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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.

Agricultural activities cannot be separated from indigenous knowledge (IK), which has developed and continues to be strongly maintained by Bugis–Makassar farmers in the face of rapidly developing science and technology. The objective of this study was to explore the IK practices in rice cultivation of the Bugis–Makassar farmer community, as well as the process of integrating IK and scientific knowledge into decision making. The study used a number of qualitative methodologies to gather information, including meetings, focus group discussions, in-depth interviews, and semi-structured interviews. Our results show that IK is slowly disappearing and being replaced by middle-aged people and youth, who seem to care more about modernization than assimilation. However, with the existence of Tudang Sipulung, the fading of local wisdom may be inhibited. Tudang Sipulung’s strength lies in the government’s involvement in integrating IK and scientific knowledge into formal forums and agreement. The Tudang Sipulung agreement is formally binding with constitutional sanctions applied. In order to provide optimum benefits, engaging young farmers in the understanding and preservation of indigenous and scientific knowledge is recommended to preserve local culture and transfer modern technology.

Agriculture is known as one of the sectors that both contributes to and is the victim of climate change. Under the adverse impacts of climate change, producing more food is an advance challenge. Rainfall and temperature controls crop growing seasons. The frequency and intensity of the occurrence of floods and droughts are increasing, which causes crop yield decline. On the other hand, climate change is also affected by agriculture sector’s emissions. Agriculture in Indonesia emitted about 104 Mt (million tonnes) CO2e in 2020 with the main sources of emission were from lowland rice, livestock enteric fermentation, direct N2O from soil and manure and indirect N2O from soil. Emission reduction strategies in Indonesia’s Nationally Determined Contribution (NDC) include the use of low-emission lowland rice crops varieties, implementation of efficient water management in lowland rice cultivation, manure management for biogas, and feed quality improvement for livestock. Moreover, for reaching the national emission reduction target some additional actions are needed, including: intensified use of organic fertilizers, organic village, balance fertilization which is mostly associated with improving N use efficiency, and raising water in peatland farming. At the national level, climate change policy is more focused on mitigation ambition above that of NDC. Therefore, the country needs to look for ways that combine climate change mitigation and ensuring food security. All policies of mitigation must be in synchrony with adaptation since adaptation is the key for successful maintenance of food security.

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).

The use of climate projections such as Cordex-SEA with crop simulation models can determine the projections of rice productivity in the future especially to meet the Indonesian vision as world food granary by 2045.This study aimed to 1) evaluate the projection of rice productivity and 2) compile a map of projected changes at the district level in Java Island in 2021-2035 and 2036-2050 for the RCP4.5 and RCP8.5 scenarios. The Agricultural Production System Simulator (APSIM) results based on the CSIROMK3.6 climate projection model for both scenarios in 2021-2035 showed that the impact of high productivity was high during the rainy season compared to the dry season. Districts with the increase of productivity indicate that these areas can be maintained or developed for rice crop. For the RCP4.5 scenario (rainy season) in the irrigated area, 41 districts out of 191 districts in Java Island had the class of up-high productivity changes. For the period of 2036 to 2050, there were fewer districts that had up-high productivity changes. The APSIM simulation results using HadGEM2 climate projections indicated more districts had decreased productivity in both year periods for both scenarios.

Peta Sumberdaya Agroklimat yang dihasilkan dari analisis ini menyajikan klasifikasi iklim Indonesia yang merupakan pemutakhiran dari Atlas Sumber Daya Iklim Pertanian Indonesia tahun 2003. Kebaruan klasifikasi ini adalah periode data 1981-2010 (mengikuti periode normal Badan Meterologi Klimatologi dan Geofisika, disusun dari 4.032 stasiun hujan yang lolos kualiti kontrol data dan pendapat pakar, dianalisis dengan metode geostatistik terpilih, yaitu teknik interpolasi co-kriging dengan parameter pendukung ketinggian tempat. Klasifikasi Peta Sumberdaya Agroklimat didasarkan pada klasifikasi curah hujan tahunan, jumlah bulan basah dan bulan kering berturut-turut sesuai kriteria Oldeman, serta ketinggian tempat untuk pilihan komoditas. Peta Sumberdaya Agroklimat Indonesia memberikan 18 Tipe Agroklimat dengan 3 tipe iklim (basah, sedang, kering), 3 kelas curah hujan, 3 kelas bulan basah bertutut-turut dan 3 bulan kering berturut-turut, dengan informasi potensi tanam dan alternatif pola tanam. Informasi potensi Indeks Pertanaman (IP) dan alternatif pola tanam dapat digunakan sebagai pedoman perencanaan tanam sehingga dapat memaksimalkan IP di suatu wilayah dan pemilihan komoditas yang sesuai. Wilayah yang IP-nya dibawah potensi IP, dapat ditingkatkan IP-nya dengan memanfaatkan potensi air yang ada, sedangkan untuk wilayah yang IP nya ingin ditingkatkan tetapi potensi IP nya tidak mendukung disarankan dapat menggunakan teknologi penyediaan air. Abstract . The Agro-climate Resource Map, resulted from this analysis, is a classification of Indonesia's climate which is a revised version of the Atlas of Indonesian Agricultural Climate Resources (2003). The novelty of this classification is the inclusion of 1981-2010 data period (following the normal MCGA period), compiled from 4.032 rainfall stations that passed the quality control data and expert judgement, analyzed by geostatistical method through co-kriging interpolation technique with the altitude used as a supporting parameter. The classification of the Agro-climate Resource Map is based on the classification of annual rainfall, the number of consecutive wet and dry months according to Oldeman's criteria, and altitude for the choice of commodities. The Agro-climate Resource Map provides 18 classifications of Agro-climate Types with 3 types of climates (wet, moderate, dry), 3 rainfall classes, 3 consecutive wet month classes and 3 consecutive dry months, with information on planting potential and alternative cropping patterns. Information on the potential for cropping index (IP) can be used as a guideline for planting planning so as to maximize the IP in an area. Areas whose IP is below the potential IP, their IP can be increased by utilizing the existing water potential, while for areas whose IP needs to be increased but is not supported by the IP potential, IP increase can be achieved by improving water availability through implementation of water engineering technology.

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.

Rendahnya produktivitas kopi merupakan salah satu permasalahan utama dalam sistem produksi kopi Indonesia. Hal ini diantaranya disebabkan tidak adanya perawatan kopi yang optimal dengan memperhatikan fase fenologi kopi, serta dampak variabilitas dan perubahan iklim. Berbagai teknologi adaptasi kopi sudah banyak dihasilkan namun langkah adaptasi dengan memanfaatkan prakiraan iklim dalam bentuk penyesuian kegiatan budidaya dengan fase fenologi atau disebut sebagai kalender budidaya belum dikembangkan. Tulisan ini memaparkan tentang dampak variabilitas dan perubahan iklim pada tanaman kopi, teknologi adaptasi kopi yang sudah tersedia, perlunya pengembangan kalender budidaya kopi sebagai bentuk strategi adaptasi dan peningkatan produktivitas serta potensi dan tantangan pengembangan kalender budidaya kopi di Indonesia. Hasil review ini menunjukkan kalender budidaya kopi berpotensi dikembangkan sebagai strategi peningkatan produktivitas serta adaptasi terhadap variabilitas dan perubahan iklim. Abstract. Low productivity is one of the main challenges in Indonesia's coffee production system .It is low due to cultivation management; most of the coffee farmer does not manage their plantation base on the coffee phenology phase. Moreover climate variability and change also have important effect on coffee productivity. Various technologies on adaptation and measurement to climate change and variability have been identified. Unfortunately, the technology which use climate forecast through adjusting cultivation activity and coffee phenology called as cultivation calendar do not exist yet. This paper provides an overview on the impact of climate variability and change to coffee production, the existing adaptation strategy, and the importance of cultivation calendar as a strategy for adapting and increasing productivity, and the potential and challenges to develop cultivation calendar in Indonesia. This review reveals that coffee cultivation calendar is a potential strategy for increaseing productivity and adapting climate change and variability.

Prediksi musim dibutuhkan untuk merencanakan waktu tanam adalah 1-2 musim ke depan. Informasi jumlah curah hujan dan deret hari kering merupakan parameter yang diperlukan dalam perencanaan pertanian. Penelitian bertujuan untuk menguji kemampuan model prediksi curah hujan musim ensemble , menentukan peluang optimal pengambilan keputusan, dan menentukan akurasi prediksi berdasarkan peluang optimal. Verifikasi model dilakukan untuk musim kemarau (MK) I (Februari-Mei) dan MK 2 (Mei-Agustus) pada daerah dengan pola hujan monsunal (Kabupaten Indramayu) dan MK 1 (Mei-Agustus) untuk pola hujan lokal (Kabupaten Bone). Keluaran prediksi musim dari Climate Forecast System (CFS) v2 digunakan untuk men- downscale jumlah curah hujan (CH) dan deret hari kering ≥15 hari (DHK15) di wilayah penelitian. Downscaling menggunakan metode Constructed Analogue dengan prediktor angin pada paras 850 hPa pada lima wilayah monsun. Metode yang digunakan untuk mengevaluasi keandalan prediksi probabilistik adalah Relative Operating Characteristics . Peluang optimal berdasarkan cut point ditentukan menggunakan Youden Indeks, dan akurasi prediksi pada peluang optimal ditentukan dengan metode Proportion of Correct . Hasil penelitian menunjukkan bahwa pengambilan keputusan menggunakan peluang optimal berdasarkan cut point untuk pengambilan keputusan dapat meningkatkan keandalan prediksi jumlah curah hujan sebesar 5-17% pada MK1 dan 3-24% pada MK2, dan frekuensi DHK15 sebesar 2-10%. The seasonal predictions are needed to adjust planting time for the following 1-2 seasons. Information on the amount of rainfall and dry spell is an appropriate parameter in agricultural planning. The research aimed to examine the skill of ensemble seasonal rainfall prediction models, to determine an optimal probability for making decisions, and to determines the skill of seasonal prediction based on optimal probability. Model verifications were assessed in Dry Season Planting (DSP)1 (February-May) and DSP2 (May-August in Monsoonal (Indramayu District) dan DSP1 (Mei-August) in Local (Bone District) Rainfall Pattern. We used Relative Operating Characteristics to evaluate the skill of probabilistic predictions. The optimal cut-point was assessed using the Youden Index, and the skill of prediction at an optimal cut point was determined using the Proportion of Correct method. In conclusion, the results show that the use of the optimal probability at the cut point in decision-making increase the skill of rainfall prediction 5-17% in DSP1 and 3-24% in DSP2. As for the frequency of DHK15, the skill increases by 2-10%.

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.

Prakiraan musim sangat penting dalam pengambilan keputusan usaha tani karena variabilitas iklim yang semakin meningkat sebagai dampak dari perubahan iklim. Prakiraan musim memiliki potensi untuk bisa membantu pengambil kebijakan dan keputusan pertanian. Kajian empirik dan literatur serta kuantifikasi nilai ekonomi pemanfaatan prakiraan iklim di Indonesia untuk pertanian masih sangat terbatas. Tulisan ini merupakan tinjauan mengenai pemanfaatan prakiraan musim dalam sistem usahatani, perkembangan terkini prakiraan musim, nilai ekonomi prakiraan musim, kendala dan tantangan ke depan. Kajian nilai ekonomi prakiraan musim untuk pertanian masih belum banyak dilakukan, kuantifikasi manfaat prakiraan musim sangat penting untuk meyakinkan pengambil kebijakan atau petani bahwa prakiraan musim memberikan manfaat bagi pertanian. Prakiraan musim masih sulit dipahami oleh petani bahkan penyuluh. Beberapa user interface telah dikembangkan untuk memudahkan memanfaatkan prakiraan musim untuk pertanian. Peningkatan akurasi prakiraan musim ke depan harus menjadi prioritas pembangunan pertanian dan merupakan salah satu investasi penting dalam adaptasi variabilitas dan perubahan iklim. Peningkatan akurasi prakiraan musim sangat tergantung pada kapasitas sumber daya manusia serta sarana pendukung seperti sistem pemantauan dan pengamatan data iklim, pengembangan model prakiraan musim .Pengembangan berbagai user interface yang lebih mudah dipahami dan diaplikasikan oleh petani harus dilakukan

Efek negatif pemanasan global akan mempengaruhi sebagian besar organisme hidup termasuk hama penyakit tanaman. Variabel iklim seperti suhu, curah hujan, kelembaban, dan atmosfer gas akan berinteraksi dengan makhluk hidup/tanaman dengan berbagai cara dengan mekanisme yang beragam. Perubahan suhu dan konsentrasi CO2 mempengaruhi mahkluk hidup/tanaman secara langsung dalam hal jaringan dan alokasi fotosintesis organ spesifik dan secara tidak langsung melalui perubahan distribusi geografis dan dinamika populasi hama penyakit tanaman. Peningkatan suhu akan menyebabkan serangga/hama menjadi lebih berlimpah, ada kaitan kuat antara ledakan (outbreak) hama penyakit tanaman dengan variabiltas iklim. Model hubungan parameter iklim dengan serangan hama penyakit tanaman dapat digunakan sebagai teknologi adaptasi peringatan dini untuk mencegah terjadinya serangan hama penyakit tanaman, agar kehilangan hasil tanaman dapat ditekan. Konsep tulisan ini adalah pentingnya membangun peringatan dini serangan OPT berbasis model persamaan iklim dengan serangan OPT, sehingga data prediksi iklim dapat dimanfaatkan, dan kehilangan hasil akibat serangan OPT dapat ditekan. Selain itu agar sistem dapat berkembang dan berkisinambungan pembangunan sistem peringatan dini perlu integrasi beberapa instansi terkait, yaitu Kementerian Pertanian dan Universitas sebagai penyusun model prediksi dan BMKG sebagai penyedia data prediksi iklimnya.

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.

Droughts and floods due to extreme climate events has caused yield loss in various regions of Indonesia, including the Provinces of Aceh and North Sumatra. An early detection model needs to be developed to anticipate the negative impacts of extreme climate event. The model may describe the association of surplus and rainfall deficits with paddy damage due to drought and flood. We used Standardized Precipitation Index (SPI) to explore drought and flood characteristics in period 1989-2016. The study aimed: (i) to analyze the relationship between SPI and paddy damage due to drought and flood events, (ii) to analyze the critical value of the duration and intensity of SPI which causes paddy damage, and (iii) to determine which districts were prone to drought and flood in the Provinces of Aceh and North Sumatra. The results concluded that SPI-3 and -6 months can better describe the frequency of drought and rice flooding. In addition, drought on paddy occured mostly if the SPI was smaller than -1 which took place within 4-5 months, whereas flood occured if the SPI was greater than 1. Short duration drought (2-3 months) were observed in five districts in Aceh (2) and North Sumatra (3). On other hand, more flood districts were identified (9 districts).

Coffee is one of the Indonesian largest export commodities and has a strategic role in the economy of nearly two million farmers’ livelihood. The potency of Indonesia’s coffee export is quite high because of its preferred taste, however the trend of national coffee production is only 1-2% per year. On the other hand, the impacts of climate change also threaten the achievement of increased production targets. This paper reviews the impact climate change on coffee production and the adaptation strategies. The main coffee producing regions in Indonesia are Aceh, North Sumatera, South Sumatera, Lampung, Bengkulu, East Java and South Sulawesi Provinces. Most of these regions are vulnerable to climate change. The increasing of extreme climate events such as drought due to El Niño causes a decline in national coffee production to 10%. On the contrary, the longer wet season due to La Niña caused the decreased coffee production to 80%. Indirect impacts due to rising temperatures are increased incidence of coffee borer and leaf rust disease which can lead to a 50% decline on coffee production. Due to rising temperatures, the projected coffee production areas are projected to shift to higher elevations. Numerous adaptive technologies have been intoduced, however adaptive capacaity of farmers are still low. This condition is exacerbated by the limited access of most farmers to climate information, markets, technology, farming credits, and climate risk management information. To overcome the problem, policy makers, stakeholders and farmers have to accelerate the adaptation practices since the climate change has occurred and will continue to happen.Keywords: Coffee, climate change, production, adaptation Top of Form AbstrakKopi merupakan salah satu komoditas ekspor yang berperan strategis dalam perekonomian hampir dua juta rumah petani di Indonesia. Potensi ekspor kopi Indonesia cukup tinggi karena cita rasanya yang disukai, namun tren peningkatan produksi kopi nasional hanya 1-2% per tahun. Di sisi lain, dampak perubahan iklim juga mengancam tercapainya target peningkatan produksi. Makalah ini merupakan tinjauan dampak perubahan iklim terhadap produksi kopi dan strategi adaptasinya di Indonesia. Daerah penghasil utama kopi seperti Aceh, Sumatera Utara, Sumatera Selatan, Lampung, Bengkulu, Jawa Timur dan Sulawesi Selatan rentan terhadap dampak perubahan iklim. Meningkatnya kejadian iklim ekstrim seperti kekeringan akibat El Niño mengakibatkan penurunan produksi kopi 10%. Sebaliknya, musim hujan yang panjang akibat La Niña menurunkan produksi kopi hingga 80%. Dampak tidak langsung perubahan iklim adalah meningkatnya serangan hama penggerek buah kopi dan penyakit karat daun yang menyebabkan penurunan produksi sekitar 50%. Akibat kenaikan suhu, sentra produksi kopi diproyeksikan akan berpindah ke wilayah dengan elevasi yang lebih tinggi. Berbagai teknologi adaptasi telah dihasilkan, namun tingkat adaptasi petani kopi umumnya masih rendah. Kondisi ini diperparah oleh terbatasnya akses sebagian besar petani terhadap informasi iklim, pasar, teknologi, kredit usaha tani, dan informasi pengelolaan risiko iklim. Untuk mengatasi masalah tersebut, pengambil kebijakan, stakeholder, dan petani harus mengakselerasi upaya adaptasi karena perubahan iklim telah terjadi dan akan terus berlangsung.Kata kunci: Kopi, perubahan iklim, produksi, adaptasi

Keragaman curah hujan yang tinggi secara spasial dan temporal akibat variabilitas iklim berpengaruh nyata terhadap produktivitas tanaman. Salah satu upaya yang efektif dan murah untuk menekan risiko terkait keragaman dan iklim ekstrem adalah menyesuaikan waktu tanam. Kriteria yang umum digunakan untuk menentukan awal musim tanam padi di Indonesia adalah awal musim hujan (MH), yaitu jika jumlah curah hujan > 50 mm dalam tiga dasarian berturut-turut. Kriteria lain yang disarankan para pakar adalah jumlah curah hujan selama beberapa hari berturut-turut, yang tidak diikuti oleh beberapa hari kering berturut-turut dalam periode setelahnya. Namun, jumlah hari hujan dan hari kering berturut-turut bervariasi. Sistem informasi untuk penentuan waktu tanam padi di Indonesia adalah Kalender Tanam (Katam). Katam memberikan informasi estimasi awal waktu tanam, potensi luas tanam, rotasi tanaman, dan intensitas tanam pada tingkat kecamatan untuk setiap musim selama satu tahun. Penentuan waktu tanam pada Katam ber-dasarkan kriteria awal MH. Namun, pertumbuhan tanaman tidak hanya ditentukan oleh curah hujan pada waktu tanam, tetapi juga jumlah dan distribusi hujan selama periode tanam. Oleh karena itu, penentuan waktu tanam perlu pula mempertimbangkan distribusi curah hujan selama musim tanam. Kendala penerapan kriteria tersebut adalah belum tersedianya prediksi curah hujan harian 1-2 bulan ke depan yang diinformasikan 1-2 sebelumnya. Namun, dengan menggunakan Global Circulation Model, prediksi curah hujan harian pada musim tanam yang akan datang dapat diberikan tepat waktu.

Changes in the frequency and severity of extreme climate events and in the variability of weather patterns will have significant consequences for stability of agricultural system. Research objectives were to a) analyze the Impact of Climate Change on Food Crops in Dryland b) develop a software prototype analysis of the impact of climate change on food production, especially upland rice and maize on dry land; c) create a simulation with multiple scenarios of the impact of climate change on dry land. The study was conducted in South Sulawesi, West Nusatenggara and East Nusatenggara.The activities were carried out by projecting precipitation using scenarios: a) SRESA2 (Scenarios of climate change by assuming economic growth is lower and population growth remains high so the rate of greenhouse gas emissions increased, b) SRESB1 (scenario of climate change by assuming mitigation efforts through expanding efficient use of energy and technology improvements so that the emission levels are lower) and making projections of production of upland rice and maize using Decission Support System for Agrotechnology Transfer (DSSAT) as resource information in the preparation of prototype software information Systems Climate Change Impacts on Crop Production (SIDaPi TaPa). The system was built based on the analysis simulation model projections of production output DSSAT. Based on SRES scenarios A2, the decline in rainfall increased until 2050 in several districts, in South Sulawesi, West Nusatenggara and East Nusatenggara.Treatment of adaptation through SRESB1 scenarios could be effective to anticipate a decrease in rainfall in some regions, either in 2025 or 2050. In general, the region experiencing a decrease in rainfall will also decrease in production of both upland rice and maize production. The decline in upland rice production by SRESA2 scenario until 2050 was between 20-25%, and by a scenario adaptation SRESB1 the decline in production could be minimized to only between 7 -10%.The decline in maize production in the plot until 2050 by SRESA2 was between 9-15%, using scenarios to reduce production SRESB1 was only 5-8%. SIDAPI TAPA is a software analysis of the impact of climate change on food production, especially upland rice and maize on dry land in South Sulawesi, West Nusatenggara and East Nusatenggara.

Studi tentang karakteristik hidro-meteorologi telah dilakukan di wilayah danau Singkarak pada 2006-2007 dengan melibatkan partisipasi masyarakat. Stasiun iklim otomatis dan pengukur tinggi muka air otomatis dipasang untuk memonitor data hidrologi dan meteorologi di wilayah cekungan Singkarak. Data meteorologi dianalisa untuk mengetahui karakteristik iklim di wilayah sekitar danau. Model hidrologi GR4J dan H2U diaplikasikan untuk simulasi discharge dan untuk mengkarakterisasi proses hidrologi di wilayah danau. Simulasi model aliran divalidasi pada musim hujan. Alternatif pengelolaan lahan diformulasikan berdasarkan karakteristik hidrologi daerah aliran sungai di sekitar cekungan Singkarak. Hasil penelitian menunjukkan bahwa daerah tangkapan di sekitar danau Singkarak memiliki respon yang tinggi terhadap jumlah dan intensitas hujan. Hidrograp menunjukkan peningkatan yang tajam dari discharge segera setelah curah hujan mulai dan menurun relative lamban ketika curah hujan berhenti. Untuk pengelolaan lahan secara berkelanjutan di wilayah danau Singkarak, konservasi lahan dan air harus menjadi prioritas utama. Wanatani dapat diimplementasikan sebagai alternatif sistem pertanaman oleh penduduk lokal. Karena potensi kelangkaan air bisa terjadi pada periode kering, panen air dan konservasi air dapat diterapkan sebagai opsi yang dapat dikombinasikan dalam sistem pengelolaan lahan. Hydro-meteorological processes of the Singkarak basin has been studied involving participatory of local community in 2006-2007. Automatic weather station (AWS) and automatic water level recorder (AWLR) were installed to record meteorological and hydrological data within the Singkarak Basin. Meteorological data was analyzed to understand the meteorological characteristic surrounding the Basin area. Model of GR4J and H2U were used to simulated discharge and to understand the hydrological processes within the basin. The validation of simulated discharge was done in the wet season. Best bet menu of land management options was formulated based on hydro-meteorological characteristics of the catchments surrounding Singkarak basin. The results showed that the catchments have high response to rainfall producing runoff that is discharged to the lake. The hydrograph data shows that the discharge sharply increased immediately after rainfall started then decreased quite slowly when rainfall ended. For sustainable land management in the Singkarak basin, land and water conservation have to be a priority options. Agro-forestry may be a better cropping system that has to be applied by local community. Since potential water scarcity during dry spell period may occur, water harvesting and water conservation are better options to be associated into the land management system.

Using deterministic model to forecast rainfall in tropical region in which its determinants quite complicated, dynamic and random is unmanageable. Therefore, it needs statistical model renewable in real time. Kalman filter combines between physical and statistical model approach to be stochastic model that has been renewable anytime for objective on line forecasting. Model validation relate rainfall and sea surface temperature Nino 3.4 gives correlation coefficient value of more than 75%. It implies that predicting model using Kalman Filter is feasible to forecast montly rainfall to design cropping pattern. Crops water balance is computed using local rainfall pattern, but a long with increased intensity and frequency of climate anomaly the computed water balance needs to be renewed more frequently through cropping pattern setting based on forecast aspects. Rainfall prediction with Kalman filtering result coeficient correlation of validation 48-92%. Results of cropping pattern scenarios based on predicted rainfall show there are periods with harvest losses more than 20% especialy in the locations with unequel annual rainfall distribution. Thus, it is not recommended to plant seasonal crops. Sukamandi show the characteristic model better than Tamanbogo, Batang and Wonosari. Date of planting that have risk decreasing of yield are 1 and 11 November (Tamanbogo), 1 November-1 January, and 21 February (Sukamandi), 1 November-1 December (Batang) and 1 November, 11 and 21 February (Wonosari). For application of cropping patterns scenario, rainfall prediction model needs to be renewed spacial and temporal based on rainfall data prediction real time supported with soil and crops data. Cover area conditional from rainfall station (topography, wind ward, etc) need considered if we want to apply cropiing pattern scenario. Penggunaan model deterministik untuk prediksi curah hujan di daerah tropik yang faktor determinannya sangat komplek, dinamis dan acak sangat rumit. Oleh karena itu diperlukan model statistik yang dapat diperbarui secara real time. Filter Kalman menggabungkan pendekatan model fisik dan statistik menjadi model stokastik yang dapat diperbarui setiap saat untuk tujuan peramalan segera (on line forecasting). Validasi model yang menghubungkan curah hujan dan suhu permukaan laut Nio 3.4 menghasilkan nilai koefisien korelasi lebih dari 75%. Artinya model prediksi dengan Filter Kalman ini dapat digunakan untuk memprakirakan curah hujan bulanan dan diaplikasikan untuk penyusunan masa tanam. Selama ini neraca air tanaman dihitung berdasarkan pola curah hujan setempat, namun dengan meningkatnya intensitas dan frekuensi anomali iklim akan menyebabkan hasil komputasi neraca air harus diperbarui setiap saat melalui penyusunan masa tanam yang memperhitungkan aspek prediksi. Prakiraan curah hujan dengan metode Filter Kalman menghasilkan nilai koefisien korelasi validasi 48-92%. Hasil skenario pola tanam berdasarkan data prediksi curah hujan memperlihatkan bahwa ditemukan periode-periode dengan persentase kehilangan hasil lebih dari 20%, terutama pada lokasi dengan distribusi curah hujan yang tidak merata sepanjang tahun. Dengan demikian tidak disarankan untuk melakukan penanaman pada periode tersebut. Lokasi Sukamandi memperlihatkan karakteristik model yang lebih bagus dibandingkan Tamanbogo, Batang, Wonosari. Tanggal tanam yang diperkirakan beresiko menurunkan hasil adalah 1 dan 11 November (di Tamanbogo), 1 November-1 Januari, dan 21 Februari (di Sukamandi), 1 November-1 Desember (di Batang) dan 1 November, 11 dan 21 Februari (di Wonosari). Untuk aplikasi skenario masa tanam, model prakiraan hujan perlu di perbarui di setiap saat dan tempat berdasarkan data prakiraan curah hujan terbaru di dukung dengan data tanah dan tanaman. Selain itu perlu diperhatikan cakupan wilayah yang bisa diwakili oleh stasiun hujan yang digunakan. Untuk itu perlu dipertimbangkan kondisi topografi, arah hadap angin dan sebagainya.

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.