Discuss How Understanding Plant Hormones Can Benefit Agriculture, Horticulture, And Plant Biotechnology
DOI:
https://doi.org/10.63682/jns.v14i32S.7837Keywords:
Plant Hormones, Phytohormones, Agriculture, Horticulture, Biotechnology, Stochastic Modeling, Large Deviation Theory, Hormone Signaling, Crop Optimization, Auxins, Cytokinins, Bifurcation AnalysisAbstract
Plant hormones: phytohormones are the key factors of plant development, physiology, and adaptive response to environmental signals. Their duty in CA appellation, expansion, senescence in addition to plant flowering, fruiting along with stress responsiveness create the biochemical anchoring of plant growth along with productivity. Riding on the complexity of plant hormone knowledge, global agriculture is also under pressure to sustainably deliver essential food supplies given the challenges of climate change as well as global population growth and a possible revolution is emerging in agriculture, horticulture and biotechnology. The paper will examine the roles that the hormone signaling pathways including auxins, gibberellins, cytokinins, abscisic acid and ethylene, can play as important regulators of growth, resistance and yield. We combine stochastic nonlinear software and simulation plans to study hormone flux amid fluctuating environmental circumstances. A big deviation theory and a bifurcation framework is adopted to elucidate the threshold behavior in the hormone driven processes such as flowering, fruit set during stress and so on. Genetic engineering of crops, processes that enhance the productivity in horticulture and micropropagation are referred to with facts and figures, and with illustrations. Moreover, the research explains the ability of mathematical modeling to maximize the exogenously applied hormones and genetic manipulation of hormones to minimize the volatility in crop products by increasing predictability of crop products. Such integrative approach shows how studies into plant hormones could fuel resilience and sustainability of practices in the field of agri-biotechnology. The results create the basis upon which future AI-integrated agronomic decision support systems will predict and optimize plant hormone case treatments.
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