Forecasting of wheat (Triticum aestivum) yield using ordinal logistic regression

In this study, uses of ordinal logistic model based on weather data has been attempted for forecasting wheat (Triticum aestivum L.) yield in Kanpur district of Uttar Pradesh. Weekly weather data (1971-72 to 2009-10) on maximum temperature, minimum temperature, morning relative humidity, evening relative humidity and rainfall for 16 weeks of the crop cultivation along with the yield data of wheat crop have been considered in the study. Crop years were divided into two and three groups based on the detrended yield. Yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors for different weeks. Data from 1971-72 to 2006-07 have been utilized for model fitting and subsequent three years (2007-08 to 2009-10) were used for the validation of the model. Evaluation of the performance of the models developed at different weeks has been done by Adj R2, PRESS (Predicted error sums of squares) and number of misclassifications. Evaluation of the forecasts were done by RMSE (Root mean square error) and MAPE (Mean absolute percentage error) of forecast

Development of a Fast SARS-CoV-2 IgG ELISA, Based on Receptor-Binding Domain, and Its Comparative Evaluation Using Temporally Segregated Samples From RT-PCR Positive Individuals

SARS-CoV-2 antibody detection assays are crucial for gathering seroepidemiological information and monitoring the sustainability of antibody response against the virus. The SARS-CoV-2 Spike protein's receptor-binding domain (RBD) is a very specific target for anti-SARS-CoV-2 antibodies detection. Moreover, many neutralizing antibodies are mapped to this domain, linking antibody response to RBD with neutralizing potential. Detection of IgG antibodies, rather than IgM or total antibodies, against RBD is likely to play a larger role in understanding antibody-mediated protection and vaccine response. Here we describe a rapid and stable RBD-based IgG ELISA test obtained through extensive optimization of the assay components and conditions. The test showed a specificity of 99.79% (95% CI: 98.82-99.99%) in a panel of pre-pandemic samples (n = 470) from different groups, i.e., pregnancy, fever, HCV, HBV, and autoantibodies positive. Test sensitivity was evaluated using sera from SARS-CoV-2 RT-PCR positive individuals (n = 312) and found to be 53.33% (95% CI: 37.87-68.34%), 80.47% (95% CI: 72.53-86.94%), and 88.24% (95% CI: 82.05-92.88%) in panel 1 (days 0-13), panel 2 (days 14-20) and panel 3 (days 21-27), respectively. Higher sensitivity was achieved in symptomatic individuals and reached 92.14% (95% CI: 86.38-96.01%) for panel 3. Our test, with a shorter runtime, showed higher sensitivity than parallelly tested commercial ELISAs for SARS-CoV-2-IgG, i.e., Euroimmun and Zydus, even when equivocal results in the commercial ELISAs were considered positive. None of the tests, which are using different antigens, could detect anti-SARS-CoV-2 IgGs in 10.5% RT-PCR positive individuals by the fourth week, suggesting the lack of IgG response

Not Available

Not AvailableIn this study, uses of ordinal logistic model based on weather data has been attempted for forecasting wheat (Triticum aestivum L.) yield in Kanpur district of Uttar Pradesh. Weekly weather data (1971-72 to 2009-10) on maximum temperature, minimum temperature, morning relative humidity, evening relative humidity and rainfall for 16 weeks of the crop cultivation along with the yield data of wheat crop have been considered in the study. Crop years were divided into two and three groups based on the detrended yield. Yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors for different weeks. Data from 1971-72 to 2006-07 have been utilized for model fitting and subsequent three years (2007-08 to 2009-10) were used for the validation of the model. Evaluation of the performance of the models developed at different weeks has been done by Adj R2, PRESS (Predicted error sums of squares) and number of misclassifications. Evaluation of the forecasts were done by RMSE (Root mean square error) and MAPE (Mean absolute percentage error) of forecast.Not Availabl

Not Available

Not AvailableThe performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models obtained at different weeks was compared using Adj R2, PRESS (Predicted error sum of square), number of misclassifications and forecasts were compared using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. Ordinal logistic regression based approach was found to be better than discriminant function analysis approach.Not Availabl

Not Available

Not AvailableSurveys are often based on complex sample designs where sampling units frequently have different probabilities of being selected. In the survey data analysis, sampling weights must be used to incorporate the sample designs. Regression coefficients are estimated to find the relationship between the study and auxiliary variables. Kish and Frankel (1974) deliberated the use of sampling weights in the estimation of regression coefficients. This paper describes calibration based approach to estimate the regression coefficient using two auxiliary variables. The variance estimation of proposed estimator is also developed. The empirical results based on synthetic and real population show that the proposed estimator, in terms of percent relative bias and percent relative root mean square error, performs better than the existing estimator. The proposed variance estimator shows a satisfactory performance in empirical evaluation.Not Availabl

Not Available

Not AvailableDiscriminant function analysis technique using Bayesian approach has been attempted for wheat forecasting in Kanpur district of Uttar Pradesh, India both qualitatively and quantitatively. Crop yield data and weekly weather data on temperature (maximum and minimum), relative humidity (maximum and minimum), rainfall for 16 weeks of the crop cultivation have been used in the study. These data have been utilized for model fitting and validation. Crop years were divided into two and three groups based on the de-trended yield. Crop yield forecast models have been developed using posterior probabilities calculated through Bayesian approach in stepwise discriminant function analysis along with year as regressors for different weeks. Suitable strategy has been used to solve the problem of number of variables more than number of data points. Performance of the models obtained at different weeks was compared using Adjusted R2, PRESS (Predicted error sum of square), number of misclassifications. Forecasts were evaluated using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. The result shows that the model based on three groups case perform better. The performance of the proposed Bayesian discriminant function analysis technique approach was better as compared to existing discriminant function analysis score based approach both qualitatively and quantitatively.Not Availabl

Not Available

Not AvailableNot AvailableNot Availabl

Applicability of QbD-assisted Analytical Method for Simultaneous Detection of Tetrahydrocurcumin and Folic Acid in Developed Nanostructured

Aims:: Applicability of QbD-assisted analytical method for simultaneous detection of tetrahydrocurcumin and folic acid in developed nanostructured. background: Background: Diabetic foot ulcer (DFU), a multifactorial disorder involves chronic inflammation, oxidative stress and neuropathy. Current treatment therapies, involving the use of growth factors and skin substitutes being costly, are out of reach for the majority of patients. The present research explored the usefulness of (5929IN008, application number 202211045937) a combination of tetrahydrocurcumin and folic acid loaded nanostructured lipidic carriers. Background:: Diabetic foot ulcer (DFU) is a multifactorial disorder that involves chronic inflammation, oxidative stress and neuropathy. Current treatment therapies involving the use of growth factors and skin substitutes being costly, are out of reach for the majority of patients. The present research explored the usefulness of (5929IN008, application number 202211045937), a combination of tetrahydrocurcumin and folic acid-loaded nanostructured lipidic carriers. Objectives:: To develop and validate a QbD-assisted method for simultaneous analysis of tetrahydrocurcumin (THC) and folic acid (FA). Applicability of the above method to determine total drug content (TDC) and entrapment efficiency (EE) of nanostructured lipid carriers (NLCs) loaded THC and FA. Methods:: A high-performance liquid chromatographic (HPLC) method was developed, optimized and validated using Box-Behnken design for improved method performance. Chromatographic separation was conducted on a Supelco 250 x 4.6 mm (5 μm) column with optimized mobile phase composition containing tetrahydrofuran: citric acid buffer pH 3.5 (50:50) at a flow rate of 0.4 mL.min-1 and diode array detection between 210 and 360 nm. Results:: The method developed in a concentration range of 1 to 100 μg.mL-1 was found to be linear (R2 0.999, p≤0.001), accurate (99.10-101.70%) and precise with high recovery values in intra and inter-day results. The system adaptability and robustness evaluation revealed that the percent recovery ranged from 96.90 to 102.80%, and the percent relative standard deviation (%RSD) values were less than 2%. Moreover, the method was further applied for the determination of TDC (86±6% and 96±8%) and drug EE (81±21% and 73±13%) for THC and FA, respectively. Conclusion:: The investigation indicated the applicability of the developed and validated method for the estimation of THC and FA in the developed nanostructured lipidic carriers

Not Available

Not AvailableAgriculture plays a vital role in the Indian economy and hence collection and maintenance of Agricultural Statistics assume great importance. During past few agricultural years it was observed that a total number of 1300000 (approx) Crop Cutting Experiments (CCE) were conducted in India every year to find out the crop yield estimates of several major and minor crops conducted under General Crop Estimation Surveys (GCES). Due to shortage of manpower and huge bulk of work day by day the data quality is becoming questionable. To tackle this problem, a pilot study was conducted by ICAR-IASRI, New Delhi sponsored by Directorate of Economics and Statistics (DES), Ministry of Agriculture and Farmers welfare (MoA&FW), Govt. of India to generate district level estimates of major crop yield from a reduced sample size of villages selected from the states. With the reduction in number of villages, the problem of no sample size in some districts were faced during the study where common design based estimates of crop yield cannot be generated. To tackle this problem Aggregate level Small Area Estimation (SAE) was used to tackle this problem. The results obtained from this pilot study in the state of Uttar Pradesh for two major crops i.e. rice and wheat for two seasons i.e. Kharif and Rabi of Agriculture Year 2015-16 and for Paddy in Assam for Kharif of the Agriculture Year (AY) 2015-16 in India were discussed. The yield estimates were compared with the estimates released under GCES for AY 2015-16. It was found that the estimates obtained from reduced sample size of number of CCEs w.r.t. GCES, produced similar estimates with acceptable level of precision.Not Availabl

Not Available

Not AvailableNot AvailableNot Availabl