A nutrition mathematical model to account for dietary supply and requirements of energy and nutrients for domesticated small ruminants: the development and evaluation of the Small Ruminant Nutrition System

A mechanistic model that predicts nutrient requirements and biological values of feeds for sheep (Cornell Net Carbohydrate and Protein System; CNCPS-S) was expanded to include goats and the name was changed to the Small Ruminant Nutrition System (SRNS). The SRNS uses animal and environmental factors to predict metabolizable energy (ME) and protein, and Ca and P requirements. Requirements for goats in the SRNS are predicted based on the equations developed for CNCPS-S, modified to account for specific requirements of goats, including maintenance, lactation, and pregnancy requirements, and body reserves. Feed biological values are predicted based on carbohydrate and protein fractions and their ruminal fermentation rates, forage, concentrate and liquid passage rates, and microbial growth. The evaluation of the SRNS for sheep using published papers (19 treatment means) indicated no mean bias (MB; 1.1 g/100 g) and low root mean square prediction error (RMSPE; 3.6 g/100g) when predicting dietary organic matter digestibility for diets not deficient in ruminal nitrogen. The SRNS accurately predicted gains and losses of shrunk body weight (SBW) of adult sheep (15 treatment means; MB = 5.8 g/d and RMSPE = 30 g/d) when diets were not deficient in ruminal nitrogen. The SRNS for sheep had MB varying from -34 to 1 g/d and RSME varying from 37 to 56 g/d when predicting average daily gain (ADG) of growing lambs (42 treatment means). The evaluation of the SRNS for goats based on literature data showed accurate predictions for ADG of kids (31 treatment means; RMSEP = 32.5 g/d; r2= 0.85; concordance correlation coefficient, CCC, = 0.91), daily ME intake (21 treatment means; RMSEP = 0.24 Mcal/d g/d; r2 = 0.99; CCC = 0.99), and energy balance (21 treatment means; RMSEP = 0.20 Mcal/d g/d; r2 = 0.87; CCC = 0.90) of goats. In conclusion, the SRNS for sheep can accurately predict dietary organic matter digestibility, ADG of growing lambs and changes in SBW of mature sheep. The SRNS for goats is suitable for predicting ME intake and the energy balance of lactating and non-lactating adult goats and the ADG of kids of dairy, meat, and indigenous breeds. The SRNS model is available at http://nutritionmodels.tamu.edu

Using System Dynamics Modelling Approach to Develop Management Tools for Animal Production with Emphasis on Small Ruminants

Small ruminants are important assets in several regions of the world. They account for more than half of the domesticated ruminants. Despite the growth in goat production in the world (more than 2% per year), research related to goat production is less than desired. One underused but potentially valuable approach for research on small ruminants is simulation modelling. Models of the components of small ruminant systems can enhance the financial returns and reduce negative environmental impacts. These models can be used to assess many dimensions of small ruminant production, from rumen dynamics to economic policies designed to support small ruminant production. Understanding the nutrition, production, and economic policy feedback signals and planning ahead is crucial to build a robust and integrated production activity that can be managed under different production scenarios. System Dynamics (SD) is a computer-aided modelling methodology that can be used to perform policy analysis and decision support system (DSS) applied to dynamic problems arising in complex social, managerial, economic, or ecological dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality. SD can be used as a modelling tool to aggregate knowledge to solve different types of problems that have a limited scope to a specific location or have broad trends of applications across locations and areas of science. Important issues of broad application include the bearings of animal production in the climate change and the impacts of climate change in animal production, alternative production scenarios of animal and crop integration, associations between animal production and business (economics, marketing). The trend of increasing small ruminants in tropical and subtropical regions and an increasing pressure on tropical and subtropical livestock systems to produce food, to feed livestock, and to produce energy crops warrants the development of DSS to address issues such as what is the “real” benefits of livestock, the negative impacts livestock can have on greenhouse-gas emissions and the environment, and the effects of climate change on livestock systems

Study of the impact of breeding seasons in the dynamics of dairy goat herds.

Abstract: The competitiveness in the animal production field has forced the smaller activities to be more efficient when compared to big business of the agro-industries. The dairy goat production is one of those market niches that need a better understand of its sector. For those reasons it has been proposed that by changing the number of reproduction seasons would increase income of the producers. The objectives of this study were to evaluate the impact of 1 or 2 annual reproductive cycles on production and economical health of dairy goats and to identify differences of production costs and revenues associated with changes in the herd dynamics as predicted by a mathematical model. A previously developed goat model using the System Dynamics approach to study long-term changes in the dynamics of the herd was used in these simulations. The model simulations used feeds, labor, and fixed costs as inputs and the outputs were revenues from milk production sales and sales of animals from all categories of the herd. The simulation time unit was ?month? and a long-term horizon of 10 years was considered for these simulations. The model was set up to simulate a freestall facility of a herd in equilibrium with 100 does in lactation. All parameters considered in this model assumed average values reported in production systems in the Southeast region of Brazil. The simulations results indicated that improvements of 10% in the fertility rate would increase the number animals in the herd up to 185% and 35% for one and two breeding season, respectively. Establishing a milk price as US$0.68 the break even for one and two breeding seasons was respectively US$0.62, and US$0.50, giving the systems with two breeding a capacity to support reductions on milk price up to 26% against 9% with one breeding season. When comparing the models with 1 or 2 breeding seasons was found that models with 2 breeding seasons was considerably more profitable and had a higher turnover than the model with 1 breeding season. The results indicate that the use of a second (artificial) breeding season might be an important management strategy to improve the efficiency of the dairy goat production systems. [Estudio del impacto de la estación reproductiva sobre la dinâmica de los rebaños lecheros]

A dairy goat model to study the impacts on herd dynamics.

The understanding of a dairy goat production system is crucial to establish a more competitive activity. Therefore, a simulation model was built to evaluate the dynamics of dairy goat herd under different scenarios of production. A System Dynamics approach was used to identify management policies that affect the behavior of the herd. All parameters considered in this model assumed average values reported in production systems in the Southeast region of Brazil. To simulate a herd in dynamic equilibrium, the culling and retention rates were used. Thereafter, simulations were performed based on changes in reproductive and mortality rates. All the simulations were planned to take the variation in herd development based on simple management strategies over 10 years of simulation. The dynamic equilibrium of 50 lactating does was obtained when fixed culling and retention rates of 20 and 70%, respectively, were assigned to the does after 36 months of simulation. A sensitivity analysis was made and indicated that an increase of 20% in the reproduction rates increased the number of animals in the herd in 56%. A decrease in the reproduction rate of 20% reduced the number of animals in 43%. A third simulation indicated that increasing mortality rate from 4 to 10% of the female kids decreased the number of lactating does by 36%. These results indicated that small changes in reproduction and mortality rates can considerably affect the dynamics of the herd, even though the herd may not be immediately affected because of the intrinsic delays in the system. This result is extremely important to justify the need of activity planning to consider the gap between a measurement taken and the consequences, preparing the producers to potential delays in the system. The use of mathematical models is important to understand the relationships between variables and the dynamic of the system and to assist in applying best management strategies to enhance productivity of dairy goats. [Modelo de cabras lecheras para evaluar el impacto de las estrategias de manejo en la dinámica del rebaño]

Effects of chemical composition variation on the dynamics of ruminal fermentation and biological value of corn milling (co)products

The objectives of this study were to evaluate the dynamics of gas production of several corn (co)products, to develop equations to predict the rate of ruminal fiber digestion, to estimate total digestible nutrients (TDN) and net energy for lactation (NEL), and to assess the stochasticity of chemical composition and nutritive value variability. Four corn milling (co)products were evaluated in this study: high protein dried distillers grains (HP-DDG), corn bran (BRAN) and dehydrated germ (GERM), and a dried distillers grains plus soluble produced with a low-heat drying process (BPX). Alfalfa hay was used as an internal standard feed in the in vitro fermentation dynamics analysis. Standard chemical analyses, in vitro digestibility, and in vitro gas production techniques were used to obtain the necessary physicochemical characterization of feeds. The in vitro dry matter digestibility at 24 and 48 h of incubation decreased exponentially as acid detergent insoluble nitrogen increased. However, the degree of in vitro dry matter digestibility reduction was more accentuated at 24 than at 48 h of incubation. The difference among these feeds regarding the dynamics of the anaerobic fermentation within different substrates (intact feed, and fiber and defatted residues) was evaluated. Results suggested that the proportion of fiber digested in the rumen was affected by the degree of sample processing and fat removal. Fractional fermentation rate (kf) of neutral detergent residue (without sodium sulfite) and defatted fiber residue for BRAN, GERM, HP-DDG, and BPX was estimated to be 0.0635 and 0.0852 h−1, 0.0803 and 0.0914 h−1, 0.118 and 0.117 h−1, and 0.0695 and 0.0844 h−1, respectively. The most influential variables affecting kfNDR of HP-DDG and BPX also affected the predicted TDN, suggesting that fiber quality is essential to ensure higher TDN values for these feeds. Our study indicated that it is possible to routinely quantify the rate of fiber digestion and this approach may be based on common analytical procedures namely estimates of neutral detergent fiber, acid detergent fiber, acid detergent insoluble nitrogen, ether extract, and acid detergent lignin. Our simulations of TDN values demonstrated that differences in fermentability and chemical composition of these corn (co)products might considerably affect the supply of energy to lactating dairy cow. The analytical methods developed in this study may serve as a valuable tool to assess nutrient quality and uniformity when samples differ in chemical composition

Effects of chemical composition variation on the dynamics of ruminal fermentation and biological value of corn milling (co)products

The objectives of this study were to evaluate the dynamics of gas production of several corn (co)products, to develop equations to predict the rate of ruminal fiber digestion, to estimate total digestible nutrients (TDN) and net energy for lactation (NEL), and to assess the stochasticity of chemical composition and nutritive value variability. Four corn milling (co)products were evaluated in this study: high protein dried distillers grains (HP-DDG), corn bran (BRAN) and dehydrated germ (GERM), and a dried distillers grains plus soluble produced with a low-heat drying process (BPX). Alfalfa hay was used as an internal standard feed in the in vitro fermentation dynamics analysis. Standard chemical analyses, in vitro digestibility, and in vitro gas production techniques were used to obtain the necessary physicochemical characterization of feeds. The in vitro dry matter digestibility at 24 and 48 h of incubation decreased exponentially as acid detergent insoluble nitrogen increased. However, the degree of in vitro dry matter digestibility reduction was more accentuated at 24 than at 48 h of incubation. The difference among these feeds regarding the dynamics of the anaerobic fermentation within different substrates (intact feed, and fiber and defatted residues) was evaluated. Results suggested that the proportion of fiber digested in the rumen was affected by the degree of sample processing and fat removal. Fractional fermentation rate (kf) of neutral detergent residue (without sodium sulfite) and defatted fiber residue for BRAN, GERM, HP-DDG, and BPX was estimated to be 0.0635 and 0.0852 h−1, 0.0803 and 0.0914 h−1, 0.118 and 0.117 h−1, and 0.0695 and 0.0844 h−1, respectively. The most influential variables affecting kfNDR of HP-DDG and BPX also affected the predicted TDN, suggesting that fiber quality is essential to ensure higher TDN values for these feeds. Our study indicated that it is possible to routinely quantify the rate of fiber digestion and this approach may be based on common analytical procedures namely estimates of neutral detergent fiber, acid detergent fiber, acid detergent insoluble nitrogen, ether extract, and acid detergent lignin. Our simulations of TDN values demonstrated that differences in fermentability and chemical composition of these corn (co)products might considerably affect the supply of energy to lactating dairy cow. The analytical methods developed in this study may serve as a valuable tool to assess nutrient quality and uniformity when samples differ in chemical composition

Development of a mechanistic model to represent the dynamics of liquid flow out of the rumen and to predict the rate of passage of liquid in dairy cattle

A mechanistic and dynamic model was developed to represent the physiological aspects of liquid dynamics in the rumen and to quantitatively predict liquid flow out of the reticulorumen (RR). The model is composed of 2 inflows (water consumption and salivary secretion), one outflow (liquid flow through the reticulo-omasal orifice (ROO), and one in-and-out flow (liquid flux through the rumen wall). We assumed that liquid flow through the ROO was coordinated with the primary reticular contraction, which is characterized by its frequency, duration, and amplitude during eating, ruminating, and resting. A database was developed to predict each component of the model. A random coefficients model was used with studies as a random variable to identify significant variables. Parameters were estimated using the same procedure only if a random study effect was significant. The input variables for the model were dry matter intake, body weight, dietary dry matter, concentrate content in the diet, time spent eating, and time spent ruminating. Total water consumption (kg/d) was estimated as 4.893 x dry matter intake (kg/d), and 20% of the water consumed by drinking was assumed to bypass the RR. The salivary secretion rate was estimated to be 210 g/min during chewing. During ruminating, however, the salivation rate was assumed to be adjusted for the proportion of liquid in the rumen. Resting salivation was exponentially related to dry matter intake. Liquid efflux through the rumen wall was assumed to be the mean value in the database (4.6 kg/h). The liquid outflow rate (kg/h) was assumed to be a product of the frequency of the ROO opening, its duration per opening, and the amount of liquid passed per opening. Simulations of our model suggest that the ROO may open longer for each contraction cycle than had been previously reported (about 3 s) and that it is affected by dry matter intake, body weight, and total digesta in the rumen. When compared with 28 observations in 7 experiments, the model accounted for 40, 70, and 90% of the variation, with root mean square prediction errors of 9.25 kg, 1.84 kg/h, and 0.013 h(-1) for liquid content in the rumen, liquid outflow rate, and fractional rate of liquid passage, respectively. A sensitivity analysis showed that dry matter intake, followed by body weight and time spent eating, were the most important input variables for predicting the dynamics of liquid flow from the rumen. We conclude that this model can be used to understand the factors that affect the dynamics of liquid flow out of the rumen and to predict the fractional rate of liquid passage from the RR in dairy cattle

Evaluation of protein fractionation systems used in formulating rations for dairy cattle

Production efficiency decreases when diets are not properly balanced for protein. Sensitivity analyses of the protein fractionation schemes used by the National Research Council Nutrient Requirement of Dairy Cattle (NRC) and the Cornell Net Carbohydrate and Protein System (CNCPS) were conducted to assess the influence of the uncertainty in feed inputs and the assumptions underlying the CNCPS scheme on metabolizable protein and amino acid predictions. Monte Carlo techniques were used. Two lactating dairy cow diets with low and high protein content were developed for the analysis. A feed database provided by a commercial laboratory and published sources were used to obtain the distributions and correlations of the input variables. Spreadsheet versions of the models were used. Both models behaved similarly when variation in protein fractionation was taken into account. The maximal impact of variation on metabolizable protein from rumen-undegradable protein (RUP) was 2.5 (CNCPS) and 3.0 (NRC) kg/d of allowable milk for the low protein diet, and 3.5 (CNCPS) and 3.9 (NRC) kg/d of allowable milk for the high protein diet. The RUP flows were sensitive to ruminal degradation rates of the B protein fraction in NRC and of the B2 protein fraction in the CNCPS for protein supplements, energy concentrates, and forages. Absorbed Met and Lys flows were also sensitive to intestinal digestibility of RUP, and the CNCPS model was sensitive to acid detergent insoluble crude protein and its assumption of complete unavailability. Neither the intestinal digestibility of the RUP nor the protein degradation rates are routinely measured. Approaches need to be developed to account for their variability. Research is needed to provide better methods for measuring pool sizes and ruminal digestion rates for protein fractionation systems

A Mechanistic model for predicting the nutrient requirements and feed biological values for sheep

The Cornell Net Carbohydrate and Protein System (CNCPS), a mechanistic model that predicts nutrient requirements and biological values of feeds for cattle, was modified for use with sheep. Published equations were added for predicting the energy and protein requirements of sheep, with a special emphasis on dairy sheep, whose specific needs are not considered by most sheep-feeding systems. The CNCPS for cattle equations that are used to predict the supply of nutrients from each feed were modified to include new solid and liquid ruminal passage rates for sheep, and revised equations were inserted to predict metabolic fecal N. Equations were added to predict fluxes in body energy and protein reserves from BW and condition score. When evaluated with data from seven published studies (19 treatments), for which the CNCPS for sheep predicted positive ruminal N balance, the CNCPS for sheep predicted OM digestibility, which is used to predict feed ME values, with no mean bias (1.1 g/100 g of OM; P > 0.10) and a low root mean squared prediction error (RMSPE; 3.6 g/100 g of OM). Crude protein digestibility, which is used to predict N excretion, was evaluated with eight published studies (23 treatments). The model predicted CP digestibility with no mean bias (-1.9 g/100 g of CP; P > 0.10) but with a large RMSPE (7.2 g/100 g of CP). Evaluation with a data set of published studies in which the CNCPS for sheep predicted negative ruminal N balance indicated that the model tended to underpredict OM digestibility (mean bias of -3.3 g/100 g of OM, P > 0.10; RMSPE = 6.5 g/100 g of OM; n = 12) and to overpredict CP digestibility (mean bias of 2.7 g/100 g of CP, P > 0.10; RMSPE = 12.8 g/100 g of CP; n = 7). The ability of the CNCPS for sheep to predict gains and losses in shrunk BW was evaluated using data from six studies with adult sheep (13 treatments with lactating ewes and 16 with dry ewes). It accurately predicted variations in shrunk BW when diets had positive N balance (mean bias of 5.8 g/d; P > 0.10; RMSPE of 30.0 g/d; n = 15), whereas it markedly overpredicted the variations in shrunk BW when ruminal balance was negative (mean bias of 53.4 g/d, P < 0.05; RMSPE = 84.1 g/d; n = 14). These evaluations indicated that the Cornell Net Carbohydrate and Protein System for Sheep can be used to predict energy and protein requirements, feed biological values, and BW gains and losses in adult sheep

An Investigation into Land Use Changes and Consequences in the Northern Great Plains Using Systems Thinking and Dynamics

From 1997 to 2007, 9.6 million hectares of grassland were converted to cropland and fifty seven percent of these conversions occurred in the Northern Great Plains (NGP). Since 2007, another 9.5 million U.S. hectares have been converted with the majority located in the NGP. Shortterm, positive benefits include increased food production and higher financial returns to farmers. However, there could be unintended consequences through loss of ecosystem services. Consequences may include compromised water quality, wildlife habitat loss/fragmentation, and decreased carbon sequestration. The principal objective of this work is to: 1) identify structural features influencing land use decisions through agricultural stakeholder engagement; and 2) to synthesize results into a causal loop diagram through a group model building process. This information can be used to construct a stock-flow model to quantify implications for land management, forecast potential unintended consequences from major land use changes, and develop strategies to minimize their impacts