https://jai.ipb.ac.id/index.php/agromet/issue/feed Agromet 2021-06-14T10:55:48+07:00 Muh Taufik mtaufik@apps.ipb.ac.id Open Journal Systems <p>Agromet publishes original research articles or reviews that have not been published elsewhere. The scope of publication includes agricultural meteorology/climatology (the relationships between a wide range of agriculture and meteorology/climatology aspects). Articles related to meteorology/climatology and environment (pollution and atmospheric conditions) may be selectively accepted for publication. This journal is published twice a year by the Indonesian Association of Agricultural Meteorology (PERHIMPI) in collaboration with the Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Sciences, IPB University.<br><br><br><br></p> https://jai.ipb.ac.id/index.php/agromet/article/view/31804 Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra 2021-01-25T17:00:34+07:00 Sri Nurdiati nurdiati@apps.ipb.ac.id Ardhasena Sopaheluwakan nurdiati@apps.ipb.ac.id Pandu Septiawan nurdiati@apps.ipb.ac.id <p>Land and forest fires in Kalimantan and Sumatra, Indonesia occurred annually at different magnitude and duration. Climate and sea interaction, like El Niño, influences the severity of dry seasons preceding the fires. However, research on the influence of El Niño intensity to fire regime in Kalimantan and Sumatra is limited. Therefore, this study aims to analyze the spatial and temporal patterns of the effects of El Niño intensity on land and forest fires in fire-prone provinces in Indonesia. Here, we applied the empirical orthogonal function analysis based on singular value decomposition to determine the dominant patterns of hotspots and rainfall data that evolve spatially and temporally. For analysis, the study required the following data: fire hotspots, dry-spell, and rainfall for period 2001-2019. This study revealed that El Niño intensity had a different impacts for each province. Generally, El Niño will influence the severity of forest fire events in Indonesia. However, we found that the impact of El Niño intensity varied for Kalimantan, South Sumatra, and Riau Province. Kalimantan was the most sensitive province to the El Niño event. The duration and number of hotspots in Kalimantan increased significantly even in moderate El Niño event. This was different for South Sumatra, where the duration and number of hotspots only increased significantly when a strong El Niño event occurred.</p> 2021-01-25T16:51:11+07:00 Copyright (c) 2021 Sri Nurdiati, Ardhasena Sopaheluwakan, Pandu Septiawan https://jai.ipb.ac.id/index.php/agromet/article/view/32038 Identification of Global Warming Contribution to the El Niño Phenomenon Using Empirical Orthogonal Function Analysis 2021-02-19T10:16:23+07:00 Mochamad Tito Julianto mtjulianto@apps.ipb.ac.id Septian Dhimas mtjulianto@apps.ipb.ac.id Ardhasena Sopaheluwakan mtjulianto@apps.ipb.ac.id Sri Nurdiati mtjulianto@apps.ipb.ac.id Pandu Septiawan mtjulianto@apps.ipb.ac.id <p>Sea surface temperature (SST) is identified as one of the essential climate/ocean variables. The increased SST levels worldwide is associated with global warming which is due to excessive amounts of greenhouse gases being released into the atmosphere causing the multi-decadal tendency to warmer SST. Moreover, global warming has caused more frequent extreme El Niño Southern Oscillation (ENSO) events, which are the most dominant mode in the coupled ocean-atmosphere system on an interannual time scale. The objective of this research is to calculate the contribution of global warming to the ENSO phenomenon.&nbsp; SST anomalies (SSTA) variability rosed from several mechanisms with differing timescales. Therefore, the Empirical Orthogonal Function in this study was used to analyze the data of Pacific Ocean sea surface temperature anomaly. By using EOF analysis, the pattern in data such as precipitation and drought pattern can be obtained. The result of this research showed that the most dominant EOF mode reveals the time series pattern of global warming, while the second most dominant EOF mode reveals the El Niño Southern Oscillation (ENSO). The modes from this EOF method have good performance with 95.8% accuracy rate.</p> 2021-02-19T10:13:00+07:00 Copyright (c) 2021 Mochamad Tito Julianto, Septian Dhimas, Ardhasena Sopaheluwakan, Sri Nurdiati, Pandu Septiawan https://jai.ipb.ac.id/index.php/agromet/article/view/30015 Planting Time and Fertilizer Validation Based on Banyudono Planting Calendar in Boyolali Regency 2021-03-31T14:16:39+07:00 Meinarti Norma Setiapermas meinarti.ns@gmail.com Ridha Nurlaily meinarti.ns@gmail.com <p>Nowadays, information technology on planting calendar and fertilizer dosage remains research challenges, in Indonesia, especially for end user farmers. Integration of the planting calendar (then called as KATAM – ‘Kalender Tanam’), has raised many benefits for users since it provides the basic recommendations for seed and fertilizer needs. This research aims to validate the benefit of using Integrated KATAM as guidance for rice planting and fertilizing in Bangak Village, Banyudono Sub-district, with an area of around 6,100 m<sup>2</sup>. Two different approaches was performed: (i) interviewing farmers about planting date, variety, growth phase, water resource, and their technology to anticipate climate change, and (ii) calculating the rice productivity under different planting date, planting pattern, fertilizer dosage, and variety. Two treatments were used simultaneously on the field within the same planting calendar based on KATAM. The first treatment was a combination of planting date and fertilizer dosage for Situ Bagendit variety, while the second was two fertilizer dosages applied on two rice varieties (Ciherang and Situ Bagendit).&nbsp; Field activity was held on May-August and June-September 2016. The results found that around 60% of the farmers in Banyudono Sub-district did not applied the integrated KATAM recommendation on planting time. During a year of validation period (2016), 80% of the farmers applied the rice-rice-rice pattern, and the remaining applied rice-rice-<em>palawija</em>. Our findings revealed that most farmers preferred to use Situ Bagendit variety as its higher tolerance to drought and higher potential yield. By applying KATAM recommendation, Situ Bagendit rice variety gave the highest productivity up to 8.89 ton/ha compared to other rice varieties. Further the research highlights the use of KATAM recommendation may increase rice productivity especially when Situ Bagendit is applied.</p> 2021-03-31T00:00:00+07:00 Copyright (c) 2021 Meinarti Norma Setiapermas, Ridha Nurlaily https://jai.ipb.ac.id/index.php/agromet/article/view/27159 Frost Predictions in Dieng using the Outputs of Subseasonal to Seasonal (S2S) Model 2021-04-22T11:39:18+07:00 Erna Nur Aini aini_ern10@apps.ipb.ac.id Akhmad Faqih akhmadfa@apps.ipb.ac.id <p>Dieng volcanic highland, where located in Wonosobo and Banjarnegara regencies, has a unique frost phenomenon that usually occurs in the dry season (July, August, and September). This phenomenon may attract tourism, but it has caused losses to farmers due to crop damage. Information regarding frost prediction is needed in order to minimize the negative impact of this extreme event. This study evaluates the potential use of the Subseasonal to Seasonal (S2S) forecast dataset for frost prediction, with a focus on two areas where frost usually occurs, i.e. the Arjuna Temple and Sikunir Hill. Daily minimum air temperature data used to predict frost events was from the outputs of the ECMWF model, which is one of the models contributed in the Subseasonal to Seasonal prediction project (S2S). The minimum air temperature observation data from the Banjarnegara station was used in conjunction with the Digital Elevation Model Nasional (DEMNAS) data to generate spatial data based on the lapse rate function. This spatial data was used as a reference to downscale the ECMWF S2S data using the bias correction approach. The results of this study indicated that the bias-corrected data of the ECMWF S2S forecast was able to show the spatial pattern of minimum air temperature from observations, especially during frost events. The S2S prediction represented by the bias-corrected ECMWF model has the potential for providing early warning of frost events in Dieng, with a lead time of more than one month before the event.</p> 2021-04-22T11:33:39+07:00 Copyright (c) 2021 Erna Nur Aini, Akhmad Faqih https://jai.ipb.ac.id/index.php/agromet/article/view/32440 Acute Respiratory Infections (Pneumonia) Incidence Rate in Children due to Climate Variables and Air Quality in Bogor 2021-06-14T10:55:48+07:00 Revia Muharrami reviamuharrami@gmail.com Rini Hidayati rinihidayati@apps.ipb.ac.id Ana Turyanti aturyanti@gmail.com <p>Pneumonia is the respiratory infection disease, which is influenced by climatic variables and air quality. However, little is known how rainfall and air humidity influence on the disease situated in a high traffic density such as in Bogor, Indonesia. The research aims to analyze the influences of rainfall, air humidity, and air pollution on the incidence rate of pneumonia under 5-year old children in Bogor. We used statistical approaches namely correlation and principal component analysis and combined with chart analysis to identify the influences. Our results revealed that high rainfall (high relative humidity) improved air quality by lowering the concentration of particulate matter. But, the indoor microorganism growth would increase, therefore it affects the incidence rate of pneumonia under 5-year old children, especially in transition season from wet to dry. In dry season, high concentration of particulate matter in the air would increase the incidence rate of pneumonia. Other findings showed that climate (through humidity) and particulate matters have regulated the pneumonia incidence rate in Bogor. The rate was higher under high humidity. On other hand, in transition from dry to wet season, concentration of particulate matters was more dominant to influence the incident rate.</p> 2021-06-09T14:51:05+07:00 Copyright (c) 2021 Revia Muharrami, Rini Hidayati, Ana Turyanti