Dr. Md. Abul Khayer Mian is presenting the paper at the Conference of Bangladesh Society of Agronomy at BARI held on 6 October 2012.
ASSESSING AGRO-CLIMATOLOGICAL MODEL OF SUMMER MUNGBEAN
M. A. K. Mian1,
M. R. Islam1, J. Hossain1 and M. S. Alam2
1RARS and 2PRC,
Ishurdi 6620
Key word: Agro-climate, model, summer mungbean and
yield
Abstract
The experiment
was conducted at the Regional Agricultural Research station, Ishurdi during
2007 to 2012 to assess the agro-climatic effect on summer mungbean. Three
mungbean cv. BARI mug 5, BARI
mug 6 and BU mug 4 were sown on 15 March, 25 March and 4 April in each year.
Average yield of six years was the highest (1306 kg/ha) in BARI
mug 6 at 25 March sowing. Developed functional yield model was Y=4645.56 + 2.42
HDDS + 6.37 Rainfall – 106.91 Tem. – 16.45 TDM + 0.33 TSSH – 63.78 RH (R2=0.67).
The effect of agro-climate can be explained about 67% by this functional model.
The co-efficients indicate the rate of change of yield due to change of one
unit of input variables. The model can be used to predict the yield of summer
mungbean or to verify the experimental results at prevailing agro-climatic condition
of a particular year.
Introduction
Climatic factors
have significant effect on crop production. Temperature, rainfall, sunshine
hours, relative humidity etc. are the main weather elements that greatly affect
the growth and development of crop plant. A good genotype can not exploit its
full yield potentially without proper growth environment (Mian et al., 2002)
since yield is the function of interaction of genotype and environment (Ullah et
al., 2002). Summer mungbean is an important pulse crop in Bangladesh.
It covers an area of 41000 ha with an annual production of 49000 tons (AIS,
2012). Its area and production is increasing day by day due to some convenience
of short duration varieties, favourable environment and fallowing of lands
between winter crops and T. aman rice (Hossain et al., 2010; BARI,
2011). Temperature is high and rainfall
is unpredictable in summer in Bangladesh.
The suitable temperature for mungbean is 25-30 0C (Afzal et al.,
2008). High temperature is harmful for proper growth and development of crop
plant. High temperature, precipitation and desiccating wind increased flower
shading in mungbean (Rainey and Griffiths, 2005). Excess rainfall exerted
vigorous growth and retarded pod formation (Mian, 2008). Sunshine hours and relative
humidity also affect the crop plant as the change of growing time. Recently,
environmental scientists and agronomists are interested to quantify the effect
of weather elements on growth and yield of crops. In this regard, functional
model can help to predict the yield and prescribe the management options to
improve yield of crops. Regression analysis is usually deals with the
functional relationship for explanation and prediction of a given variable
based on one or more variables. Long term experimental data is required to
develop a functional model (Allen et al., 1994; Panye et al., 2001). Multiple regression
analysis of yield (dependent variable) with weather elements (independent
variable) can establish a functional relationship for assessing the effect of
agro-climate on the yield of summer mungbean. Therefore, the study was
undertaken to assess the effect of agro-climate on summer munbean through
developing a functional model.
Materials and
Methods
Field experiment
was conducted at the Regional Agricultural Research station, Ishurdi during
2007 to 2012. The experiment was conducted in a RCB design with three
replications. Three mungbean cv. BARI
mug 5, BARI mug 6 and BU mug 4 were
sown on 15 March, 25 March and 4 April in each year. Unit plot size was 5 m ´ 4.5 m.
Seed was sown at 30 cm apart line following continuous seeding technique. The
soil of the experimental plot was sandy loam with PH value of 7.16. Nutrient,
20-40-20 kg ha-1 of N-P-K was applied as basal for the crop (BARI,
2011). Cultural management was done as and when necessary. Crop yield and weather
data during 2007 to 2012 was computed and summarized for developing functional
yield model (Table 1 and Table 3). Heat degree day sum (HDDS), total rainfall
(rainfall), average daily air temperature (Temperature), total sunshine hours
(TSSH) and average relative humidity (RH) during total crop growing period, and
total dry matter of crop (TDM) were regressed against yield of summer mungbean.
Attempt was made for assessing agro-climatological model of summer mungbean
following the basic principle of multiple regressions after Payne et al. (2001).
Y=a+ b1x1+b2x2+b3x3+b4x4+
b5x5+b6x6
Y=Yield (output
variable)
a=constant
(intercept)
x1............x6
are the input variables (HDDS, Rainfall, Temperature, TDM, TSSH and RH )
b1--------b6
are the coefficients.
HDDS=Heat degree
day sum was calculated as follows:
HDDS=∑(Ta-Bt)
Where
Ta = Daily air
temperature
Bt = Base temperature
(100C for mungbean, Fyfield and Grerory, 2011).
Results and Discussion
Crop yield
Seed yield
variation was noticed among the sowing times over the years (Table 1). The
highest seed yield (1485 kg ha-1) was observed in BARI
mug 6 at 25 March sowing in 2007. Similar trend of seed yield was followed in
2008, 2010 and 2012. But seed yield was the highest (1464 kg ha-1)
in BARI mug 6 at 15 March sowing in
2009. Similar trend of seed yield was observed in 2011. The highest seed yield
at 25 March and 15 March sowing in different years possibly happened due to variation
of prevailing weather condition especially of temperature and rainfall. Seed
yield was lower in 2008 as compared to other years due to unfavourable weather
condition (Table 3). Drought weather prevailed in March (8.9 mm rainfall) and
April (40.76 mm rainfall) in this year (data was presented in table). Seed
yield variation of summer mungbean in different years was also observed by Mian
(2008). Average seed yield of six years was the highest (1306 kg ha-1)
in BARI mug 6 at 25 March sowing while the lowest in BU mug 4 at 4 April sowing
(Table 1). The results indicated that 15 March to 25 March is suitable sowing
time for summer mungbean for Ishurdi region.
Crop model
Developed
functional yield model was Y=4645.56 + 2.42 HDDS + 6.37 Rainfall – 106.91 Tem.
– 16.45 TDM + 0.33 TSSH – 63.78 RH (R2=0.67) (Table 2). The effect
of agro-climate can be explained about 67% by this functional model. Similarly,
models using monthly rainfall and HDDS for wheat (R2= 0.62) and pea
(R2=0.65) predicted yields well (Payne et al., 2001). The co-efficients of developed model indicate the
rate of change of yield due to change of one unit of input variables. HDDS,
rainfall, TSSH had positive effect on seed yield of mungbean (Table 2). The
coefficient, 2.42 of HDDS indicated that seed yield increased 2.42 kg ha-1
with the increase of one unit of HDDS during the total growing period. Similarly,
seed yield increased 6.37 kg ha-1 with the increase of one mili
meter rainfall. Seed also increased 0.33 kg ha-1 with increase of
one sun shine hour during the growing period. On the contrary, air temperature,
total dry matter and relative humidity had shown negative influence on seed
yield of mungbean (Table 2). Seed yield decreased 106.91 kg ha-1
with the increase of one degree air temperature (0C) (Table 2).
Again, seed yield decreased 16.45 kg ha-1 with the increase of one
ton of dry matter of mungbean. Similarly, seed yield decreased 63.78 kg ha-1
with increase of one per cent of relative humidity. Thus the developed model
could explain the effect of weather elements on seed yield of summer mungben. Similar
functional yield model on wheat and pea was developed by Payne et al. (2001). Prevailing agro-climatic
data of another field experimental during 2012 was inserted in the developed
model for validation test. Then the predicted seed yield was 921 kg ha-1 which
was closer to experimental yield of 998 kg ha-1. Yield
variation between predicted yield and observed yield was about 7%. Therefore,
the predicted yield is consisted with the experimental yield. Similar
consistency in model validation test in corn was also described by Smart et el. (1993). However, the developed
model could be able to predict seed yield of summer mungbean satisfactorily
under a particular agro-climatic condition.
Conclusion
The developed
model could explain the effect of weather elements on seed yield of summer
mungben and it can be used to predict the seed yield of summer mungbean at
prevailing agro-climatic condition of a particular year. It also can be use to
verify the results of other similar experiments. Sowing time, 15-25 March was
suitable for higher seed yield of mungbean for Ishurdi region.
Treatment
|
Seed
yield (kg ha-1)
|
||||||
2007
|
2008
|
2009
|
2010
|
2011
|
2012
|
Mean
|
|
S1V1
|
1279
|
831
|
1370
|
1125
|
1548
|
938
|
1182
|
S1V2
|
1321
|
909
|
1464
|
1290
|
1573
|
977
|
1256
|
S1V3
|
1102
|
747
|
1296
|
1146
|
1555
|
797
|
1107
|
S2V1
|
1288
|
900
|
1101
|
1374
|
1364
|
961
|
1165
|
S2V2
|
1485
|
912
|
1296
|
1713
|
1431
|
996
|
1306
|
S2V3
|
1125
|
764
|
972
|
1195
|
1406
|
908
|
1062
|
S3V1
|
1105
|
812
|
781
|
1093
|
574
|
577
|
824
|
S3V2
|
1149
|
853
|
985
|
1259
|
648
|
616
|
918
|
S3V3
|
975
|
727
|
588
|
857
|
420
|
549
|
686
|
LS
|
**
|
**
|
**
|
**
|
**
|
**
|
-
|
LSD (0.05)
|
211
|
125
|
172
|
276
|
149
|
154
|
-
|
CV (%)
|
9.34
|
6.23
|
5.14
|
12.66
|
7.22
|
10.90
|
-
|
** indicates significant at 0.01 level of probability
S1=15 March
|
V1=BARI mug 5
|
S2=25 March
|
V2=BARI mug 6
|
S3=4 April
|
V3=BU mug 4
|
Table 2. Intercept, coefficients of input
variables and co-efficient of determination of the functional model.
Input variables
|
Co-efficient of input variables
|
Intercept (constant)
|
Co-efficient of determination (R2)
|
HDDS
|
2.42
|
4645.56
|
0.67
|
Rainfall
|
6.37
|
||
Temperature
|
-106.91
|
||
TDM
|
-16.45
|
||
TSSH
|
0.33
|
||
RH
|
-63.78
|
Table 3. Summery of weather elements during 2007 to
2012
Sowing time
|
Weather element
|
|||||
HDDS
|
Air Temperature
(0C)
|
Rainfall (mm)
|
TSSH
|
RH (%)
|
||
2007
|
S1
|
1344
|
28.67
|
219
|
583
|
79.33
|
|
S2
|
1283
|
29.74
|
238
|
539
|
79.69
|
|
S3
|
1258
|
29.96
|
261
|
478
|
81.23
|
2008
|
S1
|
1279
|
29.54
|
189
|
563
|
79.61
|
|
S2
|
1289
|
29.96
|
211
|
530
|
80.06
|
|
S3
|
1234
|
30.28
|
252
|
491
|
80.46
|
2009
|
S1
|
1430
|
29.33
|
224
|
477
|
81.75
|
|
S2
|
1353
|
29.61
|
233
|
448
|
81.92
|
|
S3
|
1372
|
29.88
|
246
|
442
|
81.99
|
2010
|
S1
|
1486
|
29.81
|
124
|
466
|
73.72
|
|
S2
|
1568
|
30.36
|
166
|
435
|
76.72
|
|
S3
|
1481
|
30.57
|
204
|
350
|
78.52
|
2011
|
S1
|
1255
|
27.42
|
235
|
368
|
76.17
|
|
S2
|
1196
|
27.59
|
235
|
342
|
77.47
|
|
S3
|
1135
|
28.02
|
226
|
340
|
77.22
|
2012
|
S1
|
1377
|
29.12
|
91
|
471
|
68.43
|
|
S2
|
1356
|
29.94
|
91
|
437
|
69.75
|
|
S3
|
1278
|
30.28
|
110
|
374
|
71.93
|
S1=15 March, S2=25
March, S3=4 April
T Table 4. Pods plant-1 of
summer mungbean as influenced by sowing time (2007-2012)
Treatment
|
Number
of pods plant-1
|
||||||
2007
|
2008
|
2009
|
2010
|
2011
|
2012
|
Mean
|
|
16.80
|
16.02
|
19.93
|
16.40
|
18.13
|
13.50
|
16.80
|
|
S1V2
|
17.13
|
18.01
|
20.53
|
15.73
|
18.10
|
13.29
|
17.13
|
S1V3
|
15.87
|
16.23
|
18.47
|
14.13
|
18.06
|
12.48
|
15.87
|
S2V1
|
16.03
|
16.32
|
19.00
|
14.93
|
17.76
|
12.12
|
16.03
|
S2V2
|
17.96
|
17.39
|
20.60
|
19.67
|
18.83
|
13.32
|
17.96
|
S2V3
|
15.34
|
15.32
|
15.68
|
16.80
|
17.46
|
11.45
|
15.34
|
S3V1
|
13.14
|
13.36
|
10.73
|
18.87
|
11.83
|
10.89
|
13.14
|
S3V2
|
14.67
|
15.39
|
12.27
|
20.27
|
12.90
|
12.54
|
14.67
|
S3V3
|
10.67
|
12.92
|
7.27
|
12.53
|
10.76
|
9.87
|
10.67
|
LS
|
**
|
**
|
**
|
**
|
**
|
**
|
-
|
LSD
(0.05)
|
2.62
|
3.21
|
4.53
|
3.09
|
0.45
|
1.82
|
-
|
CV
(%)
|
10.82
|
11.81
|
19.87
|
10.55
|
3.20
|
8.66
|
-
|
** indicates significant at 0.01 level of probability
S1=15 March
|
V1=BARI mug 5
|
S2=25 March
|
V2=BARI mug 6
|
S3=4 April
|
V3=BU mug 4
|
Reference
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This is a good paper. The learners will be benefited by this paper.It can be used to asses the ago-climate on summer mungbean. It can be used to predict yield at prevailing weather condition of a particular year.
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