对于稀薄的中国树种植园,与年度立体生长模型相兼容
Yihang Jiang1,2, Quang V Cao3, Jun Chen1
1State Key Laboratory of Efficient Production of Forest Resources, Key Laboratory of Tree Breeding and Cultivation of the National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.
Frontiers in plant science
|December 1, 2025
概括
对于中国树种植园,年度模型在稀释后的短期收益预测方面表现出色,而兼容模型提供了更好的长期稳定性. 最好的模型选择取决于所需的预测时间和稀释制度.
科学领域:
- 林业和林业 林业和林业
- 量化生态学 量化生态学
- 增长和收益率建模成长和收益率建模
背景情况:
- 中国 (Cunninghamia lanceolata) 种植园是中国亚热带地区重要的木材资源.
- 稀释显著影响仓位动态,需要准确的增长和收益预测模型.
- 为了有效的种植园管理,平衡短期响应能力和长期预测的一致性至关重要.
研究的目的:
- 为了比较不同稀释制度下的兼容和年度标准水平增长模型的表现.
- 评估模型在不同预测视界 (短期,中期和长期) 的准确性.
- 根据管理目标,确定Cunninghamia lanceolata种植园的最佳建模策略.
主要方法:
- 利用了中国南部40年稀释试验的数据.
- 使用兼容 (路径一致) 和年度 (响应) 系统建模的支架生存率和基底面积.
- 配备了连续和所有可能的增长对的模型,通过看似无关回归 (SUR) 估计参数,并通过三重交叉验证进行验证.
主要成果:
- 在未经稀释的阵列中,所有增长对的年度模型显示出优越的短期准确性;兼容的模型在中长期表现出色.
- 在稀薄的阵列中,年度模型最好地预测了短期生存,而兼容模型提供了更好的短期到中期基底面积预测.
- 所有对的估计通常提高了准确性,而连续的对则减少了短期的稀释后基底区域偏差.
结论:
- 在模型响应性 (年度) 和路径一致性 (兼容) 之间存在一个权衡.
- 年度模型最适合在稀释后立即预测,而兼容模型可以确保稳定的长期预测.
- 建议年度+所有对用于短期预测,兼容+所有对用于中长期预测,每6-8年重新校准一次.
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