Rice seed-borne pathogenic fungi are one of the causes of decreased rice productivity. Nanoemulsion of citronella oil is an alternative control for seed-borne pathogenic fungi carried by rice seeds and is effective and environmentally friendly. The research aimed to determine the effective concentration of nanoemulsions of citronella oil in controlling rice seed-borne pathogenic fungi. The study was conducted in two stages: 1. In the laboratory using a completely randomized design (CRD) with seven treatments and four replications. 2. Greenhouse uses the same design and treatment in stage one. The treatments used were control (soaking using sterile distilled water), nanoemulsion of citronella oil concentrations of 0.40%, 0.45%, 0.50%, 0.55%, 0.60%, and synthetic fungicide with propineb active site (70 WP) at a 2 g/l. The data obtained were analyzed using variance with the advanced test of Least Significance Different (LSD) at the 5% level. The parameters observed were the percentage of rice seeds that were attacked by fungi, identification of seed-borne pathogenic fungi, percentage of rice seeds that were attacked by each fungus, percentage of seedlings that appeared in the field, percentage of seedlings attacked by fungi, percentage of dead seedlings, seedling height, fresh and dry weight of seedlings. The result showed that nanoemulsion with a concentration of 0.50% was the most effective in controlling rice seed-borne pathogenic fungi, with the effectiveness of seeds being attacked suppressed by fungus, seedlings appearing in the field, seedlings being attacked by fungus, dead seedlings, seedling height, fresh and dry weight seeds were 75.00%, 60.00%, 100.00%, 100.00%, 60.83%, 141.55%, 366,67% respectively.
Heterogeneity and Complexity are the main reasons why carbonate reservoirs offer a great challenge for its characterization compared to siliciclastic reservoirs. Carbonate reservoirs are known for its variable pore type and this variability can affect the Vp value up to 40 %. Pore type can vary depending on its depositional environment and diagenetic processes and these pore types are highly correlated with permeability. Differential Effective Medium is used to model the elastic modulus of effective medium that takes into account the effect of complexity of rock pore type. This complexity, in modeling, is divided into three geophysical pore types, which are stiff pore, interparticle pore, and microcrack. The resulting rock physics model is then used to calculate the value of Vs. Pore type inversion shows that the dominant pore types in this study area are interparticle and microcrack. The results of 1D modeling are then distributed to seismic volume to map the spatial distribution of pore type. Sensitivity analysis shows that acoustic impedance, shear impedance, and porosity have a good correlation with pore type. Therefore, Probabilistic Neural Network is used to distribute 1D pore type to seismic volume by using acoustic impedance, shear impedance, and porosity as a training data. The resulting volume is then used to interpret the zones with best permeability
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