Introduction Technology of Cropping Calendar-Information System (CC-IS) for Rice Farming as A Climate Change Adaptation in Indonesia

Challenges in food production in the future are becoming more complex. Factors affect food production includes climate variability and climate change. To adapt the impact of climate change, Indonesia Agricultural Research and Development has designed a Crop Calendar-Information System. This technology contains recommendation on planting time, cropping patterns, and adaptive technology with Integrated-web-based according to climate prediction and season-to-date. Although it has been launched officially since 2011, the uses as consider for farming in sub-districts are remain low. This paper aims to present the process of designing the CCIS, the dissemination activities, and process of getting feedback to improve the CCIS implementation. Survey was done using a mail survey in 33 provinces for a year during January-December 2014. The data was collected three times for a year covered 32 provinces. The data collected consist of primary and secondary data.. Observations were carried out to determine the role of CCIS provider. Methods of data analysis use descriptive qualitative method through tabulation, data summary, data reduction, data display, then conclusion and verification. Result shows that on average, there was 10 socialization held in 10 sub-districts with 200 extension officers attend the session. The most common dissemination methods use printed media and direct communication (70-88%) and the least use visual communication-radio/television (12%) and field demonstration (6%). Furthermore, feedback of the CCIS dissemination shows that hardly any sites implement the CCIS recommendation because of unavailability of recommended variety and water availability (43,25%), whereas 52,75% said that the recommendation was less suitable with their condition and less frequency in doing socialization. According to the validation activities done in 16 provinces, the suitability of planting time vary compared to the actual planting time. In rainy season (MH), 37.5% of the sample district had the accurate prediction range from 64% up to 90%. Although the technology is widely spread, it still needed effort to ensure farmers and local stakeholders to use the CCIS as a recommendation technology to minimize the impact of the climate change.