Resource Complementarity as a Predictive Framework for Intercropping Compatibility
DOI:
https://doi.org/10.5281/zenodo.22048445Keywords:
Artificial intelligence (AI), Companion crop selection, Intercropping compatibility algorithms, Resource complementarity, Precision agricultureAbstract
Intercropping is emerging as the best solution for feeding a growing population while protecting soil health and climate resilience. It increases crop productivity compared to conventional monocropping systems. The major essential part of intercropping is the selection of crops with complementary traits for maximising productivity. Traditionally, crop combinations have been selected by farmers, but artificial intelligence (AI), machine learning, crop simulation, and agricultural informatics have enabled the development of intercropping compatibility algorithms for scientific decision-making. These algorithms evaluate crop interactions using the principle of resource complementarity by integrating information on plant functional traits, canopy architecture, rooting depth, nutrient acquisition, water-use efficiency, phenology, soil characteristics, and climatic conditions. Trait-based niche complementarity approaches focus on functional differences among crops to predict compatibility, while process-based and genomics-enabled models combine crop growth simulations with genomic information to identify suitable crop varieties for intercropping. Moreover, machine learning techniques further improve companion crop selection by analysing large datasets generated from field experiments, sensors and remote sensing platforms. Despite their potential, these approaches face challenges related to limited data availability, regional variability, model transferability and the inadequate incorporation of socio-economic and ecological factors. Thus, future developments integrating genomics, drone-based phenotyping, Internet of Things (IoT) sensors, digital twins, climate forecasting, Explainable Artificial Intelligence (XAI) and cloud-based decision-support systems are expected to improve the precision and accessibility of compatibility algorithms. Overall, intercropping compatibility algorithms provide a promising framework for designing sustainable, resource-efficient as well as climate-resilient cropping systems, supporting precision agriculture while simultaneously reducing dependence on external inputs and contributing to global food security.
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Copyright (c) 2026 Bisruti Maity, Masina Sairam, Sumit Ray, Lalichetti Sagar, Shaik Rishitha, Debanjan Guchhait, Sagar Maitra
This work is licensed under a Creative Commons Attribution 4.0 International License.
