Methane emission monitoring and reduction in dairy farms using an optical nose on chip

减少温室气体排放是减缓气候变化的优先事项。在其2030年减排计划中，加拿大的目标是到2030年将其排放量减少到2005年水平的40%，到2050年达到净零排放。 虽然甲烷占温室气体的11%，但在20年的时间里，它的变暖能力是二氧化碳的86倍。因此，减少人为甲烷排放尤为重要。2019年，农业占加拿大甲烷总排放量的29%，其中大部分来自生物来源，例如通过肠道发酵生产的牲畜。该提案的全球目标是开发一个基于芯片上光学鼻的平台，允许监测奶牛场的甲烷排放，以验证和改进农场干预措施的效果，以减少哺乳期奶牛产生的甲烷。 我们的合作伙伴Lactanet的使命是为加拿大6800家生产商提供建议，以提高奶牛场的可持续性和盈利能力。光学鼻将使用与微电子工业中使用的工艺类似的工艺在硅芯片上制造，并具有聚合物中的光学微谐振器阵列。这种方法允许低成本制造部署在生产奶牛场中所需的大量传感器。事实上，市场上可获得的商业传感器的高成本阻碍了它们的大规模使用。在这个项目中，我们将把机器学习策略应用于光学鼻，它将被放置在挤奶机器人的喂食箱中。为了开发可靠的算法来呈现24小时的甲烷排放，有必要开发将来自饲养箱中的甲烷传感器的数据与从代谢室记录的连续甲烷排放相关联的模型，所述数据每天3次，每次5分钟。后者将与加拿大农业和农业食品部合作实施。更好地了解和准确监测甲烷排放不仅对减少环境影响，而且对改善动物健康和提高生产力具有巨大潜力。事实上，甲烷的产生可以消耗牛摄入的能量的12%。

Reducing greenhouse gas emissions is a priority for mitigating climate changes. In its 2030 emissions reduction plan, Canada targets to reduce its emissions 40% below 2005 levels by 2030 and reach net-zero emissions by 2050. Although methane represents 11% of greenhouse gases, it has 86 times the warming power of carbon dioxide over a 20-year period. Therefore, it is particularly important to reduce anthropogenic methane emissions. Agriculture contributed to 29% Canada's total methane emissions in 2019, most of it coming from biological sources, such as livestock production through enteric fermentation. The global objective of this proposal is to develop a platform, based on an optical nose on chip, allowing the monitoring of methane emissions in dairy farms to validate and refine the effect of on-farm interventions to reduce methane produced by cows in lactation. The mission of our partner Lactanet is to advise 6800 producers across Canada to improve the sustainability and profitability of dairy farms. The optical nose will be fabricated on a silicon chip using similar processes as the ones used in microelectronics industry and have an array of optical microresonators in polymers. Such approach allows low-cost fabrication of large numbers of sensors necessary for the deployment in production dairy farms. Indeed, the high cost of commercial sensors available on the market are preventing their use at large scale. In this project, we will apply machine learning strategies to the optical nose, which will be placed in the feeding box of the milking robot. To develop reliable algorithms rendering a 24h methane emission, it is necessary to develop models correlating data from methane sensors in the feeding box, which are taken for 5 minutes 3 times a day, with continuous methane emission recorded from a metabolic chamber. The latter will be realized in collaboration with Agriculture and Agri-Food Canada. Better understanding and accurate monitoring of methane emissions have a great potential not only to reduce the environmental impact, but also for improved animal health, and increased productivity. Indeed, methane production can consume up to 12% of the energy ingested by cattle.