Vol.4, No.11 A, 37-54 (2013) Agricultural Sciences
http://dx.doi.org/10.4236/as.2013.411A005
Applied methods for studying the relationship
between climatic factors and cotton production
Zakaria M. Sawan
Cotton Research Institute, Agricultural Research Center, Ministry of Agriculture & Land Reclamation, Giza, Egypt;
Received 2 August 2013; revised 12 September 2013; accepted 1 October 2013
Copyright © 2013 Zakaria M. Sawan. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
This study investigates the statistical relation-
ship between climatic variables and aspects of
cotton production (G. barbadense), and the ef-
fects of climatic factors prevailing prior to flow-
ering or subsequent to boll setting on flower and
boll production and retention in cotton. The ef-
fect s of specific climatic factors durin g both pre-
and post-anthesis periods on boll production
and retention are mostly unknown. However, by
determining the relationship of climatic factors
with flower and boll production and retention,
the overall level of production can be possibly
predicted. Thus, an understanding of these rela-
tionships may help physiologists determine
control mechanisms of production in cotton
plants. Also, the study covers the predicted ef-
fects of climatic factors during convenient in-
tervals (in days) on cotton flower and boll pro-
duction compared with daily observations. Fur-
ther, cotton flower and boll production as af-
fected by climatic factors and soil moisture
status has been considered. Evaporation, sun-
shine duration, relative humidity, surface soil
temperature at 1800 h, and maximum air tem-
perature, are the important climatic factors that
significantly affect flower and boll production.
The least important variables were found to be
surface soil temperature at 600 h and minimum
temperature. The five-day interval was found to
be more adequately and sensibly r elat ed to yield
parameters. Evaporation, minimum humidity
and sunshine duration were the most effective
climatic factors during preceding and succeed-
ing periods on boll production and retention.
There was a negative correlation between flower
number and boll production and either evapora-
tion or sunshine duration, while that correlation
with minimum relative humidity was positive.
The soil moisture status showed low and insig-
nificant correlation with flower and boll produc-
tion. Higher minimum relative humidity, short
period of sunshine duration, and low tempera-
tures enhanced flower and boll formation.
Keywords: Cotton Flower and Boll Production; Boll
Retention; Evaporation; Relative Humidity; Soil
Moisture Status; Sunshine Duration; Temperature
1. INTRODUCTION
Climate affects crop growth interactively, sometimes
resulting in unexpected responses to prevailing condi-
tions. Many factors, such as length of the growing season,
climate (including solar radiation, temperature, light,
wind, rainfall, and dew), cultivar, availability of nutrients
and soil moisture, pests and cultural practices affect cot-
ton growth [1]. The balance between vegetative and re-
productive development can be influenced by soil fertil-
ity, soil moisture, cloudy weather, spacing and perhaps
other factors such as temperature and relative humidity
[2]. Weather, soil, cultivars, and cultural practices affect
crop growth interactively, sometimes resulting in plants
responding in unexpected ways to their conditions [3].
Water is a primary factor controlling plant growth.
Xiao et al. [4] stated that, when water was applied at
0.85, 0.70, 0.55 or 0.40 ET (evapotranspiration) to cotton
plants grown in pots, there was a close relationship be-
tween plant development and water supply. The fruit-
bearing branches, square and boll numbers and boll size
were increased with increased water supply. Barbour and
Farquhar [5] reported on greenhouse pot trials where
cotton cv. CS50 plants were grown at 43% or 76% rela-
tive humidity (RH) and sprayed daily with abscisic acid
(ABA) or distilled water. Plants grown at lower RH had
higher transpiration rates, lower leaf temperatures and
lower stomatal conductance. Plant biomass was also re-

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
38
duced at the lower RH. Within each RH environment,
increasing ABA concentration generally reduced stoma-
tal conductance, evaporation rates, superficial leaf den-
sity and plant biomass, and increased leaf temperature
and specific leaf area.
Temperature is also a primary factor controlling rates
of plant growth and development. Reddy et al. [6] in
growth chamber experiments found that Pima cotton cv.
S-6 produced lower total biomass at 35.5˚C than at
26.9˚C and no bolls were produced at the higher tem-
perature of 40˚C. Schrader et al. [7] stated that high tem-
peratures that plants are likely to experience inhibit pho-
tosynthesis. Zhou et al. [8] indicated that light duration is
the key meteorological factor influencing the wheat-
cotton cropping pattern and position of the bolls, while
temperature had an important function on upper (node 7
to 9) and top (node 10) bolls, especially for double crop-
ping patterns with early maturing varieties. Fisher [9]
found that high temperatures can cause male sterility in
cotton flowers, and could have caused increased boll
shedding in the late fruiting season. Zhao [10] indicated
that temperature was the main climatic factor affecting
cotton production and 20˚C - 30˚C was the optimum
temperature for cotton growth. Reddy et al. [11] found
that when Upland cotton (G. hirsutum) cv. DPL-51 was
grown in naturally lit plant growth chambers at 30/22˚C
day/night temperatures from sowing until flower bud
production, and at 20/12, 25/17, 30/22, 35/27 and
40/32˚C for 42 days after flower bud production, fruit
retention was severely curtailed at the two higher tem-
peratures compared with 30/22˚C. Species/cultivars that
retain fruits at high temperatures would be more produc-
tive both in the present-day cotton production environ-
ments and even more in future warmer world.
The objectives of this investigation: 1) Aim at pre-
dicting effects of climatic factors during different con-
venient intervals (in days) on cotton flower and boll pro-
duction compared with daily observations. The study
presents a rich effort focused on evaluating the efficacy
of regression equations between cotton crop data and
climatic data grouped at different time intervals, to de-
termine the appropriate time scale for aggregating cli-
mate data to be used for predicting flower and boll pro-
duction in cotton [12]. 2) Also, collect information about
the nature of the relationship between various climatic
factors and cotton boll development and the 15-day pe-
riod both prior to and after initiation of individual bolls
of field grown cotton plants in Egypt. This could pave
the way for formulating advanced predictions as for the
effect of certain climatic conditions on production of
Egyptian cotton. It would be useful to minimize the dele-
terious effects of the factors through utilizing proper
cultural practices which would limit and control their
negative effects, and this will lead to an improvement in
cotton yield [13]. 3) Further, provide information on the
effect of various climatic factors and soil moisture status
during the development stage on flower and boll produc-
tion in Egyptian cotton. This could result in formulating
advanced predictions as for the effect of certain climatic
conditions on production of Egyptian cotton. Minimizing
the deleterious effects of the factors through utilizing
proper cultural practices will lead to improved cotton
yield [14].
2. MATERIALS and METHODS
Two uniform field trials were conducted at the experi-
mental farm of the Agricultural Research Center, Minis-
try of Agriculture, Giza, Egypt (30˚N, 31˚: 28'E at an
altitude of 19 m), using the cotton cultivar Giza 75 (Gos-
sypium barbadense L.) in 2 successive seasons (I and II).
The soil texture was a clay loam, with an alluvial sub-
stratum (pH = 8.07, 42.13% clay, 27.35% silt, 22.54%
fine sand, 3.22% coarse sand, 2.94% calcium carbonate
and 1.70% organic matter) [14].
In Egypt, there are no rain-fed areas for cultivating
cotton. Water for the field trials was applied using sur-
face irrigation. Total water consumed during each of two
growing seasons supplied by surface irrigation was about
6000 m3·h−1. The criteria used to determine amount of
water applied to the crop depended on soil water status.
Irrigation was applied when soil water content reached
about 35% of field capacity (0 - 60 cm). In season I, the
field was irrigated on 15 March (at planting), 8 April
(first irrigation), 29 April, 17 May, 31 May, 14 June, 1
July, 16 July, and 12 August. In season II, the field was
irrigated on 23 March (planting date), 20 April (first irri-
gation), 8 May, 22 May, 1 June, 18 June, 3 July, 20 July,
7 August and 28 August. Techniques normally used for
growing cotton in Egypt were followed. Each experi-
mental plot contained 13 to 15 ridges to facilitate proper
surface irrigation. Ridge width was 60 cm and length
was 4 m. Seeds were sown on 15 and 23 March in Sea-
sons I and II, respectively, in hills 20 cm apart on one
side of the ridge. Seedlings were thinned to 2 plants per
hill 6 weeks after planting, resulting in a plant density of
about 166,000 plants ha−1. Phosphorus fertilizer was ap-
plied at a rate of 54 kg P2O5 ha−1 as calcium super phos-
phate during land preparation. Potassium fertilizer was
applied at a rate of 57 kg K2O ha−1 as potassium sulfate
before the first irrigation (as a concentrated band close to
the seed ridge). Nitrogen fertilizer was applied at a rate
of 144 kg N ha−1 as ammonium nitrate in two equal
doses: the first was applied after thinning just before the
second irrigation and the second was applied before the
third irrigation. Rates of phosphorus, potassium, and ni-
trogen fertilizer were the same in both seasons. These
amounts were determined based on the use of soil tests
[14].

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 39
After thinning, 261 and 358 plants were randomly se-
lected (precaution of border effect was taken into con-
sideration by discarding the cotton plants in the first and
last two hills of each ridge) from 9 and 11 inner ridges of
the plot in seasons I and II respectively. Pest control
management was carried out on an-as-needed basis, ac-
cording to the local practices performed at the experi-
mental. Flowers on all selected plants were tagged in
order to count and record the number of open flowers,
and set bolls on a daily basis. The flowering season
commenced on the date of the first flower appearance
and continued until the end of flowering season (31 Au-
gust). The period of whole September (30 days) until the
20th of October (harvest date) allowed a minimum of 50
days to develop mature bolls. In season I, the flowering
period extended from 17 June to 31 August, whereas in
season II, the flowering period was from 21 June to 31
August. Flowers produced after 31 August were not ex-
pected to form sound harvestable bolls, and therefore
were not taken into account [14].
For statistical analysis, the following data of the de-
pendent variables were collected: number of tagged
flowers separately counted each day on all selected
plants (Y1), number of retained bolls obtained from the
total daily tagged flowers on all selected plants at harvest
(Y2), and (Y3) percentage of boll retention ([number of
retained bolls obtained from the total number of daily
tagged flowers in all selected plants at harvest]/[daily
number of tagged flowers on each day in all selected
plants] × 100). As a rule, observations were recorded
when the number of flowers on a given day was at least 5
flowers found in a population of 100 plants and this con-
tinued for at least five consecutive days. This rule omit-
ted eight observations in the first season and ten observa-
tions in the second season. The number of observations
(n) was 68 (23 June through 29 August) and 62 (29 June
through 29 August) for the two seasons, respectively.
Variables of the soil moisture status considered were, the
day prior to irrigation, the day of irrigation, and the first
and second days after the day of irrigation [14].
The climatic factors (independent variables) consid-
ered were daily data of: maximum air temperature (˚C,
X1); minimum air temperature (˚C, X2); maximum-
minimum air temperature (diurnal temperature range)
(˚C, X3); evaporation (expressed as Piche evaporation)
(mm·day−1, X4); surface soil temperature, grass tempera-
ture or green cover temperature at 600 h (˚C, X5) and
1800 h (˚C, X6); sunshine duration (h·day−1, X7); maxi-
mum relative humidity (max RH) (%, X8), minimum
relative humidity (min RH) (%, X9) and wind speed
(m·s−1, X10) in season II only. The source of the climatic
data was the Agricultural Meteorological Station of the
Agricultural Research Station, Agricultural Research
Center, Giza, Egypt. No rainfall occurred during the two
growing seasons [13].
Daily records of the climatic factors (independent
variables), were taken for each day during production
stage in any season including two additional periods of
15 days preceding and after the production stage. Range
and mean values of the climatic parameters recorded
during the production stage for both seasons and overall
data are listed in Ta b l e 1 . Daily number of flowers and
number of bolls per plant which survived till maturity
(dependent variables) during the production stage in the
two seasons are graphically illustrated in Figures 1 and 2
[14].
3. RESULTS AND DISCUSSION
3.1. Appropriate Time Scale for Aggregating
Climatic Data to Predict Flowering and
Boll Setting of Cotton
Statistical analysis was conducted using the proce-
dures outlined in the general linear model (GLM) [15].
Data of dependent and independent variables, collected
for each day of the production stage (60 days in each
season), were summed up into intervals of 2, 3, 4, 5, 6 or
10 days. Data from these intervals were used to compute
relationships between the dependent variables (flower
and boll setting and boll retention) and the independent
variables (climatic factors) in the form of simple correla-
tion coefficients for each season. Comparisons between
the values of “r” were done to determine the best interval
of days for determining effective relationships. The α-
level for significance was P < 0.15. The climatic factors
attaining a probability level of significance not exceeding
0.15 were deemed important (affecting the dependent
variables), selected and combined with dependent vari-
able in multiple regression analysis to obtain a conven-
ient predictive equation [16]. Multiple linear regression
equations (using stepwise method) comprising selected
predictive variables were computed for the determined
interval and coefficients of multiple determinations (R²)
were calculated to measure the efficiency of the regres-
sion models in explaining the variation in data [12].
Correlation and regression analyses were computed ac-
cording to Draper and Smith [17].
3.1.1. Correlation Estimates
Significant simple correlation coefficients were esti-
mated between the production variables and studied cli-
matic factors for different intervals of days (combined
data of the 2 seasons) (Table 2) [12].
Evaporation was the most important climatic factor
affecting flower and boll production in Egyptian cotton.
The negative correlation means that high evaporation
ratio significantly reduced flower and boll production.
igh evaporation rates could result in water stress that H

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
40
Table 1. Range and mean values of the independent variables for the two seasons and over all data.
First season* Second season** Over all data (Two seasons)
Climatic factor’s
Range Mean Range Mean Range Mean
Max Temp (˚C), (X1)
Min Temp (˚C), (X2)
Max-Min Temp (˚C), (X3)♦
Evap (mm·d−1), (X4)
600 h Temp (˚C), (X5)
1800 h Temp (˚C), (X6)
Sunshine (h·d−1), (X7)
Max RH (%), (X8)
Min RH (%), (X9)
Wind speed (m·s−1), (X10)
31.0 - 44.0
18.6 - 24.5
9.4 - 20.9
7.6 - 15.2
14.0 - 21.5
19.6 - 27.0
10.3 - 12.9
62 - 96
11 - 45
ND
34.3
21.9
12.4
10.0
17.8
24.0
11.7
85.4
30.8
ND
30.6 - 38.8
18.4 - 23.9
8.5 - 17.6
4.1 - 9.8
13.3 - 22.4
20.6 - 27.4
9.7 - 13.0
51 - 84
23 - 52
2.2 - 7.8
34.1
21.8
12.2
6.0
18.0
24.2
11.9
73.2
39.8
4.6
30.6 - 44.0
18.4 - 24.5
8.5 - 20.9
4.1 - 15.2
13.3 - 22.4
19.6 - 27.4
9.7 - 13.0
51 - 96
11 - 52
ND
34.2
21.8
12.3
8.0
17.9
24.1
11.8
79.6
35.1
ND
♦Diurnal temperature range. ND not determined. *Flower and boll stage (68 days, from 23 June through 29 August). **Flower and boll stage (62 days, from 29
June through 29 August). Sawan et al. [12].
Figure 1. Daily number of flowers and bolls during the pro-
duction stage (68 days) in the first season (I) for the Egyptian
cotton cultivar Giza 75 (Gossypium barbadense L.) grown in
uniform field trial at the experimental farm of the Agricultural
Research Centre, Giza (30˚N, 31˚: 28'E), Egypt. The soil tex-
ture was a clay loam, with an alluvial substratum, (pH = 8.07).
Total water consumptive use during the growing season sup-
plied by surface irrigation was about 6000 m3·ha−1. No rainfall
occurred during the growing season. The sampling size was
261 plants [14].
Figure 2. Daily number of flowers and bolls during the pro-
duction stage (62 days) in the second season (II) for the Egyp-
tian cotton cultivar Giza 75 (Gossypium barbadense L.) grown
in uniform field trial at the experimental farm of the Agricul-
tural Research Centre, Giza (30˚N, 31˚: 28'E), Egypt. The soil
texture was a clay loam, with an alluvial substratum, (pH =
8.07). Total water consumptive use during the growing season
supplied by surface irrigation was about 6000 m3·ha−1. No
rainfall occurred during the growing season. The sampling
size was 358 plants [14].
would slow growth and increase shedding rate of flowers
and bolls [12]. Kaur and Singh [18] found in cotton that
flower number was decreased by water stress, particu-
larly when existing at flowering stage. Seed cotton yield
was decreased by about 50% when water stress was pre-
sent at flowering stage, slightly decreased by stress at
boll formation stage, and not significantly affected by
stress in the vegetative stage (6 - 7 weeks after sowing).
The second most important climatic factor was minimum
humidity, which had a high positive correlation with
flower and boll production, and retention ratio. The posi-
tive correlation means that increased humidity would
bring about better boll production. The third most im-
portant climatic factor in our study was sunshine dura-
tion, which showed a significant negative relationship
with flower and boll production only [12]. The negative
relationship between sunshine duration and cotton pro-
duction may be due to the fact that the species of the
genus Gossypium are known to be short day plants [19],
so, an increase of sunshine duration above that sufficient
to attain good plant growth will decrease flower and boll
production. Bhatt [20] found that exposure to daylight
over 14 hours and high day temperature, individually or
in combination, delayed flowering of the Upland cotton
cv. J34. Although average sunshine duration in our study
was only 11.7 h, yet it could reach 13 h, which, in com-
bination with high maximum temperatures (up to 38.8˚C),
may have adversely affected reproductive growth.
Maximum air temperature, temperature magnitude
and surface soil temperature at 1800 h show significant
negative relationships with flower and boll production
only. Meanwhile, the least important factors were surface
soil temperature at 600 h and minimum air temperature
[12].

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 41
Ta b le 2. Significant simple correlation coefficient values between the production variables and the studied climatic factors for the
daily and different intervals of days combined over both seasons.
Climatic factorsz
Air temp (˚C) Surface soil temp (˚C)Relative humidity (%)
Daily and
intervals
of days
Production
variables
Max (X1) Min (X2) Max-Min (X3)
Evap
(mm·d−1)
(X4) 600 h (X5)1800 h (X6)
Sunshine
duration
(h·d−1) (X7) Max (X8)Min (X
9)
Daily (n = 120)
Flower
Boll
Boll ret. rat.
−0.15++
NS
NS
NS
NS
NS
−0.26**
−0.25**
NS
−0.33**
−0.43**
−0.56**
NS
NS
NS
−0.20*
−0.19++
NS
−0.23*
−0.18++
NS
NS
NS
NS
0.30**
0.36**
0.34**
2 Days (n# = 60)
Flower
Boll
Boll ret. rat.
−0.31++
−0.29++
NS
NS
NS
NS
−0.32*
−0.30++
NS
−0.36**
−0.46**
−0.61**
NS
NS
NS
−0.24+
−0.21+
NS
−0.36**
−0.31*
NS
NS
NS
NS
0.37**
0.44**
0.40**
3 Days (n# = 40)
Flower
Boll
Boll ret. rat.
−0.34*
−0.32*
NS
NS
NS
NS
−0.34*
−0.32*
NS
−0.33*
−0.48**
−0.63**
NS
NS
NS
−0.28++
−0.24+
NS
−0.39*
−0.36*
NS
NS
NS
NS
0.34*
0.45**
0.40*
4 Days (n# = 30)
Flower
Boll
Boll ret. rat.
−0.31++
−0.31++
NS
NS
NS
NS
−0.35++
−0.33++
NS
−0.33++
−0.48**
−0.64**
NS
NS
NS
−0.28+
−0.23+
NS
−0.39*
−0.38*
NS
NS
NS
NS
0.34++
0.45*
0.42*
5 Days (n# = 24)
Flower
Boll
Boll ret. rat.
−0.35++
−0.33+
NS
NS
NS
NS
−0.37++
−0.35++
NS
−0.39++
−0.49*
−0.66**
NS
NS
NS
−0.39++
−0.35++
NS
−0.52**
−0.44*
NS
NS
NS
NS
0.41*
0.47**
0.43*
6 Days (n# = 20)
Flower
Boll
Boll ret. rat.
−0.37++
−0.37++
NS
NS
NS
NS
−0.41++
−0.40++
NS
−0.38++
−0.49*
−0.69**
NS
NS
NS
NS
NS
NS
−0.54**
−0.46*
NS
NS
NS
NS
0.42*
0.49*
0.45*
10 Days (n# = 12)
Flower
Boll
Boll ret. rat.
NS
NS
NS
NS
NS
NS
−0.45++
−0.43++
NS
−0.40+
−0.51++
−0.74**
NS
NS
NS
−0.55*
−0.53++
NS
−0.65*
−0.57*
NS
NS
NS
NS
0.43++
0.51++
0.55*
zWind speed did not show significant effect upon the studied production variables, so is not reported. **Significant at 1% probability level, *Significant at 5%
probability level. ++Significant at 10% probability level, +Significant at 15% probability level. NS: Means simple correlation coefficient is not significant at the
15% probability level. #n = Number of data pairs used in calculation. Sawan et al. [12].
Our results indicate that evaporation was the most ef-
fective climatic factor affecting cotton boll production.
As the sign of the relationship was negative, this means
that an increase in evaporation caused a significant re-
duction in boll number [12]. Thus, applying specific
treatments, such as an additional irrigation or the use of
plant growth regulators (PGR) that would decrease the
deleterious effect of evaporation after boll formation,
could contribute to an increase in cotton boll production
and retention, and consequently an increase in cotton
yield. In this connection, Meek et al. [21] in a field ex-
periment in Arkansas found that application of 3 or 6 kg
glycine betaine (PGR) ha−1 to cotton plants under mild
water stress increased yield.
Comparing results for the different intervals of days
with those from daily observation (Table 2), the 5-day
interval appeared to be the most suitable interval, which
actually revealed a more solid and more obvious rela-
tionships between climatic factors and production char-
acters. This was in fact indicated by the higher R2 values
obtained when using the 5-day intervals. The 5-day in-
terval may be the most suitable interval for diminishing
the daily fluctuations between the factors under study to
clear these relations comparing with the other intervals.
However, it seems that this conception is true provided
that the fluctuations in climatic conditions are limited or
minimal. Therefore, it would be the most efficient inter-
val used to help circumvent the unfavorable effect of
climatic factors. This finding gives researchers and pro-
ducers a chance to deal with condensed rather than daily
weather data [12].
3.1.2. Regression Models
Multiple linear regression equations were estimated
using the stepwise multiple regression technique to ex-
press the relation between cotton production variables
[number of flowers (Y1); bolls per plant (Y2); and boll
retention ratio (Y3)] and the studied climatic factors (Ta-
ble 3) [12].
Evaporation and surface soil temperature at 1800 h,
sunshine duration and minimum humidity accounted for
a highly significant amount of variation (P < 0.05) in
cotton production variables, with the equation obtained
for the 5-day interval showing a high degree of certainty.

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
42
Table 3. The equations obtained for each of the studied cotton
production variables for the five-day intervals and daily inter-
vals combined over both seasons.
Equationz R2 Significance
Five-day intervals
Y1 = 23.78 – 0.5362X4 – 0.1429X6
– 0.1654X7 + 0.0613X9 0.6237 **
Y2 = 15.89 – 0.4762X4 – 0.1583X6
– 0.1141X7 + 0.0634X9 0.5945 **
Y3 = 72.65 – 0.0833X4 – 0.1647X6 + 0.2278X9 0.6126 **
Daily intervals
Y1 = 19.78 – 0.181X3 – 0.069X4 – 0.164X6
– 0.182X7 + 0.010X9 0.4117 **
Y2 = 14.96 – 0.173X3 – 0.075X4 – 0.176X6
– 0.129X7 + 0.098X9 0.4461 **
Y3 = 52.36 – 3.601X4 – 0.2352X7 + 4.511X9 0.3587 **
zWhere Y1 = number of flowers per plant, Y2 = number of bolls per plant, Y3
= boll retention ratio, X3 = maximum – minimum temperature ˚C, X4 =
evaporation mm·day−1, X6 = surface soil temperature ˚C at 1800 h, X7 =
sunshine duration h·day−1, and X9 = minimum relative humidity %. Sawan
et al. [12].
The R2 values for the 5-day interval were higher than
those obtained from daily data for each of the cotton
production variables. Also, the 5-day interval gave more
efficient and stable estimates than the other studied in-
tervals (data not shown). The R
2 values for these equa-
tions clearly indicate the importance of such equations
since the climatic factors involved explained about 59 to
62% of the variation found in the dependent variables
[12].
During the production stage, an accurate weather
forecast for the next 10 days would provide an opportu-
nity to avoid any adverse effect for weather factors on
cotton production through applying appropriate cultural
practices such as adequate irrigation regime or utilization
of plant growth regulators. This proposal would be true if
the fluctuations in weather conditions were not extreme.
Our recommendation would be the accumulation 5-day
climatic data, and use this information to select the ade-
quate cultural practices (such as an additional irrigation
or utilization of plant growth regulators) that would help
circumvent the unfavorable effects of climatic factors. In
case of sharp fluctuations in climatic factors, data could
be collected daily, and when stability of climatic condi-
tions is restored, the 5-day accumulation of weather data
could be used again [12].
3.2. Response of Flower and Boll
Development to Climate Factors
before and after Anthesis Day
The effects of specific climatic factors during both
pre- and post-anthesis periods on boll production and
retention are mostly unknown. However, by determining
the relationship of climatic factors with flower and boll
production and retention, the overall level of production
can be possibly predicted. Thus, an understanding of
these relationships may help physiologists to determine
control mechanisms of production in cotton plants [13].
Daily records of the climatic factors (independent vari-
ables) were taken for each day during production stage in
any season including two additional periods of 15 days
before and after the production stage (Ta b le 4). In each
season, the data of the dependent and independent vari-
ables (68 and 62 days) were regarded as the original file
(a file which contains the daily recorded data for any
variable during a specific period). Fifteen other files be-
fore and another 15 after the production stage were ob-
tained by fixing the dependent variable data, while mov-
ing the independent variable data at steps each of 1 day
(either before or after production stage) in a matter simi-
lar to a sliding role [13]. The following is an example (in
the first season):
Data of any
dependent variable
(for each flowers
and bolls)
Any independent variable
(for each climatic factors)
Production stage
In case of original
file and files before
production stage
In case of original
file and files after
production stage
File
Date DaysDate Days Date Days
Original
file
23 Jun-
29 Aug 68 23 Jun- 29 Aug 68 23 Jun- 29 Aug 68
1st new
file
23 Jun-
29 Aug 68 22 Jun- 28 Aug 68 24 Jun- 30 Aug 68
2nd new
file
23 Jun-
29 Aug 68 21 Jun- 27 Aug 68 25 Jun- 31 Aug 68
15th new
file
23 Jun-
29 Aug 68 8 Jun- 14 Aug 68 8 Jul- 13 Sept 68
Thus, the climate data were organized into records
according to the complete production stage (68 days the
first year and 62 days the second year) and 15 days, 14
days, 13 days… and 1 day periods both before and after
the production stage. This produced 31 climate periods
per year that were analyzed for their relationships with
cotton flowering and boll production [13].
Simple correlation coefficients were computed be-
tween the original dependent variable (boll setting and
boll retention) and the independent variables for each of
the original file and the 15 new files just before or after
flowering in each season. The significance of the simple
correlation at a probability level not exceeding 5% was
ested to determine the factors affecting the dependent t

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 43
Table 4. Mean, standard deviation, maximum and minimum values of the climatic factors during the flower and boll stage (initial
time) and the 15 days prior to flowering or subsequent to boll setting for I and II season at Giza, Egypt.
First season* Second season**
Climatic factors
Mean S.Da Maxb Minc Mean S.D. Max Min
Max temp [˚C] (X1)
Min temp [˚C] (X2)
Max-Min temp [˚C] (X3)♦
Evapor [mm·d−1] (X4)
600 h temp [˚C] (X5)
1800 h temp [˚C] (X6)
Sunshine [h·d−1] (X7)
Max hum [%] (X8)
Min hum [%] (X9)
Wind speed [m·s−1] (X10)
34.1
21.5
12.6
10.6
17.5
24.2
11.7
85.6
30.2
ND
1.2
1.0
1.1
1.6
1.1
1.9
0.8
3.3
5.2
ND
44.0
24.5
20.9
16.4
21.5
32.3
12.9
96.0
45.0
ND
31.0
18.6
9.4
7.6
13.9
19.6
9.9
62.0
11.0
ND
33.8
21.4
12.4
6.0
17.6
23.7
11.7
72.9
39.1
4.6
1.2
0.9
1.3
0.7
1.2
1.1
0.4
3.8
5.0
0.9
38.8
24.3
17.6
9.8
22.4
27.4
13.0
84.0
52.0
7.8
30.6
18.4
8.5
4.1
13.3
20.6
10.3
51.0
23.0
2.2
*Flower and boll stage (68 days, from 23 June through 29 August). **Flower and boll stage (62 days, from 29 June through 29 August). ♦Diurnal temperature
range. aStandard deviation. bMaxmum. cMinimum. ND not determined. Sawan et al. [13].
variables. The relationship between the most effective
and consistent climatic factors affecting flower and boll
production and retention was computed using the step-
wise regression analysis method. Linear regression equa-
tions comprising selected predictive variables were com-
puted and coefficients of determination (r2 for simple or
R2 for multiple linear regression equations) were calcu-
lated to measure the efficiency of the regression models
in explaining the variation in the data. The statistical
analysis was carried out according to Draper and Smith
[17], by means of the computer program SAS package
using the procedures outlined in the general linear model
(GLM) [15].
3.2.1. Correlation Estimates
1) Results of the correlation between climatic factors
and each of flower and boll production during the 15
days periods before flowering day (Tables 5 and 6) re-
vealed the following [13]:
First Season
Daily evaporation and sunshine duration showed con-
sistent negative and statistically significant correlations
with both flower and boll production for each of the 15
moving window periods before anthesis (Ta b l e 5 ). Eva-
poration appeared to be the most important climate factor
affecting flower and boll production. Daily maximum
and minimum humidity showed consistent positive and
statistically significant correlations with both flower and
boll production in most of the 15 moving window peri-
ods before anthesis (Table 5). Maximum daily tempe-
rature showed low but significant negative correlation
with flower production during the 2 - 5, 8, and 10 days
periods before anthesis. Minimum daily temperatures
generally showed insignificant correlation with both pro-
duction variables. The diurnal temperature range showed
few correlations with flower and boll production. Daily
soil surface temperature at 600 h showed a significant
positive correlation with boll production during the pe-
riod extending from the 11 - 15 days period before anthe-
sis, while its effect on flowering was confined only to the
12 and the 15 days periods prior anthesis. Daily soil sur-
face temperature at 1800 h showed a significant negative
correlation with flower production during the 2 - 10 days
periods before anthesis [13].
Second Season
Daily Evaporation, the diurnal temperature range, and
sunshine duration were negatively and significantly cor-
related with both flower and boll production in all the 15
days periods, while maximum daily temperature was
negatively and significantly related to flower and boll
formation during the 2 - 5 days periods before anthesis
(Table 6) [13].
Minimum daily temperature showed positive and sta-
tistically significant correlations with both production
variables only during the 9 - 15 days periods before an-
thesis, while daily minimum humidity showed the same
correlation trend in all the 15 moving window periods
before anthesis. Daily soil surface temperature at 600 h
was positively and significantly correlated with flower
and boll production for the 12, 14, and 15 days periods
prior to anthesis only. Daily soil surface temperature at
1800 h showed negative and significant correlations with
both production variables only during the first and sec-
ond days periods before flowering. Daily maximum hu-
midity showed insignificant correlation with both flower
and boll production except for one day period only (the
15th day). Generally, the results in the two seasons indi-
cated that daily evaporation, sunshine duration and mini-
mum humidity were the most effective and consistent
climatic factors, which exhibited significant relationships
with the production variables for all the 15 days periods
before anthesis in both seasons [13].
The factors in this study which had been found to be
associated with boll development are the climatic factors
that would influence water loss between plant and at-
osphere (low evaporation demand, high humidity, and m

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
44
Table 5. Simple correlation coefficients (r) between climatic factors and number of flower and harvested bolls in initial time (0) and
each of the 15-day periods before flowering in the first season (I).
Air temp (˚C) Surface soil temp (˚C)Humidity (%)
Maxa Minb Max-Min♦
Evap
(mm·d−1)600 h 1800 h
Sunshine
duration
(h·d−1) Max Min
Climate period
(X1) (X2) (X3) (X4) (X5) (X6) (X7) (X8) (X9)
0# Flower
Boll
−0.07
−0.03
−0.06
−0.07
−0.03
−0.01
−0.56**
−0.53**
−0.01
−0.06
−0.20
−0.16
−0.25*
−0.14
0.40**
0.37**
0.14
0.10
1 Flower
Boll
−0.15
−0.07
−0.08
−0.08
−0.11
−0.02
−0.64**
−0.58**
−0.01
−0.06
−0.17
−0.10
−0.30*
−0.23*
0.39**
0.36**
0.20
0.13
2 Flower
Boll
−0.26*
−0.18
−0.10
−0.08
−0.22
−0.14
−0.69**
−0.64**
−0.07
−0.05
−0.30*
−0.21
−0.35**
−0.25*
0.42**
0.40**
0.30*
0.20
3 Flower
Boll
−0.28*
−0.19
−0.02
−0.02
−0.31**
−0.21
−0.72**
−0.65**
0.15
0.11
−0.29*
−0.20
−0.37**
−0.30*
0.46**
0.37**
0.35**
0.25*
4 Flower
Boll
−0.26*
−0.21
−0.03
−0.04
−0.26*
−0.21
−0.67**
−0.63**
0.08
0.04
−0.24*
−0.18
−0.41**
−0.35**
0.46**
0.39**
0.35**
0.29*
5 Flower
Boll
−0.27*
−0.22
−0.02
0.00
−0.27*
−0.24*
−0.68**
−0.63**
0.16
0.16
−0.29*
−0.21
−0.45**
−0.39**
0.49**
0.44**
0.38**
0.32**
6 Flower
Boll
−0.21
−0.15
0.05
0.08
−0.25*
−0.21
−0.73**
−0.67**
0.16
0.19
−0.28*
−0.19
−0.46**
−0.46**
0.47**
0.43**
0.42**
0.35**
7 Flower
Boll
−0.17
−0.11
−0.01
−0.06
−0.17
−0.15
−0.69**
−0.64**
0.10
0.14
−0.27*
−0.19
−0.43**
−0.46**
0.46**
0.43**
0.35**
0.32**
8 Flower
Boll
−0.24*
−0.14
−0.03
0.04
−0.24*
−0.17
−0.71**
−0.63**
0.09
0.16
−0.30*
−0.17
−0.44**
−0.48**
0.45**
0.44**
0.45**
0.39**
9 Flower
Boll
−0.23
−0.14
−0.10
0.04
−0.19
−0.17
−0.68**
−0.61**
0.05
0.15
−0.33**
−0.21
−0.32**
−0.40**
0.43**
0.42**
0.44**
0.41**
10 Flower
Boll
−0.26*
−0.14
0.05
0.13
−0.30*
−0.22
−0.67**
−0.58**
0.13
0.22
−0.29*
−0.17
−0.29*
−0.36**
0.40**
0.46**
0.48**
0.41**
11 Flower
Boll
−0.20
−0.04
0.10
0.22
−0.27*
−0.16
−0.62**
−0.53**
0.21
0.27*
−0.19
−0.04
−0.29*
−0.38**
0.42**
0.45**
0.44**
0.36**
12 Flower
Boll
−0.17
0.00
0.16
0.25*
−0.26*
−0.13
−0.62**
−0.51**
0.29*
0.35**
−0.15
−0.04
−0.40**
−0.45**
0.44**
0.40**
0.45**
0.30*
13 Flower
Boll
−0.13
0.00
0.16
0.22
−0.22
−0.11
−0.62**
−0.51**
0.23
0.30*
−0.12
−0.03
−0.42**
−0.49**
0.43**
0.41**
0.45**
0.33**
14 Flower
Boll
−0.08
0.01
0.18
0.21
−0.18
−0.10
−0.56**
−0.47**
0.21
0.26*
−0.15
−0.09
−0.44**
−0.49**
0.41**
0.42**
0.46**
0.33**
15 Flower
Boll
−0.08
−0.03
0.22
0.19
−0.21
−0.13
−0.51**
−0.45**
0.24*
0.24*
−0.22
−0.17
−0.42**
−0.44**
0.39**
0.43**
0.38**
0.30*
*Significant at 5% level and **significant at 1% level. #0 = Initial time. ♦Diurnal temperature range. aMaxmum. bMinimum. Sawan et al. [13].
shorter solar duration). This can lead to direct effects on
the fruiting forms themselves and inhibitory effects on
mid-afternoon photosynthetic rates even under well-wa-
tered conditions. Human et al. [22] stated that, when sun-
flower plants were grown under controlled temperature
regimes, water stress during budding, anthesis and seed
filling, the CO2 uptake rate per unit leaf area as well as
total uptake rate per plant, significantly diminished with
stress, while this effect resulted in a significant decrease
in yield per plant.
2) The correlation between climatic factors and each
of boll production and boll retention over a period of 15
days periods after flowering (boll setting) day (Ta b le s 7
and 8) [13] revealed the following:
First Season
Daily evaporation showed significant negative corre-
lation with number of bolls for all the 15 days periods
after flowering (Table 7). Meanwhile its relationship
with retention ratio was positive and significant in the 9 -
5 days periods after flowering. Daily sunshine duration 1

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 45
Table 6. Simple correlation coefficients (r) between climatic factorsz and number of flower and harvested bolls in initial time (0) and
each of the 15-day periods before flowering in the second season (II).
Air temp (˚C) Surface soil temp (˚C)Humidity (%)
Max Min Max-Min♦
Evap
(mm·d−1)600 h 1800 h
Sunshine
duration
(h·d−1) Max Min
Climate period
(X1) (X2) (X3) (X4) (X5) (X6) (X7) (X8) (X9)
0# Flower
Boll
−0.42**
−0.42**
0.00
0.02
−0.36**
−0.37**
−0.61**
−0.59**
−0.14
−0.13
−0.37**
−0.36**
−0.37**
−0.36**
0.01
0.01
0.45**
0.46**
1 Flower
Boll
−0.42**
−0.41**
0.10
0.11
−0.42**
−0.42**
−0.63**
−0.62**
−0.08
−0.07
−0.29*
−0.28*
−0.41**
−0.41**
0.05
0.05
0.48**
0.47**
2 Flower
Boll
−0.40**
−0.40**
0.08
0.08
−0.43**
−0.43**
−0.65**
−0.64**
−0.09
−0.08
−0.27*
−0.26*
−0.39**
−0.40**
0.02
0.03
0.49**
0.49**
3 Flower
Boll
−0.38**
−0.37**
0.13
0.15
−0.43**
−0.44**
−0.61**
−0.61**
−0.06
−0.05
−0.17
−0.15
−0.38**
−0.38**
0.00
0.01
0.45**
0.46**
4 Flower
Boll
−0.36**
−0.35**
0.17
0.18
−0.41**
−0.41**
−0.61**
−0.60**
−0.04
−0.03
−0.18
−0.16
−0.38**
−0.36**
0.02
0.03
0.45**
0.44**
5 Flower
Boll
−0.30*
−0.28*
0.13
0.15
−0.36**
−0.35**
−0.60**
−0.58**
−0.07
−0.05
−0.23
−0.21
−0.32**
−0.31**
−0.05
−0.05
0.43**
0.41**
6 Flower
Boll
−0.24
−0.22
0.21
0.24
−0.38**
−0.38**
−0.61**
−0.59**
−0.02
0.00
−0.12
−0.07
−0.28*
−0.29*
0.02
0.02
0.40**
0.40**
7 Flower
Boll
−0.19
−0.18
0.23
0.23
−0.29*
−0.27*
−0.54**
−0.53**
−0.03
−0.02
−0.05
−0.03
−0.26*
−0.27*
−0.04
−0.04
0.32**
0.30*
8 Flower
Boll
−0.15
−0.14
0.24
0.22
−0.25*
−0.22
−0.52**
−0.51**
−0.03
−0.03
−0.07
−0.06
−0.24*
−0.22*
−0.05
−0.05
0.28*
0.26*
9 Flower
Boll
−0.16
−0.14
0.34**
0.34**
−0.32**
−0.31**
−0.56**
−0.56**
0.08
0.09
−0.02
−0.01
−0.25*
−0.23*
0.05
0.07
0.30*
0.29*
10 Flower
Boll
−0.16
−0.14
0.31**
0.28*
−0.30*
−0.27*
−0.56**
−0.55**
0.11
0.09
−0.06
−0.07
−0.27*
−0.25*
0.11
0.09
0.33**
0.31**
11 Flower
Boll
−0.16
−0.15
0.31**
0.29*
−0.27*
−0.26*
−0.55**
−0.53**
0.10
0.10
−0.02
0.00
−0.31**
−0.29*
0.08
0.08
0.32**
0.29*
12 Flower
Boll
−0.17
−0.17
0.44**
0.42**
−0.37**
−0.36**
−0.57**
−0.55**
0.26*
0.25*
0.02
0.01
−0.36**
−0.34**
0.17
0.16
0.34**
0.32**
13 Flower
Boll
−0.14
−0.15
0.40**
0.38**
−0.33**
−0.34**
−0.56**
−0.56**
0.21
0.21
0.03
0.01
−0.28*
−0.27*
0.10
0.09
0.34**
0.33**
14 Flower
Boll
−0.19
−0.20
0.39**
0.39**
−0.38**
−0.40**
−0.59**
−0.59**
0.25*
0.26*
0.04
0.03
−0.34**
−0.36**
0.16
0.17
0.35**
0.36**
15 Flower
Boll
−0.24
−0.24
0.49**
0.51**
−0.45**
−0.48**
−0.62**
−0.63**
0.37**
0.40**
0.16
0.15
−0.38**
−0.40**
0.27*
0.26*
0.42**
0.43**
*Significant at 5% level and **significant at 1% level. #0 = Initial time. ♦Diurnal temperature range. zWind speed did not show significant effect upon the studied
production variables, so it is not reported. Sawan et al. [13].
was positively and significantly correlated with boll re-
tention ratio during the 5 - 13 days periods after flower-
ing. Daily maximum humidity had a significant positive
correlation with the number of bolls during the first 8
days periods after flowering, while daily minimum hu-
midity had the same correlation for only the 11, and 12
days periods after flowering. Daily maximum and mini-
mum temperatures and the diurnal temperature range, as
well as soil surface temperature at 1800 did not show
significant relationships with both number of bolls and
retention ratio. Daily soil surface temperature at 600 h
had a significant negative correlation with boll retention
ratio during the 3 - 7 days periods after anthesis [13].
Second Season
Daily evaporation, soil surface temperature at 1800 h,
and sunshine duration had a significant negative correla-
tion with number of bolls in all the 15 days periods after
nthesis (Table 8). Daily maximum and minimum tem- a

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
46
Table 7. Simple correlation coefficient (r) values between climatic factors and number of harvested bolls and retention ratio in initial
time (0) and each of the 15-day periods after flowering in the first season (I).
Air temp (˚C) Surface soil temp (˚C)Humidity (%)
Max Min Max-Min♦
Evap
(mm·d−1)600 h 1800 h
Sunshine
duration
(h·d−1) Max Min
Climate
period
(X1) (X2) (X3) (X4) (X5) (X6) (X7) (X8) (X9)
Retention ratio• −0.05 −0.03 −0.03 −0.10 −0.11 0.10 0.20 −0.04 −0.02
0#
No. of bolls −0.03 −0.07 −0.01 −0.53** −0.06 −0.16 −0.14 0.37** 0.10
Retention ratio −0.07 −0.08 −0.01 −0.10 −0.16 0.04 0.15 0.04 0.05
1
No. of bolls 0.02 −0.08 0.08 −0.49** −0.09 −0.05 −0.20 0.35** 0.09
Retention ratio −0.08 −0.14 0.02 −0.08 −0.19 0.03 0.17 0.02 −0.02
2
No. of bolls 0.02 −0.04 0.07 −0.46** −0.06 −0.01 −0.19 0.33** 0.09
Retention ratio −0.09 −0.21 0.06 −0.08 −0.24* 0.02 0.19 0.01 −0.10
3
No. of bolls 0.03 −0.03 0.06 −0.44** −0.04 0.05 −0.18 0.32** 0.08
Retention ratio −0.05 −0.20 0.09 −0.01 −0.24* 0.01 0.22 0.00 −0.15
4
No. of bolls 0.01 −0.05 0.05 −0.40** −0.03 0.04 −0.16 0.31* 0.08
Retention ratio −0.03 −0.21 0.13 0.07 −0.25* 0.00 0.26* −0.02 −0.22
5
No. of bolls 0.00 −0.07 0.05 −0.37** −0.02 0.03 −0.13 0.29* 0.07
Retention ratio 0.01 −0.19 0.15 0.12 −0.24* 0.02 0.27* −0.03 −0.20
6
No. of bolls −0.01 −0.08 0.04 −0.38** −0.02 0.04 −0.15 0.31* 0.13
Retention ratio 0.05 −0.17 0.17 0.18 −0.25* 0.05 0.29* −0.02 −0.21
7
No. of bolls −0.03 −0.09 0.03 −0.39** −0.04 0.06 −0.14 0.34** 0.18
Retention ratio 0.06 −0.08 0.13 0.21 −0.20 0.07 0.28* −0.06 −0.19
8
No. of bolls −0.05 −0.07 −0.01 −0.35** −0.02 0.02 −0.17 0.28* 0.17
Retention ratio 0.08 0.00 0.08 0.26* −0.14 0.08 0.29* −0.12 −0.20
9
No. of bolls −0.08 −0.06 −0.05 −0.33** −0.01 0.00 −0.23 0.20 0.16
Retention ratio 0.06 −0.02 0.05 0.27* −0.13 0.09 0.27* −0.10 −0.08
10
No. of bolls −0.11 −0.10 −0.07 −0.34** −0.03 −0.03 −0.19 0.18 0.21
Retention ratio 0.04 −0.04 0.08 0.28* −0.12 0.08 0.26* −0.09 −0.05
11
No. of bolls −0.18 −0.18 −0.06 −0.37** −0.10 −0.04 −0.14 0.15 0.28*
Retention ratio 0.02 0.01 −0.08 0.32** −0.05 0.05 0.25* −0.08 −0.03
12
No. of bolls −0.17 −0.13 −0.08 −0.32** −0.06 −0.07 −0.11 0.16 0.24*
Retention ratio −0.04 0.04 −0.09 0.38** 0.00 0.01 0.27* −0.09 −0.02
13
No. of bolls −0.15 −0.09 −0.09 −0.29* −0.03 −0.10 −0.08 0.18 0.20
Retention ratio −0.07 0.04 −0.13 0.34** 0.06 −0.02 0.18 −0.08 −0.01
14
No. of bolls −0.15 −0.10 −0.10 −0.28* −0.01 −0.10 −0.15 0.17 0.17
Retention ratio −0.13 0.03 −0.18 0.33** 0.09 −0.04 0.06 −0.07 0.00
15
No. of bolls −0.16 −0.10 −0.11 −0.28* 0.00 −0.11 −0.13 0.17 0.15
*and **Significant at 5% and 1% levels of significance, respectively. #0 = Initial time. •Retention ratio: (the number of retained bolls obtained from the total
number of each daily tagged flowers in all selected plants at harvest/each daily number of tagged flowers in all selected plants) × 100. ♦Diurnal temperature
range. Sawan et al. [13].

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 47
Table 8. Simple correlation coefficient (r) values between climatic factorsz and number of harvested bolls and retention ratio in initial
time (0) and each of the 15-day periods after flowering in the second season (II).
Air temp (˚C) Surface soil temp (˚C)Humidity (%)
Max Min Max-Min♦
Evap
(mm·d−1)600 h 1800 h
Sunshine
duration
(h·d−1) Max Min
Climate
period
(X1) (X2) (X3) (X4) (X5) (X6) (X7) (X8) (X9)
Retention ratio• −0.04 0.20 −0.31* −0.14 0.12 −0.20 0.01 −0.040.17
0#
No. of bolls −0.42** 0.02 −0.37** −0.59**−0.13 −0.36** −0.36** 0.010.46**
Retention ratio −0.10 −0.03 −0.22 −0.21 −0.15 −0.05 −0.04 −0.020.23
1
No. of bolls −0.25* −0.01 −0.36** −0.63** −0.15 −0.30* −0.25* 0.060.44**
Retention ratio −0.15 −0.06 −0.10 −0.15 −0.08 −0.21 −0.01 −0.040.12
2
No. of bolls −0.18 −0.01 −0.34** −0.65** −0.11 −0.25* −0.32* 0.130.43**
Retention ratio −0.03 −0.01 −0.02 −0.21 −0.01 −0.17 −0.08 0.090.12
3
No. of bolls −0.15 −0.06 −0.30* −0.62** −0.05 −0.28* −0.31* 0.140.33**
Retention ratio 0.08 −0.02 0.07 −0.09 −0.03 −0.09 −0.10 0.05−0.04
4
No. of bolls −0.15 −0.05 −0.28* −0.63** −0.06 −0.25* −0.33** 0.150.32*
Retention ratio 0.23 −0.03 0.12 −0.06 −0.06 −0.01 −0.11 0.01−0.16
5
No. of bolls −0.14 −0.05 −0.25* −0.62** −0.06 −0.24* −0.35** 0.150.31*
Retention ratio 0.09 −0.08 0.12 −0.09 −0.07 −0.01 −0.09 0.00−0.05
6
No. of bolls −0.15 −0.04 −0.22 −0.61** −0.08 −0.25* −0.34** 0.130.22
Retention ratio −0.03 −0.12 0.12 −0.10 −0.11 −0.01 −0.04 −0.030.02
7
No. of bolls −0.15 −0.02 −0.19 −0.60** −0.10 −0.29* −0.32* 0.100.18
Retention ratio −0.02 0.05 0.03 −0.10 −0.04 −0.03 −0.02 −0.010.01
8
No. of bolls −0.20 −0.03 −0.23 −0.61** −0.10 −0.28* −0.32* 0.190.22
Retention ratio −0.02 0.13 −0.05 −0.10 0.08 −0.05 −0.01 0.030.00
9
No. of bolls −0.24 −0.04 −0.29* −0.62** −0.11 −0.30* −0.33** 0.130.27*
Retention ratio −0.04 0.12 −0.08 −0.09 0.05 0.11 −0.02 0.040.02
10
No. of bolls −0.27* −0.07 −0.30* −0.60** −0.16 −0.34** −0.34** 0.110.26*
Retention ratio −0.07 0.10 −0.10 −0.08 0.03 0.20 −0.03 0.050.04
11
No. of bolls −0.30* −0.12 −0.30* −0.61**−0.18 −0.39** −0.36** 0.100.27*
Retention ratio −0.11 0.09 −0.14 −0.11 0.04 0.13 −0.08 0.110.09
12
No. of bolls −0.32* −0.19 −0.26* −0.60** −0.22 −0.42** −0.37** 0.090.27*
Retention ratio −0.14 0.09 −0.17 −0.18 0.06 −0.06 −0.14 0.160.12
13
No. of bolls −0.33** −0.26* −0.23 −0.59** −0.28*−0.48** −0.39** 0.080.27*
Retention ratio −0.11 −0.04 −0.10 −0.13 −0.15 −0.05 −0.09 0.010.12
14
No. of bolls −0.34** −0.32* −0.21 −0.61** −0.32*−0.48** −0.38** 0.060.27*
Retention ratio −0.08 −0.11 0.02 −0.08 −0.22 −0.05 −0.02 −0.030.12
15
No. of bolls −0.35** −0.37** −0.18 −0.61** −0.38** −0.48** −0.37** 0.030.27*
*and **Significant at 5% and 1% levels of significance, respectively. #0 = Initial time. •Retention ratio: (the number of retained bolls obtained from the total
number of each daily tagged flowers in all selected plants at harvest/each daily number of tagged flowers in all selected plants) × 100. ♦Diurnal temperature
ange. zWind speed did not show significant effect upon the studied production variables, so it is not reported. Sawan et al. [13]. r

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
48
peratures and the diurnal temperature range, and soil
surface temperature at 600 h had a negative correlation
with boll production. Their significant effects were ob-
served during the 1, and 10 - 15 days periods for maxi-
mum temperature, and the 1 - 5, and 9 - 12 days periods
for the diurnal temperatures range. Meanwhile, the daily
minimum temperature and soil surface temperature at
600 h had a significant negative correlation only during
the 13 - 15 days periods. Daily minimum humidity had a
significant positive correlation with number of bolls dur-
ing the first 5 days periods, and the 9 - 15 days periods
after anthesis. Daily maximum humidity showed no sig-
nificant relation to number of bolls produced, and further
no significant relation was observed between any of the
studied climatic factors and boll retention ratio [13].
The results in the two seasons indicated that evapora-
tion and humidity, followed by sunshine duration had
obvious correlation with boll production. From the re-
sults obtained, it appeared that the effects of air tem-
perature, and soil surface temperature tended to be
masked in the first season, i.e. did not show any signifi-
cant effects in the first season on the number of bolls per
plant. However, these effects were found to be significant
in the second season. These seasonal differences in the
impacts of the previously mentioned climatic factors on
the number of bolls per plant are most likely ascribed to
the sensible variation in evaporation values in the two
studied seasons where their means were 10.2 mm·d−1 and
5.9 mm·d−1 in the first and second seasons, respectively
[13].
There is an important question here concerning, if
there is a way for forecasting when evaporation values
would mask the effect of the previous climatic factors.
The answer would be possibly achieved through relating
humidity values to evaporation values which are natu-
rally liable to some fluctuations from one season to an-
other [13]. It was found that the ratio between the mean
of maximum humidity and the mean of evaporation in
the first season was 85.8/10.2 = 8.37, while in the second
season this ratio was 12.4. On the other hand, the ratio
between the mean minimum humidity and the mean of
evaporation in the first season was 30.8/10.2 = 3.02,
while in the second season this ratio was 6.75 (Tabl e 7)
[13]. From these ratios it seems that minimum humidity
which is closely related to evaporation is more sensitive
than the ratio between maximum humidity and evapora-
tion. It can be seen from the results and formulas that
when the ratio between minimum humidity and evapora-
tion is small (3:1), the effects of air temperature, and soil
surface temperature were hindered by the effect of
evaporation, i.e. the effect of these climatic factors were
not significant. However, when this ratio is high (6:1),
the effects of these factors were found to be significant.
Accordingly, it could be generally stated that the effects
of air, and soil surface temperatures could be masked by
evaporation when the ratio between minimum humidity
and evaporation is less than 4:1 [13].
Evaporation appeared to be the most important cli-
matic factor (in each of the 15-day periods both prior to
and after initiation of individual bolls) affecting number
of flowers or harvested bolls in Egyptian cotton. High
daily evaporation rates could result in water stress that
would slow growth and increase shedding rate of flowers
and bolls. The second most important climatic factor in
our study was humidity. Effect of maximum humidity
varied markedly from the first season to the second one,
where it was significantly correlated with the dependent
variables in the first season, while the inverse pattern
was true in the second season. This diverse effect may be
due to the differences in the values of this factor in the
two seasons; where it was on average 87% in the first
season, and only 73% in the second season (Table 4).
Also, was found that, when the average value of mini-
mum humidity exceeded the half average value of maxi-
mum humidity, the minimum humidity can substitute the
maximum humidity on affecting number of flowers or
harvested bolls. In the first season (Table 4) the average
value of minimum humidity was less than half of the
value of maximum humidity (30.2/85.6 = 0.35), while in
the second season it was higher than half of maximum
humidity (39.1/72.9 = 0.54) [13].
The third most important climatic factor in our study
was sunshine duration, which showed a significant nega-
tive relationship with boll production. The r values of
(Tables 5-8) indicated that the relationship between the
dependent and independent variables preceding flower-
ing (production stage) generally exceeded in value the
relationship between them during the entire and late pe-
riods of production stage. In fact, understanding the ef-
fects of climatic factors on cotton production during the
previously mentioned periods would have marked con-
sequences on the overall level of cotton production,
which could be predictable depending on those relation-
ships [13].
3.2.2. Regression Models
An attempt was carried out to investigate the effect of
climatic factors on cotton production via prediction equa-
tions including the important climatic factors responsible
for the majority of total variability in cotton flower and
boll production. Hence, regression models were estab-
lished using the stepwise multiple regression technique
to express the relationship between each of the number
of flowers and bolls/plant and boll retention ratio (Y),
with the climatic factors, for each of the 1) 5, 2) 10, and
3) 15 days periods either prior to or after initiation of in-
dividual bolls (Tables 9 and 10) [13].
Concerning the effect of prior days the results indi-

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 49
Table 9. The models obtained for the number of flowers and
bolls per plant as functions of the climatic data derived from
the 5, 10, and 15 days periods prior to flower opening in the
two seasons (I, II).
Season Modelz R2 Significance
Y1 = 55.75 + 0.86X3 − 2.09X4 − 2.23X7 0.51 **
Y2 = 26.76 − 5.45X4 + 1.76X9 0.42 **
First
Flower
Y3 = 43.37 − 1.02X4 − 2.61X7 + 0.20X8 0.52 **
Y1 = 43.69 + 0.34X3 − 1.71X4 − 1.44X7 0.43 **
Y2 = 40.11 − 1.82X4 − 1.36X7 + 0.10X8 0.48 **
First
Boll
Y3 = 31.00 − 0.60X4 − 2.62X7 + 0.23X8 0.47 **
Y1 = 18.58 + 0.39X3 − 0.22X4
− 1.19X7 + 0.17X9 0.54 **
Y2 = 16.21 + 0.63X3 − 0.20X4
− 1.24X7 + 0.16X9 0.61 **
Second
Flower
Y3 = 14.72 + 0.51X3 − 0.20X4
− 0.85X7 + 0.17X9 0.58 **
Y1 = 25.83 + 0.50X3 − 0.26X4
− 1.95X7 + 0.15X9 0.61 **
Y2 = 19.65 + 0.62X3 − 0.25X4
− 1.44X7 + 0.12X9 0.60 **
Second
Boll
Y3 = 15.83 + 0.60X3 − 0.22X4
− 1.26X7 + 0.14X9 0.59 **
zWhere Y1, Y2, Y3 = number of flowers or bolls per plant at the 5, 10 and 15
days periods before flowering, respectively, X2 = minimum temperature (˚C),
X3 = diurnal temperature range (˚C), X4 = evaporation (mm·day−1), X7 =
sunshine duration (h·day−1), X8 = maximum humidity (%) and X9 = mini-
mum humidity (%). Sawan et al. [13].
Table 10. The models obtained for the number of bolls per
plant as functions of the climatic data derived from the 5, 10,
and 15 days periods after flower opening in the two seasons (I,
II).
Season Modelz R2 Significance
Y1 = 16.38 − 0.41X4 0.14
**
Y2 = 16.43 − 0.41X4 0.14
**
First
Y3 = 27.83 − 0.60X4 − 0.88X9 0.15 **
Y1 = 23.96 − 0.47X4 − 0.77X8 0.44 **
Y2 = 18.72 − 0.58X4 0.34
**
Second
Y3 = 56.09 − 2.51X4 − 0.49X6 − 1.67X7 0.56 **
zWhere Y1, Y2, Y3 = number of bolls per plant at the 5, 10, and 15 days
periods after flowering, respectively, X4 = evaporation (mm·day−1), X6 = soil
surface temperature (˚C) at 1800, X7 = sunshine duration (h·day−1), X8 =
maximum humidity (%) and X9 = minimum humidity (%). Sawan et al.
[13].
cated that evaporation, sunshine duration, and the diurnal
temperature range were the most effective and consistent
climatic factors affecting cotton flower and boll produc-
tion (Table 9). The fourth effective climatic factor in this
respect was minimum humidity. On the other hand, for
the periods after flower the results obtained from the
equations (Table 10) indicated that evaporation was the
most effective and consistent climatic factor affecting
number of harvested bolls [13].
Regression models obtained demonstrate of each in-
dependent variable under study as an efficient and im-
portant factor. Meanwhile, they explained a sensible pro-
portion of the variation in flower and boll production, as
indicated by their R2, which ranged between 0.14 - 0.62,
where most of R2 prior to flower opening were about
0.50 and after flowering all but one are less than 0.50
[13].These results agree with Miller et al. [23] in their
regression study of the relation of yield with rainfall and
temperature. They suggested that the other 0.50 of varia-
tion related to management practices, which can be the
same in this study. Also, the regression models indicated
that the relationships between the number of flowers and
bolls per plant and the studied climatic factors for the 15
days period before or after flowering (Y3) in each season
explained the highly significant magnitude of variation
(P < 0.05). The R2 values for the 15 days periods before
and after flowering were higher than most of those ob-
tained for each of the 5 and the 10 days periods before or
after flowering. This clarifies that the effects of the cli-
matic factors during the 15 days periods before or after
flowering are very important for Egyptian cotton boll
production and retention. Thus, an accurate climatic fore-
cast for the effect of these 15 days periods provides an
opportunity to avoid any possible adverse effects of un-
usual climatic conditions before flowering or after boll
formation by utilizing additional treatments and/or
adopting proper precautions to avoid flower and boll
reduction.
The main climatic factors from this study affecting the
number of flowers and bolls, and by implication yield,
are evaporation, sunshine duration and minimum humid-
ity, with evaporation (water stress) being by far the most
important factor. Various activities have been suggested
to partially overcome water stress. Temperature condi-
tions during the reproduction growth stage of cotton in
Egypt do not appear to limit growth even though they are
above the optimum for cotton growth [13]. This is con-
tradictory to the finding of Holaday et al. [24]. A possi-
ble reason for that contradiction is that the effects of
evaporation rate and humidity were not taken into con-
sideration in the research studies conducted by other re-
searchers in other countries. The matter of fact is that
temperature and evaporation are closely related to each
other to such an extent that the higher evaporation rate
could possible mask the effect of temperature. Sunshine
duration and minimum humidity appeared to have sec-
ondary effects, yet they are in fact important players [13].
The importance of sunshine duration has been alluded to

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
50
by Oosterhuis [25]. Also, Mergeai and Demol [26] found
that cotton yield was assisted by intermediate relative
humidity.
3.3. Cotton Flower and Boll Production as
Affected by Climatic Factors and Soil
Moisture Status
Basic Vari ables
1) Dependant variables as defined above: (Y1) and (Y2)
[14].
2) Independent variables (Xs):
a) Irrigation on day 1 = 1. Otherwise, enter 0.0 (soil
moisture status) (X1).
b) The first and second days after the day of irrigation
(soil moisture status) = 1. Otherwise, enter 0.0 (X2).
c) The day prior to the day of irrigation (soil moisture
status) to check for possible moisture deficiency on that
day = 1. Otherwise, enter 0.0 (X3).
d) Number of days during days 1 (day of flowering)-
12 (after flowering) that temperature equaled or ex-
ceeded 37.5˚C (high temperature) (X4).
e) Range of temperature (diurnal temperature) [˚C] on
day 1 (day of flowering) (X5).
f) Broadest range of temperature [˚C] over days 1 (day
of flowering)-12 (after flowering) (X6).
g) Minimum relative humidity (min RH) [%] during
day 1 (day of flowering) (X7).
h) Maximum relative humidity (max RH) [%] during
day 1 (day of flowering) (X8).
i) Minimum relative humidity (min RH) [%] during
day 2 (after flowering) (X9).
j) Maximum relative humidity (max RH) [%] during
day 2 (after flowering) (X10).
k) Largest maximum relative humidity (max RH) [%]
on days 3 - 6 (after flowering) (X11).
l) Lowest minimum relative humidity (min RH) [%]
on days 3 - 6 (after flowering) (X12).
m) Largest maximum relative humidity (max RH) [%]
on days 7 - 12 (after flowering) (X13).
n) Lowest minimum relative humidity (min RH) [%]
on days 7 - 12 (after flowering) (X14).
o) Lowest minimum relative humidity (min RH) [%]
on days 50 - 52 (after flowering) (X15).
p) Daily light period (hour) (X16).
S tatistical Analysis
Simple correlation coefficients between the initial
group of independent variables (climatic factors and soil
moisture status) (X’s) and the corresponding dependent
variables (Y’s) were computed for each season and the
combined data of the two seasons. These correlation co-
efficients helped determine the significant climatic fac-
tors and soil moisture status affecting the cotton produc-
tion variables. The level for significance was P ≤ 0.15.
Those climatic factors and soil moisture status attaining a
probability level of significance not exceeding 0.15 were
deemed important (affecting the dependent variables)
[14]. Those factors were combined with dependent vari-
ables in multiple regression analysis to obtain a predic-
tive model as described by Cady and Allen [16]. Multiple
linear regression equations (using the stepwise method)
comprising selected predictive variables were computed
for the determined interval. Coefficients of multiple de-
terminations (R2) were calculated to measure the effi-
ciency of the regression models in explaining the varia-
tion in data. Correlation and regression analysis were
computed according to Draper and Smith [17] using the
procedures outlined in the general linear model (GLM)
[15].
3.3.1. Correlation Estimates
Simple correlation coefficients between the independ-
ent variables and the dependent variables for flower and
boll production in each season and combined data of the
two seasons are shown in Tabl es 1 1-13 [14]. The simple
correlation values indicated clearly that relative humidity
was the most important climatic factor. Relative humid-
ity also had a significant positive relationship with
flower and boll production; except for lowest min RH on
days 50 - 52 (after flowering) [14]. Flower and boll pro-
duction were positively and highly correlated with the
variables of largest max RH (X11, X13) and lowest min
RH (X14, X15) in the first season, min RH (X7, X9), larg-
est max RH (X11), and lowest min RH (X12, X14, X15) in
the second season, and the combined data of the two
seasons [14]. Effect of max RH varied markedly from the
first to the second season. Max RH was significantly
correlated with the dependent variables in the first season,
while the inverse pattern was true in the second season.
This diverse effect may be best explained by the differ-
ences of 87% in the first season, and only 73% in the
second season (Table 1). Also, when the average value of
min RH exceeded the half average value of max RH, the
min RH can substitute for the max RH on affecting num-
ber of flowers or harvested bolls. In the first season (Ta-
ble 1) the average value of min RH was less than half of
the value of max RH (30.2/85.6 = 0.35), while in the
second season it was higher than half of max RH
(39.1/72.9 = 0.54). Sunshine duration (X16) showed a
significant negative relation with fruit production in the
first and second seasons and the combined data of the
two seasons except for boll production in the first season,
which was not significant. Flower and boll production
were negatively correlated in the second season and the
combined data of the two seasons with the number of
days during days 1 - 12 that temperature equaled or ex-
ceeded 37.5˚C (X4), range of temperature (diurnal

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 51
Tab le 1 1 . Simple correlation coefficient (r) values between the
independent variables and the dependent variables in the first
season (I).
Dependent variables
(First season)
Independent variables
(Irrigation and climatic factors)
Flowers Bolls
(X1) Irrigation on day 1 −0.1282 −0.0925
(X2) Irrigation on day 0 or −1 (1st and
2nd day after irrigation) −0.1644 −0.1403
(X3) 1 is for the day prior to irrigation −0.0891 −0.0897
(X4) Number of days that temperature
equaled or exceeded 37.5˚C 0.1258 0.1525
(X5) Range of temperature [˚C] on day 1 −0.0270 −0.0205
(X6) Broadest range of temperature
[˚C] over days 1 - 12 0.0550 0.1788d
(X7) Min RH [%] during day 1 0.1492 0.1167
(X8) Max RH [%] during day 1 0.2087c 0.1531
(X9) Min RH [%] during day 2 0.1079 0.1033
(X10) Max RH [%] during day 2 0.1127 0.0455
(X11) Largest max RH [%] on days 3 - 6 0.3905a 0.2819b
(X12) Lowest min RH [%] on days 3 - 6 0.0646 0.0444
(X13) Largest max RH [%] on days 7 - 12 0.4499a 0.3554b
(X14) Lowest min RH [%] on days 7 - 12 0.3522a 0.1937d
(X15) Lowest min RH [%] on days 50 - 52 −0.3440a −0.4222a
(X16) Daily light period (hour) −0.2430b −0.1426
aSignificant at 1% probability level. bSignificant at 5% probability level.
cSignificant at 10% probability level. dSignificant at 15% probability level.
Sawan et al. [14].
temperature) on flowering day (X5) and broadest range of
temperature over days 1 - 12 (X6). The soil moisture
status showed low and insignificant correlation with
flower and boll production. The positive relationship
between relative humidity with flower and boll produc-
tion means that low relative humidity rate reduces sig-
nificantly cotton flower and boll production. This may be
due to greater plant water deficits when relative humidity
decreases. Also, the negative relationship between the
variables of maximum temperature exceeding 37.5˚C
(X4), range of diurnal temperature on flowering (X5), and
sunshine duration (X16) with flower and boll production
revealed that the increased values of these factors had a
detrimental effect upon Egyptian cotton fruit production.
Results obtained from the production stage of each sea-
son, and the combined data of the two seasons showed
marked variability in the relationships of some climatic
variables with the dependent variables [14]. This may be
Table 12. Simple correlation coefficient (r) values between the
independent variables and the dependent variables in the sec-
ond season (II).
Dependent variables
(Second season)
Independent variables
(Irrigation and climatic factors)
Flowers Bolls
(X1) Irrigation on day 1 −0.0536 −0.0467
(X2) Irrigation on day 0 or −1 −0.1116 −0.1208
(X3) 1 is for the day prior to the
day of irrigation −0.0929 −0.0927
(X4) Number of days that temperature
equaled or exceeded 37.5˚C −0.4192a −0.3981a
(X5) Range of temperature [˚C] on day 1 −0.3779a −0.3858a
(X6) Broadest range of temperature [˚C]
over days 1 - 12 −0.3849a −0.3841a
(X7) Min RH [%] during day 1 0.4522a 0.4665a
(X8) Max RH [%] during day 1 0.0083 0.0054
(X9) Min RH [%] during day 2 0.4315a 0.4374a
(X10) Max RH [%] during day 2 0.0605 0.0532
(X11) Largest max RH [%] on days 3 - 6 0.2486c 0.2520b
(X12) Lowest min RH [%] on days 3 - 6 0.5783a 0.5677a
(X13) Largest max RH [%] on days 7 - 12 0.0617 0.0735
(X14) Lowest min RH [%] on days 7 - 12 0.4887a 0.4691a
(X15) Lowest min RH [%] on days 50 - 52 −0.6246a −0.6113a
(X16) Daily light period (hour) −0.3677a −0.3609a
aSignificant at 1% probability level. bSignificant at 5% probability level.
cSignificant at 10% probability level. Sawan et al. [14].
best explained by the differences between climatic fac-
tors in the two seasons as illustrated by the ranges and
means shown in Table 1 . For example, maximum tem-
perature exceeding 37.5˚C (X4) and min RH did not
show significant relations in the first season, while that
trend differed in the second season [14].
These results indicated that relative humidity was the
most effective and consistent climatic factor affecting
boll production. The second most important climatic fac-
tor in our study was sunshine duration, which showed a
significant negative relationship with boll production
[14].
Human et al. [22] stated that, when sunflower plants
were grown under controlled temperature regimes and
water stress during budding, anthesis and seed filling, the
CO2 uptake rate per unit leaf area as well as total uptake
rate per plant, significantly diminished with stress, while
this effect resulted in a significant decrease in yield per
plant.

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54
52
Table 13. Simple correlation coefficient (r) values between the
independent variables and dependent variables in the combined
two seasons (I and II).
Dependent variables
(Combined two seasons)
Independent variables
(Irrigation and climatic factors)
Flowers Bolls
(X1) Irrigation on day 1 −0.0718 −0.0483
(X2) Irrigation on day 0 or −1 −0.1214 −0.1108
(X3) 1 is for the day prior to the day
of irrigation −0.0845 −0.0769
(X4) Number of days that temperature
equaled or exceeded 37.5˚C −0.2234b −0.1720c
(X5) Range of temperature [˚C] on day 1 −0.2551a −0.2479a
(X6) Broadest range of temperature [˚C]
over days 1 - 12 −0.2372a −0.1958b
(X7) Min RH [%] during day 1 0.3369a 0.3934a
(X8) Max RH [%] during day 1 0.0032 −0.0911
(X9) Min RH [%] during day 2 0.3147a 0.3815a
(X10) Max RH[%] during day 2 −0.0094 −0.1113
(X11) Largest max RH [%] on days 3 - 6 0.0606 −0.0663
(X12) Lowest min RH [%] on days 3 - 6 0.3849a 0.4347a
(X13) Largest max RH [%] on days 7 - 12 −0.0169 −0.1442d
(X14) Lowest min RH [%] on days 7 - 12 0.3891a 0.4219a
(X15) Lowest min RH [%] on days 50 - 52 −0.3035a −0.2359a
(X16) Daily light period (hour) −0.3039a −0.2535a
aSignificant at 1% probability level. bSignificant at 5% probability level.
cSignificant at 10% probability level. dSignificant at 15% probability level.
Sawan et al. [14].
3.3.2. Multiple Linear Regression Mod els, Beside
Contribution of Climate Factors and Soil
Moisture Status to Variations in the
Dependent Variables
Regression models were established using the step-
wise multiple regression technique to express the rela-
tionship between the number of flowers and bolls per
plant−1 (Y) with the climatic factors and soil moisture
status (Table 14). Relative humidity (%) was the most
important climatic factor affecting flower and boll pro-
duction in Egyptian cotton [min RH during day 1 (X7),
min RH during day 2 (X9), largest max RH on days 3 - 6
(X11), lowest min RH on days 3 - 6 (X12), largest max RH
on days 7 - 12 (X13), lowest min RH on days 7 - 12 (X14)
and lowest min RH on days 50 - 52 (X15)]. Sunshine du-
ration (X16) was the second climatic factor of importance
affecting production of flowers and bolls. Maximum
temperature (X4), broadest range of temperature (X6) and
soil moisture status (X1) made a contribution affecting
flower and boll production. The soil moisture variables
(X2, X3), and climatic factors (X5, X8, X10) were not in-
cluded in the equations since they had very little effects
on production of cotton flowers and bolls [14].
Relative humidity showed the highest contribution to
the variation in both flower and boll production (Table
14). This finding can be explained in the light of results
found by Ward and Bunce [27] in sunflower (Helianthus
annuus). They stated that decreases of relative humidity
on both leaf surfaces reduced photosynthetic rate of the
whole leaf for plants grown under a moderate tempera-
ture and medium light level.
Gutiérrez and López [28] studied the effects of heat on
the yield of cotton in Andalucia, Spain, during 1991-98,
and found that high temperatures were implicated in the
reduction of unit production. There was a significant
negative relationship between average production and
number of days with temperatures greater than 40˚C and
the number of days with minimum temperatures greater
than 20˚C. Wise et al. [29] indicated that restrictions to
photosynthesis could limit plant growth at high tempera-
ture in a variety of ways. In addition to increasing photo-
respiration, high temperatures (35˚C - 42˚C) can cause
direct injury to the photosynthetic apparatus. Both car-
bon metabolism and thylakoid reactions have been sug-
gested as the primary site of injury at these temperatures.
Regression models obtained explained a sensible pro-
portion of the variation in flower and boll production, as
indicated by their R2, which ranged between 0.53 - 0.72
[14]. These results agree with Miller et al. [23] in their
regression study of the relation of yield with rainfall and
temperature. They suggested that the other R2 0.50 of
variation was related to management practices, which
coincide with the findings of this study. Thus, an accu-
rate climatic forecast for the effect of the 5 - 7 days pe-
riod during flowering may provide an opportunity to
avoid possible adverse effects of unusual climatic condi-
tions before flowering or after boll formation by utilizing
additional treatments and/or adopting proper precautions
to avoid flower and boll reduction.
Temperature conditions during the reproduction grow-
th stage of cotton in Egypt do not appear to limit this
growth even though they are above the optimum for cot-
ton growth [14]. This is contradictory to the finding of
Holaday et al. [24]. A possible reason for that contradic-
tion is that the effects of soil moisture status and relative
humidity were not taken into consideration in the re-
search studies conducted by other researchers in other
countries. Since temperature and evaporation are closely
related to each other, the higher evaporation rate could
possibly mask the effect of temperature. Sunshine dura-
tion and minimum relative humidity appeared to have
secondary effects, yet they are in fact important factors.

Z. M. Sawan / Agricultural Sciences 4 (201 3) 37-54 53
Table 14 . Model obtained for cotton production variables as functions of climatic data and soil moisture status in individual and
combined seasons. All entries significant at 1% level.
Season Model R2
Y1 = − 557.54 + 6.35X6 + 0.65X7 + 1.92X11 + 4.17X13 + 2.88X14 − 1.90X15 − 5.63X16 0.63
Season I
(n = 68) Y2 = − 453.93 + 6.53X6 + 0.61X7 + 1.80X11 + 2.47X13 + 1.87X14 − 1.85X15 0.53
Y1 = −129.45 + 25.36X1 + 37.02X4 + 1.48X7 + 1.69X9 + 4.46X12 + 2.55X14 − 4.73X15 0.72
Season II
(n = 62) Y2 = − 130.23 + 24.27X1 + 35.66X4 + 1.42X7 + 1.61X9 + 4.00X12 + 2.18X14 − 4.09X15 0.71
Y1 = − 557.36 + 6.82X6 + 1.44X7 + 0.75X9 + 2.04X11 + 2.55X12 + 2.01X13 + 3.27X14 − 2.15X15 0.57
Combined data: I & II
(n = 130) Y2 = − 322.17 + 6.41X6 + 1.20X7 + 0.69X9 + 1.81X11 + 2.12X12 + 2.35X14 − 2.16X15 0.53
(Y1) Number of cotton flowers; (Y2) Number of cotton bolls. (X1) Irrigation on day 1; (X4) Number of that temperature equaled or exceeded 37.5˚C; (X6)
Broadest range of temperature [˚C] over days 1 - 12; (X7) Min RH [%] during day 1; (X9) Min RH [%] during day 2; (X11) Largest max RH [%] on days 3 - 6;
(X12) Lowest min RH [%] on days 3 - 6; (X13) Largest max RH [%] on days 7 - 12; (X14) Lowest min RH [%] on days 7 - 12; (X15) Lowest min RH [%] on days
50 - 52; (X16) Daily light period (hour). Sawan et al. [14].
4. CONCLUSIONS fecting boll production in Egyptian cotton. Our findings
indicate that increasing evaporation rate and sunshine
duration resulted in lower boll production. On the other
hand, relative humidity, which had a positive correlation
with boll production, was also an important climatic fac-
tor. In general, increased relative humidity would bring
about better boll production. Temperature appeared to be
less important in the reproduction growth stage of cotton
in Egypt than min RH (water stress) and sunshine dura-
tion. These findings concur with those of other research-
ers, except for the importance of temperature. A possible
reason for that contradiction is that the effects of evapo-
ration rate and relative humidity were not taken into con-
sideration in the research studies conducted by other re-
searchers in other countries. Since temperature and
evaporation are closely related to each other, the higher
evaporation rate could possibly mask the effect of tem-
perature.
Evaporation, sunshine duration, relative humidity, sur-
face soil temperature at 1800 h, and maximum tempera-
ture, were the most significant climatic factors affecting
flower and boll production of Egyptian cotton. Also, it
could be concluded that during the 15-day period both
prior to and after initiation of individual bolls, evapora-
tion, minimum relative humidity and sunshine duration,
were the most significant climatic factors affecting cot-
ton flower and boll production and retention in Egyptian
cotton [13]. The negative correlation between each of
evaporation and sunshine duration with flower and boll
formation along with the positive correlation between
minimum relative humidity value and flower and boll
production, indicate that low evaporation rate, short pe-
riod of sunshine duration and high value of minimum
humidity would enhance flower and boll formation [13].
Water stress is in fact the main player and other authors
have suggested means for overcoming its adverse effect
which could be utilized in the Egyptian cotton. It must be
kept in mind that although the reliable prediction of the
effects of the aforementioned climatic factors could lead
to higher yields of cotton, yet only 50% of the variation
in yield could be statistically explained by these factors
and hence consideration should also be given to the
management practices presently in use. The 5-day inter-
val was found to give adequate and sensible relationships
between climatic factors and cotton production growth
under Egyptian conditions when compared with other
intervals and daily observations [12]. It may be con-
cluded that the 5-day accumulation of climatic data dur-
ing the production stage, in the absence of sharp fluctua-
tions in these factors, could be satisfactorily used to fore-
cast adverse effects on cotton production and the appli-
cation of appropriate production practices circumvent
possible production shortage. Evaporation and sunshine
duration appeared to be important climatic factors af-
Finally, the early prediction of possible adverse effects
of climatic factors might modify their effect on produc-
tion of Egyptian cotton. Minimizing deleterious effects
through the application of proper management practices,
such as, adequate irrigation regime, and utilization of
specific plant growth regulators could limit the negative
effects of some climatic factors [14].
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