ASSESSING AGRO-CLIMATOLOGICAL MODEL OF SUMMER MUNGBEAN

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 P^H 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=∑(T_a-B_t)

Where

T_a = Daily air temperature

B_t = 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.   

 

Table 1. Seed yield of summer mungbean as influenced by sowing time (2007-2012)

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

Year	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
S1V1	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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