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Heri Santoso
Pusat Penelitian Kelapa Sawit
Agriculture, Biological Sciences & Forestry

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Karakteristik Emisi CO2 Tanah Gambut di Bawah Tegakan Kelapa Sawit Winarna Winarna; Heri Santoso
Jurnal Penelitian Kelapa Sawit Vol 28 No 1 (2020): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (717.117 KB) | DOI: 10.22302/iopri.jur.jpks.v28i1.103

Abstract

Penelitian ini bertujuan mengkaji karakteristik emisi CO2 dari tanah gambut di bawah tegakan kelapa sawit yang dihubungkan dengan faktor lingkungan spesifik lokasi penelitian di daerah Kabupaten Labuhan Batu, Sumatera Utara. Gas CO2 diambil dengan menggunakan close chamber technique, sedangkan konsentrasi gas tersebut diukur menggunakan portable micro Gas Chromatograph CP 4900. Faktor lingkungan sebagai variabel bebas yang diamati meliputi suhu tanah (TS), suhu atmosfer (TA), kelembaban tanah lapisan 0 – 5 cm (SM5), kelembaban tanah lapisan 0 – 30 cm (SM30), dan pH gambut (A). Model hubungan CO2 dengan faktor dianalisis menggunakan analisis korelasi Spearman dan regresi multivariant. Analisis regresi multivariant dilakukan menggunakan regresi stepwise untuk mengetahui faktor lingkungan yang mempunyai hubungan dengan CO2 pada taraf beda nyata 5%. Analisis variance inflation factor (vif) dilakukan untuk melihat multicollinearity dari model regresi multivariant. Berdasarkan analisis korelasi Spearman, faktor lingkungan A (pH gambut) mempunyai koefisiensi korelasi yang tegolong sedang (R = 0.637). Hasil regresi stepwise menunjukkan bahwa emisi CO2 nyata dipengaruhi oleh kelembaban tanah SM30 dan A (n = 216; p < 0.05) dengan nilai vif <3. Regresi multivariant, menghasilkan persamaan terbaik (R2 = 0.420) dimana emisi CO2 = 7.394*A – 0.008*SM30 – 16.659. Secara parsial, penelitian ini juga memperoleh model hubungan emisi CO2 dengan kelembaban tanah gambut, dimana emisi CO2 tertinggi dicapai pada kondisi kelembaban tanah sekitar kapasitas lapang (354 – 376% w w-1) dan menurun dengan meningkatnya kelembaban tanah di atas kadar air kapasitas lapangan. Emisi CO2 tanah gambut mengalami penurunan pada kondisi tanah gambut mengering di bawah zone kadar air kritis, hal ini karena hidrofobisitas tanah gambut meningkat.
Eksplorasi Pendugaan Hara Daun Tanaman Kelapa Sawit Menggunakan Pesawat Tanpa Awak dan Kamera Multispektral Heri Santoso; Winarna Winarna
Jurnal Penelitian Kelapa Sawit Vol 29 No 1 (2021): Jurnal Peneltian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v29i1.145

Abstract

Rekomendasi pemupukan umumnya disusun setiap tahun untuk mengkoreksi kebutuhan hara tanaman melalui kegiatan pemupukan. Hara daun dalam penyusunan rekomendasi pemupukan merupakan bagian kecil dari beberapa parameter yang digunakan. Beberapa peneliti telah melakukan prediksi hara daun tanaman kelapa sawit memanfaatkan data satelit multispektral dan pengukuran spectroradiometer dengan hasil yang bervariasi. Penelitian prediksi hara daun kelapa sawit dengan pesawat tanpa awak dan kamera multispektral ini dilakukan karena belum ada kajian mengenai penggunaan teknologi tersebut untuk prediksi hara daun kelapa sawit. Tujuan penelitian ini adalah membandingkan performa pendugaan hara daun dari berbagai variasi komposisi variabel berupa 3 saluran dan beberapa indeks vegetasi, serta untuk menentukan analisis regresi terbaik. Band hijau, merah, dan inframerah dekat dari kamera multispektral Mapir Survey 3, serta indeks vegetasi simple ratio, normalized difference vegetation index, dan green NDVI digunakan sebagai variabel bebas dalam analisis regresi sederhana, regresi polinomial, regresi berganda dari variabel terseleksi dengan teknik recursive feature elimination dengan metode regresi linear dan random forest, dan regresi berganda polinomial dengan variabel tidak bebas (respons) berupa analisis daun dari 20 sampel yang meliputi hara N, P, K, Ca, Mg, dan B. Model penduga terbaik dari penelitian ini adalah model regresi berganda polinomial dari variabel terseleksi menggunakan teknik RFE metode random forest. Variabel bebas hanya mampu menduga hara daun N, P, K, dan Mg dengan nilai R2 sebesar 0,9415 hingga 0,9991, Adjusted R2 sebesar 0,7223 hingga 0,9837, serta nilai residual standard error (RSE) sebesar 0,0045 hingga 0,0340.
Mitigasi Areal Banjir Pada Perkebunan Kelapa Sawit di Lahan Gambut Berdasarkan Pemodelan HEC-RAS 2D Henny Lydiasari; Heri Santoso
Jurnal Penelitian Kelapa Sawit Vol 30 No 2 (2022): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v30i2.188

Abstract

Pengembangan perkebunan kelapa sawit di areal rawa dengan tanah gambut semakin meluas dan merupakan tantangan terbesar dalam pengelolaannya. Drainase yang sangat terhambat menjadi salah satu faktor pembatas yang mempengaruhi kondisi hidrologi lahan. Kajian hidrologi ini dilakukan di salah satu perkebunan kelapa sawit Kabupaten Tapanuli Selatan Propinsi Sumatera Utara yang mengalami banjir secara periodik. Pemodelan hidrolika pada kajian ini dilakukan dalam dua skenario yaitu kondisi aktual dan redisain dengan parameter yang dibutuhkan antara lain topografi lahan, klimatologi, dan hidrologi sebagai input dalam simulasi. Pemodelan hidrolika ini untuk mensimulasikan kondisi aktual kanal dan penerapan mitigasi permasalahan banjir dengan pembuatan tanggul menggunakan HEC-RAS 2D. Hasil simulasi menunjukkan bahwa banjir terjadi sebagai akibat luapan air Aek Sibirong yang mempengaruhi kondisi kanal dalam kebun yang terkoneksi dengan sungai tersebut. Berdasarkan simulasi, penerapan tanggul setinggi 1.15 m - 3.45 m pada bagian hulu dan 1.10 m - 3 m pada bagian hilir mampu mengatasi luapan air sehingga dapat mencegah terjadinya banjir pada areal kebun.
Karakteristik Emisi CO2 Tanah Gambut di Bawah Tegakan Kelapa Sawit Winarna Winarna; Heri Santoso
Jurnal Penelitian Kelapa Sawit Vol 28 No 1 (2020): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v28i1.103

Abstract

The research aim was studying the characteristic CO2 emission from peatland under oil palm trees as a relationship with environmental factors of specific locations in the Labuhan Batu District of North Sumatra. CO2 gasses were sampling by closed chamber technique and their concentration was measured by portable micro Gas Chromatograph CP 4900. The observation of environmental factors as the variable that affected to CO2 emission included soil temperature (TS), atmosphere temperature (TA), soil moisture on 0-5 cm of soil depth (SM5), soil moisture on 0-30 cm of soil depth (SM30), and pH of peat (A). The relationship model of CO2 emission with environmental factors analyzed by Spearman correlation and multivariant regression. Stepwise regression was applied for multivariant regression to identify the environmental factors that have a relationship with CO2 emission with significant analysis in 5 % of the threshold. Variance inflation factor (vif) was applied to identify the multicollinearity problem of the model from multivariant regression. The Spearman correlation showed the A (pH) of environmental factors have the highest correlation coefficient with the middle category (R=0.637). The stepwise regression showed the CO2 emission have been significantly affected by soil moister SM30 and A (n = 216; p < 0.05) with vif value < 3. The regression model from multivariant regression was CO2 = 7.394*A – 0.008*SM30 – 16.659 with R2 = 0.420. In the partial, there was a relationship of CO2 with soil moisture in the field capacity condition. The highest CO2 emission occurred at soil moisture of 354 – 376% w w-1 and decreased while the soil moisture increases overring the field capacity condition. The CO2 emission was decreased while the peat soil was drying at under the critical of water content caused the peat soil hydrophobicity was increasing.
Performa Random Forest Group untuk Klasifikasi Penyakit Busuk Pangkal Batang yang Disebabkan oleh Ganoderma boninense pada Perkebunan Kelapa Sawit Heri Santoso
Jurnal Penelitian Kelapa Sawit Vol 28 No 3 (2020): Jurnal Peneltian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v28i3.116

Abstract

Remote sensing technologies have been used for monitoring basal stem rot (BSR) diseases in oil palm plantations. The classification performance could be improved with machine learning algorithms. The success key of BSR treatment in culture technic treatments to prolong oil palm productivity ages is early detection of BSR disease . The selection of classification models of machine learning algorithms will be affecting accuracy and classification processes time. Random forest (RF) of model classification based on several previous research has a high performance of accuracy for BSR disease classification in the oil palm plantation. Today, there were fifteen models of the variance of the random forest algorithm. The research goals were to compare the accuracy and time consumed for the fitting model from 15 model’s classification of RF group in case of identity and classify the BSR disease in the oil palm plantations. Descriptive analysis was adopted to explain the difference accuracy and time for the fitting model of fifteen model classification. The results showed the highest accuracy and Kappa value (0,914 dan 0,815) was the oblique random forest (ORF) with partial least squares (PLS) method and random ferns (Rferns) was the method with the lowest accuracy and Kappa value (0,657 dan 0,334). The highest time consumed for the fitting model was RF rule-based model (rlb) with 12.993,23 seconds and the shortest time was parallel random forest (PRF) with 13,54 seconds. The accuracy and Kappa values and time consumed for the fitting model as a consideration to selecting model classification of healthy and infected BSR in oil palm plantations.
Eksplorasi Pendugaan Hara Daun Tanaman Kelapa Sawit Menggunakan Pesawat Tanpa Awak dan Kamera Multispektral Heri Santoso; Winarna Winarna
Jurnal Penelitian Kelapa Sawit Vol 29 No 1 (2021): Jurnal Peneltian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v29i1.145

Abstract

Fertilizer recommendation is yearly activities for oil palm nutrient requirement correction by fertilization activities. Oil palm leaf nutrients content in fertilization recommendation activities is a small part of several parameters used. In a row of the remote sensing gains, the researchers have been studying the oil palm leaf nutrients content prediction from multispectral satellite data and field spectral from spectroradiometer and they have a variance of results. According to the existing research of the oil palm leaf nutrients content prediction, the unmanned aerial vehicle (UAV) assembled by the multispectral camera does not yet use for oil palm leaf nutrients. Therefore, this research is necessary to do. The objectives of this research were to compare the model predictor of oil palm leaf nutrient contents with several variations of variables from three bands of the multispectral camera and several vegetation indices and also to determine the best model predictors from them. The image from the multispectral camera of Mapir Survey 3 that consists of green, red, and near-infrared, and also simple ratio, normalized difference vegetation index, as well as green NDVI were used as the independent variable of regression analysis included simple regression, polynomial regression, multivariant regression with selected variables from linear regression and random forest methods of recursive feature elimination technic, and polynomial multivariant regression. The responses were the leaf nutrient analysis of N, P, K, Ca, Mg, and B from twenty samples. The results showed the best model predictor was the regression model from polynomial multivariant regression with variables from RF method of RFE technique. The model just predicted the N, P, K, and Mg oil palm leaf nutrients with 0.9415 to 0.9991 R2 value, 0.7223 to 0.9837 adjusted R2 value, and 0.0045 to 0.0340 residual standard error value (RSE).
Mitigasi Areal Banjir Pada Perkebunan Kelapa Sawit di Lahan Gambut Berdasarkan Pemodelan HEC-RAS 2D Henny Lydiasari; Heri Santoso
Jurnal Penelitian Kelapa Sawit Vol 30 No 2 (2022): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v30i2.188

Abstract

The development of oil palm plantations on peat swamp land is increasingly widespread and is the biggest challenge in its management. Severely obstructed drainage is one of the limiting factors affecting the hydrological conditions of the land. This hydrological study was conducted in one of the oil palm plantations in Muara Batang Toru districts, North Sumatra Province which experiences periodic flooding. The hydrological modeling was carried out in 2 scenarios: the existing condition and the redesign with the required parameters, including land topography, climate, and hydrology as input in the simulation. This hydrological modeling simulates the existing condition of drainage canals and the application of flood problem mitigation by making levee using HEC-RAS 2D. The simulation results show that flooding occurs due to the overflow of the Aek Sibirong River, which affects the condition of the drainage channel in plantation connected to the river. As Simulation, applying levee as high as 1.15 m to 3.45 m upstream and 1.10 m to 3 m downstream can overcome the overflow to prevent flooding in the plantation area.
Peningkatan Akurasi Identifikasi Penyakit Busuk Pangkal Batang di Perkebunan Kelapa Sawit Menggunakan Unmanned Aerial Vehicle (UAV) dan Machine Learning Heri Santoso
Jurnal Penelitian Kelapa Sawit Vol 31 No 2 (2023): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v31i2.218

Abstract

The basal stem rot disease caused by Ganoderma sp remains a majority disease in oil palm plantations, and there is no effective treatment. The technical culture becomes a majority treatment to prolong the oil palm life. The identification and classification accuracy of healthy and infected by BSR (unhealthy) oil palm is needed to support the technical culture of treatment. This study is based on Santoso's previous study (2020), which identifies and classifies healthy and unhealthy oil palms using remote sensing from an image of a multispectral camera with three bands and machine learning. This study aims to improve the interpretation accuracy of healthy and unhealthy oil palms by adding ten vegetation indexes from three bands (red, green, and near-infrared/NIR) of a multispectral camera and applying sixteen models of machine learning. The results showed that the random forest and stochastic gradient boosting had improved 87.18% of the interpretation accuracy by 79.49% in the previous research and 0.69 kappa value from 0.48 in the previous research. This study's accuracy and kappa value improvement may be caused by adding variables from the vegetation indexes that become variable importance besides the red band in the fitting model. The model in this study needs to validate for identifying and classifying healthy and unhealthy oil palm caused by BSR in the area with low and moderate incidence.
Pemetaan Kandungan Hara Daun Kelapa Sawit Menggunakan Citra Multispektral Berbasis Unmanned Aerial Vehicle Madiyuanto Madiyuanto; Rahmawaty Rahmawaty; Heri Santoso
Jurnal Penelitian Kelapa Sawit Vol 31 No 2 (2023): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v31i2.236

Abstract

Adequate nutrition is one of the critical factors in determining the productivity of oil palm. Usually, to obtain sufficient nutrients for plants, fertilization is performed based on the results of soil and leaf nutrient content analyses. Measuring leaf nutrient content conventionally lacks flexibility, is impractical, is labour-intensive, and takes time and money, so remote sensing can be an alternative to addressing this problem. The study used the extraction of the reflectance values of each band as well as the transformation of the NDVI and GNDVI vegetation index from 3 bands of multispectral cameras (red, green, nears infrared) as an independent variable (predictor) to estimate leaf nutrient on oil palm plantations. A multiple polynomial regression model is built from a laboratory analysis of 35 examples of oil palm leaves used as a dependent variable. Predictive models of N, P, K, Ca, and Mg using multiple polynomial regression of order 4 resulted in R2 values in the succession of 0,986; 0,975; 0,981; 0,970; and 0,968; Adjusted R2 values in the sequence of 0,861; 0,761; 0,812; 0,710; and 0,690; RSE values consecutive of 0,065; 0,003; 0,076; 0,074; and 0,036; as well as MAPE values successive of 5,23; 3,22; 10,38; 13,40; and 16,59%. The predictive value of the leaf nutrients of each tree processed and classified spatially resulted in the content of N, P, and Ca being dominated by average criteria of respectively 95,51%, 100%, and 80,58% of the total tree, while K and Mg dominated the low criteria of 77.86% and 90.39%, respectively.
Co-Authors Henny Lydiasari Madiyuanto Madiyuanto Rahmawaty, Rahmawaty Winarna Winarna