Paddy and Water Environment最新文献

The most accurate method for rice fields mapping involves a phenological approach using optical remote sensing and a multisource data integration approach. However, these approaches do not consider the two rice growing periods in tropical regions, which are the rainy and dry seasons. During the rainy season, the optical remote sensing data are affected by clouds and haze. On the other hand, during the dry season, rainfed rice fields are not planted with rice. Therefore, this study proposed a new scheme for rice fields classification in the tropical regions using data fusion between different seasonal periods. Three data fusion scenarios based on reflectance fusion, temporal feature fusion, and information fusion from remote sensing data during the rainy and dry seasons were analyzed. The results showed that the accuracy of rice fields classification increased by using the proposed scheme, rather than a single period. The best fusion scenario was the information fusion strategy with the highest increase in precision accuracy, from 92.72% in reflectance fusion and 93.17% in temporal feature fusion to 94.99%. This strategy distinguished the rice fields from the fish pond and other seasonal crops such as sugar plantations.

In Eastern India, a widespread practice known as “rice fallow pulse” (RFP) involves using the soil’s remaining moisture to grow a short-duration pulse crop. For rainfed systems, it is an excellent practice of climate adaptation. To help farmers make informed decisions about where to plant what and to help policymakers create favorable conditions for timely seed distribution, it is imperative to forecast the appropriateness of pulse crops both geographically and temporally. Using fuzzy AHP (FAHP)-based machine learning methods, we tried to detect pulse appropriateness both geographically and temporally while considering fifteen natural, climatic, environment, and soil health-related characteristics in the Western Lateritic Zone of the Indian State of West Bengal. According to the findings, all machine learning (ML) techniques identified high-suitability zones in the districts of Murshidabad, Birbhum, Paschim Bardhaman, Paschim Medinipur, and Jhargram. By using machine learning techniques such as shrinkage discriminant analysis (SDA), neural network (nnet), random forest (RF), Naive Bayes (NB), rule-based C5.0, genetic algorithm (GA), and particle swarm optimization (PSO), it was found that moderate suitability zones were visible in some areas of Murshidabad, Birbhum, Paschim Bardhaman, Paschim Medinipur, and Purulia. Additionally, it was noted that all ML approaches revealed maximum low suitability zones in certain areas of Birbhum, Bankura, Purba Bardhaman, Purulia, and Murshidabad. Finally, district-level yearly pulse yields of minor, chickpea, and pigeonpea verified the precision of the ML-based models. We have devised a structure to assess pulse suitability analysis to improve crop and land productivity. One of the world’s most populous regions can use the data to inform policy decisions that will improve food and nutritional security in the face of shifting economic and environmental conditions.

Drought is a natural phenomenon which is considered as an indicator of changing climatic conditions. The growth of crops is significantly affected by the lack of soil moisture caused by insufficient rainfall over a specific period. This study examines the occurrence of drought over seven districts in Kerala, India, by utilizing drought indices, namely the standardized precipitation index (SPI) and the agricultural standardized precipitation index (aSPI). The measured data pertaining to rainfall and computed data of crop yield of the seven districts have been gathered to analyze the teleconnections of crop yield. Modified standardized yield residual series (M-SYRS) of different crops are prepared by the proposed approach of empirical mode decomposition-based detrending. The correlation between aSPI and M-SYRS exhibits a higher magnitude compared to the correlation that SPI and M-SYRS, confirming the significance of aSPI in the analysis of agricultural yield. The wavelet coherence analysis yields the values of percentage of significant coherence (PoSC) and average wavelet coherence (AWC) for the time scales of 3, 6, and 12 months, with respect to the variables aSPI and crop yield. The crop with the greatest AWC value of 0.71 and PoSC value of 62 is banana, which holds a dominant position in the agricultural landscape of Kottayam district. It is further noted that the short to medium seasonal droughts have profound impact on the agricultural yield of the different districts.

Abstract

In the agricultural field, concrete headworks is the most important structure for the irrigation system. In recent years, a number of agricultural concrete infrastructures aging for a long-term period have been increasing. For maintenance and management, conventional inspection methods are time-consuming and costly, such as the electromagnetic wave method and elastic wave method. The detection of surface damage is more effective, safe and reliable than before since the laser scanning method provides detailed geometric information about the structure. The fundamental studies on point cloud data have been conducted in the civil engineering fields; nevertheless, the characteristics of point cloud in agricultural infrastructures, such as dam, headworks and canal, have not been discussed. In this study, 3D point clouds are generated for a concrete irrigation structure using the laser scanning method. The characteristics of surface damage which are quantitatively evaluated using point cloud information, geometric information and intensity parameter are investigated. The types of detected damage are efflorescence and cracks. It is investigated whether point clouds generated from a single scan or multiple scans are more effective for highly accurate detection. The characteristics of surface damage are evaluated by geometric features. The distance between the fitted plane and points is calculated by RANSAC algorithm and roughness parameter. The amount of efflorescence is detected by the distance between the fitted plane from RANSAC algorithm and points. The crack is detected by the local plane fitting method. The types of damage are characterized by the intensity parameter which is related to the color, roughness and moisture of the object. The surface damage and condition are evaluated by both geometric information and intensity parameter. These results show the unique parameters of point clouds from laser scanning methods, such as geometric features and intensity parameter, are useful to evaluate the characteristics of surface damage.

High-resolution topographic data are crucial for delta water management, such as hydrological modeling, inland flood routing, etc. Nevertheless, the availability of high-resolution topographic data is often lacking, particularly in low-lying regions in developing countries. This data scarcity poses a significant obstacle to inland flood modeling. However, collecting detailed topographic data is demanding, time-consuming, and costly, making remote sensing techniques a promising solution for developing flood inundation analysis models worldwide. This study presents a novel understanding for utilizing topographical elevations obtained using remote sensing techniques to create a flood inundation analysis model. In a study of three watersheds, Kameda, Niitsu, and Shirone (Japan), the assessment of digital terrain models (DTMs) showed that remote sensing-based DTMs (RS-DTMs) exhibited high reliability of coefficient of determination (R²) and root-mean-square errors, compared with the airborne LiDAR-based topography from the Geospatial Information Authority of Japan. Comparing the flood modeling results from LiDAR data and RS-DTM, with Kameda and Niitsu performing favorable outcomes, Shirone exhibited less accurate results. We hypothesized that this was caused by the topographic distortions due to lack of evenly distributed reference points. Hence, we revised the topography by adjusting both the slope and intercept from the regression equation. This verification successfully showed that the flood inundation volume correlation improved, achieving R² results for the three watersheds ranging from 0.975 to 0.997 and Nash–Sutcliffe Efficiencies ranging from 0.938 to 0.986 between the resulting flood models based on the LiDAR data and RS-DTM. Based on these findings, we recognized the significance of uniformly distributed geodetic height points. In areas lacking height references, high-precision survey instruments can be employed for achieving uniform distribution.

The objective of this study was to investigate impacts of climate disturbance and watershed management operations on hydrologic functioning of the Kordan watershed in Iran. Soil and water assessment tool was used and evaluated for an 18-year monthly stream discharge. Statistical and graphical analysis of the calibrated and validated model presented appropriate fit to the measured data (NSE and R² were more than 0.80), allowing reproduction of the historic hydrological conditions of the watershed for future analysis. A 12-month timeframe was achieved from correlation between standardized precipitation index and standardized runoff index to quantify drought characteristics and define drought disturbance scenarios. Replacing normal periods in terms of precipitation amount with extreme and severe periods up to 40% resulted in declining monthly flow out by 24% and 21%, respectively. Pasture restoring scenarios resulted in a significant decline in amount of surface runoff. Replacing 50% of poor rangeland areas with moderate rangeland caused monthly amount runoff to decline by 16%. The percentage of runoff decline raised up to 44% by replacing moderate rangeland thoroughly. Furthermore, land-use scenarios increased amount of subsurface flow considerably. Impacts of constructing hydraulic structures were assessed in each sub-basin separately. Results showed decline in yearly surface runoff varied from 50 to 97%, depending on the volume of structures and sub-basin characteristics. The findings would be beneficial to decision makers which contribute to better understanding of natural and man-made activities on hydro-power potential of watersheds.

Aerobic rice cultivation has been proposed as a water-saving option. Regional assessments are necessary to quantify its importance as such an option because aerobic rice exhibits varying effects on crop yield and irrigation water, depending on location, management, and cultivar. Currently, there is a lack of such regional assessments. In this study, we evaluated the potential of aerobic-direct-seeded rice cultivation as an alternative to the traditional flooded-transplanting system (FTS) in Golestan province, Iran. Using a bottom-up approach, rice production zones and buffers were identified, and the SSM-iCrop2 model was employed to simulate crop growth and water use for FTS and two aerobic systems in the entire province. The results revealed significant reductions in irrigation water volume for the aerobic systems, ranging from 22 to 50% compared to FTS. However, there was a trade-off in terms of crop yield, with reductions ranging from 9 to 31% in the aerobic systems. The variation was due to genotype × environment × management interactions on the performance of aerobic cultivation and emphasized the value of crop models in assessing and understanding these interactions. However, at the regional scale (Golestan province), it was found that transitioning from FTS to aerobic systems can effectively mitigate water over-withdrawal in the region, potentially saving 272–362 million m³ of water annually. This amount represents 70–90% of the current goal of reducing water withdrawal in the province. This study provides valuable insights into the water-saving potential of aerobic rice cultivation, with implications for sustainable water resource management in rice-producing regions of Iran.

Sustainable futures can be achieved by limiting non-renewable resource consumption and minimizing waste and associated emissions. Sustainable modified geofoam (MGF) blocks made of sustainable materials contribute to sustainability goals from environmental, societal, and economic perspectives. This study aims to develop MGF blocks prepared by blending cement and rice husk ash (RHA) as a binding material, water, and recycled expanded polystyrene beads. RHA is a silicon-rich agro-waste ash that is used to partially replace up to 30% of cement. MGF blocks were prepared by mixing beads at percentages of 0.50, 0.75, 1.00, 1.25, and 1.5% by the dry weight of the binding material at different water/binding material ratios. The MGF blocks were cured for 7 days, 28 days, and 56 days. This study compares the environmental impacts, energy consumption, and cost analysis of the production of traditional geofoam (TGF) and MGF blocks. MGF can reduce environmental impacts by about 80–95% compared to TGF. The MGF was found to be an eco-friendly, energy-efficient, and cost-effective material.

Abstract

The Taiwanese government introduced specialized rice production and marketing zones (SRPMZs) in 2005 as part of the structural adjustment of the rice industry. This study shows the effect of SRPMZ on leased farmland and custom farming and clarifies the role of the policy of supporting group farming operations in achieving farmland consolidation. This study uses the difference-in-differences method with and without covariates to estimate the effects of SRPMZ policy in Taiwan. This study analyzes the effect of the SRPMZ designation on the areas of leased and custom farming farmland at the village level. This study used village-level data from Taiwan’s Censuses of Agriculture from 2000 to 2015. We find that SRPMZ designation increases the area of leased farmland per village by 13.5 ha and decreases the area with custom farming by 4.86 ha when we apply difference-in-differences methods with time-varying covariates. This is the effect of farmers choosing to lease their farmland through SRPMZ instead of custom farming. While several studies discuss the SRPMZ policy, focusing on farmer productivity or SRPMZ operator efficiency, few studies have analyzed the impact of the SRPMZ policy on farmland use. The results indicate that the policy of supporting group farming operations, coupled with farmland leasing, can promote farmland consolidation and improve the agricultural structure.

Joint cracks are a type of characteristic crack that occurs in the side walls of cast-in-place reinforced concrete open channels. In this study, thermal stress analysis was performed to analyze the cause and timing of joint cracks in the side walls of such channels. The distribution of maximum principal stress indicated that previously placed spans restrain subsequently placed spans, increasing the risk of generating joint cracks. This suggests that the waterstops buried in the previously placed spans restrain the deformation of the subsequently placed spans due to the difference in strength caused by the material ages. In addition, the risk of generating joint cracks increased with the increase in the casting intervals. The maximum principal stresses exceeded the tensile strength of the concrete around the joint sections for both summer and winter construction. Thus, joint cracks may occur at both early and long-term material ages, regardless of construction season. To obtain a more realistic thermal stress analysis, outside temperature models capturing the features of temperature data in Japan were proposed, and these models gave appropriate analysis results.