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What controls post-harvest growth rates in the caatinga forest?
Agricultural and Forest Meteorology ( IF 5.6 ) Pub Date : 2020-04-01 , DOI: 10.1016/j.agrformet.2020.107906
Frans G.C. Pareyn , Walter E. Pereira , Ignacio H. Salcedo , Enrique M. Riegelhaupt , Elmo C. Gomes , Humberto T.F. Menecheli , Margaret Skutsch

Abstract All over the world, there is increasing demand for wood and other goods from seasonally dry tropical forests; the “Caatinga” forest in northeast Brazil is a case in point. In order to set up sustainable forest management protocols, a comprehensive understanding of the main drivers of forest growth is needed, but few studies have focused on this subject in the Caatinga biome. Traditionally, periodic annual increment (PAI) has been calculated by dividing standing stock by the legally imposed minimum cutting cycle of 15 years, but it is doubtful that this guarantees sustainability. We use data from 20 coupes spread over 10 managed areas and apply both multiple regression and tree regression techniques to correlate PAI with 27 environmental variables including mean annual rainfall and many soil properties. We find that neither the time since harvesting nor the stock before harvest are significantly related to PAI. Instead, using a simple linear regression model, we show that rainfall can explain most (72%) of the variation, while a tree regression model, which captures non-linear relations between rainfall and PAI, explains 96% of the variation. On the other hand, no soil factors contribute significantly to the overall explanation of growth after harvest. We conclude that planning of sustainable management could be greatly improved by use of our regression models and rainfall data which are widely available at local level across the Caatinga. Moreover, this would obviate the need for costly forest inventories.

中文翻译:

什么控制了卡廷加森林的收获后增长率?

摘要 在世界范围内,对季节性干燥热带森林的木材和其他商品的需求不断增加。巴西东北部的“Caatinga”森林就是一个很好的例子。为了制定可持续的森林管理协议,需要全面了解森林生长的主要驱动因素,但很少有研究关注卡廷加生物群落中的这一主题。传统上,定期年度增量 (PAI) 是通过将现有库存除以法律规定的 15 年最低砍伐周期来计算的,但这是否能保证可持续性值得怀疑。我们使用分布在 10 个管理区域的 20 辆轿跑车的数据,并应用多元回归和树回归技术将 PAI 与 27 个环境变量(包括年平均降雨量和许多土壤特性)相关联。我们发现收获后的时间和收获前的库存都与 PAI 没有显着关系。相反,我们使用简单的线性回归模型表明,降雨量可以解释大部分 (72%) 的变化,而捕获降雨量和 PAI 之间非线性关系的树回归模型可以解释 96% 的变化。另一方面,没有土壤因素对收获后生长的整体解释有显着贡献。我们得出的结论是,通过使用我们的回归模型和降雨数据,可以极大地改善可持续管理的规划,这些数据在卡廷加当地广泛可用。此外,这将消除对昂贵的森林库存的需要。我们表明降雨量可以解释大部分 (72%) 的变化,而捕获降雨量和 PAI 之间非线性关系的树回归模型解释了 96% 的变化。另一方面,没有土壤因素对收获后生长的整体解释有显着贡献。我们得出的结论是,通过使用我们的回归模型和降雨数据,可以极大地改善可持续管理的规划,这些数据在卡廷加当地广泛可用。此外,这将消除对昂贵的森林库存的需要。我们表明降雨量可以解释大部分 (72%) 的变化,而捕获降雨量和 PAI 之间非线性关系的树回归模型解释了 96% 的变化。另一方面,没有土壤因素对收获后生长的整体解释有显着贡献。我们得出的结论是,通过使用我们的回归模型和降雨数据,可以极大地改善可持续管理的规划,这些数据在卡廷加当地广泛可用。此外,这将消除对昂贵的森林库存的需要。我们得出的结论是,通过使用我们的回归模型和降雨数据,可以极大地改善可持续管理的规划,这些数据在卡廷加当地广泛可用。此外,这将避免对昂贵的森林库存的需要。我们得出的结论是,通过使用我们的回归模型和降雨数据,可以极大地改善可持续管理的规划,这些数据在卡廷加当地广泛可用。此外,这将消除对昂贵的森林库存的需要。
更新日期:2020-04-01
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