Agromet 2020-06-15T14:50:45+07:00 Muh Taufik 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 Indonesian Association of Agricultural Meteorology (PERHIMPI) in collaboration with Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Sciences, IPB University.<br><br><br>Agromet&nbsp; berisi naskah asli hasil penelitian atau&nbsp; telaahan (review) yang belum pernah dipublikasikan.&nbsp; Lingkup isi tulisan meliputi bidang meteorologi/klimatologi pertanian (kaitan antara pertanian dalam arti luas dengan aspek meteorologi atau klimatologi).&nbsp; Artikel yang berkaitan dengan meteorologi/klimatologi dan lingkungan (polusi dan kondisi atmosfer) dapat diterima secara selektif. Jurnal ini diterbitkan 2 (dua) kali dalam setahun oleh Perhimpunan Meteorologi Pertanian Indonesia (PERHIMPI) bekerjasama dengan Departemen Geofisika dan Meteorologi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor.<br><br><br></p> Canopy Microclimate Modification with Reflective Mulches Under Oil Palm and Its Role to Soybean Growth 2020-03-11T02:32:46+07:00 Taufan Hidayat Yonny Koesmaryono Impron Impron Munif Ghulamahdi <p>Land utilization under oil palm plantation is constrained by the condition of low light intensities. Modification of the microclimate through the use of reflective mulch, as a reflector, will increase its ability to reflect the land surface radiation under the tree stand. This modification may suitable for intercropping system between soybean and oil palm. The study aimed to determine the effect of microclimate modification, using reflective mulch, under the stand of oil palm, and to evaluate its effect on soybean productivity. The research was conducted at PTPN-VIII Cimarga Banten using a nested random design with two factors and three replications each. The first factor is the oil palm age, which consists of: (i) control (open land), (ii) 4 years, (iii) 5 years, and (iv) 8 years age of oil palm. The second factor is the reflective mulch, as a solar radiation reflector, which consists of three levels: (i) without mulch (control), (ii) inorganic reflective mulch/silver black plastic mulch, and (iii) organic reflective mulch/dried oil palm leaves. The application of inorganic and organic reflective mulch increased the distribution of reflected land surface radiation (59%-157%), reduced the soil temperature fluctuation (0.3<sup>0</sup>C-1.2<sup>0</sup>C), and maintained soil water content (45.2%-45.8%). An increased of plant growth rates (56%-86%), relative growth rates (16%-21%), and seed weight production per plant (74.8%-86.2%) also reported, as well as the reduction of the etiolation ratio (9.6%-12.5%). The use of organic and inorganic reflective mulches can improve the microclimate and increase the production of soybean under intercopping system with oil palm.</p> 2020-03-11T02:29:35+07:00 Copyright (c) 2020 Agromet Effect of Rainfall Intensity on Glyphosate Herbicide Effectiveness in Controlling Ageratum conyzoides, Rottboellia exaltata, and Cyperus rotundus Weeds 2020-04-14T06:38:06+07:00 Tumiar Katarina Manik Dad Resiworo Jekti Sambodo Dwi Saputra <p align="left">Glyphosate is one of herbicide active ingredient which is mostly used to control weeds in crops. However, in rain season herbicide effectiveness decreases as it is washed by rain. This research aimed to study effect of rainfall intensity&nbsp; on the effectiveness of herbicide (Round up 486 SL 2.5 l/ha.) with isopropilamina glyphosate as the active ingredient in controlling specific weeds <em>Ageratum conyzoides, Rottboellia exaltata, </em>and <em>Cyperus rotundus</em>. The experiment was consisted of six treatments and arranged in randomized block design with 8 replications. The treatments were level of rain intensity which were 5 mm/hour, 10 mm/hour, 20 mm/hour, 40 mm/hour, no rain and control (no herbicide no rain). Rainfall intensity was determined by conducting simulation trials prior to the treatments and applied 30 minutes after herbicide applications. The results showed that herbicide effectiveness decreased as the rainfall intensity incresed, even though with longer time the herbicide was still able to control the weeds. The effect of rainfall intensity on herbicide effectiveness was different for different weeds. Up to intensity 40 mm/hour herbicide was capable to control weeds but with level of weeds destruction 20-60%.</p> 2020-04-14T00:00:00+07:00 Copyright (c) 2020 Agromet Implementation of Bayesian Model Averaging Method to Calibrate Monthly Rainfall Ensemble Prediction over Java Island 2020-04-20T05:37:26+07:00 Robi Muharsyah Tri Wahyu Hadi Sapto Wahyu Indratno <p>Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from an ensemble prediction in the form of predictive Probability Density Function (PDF). BMA is commonly used to calibrate Ensemble Prediction System (EPS) in a shorter-range forecast. Here, we applied the BMA for a longer forecast at a seasonal interval. This study aimed to develop the implementation of the BMA method to calibrate the seasonal forecast (long range) of monthly rainfall from the RAW output of the EPS European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated with observational data from 26 stations over Java Island in 1981-2018. BMA predictive PDF was generated with a gamma distribution, which was obtained based on two training schemes, namely sequential (BMA-JTS) and conditional (BMA-JTC) training windows. Generally, both of BMA-JTS and BMA-JTC were able to produce better distribution characteristics of ensemble prediction than that of RAW model ECS4. Both BMA methods showed a good performance as indicated by a high accuracy, small bias, and small uncertainty to the observed rainfall. Our findings revealed that BMA-JTC was able to improve the quality of probabilistic forecasts of below and above normal events. The improvement was shown in most stations over Java Island, in which the model was a good skill forecast based on Brier Skill Score (BSS).</p> 2020-04-20T04:25:29+07:00 Copyright (c) 2020 Agromet The Impact of El Niño and La Nina on Fluctuation of Rice Production in Banten Province 2020-05-18T09:23:51+07:00 Tian Mulyaqin <p>Rice production in Indonesia is facing serious problem, in which the production is fluctuated causing the unstability in the food supply. One factor influencing the rice productions is climate extreme. Here, we analysed rice production in Banten Province for 2002-2015. The objective of this reasearh was to analyse the effect of climate variability on the fluctuation of rice production in Banten. We relied on data from BPS Banten, which provided timeseries of rice production for 2002-2015. We used four statistical approaches namely linear, quadratic, exponential, and moving average models to detect trend in rice production. Our results showed that Rice Production fluctuated every year indicating an increased trend for the observartion period. Based on the trend analysis, the growth rate for rice production was 1,66% per year. Climate extreme has affected on rice production, with El Niño resulted in the decreasing on rice production, whereas La Nina caused an increased of rice production. Further, to adapt climate extreme events, the government needs to encourage farmers to join the Rice Farming Insurance (AUTP) program to protect rice farming from economic losses due to the climate extreme impacts.</p> 2020-05-18T09:23:51+07:00 Copyright (c) 2020 Agromet Dynamics Modeling of CO2 in Oil Palm 2020-06-05T13:05:56+07:00 Meriana Ina Kii Tania June I Putu Santikayasa <p>Oil palm plantation has a high potency to absorb carbon. Limited observed data and expensive instrumentations to measure the absorbed carbon have caused an inaccurate estimation of carbon storage from oil palm. The objectives of this research were to develop a CO<sub>2</sub> absorption model, and to calculate the carbon cycle based on climate factors and plant age. CO<sub>2</sub> absorption was derived from gross primary production (GPP) and net primary production (NPP), which were ​​based on solar radiation. From NPP we derived net ecosystem exchange (NEE) by calculating the difference between NPP and soil respiration. Our results showed that age of oil palm has influenced the CO<sub>2</sub> absorption from 9.8 (1 year) to 117 tons ha<sup>-1</sup> year<sup>-1</sup> (19 years), with average of 86.5 tons ha<sup>-1</sup> year<sup>-1</sup> (over 25-year life cycle). We validated our NPP model with biomass that indicated a very good performance of the model with R<sup>2</sup> 0.95 and RMSE 1.81. Meanwhile, the performance of NEE model was slightly lower (R<sup>2</sup> 0.71 and 0.72, for wet and dry conditions), but the model had a similar pattern with the measured NEE. Based on the model performance, the findings imply that the model is useful to estimate CO<sub>2</sub> absorption, where there is no eddy covariance measurement. This research suggests that carbon modeling will contribute to global terrestrial carbon modeling.</p> 2020-06-05T12:59:07+07:00 Copyright (c) 2020 Agromet Modeling of Heavy Rainfall Triggering Landslide Using WRF Model 2020-06-15T14:50:45+07:00 Danang Eko Nuryanto Yuaning Fajariana Radyan Putra Pradana Rian Anggraeni Imelda Ummiyatul Badri Ardhasena Sopaheluwakan <p>This study revealed the behavior of heavy rainfall before landslide event based on the Weather Research Forecasting (WRF) model. Simulations were carried out to capture the heavy rainfall patterns on 27 November 2018 in Kulonprogo, Yogyakarta. The modeling was performed with three different planetary boundary layer schemes, namely: Yonsei University (YSU), Sin-Hong (SH) and Bougeault and Lacarrere (BL). Our results indicated that the variation of rainfall distribution were small among schemes. The finding revealed that the model was able to capture the radar’s rainfall pattern. Based on statistical metric, WRF-YSU scheme was the best outperforming to predict a temporal pattern. Further, the study showed a pattern of rainfall development coming from the southern coastal of Java before 13:00 LT (Local Time=WIB=UTC+7) and continued to inland after 13:00 LT. During these periods, the new clouds were developed. Based on our analysis, the cloud formation that generated rainfall started at 10:00 LT, and hit a peak at 13:00 LT. A starting time of cloud generating rainfall may be an early indicator of landslide.</p> 2020-06-09T00:00:00+07:00 Copyright (c) 2020 Agromet