Prediction of Metabolizable Energy Concentrations of Herbage in the Qinghai–Tibetan Plateau Using Tibetan Sheep Digestibility Data

Penghui Guo,T Yan,Fujiang Hou,Shenghua Chang,Peng Gao,Zhaofeng Wang,Fuhou Li

doi:10.3390/ani10030376

Abstract

Simple SummaryRobust prediction of herbage nutritive value is critical to improve grazing efficiency and to maintain a sustainable environment in the Qinghai–Tibet Plateau. A range of prediction equations were developed in the present study using sheep digestibility data which can produce an accurate estimation of herbage nutritive value. The adaptation of the present equations is expected to benefit local farmers with higher economical return and to improve the fragile ecological systems the Qinghai–Tibet Plateau.Due to its extremely harsh environment, including high altitude, hypoxia, long cold season, and strong ultraviolet radiation in the Qinghai–Tibet Plateau (QTP), herbage species and nutritional value of the pasture may differ considerably from elsewhere across the world. The aim of the present study was to develop biologically relevant equations for estimating the metabolizable energy (ME) value of fresh native herbages in the QTP using digestibility variables and chemical concentrations in the herbage offered to Tibetan sheep at the maintenance level. A total of 11 digestibility trials (6 sheep/trial) were performed in different grazing seasons from 2011 to 2016. The herbage was harvested daily in the morning and offered to sheep at the maintenance feeding level. Thirty-seven equations were developed for the prediction of herbage digestible energy (DE) and ME energy values. The mean prediction error for ME was the lowest when using herbage gross energy digestibility as a sole predictor. When using other digestibility variables (e.g., dry matter and organic matter) as primary predictors, addition of herbage nutrient concentration reduced the difference between predicted and actual values. When DE was used as the primary explanatory variable, mean prediction error was reduced with the addition of ash, nitrogen (N), diethyl ether extract (EE), neutral detergent fiber (NDF), and acid detergent fiber (ADF) concentrations. The internal validation of the present equations showed lower prediction errors when compared with those of existing equations for prediction of DE and ME concentrations in the herbage. Equations developed in the current study may thus allow for an improved and accurate prediction of metabolizable energy concentrations of herbage in practice, which is critical for the development of sustainable grazing systems in the QTP.

Highlights

The accurate prediction of herbage feed values is crucial for managing grassland sustainability and livestock production, especially in grazing areas with poor natural conditions and environment
This may be due to the fact that the measurement of digestible organic matter in dry matter (DOMD) takes account for the effect of ash concentration which reflects the real digestibility variables, the present study found that DOMD was a more accurate predictor for digestible energy (DE) and metabolizable energy (ME) concentrations with smaller Mean prediction error (MPE) values and higher R2 data, when compared with equations using
The present study confirms that the use of a combination of chemical composition of herbage and nutrient digestibility parameters could improve the accuracy of the prediction of energy concentrations in fresh herbage

Summary

Introduction

The accurate prediction of herbage feed values is crucial for managing grassland sustainability and livestock production, especially in grazing areas with poor natural conditions and environment. This technique has been widely used in pasture-based systems in certain countries of the world to improve nutrient utilization efficiency of the herbage, animal production, and economic performance [1,2,3]. Many strategies have been undertaken in recent years with the aim of solving these problems, including grassland improvement, establishing sown pastures, and implementing reasonable grazing systems [10,11], the trend of the grassland degradation has intensified in recent decades, even causing serious economic and environmental problems for the local ecosystem and living standard of local farmers [12,13]. A key action is the development of an effective and rapid methodology to predict herbage nutritive value; in particular, developing tools to predict metabolizable energy (ME) in fresh grass may greatly improve profitability of pasture-based systems

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