Brown oyster mushroom is one of the consumption mushrooms with high economic value, so it is important to be cultivated commercially. Several types of plant and industrial wastes, such as dried banana leaves and tofu dregs, are available abundantly in the field. The waste has the potential to be used as medium for growing consumption mushrooms. This is because dried banana leaves and tofu dregs contain enough nutrients needed for the growth and development of oyster mushrooms. The study aimed to determine the growth response and yield of brown oyster mushrooms by giving various doses of dried banana leaves and tofu dregs flour. The research was carried out using factorial completely randomized design. First factor: dosage of tofu flour: 50 g per baglog, 150 g per baglog, and 250 g per baglog. Second factor: dosage of dried banana leaves, without dried banana leaves, 100 g dried banana leaves per baglog, and 250 g dried banana leaves per baglog. The results showed that the treatment of addition of tofu dregs flour with dried banana leaves interacted very significantly in the number of mushroom caps, diameter and thickness of the caps, the length of the mycelium, the fresh weight of the fungus, and biological efficiency.

<span lang="DE">The use of location weights on the formation of the spatio-temporal<span>  </span>model contributes to the accuracy of the model formed. The location weights that are often used include uniform location weight, inverse distance, and cross-correlation normalization. The weight of the location considers the proximity between locations. For data that has a high level of variability, the use of the location weights mentioned above is less relevant. This research was conducted with the aim of obtaining a weighting method that is more suitable for data with high variability. This research was conducted using secondary data derived from 10 daily rainfall data obtained from BMKG Karangploso. The data period used was January 2008 to December 2018. The points of the rain posts studied included the rain post of the Blimbing, Karangploso, Singosari, Dau, and Wagir regions. Based on the results of the research forecasting model obtained is the GSTAR ((1), 1,2,3,12,36) -SUR model. The cross-covariance model produces a better level of accuracy in terms of lower RMSE values and higher R² values, especially for Karangploso, Dau, and Wagir areas.</span>