Weed infestation poses a critical challenge to sustainable agriculture, leading to significant crop yield losses and escalating the use of chemical herbicides, which contribute to environmental degradation and health risks. One of the most pressing issues in weed management is the declining effectiveness of traditional weed control methods, which struggle to keep pace with the growing global food demands and the challenges posed by an anticipated population of 10 billion by 2050. The focus is on Precision Weed Management (PWM), emphasizing cutting-edge technologies such as computer vision, Unmanned Aerial Vehicles (UAVs), GPS-controlled patch spraying, laser treatments, and autonomous weeding robots. Computer vision, employing image processing and deep learning, emerges as a key player in automated weed detection, challenging traditional herbicide methods. UAVs equipped with advanced sensors facilitate high-resolution weed mapping, allowing for timely interventions. Laser and thermal treatments showcase targeted and efficient weed control, while autonomous weeding robots exemplify a hands-free, precise approach. The integration of these technologies not only promises enhanced productivity but also signifies a sustainable and environmentally conscious shift in global agriculture. The article highlights the limitations of traditional weed control methods and underscores the potential of emerging technologies to revolutionize weed management, offering precise, cost-effective, and environmentally friendly solutions.

Groundnut is a favourable and profitable crop for resource-poor farmers in Africa and Asia, both for edible oil production and direct consumption. There is significant potential to breed high-yielding, better-quality groundnut cultivars by generating new variations through artificial techniques. In this study, the F2 generations of the crosses CO 7 × Chico and ICGV 07222 × Chico was analysed to assess the variability created through artificial hybridization in groundnut. The various yield and yield-related traits were analysed to estimate genetic parameters, skewness, kurtosis and subjected to principal component analysis (PCA). The variability study of the F2 population from both crosses revealed significant variations for the traits under study. The traits “days to accumulation of 25 flowers” and “shelling %” showed low GCV (genotypic coefficient of variation) and PCV (phenotypic coefficient of variation) in both populations. Most traits exhibited moderate to high heritability and genetic advance, whereas “days to accumulation of 25 flowers” and “maturity duration” had moderate heritability and low genetic advance. Only shelling % had low estimates of heritability and genetic advances. Tests for skewness and kurtosis revealed that both F2 population did not follow a normal distribution. The traits “days to maturity”, “shelling %”, “kernel yield,” and “hundred kernel weight” displayed significant positive skewness. The traits “days to accumulation of 25 flowers”, “number of matured pods”, “height of main axis”, “shelling %”, “hundred pods” and “hundred kernel weight” and “pod yield” showed platy- kurtosis, while “Kernel yield” displayed lepto-kurtosis in both populations. The first principal component explained 37 % and 32 % of the total variance in the 2 F2 populations respectively, with a focus on yield-related traits. The PCA biplot effectively clustered the genotypes based on the 10 different traits studied and clearly, grouped the population based on maturity duration. Thus, hybridization created significant variation in groundnut for all yield-related traits and yield, except for “days to maturity”. The traits require further enhancement using additional sources and could be improved through intense selection.

Global demand for livestock products increases the need for high-quality fodder. However, urbanization resulted in the depletion of agricultural lands expanding the demand for food, feed and fodder. To overcome this issue, the available fodder and agricultural wastes are preserved anaerobically to feed animals. This efficient process of preserving the fodder anaerobically throughout the year is known as ensiling and the preserved fodder is known as silage. To improve the quality and longevity of silage, various additives such as microbial inoculants (bacterial inoculants, yeast), enzymes, chemical additives (organic and mineral acids), nutrients (urea, ammonia, sodium chloride, sodium di acetate) and fermentation accelerators (molasses, starchy substance, grains) are added. Addition of these additives reduces pH to below 4.2, proteolysis up to 40 %, ammonia volatilization, population of yeasts, molds and enhances fermentation, improves the aerobic stability by 2-5 days, optimizes nutrient retention and quality of silage. By supporting better livestock nutrition, silage additives increase 5 to 20% milk production, lactation period and promote beneficial microbial populations in the rumen of cattle. This review describes the usage and effectiveness of various additives that are used to enhance the silage quality in terms of storage and nutritional content.

Maize is the third important staple food crop grown globally. The demand for maize production has increased significantly due to its multiple uses, including food, feed and various industrial applications. As a result, the area under maize cultivation is expanding, driven by its lucrative market price. Being a highly adaptive crop, the development of high-yielding hybrids better suited to climate change will help bridge the gap between demand and supply. Maize is an allogamous crop, exhibiting greater genetic diversity compared to autogamous crops. Therefore, intensified exploration of maize genetic diversity and effective utilization of germplasm will enhance the maize breeding programs. However, the domestication of maize has led to a decline in genetic diversity and the loss of valuable alleles. Human selection has significantly altered the morphology of maize from its wild ancestor. CIMMYT currently maintains around 28000 maize accessions, including landraces and wild relatives. Genetic diversity can be analysed using D2 statistics and clustering methods, employing morphological, molecular, quantitative and qualitative data. Careful consideration is needed when selecting appropriate methods and software for such analyses based on available data. In recent years, SSR markers and SNPs have gained popularity for diversity analysis. Studying genetic diversity in maize is crucial for identifying novel traits and the introgression of these traits into new hybrids using advanced technology requires further attention.