Water-driven crop simulation models are commonly employed to evaluate crop yields and irrigation management strategies to improve agricultural water productivity.  Well-tested models can serve as powerful tools for guiding agricultural practices.  The objective of this study was to assess the capability of the AquaCrop model for simulation of cotton transpiration and water use under drip irrigation conditions comparing with field sap flow measurements.  A two-year field experiment (2020-2021) in cotton was conducted in Xinjiang China including two row spacing and two topping methods.  The model adequately estimated canopy cover with a normalized root mean square error (nRMSE) of less than 5% and a model efficiency (EF) close to 1.  The model estimation of transpiration obtained a good agreement with sap flow measurements (nRMSE=22.4%) across all years and treatments.  The model simulated water use efficiency (WUE, 4.42 g m^-2 mm^-1) of cotton were lower than those calculated from actual measurements with WUE of 4.79 g m^-2 mm^-1.  The estimated transpiration was slightly higher than that measured using sap flow meter due to an 11.5% of overestimation for crop coefficient in the model when cotton grew in short and dense canopy structure under drip irrigation and plastic film cover conditions.  Air temperature, vapor pressure difference and radiation had positive effects on cotton transpiration while humidity had negative effects.  The model could capture the trends of transpiration with climate factors, but the climatic effects were stronger than that of sap flow.  In conclusion, AquaCrop model is useful tool in optimizing cotton irrigation strategies.

Chloroplasts are important organelles for photosynthesis, which is essential for increasing the yields of pak choi.  In this study, we evaluated a delayed chloroplast development mutant ‘M136’ identified during self-crossing of the pak choi inbred line ‘136’.  The young true leaves of ‘M136’ were yellow and gradually turned green with maturation.  Chloroplast development, pigment contents, and photosynthetic parameters were impaired and gradually recovered with growth in ‘M136’, and chlorophyll fluorescence parameters were also impaired in ‘M136’.  Genetic analysis and bulk segregant analysis (BSA)-seq revealed that the mutant phenotype was controlled by a single recessive gene, identified as BraA06g011520.3.5C (BrECB2), which encodes a DYW-type pentatricopeptide repeat (PPR) protein.  In ‘M136’, a T-to-C single nucleotide polymorphism (SNP) in the 4th PPR motif of BrECB2 caused a threonine-to-isoleucine amino acid substitution.  BrECB2 was mainly expressed in young leaves.  The chloroplast RNA editing efficiency of ‘M136’ was affected and significantly recovered after the leaves turned green, and the editing efficiency was partially restored in complementation lines.  The plastid-encoded RNA polymerase activity was not affected in ‘M136’.  Functional complementation analyses revealed that the transient overexpression of BrECB2 partially rescued the mutant phenotype and the RNA editing efficiency in ‘M136’.  In summary, this study indicates that BrECB2 is involved in early chloroplast development and RNA editing, providing a theoretical basis for understanding the regulatory network involved in chloroplast development in pak choi.

Leaf color directly affects the appearance quality and nutritional quality of leafy vegetables, thereby determining their economic value.  Here, we identified a golden leaf mutant, Mut298, from an ethyl methanesulfonate (EMS)-induced mutant library of Chinese cabbage.  Through the approach of forward genetics, it has been demonstrated that the phenotype of Mut298 is due to a single nucleotide substitution from C to T that changes glycine to arginine in the conserved domain of BrPRPL1, which encodes the large subunit ribosomal protein L1 of the chloroplast.  Because the PRPL1 mutation causes embryonic lethality in Arabidopsis, the function of PRPL1 in leaf development remains elusive.  In this study, the mutation of BrPRPL1 causes a substantial reduction in the expression of key chloroplast-encoded proteins (RbcL, PsaA, and PsaB) and disrupts chloroplast development.  Moreover, the chlorophyll content and photosynthetic parameters are significantly lower in Mut298 plants than in wild-type plants, resulting in golden yellow leaves in Chinese cabbage.  This study reveals the impact of PRPL1 mutation on ribosome translation within chloroplasts and provides a theoretical a foundation for future research into the regulatory roles of PRPL1 in plant growth and development.

Cucumber (Cucumis sativus L.) is a major vegetable crop worldwide, and its yield and quality are closely linked to flower development.  AGAMOUS-LIKE 6 (AGL6), a member of the ancient MADS-box transcription factor family, plays a crucial role in flower development.  However, the specific functions of its homolog in cucumber remain poorly understood.  In this study, we demonstrate that CsAGL6 is predominantly expressed in flowers, with high expression levels observed in all floral organ primordia during the early stages of floral development.  The petals of Csagl6 mutants exhibit a greener color compared to wild-type plants, along with a significant increase in total chlorophyll content.  Additionally, the mutants show abnormal petal morphology, including changes in size and shape, as well as enlarged sepals resembling leaves occasionally.  Molecular analysis reveals that CAULIFLOWER (CAL) and the E-class gene SEPALLATA 4 (SEP4) are significantly downregulated in the mutants, while the chlorophyll synthesis gene Early Light-Induced Protein 1 (ELIP1) and several stress-related genes in the chloroplasts are dramatically upregulated.  Our findings provide novel insights into the functional role of CsAGL6 in regulating sepal and petal development, and offer a potential avenue for understanding the genetic control of flower pigmentation and organ morphology in Cucumis species.

The evolutionary development of adventitious roots (ARs) in plants enhances their capacity to adapt to various stress conditions.  A thorough analysis of the influencing factors in their morphological construction holds significant theoretical value and practical guidance for overcoming rooting obstacles in cuttings, as well as for cultivating superior varieties characterized by broad adaptability and stress resistance.  In this study, we investigated the molecular mechanisms underlying the development of ARs in tomato (Solanum lycopersicum) by performing transcriptome sequencing (RNA-seq).  We analyzed the transcription profiles of relevant genes in the “Y962” strain, which exhibits spontaneous AR formation, and the “W961” strain, which does not form ARs.  Our findings indicate that the AR induction stage represents an active phase of development, during which we identified 1,676 overlapping genes across the three comparison groups, highlighting the most differentially expressed genes.  Functional enrichment analysis showed that they were most closely related to response to auxin, and were also dependent on the crosstalk between other hormones and carbohydrates.  Furthermore, through the measurement of endogenous auxin levels and the induction tests with exogenous auxin, it was established that the formation of ARs is closely linked to the accumulation and transport of auxin.  Notably, the auxin efflux SlPIN3, which was enriched in the auxin response pathway, exhibited significantly high expression during the induction phase of ARs.  The slpin3 mutant, generated using the CRISPR/Cas9 editing system, exhibited a significant reduction in the number of ARs, highlighting the close relationship between polar transport regulated by SlPIN3 and auxin-induced AR formation.  In summary, this study not only enriches the developmental network of AR formation in tomatoes with a wealth of data but also elucidates the potential mechanisms for promoting AR development by targeting SlPIN3.  

Circular RNAs (circRNAs) are a group of widely discovered non-coding RNAs in different organisms, but their biological functions are largely unknown, especially in plant–microbial interactions.  In this study, we identified an exonic circRNA (Che-circR2410) from the fungus Cochliobolus heterostrophus that, together with its corresponding linear RNA ChCYP51, synergistically regulates the virulence of C. heterostrophus to maize.  Further in situ hybridization and dual-luciferase reporter assays revealed the interaction between pathogenic circRNA Che-circR2410 and its cross-kingdom host target, zma-miR399e-5p.  Additionally, lesion areas caused by both the wild type C. heterostrophus and the circR2410 knock-out strain (ΔChcircR2410) showed no significant difference on the maize miR399e silencing mutant, providing support for the interaction between Che-circR2410 and zma-miR399e-5p.  Moreover, we found that zma-miR399e affects the expression of autophagy-related genes, regulating maize immunity.  Thus, our findings reveal a cross–kingdom interaction between the pathogenic exonic circRNA and host miRNA, modulating C. heterostrophus infection in maize.  This study broadens our understanding of the C. heterostrophus-maize interaction at the level of non-coding RNA.

Phytopathogenic fungi can weaken the effectiveness of antifungal chemicals from plants and artificial synthesis through a xenobiotic detoxification system.  Nevertheless, the transcription factors responsible for transcriptional activation of xenobiotic detoxification genes in phytopathogenic fungi are rarely reported.  Here, we show that a GATA transcription factor, SsGATA1, regulates the transcription of drug efflux pump genes, thus contributing to tolerance to various types of chemical fungicides, including propiconazole, caspofungin, and azoxystrobin in Sclerotinia sclerotiorum.  Similarly, SsGATA1 also confers tolerance to isothiocyanate and flavonols, two compounds reported as broad-spectrum antifungal chemicals, by mediating the transcription of the isothiocyanate hydrolase SsSaxA.  Importantly, SsGATA1 positively regulates pathogenicity, which is attributed to the upregulation of hydrolases and SsSaxA during infection.  Furthermore, SsGATA1 is responsible for tolerance to several stresses.  Our findings demonstrate that SsGATA1 plays roles in multidrug resistance and pathogenicity by activating the transcription of hydrolases and xenobiotic detoxification genes.

The synergistic use of chemical pesticides and biological agents poses the fundamental challenge of balancing control efficacy with ecological safety.  In recent years, nanotechnology has emerged as a promising strategy for improving pesticide performance while reducing pesticide residues and alleviating environmental contamination.  Herein, we developed an efficient nano-pesticide based on star polycation (SPc) loaded with clothianidin, which was co-applied with a widely used parasitic wasp (Aphidius colemani) to achieve synergistic pest management.  SPc at the working concentration displayed no significant impact on the eclosion or survival of parasitic wasps, whereas the oral feeding of SPc at an extremely high concentration significantly up-regulated several genes related to ribosomal protein and energy metabolism, leading to metabolic imbalance and subsequent mortality of the parasitic wasps.  The SPc could load clothianidin via hydrogen bonding and Van der Waals forces, and this spontaneous complexation achieved a reduction in particle size from 6,554.87 to 467.84 nm.  Importantly, the clothianidin/SPc complex exhibited a 16–28% increase in insecticidal activity against green peach aphids (Myzus persicae), while showing minimal adverse impacts on the eclosion and parasitism of parasitic wasps.  Finally, co-application of the clothianidin/SPc complex with parasitic wasps achieved up to 80% mortality in green peach aphids, with the promising advantages of rapid pest suppression and sustainable control.  This study proposes a synergetic pest management strategy based on nano-pesticides and natural enemies, which is beneficial for maintaining long-term agricultural ecological balance.

The intelligent pest-monitoring light trap based on machine vision employs specific light spectra to attract pests, infrared heating to eliminate pests, and artificial intelligence models to recognize and count them.  Achieving optimal model performance requires a high-quality insect annotated dataset.  However, traditional manual annotation is expert-dependent, time-consuming, and inefficient for large-scale multi-class insect labeling.  This study establishes an efficient, few-shot learning approach to construct a large-scale light-trapped insect dataset through a two-stage annotation framework: detection followed by classification.  Specifically, a MLTIDD addresses scale and receptive field disparities between large and tiny insects.  Based on a fine-tuned Grounding DINO, SAM and SAHI are integrated to detect insects at multiple scales.  Subsequently, InsectSSRL, an iBOT-based self-supervised method, learns robust insect feature representations from the extensive set of unlabeled insect sub-images detected by MLTIDD.  It enhances feature extraction capability for insect sub-images through three proxy tasks.  This feature extractor supports a classification model to pre-classify insect sub-images.  Following expert correction, labels are traced back to original images to complete annotation work for the light-trapped insect dataset.  Experimental results demonstrate that under limited samples, MLTIDD achieved 79.6% average precision (AP)_50–95 and 90.8% average recall (AR), surpassing DINO by 7.0 and 4.7 percentage points.  InsectSSRL attained 85.87% top-1 accuracy in k-NN evaluation.  In few-shot classification, Swin-T pre-trained with InsectSSRL and fine-tuned on 5% of InsectID achieved 80.35% accuracy, exceeding iBOT by 2.08 and COCO-based transfer learning by 11.3 percentage points.  The proposed pipeline improved mAP_50–95 by 10.91 and AR by 8.26 percentage points compared to DINO and iBOT, while reducing expert annotation time by approximately 80% relative to manual labeling.

Geese, descendants of migratory birds, have preserved the distinct reproductive and lipid metabolism traits of their wild ancestors.  Therefore, compared to other poultry, geese have lower egg production ability and greater susceptibility to fatty liver.  Recent research underscores the impact of lipid metabolism disorders on female reproductive health.  In this context, we observed reproductive disorders (RD) and lipid metabolism anomalies in certain geese populations.  This study systematically elucidated the differences between RD and normal geese at various levels, including genomics, transcriptomics, bile acid metabolomics, and microbiomics, revealing the crucial role of microorganisms.  Our study provides a thorough examination of the ovarian anatomical, histological, and transcriptomic profiles between normal and RD geese.  Genomic analyses pinpoint mutations in genes associated with bile acid metabolism, highlighting their potential role in RD pathogenesis.  The genomic discoveries are substantiated by precise bile acid assays and ileum transcriptome analyses, which expose a significant disruption in bile acid absorption, activation of FXR, and an increase in serum chenodeoxycholic acid (CDCA) concentrations within RD geese.  Notably, 16S rRNA sequencing uncovers significantly greater beta diversity in the ileum microbiota of RD geese than in the normal group.  Both Wilcoxon rank sum test and LEfSe analyses highlighted a marked increase in Romboutsia abundance in RD geese.  Experimental cultivation of microbiota with CDCA supplementation confirms the impact of CDCA on Romboutsia lituseburensis proliferation.  Gavage experiments with R. lituseburensis elucidate its involvement in primary follicle reduction via immune-mediated pathways.  Collectively, our multifaceted analysis unravels the intricate involvement of Romboutsia in goose RD, offering insights from genetic, physiological, and microbial dimensions.  Our findings not only deepen understanding of the etiology of RD in geese but also suggest potential avenues for therapeutic interventions targeting bile acid metabolism and modulation of specific microbiota components.