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Under low temperature stress, the molar percentage of the most abundant phospholipid PC decreased around 10%. The significantly up-regulated expression of genes encoding phospholipase [phospholipase D (PLD)] and phosphatase PAP/LPP genes implied that PC turnover was triggered by cold stress mainly via the PLD pathway. Consequently, as the central product of PC turnover, the level of PA increased drastically (63.2%) compared with the control. The gene-metabolite network and co-expression network were constructed with the prominent lipid-related DEGs to illustrate the modular regulation of metabolic changes of membrane lipids. This study will help to explicate membrane lipid remodeling and the molecular regulation mechanism in field crops encountering low temperature stress.Senescence is a highly quantitative trait, but in wheat the genetics underpinning senescence regulation remain relatively unknown. To select senescence variation and ultimately identify novel genetic regulators, accurate characterization of senescence phenotypes is essential. When investigating senescence, phenotyping efforts often focus on, or are limited to, the visual assessment of flag leaves. However, senescence is a whole-plant process, involving remobilization and translocation of resources into the developing grain. Furthermore, the temporal progression of senescence poses challenges regarding trait quantification and description, whereupon the different models and approaches applied result in varying definitions of apparently similar metrics. To gain a holistic understanding of senescence, we phenotyped flag leaf and peduncle senescence progression, alongside grain maturation. Reviewing the literature, we identified techniques commonly applied in quantification of senescence variation and developed styping methods we found most effective, encouraging their use when investigating and discriminating senescence variation of differing genetic bases, and aid trait selection and weighting in breeding and research programs alike.There is an urgent need to breed dry direct-seeded adapted rice varieties in order to address the emerging scenario of water-labor shortage. The aim of this study was to develop high-yielding, direct-seeded adapted varieties utilizing biparental to multiparental crosses involving as many as six different parents in conventional breeding programs and 12 parents in genomics-assisted breeding programs. The rigorous single plant selections were followed from the F2 generation onwards utilizing phenotypic selection and quantitative trait locus (QTL)/gene-based/linked markers for tracking the presence of desirable alleles of targeted QTL/genes. In conventional breeding, multiparent lines had significantly higher yields (2,072-6,569 kg ha-1) than the biparental lines (1,493-6,326 kg ha-1). GAB lines derived from multiparent crosses had significantly higher (3,293-6,719 kg ha-1) yields than the multiparent lines from conventional breeding (2,072-6,569 kg ha-1). Eleven promising lines from genomics-assisted breeding carrying 7-11 QTL/genes and eight lines from conventional breeding with grain-yield improvement from 727 to 1,705 kg ha-1 and 68 to 902 kg ha-1, respectively, over the best check were selected. The developed lines may be released as varieties/parental lines to develop better rice varieties for direct-seeded situations or as novel breeding material to study genetic interactions.Adjusting planting density is a common agricultural practice used to achieve maximum yields. click here However, whether the quality of medicinal herbs can be improved by implementing appropriate planting densities is still uncertain. The medicinal crop Panax notoginseng was used to analyze the effects of planting density on growth and ginsenoside accumulation, and the possible mechanisms of these effects were revealed through metabonomics. The results showed that P. notoginseng achieved high ginsenoside accumulation at high planting densities (8 × 8 and 10 × 10 cm), while simultaneously achieved high biomass and ginsenoside accumulation at moderate planting density of 15 × 15 cm. At the moderate planting density, the primary metabolism (starch and sucrose metabolism) and secondary metabolism (the biosynthesis of phytohormone IAA and ginsenoside) of the plants were significantly enhanced. However, the strong intraspecific competition at the high planting densities resulted in stress as well as the accumulation of phytohormones (SA and JA), antioxidants (gentiobiose, oxalic acid, dehydroascorbic acid) and other stress resistance-related metabolites. Interestingly, the dry biomass and ginsenoside content were significantly lower at low densities (20 × 20 and 30 × 30 cm) with low intraspecific competition, which disturbed normal carbohydrate metabolism by upregulating galactose metabolism. In summary, an appropriate planting density was benefit for the growth and accumulation of ginsenosides in P. notoginseng by balancing primary metabolism and secondary metabolism.Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases ng a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.
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