Search

Rice (Oryza sativa L.) residues (root and straw) are unique substrates for microbes in paddy soils. Microbial respiration (RH) of paddy soils is likely to be affected by substrate quality. However, no relevant study on the effects of changed rice residue chemistry by climate change on the temperature sensitivity (Q10) of the RH of soils amended with the residues is available. To fill this knowledge gap, we conducted an incubation experiment (at 15, 25, and 35 °C) with rice residues of which lignin, nonstructural carbohydrate (NSC), and carbon-to-nitrogen ratio (C/N) were modified by growing the rice plants under different CO2 and air temperature (Tair). Co-elevated CO2 and Tair decreased lignin and C/N but increased the NSC contents of the residues, leading to the greater RH in the soils amended with rice residues produced under co-elevated CO2 and Tair compared to other CO2 or Tair treatments. However, the Q10 values of the RH of soils amended with rice residues produced under different CO2 and Tair were marginally changed, probably due to other confounding factors such as soil enzyme activity and nutrient availability. Despite that, there was a negative relationship between NSC and Q10 for soils amended with straw of which C/N is lower than root, agreeing with the kinetic theory on "substrate quality–Q10 relationship". Our result suggests that residue chemistry may not affect the Q10 of soils with root residue, but for straw-amended soils, less-recalcitrant NSC rather than more-recalcitrant lignin of straw may be critical in determining the Q10 of the soils. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. Purpose: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. Material and Methods: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. Results: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64–0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45–0.58) with matched rates of 56.7%–67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. Conclusion: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG). [ABSTRACT FROM AUTHOR]

Global soil nitrogen (N) cycling remains poorly understood due to its complex driving mechanisms. Here, we present a comprehensive analysis of global soil δ15N, a stable isotopic signature indicative of the N input–output balance, using a machine‐learning approach on 10,676 observations from 2670 sites. Our findings reveal prevalent joint effects of climatic conditions, plant N‐use strategies, soil properties, and other natural and anthropogenic forcings on global soil δ15N. The joint effects of multiple drivers govern the latitudinal distribution of soil δ15N, with more rapid N cycling at lower latitudes than at higher latitudes. In contrast to previous climate‐focused models, our data‐driven model more accurately simulates spatial changes in global soil δ15N, highlighting the need to consider the joint effects of multiple drivers to estimate the Earth's N budget. These insights contribute to the reconciliation of discordances among empirical, theoretical, and modeling studies on soil N cycling, as well as sustainable N management. [ABSTRACT FROM AUTHOR]

Purpose: Soil salinity is often measured with electrical conductivity (EC) of soil–water extracts (ECw), and thus, conversion of ECw to saturated paste EC (ECe) is required for practical uses of ECw. However, conversion factors (CF, the ratio of ECe to ECw) vary widely depending on soil properties such as soil texture and ionic composition. This study was conducted to develop a novel procedure to obtain soil-specific CF by performing random forest (RF) and multiple regression (MR) modeling using EC and soil properties. Methods: Soil samples (n = 320) were collected from paddy fields in 16 reclaimed coastal areas of South Korea in 2018–2022. The samples were analyzed for EC (EC1:5 and ECe) and other soil properties including soil particle size, organic matter content, and exchangeable sodium percentage (ESP). The most important soil properties affecting the CF ratios were explored using RF modeling, and a MR equation was developed to estimate ECe using EC1:5 and the soil properties as parameters. Results: The RF model indicated that ESP and clay content are the key soil factors affecting CF variability, contributing 56.6% and 20.0% of the variability, respectively. Therefore, a multiple regression model was developed to predict CF using ESP and clay content, CF = 7.179 − 0.084 × Clay + 1.096 × ln(ESP). The predicted ECe was linearly correlated with the measured ECe (r2 = 0.84) with a marginal error (3.1%). Conclusion: Our study provides a novel method to estimate ECe from ECw by performing RF and MR modeling using soil-specific variables such as clay content and ESP. [ABSTRACT FROM AUTHOR]

According to numerous chamber and free-air CO2 enrichment (FACE) studies with artificially raised CO2 concentration and/or temperature, it appears that increasing atmospheric CO2 concentrations ([CO2]) stimulates crop yield. However, there is still controversy about the extent of the yield stimulation by elevating [CO2] and concern regarding the potential adverse effects when temperature rises concomitantly. Here, we tested the effects of natural elevated [CO2] (ca. 120 ppm above the ambient level in 100 years ago) and warming (ca. 1.7–3.2 °C above the ambient level 100 years ago) on rice growth and yield over three crop seasons via a past reference investigation and current experiment (PRICE) study. In 2020–2022, the rice cultivar Tamanishiki (Oryza sativa, ssp. japonica) was grown in Wagner's pots (1/2000 a) at the experiment fields of Chonnam National University (35°10′ N, 126°53′ E), Gwangju, Korea, according to the pot trial methodology of the reference experiment conducted in 1920–1922. Elevated [CO2] and temperature over the last 100 years significantly stimulated plant height (13.4% on average), tiller number (11.5%), and shoot biomass (10.8%). In addition, elevated [CO2] and warming resulted in a marked acceleration of flowering phenology (6.8% or 5.1 days), potentially leading to adverse effects on tiller number and grain yield. While the harvest index exhibited a dramatic reduction (12.2%), grain yield remained unchanged with elevated [CO2] and warming over the last century. The response of these crop parameters to elevated [CO2] and warming was highly sensitive to sunshine duration during the period from transplanting to heading. Despite the pot-based observations, considering a piecewise response pattern of C3 crop productivity to [CO2] of <500 ppm, our observations demonstrate realistic responses of rice crops to elevated [CO2] (+120 ppm) and moderate warming (+1.7–3.2 °C) in the absence of adaptation measures (e.g., cultivars and agronomic management practices). Hence, our results suggest that the PRICE platform may provide a promising way to better understand and forecast the net impact of climate change on major crops that have historical and experimental archived data, like rice, wheat, and soybean. [ABSTRACT FROM AUTHOR]

This review analyzes the data on co-variations in δ13C and δ15N of soils with land-use types, management, and disturbance obtained from literature to explore potential implications of the dual isotopes in the study of soil organic matter (SOM) sources and C and N processes. Overall, croplands (δ13C and δ15N were − 20.3 ± 4.4‰ and + 7.6 ± 4.4‰, respectively) had greater isotopic values than grasslands (‒26.3 ± 3.0‰ and + 5.4 ± 1.1‰, respectively) and forests (− 26.0 ± 1.1‰ and + 4.3 ± 2.2‰, respectively). For intensively managed lands such as croplands and grasslands, application of organic inputs such as manure and compost of which isotopic signatures differed from the indigenous SOM was the main driver of co-variations in the δ13C and δ15N of SOM. For natural forests, both δ13C and δ15N of SOM co-increased with soil depth, reflecting heavy isotope enrichment during microbial stabilization of SOM and the potential influence of 13C-depleted atmospheric CO2 and 15N-depleted N deposition on the upper soils. Such vertical co-enrichments of 13C and 15N were disturbed by a land-use conversion to other lands including croplands. Though there were indications that land management practices such as tillage in croplands and grazing in grasslands, land-use changes, and land disturbance including forest fire might also affect both δ13C and δ15N, more data need to be accumulated to find a general trend of the isotopic variations of SOM. Analysis of both δ13C and δ15N may enlarge understanding of changes in SOM sources and soil C and N cycling by land-use types, management, change, and disturbance. [ABSTRACT FROM AUTHOR]