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This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices. [ABSTRACT FROM AUTHOR]

After soybean and palm oil, the world's third largest oil seed crop is rapeseed mustard. Out of the seven consumable oil seed crops grown in India, rapeseed mustard is responsible for one-third of production. Mustard aphid (Lipaphis erysimi Kalt) is considered the primary pest causing mayhem in crop production. Understanding the genetics behind resistance will aid breeders in developing a resistant/tolerant strain. Appropriate parent selection and analyzing gene action contribute to economic benefit maximization. The aims of the current study are to use seven lines and five testers to determine the best-performing parents and crosses based on their general and specific capacity to combine and to examine the level of heterosis for yield and related features like mustard aphid resistance. Due to the self-pollination nature of Indian mustard, Kempthorne's line X tester method is helpful to judge the combining ability. Therefore, seven lines and five testers of Indian mustard (Brassica juncea L.) were employed in the present study. The findings suggested that there was substantial genetic variation for all traits examined. The mean oil yield of R1 B2-26 × R1 B2-25, JD6 × R1 B2-25, and JD6 × R1 B2-29 hybrids was more significant than that of the ancestors. The results show that R1 B2-26 × R1 B2-25, JD6 × R1 B2-25, and JD6 × R1 B2-29 hybrids produced more oil than their parents. The variance explained by SCA was greater than that explained by GCA, as indicated by the Ϭ2 gca/Ϭ2 sca ratio being less than one for all characters, implying that nonadditive gene actions such as dominance, epistasis, and other interaction effects played an important role in the presence of these attributes. Punjab Local was discovered to be an excellent general merge for reducing crop duration, whereas JD6 was an excellent combiner for seed yield per crop, aphid infestation indicator, seed yield per crop, and oil production per plant. The predictability ratio was found to be less than 0.5 for almost all traits, denoting that the nonadditive gene measure is involved in controlling the nature except days to 50% flowering, aphid infestation index, oil content, and oil yield per plant. Thus, based on these four traits, selection for superior plants may be practiced in later generations. [ABSTRACT FROM AUTHOR]