Dataset Information

Nitrogen dynamics in flooded soil systems: an overview on concepts and performance of models.

ABSTRACT: Extensive modelling studies on nitrogen (N) dynamics in flooded soil systems have been published. Consequently, many N dynamics models are available for users to select from. With the current research trend, inclined towards multidisciplinary research, and with substantial progress in understanding of N dynamics in flooded soil systems, the objective of this paper is to provide an overview of the modelling concepts and performance of 14 models developed to simulate N dynamics in flooded soil systems. This overview provides breadth of knowledge on the models, and, therefore, is valuable as a first step in the selection of an appropriate model for a specific application. © 2017 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

SUBMITTER: Nurulhuda K

PROVIDER: S-EPMC5814887 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Nitrogen dynamics in flooded soil systems: an overview on concepts and performance of models.

Nurulhuda Khairudin K Gaydon Donald S DS Jing Qi Q Zakaria Mohamad P MP Struik Paul C PC Keesman Karel J KJ

Journal of the science of food and agriculture 20171127 3

Extensive modelling studies on nitrogen (N) dynamics in flooded soil systems have been published. Consequently, many N dynamics models are available for users to select from. With the current research trend, inclined towards multidisciplinary research, and with substantial progress in understanding of N dynamics in flooded soil systems, the objective of this paper is to provide an overview of the modelling concepts and performance of 14 models developed to simulate N dynamics in flooded soil sys ...[more]

PMID: 28940491

Similar Datasets

Project description:In this study, the complex interactions between soil types, compaction, and moisture on nitrogen (N) transformation processes such as ammonia (NH3) volatilization, ammonification, nitrification, and denitrification were examined over a 30-day period using a simulated column approach. Two soil types: loam, and sandy loam, were subjected to three compaction treatments-control, surface, and sub-surface compaction-and two moisture regimes, dry and wet. Liquid urea ammonium nitrate (32-0-0) was used as the N fertilizer source at a rate of 200 kg N ha-1. Key indicators of N transformations were measured, including residual concentrations of ammonium (NH4-N) and nitrate (NO3-N), NO3-N leaching, NH3 volatilization, and nitrous oxide (N2O) emissions. Findings revealed that compaction significantly increased residual NH4-N concentrations in deeper soil profiles, with the highest 190.80 mg kg-1 recorded in loam soil under sub-surface compaction and dry conditions. Nitrification rates decreased across both soil types due to compaction, evidenced by elevated residual NH4-N levels. Increased NO3-N leaching was observed in loam soil (178.06 mg L-1), greater than sandy loam (81.11 mg L-1), due to initial higher residual NO3- in loam soil. The interaction of compaction and moisture most affected N2O emissions, with the highest emissions in control treatments during dry weather at 2.88 kg ha -1. Additionally, higher NH3 volatilization was noted in moist sandy loam soil under control conditions at 19.64 kg ha -1. These results highlight the necessity of considering soil texture, moisture, and compaction in implementing sustainable N management strategies in agriculture and suggest recommendations such as avoiding broadcast application in moist sandy loam and loam soil to mitigate NH3 volatilization and enhance N use efficiency, as well as advocating for readjustment of fertilizer rate based on organic matter content to reduce potential NO3-N leaching and N2O emissions, particularly in loam soil.

Project description:Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.

Dataset Information

Nitrogen dynamics in flooded soil systems: an overview on concepts and performance of models.

Publications

Nitrogen dynamics in flooded soil systems: an overview on concepts and performance of models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets