How can Punpee Tea's Trees Preserve Watershed Forests

The Mae Suai District in Chiang Rai Province exhibits two distinct approaches to tea cultivation: monoculture and intercropping with temperate or forest species. This research employs a qualitative methodology to explore various aspects of tea cultivation in the Huai Nam Khun Punpee tea plantation area. The research was conducted by three primary objectives: (1) to analyze the prevailing conditions within the tea plantation region; (2) to investigate tea planting techniques that can effectively sustain watershed forest environments in the Huai Nam Khun area; and (3) to evaluate the strengths and weaknesses of Punpee tea cultivation while identifying the challenges and obstacles faced by local growers in Tha Ko Sub-district,Mae Suai District, Chiang Rai Province. Through in-depth interviews and comprehensive document analysis, the findings aim to provide valuable insights into sustainable agricultural practices and environmental conservation in the context of tea cultivation. Data were gathered from a variety of sources, including literature, academic texts, previous research, and spatial interviews. The findings reveal that the tea cultivation area is located at an elevation of 1,100 to 1,200 meters above sea level and has a slope ranging from 50 to 60 degrees. Assam tea predominates in this region. Furthermore, the implementation of perennial tea cultivation in the Huai Nam Khun area utilizes a mixed cropping strategy that incorporates tea plants alongside temperate fruit trees and forest species. This agroecological approach not only enhances the overall green cover of the forest but also plays a crucial role in reducing soil erosion, facilitated by the extensive root systems of the tea plants.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-01-28
DOI
https://doi.org/10.5281/zenodo.14757471
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
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article

How can Punpee Tea's Trees Preserve Watershed Forests

Zenodo (CERN European Organization for Nuclear Research)
Statistical Methods and Bayesian Inference
article

How can Punpee Tea's Trees Preserve Watershed Forests

article en

Abstract

The Mae Suai District in Chiang Rai Province exhibits two distinct approaches to tea cultivation: monoculture and intercropping with temperate or forest species. This research employs a qualitative methodology to explore various aspects of tea cultivation in the Huai Nam Khun Punpee tea plantation area. The research was conducted by three primary objectives: (1) to analyze the prevailing conditions within the tea plantation region; (2) to investigate tea planting techniques that can effectively sustain watershed forest environments in the Huai Nam Khun area; and (3) to evaluate the strengths and weaknesses of Punpee tea cultivation while identifying the challenges and obstacles faced by local growers in Tha Ko Sub-district,Mae Suai District, Chiang Rai Province. Through in-depth interviews and comprehensive document analysis, the findings aim to provide valuable insights into sustainable agricultural practices and environmental conservation in the context of tea cultivation. Data were gathered from a variety of sources, including literature, academic texts, previous research, and spatial interviews. The findings reveal that the tea cultivation area is located at an elevation of 1,100 to 1,200 meters above sea level and has a slope ranging from 50 to 60 degrees. Assam tea predominates in this region. Furthermore, the implementation of perennial tea cultivation in the Huai Nam Khun area utilizes a mixed cropping strategy that incorporates tea plants alongside temperate fruit trees and forest species. This agroecological approach not only enhances the overall green cover of the forest but also plays a crucial role in reducing soil erosion, facilitated by the extensive root systems of the tea plants.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 55%
Statistical Methods and Bayesian Inference
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