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Monte Carlo-based Simulation of Oxygen in Pineapple Root Zones and Identification of Spatiotemporal Characteristics under Hypoxic Conditions
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Chun WANG1, Zhenzhen YU2, Hongxuan WANG3, Hailiang LI3, Haitian SUN3, Yunlong ZHAO4
Chinese Journal of Tropical Crops | 2026, 47(2) : 417 - 432
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Chinese Journal of Tropical Crops | 2026, 47(2): 417-432
Plant Cultivation, Physiology & Biochemistry
Monte Carlo-based Simulation of Oxygen in Pineapple Root Zones and Identification of Spatiotemporal Characteristics under Hypoxic Conditions
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Chun WANG1, Zhenzhen YU2, Hongxuan WANG3, Hailiang LI3, Haitian SUN3, Yunlong ZHAO4
Affiliations
  • 1.School of Computer Science, Shandong Xiehe University, Jinan, Shandong 250109, China
  • 2.School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, Guangdong 524088, China
  • 3.South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong 524000, China
  • 4.Engineering College, Bayi Agricultural University, Daqing, Heilongjiang 163000, China
Published: 2026-02-25 doi: 10.3969/j.issn.1000-2561.2026.02.013
Outline
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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.

pineapple root zone  /  hypoxia identification  /  Monte-Carlo  /  uncertainty sampling  /  oxygen diffusion-consumption coupling
Chun WANG, Zhenzhen YU, Hongxuan WANG, Hailiang LI, Haitian SUN, Yunlong ZHAO. Monte Carlo-based Simulation of Oxygen in Pineapple Root Zones and Identification of Spatiotemporal Characteristics under Hypoxic Conditions[J]. Chinese Journal of Tropical Crops, 2026 , 47 (2) : 417 -432 . DOI: 10.3969/j.issn.1000-2561.2026.02.013
Year 2026 volume 47 Issue 2
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Article Info
doi: 10.3969/j.issn.1000-2561.2026.02.013
  • Receive Date:2025-09-04
  • Online Date:2026-06-26
  • Published:2026-02-25
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History
  • Received:2025-09-04
  • Accepted:2025-10-27
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Affiliations
    1.School of Computer Science, Shandong Xiehe University, Jinan, Shandong 250109, China
    2.School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, Guangdong 524088, China
    3.South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong 524000, China
    4.Engineering College, Bayi Agricultural University, Daqing, Heilongjiang 163000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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