Predicting Maize Theoretical Methane Yield in Combination with Ground and UAV Remote Data Using Machine Learning.

The accurate, timely, and non-destructive estimation of maize total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) under different phenological stages is a substantial part of agricultural remote sensing. The assimilation of UAV and machine learning (ML) data may be successfully applied in predicting maize TAB and TBMP; however, in the Nordic-Baltic region, these technologies are not fully exploited. Therefore, in this study, during the maize growing period, we tracked unmanned aerial vehicle (UAV) based multispectral bands (blue, red, green, red edge, and infrared) at the main phenological stages.

Relaunch cropping on marginal soils by incorporating amendments and beneficial trace elements in an interdisciplinary approach.

In the EU and world-wide, agriculture is in transition. Whilst we just converted conventional farming imprinted by the post-war food demand and heavy agrochemical usage into integrated and sustainable farming with optimized production, we now have to focus on even smarter agricultural management. Enhanced nutrient efficiency and resistance to pests/pathogens combined with a greener footprint will be crucial for future sustainable farming and its wider environment.