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Extracting Patterns from Socioeconomic Databases to Characterize Small Farmers with High and Low Corn Yields in Mozambique: a Data Mining Approach
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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2012 (English)In: Advances in Data Mining: Workshop Proceedings / [ed] Isabelle Bichindaritz, Petra Perner, Georg Ruß, Rainer Schmidt, Ibai Publishing , 2012, 99-108 p.Conference paper, Published paper (Other academic)
Abstract [en]

Mozambique is mainly a rural country. Agriculture is a pillar of the Mozambique economy and is the main source of income for 80% of the population living in rural areas. One of the major problems in the agricultural sector is low productivity, which for most crops is the lowest in Africa. The main food crop cultivated in Mozambique is maize. This research aims to characterize households with high and low maize yields based on the National Agricultural Survey Data from 2007 and 2008 using a data mining approach. To this end, we used: a) decision trees, b) association rules, and c) classification rules. The results show that households with high maize yields are those with the capacity to generate income through the commercialization of their production and agricultural assets. Households with low maize yields are associated with production loss before harvest which results in food insecurity.

Place, publisher, year, edition, pages
Ibai Publishing , 2012. 99-108 p.
Keyword [en]
Data Mining, Maize, Decision Trees, Association Rules, Classification Rules, Mozambique
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-82235ISBN: 978-3-942952-10-0 (print) (print)OAI: oai:DiVA.org:su-82235DiVA: diva2:567212
Conference
12th Industrial Conference, ICDM 2012, Berlin, Germany, July 13-20, 2012
Note

Workshop Data Mining in Agriculture

Available from: 2012-11-12 Created: 2012-11-12 Last updated: 2013-02-28Bibliographically approved

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