Nitrogen (N) and potassium (K) are key elements for crop growth, yet studies on the impact of N–K interactions on plant N and K status and yield are lacking.  This study aimed to develop effective indicators for diagnosing N and K nutrition and predicting the yield of wheat under N–K interactions based on the theoretical framework of a critical nutrient dilution curve.  A 4-year N–K interaction experiment involving three wheat cultivars was employed for building and validating nutrient indices (NIs) based on the critical N dilution curve (CNDC) and the critical K dilution curve (CKDC).  In addition, relevant data from the literature were collected for supplementary validation.  The results revealed that changes in parameter A1 of the critical K dilution curves (CKDCs) can reflect the impact of nitrogen application on K absorption and utilization.  However, the difference in K nutrition index (KNI) values calculated by CKDC under different N levels was not significant.  Based on the aboveground biomass (AGB), a universal CKDC was established and defined as K_c=3.63AGB^–0.37 under N–K interactions.  The results showed that the direct effects of N or K deficiency on crops could be quantified by the N–K interaction index (NKI) calculated by integrating CNDC and CKDC, and the changes in crop growth in response to proportional N and K concentrations could be determined by NKI as well.  In addition, topdressing N fertilizer at the jointing stage significantly improved the N–K interaction effect on the N nutrition index (NNI) and NKI at the booting stage (P<0.05), but it had no significant N–K interaction effect on the KNI.  All indicators at the heading stage demonstrated the best predictive capability for relative yield (RY) compared to other stages.  Compared with NNI and KNI, the prediction accuracy of yield with NKI improved by 11.63 and 17.44%, respectively.  The NKI has better performance in diagnosing N and K nutrition and predicting yield under N–K interactions than either NNI or KNI.  This result enhances our understanding of the effects of N–K interactions on wheat growth and has important applications for improving the accuracy of N and K nutrition diagnosis and yield prediction.

Clostridium perfringens (Cp) is a major enteric pathogen in poultry, threatening both animal health and food safety. This study investigated the protective effects of Lactobacillus reuteri 21 (LR21) administered via in ovo injection against Cp infection in broilers. A total of 360 chicks, previously injected in ovo on embryonic day 18, were randomly allocated to four groups (n=6 replicates, 15 birds each): CON (PBS), Cp (PBS+Cp), IOF (LR21), and IOF-Cp (LR21+Cp). Birds were reared for 21 d. A two-way ANOVA was applied to determine the main and interaction effects for in vivo outcomes and one-way ANOVA for in vitro assays. Significant findings were followed by Tukey’s HSD for pairwise comparisons. Although in ovo injection of LR21 slightly mitigated Cp-induced growth suppression, it significantly increased the jejunal villus height and reduced epithelial apoptosis (P<0.05). LR21 also downregulated pro-inflammatory genes including NOD1, MyD88, NF-κB, and JNK, and inhibited the M1-type macrophage polarization in the jejunum compared to Cp challenge. Regarding gut microbiota, Cp challenge altered β-diversity and enriched Clostridium perfringens, whereas LR21 increased Roseburia, Lactobacillus, and specifically Lactobacillus reuteri. In addition, in ovo injection of LR21 enhanced the production of its signature metabolite, reuterin, in Cp-challenged broilers. In vitro, reuterin suppressed pro-inflammatory cytokines in macrophages and protected intestinal organoids from Cp-induced damage. Mechanistically, reuterin inhibited the TLR4/MAPK/NF-κB signaling pathway and activated the Nrf2/HO-1 pathway thereby alleviating inflammation response in Cp-infected macrophages. Reuterin aslo upregulated genes involved in glutathione metabolism (SLC7A11, GCLC, GCLM, GSR, PRDX6, IDH1) and increased antioxidant enzyme activities, thereby limiting ROS accumulation and cellular death of intestinal organoids. Taken together, these findings demonstrate that in ovo LR21 administration enhances intestinal resilience to Cp infection through reuterin-mediated coordination of effects on both macrophages and intestinal stem cells, leading to the attenuation of inflammatory responses and reinforcement of glutathione-dependent antioxidant defenses. 

Crop models have been widely used to optimize nitrogen (N) applications for agronomic decision-making, but uncertainties in model calibration under varying N levels, particularly the effects of phenology choice for calibration, remain underexplored. This study employed the ORYZA v3 model, coupled with a global optimization algorithm, to assess how different calibration strategies affected predictions of leaf area index (LAI) and biomass in two rice varieties under four N levels (0, 90, 180, and 270 kg ha^-1). The results indicate when the model is calibrated separately for each N level, predictive accuracy varies considerably for both LAI and biomass, reflecting the difference in crop response to N availability. When calibrating the model simultaneously with multiple N levels from a single phenology phase, variability from different selected phenology phases becomes the dominant source of model uncertainty, rather than N levels. Specifically, calibrations using measured data from the stem elongation to anthesis (SA) and anthesis to maturity (AM) phases across N levels provide the most accurate predictions for LAI (RMSE: 0.51–1.92 m² m^-²; R²≥0.88) and biomass (RMSE: 551–2619 kg ha^-1; R²≥0.96), respectively. In contrast, calibrations using measured data from early-season (transplanting to stem elongation) result in the least reliable predictions. Combined-phase calibrations using SA and AM phases result in the best predictions for both LAI and biomass, owing to their balanced representation of pre- and post-anthesis growth dynamics. This approach significantly reduces uncertainty from phase selection. However, variability in N application rates emerges as the primary uncertainty source in model simulations, emphasizing the importance of careful selection of N levels in calibration datasets, particularly when measured data span two-thirds of the growing season. These findings offer valuable insights into improved calibration practice for precise N management, highlighting the critical role of both phenology phase and N treatment selection.