Journal Paper Digests 2019 #18
- Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex
- Soil health assessment: Past accomplishments, current activities, and future opportunities
- Development of soil spectral allocation models considering the effect of soil moisture
- Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads
- Estimating Atterberg Limits of Fine-Grained Soils by Visible-Near-Infrared Spectroscopy
- pH and exchangeable aluminum are major regulators of microbial energy flow and carbon use efficiency in soil microbial communities
Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex
By:Lim, EP (Lim, Eun-Pa)[ 1 ] ; Hendon, HH (Hendon, Harry H.)[ 1 ] ; Boschat, G (Boschat, Ghyslaine)[ 2,3 ] ; Hudson, D (Hudson, Debra)[ 1 ] ; Thompson, DWJ (Thompson, David W. J.)[ 4 ] ; Dowdy, AJ (Dowdy, Andrew J.)[ 1 ] ; Arblaster, JM (Arblaster, Julie M.)[ 2,3,5 ]
NATURE GEOSCIENCE
Volume: 12 Issue: 11 Pages: 896-+ Published: NOV 2019
Document Type: Article
Abstract The occurrence of extreme hot and dry conditions in warm seasons can have large impacts on human health, energy and water supplies, agriculture and wildfires. Australian hot and dry extremes have been known to be associated with the occurrence of El Nino and other variations of tropospheric circulation. Here we identify an additional driver: variability of the stratospheric Antarctic polar vortex. On the basis of statistical analyses using observational data covering the past 40 yr, we show that weakenings and warmings of the stratospheric polar vortex, which episodically occur during austral spring, substantially increase the chances of hot and dry extremes and of associated fire-conducive weather across subtropical eastern Australia from austral spring to early summer. The promotion of these Australian climate extremes results from the downward coupling of the weakened polar vortex to tropospheric levels, where it is linked to the low-index polarity of the Southern Annular Mode, an equatorward shift of the mid-latitude westerly jet stream and subsidence and warming in the subtropics. Because of the long timescale of the polar vortex variations, the enhanced likelihood of early-summertime hot and dry extremes and wildfire risks across eastern Australia may be predictable a season in advance during years of vortex weakenings.
Soil health assessment: Past accomplishments, current activities, and future opportunities
By:Karlen, D (Karlen, Douglas)[ 1 ] ; Veum, KS (Veum, Kristen S.)[ 2 ] ; Sudduth, KA (Sudduth, Kenneth A.)[ 2 ] ; Obrycki, JF (Obrycki, John F.)[ 3 ] ; Nunes, MR (Nunes, Marcio R.)[ 4 ]
SOIL & TILLAGE RESEARCH
Volume: 195 Pages: 4365-4365 Published: DEC 2019
Document Type: Review
Abstract Global interest in soil health has increased exponentially during the past decade, with many different government, non-government, and private sector groups striving to develop monitoring and assessment protocols. This brief review focuses on developments in the United States (U.S.) with some references to activities in other countries. It also documents how the soil health concept evolved and projects what is needed to scientifically advance monitoring and assessment with a particular focus on activities in the U.S. Recommendations emphasize improving the Soil Management Assessment Framework (SMAF) and/or Comprehensive Assessment of Soil Health (CASH) assessment tools, developing protocols for national soil health monitoring, identifying and calibrating better indicators of soil biological, chemical, and physical health, and developing sensors and other tools for more rapid and in-situ assessments. Collectively, these and other research and technology transfer activities will help achieve what we suggest should be a universal goal - striving for healthy soils, healthy landscapes, and vibrant economies.
Development of soil spectral allocation models considering the effect of soil moisture
By:Wang, X (Wang, Xiang)[ 1,2 ] ; Dou, X (Dou, Xin)[ 2 ] ; Zhang, XL (Zhang, Xinle)[ 2 ] ; Liu, HJ (Liu, Huanjun)[ 1,2 ] ; Li, HX (Li, Houxuan)[ 2 ] ; Meng, XT (Meng, Xiangtian)[ 2 ]
SOIL & TILLAGE RESEARCH
Volume: 195 Pages: 4374-4374 Published: DEC 2019
Document Type: Article
Abstract Soil spectral allocation or classification is usually conducted on air-dried soils. However, the field soils are not all air-dried, and the change of soil moisture will affect soil reflectance. We introduce a soil allocation model that considers the effect of soil moisture for the purpose of eliminating the effect of soil moisture. The topsoil spectral curves of four typical soils from the Songnen Plain in Northeast China were re-sampled to 10-nm intervals and converted to first-derivative spectral curves and continuum removal curves. The spectral feature parameters were extracted from continuum removal curves in the visible-near infrared (VNIR) range (350-2500 nm), and the range of 430-2400 nm was used to build soil allocation models for reducing the effect of noise. Samples with different soil moisture were mixed into air-dried soils and we calculated the coefficient of variation (CV) of different inputs to assess the effect of soil moisture and to find allocation indices that were not affected by soil moisture. We used allocation indices of Zhang et al. (2018) because of the high accuracy of their DT (Decision Tree) model to allocate mixed-soil samples. We also used allocation indices that were not affected by soil moisture to allocate mixed-soil samples with decision tree (DT), multinomial logistic regression (MLR) and multi-layer perception neural network (MLPNN), and compared the results of the two methods. The results show the following: 1) As SFPs were built with shorter bands, SFP was less sensitive to soil moisture than PCR and PCFD and thus SFP is more suitable to build soil allocation models that consider the effect of soil moisture as input than PCR and PCFD. 2) Differences in soil moisture had little effect on absorption valley shoulders, symmetry and absorption positions, moderate effect on absorption area and depth, and a major effect on the slope of different bands. 3) The effect of soil moisture on continuum removal curves of different soil classes was variable. There was little effect on Arenosols, a moderate effect on Chernozems and Cambisols, and a large effect on Phaeozems. 4) The accuracy of the DT model using allocation indices that were not affected by soil moisture was 91.892% with a Kappa coefficient of 0.888. Our results suggest that it is feasible to build soil spectral allocation models that are not affected by soil moisture, and this improves the universality of soil spectral allocation, especially to field soils, which can be of considerable help in soil classification.
Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads
By:Kotlar, AM (Kotlar, Ali Mehmandoost)[ 1 ] ; van Uer, QD (van Uer, Quirijn de Jong)[ 1 ] ; Barros, AHC (Barros, Alexandre Hugo C.)[ 2 ] ; Iversen, BV (Iversen, Bo, V)[ 3 ] ; Vereecken, H (Vereecken, Harry)[ 4 ]
VADOSE ZONE JOURNAL
Volume: 18 Issue: 1 Pages: 90063-90063 Published: NOV 7 2019
Document Type: Article
Abstract There has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e., different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset into training and testing subsets, and the outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov-Smirnov test for the prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian process regression and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared with machine learning methods, which in their turn estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling. From these, 100 sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF-predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty.
Estimating Atterberg Limits of Fine-Grained Soils by Visible-Near-Infrared Spectroscopy
By:Rehman, HU (Rehman, Hafeez Ur)[ 1 ] ; Knadel, M (Knadel, Maria)[ 1 ] ; Kayabali, K (Kayabali, Kamil)[ 2 ] ; Arthur, E (Arthur, Emmanuel)[ 1 ]
VADOSE ZONE JOURNAL
Volume: 18 Issue: 1 Pages: 90039-90039 Published: NOV 7 2019
Document Type: Article
Abstract The Atterberg limits (shrinkage limit [SL], plastic limit [PL], and liquid limit [LL]) describe the physico-mechanical behavior of soils and thus are crucial for civil and agricultural applications. Conventional laboratory methods for measurement of these limits are tedious and costly for a large number of samples. Our objective was to develop visible-near-infrared spectroscopy (Vis-NIRS, from 400-2500 nm) based reliable models to estimate the Atterberg limits. Two conventional methods for each Atterberg limit were used to generate the reference data: paraffin wax and Hg methods for SL; rolling and motorized devices for PL; and Casagrande cup and drop-cone penetrometer methods for LL. Calibration models were built on 80% of the data using partial least squares regression and validated with the remaining 20% of the dataset. The Vis-NIRS independent validation of LL showed very good estimation with standardized RMSE (SRMSE = RMSE/Range) of 0.16 and 0.15, respectively, for LLdrop-cone and LLCasagrande methods. Similarly for PL, the Vis-NIRS estimation accuracy was quite good with SRMSE values of 0.18 and 0.22, respectively, for PLmotorized and PLrolling. Reasonably good estimation was obtained for the SLparaffin and SLHg with SRMSE of 0.25 for both methods. The results suggest that the Vis-NIRS calibration models and the accuracy following validation were similar for the pair of methods used for the SL, PL, and LL. Finally, analyses of the model regression coefficients revealed that the important wavelengths to estimate the SL, PL, and LL in the Vis-NIR regions were present across the entire Vis-NIR spectrum and were strongly related to clay type and content.
pH and exchangeable aluminum are major regulators of microbial energy flow and carbon use efficiency in soil microbial communities
By:Jones, DL (Jones, Davey L.)[ 1,2 ] ; Cooledge, EC (Cooledge, Emily C.)[ 2 ] ; Hoyle, FC (Hoyle, Frances C.)[ 1 ] ; Griffiths, RI (Griffiths, Robert I.)[ 3 ] ; Murphy, DV (Murphy, Daniel V.)[ 1 ]
SOIL BIOLOGY & BIOCHEMISTRY
Volume: 138 Pages: 7584-7584 Published: NOV 2019
Document Type: Article
Abstract The microbial partitioning of organic carbon (C) into either anabolic (i.e. growth) or catabolic (i.e. respiration) metabolic pathways represents a key process regulating the amount of added C that is retained in soil. The factors regulating C use efficiency (CUE) in agricultural soils, however, remain poorly understood. The aim of this study was to investigate substrate CUE from a wide range of soils (n = 970) and geographical area (200,000 km(2)) to determine which soil properties most influenced C retention within the microbial community. Using a C-14-labeling approach, we showed that the average CUE across all soils was 0.65 +/- 0.003, but that the variation in CUE was relatively high within the sample population (CV 14.9%). Of the major properties measured in our soils, we found that pH and exchangeable aluminum (Al) were highly correlated with CUE. We identified a critical pH transition point at which CUE declined (pH 5.5). This coincided exactly with the point at which Al3+ started to become soluble. In contrast, other soil factors [e.g. total C and nitrogen (N), dissolved organic C (DOC), clay content, available calcium, phosphorus (P) and sulfur (S), total base cations] showed little or no relationship with CUE. We also found no evidence to suggest that nutrient stoichiometry (C:N, C:P and C:S ratios) influenced CUE in these soils. Based on current evidence, we postulate that the decline in microbial CUE at low pH and high Al reflects a greater channeling of C into energy intensive metabolic pathways involved in overcoming H+/Al3+ stress (e.g. cell repair and detoxification). The response may also be associated with shifts in microbial community structure, which are known to be tightly associated with soil pH. We conclude that maintaining agricultural soils above pH 5.5 maximizes microbial energy efficiency.