Judul: Estimasi Emisi Gas Metan yang Dihasilkan dari Fermentasi Hijauan Tropis dalam Rumen In Vitro Melalui Komposisi Asam Lemak Terbang
Abstrak: Rumen is the home to billions of microbes, including bacteria, methanogens, protozoa and fungi. These microbes breakdown feed to produce volatile fatty acids (VFA), carbon dioxide, ammonia and methane (CH4). Metabolic hydrogen in the form of reduced protons (H) is used during CH4 formation as well as during VFA synthesis. Therefore, VFA molar proportion in the rumen is stoichiometrically related to CH4 emission. The aim of this study was to evaluate methane emission between experimental and model estimates. The data sets were obtained from a published literature. Samples used were leaves from 27 tropical plant species. Model comparison was based on Duncan’s test. Prediction error was conducted by computing mean square prediction error (MSPE) and root mean square prediction error (RMSPE). Results showed that linear equation of Moss model to CH4 observed before adjustment was y = 0.4231x – 3.1762 with R2 = 0.4645, whereas Hegarty model to CH4 observed before adjusment was y = 0.3735x – 3.2963 with R2 = 0.4778. Further, linear equation of Moss model to CH4 observed after adjusment was y = 0.8446x – 4.6719 with R2 = 0.6619, while Hegarty model to CH4 observed after adjustment was y = 0.7405x – 4.8011 with R2 = 0.6714. Validation assessment showed that estimation model of Moss et al. (2000) had lower RMSPE value, i.e. 8.01%, than that of Hegarty and Nolan (2007) model, i.e. 10.73%. This study demonstrated that the low or high methane emission can be estimated by VFA composition with a sufficient accuracy. Adjusment by considering H2 recovery lowered the bias significantly. Comparison of predicted versus observed CH4 production showed that Moss model had closer to the ideal line (where predicted values are the same with the observed values) than Hegarty model.
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Judul: Produksi Gas Metan dan Pola Fermentasi Rumen Domba Lokal yang Diberi Pakan Komplit Mengandung Indigofera sp. dan Limbah Tauge Menggunakan RUSITEC
Abstrak: Methane (CH4) enteric, naturally is fermentation metabolic from rumen microorganism which is in gas form and most of it will be excreted through the mouth as an eructation process. Indigofera sp. is a legume shrub which has high nutrient content; 27.97% crude protein, 19-50% NDF (neutral detergent fiber), 15% crude fiber, 0.22% calcium, 0.19% phosphor, and organic matter digestibility (in vitro) was 56-72%. Mung bean sprout waste is part of bean sprouts which is not consumed by humans, it contain green husk of bean and usually mixed by chunks of tail and head of sprouts that are no longer intact. Mung bean sprout waste contain 13-14% crude protein, crude fiber 49.44%, and 64.65% TDN. Indigofera sp. and mung bean sprout waste are potential as feed sources. Both of them could be used in ruminant ration as high protein and energy contribution. Ruminant methane production is highly influenced by nutrient composition and chemical form of ration, so any information regarding methane production determined by forms and compositions of ration are precious. This study aimed to identify the effect of complete feed for local sheep containing 30% Indigofera sp. or 30% mung bean sprout waste on methane production and obtained the rumen fermentation pattern using RUSITEC. The measurement of methane production can be performed using experimental animals (in vivo), in vitro, and semi in vitro–better known as rumen simulation technique (RUSITEC) which is a more complex rumen simulator than other in vitro analysis. Methane concentration was measured using methane analyzer. The rations were composed iso-calories and pelleted, they contained 30% Indigofera sp. and 70% concentrate (IS) or 30% mung bean sprout waste and 70% concentrate (LT). The rumen fluid were obtained from eight adult local sheep. They were kept for three months, four of them were fed IS and the other four were LT. Incubated ration in RUSITEC were same as fed for the sheep, it expected that obtained the same fermentation conditions between RUSITEC and sheep rumen. RUSITEC Analysis was conducted for eleven days, consisted of five days for adaptation period and six days (sixth to eleventh) for observation period. Responses observed were total gas production, percentage and methane production, pH, ammonia concentration, dry matter and organic matter degradation. The fermentation of responses by time were presented descriptively. Averages of responses were analyzed using student t-test. The patterns of total gas production, percentage and methane production, pH, ammonia concentration, dry matter and organic matter degradation were fluctuation by observation time. Total gas production of IS was significantly (P < 0.05) higher (1995.81 mL/day) than LT (1682.49 mL/day). Methane production of IS ration (163.58 ml/day equivalent to 9.59 mL/g incubated dry matter) was not significantly different with LT (141.43 ml/day equivalent to 8.23 mL/g incubated dry matter). The pH Values, ammonia concentration, dry matter and organic matter degradation of IS and LT were 6.78 and 6.91; 7.59 mM and 4.85 mM; 53.56% and 43.25%; 57.52% and 45.96% respectively. The pH value and ammonia concentration were not significant different in all treatments, however dry and organic matter degradation were significantly (P < 0.05) higher in
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Judul: Construction of Strongly Optimal Binary Linear Code with Minimum Distance 9 and 11
Abstrak: A binary linear code of length n over q F is a subspace of n q F . A code has three parameters that attached to it, namely length, dimension, and minimum distance. A code with length n, dimension k and minimum distance d is often called [n, k, d ]-code. Usually, when two parameters are given, then we want to find a code that has the best value for the last parameter. Based on Gilbert- Varshamov bound, if a [n, k, d ]-code exists and can not be expanded, we call it a strongly optimal code. In this paper, we construct strongly optimal code with minimum distance 9 and 11. In constructing the code, we created a theorem and algorithm based on Gilbert-Varshamov bound before we implement the algorithm to MAPLE programming language. Because of computational limitations, the program can only construct up to k = 10 for d = 9 and k = 12 for d = 11.
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