The adequacy of the mandatory two-year organic-conversion period for paddy rice was evaluated through a two-year field investigation (2022–2023) conducted in conventionally managed rice paddy in the South Korea, encompassing the simultaneous monitoring of pesticide residues in rice plants and the surrounding paddy environment. Isoprothiolane, a phosphorothiolate systemic fungicide, was foliar-applied in a 40
Conventional fertilizers in paddy fields perform low nutrient use efficiency due to significant losses through leaching, volatilization, and runoff, leading to reduced yields, higher production cost, and environmental degradation. This study aimed to (i) develop NPK-coated fertilizer formulations using natural mineral, biochar, and goat manure to increase rice yield and nutrient use efficiency, and (ii) evaluate the economic performance of the coated fertilizers compared with conventional fertilization practices. Coating materials including zeolite, guano phosphate, lime, rice husk and coconut shell biochar, and goat manure were blended in various ratios to coat the fertilizers. Nine NPK-coated formulations (F1–F8, F11) with reduced fertilizer rates were tested against dose of the national recommended fertilizer (NRFD), farmers’ practice (FEPD), and a control. Two field trials were conducted parallelly at two paddy-field sites with different soil properties, each arranged in a randomized block design with three replicates and analyzed separately. X-ray fluorescence analysis confirmed the coating materials could enrich macro- and micronutrients to conventional fertilizer. Seven formulations (F1, F2, F3, F5, F6, F7, F11) produced rice yields (5.3–6.1 t ha⁻¹) comparable to yield obtained with national fertilizer dose (6.3–6.7 t ha⁻¹), suggesting the efficiency of these formulations. Among the seven formulations, F7 (300 kg urea, 25 kg SP-36, 50 kg KCl ha⁻¹ + 270 kg coatings), and F1 (200 kg urea, 50 kg SP-36, 75 kg KCl ha⁻¹ + 335 kg coatings) give the highest profits (USD 1,239–1,457 ha⁻¹ ) with revenue–cost ratio of 2.0–2.2 across two contrasting fields experiments. In addition, the profits from F7 and F1 are much higher than the farmer’s fertilizer practices (FEPD) and control treatment, leading to recommendation of F7 and F1 as alternative fertilizer options. Overall, the findings demonstrate that NPK-coated fertilizers offer eco-friendly alternatives to enhance rice yield, reduce nutrient loss, and improve farm profitability.
The objectives of this study were to classify streamflow patterns into Hydro-Morphological Units (HMUs) using hydrodynamic factors and to evaluate stream habitats based on these HMU clusters. A 2.9 km reach of the Bokha stream was selected as the study area, where aquatic habitat enhancement is required. Stream velocity and water depth were simulated using the HEC-RAS 2D model. Based on the simulation results, the stream flow patterns were categorized into five HMU types—pools, riffles, rapids, ruffles, and runs—according to estimated probabilities derived from their respective hydrodynamic boundary ranges. Spatiotemporal analysis indicated that HMUs such as ruffle and run closely corresponded with observed fish habitat characteristics, suggesting their suitability as indicators for habitat evaluation. To quantitatively assess habitat diversity, the Normalized Shannon–Weaver Index was calculated, yielding values of 0.93 at the minimum ecological streamflow (0.48 m³/s) and 0.88 at the maximum Weighted Usable Area (WUA) flow (6.9 m³/s). Overall, the proposed approach provides a robust framework for stream habitat evaluation and contributes to the identification of streamflow ranges that are both ecologically meaningful and practically feasible, thereby supporting sustainable stream management strategies.
Accurate greenhouse gas (GHG) quantification from rice paddies is essential for climate change adaptation but is constrained by labor-intensive measurement methods. Silicate fertilizers have the potential to mitigate GHG emissions, yet predictive tools for evaluating their effectiveness remain underdeveloped. The objective of this study was to develop and validate machine learning (ML) models for predicting methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions from silicate-fertilized rice paddies using environmental covariates as input features. Field experiments were conducted over 2-year using three silicate fertilizers with different iron slag contents (i.e., 0.0
Japan is one of the most earthquake-prone countries in the world, where a significant portion of disaster-related losses can be attributed to structural collapses. Thus, the inspection of geo-structures related to these earthquakes is important. Although conventional inspections are conducted by two-dimensional analyses, the actual behaviors of the structures are affected three-dimensionally. This has led to an increase in the demand for three-dimensional analyses. A three-dimensional survey has recently been developed, using an unmanned aerial vehicle (UAV), which has made the rebuilding of the three-dimensional shape of structures easier. Despite the development of this technique, it has not been adopted for the design of geo-structures. In this study, for the purpose of incorporating the three-dimensional survey technique into the design of geo-structures, an earth-fill dam is surveyed three-dimensionally by a drone, and a three-dimensional finite element model (FEM model) of the dam is made based on the survey results and the newly developed modeling approach. The spatial distribution of the ground stiffness is estimated based on screw weight sounding (SWS) test results, and then applied to the 3-D FEM modeling. Gaussian process regression (GPR), which serves as a geostatistical interpolation framework to transform sparse field measurements into continuous three-dimensional material property distributions for the FEM model, is employed for the spatial interpolation of the ground stiffness. Finally, the FEM model is applied to a seismic response analysis, and the locations of high acceleration and high shear stress inside the earth-fill dam are identified.
