Abstract:
A novel total ensemble (TE) algorithm was developed and compared with random forest
optimization (RFO), gradient boosted machines (GBM), partial least squares (PLS), Cubist and
Bayesian additive regression tree (BART) algorithms to predict numerous soil health indicators in
soils with diverse climate-smart land uses at different soil depths. The study investigated how
land-use practices affect several soil health indicators. Good predictions using the ensemble method
were obtained for total carbon (R2 = 0.87; RMSE = 0.39; RPIQ = 1.36 and RPD = 1.51), total nitrogen
(R2 = 0.82; RMSE = 0.03; RPIQ = 2.00 and RPD = 1.60), and exchangeable bases, m3. Cu, m3. Fe, m3.
B, m3. Mn, exchangeable Na, Ca (R2 > 0.70). The performances of algorithms were in order of
TE > Cubist > BART > PLS > GBM > RFO. Soil properties differed significantly among land uses and
between soil depths. In Kenya, however, soil pH was not significant, except at depths of 45–100 cm,
while the Fe levels in Tanzanian grassland were significantly high at all depths. Ugandan agroforestry
had a substantially high concentration of ExCa at 0–15 cm. The total ensemble method showed
better predictions as compared to other algorithms. Climate-smart land-use practices to preserve soil
quality can be adopted for sustainable food production systems.
Keywords: algorithms; climate-smart; soil quality; land use
