Research on the Correlation between Tribolium castaneum and Changes in CO2 Volume Fraction in a Sealed Corn Storage Environment
摘要
储粮物性生态因子监测是保障粮食储藏安全的关键.当前以粮温为核心指标的主流粮情监控系统,受限于粮食为热的不良导体、粮堆内热量传递缓慢等因素,导致粮温响应滞后于虫害和霉变发生进程,难以及时发现储粮异常.基于 CO2 扩散速率优于粮温传递的特性,以11.7%、13.5%、15.0%三种水分含量玉米为研究对象,在30℃、25℃、20℃三种储藏温度下,分别感染 0 头/kg、5 头/kg、15 头/kg、30 头/kg 赤拟谷盗,探究虫害发生对密闭储藏环境 CO2 体积分数变化的影响,并构建关联预测模型.结果表明:高水分含量(15.0%)玉米、30℃储藏条件下的低水分含量(11.7%、13.5%)玉米感染赤拟谷盗后,密闭环境中 CO2 体积分数呈"S"型变化,且 CO2 体积分数变化幅度随水分、温度、虫口密度的升高而增大,所有害虫感染组均能在虫害发生后1 d～7 d 内检测到 CO2 的显著变化;CO2 体积分数与虫害发生时间呈极显著线性相关(R2≥0.85);基于发生时间(X1)与虫口密度(X2)构建的二元预测模型拟合度也极高(R2≥0.90),其中高温高水分下 X1 系数占主导,低温低水分下 X2 系数影响更突出.本研究证实,监测 CO2 可实现玉米储藏中赤拟谷盗发生的早期预警,构建的模型可为储粮虫害的预警监测及科学防控提供数据支撑.
Monitoring the physical and ecological factors of stored grain is essential for ensuring safe grain storage.Current grain condition monitoring systems rely mainly on grain temperature as the core indicator,but they face a key limitation:because grain is a poor thermal conductor and heat transfer within the grain bulk is slow,grain-tem-perature responses lag behind the development of insect infestation and mold growth,making it difficult to detect abnormal grain storage conditions in a timely manner.Based on the characteristic that CO2 diffuses faster than chan-ges in grain temperature are transmitted,this study used corn with moisture contents of 11.7%,13.5%,and 15.0%as the research material;the corn samples were stored at temperatures of 30℃,25℃,and 20℃and infes-ted with Tribolium castaneum at densities of 0,5,15,and 30 insects/kg to investigate the effect of insect infesta-tion on changes in CO2 volume fraction in the sealed storage environment and to construct a correlation prediction model.The results showed that for high-moisture corn(15.0%)and low-moisture corn(11.7%and 13.5%)stored at 30℃,the CO2 volume fraction in the sealed environment showed an"S"-shaped change after Tribolium castaneum infestation;moreover,the magnitude of the change in CO2 volume fraction increased with increasing moisture content,temperature,and insect density,and significant changes in CO2 were detected within 1～7 d after infestation in all insect-infested groups;there was a highly significant linear correlation between CO2 volume fraction and infestation time(R2≥0.85);the binary prediction model constructed based on infestation time(X1)and in-sect density(X2)also exhibited a very high degree of fit(R2≥0.90),with the coefficient of X1 dominating under high-temperature and high-moisture conditions,while the coefficient of X2 had a more prominent influence under low-temperature and low-moisture conditions.This study confirms that monitoring CO2 can provide early warning of Tribolium castaneum infestation in stored corn,and the constructed model can provide data support for the early warning and monitoring as well as the scientific prevention and control of stored-grain insect pests.
