4.7 Article

A Conceptual Model for Development of Small Farm Management Information System: A Case of Indonesian Smallholder Chili Farmers

期刊

AGRICULTURE-BASEL
卷 12, 期 6, 页码 -

出版社

MDPI
DOI: 10.3390/agriculture12060866

关键词

farm management information system; farmers' information needs assessment; soft system methodology; smallholder farmers; conceptual model; Indonesian chili farmers

类别

资金

  1. Sustainable Management of Agricultural Research and Dissemination (SMARTD) Project (World Bank) [P117243]

向作者/读者索取更多资源

Farm Management Information Systems (FMIS) can assist farmers in managing their farms more effectively and efficiently. However, it is currently relatively expensive for smallholder farmers to use FMIS to support crop cultivation. This study proposes a new FMIS conceptual model suitable for smallholder farmers by analyzing their information needs.
Farm Management Information Systems (FMIS) assists farmers in managing their farms more effectively and efficiently. However, the use of FMIS to support crop cultivation is, at the present time, relatively expensive for smallholder farmers. Due to some handicaps, providing an FMIS that is suitable for small-holder farmers is a challenge. To analyze this gap, this study followed 3 steps, namely: (1) identified commodity and research area, (2) performed Farmers' Information Needs Assessment (FINA), and (3) developed the conceptual model using the Soft System Methodology. Indonesian smallholder chili farmers are used as a case study. The most required information of smallholder' farmers was identified through a qualitative questionnaire. Despite this, not all identified information needs could be accurately mapped. Thus, this indicates the need for a new FMIS conceptual model that is suitable for smallholder farmers. This study proposes an FMIS conceptual model for farm efficiency that incorporates five layers, namely farmers' information needs, data quality assessment, data extraction, SMM (split, match and merge), and presentation layer. SMM layer also provides a method to comprehensively tackle three main problems in data interoperability problems, namely schema heterogeneity, schema granularity, and mismatch entity naming.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据