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Open Access
Statistical analysis of air pollution status in various urban areas of Odisha
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Kumar, Hitesh; Pradhan, P.N.; Paikaray, R.K.; Panda, Narayan
Air pollution is a major concern of new civilized world, which has a serious toxicological impact on agriculture, environment and human health. It has a number of different emission sources, but motor vehicles and industrial processes contribute the major part of air pollution. According to the World health organisation, major air pollutants include particle pollution, ground level ozone, sulphur dioxide and nitrogen oxide. Air pollutants have a negative impact on plant growth primarily through interfering with resource accumulation which affect metabolic function of the leaves and interfere with the net carbon fixation by the plant canopy. Long and short-term exposure to air suspended toxicants has a different toxicological impact on human including respiratory and cardiovascular disease, neuropsychiatric complications, eye irritation, skin diseases and long-term chronic disease such as cancer. In this present study, data from the year 2016 to 2020 is collected from the State Pollution Control Board website over eight monitored stations across Odisha. The eight monitored stations have been grouped into three divisions namely northern division, central division and southern division on the basis of RDC. The objective of this thesis is to discuss the status of four air pollutants viz., PM2.5, PM10, NO2, SO2 in the above eight monitored stations using descriptive statistics such as mean, median, range, standard deviation, correlation, etc., also draws trendlines of the air pollutants over monitored stations. Besides this, effects of four air pollutants on human health and agriculture are studied.
Open Access
Study on efficient Ratio-type exponential estimator for population variance
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Shreya, A.; Pradhan, P.N.; Paikaray, R.K.; Sarangi, Khitish Kumar
Since the ancient times, every government makes an effort to provide optimum facilities with minimum cost to their citizens for better standard of life for which government is employing multiple government and non-government organisations to undertake survey for annual and five-year plans. In this way, the horizon of research is enlarging day by day whereas money, time, manpower, equipment’s etc are posing serious constraints. In this scenario, sampling survey with applications is the only way out to handle such situations effectively with available resources at our hand. Around the world, many scientists have developed efficient methods of sampling constantly to accommodate recent problems like adequate quantity of food proportional to population in precision farming posing serious constraints. In this opportunity, we have taken four efficient sampling methods with specified margin of permissible of error at minimum cost are namely simple variance, traditional ratio estimator by Isaki (1983), Singh et al. exponential ratio-type estimator and Panda and Sen’s exponential ratio-type of estimator for our study. All the above methods are based on simple random sampling. For the above study, we took three numerical real-life examples on agriculture to compute mean square error of all estimators and subsequently also computed percentage gain in efficiency. Panda and Sen’s exponential ratio-type estimator is compared with simple variance, Isaki’s estimator and Singh et al.’s estimator numerically and finds that Panda and Sen’s estimators mean square error is less in comparison to others. Percentage gain in efficiency have also computed and it is noticed that percentage gain in efficiency is better than other estimators. We conclude here that Panda and Sen’s estimator is best among the estimators to accommodate recent problems in agriculture.
Open Access
Forecasting production of cereals and pulses in Odisha by using spline regression technique
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Basanaik, Harsha S; Dash, Abhiram; Paikaray, R.K.; Mishra, S.N.
Cereals and pulses come under most important crop groups of Odisha which are the most important determinant of agricultural status of the state. Forecasting of the production of cereals and pulses is of utmost importance to formulate the agricultural policy and strategy of the state. The ARIMA model can be reliably used to forecast for short future periods because uncertainty in prediction increases when done for longer future periods. The predictions obtained from the ordinary regression model are valid only when the relationship between the independent variables and the dependent variable does not change significantly in the future period which can be rarely assumed. Spline regression technique can be used for longer future period and also fits different curves for different section of data range without losing the continuity of the curve. In this way it is expected that the spline regression will overcome the respective discrepancies in both ARIMA and ordinary regression techniques of forecasting. With this view, the study is carried out to use spline regression model for forecasting purpose after partitioning the whole period of study, with the assumption that the future period which needs forecasting follows the same pattern as the last partitioned period. The entire period of data is split into different periods based on the scatter plot of the data and is confirmed by testing the significance of change in coefficient of variation between the different periods by chi-square test. The regression models found to be suitable are linear, compound, logarithmic and power model. The models are fitted by using the training set data. Selection of best fit model is done on the basis of overall significance of the model, model diagnostic test for error assumptions and model fit statistics. The selected best fit model is then cross validated with the testing set data. The models selected for cross validation purpose are compound spline model for area under kharif cereals and yield of rabi cereals and rabi pulses; power spline model for yield of kharif cereals and area under kharif pulses; logarithmic model for area under rabi cereals; linear spline model for yield of kharif pulses and area under rabi pulses. After successful cross validation of the selected best fit models, they are used for forecasting of the future values for their respective variables. Forecasting of area, yield and hence production of cereals and pulses for six years ahead i.e., for the year 2020-21 to 2025- 26 by using the selected best fit model after successful cross validation. The forecast values for production of kharif cereals are found to decrease despite increase in forecast values of yield which is due to decrease in forecast value of area. In case of rabi cereals, kharif and rabi pulses the forecast values for production are found to increase due to increase in forecast values of both area and yield.
Open Access
Variability analysis in rice production for the districts of western Odisha
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Das, Rakesh Ranjan; Dash, Abhiram; Paikaray, R.K.; Sarangi, K.K.
The economy of Odisha relies heavily on agriculture. Rice production is carried out in Odisha to meet the consumption needs of the growing population. The districts of western Odisha combined contribute 36.12 percentage to the total area under rice and 29.42 percentage to the total rice production in the state. To study the variation in production of kharif and rabi rice in the districts of western Odisha, an attempt is made in this study about the area, yield and production of kharif and rabi rice in these districts from the year 1993-94 to 2017-18. For this purpose, the whole period of study (1993-94 to 2017-18) is divided into two sub-periods i.e. sub-period-I (1993-94 to 2002-03) and sub-period-II (2003-04 to 2017-18) on the basis of scatter plot of area, yield and production of both kharif and rabi rice of Odisha. The study includes test of significance of change in variance of area, yield and production of kharif and rabi rice from sub-period-I to sub-period-II, by using Snedecor’s F-test. The test of significance of change in mean area, yield and production of kharif and rabi rice from sub-period-I to sub-period-II is then done by using Fisher’s t-test or t-test under Welsch approximation depending on significance of change in variance. Study of contribution of various components towards change in mean and variance of production of rice during kharif and rabi seasons in districts of western Odisha and the state as a whole is done by using Hazel′s decomposition technique. Fisher-z transformation is used to study the change in correlation between area production and yield-production of both kharif and rabi rice from sub-period-I to sub-period-II. Large and significant change in variation in area of kharif rice is found from the study which can be assigned to uneven cultivation of kharif rice. The significant variation in area leads to significant variation in production of kharif rice despite the insignificant change in yield. The significant variation in yield leads to significant variation in production of rabi rice despite the insignificant change in area. The results of the study revealed that change in yield variance, change in area variance and interaction between change in mean yield and area variance have highest contribution towards the variability in production of kharif rice in Odisha, whereas change in area yield covariance, change in area variance and interaction between change in mean area and yield and change in area-yield covariance have highest contribution towards variability in production of rabi rice in Odisha from sub-period-I to sub-period-II.
Open Access
Growth rate and instability analysis of food grain production in Odisha
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Sripriya, Jamana; Dash, Abhiram; Paikaray, R.K.; Mishra, S.N
The state of Odisha having an agrarian based economy depends largely on agriculture for the livelihood of its population. Food grains are important commodity of crop groups that provide high quality carbohydrates, protein and vitamins. A study on the compound growth rate of area, yield and production of food grains for both kharif and rabi season in the districts of Odisha and the state as a whole has been attempted in the present study which would be helpful in visualizing the progress of the state with respect to food grain cultivation and proper framing of agricultural policies of the state. The study is based secondary data for the period of 1993-94 to 2017-18 to estimate the compound growth rate and Cuddy-Della Instability Index of the districts and the state as a whole. The districts are ranked on the basis of compound growth rate and Cuddy-Della instability index in decreasing order and increasing order of their magnitudes respectively. The rank correlation between Compound Growth Rate and Cuddy-Della Instability Index of area, yield and production of food grains during kharif and rabi seasons are studied. Growth trends show that for the study period of 1993-94 to 2017-18, the highest value is found for yield and production of kharif food grains in the years 2012-13, 2014-15 and 2016- 17, where as the lowest values incurred under yield and production is in the year 2002-03. The highest values found for yield and production of rabi food grains were seen in the years 2013- 14 and the lowest values seen were in 1993-94. Among all the districts Ganjam showed significantly high mean production, Deogarh followed by Jharsuguda are among districts showing the lowest mean production under kharif food grains. Bargarh shows the highest mean production of rabi food grains among all the districts, Kandhamal and Jharsguda are the districts showing lowest mean production of rabi food grains. Study of the rank of districts on basis of compound growth rate of production of kharif food grains shows that the highest rank is of Nabarangpur district followed by Sonepur and for rabi food grainsthat is Nabarangpur followed by Nupada. Similarly the highest ranks for Cuddy-Della instability index for production of kharif is of Koraput district and for rabi food grains is of Sundargarh district respectively. The rank correlation coefficient between the compound growth rate and Cuddy-Della instability of production of kharif and rabi food grains are insignificant. The rank correlation between kharif and rabi food grains in terms of compound growth rate are significant and positive in case of area and insignificant in case of yield and production. The rank correlation between kharif and rabi food grains in terms of Cuddy-Della Instability Index is positive and insignificant.
Open Access
Decomposition analysis of production variance for pulses in coastal districts of Odisha
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Pradhan, Harekrushna; Dash, Abhiram; Paikaray, R.K.; Das, Sarbani
The economy of Odisha is based on agriculture and related industries. Pulses are nutritious, healthy, and simple to prepare. Pulses contribute to the provision of food and nutritional proteins in the diet. Green gram, black gram, horse gram, and pigeon pea are the four main pulses grown in the state. The coastal districts which are seven in number contribute respectively for about six percent and seven percent towards total pulse area and production in the state. The study of production variability of pulses in coastal districts of Odisha could help to find the reasons for such low contribution of the coastal districts towards the pulse cultivation in the state. The period from 1993-94 to 2017-18 has been considered for the study which is further divided into two sub-periods (1993-94 to 2004-05 and 2005-06 to 2017-18) on basis of scatter plot of the data. The Snedecor F-test, is used to compare the two variances of area/yield/production in two sub-periods. Fisher's t-test and t-test using Welch's approximation is used to test the significance of change in mean depending on whether the change in variance in the two sub periods are significant or not. Using Hazel′s decomposition technique, the contribution of various components towards changes in mean and variance of pulse production during the kharif and rabi seasons in coastal districts of Odisha and the state as a whole is studied. The Fisher-z transformation is used to test the significance of the difference in change of correlation coefficient between area and production and yield and production in the two sub-periods. It is found from the study that there is increase in mean area and yield of kharif pulses in Odisha from sub-period I to II resulting in an increase in the mean production from sub-period I to sub-period II. But in most of the coastal districts, both area and yield has decreased in second sub period leading to decrease in production. During rabi season, the mean area and yield of pulses in Odisha and also in most of the coastal districts increased from sub-period I to II resulting in an increase in the mean production from sub-period I to II. There is decrease in variability of area, yield and production of both kharif and rabi pulses in Odisha and also for most of the coastal districts.
Open Access
Forecasting of arrivals and prices of major vegetables in the markets around the capital region of Odisha
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Mishra, Rosni; Gupta, Akhilesh Kumar; Paikaray, R.K.; Sarangi, K.K.
The present study entitled “Forecasting of arrivals and prices of major vegetables in the markets around the capital region of Odisha” has been undertaken to study the secular trend, seasonality pattern of arrivals and prices of the major vegetables and forecast the arrivals and prices of the vegetables for the next 24 months (July 2021 to June 2023) using the best identified ARIMA models. The study showed that the arrivals of tomato and brinjal show an increasing trend in all the markets except for the Balugaon market. Whereas, the prices showed an overall increasing trend in all the markets. The analysis of the seasonal effect of arrivals of tomato shows that it is highest around the month of January for the Banki market whereas the effect is not clearly discernible for the Balugaon market. The same for prices of tomato shows that, it is highest around the month of July for both the markets of Banki and Balugaon. The analysis of the seasonal effect of arrivals of brinjal shows that, it is highest around the month of January for the Banki market, around the month of March for the Nimipara market and not clearly discernible for the Balugaon market. While the same for prices of brinjal shows that it is highest around the month of July in all the three markets. The forecast projections for tomato in Banki market suggest that highest arrivals of 33.14 tonnes/month and 34.30 tonnes/month are expected around august for both the years 2021- 22 and 2022-23 respectively. Whereas, the highest prices at Rs 4166.00/quintal and Rs 4275.07/quintal are expected around November for both the years 2021-22 and 2022-23 respectively. The same for tomato in Balugaon market suggest that highest arrivals of 13.16 tonnes/month and 9.60 tonnes/month are expected around March for both the years 2021-22 and 2022-23 respectively. Whereas, the highest prices at Rs 2929.15/quintal and Rs 3131.00/quintal are expected around June and November for the years 2021-22 and 2022-23 respectively. The forecast projections for brinjal in Banki market suggest that highest arrivals of 41.41 tonnes/month and 42.07 tonnes/month are expected around March for both the years 2021- 22 and 2022-23 respectively. Whereas, the highest prices at Rs 4249.20/quintal and Rs 4388.07/quintal are expected around November for both the years 2021-22 and 2022-23 respectively. The same for brinjal in Nimipara market suggest that highest arrivals of 65.52 tonnes/month and 62.18 tonnes/month are expected around December and July for the years 2021-22 and 2022-23 respectively. Whereas, the highest prices at Rs 4282.55/quintal and Rs 4427.65/quintal are expected around October for the years 2021-22 and 2022-23 respectively. For brinjal arrivals in Balugaon market, forecast projections could not be obtained because any of the tentative models didn’t satisfy diagnostics of model but there was no such issue for prices and forecast projections for brinjal price in Balugaon market suggest that highest prices at Rs 3151.70/quintal and Rs 3198.88/quintal are expected around August for both the years 2021-22 and 2022-23 respectively.
Open Access
Exploring appropriate model for growth rate estimation of pulse production in Odisha
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2021) Rout, Rakesh Kumar; Dash, Abhiram; Paikaray, R.K.; Das, Sarbani
Pulses come under food grain crop groups of Odisha. They are considered to be important crops for ensuring nutritional security of the state. Proper estimation of growth rate in production of pulse crops allows for more effective cropping system planning and formulation of the agricultural policy of the state. Since production depends on area and yield, the estimation of growth rates of area and yield of the crop are also important. The conventional growth models commonly used for calculating growth rate in agricultural production are based on the ordinary regression models which cannot capture any abrupt changes in the data over a long time period. As a result, these growth models frequently fail to adequately describe the data. To capture the variation in data in different phases of a long time period, spline regression is thought to be fruitful as it can fit different models in different segments of the time period as necessary without losing the continuity of the model. In the present study, to fit the spline regression model the entire period of data is divided into different segments based on the scatter plot diagram which is further confirmed by testing the significance of change in coefficient of variation between the different segments by chi square test. The regression model found to be suitable from the study of scatter plot of data are linear, compound, logarithmic, power, quadratic and compound model. The above models are fitted with both ordinary and spline regression approach. Both the groups of models are compared among each other and also within themselves. The best fit model is selected on the basis of error assumption test and model fit statistics such as R2 , adjusted R2 , MAPE, AIC and AICc. The respective selected best fit model is used for the estimation of growth rates of area, yield and production of kharif and rabi pulses in Odisha for each segment and the whole period of study. The fitted ordinary regression model could not qualify for the selection as they do not satisfy all the error assumptions and also have comparatively lower R2 and adjusted R2 values and higher values of MAPE, AIC and AICc than the spline regression models. Thus spline regression model are found to perform better than ordinary regression model. Among the spline regression models, the selected best fit model for a particular variable is used for its growth rate estimation. In both kharif and rabi season, it is found that though the growth rate in area and yield of pulses are not significant, the growth rate of production is found to be significant for the whole period of study which shows that the interaction effect of area and yield on production seems to dominate.
Open Access
Statistical trend analysis and ARIMA model of water quality variables for the Brahmani River in Odisha
(Department of Agricultural Statistics, OUAT, Bhubaneswar, 2020) K, Bhavya; Pradhan, P.N.; Paikaray, R.K.; Sarangi, Khitish Kumar
Water quality is a worldwide problem that affects the lives of human beings, plants, and animals fundamentally. Water scarcity is intensified as a result of quality deterioration. During the last decades, there has been an increasing demand for monitoring and forecasting of water quality of many rivers by regular measurements of various water quality variables. The present study provides a statistical analysis of trends and modeling of the water quality parameters of the Brahmani river, Odisha. The Station wise monthly data from six vital monitoring stations on four vital water quality variables (pH, DO mg/l, BOD mg/l, and TC MPN/100ml) for the period 2017-2019 were collected for this analysis. Time series analysis is worked out for six stations viz: Rourkela (D/S), Samal, Talcher (D/S), Dhenkanal (D/S), Pattamundai, and Aul as well as for the average monthly data of the Brahmani river. Mann Kendall non-parametric test is done using Rstudio-1.3.959 to detect trends direction and magnitude of change of the water quality parameters. The results revealed that there were both increasing and decreasing trends in the case of station wise data and monotonous decreasing trends in case of average monthly data of the Brahmani river. In case of pH of Aul and Pattamundai stations trends are increasing (Z=0.57 and 0.068 respectively), in case of DO mg/l of Dhenkanal (D/S) and Talcher (D/S) stations, there are increasing trends (Z= 2.51 and 0.74 respectively), in case of BOD mg/l of Aul, Pattamundai, Talcher (D/S) stations there are decreasing trends (Z=-1.68, -2.62 and -1.74 respectively) and whereas, in case of TC MPN/100ml monotonous decreasing trends (Z=-4.05) exists for all the stations. The ARIMA models were developed for forecasting and prediction of parameters by using the IBM SPSS software package. The order of the model for each parameter was determined using ACF and PACF of time series. The best model was selected based on the performance of selected model adequacy test criteria viz; , RMSE, MAPE, MAE, and BIC. Results revealed that ARIMA (1, 1, 1) model was found best fit for the pH of river water, the values are 0.201, 0.255, 2.686, 0.205, and, -2.430 respectively. Further, the ARIMA (2,2,5) model was found best fit for DO mg/l of water, the values are 0.190, 0.461, 4.555, 0.330 and, -0.719 respectively. Again, the best fit model for BOD mg/l was ARIMA (1,1,1), the values for the same are -0.233, 0.386, 20.989, 0.283 and -1.600 respectively. In the end, the ARIMA (3,1,1) model was pointed out as the best fit model for TC MPN/100ml of river water, the respective values are 0.256, 5016.875, 75.623, 3343.543, and 17.549. The study shows that the forecasted values of the parameters are reliable and accepted as the values lie within satisfactory limits making it suitable for irrigation purposes.

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