Climate‐Resilient Suitability Mapping of Cowpea ( <scp> <i>Vigna unguiculata</i> </scp> L. Walp.) Using Ensemble Modelling and Multi‐Criteria Evaluation

ABSTRACT Cowpea ( Vigna unguiculata L. Walp.) is a climate‐resilient legume essential for food security and dryland farming in semi‐arid regions of India. This study delineates climate‐smart suitability zones by integrating Ensemble Species Distribution Modelling (ESDM) with Analytic Hierarchy Process–Multi‐Criteria Evaluation (AHP–MCE) to incorporate climatic, soil, and land‐use determinants. Seven modelling algorithms (GLM, GAM, MARS, CTA, RF, ANN, SVM) were applied under baseline, 2050, and 2070 climates (RCP 4.5 and 8.5), achieving strong ensemble performance (AUC = 0.84–0.88; TSS = 0.62–0.67). Precipitation variables dominated current suitability, while temperature extremes shaped future patterns. Integrating soil and LULC data through AHP–MCE substantially improved spatial precision, expanding optimum suitability zones and identifying new target regions in central, southern and eastern India. The integrated framework offers practical value for breeding programs (identifying heat‐ and drought‐prone target environments), agricultural policy and investment (site‐specific irrigation, diversification, climate‐risk planning), and farm‐level decision making. By quantitatively combining climatic, soil and land‐use predictors in a unified GIS‐based ensemble system, an advancement over existing legume suitability studies, this work provides a scalable tool for climate‐resilient crop planning and adaptive agricultural development.

Authors

Institutions

Publication Details

Journal
Journal of Agronomy and Crop Science
Published
2026-01-01
DOI
https://doi.org/10.1111/jac.70156
Primary Topic
Soil and Land Suitability Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Climate‐Resilient Suitability Mapping of Cowpea ( Vigna unguiculata L. Walp.) Using Ensemble Modelling and Multi‐Criteria Evaluation

Manish Mathur, Preet Mathur
Journal of Agronomy and Crop Science
Soil and Land Suitability Analysis
article

Climate‐Resilient Suitability Mapping of Cowpea ( Vigna unguiculata L. Walp.) Using Ensemble Modelling and Multi‐Criteria Evaluation

Manish Mathur, Preet Mathur
article en

Abstract

ABSTRACT Cowpea ( Vigna unguiculata L. Walp.) is a climate‐resilient legume essential for food security and dryland farming in semi‐arid regions of India. This study delineates climate‐smart suitability zones by integrating Ensemble Species Distribution Modelling (ESDM) with Analytic Hierarchy Process–Multi‐Criteria Evaluation (AHP–MCE) to incorporate climatic, soil, and land‐use determinants. Seven modelling algorithms (GLM, GAM, MARS, CTA, RF, ANN, SVM) were applied under baseline, 2050, and 2070 climates (RCP 4.5 and 8.5), achieving strong ensemble performance (AUC = 0.84–0.88; TSS = 0.62–0.67). Precipitation variables dominated current suitability, while temperature extremes shaped future patterns. Integrating soil and LULC data through AHP–MCE substantially improved spatial precision, expanding optimum suitability zones and identifying new target regions in central, southern and eastern India. The integrated framework offers practical value for breeding programs (identifying heat‐ and drought‐prone target environments), agricultural policy and investment (site‐specific irrigation, diversification, climate‐risk planning), and farm‐level decision making. By quantitatively combining climatic, soil and land‐use predictors in a unified GIS‐based ensemble system, an advancement over existing legume suitability studies, this work provides a scalable tool for climate‐resilient crop planning and adaptive agricultural development.

Journal of Agronomy and Crop ScienceVol. 212(1)
Central Arid Zone Research Institute (IN), Thapar Institute of Engineering & Technology (IN)
Climate action
Openalex Percentile: Top 96%
Soil and Land Suitability Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.