Factor-Specific Nutrient Modeling and Management of Agricultural and Forest Crops

Dear Colleagues,

Alexander von Humboldt’s principles of biogeography, elaborated on more than 200 years ago to explain the natural occurrence of living systems based on facts and measures, can now be applied to diagnose the nutrient status of anthropic ecosystems using tools of artificial intelligence and compositional data analysis. In this Special Issue, we welcome articles (original research, reviews, modelling approaches, perspectives, opinions) focusing on the integration of factors affecting yield and quality of agricultural crops and the mineral nutrition of forest crops at local scale. Regional nutrient management recommendations often fail at local scale by not accounting for key factor interactions. Environmental and managerial factors, soil and tissue tests, remote and proximate sensing, ionomes of species down to cultivars or hybrids, biofortification and food contamination by trace metals must be addressed in detail to reach accurate predictions of crop performance and environmental quality at local scale. This warrants uniform data collection protocols, ethical data sharing, and large data sets; hence, close collaboration among stakeholders is required to trustfully collect metadata and the results of field trials, laboratory analyses, mapping, and surveys. Machine learning and compositional models are unprecedented tools to process factor interactions and to reach productive soil–plant systems at local scale.

Prof. Léon Etienne Parent
Dr. Anoop Kumar Srivastava
Guest Editors

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