In vitro evaluation of fibrolytic enzymes as additives for maize (Zea mays L.) silage - II. Effects on rate of acidification, fibre degradation during ensiling and rumen fermentation

A study was carried out to determine the influence of fibrolytic enzymes derived from mesophilic or thermophilic fungal sources, added at ensiling, on time-course fermentation characteristics and in vitro rumen degradation of maize silage. The mesophilic enzyme was a commercial product derived from Trichodenna reesei (L), whereas the thermophilic enzyme was a crude extract produced from Thermoascus aurantiacus (Ta) in this laboratory. The fungus was cultured using maize cobs as a carbon source. The resulting fermentation extract was deionised to remove sugars and characterised for its protein concentration, main and side enzymic activities, optimal pH, protein molecular mass and isoelectric point. In an additional study, both enzymes were added to maize forage (333.5 g DM/kg, 70.0, 469.8, 227.1 and 307.5 g/kg DM of CP, NDF, ADF and starch, respectively) at two levels each, normalized according to xylanase activity, and ensiled in 0.5 kg capacity laboratory minisilos. Duplicate silos were opened at 2, 4, 8, 15, and 60 days after ensiling, and analysed for chemical characteristics. Silages from 60 days were bulked and in vitro gas production (GP) and organic matter degradability (OMD) profiles evaluated using the Reading Pressure Technique (RPT), in a completely randomised design. The crude enzyme extract contained mainly xylanase and endoglucanase activities, with very low levels of exoglucanase, which probably limited hydrolysis of filter paper. The extract contained three major protein bands of between 29 and 55 kDa, with mainly acidic isoelectric points. Ensiling maize with enzymes lowered (P < 0.05) the final silage pH, with this effect being observed throughout the ensiling process. All enzyme treatments reduced (P < 0.05) ADF contents. Treatments including Ta produced more gas (P < 0.05) than the controls after 24 h incubation in vitro, whereas end point gas production at 96 h was not affected. Addition of Ta increased (P < 0.01) OMD after 12 h (410 and 416 g/kg versus 373 g/kg), whereas both L and Ta increased (P < 0.05) OMD after 24 h. Addition of enzymes from mesophilic or thermophilic sources to maize forage at ensiling increased the rate of acidification of the silages and improved in vitro degradation kinetics, suggesting an improvement in the nutritive quality. (C) 2003 Elsevier B.V All rights reserved.

Towards improving the framework for probabilistic forecast evaluation

The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast performance. The properties of several proper scores for probabilistic forecast evaluation are contrasted and then used to interpret decadal probability hindcasts of global mean temperature. The Continuous Ranked Probability Score (CRPS), Proper Linear (PL) score, and IJ Good’s logarithmic score (also referred to as Ignorance) are compared; although information from all three may be useful, the logarithmic score has an immediate interpretation and is not insensitive to forecast busts. Neither CRPS nor PL is local; this is shown to produce counter intuitive evaluations by CRPS. Benchmark forecasts from empirical models like Dynamic Climatology place the scores in context. Comparing scores for forecast systems based on physical models (in this case HadCM3, from the CMIP5 decadal archive) against such benchmarks is more informative than internal comparison systems based on similar physical simulation models with each other. It is shown that a forecast system based on HadCM3 out performs Dynamic Climatology in decadal global mean temperature hindcasts; Dynamic Climatology previously outperformed a forecast system based upon HadGEM2 and reasons for these results are suggested. Forecasts of aggregate data (5-year means of global mean temperature) are, of course, narrower than forecasts of annual averages due to the suppression of variance; while the average “distance” between the forecasts and a target may be expected to decrease, little if any discernible improvement in probabilistic skill is achieved.