The role of precision agriculture in food security

Abstract

Precision agriculture (PA) is the application of information technology to better manage crop and livestock production, heralded as another tool to advance food security around the world. Precision agriculture has the potential to increase productivity, improve resource allocation for inputs such as pesticides, fertilizers, water, feed, and labor, provide for more stable production, and reduce agricultural production's environmental effect. But PA is an approach that can be very different depending on farm characteristics, such as crops and livestock raised, farm size, management, the farms' access to technical support, and the characteristics of the operator such as age and education. As a result, the adoption of PA has been slower and less uniform compared with some other agricultural innovations. Modern PA management systems are rarely implemented on small low-mechanization farms, which comprise much of the world's agricultural production, and these farms are common in areas of the world that are the least food secure. And like other farm technology, its benefits go primarily to its adopters and society as a whole, but those who do not, or cannot adopt are left at a relative disadvantage, which unfortunately at present are most farmers around the world. Food security's four main principles according to the Food and Agriculture Organization of the United Nations are food availability, access, utilization, and stability. Precision agriculture has an aspect unlike its predecessor innovations that were primarily production related-because it is based on information, it has the capacity also to better inform and to allow adjustment all along the food supply/demand chain.

Near‐term problems in meeting world food demands at regional levels: A special issue overview

“…Experts on the panel were in 90 to 100% consensus on three hot topic issues: soil health and water usage, climate and environmental factors, and research and collaboration (more data on the topics, interdisciplinary collaboration, and sharing of information within research groups as well as with farmers). In addition, workforce training (see Erickson & Fausti, 2021), accessibility of germplasm (see Smith et al., 2021), chemical usage, (including weed resistance (see Clay, 2021 and disease problems discussed in Byamukama et al., 2021), and economic stability and governmental policy focused on the agricultural sector (discussed in Joshi et al., 2021, and Barrera et al., 2021) were recognized as urgent topics that must be addressed to continue to have sustainable production agriculture in the future.…”

“…As technology becomes more common place in crop production, a well-trained workforce is imperative to empower the best use of the technology (Erickson & Fausti, 2021). This means that users will need training in crop production best management practices, as well as when, where, and what technology is appropriate to be deployed in specific circumstances.…”

How can precision farming work on a small scale? A systematic literature review

“…In addition, socio-economic barriers do matter, such as openness to innovation, knowledge sharing, age, complexity and cost of technology, available financial support, and farm size (Blasch et al, 2022). These are among the reasons why PA adoption shows different characteristics compared to other agricultural innovations, such as a slower adoption process for some technologies and greater divergence between the different farms (Erickson and Fausti, 2021).…”

“…Lowenberg-DeBoer and Erickson (2019) identified that non-mechanized, small and medium-sized farms in developing countries have the lowest PA adoption. It should be highlighted that most of the African and Asian small-scale farms are generally non-mechanized; therefore, nondigital-based PA technologies are particularly important for them (Erickson and Fausti, 2021). Although this problem of mechanization and/or adoption is more significant in developing countries, some of the smallholdings in developed countries can also be concerned.…”

Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru

“…In the last decade, the use of technologies in agriculture has also increased significantly through the usage of geographic information systems (GIS), and global navigation satellite systems (GNSS), remote sensing, RPAS, machinery and other technologies that have supported precision agriculture [5][6][7]. The incorporation of these disciplines allows the collection, processing and analysis of temporal, spatial and individual data and combines them with other data for the implementation of adequate solutions in the use of resources, productivity, quality, profitability and sustainability of agricultural production [8][9][10][11].…”