Agromet

The Optimum Planting Time and Cropping Pattern of Potatoes and Other Horticultural Commodities based on Water Balance in Solok, Indonesia

Via Yulianti — 2023-03-03

Many mountainous regions in Indonesia have been utilized for potato cultivation. But location for the cultivation is mainly a rainfed agriculture, which greatly depend on the weather condition. Lembah Gumanti in Solok, West Sumatra is a rainfed main potato-growing area, which faced a low productivity during dry season. Therefore, efforts to optimize potato production in rainfed area remains research challenge. This study aims to
identify the optimum cropping calendar for potato and other horticultural commodities in Lembah Gumanti for 2018-2021. We used the water balance approach to derive daily water availability at field level. The approach was used to identify the planting time and pattern of potato and other horticultural commodities for 2018-2023 at dekadal (10-day) interval. The results showed that the most suitable planting time and cropping pattern
varied annually. In 2018-2019, the cropping calendar was potato (in October 1st 10-day) – shallots (in April 1st 10-day) – chilies (in July 3rd 10-day). For 2020-2021, the best cropping calendar was shallots (in November 3rd 10-day) – potato (in March 3rd 10-day) – shallots (in August 1st 10-day). The findings reveal that water availability determined the cropping calendar of each commodity.

Estimation of Oil Palm Total Carbon Fluxes Using Remote Sensing

Artika — 2023-03-31

Net primary production (NPP) is one of the approaches used to estimate the amount of carbon sequestration by plants. This research aims to estimate the total carbon flux exchanged from different ages of oil palm using remote sensing. The study site was at the PTPN VI Batang Hari, Jambi, Sumatra, Indonesia. The amount of carbon sequestration by oil palm plantations at PTPN VI Batang Hari, Jambi can be estimated using remote sensing based on the light use efficiency (LUE) model. The results showed that the oil palm age affects the amount of carbon sequestrated. The lowest Net primary production value was found at one year of planting 4.28 gCm^-2day^-1, and the highest was 9.38 gCm^-2day^-1 at 20 years of planting. The model LUE output was validated using Eddy covariance data and the results showed a low error and a high accuracy rate with RMSE = 0.05 gCMJ^-1, R² = 92%, and p-value = 0.04. We concluded that the LUE model can be used with high accuracy to estimate the amount of carbon absorption of oil palm when direct measurement is unavailable.

Statistical Assessment of High-Resolution Climate Model Rainfall Data in the Ciliwung Watershed, Indonesia

Widya Ningrum — 2023-04-19

The impact of climate change on hydrometeorological hazards pointed out the necessity for information on rainfall data. Using Climate Hazard Group InfraRed Precipitation with Station (CHIRPS) data could solve the problem of the scarcity of observed rainfall data at a finer spatial resolution. This paper examines the performance of high-resolution rainfall climate model data called CORDEX SEA and NEXGDPP in the Ciliwung watershed, Indonesia. We used CHIRPS data as observed data, which was separately divided for calibration (1981-2005) and validation (2006-2020) of the climate models. Totally 14 climate models were used, comprised of 4 CORDEX and 10 NEXGDPP. The models accuracy was assessed based on three statistical indicators: bias, mean absolute percentage error (MAPE), and mean square error (MSE). We determined the best model based on Taylor Diagram. The results showed that the bias value in the dry season was smaller than in the wet and transitional seasons. All models performed well as shown by the low bias values except for the ACCESS1-0 RCP8.5 model. The findings revealed that MRI-CGCM was the best model for calibration, whereas EC-Earth was the best model in the validation period for both RCP4.5 and RCP8.5 scenarios. Further, the choice of climate model may influence water resource management over watershed scale.

The Study of Wind Field ERA-20C in Monsoon Domains for Rainfall Predictor in Indonesia (Java, Sumatra, and Borneo)

Trinah Wati — 2023-05-31

In recent years, various research institutions have developed diverse global data reanalysis projects. This provides an opportunity to gain long-term of meteorological data for local scale. This study aims to select the potential predictor of wind fields u and v of the ERA-20C dataset, a reanalysis dataset, at 850 mb from seven domains or windows of Asian, Maritime Continent, Australian, and Western North Pacific monsoon related physically to rainfall anomaly patterns in Indonesia. The vector wind velocity scalar was obtained by using a Helmholtz decomposition to separate the total circulation v = (u,v) into the divergent component/velocity potential (χ) or Phi and rotational component/stream function (ψ) or Psi for obtaining the scalar variable of vector wind velocity. The method applied Singular value decomposition (SVD) to identify pairs of spatial patterns (expansion coefficients) between the predictors of Phi and Psi in seven domains, with rainfall data from 48 stations in Java, Sumatra, and Borneo Islands from 1981 to 2010. The results revealed that spatial patterns correlations of Java Islands were the highest in the Maritime Continent monsoon domain (80^o−150^o E and 15^oS−15^o N), while Sumatra and Borneo Island were in the Western North Pacific monsoon domain (100^o–130^o E and 5^o–15^o N) with predictor Psi. The lowest correlation for Java, Sumatra, and Borneo was the Australian monsoon domain (110^o E–130^o E and 5^o S–15^o S) with predictor Phi. In general, spatial pattern correl-ations of Java Island were higher than others, agreeing with monsoonal rainfall type dominantly in the region.