Home plant–soil feedbacks (home-PSFs) typically demonstrate negative effects in vegetable crops, substantially inhibiting their growth.  Phosphorus (P), an essential plant nutrient crucial for growth, influences vegetable crop growth patterns through soil availability levels.  However, the relationship between soil available P levels and home-PSFs in vegetable crops requires further investigation.  This study established a home PSF system incorporating 12 vegetable crops from 6 families to examine growth responses under two P conditions (low P level: 40 mg P kg^–1 soil; high P level: 200 mg P kg^–1 soil).  The findings revealed that low P conditions significantly decreased overall biomass across all vegetables, with preferential biomass allocation to root development.  Furthermore, low P conditions enhanced mycorrhizal colonization and rhizosphere acid phosphatase activity while notably decreasing root length.  While vegetables generally exhibited negative home PSFs, allium and nonmycorrhizal plants demonstrated positive responses under high P conditions.  Wild tomatoes displayed greater variation in feedback values across P levels compared to common tomatoes.  Under high-P conditions, mycorrhizal colonization showed positive correlations with feedback values of biomass and P concentration.  Root diameter and mycorrhizal colonization demonstrated distinct correlations with these feedback values under low-P conditions.  The research concludes that high P levels effectively mitigate negative home-PSFs in vegetables while increasing biomass production.  Additionally, high P levels demonstrated superior efficacy in alleviating negative home-PSFs in wild tomatoes compared to common tomatoes.

Cadmium (Cd) contamination in wheat farmland is increasing at an alarming rate, posing threats to food security and public health.  Breeding and utilizing wheat varieties characterized by low Cd accumulation levels constitute an effective strategy in the battle against wheat Cd contamination.  The adoption of molecular marker-assisted approaches can greatly expedite the selection and enhancement of wheat varieties with low Cd accumulation.  Nonetheless, research concerning the genes associated with wheat cadmium accumulation remains scarce.  In this study, a high-density 660K SNP array was employed for conducting a genome-wide association study (GWAS) on the grain Cd concentration (GCdC), bioconcentration factor (BCF) and translocation factor (TF) in 175 wheat germplasms.  The findings revealed 401 significant SNPs identified across three diverse environments.  Linkage disequilibrium analysis revealed 30 core quantitative trait loci (QTLs) capable of reliably modulating wheat Cd accumulation phenotypes.  Through gene annotation, transcriptomics, and gene molecular features, four candidate genes (TraesCS7B02G000200, TraesCS4A02G035900, TraesCS4A02G040900, and TraesCS5D02G564000) were identified as potential constituents in the biological process of wheat Cd accumulation.  Furthermore, six wheat germplasms exhibiting low grain Cd accumulation were isolated, and two kompetitive allele specific PCR (KASP) markers conducive to breeding selection were developed.  These findings provide valuable genetic resources for cultivating wheat with low Cd accumulation and establish a foundation for understanding the molecular mechanisms underlying low Cd accumulation in wheat.  The candidate genes and KASP markers elucidated in this research have potential for effective use in genetic enhancement and marker-assisted selection in the breeding of wheat with low Cd accumulation.

Adjustment of the sowing date is a widely used measure in rice production for adapting to high-temperature conditions.  However, the impact of a delayed sowing date (DS) on rice quality may vary by variety and ecological conditions.  In this study, we conducted experiments using four different sowing dates, the conventional sowing date 1 (CS1), CS2 (10 d later than CS1), DS1 (30 d later than CS1), and DS2 (30 d later than CS2), and three rice varieties, i.e., Yixiangyou 2115, Fyou 498, and Chuanyou 6203.  This experiment was conducted at four sites in the Sichuan Basin in 2018 and 2019 to evaluate the influence of DS on the pasting properties of rice, which are a proxy for the eating and cooking quality (ECQ).  In DS1 and DS2, the rice had a significantly greater amylose content (AC) but a lower protein content (PC), peak viscosity (PKV), cool paste viscosity (CPV), and hot paste viscosity (HPV) than in CS1 and CS2.  Moreover, except for CS2 and DS1 in 2018, DS1 and DS2 led to 2.15–11.19% reductions in breakdown viscosity (BDV) and 23.46–108.47% increases in setback viscosity (SBV).  However, the influence of DS on rice pasting properties varied by study site and rice variety.  In 2019, DS1 and DS2 led to BDV reductions of 2.35–9.33, 2.61–8.61, 10.03–17.78, and 2.06–8.93%, and SBV increases of 2.32–60.93, 63.74–144.24, 55.46–91.63, and –8.28–65.37% at the Dayi, Anzhou, Nanbu, and Shehong (except for SBV in CS2 and DS1) sites, respectively.  DS resulted in greater reductions in PKV, HPV, CPV, and BDV and greater increases in the AC and SBV for Yixiangyou 2115 than for Chuanyou 6203 and Fyou 498.  The correlation analysis indicated that PKV and HPV were significantly and positively related to the mean, maximum, and minimum temperatures after heading.  These temperatures must be greater than 25.9, 31.2, and 22.3°C, respectively, to increase the relative BDV and reduce the relative SBV of rice, thereby enhancing ECQ.  In conclusion, DS might contribute to a significant deterioration in ECQ in machine-transplanted rice in the Sichuan Basin.  A mean temperature above 25.9°C after heading is required to improve the ECQ of rice.

Accurately mapping the spatial distribution of soil organic carbon (SOC) is crucial for guiding agricultural management and improving soil carbon sequestration, especially in fragmented agricultural landscapes.  Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes, its relationship with SOC remains unclear.  In this study, we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions.  Taking the Qilu Lake watershed in Yunnan, China, as a case study area and based on 216 topsoil samples collected from irrigation areas, we applied the extreme gradient boosting (XGBoost) model to investigate the contributions of vegetation indices (VI), brightness indices (BI), moisture indices (MI), and spectral transformations (ST, principal component analysis and tasseled cap transformation) to SOC mapping.  The results showed that ST contributed the most to SOC prediction accuracy, followed by MI, VI, and BI, with improvements in R² of 29.27, 26.83, 19.51, and 14.43%, respectively.  The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information.  The optimal SOC prediction model integrated soil properties, topographic factors, location factors, and landscape metrics, as well as remote sensing-derived variables, and achieved RMSE and MAE of 15.05 and 11.42 g kg^–1, and R² and CCC of 0.57 and 0.72, respectively.  The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture, vegetation status, soil brightness and SOC.  Compared with traditional linear regression models, interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC.  Overall, this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy.  Through interpretable machine learning, we can further elucidate the causes of SOC variation, which is important for sustainable soil management and agricultural practices.

Flax (Linum usitatissimum L.) is a versatile crop and its seeds are a major source of unsaturated fatty acids.  Stearoyl-acyl carrier protein desaturase (SAD) is a dehydrogenase enzyme that plays a key role in oleic acid biosynthesis as well as responses to biotic and abiotic stresses.  However, the function of SAD orthologs from L. usitatissimum has not been assessed.  Here, we found that two LuSAD genes, LuSAD1 and LuSAD2, are present in the genome of L. usitatissimum cultivar ‘Longya 10’.  Heterogeneous expression of either LuSAD1 or LuSAD2 in Arabidopsis thaliana resulted in higher contents of total fatty acids and oleic acid in the seeds.  Interestingly, ectopic expression of LuSAD2 in A. thaliana caused altered plant architecture.  Similarly, the overexpression of either LuSAD1 or LuSAD2 in Brassica napus also resulted in increased contents of total fatty acids and oleic acid in the seeds.  Furthermore, we demonstrated that either LuSAD1 or LuSAD2 enhances seedling resistance to cold and drought stresses by improving antioxidant enzyme activity and nonenzymatic antioxidant levels, as well as reducing membrane damage.  These findings not only broaden our knowledge of the LuSAD functions in plants, but also offer promising targets for improving the quantity and quality of oil, and the abiotic stress tolerance of oil-producing crops, through molecular manipulation.

The rapeseed cropping system following rice in the Yangtze River Basin (YRB) universally faces the challenge of tight crop succession. To address this, integrating unmanned aerial vehicle (UAV) sowing with no-tillage practices and high-density direct seeding has been recognized as a crucial agronomic approach. However, high-density planting intensifies intraspecific competition, quantified as relative competition intensity (RCI), which impairs root-shoot development and creates a prominent contradiction between lodging resistance and yield. To investigate this, a two-year field experiment was conducted to quantify the interactive effects of tillage methods (CK, tillage with manual sowing; N, no-tillage with UAV-sowing; T, tillage with UAV-sowing) and seeding rates (S1, 3.75; S2, 5.25; S3, 6.75 kg ha⁻¹). Across the three tillage modes, sequential increases in seeding rate from S1 to S3 resulted in significant increases in population density, grain yield, and RCI, but a significant reduction in yield per plant. Integrated data from the two years revealed that the N mode significantly reduced the Relative Competition Intensity (RCI) by 32.3-37.7% compared to the CK and T modes. This management practice also optimized dry matter partitioning, increasing the root-shoot ratio and root mass fraction by 30.8-44.3%, which enhanced root anchorage. Concurrently, it reinforced stem mechanical properties; the contents of stem lignin and cellulose increased by 6.8-10.4%, leading to significantly greater stem strength and a consequent 18.6-35.8% reduction in the lodging index. Furthermore, under the N mode, moderate competitive stress activated key enzymes (phenylalanine ammonia-lyase (PAL), peroxidase (POD), cinnamyl alcohol dehydrogenase (CAD) by 7.6-46.9%) in the phenylpropanoid pathway, driving the synthesis of structural carbohydrates and enhancing mechanical support. Crucially, the no-tillage with UAV-sowing (N mode) synergistically achieved the dual objectives of high yield and lodging resistance by optimizing root-shoot coordination and reinforcing stem structure. The NS2 and NS3 treatments were identified as the optimal practices for balancing these goals, with yields comparable to or approaching the highest-yielding treatment (TS3) while offering superior lodging resistance. These findings elucidate a cascading relationship of “intraspecific competition - structural plasticity - functional enhancement - high yield and lodging resistance”, providing a precise agronomic framework for simultaneous yield increase and lodging resistance improvement in the YRB.