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Assessment of Energy Consumption Pattern and using of Nero-Fuzzy Technique in Modeling the of Forage Crops | ||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||
| Article 11, Volume 19, Issue 70, September 2018, Pages 125-138 PDF (700.24 K) | ||
| Document Type: Original Article | ||
| DOI: 10.22092/erams.2017.114591.1205 | ||
| Authors | ||
| Zeynab Ramedani* 1; Reza Abdi2; Mahmoud Omid3; Mohammadali Maysami2 | ||
| 1Department of Biosystem Engineering Faculty of Agricultural University of Tabriz, Tabriz,Iran | ||
| 2Department of Biosystem, Faculty of Agriculture, University of Tabriz | ||
| 3Department of Biosystem, Faculty of Agriculture, University of Tehran | ||
| Abstract | ||
| It is essential to have integrated information on the energy exchange of forage crops in order to allow comparisons of their energy consumption patterns. Thus the aims of this study are examination of the energy consumption pattern, estimation of the amount of produced CO2eq. and modeling between the yield and energy inputs in three forage crops. These forage crops that are fed to dairy cows were wheat straw, maize silage and alfalfa. The total amount of energy inputs in fields of wheat (and its straw), silage corn and alfalfa were calculated as: 32077.85, 93049.87 and 30208.04 MJ ha-1 respectively. The amount of produced CO2eq. in these three crops were estimated to be 2704.67, 5861.79 and 5538 respectively. The value of energy ratio in two crops of wheat and alfalfa were computed as 2.69 and 2.18 while in the silage crop due to higher amount of output energy rather than input energy was calculated less than one. Also, the adaptive neuro-fuzzy inference system was used for modeling the relation of the yield of these forage crops and the amount of energy inputs. For estimation of the model for wheat straw, the model with three 'gaussmf' memberships function for each input variables was the best among the other models. Also, the best model for maize silage and alfalfa were 'pimf' for tow memberships function and 'trapmf' for three memberships function, respectively. | ||
| Keywords | ||
| Energy Consumption Pattern; Greenhouse gases; modeling; Adaptive Neuro-Fuzzy Inference System | ||
| References | ||
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