Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

External evaluation of the prediction equation for milk fat yield by the 2021 NASEM dairy model using data from eastern Canadian dairy herds

In 2021, the National Academies of Sciences, Engineering, and Medicine (NASEM) issued an equation to predict milk fat yield using dairy cow characteristics and diet composition as input variables. This model was evaluated externally using a data set composed of 541 feed and production records obtained from 23 eastern Canadian dairy herds. The use of the developed equation requires the prediction of dry matter intake. Cow intake used in the model assessment has been obtained by NASEM equations based on (1) animal factors, or (2) a combination of feed composition and animal factors. The prediction of milk fat yield was shown to be accurate. The best prediction was obtained using intake estimated based solely on animal factors (concordance correlation coefficient = 0.68)

Conjugated linoleic acid content of milk from cows fed different sources of dietary fat

Résumé 906International audienc

Conjugated linoleic acid content of milk from cows fed different sources of dietary fat

International audienc

Supplementation of increasing amounts of linseed oil to dairy cows fed total mixed rations: Effects on digestion, ruminal fermentation characteristics, protozoal populations, and milk fatty acid composition

The effect of linseed oil (LO) supplementation on nutrient digestibility, forage (i.e., timothy hay) in sacco ruminal degradation, ruminal fermentation characteristics, protozoal populations, milk production, and milk fatty acid (FA) profile in dairy cows was investigated. Four ruminally cannulated, primiparous lactating cows were used in a 4 × 4 Latin square design (28-d periods). They were fed a total mixed ration (50:50 forage:concentrate (F:C) ratio [dry matter (DM) basis] without supplementation (control, CTL), or supplemented (wt/wt; DM basis) with LO at 2, 3, or 4%. Supplementation with LO had no effect on DM intake (19 kg/d) and apparent total-tract digestibility of nutrients (organic matter, neutral detergent fiber, acid detergent fiber, starch, and gross energy). Ruminal pH, ammonia, and total volatile FA concentrations were not changed by LO supplementation to diets. Extent of changes in volatile FA pattern and effective ruminal degradability of DM of timothy hay were minor. Neither the total numbers nor the genera distribution of protozoa was changed by the addition of increasing amounts of LO to the diet. Milk yield increased linearly (26.1, 27.3, 27.4, and 28.4 kg/d for CTL to LO4, respectively) as the amount of LO added to the diet increased. Milk fat content was not affected by LO supplementation, whereas milk protein content decreased linearly with increasing amounts of LO in the diet. Milk fat proportions of several intermediates of ruminal biohydrogenation of polyunsaturated FA (i.e., trans-10 18:1, trans-11 18:1, cis-9,trans-11 18:2, trans-11,cis-15 18:2, and cis-9,trans-11,cis-15 18:3) increased linearly with LO addition to the diet. The proportion of cis-9,cis-12 18:2 decreased linearly (2.06, 1.99, 1.91, and 1.83% for CTL to LO4, respectively) as the amount of LO in the diet increased. Milk fat content of cis-9,cis-12,cis-15 18:3 increased as the level of LO in the diet increased up to 3% but no further increase was observed when 4% of LO was fed (0.33, 0.79, 0.86, and 0.86% for CTL to LO4, respectively). A similar quadratic response to LO supplementation was also observed for cis-5,cis-8,cis-11,cis-14,cis-17 20:5 and cis-5,cis-7,cis-10,cis-13,cis-16 22:5. The results of the present study show that LO can be safely supplemented up to 4% in forage-based diets of dairy cows to enrich milk with potential health beneficial FA (i.e., n-3 FA) without causing any detrimental effects on rumen function, digestion, and milk production