Pre-harvest host-resistance to Aspergillus infection and aflatoxin B1 contaminations in groundnut (Arachis hypogaea L.) genotypes

Abstract

Groundnut (Arachis hypogaea L.) is an important oil crop in the tropical and sub-tropical countries. Pod and seed coat crack-inducing factors favour Aspergillus species infections and aflatoxin B1 (AFB1) contamination of groundnut. Aflatoxin B1 (AFB1), a toxic secondary metabolite of Aspergillus species, remains a global concern due to its human and animal health, and economic impacts. Thus, the study was conducted at Babile in 2018 with the objective to identify groundnut genotypes resistant to pre-harvest fungal infections, aflatoxin contaminations and associated effects in crop physiology. Seventeen advanced groundnut breeding lines including one commercial cultivar (Werer-961), were evaluated using randomized complete block design and completely randomized design under field and with four replications for laboratory experiments, respectively. Aflatoxin B1 analysis was carried out using Enzyme-Linked Immunosorbent Assay (ELISA) kits. Appropriate statistical procedures, including regression, were employed for data analyses. Highly significant (p<0.01) variation existed among the genotypes for A. flavus and A. niger infections, and the AFB1 contamination ranged from 13.98 (G14) to 1990.86 ppb (G12). The more A. flavus infection, the more reduction in harvest yield and seedling vigour. Fortunately, 53 % of the test materials were found to be resistant to AFB1 production, and frighteningly, none of the AFB1 contaminated genotypes were within the acceptable limit of the lenient standard (10 ppb). All in all, the groundnut genotype (G4) was identified as a good source of pre-harvest resistance to A. flavus infection, AFB1 contamination and seedling vigour so that its inclusion in breeding programs is worthwhile utmost, specifically, in the test environment as pathogen-crop-environment interaction is natural. Since the experiment was employed at one location and for only one year, it is suggested to repeat the experiment across multiple locations and over seasons for reliable recommendation.