Agricultural land use pattern is affected by many factors at different scales and effects that are separated by time and space. This will lead to simulation models that optimize or project the cropping pattern changes and incorporate complexities in terms of details and dynamics. Combining System Dynamics (SD) and a modified Conversion of Land Use and its Effects (CLUE) modelling framework, this paper suggests a new dynamic approach for assessing the demand of different crops at country-level and for predicting the spatial distribution of cultivated areas at provincial scale. As example, a case study is presented for Iran, where we have simulated a scenario of future cropping pattern changes during 2015–2040. The results indicated a change in the spatial distribution of cultivated areas during the next years. An increase in the proportion of rice is expected in northern Iran, whereas the proportion of wheat is increasing in the mountainous western areas. Wheat and barley crops are expected to become dominant within the cropping system throughout the country regions.
Agricultural development is the basis of human survival and thus, an appropriate insight into the future changes in the cultivated areas is significantly important for both whole society and policy makers. More specific in agricultural systems, an appropriate insight into the future spatial and temporal arrangements of cultivated areas have been highlighted as a crucial parameter, reaching to which requires scientific methods and models (
The simulation of land use changes has become a widely used technique in studies of land use planning and ex-ante assessment of new technologies, policy interventions and climate change on agricultural systems (
In spite of advantages of integrated models, they incorporate the complexities.
There are some dynamic simulation models on cropping pattern changes in recent years that tried to combine economic and environmental components at fine resolution (
Therefore, this study mainly contributes by describing a dynamic and integrated approach. This approach aims to allow the rapid generation and testing a range of possible scenarios at country-level to provincial scale. Generally, the suggested model can be differentiated into two scales, national and provincial. At the national scale, SD model and national agricultural statistics that are aggregated at administrative levels are used to estimate the cultivated land demand for each crop. The SD technique enables us to design and test different scenarios based on ecological and socio-economic deriving forces, including population growth, economic development, climatic changes, policy rules, and so on. At the provincial or regional scale, a modified version of Dyna-CLUE (
This paper describes the modelling approach, the inputs, outputs and processes used within the suggested approach. The model evaluation is presented through the analysis of its fitness for this purpose. Application of the model is then demonstrated with a case study on the Iranian agricultural system.
Iran is the second largest country in the west of Asia, with a population of approx. 80 million people. This county comprises 31 provinces and has a total area of 1.648 million square kilometers, of which 8.7% is cultivated land. Average annual precipitation is 228 mm and approx. 90% of the country is arid or semi-arid. In 2014, main crops included wheat, barley, rice and maize, which were grown on 53%, 16%, 5% and 2% of the country’s cultivated areas, respectively. For empirical study in this work, Iran’s 31 provinces were considered. In each province, cropping pattern of four dominant crops (wheat, barley, rice, and maize) were simulated and analysed for the next 25 years. Most of the farms in Iran are monoculture and in-rotation cultivated areas are negligible, then in this study we assumed that all agricultural areas will be cultivated monoculture.
A large amount of data, including national and provincial data, was used in this research (
The integrated model consists of two sub-models: SD sub-model and crop allocation sub-model. SD estimates the demanded area for agricultural land at national level by considering the interaction between agronomics driving forces and without spatial resolution. Crop allocation sub-model simulates distribution of demand within geographical regions of the country by considering spatial characteristics of each region.
Recently,
The stock-flow diagram (SFD) is the core of SD model, and is the process of quantization and materialization of the causal loop diagram (
(1) Demand loop: In this loop, increasing "
(2) Supply loop: In this loop, rising
(3) Trade loop: In this loop, a drop in
The main output of SD sub-model is the demanded area for each crop at country level. This output is the input of crop allocation sub-model. Complete details about the variables and formulas are available in Table S1 [suppl].
By inspiration from Dyna-CLUE (
More specific, the crop allocation sub-model like any other allocation mechanism needs to consider the following three parts: (1) cultivated land requirements (demand); (2) available land area (supply) by considering the spatial policies and restrictions; (3) allocation mechanism based on region capabilities.
(1) Demand: the area demanded to cultivate different crops will be determined by the SD sub-model described in the previous section.
(2) Supply: the total available land in each region should be calculated based on spatial policies, restrictions, conversion rules and area of protected lands. For this, we must reduce the total area of restricted lands, protected lands, and non-convertible lands from the total area of agriculturally usable lands.
(3) Allocation mechanism based on capabilities: it is assumed that crop allocation depends on the suitability of each region and current cropping pattern. In fact, crops tend to be cultivated in more suitable areas in long term due to rationality of farmers and ecological enforcements. But these changes could not be happen suddenly because of farmers’ adhesion to current situation and their resistance to change and the need for time to develop infrastructure. Then two factors influence on the future cropping pattern: (1) suitability and (2) change elasticity. In this study, two criteria have been introduced to represent these two factors (see
Parameter C is the suitability coefficient and denotes relative importance of the suitability factor to the change elasticity factor. The value of C depends on the behavior of people in every society and the time step of simulation. If people’s adherence to the current situation is low and the preferences to move towards optimality are high, then the parameter C will be higher and vice versa. Also if the time step of simulation is longer, then the time needed for change towards suitability is broader, and consequently the parameter C will be greater. This parameter should be calibrated in terms of desirable time step and the conditions of case study before starting the simulation.
In iteration t, the algorithm allocates a percentage of total available land of region i (
Competitive advantage of crop j is "relative advantage of crop j to all other crops". The
The data of 1996 to 2006 were used to calibrate the SD sub-model in terms of parameters specifying and variable settings. Econometric techniques were used to obtain the response coefficients of SD model (
The main parameter that needs to be calibrated in the crop allocation sub-model is suitability coefficient of each crop. By taking 1998 as the base year, value of parameter C was determined so that the most correlation occurs between actual cropping pattern and simulated cropping pattern in 2006. Results show that the values of C in Iran and for an 8 years’ time step is 0.3, 0.4, 0.5 and 0.3 for wheat, barley, rice and maize, respectively.
There are two ways to validate a SD model: structural validation and behavioral validation (
In addition, the data from2006 to 2014 was used to verify the whole integrated model and to evaluate its ability for projection by comparing its results to historic trends. R-squared values of the regression model between the historical data and simulated data were 0.80, 0.76, 0.61, and 0.76 (
In this study, one probable scenario is defined for the future 25 years in accordance with the combinations of exogenous variables including population growth (medium), economic growth (4.9 %) and precipitation amount (212 mm). This scenario is based on the world population prospects of the United Nations and it almost keeps the present pace with demographic and economic development. Other scenarios could be designed and compared which we leave them to future studies.
Empirical results could be presented in two categories. First, the total cultivated area of each crop that is the output of SD sub model. Second, the spatial distribution of cultivated areas in the geospatial regions.
The total area of cultivated land represents a considerable and stable growth for wheat and barley, where the area is predicted to be about 7.7 and 2.2 million hectares in 2040 for wheat and barley, respectively. Most of this increase occurs during the first period of simulation of the model and then, it will continue very slowly. Wheat shows the highest growth rates among the four crops, while the total area of rice and maize will be approximately constant.
Wheat is the most important crop in Iran, traditionally cultivated throughout the country. The growth rate of wheat cultivated lands will be higher in the western mountainous regions and less in the central arid regions. Just like wheat, barley is also a geographically ubiquitous crop; its cultivation area will be the second largest in Iran. Rice is mainly grown in northern Iran, where high temperature and high precipitation are favor to rice cultivation. Of course, some rice can be distinguished in other regions due to historic and the desire of farmers to maintain the previous state. Maize farms have the lowest share of cereal cultivated areas, and the area of its lands will decrease in all provinces. This may be due to the water shortage in Iran in the future.
This paper describes the development of a dynamic model at the provincial and national levels, combined SD sub-model with crop allocation sub-model. This model is able to assess and to analyse the changes of cropping pattern under different scenarios. Validation results demonstrate that the model is reliable for capturing the dynamics of crop demand and cropping pattern. This shows the immense potential of the model to be a decision tool, especially for policy formulations, land use change modeling, and agricultural management. Using the model in Iran’s agricultural system, future demand and spatial distribution of major crops have been calculated up to 2040 with 8th year time step. It can provide theoretical and technical support for agriculture management in Iran.
The suggested model has three major characteristics in comparison with current land use change models:
(1) Using the SD for demand estimation gives a strong to the model in relation to agricultural economics and agricultural system management theories. SD is an excellence way to separate investment and production and demand variables, and to include price effects and trade dynamics.
(2) Embedding SD model in integrated model enables us to predict the possible demand, production, trade, and geographical distribution under different ‘‘what-if’’ scenarios. This advantage allows to rapidly test a wide range of possibilities (
(3) Usually in the developing world all the data reported could be fetched at not lower than the district levels and size of those districts also may vary to a greater extent (
In summary, this study adds a noticeable contribution to land use change modeling by logically integrating the socioeconomic and spatially explicit data into a SD–CLUE framework. The characteristics of the suggested approach turn it into a useful tool for analysis of agricultural system and policy making. By successful applying the model in Iran, we believe that the model could be used in other countries because Iran is one of the best examples for displaying diversity from one place to another in terms of climate, natural and socio-economic conditions. As a general conclusion, this model not only can be utilized as an effective decision support system (DSS) for the land use planning and management of the agricultural sector, but also can be an appropriate basis to develop rather integrated models for studying agricultural and agronomics systems.