Article(id=1277254267752813067, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1277254112370623080, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-2561.2026.02.013, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756915200000, receivedDateStr=2025-09-04, revisedDate=null, revisedDateStr=null, acceptedDate=1761494400000, acceptedDateStr=2025-10-27, onlineDate=1782450747669, onlineDateStr=2026-06-26, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1782450747669, onlineIssueDateStr=2026-06-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1782450747669, creator=13701087609, updateTime=1782450747669, updator=13701087609, issue=Issue{id=1277254112370623080, tenantId=1146029695717560320, journalId=1235980609244409860, year='2026', volume='47', issue='2', pageStart='281', pageEnd='551', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1782450710622, creator='13701087609', updateTime=1782451594955, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1277257821632729830, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1277254112370623080, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1277257821632729831, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1277254112370623080, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=417, endPage=432, ext={EN=ArticleExt(id=1277254269388591632, articleId=1277254267752813067, tenantId=1146029695717560320, journalId=1235980609244409860, language=EN, title=Monte Carlo-based Simulation of Oxygen in Pineapple Root Zones and Identification of Spatiotemporal Characteristics under Hypoxic Conditions, columnId=1236256434120348225, journalTitle=Chinese Journal of Tropical Crops, columnName=Plant Cultivation, Physiology & Biochemistry, runingTitle=null, highlight=null, articleAbstract=

To identify periods of oxygen deficiency in the root zone of pineapples, understand the mechanisms of oxygen changes, and provide sufficient response time for oxygen-enriching irrigation technology management and soil regulation to address abnormal fluctuations in oxygen content in the root zone, this study utilized a soil oxygen diffusion-consumption coupling mechanism constructed based on the Monte Carlo method. In model construction, the study fully considered soil oxygen diffusion, root respiration, and microbial oxygen consumption processes. Using parameter perturbation and uncertainty sampling, the model incorporated measured boundary conditions and depth-stratified initial concentration settings. Combined with multi-layer soil profile monitoring data, the model established a simulation system for oxygen content changes at depths of 10-40 cm in three typical soil types (loam, sandy soil, and clay). Finally, the model was validated using field trial data from spring 2025. The results showed that the model demonstrated strong predictive capability under various soil and depth conditions (R²>0.95, with the lowest RMSE of 0.214 mol/m³). The error distribution fluctuated slightly with increasing depth but remained within an acceptable range overall. Further analysis revealed that the model successfully identified periods prone to oxygen deficiency within 3-12 hours after irrigation or rainfall events. In oxygen deficiency identification, the model achieved over 90% accuracy in determining responses below the critical concentration (1.5 mol/m³). The above results validate the effectiveness of the constructed model in simulating root zone oxygen dynamics and providing risk warnings, aiming to provide a theoretical basis and predictive foundation for intelligent oxygen management in pineapple fields.

, authors=null, authorsList=Chun WANG, Zhenzhen YU, Hongxuan WANG, Hailiang LI, Haitian SUN, Yunlong ZHAO, authorCompany=null, correspAuthors=Hongxuan WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1277254270214869524, articleId=1277254267752813067, tenantId=1146029695717560320, journalId=1235980609244409860, language=CN, title=基于Monte-Carlo的菠萝根区氧气模拟与缺氧时空特征识别, columnId=1236256434313286224, journalTitle=热带作物学报, columnName=作物栽培与生理生化, runingTitle=null, highlight=null, articleAbstract=

为识别菠萝根区缺氧时段、掌握氧气变化机制，并为增氧灌溉技术管理与土壤调控提供充足的反应时间，以应对菠萝根区含氧量的异常波动，本研究基于Monte-Carlo方法，融合多物理场过程，构建土壤氧气扩散-消耗耦合模型，利用参数扰动与不确定性采样，引入实测边界条件与深度分层的初始浓度设定，结合多层土壤剖面监测数据，对壤土、沙土、黏土三类典型土壤在10~40 cm深度范围内的含氧量变化进行模拟，建立了相应的模拟体系。然后通过2025年春季田间试验数据对模型进行验证。结果表明：该模型在多种土壤与深度条件下均具备较强预测能力（R²>0.95，RMSE最低为0.214 mol/m³），误差分布随土层深度增加略有波动，但整体维持在可接受范围。进一步研究表明，该模型能够成功识别灌溉与降雨事件后3~12 h内易发生缺氧的时段，在缺氧识别中，模型对临界浓度（1.5 mol/m³）以下的响应判断准确率超过90%。上述结果验证所构建模型在根区氧气动态模拟与风险预警中的有效性，研究结果旨在为菠萝大田智能化增氧管理提供理论依据与预测基础。

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汪春（1963—），男，博士，教授，研究方向：农业工程与信息技术。

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汪春（1963—），男，博士，教授，研究方向：农业工程与信息技术。

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