Search

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.

Apriyana Y, Sarvina Y, Dewi ER, Pramudia A. 2017. Farmer adaptation strategy in paddy field affected by climate variability in monsoon regions. Asian J Agric 1: 9-16. Adaptation of agriculture cultivation to climate variability and climate anomalies both in paddy field especially in monsoon regions is one of the strategies to minimize the impact of these two phenomena to reduce the economic loss, particularly for food security in Indonesia. The objectives of this study are: (i) to identify onset and cropping pattern in irrigated land, rainfed and dry land in affected area due to climate anomalies, (ii) to collect information on carrying capacity water resources and adaptation practices applied by farmers and, (iii) to identify strategies for farmers on irrigated land, rainfed and dry land in the region affected by climate variability. The desk work analysis and field survey were conducted in Serang District, Banten Province, Subang District, West Java Province and Pati District, Central Jawa Province, Indonesia. The study was undertaken in three cluster activities i.e. (i) correlation analysis of climate anomalies and rainfall, (ii) field survey and, (iii) analysis of onset planting season, cropping pattern, water availability, the best planting time and irrigation schedule. The results showed that the farmers in affected area due to climate variability could adapt by shifting the onset of planting season. Farmers in irrigated lands changed their onset around 2-4 ten-days period to October II - December II. Furthermore, in rainfed areas, the onset around 4-6 ten-days period was shifted to November I - January III. For dry land their onset around 6-8 ten-days period was moved to November II - February I. The cropping pattern rice-rice-palawija/fallow was applied on irrigated land. Furthermore, the pattern of rice-rice/palawija/fallow-fallow was carried out in rainfed. Finally, the pattern of palawija-palawija/fallow-dormant was performed on the dry land. Adaptation programs dealing with climate variability in Serang and Pati Districts varied more than in Subang District. In Serang and Pati, during the first planting season, farmers applied irrigation roughly 20%-30% of water pump from the river and during second planting season, farmers in Pati District used water from well-pump, as well as in Serang that can reach 100% of the application.

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.

Identification of climate change impacts on spatial and temporal rainfall pattern and length of growing period are very important for climate change adaptation strategy. Rainfed is an agro-ecosystem most sensitive to changes in rainfall patterns. Bone Regency is a center of agriculture in South Sulawesi and has considerable rainfed potentials. This study aims to identify changes of rainfall patterns and length of growing period (LGP) for paddy and upland rice in Bone Regency. Data of three rain stations with a 55-year period were used and Oldeman methods was applied to determine rainfall pattern and LGP. Data was separated in two period, period 1 (1961-1990) and period II (1991-2016). The pattern of changes were identified by comparing rainfall patterns in LGP I and II. This analysis was grouped by wet year, normal year and dry year with criteria developed by BMKG. The results of the analysis showed that the rainfall pattern and LGP had changed of which pattern of change varies between regions. The LGP of paddy in Macope were three months shorter in all years, while for upland rice were two months shorter in wet year, and 1 month in normal year. In Katumpi, the LGP of paddy fields were 2 months shorter comparing to the wet year and 1 month shorter in normal and dry year for upland rice. LGP for paddy in Cellu was lengthened 1 month in wet year and shortened 2 months in dry year for upland rice.

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

Climate change has significant negative impact on agriculture in tropical region. Inrecent years, research on climate change has focused mainly on food crops while horticultural crops have received little attention. This paper is an overview of Indonesian future climate projection for precipitation, temperature and extreme climate, climate change impact and adaptation strategies on vegetable and fruit crops and future challenge for horticultural development under climate change. The climate change will decrease crop productivity and quality, increase the incidence of new pest and disease, and the outbreaks on vegetable and fruit crops. Further climate change will disrupt water availability, alter climate-crop suitability and cause crop failure due to extreme climate. Several adaptation measures have been developed in farming system, among other adjustment of planting time, using resistant varieties to environmental strees, adopting irrigation technology for efficient water use, using green house and increasing farmers and extention service capacity through climate field school. For future research it is necessary to assess climate projections with several scenarios and Global Circular Models (GCMs) and their impact on future vegetable and fruits crops by developing crop modeling which should be given a priority of in agriculture. This information crucially needed for adaptation strategy and a long term agricultural planning in the future.Keywords: Vegetable, fruit, climate change, global circular model, adaptation AbstrakPerubahan iklim berdampak negatif terhadap pertanian di daerah tropis. Selama ini penelitian dampak perubahan iklim terhadap pertanian lebih banyak dilakukan pada tanaman pangan, sementara pada tanaman hortikultura, khususnya sayuran dan buah-buahan masih terbatas. Tulisan ini merupakan tinjauan tentang proyeksi dampak perubahan iklim di Indonesia yang meliputi curah hujan, suhu udara, dan iklim ekstrim terhadap produksi tanaman buah dan sayuran, di samping berbagai upaya adaptasi yang telah dilakukan dan tantangan pembangunan hortikultura ke depan. Perubahan iklim pada tanaman sayuran dan buah-buahan terbukti menurunkan kuantitas dan kualitas produksi, munculnya hama penyakit baru, meningkatnya serangan hama dan penyakit, gagal panen, penurunan kapasitas air irigasi, perubahan kesesuian lahan dan tanaman. Beberapa langkah adaptasi yang sudah dilakukan yaitu penyesuaian sistem usaha tani yang meliputi penggunaan varietas toleran cekaman lingkungan, penyesuian waktu tanam, penggunaan teknik irigasi hemat air, pengembangan teknologi pencarian sumber daya air baru, penggunaan rumah kasa/rumah plastik, peningkatan kemampuan petani dan penyuluh dalam memahami perubahan iklim melalui sekolah lapang. Ke depan masih perlu dilakukan kajian proyeksi iklim dengan berbagai skenario dan berbagai Global circular model (GCM) serta kajian dampak perubahan iklim terhadap tanaman sayur dan buah unggulan melalui pengembangan pemodelan sistem usaha tani. Informasi proyeksi dampak perubahan iklim diperlukan sebagai upaya adaptasi dan perencanaan pembangunan pertanian yang dikaitkan dengan perubahan iklim.Kata kunci: Buah-buahan, sayuran, perubahan iklim, global circular model, adaptasi

This study aims to analyze and improve modelled extreme precipitation. It was conducted in the German Federal State of Saxony using the WEREX V data set. WEREX V is a model that statistically downscales Global Circulation Model (GCM) data. Inputs for the WEREX V model included GCMs ECHAM 5, HadCM3C and HadGEM2 (sometimes downscaled with Regional Climate Models RCMs REMO, RACMO and CCLM), SRES scenarios A1B and E1, and different model runs. The output of analysis was shown by a boxplot since the WEREX V data set has 120 future projections of precipitation. The model results were verified against observed data obtained from representative meteorological stations, and systematical deviations or biases were identified. To improve the model results, two bias correction methods were applied with special emphasis given to the reproduction of precipitation extremes. Empirical quantile mapping and gamma quantile mapping methods were applied. The ability of the WEREX V ensemble to capture extreme precipitation values varied; this was described in terms of biases. All of the identified correction methods were capable of reducing the bias related to the intensity of extreme precipitation occurrence during the calibration period. The performance of empirical quantile mapping is better than gamma quantile mapping to reduce biases (median value) and uncertainty (inter quartile range value). Penelitian ini bertujuan untuk mengoreksi bias curah hujan ekstrim keluaran model. Wilayah kajian dalam penelitian ini adalah negara bagian Saxony, German sedangkan data model yang digunakan adalah data WEREX V. Dataset WEREX V adalah data GCM yang yang didownscale secara statistic. Adapun GMC yang digunakan adalah ECHAM 5, HadCM3, HadGem2 dan beberapa RCM (REMO, RAMCO, dan CCLM) dengan menggunakan skenarios SRES A1B and E1. Karena dataset WEREX V terdiri dari 120 data model, maka boxplot digunakan untuk menggambarkan hasil analisis baik untuk identifikasi maupun koreksi bias. Hasil keluaran model dibandingkan dengan data pengamatan (observasi) dari stasiun meteorologi. Dari hasil perbandingan ini, bias akan dideteksi. Untuk meningkatkan akurasi model, bias dikoreksi menggunakan dua metode yaitu Emperical quantile mappinh (EQM) dan Gamma quatile mapping (gamma). Kemampuan model (data WEREX V) untuk menggambarkan curah hujan ekstrim berbeda antar stasiun hal ini digambarkan dengan nilai bias yang berbeda. Metode EQM dan Gamma mampu mengurangi bias maupun ketidakpastian model (uncertainty). Performa EQM lebih baik dibandingkan Gamma. Secara umum EQM mampu mengurangi bias maupun ketidakpastian model.

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).

Analysis in agro-climate research often uses long and varied time series data and even involves complex simulation models. Software is required to produce information quickly, precisely, and accurately. Agro-climate research is sometime constrained by the availability proprietary software since cost of proprietary/ licensed software is relatively high. Open source software (OSS) is one solution to overcome this constrain whereas OSS can be used freely. This paper discusses the utilization of "R" for agro-climatic research that comprise of available “R” packages for agro-climate research, several studies have applied “ R” and advantage of “R” over other statistics software. Nowadays, there are many agroclimate researches and studies have utilized R both for spatial and tabular analysis. R can be used for simple statistical analysis such as variance analysis for experimental research and even for complex climate model. Many “R” packages for agro-climate research have been developed. The “R” capabilities on data management, model simulation, modelling and machine learning are “R” advantages that very useful for current agro-climate research. By using "R" researchers have greater opportunity to explore the historical agro-climate data. "R" should be developed in agro-climate research with existing packages. Researchers can develop new packages from existing packages to solve agro-climate problems and agricultural issues in general.