Gas emission and respiratory losses during feed-out of silage: a comprehensive study using novel in situ smart-sensors and data-model fusion

青贮饲料喂出过程中的气体排放和呼吸损失：利用新型原位智能传感器和数据模型融合进行的综合研究

• 批准号: 449744781
• 负责人: Professor Dr. Wolfgang Büscher
• 金额: --
• 依托单位: Institut für Landtechnik (ILT)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/449744781?language=en
• 关键词: Gas; emission; respiratory; losses; during

Livestock feeding and biogas production consume large amounts of silage. However, the gas emission of silage respiration during feed-out remains poorly characterized. This information may be directly applicable to assessment of silage degeneration and to determination of its detrimental impacts on environmental quality. Here a fundamental and comprehensive study is proposed focusing on both issues, oxidative rate/energy loss and greenhouse and precursor gas emissions, using two novel, automated multi–sensor systems to capture and decompose the signals of gas emission from silage. Two common types of silage management, bunker silos of maize silage and round bales of ryegrass, alfalfa and maize, will be assessed in situ. The positive or negative effect of chemical and biological additives on gas emission from silage will also be investigated. Digital signal processing will be used to partition CO2 into two pools, one accumulated in the silage during previous anaerobic phases and one produced real-time after opening of the silage. The formal part/pool is a noise signal and the latter part/pool, resulted from the aerobic respiration of microorganism, directly reflects the oxidative rate/energy loss of the feed-out silage. Using these novel data, we will update/extend the commonly used Pitt-Muck model to simulate CO2 flux from the open face of a bunker silo, and use a data-model fusion approach to compare and reconcile the in situ measurements from on-farm experiments with the numerical results.

牲畜饲养和沼气生产消耗大量青贮饲料。然而，饲喂过程中青贮饲料呼吸的气体排放仍然知之甚少。该信息可直接应用于评估青贮饲料退化并确定其对环境质量的有害影响。这里提出了一项基础和全面的研究，重点关注氧化率/能量损失以及温室气体和前体气体排放这两个问题，使用两种新颖的自动化多传感器系统来捕获和分解青贮饲料中气体排放的信号。两种常见的青贮饲料管理类型，即玉米青贮饲料仓和黑麦草、苜蓿和玉米的圆包，将进行现场评估。还将研究化学和生物添加剂对青贮饲料气体排放的积极或消极影响。数字信号处理将用于将二氧化碳分配到两个池中，一个是在之前的厌氧阶段在青贮饲料中积累的，另一个是在青贮饲料打开后实时产生的。前一部分/池是噪声信号，后一部分/池是微生物有氧呼吸的结果，直接反映了饲出青贮饲料的氧化率/能量损失。利用这些新颖的数据，我们将更新/扩展常用的 Pitt-Muck 模型来模拟来自燃料筒仓开口面的二氧化碳通量，并使用数据模型融合方法来比较和协调农场实验的现场测量与数值结果。