The Regional Disaster Management Agency (BPBD) and Communication and Information Agency (Diskominfo) of Garut Regency recorded seven landslide events in 2020 and nine in 2023 in the Banjarwangi District. These events were triggered by steep to very steep slopes with landslide-prone soil and rock types, and heavy rainfall. This study aimed to evaluate the accuracy of landslide hazard maps for various rainfall models, including daily, decadal, monthly, and annual rainfall, validated landslide occurrence points with the DVMBG 2004 method. The evaluation results showed that the maximum rainfall model had a hazard classification of 12,055.3 ha. The average rainfall model showed a less vulnerable classification, covering an area of 9,253.45 ha. Rainfall levels affect the classification of vulnerability, thereby impacting the accuracy. The results of evaluating the accuracy of landslide vulnerable suitability for various rainfall models showed a low accuracy of 45.5%. Therefore, further analysis is required to improve the accuracy of landslide vulnerability maps.

Rice is a major food commodity in Indonesia, including in Pangumbahan Village, Ciracap subdistrict, Sukabumi Regency, which has great potential for rice cultivation due to its favorable climate. However, the main challenges faced are climate change and the availability of water resources. This program aims to sustainably increase rice farming productivity through the dissemination of climate-friendly agricultural technology and water resources. The program is implemented in Pangumbahan Village in collaboration between IPB team and the Ciburial Berkah Farmers Group. The methods used include field observations, mapping of potentials and problems, and the implementation of technology dissemination programs. One of the technologies introduced is the Automatic Weather Station (AWS) to monitor weather conditions in real-time, helping farmers make better decisions. The program results show that the use of AWS can enhance knowledge in agricultural irrigation planning and community welfare. The collected weather data indicates a surplus of rainwater that can be utilized for irrigation. Combined with existing water sources such as springs and wells, as well as planned irrigation from rivers, farmers will be better prepared to face the challenges of climate change and support the sustainability of rice farming in Pangumbahan Village.

Greenhouse gas (GHG) emissions have become a major global environmental issue due to their significant contribution to climate change. In irrigated rice fields, methane (CH 4 ) and nitrous oxide (N 2 O) are the dominant greenhouse gases influenced by water management practices. The Alternate Wetting-Drying (AWD) irrigation system has been developed as an alternative to continuous flooding to reduce GHG emissions while maintaining rice productivity. Therefore, this study aimed to develop a GHG emission model under AWD systems using biophysical environmental parameters and an Artificial Neural Network (ANN), as well as to compare emission characteristics under different irrigation regimes. Field observations were conducted under three irrigation regimes, namely continuous flooding (FL), wetting (WT), and drying (DR) representing AWD conditions. CH 4 and N 2 O emissions were measured weekly using standard methods, while soil temperature, soil water content, electrical conductivity (EC), pH, and redox potential (Eh) were used as model input variables. The ANN model demonstrated excellent performance in predicting CH 4 emissions, with a coefficient of determination (R 2 ) of 0.92, while N 2 O prediction showed good performance with an R 2 value of 0.82. These results indicate that ANN is effective for modelling greenhouse gas emissions in irrigated rice fields, particularly for CH 4 emissions.

empor Reservoir plays a crucial role in supporting irrigation water supply, raw water provision, and flood control in Central Java. Effective reservoir operation requires reliable estimates of inflow and water availability to ensure sustainable water allocation under varying hydrological conditions. This study analyzes the daily water balance of Sempor Reservoir using the Soil and Water Assessment Tool (SWAT) model to simulate inflow and assess operational reliability. Daily rainfall and climate data for 2015–2024 were used, with streamflow observations used for model calibration (2015–2022) and validation (2023–2024). Model performance was evaluated using the Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R2). The SWAT model performed very well, achieving NSE = 0.83 and R2 = 0.87. These performance statistics indicate that the simulated inflows closely matched the observed discharge and provide confidence in the model’s application for water balance analysis. Daily water balance results show that deficit conditions occur mainly during the dry season. Annual inflow and demand were 184.02 million m3 and 186.45 million m3, resulting in a deficit of 2.43 million m3. The reservoir was full for 36 days, sufficient for 311 days, and insufficient for 18 days, indicating approximately 95% reliability. Overall, the results demonstrate that the SWAT model is an effective tool for evaluating daily reservoir water balance and operational performance. The findings provide useful information for reservoir operation, water allocation planning, and sustainable water resources management under changing hydrological conditions. The results also provide valuable information for water managers in optimizing reservoir operation, improving water allocation efficiency, and reducing the impacts of seasonal water shortages.

Kawasan Pusat Pemerintahan Kabupaten Serang (Puspemkab Serang) merupakan kawasan perkotaan yang berkembang pesat dan rentan terhadap banjir akibat meningkatnya limpasan permukaan saat terjadi hujan ekstrem. Sistem drainase yang ada belum mampu menampung debit puncak limpasan, sehingga sering terjadi genangan. Penelitian ini bertujuan untuk merencanakan penambahan kolam retensi sebagai upaya mitigasi banjir di kawasan Puspemkab Serang. Data curah hujan maksimum tahunan diperoleh dari Stasiun Hujan Pipitan dan dianalisis menggunakan metode Gumbel Tipe I untuk menentukan curah hujan rencana dengan periode ulang 25 tahun. Intensitas hujan jam-jaman dihitung menggunakan metode Mononobe 5 jam sesuai dengan SNI 03-3424-1994. Analisis debit limpasan dilakukan dengan Metode Rasional berdasarkan luas daerah tangkapan sebesar 347,66 ha dan karakteristik tata guna lahan perkotaan. Hasil analisis menunjukkan bahwa volume limpasan maksimum mencapai 163.539 m³ dan meningkat menjadi 212.601 m³ setelah diterapkan faktor keamanan sebesar 30%. Dengan kedalaman kolam retensi 3 meter, luas total kolam retensi yang dibutuhkan adalah sekitar 7,09 ha, yang dibagi menjadi dua kolam untuk meningkatkan efektivitas pengendalian banjir.

The clean water crisis and the increasing scarcity of agricultural land pose significant challenges to food security. However, household yard areas, when managed productively, have the potential to contribute to family-level food production. The Powerless Automatic Fertigator (FONi) is an appropriate technology that has been shown to enhance productivity and facilitate various horticultural cultivation practices. This study integrates aquaculture and horticulture using the FONi system (referred to as FONi Mina-horticulture) with the following objectives: (1) to develop the system design and evaluate its performance; (2) to assess the production of fish and vegetables; and (3) to analyze land and water productivity. The experiment was conducted over a two-month period in a trial garden using a fish pond connected to 30 downstream planting pots. The water level in each pot was maintained at approximately 10 cm below the soil surface. Plant growth was measured weekly and monitored using CCTV. The results demonstrate that the FONi Minahorti model achieved a fish (grass carp) growth rate of 73% with a survival rate of 100%. Land and water productivity for red spinach were 7.67 g/m² and 8.77 g/L, respectively, while for green spinach they reached 10.08 g/m² and 11.53 g/L, respectively. The crop coefficient (Kc) for both spinach varieties during the growth period ranged from 0.40 to 0.71. These findings indicate that the FONi Minahorti system is a viable approach for enhancing yard productivity by simultaneously producing fish and vegetables to support daily household needs.

Increasing water demand and the adverse effects of climate change have reduced the availability of agricultural water in regions experiencing scarcity. As a result, efficient irrigation system design and management are essential for conserving water. This study focused on optimizing subsurface drip irrigation (SDI) design and operation to enhance water use efficiency and nutrient absorption. The HYDRUS (2D/3D) simulation tool was utilized to model soil water dynamics. Calibration and validation were performed through field experiments on chili crops, comparing two SDI configurations: conventional dripline emitters and ring-shaped emitters. Soil moisture levels were tracked at depths of 15, 30, 45, and 60 cm using sensor probes. The model-generated moisture data at these depths were then compared with actual probe readings. The simulation results demonstrated that the model reliably predicted soil moisture distribution, with RMSE values ranging from 0.0504 to 0.0058. This confirms HYDRUS-2D’s accuracy in simulating subsurface water flow around emitters. Additionally, root water uptake was most effective near the SDI emitters. The study concludes that optimal SDI design should take into account plant root system size, emitter placement depth, and soil characteristics.

Methane (CH 4 ) emissions from irrigated rice fields are a major source of agricultural greenhouse gas emissions due to anaerobic conditions that stimulate methanogenesis. Organic rice cultivation combined with appropriate water management has been considered a more sustainable cultivation approach, although differences in nutrient and water management may affect methane dynamics. This study aimed to evaluate differences in CH 4 emissions between two field-relevant rice cultivation systems and examine their associated environmental and soil conditions. Field observations were conducted from August to December 2025 in Pagerageung, Tasikmalaya, comparing organic rice managed with Alternate Wetting and Drying (OR-AWD) and conventional rice under continuous flooding (CO). Methane emissions were measured alongside soil electrical conductivity (EC), pH, temperature, moisture, and water level throughout the growing season. Seasonal CH 4 emissions were 171.93 kg ha −1 season −1 in OR-AWD and 394.12 kg ha −1 season −1 in CO, representing 56.4% lower emissions in the OR-AWD system. The lower emissions were associated with differences in water-level dynamics and soil environmental conditions under the two cultivation systems. These findings highlight the potential of integrated water and nutrient management in improving the methaneemission performance of rice cultivation systems under field conditions.

The current study aims to develop a simple model for estimating greenhouse gas emissions originating from paddy fields, utilizing backpropagation neural networks. The model integrated three input parameters: soil moisture, soil temperature, and soil electrical conductivity (EC), while generating estimations for two output parameters: methane (CH4) and nitrous oxide (N2O) emissions. The model was put into practice across three different irrigation systems, i.e., continuous flooded (FL), wet (WT), and dry (DR) regimes. For model training and validation, the input parameters were measured by a single 5-TE sensor. Concurrently, CH4 and N2O emissions were determined utilizing a closed chamber, and gas samples were subjected to laboratory analysis. Findings unveiled that the developed model accurately estimated CH4 and N2O emissions, demonstrating commendable coefficient of determination (R2) values ranging from 0.60 to 0.97 for validation process. Notably, the WT irrigation system exhibited the highest precision, boasting R2 values of 0.97 for CH4 and 0.73 for N2O estimation, respectively. Conversely, the FL irrigation system has the lowest accuracy with R2 values of 0.66 and 0.60. Despite variances in accuracy across irrigation systems, the overall performance remained deemed acceptable, warranting the model's applicability for estimating greenhouse gas emissions under diverse irrigation scenarios.

As a developing nation transitions into the industrial era 4.0, the digitalization of agriculture—particularly through the integration of agrotechnology and the Internet of Things (IoT)—has emerged as a crucial component for Indonesia's agricultural advancement. Previous studies have demonstrated the effectiveness of IoT-based smart farming systems in enhancing data precision, resource efficiency, and crop monitoring (e.g., in India and China). However, despite the proliferation of such technologies, empirical data on environmental impacts, especially greenhouse gas (GHG) emissions, remain scarce in the Indonesian context. This study addresses this gap by deploying an Environmental Monitoring System (EMS) based on IoT to analyze and forecast GHG emissions in East Nusa Tenggara (NTT), a dryland area with distinct climatic conditions. The EMS, established in Kupang on July 26, 2017, integrates Field-Router, Data Logger, and sensors for solar radiation, rainfall, and soil moisture. Paddy was cultivated under the System of Rice Intensification (SRI) and conventional methods during one planting season. The findings confirm that the EMS provided reliable data for precision agriculture and dynamic tracking of environmental parameters. Notably, CH₄ emissions from SRI plots were recorded at 0.43 kg CH₄/ha/day, substantially lower than emissions from conventional fields. This value is also below the IPCC default emission factor for rice fields in tropical regions, aligning with findings from other Southeast Asian countries that highlight the emission reduction potential of intermittent irrigation in SRI. These insights can inform national and regional strategies for sustainable agriculture and climate change mitigation..

Study region: Cidanau Watershed, Banten Province, Indonesia, features a natural wetland (caldera) surrounded by mountains. Study focus: The SWAT was employed to assess future hydrological changes in water availability and seasonal patterns of dry seasons in Cidanau watershed. The dry season was determined when the rate of cumulative evapotranspiration was higher than the rate of cumulative rainfall. Using 2002 - 2021 data as a baseline, future projections were evaluated for 2030 s, 2050 s, 2070 s, 2090 s. The SWAT model has been edited with wetland input parameters and calibrated with river channel and watershed parameters. New hydrological insights: 1) The SWAT model performance marginally improved after incorporating wetland input and river channel parameters in parameterization process; 2) Projected increase in mean Tmin & Tmax range from 0.5 degrees C to 2.9 degrees C, while mean annual rainfall is expected to decrease by- 7 %- -10.3 % compare to the baseline periods; 3) As the result, the annual water yield is projected to decline by -11 % - -26 %, with the rainy season experiencing the most significant reduction; 4) The length of dry seasons is projected to increase, further impacting water availability; 5) Additionally, the frequency of extended dry seasons is expected to increase under future climate scenarios. This study highlights the anticipated decline in water availability and shifting dry season pattern, supporting decision-maker in developing adaptive and mitigation strategies for future climate challenges.

Since 2017, the installation of the Sheet-pipe system in Indonesia has marked a significant development. This initiative, a collaboration between the Ministry of Agriculture and a Japanese construction company, aims to improve water management in rice paddies. The system was specifically installed at the Sukamandi Rice Research Center in West Java, covering an area of 3600m2. Seven parallel sheet-pipe lines, each 100 meters in length and 40 cm deep, were laid out. These lines were consolidated into three drainage outlets regulated by valves. The primary goal was to evaluate the drainage capacity and land drainage modulus using the sheet-pipe system. Measurements of the drainage water exiting each pipe were taken using gravimetric methods at specific time intervals under saturated soil conditions. The results indicated a total drainage rate ranging from 0.396 l/s to 1.431 l/s. The maximum drainage rate per sheet-pipe lane was 0.20 l/s, and the drainage modulus was 275 m3 day−1 ha−1. These findings illustrate the efficiency of the sheet-pipe system in facilitating water reduction to the sheet-pipe depth, thereby enhancing soil aeration.

Water scarcity, intensified by climate change and pollution, necessitates innovative irrigation approaches to sustain agricultural productivity. The Automatic Unpowered Fertigator (FONi) represents a solution that integrates automation without electricity, using evapotranspiration-driven subsurface irrigation to deliver water and nutrients directly based on plant demand. Unlike conventional systems, FONi operates entirely without external energy input, offering a low-cost and sustainable alternative for smallholder farmers. Previous applications in various crops have demonstrated significant water savings and increased productivity, indicating its strong potential as a scalable technology for resource-limited agriculture.This study evaluated the performance of FONi in cultivating four eggplant varieties under greenhouse conditions in Bekasi City, an area facing increasing competition for water resources. Over a 118-day growing period, plant growth, water use, crop coefficients (Kc), and productivity were monitored. Results showed Kc values ranging from 0.1 to 1.8, reflecting dynamic water demand throughout plant development. The long purple variety attained the greatest height (99.8 cm), while pondoh and white varieties achieved higher water productivity (up to 4.0 g/L) and land productivity approaching 1,120 g/m². Total irrigation water use was 1,329.3 liters, with an overall application efficiency of 98.9%. These findings demonstrate that integrating FONi with appropriate crop selection provides an efficient and sustainable strategy to optimize water use and enhance yield, supporting precision agriculture and climate-resilient food systems in drought-prone regions.

Water management plays a crucial role in paddy rice cultivation. Inappropriate use of irrigation water can hinder optimal plant growth and lead to wastage. Therefore, it is essential to optimize the irrigation water delivery system. This optimization can be achieved after developing a model that identifies the relationship between the irrigation system and plant growth. This research aims to develop a plant growth model influenced by the irrigation system. An artificial neural network model with a backpropagation algorithm is employed to predict plant growth under different irrigation treatments. The model development is based on a lab-scale rice cultivation experiment conducted over two growing seasons in 2021 and 2022, comparing a flooded system (CFI) and a more water-efficient intermittent irrigation system (IIS). The first growing season was used for model training, while the second season was for model validation. The developed model incorporates three inputs: soil moisture, plant height, and the number of tillers from the previous week. The outputs are the plant height and the number of tillers for the following week. The results of the model training indicate that the neural network model accurately predicts plant height and the number of tillers, with R 2 values of 0.99 and 0.93, respectively. For validation, the R 2 values are slightly lower, at 0.97 and 0.65. These results suggest that the model can effectively predict plant height and the number of tillers.

Physical properties of peat are widely applied to detect the quality of peatland ecosystem. A comprehensive dataset on the peat properties is the foundation for the development tool and model of peat ecosystem, especially in region with frequent wildfire. Here we established a tabular dataset for physical properties of lowland tropical peatland in Indonesia. The data were obtained in dry season 2019 and 2023, respectively, at Jambi and Central Kalimantan peatlands. The dataset comprises of 66 peat samples from two land-uses namely secondary forest and ex-burned lowly vegetation. The physical properties are bulk density, porosity, water retention at four pressures (-1, -10, -25, and -1500 kPa), and water holding capacity. In addition, a set parameter of van Genuchten for water retention curve is available. The field-observed dataset provides a solid base for a better understanding of physical peat properties and can be used as a first step to develop peat water retention database in lowland tropical peatlands.

Dalam usaha peningkatan produksi padi, faktor-faktor lingkungan yang ada di sekitar tanaman perlu diperhatikan. Salah satu faktor yang berpengaruh terhadap produktivitas padi yaitu jumlah dan kualitas air yang dialirkan harus disesuaikan dengan kebutuhan padi. Kandungan oksigen yang terdapat dalam air juga perlu diperhatikan karena kebutuhannya untuk proses metabolisme dan pertumbuhan padi. Selain itu, faktor lingkungan lain seperti evapotranspirasi tanaman, suhu, kelembapan, dan konduktivitas listrik tanah juga dapat mempengaruhi produktivitas padi. Penelitian ini bertujuan untuk mengembangkan model hubungan antara tinggi muka air, oksigen terlarut, evapotranspirasi tanaman dengan produktivitas padi dengan Jaringan Saraf Tiruan (JST). Model JST digunakan untuk membantu memodelkan kompleksitas pengaruh faktor-faktor lingkungan tersebut sebagai input terhadap produktivitas padi sebagai output. Penelitian ini dilakukan dengan menggunakan 4 skenario perlakuan berdasarkan tinggi muka air, sistem pengaliran irigasi, dan penggunaan teknologi ultra fine bubble untuk meningkatkan oksigen terlarut. Hasil pemodelan JST menunjukkan bahwa model yang dikembangkan mampu menduga pertumbuhan tanaman dengan dengan koefisien determinasi (R2) sebesar 0,9991. Hasil ini menunjukkan bahwa model dapat digunakan dan dapat dijadikan acuan untuk optimasi sistem irigasi berdasarkan faktor lingkungan

Human resilience is related to the availability of natural resources, such as water, which is developed through community empowerment and technology. Nganti Village, Bojonegoro is one of the villages that is vulnerable to the availability of clean water for daily consumption. So, this research aims to (1) analyze the problems and potential of water sources that are not utilized by the Nganti community, (2) analyze community strategies in dealing with water problems, and (3) simulate alternative membrane technology based on community empowerment. This research used a combined approach, quantitative and qualitative, involving key informants, including the Village Head, Head of the Clean Water Association, Ngraho Sub-district Head, and Community Leaders, in addition to a survey of 40 Nganti-Ngraho residents. Data analysis is descriptive statistical and qualitative. The water quality of a spring in Nganti Village shows that the water is not suitable for drinking, washing and latrines (MCK) due to the high content of iron and other metals. Meanwhile, the community's strategy is to use water reservoirs as an alternative water source and introduce membrane technology through community empowerment activities.

Anaerobic digestion is a well-known biological treatment process. It uses less energy, consumes fewer nutrients, converts organic pollutants into methane gas, and produces a small quantity of biomass. The interactions among the various microbes in this complex biological system need to be better understood, and as a consequence, mathematical models need to be revised. This review discusses the principles of biokinetic models published in the literature on anaerobic fermentation as part of the anaerobic digestion process for waste-activated sludge. Biokinetic models for anaerobic fermentation have been developed to predict cell growth, substrate consumption, and gas production. This exploration delves into the incorporation of the hydrolysis stage, a multi-step process entailing the breakdown of carbohydrates, proteins, and lipids within existing biokinetic models. Because there is no single analytical method for accurately determining the biokinetics of anaerobic fermentation of waste-activated sludge incorporating hydrolysis parameters and inhibition effects are proposed to improve the estimated trends of process variables as a function of the design variables.