An increasing the volume of organic waste forming the residual impacts that can be polluted the environment. An Integrative management is needed to increase the value of organic waste and its use for several sectors such as agriculture and animal husbandry. The use of Hermetia illucens (Black Soldier Fly) larvae is an alternative for integrated waste management that can increase the value of waste processed into compost. Meanwhile, Black Soldier Fly larvae which are waste degradating agents, have the potential of high protein feed. This study was conducted to determine the effectiveness of some types of organic waste degradation using BSF larvae. The experiment was carried out using a completely randomized design with three treatments in the form of different types of organic waste. The process of waste degradation is done by placing BSF larvae taken from one broodstock. Data analysis was performed by one-way ANOVA. The results show the effectiveness of waste degradation highest value obtained on the type of rotten vegetable with 71,7 gram of media weight at the end of proccess.

This study aims to look at the application of acetoin-producing rizobacteria as biofertilizers for boosting the growth of rice plants in dissolving phosphates and fixing nitrogen. The experimental design used was a randomized block design (RBD), consisting of 6 (six) treatments, 4 (four) treatments with rizobacteria isolates (Rg21 isolates, Pd13 isolates, Pd7 isolates, and Bb7 isolates), one treatment with synthetic acetoin, and one control. Each treatment was repeated 4 (four) times so that there were 24 experimental units consisting of 10 pots of rice plants. The size of the pot used is a surface diameter of 30 cm, filled with growth media in the form of a layer of paddy soil (taken to a depth of 20 cm from the soil surface) and compost with a ratio of 3: 1. The results showed that the treatment of rhizobacteria producing acetoin was able to spur the growth of rice plants by dissolving phosphates that were still bound by colloidal soil so that it was not available for plants to become available P. Likewise, rhizobacterial treatment is able to fix Nitrogen by changing enzymatically organic N compounds into ammonium

This study aims to look at the factors that affect rice production. This research was conducted in Tanah Miring District, Merauke Regency. Site selection is done deliberately (purposive). Sampling using Purposive methods as many as 88 farmers. The data used in this study include primary data and secondary data. Data were analyzed using Multiple Linear Regression Analysis. The coefficient of determination (R2) is 0.955. This coefficient of determination shows that rice production (Y) can be explained by seed variables (X1), fertilizer (X2), pesticides (X3), and labor (X4) by 95.5% while the remaining 4.5% is influenced by factors others are not included in the equation. Significant value of F equal to 0,000, namely seed variables (X1), fertilizer (X2), pesticides (X3), and labor (X4), simultaneously have a significant effect on rice production variable (Y). Partially, seeds and pesticides affect rice production while fertilizer and labor do not affect rice production. The classic assumption test shows that there are no multicollinearity, heteroscedasticity symptoms and normally distributed regression models.

This study aims to study the response of soybean plants to various scenarios of water supply and compost using the DSSAT model. This research was conducted at the Laboratory of Agro-climatology and Statistics, Department of Agronomy, Faculty of Agriculture, Universitas Hasanuddin, Makassar. This research was conducted in February-March 2016. Simulation of the DSSAT model was determined from primary data in the form of plant management data, soil data, and other supporting data, and secondary data in the form of climate data. The results of the study, based on the paired t-test, the DSSAT model for simulating soybean plants can predict each treatment on the parameters of vegetative weight and number of pods. A t-test on leaf root weight showed that the DSSAT model could predict all treatments except in the treatment of watering every ten days and 3.0 tons/ha of compost. Based on the value of MSE, the DSSAT model for simulating soybean plants can predict the yield of each treatment for the parameters of the number of seeds. The average difference between simulation and observation on seed weight of 314.44 kg/ha and simulation results tend to predict below observation in the field.