A DISEASE PREDICTIVE MODEL FOR THE MANAGEMENT OF CHICKPEA BLIGHT (Ascochyta rabiei) (Pass.) Lab

Abstract

Chickpea blight caused by Ascochyta rabiei (Pass.) Lab. (teleomorph: Didymella rabiei) (Kovachevski) v. Arx, is the most devastating disease of gram crop in the world. Disease can induce 100% yield losses under epidemic condition. In Pakistan, chickpea blight is causing heavy yield losses annually. Due to lack of resistance in indigenous chickpea germplasm, disease is controlled through fungicides by the growers of Pakistan. Excessive use of fungicides causes resistance in the pathogen and creates fatalistic effect on the environment. Chickpea blight disease predictive model under such situation may be effective tool to predict early onset of disease. In this way excessive use of fungicides may be avoided. Plant extracts and antagonists may also provide a replacement of chemical control of chickpea blight. The objective of this study was to develop a disease predictive model based upon environmental variables i.e. maximum and minimum temperatures, relative humidity, rainfall and wind speed to predict chickpea blight and devise an eco-friendly management strategy for this disease. Correlation and regression analysis was performed to determine the relationship of environmental variables with disease severity. Significant correlation was found between all environmental variables and disease severity. Maximum temperature showed negative correlation, while minimum temperature, relative humidity, rainfall and wind speed exhibited positive correlation with disease severity. Environmental factors and disease severity data of five years (2006-10) was used to develop a disease predictive model using stepwise regression analysis. Maximum and minimum temperatures, relative humidity, rainfall and wind speed significantly contributed in disease development and explained 72% variability in disease severity. This model, based on five years data, was then validated with two years (2011-12) environmental and disease severity data. In two years model all environmental parameters explained 82% variability in blight severity. Both models i.e. based upon five and two years data validated each other on the basis of homogeneity of regression lines. Blight severity was high at maximum (20-24°C) and minimum (12-14°C) temperatures, relative humidity (65-70%), rainfall (5-6 mm) and wind speed (5-6.5 km/h), respectively. Chickpea germplasm comprised of 48 genotypes was screened against chickpea blight during years 2011-12. Advanced lines exhibited resistant and moderately resistant response were viz; K-60013, K-98008, K-96001, K-96022, D-97092, D-91055, D-90272, D-96050, D-Pb2008 and D-Pu502-362, and K-96033, K-89169, K-90395, D-91017, D-89044, D-05006, D-96018, D-86030, D-96032, D-03009 and D-1CC-5127, respectively. For the management of disease, five fungicides, five plant extracts and two bio-control agents were evaluated under in vitro conditions. The concentrations of fungicides were 0.05%, 0.10% and 0.15%, respectively while concentrations of plant extracts were 3%, 5% and 8%, respectively. Spore suspension of bio-controls was kept as; 105, 106, and 107 conidia/mL, respectively. Means of treatments were compared using least significant difference (LSD) test. Two fungicides i.e. Alliete and ThiovetJet @ 0.15%, two plants extracts i.e. Melia azedarach and Azadirachta indica @ 8% and bio-control agent (Trichoderma harzianum at 107 conidia/mL) proved significantly effective under in vitro conditions. These most effective treatments were then applied under in vivo to check their efficacy for two years. Significant disease severity was reduced by fungicides i.e. Alliete (17%) and Thiovetjet (23%) followed by plant extracts, in which M. azedarach and A. indica reduced disease severity to 50% and 56%, respectively compared to control (75%). T. harzianum proved third good against chickpea blight disease in field after fungicides and plant extracts.