Rapid soil attribute evaluation for soil security assessments in data-poor environments in the Pacific region

Abstract

Many global environments face increasing pressures on soil resources, and effective, scalable methods for assessment of soil condition and capital are essential to respond to tangible soil threats. This situation is common across Pacific Island Countries and Territories (PICTs), where high throughput soil analysis laboratories are limited, and issues such as soil organic carbon decline, acidification and fertility declines are present. Soil spectral inference presents an opportunity in such regions to provide rapid insights into soil capital and condition, though the need for robust calibration libraries remains a limiting factor. This work investigates the utility of a regionally appropriate spectral library, the New Zealand Soil Spectral Library (NZSSL) to support the development of soil spectral inference in data-poor environments, such as PICTs, through a case study on the island of Tongatapu in The Kingdom of Tonga. We contrast the performance of existing partial least squares regression (PLSR) models developed for New Zealand soils on soils from Tongatapu and explore the opportunities for enhancement of predictions formed through memory-based learning (MBL) supplemented with local data. Our work shows the potential for cost-effective and timely soil monitoring through soil spectral inference in PICTs. The work further underscores the importance of regional cooperation and data-sharing for addressing soil security.