scholarly journals Soil parameter variability affecting simulated fieldscale water balance, erosion and phosphorus losses

2009 ◽  
Vol 18 (3-4) ◽  
pp. 402-416 ◽  
Author(s):  
I. BÄRLUND ◽  
S. TATTARI ◽  
M. PUUSTINEN
Keyword(s):  
Water Balance ◽  
Management Practices ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Agricultural Soils ◽  
Data Sets ◽  
Field Scale ◽  
Lack Of Information ◽  
Scale Modelling ◽  
Phosphorus Losses

Field-scale modelling is widely used as a means to look into interdependencies of processes and to assess potential effects of agricultural management practices as well as of climate and socio-economic scenarios. Generalisation from field-scale results to cover all agricultural land in a catchment by using typical soilcrop- slope combinations has been restricted by a lack of information for the systematic parameterisation of soils. Data from single experimental fields are seldom representative for the whole respective catchment. In this study typical soil profiles for mineral agricultural soils in Finland are defined. Key parameters describing e.g. the texture and water holding capacity of soils, were generated from existing soil data using expert knowledge and are aimed to be used for field-scale modelling when the target is not to model a particular field but soils of certain type in general. Estimates for water balance and phosphorus losses, obtained with the ICECREAM model by applying these data sets, were realistic and compatible with experimental results measured in Finland.;

scholarly journals “Omics” Technologies for the Study of Soil Carbon Stabilization: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
David P. Overy ◽  
Madison A. Bell ◽  
Jemaneh Habtewold ◽  
Bobbi L. Helgason ◽  
Edward G. Gregorich
Keyword(s):  
Soil Carbon ◽  
Management Practices ◽  
Crop Residues ◽  
Agricultural Land ◽  
Agricultural Soils ◽  
Organic Fertilizers ◽  
Application Potential ◽  
And Function ◽  
Agricultural Land Management ◽  
Soil Carbon Stabilization

Evidence-based decisions governing sustainable agricultural land management practices require a mechanistic understanding of soil organic matter (SOM) transformations and stabilization of carbon in soil. Large amounts of carbon from organic fertilizers, root exudates, and crop residues are input into agricultural soils. Microbes then catalyze soil biogeochemical processes including carbon extracellular transformation, mineralization, and assimilation of resources that are later returned to the soil as metabolites and necromass. A systems biology approach for a holistic study of the transformation of carbon inputs into stable SOM requires the use of soil “omics” platforms (metagenomics, metatranscriptomics, metaproteomics, and metabolomics). Linking the data derived from these various platforms will enhance our knowledge of structure and function of the microbial communities involved in soil carbon cycling and stabilization. In this review, we discuss the application, potential, and suitability of different “omics” approaches (independently and in combination) for elucidating processes involved in the transformation of stable carbon in soil. We highlight biases associated with these approaches including limitations of the methods, experimental design, and soil sampling, as well as those associated with data analysis and interpretation.

scholarly journals A comprehensive filtering scheme for high-resolution estimation of the water balance components from high-precision lysimeters

2015 ◽  
Vol 19 (8) ◽  
pp. 3405-3418 ◽  
Author(s):  
M. Hannes ◽  
U. Wollschläger ◽  
F. Schrader ◽  
W. Durner ◽  
S. Gebler ◽  
...  
Keyword(s):  
Water Balance ◽  
High Precision ◽  
Management Practices ◽  
High Temporal Resolution ◽  
Time Interval ◽  
Data Sets ◽  
Water Balance Components ◽  
Raw Data ◽  
Terrestrial Water ◽  
External Forces

Abstract. Large weighing lysimeters are currently the most precise method to directly measure all components of the terrestrial water balance in parallel via the built-in weighing system. As lysimeters are exposed to several external forces such as management practices or wind influencing the weighing data, the calculated fluxes of precipitation and evapotranspiration can be altered considerably without having applied appropriate corrections to the raw data. Therefore, adequate filtering schemes for obtaining most accurate estimates of the water balance components are required. In this study, we use data from the TERENO (TERrestrial ENvironmental Observatories) SoilCan research site in Bad Lauchstädt to develop a comprehensive filtering procedure for high-precision lysimeter data, which is designed to deal with various kinds of possible errors starting from the elimination of large disturbances in the raw data resulting e.g., from management practices all the way to the reduction of noise caused e.g., by moderate wind. Furthermore, we analyze the influence of averaging times and thresholds required by some of the filtering steps on the calculated water balance and investigate the ability of two adaptive filtering methods (the adaptive window and adaptive threshold filter (AWAT filter; Peters et al., 2014), and a new synchro filter applicable to the data from a set of several lysimeters) to further reduce the filtering error. Finally, we take advantage of the data sets of all 18 lysimeters running in parallel at the Bad Lauchstädt site to evaluate the performance and accuracy of the proposed filtering scheme. For the tested time interval of 2 months, we show that the estimation of the water balance with high temporal resolution and good accuracy is possible. The filtering code can be downloaded from the journal website as Supplement to this publication.

scholarly journals Discharge and Nitrogen Transfer Modelling in the Berze River: A HYPE Setup and Calibration

2017 ◽  
Vol 19 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Arturs Veinbergs ◽  
Ainis Lagzdins ◽  
Viesturs Jansons ◽  
Kaspars Abramenko ◽  
Ritvars Sudars
Keyword(s):  
River Basin ◽  
Management Practices ◽  
Agricultural Land ◽  
Calibration Procedure ◽  
Point Sources ◽  
Water Bodies ◽  
Nitrogen Transfer ◽  
Data Sets ◽  
Distribution Analysis ◽  
Agricultural Field

Abstract This study is focused on water quality and quantity modelling in the Berze River basin located in the Zemgale region of Latvia. The contributing basin area of 872 km2 is furthermore divided into 15 sub-basins designated according to the characteristics of hydrological network and water sampling programme. The river basin of interest is a spatially complex system with agricultural land and forests as two predominant land use types. Complexity of the system reflects in the discharge intensity and diffuse pollution of nitrogen compounds into the water bodies of the river basin. The presence of urban area has an impact as the load from the existing wastewater treatment plants consist up to 76 % of the total nitrogen load in the Berze River basin. Representative data sets of land cover, agricultural field data base for crop distribution analysis, estimation of crop management, soil type map, digital elevation model, drainage conditions, network of water bodies and point sources were used for the modelling procedures. The semi-distributed hydro chemical model HYPE has a setup to simulate discharge and nitrogen transfer. In order to make the model more robust and appropriate for the current study the data sets previously stated were classified by unifying similar spatially located polygons. The data layers were overlaid and 53 hydrological response units (SLCs) were created. Agricultural land consists of 48 SLCs with the details of soils, drainage conditions, crop types, and land management practices. Manual calibration procedure was applied to improve the performance of discharge simulation. Simulated discharge values showed good agreement with the observed values with the Nash-Sutcliffe efficiency of 0.82 and bias of −6.6 %. Manual calibration of parameters related to nitrogen leakage simulation was applied to test the most sensitive parameters.

scholarly journals Fate of organic pollutants after sewage sludge spreading on agricultural soils: a 30-years field-scale recording

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
Dominique Patureau ◽  
Mireille Laforie ◽  
Eric Lichtfouse ◽  
Giovanni Caria ◽  
Laurence Denaix ◽  
...  
Keyword(s):  
Sewage Sludge ◽  
Agricultural Land ◽  
Agricultural Soils ◽  
Maximum Level ◽  
Dry Weight ◽  
Control Soil ◽  
Field Scale ◽  
Maize Crop ◽  
Linear Alkylbenzene ◽  
Linear Alkylbenzene Sulfonates

Toxic organic compounds, such as the surfactants linear alkylbenzene sulfonates (LAS) and nonylphenol polyethoxylates (NPE), Polycyclic aromatic hydrocarbons (PAH) and residues derived from plastics (PAE-phthalates) end up in sewage sludge. In order to evaluate and quantify the potential environmental risks associated with the xenobiotic introduction into biological life cycle, the EU BIOWASTE project (QLK5-CT-2002-01138) devotes one task to the study of the fate of xenobiotic in a sandy soil after sludge spreading on a 30-year field-scale record experiment. Experimental maize crop fields from Bordeaux (France) have been amended with 100 tons per hectare each 2 years from 1974 to 1992. From 1992 to 2004, the fields were maintained and cropped with maize. This experiment shows that the concentration fluctuations in the sludge amended soil follow the same pattern of those in the sewage sludge showing that there is a real impact of the present xenobiotics in the sewage sludge on the concentration of the xenobiotics in the soil. Nonetheless, 12 years after the last addition of sewage sludge, the residual concentrations remain from 2 to 10 times higher than the content of the control soil, even though these levels are inferior to the Predicted Non Effect Concentration (PNEC). Only LAS level went back to the level in the control soil. However, only the LAS concentration is above the PNEC during all the experiment due to the very high level of LAS in the sludge (20 g/kg dry weight). These results show that even though this compound is much more degradable than NPE and PAE, it may have a long term effect in soil if high quantities are spread. To conclude, this study underlines the importance to fix maximum level for xenobiotic compounds for sewage sludge spreading on agricultural land, and also the central role of the sewage sludge processes in reducing the xenobiotic concentrations before spreading.

The Impact of Biochar on the Soil

2018 ◽  
Vol 69 (8) ◽  
pp. 2197-2208
Author(s):  
Carmen Otilia Rusanescu ◽  
Erol Murad ◽  
Cosmin Jinescu ◽  
Marin Rusanescu
Keyword(s):  
Agricultural Land ◽  
Agricultural Soils ◽  
Biomass Gasification ◽  
High Humidity ◽  
Agricultural Ecosystems ◽  
Productive Potential ◽  
Co2 Balance ◽  
Environmentally Sound ◽  
The Impact ◽  
Organic Resources

In the present paper are presented the experimental results of biomass gasification, the biochair was produced from vineyards by controlled pyrolysis at 750 �C, in order to increase the fertility of soils, it was found the increase of the fertility produced by the development of the vegetables in the soil to which was added biochar. Soil was added to soil 4 g/dm3 biochar, 8 g/dm3 biochar, the soil had no high humidity, was taken at a time when it had not rained for at least one week, the soil pH was 8, in the soil with 8 g/dm3 biochar the plants increased compared to the soil with 4 g/dm3 and the soil without biochar. The biochar resulting from pyrolysis and gasification processes is a valuable amendment to agricultural soils and an efficient and economical way to seize carbon. Using biochar it is possible to increase the diversity of agricultural land in an environmentally sound way in areas with depleted soils, limited organic resources and insufficient water for development. Helps to soil carbon sequestration with negative CO2 balance, increases the productive potential of agricultural ecosystems.

scholarly journals Effects of Monoculture, Crop Rotation, and Soil Moisture Content on Selected Soil Physicochemical and Microbial Parameters in Wheat Fields

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
A. Marais ◽  
M. Hardy ◽  
M. Booyse ◽  
A. Botha
Keyword(s):  
Soil Moisture ◽  
Microbial Communities ◽  
Crop Rotation ◽  
Management Practices ◽  
Agricultural Soils ◽  
Soil Microbial Communities ◽  
Soil Microbial Activity ◽  
Most Probable Number ◽  
Probable Number ◽  
Soil Microbial

Different plants are known to have different soil microbial communities associated with them. Agricultural management practices such as fertiliser and pesticide addition, crop rotation, and grazing animals can lead to different microbial communities in the associated agricultural soils. Soil dilution plates, most-probable-number (MPN), community level physiological profiling (CLPP), and buried slide technique as well as some measured soil physicochemical parameters were used to determine changes during the growing season in the ecosystem profile in wheat fields subjected to wheat monoculture or wheat in annual rotation with medic/clover pasture. Statistical analyses showed that soil moisture had an over-riding effect on seasonal fluctuations in soil physicochemical and microbial populations. While within season soil microbial activity could be differentiated between wheat fields under rotational and monoculture management, these differences were not significant.

scholarly journals An FDA-Based Approach for Clustering Elicited Expert Knowledge

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 184-204
Author(s):  
Carlos Barrera-Causil ◽  
Juan Carlos Correa ◽  
Andrew Zamecnik ◽  
Francisco Torres-Avilés ◽  
Fernando Marmolejo-Ramos
Keyword(s):  
Functional Data ◽  
Expert Knowledge ◽  
Probability Distributions ◽  
Knowledge Elicitation ◽  
Parallel Analysis ◽  
Data Sets ◽  
Dimensional Problem ◽  
Prior Distributions ◽  
Infinite Dimensional ◽  
Finite Dimensional

Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

scholarly journals Why do Irish pig farmers use medications? Barriers for effective reduction of antimicrobials in Irish pig production

2021 ◽  
Vol 74 (1) ◽  
Author(s):  
Alessia Diana ◽  
Sylvia Snijders ◽  
Alison Rieple ◽  
Laura Ann Boyle
Keyword(s):  
Management Practices ◽  
Structured Interviews ◽  
Pig Production ◽  
Face To Face ◽  
Potential Barriers ◽  
Eu Countries ◽  
Lack Of Information ◽  
Personal Action ◽  
Barriers And Challenges ◽  
Attitudinal Changes

Abstract Background In addressing the threat of antimicrobial resistance, it is critical to understand the barriers to the uptake of strategies for the reduction of antimicrobial use (AMU) in the pig industry. In several EU countries, factors such as education level, habits and social pressures are recognised as affecting farmers’ decision-making process in relation to AMU. However, there is a lack of information on the Irish scenario. The aim of this study was to investigate pig farmers’ perspectives and their behaviour towards AMU to identify potential barriers to effectively reduce AMU in Irish pig production. We conducted face-to-face semi-structured interviews with 30 pig farmers, 5 pig veterinarians and 4 focus groups of pig farm personnel. We employed qualitative analyses to explore the objective of the study. Results Qualitative analysis revealed six convergent themes as potential barriers: perceptions about the need for AMU on farm, concept of animal welfare and associated management practices, legislation, culture, economics and standards of communication/type of advice-network. Overall, pig farmers believed that there is poor communication between stakeholders (i.e. farmers, vets and advisors) and a lack of reliable people to approach for advice. They considered themselves as operating responsibly in terms of AMU compared to their national and international colleagues and expressed the importance of a so-called ‘Irish solution’ to the problem of AMU because it was associated with what ‘has always been done’ and was therefore considered reliable and safe. Conclusions Barriers and challenges were in line with those identified in other EU countries highlighting similarities in behavioural and attitudinal patterns among pig farmers. Overall, farmers appeared to be more likely to rely on previous experiences or to wait for an imposed change (e.g. legislation) instead of taking personal action. Thus, considerable behavioural and attitudinal changes are needed to adopt a more responsible AMU in Irish pig production and to develop effective intervention strategies.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

scholarly journals Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 212
Author(s):  
Youssef Skandarani ◽  
Pierre-Marc Jodoin ◽  
Alain Lalande
Keyword(s):  
Deep Learning ◽  
Cardiac Mri ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Ground Truth ◽  
Cine Mri ◽  
Data Sets ◽  
Mri Segmentation ◽  
Segmentation Evaluation ◽  
Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

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