Quantifying and predicting soil buffering capacity in the African context: a multidimensional titration-based framework using chemical signatures and MIR spectroscopy

Soil acidity is a major constraint to agricultural productivity across sub-Saharan Africa, where highly weathered soils exhibit strong spatial heterogeneity in chemical reactivity. While soil pH identifies acidity status, it does not indicate how a soil will respond to corrective liming how strongly it resists pH change or how much amendment it ultimately requires. Soil buffering capacity fills this gap, yet current practice typically reduces this multidimensional behavior to a single metric that cannot simultaneously capture instantaneous reactivity, average resistance, and cumulative lime demand. This study proposes a structured framework for quantifying and predicting short-term soil buffering capacity defined here as the immediate chemical response measured during controlled calcium hydroxide titration with 25-minute equilibration intervals using three complementary indices derived from titration curves: punctual buffering capacity (BCP), quantifying instantaneous pH sensitivity at a defined titration stage; linear buffering capacity (BCL), reflecting average resistance across the liming correction interval; and integral buffering capacity (BCI), integrating the full titration response to express cumulative neutralization demand. Together, these indices characterize distinct dimensions of soil behavior under liming, providing actionable information for lime-rate calibration and acidity-management planning. A total of 543 soil samples from six African countries (Ivory Coast, Ghana, Malawi, Zimbabwe, Zambia, and Mozambique) were analyzed. Random Forest and Extreme Gradient Boosting (XGBoost) ensemble algorithms were trained to predict the three buffering indices from routine soil chemical properties (organic matter, pH, cation exchange capacity, and texture). Random Forest achieved the strongest cross-validated agreement between measured and predicted buffering values (R 2 = 0.70–0.73, RMSE = 0.26–2.99, RPIQ = 2.51–2.60), with consistent performance on independent test sets (R 2 = 0.69–0.71, RMSE = 0.24–3.20, RPIQ = 2.34–2.44). Predictor importance analysis revealed that the soil properties governing each index shifted systematically: organic matter and clay dominated BCP, reserve acidity indicators controlled BCL, and pH-related variables governed BCI. In parallel, mid-infrared (MIR) spectroscopy was evaluated as a rapid, reagent-free alternative. Absorbance spectra collected and used as inputs to Partial Least Squares Regression (PLSR) models, which relate the spectral signature of soil mineral and organic components directly to buffering behavior. PLSR models achieved R 2 up to 0.81 (RMSE = 0.23, RPIQ = 2.73) for BCP under cross-validation and R 2 = 0.78 (RMSE = 0.32, RPIQ = 2.50) on independent test sets, enabling rapid buffering assessment at scale. Classification into low, medium, and high buffering classes revealed that most soils exhibited limited short-term resistance under BCP and BCL, while BCI showed a more balanced distribution indicating that soils with modest immediate reactivity may carry substantial cumulative lime demand. This contrast underscores the agronomic value of the multidimensional approach: knowing both how quickly a soil responds and how much amendment it ultimately requires enables more precise liming, reduces over- or under-application risk, and supports cost-effective acidity management in heterogeneous smallholder systems across Africa.