Paddy and Water Environment最新文献

In the North Nile Delta, over-planting paddy fields by upstream farmers results in low canal water levels downstream during the daytime, and downstream farmers frequently cannot pump water. Consequently, downstream farmers' night irrigation has become more frequent. This study investigated daytime and nighttime irrigation practices and how they differed between upstream, midstream, and downstream tertiary canals along a selected branch canal in the Northern Nile Delta of Egypt. A scenario was developed to determine the area that could be irrigated during the daytime by comparison of actual (varied pumps’ discharge capacities) and controlled pumps’ discharge capacities (216 m³ h⁻¹ and 324 m³ h⁻¹) between all locations. Canal water levels and pump operation hours were monitored in 2013 and 2014. The period from 06:00 PM to 06:00 AM EST (Egypt Standard Time) is considered nighttime and others are daytime. Results showed that downstream received more than 30% of the total water supply during the nighttime, while upstream received 13%. Further, some upstream farmers received 6% at night during the peak water demand in July. Consequently, the nighttime irrigation days upstream were less than downstream by 11 and 20 days each year, respectively. The numbers of irrigation events downstream from 12:00 AM to 03:00 AM EST were more than upstream by eight and seven events each year, respectively. Under the conditions of controlled pumps’ discharge capacities, downstream farmers could irrigate 41% and 46% of the total irrigated area during the daytime rather than 28% and 32% under the actual conditions each year, respectively. Eventually, controlling pumps’ discharge capacities between locations could achieve equity of water distributions in terms of time and water volume during the daytime.

The excessive and imbalanced use of fertilisers in agriculture has opposite effects on human health and the environment. Punjab has the highest fertiliser consumption due to the intensive cropping system, which results in the degradation of soil and water quality. In the present study, fertiliser-induced soil contamination and nutrient imbalance, as well as the fertiliser unsuitability index, were studied in paddy-growing areas, i.e. the Bathinda and Ludhiana districts of Punjab. A total of 160 farmers in both districts were interviewed regarding fertiliser application, and soil samples from these farmers were tested. The soils of Bathinda are alkaline in reaction and neutral in reaction at Ludhiana. The labour scarcity, unavailability of improved seed materials and increased cost of inputs are the major problems for farmers at both locations. The study concluded that both districts of Punjab had relatively high soil contamination levels and nutrient imbalance values. The soil contamination level was greater in the Ludhiana district, and the nutrient imbalance was greater in Bathinda. The fertiliser unsuitability index varied from 127.7 to 164.9 in the paddy-growing soils of Punjab, with the highest value occurring in Bathinda. Hence, the judicious use of fertilisers on a soil test basis is the only solution for managing fertiliser-induced soil contamination and nutrient imbalance in paddy-growing soils in Punjab. Future research needs directions to develop this knowledge and suggest location-specific fertiliser usage patterns to minimise nutrient imbalances.

Rice, being an important global food source, is susceptible to diseases during its growth, resulting in a negative impact on its yield. Existing models for rice disease detection have limitations in recognizing small-sized and irregularly shaped disease types. To address this issue, we propose a new model called YOLOv8_Rice, specifically designed for rice leaf disease detection based on the YOLOv8n object detection model. Firstly, we conducted experimental research to investigate the influence of various common attention mechanisms on the performance of YOLOv8. The aim was to optimize the model’s ability to extract features from different types of targets. Secondly, we enhanced the model’s adaptability to target deformation and spatial changes by incorporating deformable convolutions to improve the C2f module structure in the YOLOv8 model. Furthermore, we replaced the network structure of YOLOv8 with a weighted bidirectional feature pyramid network to achieve weighted feature fusion, aiming to improve model performance and reduce computational complexity. Finally, we replaced the IOU loss function design in the YOLOv8 model with Wise IOU to provide more accurate evaluation results. In comparison to YOLOv8n, our YOLOv8_Rice model achieved an average precision increase of 15.8% and an mAP@0.5 improvement of 18.7% while reducing GFLOPs by 24.7% during testing on the rice disease dataset. These results indicate that YOLOv8_Rice has significant potential for global rice disease detection applications.

To preserve the health of aquatic ecosystems, it is important to analyze the weighted usable area (WUA) for fish species in response to flow variations and determine the suitable environmental flow ranges for their habitats. In this study, we focused on the Hwang River watershed, a part of the Nakdong River Basin known to have bad aquatic ecosystem health in Korea. The optimal and minimum environmental flows of the river were determined based on the WUA. The physical habitat simulation system (PHABSIM) model was used to estimate the environmental flows, and the Hydrological Engineering Centre-River Analysis System (HEC-RAS) program was used to obtain the input data. For the simulation, we considered the fish species that were common in the Hwang River watershed. We established flow boundary conditions for PHABSIM by analyzing flow duration by year from 2010 to 2019. Afterward, we calculated WUA that changes depending on flow using the habitat suitability indices of Pseudogobio esocinus (P. esocinus), Microphysogobio yaluensis (M. yaluensis), and Zacco platypus (Z. platypus) living in the Hwang River. The flow with the maximum WUA was calculated as the optimal flow, and the area value corresponding to the 4th quartile from the maximum WUA was calculated as the minimum flow. The optimal environmental flow ranges were 3.9–13.9 m³/s for P. esocinus, 3.2–10.4 m³/s for M. yaluensis, and 5.5–18.8 m³/s for Z. platypus. An analysis of 10 years of flow data revealed that the WUA was highest and lowest during winter and summer, respectively. If a separate nature-friendly habitat environment can be adjusted for periods of low WUA, efficient water resource management and habitat conservation will be possible. Our study provides the data essential for the water resource management of the Hwang River watershed, and the proposed method can be used to maintain the health of aquatic ecosystems.

Although numerous studies have investigated the validity of satellite-derived precipitation datasets, there has been a lack of emphasis on their practical applications. This study aims to explore the implications of such datasets in designing rain gauge networks, which are essential for acquiring reliable precipitation data. Initially, four satellite-derived precipitation datasets (PERSIANN, PERSIANN-CDR, PERSIANN-CCS, and TRMM 3B43 V.7) were statistically compared to ground-based observations from 23 synoptic stations within the Fars province in southwestern Iran, the designated study area, to assess their validity. Furthermore, to provide a technical comparison, the degree of spatial independence (variogram) derived from these datasets was compared to that obtained from ground-based observations. To meet the study's objectives, a detrending process was implemented to render the datasets isotropic and bounded. Among the aforementioned satellite-derived datasets, PERSIANN-CCS and TRMM 3B43 V.7 demonstrated promise for enhancement to be utilized in rain gauge network design through a hybrid method combining multivariate analysis incorporating factor analysis and a geostatistical approach incorporating ordinary (point and block) kriging. Based on the PERSIANN-CCS and TRMM 3B43 V.7 satellite-derived datasets, rain gauge grids containing 70 and 56 rain gauges were initially proposed using a scree diagram. However, after considering a predetermined level of accuracy (block variance of residuals set to 10 ({mm}^{2})), the numbers were subsequently reduced to 56 and 28 rain gauges, respectively. Consequently, this research sheds light on the practical utility of satellite-based precipitation datasets in the development of rain gauge networks in regions with insufficient data coverage or for evaluating existing networks.

Soil organic matter (SOM) content serves as a crucial indicator for assessing soil fertility and quality, making accurate and efficient prediction methods paramount. The application of visible near-infrared reflectance (vis–NIR) spectroscopy has been pivotal in predicting SOM content. However, utilizing soil profile data obtained during soil sample collection can provide additional insights into organic matter, suggesting that their separate use may not be optimal. This study aimed to investigate whether the fusion of vis–NIR and soil profile properties could enhance the performance of the extreme gradient boosting (XGBoost) algorithm in predicting SOM content. The sample set was sourced from paddy soils in Changsha and Zhuzhou, China. Three different modeling approaches (XGBoost constructed by LASSO feature of vis–NIR spectroscopy (LF-XGBoost), profile feature (PF-XGBoost), and fusion feature (FF-XGBoost)) were compared and evaluated using randomly split sample sets, fivefold cross-validation (fivefold CV), coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Compared to LF-XGBoost and PF-XGBoost, the FF-XGBoost model demonstrated superior prediction capabilities for SOM content, indicating that the fusion feature improved SOM content prediction. In randomly segmented datasets, FF-XGBoost achieved an R² of 0.897, RMSE of 3.746, and MAE of 2.935, with R² improvements of 31 and 24%, respectively. In fivefold CV, FF-XGBoost achieved an R²_CV of 0.806, RMSE_CV of 5.136, and MAE_CV of 1.913, with R²_CV improvements of 11 and 51%, respectively. According to Shapley additive explanations model, variations in ‘Color_class’, ‘Profile_level’, and wavelength ‘767’ within the fusion feature had the most significant impact on FF-XGBoost’s output. Compared to other commonly used regression algorithms, FF-XGBoost demonstrated higher prediction accuracy. This study only focused on paddy soils in Changsha and Zhuzhou and employed well-established modeling methods. These results can serve as a catalyst for further research into new feature fusion techniques, advanced modeling methods, and the transferability of findings to other soil landscapes.

Due to the growth of population and industrial advancements in Iran, especially Birjand, the use of groundwater makes the aquifer’s balance becomes negative. This negatively affects both quantity and quality conditions of groundwater. To prevent this, suitable water management is necessary for Birjand aquifer. In this study, to overcome negative groundwater balance, the concept of “adjustment factors” is presented. These factors are applied to all types of consumption and make the groundwater balance to be positive. For this aim, a dynamic model of groundwater resources in Birjand aquifer is created in Vensim software. This model helps to determine the groundwater balance. Then, with using dynamic model and particle filter approach in MATLAB software, minimum adjustment factors are achieved. All data and information of Birjand aquifer between 2004 and 2021 are entered into Vensim model; then, under three scenarios, the groundwater balance is computed for the next 5 years (2022–2026). Three scenarios are normal, dry, and wet conditions. In the next step, with the help of particle filter, the minimum adjustment factors for two types of consumptions including agricultural and industrial are computed. The results show that the adjustment factors for all consumptions in dry conditions are much higher than others. For instance, in 2026, the adjustment factor for agriculture in dry conditions is 0.081 while in normal and wet conditions is 0.75 and 0.031, respectively. Also, the findings indicate that applying these adjustment factors to groundwater model has successful results and make the groundwater balance to be positive.

The study presents a bibliometric analysis of scientific literature on alternate wetting and drying (AWD) as a water-saving irrigation practice for rice cultivation. Data were collected from the Web of Science, resulting in a database of 439 articles written by 2574 authors in 167 journals. The study reveals the growing importance of AWD in publications from the 90s to mid-2022, with fast growth and reaching its peak in the last 5 years, suggesting that the full potential of AWD remains yet to be realised and explored. Most papers are produced in oriental countries, except the USA and Australia. The trend of keywords in the research on AWD reveals a persistence of certain themes throughout the years while simultaneously showcasing a clear evolution of the topics being addressed. In addition to optimising productivity and agricultural yields, research now encompasses environmental issues and human and crop health, reflecting a broader trend in agriculture and research towards sustainable and environmentally responsible practices. This analysis provides insights into the development and direction of research in AWD, emphasising the need for future research to address the emerging concerns of the impact of AWD on the environment, human and crop health, and economic profitability of AWD adoption.

The global efforts on reducing methane (CH₄) emissions was emphasized in COP 28 and the potential for improved estimation became feasible through bottom-up data acquisition with advanced remote sensing technology. The objectives of this study were to extract summer rice and winter crop cultivation areas based on satellite images and to incorporate into estimating CH₄ emissions in South Korea for the year 2020. Satellite images of Sentinel-1 and Sentinel-2 were acquired from European Space Agency. Rice paddy was classified with backscattering coefficient from Sentinel-1 images, while the normalized difference vegetation index from Sentinel-2 images was used to identify winter cropping field. The equation of IPCC guidelines was used to estimate CH₄ emissions by incorporating the areas of rice paddy and winter crop extracted with the respective satellite image. National farming statistics were used to determine the scaling factors for paddy organic matter and water management practices. The estimated areas for rice paddy and winter crop cultivation were 712,237 ha and 117,840 ha, respectively. The rice paddy areas were primarily concentrated in the western regions of the Korean peninsula, whereas winter crop cultivation was predominantly found in southern part of the country. The total amount of CH₄ emissions was 6272 Gg CO₂ eq./yr when considering rice straw and winter cropping practices into estimation (modified Tier 2 method). This represents a 7% increase compared to the method that considered solely the rice straw incorporation (current Tier 2 method). The CH₄ emissions per unit area were also 8.82 tons CO₂ eq./ha/yr with the modified Tier 2 method, indicating a 10% greater compared to the current Tier 2 method. Substantial CH₄ emissions were primarily concentrated in western regions where extensive rice paddy cultivation occurs, while greater CH₄ emissions per unit area were predominantly found in southern regions with substantial winter crop cultivation. The study findings hold importance for improving the accuracy of CH₄ emissions estimation by employing bottom-up approach that utilizes satellite imagery to assess rice paddy and winter cropping areas. Further study would be needed to incorporate field-based data on rice crop management practices, such as rice straw and water management, to further refine CH₄ emission estimation method.