Fuzzy AHP-based spatial distribution of fig tree cultivation in Zaprionus indianus infection risk for sustainable agriculture development

Environ Sci Pollut Res Int. 2023 Feb;30(6):16510-16524. doi: 10.1007/s11356-022-23326-9. Epub 2022 Oct 3.

Abstract

The spatial distribution of fig trees infected by Zaprionus indianus (ZI) disease, an invasive pest, was analyzed as a control solution to determine the prone area of their growth and cultivation prevention in Southwest Iran. With this aim, the study presented the use of 9 suitability variables for fig tree cultivation mapping in 3 main steps: (i) pre-processing data of each input variable with fuzzy membership function, (ii) land suitability mapping (LSM) by using the pair-wise comparison matrix of analytical hierarchy process (AHP) method and Geographical Information System (GIS) technique, (iii) exclusion layers of Zaprionus indianus from the temperature data and growing degree days (GDD) (from April to October) with the support of inverse distance weighting (IDW) method. The results show that the central regions and parts of the east and northwest of the region (16%) are more suitable for fig cultivation. Compared to 7 growth periods, the insect is more active in the southern parts of the region than in the northern parts. Therefore, it is possible to cultivate figs with high yield in parts of the region where the land is suitable for growing this crop with the lowest activity of ZI. The overlay results show that the suitability distribution of fig cultivation in high and very high levels is mainly in the central regions (13,300 km2, 10%), parts of the east (5320 km2, 4%), and northwest (2660 km2, 2%) of the region. The proposed approach can be useful for management, planners, and local people in the development of agricultural production areas.

Keywords: Agriculture development; Fig tree cultivation; Fuzzy AHP method; Zaprionus indianus.

MeSH terms

  • Agriculture
  • Analytic Hierarchy Process
  • Animals
  • Drosophilidae*
  • Ficus*
  • Humans
  • Trees