Thumbnail Image

Dr. Rajendra Prasad Central Agricultural University, Pusa

In the imperial Gazetteer of India 1878, Pusa was recorded as a government estate of about 1350 acres in Darbhanba. It was acquired by East India Company for running a stud farm to supply better breed of horses mainly for the army. Frequent incidence of glanders disease (swelling of glands), mostly affecting the valuable imported bloodstock made the civil veterinary department to shift the entire stock out of Pusa. A British tobacco concern Beg Sutherland & co. got the estate on lease but it also left in 1897 abandoning the government estate of Pusa. Lord Mayo, The Viceroy and Governor General, had been repeatedly trying to get through his proposal for setting up a directorate general of Agriculture that would take care of the soil and its productivity, formulate newer techniques of cultivation, improve the quality of seeds and livestock and also arrange for imparting agricultural education. The government of India had invited a British expert. Dr. J. A. Voelcker who had submitted as report on the development of Indian agriculture. As a follow-up action, three experts in different fields were appointed for the first time during 1885 to 1895 namely, agricultural chemist (Dr. J. W. Leafer), cryptogamic botanist (Dr. R. A. Butler) and entomologist (Dr. H. Maxwell Lefroy) with headquarters at Dehradun (U.P.) in the forest Research Institute complex. Surprisingly, until now Pusa, which was destined to become the centre of agricultural revolution in the country, was lying as before an abandoned government estate. In 1898. Lord Curzon took over as the viceroy. A widely traveled person and an administrator, he salvaged out the earlier proposal and got London’s approval for the appointment of the inspector General of Agriculture to which the first incumbent Mr. J. Mollison (Dy. Director of Agriculture, Bombay) joined in 1901 with headquarters at Nagpur The then government of Bengal had mooted in 1902 a proposal to the centre for setting up a model cattle farm for improving the dilapidated condition of the livestock at Pusa estate where plenty of land, water and feed would be available, and with Mr. Mollison’s support this was accepted in principle. Around Pusa, there were many British planters and also an indigo research centre Dalsing Sarai (near Pusa). Mr. Mollison’s visits to this mini British kingdom and his strong recommendations. In favour of Pusa as the most ideal place for the Bengal government project obviously caught the attention for the viceroy.

Browse

2016 - 201922020 - 20229
Reset filters
Subject: Agricultural Statistics and Informatics × Start date: 1998 × Type: Thesis ×
Reset filters

Search Results

Now showing 1 - 9 of 11
  • Thumbnail Image
    ThesisItemOpen Access
    STUDY ON CROP YIELD RESPONSE TO CLIMATE VARIABILITY IN MUZAFFARPUR DISTRICT OF BIHAR
    (Dr.RPCAU, Pusa, 2022) PRAVEENKUMAR; Nidhi
    In India, a warming trend has become pronounced over the past couple of decades and is predicted to intensify in the years to come. Climate change poses an increasing threat to agricultural production. In order to project the future impact of climate change on crop production, crop models and climate change scenarios have been widely used. While climate change has limited impacts on crop production, some evidence of its impacts has been found. A variety of view points are taken into account when studying the effect of climate change in different parts of the country. According to the study, agriculture is more affected than any other sector in the country. Over the past few years, numerous studies have been conducted to illustrate the fact that annual temperature changes, changes in relative humidity, changes in evaporation, and changes in rainfall patterns have all become more evident on a global scale. Different statistical methods are used to examine the effects of climatic factors, such as Temperature (Maximum and Minimum), Rainfall, and Evapotranspiration variation on wheat and rice yields in Muzaffarpur district of Bihar. Data on wheat and rice was obtained from ICRISAT Hyderabad for period 52 years (1966-2017) and data on weather variables were obtained from Centre for Advanced Studies on Climate Change, RPCAU, Pusa. Annual data on weather variable was obtained from ICRISAT Hyderabad for time period 1958-2015. In this study, investigation of significant trends in climate variables over period of time was done for Muzaffarpur district. Trend was detected using non-parametric (Mann-Kendall) trend test. Theil Sen's slope estimator was used to determine the magnitude of the trend, and percentage change in various variables was calculated. Detection of shift in weather variables was found out using change point analysis (Pettitt test). Positive trends were observed in maximum and minimum temperature, Evapo-transpiration. Negative trend in rainfall. Weather variables showed significant change point at 1992 (Maximum temperature), 1987 (minimum temperature), 1988 (Rainfall), and non-significant change was detected in Evapo-transpiration (1991). The effect of weather variable on yield of Wheat and Rice analysed by multiple linear regression. Model explained 24.3 % and 2.9 % of variability in wheat and rice respectively.
  • Thumbnail Image
    ThesisItemOpen Access
    TREND ANALYSIS OF WHEAT PRICE IN BIHAR AND FORECASTING OF WHEAT PRODUCTIVITY BASED ON BIOMETRICAL CHARACTERS IN SIWAN DISTRICT OF BIHAR
    (DRPCAU, PUSA, 2021) kumari, Pratibha; Kumar, Mahesh
    The present study deals with the forecasting of price of wheat (Triticum aestivum L) in Bihar through Autoregressive Integrated Moving Average ARIMA model for the five year ahead forecasting. Study also deals yield forecasting of wheat in Siwan district of Bihar based on biometrical characters along with farmers appraisal with their socioeconomic condition. For this research for forecasting of price trend analysis with ARIMA model were used. There were found to be ARIMA (0 1 1) is best and it forecast model for wheat price of Bihar is as below equation -1. It were found that its statistic values are R-square=0.944, RMSE= 87.966, MAPE= 6.751, MAE-70.295 and BIC=9.47. It were found that the forecasted price of wheat for the year 2018, 2019, 2020, 2021 and 2022 were Rs.1849.87/q, Rs.1891.98/q, Rs.1970.92/q Rs.2049.86/q and Rs.2128.80/q respectively for Bihar with error percentage 5.84 % for the year 2018. For forecasting of yield of wheat in Siwan district observations on plant biometrical characters were taken, such as average plant population per m2 (X1), average plant height in cm (X2), average number of tillers per m2 (X3), average length of Panicle in cm (X4), application of nitrogen (N) in kg/ha (X5), application of phosphorus (P2O5) in kg/ha (X6), application of potassium (K2O) in kg/ha (X7), irrigation level in numbers (X8), disease infestation in percentage (X9) and average plant condition (X10) according to eye estimates of farmers.There were recorded from data from 50 farmers of Siwan district of Bihar. Multistage (three stage) sampling was used for selecting samples.. The block were first stage unit, village as second stage unit and farmers were third stage unit of selection. All possible regression analyses were carried out to select the best combination of variables on the basis of some important statistics such as , RMSE= 0.6738, R2 =0.9598, Adj-R2 =0.9568 ,CV= 5.2132,Dependent mean = 5.2132. Graph of Fit diagnostics for yield, Superimposition of Graph of model predicted value and its residual as well as its actual value , clearly indicate the suitability of model developed. Further assessment regarding socioeconomic condition of people of Siwan various statistic were used like correlation, mean, regression for assessment of income and employment opportunity in Siwan district of Bihar. Forecast Model for wheat price in Bihar: - Zt – Zt-1 = -130.681+ 0.317 (at-1 - at-2) + at ……(1) Forecast Model for wheat yield in Siwam district of Bihar: = -3.1602+ 0.0339X5 + 0.0140X7 + 2.5849X10 ……(2)
  • Thumbnail Image
    ThesisItemUnknown
    GENOTYPE × ENVIRONMENT INTERACTION BY GGE BIPLOT IN MANGO (Mangifera indica L.)
    (DRPCAU, PUSA, 2021) KRISHNA, K SAI; Choudhary, Ram Kumar
    Mango is one of the most important perennial fruit crop grown in India, with vast varieties. India has first position in mango production among the mango growing countries. Due to its taste and diverse uses it is known as “king of fruits”. The differential performance of a genotype in different environments is known as “Genotype × Environment Interaction”. Multi location trials are being carried out to study the behaviour of genotypes over different locations. Identification of stable genotypes of mango is important to increase the income of farmers. In present investigation an attempt has been made to identify stable genotypes of mango fruit crop from secondary data of multi-location trials collected from AICRP-STF and CISH, Lucknow. Data comprises of 16 genotypes grown in 4 locations over nine years have been analysed on Genotype × Environment interaction using GGE biplot. Two characters have been taken for the empirical analysis i.e. number of fruits per tree and fruit yield per tree. Results obtained by GGE biplot have been compared with results obtained by AMMI. Sixteen genotypes common across 4 locations viz., Rewa, Sabour, Sangareddy and Vengurla over a period of nine years was considered for the analysis. These genotypes, locations and years were coded accordingly. On the basis of AMMI analysis the superior genotypes were Totapari, Zardalu for Rewa; Kesar, Mankurad for Sabour; Suvarnarekha, Mankurad for Sangareddy; Alphanso, Totapari for Vengurla. AMMI analysis identified Zardalu as superior genotype for all four locations under study. Likewise, from GGE biplot analysis, genotypes Totapari, Neelum for Rewa; Kesar, Suvarnarekha for Sabour; Suvarnarekha, Mankurad for Sangareddy; Totapari, Bombai for Vengurla were found to be superior. GGE biplot analysis identified Totapuri as superior genotype for all four locations under study. Two mega environments have been identified based on GGE biplot analysis, the test locations Rewa and Sangareddy constitute first mega environment with Neelum as winner genotype; Vengurla and Sabour collectively forms second mega environment with Suvarnarekha as winner genotype. From the present study it is concluded that, GGE biplot analysis is a better approach for evaluating Genotype × environment interaction and identifying superior genotypes of mango fruit crop. Since, the interpretation of GGE biplots relatively to AMMI analysis is easier. “Which-won-where” view of GGE biplot facilitates to determine location specific genotypes which is challenging in AMMI analysis.
  • Thumbnail Image
    ThesisItemOpen Access
    TREND OF GROUNDNUT AREA, PRODUCTION, YIELD IN BIHAR AND TAMIL NADU ALONG WITH ITS YIELD FORECASTING THROUGH AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS
    (DRPCAU, PUSA, 2021) S, EZHILMATHI; Kumar, Mahesh
    The present study entitled “Trend of groundnut area, production, yield in Bihar and Tamil Nadu along with its yield forecasting through Auto-Regressive Integrated Moving Average (ARIMA) models” is based on the growth trends and ARIMA models for forecasting groundnut yield in Bihar and Tamil Nadu. The secondary data on groundnut area, production and yield were collected from the year 1980 to 2018 from the authenticated portals like Directorate of Groundnut Research, Directorate of Oilseeds Development and India Agri Stat. The data from 1980 to 2016 were used for analysis of forecasting groundnut yield and the data for 2017 to 2018 were kept for model evaluation. Trend analysis and validity tests were also calculated. With the help of above facts, it was found that the ARIMA (1,0,1) model is best fitted for Bihar among all the models namely ARIMA (0,1,1), ARIMA (0,1,2), ARIMA (0,0,1), ARIMA (1,0,0), ARIMA (1,1,1), ARIMA (2,0,0) and ARIMA (2,0,1). The ARIMA (1,0,0) model is best fitted for forecasting of groundnut yield in Tamil Nadu among all the other models namely ARIMA (0,0,1), ARIMA (1,0,1), ARIMA (0,1,1), ARIMA (1,1,1), ARIMA (0,1,2), ARIMA (2,0,0) and ARIMA (2,0,1). The parameters of all these models were computed and tested for their significance. Various statistics were also computed for selecting the adequate and parsimonious model i.e., t-test and chi-square test. This is supported by low values of MAPE, MAE, RMSE and BIC for forecasting of groundnut yield in Bihar and Tamil Nadu. Forecasting of groundnut yield for the upcoming two years was done using ARIMA models. The results showed that there was a steady increase in the yield of groundnut in Bihar as well as Tamil Nadu. In this study, lower and upper limits of the forecasted yield were also calculated with 95% of confidence interval. The forecasts done five years period ahead for the time series data of yield of groundnut by using the best fitted ARIMA (1, 0, 1) and ARIMA (1, 0, 0) models, respectively for Bihar and Tamil Nadu. Further study was done for trend analysis and it was found that the trend is likely to be in decreasing order for groundnut area and production of both the states. The trend of groundnut yield in Bihar is stable whereas it is in increasing order in case of Tamil Nadu. For the study of inter-state disparities, Compound Annual Growth Rates were also calculated and it was found that all are highly significant.
  • Thumbnail Image
    ThesisItemOpen Access
    FORECASTING OF PINEAPPLE YIELD IN TRIPURA AND BIHAR USING NON LINEAR MODELS AND IT'S TREND ANALYSIS
    (DRPCAU, PUSA, 2021) DE, ARUP; Kumar, Mahesh
    The present study is based on time series data collected for 32 years (1987-88 to 2018-19) to study the trend of area, production and productivity of pineapple in Tripura and Bihar and also to forecast the pineapple yield by using different non-linear models namely Logistic model, Gompertz model and Monomolecular model. Data from 1987-88 to 2017-18 are used for analysis purpose and 2018-19 data is used for model validation purpose. Socio economic condition of the farmers of Gomati district of Tripura, also taken as a objective of these experiment. Though the actual values of area, production and productivity were fluctuating over the period but overall trend is showing an linearly increasing pattern. Kendall, Spearman and Pearson test is done for testing the validity and result shows a highly significant positive correlated value for all the test. After comparing these three non-linear models with eleven statistics it is clear that Logistic model is most appropriate model for forecasting the pineapple yield in both the sate as it shows highest value of R2, adj. R2, R27 and R28 and lowest value of RSS, MAPE, MAE, MSE, RMSE, RSE and MSE. nn. Lowest Chi-square value of was found in Logistic model for both the state. In case of forecasting for productivity of pineapple in Tripura, the Logistic model shows closest value to actual productivity with a forecast error of 19.79% in OSAF method. In case of Tripura, percentage forecast error is some extent to high and that is due to highly variation in yield during that period of time. Whereas in case of forecasting the pineapple yield for Bihar, all the models shows almost similar result in OSAF method. Percentage forecast error for Bihar is 2.45 % for selected Logistic model. Forecasted Pineapple yield in Tripura for the year 2018 to 2022 are almost equal i.e.17.44 q/hac, and for Bihar for the year 2018-2022 are almost equal to 26.71 q/ha. Selected Logistic model for forecasting of yield of Pineapple for Tripura and Bihar are as below: Ŷ =17.4488/ (1 +3.4532 *exp(-0.2696*t) ) for Tripura Ŷ =26.72232/ (1 +10.50813*exp(-0.3022*t) ) for Bihar Annual income of the majority of the farmers of the study area are in between 1 lakh to 5 lakh and age, occupation, education, size of land holding, farming experience, farm mechanization and type of land shows a significant correlation with annual farmers income. The regression analysis shows that size of operational land shows highly significant with the farmer’s annual income.
  • Thumbnail Image
    ThesisItemOpen Access
    CHARACTERIZATION OF VARIABILITY OF SOIL PROPERTIES USING GEOSTATISTICAL APPROACH: A CASE STUDY OF MUZAFFARPUR DISTRICT IN BIHAR
    (DRPCAU, PUSA, 2022) KUMAR, ABHINEET; Nidhi, Dr.
    In this study an attempt is made to assess spatial variability of soil properties Muzaffarpur district in Bihar. The geostatistical approach has been applied to study the spatial variability of soil properties like pH, electrical conductivity (EC), organic carbon (OC), phosphorous(P), potassium(K), sulphur(S), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron(B). Data on soil properties were obtained from AICRP on micronutrients from department of soil science, RPCAU, Pusa. Variogram has been used to express the spatial variability where variogram is a plot of the variances of subsequent point in the space vs. distance. Variogram models are used to create the spatial dependence of all the soil parameters. The four variogram models are linear, spherical, exponential, gaussian. Ordinary kriging has been used to interpolate for unsampled locations. Kriging is a spatial interpolation technique that create spatial distribution map as well as map for prediction of variance of soil parameters. To check the accuracy of soil nutrient maps cross validation approach has been used where mean absolute error (MAE), Mean square error (MSE), Goodness of prediction(G) values are computed for accuracy check. It is found that pH has very lowest CV (6.37%) indicating homogeneous soil for pH across the study region while highest CV (78.44%) is found for zinc indicating high variation across the study region. EC, OC, P, K, S, Zn, Cu, Fe, Mn, and B with respect to their CV are under moderate variation across the study region. In different classes, almost 40% of soil sample have soil pH between 8.0 to 8.5 which mean moderate alkaline. Almost 85% of soil sample have OC less than 0.5%. 98% of soil sample have EC less than 1ds/m. 60% of soil sample have phosphorous between 25-50ppm. 63% of soil sample have potassium between 125 -300ppm. 82% of soil sample have Sulphur less than 22.4ppm. 54% of soil samples have Zn above 1.20ppm. 96% of soil samples have greater than1.20ppm. almost 80% of soil samples have greater than 12.0ppm. 88% of soil samples have Mn greater than 5.0ppm. 52% of soil samples have boron between 0.5-1.0ppm. Spherical and exponential model has been used to fit the experimental variogram of the soil parameters. Range of spatial dependence for each parameter has been established. values are computed to analyse the goodness of the variogram model. Based on nugget-sill ratio, the spatial dependence for each parameter has been classified as weak, moderate and strong. Mn, B, EC, OC and Zn values influenced their neighbouring values over greater distances than pH, P, K, S, Cu and Fe, all of which have range of below 10 km. The highest range of 72 km is observed for Manganese means the concentration is highly correlated spatial up to a large distance. The nugget sill ratio also proved its moderate spatial dependence in area under study. Range of Boron (B) concentration is 66 km while the largest nugget effect is observed for K and moderate dependence is observed for its concentration under study area due to large nugget effect. The goodness of fit statistic indicates a moderate fit for the variogram model. The soil pH is also observed to be spatially correlated to a distance of 10.6 km in the study area and the degree of spatial continuity is moderate. All the other parameters are observed to have low values of range varying below 10 km. It might be due to cumulative effect of climate, parent material and adopting of different land management, the range values of soil parameters are different. This observed spatial dependency can be used to support spatial sampling for detailed soil mapping in site specific soil management. Predictive spatial maps have been generated for each soil parameter by interpolating the values using ordinary kriging method using variogram model. The different distribution pattern is exhibited by the kriged surface maps developed for soil parameters. These maps could help for site specific nutrient management and also in designing future soil sampling strategies in the intensively cultivated alluvial soil of Muzaffarpur. The predictive maps produced by interpolating the values using ordinary kriging show distinct patchy distribution of pH, K, P, S, B, Zn, EC, OC and Mn across different parts of Muzaffarpur. These types of maps can help in identifying the pockets of soil available nutrients according to their concentration and may in turn help for region specific management.
  • Thumbnail Image
    ThesisItemOpen Access
    GROWTH ANALYSIS OF PIGEON PEA AND IT’S YIELD FORECASTING IN BIHAR AND KARNATAKA USING AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODEL
    (DRPCAU, PUSA, 2022) NAVEEN; Kumar, Mahesh
    The present study entitled examine “Growth Analysis of pigeon pea and it’s yield forecasting in Bihar and Karnataka using Auto-Regressive Integrated Moving Average (ARIMA) model” is based on the growth trends and ARIMA models for forecasting pigeonpea yield in Bihar and Karnataka. The secondary data for the years 1980 to 2021 was retrieved from reliable websites like the Department of Economics and Statistics and India Agri Stat. For the purpose of forecasting pigeonpea yield, data up to the year 2019 were used to build the prediction model, and data from the following two years were retained for the forecast model's validation. Trend analysis and validity tests were also calculated. With the help of above facts, it was found that the ARIMA (1,0,1) model is best fitted for pigeonpea yield in Karnataka among all the models namely ARIMA (0,1,1), ARIMA (0,1,2), ARIMA (0,0,1), ARIMA (1,0,0), ARIMA (1,1,1), ARIMA (2,0,0) and ARIMA (2,0,1). The ARIMA (1,1,1) model is best fitted for forecasting of pigeonpea yield in Bihar among all the other models namely ARIMA (0,0,1), ARIMA (1,0,1), ARIMA (0,1,1), ARIMA (1,1,1), ARIMA (0,1,2), ARIMA (2,0,0) and ARIMA (2,0,1). The parameters of all these models were computed and tested for their significance. Various statistics were also computed for selecting the adequate and parsimonious model i.e., t-test and chi-square test. This is supported by low values of MAPE, MAE, RMSE and BIC for forecasting of pigeonpea yield in Karnataka and Bihar. Forecasting of pigeonpea yield for the upcoming two years was done using ARIMA models. The results showed that there was a steady decrease in the yield of pigeonpea in Karnataka as well as Bihar. Selected ARIMA model for forecasting of yield of Pigeonpea in Karnataka and Bihar are as below: Zt – Zt-1 = 524.811 + 0.931 (zt-1 - zt-2) - 0.717 (at-1 - at-2) + at (for Karnataka) Zt – Zt-1 = 16.019 + 0.485 (zt-1 - zt-2) – 0.995 (at-1 - at-2) + at ( for Bihar) In this study, lower and upper limits of the forecasted yield were also calculated with 95% of confidence interval. The forecasts done five years period ahead for the time series data of yield of pigeonpea by using the best fitted ARIMA (1, 0, 1) and ARIMA (1,1,1) models, respectively for Karnataka and Bihar. Further study was done for the trend analysis and it is found that the trend of area, production and yield of pigeonpea in Karnataka is in increasing order whereas as in Bihar area and production shows a decreasing trend but the yield is increasing in Bihar. For accuracy coefficient of determination is calculated. Compound Annual Growth Rates were also calculated and it was found that all are highly significant. Annual income of the majority of the farmers of the study area are in between 1 lakh to 5 lakh and Age, Caste, Occupation, Education, Family size, Size of operational land holding, Farming experience shows a positive correlation with the farmer’s income. The size of operational land shows highly significant with the dependent variable which is farmers income.
  • Thumbnail Image
    ThesisItemOpen Access
    TREND ANALYSIS AND FORECASTING OF CHICKPEA YIELD IN MUNGER DISTRICT (BIHAR) AND BILASPUR DISTRICT (CHHATTISGARH) USING NON LINEAR MODELS
    (DRPCAU, PUSA, 2022) PAIKRA, KALYAN SINGH; Kumar, Mahesh
    This study is done with objective of trend analysis of area, production and productivity of chickpea for Munger district (Bihar) and Bilaspur district (Chhattisgarh) and forecast is done on chickpea yield for these districts using some nonlinear models. For conducting this study, secondary time series data is obtained from official sites of directorate of Economics and Statistics of respective states and ICRISAT Hyderabad from time period of 1990-91 to 2019-20. For achieving objective, data from 1990-91 to 2018-19 are analysed while for validation, data from year 2019-20 is taken. The graphical method is used for trend analysis for area, production and yield under chickpea. Validation of trend is checked using correlation test by Pearson and Spearman test. Forecasting of chickpea yield is done with non linear model i.e., Monomolecular, Gompertz and Logistic model and OSAF is used for validation. Although, over the time there was too much fluctuation in actual data of area, production and productivity of chickpea over all it is found decreasing for area as well as production while increasing trend for productivity for Munger district as well as for Bilaspur district. Even Pearson and Spearman coefficients are highly significant with negative values for area and production while positive values for productivity. All non-linear models are fitted to data by using Statistical software R. After fitting non-linear models, models are compared by ten different statistics R2, R27 , R28 , RSS, MAPE, MAE, MSE, RMSE, RSE and MSE. nn. So, Logistic model is found better for forecasting chickpea yield for Munger district with FE% of 22 % and Gompertz is found better for Bilaspur district with FE% of 22.3 % than other two models. Selected models for Munger district (Bihar) and Bilaspur district (Chhattisgarh) are given by Ŷ =1.16982/ (1 +(1.16982/0.60629-1) *exp(-0.10993*t)) (Munger district) Ŷ =1.12087*exp(log(0.44693/1.12087)*exp(-0.0532*t)) (Bilaspur district)
  • Thumbnail Image
    ThesisItemOpen Access
    Growth trend and yield forecasting of sugarcane for Tamil Nadu through non-linear growth models
    (DRPCAU, Pusa, 2020) R, Monisha; Kumar, Mahesh
    Sugarcane (Saccharumofficinarum),Family GRAMINEAE (POACEAE) is widely grown crop in India.It provides employment to over a million people directly and indirectly besides contributing significantly to the national exchequer. Sugarcane is the main source of sugar in India and holds a prominent position as a cash crop. India was the 2nd largest producer of sugar in the world after brazil in 2014-15.In India, sugarcane Area, Production area mainly concentrated in the states of Uttar Pradesh, Maharashtra, Tamil Nadu and Karnataka. Though Tamil Nadu accounts only for about 11% of the area under sugarcane of the country, this state has unique distinction of giving highest yield of 1,067.8 quintals/hectare. Looking to this and importance of sugarcane crop in Tamil Nadu, an attempt was made to apply some mechanistic growth models to sugarcane productivity data for complete Tamil Nadu. The present study was based on non-linear growth model such as Monomolecular, Gompertz and Logistic model. ThisThis This study is based upon the secondary data and these time series data were collected for the period from 1970-71 to 2018-19 from Directorate of Economics and Statistics Department Govt. of Tamil Nadu, (Chennai),Indian Sugar (the complete sugar journal), www.indiastat and also from Sugarcane Breeding Institute, Coimbatore(Tamil Nadu).The data from 1970-2019 were used for analysis of forecasting sugarcane yield and the data 2019-2020 were kept for model validation. Trend analysis and validity test were also calculated. The trend of sugarcane yield over a period of 1971-72 to 2018-19 seems likely to be linear in increasing order of yield (except some years). As an upward tendency seen in most of the trend data. However, the overall tendency seems to be upward. After doing trend analysis by different test like Kendall, Spearman, Pearson are found to be highly significant. The value has been obtained using the data collected and the method described Draper and Smith (1981). The three models has been compared considering R2, R27, R28, RSS, MAPE, MAE, MSE, RMSE. On the basis of these statistics, monomolecular model has been found to be most appropriate suitable for describing the sugarcane productivity data after comparison among all the models because this model have high value of R27 and R28 and low value of RSS,MAPE,MAE,MSE and RMSE and their fulfillment of assumption has been studied. After the analyzing all the model such as Monomolecular, Gompertz and logistic model, it is found that monomolecular model has good accuracy by interpreting low % FE, MAPE, RMSE. Pearson's Chi-squared test for Goodness of Fit for Monomolecular model for actual data on Area, Production & Yield of sugarcane in Tamil Nadu is highly significant. After comparison for area, production and productivity for trend and fitted for Monomolecular, Gompertz and Logistic, Monomolecular model is better superimposed to each other as compared to others model. It indicates that monomolecular model is good fitted among all three models.