Development of regression models in ber genotypes under the agroclimatic conditions of south-western region of Punjab, India

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1 Indian J. Agric. Res., 49 (3) 2015: Print ISSN: / Online ISSN: X AGRICULTURAL RESEARCH COMMUNICATION CENTRE Development of regression models in ber genotypes under the agroclimatic conditions of south-western region of Punjab, India Navjot Gupta*, K.K. Gill 1 and Ritu Babuta 2 Regional Station, PAU, Bathinda , India. Received: Accepted: DOI: / X ABSTRACT The field experiment was conducted at PAU, Regional Station, Bathinda, for consecutive 7 years ( ) to study the crop growth behavior and yield characteristics of ber fruit crop and an attempt was made to correlate the ber fruit characteristics with weather parameters over the years. The seven years data on fruit characteristics and weather parameters were taken on yearly basis and correlations were developed between ber fruit yield, fruit weight, acidity, pulp percentage, TSS and corresponding weather parameters namely minimum temperature, maximum temperature, morning relative humidity, evening relative humidity and rainfall. The regression analysis was done for four varieties of ber viz., Umran, Wailati, Chhuhara and Seb and the most suitable variety best suited for the weather conditions of southwestern region was worked out. The most sensitive period for fruit yield sensitivity appears to be during November to March months for the ber varieties under study. Different varieties responded differently with weather parameters for their different characteristics. The multiple regression models were developed on the basis of critical periods pertaining to crop yield characteristics for different varieties separately and it was found that % variation in fruit yield and yield attributing characters were explained by weather parameters. Key words: Ber varieties, Ber yield, Correlation, Regression, Weather parameters, Yield attributing characters. INTRODUCTION Weather is widely recognized as an important determinant of the level and variability of crop yield. Weather affects crop growth at different phenological stages. Therefore, fluctuations in crop behavior and yield are observed from year to year mainly due to weather parameters. A number of statistical crop models such as multiple regressions, principal component analysis and agro meteorological models have been used to quantify the response of crops to weather. Multiple regression models have been developed to forecast yields of different crops in India like Bal et al (2004) for wheat, Mallick et al (2007) for rice and Gill et al (2007) for brassica for central Punjab. Kumar and Bhar (2005) and Sharma et al (2010) for other regions have also used to quantify the response of weather towards different crops. But forecasting fruit yield by using meteorological parameters for southern region of Punjab is the first attempt of its kind. Ber (Zizyphus mauritiana), belonged to the family Rhamnaceae comprising 45 genera and 550 species, is widely distributed in tropical and subtropical climates in the world and it can tolerate very high summer temperatures (49-50 o C), fruit-set can be affected at temperatures above 35 o C. However, trees are often grown successfully at temperatures of o C. The trees shed their leaves and enter dormancy in extremely hot summers. Growth, flowering and fruiting development phases may vary depending on the temperature conditions. Ber trees can withstand very short periods of freezing temperatures, however, frost will damage the young twigs and developing fruits, and may kill the tree. For rainfed production of ber, a minimum average yearly rainfall of 400mm is required. Performance of the ber tree is adversely affected in humid areas having more than 1500mm annual rainfall. Fruit yields are higher in high rainfall years and lower in low rainfall years. The incidence of pests is also lower in low rainfall areas. Keeping the importance of fruit and its great dependence on weather in mind, an attempt has been made to develop Agrometeorological ber-yield forecasting model for southwestern region of Punjab. MATERIALS AND METHODS Correlation study between ber fruit yield and quality and corresponding weather parameters namely minimum and maximum temperature, morning and evening 1-2 School of Climate Change & Agricultural Meteorology, PAU Ludhiana , India.

2 Volume 49, Issue 3, 2015 relative humidity and rainfall was done for period from 2003 to Sensitive period for various fruit crop characteristics with respect to weather parameters were identified and the most suitable variety in terms of suitability of weather conditions was worked out by analyzing four varieties of ber viz., Umran, Seb, Chhuhara and Wailaiti planted at PAU, Regional Station, Bathinda, Punjab. The average rainfall at the experimental site was 400 mm, the annual maximum temperature was 31.5 O C and annual minimum temperature was 16.9 o C. The soil was sandy loam and characterized with ph (8.31), organic carbon (0.32%), electrical conductivity (0.23dS/ m), available N (212 kg/ha), available P (21.5 kg/ha) and available K (357.0 kg/ha). The ten years old plants of each variety was planted in randomized block design with four r epli cat ion s at t he spaci ng of 7.5 m X 7.5 m. Recommended package of practices were followed for cultivation of fruit plants. A multiple regression model was also developed between critical weather parameters during critical period of growth and yield of fruit crop. The multiple regression models were developed for different varieties according to their sensitivity towards weather parameters and critical periods were identified for different varieties. The weekly data of different weather elements for the years were collected from the Agrometeorological observatory situated in Regional Station, Bathinda for the months covering life cycle of the crop except harvesting period. Method of analysis: The traditional multiple regression technique has been employed to develop forecasting models. The average reported yield was taken as dependent variable with weather parameters as independent variables. The general form of the model is: n Y e = a 0 + Ó a 1 x 1 + Ó a 2 x 2.. i = 1 Where, Y e = Estimated yield, kg/ha a 0 = Regression constant a 1 = Regression coefficients for meteorological predictor variables x 1 = i th meteorological predictor variable i = 1, 2, n In the correlation and regression technique significant correlation between yield and the meteorological parameters were identified. The critical periods when weather parameters exert significant influence on yield were located by analyzing the correlation coefficients for statistical and phenological significance. They were then used to calculate the multiple correlations with the yield. Multiple correlations of all the combinations were calculated by dropping one or more variables, which were found less significant. In the development, those parameters (maximum temperature, minimum temperature, morning & evening relative humidity and rainfall) which are statistically significant at the mandatory levels, were used in the final equation. RESULTS AND DISCUSSION Sensitive periods and parameters: Out of all the periods, the sensitive periods of statistical and phenological significance were selected in terms of standard meteorological weeks (SMWs) for regression analysis. Umran: The Table 1 represents the correlation values fruit yield for Umran cultivar of ber for a historical period of 7 years from 2003 to Maximum temperature (Tmax, afternoon temperature) and minimum temperature (Tmin, evening temperature) was highly positively correlated with TABLE 1: The regression equations developed for fruit yield, fruit weight, TSS, acidity and pulp percentage of ber cv. Umran Fruit yield Y = X1-1.76X X X X X X X X X10 Fruit weight Y = X X X X X X X X X9 TSS Y = X1 0.51X X X X5 Acidity Y = X X X X X5 0.02X X X X X10 Pulp % Y = X X X3 1.86X X X X X X9 0.01X10 R 2 = 0.98 X1 = Tmax , X2 = 45 48, X3= 50 1,X4 = Tmin , X5 = 4 10, X6 = RHm 35-43,X7 = 46 1, X8= 7-9, X9 = RHe 1-5, X10 = Rain R 2 = 0.97 X1 = Tmax , X2 = Tmin , X3 = 8 10, X4 = RHm 31-38, X5 = 40 49, X6 = 52-6, X7 = RHe 39-44, X8 = 47 49, X9 = Rain R2= 0.99 X1= Tmax , X2= Tmin.35-36, X3= RHm 40-42, X4= RHe 45-48, X5= Rain R 2 = 0.98 X1 = Tmax , X2 = 1-3,X3 = Tmin , X4 = 45 48, X5 = RHm 23-26, X6 = 28 30, X7 = RHe 47-49, X8 = 1 4, X9 = Rain 47-49, X10 = R 2 = 0.84 X1 = Tmax , VX2 = 50 52, X3 = 7 11, X4 = Tmin. 5-7, X5 = RHm 38-41, X6 = 43 51, X7=8 12, X8 = RHe 44-46, X9 = 6 12, X10 = Rain 6-9,

3 262 INDIAN JOURNAL AGRICULTURAL RESEARCH yield correlation coefficient of r = 0.78 and 0.59 from SMW 50-1 and respectively. It was observed that the maximum % RH was negatively correlated with fruit yield during second fortnight of February with coefficient value of r = For fruit weight, maximum temperatures and morning RH were positively correlated with weight during the month of November-December with coefficient of correlation value of 0.71 and 0.68 respectively. The TSS was negatively correlated with evening RH for SMW The acidity value of fruit was positively correlated with both the maximum and minimum temperatures with value of r ranged from 0.70 to 0.83 whereas negatively correlated with evening RH (r = -0.84) for SMW 1-4. The pulp percentage was negatively correlated with maximum temperature during the month of December and positively correlated with rainfall for SMW (6-9). Wailaiti : The Table 2 represents the correlation values fruit yield for Wailaiti cultivar of ber from 2003 to The most sensitive period for fruit yield sensitivity appears to be during November-December months. Temperature maximum (Tmax) was positively and minimum (Tmin) was negatively correlated with yield with correlation coefficient of r = 0.63 and r = at SMW Jamadar et al., (2003) also observed that the peak per cent disease index of ber powdery mildew was recorded between 49 to 52 meteorological weeks with a significant relation (r= ) between PDI and minimum temperature. The afternoon RH and rainfall was negatively correlated with fruit yield, with coefficient value of r = and -0.56, respectively during the months of February-March. For fruit weight, all the weather parameters viz. maximum & minimum temperatures, morning and evening RH and rainfall was negatively correlated with coefficient of correlation value r = for Tmax (51-01), r = for Tmin (42-46), RH (r=-0.50) for 5-13 SMW and rainfall (r=0.81) for 52-2 SMW. For TSS, all the weather parameters viz. maximum & minimum temperatures, morning and evening RH and rainfall was negatively correlated with coefficient of correlation value ranging from to The acidity value of fruit was negatively correlated with the TABLE 2: The regression equations developed for fruit yield, fruit weight, TSS, acidity and pulp percentage of ber cv. Wailaiti Fruit yield Y= X X X X X X X X 8 Fruit weight Y = X X X X X X X X X X 10 TSS Y = X X X X X5 Acidity Y = X X X X X 5 R 2 = 0.95 X 1 = Tmax , X 2 = X 3 = Tmin , X 4 = 8-13 X 5 = RHm 35-39, X 6 = X 7 = RHe 6-13, X 8 = Rain 9-12 R 2 = 0.94 X 1 = Tmax , X 2 = X 3 = Tmin , X 4 = 9-12 X 5 = RHm 36-45, X 6 = 5 13 X 7 = RHe 42-46, X 8 = X 9 = Rain 47-49, X 10 = 6-9 R 2 =0.90 X1= Tmax 39-41, X2= Tmin 44-46, X3= RHm 43-45, X4= RHe 42-46, X5= Rain 8-9 R 2 = 0.86 X 1 = Tmax. 2-13, X 2 = Tmin. 3-9 X 3 = RHe 44-49, X 4 = 4-11 X 5 = Rain 52-2 Pulp % Y = X X X 3 R 2 = 0.84 X 1 = Tmin , X 2 = RHm X 3 = RHe 4 12 TABLE 3: The regression equations developed for fruit yield, fruit weight, TSS, acidity and pulp percentage of ber cv. Chhuhara Fruit yield Y = X X X X X X X X X X 10 Fruit weight Y = X X X X X X X X8 TSS Y = X X X X X5 Acidity Y = X X X X X X X X8 Pulp % Y = X X X X X X X X8 R 2 = 0.95 X1 = Tmax , X2 = 2-7, X3 = Tmin , X4 = 3 7, X5 = RHm 35-39, X6 = X7 = RHe 43-47, X8 = 4-13, X9 = Rain 38-41, X10 = R 2 = 0.94 X1 = Tmax. 5-10, X2 = Tmin , X3 = 9-13, X4 = RHm 46-6, X5 = RHe 46-50, X6 = 9-13, X7 = Rain 6-9, X8 = R 2 = 0.99 X1= Tmax 48-50, X2= Tmin 34-36, X3= RHm 47-51, X4= RHe 42-44, X5= Rain 6-7 R 2 = 0.94 X1 = Tmax.4-6, X2 = 8-12, X3 = Tmin , X4 = 4-7, X5 = RHm 7-9, X6 = RHe 42-44, X7 = 3-12, X8 = Rain 4-12 R 2 = 0.92 X1 = Tmax.39-41, X2 = Tmin , X3 = 40 46, X4 = RHm 20-26, X5 = 31 8, X6 = RHe 42-47, X7 = 8-12, X8 = Rain 10-13

4 Fruit yield Y = X X X X X X X X X9 Fruit weight Y = X X X X X5 TSS Y= X X2 0.32X X X5 Acidity Y = X X X X X X X X X X X11 Volume 49, Issue 3, 2015 TABLE 4: The regression equations developed for fruit yield, fruit weight, TSS, acidity and pulp percentage of ber cv. Seb R 2 = 0.93 X1 = Tmax , X2 = 47-50, X3 = Tmin , X4 = X5 = RHm 42-48, X6 = 7 11, X7 = RHe 44-46, X8 = Rain 39-41, X9 = 2-4 R 2 = 0.91 X1 = Tmax , X2 = RHm 38-50, X3 = 52 8, X4 = RHe 50-52, X5 = 8-11 R 2 = 0.99 X1 = Tmax , X2= Tmin , X3= RHm 51-2, X4=RHe 42-45, X5= Rain R 2 = 0.92 X1 = Tmax. 1-16, X2 = 9-13, X3 = Tmin. 4-7, X4 = 9 13, X5 = RHm 46-4, X6 = 6 13 X7 = RHe 52-3, X8 = 9-13,X9 = Rain 47-49, X10 = 52-2, X11 = 7-13 Pulp % Y = X X X3 R 2 = 0.84 X1 = Tmax , X2 = 1 3, X3 = Tmin ,X4 = RHm 46-3, X5 = RHe 44-47, X6 = 9-13 X7 = Rain maximum temperature, minimum temperatures, RH (morning and evening) and positively correlated with rainfall during the period of December- January and with value of r ranges from to The pulp percentage was positively correlated with minimum temperature with value of r= 0.64 during the sensitive period for winter months of November- December and with evening RH from the period of January- February. Chhuhara: The Table 3 represents the correlation values fruit yield for Chhuhara cultivar of ber for a period of 7 years from 2003 to The most sensitive period for fruit yield sensitivity appears to be during November and December months. Temperature maximum (Tmax, afternoon temperature) and Minimum (Tmin) was negatively correlated with yield from SMW of and 3-7 with correlation coefficient of r= It was observed that RH (evening) and rainfall was negatively correlated with fruit yield, with coefficient value of r = and respectively. The decrease in fruit yield may be due the incidence of powdery mildew. Jamadar and Jahagirdar (2004) also observed the peak incidence of powdery mildew in the month of October and the disease initiation took place at meteorological weeks. Similarly for fruit weight, maximum temperature was positively correlated whereas morning RH and rainfall were negatively correlated with weight with coefficient of correlation value of and respectively for the period of November- December and February-March. The TSS was negatively correlated morning RH (r= -0.57) for SMW and with rainfall (r=-0.69) for SMW (6-7). The acidity of fruit was negatively correlated with maximum and minimum temperatures, RH (morning) and positively with RH (evening) and rainfall during the period of February-March. The pulp percentage was negatively correlated with morning RH (r=-0.77) and positively correlated with evening RH (r = 0.85) for the months of February-March. Seb: The Table 4 represents the correlation values calculated for various environmental factors with respect to fruit yield for Seb cultivar of ber for a historical period of 7 years from 2003 to Temperature maximum (Tmax, afternoon temperature) and minimum temperature (Tmin, morning temperature) was highly positively correlated with yield from SMW and with correlation coefficient of r = 0.73 and 0.72 respectively. It was observed that RH (morning) was negatively correlated with fruit yield, with coefficient value of r = -0.75, during the month of February-March. For fruit weight, morning RH were negatively correlated with weight with coefficient of correlation value r= The TSS was negatively correlated Morning RH (-0.38). The acidity of the fruit was negatively correlated with the maximum and minimum temperatures with value of r = and for 1-6 and 4-6 SMW, respectively whereas negatively correlated with RH (morning, evening) and rainfall with value of r= 0.46, 0.68 and 0.79 respectively during the sensitive period for winter months from February- March. The pulp percentage was negatively correlated with maximum temperature (r=-0.76) for 1-3 SMW whereas it was positively correlated with morning RH (r=0.61) and rainfall (r=-0.38) during the sensitive period of December-January and February-March respectively. CONCLUSION The fruit yield in the region fluctuates from year to year and the best weather variables we examined for describing the yield characteristics were temperature, relative humidity and rainfall. The different yield characters viz. fruit yield, fruit weight, TSS, acidity and pulp percentage showed different response to different weather parameters and

5 264 INDIAN JOURNAL AGRICULTURAL RESEARCH coefficient of determination explained 84 to 98 % variation in Umran, 84 to 95 % for Wailaiti, 92 to 99% for Chhuhara and 84 to 99 % for Seb. The great relevancy has been showed by the models and in view of the above analysis, there is ample scope of these studies on which advisory systems can be developed for the farmers of the region so that timely operations can be done to reduce the losses to some extent and farmers may be benefitted. REFERENCES Bal, S. K.; Mukherjee, J.; Mallick, K. and Hundal, S. S. (2004). Wheat yield forecasting models for Ludhiana district of Punjab state. J. of Agromet. 6: Gill, K. K.; Kingra, P. K.; Mukherjee, J. and Bal, S. K. (2007). Brassica yield forecasting in central Punjab. Indian J. Ecol. 34: Jamadar, M. M. and Jahagirdar, S. (2004). Influence of pruning periods on the incidence of and severity of powdery mildew on ber in northern Karnataka. Indian Phytopath. 57: Jamadar, M. M.; Jahagirdar, S. and Venkatesh, H. (2003). Incidence and severity of powdery mildew as influenced by different pruning periods on ber in northern Karnataka. p.62. In: Paper presented in the 56th Annual Meeting of Indian Phytopathological Society and National Symposium on Crop surveillance: Disease forecasting and management, IARI, New Delhi, Feb , Kumar, A. and Bhar, L. (2005). Forecasting model for yield of Indian mustard (Brassica juncea) using weather parameters. India J. Agril. Sciences 75 : Mallick, K.; Mukherjee, J.; Bal, S. K.; Bhalla, S. S. and Hundal S. S. (2007). Real time rice yield forecasting over central Punjab region using crop weather regression model. J. of Agromet. 9: Sharma, R. C.; Gill, K. K. and Sharma, I. (2010). Forecasting paddy bunt disease in rice of Punjab-An Agrometeorological approach. Indian Phytopath. 63:

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