The spring mung bean-summer sweet maize cropping system can alleviate groundwater scarcity problems on the North China Plain. However, the effects of nitrogen application on water use efficiency (WUE) and economic benefit of spring mung bean-summer sweet maize cropping system remain unclear. This study investigated the crop yield, economic benefit and WUE of spring mung bean-summer sweet maize cropping system under four N application regimes: N0, no N for spring mung bean and summer sweet maize; N1, 60 and 60 kg·ka⁻¹ N for spring mung bean and summer sweet maize, respectively; N2, 60 and 120 kg·ka⁻¹ N for spring mung bean and summer sweet maize, respectively; and N3, 60 and 180 kg·ka⁻¹ N for spring mung bean and summer sweet maize, respectively. The results indicated that N application significantly increased annual maize equivalent yield (MEY), and N1 and N3 increased annual MEY by 8.6% and 10.7% (p < 0.05), respectively, compared to N0 in 2021. N application significantly influenced evapotranspiration (ET) in spring mung bean and summer sweet maize seasons (p < 0.05). N1 and N2 decreased ET by 15.1% and 18.9%, respectively, in spring mung bean season, and increased ET by 14.4% and 18.8%, respectively, in summer sweet maize season compared to N0 in 2021, respectively (p < 0.05). N3 improved annual WUE by 10.2% and 11.4% compared to N0 in 2020 and 2021, respectively (p < 0.05). Also, experimental year significantly affected MEY, ET and WUE in spring mung bean and summer sweet maize seasons, and annually (p < 0.001), which can be attributed to the variations in seasonal rainfall between the two experimental years. In summary, N application increased grain yield and WUE of spring mung bean-summer sweet maize cropping system, however, a lower N application rate was recommended to achieve a balance between economic benefit and WUE on the North China Plain.

To address the challenges of large model size, limited detection accuracy and poor adaptability to complex environments in tomato leaf disease detection tasks, this paper proposes a lightweight and efficient detection method based on an improved YOLO11n. First, a dynamically refined intersection over union loss function is introduced to optimize bounding box regression quality across different training stages. Subsequently, an adaptive multiscale fusion module is designed to enhance feature extraction adaptability to varying scales. To further strengthen spatial perception across lesions of different sizes, a progressive receptive field via dilated convolutions module is proposed. Finally, a detail enhanced detection head is incorporated to improve detection performance on small-scale and blurred-boundary disease regions. Extensive experiments validate the effectiveness of the proposed approach, achieving a 2.1% improvement in mean Average Precision at an Intersection-over-Union (IoU) threshold of 0.5 (mAP50) and a 2.9% improvement in mean Average Precision (mAP) averaged over Intersection-over-Union thresholds from 0.5 to 0.95 (mAP50–95) compared with the YOLO11n baseline, while boosting inference speed to 482 frames/second. The proposed method demonstrates excellent accuracy, real-time performance and lightweight deployment capability, providing a novel technical solution and practical support for intelligent agricultural disease detection.

Ultisol, characterized by acidic pH and low fertility index, present significant challenges for crop productivity. This study investigated the efficacy of three biochar types, oil palm empty fruit bunches (OPEFB-B), rice husk and maize cob (MC-B) applied at 10 t·ha^–1, with varying incubation periods (0, 10, 20 and 30-d) with three replicates, to enhance Ultisol fertility index and cucumber performance. A factorial randomized complete block design was used, which was conducted in farmer fields in Medan, Indonesia from June to October 2023. This investigation used the cucumber hybrid Metavy F1, cultivated in a composite sample of randomly collected Ultisol. Following analysis of variance, significant means were separated by Duncan’s multiple range test at a 5% level, and correlations among the variables were evaluated. Soil fertility index (SFI) of Ultisol was established and classified. Results demonstrated that MC-B was superior in boosting cucumber yield to 47.4 t·ha⁻¹. This increase is attributed to a significant elevated in the uptake of nitrogen, phosphorus and potassium by about 125%. In contrast, OPEFB-B was most effective at improving Ultisol SFI by 0.447 (a 38.8% increase). An incubation period of 20-d was identified as optimal for maximizing yield and nutrient uptake. The interaction between biochar type and incubation periods were significant effect on plant height and leaf area. Specifically, the combination of OPEFB-B with 10- or 20-d incubation enhanced soil fertility and increased the N and K content in plant shoots. Correlation analysis revealed significant positive associations between cucumber productivity and key soil properties: organic C, cation exchange capacity, base saturation, and exchangeable Ca and K with values of 0.321, 0.342, 0.420, 0.392 and 0.487, respectively. Synergistic improvements in soil fertility and crop yield require the extended incubation period of MC-B and OPEFB-B, applied separately or in combination.

Remote sensing using unmanned aerial vehicles (UAV) combined with machine learning (ML) has significantly advanced field-scale prediction of aboveground biomass. Although ensemble learning frameworks (ELFs) typically outperform individual ML algorithms in accuracy, systematic evaluations of meta learner selection and the effects of base learner quantity and diversity remain limited. Leveraging vegetation indices and texture features extracted from multi-temporal UAV imagery, ELFs were constructed incorporating nine ML algorithms with three meta learners, Linear model, Random forest and Bayesian model averaging (BMA), to systematically evaluate how base learner configuration affects prediction accuracy. Using the fused feature set of sensitive vegetation indices and texture features, Gaussian process regression (GPR) achieved the highest accuracy among all base learners, with R² = 0.769 and RMSE = 1.83 t·ha^–1. Also, the three meta learners outperformed the best base learner, with the BMA meta learner yielding the superior accuracy (R² = 0.795, RMSE = 1.73 t·ha^–1). However, meta learner performance depended strongly on the composition of the base learners pool, stability was optimal with five base learners, and maximum accuracy was achieved by hybrid ensembles that combined linear-kernel models with GPR. This study highlights the importance of both meta learner selection and base learner composition in ELFs for aboveground biomass prediction in rice. These findings offer methodological guidance for UAV-based high-precision monitoring of crop AGB, with practical implications for precision agriculture and crop management.

Investigating the potential impacts of new-type urbanization and clean energy adoption on rural carbon emissions is crucial for the advancement of urbanization and the realization of green agricultural development. Based on panel data from 30 provinces in rural China spanning the period from 2000 to 2021, this paper constructs a new-type urbanization evaluation system to assess its five dimensions: economic, ecological, social, demographic and spatial aspects. Subsequently, this paper estimates rural carbon emissions from the perspectives of production and household consumption, and uses the PVAR and spatial Durbin models to examine the interactive relationship between new-type urbanization and rural carbon emissions in China, as well as the moderating role of clean energy adoption. The study reveals firstly that, carbon emissions in rural China have a consistent upward trend from 2000 to 2021, with an overall growth rate of 28.93% and an average annual increase of 1.31%. Secondly, new-type urbanization exerts a significant negative effect on rural carbon emissions, whereas technological progress, industrialization, economic openness, agricultural marketization and agricultural mechanization have significant positive effects on rural carbon emissions. Significant heterogeneity exists in both the direction and magnitude of these impacts across different regions and categories. Thirdly, as a moderating variable, clean energy attenuates the negative effect of new-type urbanization on rural carbon emissions at the nationwide and in northeastern China, but exerts an opposite effect in eastern, and western China. Under the framework of the ecological civilization strategy, deepening the application of clean energy should be prioritized as a key initiative to advance the green development and low-carbon production, with the aim of achieving multiple objectives including carbon emission reduction, energy conservation, and pollutant emission mitigation in rural areas.

The aim of this study was to assess the current status and key constraints of rice production sustainability in the Erhai Lake Basin and to propose an effective technology package for improving sustainability performance. Emergy analysis was used to evaluate the sustainability of rice farming based on data from 171 farmer surveys and comparative experiments were conducted between the current system (CS) and a green system (GS). Unlike CS, which represents the traditional rice cultivation method in the basin, GS is a comprehensive green ecological planting model developed by the Science and Technology Backyard (STB) initiative through long-term positioning experiments. The technology package combines an optimized fertilizer application technique centered on green intelligent fertilizers with reduced mineral fertilizer and pesticide inputs, improved nutrient and water management, and better-performing rice cultivars. STB also serves as a platform linking government agencies, scientists, smallholders and fertilizer companies. Scientists are stationed in villages to carry out on-farm trials, provide technical training and offer continuous advisory services, thereby tailoring the GS to local conditions and promoting its adoption. The results revealed clear spatial heterogeneity and general low sustainability of current rice production under CS at the town scale. Yield, N and P fertilizer inputs, and manure fertilizer input were identified as the primary factors influencing farm-level emergy-based sustainability indicators and overreliance on non-renewable inputs was a key reason for low sustainability on many farms. Compared with CS, the emergy input of GS is 34.9% lower, while its emergy output is 10.2% higher, resulting in a 33.3% higher emergy sustainability index. These findings demonstrate that the GS technology package, supported by STB-mediated social and technical services, can substantially enhance the sustainability of rice production in the Erhai Lake Basin and provide a replicable model for similar rice-producing regions.