Optimizing Soil Sampling Layouts for Biotechnological Applications in Urban Landscapes Using Spatial Simulated Annealing

Abstract

This study explores the optimization of soil sampling layouts for biotechnological applications in urban landscapes, employing a spatial simulated annealing algorithm to enhance the effectiveness of integrated nutrient management. In urban environments, where precise nutrient management is critical for optimizing crop yields and supporting sustainable biotechnological interventions, traditional geostatistical approaches to soil sampling often fall short due to regional sampling constraints. By incorporating the spatial simulated annealing algorithm, our research addresses these limitations by developing both unconstrained and constrained soil sampling schemes. We demonstrate that in areas without prior data or observations, the algorithm optimizes sampling layouts based on a minimum mean distance criterion for a predetermined number of samples. Conversely, in areas with existing observational data or known variances, the sampling strategy is refined using knowledge of prior variances coupled with the minimum Kriging variance criterion. The application of the spatial simulated annealing algorithm proves to be of practical significance, not only in navigating regional obstacles inherent in urban landscape planning but also in leveraging prior knowledge to inform the sampling process. This approach is particularly beneficial in the biotechnological context, where accurate soil data is essential for managing soil health, enhancing crop biotechnology, and implementing effective bio-remediation strategies.