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Manganese (Mn) plays an important role in the oxygen-evolving complex, where energy from light absorption is used for water splitting. Although changes in light intensity and Mn status can interfere with the functionality of the photosynthetic apparatus, the interaction between these two factors and the underlying mechanisms remain largely unknown. Here, maize seedlings were grown hydroponically and exposed to two different light intensities under Mn sufficient or deficient conditions. No visual Mn deficiency symptoms appeared even though the foliar Mn concentration in the Mn deficient treatments was reduced to 2 µg g-1. However, the maximum quantum yield efficiency of photosystem II (PS II) and the net photosynthetic rate declined significantly, indicating latent Mn deficiency. The reduction in photosynthetic performance by Mn-depletion was further aggravated when plants were exposed to high light intensity. Integrated transcriptomic and proteomic analyses showed that a considerable number of genes encoding proteins in the photosynthetic apparatus were only suppressed by a combination of Mn deficiency and high light, thus indicating interactions between changes in Mn nutritional status and light intensity. We conclude that high light intensity aggravates latent Mn deficiency in maize by interfering with the abundance of PS II proteins.

Crop mixtures are often beneficial in crop rotations to enhance resource utilization and yield stability. While targeted management, dependent on the local species composition, has the potential to increase the crop value, it comes at a higher expense in terms of field surveys. As fine-grained species distribution mapping of within-field variation is typically unfeasible, the potential of targeted management remains an open research area. In this work, we propose a new method for determining the biomass species composition from high resolution color images using a DeepLabv3+ based convolutional neural network. Data collection has been performed at four separate experimental plot trial sites over three growing seasons. The method is thoroughly evaluated by predicting the biomass composition of different grass clover mixtures using only an image of the canopy. With a relative biomass clover content prediction of R2 = 0.91, we present new state-of-the-art results across the largely varying sites. Combining the algorithm with an all terrain vehicle (ATV)-mounted image acquisition system, we demonstrate a feasible method for robust coverage and species distribution mapping of 225 ha of mixed crops at a median capacity of 17 ha per hour at 173 images per hectare.

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