A Study of the Effect of Bioagent Trichoderma harzianum Rifai, the Fungicide Topsin-M and their Interaction on Root Rot Disease of Okra Abelmoschus esculentus in the Field

Abstract

<jats:p>This study aimed to investigate the effect of interaction between Trichoderma harzianum and the fungicide Topsin-M on root rot disease that infected okra in the field. Three fungi were isolated from the root of okra that infected with root rot disease: Fusarium solani, Rhizoctonia solani and Macrophomina phaseolina. The pathogenicity of these fungi was tested and found to be they cause root rot disease on okra, the disease severity was 41.7, 6.7 and 31.7% respectively. The laboratorial experiments showed that T. harzianum had a high antagonism ability with degrees of 1 and 2 against the pathogenic fungi M. phaseolina, F. solani and R. solani respectively. Also, it was found that the fungicide Topsin –M inhibited the growth of all pathogenic fungi with a percent of 100%, while it inhibited the bioagent fungus growth with a percent of 50.4 %, therefore it be recommended for the interaction experiments. The field results showed that using of bioagent T. harzianum and fungicide topsin-M significantly reduced the infection percentage and severity disease of the pathogenic fungi F. solani, R. solani and M. phaseolina to 65.3, 21.20, 13.20, 46.20, 25.70 and 18.20% respectively, compared to each pathogenic fungus alone which were 71.00, 60.20, 60.20, 66.80, 80.20 and 60.20% respectively. The interaction between the bioagent T. harzianum and topsin-M led to increase the plant height, fresh and dry weight of shoot and root systems and the fruit productivity of the examined okra plants .</jats:p>

Potential Application of Laser‐Induced Breakdown Spectroscopy (LIBS) Data for the Determination of Cation Exchange Capacity (CEC) of Agricultural Soils

“…The various difficulties in laboratory‐based CEC measurements have prompted studies on alternative or surrogate methods that could minimize labour, cost and errors in measurement. Initially, these studies involved the use of pedotransfer functions derived from soil properties that are easily measurable and correlate with CEC, such as pH, clay content and organic matter content . Soft computing techniques such as neural networks (NN) have been used to predict the CEC from easily measurable soil properties .…”