Diversity of condensed tannin structures affects rumen in vitro methane production in sainfoin (Onobrychis viciifolia) accessions

Sainfoin is a non-bloating temperate forage legume with a moderate-to-high condensed tannin (CT) content. This study investigated whether the diversity of sainfoin accessions in terms of CT structures and contents could be related to rumen in vitro gas and methane (CH4) production and fermentation characteristics. The aim was to identify promising accessions for future investigations. Accessions differed (P < 0·0001) in terms of total gas and CH4 productions. Fermentation kinetics (i.e. parameters describing the shape of the gas production curve and half-time gas production) for CH4 production were influenced by accession (P ≤ 0·038), but not by PEG. Accession, PEG and time affected (P < 0·001) CH4 production, but accession and PEG interaction showed only a tendency (P = 0·08). Increase in CH4 due to PEG addition was not related to CT content. Further analysis of the relationships among multiple traits (nutritional composition, CT structure and CH4 production) using principal component analysis (PCA) based on optimally weighted variables revealed differences among accessions. The first two principal component axes, PC1 (57·6%) and PC2 (18·4%), explained 76·0% of the total variation among accessions. Loading of biplots derived from both PCAs made it possible to establish a relationship between the ratio of prodelphinidin:procyanidin (PD:PC) tannins and CH4 production in some accessions. The PD:PC ratio seems to be an important source of variation that is negatively related to CH4 production. These results suggested that sainfoin accessions collected from across the world exhibited substantial variation in terms of their effects on rumen in vitro CH4 production, revealing some promising accessions for future investigations.

Study of the lactation curve in dairy cattle on farms in Central Mexico

Accurate knowledge of lactation curves has an important relevance to management and research of dairy production systems. A number of equations have been proposed to describe the lactation curve, the most widely applied being the gamma equation. The objective of this work was to compare and evaluate candidate functions for their predictive ability in describing lactation curves from central Mexican dairy cows reared under 2 contrasting management systems. Five equations were considered: Gaines ( exponential decay), Wood ( gamma equation), Rook ( Michaelis-Menten x exponential), and 2 more mechanistic ones (Dijkstra and Pollott). A database consisting of 701 and 1283 records of cows in small-scale and intensive systems, respectively, was used in the analysis. Before analysis, the database was divided into 6 groups representing first, second, and third and higher parity cows in both systems. In all cases except second and above parity cows in small-scale systems, all models improved on the Gaines equation. The Wood equation explained much of the variation, but its parameters do not have direct biological interpretation. Although the Rook equation fitted the data well, some of the parameter estimates were not significant. The Dijkstra equation consistently gave better predictions, and its parameters were usually statistically significant and lend themselves to physiological interpretation. As such, the differences between systems and parity could be explained due to variations in theoretical initial milk production at parturition, specific rates of secretory cell proliferation and death, and rate of decay, all of which are parameters in the model. The Pollott equation, although containing the most biology, was found to be over-parameterized and resulted in nonsignificant parameter estimates. For central Mexican dairy cows, the Dijkstra equation was the best option to use in describing the lactation curve.

Sensitivity analysis of an ocean carbon cycle model in the North Atlantic: an investigation of parameters affecting the air-sea CO2 flux, primary production and export of detritus

The sensitivity of the biological parameters in a nutrient-phytoplankton-zooplankton-detritus (NPZD) model in the calculation of the air-sea CO2 flux, primary production and detrital export is analysed. We explore the effect on these outputs of variation in the values of the twenty parameters that control ocean ecosystem growth in a 1-D formulation of the UK Met Office HadOCC NPZD model used in GCMs. We use and compare the results from one-at-a-time and all-at-a-time perturbations performed at three sites in the EuroSITES European Ocean Observatory Network: the Central Irminger Sea (60° N 40° W), the Porcupine Abyssal Plain (49° N 16° W) and the European Station for Time series in the Ocean Canary Islands (29° N 15° W). Reasonable changes to the values of key parameters are shown to have a large effect on the calculation of the air-sea CO2 flux, primary production, and export of biological detritus to the deep ocean. Changes in the values of key parameters have a greater effect in more productive regions than in less productive areas. The most sensitive parameters are generally found to be those controlling well-established ocean ecosystem parameterisations widely used in many NPZD-type models. The air-sea CO2 flux is most influenced by variation in the parameters that control phytoplankton growth, detrital sinking and carbonate production by phytoplankton (the rain ratio). Primary production is most sensitive to the parameters that define the shape of the photosynthesis-irradiance curve. Export production is most sensitive to the parameters that control the rate of detrital sinking and the remineralisation of detritus.

Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post-process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non-linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non-linear non-parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non-linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non-parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power-to-wind) transformation, simpler linear regression models with censoring perform equally or better than non-linear models with or without the frequently used wind-to-power transformation.