Substantial increase in the production of agri-food commodities over the past years has resulted in the generation of enormous volumes of wastes and by-products, thus contributing to increased environmental pollution. Being an under-exploited raw material which are rich in bioactive compounds (e.g., polyphenols, dietary fibre, oils, essential vitamins, minerals, etc), novel strategies and initiatives have been proposed and implemented for the effective management and valorization of these wastes and by-products. The proposed initiatives and strategies support the concepts of EU circular economy and green biorefinery, thus promoting sustainability. One of the strategies of management of waste and by-products includes the effectual development of nutritious low-cost sustainable animal feed. Currently, in the world market, there are a range of fruit and vegetable wastes and by-products that have been effectively introduced in animal diets. Within this context, this systematic review focuses on a diversified group of agri-food wastes (and the industrial by-products), their bioactive components, the opportunities for the development of animal feed or feed supplements (for Ruminants, Non-Ruminants and as Poultry feed) and conclusively the health benefits imparted. In addition, the safety issues and regulations aspects are also covered.

Regrouping dairy cows is a common feature of dairy farm management. Cows are grouped based on lactation stage, age, milk yield and other factors. Regrouping cows during the dry period (from far-off area to close up area and from close up area to the main herd) brings new challenges. This is especially true for heifers who, after being confirmed gravid, may be grouped into a new pen with dried off cows. The aims of this study were to determine how grouping affects activity, nearest neighbour relationships and aggression, and how heifers’ acclimatization to a new group differs from cows. Therefore, the hypotheses were that regrouping cows has less of an effect on older cows compared to heifers, and cows' individuality affects acclimatization to a new group. Aggression data were recorded using a video camera that was directed at the feed bunk, and activity was recorded with activity monitors that were attached around the right hind leg. Synchrony and distance to nearest neighbour were recorded, as was the cows' location on the first 3 d from the day they returned to the main herd. Motion index, mean number of steps and number of lying bouts were significantly higher after calving compared to the week before calving and the difference was higher amongst heifers compared to cows (P < 0.001). Both cows and heifers lay down more in the strawyard compared to cubicle housing (P < 0.01) and cows were more aggressive than heifers in both housing systems (P < 0.001 and P < 0.05, respectively). As hypothesized, heifers were more affected by regrouping and cows with more experience settled quicker to their new environment.

Numerous empirical and mechanistic models predicting methane (CH 4 ) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH 4 production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH 4 production were not included in the data set. The relationships between observed and predicted CH 4 (pCH 4 ) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH 4 (g/d) = 0.94 (±0.022) × pCH 4 (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH 4 (g/d) = CH 4 (g/d) = 0.98 (±0.019) × pCH 4 (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RM-SPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, re-spectively. The residuals of CH 4 predictions were more strongly related to factors associated with CH 4 production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH 4 yield with the Molly model. It was concluded that both models could be used for prediction of CH 4 production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.

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