IPM information network analysis: case study on information flow in integrated tomato pest management in Kenya
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | German |
| Veröffentlicht: |
Weikersheim
Margraf
2006
|
| Schriftenreihe: | Kommunikation und Beratung
73 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XI, 110 S. graph. Darst., Kt. 21 cm |
| ISBN: | 9783823614869 382361486X |
Internformat
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Table of Contents
Preface of the Editors.i
Preface of the Supervisor.ii
Acknowledgements.iii
Table of Contents.v
List of Figures.viii
List of Tables.ix
List of Acronyms.xi
1. Introduction.l
1.1 General introduction.1
1.2 Problem statement.2
1.3 Research concept.3
1.4 Organisation of the study.3
2. Integrated Pest Management (IPM) Literature Review.5
2.1 Introduction.5
2.2 Origin and development history.5
2.3 IPM success stories.6
2.4 Integrated pest management tactics.6
2.4.1 Chemical pesticides.6
2.4.2 Cultural control practices.7
2.4.3 Biological control.7
2.4.4 Host plant resistance.8
2.4.5 Legislation, codes of conduct and conventions.8
2.5 Factors leading to successful IPM.8
2.5.1 Coordinated communication and successful IPM.9
2.5.2 Extension services and models of IPM extension.9
2.5.3 Technological transfer models.9
2.5.4 Policy and successful IPM.10
2.5.5 System analysis and IPM design.10
3. IPM Practices in Kenya: Case Study Tomato.11
3.1 Introduction.11
3.2 Case study: Tomato IPM in Kenya.13
3.3 Methodology.13
3.3.1 Selection of study area.13
3.3.2 Questionnaire and interviews.14
3.4 Data processing and analysis.14
3.5 Results.15
3.5.1 Fanner characteristics: demographic profile of farmers.15
3.5.2 Land use: production systems.16
3.5.3 Tomato production constraints.17
3.5.4 Pest management practices.19
3.5.5 Information acquisition and dissemination process.21
3.5.6 Information management and information needed.22
3.5.7 Information seeking within specific IPM themes.22
3.5.8 Social responsibility and personal safety measures prognosis.23
3.6 Discussion and conclusions.24
4. Network Analysis and IPM Stakeholders.27
4.1 Introduction.27
4.2 Social network analysis and IPM stakeholders.29
4.3 Network measures selected for analysis.30
4.3.1 Quantitative network measures.30
4.3.2 Centrality and centralization measures.30
4.4 Study methodology.31
4.4.1 Study population.32
4.4.2 Sampling methodology and tools.32
4.4.3 Network study variables.33
4.5 Data entry and analysis.34
4.6 Results.35
4.6.1 Overview: IPM stakeholders' social network analysis.35
4.6.2 IPM stakeholders entire network analysis.36
4.6.2.1 Size and density.36
4.6.2.2 Clustering coefficient.39
4.6.2.3 Hierarchy and reciprocity.39
4.6.2.4 Effect of actor characteristics on interaction structure.39
4.6.2.5 Centrality.42
4.6.2.6 Centralization.44
4.6.3 Task and media cluster analyses.45
4.6.3.1 IPM tasks clusters.45
4.6.3.2 IPM actors and media of communication clusters.47
4.6.3.3 Inter-correlation between IPM tasks participation and media use.48
4.6.4 Network analysis of specific IPM tasks components.49
4.6.4.1 Quantitative network measures for the specific IPM task components.49
4.6.4.2 Centrality and centralization distribution within IPM task components.50
4.6.4.3 Statistical analysis between EPM task components.51
vi
4.6.4.4 Effect of actor characteristics on the IPM networks.53
4.6.5 Perceptions on factors facilitating or hindering IPM information flow.55
4.7 Discussion and conclusions.55
4.7.1 General discussions.55
4.7.2 SNA potential in IPM.60
4.7.3 Study limitations.61
4.7.4 Conclusions.61
5. Network Analyses and Future IPM Framework Model.63
5.1 Introduction.63
5.2 IPM and knowledge management.64
5.2.1 Knowledge types within IPM stakeholders network clusters.64
5.2.2 IPM knowledge types and innovation.65
5.2.3 Networked communities of knowledge clusters within IPM.66
5.2.4 Networked clusters' interconnections and knowledge flow.67
5.3 System dynamics and networked IPM stakeholders.68
5.3.1 Systems dynamics variables in IPM networked clusters.68
5.3.2 Feedback and causal loops structure within IPM clusters.70
5.4 Conclusions.72
6. Summary, Conclusion and Recommendations.73
6.1 Summary research problem and objectives.73
6.2 Methodology.73
6.3 Summary of the results.74
6.4 Conclusion.76
6.5 Recommendations.76
7. Abstracts.79
7.1 English short summary.79
7.2 Deutsche Kurzzusammenfassung.80
8. References.83
Appendices.91
Appendix A: Pesticides registered for tomato pests and disease control in Kenya.91
Appendix B: Exotic bio control agents of selected tomato pests in Kenya.95
Appendix C: Locally occurring biocontrol agents.95
Appendix D: Organisations surveyed for network analysis.97
Appendix E: Individual actors raw and normalized indegree and outdegree.100
Appendix F: Individual actors raw and normalized incloseness and outcloseness.102
Appendix G: Individual actors raw and normalized betwermess centrality.104
vu
Appendix H: Survey questionnaires.106
List of figures
Figure 1: Map of Kenya.13
Figure 2: Proportion of male and female respondents in the survey.15
Figure 3: Respondents by age in two areas.16
Figure4: Proportionof farmers in Mwea reporting various pests occurrence.18
Figure 5: Proportion of farmers in Bahati reporting trend of various pests.18
Figure 6: Proportion of respondents reporting various tomato pests
in the two areas.19
Figure 7: Proportion of fanners reporting various information sources in Mwea.21
Figure 8: Proportion of farmers seeking advice in case of pesticide failure.23
Figure 9: Organisations involved in agriculture information generation and
provision.27
Figure 10: Management structure of Kenya's public extension system.28
Figure 11: Network diagram for initiating contact relation.38
Figure 12: Network diagram high frequency contact relation.38
Figure 13: Normalized outdegree centrality based on organisation types.42
Figure 14: Normalized indegree centrality based on organisation types.43
Figure 15: Average incloseness centrality based on organisation types.43
Figure 16: Average outcloseness centrality based on organisation types.44
Figure 17: IPM tasks clusters.46
Figure 18: IPM communication media clusters.47
Figure 19: Specific IPM tasks component networks.52
Figure 20: How different IPM clusters interacted with each other.67
Figure 21: Illustration of feedback structure of factors affecting IPM
information flow.70
Figure 22: Illustration of stock and flow diagram using IPM variables.71
vni
List of Tables
Table 1: Tomato area, production and value trend in Kenya
between 1992 and 2002.11
Table 2: Production and value share of leading vegetables in Kenya for
1993 and 2002.11
Table 3: Commonly reported tomato pests and disease in Kenya.12
Table 4: Proportion of respondents' education levels by sex.16
Table 5: Proportion of fanners growing various crops on different acreage.17
Table 6: Proportions of farmers using various pest control practices.19
Table 7: Types of chemical substances used for various pests in the two areas.20
Table 8: Proportion of farmers reporting different communication media for
various pest control measures.21
Table 9: Information management and needed information.22
Table 10: Percent of fanners encountering challenges with pest control
products inMwea.22
Table 11: Actions farmers took after failure of control practices.23
Table 12: Prognosis on farmers' action on selected IPM themes.24
Table 13: Identified tomato EPM control practices among the stakeholders.36
Table 14: Summary of the quantitative measures for the entire IPM network.37
Table 15: Predicting probability of contacts by selected actor characteristics.40
Table 16: Predicting probability of contacts by motivation relations.40
Table 17: Autocorrelation of density based organisation types.41
Table 18: Distribution of centrality and centralization measures in
entire IPM network.45
Table 19: Difference of means test within communication media.48
Table 20: Correlation between communication media use.48
Table 21: Correlation between IPM tasks and media use.48
Table 22: Quantitative measures of IPM task component networks.49
Table 23: Distribution of centrality and centralization scores in IPM components.50
Table 24: Test for comparison of differences of means IPM tasks networks.51
Table 25: Correlations between IPM tasks.53
Table 26: Influence of selected actor characteristics on IPM task networks.54
Table 27: Influence of motivational factors on IPM task networks.54
Table 28: Example of variables to be fitted in model within each IPM task cluster.69 |
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| spelling | Raini, Rebecca Kemunto Verfasser aut IPM information network analysis case study on information flow in integrated tomato pest management in Kenya Rebecca Kemunto Raini Weikersheim Margraf 2006 XI, 110 S. graph. Darst., Kt. 21 cm txt rdacontent n rdamedia nc rdacarrier Kommunikation und Beratung 73 Zugl.: Hohenheim, Univ., Diss., 2006 Kleinbauernbetrieb (DE-588)4114221-4 gnd rswk-swf Integrierter Pflanzenschutz (DE-588)4129490-7 gnd rswk-swf Tomatenanbau (DE-588)4303768-9 gnd rswk-swf Netzwerk (DE-588)4171529-9 gnd rswk-swf Kenia (DE-588)4030236-2 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Kenia (DE-588)4030236-2 g Kleinbauernbetrieb (DE-588)4114221-4 s Tomatenanbau (DE-588)4303768-9 s Integrierter Pflanzenschutz (DE-588)4129490-7 s Netzwerk (DE-588)4171529-9 s DE-604 Kommunikation und Beratung 73 (DE-604)BV010181633 73 text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2849928&prov=M&dok_var=1&dok_ext=htm Inhaltstext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020701455&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Raini, Rebecca Kemunto IPM information network analysis case study on information flow in integrated tomato pest management in Kenya Kommunikation und Beratung Kleinbauernbetrieb (DE-588)4114221-4 gnd Integrierter Pflanzenschutz (DE-588)4129490-7 gnd Tomatenanbau (DE-588)4303768-9 gnd Netzwerk (DE-588)4171529-9 gnd |
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| title | IPM information network analysis case study on information flow in integrated tomato pest management in Kenya |
| title_auth | IPM information network analysis case study on information flow in integrated tomato pest management in Kenya |
| title_exact_search | IPM information network analysis case study on information flow in integrated tomato pest management in Kenya |
| title_full | IPM information network analysis case study on information flow in integrated tomato pest management in Kenya Rebecca Kemunto Raini |
| title_fullStr | IPM information network analysis case study on information flow in integrated tomato pest management in Kenya Rebecca Kemunto Raini |
| title_full_unstemmed | IPM information network analysis case study on information flow in integrated tomato pest management in Kenya Rebecca Kemunto Raini |
| title_short | IPM information network analysis |
| title_sort | ipm information network analysis case study on information flow in integrated tomato pest management in kenya |
| title_sub | case study on information flow in integrated tomato pest management in Kenya |
| topic | Kleinbauernbetrieb (DE-588)4114221-4 gnd Integrierter Pflanzenschutz (DE-588)4129490-7 gnd Tomatenanbau (DE-588)4303768-9 gnd Netzwerk (DE-588)4171529-9 gnd |
| topic_facet | Kleinbauernbetrieb Integrierter Pflanzenschutz Tomatenanbau Netzwerk Kenia Hochschulschrift |
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