Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts

Emerging bioeconomy sectors in energy systems modeling - Integrated systems analysis of electricity, heat, road transport, aviation, and chemicals: a case study for the Netherlands

Several studies that have assessed the role of bioenergy in the energy system have primarily focused on electricity, heat, and road transport. However, sectors that have few alternatives to biomass, namely aviation and the chemical industry, are expected to become increasingly important. We have extended a bottom‐up energy systems model with fossil‐based and bio‐based chemicals and with renewable jet fuels to assess the deployment of biomass conversion technologies in the Netherlands until 2030. The model comprises detailed cost‐structures and mid‐term developments for the energy system with detailed cost‐supply curves for biomass, renewable energy technologies, and carbon capture and storage. The framework incorporates multi‐output processes, such as biorefineries, to address cross‐sectoral synergies. To capture the uncertainty in technical progress, technology development scenarios are used to assess cost‐optimal biomass utilization pathways over time. Slow technical progress (LowTech) leads to biomass applications for heating, first‐generation biofuels from hydrotreated oils, and bio‐based chemicals based on first‐generation fermentation systems. Enhanced technology development (HighTech) allows the production of second‐generation biofuels, large volumes of diverse bio‐based chemicals and renewable jet fuels. The required biomass may range from 230 PJ (LowTech) to 300 PJ (HighTech) in 2030, supplied primarily from imported resources. Both scenarios show that, under existing policies, CO2 emissions will only gradually be reduced to reach 1990 levels (140–145 Mt CO2). Further scenario analysis is recommended to assess model sensitivity and the necessary preconditions for future biomass conversion pathways and robust directions towards the required greenhouse‐gas mitigation pathways

When food meets BRI: China's emerging Food Silk Road

One of the crucial but overlooked aspects of China's Belt and Road Initiative (BRI) is the food and agricultural cooperation. In this paper, we argue that under BRI, China is building a ‘Food Silk Road’ with which the country attempts to reconstruct global food supply chains through overseas agricultural investment, agricultural technology transfer, massive investment in infrastructure, and accelerated policy coordination. We also discuss the driving factors behind the emerging Food Silk Road, related challenges, and potential implications of China's quest for food security for global food governance