Spatial structures of soil organic carbon in tropical forests—A case study of Southeastern Tanzania

Abstract

Abstract Southeastern Tanzania serves as a typical example of soil degradation and soil organic carbon (SOC) losses on the African continent. Although sequestration of SOC through aforestation or reforestation proved favorable, these measures are restricted by the ability to produce rapid, cost-effective and precise sampling schemes. The aim of this study is to contribute to a better knowledge of the spatial distribution of soil C in tropical natural and plantation forest. This paper presents sampling strategies for estimating mean SOC values as well as for SOC mapping, based on different methods for SOC determination and on different precision levels. To do so we conducted a carbon variability study in five common forest types of Southeastern Tanzania (coastal dry forest, Miombo woodland, teak plantation, pine plantation and cashew plantation) using conventional statistical methods, as well as geostatistics. In the 5 forest types of this study, SOC stocks in the upper 5 cm ranges between 5 (in the cashew plantation) and 13 (in the coastal forest) t ha− 1. The optimal sampling distance for measuring mean SOC stocks varies between 36 m (in the patchy miombo woodland) and 422 m (in the homogenized cashew plantation). Sample sizes fluctuate between 6 and 72 (1 t ha− 1 precision) for respectively cashew plantation and coastal forest. A rectangular grid with a sample interval of 25 m can be used for SOC mapping with a point kriging estimation error of 3.0 t ha− 1 in the coastal forest, 2.6 t ha− 1 in miombo woodland, 2.2 t ha− 1 in the teak plantation and 1.1 t ha− 1 in the cashew plantation. Since the pine plantation has no spatial structure; samples can be arranged randomly and its best soil map has an average C content attributed over the whole field. Refining the sampling strategy with a new spatial variability study in other forest types can be based on a regular grid with sampling distances of half the range identified in this study. This paper proves that the optimal sampling scheme varies strongly as a result of the different spatial behavior of SOC in forests and depends on the required precision and research question. Only when the right strategy is followed, high standards of precision can be met without economic loss or risk of statistical misinterpretation.