Modeling the Decisions of Segzi Plain Farmers Based on Cultivation Type Using the Multi-Variant Logistic Regression Model

Document Type : Research Paper


1 Associate Professor, Department of GIS, Faculty of Geodesy and Geomatics Engineering and Center of Excellence in Geoinformation Technology, K. N. Toosi University of Technology, Iran

2 Assistant Professor, Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran

3 Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran

4 MSc. Student in Remote Sensing and GIS, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran


Complex interactions between human decision-makers and their biophysical environment can be observed in land-use systems. These complexities are due to the differences between biophysical and socio-economic variables. To Model human decision-making, we need to know the interactions between landscape, community, and ecosystems. In reality, humans make decisions using a variety of strategies. We need to simplify the complex interaction between all individual agents and their environment by formulating an agent typology. In this paper, given the complexity of the decision-making in agent based models, the agricultural land use changes are simulated by the multi-variant logistic regression model to determine the socio-economic and environmental factors.  The proportional random rules are used to implement the bounded rationality law for unique individual decision making. The particular environmental conditions of the region and the severity of the risk of desertification, the economic, social, and physical factors and the chemical parameters of soil were investigated with other environmental factors in the decision-making processes.
Diverse data (including GIS and household data) were used to initialize the coupled human–landscape system and farmer household decision making simulations. GIS data is consisted of landscape agents (grid cell or patch) including Land use/cover (based on Landsat 8), soil Physico-chemical properties (EC, SAR, pH), texture, and moisture), institutional variables (i.e. ownership, village territory), and topography. Household data are socioeconomic attributes including labour force, educational status, income structure, and land properties. They were derived from an intensive household survey conducted in Segzi plain in Isfahan province (central Iran) during the spring 2013. The agent-based decision-making method has been presented by Le (2005). To determine decision-making approach, a mechanism of livelihood group dynamics was considered as follows. At first, Principal Component Analysis (PCA) was used to identify key factors differentiating household characteristics. These factors were then employed to classify the population into certain household groups using K-Means Cluster Analyses. The identified agent groups were interpreted and the types specified. Regression logistic multinomial model (M-logit) was employed for land-use choices modeling in each typological household agent group. The dependent variable of the model is land-use choice by farming households (Puse). The independent variables of the M-logit model include two groups of spatial variable and socioeconomic characteristics of farming households. Environmental features of lands were defined including Pwet (soil moisture), Pslope (derived from Digital Elevation Model), Pelev (elevation), PEC (Electrical conductivity as salt factor), Pgroundwater (measuring the reduction of ground water), PPH (PH), PSAR (Sodium Absorption Ratio). Socioeconomic characteristics of farming households influencing farmers' decisions are including Hage (the age of the household head), Hedu (education of farmers), Hincome/pers (annual gross income per capita), Hholding/pers (land holding per capita), Hcultivation/pers (land cultivating per capita), Hlabor (number of workers of the household), and Hdepend (family members of the workers).
Results and Discussion
We reduced the dimensionality of 14 potential criteria by using PCA. The six principle components were extracted with total eigenvalues greater than 1.0, explaining 77.4 % of the total variance of original independent variables. The PC1 was strongly correlated to land variables: Hholding= 0.911, Hcultivation= 0.925. The principle components 2 (PC2), 3 (PC3), 4 (PC4), and 5 (PC5) were most weighted by percentage income from other off-farm activity factors (Hinother= 0.843), household size (Hsize= 0.833), percentage income from grain (HinGrain=0.773), and percentage income from wheat (HinWheat= 0.898), respectively.
The K-means run extracted three groups. The group I consists of households which are rich regarding both land resources and income. The group II includes households with average livelihood standard and the group III comprises the poorest households with the lowest amount of land and income. After the typological livelihood group determined, the variables affecting the decision-making were identified using the M-logit model. The effect coefficients were estimated with respect to the fallow land, i.e., the base case. The chi-square test shows that the empirical M-logit model is highly significant (P<0.01) to explain land-use choice by farmers of the group.
The M-Logit analysis of land use choices for household type I indicate that Pslope (-) and Hholding/pers (-) inversely are effective variables for every land use option and have statistical significance at the 0.05, 0.1 level, respectively. A similar M-logit regression was also applied for the group II (fiqure.1). With regard to the choice of grain and wheat, effective factors were Hage(+), Hlabor(-), Hdepend(+), Hcultivate_per(+), PSAR(-), PPH(+) and Hedu=0(-). The variables that significantly influence selection of other cultivations are Hlabor(-), Hdepend(+), Hcultivation_per(+), PEC(-), PSlope(-) and Pgroundwater(-) For household type III, the results demonstrated that PEC(-), PSlope(+), Pwet(+) were effective on each land use choice. In final, these coefficients are used to obtain the probability of selection for each land use according to the location of the plot in the process of implementing the model as follows:
where βik is preference coefficients, dependent variables, and I is number of land use.
In this research, the preference coefficients for household decision-making are proposed by M-Logit model. These coefficients were applied to the process of implementing agent-based model at later stages. Multi-variant logistic regression model is useful to reduce challenges in modeling farmers decision and to simplify real world and it also indicates the effective factors and its rate that predict farmer decision using empirical data. All this suggests that the presented agent typology has been able to capture the diversity of land-use decisions and strategies in rural landscapes. Regarding the sensitive areas to desertification, as expected, soil chemical factors have a key role to determine type of culture. In general, recognition of the effects of soil physical and chemical factors in the decision, terms of regional strategy in Isfahan province, agricultural sustainability policy in this area and the attempts to avoid deserted villages can be applied for the planning and management of policies and programs in the future.


Main Subjects

Ajzen, I., 1991, The theory of planned behavior, Organizational Behavior and Human Decision Processes, Vol. 50, No. 2, PP. 179-211.
An, L. and López-Carr, D., 2012, Understanding Human Decisions in Coupled Natural and Human Systems, Ecological Modeling, No. 229, PP. 1-4.
Bebbington, A., 1999, Capitals and Capabilities: A Framework for Analyzing Peasant Viability, Rural Livelihoods and Poverty, World Development, Vol. 27, No. 12, PP. 2021-2044.
Bonabeau, E., 2002, Agent-based Modeling: Methods and Techniques for Simulating Human Systems, Proceedings of the National Academy of Sciences of the United States of America, Vol. 99, No. 3, PP. 7280-7287.
Brown, D. G., Riolo, R. and Robinson, D. T. and M. North W., 2005, Spatial Process and Data Models: Toward Integrationof Agent-based Models and GIS, Journal of Geogrophical Systems, Vol. 7, No. 1, PP. 25-47.
Bui, D., 2003, Land Use Systems and Erosion in the Uplands of The Central Coast, Vietnam,Environment, Development and Sustainability, No. 5, PP. 461-476.
Campbell, B., J. A., et al., 2002, Assessing the Performance of Natural Resource Systems, Nservation Ecology, Vol. 5, No. 2, PP. 22.
Crawford, T. W., Messina, J. P., Manson, S. M. and O'Sullivan D., 2005, Complexity Science, Complex Systems, and Land-use Research, Environment and Planning, B: Planning and Design, Vol. 32, No. 6, PP. 792-798.
Entwisle, B., Malanson, G. R. R., Rindfuss and S. J., 2008, An Agent-based model of household Dynamics and Land use change, Journal of Land Use Science, Vol. 3, No. 73-93.
Evans, N. J. and Ilbery W., 1989, A Conceptual Framework for Investigating Farm-based Accommodation and Tourism in Britain, Journal of Rural Studies, Vol. 5, No. 3, PP. 257-266.
Farrington, J., Carney, D. and Ashley, C., 1999, Sustainable Livelihoods in Practice: Early Applications of Concepts in Rural Areas, Overseas Development Institute, London.
Gao, Z., Gao, W. and Chang, B., 2011, Integrating Temperature Vegetation Dryness Index (TVDI) and Regional Water Stress Index (RWSI) for Drought Assessment with the Aid of LANDSAT TM/ETM+ images, International Journal of Applied Earth Observation and Geoinformation, Vol. 13, No. 3, PP. 495-503.
Gasson, R., 1973, Goals and Values of Farmers, Journal of Agricultural Economics, Vol. 24, No. 3, PP. 521-542.
Gustafsson, L. and Sternad, M., 2010, Consistent Micro, Macro and State-based Population Modeling, Mathematical Biosciences, Vol. 225, No. 2, PP. 94–107.
Grimm, V., Railsback, B. and Steven, F., 2005, Individual-based Modeling and Ecology, Princeton University Press, Vol. 25, No. 2, PP. 485-497.
Grimm, V., et al., 2005, Pattern-oriented Modeling of Agent-based Complex Systems: Lessons from Ecology, Science, Vol. 310, No. 5750, PP. 987-991.
Ilbery, B. W., 1978, Agricultural Decision-making a Behavioural Perspective, Progress in Human Geography, Vol. 2, No. 3, PP. 448-466.
Janssen, M. A. and Ostrom, E, 2006, Empirically-based, Agent-based Models, Ecology and Society, Vol. 11, No. 2, PP. 37-50.
Janssen, M. A., Walker, B. H., Langridge, J. and Abel, N., 2000, An Adaptive Agent Model for Analyzing Co-evolution of Management and Policies in a Complex Rangeland System, Ecological Modeling, Vol. 131, No. 2, PP. 249-268.
Kintigh, K. W. and Ammerman, A. J., 1982, Heuristic Approaches to Spatial Analysis in Archaeology, American Antiquity, Vol. 47, No. 1, PP. 31-63.
Knowler, D. and Bradshaw, B., 2007, Farmers’ Adoption of Conservation Agriculture: A Review and Synthesis of Recent Research, Food Policy, Vol. 32, No. 1, PP. 25-48.
Köbrich, C., Rehman, T. and Khan, M., 2003, Typification of Farming Systems for Constructing Representative Farm Models: Two Illustrations of the Application of Multi-variate Analyses in Chile and Pakistan, Agricultural Systems, Vol. 76, No. 1, PP. 141-157.
Koczberski, G., Gibson K. and Curry, G. N., 2001, Improving Productivity of the Small Holder Oil Palm Sector in Papua New Guinea: A Socio-economic Study of the Hoskins and Popondetta Schemes, Australian National University, Research School of Pacific and Asian Studies.
Lambin, E. F., Geist, H. J. and Lepers E., 2003, Dynamics of Land-use and Land-cover Change in Tropical Regions, Annual Review of Environment and Resources, Vol. 28, No. 1, PP. 205-241.
Le, Q. B., 2005, Ecology and Development Series, No. 29, 2005 Implementation for an Upland Watershed in the Central Coast of Vietnam.
Le, Q. B., Park, S. J. and Vlek, P. L. G., 2010, Land Use Dynamic Simulator (LUDAS): A Multi-agent System Model for Simulating Spatio-temporal Dynamics of Coupled Human–Landscape System, Ecological Informatics, Vol. 5, No. 3, PP. 203–221.
Le, Q. B., Park, S. J., Vlek, P. L. G. and Cremers, A. B., 2008, Land-Use Dynamic Simulator (LUDAS): A Multi-agent System Model for Simulating Spatio-temporal Dynamics of Coupled Human–landscape System. I. Structure and Theoretical Specification, Ecological Informatics, Vol. 3, No. 2, PP. 135–153.
Li, H., Li, C., Lin, Y. and Lei, Y., 2010, Surface Temperature Correction in TVDI to Evaluate Soil Moisture over a Large Area, Journal of Food, Agriculture and Environment, Vol. 8, No. 3 & 4, PP. 1141–1145.
Le, Q. B. and Feitosa F. F, 2012, Comparison of Two Common Empirical Methods to Model Land-Use Choices in a Multi-Agent System Simulation of Landscape Transition: Implication for a Hybrid Approach.
Liu, J., et al., 2007, Complexity of Coupled Human and Natural Systems, Science, Vol. 317, No. 5844, PP. 1513-1516.
Matthews, R. B., et al., 2007, Agent-based Land-use Models: A Review of Applications, Landscape Ecology, Vol. 22, No. 10, PP. 1447-1479.
Müller, D., 2003, Land-use Change in the Central Highlands of Vietnam: A Spatial Econometricmodel Combining Satellite Imagery and Village Survey Data, Agricultural Economics, Vol. 27, No. 3, PP. 333-354.
Nelson, G., De Pinto, A., Harris V., Stone, S., 2004, Land Use and Road Improvements: A Spatial Perspective, International Regional Science Review, Vol. 27, No. 3, PP. 297-325.
Niazi, M., Hussain, A., 2011, Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey", Scientometrics (Springer), Vol. 89, No. 2, PP. 479–499.
Niknam, T., Taherianfard, E., Pourjafarian, N. and Rousta, A., 2011, An Efficient Hybrid Algorithm-based on Modified Imperialist Competitive Algorithm and K-Means for Data Clustering, Engineering Applications of Artificial Intelligence, Vol. 24, No. 2, PP. 306-317. (In Persian).
Parker, D. C., et al., 2003, Multi-agent Systems for the Simulation of Land-use and Land-cover Change: A Review, Annals of the Association of American Geographers, Vol. 93, No. 2, PP. 314-337.
Robinson, D. T., et al., 2007, Comparison of Empirical Methods for Building Agent-based Models in Land Use Science, Journal of Land Use Science, Vol. 2, No. 1, PP. 31-55.
Rokeach, M., 1968, A Theory of Organization and Change Within Value‐Attitude Systems 1, Journal of Social, Vol. 24, No. 1, PP. 13-33.
Rubinstein, A., 1998, Modeling Bounded Rationality, MIT Press.
Sandholt, I., Rasmussen K. and Andersen, J., 2002, A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status, Remote Sensing of Environment, Vol. 79, No. 2, PP. 213-224.
Sawyer, R. K., 2003, Artificial Societies Multiagent Systems and the Micro-Macro Link in Sociological Theory, Sociological Methods and Research, Vol. 31, No. 3, PP. 325-363.
Schelling, T. C., 1971, Dynamic Models of Segregation, Journal of Mathematical Sociology, Vol. 1, No. 2, PP. 143–186.
Simon, H. A., 1955, A Behavioral Model of Rational Choice, The Quarterly Journal of Economics, Vol. 69, 1, PP. 99-118.
Willock, J., et al., 1999, The Role of Attitudes and Objectives in Farmer Decision Making: Business and Environmentally‐Oriented Behavior in Scotland, Journal of Agricultural Economics, Vol. 50, No. 2, PP. 286-303.
Wu, F., 1998, An Experiment on the Generic Polycentricity of Urban Growth in a Cellular Automatic City, Environment and Planning B: Planning and Design, Vol. 25, No. 5, PP. 731-752.