The essential oils of Juniperus are highly beneficial medicinally. The present study aimed to assess the chemodiversity and bioactivity of Juniperus formosana, Juniperus przewalskii, Juniperus convallium, Juniperus tibetica, Juniperus komarovii, and Juniperus sabina essential oils from the Qinghai-Tibet Plateau. The results revealed 92 components in six essential oils: α-pinene (2.71–17.31%), sabinene (4.91–19.83%), and sylvestrene (1.84–8.58%) were the main components. Twelve components were firstly reported in Juniperus oils, indicating that the geographical location and climatic conditions of the Qinghai-Tibet Plateau produced the unique characteristics of Juniperus essential oils. The chemodiversity of Juniperus essential oils varied greatly, with J. sabina having the most recognized components (64) and the highest chemodiversity (Shannon–Wiener index of 3.07, Simpson’s diversity index of 0.91, and Pielou evenness of 0.74). According to the chemodiversity of essential oils, the six plants were decided into the α-pinene chemotype (J. formosana), hedycaryol chemotype (J. przewalskii, J. komarovii, J. convallium, J. tibetica), and sabinene chemotype (J. sabina). PCA, HCA and OPLS-DA showed that J. formosana and J. sabina were distantly related to other plants, which provides a chemical basis for the classification of Juniperus plants. Furthermore, bioactivity tests exhibited certain antioxidant and antibacterial effects in six Juniperus oils. And the bioactivities of J. convallium, J. tibetica, and J. komarovvii were measured for the first time, broadening the range of applications of Juniperus. Correlation analysis of components and bioactivities showed that δ-amorphene, β-udesmol, α-muurolol, and 2-nonanone performed well in the determination of antioxidant activity, and α-pinene, camphene, β-myrcene, as well as (E)-thujone, had strong inhibitory effects on pathogenic bacteria, providing a theoretical basis for further research on these components.

Exploring the relationship between child rearing burden and farmers’ adoption of climate adaptation technologies can be used to improve farmers’ adoption of these technologies, thus reducing the impact of climate change on agricultural production and increasing agricultural output. However, with the full implementation of the Chinese three-child policy, the number of children in families will continue to increase and the cost of raising children will rise, which will have a crowding out effect on the adoption of climate adaptive technologies. In this context, we analyzed the impact and mechanism of child rearing burden on farmers’ adoption of climate adaptive technology by Probit model and discussed its heterogeneity based on family life cycle theory. Cross-sectional survey data were collected from 511 farm households in the 3 provinces of China to produce the findings. We found that the child rearing burden had a significant negative impact on farmers’ adoption of climate adaptive technology. The impact mechanism analysis showed that the child rearing burden mainly affected farmers’ adoption of climate adaptive technology through three paths: risk appetite, economic capital and non-agricultural employment, with non-agricultural employment having the largest impact, followed by risk appetite and finally, economic capital. Furthermore, the effect of child rearing burden on the adoption of climate adaptive technology was heterogeneous amid different family life cycles: In the upbringing and burden period, the child support burden had a significant negative impact on the adoption of climate adaptive technology and the impact was greater in the upbringing period, while in the stable period, the child support burden had a significant positive impact on the adoption of climate adaptive technology. The influence mechanism was also heterogeneous in different family life cycles. This paper not only provides research evidence on the relationship between child rearing burden and farmers’ adoption of climate adaptive technology, but also has certain empirical value for the formulation and implementation of supportive measures for improving fertility policies.