There is a lack of literature reporting the methane potential of several livestock manures under the same anaerobic digestion conditions (same inoculum, temperature, time, and size of the digester). To the best of our knowledge, no previous study has reported biochemical methane potential (BMP) predicting models developed and evaluated by solely using at least five different livestock manure tests results. The goal of this study was to evaluate the BMP of five different livestock manures (dairy manure (DM), horse manure (HM), goat manure (GM), chicken manure (CM) and swine manure (SM)) and to predict the BMP using different statistical models. Nutrients of the digested different manures were also monitored. The BMP tests were conducted under mesophilic temperatures with a manure loading factor of 3.5g volatile solids (VS)/L and a feed to inoculum ratio (F/I) of 0.5. Single variable and multiple variable regression models were developed using manure total carbohydrate (TC), crude protein (CP), total fat (TF), lignin (LIG) and acid detergent fiber (ADF), and measured BMP data. Three different kinetic models (first order kinetic model, modified Gompertz model and Chen and Hashimoto model) were evaluated for BMP predictions. The BMPs of DM, HM, GM, CM and SM were measured to be 204, 155, 159, 259, and 323mL/g VS, respectively and the VS removals were calculated to be 58.6%, 52.9%, 46.4%, 81.4%, 81.4%, respectively. The technical digestion time (T80-90, time required to produce 80-90% of total biogas production) for DM, HM, GM, CM and SM was calculated to be in the ranges of 19-28, 27-37, 31-44, 13-18, 12-17days, respectively. The effluents from the HM showed the lowest nitrogen, phosphorus and potassium concentrations. The effluents from the CM digesters showed highest nitrogen and phosphorus concentrations and digested SM showed highest potassium concentration. Based on the results of the regression analysis, the model using the variable of LIG showed the best (R(2)=0.851, p=0.026) for BMP prediction among the single variable models, and the model including variables of TC and TF showed the best prediction for BMPs (R(2)=0.913, p=0.068-0.075) comparing with other two-variable models, while the model including variables of CP, LIG and ADF performed the best in BMP prediction (R(2)=0.999, p=0.009-0.017) if three-variable models were compared. Among the three kinetic models used, the first order kinetic model fitted the measured BMPs data best (R(2)=0.996-0.998, rRMSE=0.171-0.381) and deviations between measured and the first order kinetic model predicted BMPs were less than 3.0%.
