Contamination of agricultural soils by potentially toxic elements (PTEs) poses a persistent challenge to sustainable crop production and food safety. Because complete remediation of contaminated farmland is often difficult to achieve in the short term, the development and utilization of crop varieties with low PTE accumulation in edible tissues provide a practical strategy for reducing dietary exposure while maintaining agricultural productivity. However, low-accumulation traits arise from coordinated regulation across the soil–plant continuum rather than from a single uptake or detoxification process. This review integrates the major mechanisms governing PTE accumulation in food crops and proposes a regulatory framework encompassing “entry restriction–transport modulation–spatial allocation–cellular immobilization.” We first examine how soil physicochemical properties, nutrient–metal interactions, rhizosphere processes, and root structural traits regulate PTE bioavailability and root entry. We then discuss how transporter networks, metal chelation systems, xylem loading, and phloem redistribution control long-distance transport and delivery to aerial and edible organs. Tissue partitioning, cell wall immobilization, and vacuolar sequestration are further considered as key processes determining the final distribution of PTEs within crops. Importantly, similar low-accumulation phenotypes may arise through different regulatory routes, with the relative importance of individual control points varying among elements, crop genotypes, and environmental conditions. These regulatory routes are further discussed in the context of element specificity, strength of supporting evidence, potential trade-offs with mineral nutrition and crop productivity, field robustness, and food-safety relevance. Representative low-accumulation germplasm and current approaches for molecular breeding and agronomic management are also summarized. Together, these considerations position low PTE accumulation as an integrated crop phenotype resulting from multiple, potentially compensatory regulatory processes rather than simply reduced root uptake. Integrating mechanistic understanding with genotype selection and soil–crop management may facilitate the development of low-accumulation crops that combine reduced contaminant delivery to edible tissues with stable agronomic performance, thereby supporting safer and more sustainable production on contaminated agricultural land.
Whether the transposable-element layer responds non-additively to combined abiotic stress, and at what resolution that can be measured, has not been established. Whether it extends into the repetitive fraction is unknown, because a family-level effect may be an artefact of the repeat library that defines it. In a public citrus factorial, two counting rules discarding different information agreed closely (r = 0.961); resolution dependence proved a property of the library, not the plant. In one genotype, two-way non-additivity passed q < 0.05 and replicate consistency in 675 of 8106 tests (8.3%), sub-additive (391 against 284, p = 4.4 × 10−5); that excess has two opposite components: induced families lean sub-additive (93.5%), repressed families super-additive (91.5%). The gene set was non-additive in 19,508 of 61,836 (31.5%), but its direction was balanced once collapsed per gene (p = 0.52). The genotype modulated the repeat-layer interaction in 551 tests without a stable direction, and the gene-level interaction far more often (16,973 tests). The repeat layer therefore carries non-additivity but no reproducible direction: it loses its sign when restricted to intergenic insertions, while unique-only counting and the second genotype reduce its magnitude without changing its sign, so the sign is set by genomic context alone.
Plant-level canopy-size assessment is important for precision agriculture applications, including crop monitoring and variable-rate fertilization, but trait-specific annotation remains costly. This study investigates whether unsupervised β-Variational Autoencoder (β-VAE) latent maps from an image reconstruction model encode canopy-size information recoverable through compact statistical descriptors. A β-VAE was trained on the designated training partitions of a dataset of 2043 field-acquired RGB images of individual blueberry plants, without canopy-size labels. The encoder generated spatial posterior mean (μ) and log-variance (logσ2) maps, from which per-location Kullback–Leibler (KL) divergence maps were also computed. These maps were summarized into five descriptor configurations and evaluated using a separate classifier. The 300-feature reference configuration, which combined μ, logσ2, and KL descriptors, achieved 73.11% accuracy, 73.54% balanced accuracy, and 73.42% macro-F1 under a stratified-random split. Independent retraining of the complete pipeline for three acquisition session-held-out evaluations yielded 63.04 ± 4.54% balanced accuracy and 60.92 ± 6.68% macro-F1. Corresponding balanced accuracies were 78.77 ± 7.24% for ImageNet-pretrained EfficientNet-B0 and 54.13 ± 4.95% for a radial basis function support vector machine using 22 image descriptors; summaries are unweighted means ± sample standard deviations across sessions. These findings support a compact and modular framework for image-based blueberry canopy-size phenotyping, in which unsupervised representation learning is separated from downstream supervised classification, although performance remained below supervised transfer learning. The reference trait measures image-relative canopy extent, not physically calibrated plant size.
Rice (Oryza sativa L.) suffers severe yield losses from the brown planthopper (Nilaparvata lugens Stål; BPH), a phloem-feeding insect pest, and durable host resistance is widely regarded as the most economical strategy for its management. Wild relatives of rice harbor rich but largely untapped reservoirs of resistance. However, the molecular and metabolic mechanisms that underlie their contrasting resistance phenotypes remain poorly understood. Here, we integrated insect bioassays, transcriptome profiling, and widely targeted metabolomics to compare the responses of the resistant Oryza nivara and the susceptible Oryza rufipogon to BPH feeding. BPH showed significantly reduced weight gain, honeydew excretion, and survival on O. nivara, consistent with strong antibiosis-based resistance. Transcriptomic and metabolomic analyses revealed that O. nivara preferentially activated jasmonic acid signaling and phenylpropanoid-flavonoid biosynthesis and accumulated flavonoids and lignans/coumarins, whereas O. rufipogon mainly induced ribosomal and primary-metabolism genes and accumulated amino acids, nucleotides, and lipids. Integrated analyses uncovered a coordinated shikimate-phenylpropanoid-flavonoid metabolic axis in O. nivara, coupled with elevated jasmonic acid and salicylic acid signaling and suppressed auxin biosynthesis. Exogenous application of jasmonic or salicylic acid enhanced resistance in the susceptible O. rufipogon. Our results reveal a coordinated defense strategy in wild rice against BPH and highlight key metabolic and regulatory nodes that can be targeted to breed durable resistance into cultivated rice.
Soil salinity substantially modulates soil nitrogen (N) transformation processes, yet how soil texture mediates the salinity-induced changes in soil nitrogen transformation remains poorly quantified. To evaluate the interactive effects of soil identity and salt stress on inorganic N concentrations, a 56-day column incubation experiment was conducted using three typical soil textures (loamy sand, silt loam, and clay loam) with a salinity gradient (0, 3, and 6 g kg−1). Uniform urea application (215 mg N kg−1 soil) was performed at 25 ± 1 °C and approximately 60% water-holding capacity, and dynamic variations in soil inorganic N fractions and urease activity were monitored throughout incubation. The results revealed distinct soil-specific inorganic N concentration patterns under salt perturbation. Loamy sand soil exhibited prominent NO3--N accumulation, while clay loam soil showed stronger NH4+-N retention and more significant transient NO2--N accumulation. In the non-saline control treatment, the final concentration of NO3--N reached 202.14 mg kg−1 in loamy sand, which was 48.4% higher than that in clay loam soil. Increasing soil salinity generally prolonged NH4+-N retention and transient NO2--N accumulation while limiting NO3--N formation, with the magnitude of such salt-induced changes highly dependent on soil texture and incubation stage. Specifically, soil salinity at 3 and 6 g kg−1 reduced the final nitrate content of clay loam soil by 14.3% and 15.4%, respectively. Urease activity consistently ranked as clay loam > silt loam > loamy sand across treatments, and this enzymatic difference was decoupled from soil nitrate accumulation capacity. RDA further indicated date-dependent associations of EC with NH4+-N and NO2--N retention, while pH was more closely related to NO3--N accumulation. The results indicate that soil texture is associated with heterogeneous inorganic N responses to salt stress, although initial chemical differences preclude attribution to texture alone. These findings describe measured inorganic N pools and urease activity rather than directly measured nitrification rates or microbial mechanisms. Validation with plants, mixed salts, and field conditions is required before management recommendations can be established.
