Journal Paper Digests

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Journal Paper Digests 2022 #14

  • Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies
  • Same soil, different climate: Crop model intercomparison on translocated lysimeters
  • Machine learning in vadose zone hydrology: A flashback
  • Soil water potential: A historical perspective and recent breakthroughs
  • Estimating textural fractions of the USDA using those of the International System: A quantile approach
  • Will fungi solve the carbon dilemma?
  • Realistic rates of nitrogen addition increase carbon flux rates but do not change soil carbon stocks in a temperate grassland

Realistic rates of nitrogen addition increase carbon flux rates but do not change soil carbon stocks in a temperate grassland

Changes in the biosphere carbon (C) sink are of utmost importance given rising atmospheric CO2 levels. Concurrent global changes, such as increasing nitrogen (N) deposition, are affecting how much C can be stored in terrestrial ecosystems. Understanding the extent of these impacts will help in predicting the fate of the biosphere C sink. However, most N addition experiments add N in rates that greatly exceed ambient rates of N deposition, making inference from current knowledge difficult. Here, we leveraged data from a 13-year N addition gradient experiment with addition rates spanning realistic rates of N deposition (0, 1, 5, and 10 g N m−2 year−1) to assess the rates of N addition at which C uptake and storage were stimulated in a temperate grassland. Very low rates of N addition stimulated gross primary productivity and plant biomass, but also stimulated ecosystem respiration such that there was no net change in C uptake or storage. Furthermore, we found consistent, nonlinear relationships between N addition rate and plant responses such that intermediate rates of N addition induced the greatest ecosystem responses. Soil pH and microbial biomass and respiration all declined with increasing N addition indicating that negative consequences of N addition have direct effects on belowground processes, which could then affect whole ecosystem C uptake and storage. Our work demonstrates that experiments that add large amounts of N may be underestimating the effect of low to intermediate rates of N deposition on grassland C cycling. Furthermore, we show that plant biomass does not reliably indicate rates of C uptake or soil C storage, and that measuring rates of C loss (i.e., ecosystem and soil respiration) in conjunction with rates of C uptake and C pools are crucial for accurately understanding grassland C storage.

Will fungi solve the carbon dilemma?

Soils are hotspots of diversity and sustain many globally important functions. Here we focus on the most burning issue: how to keep soils as carbon sinks while maintaining their productivity. Evidence shows that life in soils plays a crucial role in improving soil health yet soil ecological processes are often ignored in soil sciences. In this review, we highlight the potential of fungi to increase soil carbon sequestration while maintaining crop yield, functions needed to sustain human population on Earth and at same time keep the Earth livable. We propose management strategies that steer towards more fungal activity but also high functional diversity of fungi which will lead to more stable carbon sources in soil but also affects the structure of the soil food web up to ecosystem level. We list knowledge gaps that limit our ability to steer soil fungal communities such that stabilising carbon in top soils becomes more effective. Using the natural capacity of a biodiverse soil community to sequester carbon delivers double benefit: reduction of atmospheric carbon dioxide by storing photosynthesized carbon in soil and increasing agricultural yields by restoring organic matter content of degraded soils.

Estimating textural fractions of the USDA using those of the International System: A quantile approach

In soil science, the two most frequently used classification systems for the soil particle size distribution are the schemes by the United States Department of Agriculture (USDA) and the so-called International System (IS), whose difference is the upper particle size limit of the silt fraction, namely, 0.02 mm for the IS and 0.05 mm for the USDA system. The existence of these and other systems creates a disparity that hinders and prevents the use and exchange of soil information worldwide. To solve this problem, it is necessary to devise methodologies for the conversion of textural fractions between the different classification systems. This work focuses on the estimation of the USDA silt fraction from the basic textural fractions (sand, silt and clay) in the IS. Five models are currently available for that purpose: the log-linear interpolation method, the Minasny-McBratney-Bristow regression formula, the Shirazi-Boersma-Johnson interpolation method, the Minasny-McBratney regression formula, and the Padarian-Minasny-McBratney regression formula. The accuracy of some of these methods has already been assessed, but in this work we develop a new methodology, based on a local quantile regression, which improves and enriches this evaluation, providing both the regions of the textural triangle where the predictions of the models are acceptable, and the regions where each model is most appropriate. The data used were taken from the publicly available National Cooperative Soil Survey Soil Characterization Database, from which more than 270,000 soil horizon samples were selected for having valid texture data. The analysis carried out concludes that the Padarian-Minasny-McBratney regression formula is the best model of those evaluated. In addition, the tool developed for the evaluation of the models becomes a new model that provides point estimates of the USDA silt fraction from the basic textural fractions in the IS, with further improvement, compared to the 5 models evaluated, as it also provides a prediction interval for those estimates.

Soil water potential: A historical perspective and recent breakthroughs

Soil water potential is a cornerstone in defining the thermodynamic state of soil water required to quantify phenomena such as water phase change, water movement, heat transfer, electric current, chemical transport, and mechanical stress and deformation in the earth’s shallow subsurface environment. This potential has historically been conceptualized as free energy stored in a until volume of soil water. Though the concept of soil water potential has been evolving over the past 120 yr, a consensual definition is still lacking, and answers to some fundamental questions remain controversial and elusive. What are the origins and mechanisms for the free energy of soil water? Can the common mathematical expression of soil water potential as superposition of gravitational, osmotic, and matric potentials be used to define water phase transitions in soil? Are these major components of soil water potential independent or coupled? Is pore water pressure always tensile under unsaturated conditions? If so, how can soil water density be as high as 1.7 g cm(-3)? How do adsorptive soil-water interactions originating from the electromagnetic field around and within soil particles transfer to mechanical pore pressure? In this review, the authors (a) provide critical analysis of historical definitions of soil water potential to identify their strengths, limitations, and flaws; (b) synthesize the origins of electromagnetic energies in soil to clarify the fundamental differences between adsorptive and capillary soil water potential mechanisms; (c) introduce a recently emerging concept of soil matric potential that unifies contributions of adsorption and capillarity to soil water potential; and (d) illustrate the generality and promise of the unified definition of soil water potential for answering some of the fundamental questions that remain elusive to the hydrology, geoengineering, and geoscience communities.

Machine learning in vadose zone hydrology: A flashback

Artificial intelligence (AI) and machine learning (ML) have been recently applied extensively in various disciplines of vadose zone hydrology. However, not much attention has been paid to their database-dependent accuracy and uncertainty, reproducibility, and delivery, which undermines their applications to real-world problems. We discuss lessons from the past and emphasize the need for and lack of fundamental protocols (i.e., detailed clarification on data processing, ML models accessibility, and a clear path for reproducing results).

Same soil, different climate: Crop model intercomparison on translocated lysimeters

Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux-related performance of a set of crop models. The aim was to predict weighing lysimeter-based crop (i.e., agronomic) and water-related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014-2018) were from the Dedelow (Dd), Bad Lauchstadt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop- and soil-related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi-model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site-specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil-related data (i.e., water fluxes and system states) when simulating soil-crop-climate interrelations in changing climatic conditions.

Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies

Soil moisture information is key to irrigation water management, drought monitoring, and yield prediction. It plays a vital role in the water cycle and energy budget between the earth’s surface and atmosphere. Hence, its monitoring is crucial for both natural and anthropogenic environments. While the current remote sensing-based global SM products available at coarser resolution (3/15 km) are unsuitable for field-level operations, the most widely used microwave remote sensing suffers from model complexities and in-situ data requirements. Weather conditions limit the alternate approaches such as optical/thermal. This study aims to map surface soil moisture (SSM) at 30 m spatial resolution in a semi-arid region by fusing optical, thermal, and microwave remote sensing data using bagging, boosting, and stacking machine learning approaches. The reference data were collected using a soil moisture meter. The covariates included radar backscatter from Sentinel-1, visible, near-infrared, shortwave infrared, land surface temperature, and spectral indices derived from Landsat 8. Boruta algorithm was used for feature selection which identified radar backscatter, modified normalized difference water index, and land surface temperature as the most critical covariates impacting the SSM. The random forest (RF) showed the highest correlation coefficient (r = 0.71), and least root mean square error (RMSE = 5.17%). The cubist model had the least mean bias error (MBE = 0.21%) during independent validation. Stacking of cubist, gradient boosting machine (GBM), and RF using elastic net (ELNET) as meta-learner further reduced the MBE (0.18%) and RMSE (5.03%) during the validation. Overall, stacking multiple machine learning models improved model prediction and can be recommended to improve the digital soil moisture mapping.

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