Search Results (9)

From May 2019 to May 2022, a field experiment was conducted to clarify the effects of paddy–upland rotations on rice yield and greenhouse gas emissions in winter paddy fields. Four types of rotation pattern, rice–oilseed rape, rice–radish, rice–faba bean, and rice–fallow (flooded), were investigated and the N₂O, CH₄, and CO₂ emissions in situ and rice yield were determined. The results showed that the paddy–upland rotation mode required fertilization during the winter cropping season. Compared with the rice–fallow (flooded) mode, the flux rate and annual cumulative emissions of N₂O were significantly higher in the paddy–upland rotation modes. The rice–radish mode had the highest flux rate and annual cumulative emissions of N₂O. When the soil in each paddy–upland rotation mode was exposed to air in winter, the soil redox potential increased and reducing substances were oxidized. Compared with the rice–fallow (flooded) mode, the flux rate and annual cumulative emissions of CH₄ significantly decreased in the paddy–upland rotation modes, with the rice–radish mode producing the lowest flux rate and annual cumulative emissions of CH₄. Fertilization and crop planting were conducted in winter, and the soil moisture was low. Compared with the rice–fallow (flooded) mode, the flux rate of CO₂ of the paddy–upland rotation modes increased significantly. The flux rate of CO₂ in the rice–oilseed rape mode was the highest. Furthermore, the N₂O and CH₄ emissions produced during the rice season and annually were significantly positively correlated with those in the winter season, indicating that the winter season had a significant effect on greenhouse gas emissions from winter paddy fields. Because of the significantly higher annual cumulative emissions of CH₄ and the significantly lower rice yield in the rice–fallow (flooded) mode than in the paddy–upland rotation modes, this mode’s global warming potential (GWP) and greenhouse gas intensity (GHGI) during the rice season are significantly higher than those of the paddy–upland rotation modes. Full article

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country’s economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix. Full article

Rice is a staple food in Asia, and its impact on the environment is considerable, such as chemical input concerns. Organic rice farming represents an alternative approach to reducing environmental concerns throughout rice production. However, the precise nutrient management to optimize organic rice production while recovering soil residual nitrogen (N) for the subsequent crops remains unclear. This study aims to: (1) assess nutrient recovery in soil cultivated with cover crops, including Italian ryegrass and hairy vetch, and (2) investigate the optimization of nutrient management in organic rice farming using cover crops. An experiment was conducted in a paddy field adopting cover crop plots and fallow (FA) plots in four replicates each from 2021 to 2023. In addition, incubation studies were conducted in 2021 and 2022. The incubation study included various treatments: (1) soil from cover crop or FA plots, (2) with or without cover crop residues, (3) with or without weed input (2021). In 2022, fertilizer input replaced weed input. The field study indicated cover crop biomass was larger than that of weeds. Furthermore, it can determine cover crops have more recyclable plant N compared to weeds when incorporated into the soil. In contrast, there was no noticeable difference in soil inorganic N and soil total organic carbon (C) contents between cover crop and FA plots at the 0–90 cm depth. In the incubation study, we found the soil of cover crop plots and cover crop input show less inorganic N than the soil of FA plots and cover crop input during the incubation period. However, the soil of the cover crop plots and cover crop input showed a high inorganic N content after setting the flooded condition. It indicates the soil of cover crop plots, and cover crop input provides N to the soil for a longer period. Overall, our results show that winter cover crop application in paddy fields contributes to N recovery and helps maintain soil fertility. Specifically, the occasional cultivation of a combination of Italian ryegrass and hairy vetch as winter cover crops can contribute to reducing the reliance on chemical fertilizers. This practice also promotes sustainable rice farming in paddy fields. Full article

Habitat fragmentation and degradation in natural wetlands has resulted in declines in the populations of shorebirds in the Indian subcontinent. Shorebirds rely on these wetlands as wintering or stop-over sites along the southern extent of the Central Asian Flyway. Shorebirds are known to utilize agroecosystems as alternate foraging habitats. The suitability of agroecosystems as foraging areas for overwintering migratory shorebirds has not been well studied in the Indian subcontinent. We conducted a comprehensive assessment of published literature and compiled field observations to investigate the importance of inland and coastal agroecosystems for shorebirds in India. We assessed the shorebird populations at natural wetlands: mudflats and mangroves of Kadalundi Vallikkunnu Community Reserve (KVCR) and Puthuvypu sand beach, as well as adjacent agroecosystems on the west coast of India, including Sanketham Wetlands, Manthalakkadavu, Vazhakkad, Elamaram, Kodinhi, and Kooriyad. On the east coast, we assessed the natural wetland habitats of Valinokkam, Point Calimere, and Pichavaram and evaluated inland agroecosystems in regions, such as Jammu and Kashmir, Punjab, Rajasthan, Gujarat, Uttar Pradesh, Tamil Nadu, and Kerala. Fifty-three shorebird species utilize diverse agroecosystems from various parts of India. While studies on the use of agroecosystems by shorebirds are limited on the east coast, evidence suggests that major wintering sites are adjacent to paddy fields, fostering substantial shorebird diversity. In Pichavaram, Point Calimere, and Gulf of Mannar regions, 22 shorebird species utilize agroecosystems, including the notable near-threatened Eurasian Curlew. Seventeen of these species are winter arrivals, highlighting the crucial role agroecosystems play as stopover areas. On the west coast, 19 shorebird species appear to utilize agroecosystems in Kooriyad, Manthalakkadavu, Vazhakkad, Sanketham Wetlands, Elamaram, and Kodinhi. Few species use agricultural fields in the north (Jammu and Kashmir). Paddy fields, that are flooded as part of the cropping cycle, support diverse prey species, such as macroinvertebrates, amphibians, and small fish, that could attract and support migratory shorebirds. Agricultural practices like fallowing, flooding, and ploughing could further increase the abundance and accessibility of prey for shorebirds, drawing them in greater numbers. It is crucial to recognize that unsustainable and unethical agricultural methods could detrimentally affect shorebird numbers. The accumulation of pesticide residues and the contamination from heavy metals could also threaten shorebirds. As a result, there is an urgent need for detailed research to better evaluate the importance of agroecosystems in supporting resident or migratory shorebirds. Systematic studies that explain the population dynamics, habitat selection trends, habitat utilization, and the over-summering behavior of the migratory birds at agroecosystems are needed. Implementing sustainable conservation strategies and adopting environmentally friendly agricultural practices are essential to support the rich biodiversity of the region. Full article

Paddy rice cropping systems play a vital role in food security, water use, gas emission estimates, and grain yield prediction. Due to alterations in the labor structure and the high cost of paddy rice planting, the paddy rice cropping systems (single or double paddy rice) have drastically changed in China in recent years; many double-cropping paddy rice fields have been converted to single-cropping paddy rice or other crops, especially in southern China. Few maps detect single and double paddy rice and cropping intensity for paddy rice (CIPR) in China with a 30 m resolution. The Landsat-based and effective flooding signal-based phenology (EFSP) method, which distinguishes CIPR with the frequency of the effective flooding signal (EF_e), was proposed and tested in China. The cloud/ice/shadow was excluded by bit arithmetic, generating a good observation map, and several non-paddy rice masks were established to improve the classification accuracy. Threshold values for single and double paddy rice were calculated through the mapped data and agricultural census data. Image processing (more than 684,000 scenes) and algorithm implementation were accomplished by a cloud computing approach with the Google Earth Engine (GEE) platform. The resultant maps of paddy rice from 2014 to 2019 were evaluated with data from statistical yearbooks and high-resolution images, with producer (user) accuracy and kappa coefficients ranging from 0.92 to 0.96 (0.76–0.87) and 0.67–0.80, respectively. Additionally, the determination coefficients for mapped and statistical data were higher than 0.88 from 2014 to 2019. Maps derived from EFSP illustrate that the single and double paddy rice systems are mainly concentrated in the Cfa (warm, fully humid, and hot summer, 49% vs. 56%) climate zone in China and show a slightly decreasing trend. The trend of double paddy rice is more pronounced than that of single paddy rice due to the high cost and shortages of rural household labor. However, single paddy rice fields expanded in Dwa (cold, dry winter, and hot summer, 11%) and Dwb (cold, dry winter, and warm summer, 9%) climate zones. The regional cropping intensity for paddy rice coincides with the paddy rice planting area but shows a significant decrease in south China, especially in Hunan Province, from 2014 to 2019. The results demonstrate that EFSP can effectively support the mapping of single and double paddy rice fields and CIPR in China, and the combinations of Landsat 7 and 8 provide enough good observations for EFSP to monitor paddy rice agriculture. Full article

Soil seedbanks are defined in composition and quantity by many environmental factors inherent to a specific area, and they can be an indicator of the potential problems of weeds in crops. In Valencia (Spain), rice is cultivated with continuous flooding during the growing season, and after harvesting, many of the paddy fields are flooded again during the winter. This study investigates the paddy fields’ soil seedbank composition in this Mediterranean paddy area and the effect of winter flooding on the soil seedbank. Multispectral images from the Sentinel-2 satellite were used to characterise the water level of paddies in winter. Satellite images facilitated the characterisation of winter flooding in fields. Soil samples from sixty-nine points distributed over 15,000 ha of paddies were used to determine the composition of the seedbank plots. The data were spatially represented by geographic information systems. The species that contributed most to the paddy seedbank were Cyperus difformis L., an important rice weed in the Mediterranean area, and other rice weeds such as Echinochloa sp. and Leptochloa fusca subspecies. Other species with a great contribution to the seedbank are species that develop in paddy fields that produce a large quantity of small seeds, such as Lemna sp., Polypogon monspeliensis (L.) Desf., and Nasturtium officinale R. Br. These species interfere little or do not interfere with the rice crop. The study revealed that in general, flooding reduced seedbank density with differences between species. Furthermore, the influence of winter flooding on the different plant species obtained as well as their distribution maps are a further step in this protected area from the point of view of weed management in rice crop, as well as in the management of this Mediterranean wetland. Full article

Converting natural ecosystems to cultivated land, driven by human activities, has been considered a significant driver of limiting the delivery of ecosystem services (ES). The ES loss in the past was mainly caused by agricultural activities that have been taken to meet people’s needs in Northeast China. Quantifying historical declining ecosystem service values is essential to facilitate sustainable development. In this study, remote sensing images were used to investigate the history of cultivated land expansion over the last five decades. Additionally, ES variations caused by agricultural expansion since 1965 were quantified in the Small Sanjiang Plain (SSP), Northeast China. From the results, cultivated land expanded from 3.97% of the total SSP area to 66.40% from 1965 to 2015 (approximately 898.23 million ha), of which paddy field expanded drastically from 0% to 55.93%. Variations in cultivated land resulted in a loss of ecosystem service values by 11,893.85 million dollars, of which 62.98 million dollars were caused by the internal conversion between cultivation during 1965–2015. Agricultural expansion accelerated the export of agricultural products function, while it decreased almost all other functions, especially hydrological regulation and freshwater supply function. For future sustainability of the SSP, some suggestions, such as restoring natural ecosystems, planting trees between cultivated land, coculture systems, and winter-flooding of paddy rice were provided in our study. Full article

The Red River Delta (RRD), including 11 provinces, is one of the four largest rice-growing areas in Vietnam. Tropical storms often occur and cause serious flooding from May to October annually in the RRD, which strongly affects the productivity of the summer–autumn rice, one of two main rice crops. Therefore, the rapid assessment of damaged rice area by flooding inundation is critical for farmers and the government. In this study, we proposed a methodology for quick estimation of rice areas damaged by flooding using Sentinel 1A (S1A) imagery. Firstly, the latest rice map was produced. Then, a Near Real-Time (NRT) flood map, which is estimated from S1A images at the closest time to a flooding event, was generated by excluding the yearly permanent map from the temporal water map. Our experiment was conducted for the assessment of damaged rice area by flooding from the tropical storm named Son-Tinh, which happened on 19–21 July 2018. A Support Vector Machine (SVM) classifier was applied on time-series of S1A VV with VH data (VVVH) to obtain a rice map for the winter-spring season of 2018 with 90.5% Overall Accuracy (OA) and 2.37% difference (12,544 ha) from the General Statistics Office (GSO) of Vietnam’s reports for the whole region. Then, the Otsu thresholding method was applied for permanent water surface extraction and NRT flood mapping. The estimated damaged area was compared to available provincial and communal statistics for validation and further analysis. Right after the Son-Tinh storm, the estimation of inundated rice was approximately 50% of the total rice area in the RRD (271,092 ha). As a result, rice damage level strongly corresponds to the inundation period. In addition, the rice-flooding frequency map over the RRD was estimated to show rice fields suffering a high risk of flooding during the rainy season in the RRD. Our experiment’s results highlight the potential of using Synthetic-Aperture Radar (SAR) imagery for fast monitoring and assessment of paddy rice areas affected by flooding at a large scale in the RRD region. Full article

Dynamic monitoring of the spatial pattern of winter continuously flooded paddies (WFP) at regional scales is a challenging but highly necessary process in analyzing trace greenhouse gas emissions, water resource management, and food security. The present study was carried out to demonstrate the feasibility of extracting the spatial distribution of WFP through time series imagery of volumetric surface soil moisture content (θ_v) at the field scale (30 m). A trade-off approach based on the synergistic use of tasseled cap transformation wetness and temperature vegetation dryness index was utilized to obtain paddy θ_v. The results showed that the modeled θ_v was in good agreement with in situ measurements. The overall correlation coefficient (R) was 0.78, with root-mean-square ranging from 1.96% to 9.96% in terms of different vegetation cover and surface water status. The lowest error of θ_v estimates was found to be restricted at the flooded paddy surface with moderate or high fractional vegetation cover. The flooded paddy was then successfully identified using the θ_v image with saturated moisture content thresholding, with an overall accuracy of 83.33%. This indicated that the derived geospatial dataset of WFP could be reliably applied to fill gaps in census statistics. Full article