Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized ...by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.
What is microbial dormancy? McDonald, Mark D.; Owusu-Ansah, Carlos; Ellenbogen, Jared B. ...
Trends in microbiology (Regular ed.),
February 2024, 2024-02-00, 20240201, Letnik:
32, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The ability of microorganisms to resuscitate from dormancy has major implications for ecosystem function.By conceptualizing the space of possible definitions of dormancy as a multidimensional trait ...space, we construct a pluralistic concept of dormancy that accommodates and allows intercomparison between distinct definitions of dormancy from a variety of disciplines.Our framework for describing and comparing definitions of dormancy facilitates cross-communication between subfields of microbiology.Our framework can be applied to extreme cases, such as permafrost, where it allows for discriminating between microorganisms that are adaptively dormant, and those that are simply stressed.
Life can be stressful. One way to deal with stress is to simply wait it out. Microbes do this by entering a state of reduced activity and increased resistance commonly called ‘dormancy’. But what is dormancy? Different scientific disciplines emphasize distinct traits and phenotypic ranges in defining dormancy for their microbial species and system-specific questions of interest. Here, we propose a unified definition of microbial dormancy, using a broad framework to place earlier discipline-specific definitions in a new context. We then discuss how this new definition and framework may improve our ability to investigate dormancy using multi-omics tools. Finally, we leverage our framework to discuss the diversity of genomic mechanisms for dormancy in an extreme environment that challenges easy definitions – the permafrost.
Life can be stressful. One way to deal with stress is to simply wait it out. Microbes do this by entering a state of reduced activity and increased resistance commonly called ‘dormancy’. But what is dormancy? Different scientific disciplines emphasize distinct traits and phenotypic ranges in defining dormancy for their microbial species and system-specific questions of interest. Here, we propose a unified definition of microbial dormancy, using a broad framework to place earlier discipline-specific definitions in a new context. We then discuss how this new definition and framework may improve our ability to investigate dormancy using multi-omics tools. Finally, we leverage our framework to discuss the diversity of genomic mechanisms for dormancy in an extreme environment that challenges easy definitions – the permafrost.
•This paper reviewed the changes made in China-DNDC, a new version of DNDC.•The scientific basis and the new features of China-DNDC model were described in detail.•How this model was tested and ...applied to serve the sustainability of Chinese agriculture was reported.•The weaknesses and potential improvements for the model were discussed.
During the past century Chinese agriculture has been struggling to produce more food to support the ever growing population, while dealing with the increased degradation of air, water and soil quality in the agricultural regions. Lessons learnt from the long-term efforts indicated that scientifically sound methodology could play a crucial role in understanding the complex interactions among climate, soil, water and farming management practices, all of which collectively control the agroecosystem services including crop yield, greenhouse gas emissions, nutrient loading and other environmental issues. Therefore, the development of a more process-based approach is needed. The process-based model, DNDC (Denitrification-Decomposition) has been widely used internationally to simulate detailed carbon and nitrogen biogeochemical cycles occurring in agricultural systems. However, this model is not fully suitable for China as it lacks a number of features which are crucial for representing Chinese agro-ecosystems, including paddy rice cultivation, complex and multiple cropping systems, and intensive management practices, etc. Recently a new version of DNDC, China-DNDC, was developed. The new model improved its capability of predicting the fluxes of all three terrestrial greenhouse gases: nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4), as well as other important indicators such as crop phenology, soil C sequestration, and nutrient leaching from different cropping systems across all the major agricultural regions in China. This paper reported how China-DNDC was developed, tested and applied for sustaining Chinese agriculture. And then, it identified the weaknesses and potential improvements for the model.
Climate change threatens to release abundant carbon that is sequestered at high latitudes, but the constraints on microbial metabolisms that mediate the release of methane and carbon dioxide are ...poorly understood
. The role of viruses, which are known to affect microbial dynamics, metabolism and biogeochemistry in the oceans
, remains largely unexplored in soil. Here, we aimed to investigate how viruses influence microbial ecology and carbon metabolism in peatland soils along a permafrost thaw gradient in Sweden. We recovered 1,907 viral populations (genomes and large genome fragments) from 197 bulk soil and size-fractionated metagenomes, 58% of which were detected in metatranscriptomes and presumed to be active. In silico predictions linked 35% of the viruses to microbial host populations, highlighting likely viral predators of key carbon-cycling microorganisms, including methanogens and methanotrophs. Lineage-specific virus/host ratios varied, suggesting that viral infection dynamics may differentially impact microbial responses to a changing climate. Virus-encoded glycoside hydrolases, including an endomannanase with confirmed functional activity, indicated that viruses influence complex carbon degradation and that viral abundances were significant predictors of methane dynamics. These findings suggest that viruses may impact ecosystem function in climate-critical, terrestrial habitats and identify multiple potential viral contributions to soil carbon cycling.
Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we ...characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.7 Pg C y(-1) over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.2 Pg C y(-1), and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.004 Pg C y(-1). Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink.
The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of ...canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.
As the Earth's population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is ...becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA's (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.
Peatlands occupy a relatively small fraction of the Earth's land area, but they are a globally important carbon store because of their high carbon density. Undisturbed peatlands are currently a weak ...carbon sink (~0.1 Pg C y−1), a moderate source of methane (CH4; ~0.03 Pg CH4y−1), and a very weak source of nitrous oxide (N2O; ~0.00002 Pg N2O–N y−1). Anthropogenic disturbance, primarily agriculture and forestry drainage (10%–20% of global peatlands), results in net CO2emissions, reduced CH4emissions, and increased N2O emissions. This likely changes the global peatland greenhouse gas balance to a C source (~0.1 Pg C y−1), a 10% smaller CH4source, and a larger (but still small) N2O source (~0.0004 Pg N2O–N y−1). There is no strong evidence that peatlands significantly contributed to 20th century changes in the atmospheric burden of CO2, CH4, or N2O; will this picture change in the 21st century? A review of experimental and observational studies of peatland dynamics indicates that the main global change impacts on peatlands that may have significant climate impacts are (1) drainage, especially in the tropics; (2) widespread permafrost thaw; and (3) increased fire intensity and frequency as a result of drier climatic conditions and (or) drainage. Quantitative estimates of global change impacts are limited by the sparse field data (particularly in the tropics), the large variability present in existing data, uncertainties in the future trajectory of peatland use, interactive effects of individual impacts, and the unprecedented rates of climate change expected in the 21st century.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Unsustainable water use challenges the capacity of water resources to ensure food security and continued growth of the economy. Adaptation policies targeting future water security can easily overlook ...its interaction with other sustainability metrics and unanticipated local responses to the larger-scale policy interventions. Using a global partial equilibrium grid-resolving model SIMPLE-G, and coupling it with the global Water Balance Model, we simulate the consequences of reducing unsustainable irrigation for food security, land use change, and terrestrial carbon. A variety of future (2050) scenarios are considered that interact irrigation productivity with two policy interventions- inter-basin water transfers and international commodity market integration. We find that pursuing sustainable irrigation may erode other development and environmental goals due to higher food prices and cropland expansion. This results in over 800 000 more undernourished people and 0.87 GtC additional emissions. Faster total factor productivity growth in irrigated sectors will encourage more aggressive irrigation water use in the basins where irrigation vulnerability is expected to be reduced by inter-basin water transfer. By allowing for a systematic comparison of these alternative adaptations to future irrigation vulnerability, the global gridded modeling approach offers unique insights into the multiscale nature of the water scarcity challenge.
Agricultural water use accounts for around 70% of the total water that is withdrawn from surface water and groundwater. We use a new, gridded, global‐scale water balance model to estimate interannual ...variability in global irrigation water demand arising from climate data sets and uncertainties arising from agricultural and climate data sets. We used contemporary maps of irrigation and crop distribution, and so do not account for variability or trends in irrigation area or cropping. We used two different global maps of irrigation and two different reconstructions of daily weather 1963–2002. Simulated global irrigation water demand varied by ∼30%, depending on irrigation map or weather data. The combined effect of irrigation map and weather data generated a global irrigation water use range of 2200 to 3800 km3 a−1. Weather driven variability in global irrigation was generally less than ±300 km3 a−1, globally (<∼10%), but could be as large as ±70% at the national scale.
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