Preserving traditional systems: Identification of agricultural heritage areas based on agro-biodiversity

Societal Impact StatementWith the rapid development of modern agriculture and its reliance on high-yielding and genetically uniform varieties, many traditional agricultural systems are gradually being abandoned. The genetic diversity contained in landraces is crucial for modern eco-agriculture. An indicator evaluation model combined with machine learning could help to locate and conserve these existing traditional agricultural systems, called agricultural heritage systems (AHS). Here, this method provided the first map of potential areas of Tea-AHS in China. These results could help policymakers to confirm priorities and rationally allocate conservation resources based on the distribution status and endangerment of AHS. This could also help local people to receive additional support for the transfer of germplasm resources and indigenous knowledge.SummaryModern agriculture is overly dependent on high-yielding and genetically uniform varieties, whereas traditional agricultural systems contain a large number of genetically diverse landraces and the indigenous knowledge associated with them. We call traditional agricultural systems that survive to the present-day agricultural heritage systems (AHS). Under the impact of modernization, AHS are gradually disappearing. Identifying these systems is the first step towards conserving them, but the potential areas of AHS related to agro-biodiversity are not yet clear.Using Chinese tea as an example, this paper provides the first universal method for identifying potential areas of AHS based on agro-biodiversity and the first map of potential areas of Tea-AHS in China. The map is constructed based on the maximum entropy model (Maxent) of tea germplasm resources and related indicator functions and has been validated by existing Tea-AHS in China.The study identified 54 potential areas of Tea-AHS. These potential areas are mainly concentrated in the southern region, in 15 provinces, including Anhui, Fujian, Guangdong, Yunnan, Guizhou, Guangxi, Hubei, and Hunan. Mangshi, Qimen County, and Chaisang District are among the high potential areas for Tea-AHS and are the next priority for exploration and conservation work.We have verified the validity of the proposed method, which can help conserve the germplasm resources and traditional wisdom in the global AHS in a timely manner, and contribute to the development of modern and eco-agriculture. With the rapid development of modern agriculture and its reliance on high-yielding and genetically uniform varieties, many traditional agricultural systems are gradually being abandoned. The genetic diversity contained in landraces is crucial for modern eco-agriculture. An indicator evaluation model combined with machine learning could help to locate and conserve these existing traditional agricultural systems, called agricultural heritage systems (AHS). Here, this method provided the first map of potential areas of Tea-AHS in China. These results could help policymakers to confirm priorities and rationally allocate conservation resources based on the distribution status and endangerment of AHS. This could also help local people to receive additional support for the transfer of germplasm resources and indigenous knowledge.image