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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.

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2019 - 201922020 - 20233
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Subject: Statistics × End date: 2023 ×
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    ThesisItemOpen Access
    FORECASTING OF RICE YIELD BASED ON BIOMETRICAL CHARACTERS AND ANALYSE THE PERFORMANCE OF PRADHAN MANTRI FASAL BIMA YOJANA IN WEST GODAVARI DISTRICT OF ANDHRA PRADESH
    (RPCAU, Pusa, 2023) KRISHNA, GAJJARAPU JAYA; KUMAR, MAHESH
    This study, conducted in West Godavari district, Andhra Pradesh, a prominent rice-producing region, employs biometrical characteristics to forecast rice yield and assesses the performance of the Pradhan Mantri Fasal Bima Yojana (PMFBY) program. Time series analysis reveals stable rice-growing area but increasing production and yield. The study identifies ten biometrical characteristics affecting rice yield, including plant population(X1), plant height(X2), tiller count(X3), panicle length(X4), nitrogen(X5), phosphorus(X6), potassium levels(X7), irrigation frequency(X8), disease infestation(X9), and plant condition(X10). Multiple regression analyses were conducted using these variables, resulting in a model selection process which includes R-square, adjusted R-square, Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Coefficient of Variation (CV) based on these 5 models are selected from overall possible models. Residual analysis of these 5 models favoured a third model of the selected five models for forecasting rice yield. The chosen model (Ŷ = 46.08838 + 0.80226X₁ + 0.14708X₂ - 0.36854X₃ - 0.02404X₅ + 0.02418X₇ + 1.28461X₈) having yield affecting characters such as plant population, plant height, tiller count, nitrogen application, potassium application and irrigation frequency adeptly forecasts rice yield (94.01 quintals/ha) and accounts for 52.6% of yield variation. These models were rigorously assessed for validity. The final selection was based on residual analysis, with a preference for models demonstrating the least Mean Absolute Percentage Error (MAPE), ensuring robust predictive performance. Furthermore, the study highlights the robust growth of PMFBY insurance scheme metrics, emphasizing higher Compound Annual Growth Rates (CAGRs) during Rabi seasons. In the 2022 Kharif season, 604,529 farmers were insured, covering 180.5 thousand hectares, with a gross premium of 105.07 crores and a sum insured of 1,756 crores. In conclusion, this research contributes significantly to precise rice yield prediction and showcases the effectiveness of PMFBY in West Godavari district, crucial for enhancing food security and economic stability.
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    ThesisItemOpen Access
    FORECASTING OF COCONUT YIELD AND PERFORMANCE OF KISAN CREDIT CARD SCHEME IN MALAPPURAM DISTRICT OF KERALA
    (RPCAU, Pusa, 2023) M, MOHAMMED HISHAM; Kumar, Mahesh
    The current study entitled with “Forecasting of Coconut Yield and Performance of Kisan Credit Card Scheme in Malappuram district of Kerala” is based on the objectives of analyzing the coconut cultivation in Malappuram district of Kerala, Forecast coconut yield using nonlinear growth models for the district and assess the effect of KCC scheme for coconut growers of the same district. The trend analysis and forecast relies on secondary time series data of coconut, which is obtained from Kerala State Economics and Statistics Department and Coconut Development Board. The time series data spans from 1971-72 to 2020-21. The data from 1971-72 to 2015-16 is used for forecasting while the data from the year 2016-17 to 2020-21 is used for the validation process. For the assessment of KCC scheme, primary data collected from the beneficiaries is used. Graphical method is employed to analyze the trend. The result indicates that the trend of coconut area, production, and productivity in Malappuram district of Kerala is increasing. The accuracy of the trend analysis was done using the coefficient of determination values. Compound Annual Growth Rates were also calculated and it also found positive. Monomolecular, Logistic, and Gompertz models were utilized to forecast the coconut yield and the data is fitted using R software. A comparison of models using ten different statistics, including R^2, R_7^2, R_8^2, RSS, MAPE, MAE, MSE, RMSE, RSE and MSE.nn was conducted and Monomolecular model found to be the better model for forecasting coconut yield in Malappuram district. Selected model for the coconut yield in Malappuram district is given by Y ̂=2.817738-(2.817738-3.810496)×exp⁡(0.043186×t) Kisan Credit Card scheme emerged as an innovative means of delivering credit to meet the production related needs of the farmers ensuring timely and sufficient manner. The primary data were collected from 50 KCC beneficiary coconut farmers from Malappuram district. The collected data were interpreted using frequency tables and percentage analysis. Beneficiaries found KCC loan availability relatively easier compared to other loan processes. 86% of the beneficiaries obtained loans up to maximum limit, 94% used funds for their intended purpose and 78% repaid the loan amount using the intended earnings. Overall, the KCC scheme positively influenced farming practices, leading to increased yield and income.
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    ThesisItemOpen Access
    ASSESSMENT OF GENOTYPE × ENVIRONMENT INTERACTION USING MULTI-LOCATION TRIAL DATA OF SUGARCANE
    (RPCAU, Pusa, 2023) M, RANJAN L; Choudhary, Ram Kumar
    Sugarcane is one of the most important commercial crops grown in India, with vast varieties. India has second position in sugarcane production among the sugarcane growing countries. Primary industries such as sugar mills, jaggery producing units, and chemical industries are largely depend on this crop. The differential performance of genotypes over different environments is known as “Genotype × Environment Interaction” (GEI). Multi Location Trials (MLT) are being carried out for performance testing of genotypes over different locations. Identification of stable genotypes of sugarcane is important to increase the income of farmers as well meeting the input requirement of sugarcane based industries. In present investigation an attempt has been made to identify stable genotypes of sugarcane crop from secondary data of MLT data collected from G. B. Pant University of Agriculture and Technology, Pantnagar. Data comprises of 17 sugarcane genotypes grown in 6 locations with two replication using Randomizes Block Design. Nine characters were investigated for the assessment of GEI through regression models and stability analysis through 9 stability measures bi, Bi, S2di, Di, ri, Wi, ASI, ASTABi including mean yield. Results obtained by assessment of GEI were compared with results obtained by stability measures using rank correlation and cluster analysis. The analysis was carried out using R-studio, Ms- Excel, & GEA-R. The genotypes were coded from coded from G1 to G17 and locations from E1 to E6. Analysis of variance showed that genotypes were significant for all the 9 characters in all the locations. Pooled analysis confirms significant GEI for six characters under study namely cane yield, single cane weight, sugar content, germination%, number of tillers and number of millable canes. However for characters like brix%, juice extraction % and polarization% GEI was found non-significant. The contribution of variation in total variation due to G×E Interaction for characters under study varies from 11.37% to 25.75% and it was maximum for number of tillers (25.75%) and minimum for single cane weight (11.37%). Further, presence of significant genotype × environment interaction at 5% level of significance in sugarcane was confirmed by joint regression models namely Eberhart and Russell model and Perkins and Jink Model for the same 6 characters as confirmed by Pooled analysis. The contributions of variation in total variation by G×E (linear) Interaction for characters were varied from 4.2% to 17.82%. The minimum contribution by G×E (linear) Interaction in total variation was found in cane yield at harvest (4.2%) and maximum in number of millable canes at harvest (17.82%). Stability measures such as bi & Bi, S2di & Di and Wi & ri showed perfect positive correlation (r = 1) for all the traits. It was observed that mean was significantly correlated with only Eberhart’s regression co-efficient (bi) and Perkin’s co-efficient (Bi). The correlation were varied character to character from 0.67 to 0.92. Hence, they may be used for selecting stable as well high yielding genotypes. ASTABi highly positively correlated with shukla’s stability variance and Wricke’s ecovalence index and moderately correlated with S2di & Di with little variation character to character varied. The superior genotypes were identified for the characters namely cane yield, single cane weight, sugar content germination%, number of tillers character, number of millable as G5, G4, G5, G6, G14 and G5 respectively. While poorest genotypes identified for cane yield, single cane weight, sugar content germination%, number of tillers character, number of millable canes character were G16, G3, G10,G16, G10,and G10 respectively. From this study we can conclude that we can use either of Eberhart and Russell model or Perkins and Jinks model and Wricke’s ecovalence index or shukla’s stability variance for selecting the stable genotypes, since perfect positive correlation (r = 1) was found between them. All six character’s mean was significantly correlated with only regression co-efficients of Eberhart and Russell model (bi) & Perkins and Jinks model (Bi). So these regression co-efficient can be used for selection of stable genotypes with high or above average yield. These results were supported by dendrogram obtained through cluster analysis. Six clusters were identified. ASI performed similarly for the characters namely cane yield, cane weight and Sugar contents while for the traits namely germination %, number of tiller and number of millable canes performed similarly as stability measures shukla's stability value and Wricke’s ecovalence index and AMMI Stability measure (ASTABi).
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    ThesisItemOpen Access
    Yield forecasting of sugarcane in Bihar based on biometrical characters
    (Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur (Bihar), 2019) M, Muhammed Irshad; Kumar, Mahesh
    The present study deals with the development of yield forecast models for sugarcane (Saccharum officianarum) in Bihar based on Biometrical characters. For this research observations on plant biometrical characters such as number of millable canes per 100 m2 (X1), average plant height in cm (X2), average cane girth in cm (X3), average length of third leaves cm (X4), average width of third leaves in cm (X5), average cane perimeter in cm (X6), single cane weight in kg (X7), average plant population per 100 m2(X8), number of irrigations in entire crop season(X9), average number of tillers per 100 m2(X10), application of nitrogen (N) in kg/ha (X11), application of phosphorus (P2O5) in kg/ha (X12), application of potassium (K2O) in kg/ha (X13), disease infestation in percentage (X14) and average plant condition (X15) according to eye estimate, were recorded from 50 farmers fields in which 30 farmers were selected from Samastipur, 10 farmers were selected from West Champaran and 10 farmers were from East Champaran districts of Bihar. Simple random sampling was used for selecting farmer‟s field. All possible regression analyses were carried out to select the best combination of variables on the basis of some important statistics such as, RMSE, CV, R2 and adj- R2 . ̂ -596.51888+ 1. 35081X1-0.84646X2+ 1.08494X4 + 32.1379X5 + 423.25714X7- 6.40145X9 + 0.79303X13-13.20593X15 CV= 4.525, R2 = 0.9385, Adj R2=0.9248, RMSE = 32.269, Standard Error residuals=23.444 Further assessment regarding accuracy of model has been done on comparing the actual yield from 10 % of the observations not included in the model development with their predicted value and results shows close resemblance with the margin of error ranging from (5.91-8.36%). Forecasted yield of sugarcane has been worked out as 847.82 q/ha in Bihar with the help of proposed model. Forewarning models for wilt disease on sugarcane based on climate factors was mainly aimed at to study the behavior of climate factors on wilt of sugarcane, to establish association between climatic factors and wilt diseases of sugarcane in different years in sugarcane growing seasons, to generate forewarning statistical models for prediction of wilt diseases based on climatic factors. Data collection was done on the basis of major sugarcane grown area and also compatibility. The secondary data on wilt incidence (%) of sugarcane along with climate factors were collected for the period from 2008 to 2017 during crop seasons. The climatic factors from 2008 to 20117 in sugarcane growing seasons, the average rainfall distribution varied greatly within sugarcane growing seasons over years (19.5 mm – 78.5 mm). The average minimum temperatures (18.70C – 260C), maximum temperature (300C -310C), morning relative humidity (83.7-87.6%) and evening relative humidity (51.8-85.8%) were observed. Correlation studies revealed that there was positive association between the wilt infestation and weather factors morning relative humidity (0.23) while significant positive correlation with minimum temperature (0.89) and negative association with evening relative humidity (-0.03) while significant positive correlation was showed with maximum rainfall (0.97), maximum temperature (0.629). The Multiple Linear Regression (MLR) model was developed with respect to these factors with R2= 0.969. The MLR models for between years found to be useful in the prediction of wilt incidence of sugarcane. (Ŷ) = -99.498+0.142X1+0.345X2+0.601X3-0.05X4+0.46X5+6.252X6 Data was collected using structured schedule and procedure for quantifying Socio-economic status of sugarcane farmers. The data revealed that more than half of the respondents (52%) were small landholders, respondents had high school and above (86%) education, Majority (78%) of the respondents were belonged to UR category, majority (66%) of the farmers had joint family (less than five Members) and Cent percent of the respondents were following agriculture as their main Occupation. There is a positive correlation between income of the farmer with land holding and education Validation of selected forecasting model (5th model) was done by using forecast error (FE), mean absolute error (MAE), Mean absolute percentage error (MAPE),and mean square error (MSE). All the parameters shows a minimum value for the model 5 Key Word: Pre harvest forecast of sugarcane yield, Biometrical Characters of sugarcane, Farmers appraisal, forewarning of disease, socioeconomic condition of the farmer.
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    ThesisItemOpen Access
    Geospatial variability of soil characteristics and their relationship with crop yield in Samastipur district of Bihar
    (Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur (Bihar), 2019) Katiyar, Mohit; Nidhi
    Crop production is a function of three factors, namely, soil, inputs and climatic condition.The soil preparation is the first step to ensure that the field is ready for growing a crop. The quality of soil plays a critical role in enhancing the crop yield. Soil quality is usually assessed in terms of presence of several macronutrients as well as micronutrients. Micronutrients are essential for plant growth and play an important role in balanced crop nutrition. They include boron (B), copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo), zinc (Zn), nickel (Ni) and chloride (Cl). The knowledge on spatial variability of soil characteristics are expected to serve as a guide for site specific management. Several studies have documented the important role of spatial variability in soil properties in determining the site specific agricultural management strategies. This study is an attempt to assess spatial variability of soil characteristics of Samastipur district. Geo-statistical analysis has been used to study the spatial continuity of soil parameters like pH, electrical conductivity, organic carbon and soil available micronutrients viz., zinc, copper, iron and boron. Spatial variability has been expressed by variogram ̂ which is a plot of the variances of subsequent points in the space vs. distance. It measures the average dissimilarity between data separated by a vector h (Jounel and Huijbregts 1978). Variogram models are used to establish the spatial dependence of all the soil parameters. Based on the variogram model, soil parameters have been interpolated for un-sampled locations using ordinary kriging. Kriging is a spatial interpolation technique which generates a smooth predictive map along with the map representing the variance of prediction. Soil pH value ranged from 6 to 8.9, with a mean 8.1 and median 8.2.pH is observed to have very low CV indicating homogeneous soil with respect to pH across the study region.EC is observed to havemoderate variation across the study area with CV of 77%.The spatial distribution of EC in the study area has the mean value 0.56 dsm-1 and ranges from 0.1 to 3.59dsm-1.EC is highest (3.59 dsm-1) at the Ladaura village in Kalyanpur block.OC is observed to have moderate variability across the study area with CV of 41.07%.Zn exhibited moderate variability (53.19%) across the study area. Rest of micronutrients is observed to be moderate variability across study area.In different classes, 33% of the soil samples have Zn concentration above 1.5 ppm.Almost 90% of the soil samples collected across the study area have Cu concentration more than 1.5 ppm.Iron concentration was observed to be above 11.5 ppm at 36% of the sampled locations.Spherical model has been used to fit the experimental variogram of the soil parameters. pH, Fe and Mn values influenced their neighbouring values over greater distances than EC, OC, Zn and Cu, all of which have range below 20 km.Manganese concentration is revealed to have highest range of 844 km i.e., its concentration is highly correlated spatially up to a large distance.The nugget-sill ratio also proved its strong spatial dependence in the study area. Range of iron concentration is 292 km;however, it exhibits large nugget effect which is a representative of micro-variability in the soil.Due to large nugget effect, its concentration in the study area is observed to have moderate dependence.Soil pH is also observed to be spatially correlated to a large distance of 223 km in the study area.All the other parameters are observed to have low values of range varying between 5 km to 16 km.The predictive maps ofkriged surface produced by interpolating the values using ordinary kriging show distinct patchy distributions of Zn, Cu and Mn across different parts of Samastipur.70 % of farmers reported to have upland, whereas 52.5% farmers and 42.5 % of farmers are having midland and upland respectively. Farmers follow rice+wheat, rice+maize+potatoand rice+vegetable cropping pattern in their field.The average yield for cereal is reported to be approximately 79.68 qha-1 and the average yield of vegetables is 94.02 qha-1 observed in both seasons. Principal component analysis yielded two principal components that explained 68% of variation in the entire set of variables. These two principal components have been used in regression model to establish the relationship between yield and soil parameters for each of the three topographies.For low land and mid-land, the second principal component (high loading with OC, pH and Zn) is observed to have higher contribution towards explaining REY. For upland, first principal component having high loadings with EC, Cu, Fe and Mn is observed to have significant role in explaining the variability in REY. Based on these principal components, soil quality index can be developed for different topographies. Thus the study demonstrated that influence of soil properties on variability of crop yield depends on the micronutrient concentration as well as varying topography. Key words: geospatial variability, variogram, kriging, spatial interpolation