Judul: Characterizing Temporal Dynamic of Weather Variability to Support Decision Making on Weed Control
Abstrak: Herbicide is the dominant tool used for weed control in modern agriculture. Although herbicide has positive benefit in killing the target weeds, it potential becomes negative impact to the environment if some remains in the air and drift. Spray drift can happen due to unsuitable weather. The knowledge of weather condition will help farmer and decision maker to decide the appropriate technology and method for eradicating weed which minimize drift and other potential waste. The progress of information technology has been applied widely in agriculture such as precision agriculture. Solahudin (2013) developed a weed control method using multi-agents based. That system has two functions i.e. consultation function by giving recomendation before spraying (off-farm) and controlling during spray application (on-farm) using multi-intelligent agents which applied for groundnut farming. Relationship with this research is the system built by Solahudin (2013) needs to be improved in knowledge that related with weather. Weather data can be used to optimize spray scheduling on weed control activities that safe to the environment and for setup the equipment and machinery which prepared earlier before application day. The spatial and temporal variability weather conditions are important sources for agricultural activities such as spray application. Integration meteorological satellite with numerical weather prediction (NWP) product is promising in find timely weather variables as input for decision making to resolve problems in spray application especially for area which sparse coverage of weather stations. The objective of this research is to develop a decision support system (DSS) for schedulling of weed control and for selecting the proper nozzle size of the sprayers that introduce minimum negative impact to the environment. The main set of data required for our proposed system includes the set of 10 years weather data series acquired from remote sensing such as the National Oceanic & Atmospheric Administration (NOAA) and the Tropical Rainfall Measuring Mission (TRMM) and a set of vegetation index from the MODerate Resolution Imaging Spectroradiometer (MODIS). The vegetation index data utilized to determine the planting period of paddy and weather data set utilized to determine spray schedule and to determine the proper size of the sprayers for weed control. In precision farming, weed control is done two times i.e. pre-planting and post-emergence. To know these times, we used planting time as reference. We used paddy for this study. Paddy planting time is easy to identify through multi-temporal analysis of vegetation index. Enhanced vegetation index (EVI) of paddy field in study area shows the annual paddy growth cycle, it representing intensive cropping with multiple harvests. Based on analysis of EVI, we estimated the spraying times for paddy are April to May and October to November for cycle 1 and cycle 2, respectively. We study weather pattern in Jonggol during ten years. Every parameter have own characteristic and generally in same fluctuated pattern form. Generally, during ten years rainfall is high in year end to early year while low in mid-year. Wind speed is fluctuates. Wind is high in year end to January every year about more than 10 km/s. For ten years, minimum and maximum temperatures are 23.57°C and
Keyword: DSS, herbicides, spray drift, weather pattern, weed control

Judul: Pemodelan Decision Network untuk Menentukan Pola Tanam Pertanian Dinamis
Abstrak: Plants require different climatic conditions to grow well. The knowledge of optimum climate for plant to grow is essentially needed. The El Nino and La Nina phenomena affect the climate changes variability in Indonesia. El Nino causes a decrease in agricultural productivity due to the reduction in availability of water for crops. However, the La Nina phenomenon increases agricultural productivity due to the abundant availability of water. The strategy that can be done to solve the problem of climate change are to adjust the shape of cropping patterns and to forecast the season by using a Decision Network. Based on the Decision Network, the decision to select established cropping patterns is done using climate information and other obtained information before the decision is made. In this study, there are four information to be used. They are SOI index in August which can be used as an early possible indicator of rainy season changes, the length of rainy season, the rainfall in dry season, and the land area affected by drought. The objective of this research was to build the information model for agricultural cropping pattern by decision network. The data in this research were from 1965 to 2010. Cropping pattern can be used to produce a proper and accurate decision regarding to possibility of drought in areas. It also can be used to predict the possibility of loss amount that may arise. This study is conducted Indramayu District. The decisions are based on cropping patterns in Indramayu. The result showed that the model accuracy was 61.9% based on the relationship between the possibility of drought and drought data record in Indramayu from 1989 until 2009.
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Judul: Embriogenesis ikan buta (Astyanax fasciatus)
Abstrak: Ikan buta Astyanax fasciatus adalah spesies ikan unik dari famili Characidae yang menempati perairan dalam gua di Wilayah La Cueva Chica-San Luis Potasi-Mexico. Ikan ini memiliki mata yang tereduksi (Blind Cavefish) dan warna kulit merah muda, pucat serta mengkilap.
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