[Database] Precision Agriculture in the United States: A Comprehensive Meta-Review Inspiring Further Research, Innovation, and Adoption
Description
Precision agriculture has emerged as a dominant force in the United States, with widespread adoption of advanced technologies and decision support systems (DSS) since the 1980s. Key tools such as variable rate application (VRA), autopilot systems, and remote sensing have become integral for U.S. farmers, offering invaluable insights from crop, soil, and weather information to optimize agricultural production while minimizing environmental impact. To synthesize and categorize the extensive research available on precision agriculture, a systematic review protocol has been designed. Our objective is to offer clear and authoritative insights into the nature, scope, and volume of this field. Implementing a rigorous search strategy, we utilized renowned databases such as ScienceDirect® and Web of ScienceTM to gather relevant and significant materiality. The retrieval process involved the use of indexing terms and Boolean operators, with a focus on 'precision agriculture' and 'precision farming', striking a balance between specificity and comprehensiveness. To ensure the credibility of our findings, only peer-reviewed papers authored by individuals affiliated with U.S. institutions have been included. Expert reviewers with deep knowledge in the field independently assessed the selected papers, thoroughly evaluating titles, abstracts, keywords, methods, conclusions, and declarations. Consistency and eligibility were paramount in determining which papers met the criteria for inclusion. Any discrepancies or disagreements were resolved through rigorous consensus-building discussions among the reviewers. Through this comprehensive meta-review, we provide a scientific contribution that enhances our understanding of precision agriculture, highlighting focus areas for further research and development (R&D). By synthesizing and categorizing the existing literature, we offer authoritative insights into the research landscape, informing future investigations and fostering innovation. Focusing specifically on the U.S., we shed light on the unique aspects and pioneering advancements in precision agriculture within the country. Ultimately, our findings have the potential to drive progress, contributing to sustainable development, increased productivity, enhanced environmental sustainability, and responsible agricultural practices.
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To ensure the inclusivity of relevant scholarly materials, we carefully selected ScienceDirect® and Web of ScienceTM as the primary databases for retrieving academic articles. By employing a meticulously crafted search strategy, we combined appropriate indexing terms and utilized Boolean operators to construct a robust and representative research-engining string, specifically [title-abstract-keyword = ("precision agriculture" OR "precision farming")]. This deliberate choice was intended to yield a comprehensive compilation of articles that authentically reflect the core essence of precision agriculture, while consciously excluding topics related to digital agriculture or digital farming, such as deep learning (DL) for artificial intelligence (AI)-intensive machines (e.g., agribots), strategies, practices, and actions. Our goal has been to focus on the specific context of digitized on-farm operations, including remote sensors for soil, crop, and weather data collection, predictive maps, and variable rate applications (VRA) for water, pesticides, and fertilizers. To establish a focused analysis within the specific context of the U.S., we meticulously filtered the retrieved literature, ensuring that only peer-reviewed papers were selected where at least one author affiliated with a U.S. institution made a substantive intellectual contribution to the study. This rigorous approach guarantees the reliability and accuracy of our findings, as we sought to present a comprehensive overview of the state of precision agriculture research. To accomplish this, we assembled a team of accomplished reviewers, each possessing expertise in the field, who were tasked with independently assessing the selected articles for consistency and eligibility. They selected only studies fitting within our scope of precision agriculture, consciously excluding experiments on duplicates to prevent bias, not related to precision agriculture and any “gray literature” to ensure the soundness of our approach. Through a meticulous screening process, our reviewers critically evaluated various aspects of each article, including titles, highlights, abstracts, keywords, methods, conclusions, and declarations. This rigorous evaluation ensured that only articles directly aligned with the subject matter of precision agriculture were included in our review. To enhance the reliability and robustness of our synthesis, our reviewers engaged in regular discussions, meticulously resolving any divergences through a consensus-based approach. Additionally, we synthesized and assimilated all pertinent information associated with each subject to formulate the results and provide a comprehensive discussion.