Prediction of the relationship between body weight and body condition score in sheep

During the whole production cycle it is important to monitor the energy balance and to quantify body reserve changes of the ewes. This can be done, both in experimental settings and in the field, by estimating the body condition score (BCS) of the ewes and its variations. However, if this tool is used to balance the diets it is necessary to know the relationship between BCS and body weight (BW), which varies depending on the mature size of the breed and of the population considered within each breed. The relationship between BW and BCS has been studied only for some sheep breeds and populations. For this reason, this research aimed to develop a prediction model of this relationship in ewes for any breed or population

Development and evaluation of a model to predict sheep nutrient requirements and feed utilisation

A new feeding system for sheep, called MIPAF, was developed by integrating previously published equations with new ones to predict energy and protein requirements as well as feed utilization of sheep. Special emphasis was given to dairy sheep, whose specific needs are not considered by most sheep feeding systems, and to some of the environmental factors that affect requirements. Original equations were added to predict fluxes in body energy reserves from body weight (BW) and body condition score. The prediction of supply of nutrients was based on the discount system of Van Soest. Thus, the MIPAF system predicts feed value as a function of the specific feeding level of the sheep that receive the ration. The ability of the MIPAF model to predict BW variations was evaluated using data from six studies with adult sheep (13 treatments with lactating ewes and 15 with dry ewes or wethers). The model predicted the variations of BW in sheep with no bias, but with high rooted mean squared prediction error (RMSPE) (mean bias = -0.1 g/d; P > 0.1; RMSPE = 44.9 g/d; n = 28). Three extreme outliers were discarded because the treatment diets, made only of wheat straw and supplied to mature wethers, had very low CP concentrations (less than 3.25%, DM basis). After the outliers were removed, the prediction error improved but the mean bias became significantly different from zero (mean bias = -12.3 g/d; P < 0.05; RMSPE = 29.6 g/d; n = 25). Prediction accuracy was different between lactating and non lactating sheep. Variations of BW in lactating ewes were predicted with high accuracy (mean bias = 6.8 g/d; P > 0.1; RMSPE = 18.7 g/d; n = 13), while for dry ewes the model was less accurate, under predicting the variations in BW (mean bias = -33.0 g/d, P < 0.001; RMSPE = 38.1 g/d; n = 12). The evaluations included published experiments with sheep of diverse body sizes and physiological stages fed diverse diets at various levels of nutrition. This suggests that the MIPAF model can be used to evaluate diets and animal performance in a variety of production settings with good accuracy

Nutritional strategies to improve lactation persistency in dairy ewes

Milk production is largely dependent on the shape of the lactation curve. Relevant elements of the lactation pattern are the peak yield, which represents the maximum milk yield during the lactation, and the lactation persistency, which expresses the ability of animals to maintain a reasonably constant milk yield after the lactation peak. Thus, persistent animals are those that show flatter lactation curves. Several measurements of persistency have been proposed (Broster and Broster, 1984; Gengler, 1996): the rate of fall of milk yield per week or per month; combinations of parameters of mathematical functions used to model the lactation curve; the variation of test day yields throughout the whole lactation or part of it; the proportion of total milk yield achieved in a certain period (e.g. second half of lactation). However, none of the above mentioned measurements seems to be able to become the reference method (Grossman et al., 1999). For example, the definition of persistency as the rate of fall of milk yield per unit of time can be misleading if the absolute level of production is not considered. Usually curves with high peak yield show low persistency because the rate of milk yield declines faster in animals that have a fast milk yield increase after calving. Thus in this review, we will consider persistency in a broad sense, and we will analyze the nutritional and nonnutritional factors that affect and limit milk production in mid-late lactation in sheep

Effects of heat stress and diet on milk production and feed and energy intake of Sarda ewes

Ten Sarda dairy ewes (5th-6th month of lactation; 1995 ± 353 g/d of milk yield) were divided into two isoproductive groups and fed two different diets (high and low fiber) from May 20th to June 18th 2003, to evaluate diet effects on milk yield and intake. In addition, the relationships between meteorological conditions, measured during that unusually hot period, and milk yield and quality, dry matter intake, NDF or NDL were determined, to study animal responses to heat stress. Diet did not have any significant effect on the evaluated parameters. Milk yield was reduced by 20% (0.39 kg/d per head) as minimum temperatures changed from 9-12 °C to 18-21 °C. Similar milk yield reduction was observed as mean temperature-humidity index (THI) went from 60-65 to 72- 75. As wind speed increased from 1.5-2.5 m/s to 2.5-4 m/s, milk yield increased by 10%. Milk composition was not affected by heat stress throughout the experiment except for milk somatic cell count, which was increased by high temperatures. Dry matter, fibre and net energy intake varied significantly during the trial, with consistent and marked decreases as heat load increased

Effect of the feeding method of a complete pelleted feed (Unipellet) as supplement of grazing in dairy ewes

This trial was carried out to compare, in grazing ewes, two feeding techniques of the Unipellet, which is a complete pelleted feed with a high un-milled fibre content and a high energy concentration, obtained using fatty acid calcium soaps. 100 Sardinian dairy ewes were divided in 2 groups (A and B) and fed, during l0 weeks, at grazing during the day + either 500 g/d of Unipellet (group A), given twice/d at milking time, or Unipellet ad libitum during the night (group B). The group A ate completely the Unipellet supplied (454 g/d of DM), whereas the group B ate as average 1241 g/d of Unipellet (1121 g/d of DM). The milk yield did not differ significantly between the two groups (g/d 1507 vs 1414). The milk fat content was higher in the group B (5.89% vs 6.31%, P ≤ 0.01) whereas the milk protein content and the somatic cells count did not vary at allo The body weight increased more in the group B (+3.79 kg vs +5.73 kg, P ≤ 0.01)

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

A multivariate and stochastic approach to identify key variables to rank dairy farms on profitability

The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21 farms had dRI-1 SD, 32 farms were between -1 SD and 0, 67 farms were between 0 and +1 SD, and 15 farms had dRI+1 SD. The top 10% of the farms had a dRI greater than 0.170 €/L, whereas the bottom 10% farms had a dRI lower than 0.116 €/L. This stochastic approach allowed us to understand the relationships among the inputs of the studied dairy farms and to develop a ranking index for comparison purposes. The developed methodology may be improved by using more inputs at the dairy farm level and considering the actual cost to measure profitability

Trial on use of a complete pelleted feed (Unipellet) in lactating ewes: metabolic profile results

A trial was carried out to examine the metabolic and productive effects of a complete pelleted feed (Unipellet) in dairy ewes feeding. 24 Sardinian lactating ewes were divided in 3 groups (A, B, C) and fed with: group A = pasture + pelleted concentrate; group B = alternatively either pasture + Unipellet ad libitum or only Unipellet ad libitum; group C = pasture + Unipellet ad libitum. The intake of concentrate was 756 g/d in the group A, whereas the intake of Unipellet was 998 g/d in the group C and 858 g/d in the group B when the ewes grazed and 2277 g/d when the Unipellet was the only fed. The milk yie1d of the 3 groups did not differ significative1y (g/d 906 vs 1044 vs 975); the milk fat content was highest in the group B (6.42% vs 7.08% vs 6.33%); the milk protein content was highest in the group A and lowest in the group B (6.32% vs 5.55% vs 5.92%). The body weight increased more in the group B. The metabolic profile showed that the Unipellet did not seem to have an adverse effect on the alimentary canal, whereas the function of the liver appeared to be slight1y impaired

Effects of triticale cultivars grown in a Mediterranean environment on biomass yield and quality

Triticale is a valuable crop in Mediterranean environments because its growth capacity at low temperatures and its precocity make it possible to obtain high biomass yields in early spring. Precocity of triticale is particularly appreciated in Mediterranean environment, where irrigation allows the sowing of a spring–summer corn crop after a winter cereal crop has been harvested for silage. In these conditions, early planted corn can take advantage of both longer-cycle cultivars and of the lower incidence of the European corn borer Ostrinia nubilalis attacks. Nutritional quality of triticale as forage is related to the phenological stage at harvest, cultivar choice, seeding rate and environmental conditions. The work reported in this paper was aimed at verifying if the hypothesized effects of the different habitus (cold requirement) of triticale cultivar grown at different seeding rates affect biomass quantity and quality at the stages of flowering and milk-waxy-maturity, which are the most relevant for triticale silage production

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