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Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms
Thesis (MEM)--Stellenbosch University, 2023.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2023
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| _version_ | 1867614136365481984 |
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| access_status_str | Open Access |
| author | Bekker, Gretchen |
| author2 | Jooste, Johannes L. |
| author_browse | Bekker, Gretchen Jooste, Johannes L. |
| author_facet | Jooste, Johannes L. Bekker, Gretchen |
| author_sort | Bekker, Gretchen |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEM)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/128693 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:47:14.760Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/128693 Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms Bekker, Gretchen Jooste, Johannes L. Hummel, Vera Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM). Agricultural innovations Decision support systems Smart farming Sustainable agriculture Thesis (MEM)--Stellenbosch University, 2023. ENGLISH ABSTRACT: The Industrial Revolution precedes the development of the Agricultural Revolution. As revolutions unfold, the agricultural industry is experiencing increasing pressure due to population growth, diminishing resources, decreasing yields, and the growing emissions footprint produced by the agricultural sector. SSFs manage 80% of sub-Saharan African and Asian farmland that experiences these elements but does not have the capital capacity to invest in new developments or risk mitigation plans. However, an Agriculture 4.0 characteristic is digitalisation, and this revolution provides technologies and DAQ-sensor technologies, that require little to no capital investment, which can help SSFs manage the elements that cause negative pressure on their farms. The research problem is that Agriculture 4.0 practices have various technological considerations, and there is limited decision support for small-scale farmers (SSF) to gain knowledge and adapt Agriculture 4.0 data acquisition technologies effectively. The thesis adopts the Hutter-Hennink Qualitative Research Cycle as the research methodology and collects data through validation and veri_cation via semi-structured interviews. Developing a decision support framework (DSF) in combination with an analytic hierarchy process (AHP) successfully addresses the research problem. The DSF is developed in combination with AHP, which enables the framework to process user inputs and use SMEs from the agricultural technology industry. Regarding the research problem, Agriculture 4.0 practices are precision agriculture, digital farming and smart farming. All of these practices use technology to build a more sustainable and Agriculture 4.0 industry. Within each practice, there are countless crops, but the thesis focuses on horticulture crops. Land for horticulture crops must be planned and preprepared before planting, and the crops go through multiple growth stages before _nally being harvested. For SSFs, these activities, from ground preparation to harvesting, are the most important. Therefore the DSF is the research product and uses the farm activities (FA) and the key performance indicators (KIPs) as criteria. The DSF consists of _ve phases: (1) Awareness, (2)Criteria, (3)DAQ-sensor technology, (4)Adapt, and (5)Adoption. The _rst phase includes farm characteristics and actions that ought to be done prior to technology adoption. Phase one presents valuable information for the user. Phase two uses FAs and KPIs as criteria to individualise the decision support provided by the framework. The criteria capture users' input for the DSF to identify the users' needs and to align these with suitable DAQ-sensor technologies. The criteria are used similarly in phase 3 by SMEs to indicate the importance of a DAQ-sensor technology concerning each criterion. Phases 2 and 3 perform the AHP and present AHP output calculated by XLSTAT, an additional functionality in MS Excel. In phase 4, the characteristics of the most suitable DAQ-sensor technology according to the AHP output and the capability of each DAQ-sensor technology regarding technologies that enhance compatibility are indicated. The criteria and the technology characteristics both capture the technology considerations from past agricultural frameworks and Agriculture 4.0 practices. Finally, phase 5 presents the DSF's synopsis of phases one to four. The DSF can solve the research problem and has the potential to be developed further. AFRIKAANS OPSOMMING: Die Industriële Revolusie het die ontwikkeling van die Landbou-revolusie voorafgegaan. Soos die revolusie ontplooi, beleef die landboubedryf toenemende druk weens bevolkingsaanwas, krimpende hulpbronne, kleiner opbrengste, en die groeiende vrystellingsvoetspoor van die landbousektor. Kleinskaalboere (KSB'e) behartig 80% van die landbougrond in Afrika suid van die Sahara en Asië, waar hierdie elemente ervaar word, maar het nie die kapitaalvermoë om in nuwe ontwikkelings of risikoversagtingsplanne te belê nie. Een van die kenmerke van Landbou 4.0 is egter digitalisering, en hierdie revolusie bied tegnologieë en dataverkrygingsensortegnologie (DVS-) wat min of geen kapitaalbelegging vereis (nie) en wat KSB'e kan help om die oorsake van negatiewe druk op hulle plase te bestuur. Die navorsingsprobleem is dat Landbou 4.0- praktyke verskeie tegnologiese oorwegings behels en dat daar beperkte besluitsteun is vir KSB'e om kennis op te doen en die DVS-tegnologie doeltre_end vir Landbou 4.0 aan te pas. Die navorsingsmetodologie van hierdie tesis is Hutter-Hennink se kwalitatiewe navorsingsiklus. Data word deur bevestiging en stawing via semigestruktureerde onderhoude ingesamel. Die navorsingsprobleem word suksesvol aangepak deur die ontwikkeling van 'n besluitsteunraamwerk (BSR) in kombinasie met 'n analitiese hiërargieproses (AHP). Die BSR word in kombinasie met die AHP ontwikkel, wat die raamwerk in staat stel om gebruikersinsette te verwerk en klein- tot mediumondernemings (KMO's) uit die landboutegnologiebedryf te gebruik. Die Landbou 4.0-praktyke wat met die navorsingsprobleem verband hou, is presisie-landbou, digitale boerdery en slimboerdery. Hierdie praktyke gebruik tegnologie om 'n meer volhoubare en Landbou 4.0-bedryf op te bou. Daar is talle gewasse binne elke praktyk betrokke, maar hierdie tesis fokus op tuinbougewasse. Grond vir tuinbougewasse moet voor beplanting beplan en voorberei word, en die gewasse ondergaan veelvuldige groeifases, voordat dit geoes word. Vir KSB'e is hierdie werksaamhede, van grondvoorbereiding tot oes, die belangrikste. Die BSR is dus die navorsingsproduk en gebruik die boerderywerksaamhede (BW'e) en die sleutelprestasie-aanduiders (SPA's) as kriteria. Die BSR behels vyf fases: (1) Bewustheid; (2) Kriteria; (3) DVS-tegnologie; (4) Aanpassing; en (5) Aanneming. Die eerste fase sluit in plaaskenmerke en aksies wat voor tegnologie-aanneming moet plaasvind. Fase een bied waardevolle inligting aan die gebruiker. Fase twee gebruik BW'e en SPA's as kriteria om die besluitsteun van die raamwerk te individualiseer. Die kriteria lê die gebruikersinsette vas sodat die BSR die gebruikers se behoeftes kan identi_seer en dit met die geskikte DVS-tegnologieë kan belyn. In fase drie word die kriteria insgelyks deur KMO's gebruik om die belangrikheid van DVS-tegnologie ten opsigte van elke kriteria aan te dui. Fase twee en drie voer die AHP uit en lewer die AHP-uitset, soos deur XLSTAT, 'n bykomende funksie in MS Excel, bereken. In fase vier word die kenmerke van die mees geskikte DVS-tegnologie volgens die AHP-uitset en die versoenbaarheidsvermoë van elke soort DVStegnologie aangedui. Die kriteria en die tegnologiekenmerke lê die tegnologiese oorwegings van vorige landbouraamwerke en Landbou 4.0-praktyke vas. Laastens bied fase vyf die BSR se opsomming van fase een tot vier aan. Die BSR kan die navorsingsprobleem oplos en het die potensiaal om verder ontwikkel te word. Masters 2023-02-10T17:39:16Z 2023-11-16T08:32:48Z 2023-02-10T17:39:16Z 2023-11-16T08:32:48Z 2023-03 Thesis https://scholar.sun.ac.za/handle/10019.1/128693 en_ZA Stellenbosch University xviii, 232 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Agricultural innovations Decision support systems Smart farming Sustainable agriculture Bekker, Gretchen Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms |
| title | Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms |
| title_full | Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms |
| title_fullStr | Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms |
| title_full_unstemmed | Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms |
| title_short | Development of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms |
| title_sort | development of an agriculture 4 0 data acquisition technology decision support framework for small scale farms |
| topic | Agricultural innovations Decision support systems Smart farming Sustainable agriculture |
| url | https://scholar.sun.ac.za/handle/10019.1/128693 |
| work_keys_str_mv | AT bekkergretchen developmentofanagriculture40dataacquisitiontechnologydecisionsupportframeworkforsmallscalefarms |