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Coffee is an annual crop sensitive to climate variability. Most Indonesian coffee is cultivated on marginal lands that are vulnerable to environmental changes, including climate. Indonesia's climate variability is influenced by several factors, including the monsoon, local aspects, and global climate oscillations such as the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). It is crucial to identify the impacts of climate variability on both the production and the economy to develop adaptative measures. This study aims to determine the effects of global climate variability, namely ENSO and IOD, on coffee production in several Indonesian production centers. It uses annual coffee production data in the five major production centers in Indonesia. The ENSO indicators used in this study were the Oceanic Nino Index (ONI) in the Nino 3.4 region and the IOD indicator in the Dipole Mode Index (DMI). The production anomaly analysis approach in neutral years and the extreme ENSO and IOD phases were applied in this study. The results showed that the effects of ENSO and IOD were different in each region. The highest decline in production occurred in the La-Nina year in almost all production centers. The decline in output in the La-Nina year ranged from 6 to 22%. Meanwhile, the IOD that had a decreasing effect on production was positive IOD with a decrease ranging from 1 to 15%. Key words: ENSO; IOD; Economy; Climate change.

Climate forecasts have shown the potential for improving the resilience of agriculture to climate change. The usefulness of climate forecasts for applications in agriculture can be enhanced if the forecasts are translated into agricultural outlooks, in which the information is targeted for decision-making. The sequence of dry periods is necessary for successful crop management decisions, especially in dry season planting. This paper investigates how well the Climate Forecast System version 2 (CFSv2) seasonal forecasts predict the dry spell (DS) over the Indonesia region. The seasonal forecasts were downscaled using the constructed analogue method and, in turn, were corrected with TRMM 3B42 rainfall data to match daily precipitation totals. The DS is defined as rainfall less than 5 mm day−1 for ≥10 consecutive days. Accuracy of the DS prediction was assessed using the Brier Score (BS) method for December-January-February (DJF) and June-July-August (JJA) periods. The results demonstrate that the highest accuracy of the DS forecast occurred in JJA in southern part Indonesia with a range of the BS value between 0-0.2 (>80%). The operational DS seasonal forecasts are needed to manage agriculture practices for the upcoming planting season such as the choice of a crop/variety, supplementary irrigation, and crop water requirement.

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.

The relevance of climate extreme events, such as droughts, is well recognized as one of the impacts of climate change. The serious paddy droughts that occur during El Niño climate cycles has increased political awareness of the need to incorporate these in drought-forecasting and warning systems for effective drought management. In this study, we develop a paddy drought hazard forecasting system (PDFS) based on a web-based geographic information system (GIS). The Drought Hazard Index (DHI) was used to derive a deterministic forecast model, which is a simplified version of the probabilistic forecast model used in the previous version of this system. Based on the DHI, rice field areas in Indonesia were classified into four categories: low (1.0< DHI< 3.3), moderate (3.33 < DHI

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.

Droughts have severe consequences for rice crops in Indonesia, as these occur annually and increase during El Niño phenomena. Accordingly, paddy drought assessment is necessary for developing adaptation strategies for successful crop production. We conducted a detailed assessment of paddy drought-climate indices in Indonesia. This was done by looking at the onset and trends of the Standardized Precipitation Index (SPI) in a three-month time scale (SPI-3) and exploring their relationship with paddy drought-affected areas. The Cartesian quadrant was used to illustrate the combination of SPI-3 onset and trend on the paddy drought affected area. This gave four different drought risk levels: low, moderate, high, and very high. The hit rate (HR) as the proportion of drought occurrences were correctly hindcast and percent of correct (PC) as the total number of correct hindcast divided by the total number of hindcast was used to verify the accuracy of drought effect prediction on paddy rice. The results demonstrate that the highest accuracy of paddy drought predictions occurred in the peak of dry season in July, while the accuracy of drought and non-drought occurrences (PC) was higher in the non-peak months, April through September, excluding July.

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.