College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA.
Departamento de Topografía, Facultad de Ciencias del Hábitat, Diseño e Infraestructura, Universidad del Tolima, Ibagué, Colombia.
Sci Rep. 2023 Feb 3;13(1):1965. doi: 10.1038/s41598-023-28918-0.
Illicit cattle ranching and coca farming have serious negative consequences on the Colombian Amazon's land systems. The underlying causes of these land activities include historical processes of colonization, armed conflict, and narco-trafficking. We aim to examine how illicit cattle ranching and coca farming are driving forest cover change over the last 34 years (1985-2019). To achieve this aim, we combine two pixel-based approaches to differentiate between coca farming and cattle ranching using hypothetical observed patterns of illicit activities and a deep learning algorithm. We found evidence that cattle ranching, not coca, is the main driver of forest loss outside the legal agricultural frontier. There is evidence of a recent, explosive conversion of forests to cattle ranching outside the agricultural frontier and within protected areas since the negotiation phase of the peace agreement. In contrast, coca is remarkably persistent, suggesting that crop substitution programs have been ineffective at stopping the expansion of coca farming deeper into protected areas. Countering common narratives, we found very little evidence that coca farming precedes cattle ranching. The spatiotemporal dynamics of the expansion of illicit land uses reflect the cumulative outcome of agrarian policies, Colombia's War on Drugs, and the 2016 peace accord. Our study enables the differentiation of illicit land activities, which can be transferred to other regions where these activities have been documented but poorly distinguished spatiotemporally. We provide an applied framework that could be used elsewhere to disentangle other illicit land uses, track their causes, and develop management options for forested land systems and people who depend on them.
非法养牛和古柯种植对哥伦比亚亚马逊土地系统造成了严重的负面影响。这些土地活动的根本原因包括殖民化、武装冲突和毒品走私的历史进程。我们旨在研究非法养牛和古柯种植在过去 34 年（1985-2019 年）期间如何推动森林覆盖变化。为了实现这一目标，我们结合了两种基于像素的方法，使用假设的非法活动观察模式和深度学习算法来区分古柯种植和养牛。我们有证据表明，在合法农业前沿之外，养牛而不是古柯是森林流失的主要驱动因素。自和平协议谈判阶段以来，有证据表明，在农业前沿之外和保护区内，森林最近迅速转化为养牛场。相比之下，古柯的持久性非常显著，这表明作物替代计划在阻止古柯种植向保护区内部扩张方面一直没有效果。与常见的说法相反，我们几乎没有发现古柯种植先于养牛的证据。非法土地利用的扩张的时空动态反映了农业政策、哥伦比亚的禁毒战争以及 2016 年和平协议的累积结果。我们的研究能够区分非法土地活动，这些活动可以转移到其他有记录但在时空上区分不佳的地区。我们提供了一个应用框架，可在其他地方用于解开其他非法土地利用，追踪其原因，并为森林土地系统和依赖这些系统的人制定管理方案。
