trabalho completo - 52ª Reunião Anual da Sociedade Brasileira de

52a Reunião Anual da Sociedade Brasileira de
Zootecnia
Zootecnia: Otimizando Recursos e Potencialidades
Belo Horizonte – MG, 19 a 23 de Julho de 2015
Modelo empírico para predição do consumo diário de ração em suínos nas fases de crescimento e terminação
em clima tropical
Dani Perondi1, Luciano Hauschild2, Jaqueline de Paula Gobi1, Alini Mari3, Cintia Francarolli3, Luan Souza1, Inês
Andretta4
Doutorando do Programa de Pós-Graduação em Zootecnia – FCAV – UNESP, Jaboticabal - SP; E-mail: [email protected]
Professor assistente doutor do Programa de Pós-Graduação em Zootecnia– FCAV – UNESP, Jaboticabal - SP;
3
Mestrando do programa de Pós-Graduação em Zootecnia– FCAV – UNESP, Jaboticabal - SP ;
4
Professora assistente doutora do Programa de Pós-Graduação em Zootecnia-PPZ-UFRGS, Porto Alegre – RS;
1
2
Resumo: O desenvolvimento de modelos matemáticos na produção de suínos é constante, porém, alguns são
desenvolvidos utilizando populações homogêneas e desta forma, pouco flexíveis. Assim, o desenvolvimento de
modelos empíricos utilizando banco de dados heterogêneos, torna-se fundamental. Foi elaborada uma equação
utilizando um banco de dados com 5.060 observações do consumo diário de ração de suínos (CDR) em fase de
crescimento e terminação (40-120 kg) mantidos em clima tropical (22 e 31°C). O modelo considerou o peso vivo
(PV, kg0.803) e a temperatura (T,°C), como variáveis de entrada e o CDR (kg/dia) como resposta. O modelo
demonstrou ajuste no consumo diário predito (CDRP) com média de 2,60 kg/dia, tendo elevada correlação (0,71)
do predito ao observado no banco de dados. O modelo empírico explicou 52% da variância contida nos dados,
sendo o restante considerado aleatório. Foi verificado que o aumento em 1°C na temperatura ambiente reduziu em
53 g/dia o CDR. A utilização do modelo empírico prediz de forma eficaz o CDR em uma população heterogênea
em clima tropical.
Palavras–chave: alimentação de precisão, temperatura, nutrição
Empiric model for daily feed intake prediction in growing and finishing pigs in tropical climate
Abstract: The development of mathematical models in swine production is constant, however, some are developed
using homogeneous populations and thus inflexible. Thus, the development of empirical models using
heterogeneous database, it is essential. An equation using a database with 5060 observations of the DFI of pigs in
growing and finishing was prepared (40-120 kg) kept in tropical climate (22 and 31°C). The model considered BW
(kg0.803) and T (°C) as input variables and DFI (kg/day) in response. The model showed adjustment in the prediction
of PDFI (2.60 kg/day), having high correlation (0.71) predicted that observed in the database. The empirical model
accounted for 52% of the variance contained in the data, with the remainder considered random. It has been found
that increased by 1°C at room temperature reduced by 53 g/day DFI. The use of empirical model predicts
effectively the DFI in a heterogeneous population in tropical climate.
Keywords: nutrition, precision feeder, temperature
Introduction
World production of pigs is distributed geographically in varied climates. Where 50% is located in a tropical
climate and is characterized by high temperatures. Second, St-Pierre et al. (2003) related losses high temperature in
the EUA reach 300 million dollars a year. The temperature increase reduces daily feed intake (DFI) animals, and
thus affects performance (Renaudeau et al., 2011). The use of empirical models together with other models can help
understand the change of DFI according to temperature change and body weight of the animal. However, empirical
models in their great majority are developed homogeneous populations, exploring very little the animal's response
to the environment. Thus, utilizing one database for DFI, empirical equation was determined in order predict PDFI
for growing and finishing pigs in tropical climate.
Material and Methods
A study was conducted at the Universidade Estadual Paulista Julio de Mesquita Filho (FCAV/UNESP) in
pig farming laboratory. Were used 68 pigs with high genetic potential, with initial weight of 38.98 kg and final of
128.50 kg. The animals were kept into a single group, with free access to feed and water. The experimental period
was be January to March 2015, totaling 76 experimental days. Daily collection was carried out (time) the
temperature of the experimental environment using a datalogger (HT-500). The data of daily feed intake of real
time (DFI) of each animal were collected by an automated device AIPF (Automatic and Intelligent Precision
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52a Reunião Anual da Sociedade Brasileira de
Zootecnia
Zootecnia: Otimizando Recursos e Potencialidades
Belo Horizonte – MG, 19 a 23 de Julho de 2015
Feeder). Diets were formulated order to meet requirements, recommended by the NRC (2012). Weigh of the
animals was performed weekly. Data were analyzed using the statistical package SAS 9.0. Data normality analysis
was removed from the database with residue greater than 3 and less than -3. Were submitted to multivariate analysis
order to get the model that best fit the data. Was adopted 20% significance, with 80% confidence to accept the
parameters used in the empirical model.
Results and Discussion
For processing model were utilized 5062 observations of DFI in pigs with different body weights and
temperatures. The maximum, minimum and mean temperature, body weight and daily feed intake observed (DFI)
and daily feed intake estimated by the model (PDFI) are present in Table 01. The average temperature during the
day showed maximum and minimum 30°C overnight 20°C, with amplitude of 10°C. The highest temperature was
15:00 hours and lower temperature values were 18:00. The considered ideal temperature value for pigs with average
weight of 81.27 kg is 20°C, according Whittemore and Kyriazakis (2008).
Table 01. Body weight (BW), temperature (T), daily feed intake observations (DFI) and predict
daily feed intake model (PDFI), in growing and finishing pigs in experimental periods
(76 days).
Items
Observations
Mean
Minimum
Maximum
SEM
BW, kg
T, °C
5062
5062
81.27
26.78
29.16
22.75
147.01
31.16
0.010
0.384
DFI, kg
5062
2.60
0.40
4.80
0.022
PDFI
5062
2.60
1.49
3.70
0.007
The model that best adjusted to the DFI data is as follows:
The BW is body weight (kg) was elevated in the exponent 0.803, it is difficult to define the best exponent
because there is an exponent that adequately describes the maintenance and production costs (Whittemore et al.,
2001). T is the temperature in °C of the breeding environment, the intercept and the independent variables BW and
T, were significant (P=0.001). The correlation It was verified between the observed and estimated data found high
correlation coefficient (0.71). The presented empirical model demonstrated accuracy in determining the PDFI
compared to DFI observed data. The average DFI observed in the population was 2.60 kg/day, similar to that
estimated by the model propost (2.60 kg/day). The model showed R-square 0.52, representing 52% of the data
variance and the rest remained in random error 48%. Was verified that increasing by 1°C at room temperature, on
average 53 grams reduces the DFI in growing and finishing pigs. As the DFI data were obtained from a population
with high variability, it is presumed that the model has not have power to appropriate adjustment in PDFI.
Furthermore, the DFI is influenced by a number of physiological, biochemical factors (animal) animal interrelation
environment (Wellock et al., 2003) and extrinsic factors related to the authoring environment. The model response
in predicting the DFI demonstrated ways near the average of the population (Figure 01), however because it is a
large and heterogeneous database is difficult to model accurately predict.
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52a Reunião Anual da Sociedade Brasileira de
Zootecnia
Zootecnia: Otimizando Recursos e Potencialidades
Belo Horizonte – MG, 19 a 23 de Julho de 2015
Figure 1. Graphic demonstration of the daily feed consumption curves over time (Day) using the
observed data DFI (DFI) and the estimated by the model DFI (PDFI).
The utilization of empirical models include contradictions, because it is considered that they do not show
biological relationship with the animal. The use of mathematical models is critical to understanding the dynamics of
the population. In this way, new models have been proposed in the literature, it is difficult to adopt a model as the
ideal, for some traits the population in which it was developed prevail in the model answer. Thus, the search for
models that take into account the variability of populations becomes critical. However, this should be tested in a
different database in which it was developed.
Conclusions
Empirical model used demonstrated accuracy in predicting the DFI values in heterogeneous population of
growing and finishing pigs in tropical climate.
Acknowledgements
The authors thanks (Grant nº. 2012/03781-0) São Paulo Research Foundation (FAPESP) (Brazil) for
financial support of this project.
References
NRC. 2012. Nutrient requirements of swine. 11th ed. Natl. Acad. Press, Washington,DC.
RENAUDEAU, D.; GOURDINE, J. L.; ST-PIERRE, N. R. A meta-analysis of the effects of high ambient
temperature on growth performance of growing-finishing pigs. Journal of Animal Science, v. 89, n. 7, p. 22202230, 2011.
ST-PIERRE, N.; COBANOV, B.; SCHNITKEY, G. Economic losses from heat stress by US livestock industries.
Journal of dairy science, v. 86, p. E52-E77, 2003.
WELLOCK, I. J.; EMMANS, G. C.; KYRIAZAKIS, I. Predicting the consequences of social stressors on pig food
intake and performance. Journal of Animal Science, v. 81, n. 12, p. 2995-3007, 2003.
WHITTEMORE, C. T.; KYRIAZAKIS, I. Whittemore's science and practice of pig production. John Wiley &
Sons, 2008.
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