Prognostication of total greenhouse gas emissions and economic indices for hazelnut production in Guilan province, Iran
Journal Title: Journal of Biodiversity and Environmental Sciences (JBES) - Year 2015, Vol 6, Issue 2
Abstract
In this study, Artificial Neural Network (ANN) was developed to estimate the total GHG emissions and economic indices of hazelnut production in Guilan province of Iran. In this regard, the data collected from 120 orchardists in the studied region during plant cultivation in 2012-2013 using face-to-face questionnaires. The results indicated that total GHG emissions and hazelnut yield was 77.66 kgCO2eq. ha-1 and 450.20 kg ha-1, respectively. Based on grouping of hazelnut orchards according to three sizes level, the large size had the highest emissions and yield compare to another sizes. Moreover, the GHG ratio was 0.17 for all orchards. The economic indices including gross production value, benefit to cost ratio, productivity and net return were calculated as 1575.70 $ ha-1, 1.64, 0.47 kg $-1 and 615.34 $ ha-1, respectively. In this research, the Levenberg-Marquardt learning algorithm was applied for determination of ANN model. With respect to result, the ANN model with 7-4-4-5 structure was determined as best topology which the highest R2 and lowest RMSE showed the robust model for prediction. In the last section sensitivity analysis was done and its results demonstrated potassium had the highest sensitivity on total GHG emissions and benefit to cost ratio; while nitrogen was the most sensitive input on gross production value, productivity and net return.
Authors and Affiliations
Alireza Sabzevari, Ashkan Nabavi-Pelesaraei
Keywords
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