Unraveling the impact of wildfires on permafrost ecosystems: Vulnerability, implications, and management strategies.

Permafrost regions play an important role in global carbon and nitrogen cycling, storing enormous amounts of organic carbon and preserving a delicate balance of nutrient dynamics. However, the increasing frequency and severity of wildfires in these regions pose significant challenges to the stability of these ecosystems. This review examines the effects of fire on chemical, biological, and physical properties of permafrost regions. The physical, chemical, and pedological properties of frozen soil are impacted by fires, leading to changes in soil structure, porosity, and hydrological functioning. The combustion of organic matter during fires releases carbon and nitrogen, contributing to greenhouse gas emissions and nutrient loss. Understanding the interactions between fire severity, ecosystem processes, and the implications for permafrost regions is crucial for predicting the impacts of wildfires and developing effective strategies for ecosystem protection and agricultural productivity in frozen soils. By synthesizing available knowledge and research findings, this review enhances our understanding of fire severity's implications for permafrost ecosystems and offers insights into effective fire management strategies.

High-throughput phenotyping of nutritional quality components in sweet potato roots by near-infrared spectroscopy and chemometrics methods.

The lack of an efficient approach for quality evaluation of sweet potatoes significantly hinders progress in quality breeding. Therefore, this study aimed to establish a near-infrared spectroscopy (NIRS) assay for high-throughput analysis of sweet potato root quality, including total starch, amylose, amylopectin, the ratio of amylopectin to amylose, soluble sugar, crude protein, total flavonoid content, and total phenolic content. A total of 125 representative samples were utilized and a dual-optimized strategy (optimization of sample subset partitioning and variable selection) was applied to NIRS modeling. Eight optimal equations were developed with an excellent coefficient of determination for the calibration (R2C) at 0.95-0.99, cross-validation (R2CV) at 0.93-0.98, external validation (R2V) at 0.89-0.96, and the ratio of prediction to deviation (RPD) at 6.33-11.35. Overall, these NIRS models provide a feasible approach for high-throughput analysis of root quality and permit large-scale screening of elite germplasm in future sweet potato breeding.

Nanotechnology for endorsing abiotic stresses: a review on the role of nanoparticles and nanocompositions.

Environmental stresses, including the salt and heavy metals contaminated sites, signify a threat to sustainable crop production. The existence of these stresses has increased in recent years due to human-induced climate change. In view of this, several remediation strategies including nanotechnology have been studied to find more effective approaches for sustaining the environment. Nanoparticles, due to unique physiochemical properties; i.e. high mobility, reactivity, high surface area, and particle morphology, have shown a promising solution to promote sustainable agriculture. Crop plants easily take up nanoparticles, which can penetrate into the cells to play essential roles in growth and metabolic events. In addition, different iron- and carbon-based nanocompositions enhance the removal of metals from the contaminated sites and water; these nanoparticles activate the functional groups that potentially target specific molecules of the metal pollutants to obtain efficient remediation. This review article emphasises the recent advancement in the application of nanotechnology for the remediation of contaminated soils with metal pollutants and mitigating different abiotic stresses. Different implementation barriers are also discussed. Furthermore, we reported the opportunities and research directions to promote sustainable development based on the application of nanotechnology.

Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy.

Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Fourier-transform (FT) NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by FT-NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. This study aimed to provide a reference for the evaluation of sorghum grain biochemicals for food, feed, and fuel without destruction and complex chemical analysis.