The American Journal of Agriculture is a monthly journal that publishes original and innovative research on various aspects of agricultural sciences. The journal aims to facilitate interdisciplinary collaboration and communication among researchers, practitioners, and policy makers. The journal has a high-quality and transparent peer review process, and is indexed by several reputable databases. The journal respects the authors' intellectual property rights and ethical standards.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Revolutionizing Smallholder Agriculture with AI: Intelligent Sensor Networks for Real-Time Climate
Purpose: Smallholder agriculture forms the backbone of global food security; however, it has been considered highly vulnerable to the impacts of climate variability. The present study explores the role of Artificial Intelligence in integrating intelligent sensor networks for real-time climate monitoring and improving resilience among smallholder farmers. Materials and Methods: The present research will employ a mixed-methods approach, combining quantitative analysis of climate data with qualitative interviews of farmers regarding the efficiency of AI-driven strategies for climate adaptation. Findings: The findings of this study have shown that intelligent sensor networks greatly enhance precision and timeliness of real-time climate data. These help the smallholder farmers in making very precise decisions regarding irrigation, pest control, and crop management that eventually lead to increased productivity and reduced vulnerability to climate risks. The quantitative results show that farmers adopting AI-driven interventions are likely to have a 50% better yield compared to those dependent on conventional methods. Farmers reported qualitative insights into the transformative potential of these technologies by way of improved confidence in decision-making processes and increased resilience against adverse climatic conditions. Implications to Theory, Practice and Policy: Financial constraints, technical difficulties, and the need for capacity building in facilitating technology adoption comprise some important challenges that emanate from the study. All these barriers, once overcome, will see the integration of AI and sensor networks realize benefits not only at an individual farmer level but also at the global agricultural sustainability level. This research contributes to the increasing literature on climate-smart agriculture and gives actionable recommendations for policymakers, practitioners, and stakeholders. The potential of AI-driven intelligent sensor networks can be leveraged to empower smallholder farmers toward sustainable agricultural development that would meet the challenges of food security and economic stability in a changing climate.
Copyright (c) 2025 Mustapha Diyaol https://creativecommons.org/licenses/by/4.0
Growth and Tuberization in Seed-Propagated Cassava
Purpose: This study aimed to assess the growth and tuberization dynamics in seed-propagated cassava in order to offer insights into the morphological and anatomical characteristics of cassava during various stages of growth. Materials and Methods: Cassava seeds of line TMS 30555 obtained from IITA were planted at a distance of 25 cm by 25 cm in prepared beds. Regular observations were made on plant morphological characteristics, tuber formation, stem height, taproot length and lateral root length with data obtained analysized using Microsoft Excel. Microscopic analysis was conducted on root samples to assess tuberization using a detailed slide preparation procedure involving fixation, dehydration, infiltration with paraffin wax, embedding, sectioning with a microtome, staining and mounting on slides for examination under a microscope. Findings: There was a steady weekly increase in the average number of nodes with an almost constant number of lobes and number of adventitious roots on the hypocotyl. The true leaf produced on the second node was simple and the shape was ovate. The study confirmed that starch synthesis begins in cassava roots around six weeks post-sowing, initially visible in cortical cells before spreading to xylem arms and lateral roots. By eight weeks after sowing, there was a notable increase in starch grain accumulation across various parts of the lateral roots. There was also strong positive correlations among stem height, taproot length and lateral root length, suggesting that these traits are interdependent and influenced by shared genetic factors and environmental conditions. Unique Contribution to Theory, Practice and Policy: The findings provide valuable insights into the growth and development of seed-propagated cassava plants, which can help guide breeding efforts aimed at improving cassava yield and quality. The understanding gained in the growth and development of seed-propagated cassava plants, including factors influencing tuberization and starch accumulation, is essential as it informs breeding efforts aimed at improving crop yield, resilience and nutritional content, partly contributing to resolutions towards food insecurity.
Copyright (c) 2025 Andrews Danquah, Emmanuel Plas Otwe, Isaac Kojo Angnangsoore Galyuon, Kofi Atiah, Clement Napo https://creativecommons.org/licenses/by/4.0