Calibrating SoilGen2 for interglacial soil evolution in the Chinese Loess Plateau considering soil parameters, and the effect of uncertain forcings’

<p>To better understand interglacial paleosol formation by quantifying the paleosol formation processes on the Chinese Loess Plateau (CLP), we need a soil genesis model calibrated for long timescales.  Here, we calibrate a process-based soil genesis model, SoilGen2, by confronting simulated and measured soil properties for interglacial soils formed in the CLP for various parameter settings. After the calibration of the intrinsic soil process parameters, the effect of uncertainty of external forcings (e.g. dust deposition) on calibration results was assessed.</p><p>This calibration comprises three major soil process formulations, represented by various process parameters. Sequentially : [1]. decalcification by tuning (i) the dissolution constant of calcite (ii) the interception evaporation fraction [2]. clay migration by tuning (iii) the volume of clay in-contact with macropores (iv) the filter coefficient (v) physical weathering (vi) the ectorganic layer thickness [3]. soil organic carbon by tuning the decay rates of (vii) humus and (viii) resistant plant material, and (ix) the ratio of ectorganic/endorganic litter (natural vegetations) (x) the ratio of carbon mineralized (CO<sub>2</sub>) over that still in the food web (biomass and humus) during decomposition. The order of the tuned parameters was based on sensitivity analyses on parameters for modelling (de-)calcification and clay migration done for West European leaching climates, and on C-cycling parameters done for both West European and Chinese circumstances. These parameters, [1 and 3] and [2] were successfully calibrated to the Holocene and the Marine Isotope Stage (MIS) 13 climate evolution of the CLP, respectively. After calibration, soil properties show a strong response to 10 reconstructed dust deposition scenarios reflecting the propagation of uncertainty in dust deposition.</p><p>Our results emphasize the equal importance of calibrating soil process parameters and defining correct external forcings in the future use of soil models. Nevertheless, this calibrated model permits interglacial soil simulation in the CLP over long timescales.</p>

Stabilization of MSW by using Cowurine

This paper inspected the fluctuation which happens in key boundaries like pH, temperature, dampness content, natural carbon, nitrogen, phosphorous and so on during the 30 days standard observing of fertilizing the soil process.5 kg of city strong waste, old fertilizer, straw and soil, was blended in with 5%, 10%, 15% of cow urine of 3 kg civil strong waste for treating the soil. Treating the soil was finished by utilizing sixteenth containers model composter made up with legitimate air circulation and waste office and was kept in semi sun beams condition. Ph running 7.6 to 8.9 in the main stage, Temperature ascends from the primary day of process and become 55°C on18 day. Dampness content in manure was insecure all through the procedure because of changing microbial populace. The NPK substance of conclusive fertilizer are discover after finding the aftereffects of NPK got from fertilizing the soil treatment given to MSW and Cow urine are demonstrate that consolidated fertilizing the soil are an appealing technique for the executives of city strong waste.

A nonlinear dynamics approach to data-enabled science: Reconstructing soil-moisture dynamics from big data collected by wireless sensor networks

<p>The complex soil biome is a center piece in providing essential ecosystem services that humans rely on (carbon sequestration, food security, one-health interactions).  Agricultural engineers and soil scientists are developing wireless sensor networks (WSN) that collect large/big data on the soil key state variables (water content, temperature, chemistry) to better understand the soil biome primary environmental drivers. The profession extracts information from WSN records with methods including soil-process modeling and artificial-intelligence (AI) algorithms.  However, these approaches carry their own limitations.  A recent review article faulted current soil-process modeling for inadequately detecting and resolving model structural (abstraction) errors.  AI experts themselves caution against indiscriminant use of AI methods because of: a) problems including replication of past results due to inconsistent experimental methods; b) difficulty in explaining how a particular method arrives at its conclusions (the black box problem) and thus in correcting algorithms that learn ‘bad lessons’; and c) lack of rigorous criteria for selecting AI architectures.  An alternative approach to address these limitations is to investigate new strategies for reducing large/big data problems into smaller, more interpretable causal abstractions of the soil system.  </p><p>We develop an innovative data diagnostics framework—based on empirical nonlinear dynamics techniques from physics—that addresses the above concerns over soil-process modeling and AI algorithms.  We diagnose whether WSN and other similar environmental large/big data are likely generated by dimension-reducing (i.e., dissipative) nonlinear dynamics.  An n-dimensional nonlinear dynamic system is dissipative if long-term dynamics are bounded within m<<n dimensions, so that the problem of modeling long-term dynamics shrinks by the n-m inactive degrees of freedom.  If so, long-term system dynamics can be investigated with relatively few degrees of freedom that capture the complexity of the overall system generating observed data.  To make this diagnosis, we first apply signal processing to isolate structured variation (signal) from unstructured variation (noise) in large/big data time series records, and test signals for nonlinear stationarity.  We resolve the structure of isolated signals by distinguishing between stochastic-forcing and deterministic nonlinear dynamics; reconstruct phase space dynamics most likely generating signals, and test the statistical significance of reconstructed dynamics with surrogate data.  If the reconstructed phase space is dimension-reducing, we can formulate low-dimensional (phenomenological) ODE models to investigate nonlinear causal interactions between key soil environmental driving factors.  When we do not diagnose dimension-reducing nonlinear real-world dynamics, then underlying dynamics are most likely high dimensional and the information-extraction problem cannot be shrunk without losing essential dynamic information. In this case, other high-dimensional analysis techniques like AI offer a better modeling alternative for mapping out interactions.  Our framework supplies a decision-support tool for data practitioners toward the most informative and parsimonious information-extraction method—a win-win result.       </p><p>We will share preliminary results applying this empirical framework to three soil moisture sensor time series records analyzed with machine learning methods in Bean, Huffaker, and Migliaccio (2018).</p>

Influence of the Plough with Tekrone Mouldboards and Landsides on Ploughing Parameters

This paper is dedicated to Tekrone composite material utilization in agricultural machinery. In terms of its technical properties, tekrone is very similar to steel 60 that is used for production of plough mouldboards and landsides. However, Tekrone shows more preferable characteristics, because its friction coefficient (kf) is 2.6 times lower in contrast to steel 60. This fact indicates that the draft resistance can be decreased by replacing the plough mouldboards and landsides made of steel 60 with their counterparts made of Tekrone. This science hypothesis was confirmed by experimental investigation results. Analyses showed that utilization of plough with Tekrone mouldboards and landsides instead of steel ones significantly reduces their sticking to the wet soil. This results in a “soil moves over plough mouldboard surface” process instead of a “soil moves over soil” process. The plough draft resistance was decreased by 13.6% after replacement of the steel equipment with Tekrone one. Simultaneously, the performance of new tractor-plough aggregate was increased by 12.6%, the specific fuel consumption was reduced by 11.8%, and the preserving probability of the agrotechnological ploughing depth tolerance (±2 cm) was increased from 88% to 93%.

Analysis of strength characteristics of carbon fiber–reinforced microbial solidified sand

Microbial solidified sand effectively enhances the strength of the soil, but it will cause brittle failure. In order to reduce the impact of microbial solidification sand brittleness, an improved method for adding carbon fiber to microbial solidified sand is proposed. The qualitative analysis was based on unconfined compressive strength test, calcium carbonate content determination, and penetration test. The results show that the addition of fiber in the microbial solidified sand can significantly increase the unconfined compressive strength of the sample. The unconfined compressive strength of the sample increases first and then decreases with the increase of fiber addition. The addition of fibers during the soil process enhances the toughness of the specimen and causes plastic damage during the failure of the specimen. Based on the analysis of the microstructure of the sample, the effect of fiber bundles on the strength characteristics of the sample is discussed when the fiber content is higher than the optimal fiber content. The addition of carbon fiber to microbial solidified sand can greatly improve the strength of the sample and increase the toughness, which plays a positive role in improving the safety and stability of the project.

Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China

Risk assessment lays a foundation for disaster risk reduction management, especially in relation to climate change. Intensified extreme weather and climate events driven by climate change may increase related disaster susceptibility. This may interact with exposed and vulnerable socioeconomic systems to aggravate the impacts and impede progress towards regional development. In this study, debris flow risk under climate change was assessed by an integrated debris flow mechanism model and an inclusive socioeconomic status evaluation. We implemented the method for a debris flow-prone area in the eastern part of the Qinghai-Tibet Plateau, China. Based on the analysis of three general circulation models (GCMs)—Beijing Climate Center Climate System Model version 1 (BCC_CSM), model for Interdisciplinary Research on Climate- Earth System, version 5 (MIROC5, and the Community Climate System Model version 4 (CCSM4)—the water–soil process model was applied to assess debris flow susceptibility. For the vulnerability evaluation, an index system established from the categories of bearing elements was analyzed by principle component analysis (PCA) methods. Our results showed that 432 to 1106 watersheds (accounting for 23% to 52% of the study area) were identified as debris-flow watersheds, although extreme rainfall would occur in most of the area from 2007 to 2060. The distributions of debris flow watersheds were concentrated in the north and transition zones of the study area. Additionally, the result of the index and PCA suggested that most areas had relatively low socioeconomic scores and such areas were considered as high-vulnerability human systems (accounts for 91%). Further analysis found that population density, road density, and gross domestic production made great contributions to vulnerability reduction. For practical mitigation strategies, we suggested that the enhancement of road density may be the most efficient risk reduction strategy.