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An accurate and spatially explicit large-scale rice calendar can enhance the understanding of agricultural practices and their ecological services, particularly in monsoon Asia. However, currently available global- or continental-scale rice calendars suffer from coarse resolution, poor recording, and outdated information; thus, they do not provide detailed and consistent information on rice phenology. To address this limitation, this study mapped a new (2019–2020) gridded (0.5°×0.5° resolution) rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 satellite images. The novelty of this rice calendar lies in the development of a consistent optimal methodological framework that allows the spatially explicit characterization of the rice transplanting date, harvesting date, and number of rice cropping seasons. The methodological framework incorporates two steps: (1) detection of rice phenological dates and number of rice cropping seasons via the combination of a feature-based algorithm and the fitted Weibull function; (2) spatiotemporal integration of the detected transplanting and harvesting dates derived from Step 1 using von Mises maximum likelihood estimates. Results revealed that the proposed rice calendar can accurately identify the rice phenological dates for three croppings in monsoon Asia. When compared with single rice data from the census-based RiceAtlas calendar, the proposed calendar exhibited better results than the MODIS-based Rice Crop Calendar for Asia (RICA). It exhibited biases of 4 and - 6 d for the transplanting and harvesting dates, respectively, with 10 and 15 d lower MAE values and 6 and 15 d lower RMSE values for the transplanting and harvesting dates, respectively. In total, the proposed rice calendar can detect single-, double-, and triple-rice-cropping seasons with an area of 0.53×106 , 0.45×106 , and 0.09×106 km 2 , respectively. This novel gridded rice calendar fills the gaps in 0.5° rice calendars across major global rice production areas, facilitating research on rice phenology that is relevant to climate change. The developed gridded Monsoon Asia Rice Calendar (MARC) is available at 10.17595/20230728.001 (Zhao and Nishina, 2023). [ABSTRACT FROM AUTHOR]

Reducing synthetic nitrogen fertilizer (SNF) use is critical for mitigating environmental nitrogen (N) pollution caused by excessive reactive nitrogen and lowering the cost of chemical N fertilizer inputs in agricultural management. In response, Japan has set a national target to reduce chemical fertilizer inputs, with a goal of 30% reduction in SNF use. To assess the feasibility of this target, we collected ca. 1700 records from domestic SNF reduction experiments and conducted a meta-analysis to examine the effects of SNF reduction on crop yield. Using a linear plateau response model of yield to SNF application in the experiments, we assumed that the conventional SNF rates used in control plots were sufficient to reach maximum yield. Our meta-analysis indicates that a 30% SNF reduction in SNF is achievable without significant yield loss on average. This reduction can be facilitated through the use of functional fertilizers and replacement of SNF with organic materials. Additionally, we evaluated changes in nitrogen waste from domestic cropland under the new national target using simple scenarios. Our results show that a 30% SNF reduction, without substituting by organic materials, would only decrease N waste by 8.8%. However, our meta-analysis finds that greater reductions in nitrogen input are possible, indicating potential for further optimization of N management, including the use of organic amendments. [Display omitted] • A 30% SNF reduction is achievable without major yield loss, per meta-analysis. • Functional fertilizers and organic inputs can help meet Japan's SNF reduction goal. • Reducing SNF by 30% would reduce nitrogen waste from Japanese cropland by 8.8%. [ABSTRACT FROM AUTHOR]

Abstract Predicting the environmental fate of 137Cs in forest ecosystems along with the concentrations of 137Cs in tree parts are important for the managements of radioactively contaminated forests. In this study, we calibrate the Forest RothC and Cs model (FoRothCs), a forest ecosystem 137Cs dynamics model, using observational data obtained over six years from four forest sites with different levels of 137Cs contamination from Fukushima Prefecture. To this end, we applied an approximate Bayesian computation (ABC) technique based on the observed 137Cs concentrations (Bq kg−1) of five compartments (leaf, branch, stem, litter, and soil) in a Japanese cedar plantation. The environmental decay (increment) constants of the five compartments were used as the summary statistics (i.e., the metric for model performance) to infer the five parameters related to 137Cs transfer processes in FoRothCs. The ABC technique successfully reconciled the model outputs with the observed trends in 137Cs concentrations at all sites during the study period. Furthermore, the estimated parameters are in agreement with the literature values (e.g., the root uptake rates of 137Cs). Our study demonstrates that model calibration with ABC based on the trends in 137Cs concentrations of multi compartments is useful for reducing the prediction uncertainty of 137Cs dynamics in forest ecosystems. Highlights • Forest 137Cs dynamics model "FoRothCs" is calibrated with six-years observations. • Approximate Bayesian computation with observable data improved model perfor-mance. • Trends of 137Cs activities of forest parts can be good metric for model assessment. [ABSTRACT FROM AUTHOR]

This study assesses the potential impacts of future global warming on the carbon budget of terrestrial ecosystems across monsoon Asia using the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) dataset. We used simulation results of two emission pathways (RCP2.6 and RCP8.5), climate projections of five climate models, and seven terrestrial biome models to analyze the changes in net primary production and carbon stocks in the South, Southeast, and East Asian subregions during the period 1981-2099. The simulations indicated that by the end of the 21st century, net primary production would increase by 9-45 % and ecosystem carbon storage would increase by 42-86 Pg C. The clearest climatic impacts were found when using the adaptation-oriented emission scenario (RCP8.5), which assumes a greater CO increase and a larger change in climatic conditions. Substantial disparities in temporal trajectories and spatial patterns were found in the estimated changes, owing to the uncertainties in the emission scenarios, climate projections, and ecosystem models. We attempted to derive consistent patterns throughout the simulations to specify potential hotspots of climatic impacts (e.g., soil carbon change in the southern Tibetan Plateau). Finally, we discuss changes to the climatic characteristics in the study region (e.g., a change in the rainy season), the implications for ecosystem services, and the need for collaborative field monitoring studies. [ABSTRACT FROM AUTHOR]

• A new satellite-based algorithm for detecting rice phenology is proposed. • Feature-based VH, EVI, and NDYI predict rice planting, heading, and harvest dates. • Robustness was confirmed for multiple scales and cropping systems in monsoon Asia. • Algorithm is promising in mapping large-scale high resolution rice phenology map. Knowledge of rice phenology is essential for understanding the agricultural practices and studying its impact on ecosystem services. However, so far, available global-scale rice phenology maps do not provide fine spatiotemporal details of rice phenology in a consistent framework because they rely on the compilation of statistical data. Thus, this paper proposes an algorithm that combines the complementary advantages of Sentinel-1 and Sentinel-2 satellite images to produce large-scale maps that depict rice phenology dynamics. The novelty of this algorithm lies in the correlation with rice phenology features, i.e., rice in water condition and rice color change. The time series of backscattering at Vertical-Horizontal (VH) polarization and Enhanced Vegetation Index (EVI) are proposed to recognize rice planting and heading dates, respectively. For the same time, the Normalized Difference Yellow Index (NDYI) is utilized to detect the rice harvest date for the first time. The proposed algorithm is applied to multiple spatial scales (prefecture, 0.5° gridcell, and site scales) and to multiple rice cropping systems (single, double, and triple croppings) in monsoon Asia. Results reveal that the algorithm is able to accurately detect the rice planting and harvest dates across two rice paddy field distribution maps with moderate-to-high spatial resolution, different validation data, and different rice cropping systems. The bias values of detected planting dates are 2, 0, and 4 days, while that of harvest dates are -2, -5, and -13 days at the prefecture, 0.5° gridcell, and site scales, respectively. These results highlight the potential of this algorithm to generate national, continental, or even global maps of rice phenology dynamics in an efficient manner, which can facilitate research on the impact of rice phenology on rice ecosystem services that echoes environmental and climate change. [ABSTRACT FROM AUTHOR]

Topographic factors affect nitrogen cycling in forest soils, including nitrous oxide (N2O) emissions, which contribute to the greenhouse effect. We measured the N2O flux at 14 chambers placed along a 65-m transect on a slope for 1 year at 2- to 3-week intervals. We applied a hierarchical Bayesian model with a conditional autoregressive (CAR) model to assess the spatiotemporal N2O flux along a slope and quantify the effects of environmental factors on N2O emissions. N2O fluxes at chambers located at lower positions along the slope were relatively greater than those at higher positions. During the non-soil-freezing period, N2O fluxes fluctuated seasonally depending on soil temperature. The soil temperature dependency of N2O fluxes at each chamber increased with descending slope position (the median of the Q10 equivalent simulated from posterior distribution ranged from 1.18 to 3.64). According to the Bayesian hierarchical model, this trend could be partially explained by the C/N ratio at each chamber position. During the soil-freezing period, relatively high N2O fluxes were observed at lower positions along the slope. [ABSTRACT FROM AUTHOR]

Excessive anthropogenic nitrogen (N) inputs to the biosphere have disrupted the global nitrogen cycle. To better quantify the spatial and temporal patterns of anthropogenic N inputs, assess their impacts on the biogeochemical cycles of the planet and the living organisms, and improve nitrogen use efficiency (NUE) for sustainable development, we have developed a comprehensive and synthetic dataset for reconstructing the History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere. The HaNi dataset takes advantage of different data sources in a spatiotemporally consistent way to generate a set of high-resolution gridded N input products from the preindustrial period to the present (1860–2019). The HaNi dataset includes annual rates of synthetic N fertilizer, manure application/deposition, and atmospheric N deposition on cropland, pasture, and rangeland at a spatial resolution of 5 arcmin × 5 arcmin. Specifically, the N inputs are categorized, according to the N forms and land uses, into 10 types: (1) NH4+ -N fertilizer applied to cropland, (2) NO 3- -N fertilizer applied to cropland, (3) NH4+ -N fertilizer applied to pasture, (4) NO 3- -N fertilizer applied to pasture, (5) manure N application on cropland, (6) manure N application on pasture, (7) manure N deposition on pasture, (8) manure N deposition on rangeland, (9) NH x -N deposition, and (10) NO y -N deposition. The total anthropogenic N (TN) inputs to global terrestrial ecosystems increased from 29.05 Tg N yr -1 in the 1860s to 267.23 Tg N yr -1 in the 2010s, with the dominant N source changing from atmospheric N deposition (before the 1900s) to manure N (in the 1910s–2000s) and then to synthetic fertilizer in the 2010s. The proportion of synthetic NH4+ -N in fertilizer input increased from 64 % in the 1960s to 90 % in the 2010s, while synthetic NO 3- -N fertilizer decreased from 36 % in the 1960s to 10 % in the 2010s. Hotspots of TN inputs shifted from Europe and North America to East and South Asia during the 1960s–2010s. Such spatial and temporal dynamics captured by the HaNi dataset are expected to facilitate a comprehensive assessment of the coupled human–Earth system and address a variety of social welfare issues, such as the climate–biosphere feedback, air pollution, water quality, and biodiversity. The data are available at 10.1594/PANGAEA.942069 (Tian et al., 2022). [ABSTRACT FROM AUTHOR]

Rationale: Oxygen isotope ratio measurements of NO2− and NO3− by the azide method and denitrifier method are sensitive to the δ18O value of the sample water. However, the influence of δ18OH2O on those measurements has not been quantitatively evaluated and documented so far. Therefore, we investigated the influence of δ18OH2O of a sample on the δ18O analysis of NO2− and NO3−. Methods: We prepared NO2− and NO3− standards (with known δ18ONO2‐ and δ18ONO3‐ values) dissolved in waters having different δ18OH2O values (δ18OH2O = −12.6, 25.9, 56.7, and 110.1‰). Nitrite and nitrate were converted into N2O using the azide method and the denitrifier method, respectively. The isotope ratios of the generated N2O were measured with a Sercon purge‐and‐trap gas chromatography/isotope ratio mass spectrometry (PT‐GC/IRMS) system. The measured δ18O values of the produced N2O were plotted against known δ18ONO2‐ and δ18ONO3‐ values to evaluate the influence of exchange of an oxygen atom with H2O during the conversion of NO2− into N2O and NO3− into N2O, respectively. Results: The degree of oxygen isotope exchange was 10.8 ± 0.3% in the azide method and 5.5 ± 1.0% in the denitrifier method, indicating that the azide method is more susceptible to artifacts arising from differences in the δ18OH2O value of water than the denitrifier method. Thus, the intercept of the standard calibration curve must be corrected to account for differences in δ18OH2O. Abiotic NO2–H2O equilibrium isotope effect experiments yielded a rate constant of (1.13 ± 007) × 10−2 (h−1) and an equilibrium isotope effect of 11.9 ± 0.1‰ under the condition of pH = 7.5, 30°C, and 2.5% salinity. Conclusions: Oxygen isotope ratio measurements of NO2− by the azide method are highly sensitive to δ18OH2O as a result of significant oxygen isotope exchange between NO2− and H2O. Therefore, to obtain the most accurate measurements water with the same δ18OH2O value as that of the sample must be used to make the NO2− and NO3− standards. [ABSTRACT FROM AUTHOR]

Most of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident is covered by forest. In this paper, we updated model predictions of temporal changes in the 137Cs dynamics using the latest observation data and newly provided maps of the predicted 137Cs activity concentration for wood, which is the most commercially important part of the tree body. Overall, the previous prediction and latest observation data were in very good agreement. However, further validation revealed that the migration from the soil surface organic layer to the mineral soil was overestimated for evergreen needleleaf forests. The new prediction of the 137Cs inventory showed that although the 137Cs distribution within forests differed among forest types in the first 5 years, the difference diminished in the later phase. Besides, the prediction of the wood 137Cs activity concentrations reproduced the different trends of the 137Cs activity concentrations for cedar, oak, and pine trees. Our simulation suggests that the changes of the wood 137Cs activity concentration over time will slow down after 5–10 years. Although the model uncertainty should be considered and monitoring and model updating must continue, the study provides helpful information on the 137Cs dynamics within forest ecosystems and the changes in wood contamination. [ABSTRACT FROM AUTHOR]

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) are major drug targets, and their ligands are currently being explored and developed by many pharmaceutical companies and independent researchers. Class A (rhodopsin-like) GPCRs compose a predominant GPCR family; therefore, class A GPCR ligands are in demand. Growth hormone secretagogue receptor (GHS-R) is a class A GPCR that stimulates food intake by binding to its peptide ligand, ghrelin. Therefore, antagonists of GHS-R are expected to exert antiobesity function. In this article, we describe the use of cDNA display to screen for successfully and identify an antagonistic peptide of GHS-R. The antagonistic peptide inhibited the ghrelin-induced increase in intracellular Ca2+ in vitro (IC50 = approximately 10/

Forests cover approximately 70% of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident in 2011. Following this severe contamination event, radiocaesium (137Cs) is anticipated to circulate within these forest ecosystems for several decades. Since the accident, a number of models have been constructed to evaluate the past and future dynamics of 137Cs in these forests. To explore the performance and uncertainties of these models we conducted a model inter-comparison exercise using Fukushima data. The main scenario addressed an evergreen needleleaf forest (cedar/cypress), which is the most common and commercially important forest type in Japan. We also tested the models with two forest management scenarios (decontamination by removal of soil surface litter and forest regeneration) and, furthermore, a deciduous broadleaf forest (konara oak) scenario as a preliminary modelling study of this type of forest. After appropriate calibration, the models reproduced the observed data reliably and the ranges of calculated trajectories were narrow in the early phase after the fallout. Successful model performances in the early phase were probably attributable to the availability of comprehensive data characterizing radiocaesium partitioning in the early phase. However, the envelope of the calculated model end points enlarged in long-term simulations over 50 years after the fallout. It is essential to continue repetitive verification/validation processes using decadal data for various forest types to improve the models and to update the forecasting capacity of the models. [Display omitted] • The first inter-comparison of forest radiocaesium models using Fukushima data. • The models reproduced the observed data reliably. • The long-term simulations of inventory in tree and soil showed a general agreement. • The long-term simulations of activity concentrations diverged. [ABSTRACT FROM AUTHOR]

• We quantified stocking effectiveness of hybrid domesticated/native salmonids. • We used a Bayesian multi-variate state-space model to evaluate survivability. • Hybrid fish exhibit a 2.5-times higher survival rate than domesticated fish. • One-time crossbreeding can improve stocking effectiveness. Streams depleted of their native salmonid populations are often stocked with domesticated fish for supplementation. However, these domesticated salmonids have a lower adaptability to environmental conditions and thus have a lower survival rate than "native" salmonids. Recent studies suggest the stocking of "semi-native" strains of fish (hybrids between native males and domesticated females) as an alternative to domesticated fish. We evaluated the difference in the apparent survival rates of semi-native and domesticated juvenile char after their stocking in mountain streams. The analysis used multi-site variable time-series data with a Bayesian multi-variate state-space model reflecting the characteristics of the data, including time-series variation, site-specific effects, and observation error. The resulting odds ratio, which represents the general and potential strain effect of the semi-native char stocking, was 2.49 (95% CI = 2.12–2.94), implying that the apparent survival rate of the semi-native char was approximately 2.5 times higher than that of the domesticated char in the mountain streams studied. The results suggest that hybridization might be a viable approach for stocking and harvest augmentation of the commercial and recreational fisheries in fragmented populations. [ABSTRACT FROM AUTHOR]