International Journal of Chemical Studies 2019; 7(3): 320-322 P-ISSN: 2349–8528 E-ISSN: 2321–4902 IJCS 2019; 7(3): 320-322 Estimation of the effect of weather parameters on © 2019 IJCS Received: 18-03-2019 castor yield of Banaskantha district in Gujarat Accepted: 22-04-2019 AG Sabhaya AG Sabhaya, DV Patel and PB Marviya Department of Agricultural Statistics, Junagadh Agricultural University, Junagadh, Gujarat, Abstract India In the present study, “Estimation of the effect of weather parameters on castor yield in Gujarat” attempts have been made to develop models for forecasting castor yield at Banaskantha district on the basis of DV Patel weather variables. Weekly data from 32nd meteorological standard week (MSW) to 9th standard week of Associate Professor, Department next year. The weekly average of weather variables (rainfall, maximum and minimum temperature, of Agricultural Statistics, morning and afternoon relative humidity and sunshine hours) over a span of 31 years period (1981-82 to Junagadh Agricultural 2011-12) has been used along with the annual castor production data for Banaskantha district of Gujarat University, Junagadh, Gujarat, state. For early forecast, 12, 9, 6 and 3 weeks intervals were considered. The stepwise regression India procedure was adopted using 31 years data for selection of variables. These models for respective districts can be used for providing pre-harvest forecast, 9 weeks before expected harvest in case of PB Marviya Banaskantha districts. The study showed that models selected for pre-harvest forecasts explained more Assistant Professor, Department than 90% for Banaskantha district. The errors of simulated forecasts were less than 3 per cent in model. of Agricultural Statistics, Junagadh Agricultural University, Junagadh, Gujarat, Keywords: Statistical model, weather forecasting, castor, regression analysis India Introduction Castor is an important crop grown under rain fed as well as irrigated condition. India ranks first in castor production in the world about 80% per cent of total world castor demands. The area, production and productivity of the country during 2012 were 8.59 lakh ha. 11.90 lakh [2] tones and 1385.0 kg per ha. Respectively (Anonymous 2012-13) . The major growing states in India are Gujarat, Rajasthan, Andhra Pradesh, Tamil Nadu, Karnataka and Orissa. Gujarat plays significant role in castor by contributing 65 per cent of the total production in the country. The area, production and productivity of the state during 2012 were 4.90 lakh ha. 9.86 lakh tonnes and 2010.0 kg per ha. Respectively (Anonymous 2012-13) [2] . In Gujarat, Banaskantha, Mehsana, Sabarkantha are major castor growing district. The importance of castor crop to agricultural economy of India can hardly be over emphasized. Crop production as specially for kharif crop is highly depends on Weather variables. The present investigation is proposed to develop pre-harvest forecast models for castor yield based on weather parameters like rain fall, maximum and minimum temperature, morning and evening relative humidity, bright sunshine hours which constitute the major climatic influence contributing to the growth and development of the crop directly or indirectly, covering castor growing region and district of Gujarat state. Material and Methods The weather variables like like rainfall, maximum and minimum temperature, relative humidity and sunshine hours affect growth and development in different ways and at different times during the growth cycle of the crop. The relationship between crop yields and weather parameters can be identified with the help of multiple regression models (Agrawal and Mehta, 2001) [1]. With a view to development of forecasting model of castor for Banaskantha district of Gujarat by using combined effect of weather parameters, the castor yield data of Banaskantha district for the years 1981-82 to 2011-12 was obtained from the Department of Agriculture and Correspondence cooperation, Government of Gujarat (Anonymous 2012-13) [2], and the meteorological data of AG Sabhaya Department of Agricultural Banaskantha (Lat: 24.34° N, Long: 71.76° E) was collected from Banaskantha for the Statistics, Junagadh Agricultural corresponding period. University, Junagadh, Gujarat, Weekly weather variables viz. (X1) weekly total rain fall, (X2) Maximum temperature, (X3) India Minimum temperature, (X4) Morning relative humidity, (X5) After noon relative humidity and ~ 320 ~ International Journal of Chemical Studies (X6) Sunshine hours were collected for growing season of aij and b = Partial regression coefficient associate with each castor in Banaskantha district for the years under Xij and time trend, respectively. consideration. The sowing of castor mainly around second To determine the effect of week wise weather variables on week of August in Gujarat. Hence data pertaining to weather castor yield, the variables which appeared in the equation and parameters for the period 32nd week to 9th meteorological significant partial regression coefficient were considered to standard week of succeeding year. have influence for each crop period for the model. First part For selecting the best regression equation with significant deals with fitted regression equations and second part deal independent variables among number of independent, the with their corresponding simulated forecast subsequent years stepwise regression procedure was adopted (Draper and not including for obtaining the regression. Smith, 1966) [3]. Statistical computer software SPSS was used for the analysis of the data with the probability level 0.05 and Result and Discussion 0.1 to remove the variables using weekly weather variables. The result related to 18 week crop period model, indicated th With a view to assess the accuracy and capability of earlier that time trend(T), rainfall of 11 week (X111), morning th forecasts at an interval of weeks, four models were fitted, relative humidity of 5 week (X405), afternoon relative th th considering up to 18, 21, 24 and 27 weeks after sowing during humidity of 11 week (X511) and sunshine hours of 5 week the crop period. The time trend variable was included in this (X605) were significantly and positively influenced in all four analysis as an explanatory variable. models. The weeks correspond vegetative to flowering stage The mathematical expression of this model is. of castor crop. The variation explained by these variables in fitted models of 18 week period data ranged from 87.92% to P w 88.71%. The simulated forecasts showed 2.35 to 22.62 per cent deviations from the actual castor yield. Y = A + ∑ ∑ aijXij + bT The result presented in case of 21 week crop period model, revealed that variables time trend (T), rainfall of 3rd week i=1 j=1 th (X103), morning relative humidity of 21 week (X421), afternoon relative humidity X and sunshine hours of 5th Where 511 week (X605) were positive and significant in all four model. Y = Average castor yield of district kg/ha th The maximum temperature of 12 week (X212) was negative A = Constant and significantly predicted the yield. The weeks correspond to th th Xij = Observed value of i weather variable in j week, i = 1, germination to spike formation stages of castor crop. The 2… P = 6 and coefficient of determination (R2) varied from 90.14% to j = 1, 2, ….w = 18, 21, 24 and 27 90.90%. The simulated forecasts showed deviation from T = Year, (T = 1, 2, 3, ……..31) observed yield ranged from 3.73 to 24.14 per cent (Table-2). Table 1: Regression equations for 21 week crop period of Banaskantha district Models for different years Variables in model Model-I 2007-08 (27 year) Model-II 2008-09 (28 year) Model-III 2009-10 (29 year) Model-IV 2010-11 (30 year) Constant -2361.75 -2453.31 -2453.89 -2299.86 T 29.50** 28.78** 28.15** 27.98** X103 78.36** 80.54** 77.77** 79.24** X212 -1.49* -1.51* -1.43* -1.39* X421 0.20* 0.20* 0.32* 0.28* X511 0.12* 0.12* 0.10* 0.13* X605 4.72** 4.75* 4.21** 4.83** S.E. 143.62 139.71 138.67 129.96 R2 (%) 90.90 90.14 90.72 90.21 *Significant at 5% level. ** Significant at 1% level. Table 2: Simulated forecast values for 21 week crop period of Banaskantha district Predicted values (kg/ha) Observed yield Year Model-I 2007-08 (27 Model-II 2008-09 (28 Model-III 2009-10 (29 Model-IV 2010-11 (30 (kg/ha) year) year) year) year) 2008-09 1995 2300.20(15.30) -- -- -- 2009-10 2101 1811.20(13.79) 1788.32(14.88) -- -- 2010-11 1970 2428.1(23.25) 2410.21(22.35) 2445.52(24.14) -- 2011-12 2120 2210.21(4.25) 2199.12(3.73) 2229.01(5.14) 2001.30(5.60) 2012-13 2413 2024.21(16.11) 1998.25(17.19) 1969.95(18.36) 2010.91(16.66) Figures in () are percent deviation from observed yield. The result presented in case of 24 week crop period model, forecasts obtained from these prediction equations showed revealed that variables time trend, X103 and X511 were 3.22 to 21.38 per cent deviations from the recorded castor significantly and positively affected in all four models but in yields of the district. th case of X212, minimum temperature of 21 week (X321) and The fitted regression equation related to 27 week crop period X421 were negatively influenced on yield of castor. The weeks model, indicated that influence of variable positively and correspond to germination to spike formation stages of castor significant for (X225) in all four models, but in case of rainfall th st crop. The variation explained by these variables in fitted of 22 week (X122) and minimum temperature of 1 week models ranged from 89.89% to 90.41%. The simulated (X301) were negatively and significantly influenced in Spike ~ 321 ~ International Journal of Chemical Studies formation stage on the castor yield.