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利用模糊逻辑和菌根接种模拟高浓度二氧化碳条件下的小麦生产力

Modeling wheat productivity under elevated CO using fuzzy logic and mycorrhizal inoculation.

作者信息

Sobrinho Renato Lustosa, de Oliveira Bruno Rodrigues, Zuffo Alan Mario, Filho Marcelo Carvalho Minhoto Teixeira, Filho Aldir Carpes Marques, Zoz Tiago, Okla Mohammad K, Alaraidh Ibrahim A, Alwasel Yasmeen A, Alhaj Hamoud Yousef, El-Keblawy Ali, Sulieman Saad, Askri Amira, Alyafei Mohammed, Sheteiwy Mohamed S

机构信息

Information Technology - Technical Department, State University of Mato Grosso do Sul (UEMS), Paranaiba, Mato Grosso do Sul, Brazil.

Agricultural Engineering Department, Federal University of Lavras, Lavras, Brazil.

出版信息

BMC Plant Biol. 2025 Jun 4;25(1):756. doi: 10.1186/s12870-025-06642-3.

Abstract

BACKGROUND

Understanding the complex interactions between plants, Arbuscular Mycorrhizal Fungi (AMF), and elevated atmospheric CO (eCO) is crucial for enhancing agricultural sustainability and productivity, particularly in the face of future climate change. While elevated CO concentrations can influence AMF colonization development, AMF are known to benefit plants by improving nutrient uptake, especially phosphorus, enhancing drought tolerance, and increasing resistance to certain soil-borne pathogens. These beneficial effects of AMF can potentially mitigate some of the negative impacts of climate change on crop yields. This study explores the interplay between wheat (Triticum aestivum L.), AMF inoculation, and eCO levels using the Mamdani Fuzzy Inference System (MFIS), a tool well-suited to handle uncertainties in modeling complex plant responses to environmental changes. By integrating fuzzy logic-based approaches, this research aims to elucidate how AMF inoculation can modulate wheat productivity under projected future elevated CO levels, thereby providing insights into strategies for maintaining or improving crop yields in changing climatic conditions. The goal was to explore the relationship between CO levels, AMF inoculation, and wheat yield, specifically investigating the potential of AMF to enhance wheat performance under elevated CO.

RESULTS

Statistical analyses revealed that eCO significantly increased ear length (p < 0.05), while AMF inoculation significantly enhanced the number of spikelets per ear (p < 0.05), number of grains per ear (p < 0.05), and weight of 1000 seeds (p < 0.05). The Mamdani Fuzzy Inference System (MFIS) models demonstrated that under eCO conditions, the predicted 1000-seed weight stabilized around 40 g/plant in AMF-inoculated wheat, compared to approximately 37 g/plant in uninoculated plants. Similarly, ear length simulations showed stabilization at around 14 cm with AMF inoculation under eCO, versus 12.2 cm without AMF. These results highlight the synergistic effects of eCO and AMF inoculation on key wheat productivity parameters.

CONCLUSION

This study underscores the importance of integrating fuzzy logic-based approaches into agricultural management strategies to optimize crop yields while minimizing environmental impacts. The findings encourage further research into refining experimental designs and expanding datasets to enhance our understanding of plant responses to changing environmental conditions.

摘要

背景

了解植物、丛枝菌根真菌(AMF)与大气CO浓度升高(eCO)之间的复杂相互作用,对于提高农业可持续性和生产力至关重要,尤其是面对未来气候变化时。虽然CO浓度升高会影响AMF的定殖发育,但已知AMF可通过改善养分吸收,特别是磷的吸收、增强耐旱性以及提高对某些土传病原体的抗性来使植物受益。AMF的这些有益作用有可能减轻气候变化对作物产量的一些负面影响。本研究使用Mamdani模糊推理系统(MFIS)探索小麦(Triticum aestivum L.)、AMF接种与eCO水平之间的相互作用,MFIS是一种非常适合处理模拟复杂植物对环境变化响应中不确定性的工具。通过整合基于模糊逻辑的方法,本研究旨在阐明在预计未来CO水平升高的情况下,AMF接种如何调节小麦生产力,从而为在不断变化的气候条件下维持或提高作物产量的策略提供见解。目标是探索CO水平、AMF接种与小麦产量之间的关系,特别研究AMF在CO浓度升高时提高小麦性能的潜力。

结果

统计分析表明,eCO显著增加了穗长(p < 0.05),而AMF接种显著提高了每穗小穗数(p < 0.05)、每穗粒数(p < 0.05)和千粒重(p < 0.05)。Mamdani模糊推理系统(MFIS)模型表明,在eCO条件下,接种AMF的小麦预测千粒重稳定在约40克/株,而未接种的植株约为37克/株。同样,穗长模拟显示,在eCO条件下接种AMF时稳定在约14厘米,未接种AMF时为12.2厘米。这些结果突出了eCO和AMF接种对关键小麦生产力参数的协同作用。

结论

本研究强调了将基于模糊逻辑的方法整合到农业管理策略中的重要性,以优化作物产量同时将环境影响降至最低。研究结果鼓励进一步研究完善实验设计并扩大数据集,以增进我们对植物对不断变化的环境条件响应的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9632/12135296/3f5d673517ff/12870_2025_6642_Fig1_HTML.jpg

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