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

Yunxiao Bai, Xiaoshuang Li, Yuqing Feng, Moucheng Liu, Cheng Chen
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Abstract

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. Modern 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.
保护传统系统:根据农业生物多样性确定农业遗产区
随着现代农业的快速发展及其对高产和基因统一品种的依赖,许多传统农业系统正逐渐被抛弃。土地品种所包含的遗传多样性对现代生态农业至关重要。一种与机器学习相结合的指标评估模型有助于定位和保护这些现存的传统农业系统,即农业遗产系统(AHS)。在此,该方法首次提供了中国茶叶-农业遗产系统的潜在区域图。这些结果有助于政策制定者根据茶树-高山植物的分布状况和濒危程度,确定保护的优先次序并合理分配保护资源。现代农业过度依赖高产和基因统一的品种,而传统农业系统则包含大量基因多样的地方品种以及与之相关的本土知识。我们把存活至今的传统农业系统称为农业遗产系统(AHS)。在现代化的冲击下,农业遗产系统正在逐渐消失。本文以中国茶叶为例,首次提出了基于农业生物多样性的农业遗产系统潜在区域识别方法,并首次绘制了中国茶叶农业遗产系统潜在区域图。该地图是基于茶叶种质资源的最大熵模型(Maxent)和相关指标函数构建的,并通过中国现有的茶叶AHS进行了验证。这些潜在区域主要集中在南方地区,包括安徽、福建、广东、云南、贵州、广西、湖北和湖南等 15 个省。芒市、祁门县和柴桑区是茶树-AHS 的高潜力区,也是下一步勘探和保护工作的重点。我们验证了所提出方法的有效性,该方法有助于及时保护全球 AHS 的种质资源和传统智慧,为现代农业和生态农业的发展做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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