Landscape-scale variability of soil health indicators: effects of cultivation on soil organic carbon in the Usambara Mountains of Tanzania

• Published in: Nutrient cycling in agroecosystems Vol. 105; no. 3; pp. 263 - 274
• Main Authors: Winowiecki, Leigh, Vågen, Tor-Gunnar, Massawe, Boniface, Jelinski, Nicolas A., Lyamchai, Charles, Sayula, George, Msoka, Elizabeth
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2016; Springer Nature B.V
• Subjects: Agriculture; Biodiversity; Biomedical and Life Sciences; Cultivation; cultivation area; Ecosystem services; Farming systems; Farms; High resolution; Land use; land use change; Landscape; landscapes; Life Sciences; Mapping; model validation; monitoring; Mountains; Organic carbon; Original Article; Prediction models; Remote sensing; Soil erosion; soil heterogeneity; soil organic carbon; soil quality; spatial data; surveys; Tanzania; tillage; Topsoil; trees; Tanzania; Cropping diversity; Land-use change; Land health
• ISSN: 1385-1314; 1573-0867
• DOI: 10.1007/s10705-015-9750-1

Land-use change continues at an alarming rate in sub-Saharan Africa adversely affecting ecosystem services provided by soil. These impacts are greatly understudied, especially in biodiversity rich mountains in East Africa. The objectives of this study were to: conduct a biophysical baseline of soil and land health; assess the effects of cultivation on soil organic carbon (SOC); and develop a map of SOC at high resolution to enable farm-scale targeting of management interventions. Biophysical field surveys were conducted in a 100 km 2 landscape near Lushoto, Tanzania, with composite soil samples collected from 160 sampling plots. Soil erosion prevalence was scored, trees were counted, and current and historic land use was recorded at each plot. The results of the study showed a decline in SOC as a result of cultivation, with cultivated plots (n = 105) having mean topsoil OC of 30.6 g kg −1 , while semi-natural plots (n = 55) had 71 g OC kg −1 in topsoil. Cultivated areas were also less variable in SOC than semi-natural systems. Prediction models were developed for the mapping of SOC based on RapidEye remote sensing data for January 2014, with good model performance (RMSEP cal  = 8.0 g kg −1 ; RMSEP val  = 10.5 g kg −1 ) and a SOC map was generated for the study. Interventions will need to focus on practices that increase SOC in order to enhance productivity and resilience of the farming system, in general. The high-resolution maps can be used to spatially target interventions as well as for monitoring of changes in SOC.