Food security in climate mitigation scenarios

Using Science and Technology to Unveil The Hidden Delicacy Terfezia arenaria, a Desert Truffle

Terfezia arenaria is a desert truffle native to the Mediterranean Basin region, highly appreciated for its nutritional and aromatic properties. Despite the increasing interest in this desert truffle, T. arenaria is not listed as an edible truffle authorized for trade in the European Union. Therefore, our objective was to showcase T. arenaria's nutritional and chemical composition and volatile profile. The nutritional analysis showed that T. arenaria is a good source of carbohydrates (67%), proteins (14%), and dietary fibre (10%), resulting in a Nutri-Score A. The truffle's volatile profile was dominated by eight-carbon volatile compounds, with 1-octen-3-ol being the most abundant (64%), and 29 compounds were reported for the first time for T. arenaria. T. arenaria's nutritional and chemical compositions were similar to those of four commercial mushroom and truffle species, while the aromatic profile was not. An electronic nose corroborated that T. arenaria's aromatic profile differs from that of the other four tested mushroom and truffle species. Our data showed that T. arenaria is a valuable food resource with a unique aroma and an analogous composition to meat, which makes it an ideal source for plant-based meat products. Our findings could help promote a sustainable future exploitation of T. arenaria and ensure the quality and authenticity of this delicacy.

Assessment and Prediction of Grain Production Considering Climate Change and Air Pollution in China

This study examines the spatial and temporal impacts of climate change on grain production in China. This is achieved by establishing a spatial error model consisting of four indicators: the climate, air pollution, economic behavior, and agricultural technology, covering 31 provinces in China from 2004 to 2020. These indicators are used to validate the spatial impacts of climate change on grain production. Air pollution data are used as instrumental variables to address the causality between climate and grain production. The regression results show that: First, climatic variables all have a non-linear “increasing then decreasing” effect on food production. Second, SO2, PM10, and PM2.5 have a negative impact on grain production. Based on the model, changes in the climatic production potential of grain crops can be calculated, and the future spatial layout of climate production can also be predicted by using random forests. Studies have shown that the median value of China’s grain production potential is decreasing, and the low value is increasing.