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The El Nino Southern Oscillation (ENSO) strongly influences rainfall extremes in Indonesia with major impacts on droughts and floods and potential consequences for rice production. The Southern Oscillation Index (SOI) is an indicator used to detect the occurrence of ENSO events. A consistently negative (phase 1) and a rapidly falling SOI (phase 3) (indicating an El Nino cycle) were related to high probability of below-average rainfalls in the Ciparay and Bojongsoang areas of Bandung District. Therefore, the use of SOI phase information prior to the planting season would assist farmers in making optimum planting decisions. This study attempted to evaluate the economic benefits of using SOI phase information in March/April to make informed agricultural decisions for the second crop planting (April/May). The use of the SOI phases in conjunction with a crop simulation model would facilitate an objective evaluation of other cropping options. The results indicated that farmers who switched from rice to soybean or maize for the May planting season, following the March/April SOI phase I and III, earned higher incomes. The cumulative net income differences over the 63 years for soybean was about USD 1700 (27% higher at Ciparay) and USD 2350 (45% higher at Bojongsoang) and for maize was about USD 1524 (19% higher at Ciparay) and USD 1970 (35% higher at Bojongsoang).

Climate-change related hazards, including drought, floods, extreme temperatures, and sea-water level rise have impacted Indonesia’s agriculture and these associated with economic losses. Therefore, it is increasingly important for farmers to be able to proactively anticipate the impact of weather and climate risks to protect their livelihoods through climate risk management (CRM) and to practice the sustainable agricultural production systems. Sustainable agriculture practices are needed to enhance resilience to adverse climate change events. This paper attempts to provide a review of agricultural risks related to climate change, principles and current CRM practices, and CRM practices at farm level based on agroecosystems, as well as approaches in enhancing agriculture CRM for sustainable agriculture development. The key technologies for lowland rice farming include alternate wetting and drying irrigation systems, and the use of drought, saline, and submergence tolerant rice varieties. For upland farming, water storage facilities such as water retardation pond, long storage, and channel reservoir are important. Subsequently, efficient water distribution systems such as drip irrigation, sprinkler irrigation, as well as capillary irrigation need enhancement. Various soil management technologies including minimum tillage and organic matter application are essential. For swampland one-way water management and conservation blocks, the “surjan” system, planting of adaptive varieties, and soil amelioration and fertilization are among the key treatments. Accurate climate forecasts may allow decision makers and farmers to make decisions to reduce negative impacts or take advantage of expected favorable climate. Finally, engagement of various actors, and capacity building is an integral part of CRM.Keywords: Climate, management, agriculture, sustainable, agroecosystem. AbstrakBencana iklim seperti kekeringan, banjir, suhu ekstrem dan kenaikan muka air laut berdampak negatif terhadap pertanian dan menimbulkan kerugian ekonomi. Oleh karena itu menjadi semakin penting bagi petani untuk proaktif mengantisipasi dampak risiko cuaca dan iklim untuk melindungi kehidupan mereka melalui pengelolaan risiko iklim dan menerapkan sistem produksi pertanian berkelanjutan. Praktik budi daya pertanian berkelanjutan memerlukan upaya peningkatan ketangguhan tanaman terhadap dampak kejadian iklim ekstrem. Tulisan ini merupakan tinjauan risiko pertanian terhadap perubahan iklim, prinsip dan praktik pengelolaan risiko iklim, dan praktik pengelolaan risiko iklim di tingkat petani berdasarkan agroekosistem, serta pendekatan untuk mendorong praktik pengelolaan risiko iklim untuk pertanian berkelanjutan. Teknologi utama untuk pertanian padi sawah termasuk pengairan berselang dan penggunaan varietas toleran kekeringan, salinitas, dan rendaman. Untuk pertanian lahan kering diperlukan bangunan pemanen air seperti embung, long storage, dan dam parit untuk pengairan tanaman. Selain itu, sistem distribusi air yang efisien seperti irigasi tetes, irigasi sprinkler, dan irigasi kapiler juga diperperlukan. Berbagai teknologi pengelolaan tanah termasuk pengolahan tanah minimum dan penggunaan bahan organik sangat penting. Pada lahan rawa pasang surut, pengelolaan air satu arah dan blok penyimpan air, sistem surjan, penanaman varietas adaptif, dan penggunaan amelioran dan pemupukan merupakan perlakuan utama. Prediksi iklim yang akurat dapat digunakan pengambil kebijakan dan petani dalam mengambil keputusan untuk mengurangi dampak negatif atau memanfaatkan kondisi iklim. Pelibatan berbagai aktor dan peningkatan kapasitas merupakan bagian integral dari pengelolaan risiko iklim.Kata kunci: Iklim, pengelolaan, pertanian, berkelanjutan, agroekosistem.

El Nino events have been frequently marked by drought occurrences with severe consequences for agricultural production in Indonesia. Paddy drought occurs almost every year and extends during El Nino phenomena. The Nino 3.4 index is commonly used as an important tool for managing a food security policy. However, there are no details regarding the impact of El Nino on drought-induced paddy damage. We developed the Paddy Drought Impact Index (PDII), which is the ratio of drought-induced paddy damaged area to the total paddy area planted in order to investigate the impact of drought on paddies among 335 districts in Indonesia. Unlike other agricultural drought indices, this index represents real-life percentage of drought-induced paddy damage to indicate each district’s relative severity to drought, which can be easily understood by practical users. The connection between the Nino 3.4 index and PDII was assessed using cross correlation analysis. Scatter plots of best lag time Nino 3.4 index against PDII were examined. The findings show that with 2 months lag of Nino 3.4 prior to PDII, March and June Nino 3.4 indices can be used to predict May–July and August–October PDII, respectively. Critical thresholds of the March Nino 3.4 index were found to range from 0.0 to 0.5 °C, which is associated with a 0.57 probability of weak El Nino occurrence during the subsequent 5 months. On the other hand, a higher probability of 0.67 for occurrences of moderate El Nino is associated with the critical thresholds of June Nino 3.4 index, which ranges from 0.5–1.0 °C. This study has found that the potential impact of drought due to the weak and moderate El Nino occurrences in Indonesia is such that yields are reduced by about 40 % in average. We also found that the most drought-prone areas are located in West Java for both May–July and August–October and in South Sulawesi for August–October.

Indonesia consistently experiences dry climatic conditions and droughts during El Niño, with significant consequences for rice production. To mitigate the impacts of such droughts, robust, simple and timely rainfall forecast is critically important for predicting drought prior to planting time over rice growing areas in Indonesia. The main objective of this study was to predict drought in rice growing areas using ensemble seasonal prediction. The skill of National Oceanic and Atmospheric Administration’s (NOAA’s) seasonal prediction model Climate Forecast System version 2 (CFSv2) for predicting rice drought in West Java was investigated in a series of hindcast experiments in 1989-2010. The Constructed Analogue (CA) method was employed to produce downscaled local rainfall prediction with stream function (y) and velocity potential (c) at 850 hPa as predictors and observed rainfall as predictant. We used forty two rain gauges in northern part of West Java in Indramayu, Cirebon, Sumedang and Majalengka Districts. To be able to quantify the uncertainties, a multi-window scheme for predictors was applied to obtain ensemble rainfall prediction. Drought events in dry season planting were predicted by rainfall thresholds. The skill of downscaled rainfall prediction was assessed using Relative Operating Characteristics (ROC) method. Results of the study showed that the skills of the probabilistic seasonal prediction for early detection of rice area drought were found to range from 62% to 82% with an improved lead time of 2-4 months. The lead time of 2-4 months provided sufficient time for practical policy makers, extension workers and farmers to cope with drought by preparing suitable farming practices and equipments.