Robin R. White

Nutritional and greenhouse gas impacts of removing animals from US agriculture

Significance US agriculture was modeled to determine impacts of removing farmed animals on food supply adequacy and greenhouse gas (GHG) emissions. The modeled system without animals increased total food production (23%), altered foods available for domestic consumption, and decreased agricultural US GHGs (28%), but only reduced total US GHG by 2.6 percentage units. Compared with systems with animals, diets formulated for the US population in the plants-only systems had greater excess of dietary energy and resulted in a greater number of deficiencies in essential nutrients. The results give insights into why decisions on modifications to agricultural systems must be made based on a description of direct and indirect effects of change and on a dietary, rather than an individual nutrient, basis. As a major contributor to agricultural greenhouse gas (GHG) emissions, it has been suggested that reducing animal agriculture or consumption of animal-derived foods may reduce GHGs and enhance food security. Because the total removal of animals provides the extreme boundary to potential mitigation options and requires the fewest assumptions to model, the yearly nutritional and GHG impacts of eliminating animals from US agriculture were quantified. Animal-derived foods currently provide energy (24% of total), protein (48%), essential fatty acids (23–100%), and essential amino acids (34–67%) available for human consumption in the United States. The US livestock industry employs 1.6 × 106 people and accounts for

The transcriptome of mouse central nervous system myelin

31.8 billion in exports. Livestock recycle more than 43.2 × 109 kg of human-inedible food and fiber processing byproducts, converting them into human-edible food, pet food, industrial products, and 4 × 109 kg of N fertilizer. Although modeled plants-only agriculture produced 23% more food, it met fewer of the US population’s requirements for essential nutrients. When nutritional adequacy was evaluated by using least-cost diets produced from foods available, more nutrient deficiencies, a greater excess of energy, and a need to consume a greater amount of food solids were encountered in plants-only diets. In the simulated system with no animals, estimated agricultural GHG decreased (28%), but did not fully counterbalance the animal contribution of GHG (49% in this model). This assessment suggests that removing animals from US agriculture would reduce agricultural GHG emissions, but would also create a food supply incapable of supporting the US population’s nutritional requirements.

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations

Rapid nerve conduction in the CNS is facilitated by insulation of axons with myelin, a specialized oligodendroglial compartment distant from the cell body. Myelin is turned over and adapted throughout life; however, the molecular and cellular basis of myelin dynamics remains elusive. Here we performed a comprehensive transcriptome analysis (RNA-seq) of myelin biochemically purified from mouse brains at various ages and find a surprisingly large pool of transcripts enriched in myelin. Further computational analysis showed that the myelin transcriptome is closely related to the myelin proteome but clearly distinct from the transcriptomes of oligodendrocytes and brain tissues, suggesting a highly selective incorporation of mRNAs into the myelin compartment. The mRNA-pool in myelin displays maturation-dependent dynamic changes of composition, abundance, and functional associations; however ageing-dependent changes after 6 months were minor. We suggest that this transcript pool enables myelin turnover and the local adaptation of individual pre-existing myelin sheaths.

An environmental, economic, and social assessment of improving cattle finishing weight or average daily gain within U.S. beef production

The objective was to summarize the literature and derive equations that relate the chemical composition of diet and rumen characteristics to the intestinal supply of microbial nitrogen (MicN), efficiency of microbial protein synthesis (EMPS), and flow of nonammonia nonmicrobial N (NANMN). In this study, 619 treatment means from 183 trials were assembled for dairy cattle sampled from the duodenum or omasum. Backward elimination multiple regression was used to derive equations to estimate flow of nitrogenous components over a large range of dietary conditions. An intercept shift for sample location revealed that omasal sampling estimated greater MicN flow relative to duodenal sampling, but sample location did not interact with any other variables tested. The ruminal outflow of MicN was positively associated with dry matter intake (DMI) and with dietary starch percentage at a decreasing rate (quadratic response). Also, MicN was associated with DMI and rumen-degraded starch and neutral detergent fiber (NDF). When rumen measurements were included, ruminal pH and ammonia-N were negatively related to MicN flow along with a strong positive association with ruminal isovalerate molar proportion. When evaluating these variables with EMPS, isovalerate interacted with ammonia such that the slope for EMPS with increasing isovalerate increased as ammonia-N concentration decreased. A similar equation with isobutyrate confirms the importance of branched-chain volatile fatty acids to increase growth rate and therefore assimilation of ammonia-N into microbial protein. The ruminal outflow of NANMN could be predicted by dietary NDF and crude protein percentages, which also interacted. This result is probably associated with neutral detergent insoluble N contamination of NDF in certain rumen-undegradable protein sources. Because NANMN is calculated by subtracting MicN, sample location was inversely related compared with the MicN equation, and omasal sampling underestimated NANMN relative to duodenal sampling. As in the MicN equation, sampling location did not interact with any other variables tested for NANMN. Equations derived from dietary nutrient composition are robust across dietary conditions and could be used for prediction in protein supply-requirement models. These empirical equations were supported by more mechanistic equations based on the ruminal carbohydrate degradation and ruminal variables related to dietary rumen degradable protein.

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate

The objective of this study was to assess environmental impact, economic viability, and social acceptability of 3 beef production systems with differing levels of efficiency. A deterministic model of U.S. beef production was used to predict the number of animals required to produce 1 × 10(9) kg HCW beef. Three production treatments were compared, 1 representing average U.S. production (control), 1 with a 15% increase in ADG, and 1 with a 15% increase in finishing weight (FW). For each treatment, various socioeconomic scenarios were compared to account for uncertainty in producer and consumer behavior. Environmental impact metrics included feed consumption, land use, water use, greenhouse gas emissions (GHGe), and N and P excretion. Feed cost, animal purchase cost, animal sales revenue, and income over costs (IOVC) were used as metrics of economic viability. Willingness to pay (WTP) was used to identify improvements or reductions in social acceptability. When ADG improved, feedstuff consumption, land use, and water use decreased by 6.4%, 3.2%, and 12.3%, respectively, compared with the control. Carbon footprint decreased 11.7% and N and P excretion were reduced by 4% and 13.8%, respectively. When FW improved, decreases were seen in feedstuff consumption (12.1%), water use (9.2%). and land use (15.5%); total GHGe decreased 14.7%; and N and P excretion decreased by 10.1% and 17.2%, compared with the control. Changes in IOVC were dependent on socioeconomic scenario. When the ADG scenario was compared with the control, changes in sector profitability ranged from 51 to 117% (cow-calf), -38 to 157% (stocker), and 37 to 134% (feedlot). When improved FW was compared, changes in cow-calf profit ranged from 67% to 143%, stocker profit ranged from -41% to 155% and feedlot profit ranged from 37% to 136%. When WTP was based on marketing beef being more efficiently produced, WTP improved by 10%; thus, social acceptability increased. When marketing was based on production efficiency and consumer knowledge of growth-enhancing technology use, WTP decreased by 12%-leading to a decrease in social acceptability. Results demonstrated that improved efficiency also improved environmental impact, but impacts on economic viability and social acceptability are highly dependent on consumer and producer behavioral responses to efficiency improvements.

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirements series is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition.

Complementary transcriptomic and proteomic analyses reveal regulatory mechanisms of milk protein production in dairy cows consuming different forages.

Several attempts have been made to quantify microbial protein flow from the rumen; however, few studies have evaluated tradeoffs between empirical equations (microbial N as a function of diet composition) and more mechanistic equations (microbial N as a function of ruminal carbohydrate digestibility). Although more mechanistic approaches have been touted because they represent more of the biology and thus might behave more appropriately in extreme scenarios, their precision is difficult to evaluate. The objective of this study was to derive equations describing starch, neutral detergent fiber (NDF), and organic matter total-tract and ruminal digestibilities; use these equations as inputs to equations predicting microbial N (MicN) production; and evaluate the implications of the different calculation methods in terms of their precision and accuracy. Models were evaluated based on root estimated variance σˆe and concordance correlation coefficients (CCC). Ruminal digestibility of NDF was positively associated with DMI and concentrations of NDF and CP and was negatively associated with concentration of starch and the ratio of acid detergent fiber to NDF (CCC=0.946). Apparent ruminal starch digestibility was increased by omasal sampling (compared with duodenal sampling), was positively associated with forage NDF and starch concentrations, and was negatively associated with wet forage DMI and total dietary DMI (CCC=0.908). Models were further evaluated by calculating fit statistics from a common data set, using stochastic simulation, and extreme scenario testing. In the stochastic simulation, variance in input variables were drawn from a multi-variate random normal distribution reflective of input measurement errors and predicting MicN while accounting for the measurement errors. Extreme scenario testing evaluated each MicN model against a data subset. When compared against an identical data set, predicting MicN empirically had the lowest prediction error, though differences were slight (σˆe 23.3% vs. 23.7 or 24.3%), and highest concordance (0.52 vs. 0.48 or 0.44) of any approach. Minimal differences were observed between empirical MicN prediction (σˆe 25.3%; CCC 0.530) and MicN prediction (σˆe 25.3%; CCC 0.532) from rumen carbohydrate digestibility in the stochastic analysis or extreme scenario testing. Despite the hypothesized benefits of a more mechanistic prediction approach, few differences between the calculation approaches were identified.

Methionine, leucine, isoleucine, or threonine effects on mammary cell signaling and pup growth in lactating mice

Forage plays a critical role in the milk production of dairy cows; however, the mechanisms regulating bovine milk synthesis in dairy cows fed high forage rations with different basal forage types are not well-understood. In the study, rice straw (RS, low-quality) and alfalfa hay (AH, high-quality) diets were fed to lactating cows to explore how forage quality affected the molecular mechanisms regulating milk production using RNA-seq transcriptomic method with iTRAQ proteomic technique. A total of 554 transcripts (423 increased and 131 decreased) and 517 proteins (231 up-regulated and 286 down-regulated) were differentially expressed in the mammary glands of the two groups. The correlation analysis demonstrated seven proteins (six up-regulated and one down-regulated) had consistent mRNA expression. Functional analysis of the differentially expressed transcripts/proteins suggested that enhanced capacity for energy and fatty acid metabolism, increased protein degradation, reduced protein synthesis, decreased amino acid metabolism and depressed cell growth were related to RS consumption. The results indicated cows consuming RS diets may have had depressed milk protein synthesis because these animals had decreased capacity for protein synthesis, enhanced proteolysis, inefficient energy generation and reduced cell growth. Additional work evaluating RS- and AH-based rations may help better isolate molecular adaptations to low nutrient availability during lactation.

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 2. Rumen degradable and undegradable protein

Two studies were undertaken to assess the effects of individual essential AA supplementation of a protein-deficient diet on lactational performance in mice using litter growth rates as a response variable. The first study was designed to establish a dietary protein response curve, and the second to determine the effects of Leu, Ile, Met, and Thr supplementation of a protein-deficient diet on lactational performance. In both studies, dams were fed test diets from parturition through d 17 of lactation, when the studies ended. Mammary tissue was collected on d 17 from mice on the second experiment and analyzed for mammalian target of rapamycin (mTOR) pathway signaling. Supplementation with Ile, Leu, or Met independently increased litter weight gain by 11, 9, and 10%, respectively, as compared with the protein-deficient diet. These responses were supported by independent phosphorylation responses for mTOR and eIF4E binding protein 1 (4eBP1). Supplementation of Ile, Leu, and Met increased phosphorylation of mTOR by 55, 34, and 47%, respectively, as compared with the protein-deficient diet. Phosphorylation of 4eBP1 increased in response to Ile and Met supplementation by 60 and 40%, respectively. Supplementation of Ile and Met increased phosphorylation of Akt/protein kinase B (Akt) by 41 and 59%, respectively. This work demonstrated that milk production responds nonlinearly to protein supply, and milk production and the mTOR pathway responded independently to supplementation of individual AA. The former demonstrates that a linear breakpoint model is an inappropriate description of the responses, and the latter demonstrates that no single factor limits AA for lactation. Incorporation of a multiple-limiting AA concept and nonlinear responses into milk protein response models will help improve milk yield predictions and allow derivation of diets that will increase postabsorptive N efficiency and reduce N excretion by lactating animals.

Evaluating equations estimating change in swine feed intake during heat and cold stress

This work evaluated the National Research Council (NRC) dairy model (2001) predictions of rumen undegradable (RUP) and degradable (RDP) protein compared with measured postruminal non-ammonia, nonmicrobial (NANMN) and microbial N flows. Models were evaluated using the root mean squared prediction error (RMSPE) as a percent of the observed mean, mean and slope biases as percentages of mean squared prediction error (MSPE), and concordance correlation coefficient (CCC). The NRC (2001) over-estimated NANMN by 18% and under-estimated microbial N by 14%. Both responses had large mean biases (19% and 20% of MSPE, respectively), and NANMN had a slope bias (22% of MSPE). The NRC NANMN estimate had high RMSPE (46% of observed mean) and low CCC (0.37); updating feed library A, B, and C protein fractions and degradation rate (Kd) estimates with newer literature only marginally improved fit. The re-fit NRC models for NANMN and microbial N had CCC of 0.89 and 0.94, respectively. When compared with a prediction of NANMN as a static mean fraction of N intake, the re-derived NRC approach did not have improved fit. A protein system of intermediate complexity was derived in an attempt to estimate NANMN with improved fit compared with the static mean NANMN model. In this system, postruminal appearance of A, B, and C protein fractions were predicted in a feed-type specific manner rather than from estimated passage and degradation rates. In a comparison to independent data achieved through cross-validation, the new protein system improved RMSPE (34 vs. 36% of observed mean) and CCC (0.42 vs. 0.30) compared with the static mean NANMN model. When the NRC microbial N equation was re-derived, the RDP term dropped from the model. Consequently, 2 new microbial protein equations were formulated, both used a saturating (increasing at a decreasing rate) form: one saturated with respect to TDN and the other saturated over increasing intakes of rumen degraded starch and NDF. Both equations expressed maximal microbial N production as a linear function of RDP intake. The function relating microbial N to intake of rumen degradable carbohydrate improved RMSPE (24 vs. 28% of the observed mean) and CCC (0.63 vs 0.30) compared with the re-derived NRC model. The newly derived equations showed modest improvements in model fit and improved capacity to account for known biological effects; however, substantial variability in NANMN and microbial N estimates remained unexplained.