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In sub‐Saharan Africa (SSA), few studies have quantified greenhouse gas (GHG) emissions following application of soil amendments, for development of accurate national GHG inventories. Therefore, this study quantified soil GHG emissions using static chambers for two maize cropping seasons (one full year) of four different soil amendments in the central highlands of Kenya. The four treatments were (i) animal manure, (ii) inorganic fertilizer, (iii) combined animal manure and inorganic fertilizer, and (iv) a no‐N control (no amendment) laid out in a randomized complete block design. Cumulative annual soil fluxes (February 2017 to February 2018) ranged from −1.03 ± 0.19 kg CH4‐C ha−1 yr−1 from the manure inorganic fertilizer treatment to −0.09 ± 0.03 kg CH4‐C ha−1 yr−1 from the manure treatment, 1,391 ± 74 kg CO2‐C ha−1 yr−1 from the control treatment to 3,574 ± 113 kg CO2‐C ha−1 yr−1 from the manure treatment, and 0.13 ± 0.08 to 1.22 ± 0.12 kg N2O‐N ha−1 yr−1 in the control and manure treatments, respectively. Animal manure amendment produced the highest cumulative CO2 emissions (P < 0.001), N2O emissions (P < 0.001), and maize yields (P = 0.002) but the lowest N2O yield‐scaled emission (YSE) (0.5 g N2O–N kg−1 grain yield). Manure combined with inorganic fertilizer had the highest cumulative CH4 uptake (P < 0.001) and N2O YSE (2.2 g N2O–N kg−1 grain yield). Our results indicate that while the use of animal manure may increase total GHG emissions, the concurrent increase in maize yields results in reduced yield‐scaled GHG emissions.

This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. Spline, Kriging, and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model using ArcGIS. Validation of these interpolation methods was evaluated by comparing the modelled/generated rainfall values and the observed daily rainfall data using root mean square errors and mean absolute errors statistics. Results showed 90% chance of below cropping threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for one year return period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59) and in number of rainy days (CV = 0.88, 0.49, and 0.53) in Machang’a, Kiritiri, and Kindaruma, respectively. Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a and Kindaruma. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60%) in Kiambere, Kindaruma, Machang’a, and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.