David M. Mburu

The application of mineral fertilizers can effectively enhance crop yields. However, this potential benefit may be diminished if the use of mineral fertilizers leads to a substantial decline in soil organic carbon (SOC) and an increase in soil greenhouse gas (GHG) emissions. This study aimed to determine the optimal fertilizer combinations and rates for improving SOC and maize yield while reducing GHG emissions in the semi-arid uplands of Kenya. Data were collected from five different fertilizer treatments (N50, N100, N150, N100+manure, and N100+straw) compared to a control (N0) in a long-term experimental field, which was used to run and validate the DNDC model before using it for long-term predictions. The results showed that the combination of mineral fertilizer and straw resulted in the highest SOC balance, followed by that of fertilizer and manure. All fertilized treatments had higher maize grain yields compared to low-fertilizer treatment (N50) and control (N0). Daily CO₂ fluxes were highest in the treatment combining mineral fertilizer and manure, whereas there were no significant differences in N₂O fluxes among the three tested treatments. The findings of this study indicate that the judicious application of mineral fertilizer, animal manure, and straw has great potential in enhancing SOC and maize yields while reducing GHG emissions, thereby providing practical farming management strategies in semi-arid Kenya.

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.