Agronomy

As a critically important global food crop, wheat has been increasingly threatened by the frequent occurrence of extreme high-temperature events, which impairs its growth and development, resulting in reduced seed-setting rate, compromised grain quality and diminished yield. Therefore, identifying heat-tolerant genes and enhancing thermotolerance through molecular breeding are essential strategies for wheat improvement. In this study, we retrieved spatial transcriptomic data from the public database PRJNA427246, which captured gene expression profiles in flag leaves and grains of the heat-sensitive wheat cultivar Chinese Spring (CS) under 37 °C heat stress at time points of 0 min, 5 min, 10 min, 30 min, 1 h, and 4 h. Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression networks for flag leaf and grain transcriptomes. One highly significant module was identified in each tissue, along with 35 hub genes that showed a strong temporal association with heat stress progression. Notably, both modules contained the previously characterized thermotolerance gene TaMBF1c, suggesting that additional heat-responsive genes may be present within these modules. Simultaneous analysis of the expression data from four groups (encompassing different tissues and high-temperature treatments) for the 35 core genes revealed that genes from the TaHSP20 family, TaMBF1c family, and other related genes exhibit coordinated expression patterns in terms of the temporal dynamics and tissue distribution of stress responses. Additionally, 27 genes of the small heat shock protein (HSP20) family are predicted to be involved in the endoplasmic reticulum-associated degradation (ERAD) pathway. They assist in clearing misfolded proteins induced by stress, thereby helping to maintain endoplasmic reticulum homeostasis and cellular functions under stress conditions. Finally, the expression levels of three core genes, TaHSP20-1, TaPCDP4, and TaMBF1c-D, were validated by qRT-PCR in two wheat cultivars with distinct thermotolerance: S116 (Zhehuamai 2008) and S128 (Yangmai 33). These findings provide new insights into the molecular mechanisms underlying heat tolerance in wheat and offer valuable genetic resources for breeding thermotolerant varieties.

Rice is a vital staple food crop worldwide and also one of the major sources of greenhouse gas (GHG) emissions, generating substantial methane (CH₄) and nitrous oxide (N₂O). As one of the key management practices for rice production, the GHG mitigation potential of water management has attracted extensive attention, whereas its global scalability remains to be further investigated. Based on 15,458 global observations of field experimental data, we employed advanced machine learning methods to quantify the GHGs and soil carbon sequestration of global rice systems around 2020. Furthermore, we identified the optimal spatial distribution of GHG mitigation for five rice water management practices (continuous flooding (CF), flooding–midseason drainage–reflooding (FDF), alternate wetting and drying irrigation (AWD), flooding–midseason drainage–intermittent irrigation (FDI), and rainfed cultivation (RF)) through scenario simulation, under the premise of no yield reduction. The results of machine learning simulation showed that optimizing water management could reduce global rice greenhouse gas emissions by 39.17%, equivalent to 340.46 Mt CO₂ eq, while increasing rice yields by 3.55%. This study provides valuable insights for the optimization of agricultural infrastructure and the realization of agricultural sustainable development.

China boasts abundant cultivated resources of pitahaya, with Guizhou Province being one of its core producing areas. Quality differences in red-fleshed pitahaya among local producing areas have not been fully clarified, and a standardized quantitative evaluation system for these differences remains lacking. This study seeks to identify the key factors influencing regional variations in quality and establish a comprehensive evaluation standard. In this study, 15 samples of red-fleshed pitahaya were collected from four major producing areas in Guizhou and used as research materials. Based on 15 quality characteristic indicators of the fruits, an analysis of quality differences and establishment of an evaluation system were carried out using multivariate statistical analysis. The results showed that 14 of the 15 quality indicators exhibited significant differences among pitahaya samples from different producing areas (p < 0.05), with the a* value being the sole exception. Cluster analysis classified the 15 samples into four groups. Principal component analysis (PCA) extracted four principal components, with a cumulative variance contribution rate of 81.07%, which clearly identified betacyanin, betaxanthin, 1,1-diphenyl-2-picrylhydrazyl (DPPH) free-radical scavenging rate, vitamin C, fruit shape index, and transverse diameter as the core evaluation indicators. This study systematically clarifies the differences in quality characteristics and the internal correlations among quality indicators of red-fleshed pitahaya from different major producing areas in Guizhou. It further provides an important scientific basis for pitahaya variety breeding, cultivation regulation, and market positioning in this region and fills the research gap existing in the field of comprehensive quality evaluation of pitahaya. This is of significant practical importance for promoting the standardized upgrading of local specialty fruit industries, enhancing the market competitiveness of products, and facilitating the high-quality development of the agricultural economy.