otential of remote and proximal sensing, publicly available datasets and machine learning for site-specific management in Australian irrigated cotton systems

Abstract

Agricultural fields are inherently variable across both space and time but are commonly managed uniformly. Uniform management can simultaneously lead to an under and over-application of resources (e.g. fertiliser) within the same field, resulting in poor resource efficiency and reduced profit margins. This research demonstrated the potential of publicly available datasets (i.e. remote sensing, digital soil maps, weather), machine learning techniques and crop models to inform management at a sub-paddock scale. These findings will help provide a cost-effective and efficient approach to improving farm productivity, profitability and sustainability in Australian irrigated cotton systems.