Current World Environment

Introduction

Rainfall is the single most important factor in crop production programme particularly under dry lands areas. Of all the climatic factors, rainfall is of greatest concern to population in rainfed agriculture. The variation of monsoonal and annual rainfall in space and time are well known and this inter-annual variability of monsoonal rainfall has considerable impact on agricultural production, water management and energy generation. Analysis of annual, seasonal and monthly rainfall of a region is useful to design water harvesting structure. Similarly weekly rainfall analysis give more useful information in crop planning (Sharma et al.,1979). Earlier workers have worked out the weekly rainfall probabilities for different agro climatic regions (Ray et al.1980 and Agnihotri et al.,1986). Gupta et al.(1975) suggested that the rainfall at 80 per cent probability can safely be taken as assured rainfall, while that of 50 per cent probability is the medium limit for taking dry risk. According to Mulat et al., 2004, the quantum of rainfall during crop growing season and temporal distribution of rainfall is a crucial factor deciding  interannual fluctuations in national crop production security. On the similar lines, Von Braun (1991) also commented that a 10% decrease in monsoonal rainfall from the long-period average generally translates into a 4.4% decrease in the national food production. Rainfall in much of the country is, on the other hand, often erratic and unreliable and can be considered as gamble; and  resultant  droughts due to negative departure of rainfall have historically been major causes of food shortages and famines (Wood, 1977; Pankhurst and Johnson, 1988).

There have been different techniques for determining probability distribution on rainfall analysis and best fit probability distribution function such as normal, log-normal, gumbel, weibull and Pearson type distribution have been identified as per different research studies. Fisher (1924) studied the influence of rainfall on the yield of wheat in Rothamasted and concluded that distribution of rainfall during a season is determining factor rather than  total amount of rainfall which influences the crop yield. Another useful line of work relating to the study of rainfall distribution was introduced by Manning (1950) and a transformation of  the skew frequency distribution of rainfall was performed to close down to the theoretical normal distribution.

Bhargava et al. (1971) also showed that for a number of crops under Indian situation the distribution of rainfall over the season has a great influence on the yield. Ramana Rao et al. (1975) analyzed the daily rainfall data of Bijapur for the period 1921 - 1970. Biswas and Khambete (1989) computed the lowest amount of rainfall at different probability levels by fitting gamma distribution probability model on weekly basis of 82 stations in dry farming tract of Maharashtra. Upadhaya and Singh (1998) stated that it is possible to predict rainfall fairly accurately using various probability distributions for certain returns periods although the rainfall has inconsistent nature both spatially and temporarily.

In Chhattisgarh state, there are large variations in annual rainfall and these variations often result in reduced crop productivity especially rice crop. Rice is grown in approximately 3.6 million hectares area and farmers take tall and photo-sensitive varieties for some reasons which flower by mid-October and mature by mid-November. Terminal drought is a recurring feature for rice crop in this region. Also intermittent dry spells make the crop operations dealyed as 80 per cent of the area in this region is under rainfed conditions. Thus, the success of rice crop depends upon not only the monsoonal rainfall but also on the October rainfall which occurs due to cyclonic activity in the Bay of Bengal. As the Chhatisgarh plains in central India are mainly dependant on monsoon rains (south-west), the precise knowledge of amount of rainfall that can be expected at different probability levels will go a long way in helping farmers to plan their agricultural operations (Rao et al. 1968). Therefore, an attempt has been made for probability analysis of long term rainfall data of Labandi station for developing risk proof technologies for rainfed agriculture of Raipur district.

Materials and Methods

Daily rainfall data of the recent past 43 years (1971-2013) of Labhandi station, Indira Gandhi Krishi Vishwavidhyalaya Raipur, Chhattisgarh has been scrutinized for correctness of data and converted into weekly, monthly, seasonal and annual format using weathercock software. The weekly rainfall data then analyzed with the help of Weibulls formula as followed by Jadav et al.1999

P= {(m/n+1)/100}

Where, P is plotting position in percentage, m is the rank of magnitude and n is number of year for which the data are taken. Rainfall at various probability levels (25, 50, 75 and 90 percent) for weekly, monthly, seasonal and annual basis has been worked out and are presented in Table 1 and 2.

Results and Discussion

Analysis of rainfall data showed that on an annual basis, rainfall of the Labhandi station is 1202 mm received in 61 rainy days. The mean monthly rainfall of labhandi is 189, 337, 337 and 191 mm in the month of June, July, August and September, respectively with a total monsoon rainfall of 1054 mm on the data base of 1971-2013.

However when the monthly statistics has been put to probability analysis the rainfall quantity comes down drastically at 90%. 90 per cent probability means that we can expect this rainfall quantity in 9 out of 10 years. However, the assured probability level of 75% which is considered as assured rainfall in 3 out of 4 years. On annual basis, this quantity 1023.2 mm, at 50 probability annual rainfall quantity is 1231.8 mm and at 25% probability, annual rainfall quantity is 1397.5 mm. Moreover, August happens to be wettest month with rainfall quantity of 160, 228, 349, 457 mm at 90%, 75%, 50% and 25% probability level and its quantity is more than July month. It leads to conclusion that there is need of more preparedness on part of the farmers to go for water management (conservation & harvesting) practices in the months of July and August. Moreover, there is water conservation scope in the month of August which can later be utilized for rice crop at reproductive phase in the month of October. Rainfall quantity is falling drastically in the month of October and at 50 per cent probability, the expected rainfall is 34 mm only.

Similarly seasonal basis analysis has been carried out at different probability levels.  At 50 per cent probability, there is expected rainfall of 1040 mm in south-western monsoon season while at 75% probability level assured rainfall of 795 mm which is not sufficient for meeting the water requirement of long duration rice variety and even medium duration rice variety. Therefore, here lies risk in rainfed rice production as well as need for water harvesting in On Farm Reservoirs (OFRs) ),farm ponds  and other harvesting structures as per farming situation basis.

However, it is the distribution of rainfall on weekly basis which is very important and rainfall quantity expected at 75% probability is less than 50 mms in almost all the weeks south west monsoon period which adds uncertainty factor in rainfed rice production. Standard week number 28 is receiving highest rainfall quantity of 43 mm which can satisfy minimum water requirement of percolation and ET losses for rice crop. However at 50% probability which means on every alternate year basis, there is definite rainfall receipt of more than 45 mm per week during the period between 25 and 36 standard weeks and this period is in between 18 June and 9 September. Agricultural strategies, farming operations need to be based on this type of analysis and advisories should be planned accordingly.

Table 1: Monthly and Seasonal Expected Rainfall Amount (Mm) at Different Probability
Levels at Labhandi Station, Raipur.

Months	Probability levels
	90 %	75%	50%	25%
January	0	0	6	26
February	0	1	11	23
March	0	0	10	19
April	0	3	12	20
May	2	9	16	36
June	76	101	144	221
July	136	214	321	451
August	160	228	349	457
September	62	100	192	232
October	1	14	34	68
November	0	0	0	16
December	0	0	0	4
Season
Winter (Jan-Feb)	0	8	19	40
Summer (Mar-May)	16	25	40	70
Southwest monsoon (Jun-Sep)	702	795	1040	1207
Northeast monsoon (Oct-Dec)	8	23	52	120

 
Table 2: Expected Weekly Rainfall Amount (Mm) at Different Probability Levels

Standard week	Probability levels
	90 %	75%	50%	25%
23 (4th to 10 June)	0	0	6	32
24 (11th  to 17th June)	1	7	28	64
25 (18th  to 24th June)	5	17	46	80
26 (25th  to 1st July)	11	19	54	98
27 (2nd to 8th July)	10	24	53	78
28(9th to 15th July)	14	43	74	99
29(16th  to 22nd July)	17	38	58	111
30(23rd  to 29th July)	6	23	60	95
31(30th  to 5th August)	10	35	62	110
32(6th  to12th August)	16	36	71	104
33(13th  to 19th August)	9	27	77	126
34(20th  to 26th August)	6	26	47	84
35(27th  to 2nd September)	4	23	56	93
36(3rd  to 9th September)	3	22	45	82
37(10th  to 16th September)	11	19	29	61
38(17th  to 23rd September)	0	1	12	61
39(24th  to 30th September)	0	3	13	47

References
 

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