The implementation of climate change adaptation for crop production is often ineffective among farmers due to a lack of access to climatic data and inadequate knowledge on how to use the available data. The project introduced the utilization of climate data via the Climate Smart Action "Saung Iklim" application to targeted stakeholders (i.e., agricultural extension workers and farmer representatives) designed to improve farm management. The term “Saung Iklim” originally means a place where people can learn about the use of climate information for farming activities. Therefore, strengthening stakeholders’ capacity is an essential element of "Saung Iklim", which has been conducted via a series of training activities using various modules and focus group discussions. The project selected one of the major production centres of rice in Indonesia, namely the District of Subang. The district government formed a task force named “Tim Iklim”, consisting of the targeted stakeholders, to assist the project team in delivering the targeted project outputs. The project team, in collaboration with Tim Iklim, produced the modules on utilizing simulations of crop models for managing climate risks. The involvement of the stakeholders was to accommodate their input and understanding so that the modules are ready for practical purposes. The project also equipped Tim Iklim with a dedicated website, containing crop simulation model (CSM) outputs, modules, online surveys, and forums, to facilitate information delivery on managing farm risks to climate exposures.

Mangrove plays important role in the coastal ecosystem worldwide, and Indonesia as an archipelagic country occupies about 27% of the global mangrove area. Unfortunately, about 48% of areas of Indonesia’s mangroves have been damaged. Human, biophysics and climate stressors are found to affect the mangrove damages. Using a case study of Pekalongan and Demak, i.e., the prioritized locations for mangrove rehabilitation in Indonesia, this study translated the contributing stressors into quantifiable indicators that can be used to measure the risks. The risk framework on climate change assessment and ecological sensitive evaluation was employed to define the measured indicators and parameters. The indicators were classified into distinctive groups of hazards, exposures, and vulnerability which were composed of sensitivity and capacity. The indicators are then defined with regards to the existence of mangroves as an individual (vegetation), habitat, and ecosystem, and a set of parameters constitute the indicators measured the stressors of socio-economics, biophysics, and climate were determined with regards to the data availability and requirements. The measured indicators can be helpful to identify what strategies or actions should be devised to address the most contributing indicators to the mangrove risks and may be replicated to other coastal areas in the tropical regions.

Designing climate change adaptation actions are considerably a challenge, as the actions should be targeted uniquely addressing climate change impacts. One of the challenges is to determine climate change adaptation sites. The complexity raises considering climate change impact a wide range of economic sectors, which require a lot of resources to conduct a comprehensive climate change assessments. This study proposes the use of climate change hotspots as an initiative to firstly consider the potential targeted sites. The target of global efforts to maintain air temperature under 2°C was employed as a clue to prioritize areas that air temperature is increasing beyond the thresholds to which can affect human activities. This study employed spatial and threshold analysis to develop climate change hotspots of projected temperature change for 2021-2050 over Indonesia. The thresholds were defined by considering the effects of base temperature of 32 °C, 35 °C, and 38 °C on agriculture, environment, and human health in combination with elevated temperature from 0.75 to 2 °C. The initiative method was applied to the baseline and projected air temperature obtained from higher resolution of climate model outputs simulated under representative carbon pathway scenario of 4.5 (RCP 4.5 and 8.5) as a case study. The maps of climate change hotspots provide the potential targeted areas for climate change adaptation actions. Referring to the target of suppressing global temperatures below 2°C, we identified the distribution of climate change hotspots in Indonesia with a scenario of increasing temperature of 2°C from baseline conditions so that future air temperatures will be more than 35°C. The maps can also be combined with the other maps related to climate change analyses, which are available in Indonesia such as SIDIK to refine the priority areas and/or more general geographic information such as city location. As an example, the overlay of climate change hotspots and city location can provide early anticipation on which city will experience urban heat island. The development of climate change hotspots nationally is also expected to initiate climate change services that can be provided to the end users to ease them in defining suitable actions to adapt to the impacts of climate change.

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Changes in climate characteristics affect the growth and development of paddy and therefore affect rice productivity. Simulation model can be used to study the effect of climate change on rice productivity. In this study, combination of crop simulation model and field observation are used to comprehend the effect of climate change on rice productivity. The study uses field observation data. Climate, soil, and crop management input variable are similar to observation conditions. This research used default Aquacrop to estimate rice productivity in response to climate changes in Subang and also used DSSAT to estimate rice productivity and biomass components. The observation results showed that rice productivity in Pamanukan, Binong, Pagaden, Purwadadi, and Cijambe are about 6, 9, 7, 7, and 4 ton/ha. The output of the crop simulation model shows that rice productivity estimation based on the model have similar amount to rice productivity based on the field observation. Productivity estimation of simulation models is based on climate variations distinguished by temperature and rainfall accumulation. In the applications, it can be used to prepare mitigation and adaptation actions