000			04350nam a22002653a 4500
001			UPMIN-00009072458
003			UPMIN
005			20230327105300.0
008			230327b |||||||| |||| 00| 0 eng d
040			_cUP Min
090		0	_aLG 993.5 2016 A3 _bB33
100	1		_aBachanicha, Mary Jane T., _eauthor _93119
245	1	0	_aAnalyzing ties, nodes, and networks in the mango supply chain in Island Garden City of Samal, Davao del Norte, Philippines : _ba social network analysis approach / _cMary Jane T. Bachanicha; Nikko L. Laorden, adviser
260			_aDavao City: _bSchool of Management, University of the Philippines Mindanao, _cc2016
300			_a59 leaves
500			_aThesis (BS Agribusiness Economics)-- University of the Philippines Mindanao, June 2016
506			_aNot available to the general public.
520			_aSupply chain actors or nodes have different ties with each other that make up the chain's social network. In this study, the mango supply chain in the Island Garden City of Samal was analyzed to determine how chain performance in terms of productivity and profitability can be affected by the weight and direction of ties, position of the chain actors in the social network, and geodesic distance of farmers to focal buyers. A social network analysis approach was used to analyze the nature of ties, node properties and network properties of the mango supply chain. Results show that farmers with a direct support from their buyers (direct ties) have higher net margins compared to those farmers that are self-financing (undirected ties). The weight of these ties implies that directed ties equate to a strong tie and undirected ties to a weak tie. In the mango supply chain, undirected (weak) ties are dominant than directed (strong) ties. Moreover, farmers with high degree certainly (many buyers) were observed to be more productive and profitable than farmers with low degree centrality (only one buyer). However, mango farmers with low degree centrality are large in number. Lastly, the mango supply chain in Samal has low network density, which implies that many potential connections among actors were not achieved. These findings suggest that ties between mango farmers and a variety of downstream buyers should be improved as nodes with directed ties and high degree centrality are characterized with more productivity and profitability. Astronomo, IJT Layno, KGV In the Philippines, rice is a staple food and 90% consumes rice. The two most adapted rice farming method are conventional and organic farming. The local demand for organic rice in the Philippines reached up to 800 tons per month. However, there are less supply of organic rice because only few farmers adopt organic farming method. In this study, the factors that affect the farmers? willingness to adopt organic farming in Bislig City, Surigao del Sur were identified through key informant and survey interview. The logit regression analysis and profitability analysis were used to analyze the data from 146 farmers? socio-economic and other farm-related indicators. Results showed that among the respondents 115 farmers (78.67%) are willing to adopt while 31 farmers (21.23%) are not willing to adopt organic farming. Farmers who are willing to adopt have an average of 20 years of rice farming experience and reached 2nd year high school while farmers who are not willing to adopt have an average of 14 years of rice farming experience and reached 1st year high school level of education. Therefore, farmers who spend more years in rice farming and attained higher level of education are more willing to adopt organic farming. The factors that affect farmers? decision to adopt organic farming are low production cost, health benefits and environment-friendly method while the factors that hinder their adoption are poor irrigation, low yield and intensive labor requirement. The ROI of the farmers who are not willing to adopt. Finally, identified socio-economic indicators and other farm-related indicators showed no significant effect on the willingness of the farmers to adopt organic rice farming in Bislig City, Surigao del Sur.
650		0	_aMango industry _93120
658			_aUndergraduate Thesis, _cABE 200b
700	1		_aLaorden, Nikko L., _eadviser _91605
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c3262 _d3262