Probability modelling to reduce decision uncertainty in environmental niche identification and driving factor analysis: CaNaSTA case studies

Hillside agro-ecosystems have a complex spatial and temporal distribution of natural resources. Farmers generally possess a vast bodyof knowledgeabout environmental resources on their farms but this knowledge is largely based on locally observable features rather than generalized knowledge. The lack of process-based knowledge concerning agroecosystemfunction creates uncertainty that obstructs sound decision-making under conditions of rising economic and ecologic pressure in many developing countries. Since the past decade, Precision Agricultureprovides tools to reduce uncertainty caused by environmental variation. By describing spatial and temporal variation of the environment, Geographic Information Systems help to detect suitable crops for specific environmental niches and support farmers to find optimal management practices for their plot of land. Hence Precision Agriculturehelps to raise the economic benefits of farming, ensures consistent product quality and reduces negative environmental impacts caused by inappropriate management practices.