A System Dynamics Model of Land-use Change for Climate Change

Adaptation: The Case of Uganda

Isdore Paterson Guma

, Agnes Semwanga Rwashana

and Benedict Oyo

Department of Computer Science, Gulu University, Gulu, Uganda

College of Computing and Information Science, Makerere University, Kampala, Uganda

Keywords: System Dynamics Model, Land-Use Change, Climate, Climate Change Adaptation.

Abstract: System dynamics models in land use change are useful tools for understanding the cause and effect of land

use changes, assessing the impacts of land use systems on the environment, and supports land use planning

and policy dimensions. Several studies have used different methods to examine the drivers of land-use change

in understanding the interactions of land-use change as a result of human activities. However, much less work

has been undertaken to model the future of a suite of ecosystem services in a holistic way. These studies have

been conducted with minimum emphasis on the systemic structures or feedback processes of land-use

decisions. A system dynamics model will be used to model ecosystem services to understand complex

interactions using dynamic synthesis methodology. Questionnaires and interviews will be used for data

collection. The study will explore viable policies for optimal land use to mitigate the degree of future climate

change and risks. Projections of future resource requirements and environmental stress are alarming as a result

of poorly planned economic development. Unless significant measures are taken to incorporate environmental

concerns, the situation is likely to worsen in the future. Modeling complex natural-human systems remains

an important research area.

1 INTRODUCTION

System Dynamics (SD) is a tool for understanding

complex system interactions that deal with dynamical

processes with feedback (Rasmussen et al., 2012).

Besides, SD predicts the complex system changes

under different "what-if" scenarios, making it a good

tool and is widely used in different fields of natural

science, social science, and engineering technology

(Rasmussen et al., 2012). This is because the

complexities of the systems are beyond the grasp of

human mental models. Such a systems-oriented

stance suggests a means of untangling the

complexities of the biophysical and socio-economic

systems. SD places special emphasis on explicit

representation and simulation of non-linear feedback

mechanisms when addressing complex problems

(Siregar et al., 2018).

It helps to identify leverage factors (population

pressure, socio-economic pressure), predicts changes

in the future such as climate variability, floods

(Siregar et al., 2018), appreciate how systems change

https://orcid.org/0000-0002-8282-2993

over time, and a method for studying complex

systems based on the theory of non-linearity,

dynamics, and feedback control (Liu et al., 2017).

Hence, SD is a valuable approach that allows

exploration of how the land systems work, and more

critically, to assess the drivers of environmental

degradation and its contribution to climate change

(Josephat, 2018).

Land-use change (LUC) is a process of

transforming the natural ecosystem by human

activities, causing a significant impact on the

environmental systems (Worku, 2020). LUCs are

often nonlinear and might trigger feedback to the

system, stress living conditions, and threaten people

with vulnerability (Siregar et al., 2018).

The land degradation and loss of biodiversity have

underprivileged human communities of important

ecosystem services (Businge et al., 2017). If this trend

continues, the world will face a very serious challenge

to meet the global goals on water and sanitation, food

security, climate change action, affordable and clean

energy.

Guma, I., Rwashana, A. and Oyo, B.

A System Dynamics Model of Land-use Change for Climate Change Adaptation: The Case of Uganda.

DOI: 10.5220/0010342101910198

In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021), pages 191-198

ISBN: 978-989-758-528-9

191

For example, agricultural expansion in the

equatorial forest in the Democratic Republic of

Congo is the main cause of deforestation (Samndong

et al., 2018) and in the Atlantic Forest in Northern

Brazil, 76% of the households use wood fuel

regularly, and consume on average

686kg/person/year of tree biomass; while poorer

people consume 961kg/person/year (Specht et al.,

2015).

Similarly, over 80% of the households in Uganda

live adjacent to wetland areas and directly use

wetland resources for their household food security

needs (Willbroad & Kiyawa, 2019). The occupants

only associate the importance of wetlands to

consumptive use value like crop cultivation, animal

grazing, human settlement, and extraction of useful

materials while least recognizing stabilization of

hydrological cycle and microclimates, protection of

riverbanks, nutrient and toxin retention, and sewage

treatment (Willbroad & Kiyawa, 2019).

At least 20% of wetlands in eastern Uganda have

been destroyed, depleted, and diminished for rice

plantations (Willbroad & Kiyawa, 2019). Currently,

land resources conversion is a critical challenge for

Uganda driven by the need to meet the livelihoods of

smallholders, high demand for forest products,

urbanization increasing at the rate of 6.6%

(Mwanjalolo et al., 2018; Willbroad & Kiyawa, 2019)

among others.

As a result, Uganda lost an estimated 16.5% of

forests and woodlands (Josephat, 2018) and, a decline

in wetlands by 30% (Willbroad & Kiyawa, 2019)

between 1994 and 2014 causing erratic behaviour in

climate change variability (Boston & Lawrence,

2018). These indicators all point to serious

environmental concerns affecting the livelihoods of

human societies which depend on a wide range of

ecosystem services.

To understand the impacts on the natural

landscape and feedback onto humanity, this study

aims to develop a system dynamics model of land-use

change for climate change adaptation.

1.1 Reference Modes

System dynamics models represent problems but not

systems, and the first step in the modeling process is

to define the problem (Saeed, 1998). The problem is

defined by the reference mode based on historical

information and is often described in a graphical form

(Saeed, 1998). Available data for this model's

reference modes are in two areas namely:

deforestation and carbon emission. In system

dynamics, a problem is defined as an internal

behavioural tendency found in a system (Saeed,

1998).

Deforestation. Uganda lost on average 844kha of

tree cover equivalent to 11% since 2000 (Pendrill et

al., 2019), translating to 218Mt of CO

emissions. In

2001, the tree cover loss was recorded at 29.7kha

representing 0.38%; 65.1kha, 117kha, and 63.3kha

were recorded representing 0.84%, 1.5%, and 0.81%

in the year 2011, 2017, and 2019 respectively as

depicted in Figure 1.

Figure 1: Deforestation trends – 2001 to 2019 (Source:

Globalforestwatch.org).

Greenhouse Gas Emissions. Forest is a net source of

, emitting on average 25.5t of CO

per year from

1990 to 2016 (Dou et al., 2016), representing 44%

greenhouse gas emissions over the same period. For

instance, in 2001, 29.7kha of tree cover was lost and

10.8Mt of carbon was emitted. A total of 65.1kha and

20.9Mt of tree cover losses and carbon emissions

recorded in 2011 respectively. In 2019, 63.3kha of

tree cover equivalent to 12.6Mt of carbon emissions

as shown in Figure 2.

Figure 2: Carbon emission trends (2001-2019) (Source:

Globalforestwatch.org).

1.2 Dynamic Hypothesis

This is a theory of how structure, decisions, and

policies can generate the observed behaviour (Oliva,

2003). The model theory explains the causal link

between structure and the simulated behavioural

output arising from the interaction of the equations

and initial conditions. The dynamic hypothesis

SIMULTECH 2021 - 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

192

explains the problematic behaviour shown by

balancing loops B1, B2, and B3 by providing an

explanation of the dynamics, characterizing the

problem in terms of feedback and delays in the

structures by the system as in Figure 3.

In loop B1, an increase in population increases

demand for ecosystem services leading to ecosystem

degradation, which in turn increases environmental

hazards thereby affecting food security in the long

run. In loop B2, increasing ecosystem degradation

leads to an increase in environmental hazards

negatively affecting economic growth, implying that

government has to spend a lot to minimize

environmental challenges affecting the population.

The negative effect of economic growth widens the

poverty gap among the population, further

exacerbating ecosystem degradation.

While in loop B3, increasing ecosystem

degradation increases economic growth as the

ecosystem provides a source of income to the poor

population. At the same time, expanding agricultural

land as a result of forest and wetland degradation with

other economic activities improves the livelihoods of

the poor as in Figure 3.

Figure 3: Dynamic hypothesis of land-use change.

2 GAP ANALYSIS

Several studies have used different methods to

examine the drivers of LUC such as statistical

methods (Gray & Bilsborrow, 2014); spatial analysis

and remote sensing (Call et al., 2017); and system

dynamics (SD) models (Bastan et al., 2018; Turner &

Kodali, 2020) in understanding the interactions of

LUC as a result of human activities. Other models

include: carbon sequestration (Boysen et al., 2020;

Lawrence et al., 2020), biodiversity (Di Marco et al.,

2019; Hof et al., 2018) among others to examine how

future land-use changes affect individual ecosystem

services.

However, much less work has been undertaken to

model the future of a suite of ecosystem services

holistically and have been conducted with minimum

emphasis on the systemic structures or feedback

processes of land-use decisions (Dang & Kawasaki,

2017; Krause et al., 2017; Molotoks et al., 2018;

Rabin et al., 2020).

3 RELATED LITERATURE

Uganda like any other country in Sub-Saharan Africa

and the world experiences environmental and socio-

economic changes as a result of land-use changes.

Land use and its exploitations are critical links

between human activities and the natural

environment contributing to regional and global

climate change by driving energy recycling and

material exchange on the land surface (Liu et al.,

2017). Land-use is any form of human activity on the

land to benefit from the land resources (Liu et al.,

2017).

This interplay between population growth,

resource depletion, and environmental degradation

has been a matter of debate for decades (Creutzig et

al., 2019). The common position is centred on both

population growth and unsustainable development as

the cause for concern.

3.1 Drivers and Implications of

Land-use Change

The key driver of land-use change according to

researchers is the population growth and derived

human activities (Fang et al., 2019; Mwanjalolo et al.,

2018; Willbroad & Kiyawa, 2019). Population

explosion drives encroachments into forest reserves

(Mwanjalolo et al., 2018), wetlands for agriculture

(Baker et al., 2019); settlement (Liu et al., 2017),

mining sand and clay (Willbroad & Kiyawa, 2019).

These human-induced activities of land degradation

not only exacerbate global warming through

increasing greenhouse gas emissions, rather

persistently causing irreversible biological diversity

losses across the globe (Liu et al., 2017).

Urbanization and socio-economic development

have increased human-environment interactions (Yao

et al., 2018) as more than 50% of the world's

population lived in urban areas, a number that will

likely reach over 70% by 2050.

The prevailing poverty in low-income developing

countries is another contributor to environmental

threats. Poor farmers in rural areas live in the most

marginal, fragile environments, forcing them to

A System Dynamics Model of Land-use Change for Climate Change Adaptation: The Case of Uganda

193

sacrifice long-term sustainability for short-term

survival (Izazola & Jowett, 2010). Poverty accounts

for 21.4% of the population in Uganda (Izazola &

Jowett, 2010).

In this context, some studies have reported a

decline of over 53.8% of wetlands in the Lake

Victoria basin and 14.7% in the Lake Albert basin

(Businge et al., 2017).

3.2 Climate Change Adaptation

In Uganda and elsewhere in the world, current

climatic events could have led some individuals to

conclude that "unprecedented is the new normal". For

instance, the floods experienced in New Zealand

during 2017 were recorded as the most expensive on

record costing $243M of insured losses (Lawrence et

al., 2018). Similarly, floods have occurred in

Auckland in May 2018 costing $72M with the 2018

total already at $173M. These indicators in damage

and costs are reflections of our changing climate and

evidence of a more volatile and dynamic

environment.

4 RESEARCH METHODOLOGY

The research methodology adopted will be dynamic

synthesis methodology (DSM) (Williams, 2002) and

employs a research design that combines two

powerful research methods; case study research

(qualitative) and system dynamics methods

(quantitative) to provide solutions to problems

(Sooka & Semwanga, 2011). The two methods

complement each other in terms of theory building,

testing, and theory extension (Williams, 2002).

A combination of qualitative and quantitative

research methods increases the robustness of results

which can be strengthened through cross-validation

(Williams, 2002). Methodological pluralism is

important in research as it eliminates personal bias

(Williams, 2002).

4.1 System Dynamics Method

The SD method provides tools capable of

incorporating mental models into stock-and-flow-

based simulations linking physical materials, delays,

and information flows (Sweeney & Sterman, 2000).

SD method investigates complex systems whose

models are both descriptive and behavioural as they

attempt to represent the physical world relevant to a

specific problem (Sweeney & Sterman, 2000).

Systems theory explains the behaviour of

complex dynamic systems endogenously; identifying

feedback effects most often hidden because of delays

at large time scales. SD illuminates three principal

effects: exogenous shocks, systemic feedback loops,

systemic delays and unintended consequences

(Rwashana et al., 2009).

4.2 Case Study Method

A case study is an empirical investigation that probes

and examines responses of convenient influences

within the real operational environment of the task,

user, and system (Williams & Kennedy, 2012). Case

study approach refers to group methods that

emphasizes qualitative analysis (Yin et al., 1985).

Case study is quantitatively used to validate and

evaluate SD simulation models (Yin et al., 1985).

Similarly, the case study method emphasizes the

study of a phenomenon within its real-world context

favouring the collection of data in natural settings,

compared with relying on "derived" data (Yin et al.,

1985).

5 RESEARCH STRATEGY

The research strategy is a step-by-step approach for

data collection and analysis (Rwashana et al., 2009).

It follows a six (6) step process as illustrated in Figure

Figure 4: Research Design Strategy (Source: (Rwashana et

al., 2009).

Stage 1: Problem Statement. The stage of this

process requires solving problems rather than

answering questions. It identifies key stakeholders,

problems, and their owners.

SIMULTECH 2021 - 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

194

Stage 2: Case Studies. It is used to validate and

evaluate the SD model and provide a deeper

understanding of the problem being investigated.

Stage 3: Field Studies. Provides tools and techniques

for conducting studies of users, their tasks, and their

work environments (Rohn et al., 2002).

Sampling Procedure. Purposive sampling will be

adopted by the researcher, enabling the selection of

cases that are both easy to get to and hospitable to the

research inquiry (Kazerooni, 2001). It is flexible in

meeting multiple needs and interests based on the

purpose of the study and knowledge of a population

(Tongo, 2007).

Interviews. Semi-structured interviews will be used

to interview key stakeholders and occupants of

research sites.

Questionnaires and Data Collection. A tool for

collecting and recording information which provides

the missing data for the model. The sample size will

be determined using the Krejcie & Morgan (1970)

table.

Data Analysis. Analysis will be done using SPSS

version 22.

Stage 4: Model Building. Detailed model structure

comprising influence diagrams (CLD) using Vensim

software. The stocks and flows provide a richer visual

language for the quantitative representation using

STELLA Architect, and mathematical relationships

between and among variables are defined.

Stage 5 & 6: Model Testing, Validation and Policy

Analysis. Both model structure and model behaviour

tests will be performed involving different

stakeholders. Success in the testing of the model

creates confidence in the model. While determining

whether the model is valid or not, the following

questions can be asked:

• Does the model represent the real-life situation?

• Do the specifications of requirements satisfy the

system's needs?

5.1 Expected Outcomes

The following will be the expected outcomes of the

research:

First, design of CLD showing relationships

among the interacting variables, generating a theory

of the observed behaviour of the dynamics of land-

use change.

Secondly, design of quantitative models using

Stella architect software.

Lastly, formulation of suitable policies from

simulation runs.

6 DISCUSSION

Sustainable land-use systems planning and

management require a thorough understanding of the

human ecosystem interactions across any landscape.

However, the contributing factors must be interpreted

carefully given the multiple socio-economic and

methodological perspectives in which related studies

have been conducted. Besides, interactions between

contributing factors add to the complexity of land use

change processes.

Rapid population growth drives depletion of

forest resources owing to the increasing demand for

productive land for agriculture, forest products by

clearing more forests. Deforestation reduces species

diversity and erodes the genetic base of tropical trees.

Another driver of land-use change is the weak

environmental laws and policies leading to illegal pit

sawing and timber harvesting activities in tropical

high forest (Mwanjalolo et al., 2018).

Similarly, wetlands provide important socio-

economic value ranging from fish breeding, crop and

livestock farming, non-use values such as micro-

climate regulation, flood control, water regulation,

habitat and eco-tourism, and food security (Kakuru et

al., 2013). The economic value of wetlands through

crop production is attributed to reliable moisture for

crop growth. However, this practice causes

overgrazing and leads to the removal of vegetation,

soil compaction, and destabilization of river banks

and lakeshores, affecting filtering capacity of

wetlands, flood control abilities, water recharge, and

wildlife habitat.

7 CONCLUSION

The exploration of the dynamic land system reveals

important dynamics that would be missed even by far

more complex models that treat climate change

adaptation variability exogenously. This calls for

engagement of different stakeholders who play a key

role in adaptation to climate change including

securing ecosystem service provision, the

dissemination of effective adaptation strategies, and

A System Dynamics Model of Land-use Change for Climate Change Adaptation: The Case of Uganda

195

smoothing out shocks. This interplay between

government agencies, the private sector, and

occupants of the affected land ecosystems are

important aspects in determining the adaptive

capacity of land-use changes.

However, the design of interventions is hindered

by the uncertainty of climate change and population

dynamics, untested strategies, and time lags in

implementation (Koontz et al., 2015; Lyle, 2015). At

the same time, actions may have severe unintended

effects on the provision of ecosystem services when

legacies of a previous policy supporting a certain land

use prevent future additional holistic interventions

(Holzhauer et al., 2019). Understanding the interplay

of human actions within the land system is therefore

challenging and projecting their impacts becomes

even more difficult.

System dynamics supports exploration of various

socio-economic and environmental scenarios by

representing different stakeholder viewpoints leading

to fair or better simulation results and fair policy

(Balint et al., 2016).

In this regard, effective governance requires

adaptive and pro-active processes of policy design

and actions to reconfigure incentives that support

policy design. These in turn require integrated

analysis of multiple policies that support an

understanding of different options, risks, stresses, and

outcomes of such policies.

Effective systems and policy design require the

knowledge and ability to examine and understand,

evaluate, and then manage the complex, dynamic

(non-linear) trade-offs existing at the structural level

of land-use changes including climate change

adaptation and mitigation. Future research requires

integrating system dynamics method with other

methods in participative modeling of a suite of

ecosystem services, taking into account all

stakeholder viewpoints.

ACKNOWLEDGMENT

The authors appreciate the financial support from

Building Stronger Universities (BSU) towards

publication of this work. More appreciation goes to

the course facilitator who has been instrumental in

guidance and production of this paper. We are also

indebted to the Almighty God for His protection and

wisdom. Not forgetting the course mates who have

contributed ideas as far as this work is concerned.

REFERENCES

Baker, M., Sarfo, I., Darko, G., & Bi, S. (2019). Loss of

wetland resources in Uganda: The case of lake Wamala

in Mityana District. International Research Journal of

Public and Environmental Health, 6(8), 170–190.

https://doi.org/10.15739/irjpeh.19.021

Balint, T., Lamperti, F., Mandel, A., Napoletano, M.,

Roventini, A., Sapio, A., Balint, T., Lamperti, F.,

Mandel, A., Napoletano, M., & Roventini, A. (2016).

Complexity and the Economics of Climate Change : a

Survey and a Look Forward Centre d ’ Economie de la

Sorbonne Documents de Travail du.

Bastan, M., Ramazani Khorshid-Doust, R., Delshad Sisi, S.,

& Ahmadvand, A. (2018). Sustainable development of

agriculture: a system dynamics model. Kybernetes,

47(1), 142–162. https://doi.org/10.1108/K-01-2017-

0003

Boston, J., & Lawrence, J. (2018). Funding Climate Change

Adaptation. Policy Quarterly, 14(2). https://doi.org/

10.26686/pq.v14i2.5093

Boysen, L., Brovkin, V., Pongratz, J., Lawrence, D.,

Lawrence, P., Vuichard, N., Peylin, P., Liddicoat, S.,

Hajima, T., Zhang, Y., Rocher, M., Delire, C., Séférian,

R., Arora, V., Nieradzik, L., Anthoni, P., Thiery, W.,

Laguë, M., Lawrence, D., & Lo, M.-H. (2020). Global

climate response to idealized deforestation in CMIP6

models. Biogeosciences Discussions, July, 1–35.

https://doi.org/10.5194/bg-2020-229

Businge, Z., District, K., Government, L., Madrigal, V., &

Barrio, I. C. (2017). Drivers of Wetland Degradation in

Western Uganda and Iceland , and How They Are

Addressed in Current Policies. 2017. http://www.

unulrt.is/static/fellows/document/businge2017.pdf

Call, M., Mayer, T., Sellers, S., Ebanks, D., Bertalan, M.,

Nebie, E., & Gray, C. (2017). Socio-environmental

drivers of forest change in rural Uganda. Land Use

Policy, 62, 49–58. https://doi.org/10.1016/j.landuse

pol.2016.12.012

Creutzig, F., Bren D’Amour, C., Weddige, U., Fuss, S.,

Beringer, T., Gläser, A., Kalkuhl, M., Steckel, J. C.,

Radebach, A., & Edenhofer, O. (2019). Assessing

human and environmental pressures of global land-use

change 2000-2010. Global Sustainability, 2, 1–17.

https://doi.org/10.1017/sus.2018.15

Dang, A. N., & Kawasaki, A. (2017). Integrating

biophysical and socio-economic factors for land-use

and land-cover change projection in agricultural

economic regions. Ecological Modelling, 344, 29–37.

https://doi.org/10.1016/j.ecolmodel.2016.11.004

Di Marco, M., Harwood, T. D., Hoskins, A. J., Ware, C.,

Hill, S. L. L., & Ferrier, S. (2019). Projecting impacts

of global climate and land-use scenarios on plant

biodiversity using compositional-turnover modelling.

Global Change Biology, 25(8), 2763–2778.

https://doi.org/10.1111/gcb.14663

Dou, X., Zhou, W., Zhang, Q., & Cheng, X. (2016).

Greenhouse gas (CO2, CH4, N2O) emissions from soils

following afforestation in central China. Atmospheric

SIMULTECH 2021 - 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

196

Environment, 126, 98–106. https://doi.org/10.1016/

j.atmosenv.2015.11.054

Fang, C., Cui, X., Li, G., Bao, C., Wang, Z., Ma, H., Sun,

S., Liu, H., Luo, K., & Ren, Y. (2019). Modeling

regional sustainable development scenarios using the

Urbanization and Eco-environment Coupler: Case

study of Beijing-Tianjin-Hebei urban agglomeration,

China. Science of the Total Environment, 689(June),

820–830. https://doi.org/10.1016/j.scitotenv.2019.06.

430

Gabiri, G., Leemhuis, C., Diekkrüger, B., Näschen, K.,

Steinbach, S., & Thonfeld, F. (2019). Modelling the

impact of land use management on water resources in a

tropical inland valley catchment of central Uganda,

East Africa. Science of the Total Environment, 653,

1052–1066. https://doi.org/10.1016/j.scitotenv.2018.1

0.430

Gray, C. L., & Bilsborrow, R. E. (2014). Consequences of

out-migration for land use in rural Ecuador. Land Use

Policy, 36, 182–191. https://doi.org/10.1016/

j.landusepol.2013.07.006

Hof, C., Voskamp, A., Biber, M. F., Böhning-Gaese, K.,

Engelhardt, E. K., Niamir, A., Willis, S. G., & Hickler,

T. (2018). Bioenergy cropland expansion may offset

positive effects of climate change mitigation for global

vertebrate diversity. Proceedings of the National

Academy of Sciences of the United States of America,

115(52), 13294–13299. https://doi.org/10.1073/pnas.1

807745115

Holzhauer, S., Brown, C., & Rounsevell, M. (2019).

Modelling dynamic effects of multi-scale institutions

on land use change. Regional Environmental Change,

19(3), 733–746. https://doi.org/10.1007/s10113-018-

1424-5

Izazola, H., & Jowett, A. (2010). SA NE M SC PL O E – C

EO AP LS TE S PL O E –. II.

Josephat, M. (2018). Deforestation In Uganda: Population

Increase, Forests Loss And Climate Change.

Environmental Risk Assessment and Remediation,

02(02), 46–50. https://doi.org/10.4066/2529-8046.100

040

Kakuru, W., Turyahabwe, N., & Mugisha, J. (2013). Total

economic value of wetlands products and services in

Uganda. The Scientific World Journal, 2013.

https://doi.org/10.1155/2013/192656

Kazerooni, E. A. (2001). Population and sample. American

Journal of Roentgenology, 177(5), 993–999.

https://doi.org/10.2214/ajr.177.5.1770993

Koontz, T. M., Gupta, D., Mudliar, P., & Ranjan, P. (2015).

Adaptive institutions in social-ecological systems

governance: A synthesis framework. Environmental

Science and Policy, 53, 139–151. https://doi.org/

10.1016/j.envsci.2015.01.003

Krause, A., Bayer, A. D., Pugh, T. A. M., Doelman, J. C.,

Humpenöder, F., Anthoni, P., Olin, S., Bodirsky, B. L.,

Popp, A., Stehfest, E., & Arneth, A. (2017). Global

consequences of afforestation and bioenergy cultivation

on ecosystem service indicators. Biogeosciences

Discussions, 1–42. https://doi.org/10.5194/bg-2017-

160

Krejcie, R. V., & Morgan, D. W. (1970). Determining

Sample Size for Research Activities. Educational and

Psychological Measurement, 30(3), 607–610.

https://doi.org/10.1177/001316447003000308

Lawrence, J., Blackett, P., & Cradock-Henry, N. A. (2020).

Cascading climate change impacts and implications.

Climate Risk Management, 29. https://doi.org/10.1016/

j.crm.2020.100234

Lawrence, P. j., Lawrence, D. M., & Hurtt, G. C. (2018).

Attributing the Carbon Cycle Impacts of CMIP5

Historical and Future Land Use and Land Cover

Change in the Community Earth System Model

(CESM1). Journal of Geophysical Research:

Biogeosciences, 123(5), 1732–1755. https://doi.org/

10.1029/2017JG004348

Liu, D., Zheng, X., Zhang, C., & Wang, H. (2017). A new

temporal–spatial dynamics method of simulating land-

use change. Ecological Modelling, 350, 1–10.

https://doi.org/10.1016/j.ecolmodel.2017.02.005

Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S.,

Wang, S., & Pei, F. (2017). A future land use simulation

model (FLUS) for simulating multiple land use

scenarios by coupling human and natural effects.

Landscape and Urban Planning, 168(July 2016), 94–

116. https://doi.org/10.1016/j.landurbplan.2017.09.019

Lyle, G. (2015). Understanding the nested, multi-scale,

spatial and hierarchical nature of future climate change

adaptation decision making in agricultural regions: A

narrative literature review. Journal of Rural Studies, 37,

38–49. https://doi.org/10.1016/j.jrurstud.2014.10.004

Molotoks, A., Stehfest, E., Doelman, J., Albanito, F.,

Fitton, N., Dawson, T. P., & Smith, P. (2018). Global

projections of future cropland expansion to 2050 and

direct impacts on biodiversity and carbon storage.

Global Change Biology, 24(12), 5895–5908.

https://doi.org/10.1111/gcb.14459

Mwanjalolo, M. G. J., Bernard, B., Paul, M. I., Joshua, W.,

Sophie, K., Cotilda, N., Bob, N., John, D., Edward, S.,

& Barbara, N. (2018). Assessing the extent of

historical, current, and future land use systems in

Uganda. Land, 7(4), 1–17. https://doi.org/10.3390/

land7040132

Oliva, R. (2003). Model calibration as a testing strategy for

system dynamics models. European Journal of

Operational Research, 151(3), 552–568.

https://doi.org/10.1016/S0377-2217(02)00622-7

Paul, B. K., & Rashid, H. (2017). Land Use Change and

Coastal Management. In Climatic Hazards in Coastal

Bangladesh (pp. 183–207). https://doi.org/10.1016/

b978-0-12-805276-1.00006-5

Pendrill, F., Persson, U. M., Godar, J., & Kastner, T.

(2019). Deforestation displaced: Trade in forest-risk

commodities and the prospects for a global forest

transition. Environmental Research Letters, 14(5).

https://doi.org/10.1088/1748-9326/ab0d41

Rabin, S. S., Alexander, P., Henry, R., Anthoni, P., Pugh,

T. A. M., Rounsevell, M., & Arneth, A. (2020). Impacts

of future agricultural change on ecosystem service

indicators. Earth System Dynamics, 11(2), 357–376.

https://doi.org/10.5194/esd-11-357-2020

A System Dynamics Model of Land-use Change for Climate Change Adaptation: The Case of Uganda

197

Rasmussen, L. V., Rasmussen, K., Reenberg, A., & Proud,

S. (2012). A system dynamics approach to land use

changes in agro-pastoral systems on the desert margins

of Sahel. Agricultural Systems, 107, 56–64.

https://doi.org/10.1016/j.agsy.2011.12.002

Rohn, J. A., Spool, J., Ektare, M., Koyani, S., Muller, M.,

& Redish, J. (2002). Usability in practice: Alternatives

to formative evaluations - Evolution and revolution.

Conference on Human Factors in Computing Systems -

Proceedings, 891–897.

Rwashana, A. S., Williams, D. W., & Neema, S. (2009).

System dynamics approach to immunization healthcare

issues in developing countries: A case study of Uganda.

Health Informatics Journal, 15(2), 95–107.

https://doi.org/10.1177/1460458209102971

Saeed, K. (1998). Defining a Problem or Constructing a

Reference Mode. 16th International Conference of the

System Dynamics Society, Québec City, Canada, May

1998, 1–29.

Samndong, R. A., Bush, G., Vatn, A., & Chapman, M.

(2018). Institutional analysis of causes of deforestation

in REDD+ pilot sites in the Equateur province:

Implication for REDD+ in the Democratic Republic of

Congo. Land Use Policy, 76(March), 664–674.

https://doi.org/10.1016/j.landusepol.2018.02.048

Siregar, P. G., Supriatna, J., Koestoer, R. H., & Harmantyo,

D. (2018). System dynamics modeling of land use

change in West Kalimantan, Indonesia. Biotropia,

25(2), 103–111. https://doi.org/10.11598/btb.2018.2

5.2.792

Sooka, C., & Semwanga, A. R. (2011). Modeling the

dynamics of maternal healthcare in Uganda: A system

dynamics approach. World Journal of Modelling and

Simulation, 7(3), 163–172.

Specht, M. J., Pinto, S. R. R., Albuqueque, U. P., Tabarelli,

M., & Melo, F. P. L. (2015). Burning biodiversity:

Fuelwood harvesting causes forest degradation in

human-dominated tropical landscapes. Global Ecology

and Conservation, 3, 200–209. https://doi.org/10.1016/

j.gecco.2014.12.002

Sweeney, L. B., & Sterman, J. D. (2000). Bathtub

dynamics: Initial results of a systems thinking

inventory. System Dynamics Review, 16(4), 249–286.

https://doi.org/10.1002/sdr.198

Tongo, D. M. C. (2007). Purposive Sampling as a Tool for

Informant Selection. 5, 147–158.

Turner, B. L., & Kodali, S. (2020). Soil system dynamics

for learning about complex, feedback-driven

agricultural resource problems: model development,

evaluation, and sensitivity analysis of biophysical

feedbacks. Ecological Modelling, 428(May), 109050.

https://doi.org/10.1016/j.ecolmodel.2020.109050

Vance, C., & Iovanna, R. (2006). Analyzing spatial

hierarchies in remotely sensed data: Insights from a

multilevel model of tropical deforestation. Land Use

Policy, 23(3), 226–236. https://doi.org/10.1016/

j.landusepol.2005.02.002

Willbroad, B., & Kiyawa, S. A. (2019). Sustainable

Management and Conservation of Wetland Resources

in Uganda : A Review. Journal of Environment and

Health Science, 5(1), 47–51. https://doi.org/10.154

36/2378-6841.19.2479

Williams, D. (2002). Integrating System Dynamics

Modelling and Case Study Research Method : A

theoretical framework for process improvement.

Proceedings from the 20th International Conference of

the System Dynamics Society, January 2005, 1–27.

Williams, D., & Kennedy, M. (2012). Towards a Model of

Decision-Making for Systems Requirements

Engineering Process Management. Bi-Annual

Conference on Requirement Engineering, 1–15.

Worku, A. (2020). Assessment of Land Use Land Cover

Change and Its Implication on Agro-Pastoral Area of

Gode District , Somali Regional State ,. Assessment,

8(1), 80–90. https://pdfs.semanticscholar.org/b875/

efe232ca1c1657db67fa37668347676afe0c.pdf

Yao, J., Zhang, X., & Murray, A. T. (2018). Spatial

Optimization for Land-use Allocation: Accounting for

Sustainability Concerns. International Regional

Science Review, 41(6), 579–600. https://doi.org/

10.1177/0160017617728551

Yin, R. K., Bateman, P. G., & Moore, G. B. (1985). Case

Studies and Organizational Innovation: Strengthening

the Connection. Science Communication, 6(3), 249–

260. https://doi.org/10.1177/107554708500600303

SIMULTECH 2021 - 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

198