Soybean Yield and Seed Composition Changes in Response to Increasing Atmospheric CO2 Concentration in Short-Season Canada

Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms

Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.

Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change

The rapid expansion of soybean-growing areas across Europe raises questions about the suitability of agroclimatic conditions for soybean production. Here, using data-driven relationships between climate and soybean yield derived from machine-learning, we made yield projections under current and future climate with moderate (Representative Concentration Pathway (RCP) 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The selected model showed high R² (>0.9) and low root-mean-squared error (0.35 t ha^-1) between observed and predicted yields based on cross-validation. Our results suggest that a self-sufficiency level of 50% (100%) would be achievable in Europe under historical and future climate if 4-5% (9-11%) of the current European cropland were dedicated to soybean production. The findings could help farmers, extension services, policymakers and agribusiness to reorganize the production area distribution. The environmental benefits and side effects, and the impacts of soybean expansion on land-use change, would need further research.

Symbiotic integration of bioprocesses to design a self-sustainable life supporting ecosystem in a circular economy framework

Climate change, resource depletion and unsustainable crop productivity are major challenges that mankind is currently facing. Natural ecosystems of earth's biosphere are becoming vulnerable and there is a need to design Bioregenerative Life Support Systems (BLSS) which are ecologically engineered microcosms that could effectively deal with problems associated with urbanization and industrialization in a sustainable manner. The principles of BLSS could be integrated with waste fed biorefineries and solar energy to create a self-sustainable bioregenerative ecosystem (SSBE). Such engineered ecosystems will have potential to fulfil urban life essentials and climate change mitigation thus generating ecologically smart and resilient communities which can strengthen the global economy. This article provides a detailed overview on SSBE framework and its improvement in the contemporary era to achieve circular bioeconomy by means of effective resource recycling.

Genetic Engineering for Global Food Security: Photosynthesis and Biofortification

Increasing demands for food and resources are challenging existing markets, driving a need to continually investigate and establish crop varieties with improved yields and health benefits. By the later part of the century, current estimates indicate that a >50% increase in the yield of most of the important food crops including wheat, rice and barley will be needed to maintain food supplies and improve nutritional quality to tackle what has become known as 'hidden hunger'. Improving the nutritional quality of crops has become a target for providing the micronutrients required in remote communities where dietary variation is often limited. A number of methods to achieve this have been investigated over recent years, from improving photosynthesis through genetic engineering, to breeding new higher yielding varieties. Recent research has shown that growing plants under elevated [CO2] can lead to an increase in Vitamin C due to changes in gene expression, demonstrating one potential route for plant biofortification. In this review, we discuss the current research being undertaken to improve photosynthesis and biofortify key crops to secure future food supplies and the potential links between improved photosynthesis and nutritional quality.