Department of Animal Medicine, Production, and Health, University of Padova, Padova, Italy.
PLoS One. 2024 Sep 18;19(9):e0308627. doi: 10.1371/journal.pone.0308627. eCollection 2024.
Maize silage serves as a significant source of energy and fibre for the diets of dairy and beef cattle. However, the quality of maize silage is contingent upon several crucial considerations, including dry matter loss, fermentative profile, pH level, ammonia content, and aerobic stability. These aspects are influenced by a multitude of factors and their interactions, with seasonality playing a crucial role in shaping silage quality. In this study an open-source database was utilised to assess the impact of various pre-ensiling circumstances, including the diversity of the chemical composition of the freshly harvested maize, on the silage quality. The findings revealed that seasonality exerts a profound influence on maize silage quality. Predictive models derived from the composition of freshly harvested maize demonstrated that metrics were only appropriate for screening purposes when utilizing in-field sensor technology. Moreover, this study suggests that a more comprehensive approach, incorporating additional factors and variability, is necessary to better elucidate the determinants of maize silage quality. To address this, combining data from diverse databases is highly recommended to enable the application of more robust algorithms, such as those from machine learning or deep learning, which benefit from large data sets.
青贮玉米是奶牛和肉牛日粮的重要能量和纤维来源。然而，青贮玉米的质量取决于几个关键因素，包括干物质损失、发酵特性、pH 值、氨含量和有氧稳定性。这些方面受到多种因素及其相互作用的影响，季节性在塑造青贮玉米质量方面起着至关重要的作用。在这项研究中，利用了一个开源数据库来评估各种青贮前情况（包括新鲜收获的玉米化学成分的多样性）对青贮质量的影响。研究结果表明，季节性对青贮玉米质量有深远的影响。从新鲜收获的玉米成分中得出的预测模型表明，当使用田间传感器技术时，度量标准仅适用于筛选目的。此外，本研究表明，需要采用更全面的方法，纳入其他因素和变异性，以更好地阐明青贮玉米质量的决定因素。为了解决这个问题，强烈建议结合来自不同数据库的数据，以应用更强大的算法，如机器学习或深度学习算法，这些算法受益于大数据集。
