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Impact of temperature on yield and related traits in cotton genotypes
Journal of Integrative Agriculture 2016, 15(3): 678–683 Available online at www.sciencedirect.com
ScienceDirect
RESEARCH ARTICLE
Impact of temperature on yield and related traits in cotton genotypes Kalim Ullah1, Niamatullah Khan1, Zahid Usman1, Rehmat Ullah2, Fazal Yazdan Saleem3, Syed Asif Imran Shah1, Muhammad Salman1 1
Pakistan Central Cotton Committee, Cotton Research Station, Dera Ismail Khan 29050, Pakistan Department of Agricultural Extension, Education & Communication, The University of Agriculture, Peshawar 25000, Pakistan 3 Oil Seed Programme, National Agriculture Research Center, Islamabad 44000, Pakistan 2
Abstract Cotton growth and development is influenced by various uncontrollable environmental conditions. Temperature variations in the field can be created by planting at different dates. The objective of the present study was to evaluate the effect of planting dates and thermal temperatures (growing degree days) on yield of 4 cotton genotypes, viz., CIM-598, CIM-599, CIM602 and Ali Akbar-703. Plants were subjected to 6 planting dates during 2013 and 2014 in a trial conducted in randomized complete block design with four replications. For boll number, boll weight and seed cotton yield, cotton genotypes exhibited significant differences, CIM-599 produced the highest seed cotton yield of 2 062 kg ha–1 on account of maximum boll number and boll weight. The highest seed cotton yield was recorded in planting dates from 15th April to 1st May whereas early and delayed planting reduced the yield due to less accumulation of heat units. Regression analysis revealed that increase of one unit (15 days) from early to optimum date (15th March to 15th April) increased yield by 93.58 kg ha–1. Delay in planting also decreased the seed cotton yield with the same ratio. Thus it is concluded that cotton must be sown from 15th April to 1st May to have good productivity in this kind of environment. Keywords: Gossypium hirsutum, planting dates, growing degree days, genotypes
1. Introduction Cotton growth, yield, composition and quality are influenced by various factors such as genotype, environment and agronomic practices. Environmental factors are classified into predictable and unpredictable variables (Allard and Bradshaw 1964). Sowing time is among one of the predictable factors, it is under human control and can be slightly
Received 6 February, 2015 Accepted 6 May, 2015 Correspondence Kalim Ullah, E-mail: [email protected] © 2016, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(15)61088-7
changed as per requirements, therefore it is decleared as predictable factor. Planting time is a major agronomic factor that affect growth and yield (Gecgel et al. 2007). Therefore, determination of optimum sowing time and selection of suitable cultivar for specific growing areas are of utmost importance for high yield and quality cotton. Environmental factors determine the good growing season for a specific crop. It also reveals the adoptive potential of a genotype. Temperature is a major environmental factor which reveals the growth, development and yield. All over the world, an increase in global warming increases heat stress which is a serious threat to crop productivity. Documented rise in global temperature that has been forecasted by several climatic models, have direct effect on plant growth, yield and quality. This type of temperature variation in the field can be created by cultivating the crops at dif-
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Kalim Ullah et al. Journal of Integrative Agriculture 2016, 15(3): 678–683
ferent sowing times and the crop will thus grow at different temperatures and relative humidities. Cotton genotypes that have wide range of adoptability need different total numbers of cumulative heat units (CHUs) or growing degree days (GDDs) for their growth, development, yield and maturity. The CHUs or GDDs are the most common indexs used to estimate the development of a plant. CHUs accumulation determines the crop maturity along with the fiber quality of cotton crop. In terms of quality and quantity of the product, the genotype-environment interaction is a key factor in the performance assessment of a crop cultivar. All the environmental factors such as temperature, sunshine, humidity and rainfall affect differently the growth and development of a crop. As the rate of plant growth is mainly driven by temperature (Ritche and Ne Smith 1991), the gap between the actual and potential yield needs to be closed via modeling the impact of temperature variation on yield and quality of genotypes. In this context, the objectives of the study were to (i) evaluate the impact of planting dates on seed cotton yield and (ii) modeling the association between heat unit accumulation and yield-related traits of cotton genotypes planted at different times.
future research for several breeding purposes. The G×D was also significant for number of bolls per plant indicating significant G×environment (E) interaction. It is thus depicted that environmental effect in understanding plant growth must be considered due to its importance in cotton breeding programme. These results are in complete analogy with the Machado et al. (2002), who also observed significant genotype×year and genotype×year×location interaction. In different environments, the genotypes performed differently and revealed significant genotype×environment interaction in upland cotton genotypes (Unay et al. 2004; Satish et al. 2009). Similarly, mean squares due to G, E and G×E interaction were highly significant for different morphological traits in upland cotton (Khan et al. 2008; Gul 2013).
2.2. Cotton development and crop phenology
2. Results and discussion
Environmental factors, particularly temperature, are the key components that affect the plant growth and development. Significant differences for GDDs/CHUs in various genotypes depicted that these genotypes have varying maturity periods. However, higher GDDs/CHUs accumulated in the crop that planted on 15th April and 1st May in 2013–2014 reflected that these planting dates are optimum planting dates to have good output.
2.1. Combined analysis of variance
2.3. Number of bolls per plant
The analysis of variance revealed significant differences (P≤0.01) among genotypes (G) and planting dates (D) for the tested parameters (Table 1), indicating the presence of variability among genotypes as well as environments. It further suggests that some of the genotypes were superior to others in these traits. The interaction of G×D for number of bolls per plant was also significant, showing that different genotypes performed differently in different environments (Table 1). Significant mean squares of genotypes also indicate higher degree of genetic variability among the material used in the study. This significant variation was observed in all the studied traits which are valuable sources used in
Bolls per plant is the major independent yield component and plays a major role in managing the variations in seed cotton yield. Hence selection for larger number of bolls per plant must receive emphasis on cotton improvement. Statistical analysis of the data revealed that planting dates significantly influenced number of bolls per plant (Table 2). Number of bolls per plant enhanced with delay in planting date, maximum number of bolls per plant (26.50) was recorded in the crop planted on 15th April, whereas further delay in planting dates reduced the number of bolls per plant and thus minimum bolls per plant was recorded in the crop planted either very early (19.50) on 1st March or delayed planting (17.42) on
Table 1 Combined analysis of variance of studied crop phenology and yield components of 4 cotton genotypes evaluated for 2 years and 6 planting dates Source of variability Years (Y) Planting dates (D) Y×D Genotypes (G) Y×G D×G Y×D×G Error (R×Y×D×G) *
df 1 5 5 3 3 15 15 80
Number of bolls plant–1 324** 249.45** 0.0086 229.06** 0.0013 1.51** 0.0012 0.331
and **, significance at 5 and 1% level of probability respectively using the F test.
Boll weight (g) 0.0386* 1.6550** 0.0510** 0.2710** 0.0184 0.0138 0.0061 0.0112
Seed cotton yield (kg ha–1) 495 030 1 889 354** 101 291** 879 197** 27 275 23 543 16 530 21 459
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1st June. This reduction in early and delayed planting might be attributed to the variation in temperature prevailed by the crop growth. The studied genotypes also beared different number of bolls per plant. The mean comparison showed significant differences at 5% probability level among the 4 cotton genotypes (Table 2), CIM-599 had the highest (24.44) bolls per plant followed by CIM-598 (21.94), while the lowest was recorded in genotype AliAkbar-703 (18.39). The significant G×D interaction across the environments indicated existence of variation for boll setting (Ali et al. 2005) and positive relation with yield in cotton (Makhdoom et al. 2010; Gul 2013).
2.4. Boll weight Analysis of data revealed significant differences for boll weight in different planting dates. Crops planted on 15th April showed maximum boll weight (3.07 g) followed by the crops planted on 1st May (Table 2). Boll weight increased with delay in planting date till 15th April, however further delay reduced the trait which might be attributed to the unfavorable photoperiod and high temperature conditions at early growth and development stage that forced the crop to end up the life cycle rapidly at the cost of reduction in yield and yield components. The genotypes also differed significantly regarding boll weight. Genotype CIM-599 showed the highest boll weight of 2.82 g followed by CIM-598 (2.72 g). The minimum boll weight was recorded in the genotype AliAkbar-703. Seed cotton yield was highly affected directly and indirectly both by boll number and boll weight in cotton (Rauf et al. 2004; Batool et al. 2013; Khan 2013).
2.5. Seed cotton yield Genotypes and planting dates play a vital role in yield and yield-related attributes. The crop planted on 15th April
depicted the highest seed cotton yield (2 228 kg ha–1) on account of increased number of bolls, boll weight and favorable environmental growing conditions compared to other planting dates evaluated in the study. Crop planted on 1st May ranked the second regarding the seed cotton yield (2 028.8 kg ha–1). As shown in Table 2, the seed cotton yield was significantly influenced by planting dates and genotypes. The seed cotton yield increased from 1 738.5 to 2 228 kg ha–1 as planting date delayed from 1st March to 15th April, whereas further delay reduced the seed cotton yield, delayed planting from 15th April to 1st June reduce dit to 1 404.2 kg ha–1, showing 489.5 kg ha–1 increment up to 15th April and then 823.8 kg ha–1 decrement in seed cotton yield respectively (Fig. 1-A). Sowing time is a major agronomic factor, therefore determining optimum sowing time and suitable cultivar for certain region are necessary to obtain higher yield. The yield ability of the present cotton genotypes was the result of various interactions (G×D, G×Y and G×D×Y) factors (Khan et al. 2008). Maximum year to year variation for seed cotton yield was recorded in different growing conditions. Both genotypes and environments contributed to yield variations; however the environmental complex showed primary effect on yield (Best 2005; Ahmad et al. 2006). In the present, the genotypes and environments were predominant for yield variation. Different cotton genotypes showed significant differences regarding seed cotton yield. The highest seed cotton yield of 2 062 kg ha–1 was recorded in CIM-599 followed by CIM598 (Table 2, Fig. 1-B). The lowest seed cotton yield was observed in genotype AliAkbar-703. Cotton genotypes revealed significant differences through different environments and their interaction on seed cotton yield, indicating variability among genotypes as well as environments (Unay et al. 2004; Gul 2013). Regression analysis of planting date (x) and seed cotton
Table 2 Means of 6 planting dates and 4 cotton genotypes for some of the studied traits Factor Planting dates 15th March 1st April 15th April 1st May 15th May 1st June LSD0.05 Genotypes CIM-598 CIM-599 CIM-602 AliAkbar-703 LSD0.05
Number of bolls plant–1
Boll weight (g)
19.50 d 22.00 c 26.50 a 23.16 b 19.67 d 17.42 e 0.33
2.46 e 2.63 d 3.07 a 2.93 b 2.76 c 2.40 e 0.061
1 738.5 d 1 920.9 c 2 228.0 a 2 028.8 b 1 798.4 d 1 404.2 e 84.15
21.94 b 24.44 a 20.72 c 18.39 d 0.259
2.72 b 2.82 a 2.62 c 2.67 bc 0.0801
1 868.7 b 2 062.0 a 1 786.8 b 1 695.1 c 84.065
Means followed by same letters in the same column are not significantly different at 5% level of probability.
Seed cotton yield (kg ha–1)
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Seed cotton yield (kg ha–1)
Seed cotton yield (kg ha–1)
A
2 500 2 000 1 500 1 000 500 0
15th March
Seed cotton yield (kg ha–1)
B
1st April
15th 1st April May Planting date
15th May
2 500 2 000 1 500
1st June
2 500
y=–93.589x2+589.69x+1 217.3 R2=0.9242
1 000 500 0
0
15th 1st March April
15th April
1st May
15th May
1st June
Planting date
Fig. 2 Relationship between planting dates and seed cotton yield.
2 000 1 500 1 000
governs the crop growth rate (Kaleem et al. 2009).
500 0
CIM-598
CIM-599
CIM-602 Ali Akbar-703
Genotypes
Fig. 1 Seed cotton yield under six planting dates (A) and four different cotton genotypes (B).
yield (y) indicated highly significant association between these attributes. Besides, R2 (coefficient of determination) revealed that up to 92% of the increase till 15th April and later on decrease in seed cotton yield might be under the influence of planting date. The regression between seed cotton yield and planting date was negative, regression equation for planting date suggested that increase in one unit (15 days) of planting date increased/decreased seed cotton yield by 93.58 kg ha–1 (Fig. 2). Environmental factors, especially temperature, are the main attributes that affect the plant growth and development. Significant differences among genotypes for GDD were observed, which clearly depicted that various genotypes have varying maturity period. Accumulation of higher GDD for the 15th April and 1st May planting during 2013 and 2014 provided a clue that 15th April to 1st May is the best planting time for cotton crop to have good yield (Table 3). The accumulation of GDD determined the yield and yield-related attributes because temperature is the primary factor which
3. Conclusion The instant results indicated that number of bolls per plant, boll weight and seed cotton yield have been significantly influenced by the genotypes and growing conditions under the different planting dates. Accumulation of GDD played a vital role in modeling cotton to the expected global warming in the coming years. Differences among cotton attributes might be attributed to the temperature-based different climatic conditions prevailed during the crop life cycle. It is therefore concluded that 15th April to 1st May is the optimum date, early/delayed planting results in reduction of seed cotton yield as the crop completes its life cycle in short duration and accumulates less CHUs.
4. Materials and methods 4.1. Experimental location and plant materials The experiment was conducted at Cotton Research Station, D.I.Khan, Pakistan (situated at 31°49´N latitude and 70°55´E longitude) during the crop season in 2013 and 2014. Four genotypes, viz., CIM-598, CIM-599, CIM-602 and AliAkbar-703 were used in this study. These genotypes were sown at different sowing dates so as to create difference
Table 3 Relationship between growing degree days and seed cotton yield (kg ha–1) Planting date 15th March 1st April 15th April 1st May 15th May 1st June LSD0.05
2013 2 224.5 2 420.5 2 639.5 2 564.0 2 516.0 2 408.5
Growing degree days (d) 2014 2 141.7 2 333.7 2 601.2 2 482.2 2 451.7 2 387.7
Mean 2 183.10 2 377.10 2 630.35 2 523.10 2 483.85 2 398.10
Seed cotton yield (kg ha–1) 1 738.5 d 1 920.9 c 2 280.0 a 2 028.8 b 1 798.4 d 1 404.2 e 84.15
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Kalim Ullah et al. Journal of Integrative Agriculture 2016, 15(3): 678–683
in temperature for growth, development and maturity, thus giving a wide range of temperature from sowing till maturity. Meteorological data were obtained from Arid Zone Research Institute (AZRI), Pakistan Agricultural Research Council (PARC), D.I.Khan, Pakistan. Monthly maximum, minimum and mean temperature along with rainfall during 2013 and 2014 are shown in Fig. 3. The baseline temperature used for growing degree days (GDDs) calculation was 16°C. The cumulative heat units (CHUs) for various stages were calculated using the equation as suggested by Dwyer and Stewart (1986). (Tmax–Tmin) –Tb CHU= 2 Where, Tmax, Tmin and Tb are maximum daily temperature, minimum daily temperature and baseline temperature below which development ceases, respectively.
4.2. Experimental environments The experiment was conducted at 12 environments (2 years×6 sowing dates); details of these are given below (Table 4).
4.3. Experimental layout and crop management The four cotton genotypes (CIM-598, CIM-599, CIM-602, AliAkbar-703) were investigated at six different dates of planting for two consecutive years of 2013 and 2014. These genotypes were planted in randomized complete block (RCB) design with 4 replications. Planting was done in a 4-row plot with 10 m length, the row to row and plant to plant distance was 0.75 and 0.30 m, respectively. The plot size was 40 m2. Seeds were planted in hills and each hill
Minimum
50
Maximum
45
Temperature (°C)
40 35 30 25 20 15 10
November
Octobor
August
July
June
May
April
March
Octobor
November
2014
September
2013
September
August
July
June
May
April
0
March
5
Fig. 3 Maximum and minimum temperature of the years 2013 and 2014. Table 4 Twelve manipulated environments used in the study
received 5 seeds which were irrigated immediately. In these
Environment1) E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12
experiments, 60 kg ha–1 of P2O5 as single super phosphate
1)
Cropping season 2013 2013 2013 2013 2013 2013 2014 2014 2014 2014 2014 2014
Planting date 15th March 1st April 15th April 1st May 15th May 1st June 15th March 1st April 15th April 1st May 15th May 1st June
E1 is Environment 1, E2 is Environment 2 and so on.
and 50 kg ha–1 of nitrogen as urea were applied prior to
sowing, 50 kg ha–1 nitrogen as urea used at the flowering stage and 50 kg ha–1 at boll formation stage. After 3 weeks of planting seedlings were thinned to single plant per hill. Picking was done 130 and 150 days after planting. All the cultural practices were done according to recommendations.
4.4. Recording of observations and statistical analysis Data of number of bolls per plant, boll weight (g) and seed cotton yield (kg ha–1) were recorded. The data recorded
Kalim Ullah et al. Journal of Integrative Agriculture 2016, 15(3): 678–683
were subjected to analysis of variance technique appropriate for RCB design as suggested by Steel et al. (1997). After having homogeneity test for error variances by using Bartlett’s tests (Snedecor and Cochran 1983), combined analysis of variance was performed. To determine the statistical differences in means, the least significant difference (LSD) test at 5% level of probability was used when the F-value was significant. For this purpose, MSTAT-C (Freed et al. 1989) statistical package was utilized. Microsoft Excel 2007 was used for diagrams and regression analysis (Steel et al. 1997).
Acknowledgements Pakistan Central Cotton Committee (PCCC) is highly acknowledged for the financial support of this work.
References Ahmed A E, Abdalla A H, Fadlalla A S. 2006. A note on the stability of five medium staple cotton (Gossypium hirsutum L.) varieties for some fiber properties in the Gezira scheme of the Sudan. University of Khartoum Journal of Agricultural Science, 14, 313–319. Ali Y, Aslam Z, Hussain F. 2005. Genotype and environment interaction effect on yield of cotton under naturally salt stress condition. International Journal of Environmental Science and Technology, 2, 169–173. Allard R W, Bradshaw A D. 1964. Implications of genotypeenvironment interaction in applied plant breeding. Crop Science, 4, 503–507. Batool S, Khan N U, Gul S, Baloch M J, Turi N A, Taran S A, Saeed M. 2013. Genetic analysis for yield and yield contributing variables in upland cotton. Journal of Food, Agriculture and Environment, 11, 624–630. Best E C. 2005. Source-sink relations in cotton: Genetic and environmental affectors. MSc thesis, Texas Tech University, Texas, USA. Dwyer L M, Stewart D W. 1986. Leaf area development in field grown maize. Agronomy Journal, 78, 334–343. Freed R, Einensmith S P, Gutez S, Reicosky D, Smail V W, Wolberg P. 1989. User’s Guide to MSTAT-C Analysis of Agronomic Research Experiments. Michigan State University, East Lansing, USA. Gecgel U, Demirci M, Esendal E. 2007. Fatty acid composition of the oil from developing seeds of different varieties of
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safflower (Carthamus tinctorius L.). Journal of American Oil & Chemical Society, 84, 47–54. Gul S. 2013. Genotype by environment interaction for morphoyield traits in upland cotton. MSc thesis, The University of Agriculture, Peshawar, Pakistan. Kaleem S, Hassan F U, Saleem A. 2009. Influence of environmental variations on physiological attributes of sunflower. African Journal of Biotechnology, 8, 3531–3539. Khan N G, Naveed M, Khan N I. 2008. Assessment of some novel upland cotton genotypes for yield constancy and malleability. International Journal of Agriculture & Biology, 10, 109–111. Khan N U. 2013. Diallel analysis of cotton leaf curl virus (CLCuV) disease, earliness, yield and fiber traits under CLCuV infestation in upland cotton. Australian Journal of Crop Science, 7, 1955–1966. Machado J R D A, Penna J C V, Fallieri J, Santos P G, Lanza M A. 2002. Stability and adaptability of seed cotton yields of upland cotton genotypes in the state of Minas Gerais, Brazil. Crop Breeding & Applied Biotechnology, 2, 401–410. Makhdoom K, Khan N U, Batool S, Bibi Z, Farhatullah, Khan S, Mohammad F, Hussain D, Raziuddin, Sajjad M, Khan N. 2010. Genetic aptitude and correlation studies in G. hirsutum L. Pakistan Journal of Botany, 42, 2011–2017. Rauf S, Khan T M, Sadaqat H A, Khan A I. 2004. Correlation and path coefficient analysis of yield components in cotton (G. hirsutum L.). International Journal of Agriculture & Biology, 6, 686–688. Ritchie J T, Ne Smith D S. 1991. Temperature and crop development. In: Hanks J, Ritchie J T, eds., Modeling Plant and Soil Systems. American Society of Agronomy, Madison, USA. pp. 5–29. Satish Y, Jain P P, Chhabra B S. 2009. Stability analysis for yield and its component traits in American cotton (G. hirsutum L.). Journal of Cotton Research & Development, 23, 175–182. Snedecor G W, Cochran W G. 1983. Statistical Methods. 6th ed. Oxford and IBH, New Delhi, India. Steel R G D, Torrie J H, Dickey D A. 1997. Principles and Procedures of Statistics: A Biometrical Approach. 3rd ed. McGraw-Hill, New York, USA. Unay A, Basal H, Erkul A, Yuksekkaya Z. 2004. Stability analysis of upland cotton genotypes to the Aegean Region in Turkey. Asian Journal of Plant Science, 3, 36–38. (Managing editor SUN Lu-juan)
ScienceDirect
RESEARCH ARTICLE
Impact of temperature on yield and related traits in cotton genotypes Kalim Ullah1, Niamatullah Khan1, Zahid Usman1, Rehmat Ullah2, Fazal Yazdan Saleem3, Syed Asif Imran Shah1, Muhammad Salman1 1
Pakistan Central Cotton Committee, Cotton Research Station, Dera Ismail Khan 29050, Pakistan Department of Agricultural Extension, Education & Communication, The University of Agriculture, Peshawar 25000, Pakistan 3 Oil Seed Programme, National Agriculture Research Center, Islamabad 44000, Pakistan 2
Abstract Cotton growth and development is influenced by various uncontrollable environmental conditions. Temperature variations in the field can be created by planting at different dates. The objective of the present study was to evaluate the effect of planting dates and thermal temperatures (growing degree days) on yield of 4 cotton genotypes, viz., CIM-598, CIM-599, CIM602 and Ali Akbar-703. Plants were subjected to 6 planting dates during 2013 and 2014 in a trial conducted in randomized complete block design with four replications. For boll number, boll weight and seed cotton yield, cotton genotypes exhibited significant differences, CIM-599 produced the highest seed cotton yield of 2 062 kg ha–1 on account of maximum boll number and boll weight. The highest seed cotton yield was recorded in planting dates from 15th April to 1st May whereas early and delayed planting reduced the yield due to less accumulation of heat units. Regression analysis revealed that increase of one unit (15 days) from early to optimum date (15th March to 15th April) increased yield by 93.58 kg ha–1. Delay in planting also decreased the seed cotton yield with the same ratio. Thus it is concluded that cotton must be sown from 15th April to 1st May to have good productivity in this kind of environment. Keywords: Gossypium hirsutum, planting dates, growing degree days, genotypes
1. Introduction Cotton growth, yield, composition and quality are influenced by various factors such as genotype, environment and agronomic practices. Environmental factors are classified into predictable and unpredictable variables (Allard and Bradshaw 1964). Sowing time is among one of the predictable factors, it is under human control and can be slightly
Received 6 February, 2015 Accepted 6 May, 2015 Correspondence Kalim Ullah, E-mail: [email protected] © 2016, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(15)61088-7
changed as per requirements, therefore it is decleared as predictable factor. Planting time is a major agronomic factor that affect growth and yield (Gecgel et al. 2007). Therefore, determination of optimum sowing time and selection of suitable cultivar for specific growing areas are of utmost importance for high yield and quality cotton. Environmental factors determine the good growing season for a specific crop. It also reveals the adoptive potential of a genotype. Temperature is a major environmental factor which reveals the growth, development and yield. All over the world, an increase in global warming increases heat stress which is a serious threat to crop productivity. Documented rise in global temperature that has been forecasted by several climatic models, have direct effect on plant growth, yield and quality. This type of temperature variation in the field can be created by cultivating the crops at dif-
679
Kalim Ullah et al. Journal of Integrative Agriculture 2016, 15(3): 678–683
ferent sowing times and the crop will thus grow at different temperatures and relative humidities. Cotton genotypes that have wide range of adoptability need different total numbers of cumulative heat units (CHUs) or growing degree days (GDDs) for their growth, development, yield and maturity. The CHUs or GDDs are the most common indexs used to estimate the development of a plant. CHUs accumulation determines the crop maturity along with the fiber quality of cotton crop. In terms of quality and quantity of the product, the genotype-environment interaction is a key factor in the performance assessment of a crop cultivar. All the environmental factors such as temperature, sunshine, humidity and rainfall affect differently the growth and development of a crop. As the rate of plant growth is mainly driven by temperature (Ritche and Ne Smith 1991), the gap between the actual and potential yield needs to be closed via modeling the impact of temperature variation on yield and quality of genotypes. In this context, the objectives of the study were to (i) evaluate the impact of planting dates on seed cotton yield and (ii) modeling the association between heat unit accumulation and yield-related traits of cotton genotypes planted at different times.
future research for several breeding purposes. The G×D was also significant for number of bolls per plant indicating significant G×environment (E) interaction. It is thus depicted that environmental effect in understanding plant growth must be considered due to its importance in cotton breeding programme. These results are in complete analogy with the Machado et al. (2002), who also observed significant genotype×year and genotype×year×location interaction. In different environments, the genotypes performed differently and revealed significant genotype×environment interaction in upland cotton genotypes (Unay et al. 2004; Satish et al. 2009). Similarly, mean squares due to G, E and G×E interaction were highly significant for different morphological traits in upland cotton (Khan et al. 2008; Gul 2013).
2.2. Cotton development and crop phenology
2. Results and discussion
Environmental factors, particularly temperature, are the key components that affect the plant growth and development. Significant differences for GDDs/CHUs in various genotypes depicted that these genotypes have varying maturity periods. However, higher GDDs/CHUs accumulated in the crop that planted on 15th April and 1st May in 2013–2014 reflected that these planting dates are optimum planting dates to have good output.
2.1. Combined analysis of variance
2.3. Number of bolls per plant
The analysis of variance revealed significant differences (P≤0.01) among genotypes (G) and planting dates (D) for the tested parameters (Table 1), indicating the presence of variability among genotypes as well as environments. It further suggests that some of the genotypes were superior to others in these traits. The interaction of G×D for number of bolls per plant was also significant, showing that different genotypes performed differently in different environments (Table 1). Significant mean squares of genotypes also indicate higher degree of genetic variability among the material used in the study. This significant variation was observed in all the studied traits which are valuable sources used in
Bolls per plant is the major independent yield component and plays a major role in managing the variations in seed cotton yield. Hence selection for larger number of bolls per plant must receive emphasis on cotton improvement. Statistical analysis of the data revealed that planting dates significantly influenced number of bolls per plant (Table 2). Number of bolls per plant enhanced with delay in planting date, maximum number of bolls per plant (26.50) was recorded in the crop planted on 15th April, whereas further delay in planting dates reduced the number of bolls per plant and thus minimum bolls per plant was recorded in the crop planted either very early (19.50) on 1st March or delayed planting (17.42) on
Table 1 Combined analysis of variance of studied crop phenology and yield components of 4 cotton genotypes evaluated for 2 years and 6 planting dates Source of variability Years (Y) Planting dates (D) Y×D Genotypes (G) Y×G D×G Y×D×G Error (R×Y×D×G) *
df 1 5 5 3 3 15 15 80
Number of bolls plant–1 324** 249.45** 0.0086 229.06** 0.0013 1.51** 0.0012 0.331
and **, significance at 5 and 1% level of probability respectively using the F test.
Boll weight (g) 0.0386* 1.6550** 0.0510** 0.2710** 0.0184 0.0138 0.0061 0.0112
Seed cotton yield (kg ha–1) 495 030 1 889 354** 101 291** 879 197** 27 275 23 543 16 530 21 459
680
Kalim Ullah et al. Journal of Integrative Agriculture 2016, 15(3): 678–683
1st June. This reduction in early and delayed planting might be attributed to the variation in temperature prevailed by the crop growth. The studied genotypes also beared different number of bolls per plant. The mean comparison showed significant differences at 5% probability level among the 4 cotton genotypes (Table 2), CIM-599 had the highest (24.44) bolls per plant followed by CIM-598 (21.94), while the lowest was recorded in genotype AliAkbar-703 (18.39). The significant G×D interaction across the environments indicated existence of variation for boll setting (Ali et al. 2005) and positive relation with yield in cotton (Makhdoom et al. 2010; Gul 2013).
2.4. Boll weight Analysis of data revealed significant differences for boll weight in different planting dates. Crops planted on 15th April showed maximum boll weight (3.07 g) followed by the crops planted on 1st May (Table 2). Boll weight increased with delay in planting date till 15th April, however further delay reduced the trait which might be attributed to the unfavorable photoperiod and high temperature conditions at early growth and development stage that forced the crop to end up the life cycle rapidly at the cost of reduction in yield and yield components. The genotypes also differed significantly regarding boll weight. Genotype CIM-599 showed the highest boll weight of 2.82 g followed by CIM-598 (2.72 g). The minimum boll weight was recorded in the genotype AliAkbar-703. Seed cotton yield was highly affected directly and indirectly both by boll number and boll weight in cotton (Rauf et al. 2004; Batool et al. 2013; Khan 2013).
2.5. Seed cotton yield Genotypes and planting dates play a vital role in yield and yield-related attributes. The crop planted on 15th April
depicted the highest seed cotton yield (2 228 kg ha–1) on account of increased number of bolls, boll weight and favorable environmental growing conditions compared to other planting dates evaluated in the study. Crop planted on 1st May ranked the second regarding the seed cotton yield (2 028.8 kg ha–1). As shown in Table 2, the seed cotton yield was significantly influenced by planting dates and genotypes. The seed cotton yield increased from 1 738.5 to 2 228 kg ha–1 as planting date delayed from 1st March to 15th April, whereas further delay reduced the seed cotton yield, delayed planting from 15th April to 1st June reduce dit to 1 404.2 kg ha–1, showing 489.5 kg ha–1 increment up to 15th April and then 823.8 kg ha–1 decrement in seed cotton yield respectively (Fig. 1-A). Sowing time is a major agronomic factor, therefore determining optimum sowing time and suitable cultivar for certain region are necessary to obtain higher yield. The yield ability of the present cotton genotypes was the result of various interactions (G×D, G×Y and G×D×Y) factors (Khan et al. 2008). Maximum year to year variation for seed cotton yield was recorded in different growing conditions. Both genotypes and environments contributed to yield variations; however the environmental complex showed primary effect on yield (Best 2005; Ahmad et al. 2006). In the present, the genotypes and environments were predominant for yield variation. Different cotton genotypes showed significant differences regarding seed cotton yield. The highest seed cotton yield of 2 062 kg ha–1 was recorded in CIM-599 followed by CIM598 (Table 2, Fig. 1-B). The lowest seed cotton yield was observed in genotype AliAkbar-703. Cotton genotypes revealed significant differences through different environments and their interaction on seed cotton yield, indicating variability among genotypes as well as environments (Unay et al. 2004; Gul 2013). Regression analysis of planting date (x) and seed cotton
Table 2 Means of 6 planting dates and 4 cotton genotypes for some of the studied traits Factor Planting dates 15th March 1st April 15th April 1st May 15th May 1st June LSD0.05 Genotypes CIM-598 CIM-599 CIM-602 AliAkbar-703 LSD0.05
Number of bolls plant–1
Boll weight (g)
19.50 d 22.00 c 26.50 a 23.16 b 19.67 d 17.42 e 0.33
2.46 e 2.63 d 3.07 a 2.93 b 2.76 c 2.40 e 0.061
1 738.5 d 1 920.9 c 2 228.0 a 2 028.8 b 1 798.4 d 1 404.2 e 84.15
21.94 b 24.44 a 20.72 c 18.39 d 0.259
2.72 b 2.82 a 2.62 c 2.67 bc 0.0801
1 868.7 b 2 062.0 a 1 786.8 b 1 695.1 c 84.065
Means followed by same letters in the same column are not significantly different at 5% level of probability.
Seed cotton yield (kg ha–1)
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Seed cotton yield (kg ha–1)
Seed cotton yield (kg ha–1)
A
2 500 2 000 1 500 1 000 500 0
15th March
Seed cotton yield (kg ha–1)
B
1st April
15th 1st April May Planting date
15th May
2 500 2 000 1 500
1st June
2 500
y=–93.589x2+589.69x+1 217.3 R2=0.9242
1 000 500 0
0
15th 1st March April
15th April
1st May
15th May
1st June
Planting date
Fig. 2 Relationship between planting dates and seed cotton yield.
2 000 1 500 1 000
governs the crop growth rate (Kaleem et al. 2009).
500 0
CIM-598
CIM-599
CIM-602 Ali Akbar-703
Genotypes
Fig. 1 Seed cotton yield under six planting dates (A) and four different cotton genotypes (B).
yield (y) indicated highly significant association between these attributes. Besides, R2 (coefficient of determination) revealed that up to 92% of the increase till 15th April and later on decrease in seed cotton yield might be under the influence of planting date. The regression between seed cotton yield and planting date was negative, regression equation for planting date suggested that increase in one unit (15 days) of planting date increased/decreased seed cotton yield by 93.58 kg ha–1 (Fig. 2). Environmental factors, especially temperature, are the main attributes that affect the plant growth and development. Significant differences among genotypes for GDD were observed, which clearly depicted that various genotypes have varying maturity period. Accumulation of higher GDD for the 15th April and 1st May planting during 2013 and 2014 provided a clue that 15th April to 1st May is the best planting time for cotton crop to have good yield (Table 3). The accumulation of GDD determined the yield and yield-related attributes because temperature is the primary factor which
3. Conclusion The instant results indicated that number of bolls per plant, boll weight and seed cotton yield have been significantly influenced by the genotypes and growing conditions under the different planting dates. Accumulation of GDD played a vital role in modeling cotton to the expected global warming in the coming years. Differences among cotton attributes might be attributed to the temperature-based different climatic conditions prevailed during the crop life cycle. It is therefore concluded that 15th April to 1st May is the optimum date, early/delayed planting results in reduction of seed cotton yield as the crop completes its life cycle in short duration and accumulates less CHUs.
4. Materials and methods 4.1. Experimental location and plant materials The experiment was conducted at Cotton Research Station, D.I.Khan, Pakistan (situated at 31°49´N latitude and 70°55´E longitude) during the crop season in 2013 and 2014. Four genotypes, viz., CIM-598, CIM-599, CIM-602 and AliAkbar-703 were used in this study. These genotypes were sown at different sowing dates so as to create difference
Table 3 Relationship between growing degree days and seed cotton yield (kg ha–1) Planting date 15th March 1st April 15th April 1st May 15th May 1st June LSD0.05
2013 2 224.5 2 420.5 2 639.5 2 564.0 2 516.0 2 408.5
Growing degree days (d) 2014 2 141.7 2 333.7 2 601.2 2 482.2 2 451.7 2 387.7
Mean 2 183.10 2 377.10 2 630.35 2 523.10 2 483.85 2 398.10
Seed cotton yield (kg ha–1) 1 738.5 d 1 920.9 c 2 280.0 a 2 028.8 b 1 798.4 d 1 404.2 e 84.15
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in temperature for growth, development and maturity, thus giving a wide range of temperature from sowing till maturity. Meteorological data were obtained from Arid Zone Research Institute (AZRI), Pakistan Agricultural Research Council (PARC), D.I.Khan, Pakistan. Monthly maximum, minimum and mean temperature along with rainfall during 2013 and 2014 are shown in Fig. 3. The baseline temperature used for growing degree days (GDDs) calculation was 16°C. The cumulative heat units (CHUs) for various stages were calculated using the equation as suggested by Dwyer and Stewart (1986). (Tmax–Tmin) –Tb CHU= 2 Where, Tmax, Tmin and Tb are maximum daily temperature, minimum daily temperature and baseline temperature below which development ceases, respectively.
4.2. Experimental environments The experiment was conducted at 12 environments (2 years×6 sowing dates); details of these are given below (Table 4).
4.3. Experimental layout and crop management The four cotton genotypes (CIM-598, CIM-599, CIM-602, AliAkbar-703) were investigated at six different dates of planting for two consecutive years of 2013 and 2014. These genotypes were planted in randomized complete block (RCB) design with 4 replications. Planting was done in a 4-row plot with 10 m length, the row to row and plant to plant distance was 0.75 and 0.30 m, respectively. The plot size was 40 m2. Seeds were planted in hills and each hill
Minimum
50
Maximum
45
Temperature (°C)
40 35 30 25 20 15 10
November
Octobor
August
July
June
May
April
March
Octobor
November
2014
September
2013
September
August
July
June
May
April
0
March
5
Fig. 3 Maximum and minimum temperature of the years 2013 and 2014. Table 4 Twelve manipulated environments used in the study
received 5 seeds which were irrigated immediately. In these
Environment1) E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12
experiments, 60 kg ha–1 of P2O5 as single super phosphate
1)
Cropping season 2013 2013 2013 2013 2013 2013 2014 2014 2014 2014 2014 2014
Planting date 15th March 1st April 15th April 1st May 15th May 1st June 15th March 1st April 15th April 1st May 15th May 1st June
E1 is Environment 1, E2 is Environment 2 and so on.
and 50 kg ha–1 of nitrogen as urea were applied prior to
sowing, 50 kg ha–1 nitrogen as urea used at the flowering stage and 50 kg ha–1 at boll formation stage. After 3 weeks of planting seedlings were thinned to single plant per hill. Picking was done 130 and 150 days after planting. All the cultural practices were done according to recommendations.
4.4. Recording of observations and statistical analysis Data of number of bolls per plant, boll weight (g) and seed cotton yield (kg ha–1) were recorded. The data recorded
Kalim Ullah et al. Journal of Integrative Agriculture 2016, 15(3): 678–683
were subjected to analysis of variance technique appropriate for RCB design as suggested by Steel et al. (1997). After having homogeneity test for error variances by using Bartlett’s tests (Snedecor and Cochran 1983), combined analysis of variance was performed. To determine the statistical differences in means, the least significant difference (LSD) test at 5% level of probability was used when the F-value was significant. For this purpose, MSTAT-C (Freed et al. 1989) statistical package was utilized. Microsoft Excel 2007 was used for diagrams and regression analysis (Steel et al. 1997).
Acknowledgements Pakistan Central Cotton Committee (PCCC) is highly acknowledged for the financial support of this work.
References Ahmed A E, Abdalla A H, Fadlalla A S. 2006. A note on the stability of five medium staple cotton (Gossypium hirsutum L.) varieties for some fiber properties in the Gezira scheme of the Sudan. University of Khartoum Journal of Agricultural Science, 14, 313–319. Ali Y, Aslam Z, Hussain F. 2005. Genotype and environment interaction effect on yield of cotton under naturally salt stress condition. International Journal of Environmental Science and Technology, 2, 169–173. Allard R W, Bradshaw A D. 1964. Implications of genotypeenvironment interaction in applied plant breeding. Crop Science, 4, 503–507. Batool S, Khan N U, Gul S, Baloch M J, Turi N A, Taran S A, Saeed M. 2013. Genetic analysis for yield and yield contributing variables in upland cotton. Journal of Food, Agriculture and Environment, 11, 624–630. Best E C. 2005. Source-sink relations in cotton: Genetic and environmental affectors. MSc thesis, Texas Tech University, Texas, USA. Dwyer L M, Stewart D W. 1986. Leaf area development in field grown maize. Agronomy Journal, 78, 334–343. Freed R, Einensmith S P, Gutez S, Reicosky D, Smail V W, Wolberg P. 1989. User’s Guide to MSTAT-C Analysis of Agronomic Research Experiments. Michigan State University, East Lansing, USA. Gecgel U, Demirci M, Esendal E. 2007. Fatty acid composition of the oil from developing seeds of different varieties of
683
safflower (Carthamus tinctorius L.). Journal of American Oil & Chemical Society, 84, 47–54. Gul S. 2013. Genotype by environment interaction for morphoyield traits in upland cotton. MSc thesis, The University of Agriculture, Peshawar, Pakistan. Kaleem S, Hassan F U, Saleem A. 2009. Influence of environmental variations on physiological attributes of sunflower. African Journal of Biotechnology, 8, 3531–3539. Khan N G, Naveed M, Khan N I. 2008. Assessment of some novel upland cotton genotypes for yield constancy and malleability. International Journal of Agriculture & Biology, 10, 109–111. Khan N U. 2013. Diallel analysis of cotton leaf curl virus (CLCuV) disease, earliness, yield and fiber traits under CLCuV infestation in upland cotton. Australian Journal of Crop Science, 7, 1955–1966. Machado J R D A, Penna J C V, Fallieri J, Santos P G, Lanza M A. 2002. Stability and adaptability of seed cotton yields of upland cotton genotypes in the state of Minas Gerais, Brazil. Crop Breeding & Applied Biotechnology, 2, 401–410. Makhdoom K, Khan N U, Batool S, Bibi Z, Farhatullah, Khan S, Mohammad F, Hussain D, Raziuddin, Sajjad M, Khan N. 2010. Genetic aptitude and correlation studies in G. hirsutum L. Pakistan Journal of Botany, 42, 2011–2017. Rauf S, Khan T M, Sadaqat H A, Khan A I. 2004. Correlation and path coefficient analysis of yield components in cotton (G. hirsutum L.). International Journal of Agriculture & Biology, 6, 686–688. Ritchie J T, Ne Smith D S. 1991. Temperature and crop development. In: Hanks J, Ritchie J T, eds., Modeling Plant and Soil Systems. American Society of Agronomy, Madison, USA. pp. 5–29. Satish Y, Jain P P, Chhabra B S. 2009. Stability analysis for yield and its component traits in American cotton (G. hirsutum L.). Journal of Cotton Research & Development, 23, 175–182. Snedecor G W, Cochran W G. 1983. Statistical Methods. 6th ed. Oxford and IBH, New Delhi, India. Steel R G D, Torrie J H, Dickey D A. 1997. Principles and Procedures of Statistics: A Biometrical Approach. 3rd ed. McGraw-Hill, New York, USA. Unay A, Basal H, Erkul A, Yuksekkaya Z. 2004. Stability analysis of upland cotton genotypes to the Aegean Region in Turkey. Asian Journal of Plant Science, 3, 36–38. (Managing editor SUN Lu-juan)