Millets, a group of climate-resilient cereals, have been cultivated for over 10,000 years, primarily in arid and semi-arid regions. Recognized for their superior nutritional profile, including high fiber, protein, vitamins, minerals, and bioactive compounds, millets contribute significantly to global food security. Despite their historical significance, millet cultivation has declined due to the dominance of major cereals like wheat and rice, driven by the Green Revolution. However, recent global challenges such as climate change, water scarcity, malnutrition and health awareness have renewed interest in these nutrient-dense grains. This review critically examines global millet production, market trends, consumption patterns, and trade dynamics. India, Niger, and China dominate millet cultivation, collectively accounting for 60% of global production. Emerging markets have witnessed increasing consumer interest in millet-based gluten-free products and functional foods. Government policies and international initiatives, such as India's National Year of Millets (2018) and the United Nations' International Year of Millets (2023), have played a crucial role in promoting millet production and trade. Despite these positive trends, the millet market faces significant challenges, including supply chain inefficiencies, limited consumer awareness, and inconsistent policy support. Additionally, successful case studies demonstrate innovative approaches to overcoming the challenges and fostering millet commercialization. This review highlights the need for investment in value addition, improved processing technologies, and enhanced global marketing strategies. By harnessing these insights, this review provides a strategic outlook on how millets can be sustainably integrated into the global food system, contributing to food security, nutrition, and climate resilience. Graphical Abstract

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.

Background: The increasing impacts of heat stress on wheat production due to climate change has entailed the development of heat-resilient crop varieties. To address this, two hundred recombinant inbred lines (RILs) derived from a cross between WH711/WH1021 were evaluated in a randomized block design (RBD) with two replications at CCSHAU, Hisar, during 2018-19 under heat stress and non-stress conditions. Heat stress was induced by altering the date of sowing so that the grain filling stage coincide with heat stress. Results: Heat stress adversely affects RILs performance, as illustrated by alterations in phenotypic traits. Highest coefficients of variations were recorded for TAA, CTD 1, WUE, CTD 2, Cc and A under non-stress and heat stress conditions whereas gs, WUEi and GY under non-stress and SPAD 1, SPAD 2, GY and NDVI 2 under heat-stress conditions recorded moderate estimates of coefficient of variations. CTD 2, TAA, E, WUE and A displayed a significant occurrence of both high heritability and substantial genetic advance under non-stress. Similarly, CTD 2, NDVI 2, A, WUEi, SPAD 2, gs, E, Ci, MDA and WUE exhibited high heritability with high genetic advance under heat-stress conditions. Conclusions: Complementary and duplicate types of interactions with number of controlling genes were observed for different parameters depending on the traits and environments. RILs 41, 42, 59, 74, 75, 180 and 194 were categorized as heat tolerant RILs. Selection preferably for NDVI 1, RWC, TAA, A, E and WUEi to accumulate heat tolerance favorable alleles in the selected RILs is suggested for development of heat resilient genotypes for sustainable crop improvement. The results showed that traits such as such as NDVI, RWC, TAA, A, E, and WUEi, can be effective for developing heat-resilient wheat genotypes and ensuring sustainable crop improvement.