Using indigenous knowledge to model land suitability for crops in Amuru District, Northern Uganda

Abstract

Locally-generated agricultural land management models may be more effective in achieving sustainable agricultural production and environmental management because they operate within the eco-cultural context of the farmers, therefore easily useable. The present study uses locally generated indigenous knowledge to model the suitability for cultivation of maize, rice and beans and compares the results obtained with those of a modern scientific land suitability model in Amuru district, northern Uganda. Georeferenced indigenous knowledge-based land suitability data was collected from farmers' fields using a Global Positioning System handset, questionnaires and focus group discussions were also used to develop an indigenous knowledge-based agricultural land resource database. The Analytical Hierarchical Process method was applied to sort the data to determine the relative importance of the various indigenous land evaluation parameters. For the modern scientific land suitability evaluation data set, a land resource database with climate and soil physical and chemical parameters was developed to model the scientific land suitability evaluation. The two data sets ere analyzed using Automated Land Evaluation System software based on the principles of the FAO framework for land evaluation. Two spatial land suitability models were generated for the two data sets. The two spatial models were matched using ArcGIS software applications to obtain land suitability comparisons. Results produced more than 70% agreement between the indigenous and the modern scientific evaluations. The evaluation for maize and rice produced 75% agreement. Both scientific and indigenous land evaluations for maize and rice showed that the two crops almost have similar land use requirements. On the other hand, land suitability comparisons for beans produced 71% level of agreement. It is concluded that locally generated land evaluation systems based on farmers' indigenous knowledge are comparable to modern scientific ones, and may be relied upon where technical land evaluations are not readily available or useable by rural indigenous farmers.