Mastitis is one of the important animal disease which causes huge economic loss. The present study was aimed to find the best lactation curve model that describes the milk production pattern of Vrindavani cattle suffered from mastitis. In the study, test day milk yield data of 161 mastitic Vrindavani cattle (incidence of the disease occurring within the three months of parturition) was collected from Cattle and Buffalo Farm, LPM section, ICAR‑IVRI over 5 years (2009-2014). Eight lactation curve models [Inverse quadratic polynomial model (ND), Incomplete Gamma function (WD), Linear decline model (CL), Wilmink model (WL) Mixed log model (ML), Mitscherlich x Exponential (ME), Morant and Gnanasakthy model (MG) and Ali & Schaeffer lactation curve model (AS)] were fitted. The best model was selected based on different goodness of fit criteria and the Durbin- Watson test was used to examine the presence of autocorrelation present in the data set. Kolmogorov-Smirnova test and Shapiro-Wilk were used to test the normality of the residuals. Based on the various goodness of fit test it was observed that the Mixed Log (ML) model was the best fitted model to describe the lactation pattern of mastitic Vrindavani cattle.

  General Crop Estimation Surveys (GCES) based on Crop Cutting Experiments (CCEs) are conducted for estimation of crop yield following random sampling approach for almost all major crops. About 13 lakh CCEs are conducted every year which has now increased rapidly due to the Pradhan Mantri Fasal Bima Yojana (PMFBY) which is yield based insurance scheme. As suggested by Ministry of Agriculture and Farmers? Welfare (MoA&FW), this number needs to be reduced drastically by developing sampling procedures based on the use of advanced technologies and advanced survey techniques for crop yield estimation. In this study, an attempt has been made to develop crop yield estimation procedures using Random Forest Spatial Interpolation (RFSI) technique including the spatial variables like spatial distance and nearest neighbours as covariates. RFSI is one of the most adaptable and user-friendly interpolation techniques, as well as one of the fastest in large training datasets. Estimates of yield of wheat were obtained for all the six tehsils of Barabanki district using the estimator under stratified two stage sampling technique. The district level estimates were also obtained by pooling area under wheat crop in each tehsil along with the district level estimate of crop yield, estimate of variance, estimate of standard error (SE) and percentage SE (%SE) of these estimates were also computed in order to make comparison. The results of this study suggest that the estimates derived using RFSI are comparable to kriging and superior to inverse distance weighting (IDW) for the prediction of yield at unknown locations using distance and nearest neighbours.

  The present investigation was undertaken with a view to identify the models for predicting the hectareage of paddy crop of the middle Gujarat region. The investigation was carried out on the basis of secondary data covering the period of nineteen years, (1998-99 to 2016-17). The District level data relating to hectareage, production, productivity and farm harvest prices of paddy were obtained from the published and compiled information by Directorate of Agriculture, Gujarat State, Gandhinagar. The linear multiple regression technique (basically Nerlovian type) was employed. The eight single equation and four simultaneous equation (SE) models were tried for paddy crop, the following models were selected on the basis of the values of adjusted coefficient of multiple determination. SE model-III for paddy is given below. HEPD = 40960.532**** - 10.414*** HEBJ + 0.784 HEMZ - 1.187**** HEPDL + 3.720*** HEBJL + 5.588**** EYPD + 0.866 EYBJ - 6.205*** EYMZ - 6.833**** EPPD + 1.502 EPBJ (R2= 0.946) HEBJ = 3261.298 - 0.061 HEPD + 0.108 HEMZ - 0.093 HEPDL + 0.337 HEBJL + 0.441 EYPD + 0.220 EYBJ - 0.619 EYMZ - 0.594 EPPD + 0.227 EPBJ (R2= 0.960) HEMZ = 1816.343 + 0.028 HEPD + 0.147 HEBJ + 0.220 HEBJL + 0.649 HEMZL - 0.120 EYPD - 0.176 EYBJ - 0.092 EYMZ - 0.226 EPMZ - 0.106 EPBJ (R2= 0.850) *, **, ***, **** Significant at the 20, 10, 5, 1 percent level of significance, respectively For the selected crops, SE model was recommended for prediction of the current hectareage on the basis of the adjusted coefficient of multiple determination ( R 2). For Paddy hectareage the main affecting factors viz., bajra hectareage, lagged hectareage of paddy, expected yield of maize and expected price of paddy. Expected yield and expected price of paddy were determining factors of bajra hectareage.

  Calibration approach is a popular technique in sample surveys which incorporates auxiliary information in the estimation process assuming that population aggregates of auxiliary variable are available. Many often under two-stage sampling design, such population aggregates of auxiliary variable, i.e., population mean or total are unavailable and under such situations, estimation of population total has been limited to the use of two phase sampling. Therefore, in the present study, efficient estimator of population total is developed under two-stage sampling design when population aggregates of auxiliary variable are unavailable for the selected psu?s. The calibrated estimator is developed using the information on known population aggregates of additional auxiliary variable which is less linearly related to the study variable through two step calibration approach. The approximate variance and variance estimator of the proposed calibrated estimator has also been developed. Empirical evaluations using both real and simulated data shows the superior performance of the developed calibrated estimator in comparison to the existing estimators.

  A procedure of obtaining efficient block designs in block size 2 for n-factor mixed level factorial microarray experiments based on baseline parameterization has been given. A software module has been developed using C# programming language with ASP.NET platform for generation of efficient block designs in block size 2 for s1 ? s2 ?? sn factorial experiments in v −1 arrays, where j s denotes the number of levels of jth factor and n denotes the number of factors and 1 n j j v s = = Π , the total number of treatment combinations. For n = 2, the software developed can also generate efficient block designs for 1 2 v −1≤ b ≤ ( v −1) + ( s −1)( s −1) , where b is the number of arrays.

  The rural unemployment rate is a critical economic indicator used to assess strength of rural economy in India. Annual estimates of rural UR are released in both usual status (ps+ss) as well as current weekly status (CWS) at the state and national level in India by National Statistics Office (NSO) through Periodic Labour Force Survey (PLFS). At present, the annual rural UR estimates are available for the state of West Bengal from the year 2017-18 to 2021-22. However, there is a notable delay in the publication of UR estimates as compared to the reference period. Therefore, accurate forecasting of the UR is crucial for timely and targeted interventions, and effective policy planning. Conventional forecasting models fail to provide accurate predictions of UR in these type of small time series due to the violation of requirement of number of data points. In contrast, the Grey model requires limited data to establish a differential forecasting model. In this study, application of grey model has been considered to forecast annual rural UR in West Bengal for different age groups as well as gender, and it was found that grey model provides satisfactory forecast.

  Forecasting agricultural commodity prices is regarded as a challenging task due to its non-linear and non-stationary nature. As agriculture production is highly reliant on various biological and agro-meteorological factors, traditional smoothing techniques as well as statistical models often fail to model such series satisfactorily. To capture such complex patterns effectively, different data-driven and self-adaptive techniques have been developed time-to-time. Against this backdrop, in this paper, we have assessed the suitability of empirical mode decomposition (EMD)-based neural network and support vector regression (SVR) approaches for forecasting wholesale prices of three major potato markets namely, Agra, Bangalore, and Mumbai. As the benchmark models, autoregressive integrated moving average (ARIMA), time delay neural network (TDNN) and SVR models have been employed for the comparative evaluation. The experimental results clearly reveal the comparative superiority of the EMD-SVR model for the Agra and Bangalore markets and the EMD-TDNN model for the Mumbai market in terms of root mean squared error values and turning point predictions. Moreover, all the EMD-based models have performed better than the other competing models.

  Orthogonal Latin hypercube designs are becoming popular in designing computer experiments. Available literature on construction of orthogonal or nearly orthogonal LHDs has one or more restriction in terms of either runs or factors. In this article, we have proposed a method of construction for obtaining nearly orthogonal Latin hypercube designs capable of accommodating flexible number of runs or factors.

  Rice, a crucial global staple, is integral to food security. Precise identification of paddy growth stages, booting, heading, anthesis, grain filling, and grain maturity is vital for agricultural decisions. However, a gap exists in recognizing these stages using red-green-blue (RGB) images. This study uses state-of-the-art computer vision and deep learning classification (Convolutional Neural Networks) algorithms to address this gap. Among the studied algorithms, EfficientNet_B0 achieved an impressive 82.8% overall accuracy. Notably, increasing image size from 64X64 pixels to 128X128 pixels significantly enhanced accuracy. A detailed assessment of growth stages revealed varying accuracy levels, with boot leaf being the most accurately detected (95.1%) and anthesis being the most challenging (72.28%). This work significantly advances automated monitoring, empowering researchers in real-time decision-making.

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