The model for farm management information system for smallholder farmers | |
| Author | Henriyadi |
| Call Number | AIT Diss no.IM-23-01 |
| Subject(s) | Farms, Small--Management--Mathematical models Farm management--Mathematical models Farm management--Data processing Management information systems Ontology |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | The complexity of crop production drives farmers to utilize a Farm Management Information System (FMIS) to manage their farms more effectively and efficiently. However, the existing FMIS are prohibitively expensive, particularly for smallholder farmers, the majority of farmers in the world. Providing an FMIS application that conforms to the smallholder farmers' needs is a very tough task. Three approaches could be considered in providing FMIS that conforms to smallholder farmers, namely: (1) functionalities they require; (2) the use of free external open data sources; and (3) a mobile apps platform. However, when utilizing external data sources, four main problems may arise, namely: (1) schema heterogeneity, (2) schema granularity, (3) mismatched entity naming or data unit, and (4) inconsistency of data. The ultimate goal of this study is to develop a conceptual model of FMIS for smallholder farmers. This study's specific objectives are to (1) identify farmers’ information needs, (2) construct an ontology for smallholder farmers, (3) construct a conceptual model (4) create an algorithm to address the data interoperability problems, (5) develop a prototype application, and (6) conduct application testing and impact analysis. The research uses two districts of West Java Province as the case study, with a total of 50 smallholder chili farmers as respondents. Farmers’ Information Needs Assessment (FINA) qualitative data analysis results in the ten most important farmers’ information needs. In addition, according to the in-depth discussion, farmers need five pieces of information. This study proposes an ontology called OntoFMIS specifically designed for smallholder farm management information systems. The general conceptual model of FMIS consists of five layers, namely: (1) farmers’ information needs, (2) assessing the quality of external data sources, (3) extraction of the external data sources, (4) split-match-merge, and (5) presentation layer. The split-match method layer includes two groups of algorithms: one algorithm to extract data from external data sources and another algorithm to address issues with data interoperability. The algorithm to extract external data sources includes four different types of data extractors. In addition, this study employs the combination of four matchers' methods and five similarity-matchers' algorithms to transform and load data into the application database. Additionally, this study uses a split-match-merge method to speed up the matching process. The prototype application, called SIMUSTI, is downloadable from the Google Play Store. The result of black-box testing shows that 90% of testers could execute all scenarios without any problems. Furthermore, the user-experience testing showed that the SIMUsTi Android application received a positive evaluation for all categories. The usability testing shows that the respondents do not face any difficulties in using the application. According to the impact analysis, the respondents used SIMUsTi apps besides traditional channels to find information. Regarding the financial aspect, the analysis shows a slight increase in average total income for a single crop production cycle, although it is not statistically significant. The proposed conceptual model is the first to explicitly apply to smallholder farmers based on a data interoperability framework, which is the contribution of the research. In terms of the practical contribution, by using SIMUsTi, farmers were able to find the information they did not previously have, namely other locations growing the same crop. |
| Year | 2023 |
| Type | Dissertation |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Vatcharaporn Esichaikul;Chutiporn Anutariya (Co-Chairperson) |
| Examination Committee(s) | Dailey, Matthew N.;Himanshu, Sushil Kumar; |
| Scholarship Donor(s) | SMARTD Project, Indonesian Agency for Agriculture Research and Development (IAARD), The Ministry of Agriculture; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2023 |