Toward a global digital commons for maize breeding
Year: 2026
Type: article
Abstract: Maize (Zea mays L.) is a crucial C4 crop with extremely high photosynthetic efficiency, and provides a significant source of calories worldwide. However, under the traditional breeding model, it is facing frequent environmental pressures caused by extreme climates. In response to the pressure of global food demand, the existing breeding system is unable to cope. From the perspective of maize breeders, this study proposes a new collaborative and interdependent cooperation model for breeding. China and the U.S. are the two biggest maize-producing countries; therefore, we analyzed the complementary mechanism between the density tolerance characteristics of the germplasm in the U.S. and the wide adaptability of the germplasm in China. A theoretical framework integrating a crop growth model - whole genome prediction (CGM-WGP) and environmental typing had been proposed. This framework not only means a technological iteration, but also represents a strategic response to the global food crisis. To overcome geopolitical and intellectual property barriers, we proposed to establish a data-sharing strategy based on the concept of federated learning (FL) and homomorphic encryption to construct a global digital maize common. This approach aims to integrate collective expertise among major maize-producing countries, develop new varieties adapted to new climate conditions, and provide both biological and digital support for sustainable global food production, while extending these digital dividends to countries in the Global South to build an inclusive global food security defense line.
Authors: Yaqi Bi, Xingming Fan
Institution: Yunnan Academy of Agricultural Sciences
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,530
OpenAlex ID: W7223021442