Dalam dua dekade terakhir, berbagai program intensifikasi penggunaan sarana produksi pertanian (misal: bantuan benih, pupuk bersubsidi, pupuk organik, dan perbaikan irigasi) telah berdampak terhadap peningkatan produksi beras nasional. Di balik keberhasilan program tersebut, fluktuasi kondisi iklim memberikan tantangan dalam mempertahankan stabilitas produksi nasional. Kondisi tersebut dapat diperparah dengan adanya potensi dampak negatif perubahan iklim yang berakibat pada penurunan produktivitas ataupun peningkatan serangan hama dan penyakit. Ancaman lainnya adalah peningkatan fenomena iklim ekstrem yang dapat menyebabkan bencana banjir dan kekeringan, sehingga berimplikasi pada gagal panen ataupun gagal tanam. Memperhatikan kondisi tersebut, tulisan ini membahas berbagai inisiatif adaptasi yang dilakukan melalui langkah praktis dan didorong oleh regulasi yang dikeluarkan pemerintah Indonesia. Praktik adaptasi dilakukan melalui insiatif mandiri berdasarkan kearifan lokal maupun bantuan pemerintah. Iniastif pemerintah terkait adaptasi dilakukan melalui Pedoman Umum Langkah-Langkah Adaptasi Perubahan Iklim (Pedum) dan langkah praktis dalam strategi budidaya yang responsif terhadap perubahan iklim.

Abstract. Bali has been open to tourism since the beginning of the 20th century and is known as the first tourist destination in Indonesia. The Denpasar, Badung, Gianyar, and Tabanan (Sarbagita) areas experience the most rapid growth of tourism activity in Bali. This rapid tourism growth has caused land use and land cover (LULC) to change drastically. This study mapped the land-use change in Bali from 2000 to 2025. The land change modeller (LCM) tool in ArcGIS was employed to conduct this analysis. The images were classified into agricultural land, open area, mangrove, vegetation/forest, and built-up area. Some Landsat images in 2000 and 2015 were exploited in predicting the land use and land cover (LULC) change in 2019 and 2025. To measure the accuracy of prediction, Landsat 8 OLI images for 2019 were classified and tested to verify the LULC model for 2019. The Multi-Layer Perceptron (MLP) neural network was trained with two influencing factors: elevation and road network. The result showed that the built-up growth direction expanded from the Denpasar area to the neighbouring areas, and land was converted from agriculture, open area and vegetation/forest to built-up for all observation years. The built-up was predicted growing up to 43 % from 2015 to 2025. This model could support decision-makers in issuing a policy for monitoring LULC since the Kappa coefficients were more than 80% for all models.

Abstract. Climate change and current susceptibilities exacerbated the coastal flood loss and damage resulting in livelihoods and property damage. Urban areas in the Low to Lower-Middle Income Countries are expected to be disproportionately impacted by the disaster, given a higher share of citizens living in the Low Elevation Coastal Zone, limited financial resources, and poorly constructed disaster protection. Documentation of historical coastal floods, population, and property affected, could advance the assessment by considering those parameters in risk analysis. Besides, incorporating such geographic features e.g., mangroves as the ecological solution for alternative coastal flood protection in the prediction is also essential. Mangrove is considered fit for the LLMIC primarily situated in the tropical zone. The prediction utilizing spatial Machine Learning (ML) could aid climate-related disaster risk analysis and contribute to risk reduction and policy suggestions to improve disaster resilience. The research aims to archive recent studies on the application of geospatial science empowering Artificial Intelligence, notably ML in coastal flood risk assessment, so-called GIS-based AI. Another aim is to document population, property, and mangrove distribution across the LLMIC. Artificial Neural Networks were mostly utilized for disaster risk assessment in past research. The number of 58 historical coastal flood events and 908 expected coastal flood hotspots for 2006 to 2021 has been documented. Over 1,2 million Km2 falls under vulnerable areas toward coastal flood in LLMIC under different settlement types where Large City (urban areas) dominates it. Mangrove distribution is mainly distributed across tropical regions mostly distributed along the Southeast Asia coast.

Abstract. Urban farming is recently acknowledged as a strategy with various services in improving cities resilience but facing cons such as land competition and rapid urbanization. The study attempts to inventory available areas for urban farming implementation and estimate the total values with case study in Malang city, Indonesia. The study divided urban farming into five forms i.e. nursery, allotment, residential, institutional and rooftop farming based on its characteristics. Land inventory has been done by estimating existing and potential areas. Existing area was manually delineated by Field Area Measure App through field visit and visualized by ArcGIS. Potential area was identified through geospatial assessment considering land use and land cover map provided by the Government of Indonesia and parcel zoning based on Guideline of Urban Farming development and literatures. The study employed Contingent Valuation Method (CVM) and Market Price Method to estimate total values of urban farming. Currently there is 1.38 ha of urban farming which is equal to 0.01 % of city’s area distributed in 21 plots and 211.46 ha potential area or equal to 1.92 % of city’s area. Urban farming has services for amount of US$ 28.68 m−2 yr−1, specifically 22.86, 3.60, 0.80, 1.10 and 0.34 US$ m−2 yr−1 in terms of provisioning food; income generation; recreation and community building; education and learning; and maintenance urban comfort, respectively. If existing and potential area used for urban farming, then it could contribute to US$ 395,095.68 annually for existing and potentially up to US$ 60,646,800.35 annually for entire city.

Currently, urban vulnerability has been exposed by catastrophic and unpredictable events which required cities to improve their resilience. Urban farming is promoted as one of the alternative strategies that could improve resilience through community empowerment aligned with re-naturing the environment. This study highlights the role of urban farming as community empowerment activities which could develop community resilience in the context of food and nutrition security specifically as emergency response. The study utilized an in-depth field survey to develop the database. The study found that urban farming could contribute to community resilience for feeding potential and nutrient sufficiency especially for targeted population who has highest risk during emergency such as the COVID-19 case. Urban farming in Malang could feed up to 50,000 inhabitants which cover only an age range of 60-64 years old. To provide sufficient vegetables for targeted population, there was a need for 1.91% (211 ha), 1.09% (120 ha), and 0.82% (91 ha) of area given each production scenarios such as normal, medium, and intensive management, respectively. The most important nutrient needs were Vitamin B1, B2, B12, D; Niacin eq.; and Folic acid due to only 10% sufficiency in the average. The study recommended specific additional vegetables to be cultivated such as red spinach and long beans since they have the highest nutrients content based on scoring.