Thirty‐year variations of above‐ground net primary production and precipitation‐use efficiency of an alpine meadow in the north‐eastern Qinghai‐Tibetan Plateau

Above‐ground net primary production (ANPP) and precipitation‐use efficiency (PUE) are key factors that can clarify the response of grassland ecosystem carbon and water cycles to ongoing climate change. The variations of ANPP and PUE were analysed based on long‐term in situ observations of a species‐rich alpine meadow in the north‐eastern Qinghai‐Tibetan Plateau from 1981 to 2010. ANPP and PUE increased markedly over time. ANPP was significantly controlled by post‐growing season length (from 1 September to the end of growing season in the previous year, R² = 0·31, P < 0·01). Regression trees showed that air temperature during October of the previous year played a predominant role in ANPP annual variations. Results indicated that a strong thermal‐lagging effect on ANPP variations was present in the alpine meadow ecosystem. ANPP variations were undetectable during wet, normal and dry years (P = 0·25). Our finding supported the hypothesis that temporal site‐specific ANPP variations were less regulated by a single factor. The temporal PUE declined linearly with increasing annual precipitation, and the slope was obviously steeper than that of spatial patterns. More ANPP variability in an alpine meadow under warming conditions might occur via community transition in the north‐eastern Qinghai‐Tibetan Plateau.     

Evaluation of the GrazeIn model of grass dry‐matter intake and milk production prediction for dairy cows in temperate grass‐based production systems. 2 – Animal characteristics

This study evaluated the prediction accuracy of grass dry‐matter intake (GDMI) and milk yield predicted by the GrazeIn model using a large database representing 8787 per cow GDMI measurements. In this study, the animal input variables (age, parity, week of lactation, potential peak milk yield, milk fat content, milk protein content, bodyweight, body condition score (BCS), week of conception, BCS at calving and calf birth weight) were investigated. The mean actual GDMI of the database was 15·9 kg DM per cow d^?1 and GrazeIn predicted a mean GDMI for the database of 15·5 kg DM per cow d^?1. The mean bias was ?0·4 kg DM per cow d^?1. GrazeIn predicted GDMI for the total database with an RPE of 15·5% at cow level. The mean actual daily milk yield of the database was 21·3 kg per cow d^?1 and GrazeIn predicted a daily milk yield for the database of 22·2 kg per cow d^?1. The mean bias was +0·9 kg per cow d^?1. GrazeIn predicted milk yield for the total database with an RPE of 16·7% at cow level. From the evaluation, GrazeIn predicted milk yield of all cows in late lactation with a larger level of error than in early and mid‐lactation. This error appears to be due to the persistency of the lactation curve used by the model, which results in a higher predicted milk yield in late lactation compared with the actual milk yield.