In 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.

Aim: 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.

This 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 Telangana state 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 was used 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 months showed a non-significant trend and no significant trend in the monsoon periods. These accurate identification of rainfall patterns over the area may help to create the appropriate policy measures in advance to plan the future climate uncertainties.

Rice germplasm has abundant genetic diversity, which provides a feasible solution for mapping loci of multiple traits simultaneously. In this study, a set of 72 rice germplasm lines were evaluated for yield and yield-related traits, and significant phenotypic variation was observed among the lines. Three accessions with high yield performance were identified. The germplasm set comprised five sub-populations and genome-wide association study (GWAS) identified a total of 6 marker-trait associations (MTAs) for the studied traits. These MTAs were located on rice chromosomes 1, 3, 7, 9, and 12 and explained the trait phenotypic variances ranging from 17.8 to 26.3%. Six novel MTAs were identified for yield and yield-related traits. A total of 28 putative annotated candidate genes were identified in a genomic region spanning ~200 kb around the MTAs respectively. Among the important genes underlying the novel MTAs were OsFBK12, bHLH, WRKY, HVA22, and ZmEBE-1, which are known to be associated with the identified novel QTLs. These MTAs provide a pathway for improving high yield in rice genotypes through molecular breeding.

This study attempted to explore the interactive relations among agricultural labour wage rates in five neighbouring Indian states viz., Andhra Pradesh, Karnataka, Tamilnadu Telangana and Chhattisgarh using monthly time series data of 2005-2020. The objective of this study was to examine the degree of integration among wage rates of agricultural labourers in neighbouring states. Integration with outside markets may partly mitigate the costs of climate change, as individuals respond to warming temperature by migrating to urban areas and internationally in search of employment. We built vector error correction model (VECM) by conducting stationarity test and cointegration test. The Granger Causality test was employed to check whether the wage rates among different states influence each other. For building the VEC Model, the complete data set (180 data points) was split into training (168 data points) and testing (12 data points) data sets. The nonstationarity of the data was established by the Augmented Dickey Fuller test. For the purpose of forecasting, VECM (1) was built and tested for goodness of fit using Mean Absolute Percentage Error (MAPE) which were found to be < 10% for all the states suggesting good fit of the VECM model. A growing body of literature suggests that the economic costs of climate change may be substantial and far‐reaching, impacting agriculture, mortality, labour productivity, economic growth, civil conflict and migration.