Principles of group model building and spatial group model building

Principles of group model building and spatial group model building
Principles of group model building and spatial group model building Better lives through livestock O K A PiS Training course on Systems Thinking, Participatory Modeling, and Value Chains Materials prepared and presented by Karl M. Rich (with contributions from Jared Berends, Greg Cooper, Chisoni Mumba, Magda Rich, Helene Lie, Kanar Dizyee, and Sirak Bahta) Foresight Modeling & Policy Team, Policies, Institutions, and Livelihoods International Livestock Research Institute (ILRI) Version April 2020 (draft)
2 Outline Overview of group model building (and its cousin, mediated modeling) Spatial group model building – how it extends GMB and how it’s different Examples • Urban agriculture (NZ) • East coast fever (Zambia) • Value chain upgrading - pigs (Myanmar) • Aggregation systems in horticulture (Bihar) qualitative quantitative
3 Session goals An emerging understanding of (spatial) group model building and why we use it An appreciation of the potential of participatory processes based on previous work A desire to learn more on implementing SGMB in practice! Picture credit: K.M. Rich 2012 (Maroantsetra, Madagascar)
4 Overview SD models, given their graphical and intuitive nature, can be developed in collaboration with a variety of groups Group model building (GMB) is one way to both obtain information and parameterize relationships that exist in the system in question.
5 What is group model building? Group Model Building or GMB “focuses on building system dynamics models with teams in order to enhance team learning, to foster consensus and to create commitment with a resulting decision.” Source: Vennix (1996) Picture credit: K.M. Rich 2019 (Myeik, Myanmar)
6 What is group model building? A participatory process aimed at: • Identifying and prioritizing the key problems in the system • The causes of these problems • The consequences of these problems • Feedbacks between consequences and causes • Development of models from these sessions (qualitative or quantitative) Use of SD principles and language (stocks/flows/feedbacks) to facilitate this discussion. Involvement of stakeholders in the model building process to increase the effectiveness and ownership of the final product Source: https://commons.wikimedia.org/wiki/File:Kelly%27s_Kin dergarten_(1898-11-27).jpg, Public Domain
7 GMB and participatory modeling GMB is not the only type of participatory modeling technique that uses systems thinking/dynamics tools. Mediated modeling (van den Belt 2004) is a similar concept – it involves a wider range of stakeholders in the process of model building, rather than a smaller client group (Antunes et al. 2006). Mediated modeling has been used primarily in environmental applications.
8 Why GMB? Messy problems: Problems are usually complex and not easily defined. In complex problems, individuals have a limited (or narrow) view of the problem (silo thinking). Our mental models are limited by our individual ability to process information: role of groups Difficulties in identifying multiple causes of complex problems their interconnections Source: Vennix (1996) Picture credit: S.Bahta 2019 (New Delhi, India)
9 GMB process Source: Rich, Rich, and Hamza (2015)
10 Source: Lie et al. (2017)
11 Source: https://exchange.iseesystems.com/public/helene-lie/dairy-value-chain-development-in- nicaragua/index.html#page1
14 Client group has problem Is SD appropriate? Use preliminary model? Yes No Use something else Questions to consider: • Is problem dynamic? • Short vs. long term effects • Reference mode of behavior • Qualitative or quantitative • Who to involve? Yes: model based on: No: start from scratch Informal interviews GMB sessions Conclusions Interviews Documents Questionnaires/ Workbooks Source: Adapted from Vennix (1996), figure 4.1, p. 103 Note: questionnaires both inform and triangulate our GMB sessions A way to “short-cut” the process if time or resources are scarce, but can reduce ownership of model and power of participatory process (Vennix 1996)
15 GMB design issues • Small (5-12) or large (12+) group? • Type of participants - homogenous vs heterogeneous groups? • Level of model complexity needed: problem conceptualization vs. full model development with clients/stakeholders? Picture credit: K.M. Rich 2019 (Jessore, Bangladesh)
16 Limitations of conventional GMB (1) Lack of use of value chain/natural resource/LDC settings (an exception: Lie Ph.D., see Lie and Rich 2016) Issues of comparability/replication (Scott et al. 2016) Issues of scaling across contexts
17 Limitations of conventional GMB (2) GMB sessions do not focus on spatial dynamics However, the processes that generate change within systems could have important spatial dimensions (land use, population dynamics, etc.) The “where” of the system matters as much as the “what”, “how”, and “why” Picture credit: Ingrid Kallick (http://www.ikallick.com) / Public domain; source https://upload.wikimedia.org/wikipedia/commons/2/ 23/SphericalCow2.gif
18 Spatial group model building: GMB with spatial attributes Key characteristics • Grounding problems, causes, and consequences spatially • Identifying spatial and temporal changes and their co-evolution • Using maps and GIS concepts to facilitate model and system building through physical platforms such as LayerStack (and eLayerstack using Vecta) or other related tools Picture credit: K.M. Rich 2016 (Lincoln, New Zealand)
19 Spatial group model building: toolkits (1) LayerStack: an offline, participatory GIS-type facilitation platform (funded by KiwiNet) Use of plastic acetates as data layers (land use, VC actors, climate, disease patterns, production characteristics) over a map Use of variety of consumables (stickers, markers) to denote physical location and temporal/spatial movement Improves visualization of system and facilitation of model development Simple, low-tech, hands-on, easy to store information
Picture credit: K.M. Rich 2016 (Monze, Zambia)
22 Spatial group model building: toolkits (2) “Necessity is the mother of all innovation” COVID-19 has made face-to-face participatory processes challenging “eLayerstack” – an online means of conducting SGMB with groups online using the web-based Vecta platform (http://vecta.io) Same principles of layers and consumables to “draw” on base maps, but in real-time with stakeholders Picture credit: K.M. Rich 2020 (online snapshot)
23 Spatial group model building Process for model development outlined in Rich, Rich, and Dizyee (2018) Eight-step process, but flexible depending on use for qualitative or quantitative modelling. In the next presentation, we will demonstrate how we implement in practice (offline and online). Picture credit: K.M. Rich 2019 (Dakar, Senegal)
24 Spatial group model building: process (1) Source: Rich, Rich, and Dizyee (2018)
30 Spatial group model building: process (6) Source: Rich, Rich, and Dizyee (2018) Note: spatial co-evolution of models remains an important area of future research; we’re not there yet, but hope to go in that direction
31 Example #1: urban agriculture (UA) in Christchurch, NZ UA has a long tradition in Christchurch (WWII, Vegetable Campaigns) Since the earthquakes in 2010 and 2011, Christchurch has experienced a revival in UA More complex situation: High prices of fresh produce Psychological and emotional impact of the earthquake UA as a way to reconnect with the city Divergence between planners and practitioners: role of SGMB to articulate key spatial issues and leverage points Picture credit: M. Rich 2017 (Christchurch, New Zealand)
Source: Rich, Rich, and Dizyee (2018)
Source: Rich, Rich, and Dizyee (2018)
POPULATION LAND/PRODUCTION O1 O2 O3 O4 O5 O7 O8 O9 O10 O11 O11 O12 O13 O14 O6 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 L1 L2 L3 L3 L4 L5 L6 L7 L8 L9 PO1 PO2 PO3 PO4 PO5 PO5 PO5 PO6 PO7 PO8 PO9 PO10 LEGEND IN THE MODEL IN THE MAP Source: Rich, Rich, and Dizyee (2018)
Production Number of market outlets O1 O14 P1 P12 L1 L9 PO1 PO10 Land for urban agriculture Population LEGEND IN THE MODEL IN THE MAP Profits Demand Distance to market - Growth rate of market outlets - Factors to promote awareness in UA + Actual UA participants Number of market outlets + Selling to consumers + + Community & consumer awareness of UA + + Production Land for urban agriculture + + + + + + Population + + Production Number of market outlets O11 O11 O12 O1 O14 P4 P5 P6 P7 P8 P1 P12 L4 L5 L6 L7 L1 L9 PO1 PO10 Land for urban agriculture Population LEGEND IN THE MODEL IN THE MAP Profits Demand Distance to market - Growth rate of market outlets - Factors to promote awareness in UA + Actual UA participants Number of market outlets + Selling to consumers + + Community & consumer awareness of UA + + Production Land for urban agriculture + + + + + + Population + + Source: Rich, Rich, and Dizyee (2018)
37 Urban agriculture in Christchurch: insights Spatial dimension of UA in Christchurch extremely important – land use patterns vs. population movement patterns. An opportunity: How to bring UA products from producers to consumers? Model remained qualitative – not parameterized quantitatively Picture credit: M. Rich 2017 (Christchurch, New Zealand)
38 Example #2: East coast fever in Zambia ECF – an important livestock disease in East Africa, including Zambia. Recent field work (Mumba 2018) highlighted importance of ECF relative to other government priorities (e.g. FMD) Little known about drivers/context of control and how this differs across space. How to identify and quantify impact of interventions that would both improve communal involvement in the chain and reduce disease? First “live” test of Layerstack and (qualitative) SGMB in the field Picture credit: K.M. Rich 2016 (Monze, Zambia)
Source: Mumba et al. (2017)
Source: Mumba et al. (2017)
43 East coast fever in Zambia: insights Drivers of ECF have distinct spatial patterns • Competition between land • Market differences (external – Lundazi vs. local – Monze) • Cultural norms against mixing animals at dip tanks based on social status/class • Variations in herding practices Spatial differences highlight the need for developing locally relevant, fit-for-purpose control strategies. Picture credit: K.M. Rich 2016 (Monze, Zambia)
44 Example #3: upgrading pig value chains in Myanmar Project at a glance… Focus: Pro-poor interventions to upgrade pork and rice value chains in Tanintharyi region, Myanmar Duration: 5 years, from November 2017 to October 2022 Client: MFAT, Partnership for International Development (PfID) fund Contract value: NZ$4.1 million. Partners World Vision (WV): Contract holder, personnel, resources and logistical support for project implementation and monitoring Vision Fund (VF): Micro-finance for households and farmers, new financial products to co- finance value-adding ventures Lincoln University & ILRI: Value chain research and design, steer project implementation and monitoring, impact assessment Source: Slide courtesy of Berends and Esnard (2020)
45 Process Three field visits to Myanmar: • Five pig SGMB workshops (avg. 13 participants and 50% female) with farmers, brokers, slaughterhouse owners, and wholesalers • Five rice SGMB workshops (avg. 14 participants and 40% female) with paddy farmers, millers, wholesalers • Six Reference Group workshops (avg. 6 participants) with government officers, NGO staff, and lead farmers • Two Project Advisory Committee(PAC) meetings to review results and decide on interventions Source: Slide courtesy of Berends and Esnard (2020)
46 Tools and outputs: Layerstack for VC dynamics • • •HHHH • • • • • • •HHHH • • • • Layer 2: Input, service and product flows in the pig value chain Layer 1: Livelihood zones Source: Slide courtesy of Berends and Esnard (2020); picture credit J. Berends (2019)
47 Tools and outputs: causes and consequences • Value chain problems prioritised and then explored by developing reference nodes, and cause and consequence maps • Goal of identifying causal relationships that determine dynamic behaviours in the chain Source: Slide courtesy of Berends and Esnard (2020); picture credit J. Berends (2019)
48 Tools and outputs: concept model • Based on common themes and critical feedback loops from cause and consequence mapping, develop concept model that contains feedback loops and structure which determine dynamic behaviour in the chain Source: slide courtesy of Berends and Esnard (2020); picture credit J. Berends (2019)
49 Tools and outputs: modules for scenario analysis • Concept model then divided into modules • Each module structure is further developed in Stella Architect (SD software package) and parametrised • Modules are then connected through material flows and information flows to form a functioning baseline model Source: Slide courtesy of Berends and Esnard (2020)
50 Tools and outputs: scenarios Model scenarios • Baseline: No project interventions • Scenario 1: Project interventions cover all pig producers in target villages • Scenario 2: Project establishes Producer Groups (PGs) and targets PG members for interventions • Scenario 3: PGs are upgraded to Producer Organizations (POs) with the institutional arrangements to support ongoing capacity investments Interventions (within scenarios) • Microfinance loans • Good Animal Husbandry Practices (Animal Health Workers and biosecurity) • Training on hybrid pig production and commercial pig feed • Artificial insemination • Combination of interventions Source: Slide courtesy of Berends and Esnard (2020)
51 Key findings Source: Slide courtesy of Berends and Esnard (2020); photo credit J. Berends (2019) • Establish PGs with a mix of hybrid Farrow-to-Finish (high profits) and Wean-to-Finish (moderate profits) farming systems that can collectively supply slaughterhouses with a consistent high-quality fattener • To sustain investments in hybrid breeds a rank order for project interventions is recommended: 1. Improved credit facilities (high priority) 2. Good Animal Husbandry Practices (high priority) 3. Training and the introduction of commercial pig feed (medium priority) 4. Artificial insemination (low priority) • Interaction effects: individual activities are negative or barely positive but 1+2+3 = 47% increase in profits • Focus on functional PGs: Institutional arrangements that reward small-scale farmers in proportion to their patronage and investment delivered higher reinvestment in PGs and larger profits for members • Co-investment between PG/PO members and a strategic partner in a hygienic slaughterhouse facility is a high-impact intervention that widens and deepens the medium and long-term results of the project. • Potential negative impacts for smaller farmers if disease outbreak occurs during upgrading. Improved loan product, subsidize introduction of GAHPs, keep funds in reserve to cover loan defaults.
52 Example #4: Aggregation systems for horticulture in Bihar People in Bihar consume less than half of the global recommendation of 400 grams/capita/day (FAO and WHO, 2014) Consumers dependent upon nutritionally vulnerable markets (i.e. traditional, small and often rural) likely to face the greatest challenges to F&V access and affordability. Do aggregation systems like LOOP (a program of Digital Green) improve availability/accessibility for poorer, more remote HHs? Can they be made more nutritionally- sensitive? Are there trade-offs in doing so? Picture credit: K.M. Rich 2019 (Muzzafapour, India) Source: Slide courtesy of Cooper et al. (2020)
53 The LOOP aggregation scheme 1. F&V aggregation from farmers 2. Aggregator sells F&V at market 3. Aggregator collects money and receipts 4. Returns revenues and receipts to farmers LOOP LOOP: a mobile app-based aggregation service that has collected and sold the F&V supplies of over 28,000 farmers in Bihar, India Key farmer-facing benefits:  Cut transport costs (1.5 Rs/kg  0.5-1 Rs/kg)  Market access  Increased bargaining power  Time-savings BUT … The combination of lower transport costs and access to higher capacity vehicles has contributed to aggregation pathways clustering around large urban markets (occasionally bypassing smaller rural markets) Source: Slide courtesy of Cooper et al. (2020)
54 Approach Example: the total number of farmers registered to LOOP in Koilwar block, Bihar Spatial group model building (SGMB): involving stakeholders in model conceptualisation, formulation, analysis, evaluation and decision- making (Mumba et al. 2017); using the participatory GIS tool ‘LayerStack’ (Rich et al. 2018) LOOP dashboard data: real-time market transaction data covering LOOP supply quantities, F&V types, prices and associated meta-data Household survey data: 360 farming household surveys on production and marketing habits Source: Slide courtesy of Cooper et al. (2020)
55 Output timeseries Reference Extension Quota Cold storage Consumer demand LOOP farmers LOOP profits LOOP sales Small market F&V retail purchases per customer Source: Slide courtesy of Cooper et al. (2020)
56 Nutrition trade-offs LOSE-WIN WIN-WIN WIN-LOSE LOSE-LOSE How do these scenarios plot on the trade-off space? LOSE-WIN WIN-WIN WIN-LOSE LOSE-LOSE Source: Slide courtesy of Cooper et al. (2020)
57 Wider trade-offs -1 0 1 2 3 4 LOOP extension -1 0 1 2 3 4 Small market quota -1 0 1 2 3 4 Cold storage -1 0 1 2 3 4 Retail demand growth Reference baseline Outcome relative to reference Outcome relative to reference Source: Slide courtesy of Cooper et al. (2020)
58 Implications Aggregation systems: real potential to improve the availability and affordability of F&V in small, rural markets. However, nutrition-facing benefits may come at the expense of producer-facing financial outcomes. Likewise, changes in the wider enabling environment may compound these trade-offs (e.g. cold storage stabilising prices in smaller markets) Picture credit: K.M. Rich 2019 (Muzzafapour, India) Source: Slide courtesy of Cooper et al. (2020)
59 References Antunes, P., Santos, R., & Videira, N. (2006). Participatory decision making for sustainable development—the use of mediated modelling techniques. Land Use Policy, 23(1), 44-52. Berends, J., Rich, K.M., & Lyne, M.C. (2020). A pro-poor approach to upgrade value chains in Tanintharyi region of Myanmar. Oral presentation for the 3rd Asia-Pacific System Dynamics Society Conference, Brisbane, Australia, 4 February 2020. Cooper, G.S., Rich, K.M., Shankar, B., Rana, V., Ratna, N., Kadiyala, S., Alam, D. & Nadagouda, S.B. (in review).Identifying ‘win-win-win’ futures from inequitable value chain trade-offs: a system dynamics approach. Submitted to Agricultural Systems. Lie, H., Rich, K.M., & Burkart, S. (2017). Participatory system dynamics modelling for dairy value chain development in Nicaragua. Development in Practice 27 (6), 785- 800. Lie, H., Rich, K.M., van der Hoek, R., & Dizyee, K. (2018). Quantifying and evaluating policy options for inclusive dairy value chain development in Nicaragua: A system dynamics approach. Agricultural Systems 164, 193-222. Mumba, C., Skjerve, E., Rich, M., & Rich, K.M. (2017). Application of System Dynamics and Participatory Spatial Group Model Building in Animal Health – A Case Study of East Coast Fever Interventions in Lundazi and Monze Districts of Zambia. PLOS One, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189878. Rich, K.M., Rich, M., & Dizyee, K. (2018). Participatory system approaches for urban and peri-urban agriculture planning: the role of system dynamics and spatial group model building. Agricultural Systems 160, 110-123. Scott, R. J., Cavana, R. Y., & Cameron, D. (2016). Recent evidence on the effectiveness of group model building. European Journal of Operational Research, 249(3), 908- 918. Vennix, J. A. M. (1996). Group Model Building. Facilitating Team Learning Using System Dynamics. New York: Wiley & Sons
THANK YOU Picture credit: K.M. Rich 2018 (Dakar, Senegal)

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