ブロイラー鶏糞のバーミコンポスト化における炭素窒素比の最適化
Carbon-to-nitrogen optimization for vermicomposting broiler poultry litter (原題)
Saravanan Narayanan Ramanathan, Sivakumar Karuppusamy, Anandha Prakash Singh Dharmalingam, Prabhu Murugan, Bharathy Nallathambi, Sakthivadivu Rathinasamy, Thirunavukkarasu Maruthamuthu, D Kannan, Sri Balaji Nagarajan, Vidya de Gannes, Thiruvenkadan Aranganoor Kannan
🤖 gxceed AI 要約
日本語
ブロイラー鶏糞(PL)をミミズ(Eudrilus eugeniae)でバーミコンポスト化する際、炭素窒素比(C/N)を最適化する研究。ココナッツコイアピート(CP)と農場堆肥(FM)を副資材として用い、C/N比25,30,35で比較。90日間の処理で、FM処理区で最も高い転換率(54.1)を示し、C/N比30以上で良好な堆肥化が可能と判明。栄養塩類の増加や植物毒性のない高品質な堆肥が得られた。
English
This study optimizes the carbon-to-nitrogen ratio for vermicomposting broiler poultry litter using Eudrilus eugeniae. Coir pith and farmyard manure were used as co-substrates. The highest conversion ratio (54.1) was in the farmyard manure treatment, and C/N ratios of 30 and 35 improved compostability. The resulting vermicompost had favorable physicochemical properties, including nutrient mineralization and no phytotoxicity.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では畜産廃棄物の堆肥化は循環型農業の一環として注目されるが、GX政策との直接的な連携は薄い。ただし、廃棄物削減や資源循環の観点で、日本の農業分野における持続可能性向上に寄与する可能性がある。
In the global GX context
Globally, this research contributes to sustainable agriculture by improving waste management and producing organic fertilizer, aligning with circular economy principles. However, it lacks direct links to climate disclosure or decarbonization frameworks.
👥 読者別の含意
🔬研究者:This study provides empirical data on optimizing vermicomposting parameters, useful for researchers in waste management and sustainable agriculture.
🏢実務担当者:Poultry farmers and compost producers can apply the C/N optimization to improve waste valorization and produce high-quality organic fertilizer.
📄 Abstract(原文)
The potential benefits of bioconversion of commercial broiler poultry litter (PL) to vermicompost (with Eudrilus eugeniae ) were studied by optimizing the carbon-nitrogen ratio (C/N) using coconut ( Cocos nucifera ) coir pith (CP) and farmyard manure (FM) as co-substrate. In this experiment, after optimizing the carbon to nitrogen ratio (C/N) at the levels of 25, 30, and 35, the pre-composting of PL followed by vermicomposting was done, and PL and FM alone were used as the control group. After pre-composting, the earthworms ( Eudrilus eugeniae ) were introduced, and the vermicomposting process was continued for 90 days, and the samples were analysed on days 45 and 90 of vermicomposting. The study revealed a significantly ( P < 0.01 ) higher Feedstock Vermicompost Conversion Ratio (FVCR) of 54.1 ± 0.9 in FM treatment group. This was followed by Broiler poultry litter + coir pith + FM at C/N 35 (PLCPFM35: 47.5 ± 0.6), Broiler poultry litter + coir pith + FM at C/N 30 (PLCPFM30: 45.3 ± 0.4), Broiler poultry litter + coir pith at C/N 35 (PLCP35: 45.1 ± 1.2) and Broiler poultry litter + coir pith at C/N 30 (PLCP30: 41.8 ± 0.6). An increase in total Ca (g kg −1 ) was recorded during 90th day sampling and ranged between 27.0 ± 5.4 and 21.6 ± 2.6 g kg −1 in treatment groups, where earthworm activity was high. The total K level (g kg −1 ) at the end of 90th day ranged between 12.1 ± 0.1 and 21.6 ± 0.4 with increasing trend. PL vermicompost exhibited favourable physicochemical features, including significant ( P < 0.01 ) nitrogen mineralization and the absence of phytotoxic compounds. The experiment revealed that commercial PL either in its raw form or at a C/N of 25, was unsuitable for vermicomposting, but vermicompostability improved after optimizing the C/N to 30 and 35 respectively. These findings indicate that Eudrilus eugeniae (African nightcrawler) can effectively maximize the bioconversion of PL into high-quality vermicompost, provided the substrate materials are carefully balanced.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.heliyon.2026.e45347first seen 2026-08-23 04:50:56
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。