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Land Management of Tidal Swamp Type B with Surjan System as Climate Change Anticipation
Full text linkAgriculture is one of the most vulnerable sectors to climate change, which can significantly impact national food security. In addition to climate change, agricultural development faces challenges, including the conversion of agricultural land for non-agricultural purposes. As a result, agricultural extensification has expanded into marginal lands, such as tidal swamplands. This paper presents a literature review on the characteristics of tidal swamplands, the principles of the surjan system, and its relevance in addressing climate change, particularly in the context of food security and ecosystem sustainability. Various literature sources were analyzed to assess the advantages, challenges, and sustainable management strategies of tidal swamplands. The review highlights the importance of effective land management to create suitable soil conditions for optimal plant growth and increased productivity. The surjan system, a land management approach practiced by tidal swampland farmers, demonstrates high adaptability in mitigating the impacts of climate change. This system integrates cultural, ecological, and economic perspectives by combining local knowledge with technological advancements. Key components of the surjan system include a one-way water management system with flap-gates and stoplogs, as well as the use of climate-adaptive crop varieties on tidal swamplands
Water Use Efficiency and Adaptive Responses of Oil Palm under El Niño-Induced Drought and Haze
No full textOil palm plays an important role in the tropical carbon cycle but is highly sensitive to climatic variability. Understanding the coupled dynamics of carbon and water fluxes in such ecosystems essential for sustainable management under variable climatic conditions. This study analyses the Water Use Efficiency (WUE) and coupled carbon–water fluxes of an 18-year-old oil palm plantation in Jambi, Indonesia, during the 2015 El Niño event using data from an eddy covariance flux tower. The analysis focused on the diurnal variations of Net Ecosystem Production (NEP), evapotranspiration (ET), and water use efficiency (WUE) during wet, dry, and dry with haze periods, which were determined based on rainfall data. Our results show that WUE reached its highest value during the dry-with-haze period (7,484 g CO₂ kg⁻¹ H₂O), more than double that of the wet (3,440) and dry (3,347) periods. This increase resulted from reduced evapotranspiration (ET) due to stomatal regulation, despite lower Net Ecosystem Production (NEP) caused by light limitation from haze. Diurnal analyses showed WUE peaking in the morning and declining at midday as the Vapor Pressure Deficit (VPD) increased (up to 0.88 kPa under haze). These findings highlight oil palm’s adaptive strategy to conserve water under stress while maintaining productivity. However, severe haze markedly weakens carbon sequestration. The results provide critical insights for optimizing irrigation and water management in the face of increasing climate variability
Flood Management Strategy Based on Analysis of Regional Characteristics and Causal Factors in Kendari City
No full textFlooding is a disaster that causes environmental damage, economic losses, and social problem. Kendari City is one of the Indonesian cities that frequently experiences floods due to various factors, including high rainfall, land use changes, poor drainage conditions, and improper spatial management. This study aims to (1) assess the factors contributing to flooding, (2) analyze government governance and community participation in flood management, and (3) formulate an integrated flood management strategy. The methods used were descriptive analysis, spatial approach, SWOT analysis, and Quantitative Strategic Planning Matrix (QSPM). The results showed that there is evidence of land use changes between 2013 – 2023 based on spatial image analysis. We found there were three sub-districts, which is categorized on high flood vulnerability namely Baruga, Kadia, and Kambu sub-districts. Based on the level of community preparedness parameters including knowledge and attitudes, emergency plans, early warning, and resource mobilization, Baruga belongs to the medium category (74.60%), while Kambu (57.42%) and Kadia (59.58%) were in low category. QSPM analysis recommends two priority strategies to reduce flood vulnerability namely accelerating drainage system improvements and replicating the Baruga model in other areas. Future research should focus on climate change-induced flood modeling, gender-sensitive vulnerability assessments, and economic loss estimation to enhance the effectiveness of flood management strategies
Water Use Efficiency in Pineapple Plants Fertilization Based on Water Balance Analysis
No full textMost fertilization in pineapple cultivation occurs through foliar application, which involves dissolving fertilizers in water and spraying them on the pineapple plants. The provision of irrigated water to plants refers to water balance analysis, which corresponds to the available water supply. The research aims to determine the effect of various foliar volumes on the growth and production of pineapple plants. The research was carried out in April 2022 - July 2023 at PT. Great Giant Pineapple Lampung using a Completely Randomized Block Design (CRBD). We applied four treatments of foliar water, which comprised of 1500 l/ha, 2000 l/ha, 3000 l/ha (as control), and based on water balance analysis. The treatment has three replication each. The results showed there was not significant different of growth and yield between treatments of water balance approach and the 3000 l/ha foliar water volume (control), in which both have reduced water usage by 22% for one cycle of pineapple cultivation. The findings provide a more efficient water manage-ment strategy for foliar fertilization, reducing water usage without affecting plant performance, and supporting sustainable agricultural practices in pineapple cultivation. Further, the findings can serve as a reference for optimizing irrigation scheduling and input costs in large-scale plantations
Assessment of Rice Crop Water Requirements for Planting Season in Moderate Agroclimatic Area of West Sumatra
Full text linkIn changing climate, uncertainty in rice production becomes more frequent leading to threat of food security. However, research on rice cultivation in the rainfed agricultural areas of West Sumatra remains limited. The objectives of the study are to analyze the crop water requirements of rainfed rice and to determine rice planting patterns. The study was conducted in a moderate agro-climate area of West Sumatra based on oldeman agroclimate zone that experienced changes in planting patterns. We used climate data for 1991 – 2020 obtained from TerraClimate, which were utilized for monthly water balance computation based on the Thornwhite and Matter approach. The analysis focused on four major rice production centers, namely: Panti in Pasaman, Lima Kaum in Tanah Datar, Luak in Lima Puluh Kota and Sijunjung. The results showed change in water deficit periods across the study sites have changed planting season. Based on our analysis site in Lima Kaum, Tanah Datar experienced the longest deficit period, which lasted 5 months from May to September. This situation may not suitable to plant rice throughout the year without additional irrigation. Further, adjusting to the secondary crop may be considered to optimize agricultural productivity. These findings can serve as a reference for determining planting seasons and improving water use and distribution strategies in rainfed agricultural systems
Contribution of Small Rainwater Reservoirs to Performance Off-season Vegetable Farming
Full text linkClimate Smart Agriculture (CSA) has become essential in ensuring sustainable agricultural production amidst ongoing climate change. This study aims to analyze the impact of small rainwater reservoirs (SRRs) on the performance of off-season vegetable farming in Netpala Village, East Nusa Tenggara, Indonesia. The SRRs, constructed using plastic tarpaulin with storage capacities of 3.6 m³ and 4.3 m³, were applied to support the cultivation of mustard greens, Chinese cabbage, cabbage, carrots, and eggplant during the December 2017 to April 2018 growing season. An on-farm research (OFR) approach was used to assess the effects of SRR implementation compared to traditional water management practices. Key performance indicators include cultivated land area, planting index, crop diversification, and farmer income. Results revealed that SRRs expanded the cultivated area by 21.07%, increased the planting index by 0.52 for mustard greens and 0.64 for cabbage, and boosted farmer income by 29.38%. Income levels were also influenced by factors such as market absorption, commodity prices, and land availability. These findings demonstrate that SRRs can enhance the resilience and productivity of smallholder vegetable farming systems by improving water availability during the rainy or off-season. SRRs offer a practical and scalable solution to address water scarcity and promote sustainable intensification in vulnerable agricultural regions
The Use of Artificial Neural Networks to Estimate Reference Evapotranspiration
Full text linkEvapotranspiration is defined as the loss of water from soil and vegetation to the atmosphere, driven by weather conditions. It reduces the availability of water for agricultural purposes, which affects the amount of irrigation water, particularly during the dry season. The objective of this paper is to present a comparative analysis of the estimated reference evapotranspiration value based on artificial neural networks (ANN) with backpropagation bias 1 (BP-1) and backpropagation bias 0 (BP-0) architectures. The model was fed with data of air temperature, relative humidity, and solar radiation. The model is utilized to calculate the evapotranspiration using the Hargreaves method as the training data. The performance of ANN model was evaluated using the mean square error (MSE), root mean square error (RMSE), and coefficient determination (R2). Our results showed that both ANN models performed well as indicated by low error (MSE < 0.01) and high R2 (>0.99). Also, we found that air temperature and relative humidity determine the optimal prediction. Further, this proposed model can serve as a reference for other models seeking to determine the most appropriate computational model for evapotranspiration value estimation
Climate Change Impact On Rice Productivity Using FAO AquaCrop Model: A Case Study in Lampung
No full textClimate change impacts global food productions, but its local effect on rice production, particularly on monsoon dominated regions remain less understood. Here, we assess climate change impact on rice production situated on Lampung Province, as one of the largest rice productions in Indonesia. The FAO AquaCrop model was used to predict rice productivity, incorporating climate projections from the CMIP5 model under medium (RCP 4.5) and high (RCP 8.5) emission scenarios. We simulated the model for fifteen locations representing districts in Lampung Province. Our results show that by 2050, the decreased rainfall is projected during the dry season and early rainy season, but the average monthly temperatures and evapotranspiration rates are expected to increase across all districts. AquaCrop simulated an increased rice productivity by +0.25 and +0.74 tons/ha for both scenarios in April planting season, but it decreased by -0.41 and -0.75 tons/ha in November planting season due to water stress. This research is important to provide a deeper understanding of the impact of climate change on rice productivity in Lampung Province. These findings highlight the need for adaptive strategies to sustain rice production under future climate conditions
ECMWF SEAS5 Seasonal Rainfall Assessment: A Study Case in Papua
No full textPapua, Indonesia’s easternmost island, is prone to seasonal hydrometeorological disasters, necessitating high-quality climate forecasts for effective risk management. This study evaluates the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecast System 5 (SEAS5) in predicting seasonal (3-monthly) rainfall across Papua from 1982 to 2016, using the blended Climate Hazards group InfraRed Precipitation with in-situ rainfall data (CHIRP+Pos) as the observational reference. SEAS5 forecasts at 1 to 3 month lead times were assessed across seasons which defined as July-August-September (JAS), August-September-October (ASO), September-October-November (SON), and December-January-February (DJF), and El Niño-Southern Oscillation (ENSO) phases (El Niño, La Niña, Neutral), using Pearson correlation coefficient (Corr), root mean square error (RMSE), and Kling-Gupta Efficiency (KGE) metrics. Results show stronger SEAS5 skill in JAS–SON (Corr up to 0.939) compared to DJF-JFM (Corr as low as -0.208), with a robust ENSO-rainfall relationship in JJA-SON. SEAS5 performed best during El Niño, particularly in lowlands and exhibited greater variability skill during La Niña and Neutral phases. Benchmarking against a linear regression baseline showed SEAS5’s superior Corr in 76.2% of grids but higher RMSE in 60.6%. Despite limitations in mountainous regions and at longer lead times, SEAS5 offers reliable forecasts for lowland areas during JAS-SON under El Niño, supporting operational applications like drought preparedness and agricultural planning in the regions
Bias Correction of CMIP6 Models for Assessment of Wet and Dry Conditions Over Sumatra
Full text linkThe performance of CMIP6 models in capturing local and regional precipitation patterns often requires refinement due to inherent biases. This study evaluates eleven CMIP6 models for their applicability over Sumatra Island and applies two bias correction methods namely Linear Scaling (LS) and Quantile Delta Mapping (QDM). We used ERA5 precipitation datasets as a reference bias correction during 1981-2014. The performance was assessed using MAE, correlation, and PBIAS. Results reveals that raw model of CMIP6 generally underestimate precipitation, particularly during the DJF and SON seasons, with the largest errors over the mountainous western Sumatra. LS tends to overcorrect and shift precipitation estimates toward a wetter bias, while QDM significantly improves the accuracy and seasonal consistency of the simulations. The multi-model ensemble mean (CMIP6-avg) outperforms individual models, and its performance is further enhanced with QDM, yielding higher correlation and lower error metrics. Spatial and seasonal analyses demonstrate that QDM more effectively reduces both dry and wet biases, especially during peak rainfall seasons. These findings underscore the importance of robust bias correction techniques to improve climate projections for hydrological and climate impact studies in Sumatra and other tropical regions with complex terrain