Recovering unrealized crop production through improved field management is important for sustainable agriculture, but the benefits may depend on soil conditions. We aimed to determine how soil quality modifies the yield benefits of management improvement in China’s maize, wheat and rice systems. We test two hypotheses: (1) that higher soil quality is associated with smaller relative yield shortfalls, and (2) that the gains associated with improved technical efficiency (TE) depend on soil quality. Using field observations across China, we quantified the difference between cultivar-specific attainable and actual field yields and combined stochastic frontier estimation, random forest modeling and scenario analysis. The relative shortfall was 8.4 percentage points (pp) lower in maize, 11.0 pp lower in wheat and 11.8 pp lower in rice on high- than low-quality soils. However, its sensitivity to TE improvement declined with soil quality in maize and wheat but increased in rice. Better soils therefore partly compensated for inefficient management in maize and wheat, whereas they enabled greater returns to management improvement in rice. Soil organic matter and available phosphorus were the principal soil attributes associated with lower shortfalls in maize and wheat, whereas available potassium and pH were more influential in rice. Scenarios analyses showed that combined soil and TE improvement reduced predicted relative shortfall by 8.7 pp in maize, 8.9 pp in wheat and 15.1 pp in rice. Spatial prioritization further identified where soil-focused, management-focused or integrated interventions offered the greatest potential. These findings identify crop-specific associations between soil conditions and TE-related yield-shortfall reduction and provide model-based priorities for evaluating soil and management interventions through field testing.
The stability of large-span Chinese solar greenhouses depends not only on snow-load distribution but also on support continuity and the progressive engagement of as-built joints. To distinguish these effects, this study presents a multilevel investigation combining geometrically and materially nonlinear frame analysis, a full-scale sequential hanging-load test with shell-model comparison, and refined solid modelling of design-reference and as-built joints. These three levels are complementary but mechanically uncoupled. Four global cases were analysed: supported-uniform (SU), supported-non-uniform (SN), unsupported-uniform (UU), and unsupported-non-uniform (UN). Their ultimate total vertical loads were 12.336, 11.594, 2.592, and 2.069 kN, respectively. Non-uniform snow reduced the ultimate load by 6.0% for the supported frame and by 20.2% for the unsupported frame, while removal of the intermediate columns reduced the capacity by 79.0% under uniform snow and by 82.2% under non-uniform snow. For the SN case, changing the initial-imperfection amplitude from L/2000 to L/1000 reduced the ultimate load by only 0.285%, indicating that the principal capacity hierarchy was insensitive to the investigated amplitude range. The shell model reproduced the measured strain signs and principal loading trends. At the final 40 kg state, the absolute strain differences ranged from 0.091 to 0.931 µε, whereas the sequence-level root mean square errors ranged from 1.289 to 6.361 µε, indicating larger path-dependent discrepancies at some intermediate loading stages. The refined as-built joint model indicated progressive closure of the column-top–longitudinal-member gap, with an interpolated contact-onset load of approximately 129.7 N and non-zero normal contact pressure at the next prescribed load of 149.4 N. Under the baseline truncation boundary, the simulated column-reaction share increased to 87.8% at 1000 N; alternative spring boundaries produced shares of 93.9–97.4%, demonstrating that the exact value was boundary-dependent while the progressive engagement mechanism remained consistent. Overall, support continuity dominated the stability capacity of the investigated frame, while as-built gaps and contact conditions governed the delayed mobilisation of the local support path. The local joint results are used for mechanism interpretation and are not propagated into the global ultimate-capacity calculation.
Shot hole disease is one of the major diseases affecting the health of peach trees. To achieve effective grading-based prevention and control and enable precise grading of peach leaf shot hole disease severity, this study proposed a disease grading method based on hyperspectral imaging (HSI) combined with machine learning and deep learning. First, hyperspectral imaging equipment was used to acquire region-of-interest (ROI) information from leaves at different disease grades; after calibration, shot hole disease was divided into five disease grades from Grade 0 to Grade 4. Second, to fully exploit the spectral information, multiple preprocessing methods were systematically compared, and the grading performance of seven machine learning classifiers and two deep learning classifiers under full-spectrum conditions was evaluated. To reduce data dimensionality and redundancy, eight feature wavelength selection methods were introduced, and the performance metrics of the classifiers under different feature wavelengths were compared and analysed. The experimental results showed that scatter correction was the most effective preprocessing strategy at the leaf level: the standard normal variate transformation (SNV) combined with the Elman recurrent neural network reached the highest spectrum-level test accuracy, 77.17%, together with 100% at the leaf level and 94.12% for the leaf-mean spectra, and the linear classifiers logistic regression and linear discriminant analysis reached 94.12% at the leaf level under several preprocessing schemes. Savitzky–Golay smoothing combined with the first derivative (SG+D1) remained clearly superior to the raw spectra, and its combination with the support vector machine (SVM) reached 70.08% at the spectrum level and 76.47% at the leaf level. In terms of feature wavelength selection, eight variable selection methods were compared. The experimental results showed that feature wavelength selection effectively reduced data dimensionality while causing a certain degree of accuracy loss; among the feature-wavelength configurations, the best spectrum-level test accuracy was obtained with the stepwise informative spectral screening (SISS) subset combined with logistic regression (66.93%, and 82.35% at the leaf level), whereas the best leaf-level result of the ten-band subsets was obtained by the synergy interval partial least squares (siPLS) subset with the k-nearest neighbours classifier (94.12%); all the feature subsets remained clearly below the best full-spectrum result, making the accuracy–dimensionality trade-off explicit. Finally, the results indicated that HSI combined with spectral preprocessing can effectively achieve accurate identification of peach leaf shot hole disease grades under full-spectrum conditions, and feature wavelengths can effectively reduce data dimensionality while maintaining a certain level of accuracy. This study provides methodological support and an experimental basis for the precise grading, prevention, and control of peach leaf shot hole disease.
Soil acidification and nutrient imbalances threaten mountain tea plantation sustainability, while nutrient-driven acidification mechanisms along elevation gradients in deep soil profiles remain poorly understood. This study examined three tea plantations at different elevations (107, 400, and 711 m) on Mount Lu, Jiangxi Province, sampling soils at five depths to 100 cm. Objectives were to quantify vertical differentiation of acidity, base ions, and nutrients, reveal altitudinal conversion patterns of acidification drivers, and guide fertilizer management. Soils exhibited strongly acidic conditions (pH 3.86–5.46), with the most severe acidification at the mountain foot and surface layer (0–20 cm). Potential acid accumulation in deep soils exceeded 75% (exchangeable aluminum/exchangeable acidity: 80.61%, 77.42%, 80.10% at mountain foot, mountainside, and mountaintop, respectively), and acidification spread was most pronounced at the mountain foot. The mountaintop and surface layer showed strongest acid–base resistance. Cation exchange capacity and base cations decreased with depth; base saturation in surface soil was 47.12% higher than in deeper layers. Organic matter, nitrogen, phosphorus, and available potassium were surface-enriched, whereas total potassium accumulated in deeper layers. Correlation analysis showed that pH was significantly negatively correlated with exchangeable acidity, exchangeable aluminum, organic matter, total nitrogen, and alkaline-hydrolyzable nitrogen, and significantly positively correlated with exchangeable calcium, exchangeable magnesium, and total potassium. Structural equation modeling revealed that nitrogen accumulation was the primary acidification driver, with total effect sizes ranking as total nitrogen (0.549) > organic matter (0.319) > total potassium (0.247) > alkaline-hydrolyzable nitrogen (0.156). Nitrogen drove soil acidification via direct and indirect pathways by increasing exchangeable acidity and depleting base cations dominated by Ca2+. Organic matter exerted bidirectional regulation on soil pH, while total potassium indirectly alleviated soil acidification. These findings underscored the need to adjust the N:K2O ratio from 2:1 to approximately 1.5:1; Organic fertilizers and soil amendments should be applied as base fertilizers, with the mountainside (400 m) being the most suitable elevation for tea cultivation.
Monolepta signata (Coleoptera: Chrysomelidae) is a major agricultural pest in East and Southeast Asia. Cytochrome P450 genes are commonly involved in xenobiotic detoxification, but their roles in the response of this pest to neonicotinoid insecticides remain unclear. In this study, we integrated resistance bioassays with transcriptomic analysis to investigate gene expression patterns associated with thiamethoxam and imidacloprid stress. GO and KEGG analyses indicated that metabolic and cellular process-related pathways were affected by insecticide exposure. Seven differentially expressed P450 genes, belonging to the CYP3, CYP4, and CYP12 clades, were identified. Across the six field populations, the basal expression of CYP4C1 and CYP6a23 showed significant positive correlations with thiamethoxam relative resistance ratios, whereas no significant correlation was detected between their expression levels and imidacloprid relative resistance ratios. However, after excluding the BETL population, neither correlation remained significant, indicating that the correlation was largely driven by this single population. Constitutive overexpression of these genes may be associated with the thiamethoxam resistance phenotype. Functional validation is still required to clarify their specific roles. This study provides a valuable transcriptomic resource for resistance monitoring and pest management of this pest.
