scholarly journals Quantifying denitrification on a field scale in hummocky terrain

1992 ◽  
Vol 72 (1) ◽  
pp. 21-29 ◽  
Author(s):  
J. A. Elliott ◽  
E. de Jong
Keyword(s):  
Simulation Model ◽  
Soil Properties ◽  
Key Words ◽  
Climatic Data ◽  
Regression Equations ◽  
Landscape Classification ◽  
Field Scale ◽  
Landscape Elements ◽  
Moisture Contents ◽  
Key Words Denitrification

In this study a landscape classification and daily climatic data were used to extrapolate from a number of discrete denitrification rate measurements to an annual field average. Denitrification rates were calculated from modelled daily moisture contents and observed daily temperature using regression equations. Different regression equations were used for crop and fallowed fields, and for three distinct landscape groups; depositional, intermediate, and erosional. Good correlations were found between predicted and measured denitrification rates. Annual denitrification losses were greatest under fallow, ranging from 50.5 kg N ha−1 on the low level landscape element to 3.1 kg N ha−1 on the diverging blackslope. Losses from cropped soil ranged from 12.8 kg N ha−1 on the diverging footslope in 1988 to 0.7 kg N ha−1 on the diverging backslope in 1986. Field average denitrification losses were estimated to be 3 kg N ha−1 in 1986 (wheat), 16 kg N ha−1 in 1987 (the fallow year) and 4 kg N ha−1 in 1988 (canola). Key words: Denitrification, annual field averages, landscape elements, soil properties, simulation model

SIMULATION OF A SEA CARGO PORT AS A QUEUING SYSTEM IN ANYLOGIC

2020 ◽  
Vol 4 (26) ◽  
pp. 59-66
Author(s):  
A. G. Morozkov ◽  
◽  
M. R. Yazvenko ◽  
Keyword(s):  
Simulation Model ◽  
Key Words ◽  
Simulation Modeling ◽  
Queue Length ◽  
System Simulation ◽  
Queuing System ◽  
System Model ◽  
System Capacity ◽  
Mathematical Solution ◽  
Structure Simulation

The article presents simplified queuing system model of freight marine port. The article discusses the basic elements of queuing system, its mathematical solution and structure. Simulation model was created using AnyLogic to analyze an effect of system capacity on queue length. The results were analyzed and the solution for queue optimization was proposed. Key words: queuing system, simulation modeling, AnyLogic, marine port, servers, queue.

scholarly journals Model Simulasi dan Visualisasi Prediksi Potensi Hasil dan Produksi Kedelai di Jawa Timur

2016 ◽  
Vol 34 (3) ◽  
pp. 195
Author(s):  
Bambang S. Koentjoro ◽  
Imas S. Sitanggang ◽  
Abdul Karim Makarim
Keyword(s):  
Simulation Model ◽  
Systems Approach ◽  
Yield Potential ◽  
Soybean Plant ◽  
Climatic Data ◽  
Observation Data ◽  
Potential Yield ◽  
Soybean Yield ◽  
Soybean Production ◽  
Growth Simulator

<p>The prediction of national soybean yield and production could be improved its accuracy by integrating a simulation model and Geographic Information Systems (GIS). The objective of this research was to integrate a simulation model with a GIS, to predict the potential yield and production of soybean in the soybean production centers of East Java. This study was conducted from December 2013 till May 2014. The approach used in this study was a systems approach using a simulation model as solution to the problem. The model is SUCROS.SIM (Simple Universal Crops Growth Simulator), which was written using Powersim software and Spreadsheet in order to be fully integrated with GIS. The initial phase of the integration process between SUCROS.SIM and GIS are as follows (a) model validation, using input data of soybean plant assimilate partitioning, (b) climatic data (solar radiation, maximum and minimum temperatures) collected from the climatological station (BMKG) Karangploso Malang and (c) observation data of soybean yields of two varieties (Wilis and Argomulyo) at Muneng Experiment Station. It was found that the coefficients of determination of simulation model of soybean yield potential (R2) range from 0.945-0.992 and RMSE (Root Mean Square Error) values range from 0.11 to 0.25 t/ha. The average of soybean yield potential and production in 2012 at soybean production centers of East Java were 1.94 t/ha and 293,459 ton, respectively. The conclusion is SUCROS.SIM valid to be integrated with GIS.</p>

scholarly journals Microbial soil characteristics of grassland and arable soils linked to thermogravimetry data: correlations, use and limits

2021 ◽  
Author(s):  
Helena Doležalová-Weissmannová ◽  
Stanislav Malý ◽  
Martin Brtnický ◽  
Jiří Holátko ◽  
Michael Scott Demyan ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Parameters ◽  
Organic Carbon Content ◽  
Regression Equations ◽  
Arable Soils ◽  
Grassland Soils ◽  
Simple Method ◽  
Organic C ◽  
Mass Losses ◽  
C And N

Abstract. Thermogravimetry (TG) is a simple method that enables rapid analysis of soil properties such as the content of total organic C, nitrogen, clay and C fractions with different stability. However, the possible link between TG data and microbiological soil properties has not been systematically tested yet and limits TG application for soil and soil organic matter assessment. This work aimed to search and to validate relationships of thermal mass losses (TML) to total C and N contents, microbial biomass C and N, basal and substrate-induced respiration, extractable organic carbon content, anaerobic ammonification, urease activity, short-term nitrification activity, specific growth rate, and time to reach the maximum respiration rate for two sample sets of arable and grassland soils. Analyses of the training soil set revealed significant correlations of TML with basic soil properties such as carbon and nitrogen content with distinguishing linear regression parameters and temperatures of correlating mass losses for arable and grassland soils. In a second stage the equations of significant correlations were used for validation with an independent second sample set. This confirmed applicability of developed equations for prediction of microbiological properties mainly for arable soils. For grassland soils was the applicability lower, which was explained as the influence of rhizosphere processes. Nevertheless, the application of TG can facilitate the understanding of changes in soil caused by microorganism’s activity and the different regression equations between TG and soil parameters reflect changes in proportions between soil components caused by land use management.

The Effect of Soil Properties on Metal Bioavailability: Field Scale Validation to Support Regulatory Acceptance

2013 ◽  
Author(s):  
Amy Hawkins ◽  
Mark Barnett ◽  
Nick Basta ◽  
Elizabeth Dayton ◽  
Roman Lanno ◽  
...  
Keyword(s):  
Soil Properties ◽  
Scale Validation ◽  
Metal Bioavailability ◽  
Field Scale ◽  
Regulatory Acceptance

Prediction of available potassium in Ontario soils by electro-ultrafiltration and chemical extraction

1992 ◽  
Vol 72 (2) ◽  
pp. 159-167 ◽  
Author(s):  
Liangxue Liu ◽  
T. E. Bates ◽  
T. S. Tran
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Key Words ◽  
Soil Ph ◽  
Chemical Extraction ◽  
K Uptake ◽  
Chemical Methods ◽  
Exchangeable Calcium ◽  
Time Required

The extractions of soil K by electroultrafiltration (EUF) and by chemical methods were compared as predictors of plant-available K for greenhouse-grown alfalfa on 38 Ontario soils. The relation of soil properties to the amount of K extracted by EUF fractions was also examined. The contents of silt and clay were negatively correlated with EUF-K at 50 V and 22 °C and positively correlated with EUF-K at 400 V and 80 °C. Soil pH and organic matter were not significantly correlated with the amounts of K extracted in the EUF-K fractions. The EUF extraction of K was influenced by the presence of carbonate or high exchangeable calcium in soils. Similar correlations were obtained between K uptake and K extracted by chemical methods and the sum of EUF-K fractions. When used along with other soil properties, EUF-K fractions and K extracted by chemical methods predicted availability of soil K with roughly equal ability. The model using the sum of K extracted by EUF at 50, 200 and 400 V is the simplest one and contains three variables, K, K2 and Ca2. The use of EUF is limited due to cost of equipment and time required for analysis, unless a number of nutrients can be accurately determined on one extract. Among the chemical methods, equations developed using three nonacidic extradants, NaCl, ABDTPA and NH4OAc explained more variation in K uptake than two acidic extractants, Mehlich 3 and 0.1 M HNO3. The NaCl model, ABDTPA model and NH4OAc model contained the same variables and had similar R2 values (0.88–0.91). Key words: Available K, chemical methods, electro-ultrafiltration, EUF-K fractions, K uptake

Volume change indices during loading and unloading of an unsaturated soil

1992 ◽  
Vol 29 (2) ◽  
pp. 195-207 ◽  
Author(s):  
D. Y. F. Ho ◽  
D. G. Fredlund ◽  
H. Rahardjo
Keyword(s):  
Soil Properties ◽  
Key Words ◽  
Volume Change ◽  
Unsaturated Soil ◽  
Constitutive Relations ◽  
Change Theory ◽  
Glacial Till ◽  
Testing Procedures ◽  
Loading And Unloading ◽  
Oedometer Tests

The paper presents the volume change theory and the designation of associated soil properties that must be measured for an unsaturated soil. The equipment required for the measurement of each of the relevant volume relationships is described. Several testing procedures for obtaining the volume change indices during loading and unloading of an unsaturated soil are presented. Typical results from loading and unloading tests on compacted silt and compacted glacial till specimens are presented and analyzed. The analysis is given in order to illustrate the application of the volume change theory to practical problems. Key words : unsaturated soil, volume change indices, constitutive relations, coefficients of volume change, oedometer tests.

Sensitivity of selected water retention functions to compaction and inherent soil properties

Soil Research ◽  
1998 ◽  
Vol 36 (2) ◽  
pp. 317 ◽  
Author(s):  
V. Rasiah ◽  
L. A. G. Aylmore
Keyword(s):  
Soil Properties ◽  
Water Retention ◽  
Linear Regression Analysis ◽  
Coefficient Of Determination ◽  
Strongly Correlated ◽  
Limited Information ◽  
Field Scale ◽  
Soil Water Retention ◽  
Landscape Unit ◽  
Retention Characteristics

It is known that field-scale variations in subsurface hydraulic characteristics are influenced, to a large extent, by soil properties. Limited information, however, exists on the sensitivity of hydraulic functions to field-scale variations in soil properties. The sensitivity of 4 soil water retention functions, θ(h), to variations in soil properties and changes in bulk density (ρ) across and within soils along a 500-m transect has been assessed in this study. The θ(h) functions compared are those of van Genuchten, Brooks and Corey, Campbell, and Gardner. Water retention characteristics for 7 soils, each packed to 2 relative ρ, were established for each function. The coefficient of determination, R 2 , for the best fit of water retention ranged from 0·79 to 0· 98 for the Gardner and Campbell functions, from 0· 92 to 0·99 for the Brooks and Corey function, and from 0·83 to 0·99 for the van Genuchten function. Simple linear regression analysis indicated the nonlinear slope parameters of the 4 functions were more strongly correlated with soil properties. However, only the van Genuchten slope parameters were sensitive to changes in ρ. No consistency existed between the sensitivity of the linear parameters of the 4 functions and soil properties, and none were sensitive to changes in ρ. Except for the a parameter in the van Genuchten function, all the parameters in this function can be predicted with satisfactory confidence from soil properties and ρ. The results indicate that, of the 4 functions assessed, the van Genuchten θ(h) function is the most sensitive to field-scale variations in soil properties along a transect in a landscape unit and to changes in ρ.

Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Geoderma ◽  
2020 ◽  
Vol 366 ◽  
pp. 114253 ◽  
Author(s):  
Yakun Zhang ◽  
Wenjun Ji ◽  
Daniel D. Saurette ◽  
Tahmid Huq Easher ◽  
Hongyi Li ◽  
...  
Keyword(s):  
Soil Properties ◽  
Three Dimensional ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Regression Kriging ◽  
Field Scale

Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non-spatial techniques

2014 ◽  
Vol 65 (6) ◽  
pp. 842-851 ◽  
Author(s):  
F. Castaldi ◽  
R. Casa ◽  
A. Castrignanò ◽  
S. Pascucci ◽  
A. Palombo ◽  
...  
Keyword(s):  
Soil Properties ◽  
Satellite Data ◽  
Field Scale
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