Comparing indigenous and scientific (Palin test) soil fertility assessment approaches for better soil health monitoring, management, and farm decision-making in Uganda

This paper evaluated the accuracy of indigenous soil fertility assessment approaches commonly used by smallholder farmers in comparison to scientific soil testing to determine better performing indigenous approach for soil fertility assessment. The extent of soil fertility misclassification and its correlates were also assessed from a sample of 461 input subsidy project beneficiaries in Uganda using descriptive statistics, a binary probit model, and a multivariate probit model. Our findings reveal that vegetation cover, weeds, soil colour, and previous yield indicators were associated with 71%, 67%, 66%, and 61% levels of misclassification, respectively. The Probit model results revealed that the plot size and land tenure system had a significant positive association with misclassification. These results suggest that the farmers' likelihood of misclassifying soil fertility using indigenous approaches significantly increased as plot sizes increased. Additionally, farmers who owned land were more likely to misclassify their soil fertility status than those who rented it. This implies that the most reliable soil fertility assessment approach is the objective scientific soil testing for better soil monitoring and health management decisions.