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  How far does the tree affect the crop in agroforestry? New spatial analysis methods in a Faidherbia parkland

  The trees in agroforestry plots create spatial heterogeneity of high interest for adaptation, mitigation, and the provision of ecosystem services. But to what distance, exactly, from the tree? We tested a novel approach, based upon geostatistics and Unmanned Aerial Vehicle (UAV) sensing, to infer the distance at which a single agroforestry tree affects the surrounding under-crop, to map yield, litter (i.e. stover) and compute crop-partial Land Equivalent Ratio (LERcp) at the whole-plot level. In an agro-silvo-pastoral parkland of semi-arid western Africa dominated by the multi-purpose tree Faidherbia albida, we harvested the pearl-millet under-crop at the whole-plot scale (ca. 1 ha) and also in subplot transects, at three distances from the trunks. We observed that the yield was three times higher below the tree crown (135.6 g m−2) than at a distance of five tree-crown radii from the trunk (47.7 g m−2). Through geostatistical analysis of multi-spectral, centimetric-resolution images obtained from an UAV overflight of the entire plot, we determined that the ‘Range’ parameter of the semi-variogram (assumed to be the distance of influence of the trees on the Normalized difference vegetation index (NDVI)) was 17 m. We correlated the yield (r2 = 0.41; RRMSE = 48 %) and litter production (r2 = 0.46; RRMSE = 35 %) in subplots with NDVI, and generated yield and litter maps at the whole-plot scale. The measured whole-plot yield (0.73 t ha-1) differed from the one estimated via the UAV mapping by only 20 %, thereby validating the overall approach. The litter was estimated similarly at 1.05 tC ha-1 yr-1 and mapped. Using a geostatistical proxy for the sole crop, LERcp was estimated 1.16, despite the low tree density. This new method to handle heterogeneity in agroforestry systems is a first application. We also propose strategies for extension to the landscape level.
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  Mapping topsoil organic carbon concentrations and stocks for Tanzania

  Tanzania is one of the countries that has embarked on a national programme under the United Nations collaborative initiative on Reducing Emissions from Deforestation and forest Degradation (REDD). Tanzania is currently developing the capacity to enter into a carbon monitoring REDD+ regime. In this context spatially representative soil carbon datasets and accurate predictive maps are important for determining the soil organic carbon pool. The main objective of this study was to model and map the SOC stock for the 0–30-cm soil layer to provide baseline information for REDD+ purposes. Topsoil data of over 1400 locations spread throughout Tanzania from the National Forest Monitoring and Assessment (NAFORMA), were used, supplemented by two legacy datasets, to calibrate simple kriging with varying local means models. Maps of SOC concentrations (g kg−1) were generated for the 0–10-cm, 10–20-cm, 20–30-cm, 0–30-cm layers, and maps of bulk density and SOC stock (kg m−2) for the 0–30-cm layer. Two approaches for modelling SOC stocks were considered here: the calculate-then-model (CTM) approach and the model-then-calculate approach (MTC). The spatial predictions were validated by means of 10-fold cross-validation. Uncertainty associated to the estimated SOC stocks was quantified through conditional Gaussian simulation. Estimates of SOC stocks for the main land cover classes are provided. Environmental covariates related to soil and terrain proved to be the strongest predictors for all properties modelled. The mean predicted SOC stock for the 0–30-cm layer was 4.1 kg m−2 (CTM approach) translating to a total national stock of 3.6 Pg. The MTC approach gave similar results. The largest stocks are found in forest and grassland ecosystems, while woodlands and bushlands contain two thirds of the total SOC stock. The root mean squared error for the 0–30-cm layer was 1.8 kg m−2, and the R2-value was 0.51. The R2-value of SOC concentration for the 0–30-cm layer was 0.60 and that of bulk density 0.56. The R2-values of the predicted SOC concentrations for the 10-cm layers vary between 0.46 and 0.54. The 95% confidence interval of the predicted average SOC stock is 4.01–4.15 kg m−2, and that of the national total SOC stock 3.54–3.65 Pg. Uncertainty associated with SOC concentration had the largest contribution to SOC stock uncertainty. These findings have relevance for the ongoing REDD+ readiness process in Tanzania by supplementing the previous knowledge of significant carbon pools. The soil organic carbon pool makes up a relatively large proportion of carbon in Tanzania and is therefore an important carbon pool to consider alongside the ones related to the woody biomass. Going forward, the soil organic carbon data can potentially be used in the determination of reference emission levels and the future monitoring, reporting and verification of organic carbon pools.