Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (Lablab purpureus L.): Role of OMICS Based Biology, Biotic and Abiotic Elicitors

Hyacinth bean also known as Indian bean is multipurpose legume crops consumed both as food by humans and as forage by animals. Being a rich source of protein, it also produces distinct secondary metabolites such as flavonoids, phenols and tyrosinase which not only help strengthened plant’s own innate immunity against abiotic/biotrophic attackers but also play important therapeutic role in the treatment of various chronic diseases. However, despite its immense therapeutic and nutritional attributes in strengthening food, nutrition and therapeutic security in many developing countries, it is still considered as an “orphan crop” for unravelling its genetic potential and underlying molecular mechanisms for enhancing secondary metabolite production. Several lines of literatures have well documented the use of OMICS based techniques and biotic and abiotic elicitors for stimulating secondary metabolite production particularly in model as well as in few economically important crops. However, only limited reports have described their application for stimulating secondary metabolite production in underutilised crops. Therefore, the present chapter will decipher different dimensions of multi-omics tools and their integration with other conventional techniques (biotic and abiotic elicitors) for unlocking hidden genetic potential of hyacinth bean for elevating the production of secondary metabolites having pharmaceutical and therapeutic application. Additionally, the study will also provide valuable insights about how these advance OMICS tools can be successfully exploited for accelerating functional genomics and breeding research for unravelling their hidden pharmaceutical and therapeutic potential thereby ensuring food and therapeutic security for the betterment of mankind.

Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p < 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p < 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.

The Distribution of Enset Pests and Pathogens and a Genomic Survey of Enset Xanthomonas Wilt

Mapping the distribution of crop pests and pathogens is essential to safeguard food security and sustainable livelihoods. However, these data are unavailable for many neglected and underutilised crops, particularly in developing countries. In Ethiopia, the world’s largest historic recipient of food aid, the indigenous banana relative enset (Ensete ventricosum) is threatened by multiple pests and pathogens whilst providing the staple starch source for 20 million people. Foremost among these is Xanthomonas Wilt of enset (EXW), caused by Xanthomonas vasicola pv. musacearum (Xvm), a globally important disease of bananas (Musa sp.) that likely originated in enset. Here we collate 1069 farm surveys to map the distribution and relative prevalence of enset pests and pathogens across the entire enset growing region. We find that EXW is the most frequently encountered pathogen, and that farmers consistently ranked EXW as the most significant constraint on enset agriculture. Our surveys also showed that corm rot, and the pests root mealybug, mole rat and porcupine are all virtually ubiquitous. Finally, we apply genotyping-by-sequencing to the detection of Xvm and demonstrate that it is present even in asymptomatic domesticated and wild enset samples, suggesting that management of plants displaying symptoms alone may not be sufficient to reduce disease transmission. Holistic understanding of pests and pathogen distributions in enset may have significant benefits for both food security in Ethiopia, and preventing proliferation in related crops such as banana across central and east Africa.

Use of Unmanned Aerial Vehicles (UAVs) Imagery in Phenotyping of Bambara Groundnut

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 &lt; 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 &lt; 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.

Characteristics of the Underutilised Pulse Bambara Groundnut (Vigna subterranea (L.) Verdc.) Relevant to Food & Nutritional Security

Underutilised crops contribute only marginally to global food and nutritional security, despite their regional nutritional, economic, and cultural significance. Their potential to make a greater contribution is often overlooked, in part due to the scarcity of data reflecting the range of nutritional variation. Bambara groundnut (BG; Vigna subterranea (L.) Verdc.), a pulse native to West Africa has been proposed as an exemplar underutilised crop. We analysed variation in the major nutritional components of 100 BG accessions of diverse geographical origins and compared these values with representatives of four related pulse crops. We found considerable intra-species variation for major components: 13.8–26.4% protein, 4.6–8.2% oil, 0.9–12.9% total dietary fibre, and 47.8–71.4% available carbohydrate. The range in seed protein and oil concentrations was similar to that measured in chickpea and mungbean. Linoleic (C18:2, n-6) accounted for up to 48% of total fatty acids in BG seed oil, and oleic (C18:1, n-9) up to 28%, with palmitic (C16:0) representing the other major component. Principal component analysis indicated that oleic, behenic (C22:0) and lignoceric (C24:0) acids are strongly associated with seed oil concentration. These data were obtained from material where there has been no systematic selection for enhancement of nutritional composition. The range of variation suggests that there is potential within the BG genepool to develop distinct sets of high-protein, high-oil cultivars. With the addition of more detailed genomic information, BG could benefit from similar breeding approaches that have been successfully adopted for crops such as cowpea and soybean.

A Systematic Approach to Defining Nutritional Quality of Underutilised Crops

Underutilised crops have potential to play an increasing role in the diet of many who may lack adequate nutrition. At present, there appears to be no comprehensive or systematic effort to collate and analyse nutritional composition data, although this is likely to make a significant contribution to global food and nutritional security. For example, determining intra- and inter- species variation for nutritional components would enable direct comparison with commodity crops. Using bambara groundnut (Vigna subterranea; BG) as a use-case, we present a systematic work-flow that facilitates comparison of nutritional composition and function for underutilised crops. This includes (i) critical literature review of available data sources for the target and comparator crops, to determine the extent of variation reported for a range of nutritional components; (ii) development of a Crop Dietary Nutritional Data Framework (CDN-DF) to assist in data curation; (iii) de novo analysis of seed nutritional components for a subset of BG accessions selected to represent the global genepool. The CDN-DF includes controlled vocabularies organised in a hierarchical structure that represent a simplified subset of relationships for nutritional composition and dietary function This facilitates comparison of datasets between species and can help identify data gaps. This framework is currently guiding establishment of a formal nutritional ontology that more fully represents the complex relationships between nutritional components. Based on our analysis, it appears that BG seed protein, lipid and fibre concentrations cover a similar range in chickpea and mungbean. Variation in BG protein concentration indicates scope for developing high protein cultivars.