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: Prediksi Emisi Gas Metana pada Ransum yang Mengandung Tanin dalam Sistem RUSITEC Melalui Komposisi Asam Lemak Terbang
Abstrak: Greenhouse gas (GHG) emissions from agricultural sector account for about 25.5% from total global anthropogenic emission. While CO2 receives most of the attention as a factor relative to global warming, CH4 is one of the most important GHGs with the relative global warming potential of 25 time higher than that of CO2. Ruminant animals contribute towards the greenhouse effect through methane emission as a result of fermentation processes occurring in the rumen through microbial activities. This study is aimed to predict CH4 emissions from volatile fatty acid (VFA) profiles in the rumen simulation technique system. Data from a total of 6 experiments comprising of 25 treatments were entered in a database. Rumen simulation technique measurements were distinguished as experimental approaches. Methane was predicted from volatile fatty acid (VFA) profiles, i.e. acetate, propionate and butyrate based on rumen stoichiometry and compared with the measured CH4 emission. Prediction of enteric CH4 emissions was according to Hegarty or Moss model. The results predicted by both Hegarty and Moss models showed closer regression lines to the ideal line measurement of CH4 (where the observed values are equal to the predicted values) after adjustment by accounting for the actual hydrogen recovery. Comparing the both models, Moss equation (R2 = 0.766) is closer to the ideal line than that of Hegarty (R2 = 0.763). It was concluded that prediction of CH4 emissions in the Rusitec system via VFA composition could explain the variation of CH4 quite accurately but there was a considerable bias from the ideal line.
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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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