Impact of Mission Kakatiya on Area under Tank Irrigation in Southern Telangana Zone, India

Aim: The study was done with an aim to find out whether there is any shift in major crops in Southern Telangana Zone with respect to area, production and yield due to the restoration of tanks with the Mission Kakatiya program and to study the growth in tank irrigated area. Data Description: Time series data of 15 years from 2005-10 to 2015-20 which consists of area, production and yield of major crops (Paddy, Maize, Cotton and Groundnut) and area under tank irrigation in Southern Telangana Zone were utilized for the study and was collected from Statistical Year Books published by Directorate of Economics and Statistics. Methodology: Analysis was done with the help of analysis platforms like SPSS and Excel using statistical tools which include linear and compound growth rates. Results: Results revealed that there was a considerable and significant growth observed in area under tank irrigation (29.69%) in Southern Telangana Zone after Mission Kakatiya. With the increase in tank irrigated area, this zone showed a shift towards irrigated and commercial crops like Paddy, Cotton and Maize from the rainfed crops. Conclusion: During the period before Mission Kakatiya there was a negative growth observed in tank irrigated area whereas both the growth rates have turned to positive in the period after implementation of Mission Kakatiya. This study concluded that there is a positive impact on crop characteristics in this zone due to Mission Kakatiya program. As a whole Mission Kakatiya is one of the outstanding projects whose achievements are incomparable and is a blessing for the farmers of Telangana State

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Not AvailableThis study was carried out to analyse the trend analysis of the long-term annual and seasonal rainfall pattern in Telangana state, India. For this study monthly rainfall data of Telanganastate from January 1982 to December 2021 was collected from the NASA power website (https://power.Iarc.nasa.gov). The linear regression trend line and the non-parametric tests, such as Mann-Kendall test, Modified-Mann Kendall test and Innovative trend analysis tests, were used to understand the trend present in the rainfall data of Telangana. Wallis and Moore test wasused to test the randomness of the rainfall data under consideration. Both increasing and decreasing trend was seen in linear regression trend method for Telangana rainfall data. The significant result was found in the month of May which showed an increasing trend, whereas remaining months showed the non-significant trend in the Modified Mann Kendall test as well as in the Innovative trend analysis. The pre-monsoon, monsoon and post-monsoon periods showed a non-significant trend in the rainfall pattern of Telangana state. The annual rainfall of Telangana showed a non-significant trend pattern by Modified Mann-Kendall test. There was a significant increasing trend of rainfall in the month of May and remaining monthsshowed a non-significant trend. Accurate identification of rainfall patterns over the area may help to create the appropriate policy measures in advance to plan the future climate uncertaintiesNot Availabl

Modelling the Relationship between Weather Variables and Rice Yellow Stem Borer Population: A Count Data Modelling Approach

Not AvailableAim: This study was conducted to model the relationship between discrete dependent variable (yellow stem borer population) and continuous weather variables. Data Description: The yellow stem borer (YSB) population and standard meteorological week (SMW) wise weather variables (temperature, relative humidity, rainfall and sunshine hours) data of Warangal centre (Telangana state) generated under All India Co-Ordinated Rice Improvement Project (AICRIP) from 2013-2021 were considered for the study. The YSB population were recorded daily using light trap with an incandescent bulb and are counted as weekly cumulative catches. Methodology: The weekly cumulative trapped YSB populations and weekly averages of climatological data were considered as inputs to the models under consideration. In this study the classical linear regression i.e. step-wise multiple linear regression and count regression models such as Poisson, negative binomial, zero inflated Poisson and zero inflated negative binomial regression models were employed. Result: The empirical results revealed that the zero inflated count regression models viz., zero inflated Poisson regression and zero inflated negative binomial regression models performed better compared to the classical linear regression, Poisson and negative binomial regression models, further the negative binomial regression model outperformed all models as it yielded lowest mean square error (MSE) and highest R2 values. The average percentage reduction in accuracy of zero-inflated negative binomial regression model over classical model was around 4 percent. Conclusion: Based on the results obtained in this study, it is concluded that the zero inflated models performs better compared to classical models as they are unable to handle the presence of excess zeroes, as a result provides more prediction error and lower R2 values. Further, the models developed in this study will be of great assistance in identifying the factors influencing occurrence of YSB population in rice.Not Availabl

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Not AvailableIn agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.Not Availabl

Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models

Not AvailableIn agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.Not Availabl