Estimates of plant biomass are helpful for many applications in invasive plant science and management, but measuring biomass can be time-consuming, costly, or impractical if destructive sampling is inappropriate. The objective of this study was to assess feasibility of developing regression equations using a fast, nondestructive measure (cover) to estimate aboveground biomass for red brome (Bromus rubens L.), a widespread nonnative annual grass in the Mojave Desert, USA. At three study sites, including one measured for three consecutive years, B. rubens cover spanned 0.1% to 85% and aboveground biomass 1 to 321 g m−2. In log10-transformed linear regressions, B. rubens cover accounted for 68% to 96% of the variance in B. rubens biomass among sites, with all coefficients of determination significant at P < 0.05. For every doubling of percent cover, biomass was predicted to increase by 78%, 83%, and 144% among the three sites. At the site measured for three consecutive years, which ranged in rainfall from 65% to 159% of the long-term average, regression slopes each year differed from other years. Regression results among sites were insensitive to using cover classes (10 classes encompassing 0% to 100% cover) compared with simulated random distribution of integer cover within classes. Biomass of B. rubens was amenable to estimation in the field using cover, and such estimates may have applications for modeling invasive annual plant fuel loads and ecosystem carbon storage.

Breeding efforts over the last decades altered markedly empty body (EB) composition of pigs. This study aimed to re-evaluate the dynamics of changes in the composition and deposition rate of fat, protein and amino acids (AA) in the EB from birth to 140 kg BW depending on the dietary CP and AA supply in a current pig genotype. In the experiment 66 entire male, 58 castrated and 66 female Swiss Large White pigs were used. From 20 kg BW onwards, they had either ad libitum access to a control (C) diet or a diet (LP) compared to diet C only 80% of CP, lysine, methione+cystine, threonine and tryptophan. The EB composition was determined at birth on eight boars and eight females, at 10 and 20 kg BW on two boars, two castrates and two females, and at 20 kg intervals from 40 to 140 kg BW, on four pigs per gender and dietary treatment. Each EB fraction was weighed and analysed for protein, fat and AA profile. The AA-to-lysine ratio was calculated and the different chemical component contents were fitted to allometric regressions. Overall, C-boars had the greatest EB protein and AA content and deposition rates, and lowest fat content and deposition rates. At the beginning of the grower period, LP-castrates and females displayed the lowest protein and AA and the highest fat deposition rates. However, compared with their counterparts in the C-group, in LP-castrates and females protein and AA deposition rates were greater above 64 and 40 kg EB weight, respectively, whereas fat deposition rates was lower above 80 kg EB weight. Thus, there seems a great potential to optimise protein and AA efficiency especially in the finisher period in castrates and females. Important individual variations were found in the essential AA-to-lysine ratio of the EB. Phenylalanine and threonine-to-lysine ratios decreased with increasing EB weight. Valine- and threonine-to-lysine ratios in C-castrates and C-females were 5% and 4% greater than recently reported by the National Research Council (NRC) whereas cysteine-, methionine- and tyrosine-to-lysine ratios were lower by 34%, 25% and 10%, respectively. The clear differences found between the EB AA-to-lysine ratios in the present study and the NRC might partly be explained by the genotype and the temporal changes in the relative weight of each EB fraction or changes in the AA profile. Nevertheless, these findings on changes in the essential AA profile of tissue protein warrant further studies.

Reports concerning maintenance energy requirements (MER) in dogs are common but most of the data cover laboratory or utility dogs. This study establishes those of healthy adult pet dogs and the factors which cause these energy requirements to vary. Within the framework of a nutrition teaching exercise, each student followed a pet from his entourage and gathered accurate records of its feeding habits. Data have been restricted to healthy adult dogs with an ideal body weight (BW) which did not vary more than 5 % during the study period. A total of 319 eligible records were analysed using multiple linear regression. Variation factors such as ownership, breed, sex and neutered status, bedding location, temperament and feeding habits were then analysed individually using a non-parametric model. Two models result from this study, one excluding age (r2 0·813) and a more accurate one which takes into consideration the age in years (r2 0·816). The second model was assessed with the main variation factors and shows that: MER (kcal) = k1 × k2 × k3 × k4 × k5 × 128 × BW0·740 × age−0·050/d (r2 0·836), with k1 the effect of the breed, k2 the effect of sex and neutered status, k3 the effect of bedding location, k4 the effect of temperament and k5 the effect of the type of feed. The resulting model is very similar to the recommendations made by the National Research Council (2006) but a greater accuracy was obtained using age raised to a negative power, as demonstrated in human nutrition.

A meta-analysis was carried out in order to establish the energy requirements of adult cats. Publications that identified cat body weight (BW) were used to generate allometric relationships between energy requirements and BW of healthy adult cats, using log-log linear regression. Energy requirements were expressed in kcal/kg BW to be consistent with those reported by the National Research Council. Mean maintenance energy requirements were 55·1 (se 1·2) kcal/kg BW (115 treatment groups). Three allometric equations were identified to predict the energy requirements for maintenance of BW in the cat based on BW: light (53·7 kcal/kg BW− 1·061), normal (46·8 kcal/kg BW− 1·115) and heavy (131·8 kcal/kg BW− 0 ·366). When reported on lean mass, the allometric equation revealed maintenance requirements were 58·4 kcal/kg lean mass− 1·140 (adjusted R2 0·694; thirty-six treatment groups). The present review suggests that values for maintenance energy requirements based on BW alone may not be an accurate prediction and more detailed information on the age, sex and neuter status, BW and composition would enhance the ability to interpret the maintenance energy requirements of cats.