scholarly journals Tea Bags—Standard Materials for Testing Impacts of Nitrogen Addition on Litter Decomposition in Aquatic Ecosystems?

Nitrogen ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 259-267
Author(s):  
Taiki Mori
Keyword(s):  
Time Series ◽  
Litter Decomposition ◽  
Green Tea ◽  
Aquatic Ecosystems ◽  
Time Series Data ◽  
Nitrogen Addition ◽  
Series Data ◽  
N Addition ◽  
Rooibos Tea ◽  
Tea Bag Index

How the anthropogenic addition of nutrients, especially nitrogen (N), impacts litter decomposition has attracted extensive attention, but how environmental factors other than nutrients affect the impacts of N addition on litter decomposition is less understood. Since different local litters could respond differently to N addition, standard materials are necessary for comparing the impacts among various environments. The present study tested if tea bags used for the Tea Bag Index (TBI) approach, i.e., constructing an asymptote model by using a green tea decomposition datum and a rooibos tea decomposition datum (single measurement in time), can be standard materials for testing the impacts of N addition on litter decomposition in aquatic ecosystems. A laboratory incubation experiment was performed using a water sample taken from a stream in Kumamoto, Japan. Since a recent study suggested that the TBI approach may be inapplicable to aquatic ecosystems, a time-series data approach, i.e., fitting models to time-series mass loss data of tea bags, was also used for testing if tea bag decomposition can pick up the impacts of N addition on aquatic litter decomposition. The time-series data approach demonstrated that N addition significantly suppressed rooibos tea decomposition, whereas green tea decomposition was not affected by N addition. The TBI approach was unsuitable for testing the sensitivity of the response of tea bag decomposition to N addition because the TBI-based asymptote model failed to predict the observed data, confirming the suggestion by a previous study. Overall, the present study suggested that the tea bags can be used as standard materials for testing the impacts of N addition on litter decomposition in aquatic ecosystems, but only when using a time-series measurement and not the TBI.

scholarly journals Testing the Tea Bag Index as a potential indicator for assessing litter decomposition in aquatic ecosystems

2021 ◽  
Author(s):  
Taiki Mori ◽  
Kenji Ono ◽  
Yoshimi Sakai
Keyword(s):  
Litter Decomposition ◽  
Green Tea ◽  
Aquatic Ecosystems ◽  
Terrestrial Ecosystems ◽  
Terrestrial Environment ◽  
Validation Data ◽  
Standard Material ◽  
Tea Bag Index ◽  
Potential Indicator

AbstractThe Tea Bag Index (TBI) approach is a standardized method for assessing litter decomposition in terrestrial ecosystems. This method allows determination of the stabilized portion of the hydrolysable fraction during the decomposition process, and derivation of a decomposition constant (k) using single measurements of the mass-loss ratios of green and rooibos teas. Although this method is being applied to aquatic systems, it has not been validated in these environments, where initial leaching tends to be higher than in terrestrial ecosystems. Here, we first validated a critical assumption of the TBI method that green tea decomposition plateaus during the standard incubation period of 90 days, and then tested the accuracy of a TBI-based asymptote model using a second model obtained from fitting actual decomposition data. Validation data were obtained by incubating tea bags in water samples taken from a stream, a pond, and the ocean in Kumamoto, Japan. We found that green tea decomposition did not plateau during the 90-day period, contradicting a key assumption of the TBI method. Moreover, the TBI-based asymptote models disagreed with actual decomposition data. Subtracting the leachable fraction from the initial tea mass improved the TBI-based model, but discrepancies with the actual decomposition data remained. Thus, we conclude that the TBI approach, which was developed for a terrestrial environment, is not appropriate for aquatic ecosystems. However, the use of tea bags as a standard material in assessments of aquatic litter decomposition remains beneficial.

scholarly journals Tea Bag Index to Assess Carbon Decomposition Rate in Cranberry Agroecosystems

Soil Systems ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 44
Author(s):  
Wilfried Dossou-Yovo ◽  
Serge-Étienne Parent ◽  
Noura Ziadi ◽  
Élizabeth Parent ◽  
Léon-Étienne Parent
Keyword(s):  
Litter Decomposition ◽  
Green Tea ◽  
Decomposition Rate ◽  
Terrestrial Ecosystems ◽  
Carbon Accumulation ◽  
N Leaching ◽  
Decomposition Rates ◽  
Rooibos Tea ◽  
Tea Bag Index ◽  
Stabilization Factor

In cranberry production systems, stands are covered by 1–5 cm of sand every 2–5 years to stimulate plant growth, resulting in alternate layers of sand and litter in soil upper layers. However, almost intact twigs and leaves remain in subsurface layers, indicating a slow decomposition rate. The Tea Bag Index (TBI) provides an internationally standardized methodology to compare litter decomposition rates (k) and stabilization (S) among terrestrial ecosystems. However, TBI parameters may be altered by time-dependent changes in the contact between litter and their immediate environment. The aims of this study were to determine the TBI of cranberry agroecosystems and compare it to the TBI of other terrestrial ecosystems. Litters were standardized green tea, standardized rooibos tea, and cranberry residues collected on the plantation floor. Litter decomposition was monitored during two consecutive years. Added N did not affect TBI parameters (k and S) due to possible N leaching and strong acidic soil condition. Decomposition rates (k) averaged (mean ± SD) 9.7 × 10−3 day−1 ± 1.6 × 10−3 for green tea, 3.3 × 10−3 day−1 ± 0.8 × 10−5 for rooibos tea, and 0.4 × 10−3 day−1 ± 0.86 × 10−3 for cranberry residues due to large differences in biochemical composition and tissue structure. The TBI decomposition rate (k) was 0.006 day−1 ± 0.002 in the low range among terrestrial ecosystems, and the stabilization factor (S) was 0.28 ± 0.08, indicating high potential for carbon accumulation in cranberry agroecosystems. Decomposition rates of tea litters were reduced by fractal coefficients of 0.6 for green tea and 0.4 for rooibos tea, indicating protection mechanisms building up with time in the tea bags. While the computation of the TBI stabilization factor may be biased because the green tea was not fully decomposed, fractal kinetics could be used as additional index to compare agroecosystems.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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