A Dairy Herd Model for Use in Whole Farm Simulations

Rotz, C. Alan; Mertens, D.R.; Buckmaster, Dennis R.; Allen, Michael S.; Harrison, Joe

doi:10.3168/jds.s0022-0302(99)75541-4

Cited by 79 publications

(76 citation statements)

References 21 publications

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“…Perhaps the most undesirable characteristic of this model was the nil of the intercept model. Nevertheless, for the use in milk production simulation systems, such as those by Rotz et al (1999), Rotz et al (2005), Rennó et al (2008a, b, c), in which the time interval used in each interaction is greater than one day, this may not present serious problems.…”

Section: Resultsmentioning

confidence: 99%

Selection of models of lactation curves to use in milk production simulation systems

Cunha¹,

Pereira²,

Silva³

et al. 2010

R. Bras. Zootec.

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-The objective of this study was to select models of lactation curves with a better adjustment to the observed data in models of milk production simulation systems. A data base on 6,459 recordings of daily milk production was used. These data were obtained from monthly and fortnightly controls of milk between 2004 and 2007, from 472 lactations of animals from ten different milking cow herd farms. Based on rolling averages of milk production (MP-L/day) per cow, the ten herd farms were divided into low (L < 15), medium (15 20). Data were also divided according to the lactation numbers in first, second, third or greater. Eight lactation curve models commonly used in literature were compared. The models were individually adjusted for each lactation. The goodness of fit used for comparison of those models was the coefficient of determination, mean square error, mean square prediction error and the Bayesian information criterion. The values for the goodness of fit obtained in each model were compared by using 95% probability confidence interval. Wilmink (1987) model showed a better adjustment for cows of the first lactation numbers, whereas the Wood (1967) model showed a better adjustment for cows of the third or greater lactations numbers for the low milk production groups. Wood model showed a better adjustment for all the lactation numbers for the medium milk production group. Dijkstra (1997) model showed a better adjustment for all lactation numbers for the high milk production group. Despite of being more recent, the model by Pollott (2000), mechanist based and with a higher number of parameters, showed a good convergence for the used data.

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Section: Resultsmentioning

confidence: 99%

Selection of models of lactation curves to use in milk production simulation systems

Cunha¹,

Pereira²,

Silva³

et al. 2010

R. Bras. Zootec.

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“…No cálculo das exigências nutricionais de mantença, o peso corporal variou conforme equação proposta por Rotz et al (1999) e o peso corporal segundo os dias de lactação. Utilizou-se o software CNCPS, versão 5.0 (Fox et al, 2003), para cálculo das dietas e estimativa das exigências nutricionais e do valor nutritivo dos alimentos.…”

Section: Methodsunclassified

Eficiência bioeconômica de estratégias de alimentação em sistemas de produção de leite: 1. Produção por animal e por área

Rennó¹,

Pereira²,

Leite³

et al. 2008

R. Bras. Zootec.

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RESUMO -Objetivou-se aplicar um modelo de simulação para avaliação bioeconômica de estratégias de alimentação para rebanhos leiteiros e avaliar a produtividade física e a eficiência bioeconômica de sistemas de alimentação com diversas estratégias de alimentação à base de volumosos para vacas de cinco níveis de produção de leite. Utilizou-se uma plataforma computacional desenvolvida com os programas CNCPS v.5.0 e planilhas eletrônicas do Microsoft Excell ® , de forma a simular a produção e as exigências de nutrientes de uma lactação completa para vacas de diferentes níveis de produção. Foram realizadas análises econômicas em sete estratégias de alimentação. A avaliação da receita subtraída dos custos com alimentação (RMCA) comprovou interação entre a estratégia de alimentação e o nível de produção de leite. As estratégias com alimentação à base de silagem de milho durante a época da seca e pastagens na época das águas resultaram em maiores RMCA para todos os níveis de produção de leite, apesar de as demais estratégias apresentarem resultados próximos dependendo do nível de produção de leite. Nas estratégias avaliadas, quanto maior a produção de leite por vaca maior a produtividade (PROD/ha) e a RMCA por área (RMCA/ha). Quanto maior a capacidade de suporte dos volumosos, ou quanto maior a taxa de lotação que determinada área foi submetida, considerando determinada estratégia de alimentação e determinado nível de produção de leite, maior a PROD/ha e RMCA/ha. Para a RMCA por vaca, volumosos de maior densidade energética resultam em diminuição dos custos de alimentação e aumento da receita por animal. A RMCA/ha é fortemente influenciada pela capacidade de suporte das forrageiras em todos os níveis de produção.Palavras-chave: bovinos de leite, eficiência bioeconômica, simulação Bioeconomic evaluation of feeding strategies in milk production systems. Production per animal and per areaABSTRACT -This work was carried out to apply a simulation model for the bioeconomic evaluation of feeding strategies for dairy herds and evaluate the physical productivity and the bioeconomic efficiency of feeding systems for dairy cows using feeding forage based strategies for cows with five levels of milk yield. An computational platform developed with the programs CNCPS v5.0 and electronic spreadsheets of Microsoft Excell ® was used, in way to simulate the production and demands of nutrients of a complete lactation for cows of different milk yield levels. Economic analyses in seven strategies of feeding were carried out. The income over feed costs (RMCA) showed interaction among the feeding strategy with the milk yield levels. The strategies based on corn silage during the dry season and pastures during rain season resulted in higher RMCA for all milk yield levels, although the other feeding strategies present closed results, depending on the milk yield level. In the evaluated strategies, as higher was the milk yield per cow, greater was the productivity (PROD/ha) and the RMCA per area (RMCA/ha). As higher was the carrying capacity of f...

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“…Therefore, an accurate and rapid estimate of these parameters is critical to formulate diets that meet animal energy requirements (De Boever et al, 1996;Weiss, 1998;Aguiar et al, 2011). Historically, the energy content of forages was estimated using empirical singlecomponent equations based on specific nutrients such as NDF, ADF or crude fibre (Mertens, 1992;Rotz et al, 1999;Ferreira and Mertens, 2005). A summative approach was adopted by dynamic nutritional models (Sniffen et al, 1992; -E-mail: francesco.masoero@unicatt.it National Research Council (NRC), 2001; Robinson et al, 2004) to estimate the net energy for lactation (NE L ) content of forage on the basis of their total digestible nutrient (TDN) values and considering the level of intake of animals (Weiss et al, 1992;Weiss, 1998).…”

Section: Introductionmentioning

confidence: 99%

Use of principal component analysis to classify forages and predict their calculated energy content

Gallo

Moschini

Cerioli

et al. 2013

Animal

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A set of 180 forages (47 alfalfa hays, 26 grass hays, 52 corn silages, 35 small grain silages and 20 sorghum silages) were randomly collected from different locations of the Po Valley (Northern Italy) from 2009 to 2010. The forages were characterised for chemical composition (11 parameters), NDF digestibility (five parameters) and net energy for lactation (NE L ). The latter was calculated according to the two approaches adopted by the 2001 Nutrient Research Council and based on chemical parameters either alone (NE L3x-Lig ) or in combination with 48 h NDF degradability in the rumen (NE L3x-48h ). Thereafter, a principal component analysis (PCA) was used to define forage populations and limit the number of variables to those useful for obtaining a rapid forage quality evaluation on the basis of the calculated NE L content of forages. The PCA identified three forage populations: corn silage, alfalfa hay and a generic population of so-called 'grasses', consisting of grass hays, small grain and sorghum silages. This differentiation was also confirmed by a cluster analysis. The first three principal components (PC) together explained 79.9% of the total variation. PC1 was mainly associated with protein fractions, ether extract and lignin, PC2 with ash, starch, NDF and indigestible NDF (iNDF) and PC3 with NDF digestibility. Moreover, PC2 was highly correlated to both NE L3x-Lig ( r 5 20.84) and NE L3x-48h ( r 5 20.94). Subsequently, forage-based scores (FS) were calculated by multiplying the original standardised variables of ash, starch, NDF and iNDF with the scoring factors obtained from PCA (0.112, 20.141, 0.227 and 0.170, respectively). The FS showed a high determination coefficient for both NE L3x-Lig ( R 2 5 0.86) and NE L3x-48h ( R 2 5 0.73). These results indicate that PCA enables the distinction of different forage classes and appropriate prediction of the energy value on the basis of a reduced number of parameters. With respect to the rumen in situ parameters, iNDF was found to be more powerful at discriminating forage quality compared with NDF digestibility at different rumen incubation times or rates of NDF digestion.

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A Dairy Herd Model for Use in Whole Farm Simulations

Cited by 79 publications

References 21 publications

Selection of models of lactation curves to use in milk production simulation systems

Selection of models of lactation curves to use in milk production simulation systems

Eficiência bioeconômica de estratégias de alimentação em sistemas de produção de leite: 1. Produção por animal e por área

Use of principal component analysis to classify forages and predict their calculated energy content

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