Spatial Zoning of Agricultural Tourism Capabilities in Rural Areas of Lorestan Province

Given the heterogeneous spatial distribution of opportunities for agricultural tourism development and the close relationship between this type of tourism and spatial conditions, this study is an attempt to fill the gap of spatial and location-based analysis in agricultural tourism studies as a strategy to solve agricultural and rural challenges. For the development and sustainability of agricultural tourism opportunities, it is necessary to recognize the existing capabilities in each geographical space and villages with more capabilities. This issue is especially important for planners, decision makers and investors, considering the risk of developing tourism businesses in different space units. Therefore, in support of spatial planning of agricultural tourism activities, the present study was conducted to make spatial zoning of agricultural tourism development capabilities and rank the villages targeted for agricultural tourism in susceptible areas. In this study, spatial data including natural resources, access, preconditions for tourism development, data related to rural capacities, which can be divided into three groups of agricultural capacity, services required by tourists and tourist attractions located in the villages of the study area, have been used. Spatial analysis was performed with Fuzzy-TOPSIS hierarchical analysis model. The results show that the criteria of distance from agricultural plots and distance from historical villages are the most important and the least important among the criteria used, respectively. Also, only 35% of the total area of the study area has high potential (suitable and very suitable class) for the development of agricultural tourism. The ranking of the villages located in the very suitable class reveal that Nasrabad village with a relative distance of 0.779 is the most capable village for the development of agricultural tourism. In general, the findings indicate that the susceptible areas to agricultural tourism development are stretched from the southeast to the northeast of the study area.
Extended Abstract
1-Introduction
The decline in the agricultural sector's capacity to produce and generate sufficient income forced many farmers to sell their farms; they migrate to other places seeking jobs and income, or applying alternative economic strategies to diversify their local economies and livelihoods. These strategies include expanding farm size, specialized production, non-agricultural employment, or diversifying farms through entrepreneurship and developing rural and agricultural businesses. In this regard, agricultural tourism is one of the strategies that has been proposed in recent decades to diversify the economy and sustainable rural development. Most countries of the world have considered this type of tourism as a new strategy for socio-economic development, revitalization and reconstruction of rural areas. Due to the novelty of this strategy and the need to preserve non-renewable resources, the development of agricultural tourism requires the existence of favorable infrastructure. This issue has become more important due to issues such as unbalanced distribution of welfare infrastructure and spatial differences for the development of agricultural tourism.
2-Materials and Methods
In this study, first, using the opinion of experts and research background, the criteria needed to prepare an agricultural tourism map were selected. In the second step, the weight of the criteria was calculated using the opinion of experts and using the Fuzzy-Analytic Hierarchy Process (FAHP) method. Then, using the weight of the criteria and the criteria map, the agricultural tourism zoning map was prepared using the WLC method. Finally, in the fourth stage, the villages that were in the very suitable class were ranked using the TOPSIS method.
3-Results and Discussion
Using the opinion of experts (experts in the field of tourism, environment and urban and rural planners) and the GIS, the most effective (best) and least effective (worst) criteria and the weight of the criteria were determined. The most effective criterion is the criterion that is more important in decision making than other criteria, and the least effective criterion is the criterion that is less important than other criteria. Fuzzy hierarchical analysis method was used to obtain the weight of the criteria. Finally, the weights extracted from the FAHP method were applied to produce the final map. According to experts, the best criteria (highest weight and importance) were the distance from agricultural plots and the distance from garden lands, respectively, and the worst criteria (lowest weight and importance) were the distance from historical villages and the distance from water areas, respectively. According to the results, the inconsistency rate to calculate the weight of the criteria based on expert opinions is less than 0.1. In other words, it shows the acceptability and compatibility of experts' opinions.
Northeastern and southeastern regions have a high potential for the development of agricultural tourism. This is important because in these areas, due to the temperate and humid climate, agricultural lands have a significant area. Northwest and southwest regions are in a very unsuitable and unsuitable class because they are rugged and hard to reach. Of the total area of the study area, 1103358.87 hectares (suitable and very suitable) is equivalent to 0.34% suitable for the development of agricultural tourism and 0.35% (inappropriate and very inappropriate) is unsuitable.
According to the findings, Nasrabad village with the lowest distance from the positive ideal and the highest distance from the negative ideal and the relative distance of 0.779 is the most capable village for the development of agricultural tourism and Karimaabad and Kamandan villages are in the rankings, respectively. The second and third are Sarnaveh village, in the lowest rank, has the highest distance from the positive ideal and the lowest distance from the negative ideal.
4-Conclusion
According to the results, a practical approach for spatial analysis of tourism potential based on the fuzzy-TOPSIS hierarchical model was presented in order to enable the development of agricultural tourism in Lorestan province. The ability of the used model, compared to other models, is that it considers the uncertainty in the weight of the criteria. According to experts, the best criteria (highest weight and importance) were the distance from agricultural plots and the distance from garden lands, respectively, and the worst criteria (lowest weight and importance) were the distance from historical villages and the distance from water areas, respectively. Finally, the final map of agricultural tourism development was prepared using the standardized criteria map and weight of criteria. The results show that the villages with higher potential are stretched in a strip from the southeast to the northeast of the province. Also, the study of the area of ​​each floor showed that of the total area of ​​the study area 14.34% in the most unsuitable category, 20.87% in the inappropriate, 29.94% in the average, 23.83% in the appropriate and 11.02% are located on a very convenient floor. Villages located on the most suitable floor were ranked using the TOPSIS method. The findings reveal that Nasrabad village is the most powerful village with the lowest distance from the positive ideal and the highest distance from the negative ideal, and Karimaabad and Kamandan villages were ranked second and third, respectively. The village of Sernaveh, which is in the lowest rank, has the highest distance from the positive ideal and the lowest distance from the negative ideal. Findings of this study help investors, planners and decision makers in choosing the optimal location and the most capable villages for the development of agricultural tourism. It also makes suggestions for future studies; it is suggested that in future studies, the weighted linear combination process model with data with higher spatial resolution be used to reinforce the results. Future studies can combine the capability of a fuzzy hierarchical analysis model with an ordered weight combination model to apply the risk parameter to decisions.