Optimizing Jatropha curcas bioenergy plantations in Pakistan: A geospatial suitability analysis using advanced spatial modeling

Bioenergy from energy plants, an alternative fuel, is projected to increasingly meet future worldwide energy demands as well as those of a developing nation like Pakistan. Jatropha curcas (JC) has been found to possess many desirable qualities, including high oil content seeds (27–40 %), rapid growth, ease of growing, tolerance to drought, ability to grow on poor soil and wasteland, needing less nutrient input and management, and not interfering with current food crops, insects, or pest resistance. Hence, growing JC in highly appropriate barren lands could reduce the global reliance on fossil fuels. However, an evaluation of the suitability of the land would be required for tree establishment to be effective. In this study, we integrated an Analytical Hierarchy Process (AHP) with the ArcGIS tool for assessing suitable available sites to cultivate JC across Pakistan. Using geospatial technologies, this study intended to assess and map possible areas appropriate for JC bioenergy plantations with the necessary climate, soil type, and topography factors. Pakistan was classified into 3 levels of suitability for JC plantations based on the results of a spatial analysis that integrated various data sets and varied evaluation criteria: Most Suitable, Moderately Suitable, and Less Suitable. Our results showed that out of the total area of Pakistan, around 54,392,075 hectares (57 %), 22,516,700 hectares (23 %), and 19,594,100 hectares (20 %) were identified as highly suitable, moderately suitable, and less suitable, respectively, for JC bioenergy plantations. We conclude that the methods used in this study provided a considerably reliable estimate of suitable sites for JC production in Pakistan. It can assist smallholder-based initiatives to promote JC cultivation on farmer-owned to enhance their living circumstances. Multi-criteria decision analysis (MCDA) was found to be a beneficial tool in breaking down the issues of identifying and classification of the locations for afforestation in the management of forests.