ArticleScientific reports2025
Crop suitability analysis for the coastal region of India through fusion of remote sensing, geospatial analysis and multi-criteria decision making.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crop suitability analysis plays an important role in identifying and utilizing the areas suitable for better crop growth and higher yield without deteriorating the natural resources. The present study aimed to identify suitable areas for rice and coconut cultivation across the coastal region of India using the analytic hierarchy process (AHP) integrated with geographic information systems (GIS) and remote sensing. A total of nine parameters were selected for suitability analysis including elevation, slope, soil depth, drainage, texture, pH, soil organic carbon, rainfall, temperature and a land use land cover (LULC) constraint map. This study represents the first-ever application of an integrated approach combining AHP, GIS, and remote sensing for crop suitability analysis in entire coastal region of India. The weights for the parameters and subclasses were assigned using the AHP method based on experts' opinions. Subsequently, all the thematic maps were overlaid using the weighted overlay analysis to generate a land suitability map. Separately, the LULC crop mask map was used to extract suitable areas for rice and coconut cultivation to create crop-specific suitability maps. The final suitability maps were classified into four different classes: highly suitable, moderately suitable, marginally suitable, and not suitable for crop production. The findings revealed that approximately 13.68% of the study area was highly suitable, with around 19.26% and 18.35% being moderately and marginally suitable, respectively, and 13.76% was not suitable for rice cultivation. Similarly, for coconut cultivation, approximately 11% were highly suitable, with 27.40% and 18.34% being moderately and marginally suitable. However, about 35% of the total study region was deemed permanently unsuitable for any type of cultivation. The suitability maps were validated using area under receiver operating characteristic curve (AUROC). The AUROC values for rice and coconut were found to be 0.764 and 0.740 indicating high accuracy. By strategically cultivating rice and coconut in highly and moderately suitable locations identified in the current study, and utilizing marginally suitable areas for other crops, it is possible to achieve financial viability in agricultural production by increasing crop yield without causing harm to natural resources.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.