• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1195-1206
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• 2011

This study was conducted to apply LCA (Life cycle assessment) methodology to lettuce (Lactuca sativa L.) production systems in Namyang-ju as a case study. Five lettuce growing farms with three different farming systems (two farms with organic farming system, one farm with a system without agricultural chemicals and two farms with conventional farming system) were selected at Namyangju city of Gyeonggi-province in Korea. The input data for LCA were collected by interviewing with the farmers. The system boundary was set at a cropping season without heating and cooling system for reducing uncertainties in data collection and calculation. Sensitivity analysis was carried out to find out the effect of type and amount of fertilizer and energy use on GHG (Greenhouse Gas) emission. The results of establishing GTG (Gate-to-Gate) inventory revealed that the quantity of fertilizer and energy input had the largest value in producing 1 kg lettuce, the amount of pesticide input the smallest. The amount of electricity input was the largest in all farms except farm 1 which purchased seedlings from outside. The quantity of direct field emission of $CO_2$, $CH_4$ and $N_2O$ from farm 1 to farm 5 were 6.79E-03 (farm 1), 8.10E-03 (farm 2), 1.82E-02 (farm 3), 7.51E-02 (farm 4) and 1.61E-02 (farm 5) kg $kg^{-1}$ lettuce, respectively. According to the result of LCI analysis focused on GHG, it was observed that $CO_2$ emission was 2.92E-01 (farm 1), 3.76E-01 (farm 2), 4.11E-01 (farm 3), 9.40E-01 (farm 4) and $5.37E-01kg\;CO_2\;kg^{-1}\;lettuce$ (farm 5), respectively. Carbon dioxide contribute to the most GHG emission. Carbon dioxide was mainly emitted in the process of energy production, which occupied 67~91% of $CO_2$ emission from every production process from 5 farms. Due to higher proportion of $CO_2$ emission from production of compound fertilizer in conventional crop system, conventional crop system had lower proportion of $CO_2$ emission from energy production than organic crop system did. With increasing inorganic fertilizer input, the process of lettuce cultivation covered higher proportion in $N_2O$ emission. Therefore, farms 1 and 2 covered 87% of total $N_2O$ emission; and farm 3 covered 64%. The carbon footprints from farm 1 to farm 5 were 3.40E-01 (farm 1), 4.31E-01 (farm 2), 5.32E-01 (farm 3), 1.08E+00 (farm 4) and 6.14E-01 (farm 5) kg $CO_2$-eq. $kg^{-1}$ lettuce, respectively. Results of sensitivity analysis revealed the soybean meal was the most sensitive among 4 types of fertilizer. The value of compound fertilizer was the least sensitive among every fertilizer imput. Electricity showed the largest sensitivity on $CO_2$ emission. However, the value of $N_2O$ variation was almost zero.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.