It is critical to develop technologies that simultaneously improve agricultural production, offset impacts of climate change, and ensure food security in a changing climate. Within this context, considerable attention has been given to climate-smart agricultural practices (CSA). This study was conducted to investigate the effects of integrating different CSA practices on crop production, soil fertility, and carbon sequestration after being practiced continuously for up to 10 years. The CSA practices include use of soil and water conservation (SWC) structures combined with biological measures, hedgerow planting, crop residue management, grazing management, crop rotation, and perennial crop-based agroforestry systems. The landscapes with CSA interventions were compared to farmers’ business-as-usual practices (i.e., control). Wheat (Triticum sp.) yield was quantified from 245 households. The results demonstrated that yield was 30–45% higher under CSA practices than the control (p < 0.05). The total carbon stored at a soil depth of 1 m was three- to seven-fold higher under CSA landscapes than the control. CSA interventions slightly increased the soil pH and exhibited 2.2–2.6 and 1.7–2.7 times more total nitrogen and plant-available phosphorus content, respectively, than the control. The time series Normalized Difference Water Index (NDWI) revealed higher soil moisture content under CSA. The findings illustrated the substantial opportunity of integrating CSA practices to build climate change resilience of resource-poor farmers through improving crop yield, reducing nutrient depletion, and mitigating GHG emissions through soil carbon sequestration.

Climate-Smart Villages (CSVs) were established by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) in the East African countries of Kenya, Tanzania and Uganda to test and promote a portfolio of climate-smart agriculture (CSA) practices that have climate change mitigation potential. This study evaluated the soil carbon sequestration potential of these CSVs compared to the control land use that did not have CSA practices. At the one-meter depth, soil carbon stocks increased by 20–70%, 70–86%, and 51–110% in Kenya, Tanzania and Uganda CSVs, respectively, compared to control. Consequently, CSVs contributed to the reduction of emissions by 87–420 Mg CO₂ eq ha⁻¹. In the topsoil (0–15 cm), CSVs sequestered almost twice more soil carbon than the control and subsequently emissions were reduced by 42–158 Mg CO₂ eq ha⁻¹ under CSVs. The annual increase in carbon sequestration under CSVs ranged between 1.6 and 6.2 Mg C ha⁻¹ yr⁻¹ and substantially varied between the CSA land use types. The forests sequestered the highest soil carbon (5–6 Mg C ha⁻¹ yr⁻¹), followed by grasslands and croplands. The forest topsoil also had lower bulk density compared to the control. The findings suggest that CSA practices implemented through the CSVs approach contribute to climate change mitigation through soil carbon sequestration.

Short-term and plot-level trials mostly produce data on the advantages of climate-smart agriculture (CSA) practices on food security in a changing climate. Previous studies evaluated only one or a combination of a few CSA practices that improved soil nutrients, particularly in the landscapes of East Africa; hence, it is difficult to draw general conclusions. In this study, we evaluated the effect of CSA practices portfolio on soil macronutrient (nitrogen, phosphorus, and potassium) and micronutrient (manganese and zinc) levels in climate-smart villages (CSVs) in Uganda, Kenya, and Tanzania over a six-year period across different land uses such as agroforestry, cropland, grassland, forest, and control (without CSA practices). A total of 432 soil samples were collected at depths of 0–15, 15–45, and 45–100 cm, and analyzed for macro- and micronutrients. CSA practices increased total nitrogen (TN), phosphorus (P), and potassium (K) regardless of land use type or soil depth. TN, P, and K were mainly stored in surface soil (0–15 cm), accounting for 50.8–52.5%, 47.0–79.5%, and 34.2–65.5% respectively. Concentrations of Mn and Zn were 1.5–3.6 and 5.1–15.6 times greater under CSA than those under the control, respectively, at all soil depths. Results suggest that CSA practices implemented using the landscape approach contributed to improved soil fertility, which is critical in developing more sustainable and resilient production systems among smallholder farmers.

The present study assessed soil physical-chemical characteristics as reliable soil health indicators in six climate-smart land use types; agroforestry, community forest, cropland with soil and water conservation (SWC), crop land without SWC, grassland and control across climate-smart villages (CSVs) in Lushoto (Tanzania), Hoima (Uganda), Wote and Nyando (Kenya). Soils were sampled at three depths; 0–15 cm, 15–45 cm and 45–100 cm and then analyzed for bulk density (BD), pH, exchangeable bases (Ca, Mg, K, Na), extractable Fe, Mn, Zn, exchangeable acidity (ExAc), Electrical conductivity (EC), total carbon (TC), total nitrogen (TN) and cation exchange capacity (CEC). Land use types and sampling depths significantly affected soil properties (p < 0.05), High bulk density (BD) was measured at 45–100 cm depth in grassland (1.47 g/cm³) and crop land (1.50 g/cm³) in Kenya and Tanzania, respectively. BD in Ugandan grasslands was statistically lower (p < 0.05) than BD in other land use types at all depths. Soil pH of surface soil (0–15 cm) ranged from 6.67 ± 0.67 (agroforestry) to 6.27 ± 0.85 (grassland). Ex. bases (Ca, Mg, K and Na) and extractable Fe, Mn, Zn, ExAc, EC, TC, TN and CEC were significantly affected by land uses (p ≤ 0.05). Soil properties were significantly correlated, a positive correlation between silt % (p < 0.01) and pH, sand and Ca (p < 0.05). EC and pH, exchangeable Ca, exchangeable bases, exchangeable K and C: N ratio was observed. There was a negative correlation (p < 0.05) between pH and clay. The study has shown that improving soil properties using land use systems leads to an increase in soil nutrients.

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.