A study was carried out in Kenya Cereal Enhancement Project site in Western region of Kenya to examine the soil fertility status in relation to the current blanket fertilizer recommendations and farmers’ practices across the four wards, namely: Motosiet, Keiyo, Cherangani and Kwanza. The baseline fertility status in different soil mapping units was assessed in terms of soil productivity index with a view of analyzing the levels of nutrients and yield gaps. Using the standard soil survey procedures, six soil mapping units were identified as UUr1, UUr2, UUr3, RUd, RUrb, and BU1.. The results showed that the highest productivity index was in unit BU1, followed by UUr1, UUr2, UUr3, and RUrb with values of 40.5, 29.4, 25.0, 16.0 and 8.9% respectively. Keiyo Ward had the highest level of nitrogen, being 125.82, followed by Motosiet, Cherangani and Kwanza with values of 99.92, 97.12, and 81.12 kg/ha respectively. Phosphorous level was highest in Kwanza (136.41 kg/ha), followed by Cherangani (106.82 kg/ha) and Keiyo Ward (76.08 kg/ha). The lowest level was recorded in Motosiet with the value of 72.56 kg/ha. Potassium was found to be adequate in all the four Wards with values ranging between 347.67 and 410.34 kg/ha. The maximum maize production recorded in the project sites was 9,000 kg/ha, with a yield gap of 1,000 kg/ha. This was achieved through application of 100 and 50 kg/ha of DAP and CAN respectively. This was followed by 6,750 kg/ha obtained through application of 50 kg/ha of DAP and CAN. The yields from the rest of the sites ranged between 1,800 and 4,500 kg/ha with yield gaps varying from 3,250 to 8,650 kg/ha. The lowest yields were obtained in Keiyo, followed by Kwanza Ward despite the relatively high macro- nutrient levels in the soils of the two Wards. This was attributed to soil-related constraints caused by the increased soil structural degradation and loss of soil tilth. Therefore, it is recommended that the envisaged climate smart technologies be geared towards enhancement of nutrient and water use efficiency through improved soil structure and tilth.
Assessment of the soil fertility status in Benin (West Africa) – Digital soil mapping using machine learning
