Co-applied magnesium nanoparticles and biochar modulate salinity stress via regulating yield, biochemical attribute, and fatty acid profile of Physalis alkekengi L.

While previous studies have addressed the desirable effects of biochar (BC) or magnesium nanoparticles (Mg NPs) on salinity stress individually, there is a research gap regarding their simultaneous application. Additionally, the specific mechanisms underlying the effects of BC and Mg NPs on salinity in Physalis alkekengi L. remain unclear. This study aimed to investigate the synergistic effects of BC and Mg NPs on P. alkekengi L. under salinity stress conditions. A pot experiment was conducted with salinity at 100 and 200 mM sodium chloride (NaCl), as well as soil applied BC (4% v/v) and foliar applied Mg NPs (500 mg L-1) on physiological and biochemical properties of P. alkekengi L. The results represented that salinity, particularly 200 mM NaCl, significantly reduced plant yield (58%) and total chlorophyll (Chl, 36%), but increased superoxide dismutase (SOD, 82%) and catalase (CAT, 159%) activity relative to non-saline conditions. However, the co-application of BC and Mg NPs mitigated these negative effects and improved fruit yield, Chl, anthocyanin, and ascorbic acid. It also decreased the activity of antioxidant enzymes. Salinity also altered the fatty acid composition, increasing saturated fatty acids (SFAs) and polyunsaturated fatty acids (PUFAs), while decreasing monounsaturated fatty acids (MUFAs). The heat map analysis showed that fruit yield, anthocyanin, Chl, and CAT were sensitive to salinity. The findings can provide insights into the possibility of these amendments as sustainable strategies to mitigate salt stress and enhance plant productivity in affected areas.

Arbuscular mycorrhizal fungi species improve the fatty acids profile and nutrients status of soybean cultivars grown under drought stress.

AIMS: To investigate the effects of arbuscular mycorrhizal fungi (AMF) species on the absorption and distribution of mineral nutrients in soybean cultivars under drought stress, an experiment was carried out through a factorial method in the form of randomized complete blocks with six replicates in 2020. METHODS AND RESULTS: Experimental factors include: drought stress at three irrigation levels (well-watered [WW], medium stress [MS] and severe stress [SS], i.e., 100%, 70% and 40% FC), soybean cultivars at two levels (Sepideh and Williams), and mycorrhizae application at four levels (nonapplication, Funneliformis mosseae, Glomus hoi, Rhizophagus intraradices). The results indicated that drought stress increased the concentration of grain potassium (K) and sodium (Na), leaf K, stem and leaf Na, and decreased the concentrations of grain phosphorus (P), copper (Cu), and zinc (Zn), leaf P, stem and leaf Cu, and manganese (Mn). In addition, there was no significant difference in terms of concentrations of grain K. The highest reduction was observed under SS conditions (40% FC). G. hoi colonization increased the concentrations of grain P and Zn, leaf K and Cu and stem Mn under SS conditions (40% FC). In addition, compared to Sepideh cv., Williams cv. showed higher stress resistance. Seed oil content decreased in the plants exposed to drought stress. Severe drought treatments have a deleterious effect on seed fatty acid composition, resulting in enhanced linoleic, oleic and linolenic acids. CONCLUSION: AMF colonization is a useful tool for improving the plant nutrient uptake, fatty acid profile, efficiency of resource utilization and stabilizing yield, hence reducing the production risks of crops grown under drought stress conditions. It was concluded that AMF colonization should be employed to help alleviate the adverse effects of drought stress. SIGNIFICANCE OF STUDY: AMF colonization is an effective biotechnological strategy that can alter nutrient uptake and fatty acid composition and enhance oil quality in soybean cultivars under drought conditions.

Using artificial neural network in determining postharvest LIFE of kiwifruit.

BACKGROUND: Artificial intelligence systems have been employed for the development of predictive models that estimate many agricultural processes. RESULTS: In present study, the predictive capabilities of artificial neural networks (ANNs) were evaluated with respect to assessing fruit firmness as a postharvest life index, with determinations made at four stages of storage: 1, 60, 120 and 180 days after harvesting. Single concentrations of nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg) on fruit (D1 ), all of these nutrient concentrations (D2 ), the ratios of the nutrient concentrations alone (D3 ), and a combination of nutrient concentrations and their ratios (D4 ), were considered. CONCLUSION: The results obtained showed that fruit firmness at 1 and 60 days after harvesting was not influenced by nutrients. However, the ANN model estimated fruit firmness of 120 and 180 days, respectively, for D1 and D3 more accurately than for the D2 and D4 datasets. Application of D3 (nitrogen/calcium ratio) as the input dataset improved predictions of fruit firmness, with a correlation coefficient of 0.85 between the measured and estimated data. © 2019 Society of Chemical Industry.