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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Ilmu Tanah dan Lingkungan (Journal of Soil Science and Environment) Jurnal Planologi Sultan Agung Journal of Degraded and Mining Lands Management Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) JURNAL ILMIAH GEOMATIKA MAJALAH ILMIAH GLOBE SAINS TANAH - Journal of Soil Science and Agroclimatology Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Agricultural Science and Technology Makara Journal of Technology Indonesian Journal of Sustainable Agriculture and Environmental Sciences The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Bambang Hendro Trisasongko
Department Of Soil Science And Land Resources And P4W/CRESTPENT, Bogor Agricultural University. Jalan Meranti, Dramaga Campus. Bogor 16680. Indonesia.
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Spatial Heterogeneity of Rice Production Responses to ENSO Anomalies in Banten Province, Indonesia Tian Mulyaqin; Rita Nurmalina; Nunung Kusnadi; Bambang Hendro Trisasongko
Journal of Applied Agricultural Science and Technology Vol. 10 No. 1 (2026): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v10i1.503

Abstract

El Niño–Southern Oscillation (ENSO) anomalies are significant drivers of climate variability affecting agricultural production, although their impacts exhibit high spatial and temporal complexity. This study investigates rice production responses to ENSO phases in four districts of Banten Province, Indonesia—Pandeglang, Serang, Lebak, and Tangerang—during the 2000–2024 period. Utilizing descriptive statistics, OLS regression, and comparative time-series models (linear, quadratic, exponential, and moving average), the study evaluates how climatic signals are translated into production outcomes. Results reveal significant spatial heterogeneity. Although El Niño generally suppressed yields, regression analysis identifies Tangerang as the only district with a statistically significant vulnerability to drought-induced losses (β= -33,371 t/year). Conversely, the study identifies a "Triple-Dip" La Niña anomaly (2020–2023) where excessive rainfall reduced production in flood-prone districts such as Pandeglang, challenging the assumption that La Niña universally benefits rice yields. Methodologically, second- and third-order moving average models (MA(2) and MA(3)) consistently outperformed alternative specifications in capturing stochastic fluctuations. These findings underscore the localized nature of ENSO impacts and the inadequacy of generalized policies. The study therefore advocates spatially differentiated adaptation strategies, including localized early warning systems and improved drainage infrastructure, to mitigate drought and flood risks in Banten’s rice systems.
Modeling the activity ratio of soil potassium using machine learning approach Desi Nadalia; Arief Hartono; Heru Bagus Pulunggono; Bambang Hendro Trisasongko; Widiatmaka Widiatmaka; Muhammad Fuady Emzir
SAINS TANAH - Journal of Soil Science and Agroclimatology Vol 22, No 2 (2025): December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/stjssa.v22i2.100102

Abstract

The potassium (K) Quantity-Intensity (Q-I) relationship results in important parameters, including the activity ratio of potassium at equilibrium (AReK), which indicates potassium availability in soil. Experiments to observe soil Q-I K relationship parameters are often complex, time-consuming, and do not include environmental variables. This research aims to model AReK using a machine learning (ML) approach. ML models applied are Random Forest (RF), Cubist, and Support Vector Machine (SVM) as the primary approaches, with Multiple Linear Regression (MLR) serving as a baseline. The dataset was derived from sixty-one observation points in Brebes, Central Java. The predictors were pH, organic carbon, clay, cation exchange capacity (CEC), exchangeable cations (Exc-Ca, Mg, K, Na), water soluble K, available K, K saturation, potential K, non-exchangeable K (NE-K), elevation, and slope. The response variable was the AReK. Variable selection was performed using Pearson correlation to eliminate highly correlated predictors and reduce multicollinearity. Exactly 75% of the data was utilized as the training set and 25% as the test set. Three metrics, i.e., MAE, RMSE, and R², were used in model evaluation. The results showed that the Cubist model could predict AReK with high accuracy (R2=0.9437) and low RMSE (0.5701) and MAE (0.3514). Based on the Cubist model, Exc-K, Exc-Mg, CEC, and Exc-Ca were the most important variables for predicting AReK. This model can be employed to support site-specific fertilizer recommendation strategies. To improve the performance of the model, it is necessary to add other predictor variables (e.g., soil physical properties, clay minerals, rainfall, temperature and soil moisture).
Andosols property dynamics under intensive tea cultivation in West Java: Implications for sustainable management Rachmat Abdul Gani; Bambang Hendro Trisasongko; Budi Mulyanto; Sukarman Sukarman; Edi Yatno; Rufaidah Qonita Muslim; Haikal Caesa Prayudi; Heppy Suci Wulanningtyas; Destika Cahyana
SAINS TANAH - Journal of Soil Science and Agroclimatology Vol 23, No 1 (2026): June (in Progress)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/stjssa.v23i1.108781

Abstract

Despite its significant economic value, tea cultivation in Indonesia is experiencing a continuous decline in plantation area, primarily due to changes in land use. Soil fertility degradation and reduced yields present a significant threat to the long-term sustainability of tea production. A comprehensive understanding of the dynamic nature of tea plantation soil properties is essential for developing sustainable land management strategies aimed at enhancing and maintaining the productivity and sustainability of healthy tea cultivation. This study investigates the properties of Andosols formed from andesite tuff in a heavily managed tea plantation in Cisarua, Bogor, West Java, Indonesia. Four vertical soil profiles and ten composite soil samples were collected from depths of 0-20 cm and 20-40 cm across varying tea vigor and slope class gradients in block plantations. The ongoing production of tea on Andosols has led to significant chemical decline, evidenced by decreased organic matter, increased acidity, reduced cation exchange capacity, and compromised andic properties. The observed rise in base saturation primarily indicates a reduction in CEC, rather than an improvement in fertility. The observed patterns indicate progressive soil weathering and reduced resilience in monoculture systems. Restorative management, which encompasses the incorporation of organic matter and a balanced nutrient supply, is essential for maintaining soil functionality and securing long-term tea productivity. This article synthesizes key findings regarding soil properties, anthropogenic impacts, and strategies for sustainable management. Understanding these dynamics is crucial for optimizing good soil management practices and enhancing tea productivity in volcanic areas.
Artificial Intelligence Adoption in Smart Agriculture: A PRISMA-Based Systematic Comparative Review across Australia, South Korea, Indonesia and Pakistan Muhammad Faizan Khan; Muhammad Tariq Nawaz; Bambang Hendro Trisasongko; Muhammad Madnee; Nimrah Ameen
Indonesian Journal of Sustainable Agriculture and Environmental Sciences (IJSAES) Vol. 2 No. 2 (2026): Indonesian Journal of Sustainable Agriculture and Environmental Sciences (IJSAE
Publisher : CV. Truly Science Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65896/ijsaes.v2i2.38

Abstract

Background: Artificial intelligence (AI) is steadily changing the agricultural systems around the world. However, its adoption remains uneven between developed and developing economies, creating significant inequality in productivity, technological access and food security. Aim: This systematic review determines the adaptation of AI in agriculture across Australia, South Korea, Indonesia and Pakistan. Prior systematic reviews of AI in agriculture have largely examined technical performance within single countries or technology categories, without directly comparing adoption pathways across countries at different stages of economic development. This review addresses that gap. Methods: This study addresses a critical research gap by following PRISMA 2020 guidelines. A systematic search of the Scopus database showed 940 records. Out of these, only 47 peer-reviewed studies (2015-2024) were included after screening for qualitative synthesis and thematic analysis. Extracted data were coded and grouped into four themes aligned with the review objectives, and the percentages reported below reflect the proportion of the 47 included studies in which each theme, technology, barrier or benefit was identified during coding. Results: The most prominently reported AI approaches in the studies were Machine learning (51.1%), precision farming (48.9%) and computer vision (46.8%). Developed economies demonstrated advanced integration of precision agriculture and automation. Developing economies mainly use AI for disease detection, crop monitoring and yield forecasting. The most common challenges reported were the high cost of implementation (38.3%), limited infrastructure (36.2%) and poor data quality (31.9%). The most frequently reported benefits were improved crop yields (61.7%), better resource use efficiency (51.1%) and more effective disease management (46.8%). These percentages show how many studies reported each benefit, but they do not indicate the actual level of improvement achieved. Conclusion: Overall, the findings suggest that the adoption of AI depends not only on the availability of technology but also on infrastructure readiness, supportive policies and strong institutional capacity. This review highlights the need for context-specific strategies, greater investment in rural digital infrastructure and inclusive innovation frameworks to support fair and sustainable agricultural development.
Co-Authors A Kasno Achmad, Alfredian Adi F. Ramly, Adi F. Alinda F. M. Zain Andhi Trisnaputra Andi Tahir ANTONIUS KASNO Arief Hartono Aufa H. A. Syafril, Aufa H. A. Baba Barus Boedi Tjahjono Budi Mulyanto Chiharu Hongo, Chiharu Desi Nadalia Destika Cahyana Dyah R Panuju Dyah R. Panuju Dyah R. Panuju Dyah R. Panuju Dyah Retno Panuju Edi Yatno Febria Heidina, Febria Firdiana, Indah H.A. Syafril, H.A. Haikal Caesa Prayudi Hari Wijayanto Harimurti Harimurti Heppy Suci Wulanningtyas Heru Bagus Pulunggono Heru Subroto, Heru Irzaman, Irzaman Khursatul Munibah La Ode Syamsul Iman Luluk Dwi Wulan Handayani Mahmud A. Raimadoya Mahmud A. Raimadoya Mahmud Arifin Raimadoya Muhammad Faizan Khan Muhammad Fuady Emzir Muhammad Madnee Muhammad Tariq Nawaz Nimrah Ameen Nindita, Wida Nunung Kusnadi Nur Etika Karyati Panuju, Dyah R. Rachmat Abdul Gani Ramdhani Reyna Prachmayandini Reyna Prachmayandini, Reyna Rita Nurmalina Rizqi I’anatus Sholihah Rufaidah Qonita Muslim Sigit, Gunardi Sukarman Sukarman Suria Darma Tarigan Tian Mulyaqin Uciningsih, Winda Wahyudi Hasbi Widiatmaka Widiatmaka Widiatmaka Yunito, Muhammad Rahmanda