Food and Energy Security最新文献

Modern agriculture is a complex system that demands real-time and large-scale quantification of trait values for evidence-based decisions. However, high-profile traits determining market values often lack high-throughput phenotyping technologies to achieve this objective; therefore, risks of undermining crop values through arbitrary decisions are high. Because environmental conditions are major contributors to performance fluctuation, with the contemporary informatics infrastructures, we proposed enviromic prediction as a potential strategy to assess traits for informed decisions. We demonstrated this concept with wheat falling number (FN), a critical end-use quality trait that significantly impacts wheat market values but is measured using a low-throughput technology. Using 8 years of FN records from elite variety testing trials, we developed a predictive model capturing the general trend of FN based on biologically meaningful environmental conditions. An explicit environmental index that was highly correlated (r = 0.646) with the FN trend observed from variety testing trials was identified. An independent validation experiment verified the biological relevance of this index. An enviromic prediction model based on this index achieved accurate and on-target predictions for the FN trend in new growing seasons. Two applications designed for production fields illustrated how such enviromic prediction models could assist informed decision along the food supply chain. We envision that enviromic prediction would have a vital role in sustaining food security amidst rapidly changing climate. As conducting variety testing trials is a standard component in modern agricultural industry, the strategy of leveraging historical trial data is widely applicable for other high-profile traits in various crops.

The deposition of atmospheric nitrogen can significantly boost the amount of nitrogen available in various ecosystems, potentially altering the mutualistic association between arbuscular mycorrhizal fungi (AMF) and their host plants. Nevertheless, the precise mechanisms and the degree to which externally induced nitrogen-related changes in AMF functionality might impact Sorghum bicolor (L.) Moench, a plant known for its high mycorrhizal colonization, remains unclear. In this study, the mycorrhizal response affected by environmental N enrichment was addressed by conducting a glasshouse experiment, and four fertilization treatments (N1, N2, N3, and N4, 0, 15, 30, and 60 kg N hm⁻¹ a⁻¹, respectively) were used to simulate N deposition differences over the mycorrhizal response. The changes in mycorrhizal colonization and plant variables during different AMF and N fertilizer applications were investigated. When the gradient's nitrogen levels increased, the mycorrhizal growth response and mycorrhizal nitrogen response showed a pattern of first dropping and then increasing. N-induced changes in the mycorrhizal response were associated with vesicular colonization, arbuscular colonization, and root-length colonization. The variation in the mycorrhizal response over the N concentration gradient highlights the critical role of AMF in agroecosystems.

Crop growth models, such as the WOrld FOod STudies (WOFOST) model, mimic the mechanistic processes involved in crop development, growth, and yield production. The accuracy of simulation is decreased in unfavorable low-temperature settings because these models do not accurately represent crop response processes in low-temperature stress. Enhancing the WOFOST crop growth model's accuracy in simulating crops' responses to cold temperatures is the aim of this work. Given its vulnerability to low temperatures, the inquiry uses winter wheat in Henan Province as a focal point. It integrates the WHEATGROW wheat phenology model with the Frost model of Lethal Temperature 50 (FROSTOL) inside the framework of the crop growth model. This link aims to improve simulation accuracy and supplement the model's mechanisms, particularly when it comes to the impact of low temperatures on crop development. The study uses Long Short-Term Memory networks to build a yield model that integrates remote sensing data with information from simulated crop models. Under low temperatures, the leaf area index, total above ground biomass, and total weight of storage organs of the model WWF—which combines FROSTOL and WHEATGROW with WOFOST—show a considerable decline. It was discovered that there is a greater improvement in simulation accuracy of the linked model WWF relative to the WOFOST model in frost years than in normal years, based on a comparison analysis between typical frost years and normal years. To be more precise, the improvement is 8.03% in frost years and 1.98% in regular years. When all is said and done, the coupled model advances our knowledge of how winter wheat is impacted by low temperatures.

The cover image is based on the Review Article Building forests for the future by A. Robert MacKenzie et al., https://doi.org/10.1002/fes3.518. Image Credit: Ian Crompton.

Xinyu Li, Wenzhen Liu, Gaoliang Wang, Samuel Sai-Ming Sun, Ling Yuan, Jingxue Wang. Food Energy Security. (2023) 00:e506.

The original version of this article contained an error in Figure 2, where the graph of Figure 2d is incorrect and does not match the figure legend. The error does not affect the rest of the article.

The corrected Figure 2 and accompanying legend appear below.

Increasing the application of nitrogen fertilizer is the main approach to increase rice production, but it also brings problems of environmental pollution and increases agricultural production costs. Cultivating high-yielding and high nitrogen use efficiency (NUE) rice varieties is an important approach to solving this problem. The rice varieties carrying dep1 (dense and erect panicle 1) have both high grain yield and high NUE. However, their plant traits have not been fully explored. In this study, two rice near-isogenic lines carrying dep1 (NIL-DEP1 and NIL-dep1) were grown in paddy fields under 0, 120 and 270 kg N ha⁻¹. We analyzed agronomic traits of panicle type, plant type, leaves and roots, and physiological traits of vascular bundles, photosynthetic rate and carbon and nitrogen transport. The results showed that the NIL-dep1 exhibited higher grain yield and NUE than NIL-DEP1, mainly due to the higher spikelet number per panicle, grain filling percentage and dry matter production. Compared with NIL-DEP1, NIL-dep1 had improved flag leaf morpho–physiological traits, including erect flag leaves, greater leaf thickness and specific leaf weight, higher root dry weight, root length, root volume and root surface area, and a better canopy structure, as reflected by a lower light interception percent and canopy extinction coefficient, leading to better photosynthetic performance and dry matter production. In addition, NIL-dep1 exhibited better vascular bundle traits of peduncle and enhanced dry matter, stem carbon and nitrogen translocation during grain filling. In conclusion, NIL-dep1 had high grain yield and NUE by improved agronomic and physiological traits and increasing carbon and nitrogen translocation during grain filling. These traits mentioned above could be used to select and breed high grain yield with high NUE rice varieties.

This review explores the potential of genetically engineering cyanobacteria with the aim of synthesizing high-value protein directly from atmospheric nitrogen. The article examines numerous techniques that may enhance protein synthesis in cyanobacteria, and discusses advantages, barriers, and opportunities for this strategy going forward. Genetic manipulation of cyanobacteria shows promise in sustainably raising protein production via reduced greenhouse gas emissions and lower dependence on synthetic fertilizers, but also potentially fewer environmental implications traditionally caused by conventional protein production methods. The article uncovers many difficulties in genetically modifying cyanobacteria for protein production. For example, genetically modified organisms (GMOs) have legal and regulatory ramifications that must be accounted for if ethical, moral and secure use of these technologies is to be ensured. Economic viability, too, must be evaluated, taking into consideration production costs, scalability, market demand and future market potential. We suggest that processing of cyanobacterial proteins in downstream stages need further development. Effective and economical methods are needed for protein extraction, purification, and formulation into commercially viable products. For successful application of cyanobacterial protein production at scale, such obstacles must be overcome. We conclude that genetic engineering of cyanobacteria for protein synthesis has a great deal of potential to offer a resource-effective and sustainable replacement for the synthesis of high-value proteins, so promoting a more sustainable and environmentally conscious future.

Fusarium disease and the consequent mycotoxin accumulation pose significant problem in maize cultivation, with fumonisins produced by Fusarium verticillioides posing a global health concern. To address this issue, a range of preventive measures (e.g. crop management techniques) can be implemented to minimize fungal infections. A promising strategy to counteract this issue involves the selection of genotypes with greater resistance to fungal pathogens. This approach has the potential to reduce the reliance on chemical inputs for controlling fungus growth or indirect infection vectors. Leveraging genetic approaches can help improve the economic sustainability of agriculture in the face of climate change challenges. In the present work, we assessed the importance of two husk leaf traits (coverage and number), their association with F. verticillioides infection, fumonisin content, and their potential influence on crop yield. The study was conducted in three locations in the North of Italy and 38 hybrids with varying resistance to F. Verticillioides were compared. The results obtained showed that husk coverage has a pivotal role not only in protecting maize ears from Fusarium infection but have also a significant impact on crop yield: a significant positive correlation was found between husk coverage and yield in all three locations (r = 0.33185; r = 0.51327 and r = 0.51207, respectively). Furthermore, in the field of Vicenza, a significant negative correlation was found between husk coverage and Fusarium severity (r = −0.41492). Husk coverage emerges as an important trait that merits inclusion in maize breeding programs, given its protective role against fungal infections and its favourable influence on both yield and grain quality.

Agricultural intensification affects natural and crop ecosystems, and increases the risk of agricultural pests in agroecosystems. Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) is an important pest that damages a wide range of crops. However, the effect of agricultural intensification on the genetic diversity of this pest is still unclear. In this study, we investigated the effects of the composition and configuration of the landscape on the genetic diversity of the agricultural pest H. armigera based on cytochrome oxidase subunit I (COI) analyses. In total, 10 haplotypes were found in 2016 and 15 haplotypes in 2021 based on COI genes. The haplotype diversity and nucleotide diversity were the highest in the Anqiu (AQ) region during 2016 and in the Bincheng (BC) region during 2021. Haplotype 2 and haplotype 3 (Hap2 and Hap3) were the dominant haplotypes in the H. armigera population. Agricultural intensification had no effect on the genetic diversity of H. armigera between 2016 and 2021. Our study highlights the effect of agricultural intensification on the genetic diversity of H. armigera. Understanding the genetic consequences of agricultural intensification is essential for the green control of agricultural pests and the sustainable development of agriculture.