Background: Unfavorable climatic changes have led to an increased threat of several biotic and abiotic stresses over the past few years. Looking at the massive damage caused by these stresses, we undertook a study to develop high yielding climate-resilient rice, using genes conferring resistance against blast (Pi9), bacterial leaf blight (BLB) (Xa4, xa5, xa13, Xa21), brown planthopper (BPH) (Bph3, Bph17), gall midge (GM) (Gm4, Gm8) and QTLs for drought tolerance (qDTY 1.1 and qDTY 3.1 ) through marker-assisted forward breeding (MAFB) approach. Result: Seven introgression lines (ILs) possessing a combination of seven to ten genes/QTLs for different biotic and abiotic stresses have been developed using marker-assisted selection (MAS) breeding method in the background of Swarna with drought QTLs. These ILs were superior to the respective recurrent parent in agronomic performance and also possess preferred grain quality with intermediate to high amylose content (AC) (23-26%). Out of these, three ILs viz., IL1 (Pi9+ Xa4+ xa5+ Xa21+ Bph17+ Gm8+ qDTY 1.1 + qDTY 3.1 ), IL6 (Pi9+ Xa4+ xa5+ Xa21+ Bph3+ Bph17+ Gm4+ Gm8+ qDTY 1.1 + qDTY 3.1 ) and IL7 (Pi9+ Xa4+ xa5+ Bph3+ Gm4+ qDTY 1.1 + qDTY 3.1 ) had shown resistance\tolerance for multiple biotic and abiotic stresses both in the field and glasshouse conditions. Overall, the ILs were high yielding under various stresses and importantly they also performed well in non-stress conditions without any yield penalty. Conclusion:The current study clearly illustrated the success of MAS in combining tolerance to multiple biotic and abiotic stresses while maintaining higher yield potential and preferred grain quality. Developed ILs with seven to ten genes in the current study showed superiority to recurrent parent Swarna+drought for multiple-biotic stresses (blast, BLB, BPH and GM) together with yield advantages of 1.0 t ha − 1 under drought condition, without adverse effect on grain quality traits under non-stress.

The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between the period from 2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and (ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were measured at four intensive rice cropping agroecosystems that are endemic for gall midge incidence in India. In addition, weekly cumulative trapped gall midge populations and weekly averages of climatological data were subjected to count time series (Integer-valued Generalized Autoregressive Conditional Heteroscedastic—INGARCH) and machine learning (Artificial Neural Network—ANN, and Support Vector Regression—SVR) models. The empirical results revealed that the ANN with exogenous variable (ANNX) model outperformed INGRACH with exogenous variable (INGRCHX) and SVR with exogenous variable (SVRX) models in the prediction of gall midge populations in both training and testing data sets. Moreover, the Diebold–Mariano (DM) test confirmed the significant superiority of the ANNX model over INGARCHX and SVRX models in modeling and predicting rice gall midge populations. Utilizing the presented efficient early warning system based on a robust statistical model to predict the build-up of gall midge population could greatly contribute to the design and implementation of both proactive and more sustainable site-specific pest management strategies to avoid significant rice yield losses.

Influence of weather variables on occurrence of spiders in pigeon pea across locations of seven agro-climatic zones of India was studied in addition to development of forecast models with their comparisons on performance. Considering the non-normal and nonlinear nature of time series data of spiders, non-parametric techniques were applied with developed algorithm based on combinations of wavelet–regression and wavelet–artificial neural network (ANN) models. Haar wavelet filter decomposed each of the series to extract the actual signal from the noisy data. Prediction accuracy of developed models, viz., multiple regression, wavelet–regression, and wavelet–ANN, tested using root mean square error (RMSE) and mean absolute percentage error (MAPE), indicated better performance of wavelet–ANN model. Diebold Mariano (DM) test also confirmed that the prediction accuracy of wavelet–ANN model, and hence its use to forecast spiders in conjunction with the values of pest–defender ratios, would not only reduce insecticidal sprays, but also add ecological and economic value to the integrated pest management of insects of pigeon pea.

Agrometeorological Advisory Services (AAS) are being offered by India Meteorological Department (IMD), Ministry of Earth Sciences (MoES) under Gramin Krishi Mausam Sewa (GKMS) scheme as a step towards providing weather information-based crop or livestock management strategies and operations dedicated to enhancing crop production and food security. The Government of India has entrusted IMD the task of establishing weather observing system and development of GKMS in the country. In pursuance, IMD has set up a network of District Agro-Meteorology Unit (DAMU) in 530 districts in the country. The DAMU project was started at PJTSAU- Mahabubabad, Krishi Vigyan Kendra, Malyal during 2019 with operational districts of Mahabubabad, Warangal (Rural), Jangaon, Jayashankar Bhupalapalli with an aim to disseminate weather based agro advisory bulletins to locations. The present study was conducted to determine the impact of DAMU project and importance of ICT Tools in dissemination of AAS among farmers by conducting a feedback survey through Google form in Telugu language by randomly selecting 200 beneficiaries belonging to Mahabubabad district. The study revealed that WhatsApp is the most convenient and widely used medium for weather information followed by personal visit to the Krishi Vigyan Kendra, (i.e, Location of observatory). It was observed that majority of the farmers i.e., 74.6 per cent get weather information through WhatsApp, using these bulletins, 64.5% respondents could save Rupees 2,500-5000 while 35.5% respondents could save Rupees 5000-20,000 in a season by either preponing or postponing the agriculture operations based on weather.

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.