55 research outputs found
Not Available
No full textNot 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
Forecasting of wheat (Triticum aestivum) yield using ordinal logistic regression
No full textIn 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
Weather based fuzzy regression models for prediction of rice yield
No full textFuzzy regression models for forecasting rice yield in Kanpur district were developed and compared with the weather indices-based regression model. For this, weekly (23-35 SMW) weather data (1971, 1973-2011) were utilized. Significant variables in fuzzy approach were selected based on index of confidence (IC) and adequacy of models was compared with the weather indices-based regression
models. It was found that variables such as total accumulation of minimum temperature, weighted interaction of bright sunshine hours and rainfall, weighted interaction of minimum and maximum temperature, unweighted interaction of maximum temperature and relative humidity in morning and weighted interaction of relative humidity in morning and evening respectively, are significant based on their IC and SSE (sum of square error) values. The validations of models were also attempted for three years (2008-09, 2010-11 and 2011-2012).This study also reveals that the parameters for adequacy of models for linear regression models vis-a-vis their fuzzy counterparts are much higher for all values of fitness criterion (h). Thus, fuzzy regression methodology is more efficient than linear regression technique.
Predictive modeling of Pan Evaporation using Random Forest Algorithm along with Features Selection
No full textForecasting podfly (Melanogromyza obtusa) in late pigeonpea (Cajanus cajan)
No full textQualitative and quantitative models were developed for damage due to podfly (Melanogromyza obtusa) on late maturing pigeonpea [Cajanus cajan (L.) Millsp] in Kanpur. Historical data from 1987-88 to 2009-10 on per cent pod damage and weekly weather variables were considered for model fitting. Weather based indices were generated which were used as explanatory variables. Models were validated on subsequent periods (2010-11 and 2011-12) data and found to be satisfactory for both qualitative (epidemic/non-epidemic year) and quantitative (extent of damage)forewarning of damage due to podfly in late pigeonpea at Kanpur
Long term performance of mango varieties on five polyembryonic rootstocks under subtropical conditions: effect on vigour, yield, fruit quality and nutrient acquisition
No full textNot Available
No full textNot AvailableUnder Online Pest Monitoring and Advisory Services (OPMAS) program, huge information/data on cotton pest along with weather were collected in three intensive cotton growing zones, viz. the North Zone (Punjab, Haryana and Rajasthan), the Central Zone (Maharashtra, Madhya Pradesh and Gujarat), and the Southern Zone (Andhra Pradesh, Telangana, Karnataka and Tamil Nadu), in India. Based on pest monitoring weekly advisory services were issued to extension agencies and farmers for control measures of pests in the cotton crop. Under the project extraction system was developed which was based on three tier architecture, i.e. presentation, application and data tier to reduce the effort for searching a huge set of data for desired information on real time points. In the system, the central value of pest (mean, maximum and minimum) and spread of the pest in terms of variance and standard deviation may be obtained. These results can provide the epidemic status of the pest based on the threshold values which can be utilized to issue advisories to farmers about the pest control. In future the data extracted from this system can be used for pattern development using pest population as a character under study and time variable as an independent/ explanatory variable.Not Availabl
Not Available
No full textNot AvailableUnder Online Pest Monitoring and Advisory Services (OPMAS) program, huge information/data on cotton pest
along with weather were collected in three intensive cotton growing zones, viz. the North Zone (Punjab, Haryana and Rajasthan), the Central Zone (Maharashtra, Madhya Pradesh and Gujarat), and the Southern Zone (Andhra Pradesh, Telangana, Karnataka and Tamil Nadu), in India. Based on pest monitoring weekly advisory services were issued to extension agencies and farmers for control measures of pests in the cotton crop. Under the project extraction system was developed which was based on three tier architecture, i.e. presentation, application and data tier to reduce the effort for searching a huge set of data for desired information on real time points. In the system, the central value of pest (mean, maximum and minimum) and spread of the pest in terms of variance and standard deviation may be obtained. These results can provide the epidemic status of the pest based on the threshold values which can be utilized to issue advisories to farmers about the pest control. In future the data extracted from this system can be used for pattern development using pest population as a character under study and time variable as an independent/ explanatory variable.Not Availabl
Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India
No full text- …
