WE-119
First estimation and validation of a new model FirstFirst estimation and validation of a new model to predict estimation and validation of a new model to predict dry matter loss based on dry matter loss based on temperature changes – III. totemperature predict dry changes matter loss based on – III. Validation of model Validation of model in a crop with low ensilability. temperature changes – III. Validation of model in a crop with low ensilability a crop K.L. Witt in , J. N. Joergensen , N. G.with Nielsen , K. low A. Bryan , ensilability G. Copani , S. Pires , V. Vrotniakiene , J. Jatkauskas 1
1, N. G. Nielsen 1, K. A. Bryan 1, G. Copani 1, S. Pires 1 1 1 1 1 1, V. Vrotniakiene22, J. Jatkauskas2 K.L. Witt1 , J. N. Joergensen
2
1, 10/12 1, K. A. Bryan 1, S. Pires1, V. Vrotniakiene2, J. Jatkauskas2 Chr. 1Hansen - Boege Alle - 2970 Hoersholm – 1Denmark,
[email protected] K.L.1 Witt , J. N. Joergensen N. G. Nielsen , G. Copani 2 Institute of Animal Science of Lithuanian University of Health Sciences, Baisogala, 82317, Lithuania 1 Chr. Hansen Boege -Alle 10/12 - 2970 Hoersholm – Denmark, 1 Chr.- Hansen Boege Alle 10/12 - 2970 Hoersholm – Denmark,
[email protected] [email protected] 2 Institute of Science Animal Science of Lithuanian University HealthSciences, Sciences, Baisogala, 82317, Lithuania Institute 2of Animal of Lithuanian University of of Health Baisogala, 82317, Lithuania
Keywords: aerobic stability, dry matter loss estimation, spoilage control, temperature increase
Keywords:Keywords: Aerobic stability, dry matter loss estimation, spoilage control, temperature increase aerobic stability, dry matter loss estimation, spoilage control, temperature increase
INTRODUCTION It was recently demonstrated that a linear regression correlation exists, between dry matter (DM) loss and temperature development during INTRODUCTION It was challenge recently demonstrated that The a linear regression correlation aerobic stability (Pires et al. 2018). model was validated and exists, between dry matter (DM) loss and temperature development during provedIt useful to estimate DM loss inthat different storage structures (mini exists, was recently demonstrated a linear regression correlation aerobic stability challenge (Pires et al. 2018). The model was validated between dry matter (DM) losssensitivity and development during silos and big bales) and showed no to indifferent treatments and proved useful to estimate DMtemperature loss different storage structures aerobic challenge (Piresand et FC al.showed 2018).etThe model was to validated (mini silos and big SiloSolve® bales) no sensitivity different (untreated andstability inoculated with (Witt al. 2018)).
andtreatments proved useful to estimate DM loss in different storage (untreated and inoculated with SiloSolve® FCstructures (Witt et al. (mini silos and big bales) and showed no sensitivity to different 2018)). treatments (untreated andtoinoculated withnew SiloSolve® FC (Witt The purpose of this study was test how the model could be et al. The purpose of this study was to test how the new model could be 2018)). used for DM prediction in alfalfa/grass silages during usedloss for DM loss prediction in alfalfa/grass silagesaerobic during aerobic Thevs. purpose of the this study described was to test the new model could spoilage the model described by McDonald et al. (1991), using the spoilage vs. model by how McDonald et al. (1991), usingbe the used for DM loss prediction in temperature alfalfa/grass during difference between recorded thethe siloambient and theaerobic ambient difference between recorded temperature in the siloinsilages and spoilage vs. the model described by McDonald et al. (1991), using the temperature. temperature. difference between recorded temperature in the silo and the ambient MATERIAL AND METHODS temperature.
alfalfa:ryegrass MATERIAL AND METHODS (50:50) (35.1% DM) alfalfa:ryegrass MATERIAL METHODS SiloSolve® FC (FC) Untreated AND control (50:50) (35.1% DM) 150,000 cfu/g of forage Untreated control
n=5
SiloSolve®buchneri FC (FC) DSM22501 Lactobacillus Alfalfa:Ryegrass 150,000 cfu/g of forage Lactococcus lactis O224 DSM11037 (50:50) (35.1% DM) Lactobacillus buchneri DSM22501 Mini silo Lactococcus lactis O224n=5 DSM11037 3 l, (1.8-1.9 kg forage)/silo
RESULTS RESULTS
Table 1. DM loss recorded vs. calculated using 2 different models based on temperature in the difficult to ensile alfalfa/grass crop, comparing 2 different treatments (TRT) (untreated (C) and SiloSolve® FC (SSFC)). Table 1. . DM loss recorded vs. calculated using 2 different models based on temperature in the difficult to ensile alfalfa/grass crop, comparing 2 different treatments (TRT) (untreated (C) and SiloSolve® FC (SSFC)).
RESULTS
Calculated DM
Table 1. . DM loss recorded vs. calculated using 2 different models based on temperature inCalculated the difficult to ensile alfalfa/grass crop, Ambient DM loss (%) using comparing 2 different treatments (TRT) (untreated (C) and SiloSolve® FC (SSFC)).
T at max
Crop
Crop
Max T (°C)
DM loss (%)
loss (%) using
Ambient T during
reached after
recorded after AS
Calculated DM delta vs. ambient
T atAS max test
Max Tof(°C) [hours] AS test
DM test loss (std.(%) dev.)
loss (%) using T according to
T during (°C)
reached after
recorded after AS
delta vs. ambient McDonald (1991)
AS test
[hours] of AS test
test (std. dev.)
T according to
Calculated DM linear regression loss (%) using to model according linear et regression Pires al. (2018) model according (std. dev.) to
Pires et al. (2018) TRT C SSFC C SSFC McDonald C SSFC C SSFC (°C) (1991) (std. dev.) Alfalfa/grass 3.1 1.9 2.6 1.0 25.3 22.1 TRT C SSFC C SSFC C SSFC C SSFC 20.6 10 10 [204] [240] Mini silos (+/- 0.21) (+/- 0.21) (+/- 0.4) (+/- 0.4) Alfalfa/grass 3.1 1.9 2.6 1.0 25.3 22.1 Figure 1. Continuous20.6 temperature recording during aerobic exposure of alfalfa/grass crop,10 comparing 102 different 2 different treatments (TRT) ® [204]**(P
