Farmland Irrigation Research Institute

A water conveyance open channel project in Xinjiang passes through a vast region of collapsible loess. Under intermittent water supply conditions and the environmental effect of seasonal temperature variations, the sliding failure of the canal slope often occurs. To analyze the influence of environmental effects on collapsible loess, this study examines the variation law of mechanical properties through triaxial tests subjected to dry–wet–freeze–thaw cycles. An elastoplastic constitutive model suitable for structural loess is proposed based on the modified Cam-clay model. The results indicate that as the number of dry–wet–freeze–thaw cycles increases, the stress–strain curve of loess decreases, while the volume change curve rises, with a progressively smaller range of variation. The cohesion and internal friction angle show an exponentially decreasing trend, greatly affecting cohesion by the dry–wet–freeze–thaw cycle. Three failure modes are observed in loess soil samples. The stress–strain curve corresponding to shear failure exhibits a softening type, the curve for bulging failure shows a weak hardening type, and the curve belonging to compaction failure demonstrates a strong hardening type. Based on the modified Cam-clay model, the concept of upper and lower loading surfaces is introduced to account for the structure of loess, and a nonassociated flow criterion is used to develop a boundary surface elastoplastic constitutive model suitable for loess. The fitting results are in good agreement with triaxial test data obtained under dry–wet–freeze–thaw cycles. These findings provide theoretical support for the safe operation of open channels.

Soil cadmium (Cd) contamination poses serious risks to plant productivity and human food safety. Farmlands contaminated with Cd are often simultaneously affected by microplastics (MPs) and low phosphorus (LP) availability. This study investigated the combined effects of Cd, LP, and polyethylene microplastics (PE) on Cd uptake in two ryegrass (Lolium rigidum) genotypes, WALR60 and SULR1, contrasting in their sensitivity to PE stress. Both LP and PE treatments significantly enhanced Cd accumulation in plants. A synergistic interaction between LP and PE further increased Cd uptake and impaired plant growth, with the genotype SULR1 showing particular sensitivity to PE stress. Mechanistically, LP and PE promoted Cd2 + influx, likely through Ca2+-permeable channels, and upregulated key metal transporter genes such as NRAMP5 and IRT1. Furthermore, PE altered the subcellular distribution of Cd in the sensitive genotype by reducing its sequestration in the cell wall and increasing its accumulation in organelles, thereby enhancing cellular toxicity. Interestingly, for elements such as P and Ca, no significant three-way interaction (Cd × LP × MP) was observed; instead, the combined effect was best presented as Cd + LP × MP. The significant Cd × LP × MP effect on biomass of the sensitive genotype may result from the integrated regulation of multiple physiological networks. Overall, this study provides new insights into plant adaptive responses under combined Cd, LP, and MP stresses, contributing to a better understanding of complex pollution scenarios in agricultural soils.

Accurate diagnosis of nitrogen status is essential for precision fertilization in winter wheat. Single-modal or single-temporal remote sensing often fails to capture the multidimensional crop responses to nitrogen stress. In this study, we propose a hybrid framework based on CNN-LSTM-XGBoost for interpretable classification of wheat nitrogen stress gradients using multimodal unmanned aerial vehicle (UAV) multispectral and thermal infrared (TIR) imagery. Field experiments were conducted at the Xinxiang base in Henan Province during the 2023–2024, following a randomized block design involving 10 cultivars, four nitrogen levels, and four water treatments. Multisource UAV images acquired at jointing, heading, and filling stages were used to construct a multimodal feature set consisting of manual features (spectral bands, vegetation indices (VIs), TIR, and their interaction terms) and seven temporal statistical features. A deep learning model (CNN-LSTM) was utilized to further extract deep spatiotemporal features, and its performance was systematically compared with traditional machine learning models. The results show that multimodal feature fusion significantly enhanced classification performance. The CNN-LSTM model achieved an accuracy of 89.38% with fused multimodal features, outperforming all traditional machine learning models. Incorporating multi-temporal features improved the F1macro of the XGBoost model to 0.9131, a 9.42 percentage-point increase over using the single heading stage alone. The hybrid model (CNN-LSTM-XGBoost) achieved the highest overall performance (Accuracy = 0.9208; F1macro = 0.9212; AUCmacro = 0.9879; Kappa = 0.8944). SHAP analysis identified TIR × NDRE as the most influential indicator, reflecting the coupled physiological response of reduced chlorophyll content and increased canopy temperature under nitrogen deficiency. The proposed multimodal, multi-temporal, and interpretable framework provides a robust technical foundation for UAV-assisted precision nitrogen management.

Drought is a significant challenge to winter wheat production. Its impact can be mitigated by preventing plant moisture stress through precision agriculture. Remote sensing and machine learning have proven effective for managing moisture stress in winter wheat. This study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat under irrigation treatments: W0 (no irrigation), W1 (45-65%), W2 (55-75%), W3 (65-85%), W4 (75-95%) of field capacity, and Z (irrigation and rainfall). Our findings show that the ratio stress index (RSI), with band combinations such as RSI7(650, 428), RSI8(663, 422), and RSI9(671, 450), performs better in tracking PMC and LMC, demonstrating high correlation and improved average prediction metrics for vegetation index (VI) models with R2, RMSE, and MAE of 0.838, 2.791, and 2.093 respectively, for LMC and VI-Tc input models with 0.850, 2.731, and 2.105 for PMC. Incorporating Tc into RSI models enhances prediction accuracy, increasing R² by up to 13.82% in the RSI-Tc-SVM-PMC model and decreasing RMSE and MAE by 15.89% and 18.33%, respectively. Therefore, a combination of RSI-Tc-SVM-ANN is recommended to monitor winter wheat moisture stress.

ABSTRACT Lightweight energy‐absorbing structures are critically needed across engineering sectors. Conventional energy‐absorbing structures have an initial peak crush force that is too high and insufficient lightweighting. This study integrates additive manufacturing with biomimetic design to develop bamboo‐inspired vascular bundle tubes(VBT). Specimens fabricated from multiple materials underwent quasi‐static axial and radial compression testing. The results demonstrate superior energy absorption performance in carbon fiber–reinforced Polyethylene Terephthalate Glycol (PETG/CF) specimens. Structural optimization incorporating bamboo‐mimetic axial vascular arrangements enhanced specific energy absorption (SEA) through improved biomimetic fidelity. Process parameter studies revealed that reducing the layer height from 200 to 100 μm simultaneously increased SEA and reduced peak crushing force (PCF) under both loading conditions. Axial loading increased SEA by 30% and reduced PCF by 15%. In contrast, radial loading resulted in a 15% increase in SEA and a 20% reduction in PCF. This work establishes a synergistic material‐structure‐process strategy for high‐performance bio‐inspired energy absorbers, advancing impact protection technology.

This study aimed to elucidate the effects of saline-fresh water rotational irrigation on the root microenvironment of tomatoes, which is crucial for optimizing the saline water irrigation strategies. A two-season tomato experiment was conducted in a greenhouse in southern Xinjiang, with three saline-fresh water rotational irrigation treatments (W1, W2, W3) and a freshwater control (CK). The main focus was on the effects of saline-fresh water rotation irrigation on the physicochemical properties, enzyme activity, and microbial community structure of the tomato rhizosphere soil. The implementation of saline-fresh water rotational irrigation strategies was an effective means of maintaining the abundance and diversity of beneficial microorganisms in the tomato rhizosphere soil, positively impacting soil nutrient availability and enzyme activity. Soil enzyme activity and nutrient content showed a significant increase in the W1 and W3 treatments compared to the control treatment. The W3 treatment significantly improved the soil microenvironment and conserved freshwater, while ensuring no significant yield reduction compared with the CK treatment. The relative abundance of Actinobacteriota was reduced to varying degrees in different saline-fresh water rotation irrigation treatments, while that of beneficial microorganisms such as Firmicutes, Bacteroidetes, Verrucomicrobiota, and Bacillus increased. In autumn, cold-loving bacteria such as Paenisporosarcina were prominent. These beneficial microorganisms enhance tomatoes' ability to resist saline-alkali stress. The W3 treatment demonstrated a stronger ability to recruit beneficial microorganisms, likely due to host specificity and higher productivity. Therefore, the W3 treatment is recommended as an effective saline water application method for improving the root microenvironment of greenhouse tomatoes.