Shortlisting ecologically adaptable plant species can be a starting point for agricultural diversification projects. We propose a rapid assessment framework based on an ecological model that can accelerate the evaluation of options for sustainable crop diversification. To test the new model, expert-defined and widely available crop requirement data were combined with more than 100,000 occurrence data for 40 crops of different types (cereals, legumes, vegetables, fruits, and tubers/roots). Soil pH, texture, and depth to bedrock data were obtained and harmonised based on the optimal rooting depths of each crop. Global baseline temperature and rainfall data were used to extract averages at each location. To evaluate the ability of the method to capture intraspecies variation, a test was performed using more than 1000 accession records of bambara groundnut (Vigna subterranea (L.) Verdc.) as an exemplar underutilised crop. Results showed that a suitability index based on soil pH and an index that combines the thermal suitability moderated by the soil pH, texture, and depth suitability have the potential to predict crop adaptability. We show that the proposed method can be combined with traditional land use and crop models to evaluate diversification options for sustainable land and agrobiodiversity resources management.

Bambara groundnut (Vigna subterranea L.) is a high potential indigenous legume with origins from East Africa. Desirable characteristics such as high tolerance to drought, and a more complete nutritional profile nutritional profile has made this legume, a candidate underutilised crop. Underutilised or forgotten crops play an important role in food and nutrition security, agricultural diversification, minimising environmental impacts and mitigating climate variability. Despite having full potential, Malaysia, with fairly established agricultural industry, is not exploiting bambara groundnut or Kacang Poi benefits. Malaysia is predicted to face hotter climates in the future which may affect with potential impact on oil palm and rice production. Therefore, research on alternative or complementary crops such as bambara groundnut is relevant. Major challenge that is limiting the full utilisation of this crop is limited knowledge on where it is suitable to grow based on soil and climate conditions. In this study we delineated suitable areas for the bambara groundnut in Malaysia using available soil, climate, land use/land cover and topography factors. The combination of GIS-based Multi Criteria Decision Analysis (MCDA) with a novel joint soil-climate suitability criteria for bambara groundnut was used in Land Suitability Assessment (LSA) on the areas that are suitable to grow bambara groundnut. Data on soil physical and chemical properties were acquired from ISRIC global soil database. Climate dataset was retrieved from Worldclim which includes monthly temperature and rainfall. The suitability of bambara groundnut was field tested in a highly suitable area (more than 70%) in Semenyih, Malaysia where fresh pod yield of 2.54 MT/ha is reported. The result shows the LSA technique provides an effective assessment tool for land managers and farmers to identify potential areas for potential underutilised crops in agriculture to support Malaysian agriculture.

In this experiment, proximal measurements and Unmanned Aerial Vehicle (UAV) imagery was used to determine growth stages for bambara groundnut (Vigna subterranea (L.) Verdc.). The crop is a high potential crop due to its ability to yield in marginal environments, but neglected and underutilised due to lack of information on its growth in different environments. This study evaluated the correlation between Normalised Difference Vegetation Index (NDVI) derived from the ground as well as airborne sensors to test the ability of remotely sensed data to identify growth stages. NDVI and chlorophyll content of bambara groundnut leaves were measured at ground level at 18, 32, 46 and 88 days after planting (DAP) comprising vegetative, flowering, pod formation and maturity growth stages. The UAV imagery for the experimental plots was acquired with 0.2m resolution at maturity. The result showed a significant (p < 0.05) linear relationship between proximal NDVI and chlorophylls content at all growth stages ofgrowth. The R2 varied from 0.57 in the vegetative stage to 0.78 in the flowering stage. Furthermore, NDVI derived from proximal measurements and UAV data showed a significant (p < 0.05) correlation. The observed high correlation between proximal sensors, UAV data and crop parameters suggest that remote sensing technologies can be used for rapid phenotyping to hasten the development of models to assess the performance of underutilised crops for food and nutrition security.