The application of near-infrared spectroscopy to predict composition, gross energy yield, and methane production of natural forages on the Qinghai–Tibet Plateau

Runze Wang, Huakun Zhou, Yayu Huang, Allan Degen, Xueyan Du, Muhammad Irfan Malik, Rongzhen Zhong, Binqiang Bai, Lizhuang Hao
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Abstract

Background

Yak (Poephagus grunniens) production on the Qinghai–Tibet Plateau is influenced heavily by the quality of the natural forage, which can vary significantly in both quality and quantity. Therefore, timely and accurate monitoring of forage variables is essential for optimizing livestock production in this region.

Methods

This study investigated the use of near-infrared spectroscopy (NIRS) as a tool for estimating the composition and quality of natural forage. A total of 301 natural forage samples were collected, and their spectral data were acquired using NIRS. Conventional methods were used to measure the forage composition, and predictive models were developed based on the spectral data.

Results

Our findings indicate that NIRS can accurately predict the contents of crude protein, acid detergent fiber, and neutral detergent fiber. However, it demonstrated less accuracy in predicting dry matter digestibility, gross energy yield, and methane production.

Conclusions

The application of NIRS for assessing the nutritional composition of forages on the Qinghai–Tibet Plateau is a key advancement for the livestock industry. Understanding forage nutrition enables informed feeding strategies and improvement of livestock production. Future research should refine predictive models to ensure sustainable forage management and enhance livestock productivity in this unique ecological environment.

Abstract Image

近红外光谱技术在青藏高原天然牧草组成、总能产和甲烷产量预测中的应用
青藏高原牦牛产量受天然牧草质量的影响较大,天然牧草的质量和数量差异较大。因此,及时准确地监测饲料变量对优化该地区畜牧业生产至关重要。方法利用近红外光谱(NIRS)技术对天然牧草的成分和品质进行评价。采集了301份天然牧草样品,利用近红外光谱技术获取了其光谱数据。采用常规方法测量牧草成分,并基于光谱数据建立预测模型。结果近红外光谱可以准确预测粗蛋白质、酸性洗涤纤维和中性洗涤纤维的含量。然而,它在预测干物质消化率、总能量产量和甲烷产量方面的准确性较低。结论应用近红外光谱技术评价青藏高原牧草营养成分是畜牧业发展的关键技术。了解饲料的营养可以使饲养策略更明智,提高牲畜产量。未来的研究应完善预测模型,以确保在这种独特的生态环境下可持续的饲料管理和提高畜牧业生产力。
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