years. Late sown crop December 10th recorded maximum protein content (16.32%), starch contents (55.24%), and gluten content (34.19%) while early sown crop showed maximum moisture content (23.33%). Cultivar ASS-2011 demonstrated maximum protein content (13.47%), moisture content (10.73%), and starch content (55.09% and gluten content (33.66%). Year-II recorded the maximum protein content 11.95% moisture content 13.91%, starch content (53.89%), and gluten content 4.90% as compared 1st year in case of wheat crop. Wheat cultivar AAS-2011 showed best results regarding quality attributes in case of the late sown condition
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41
Pak. j. life soc. Sci. (2020), 18(1): 41-50
E-ISSN: 2221-7630; P-ISSN: 1727-4915
Pakistan Journal of Life and Social Sciences
www.pjlss.edu.pk
RESEARCH ARTICLE
Effect of Different Sowing Times and Cultivars on Wheat Grain Quality
under Cotton-Wheat Cropping System in Southern Punjab, Pakistan
Qaiser Abbas1, Atique-ur-Rehman1, Naeem Sarwar1, Sajjad Hussain1, Ghulam Abbas,
Muhammad Nazer Khan 1, Zarta sh Fatima1, Sahrish Naz 1, Haseeb Yo unis1, Mukhtar Ahmed2,
Haseeb Ullah3, Pake eza Iqbal3, Muha mmad Iqbal 4, Mustafa Kan 5 and Sha keel Ahmad 1
1Bahauddin Zakariya Un iversity, Mult an -60800, Pakistan
2Pir Mehar Ali Shah, Arid Agriculture University, Rawalpind i-46300, Pakistan
3University of Agriculture, Faisalabad , Pakistan
4Cotton Research In stitute, Old Shu jaabad Road, Multan, Paki stan
5Kirsehir Ahi Evran University, Agr icultural Faculty, Department of Agricul tural Economi cs,
Turkey
ARTICLE INFO
ABSTRACT
Jan 23, 2019
Apr 18, 2020
The quality of wheat grain can impact the farmer’s income and also affect human
health. The interaction between the planting time and genetic information indicated a
significant effect. Determining correct planting time that can exploit the result of
interaction between genotypes and atmosphere that enhance the production and grain
quality. The main objective of this study was to choose the more suitable planting
dates that maximize the wheat production and quality of wheat grain in cotton-wheat
cropping system at different locations. Four wheat cultivars were planted at
November 10th ,20th, December 1st, and December 10th at three different cotton-
wheat system growing districts, i.e., Bahawalpur, Khanewal, and Multan during both
years. Late sown crop December 10th recorded maximum protein content (16.32%),
starch contents (55.24%), and gluten content (34.19%) while early sown crop
showed maximum moisture content (23.33%). Cultivar ASS-2011 demonstrated
maximum protein content (13.47%), moisture content (10.73%), and starch content
(55.09% and gluten content (33.66%). Year-II recorded the maximum protein
content 11.95% moisture content 13.91%, starch content (53.89%), and gluten
content 4.90% as compared 1st year in case of wheat crop. Wheat cultivar AAS-2011
showed best results regarding quality attributes in case of the late sown condition.
Keywords
Gluten
Moisture
Protein
Starch
Triticum aestivum L
*Corresponding Author:
shakeelahmad@bzu.edu.pk
INTRODUCTION
Wheat (Triticum aestivum L) is an important food crop
cultivated for feed grain on the earth (Abbas and
Ahmad, 2018; Ahmad et al., 2019). Approximately, 60-
80% carbohydrates, 8-15 % protein, 1.5-2.0% fat, 1.5-
2.0% inorganic ions along with vitamin (B-complex &
E) are present in grain (Shahzad et al., 2007; Ahmed et
al., 2019; Jabran et al., 2020; Khan et al., 2019). Wheat
offers more than 50 percent of the total calories and 60
percent of the total protein used by people of the world.
With the increase in temperature, physiology of the
wheat plant, grain yield and quality of grain are
changed. High temperature (>35°C) after the anthesis
period reduced the grain quality (Sial et al., 2005).
Wheat is an abundant source of food for people of the
world as compared to any other food entity. Wheat
grain storage and conversion of grain into flour are very
simple and easy. Digestion of protein is very simple and
easy and is similar to starch. Wheat grain quality is a
role of grain composition, mainly in proteins, which is
determined by cultivars and climate. The effect of
inheritance is largely reflected by quality differences
such as protein polymorphism followed by quantity
dissimilarity of total protein units and subunits.
Climatic effects were reflected by quantity differences
in total protein, protein units, and subunits. Grain
quality composition is controlled by genetics and
cultural practices. It was concluded that conditions that
condense grain filling i.e. maximum temperature or
water deficit condition influenced the balance protein
fractions. The protein content is considered a key
Abbas et al
42
feature that describes the fitness of wheat as an ideal
food. Qualitative and quantitative protein is significant
in determining the suitability of flour for its end-
produce superiority (Ferdous and Rehman, 2013). Grain
yield, growth, and quality can be influenced by sowing
wheat crop before or after the best time (Seleiman et al.,
2011).
Getting more production and baking quality is critical
for wheat crop competition. Planting time influenced
the baking quality and it is very different task to
increase grain yield and baking quality because a
negative correlation is observed between these two
factors. Suitable planting time influenced the water,
temperature and solar radiations accessible for a crop.
Unfavorable ecological conditions at the reproductive
stage and grain development stage are significant
factors in the baking superiority categorization of wheat
(Silva et al., 2014). Wheat quality attributes especially
flour protein, moisture content, starch content and
gluten content, milling-yield, rheological properties
along with bread making properties are affected by
cultivars plus sowing time and their interaction (Spiertz
et al., 2006). Higher protein, starch and gluten contents
and smaller grain size were observed in the case of the
late sown wheat crop due to higher temperature at/after
anthesis stage and ultimately wheat reduced grain
development with less endosperm and grain weight
(Eslami et al., 2014). The productivity of wheat and
grain quality is mainly affected by cultivar inheritance
and different agronomic practices especially at the time
of sowing (Sohrabi et al., 2010). Due to its high protein,
starch and gluten values, wheat is used in various food
industries. The quality of the wheat grain is influenced
by unfavorable climatic conditions. Grain protein
content and other quality characteristics might be
significantly influenced by the host of ecological
factors, with cultivating zone along with environmental
variables indicating major effects (Saeed et al., 2014).
Temperature stress decreased grain weight and
impaired grain quality during the reproductive stage.
Grain quality is a significant determinant in genetics
and influences the commercial values of cultivars. The
presence of specific alleles at loci enhanced the quality
of grain. If a cultivar has some special allele
combination at critical loci then it represents the quality
of the end product. Production of cultivars with the best
quality demanded climate which enhanced the genetic
potential (Bagulho et al., 2015). Maximum temperature
after the anthesis stage especially in late sowing
indicated smaller endosperm, less grain weights, rose
protein, starch and gluten contents (Abdullah et al.,
2007).
This study was focused to investigate the impacts of
appropriate sowing dates and different cultivars and
their parallel interactions on wheat grain quality under
the cotton-wheat cropping systems at different locations
in southern Punjab, Pakistan.
MATERIALS AND METHODS
Four wheat cultivars viz. AAS-2011, AARI-2011,
PUNJAB-2011 and MILLAT-2011 were sown at four
different times i.e. November 10th, November 20th,
December 1st and December 10th during the years 2015-
16 and 2016-17.
Wheat grain quality
Grain quality parameters including grain protein,
moisture, starch and gluten contents were determined
by the following methods:
Protein contents
Nitrogen contents were quantified by Micro-Kjeldahl
distillation method (Tecator, 1991). For titration
purpose, sulphuric acid was taken which showed pink
color as end point indicator . The nitrogen and protein
contents were estimated as follows:
100 x 0.141 x Nx
WV - V
(%)Nitrogen 12
=
Where, V1 = Sulphuric acid amount used for titration;
V2 = Amount of normal sulphuric acid utilized for
titration of vacant solution; N = Normality of sulphuric
acid; and W = sample weight.
Moisture contents
Wheat grains were changed into powder form by
grinding to determine the moisture contents. We took
weighed homogenous sample in weighed flat bottom
dish and re-weighed again. This weighed sample dish
was placed on heat at 100 °C for 240 minutes and
placed the sample in desiccators for cooling purpose.
The sample was reweighed after cooling and placed this
sample in oven again for 120 minutes. The weight of
samples was calculated after regular intervals until it
achieved a constant weight. The moisture contents of
wheat were calculated as followed.
sample ofWeight dryingafter sample of Weight - samplefresh ofWeight
x 100 (%) Moisture =
Starch contents
A total of 100 mg precisely weighed sample was placed
in a test tube followed by addition of aqueous ethanol
(0.2 mL) to soak dispersed particles. Whirlpool food
mixer was used to mix the tube. Thermostable α-
amylase (3 mL) was mixed and tube was placed in
boiling water for 6 minutes approximately with
continuous stirring. Amyloglucosidase (330 U) was
added with 0.1 mL starch and incubated in water bath at
50 °C for half hour, stirring tube kept on vortex blender
moved material of tube in flask. The volume was
regulated with distilled water. This material was
centrifuged for 10 minutes at 3000 rpm. Then, shifted
the 0.1 ml extra aliquot of adulterate liquid to beaker
test tube. Thereafter, 3.0 ml of GOPODN reagent was
mixed to every tube holding D-glucose controls and
GOPODN as a reagent vacant, placed to heat tube at
50°C for twenty minutes. Absorption for every sample
Effect of different sowing times and cultivars on wheat grain quality
43
was pointed out and D-glucose was managed at 510 nm
beside vacant reagent. The starch contents were
calculated by using the formula as follows:
0.9 x FVx
W
F
A x (%)Starch =
Glucose - D of Absorbance 100
F =
Where ΔA = Absorption (reaction) study beside the
digested sample; FV = Volume final for example
equivalent 100 mL or 10 mL; and W = Weight of
sample (in milligram)
Gluten content
Flour sample (25 gm) was obtained in porcelain cup
and water was added to form strong dough bowl. The
dough waas kept in H2O at 25 °C for 30 minutes. The
dough was pushed efficiently in brook of water tapped
to exclude starch and all soluble materials. Though
extra starch excluded gluten ball changed into black and
obtained on a web like structure in 30 minutes. Then we
noted weight of gluten bowl and it was regarded as wet
gluten (W1). After this soaked gluten was shifted into
glutork for almost four minutes for drying and recorded
weight known as dry gluten (W2).
100x
sample ofWeight
W
(%)gluten Wet 1
=
100x
sample ofWeight
W
(%)gluten Dry 2
=
RESULTS
Protein content (%)
Protein is very significant part of our body. Effect of
years remained significant on protein content at all
experimental sites (Table 1). Maximum protein content
was found 12.13% during 2nd year trial which was
11.95% more than 1st year (13.58%). Wheat protein
was significant affected by cultivars at all sites.
Maximum protein content was achieved by cultivar
AAS-2011 (13.47%) which was 2.75% more than
ARRI-2011(13.10%), 6.01% more than Punjab-2011
(12.66%) and Millat-2011 which achieved the
minimum protein content (12.18%) which was 9.58 less
than AAS-2011. Sowing time showed significant
results regarding protein contents at all sites. Maximum
protein content was achieved by late sown crop
December 10th (16.32%). December 1st represented
protein content 13.48% and November 20th showed
12.17% and minimum protein content was observed in
early sowing November 10th (10.48%). Non-significant
interaction between season and time of sowing, along
with years, cultivars along with sowing dates was
observed on all sites. Highly significant response
regarding protein content was shown by years and
cultivars at Bahawalpur, Khanewal along with Multan.
Interaction between cultivars along with sowing dates
was represented significant at Khanewal along with
Multan but non-significant results at Bahawalpur. The
highest protein contents 16.14, 14.33 and 13.20% in
second year were displayed by cultivar AAS-2011. The
cultivar AAS-2011 showed minimum protein content
11.40 during 1st year at Bahawalpur and Millat-2011
represented 10.87 and 10.30% during 2nd year at
Khanewal along with Multan, respectively (Table 2).
Cultivar AAS-2011 recorded the highest protein content
16.97 and 14.79% at December 10th sowing and Millat-
2011 displayed the lowest protein content 8.88 and
9.22% in the wheat crop which sown in 1st week of
November at Khanewal and Multan, respectively
(Table 3). Average values for protein contents were
noted 14.38 at Bahawalpur 12.55% at Khanewal and
minimum protein content was seen 11.63% at Multan
(Table 1).
Moisture content (%)
Table 1 showed that moisture content in wheat grain
was significantly affected by the years at all sites.
Maximum moisture content was recorded during 2nd
year of trial than 1st year which exceeded 13.91% more
(11.43 vs. 9.84%). Cultivars indicated significant
results regarding moisture content at Bahawalpur,
Khanewal, and Multan. Cultivar ASS-2011 produced
the highest moisture content (10.73%) which was
almost equal to ARRI-2011 (10.72%). Cultivar Punjab-
2011 and Millat-2011 displayed moisture content with
little difference 10.08 % and 9.77%, However, Millat-
2011 showed minimum values. Sowing time displayed
significant results regarding moisture content at all
sites. Maximum moisture content was shown in early
sown crop which was 23.33% more (11.79 vs. 9.04%)
than late sowing. Years, cultivars along with sowing
dates represented non-significant behavior regarding
moisture content at Bahawalpur and Multan but highly
significant results were noted at Khanewal. Significant
results were observed among years and cultivars, years
along with sowing dates on all sites. Interaction
between cultivars and sowing dates remained non-
significant on all sites. Cultivar AAS-2011 recorded the
highest moisture content 12.92, 11.70, and 9.33%
during the second year and Millat-2011 represented
minimum moisture content 11.47, 8.51, and 8.28% for
1st year on Bahawalpur, Khanewal along with Multan
(Table 6(b)). All-out moisture contents 14.19% was
observed by early sowing at Bahawalpur, 11.98% at
Khanewal, and 11.17% at Multan during 2nd year, and
minimum moisture content was noted 10.44%, 7.63%,
and 7.58% during 1st year by late sowing on
Bahawalpur, Khanewal along with Multan, respectively
(Table 2. Cultivar AAS-2011 displayed maximum
moisture content 12.95% in November 10th sowing in
2nd year and Punjab-2011 displayed minimum moisture
content 7.29% in December 10th sowing in 1st year at
Abbas et al
44
Table 1: Wheat quality parameters (protein, moisture, starch and gluten contents) as effected by cultivars and sowing
dates
Treatments
Protein Contents (%)
Moisture Contents (%)
Starch Contents (%)
Gluten Contents (%)
BWP
KWL
MLN
Mean
BWP
KWL
MLN
Mean
BWP
KWL
MLN
Mean
BWP
KWL
MLN
Mean
A. Years
Year-I
12.92b
12.22b
11.24b
12.13
11.64b
9.20b
8.69b
9.84
53.63b
53.57b
51.96b
53.05
34.75b
30.33b
27.48b
30.58
Year-II
15.84a
12.88a
12.01a
13.58
12.34a
10.51a
9.55a
11.43
54.58a
54.55a
52.54a
53.89
35.81a
31.99a
28.43a
32.08
LSD %
0.44
0.20
0.25
0.22
0.37
0.25
0.17
0.22
0.28
0.58
0.35
0.71
Significance
**
**
**
**
**
**
**
**
**
**
**
**
B. Cultivars
AAS-2011
13.77b
13.77a
12.87a
13.47
12.34a
10.66a
9.19ab
10.73
55.09a
54.23a
53.24a
54.19
35.36a
33.66a
29.48a
32.83
AARI-2011
14.39ab
13.15b
11.75b
13.10
12.29a
10.37a
9.50a
10.72
54.92a
54.09a
52.82a
53.94
35.30a
33.43a
28.67a
32.47
PUNJAB-2011
14.46a
12.13c
11.40b
12.66
11.83b
9.59b
8.81c
10.08
53.79b
54.01a
51.97b
53.26
35.31a
30.71a
27.65b
31.22
MILLAT-2011
14.89a
11.14d
10.50c
12.18
11.50b
8.82c
8.99bc
9.77
52.63c
53.90a
50.98c
52.50
35.14a
26.83a
26.02c
29.33
LSD %
0.62
0.29
0.36
0.32
0.42
0.36
0.24
0.32
0.39
0.82
0.50
1.01
Significance
**
**
**
**
**
**
**
NS
**
NS
**
**
C. Sowing Dates
Nov. 10th
11.58d
9.83d
10.04d
10.48
13.55a
11.49a
10.34a
11.79
52.05d
52.15d
50.45d
51.55
32.68d
27.89d
25.49d
28.69
Nov. 20th
13.28c
12.11c
11.13c
12.17
12.33b
10.07b
9.45b
10.62
53.50c
53.54c
51.68c
52.91
34.33c
30.31c
26.91c
30.52
Dec. 1st
15.25b
13.03b
12.15b
13.48
11.41c
9.25c
8.86c
9.84
54.85b
54.87b
52.87b
54.20
36.60b
32.09b
28.70b
32.46
Dec. 10th
17.41a
15.22a
13.90a
16.32
10.66d
8.62d
7.83d
9.04
56.02a
55.67a
54.03a
55.24
37.50a
34.35a
30.72a
34.19
LSD %
0.62
0.29
0.36
0.32
0.42
0.36
0.24
0.35
0.39
0.82
0.50
1.01
Significance
**
**
**
**
**
**
**
**
**
**
**
**
Interactions
A x B
**
*
**
**
**
*
**
NS
NS
NS
NS
NS
A x C
NS
NS
NS
*
*
**
**
NS
NS
NS
NS
NS
B x C
NS
**
**
NS
NS
NS
NS
**
NS
NS
.*
*
A x B x C
NS
NS
NS
NS
**
NS
NS
NS
*
NS
NS
NS
Mean
14.38
12.55
11.63
11.99
9.86
9.12
54.11
54.06
52.25
35.28
31.16
27.96
Means sharing different letters in a column differ significantly at P = 0.05; BWP = Bahawalpur; KWL = Khanewal; MLN =
Multan.
Table 2: Interactive effects of years and cultivars on wheat protein and moisture contents (%)
Year x Cultivar
Bahawalpur
Khanewal
Multan
(a)-Protein contents
Year-I
Year-II
Mean
Year-I
Year-II
Mean
Year-I
Year-II
Mean
V1
11.40d
16.14a
13.77
13.21b
14.33a
13.77
12.54b
13.20a
12.87
V2
12.68c
16.10a
14.39
13.01b
13.29b
13.15
10.67de
12.82ab
11.75
V3
13.33c
15.59a
14.46
11.78d
12.48b
12.13
11.07d
11.72c
11.40
V4
14.24b
15.54a
14.89
10.87e
11.42d
11.15
10.69de
10.30e
10.50
Mean
12.91
15.84
12.22
12.88
11.24
12.01
LSD %
0.88
0.40
0.50
(b)-Moisture contents
Year x Cultivar
Bahawalpur
Khanewal
Multan
Year-I
Year-II
Mean
Year-I
Year-II
Mean
Year-I
Year-II
Mean
V1
11.76bc
12.92a
12.34
9.61bc
11.70a
9.04c
9.33bc
9.19
V2
11.69bc
12.88a
12.29
9.45bc
11.29a
9.10c
9.89a
9.50
V3
11.63bc
12.02b
11.83
9.24c
9.94b
8.35d
9.27bc
8.81
V4
11.47c
11.54c
11.51
8.51d
9.13c
8.28d
9.68ab
8.98
Mean
11.64
12.34
9.20
10.52
8.69
9.54
LSD %
0.45
0.60
0.51
(c)-Moisture contents
Year x Sowing dates
Year-I
Year-II
Mean
Year-I
Year-II
Mean
Year-I
Year-II
Mean
D1
12.91b
14.19a
13.55
10.99b
11.98a
11.49
9.52bc
11.17a
10.35
D2
12.06c
12.60b
12.33
9.64d
10.50bc
10.07
8.98d
9.91b
9.45
D3
11.13d
11.70c
11.42
8.54e
9.95cd
9.25
8.70d
9.02cd
8.86
D4
10.44e
10.87de
10.66
7.63f
9.62d
8.63
7.58e
8.08e
7.83
Mean
11.64
12.34
9.20
10.51
8.82
9.42
LSD %
0.45
0.60
0.51
Means sharing different letters in a column differ significantly at P = 0.05.
Effect of different sowing times and cultivars on wheat grain quality
45
Table 3: Interactive effect of cultivars and sowing dates on wheat protein, starch and gluten contents (%)
Cultivar x Sowing dates
V1
V2
V3
V4
Mean
(a)-Protein contents
Khanewal
D1
10.56i
10.19ij
9.67j
8.88k
9.83
D2
13.33e
12.71fg
11.76h
10.67i
12.12
D3
14.22cd
13.67de
12.46g
11.77h
13.03
D4
16.97a
16.03b
14.62c
13.26ef
15.22
Mean
13.77
13.15
12.13
11.15
LSD 5%
0.57
Multan
D1
10.22hi
10.30hi
10.40g-i
9.22j
10.04
D2
12.69cd
11.05fg
10.87gh
9.88ij
11.12
D3
13.77b
12.34de
11.75ef
10.75gh
12.15
D4
14.79a
13.28bc
12.56d
12.12de
13.19
Mean
12.87
11.74
11.40
10.49
LSD 5%
0.71
(b)-Starch contents
Khanewal
D1
51.60h
52.21gh
52.83fg
52.97h
52.40
D2
53.67e
53.47e
53.43ef
53.60e
53.54
D3
55.27bc
54.93cd
54.57d
54.73cd
54.88
D4
56.40a
55.77ab
55.20bc
55.31bc
55.67
Mean
54.24
54.10
54.01
54.15
LSD 5%
0.63
(c)-Gluten contents
Khanewal
D1
29.55gh
30.22gh
27.97i
23.83k
27.89
D2
32.95e
33.08e
29.38h
25.82j
30.31
D3
35.02c
34.39cd
31.78f
27.13i
33.73
D4
37.12a
36.05b
33.72de
30.50g
35.63
Mean
33.66
33.44
30.71
24.83
LSD 5%
1.00
Multan
D1
27.56de
24.64f
24.82f
24.94f
25.49
D2
28.50cd
27.45de
26.28ef
25.39f
26.91
D3
30.11bc
29.97bc
28.51cd
26.21ef
28.70
D4
31.75ab
32.62a
30.97ab
27.55de
30.72
Mean
29.48
28.67
27.65
26.02
LSD 5%
2.01
Means sharing different letters in a column differ significantly at P = 0.05.
Khanewal (Table 4(a)). Average values for moisture
content highest values were obtained 11.99 at
Bahawalpur followed by 9.86 at Khanewal and
minimum moisture content was noted 9.12% at Multan
(Table 1).
Starch content (%)
Data in Table 1 showed that influence of years
regarding starch content was significant on all sites.
Average starch content values were observed highest in
2nd year of experiment (53.89%) than 1st year (53.05%).
Significant response was demonstrated by cultivars
regarding starch content at Bahawalpur along with
Multan and remained non-significant in Khanewal.
Highest starch content (55.09% and 53.24) was
observed by AAS-2011 tracked by AARI-2011
(54.92% and 52.82) but difference is minute. Cultivar
Punjab-2011 represented (53.79 and 51.97 %) and
Millat-2011 showed minimum starch content (52.63%
and 50.98) at Bahawalpur and Multan, respectively.
Starch content was significantly affected by time of
sowing at all sites. Maximum starch content was
demonstrated by delayed sowing (55.24%) while timely
sown crop displayed minimum values for starch content
(51.55%). Non-significant interaction was noted
between seasons, varieties and time of sowing at
Bahawalpur and Khanewal while significant results at
Multan site. Seasons and varieties along with seasons
and time of sowing represented significant results
regarding starch content at Bahawalpur. Significant
results were observed between varieties and time of
sowing concerning starch content at Khanewal. Highest
starch content 56.22 % were demonstrated by delayed
sowing during 2nd year of experiment and timely
sowing showed lowest starch content 51.18% during 1st
year at Bahawalpur (Table 5). Maximum starch content
55.59% was introduced by cultivar AAS-2011 during
second year of trial and Millat-2011 produced
minimum starch 52.45% in 1st year in Bahawalpur
(Table 9). At Khanewal AAS-2011 represented highest
starch 56.40 % in delayed sowing and lowest starch
content 51.60% in early planted crop (Table 3). Cultivar
AAS-2011 recorded highest starch 55.63% in delayed
sowing during 2nd year along with Millat-2011 gave the
lowest starch 48.27% in timely sowing during 1st year
at Multan (Table 4(b)). Average values for starch
content remained highest 54.11 at Bahawalpur followed
by 54.06 at Khanewal and lowest starch content was
observed 52.25% at Multan, respectively (Table 1).
Abbas et al
46
Table 4: Interactive effect of years, cultivars and sowing dates on wheat moisture and starch contents (%)
Year x Cultivar x
Sowing date
Year-I
Year-II
V1
V2
V3
V4
Mean
V1
V2
V3
V4
Mean
(a)-Moisture contents
Khanewal
D1
12.42a-c
11.20de
10.97d-f
9.40g-j
11.00
12.95a
12.47ab
11.53b-d
10.98d-f
11.98
D2
10.14e-h
10.51d-g
9.81f-i
8.12k-n
9.65
11.57b-d
11.06de
10.13e-h
9.24h-k
10.50
D3
8.57j-l
8.74i-l
8.88i-l
7.97l-n
8.54
11.23c-e
10.86d-f
9.21h-k
8.52j-m
9.96
D4
7.32n
7.36mn
7.29n
8.56j-l
7.63
11.05de
10.75d-f
8.90i-l
7.76l-n
9.62
Mean
9.61
9.45
9.24
8.51
11.70
11.29
9.94
9.13
LSD 5%
1.19
(b)-Starch contents
Multan
D1
51.07l-n
51.87h-l
48.50o
48.27o
49.93
51.47j-m
51.27k-m
51.02l-n
50.10n
50.97
D2
52.33g-k
52.45f-j
50.70mn
50.13n
51.40
52.63f-i
52.50f-j
51.93h-l
50.73mn
51.95
D3
53.53c-f
53.07e-g
52.43f-j
51.60i-m
52.66
54.43b-d
54.40b-g
52.87f-h
51.60i-m
53.33
D4
54.83ab
53.47c-f
54.27b-d
52.90f-g
53.87
55.63a
54.54a-c
54.02b-e
52.53f-j
54.18
Mean
52.94
52.72
51.48
50.73
53.54
53.18
52.46
51.24
LSD 5%
1.11
Means sharing different letters in a column differ significantly at P = 0.05; *, ** = Significant at 5% and 1%, respectively; NS =
Non-significant.
Table 5: Interactive effect of years and cultivars, years
and sowing dates on wheat starch contents (%)
Year x Cultivar
Bahawalpur
Year-I
Year-II
Mean
V1
54.58b
55.59a
55.09
V2
54.42d
55.40a
54.91
V3
53.05c
54.53b
53.79
V4
52.45d
52.81
52.63
Mean
53.63
54.58
LSD 5%
0.34
Year x Sowing date
D1
51.18g
52.92f
52.05
D2
52.91f
54.09e
53.50
D3
54.60d
55.10c
54.85
D4
55.81b
56.22a
56.02
Mean
53.63
54.58
LSD 5%
0.34
Means sharing different letters in a column differ significantly
at P = 0.05; *, ** = Significant at 5% and 1%, respectively;
NS = Non-significant.
Gluten content (%)
The effect of season on gluten content remained
significant at all sites (Table 1). Gluten content in the
second year of experiment exceeds 4.90% (32.08 vs.
30.58) than 1st year. Wheat gluten content was
significantly affected by cultivars at Khanewal along
with Multan but showed non-significant results in
Bahawalpur. Maximum gluten was witnessed by AAS-
2011 (33.66% and 29.48%) and minimum gluten
content was noted (26.83% and 26.02%) in Millat-2011
at Khanewal and Multan, respectively. Sowing time
significantly affected gluten content at all sites. Delayed
sowing December 10th represented the highest gluten
content (34.19%) which exceeds 5.05, 10.73 and
16.10% as compared to December 1st (32.46%),
November 20th (30.52%) and November 10th sowing
(28.69%). Interaction between cultivars along with the
time of sowing displayed significant results in
Khanewal and Multan, while other all kinds of
interactions remained non-significant at all sites.
Maximum gluten content was obtained by ASS-2011
(37.12) at Khanewal and by AARI-2011 (32.62%) at
Multan in delayed sowing and Millat-2011 displayed
minimum gluten content (23.83 at Khanewal and
ARRI-2011 showed lowest gluten content 24.64% at
Multan in early sown condition (Table 3). Average
values for gluten content were recorded maximum
35.28 at Bahawalpur followed by 31.16 at Khanewal
and minimum were noted 27.96% at Multan (Table 1).
DISCUSSION
Late sown crop resulted more water uptake and dough
stability time but reduced the dough development time
and dough weakness. Wheat dough properties were
increased in case of late sowing of wheat crop. Late
sown crop resulted in the maximum percentage of
protein and wet and dry gluten in the grains which
increased the water uptake and dough stability time
(Abbas et al., 2020; Ahmad and Hasanuzzaman, 2020;
Ahmad et al., 2015a, b; Ahmad et al., 2012; Ahmad et
al., 2014; Ahmad et al., 2018; Ahmad et al., 2017; Ali
et al., 2020; Atique-ur-Rehman et al., 2020). An
increase in temperature during March displayed higher
evapotranspiration along with evaporation which
decreased the available water for crop. Rainfall in last
week of March may recompense water scarcity
(Seleiman et al., 2011). The quality of grain is
determined by environmental factors with different
planting time. During the grain filling process rising
temperature initially showed positive impact on seed
protein content (Bagulho et al., 2015). Increase in soil
temperature showed more uptake of nitrogen which
enhanced the vegetative stage to reproductive stage and
the required temperature for protein formation is more
than starch. Cultivar ASS-2011 is an early maturing
cultivar and displayed more protein content because of
Effect of different sowing times and cultivars on wheat grain quality
47
the inherent capacity of cultivar (Akhtar et al., 2012).
Grain development stage is enhanced with increasing
temperature leading to less concentration of
carbohydrates and starch content in grain. The rising
content of protein resulted less deposition of starch in
grain (Silva et al., 2014). Higher temperature influenced
the growth and development of wheat crop and also
affected grain quality. Heat stress keeps negative effects
on grain weight and grain quality. Elevated temperature
35-40 °C at grain filling stage deteriorates grain quality
and some study resulted that heat shock has positive
effect. Heat stress improved seed protein percentage in
spite of reality that protein content per grain is
decreased. More temperature at grain development
stage increased protein concentration however the
decreasing the function of protein. Gluten particle size
was influenced by cultivar and environment interaction
(Abdulah et al., 2007). The cultivar which keeps higher
molecular weight gluten subunits allele was most
susceptible to heat shock. Cultivar varies in quality
characteristics to heat-shock based on ambient
temperature at anthesis stage. Quality characteristics of
heat susceptible cultivar were influenced strongly by
temperature. The interaction between the cultivars and
temperature in response to heat represented that
potential for choice of cultivars with enhanced
constancy of production in addition to grain quality at
high temperature at the grain development stage. High
temperature increased protein content and other related
quality traits. Heat influenced rate of carbon and N
accumulation in grain as an addition to synthesis of
high molecular of starch along with protein particles.
Differences with in the cultivars were greater than
between the cultivars. Negative relationship exists
between seed dry mass and flour protein concentration
(Sial et al., 2005). The extent of negativity is primarily
based on the sensitivity and acceptance of cultivars
(Farooq et al., 2020; Ghaffar et al., 2020; Khan et al.,
2020; Matloob et al., 2020; Munir et al., 2020; Tariq et
al., 2017; Tariq et al., 2020a, b; Usman et al., 2010;
Wajid et al., 2010). The flour protein concentration of
heat-sensitive cultivar Millat-2011 decreased
considerably with an enlargement of individual seed
dry mass, while for heat tolerant cultivar AAS-2011
remained approximately stable (Spiertz et al., 2006).
Rising temperature showed better results on grain
protein content at grain development stage which
indicated that 0.286% more grain protein content when
increase in temperature 1 °C and when average annual
monthly increase in temperature 1 °C then 0.425%
raised protein content and 0.435 more when daily
average increase in temperature 1 °C. When daily mean
temperature rises from 20 °C to 28 °C at the grain
development stage showed more grain protein content,
dough stability time, and strong wheat gluten is
observed (Sial et al., 2005). Rising temperature showed
good results on protein content representing grain
protein content is directly associated with low humidity.
The mechanisms in which temperature regimes affect
grain protein content, gluten content and starch content
investigated the reasonably elevated soil temperature
increases N uptake from soil and N movement from
tissue parts to wheat seed along with the best
temperature for protein . High temperatures during the
grain development stage may motivate grain protein
formation along with protein retransfer from vegetable
tissue to wheat seeds along with decreasing rate of
photosynthesis and stop both the change of sucrose into
starch along with the movement of carbohydrate
reserves from tissue organs into the wheat grain.
Though, daily maximum temperatures increasing 32°C
would decreased the period of seed ongenesis, indicate
variation in protein composition, create shrunken wheat
seeds comprising high quantity of bran and
consequently decrease wheat grain quality (Kong et al.,
2013). Precipitation effects on grain quality differ from
site to site and showed bad effects on grain protein
content and grain processing quality. If the quantity of
collective post-anthesis rainfall is less than 50 mm
indicated good results on seed protein content, grain
gluten plus grain moisture content. If collective post-
anthesis rainfall is greater than 50 mm negative effects
on protein content was observed (Kong et al., 2013;
Sohrabi et al., 2010). Rainfall before the start of the
grain development stage decreased the grain protein
content because of more nitrogen losses by leaching
and diluting the nitrogen content in the vegetative tissue
of plant. More rainfall at maturity resulted in the grain
sprouting and attack of fungal disease leading to reduce
the processing quality. If more than 30 mm d-1 rainfall
at seed development stage followed by increase in
temperature resulted dying of root of wheat, excessive
loss of water from grain and shriveled wheat grain and
finally deteriorated grain quality. Wheat grain quality is
affected by period of sunshine and is linearly associated
to grain protein content, starch content, moisture
content, and wheat gluten content. Sunlight
concentration may influence grain protein content
because less sunlight intensity improved protein content
(Kong et al., 2013). Grain protein content and gluten
content were observed higher in fluvoaquic soil as
compared to brown soil, black soil or cinnamon soil.
Weak- and medium-gluten wheat cultivars cultivated in
loamy soil showed more gluten content as compared to
cultivated in clay and sandy soil and high gluten wheat
cultivar cultivated in clay-soil resulted high protein
content as compared to loam and sandy soil. High and
low gluten content sown in clay soil and showed higher
flour extraction as compared to in sandy soil and loam
soil. Type of soil changes response of wheat quality
attributes because of various factors i.e. soil nutrients
soil moisture and precipitation (Silva et al., 2014).
Abbas et al
48
New cultivars resulted in better desired quality as
compared to conventional cultivars because of
adaptableness with changing climate and good inherited
characters. Genetic modification displayed better results
for quality grain produce. Best application of genes
variation i.e. maximum sprouting resistance and early
maturity escaping of high temperature at grain
development stage. Greater than forty percent progenies
resulted more grain protein content, gluten content,
moisture content and starch content than their parents
(Ferdous and Rehman, 2013). High gluten and starch
varieties which showed better gluten and starch in
subunit genes well-matched with sprouting resistance
have enhanced the processing quality (Kong et al.,
2013). In conclusion, late sown wheat crop enhanced all
quality characteristics of grain i.e. protein, starch gluten
contents and less moisture content during 2nd year at
Bahawalpur, Khanewal and Multan locations. Climatic
condition i.e. temperature and effective rainfall during
2nd year was more appealing for the production of good
quality wheat grain. Wheat cultivar AAS-2011 recorded
best quality grains as compared to other cultivars at all
locations during 2nd year. Late sowing is best for the
wheat crop to enhance the quality characteristics of
wheat which is very much essential for the human
body.
Authors’ Contribution
QA did field experimentation. GA, MNK, ZF, SN, HY,
HU, PI reviewed literature. AR, NS, SH, MA, MI, MK
analyzed data, and SA supervised the whole study. All
the authors read and approved the manuscript before
publication.
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... The reduction in grain starch deposition might be due to the reduced activity of soluble starch synthase at high temperatures around 40 °C. Similar results were reported by Chauhan et al. (2011) [12] , and Abbas et al. (2020) [13] . Supporting the results of the study, Liu et al. (2011) [16] observed that at high temperatures, starch content in grain reduces up to one-third of total endosperm starch, due to the decreased efficiency of enzymes involved in starch biosynthesis. ...
... A maximum of 25 per cent improvement over the timely sown trial was recorded. Abbas et al. (2020) [13] reported similar findings where late sown crop showed higher protein content compared to the timely sown. During the grain filling process, the rising temperature positively impacted seed protein content initially (Bagulho et al., 2015) [19] . ...
... A maximum of 25 per cent improvement over the timely sown trial was recorded. Abbas et al. (2020) [13] reported similar findings where late sown crop showed higher protein content compared to the timely sown. During the grain filling process, the rising temperature positively impacted seed protein content initially (Bagulho et al., 2015) [19] . ...
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C4 summer cereals are important fodder crops across the world. These cereals are being grown during the summer season in South Asia. The crops included in this group are maize, millet, and sorghum. These fodder crops are being grown in the following cropping systems, i.e., maize-sorghum, maize-millet, maize-maize, sorghum-millet, sorghum-sorghum, and millet-millet. Overall, productivity of C4 crops is better than C3 crops. But due to climatic uncertainty, the production of C4 cereal fodder is lower than the potential yield. Therefore, climate-smart production practices must be adopted for these crops for harvesting higher yields.
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Cropping systems based on pulses, such as chickpeas and mung beans, are significant all throughout the world. Chickpeas and mung beans are two of the most significant pulse crops all over the world, and they are high in protein. High-temperature stress is becoming a severe hazard to the chickpea and mung bean cropping systems as a result of global climate change, impacting plant development and eventually culminating in considerable yield losses. Global climate change has resulted in significantly reduced productivity in the chickpea and mung bean cropping systems throughout the world, threatening food security in the future. Climate change has a significant influence on food production in Asia and Africa, particularly in the chickpea and mung bean cropping systems. Climatic changes are expected to have a detrimental influence on the production of chickpeas and mung beans. Chickpeas and mung beans are legumes that thrive on residual soil moisture. With various intensities and frequencies, high temperatures and terminal heat stress are typical in diverse locations of chickpea and mung bean cropping system production. As a result, consistent chickpea and mung bean production will be dependent on the emergence of new cultivars with higher heat tolerance. Significant advances in chickpea and mung bean breeding have boosted the efficiency with which genetic diversity in germplasm collections can be assessed. The chickpeamung bean farming system may be in risk of extinction due to climate change, according to certain anticipated forecasts. At this time, the most urgent global issues are those related to food security and ecological resilience. The only way to lessen the negative impact of climatic variations on crop adaptation before they have a large impact on global crop yields is through the use of chickpea-mung bean cropping systems that are climate-smart.
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On the basis of predicted responses of crop to managing, weather, and soil procedures, models for groundnut and canola crops may be used for estimation variations in future productivity. Crop models are practical tools for examining how climate change may affect crops and developing effective adaptation measures. Projected crop yields, however, are highly dependent on a wide variety of conceivable climatic and field management circumstances, and they differ significantly between crops and places. Projections of crop production are also affected by model parameter errors and the depiction of biophysical processes in various crop models. Experts have been more worried about threats posed by shifting global climate as these variations are having a detrimental effect on agrarian productivity and jeopardizing global food safety and security. The groundnut-canola cropping system is thought to be the activity most endangered by climate change, according to certain forecast reports. The two issues that worry the world’s people the most right now are food security and ecological resilience. The only way to prevent climatic changes from having a significant detrimental influence on crop adaptability is to use a climatesmart groundnut-canola cropping system before it is too late. In order to create climate-resistant crops, we describe the effect of climate change, pressures brought on by it, its effects on the groundnut-canola cropping system, and coping mechanisms in this chapter.
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Sugarcane is an important cash and energy crop in the world. Overall, the major component of sugar is being extracted from this crop. However, the water and fertilizer requirements of this crop are more as compared to other agronomic crops. It is a water-loving crop. The crop phenology and productivity of sugarcane are affected by husbandry practices along with climate change across various regions of the world. Climate change has adverse effects on the growth and development of crop. In order to feed the burgeoning population of the world, there is dire need of adaptation of climate-smart agricultural practices for sugarcane crop along with dissemination of these practices to growers.
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The potato is a staple crop for many people around the world, and it plays a vital part in achieving Millennium Development Goals of ending hunger, poverty, and malnutrition. CO2 levels in the atmosphere are expected to rise, which gives a chance to boost potato harvests. This, however, can only happen if the crop is resilient enough to endure the additional impacts of climate change caused by the increase in CO2 concentrations. Climate change may positively or negatively impact biotic stress; however, it is anticipated that abiotic stress will increase significantly and threaten the production of potatoes. Changing climatic conditions present a significant challenge to the cultivation of potatoes because they increase the risk of crop failure, an extended growing season, low yields, poor quality, host-pathogen interactions, insect dispersal, and insect ecology are negatively impacted. Consequently, it is necessary to implement adaptation strategies such as modifying cropping systems and other inputs, planting dates, and growing regions. In addition, better water management practices need to be implemented, and resistant potato cultivars need to be developed.
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Global climate change will have an influence on agriculture and, as a result, the availability of food. The plant growth, time of flowering, pollination, fruit set, and development of vegetable crops can all be negatively impacted by the rapid rise in temperature and erratic rainfall at any stage of crop development, reducing crop production. One of the most widely consumed vegetable crops worldwide are sweet pepper and sweet corn; however, climate variations have an impact on how productive they are. Various crop models are used to assess climate change and its effects on vegetables in the sweet corn and sweet pepper cropping systems. This chapter examines how increased atmospheric carbon dioxide, warming temperatures, and heat stress affect sweet corn and sweet pepper crops’ physiology, phenology, growth, and yield. Additionally, implementing novel adaptation tactics improves crop output in the sweet corn and sweet pepper cropping systems and helps to adapt to upcoming climate changes.
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Important cropping system in Pakistan, India, China, and other nations is the maize-maize cropping system. The burgeoning populations in developing nations, especially in Asia and Africa, are fed by this cropping system. South Asia is the main region covered by this cropping technique. The productivity of this farming method is currently declining due to climate change. Climate change is just one of the many factors contributing to this yield stagnation. Food security in underdeveloped nations is threatened due to change in climate. The effect of the climate changes on this system’s phenology and yield is being quantified by scientists. Additionally, there are numerous regional and international initiatives including stakeholders that aim to recommend climate change adaptation options for maize-maize cropping system and secure regional and global food security.
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Coping with the threat of climate change and food security, crop productivity needs improvement. Low-price situation for cotton in some developing countries is discouraging cotton cultivation. Cotton-based intercropping offers a better solution to cope with the situation. The system of companion plantation of one crop with another during the same growing season on the same piece of land offers an option. The basic objective is to augment yields by multiplying the available growing area. Across other merits of intercropping, the biodiversity of growing crops attracts a variety of predatory insects making possible an integrated pest control of cotton. Intercropping may be classified into three types, viz., mixed cropping, which includes planting a variety of harmonious plants together; alley or row cropping, in which diverse crops are grown with each other in rows; or temporal intercropping, that is a system of the fast-growing crop with a slow growth. Due to a relatively longer duration with slow-growing habit during initial stages, cotton-based cropping system is ideally suitable for intercropping. The cotton-based intercropping aims to maximize the yield of cotton along with extra profits from intercrops. There is a wide range of crops including cereals, legumes, and vegetables, which are possible to grow with or in standing cotton crop. Each group offers versatile advantages in terms of yields of both companion crops and monetary returns of the system. However, there is a crop-wise variation in monetary advantages of diverse intercropping systems.
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Agriculture contributes mainly to national economies specifically in developing countries, and cotton is an important cash crop. In certain countries, it is recognized as “white gold” since it is earning foreign exchange. In the world, cotton fiber is a distinguished fiber that serves as a raw material for textile industries having a yearly significant economic impact of at least $600 billion. Genetic diversity and its usage in getting sustainability of lint cotton and cotton yield, and usage of bio-based substitute such as procession and change in various biochemical, physiological, morphological and genetically significant traits. Nearly 25 M tons of total cotton is produced worldwide annually. Top ten cotton-producing countries are India, China, the United States, Pakistan, Brazil, Australia, Uzbekistan, Turkey, Turkmenistan, and Burkina Faso.
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This book provides a comprehensive and systematic overview of the recent developments in cotton production and processing, including a number of genetic approaches, such as GM cotton for pest resistance, which have been hotly debated in recent decades. In the era of climate change, cotton is facing diverse abiotic stresses such as salinity, drought, toxic metals and environmental pollutants. As such, scientists are developing stress-tolerant cultivars using agronomic, genetic and molecular approaches. Gathering papers on these developments, this timely book is a valuable resource for a wide audience, including plant scientists, agronomists, soil scientists, botanists, environmental scientists and extention workers
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Sustainable cotton production in current environmental conditions is under threat due to climatic variability and shortage of ever-decreasing resources for agricultural crops. There is dire need to improve the cotton production to fulfill increasing demands of the ever increasing world population which will rise up to nine billion till 2050. Poor soil health, poor water quality and water shortage, insect pest complex, and unpredictable climatic patterns are predominant problems to cotton production. Hence, there is a great challenge to manage cotton crop in a sustainable fashion without the degradation of soil, water, and environment due to climate variability. There are several factors associated with low production of cotton including improper sowing and picking, poor pesticide spraying approaches, inappropriate amount and time of irrigation, processing and ginning through inappropriate and primitive procedures, heat stress, lack of disease- and pest-tolerant varieties, improper nutrient management, improper disease management, and improper weed management. It is the need of the hour to adopt the modern technologies and applications for sustainable cotton production. There are several modern technologies which can increase the production of cotton and make the idea of sustainability feasible because of their site-specific management of all agricultural inputs. GPS, GIS, and remote sensing technologies make the precise seeding of cotton seed, fertilizers, and pesticides. IPM, IWM, and INM are the well-developed modern concepts which not only reduce the cost of production but also mitigate the emission of greenhouse gases. For sustainable cotton production, implementation of these modern concepts is crucial so that the human beings will get benefits in the future. Therefore, this chapter will be focused on the recently developed technologies which can be sustainably utilized for the better management of cotton crop across the world. This chapter will explore the importance of Decision Support system (DSS) for sustainable cotton production; role of GPS, GIS, and remote sensing for identifying site-specific factors such as soil quality indicators; importance of transgenic cotton; impact of mechanical sowing and picking on sustainable cotton production; use of UAVs for nutrient and pesticide management; and impacts of modern concepts on increasing agronomic production and advancing global fiber and oil security.
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Cotton farming symbolizes single largest use of arable land for fiber production on earth, and cotton-based cropping systems are practiced under diverse agro-climatic environments in more than 100 countries. World cotton production has escalated in recent past and has undergone numerous technological transformations and socioeconomic interventions in quest of productivity and sustainability. Cotton-based cropping systems range from low-input rainfed systems in Australia and Africa to highly mechanized intensive farming systems in the United States, Brazil, and China. In India and Pakistan, multiplicity of cotton varieties, weather extremes, uncertainty of climatic optima, spurious seeds, non-remunerative markets, and low quality plus adulterated chemicals or pesticides are key problems leading to low yields besides net profits in otherwise high productivity cotton-based cropping systems. Resource conserving, eco-efficient, climate smart, and economically viable cropping systems that rotate/intercrop cotton with cereals, oilseeds, and legumes are required. Relay or intercropping and crop rotations will lead to the ecological intensification of cotton-based cropping systems. An ideal cotton-based cropping system should aim at higher yields and net profits per unit area, bring stability into the production system, ensure optimal utilization of the available resources, be able to meet domestic requirements of farmer, and avoid ecological uncertainty in the form of shifts in insect pests or weed populations or evolution of pesticide resistance in the long run. Another area requiring significant improvement is integrating current curative pest management options with other cultural methods to avoid insecticide/herbicide resistance development in an era of transgenics. The transgenics have their own pros and cons, and due deliberations in the best interest of agro-ecosystem sustainability and small landholders be made with involvement of all stakeholders. Biotech seed industry should plan safe mechanisms for herbicide-tolerant crop development to evade resistance development or gene introgression in weeds. Productivity and profitability of cotton-based cropping systems needs to be explored with greater ecological orientation under conventional and organic management systems. This chapter documents the productivity and resource use efficiency of cotton-based cropping systems based on existing agronomic and experimental evidences. Crop growth and development, productivity, quality, resource use efficiencies, and profitability of various systems have been discussed at the plant, field, and system levels.
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The weeds are becoming a major challenge for cotton production across the world, and the crop is infested by broad leaves, grasses, and sedges. Although the presence of weeds is deleterious for potential yield, the extent of losses depends on weed type, density, duration, and crop growth stage. Some weeds like Datura stramonium L., Amaranthus palmeri L., Amaranthus retroflexus L., and Ambrosia trifida L. cause significant yield losses at very low densities in comparison with Cucumis melo L. and Eleusine indica L. The cotton crop is very sensitive at early growth stages where weed presence during the first 2 months of growth may reduce yield from 10% to 90%. The most common effects of weeds on cotton quality are higher trash contents and lint staining problem. The various weed control options have been used for weed management in cotton; however, the efficacy of weed control methods remains low, probably by practicing weed control out of the critical period of weed-crop competition and selection of inappropriate method. The cost of weed control ranges from 54.5 to 320.6 US$ ha⁻¹ in various cotton-growing countries of the world. The use of herbicides for weed control is common in the world; however, excessive and non-judicious use of herbicides led to the evolution of resistant weed biotypes. It provided the basis for development of glyphosate-tolerant cotton genotypes. The big change has been made for weed management with the advent of genetically modified glyphosate-tolerant cotton. It offers flexibility in herbicide use and time of application and improves weed control efficiency and economic returns. The evolution of herbicide resistance has been reported in Amaranthus palmeri L., Commelina benghalensis L., Conyza bonariensis L., Conyza canadensis L., and Sorghum halepense L. Some new technologies like Roundup Ready Xtend cotton and Enlist technology are being developed to cope to the challenge of glyphosate resistance.
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Among responsible factors for getting maximum yield of cotton, appropriate sowing method has prime importance, as it plays a decisive role in proper crop stand and root penetration and development. Furthermore, suitable plant-to-plant and row-to-row adjustment accelerates the utilization of available resources. Cotton is an important cash crop besides being a big earning source for many leading cotton-producing countries of the world. Various planting techniques including flat planting by drilling in differently spaced lines, ridge sowing and raised bed planting are being practised in different countries of the world. Among these methods, each has associated merits and demerits under prevailing soil and climatic conditions. Studies have revealed that higher yield targets can be achieved through exploiting all aforementioned planting techniques, but management factor makes a real difference under all conditions. Due to specific climatic requirement, cotton crop has to deal with a variety of pests including insect, disease-causing pathogens and weeds, so, through good management practices, we can save this white gold from yield losses and get targeted yield.
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Cotton growth models are being generally used by cotton scientists as well as policy makers across globe as an important and effective research tool. Cotton simulation models have been applied during last and current decades for the analysis of the cotton plant responses to drought, heat, and nutrients stress as well as to test the alternating optimum sowing window under climate warming trend in cotton belt. Cotton growth models are useful research tools in worldwide. Mostly cotton models were applied for climatic changes, cotton management practices, and irrigation strategies on lint and cottonseed yield in worldwide. All cotton models were successfully used at local, regional, and national levels in worldwide, but among all cotton growth models, CROPGRO-Cotton model was mostly used by researchers and policy makers. For irrigation management strategies, mostly AquaCrop model was used by researchers.
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Cotton-wheat is an important cropping system of the world in which cotton is sown after harvest of wheat in the start of summer season. In the recent decades, Bt cotton cultivars have been introduced to better combat the bollworms. However, the Bt cotton cultivars have a growth period longer than the conventional cotton cultivars. This situation pressured the farmers to opt to grow either wheat or cotton in a single year. This not only could result in economic loss to farmers but also could threaten the food security of the cropping region. Relay cropping of cotton in wheat was suggested by an innovative solution for maintaining the productivity and sustainability of cotton-wheat cropping system. Relay cropping of cotton in wheat could be done either by inter-seeding the seeds of cotton in free space between the wheat strips (while wheat is at reproductive phase) or by transplanting the 5–7-week-old cotton seedlings between the wheat strips. Subsequent research work indicated that relay cropping could improve the resource use efficiency and overall productivity of the cotton-wheat cropping system. In a 2-year study in Punjab, Pakistan, conducted at two locations, intercropping cotton in bed−/ridge-sown wheat in early March improved the overall system productivity and cotton fiber quality as compared with conventionally tilled cotton sown after harvest of flat-sown wheat in late April. Future research may investigate the weed control and incorporation of conservation agricultural practices in the cotton-wheat relay intercropping systems.
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For cultivation of crops, among all soil adaptive practices, tillage has been considered a fundamental crop-growing practice for centuries to clear and soften the soil. Due to changing climatic conditions and perturbation of resources, there is a need to implement soil adaptive practices and improve tillage practices to ensure security of food and guaranteed fiber production to achieve zero hunger. This chapter covers the influences of climate change and important soil adaptation and tillage practices especially for cotton crop. Our goal was to provide a framework regarding factors responsible for low cotton yield and soil adaptations that can improve cotton productivity. We attempt to highlight possible negative effects of climate change, i.e., high temperature, greenhouse gas emission, drought stress, salinity stress, insect/pest/disease attack, and primary techniques to mitigate climatic adverse effects on cotton crop. Keeping the current scenario, we suggest that advance research is still required to address the adverse effects of climate through better implementation of soil adaptations.