Foliar application of iron-lysine to boost growth attributes, photosynthetic pigments and biochemical defense system in canola (Brassica napus L.) under cadmium stress.

In the current industrial scenario, cadmium (Cd) as a metal is of great importance but poses a major threat to the ecosystem. However, the role of micronutrient - amino chelates such as iron - lysine (Fe - lys) in reducing Cr toxicity in crop plants was recently introduced. In the current experiment, the exogenous applications of Fe - lys i.e., 0 and10 mg L - 1, were examined, using an in vivo approach that involved plant growth and biomass, photosynthetic pigments, oxidative stress indicators and antioxidant response, sugar and osmolytes under the soil contaminated with varying levels of Cd i.e., 0, 50 and 100 µM using two different varieties of canola i.e., Sarbaz and Pea - 09. Results revealed that the increasing levels of Cd in the soil decreased plant growth and growth-related attributes and photosynthetic apparatus and also the soluble protein and soluble sugar. In contrast, the addition of different levels of Cd in the soil significantly increased the contents of malondialdehyde (MDA) and hydrogen peroxide (H2O2), which induced oxidative damage in both varieties of canola i.e., Sarbaz and Pea - 09. However, canola plants increased the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and non-enzymatic compounds such as phenolic, flavonoid, proline, and anthocyanin, which scavenge the over-production of reactive oxygen species (ROS). Cd toxicity can be overcome by the supplementation of Fe - lys, which significantly increased plant growth and biomass, improved photosynthetic machinery and sugar contents, and increased the activities of different antioxidative enzymes, even in the plants grown under different levels of Cd in the soil. Research findings, therefore, suggested that the Fe - lys application can ameliorate Cd toxicity in canola and result in improved plant growth and composition under metal stress.

Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding.

MAIN CONCLUSION: Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.

Evaluation of disease outbreak in terms of physico-chemical characteristics and heavy metal load of water in a fish farm with machine learning techniques.

Diseases are quite common in fish farms because of changes in physico-chemical characteristics in the aquatic environment, and operational concerns, i.e., overstocking and feeding issues. In the present study, potential factors (water physico-chemical characteristics and heavy metal load) on the disease-causing state of the pathogenic bacteria Lactococcus garvieae and Vagococcus sp. were examined with machine learning techniques in a trout farm. Recording of physico-chemical characteristics of the water, fish sampling and bacteria identification were carried out at bimonthly intervals. A dataset was generated from the physico-chemical characteristics of the water and the occurrence of bacteria in the trout samples. The eXtreme Gradient Boosting (XGBoost) algorithm was used to determine the most important independent variables within the generated dataset. The most important seven features affecting bacteria occurrence were determined. The model creation process continued with these seven features. Three well-known machine learning techniques (Support Vector Machine, Logistic Regression and Naïve Bayes) were used to model the dataset. Consequently, all the three models have produced comparable results, and Support Vector Machine (93.3% accuracy) had the highest accuracy. Monitoring changes in the aquaculture environment and detecting situations causing significant losses through machine learning techniques have a great potential to support sustainable production.