In vitro gas production kinetics and digestibility in ruminant diets with different levels of cashew nut shell liquid

Díaz, Tatiana García; Branco, Antônio Ferriani; Ítavo, Luís Carlos Vinhas; Santos, Geraldo Tadeu dos; Carvalho, Silvana Teixeira; Teodoro, Ana Lúcia; Oliveira, Ronaldo Lopes

doi:10.5433/1679-0359.2018v39n4p1669

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…The higher concentration of lignin and cellulose in the diet with Gliricidia silage also explains the longer time taken to obtain the maximum rate of substrate degradation, in that diet. Lignin works as a mechanical barrier against the action of rumen microorganisms (DÍAZ et al, 2018), which can increase time spent by microorganisms to colonize the particle (OLIVEIRA et al, 2017) and; consequently, the time for microorganisms to reach maximum activity.…”

Section: Discussionmentioning

confidence: 99%

Mathematical models to adjust the parameters of in vitro cumulative gas production of diets containing preserved Gliricidia

et al. 2021

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This study examined the use of the Gompertz, Groot, monomolecular, Richards and two-compartment-logistic mathematical models to investigate the kinetics of in vitro gas production of diets composed of combinations of Gliricidia hay or silage. In addition, the effects of Gliricidia hay or silage inclusion on the in vitro cumulative gas production of these diets were evaluated. Rumen fermentation kinetics were analyzed by the in vitro cumulative gas production methodology. The model parameters were estimated using the Gauss Newton method, with the exception of the Richards model, which was used by Marquardt’s algorithm. Model fit was assessed using the determination coefficient, F test for parameters identity, concordance correlation coefficient, root mean square error of prediction, and decomposition of mean square error of prediction into mean error, systematic bias and random error. The models were compared for accuracy (pairwise mean square error of prediction) and precision (delta Akaike’s information criterion). All model evaluation and comparison statistics were calculated using Model Evaluation System software version 3.2.2. The Groot and Richards models did not differ from each other (P>0.05) and were the most precise and accurate (P<0.05). Therefore, the Groot model was selected due to its better accuracy and precision and easier access to the parameters. The inclusion of Gliricidia silage in the diet resulted in an increase in the time to obtain the maximum rate of degradation and in the time after incubation when half of the asymptotic level was reached. The Groot model is recommended to estimate the average curve. Dietary inclusion of Gliricidia silage alters the gas production curve due to the longer time required for the diet to reach the maximum rate of degradation, this can increase the time the diet remains in rumen and promote a reduction in the consumption.

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Section: Discussionmentioning

confidence: 99%

Mathematical models to adjust the parameters of in vitro cumulative gas production of diets containing preserved Gliricidia

et al. 2021

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“…To start the incubation, one jar at a time was taken from the incubator, where 16 TNT bags were placed, 4 bags of each forage: concentrate ratio, randomly distributed on both sides of the jar partition, and other 2 bags without sample (blank) was then added to the buffer solution (1600mL) and ruminal fluid (400mL), applying the CO 2 for thirty seconds and closing the jar lid, which was returned to the incubator. Each jar contained all F: C ratios and the doses determined as recommended by Dias et al (2017) and Diaz et al (2018). The first jar was the control (C), without additives, the second with 0.11g chitosan -Q (900mg/kg DM), the third with the addition of 0.08g technical cashew nut shell liquid -LCC (600mg/kg DM), and the fourth with 0.11g chitosan + 0.08g technical cashew nut shell liquid -Q + LCC (900mg/kg Q DM + 600mg/kgLCC DM).…”

Section: Methodsmentioning

confidence: 99%

“…According to Van Der Werf et al (1996), when the crude protein digestibility rate is lower, there is better nitrogen maintenance and elevation of amino acids in the small intestine, resulting larger amounts of amino acids to be used in reproduction, muscle and milk protein. Nevertheless, in in vitro studies, Diaz et al (2018)found that the inclusion of up to 0.5g/kg LCC DM increased IVDMD, and values higher than this reduced IVDMD. As for pH (Table 5), there was an effect of the diet (P <0.001), interaction (P <0.001) and additive (P <0.001).…”

Section: Animal Nutritionmentioning

confidence: 97%

“…Cashew nut shell liquid (LCC) at 550µg/mL in diets containing 30% forage can reduce the production of methane, acetate and butyrate and increase propionate concentration (WATANABE et al 2010). According to Diaz et al (2018), the inclusion level of up to 0.5g/kg technical LCCDM increased the IVDMD, and values higher than this resulted in a decrease in IVDMD. Based on this, the goal of this study was to evaluate in vitro the inclusion of chitosan and technical cashew nut shell liquid in ruminant diets, regarding nutrient digestibility and fermentation parameters.…”

Section: Introductionmentioning

confidence: 99%

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In vitro evaluation of the association of chitosan and cashew nut shell liquid as additives for ruminants

Pereira

Goes

Martinez

et al. 2019

Rev. bras. saúde prod. anim.

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This study evaluated the in vitro digestibility of nutrients from different diets added with chitosan (Q), technic cashew nut shell liquid (LCC) and the association of Q and LCC. The treatments used consisted of 4 diets (forage: concentrate ratio of 100: 0, 50:50, 40:60 and 20:80) associated with 4 additives (control, chitosan, LCC and the association of Q + LCC), totaling 16 treatments, in a 4x4 factorial randomized block design. The dosages used were: Control (without additives), LCC (600mg/kg DM), Chitosan (900mg/kg DM), and LCCQ (600mg/kg LCC DM + 900mg/kg Chitosan DM). In the laboratory, samples were analyzed for IVDMD, IVNDFD, IVCPD, pH and RAN (ruminal ammonia nitrogen). For pH and RAN analyses, samples were taken at 0, 2, 4, 6 and 8 hours after incubation. The results showed higher digestibility of DM, NDF and CP for diets with chitosan and technic cashew nut shell liquid alone and higher pH and RAN values in the diets containing the two additives. The association of additives brings better results for animal nutrition and increases ruminant productivity.

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“…This variety of mathematical models is still used to fit in vitro gas production of cattle feeds, additives or diverse conditions due to that its complexity of biological factors fit perfectly in one single model for posterior statistical analysis of the studied treatments. However, the Dual-pool Logistic model [8] has been widely used to fit the in vitro gas production of feedstuffs and diets for ruminants [9][10][11][12][13]. Nevertheless, it should be noted that a single model should not be used for all types of feed; rather, it is essential that different models be adjusted for each nutritional situation [14].…”

Section: Introductionmentioning

confidence: 99%

Kinetics of In Vitro Gas Production and Fitting Mathematical Models of Corn Silage

Zornitta

Ítavo

et al. 2021

Fermentation

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This study aimed at examining the effects of rumen inoculum of steers receiving different combinations of ionophore and probiotics in their diets on in vitro gas production of corn silage. The fitting of gas production was performed with five mathematical models and its kinetics was evaluated. Four crossbred steers (403.0 ± 75.5 kg body weight) with ruminal cannula were assigned to a 4 × 4 Latin square design. The additives used were Monensin sodium (Rumensin® 100, 3 g/day), Bacillus toyonensis (Micro-Cell Platinum® 109, 1 g/day) and Saccharomyces cerevisiae boulardii (ProTernative®20, 0.5 g/day). Additives were arranged into the following treatments, supplied daily into total mixed diet: (1) Monensin; (2) Monensin + B. toyonensis; (3) Monensin + S. boulardii; and (4) B. toyonensis + S. boulardii. The gas production data were fitted into the models of Gompertz, Groot, Ørskov, Brody, Richards, and Dual-pool Logistic. A perfect agreement between observed and predicted values in curves of accumulated in vitro gas production was observed in the Groot and Richards models, with higher coefficient of determination (R2 = 0.770 and 0.771, respectively), concordance correlation coefficient (CCC = 0.871 and 0.870, respectively), and root mean square error of prediction (RMSEP = 1.14 and 1.15, respectively). Evaluating the feed additives throughout the Groot model, the B. toyonensis + S. boulardii treatment presented higher VF (12.08 mL/100 mg of DM; p = 0.0022) than Monensin and Monensin + S. boulardii (9.16 and 9.22 mL/100 mg of DM, respectively). In addition, the fractional rate of gas production (k) was higher (p = 0.0193) in B. toyonensis + S. boulardii than in Monensin, not presenting a statistical difference (p > 0.05) from the other two treatments. Additionally, with the time of beginning to gas production, the lag time (λ), was greater (p < 0.001) with Monensin and Monensin + B. toyonensis than with Monensin + S. boulardii and B. toyonensis + S. boulardii. The combination of Monensin and probiotics (B. toyonensis + S. boulardii) resulted in better kinetics of degradation of corn silage, being that the Groot and Richards models had the best fit for estimates of the in vitro gas production data of corn silage tested with different feed additive combinations.

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In vitro gas production kinetics and digestibility in ruminant diets with different levels of cashew nut shell liquid

Cited by 5 publications

References 19 publications

Mathematical models to adjust the parameters of in vitro cumulative gas production of diets containing preserved Gliricidia

Mathematical models to adjust the parameters of in vitro cumulative gas production of diets containing preserved Gliricidia

In vitro evaluation of the association of chitosan and cashew nut shell liquid as additives for ruminants

Kinetics of In Vitro Gas Production and Fitting Mathematical Models of Corn Silage

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