Soil organic carbon baselines for land degradation neutrality: map accuracy and cost tradeoffs with respect to complexity in Otjozondjupa, Namibia.
Soil organic carbon baselines for land degradation neutrality: map accuracy and cost tradeoffs with respect to complexity in Otjozondjupa, Namibia.
复制标题
土地退化中和的土壤有机碳基线：纳米比亚奥乔宗朱帕复杂性的地图准确性和成本权衡。
DOI：
10.3390/su10051610
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
J. Mutua
中科院分区：
文献类型：
--
作者：
R. Nijbroek;K. Piikki;M. Söderström;B. Kempen;K. Turner;S. Hengari;J. Mutua
最近的估计表明，世界上三分之一的土地和水资源高度或中度退化。全球每年因土地退化造成的经济损失高达10.6万亿美元。这些趋势催生了避免未来土地退化的呼吁，减少正在进行的土地退化，扭转过去的土地退化，最终通过了旨在到2030年实现全球土地退化中和的可持续发展目标(SDG)指标15.3。政治势头和越来越多的科学文献促使人们呼吁建立“新的LDN科学”，并强调了实施LDN所面临的实际挑战。本研究的目的是通过比较不同的数字土壤制图(DSM)方法和采样密度，以纳米比亚Otjozondjupa为例，得出LDN土壤有机碳(SOC)储量基线图，并从复杂性、成本和地图精度方面对每种方法进行评估。在DSM模型中包含了100个样本后，平均绝对误差(MAE)趋于稳定，这导致了额外采集土壤样本的成本权衡。在容量足够的情况下，随机森林DSM方法优于其他方法，但与用普通克里格法对土壤样本数据进行内插相比，使用这种更复杂的方法的改进微乎其微。在开发Otjozondjupa LDN SOC基线时吸取的经验教训为其他地方负责开发LDN基线的其他人提供了宝贵的见解。
Recent estimates show that one third of the world’s land and water resources are highly or moderately degraded. Global economic losses from land degradation (LD) are as high as USD $10.6 trillion annually. These trends catalyzed a call for avoiding future LD, reducing ongoing LD, and reversing past LD, which has culminated in the adoption of Sustainable Development Goal (SDG) Target 15.3 which aims to achieve global land degradation neutrality (LDN) by 2030. The political momentum and increased body of scientific literature have led to calls for a ‘new science of LDN’ and highlighted the practical challenges of implementing LDN. The aim of the present study was to derive LDN soil organic carbon (SOC) stock baseline maps by comparing different digital soil mapping (DSM) methods and sampling densities in a case study (Otjozondjupa, Namibia) and evaluate each approach with respect to complexity, cost, and map accuracy. The mean absolute error (MAE) leveled off after 100 samples were included in the DSM models resulting in a cost tradeoff for additional soil sample collection. If capacity is sufficient, the random forest DSM method out-performed other methods, but the improvement from using this more complex method compared to interpolating the soil sample data by ordinary kriging was minimal. The lessons learned while developing the Otjozondjupa LDN SOC baseline provide valuable insights for others who are responsible for developing LDN baselines elsewhere.