农业农村日报 · 2026-11-12

Plants continuously adapt to environmental changes, and temperature is a key signal for growth and flowering. How plant thermometers, or "thermosensors," work at the molecular level is not well understood.

11月7日，中国农科院作科所联合中科院动物所对60份谷子种质开展基因组与甲基化分析，鉴定出76815个差异甲基化区域，阐明转座子驱动驯化性状的因果链条，为分子设计育种提供新靶点。

世界各地的农场都依赖合成氮肥来提高作物产量，但这些化学品的生产和使用每年会产生数十亿吨的温室气体排放。如今，科学家在一种更环保的替代方案——生物工程细菌的研究上取得了进展。...

11月11日报道，山东省农科院耐盐花生‘花育9118’在3.3‰盐碱地实收亩产694.57公斤，刷新东营盐碱地花生纪录；耐盐粳稻‘鲁盐稻13’‘鲁盐稻22’在4‰盐度下亩产分别达693.77、719.30公斤。

丘陵山地轻量化智能农机加速落地，江西兴国无人微耕机适配“巴掌大”田块；河南5G+氢燃料电动拖拉机零排放、续航4小时，芦笋采收机器人识别率超95%。

The Sunday roast and the backyard barbecue have long been Australian rituals. The country is one of the world's biggest consumers of red meat. The average Australian ate about 22 kilograms (49 pounds) of beef in 2024.

Researchers from Adelaide University and Plant Synthetic Biology Australia have uncovered new insights into the molecular machinery controlling the dormancy-to-germination transition in barley, potentially paving the way for improved crop varieties and better outcomes for farmers, maltsters and brewers.

11月10日，以“生物智能育种与绿色低碳农业”为主题的2026年大湾区（广东）农业科技论坛在广州开幕。中国工程院院士罗锡文、张新友、李洪文出席论坛。 签约现场。主办方供图，...

11月5日，湖南省农科院加工所单杨院士团队受微生物摄碳启发构建淀粉酶响应抗菌体系，5%肉桂醛纳米乳液包封率达98.66%，使鲜切猕猴桃等货架期延长1.5—2.0倍，成果发表于《Food Hydrocolloids》。

ABSTRACT Irrigation is vital for climate resilience and food security in Sub‐Saharan Africa (SSA), yet its expansion remains constrained by climatic and structural barriers. Although information and communication technologies (ICTs) offer opportunities for climate‐smart irrigation, evidence across diverse SSA remains limited. Using panel data from 20 SSA countries (2000–2023), this study employs fixed‐effects (FE) and dynamic system–GMM (Generalized Method of Moments) estimators to examine the ICT–irrigation relationship, accounting for persistence and potential endogeneity. The findings reveal that ICT effects vary by technology and income level. Internet access consistently promotes irrigation development, particularly in low‐ and upper‐middle‐income economies, potentially by reducing information gaps and improving water management. Conversely, mobile phone penetration does not automatically generate irrigation gains and is negatively associated with irrigation development in lower‐middle‐income economies. Complementary conditions are also crucial: Electricity access and agricultural capital support irrigation development, whereas higher rainfall is associated with lower irrigation intensity, reflecting reduced reliance on irrigation. These findings reveal a development gradient in digital benefits, demonstrating that connectivity alone cannot drive irrigation expansion. Policies should therefore prioritize rural internet access alongside electrification, irrigation infrastructure and agricultural finance, supported by context‐specific digital agriculture strategies to advance climate resilience and sustainable agriculture across SSA.

基于四川一手微观调查，实证检验新型农村集体经济组织提供农业社会化服务对小农户增收的影响与机制。

用省级面板数据检验中国多元化食物供给能力与农村居民收入的关系。

Automated tomato ripeness and quality grading is essential for precision agriculture and industrial packing lines, replacing slow, subjective manual sorting. Most reported systems, however, rely on heavy deep networks or a single accuracy figure without ablation, statistical validation, or an explicit accuracy–interpretability–latency trade-off. This study proposes HHO-XQI, a Harris Hawks Optimization (HHO)-optimized Explainable Quality Index that fuses interpretable color-statistical features into a continuous Tomato Quality Index for four-class grading (Damaged, Old, Ripe, Unripe). The framework extracts 125 RGB/HSV/Lab colorstatistical descriptors, fuses four base learners (logistic regression, random forest, extra trees, k-nearest neighbors) via HHO-optimized soft voting on 7,226 images, and is benchmarked against a fine-tuned ResNet18 CNN with Gradient-weighted Class Activation Mapping (Grad-CAM). Evaluation combined held-out accuracy/macro-F1, repeated cross-validation with Holm-corrected Wilcoxon tests, feature- and ensemble-composition ablation, HHO sensitivity/convergence analysis, SHapley Additive exPlanations (SHAP) attribution, and latency/training-time profiling. HHO-XQI reached 94.62% accuracy (92.90% macro-F1), significantly beating three baselines (p < 10−4 ) but not extra trees alone (p = 0.071); the CNN reached 99.31% accuracy at comparable CPU latency (11.31 vs. 12.05 ms) yet required approximately 147 × longer training (575.7 vs. 3.92 s). These findings support a two-tier deployment: HHO-XQI as a near-instantly retrainable, SHAP-native first-stage screener, and the CNN + Grad-CAM branch as a slower-to-retrain but more accurate second-stage auditor. The study contributes a statistically validated, fully reproducible metaheuristic-fused explainable grading pipeline that fills a rigor gap common in agricultural swarm-intelligence literature. Received: 29 July 2026 | Revised: 7 September 2026 | Accepted: 29 September 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The tomato image dataset used in this study is the publicly available Tomatoes dataset published by Enalis (2023) on Kaggle: https://www.kaggle.com/datasets/enalis/tomatoes-dataset. Per the journal’s editorial guidance, this dataset is not listed as a numbered reference in the reference list; the full dataset citation and access link are provided here instead. The feature-extraction, HHO optimization, and CNN training code are available from the corresponding author upon reasonable request. Author Contribution Statement Van Van Chau: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization. Minh Tuan Nguyen: Conceptualization, Methodology, Validation, Data curation, Writing – review & editing, Project administration. Phayung Meesad: Resources, Writing – review & editing, Supervision, Project administration.

该综述回顾2017年以来精准畜牧养殖（PLF）的技术演进，指出RGB/深度相机、3D成像与物联网平台可实时获取生物特征与行为数据，AI实现早期疾病检测、精准饲喂与繁殖管理。未来将融合大语言模型与联邦学习，并需解决数据标准化、农村连接与成本问…

Cover crops, cultivated for agroecological benefits, must be terminated before sowing the subsequent cash crop. Frost winterkill is a cost-effective, herbicide-free termination method of interest in conservation agriculture, where mechanical termination methods are limited and not always effective. Frost termination depends on interactions between weather and crop development stage, and affects soil nitrogen and water cycles differently from mechanical or chemical termination. Because many interacting factors are involved, simulation models can support cropping system analysis and water, nutrient and weed management decisions. Existing frost damage models developed for cereals have many parameters, limiting their transferability to cover crops. To address this gap, we developed a simplified frost damage model that simulates frost resistance temperature over time, and biomass frost damage after frost events. The model was calibrated using white mustard ( Sinapis alba L.) field data from Lombardy, Northern Italy, and evaluated against a four-year dataset of satellite-derived observations. Under the conditions examined, early sowing maximised biomass production (reaching 3.0–3.5 t DM ha −1 ) and ensured complete frost termination. Conversely, late-sown crops remained in a frost-resistant state and survived sub-zero temperatures down to −5.6 °C. The simulation of frost-damaged biomass had a modelling efficiency of 0.93 on the calibration dataset. After calibration, the model was independently evaluated and predicted first frost damage within the reference frost window with 89% accuracy, while late predictions had a median delay of 3 d. In future work, using broader datasets, its integration in cropping system models will enable decision support under different cover crop management scenarios.