Spatial data infrastructure as the means to assemble geographic information necessary for effective agricultural policies in Pakistan

Bibliographic Data

ID21500802
AuthorsAsmat Ali (0000-0002-1987-0466, Survey of Pakistan, Rawalpindi, Pakistan, corresponding author), Munir Ahmad (0000-0002-4376-8410, Survey of Pakistan, Rawalpindi, Pakistan), Muhammad Nawaz (0000-0002-3719-7914, National University of Singapore), Farha SATTAR (0000-0001-7070-8715, Charles Darwin University)
Year2026
Volume42
Issue2
Pages805-827
Publication date2026-06-01
Open AccessYes
TypeARTICLE
VenueInformation Development (JOURNAL)
Journal identifiersISSN: 0266-6669 • E-ISSN: 1741-6469
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/02666669241244503
OpenAlexW4393943638
LanguageEN
Citations received1
References cited73

Geospatial information is used to regularly estimate agricultural production for improving food security and economic indicators. Particularly, such estimates are vital for agriculture-based economies like Pakistan. However, poorly managed spatial information causes inaccurate agricultural estimates. Consequently, public policies such as agriculture policies often remain unsustainable to secure enough food and to uplift the rural economy. Against this backdrop, the main objective of this paper is to identify types of spatial datasets, categorize them based on relative importance, and propose a framework to seamlessly disseminate those datasets to agricultural policy-makers in Pakistan. To do so, first of all, the literature is reviewed and a preliminary list of data is prepared. Then we make use of the Delphi survey to prepare the final list of the data. The data are also categorized into most important, very important, and important datasets. The results of the study revealed that the four most important spatial datasets include; hydrological, land use, agricultural census, and meteorological data. The datasets in the category of very important include six datasets; cadaster, crops, soil, pest and disease, natural hazards, and climate change data. The three datasets; remote sensing, research, and agroecological zones data fall under the category of important spatial datasets. Through implementing SDI, the identified data can be made available in one place to find and access to inform policies, the paper concludes

Agriculture · Business · Economics · Environmental planning · Environmental resource management · Geographic information system · Geography · Regional science · Remote sensing · Spatial analysis · Spatial data infrastructure · Agricultural Innovations and Practices · Climate change impacts on agriculture · Soil and Land Suitability Analysis

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