Agrivoltaic (AV) systems increase land productivity through the combined production of renewable energy and food. Although several studies have addressed their impact on crop production, many aspects remain unexplored. The objective of this study was to determine the effects of AV on the cultivation of celeriac, a common root vegetable in Central Europe. Celeriac was cultivated in 2017 and 2018 as part of an organically managed on-farm experiment, both underneath an AV system and in full-sun conditions. Under AV, photosynthetic active radiation was reduced by about 30%. Monitoring of crop development showed that in both years, plant height increased significantly under AV. Fresh bulb yield decreased by about 19% in 2017 and increased by about 12% in 2018 in AV, but the changes were not significant. Aboveground biomass increased in both years under AV, but only increased significantly in 2018. As aboveground biomass is a determinant of root biomass at harvest in root vegetables, bulb yields may be further increased by a prolonged vegetation period under AV. Compound analysis of celeriac bulbs did not show any clear effects from treatment. As harvestable yields were not significantly reduced, we concluded that celeriac can be considered a suitable crop for cultivation under AV.

Elevated carbon dioxide (eCO₂) stimulates wheat grain yield, but simultaneously reduces protein/nitrogen (N) concentration. Also, other essential nutrients are subject to change. This study is a synthesis of wheat experiments with eCO₂, estimating the effects on N, minerals (B, Ca, Cd, Fe, K, Mg, Mn, Na, P, S, Zn), and starch. The analysis was performed by (i) deriving response functions to assess the gradual change in element concentration with increasing CO₂ concentration, (ii) meta-analysis to test the average magnitude and significance of observed effects, and (iii) relating CO₂ effects on minerals to effects on N and grain yield. Responses ranged from zero to strong negative effects of eCO₂ on mineral concentration, with the largest reductions for the nutritionally important elements of N, Fe, S, Zn, and Mg. Together with the positive but small and non-significant effect on starch concentration, the large variation in effects suggests that CO₂-induced responses cannot be explained only by a simple dilution model. To explain the observed pattern, uptake and transport mechanisms may have to be considered, along with the link of different elements to N uptake. Our study shows that eCO₂ has a significant effect on wheat grain stoichiometry, with implications for human nutrition in a world of rising CO₂.

Elevated carbon dioxide (eCO₂) is well known to stimulate plant photosynthesis and growth. Elevated carbon dioxide’s effects on crop yields are of particular interest due to concerns for future food security. We compiled experimental data where field-grown wheat (Triticum aestivum Linnaeus) was exposed to different CO₂ concentrations. Yield and yield components were analyzed by meta-analysis to estimate average effects, and response functions derived to assess effect size in relation to CO₂ concentration. Grain yield increased by 26% under eCO₂ (average ambient concentration of 372 ppm and elevated 605 ppm), mainly due to the increase in grain number. The response function for grain yield with CO₂ concentration strongly suggests a non-linear response, where yield stimulation levels off at ~600 ppm. This was supported by the meta-analysis, which did not indicate any significant difference in yield stimulation in wheat grown at 456–600 ppm compared to 601–750 ppm. Yield response to eCO₂ was independent of fumigation technique and rooting environment, but clearly related to site productivity, where relative CO₂ yield stimulation was stronger in low productive systems. The non-linear yield response, saturating at a relatively modest elevation of CO₂, was of large importance for crop modelling and assessments of future food production under rising CO₂.

The demand for food and renewable energy is increasing significantly, whereas the availability of land for agricultural use is declining. Agrivoltaic systems (AVS), which combine agricultural production with solar energy generation on the same area, are a promising opportunity with the potential to satisfy this demand while avoiding land-use conflicts. In the current study, a Consequential Life-Cycle Assessment (CLCA) was conducted to holistically assess the environmental consequences arising from a shift from single-use agriculture to AVS in Germany. The results of the study show that the environmental consequences of the installation of overhead AVS on agricultural land are positive and reduce the impacts in 15 of the 16 analysed impact categories especially for climate change, eutrophication and fossil resource use, as well as in the single score assessment, mainly due to the substitution of the marginal energy mix. It was demonstrated that, under certain conditions, AVS can contribute to the extension of renewable energy production resources without reducing food production resources. These include maintaining the agricultural yields underneath the photovoltaic (PV) modules, seeking synergies between solar energy generation and crop production and minimising the loss of good agricultural land.

Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features.