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Climate change and its variability are some of the most critical threats to sustainable agriculture, with potentially severe consequences on Indonesia’s agriculture, such as changes in rainfall patterns, especially the onset of the wet season and the temporal distribution of rainfall. Most Indonesian farmers receive support from agricultural extension services, and therefore, design their agricultural calendar based on personal experience without considering global climate phenomena, such as La Niña and El Niño, which difficult to interpret on a local scale. This paper describes the Integrated Cropping Calendar Information System (ICCIS) as a mechanism for adapting to climate variability. The ICCIS contains recommendations on planting time, cropping pattern, planting area, varieties, fertilizers, agricultural machinery, potential livestock feed, and crop damage due to climate extremes for rice, maize, and soybean. To accelerate the dissemination of information, the ICCIS is presented in an integrated web-based information system. The ICCIS is disseminated to extension workers and farmers by Task Force of the Assessment Institute for Agricultural Technology (AIAT) located in each province. Based on the survey results, it is known that the ICCIS adoption rate is moderate to high. The AIAT must actively encourage and support the ICCIS Task Force team in each province. Concerning the technological recommendations, it is necessary to update the recommendations for varieties, fertilizer, and feed to be more compatible with local conditions. More accurate information and more intensive dissemination can enrich farmers’ knowledge, allowing for a better understanding of climate hazards and maintaining agricultural production.
Yayan Apriyana; Elza Surmaini; Woro Estiningtyas; Aris Pramudia; Fadhlullah Ramadhani; Suciantini Suciantini; Erni Susanti; Rima Purnamayani; Haris Syahbuddin. The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia. Sustainability 2021, 13, 6495 .
AMA StyleYayan Apriyana, Elza Surmaini, Woro Estiningtyas, Aris Pramudia, Fadhlullah Ramadhani, Suciantini Suciantini, Erni Susanti, Rima Purnamayani, Haris Syahbuddin. The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia. Sustainability. 2021; 13 (11):6495.
Chicago/Turabian StyleYayan Apriyana; Elza Surmaini; Woro Estiningtyas; Aris Pramudia; Fadhlullah Ramadhani; Suciantini Suciantini; Erni Susanti; Rima Purnamayani; Haris Syahbuddin. 2021. "The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia." Sustainability 13, no. 11: 6495.
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%.
Surmaini Elza; Tri Wahyu Hadi. VERIFIKASI PREDIKSI CURAH HUJAN ENSEMBLE MENGGUNAKAN METODE ROC. Jurnal Meteorologi dan Geofisika 2020, 21, 37 -44.
AMA StyleSurmaini Elza, Tri Wahyu Hadi. VERIFIKASI PREDIKSI CURAH HUJAN ENSEMBLE MENGGUNAKAN METODE ROC. Jurnal Meteorologi dan Geofisika. 2020; 21 (1):37-44.
Chicago/Turabian StyleSurmaini Elza; Tri Wahyu Hadi. 2020. "VERIFIKASI PREDIKSI CURAH HUJAN ENSEMBLE MENGGUNAKAN METODE ROC." Jurnal Meteorologi dan Geofisika 21, no. 1: 37-44.
The El Niño 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 Niño 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).
Rizaldi Boer; Elza Surmaini. Economic benefits of ENSO information in crop management decisions: case study of rice farming in West Java, Indonesia. Theoretical and Applied Climatology 2019, 139, 1435 -1446.
AMA StyleRizaldi Boer, Elza Surmaini. Economic benefits of ENSO information in crop management decisions: case study of rice farming in West Java, Indonesia. Theoretical and Applied Climatology. 2019; 139 (3-4):1435-1446.
Chicago/Turabian StyleRizaldi Boer; Elza Surmaini. 2019. "Economic benefits of ENSO information in crop management decisions: case study of rice farming in West Java, Indonesia." Theoretical and Applied Climatology 139, no. 3-4: 1435-1446.
E Surmaini; E Susanti; M R Syahputra; Tri Hadi. Exploring Standardized Precipitation Index for predicting drought on rice paddies in Indonesia. IOP Conference Series: Earth and Environmental Science 2019, 303, 1 .
AMA StyleE Surmaini, E Susanti, M R Syahputra, Tri Hadi. Exploring Standardized Precipitation Index for predicting drought on rice paddies in Indonesia. IOP Conference Series: Earth and Environmental Science. 2019; 303 ():1.
Chicago/Turabian StyleE Surmaini; E Susanti; M R Syahputra; Tri Hadi. 2019. "Exploring Standardized Precipitation Index for predicting drought on rice paddies in Indonesia." IOP Conference Series: Earth and Environmental Science 303, no. : 1.
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.
Elza Surmaini; Tri Wahyu Hadi; Kasdi Subagyono; Nanang Tyasbudi Puspito. PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD. Indonesian Journal of Agricultural Science 2015, 16, 21 .
AMA StyleElza Surmaini, Tri Wahyu Hadi, Kasdi Subagyono, Nanang Tyasbudi Puspito. PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD. Indonesian Journal of Agricultural Science. 2015; 16 (1):21.
Chicago/Turabian StyleElza Surmaini; Tri Wahyu Hadi; Kasdi Subagyono; Nanang Tyasbudi Puspito. 2015. "PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD." Indonesian Journal of Agricultural Science 16, no. 1: 21.
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.
Elza Surmaini; Tri Wahyu Hadi; Kasdi Subagyono; Nanang Tyasbudi Puspito. PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD. Indonesian Journal of Agricultural Science 2015, 16, 21 .
AMA StyleElza Surmaini, Tri Wahyu Hadi, Kasdi Subagyono, Nanang Tyasbudi Puspito. PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD. Indonesian Journal of Agricultural Science. 2015; 16 (1):21.
Chicago/Turabian StyleElza Surmaini; Tri Wahyu Hadi; Kasdi Subagyono; Nanang Tyasbudi Puspito. 2015. "PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD." Indonesian Journal of Agricultural Science 16, no. 1: 21.
El Niño 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 Niño phenomena. The Niño 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 Niño 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 Niño 3.4 index and PDII was assessed using cross correlation analysis. Scatter plots of best lag time Niño 3.4 index against PDII were examined. The findings show that with 2 months lag of Niño 3.4 prior to PDII, March and June Niño 3.4 indices can be used to predict May–July and August–October PDII, respectively. Critical thresholds of the March Niño 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 Niño occurrence during the subsequent 5 months. On the other hand, a higher probability of 0.67 for occurrences of moderate El Niño is associated with the critical thresholds of June Niño 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 Niño 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.
Elza Surmaini; Tri Hadi; Kasdi Subagyono; Nanang Tyasbudi Puspito. Early detection of drought impact on rice paddies in Indonesia by means of Niño 3.4 index. Theoretical and Applied Climatology 2014, 121, 669 -684.
AMA StyleElza Surmaini, Tri Hadi, Kasdi Subagyono, Nanang Tyasbudi Puspito. Early detection of drought impact on rice paddies in Indonesia by means of Niño 3.4 index. Theoretical and Applied Climatology. 2014; 121 (3-4):669-684.
Chicago/Turabian StyleElza Surmaini; Tri Hadi; Kasdi Subagyono; Nanang Tyasbudi Puspito. 2014. "Early detection of drought impact on rice paddies in Indonesia by means of Niño 3.4 index." Theoretical and Applied Climatology 121, no. 3-4: 669-684.