scholarly journals Reconstruction of spatially detailed global map of NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>−</sup> application in synthetic nitrogen fertilizer

2016 ◽  
Author(s):  
Kazuya Nishina ◽  
Akihiko Ito ◽  
Naota Hanasaki ◽  
Seiji Hayashi
Keyword(s):  
Multiple Imputation ◽  
Nitrogen Fertilizer ◽  
Time Series Data ◽  
Fertilizer Application ◽  
Imputation Method ◽  
N Fertilizer ◽  
Series Data ◽  
Specific Information ◽  
Model Studies ◽  
Multiple Imputation Method

Abstract. This paper provides a method for constructing a new historical global nitrogen fertilizer application map (0.5° × 0.5° resolution) for the period 1961–2010 based on country-specific information from Food and Agriculture Organization statistics (FAOSTAT) and various global datasets. This new map incorporates the fraction of NH4+ (and NO3−) in N fertilizer inputs by utilizing fertilizer species information in FAOSTAT, in which species can be categorized as NH4+ and/or NO3−-forming N fertilizers. During data processing, we applied a statistical data imputation method for the missing data (19 % of national N fertilizer consumption) in FAOSTAT. The multiple imputation method enabled us to fill gaps in the time-series data using plausible values using covariates information (year, population, GDP, and crop area). After the imputation, we downscaled the national consumption data to a gridded cropland map. Also, we applied the multiple imputation method to the available chemical fertilizer species consumption, allowing for the estimation of the NH4+/NO3− ratio in national fertilizer consumption. In this study, the synthetic N fertilizer inputs in 2000 showed a general consistency with the existing N fertilizer map (Potter et al., 2010) in relation to the ranges of N fertilizer inputs. Globally, the estimated N fertilizer inputs based on the sum of filled data increased from 15 Tg-N to 110 Tg-N during 1961–2010. On the other hand, the global NO3− input started to decline after the late 1980s and the fraction of NO3− in global N fertilizer decreased consistently from 35 % to 13 % over a 50-year period. NH4+ based fertilizers are dominant in most countries; however, the NH4+/NO3− ratio in N fertilizer inputs shows clear differences temporally and geographically. This new map can be utilized as an input data to global model studies and bring new insights for the assessment of historical terrestrial N cycling changes. Datasets available at doi:10.1594/PANGAEA.861203.

scholarly journals Reconstruction of spatially detailed global map of NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>−</sup> application in synthetic nitrogen fertilizer

2017 ◽  
Vol 9 (1) ◽  
pp. 149-162 ◽  
Author(s):  
Kazuya Nishina ◽  
Akihiko Ito ◽  
Naota Hanasaki ◽  
Seiji Hayashi
Keyword(s):  
Multiple Imputation ◽  
Nitrogen Fertilizer ◽  
Input Data ◽  
Fertilizer Application ◽  
Imputation Method ◽  
N Fertilizer ◽  
Series Data ◽  
Biogeochemical Model ◽  
Specific Information ◽  
Multiple Imputation Method

Abstract. Currently, available historical global N fertilizer map as an input data to global biogeochemical model is still limited and existing maps were not considered NH4+ and NO3− in the fertilizer application rates. This paper provides a method for constructing a new historical global nitrogen fertilizer application map (0.5°  ×  0.5° resolution) for the period 1961–2010 based on country-specific information from Food and Agriculture Organization statistics (FAOSTAT) and various global datasets. This new map incorporates the fraction of NH4+ (and NO3−) in N fertilizer inputs by utilizing fertilizer species information in FAOSTAT, in which species can be categorized as NH4+- and/or NO3−-forming N fertilizers. During data processing, we applied a statistical data imputation method for the missing data (19 % of national N fertilizer consumption) in FAOSTAT. The multiple imputation method enabled us to fill gaps in the time-series data using plausible values using covariates information (year, population, GDP, and crop area). After the imputation, we downscaled the national consumption data to a gridded cropland map. Also, we applied the multiple imputation method to the available chemical fertilizer species consumption, allowing for the estimation of the NH4+ ∕ NO3− ratio in national fertilizer consumption. In this study, the synthetic N fertilizer inputs in 2000 showed a general consistency with the existing N fertilizer map (Potter et al., 2010) in relation to the ranges of N fertilizer inputs. Globally, the estimated N fertilizer inputs based on the sum of filled data increased from 15 to 110 Tg-N during 1961–2010. On the other hand, the global NO3− input started to decline after the late 1980s and the fraction of NO3− in global N fertilizer decreased consistently from 35 to 13 % over a 50-year period. NH4+-forming fertilizers are dominant in most countries; however, the NH4+ ∕ NO3− ratio in N fertilizer inputs shows clear differences temporally and geographically. This new map can be utilized as input data to global model studies and bring new insights for the assessment of historical terrestrial N cycling changes. Datasets available at doi:10.1594/PANGAEA.861203.

scholarly journals Asymptotic Standard Errors of Parameter Scale Transformation Coefficients in Test Equating Under the Nominal Response Model

2020 ◽  
pp. 014662162096574
Author(s):  
Zhonghua Zhang
Keyword(s):  
Multiple Imputation ◽  
Imputation Method ◽  
Delta Method ◽  
Scale Transformation ◽  
Standard Errors ◽  
Response Model ◽  
Test Equating ◽  
Nominal Response Model ◽  
Multiple Imputation Method ◽  
Nominal Response

Researchers have developed a characteristic curve procedure to estimate the parameter scale transformation coefficients in test equating under the nominal response model. In the study, the delta method was applied to derive the standard error expressions for computing the standard errors for the estimates of the parameter scale transformation coefficients. This brief report presents the results of a simulation study that examined the accuracy of the derived formulas and compared the performance of this analytical method with that of the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion standard errors as well as those yielded by the multiple imputation method under all the simulation conditions.

A New Imputation Method for Treating Missing Precipitation Records

2014 ◽  
Vol 635-637 ◽  
pp. 1488-1495
Author(s):  
Yu Liu ◽  
Feng Rui Chen
Keyword(s):  
Time Series Data ◽  
Absolute Error ◽  
Imputation Method ◽  
Temporal Information ◽  
Series Data ◽  
Precipitation Trend ◽  
Monthly Total Precipitation ◽  
The Mean ◽  
Spatio Temporal ◽  
Precipitation Records

This study aims to present a new imputation method for missing precipitation records by fusing its spatio-temporal information. On the basis of extending simple kriging model, a nonstationary kriging method which assumes that the mean or trend is known and varies in whole study area was proposed. It obtains precipitation trend of each station at a given time by analyzing its time series data, and then performs geostatistical analysis on the residual between the trend and measured values. Finally, these spatio-temporal information is integrated into a unified imputation model. This method was illustrated using monthly total precipitation data from 671 meteorological stations of China in April, spanning the period of 2001-2010. Four different methods, including moving average, mean ratio, expectation maximization and ordinary kriging were introduced to compare with. The results show that: Among these methods, the mean absolute error, mean relative error and root mean square error of the proposed method are the smallest, so it produces the best imputation result. That is because: (1) It fully takes into account the spatio-temporal information of precipitation. (2) It assumes that the mean varies in whole study area, which is more in line with the actual situation for rainfall.

scholarly journals Multiple Imputation for Missing Values with an Empirical Application

2021 ◽  
pp. 1-18
Author(s):  
Theodora Sotiropoulou ◽  
Stefanos Giakoumatos ◽  
Antonios Georgopoulos
Keyword(s):  
Multiple Imputation ◽  
Financial Development ◽  
Missing Values ◽  
Time Series Data ◽  
Trade Openness ◽  
Banking System ◽  
Series Data ◽  
Diagnostic Tools ◽  
The European Union ◽  
Economic Data

Abstract Missing data are the most common problem in many research areas. For cross-section and time-series data, imputation can be a challenging problem. The most widely used method for filling missing observations is the multiple imputation which increase the number of the available data and thereby reducing biases that may occur when observations with missing values are simply deleted. The main purpose of this paper is to employ a bootstrapping expectation–maximization (EM) algorithm in order to impute missing values mainly to economic data. In the application we use a dataset that is consisted by annual panel data for the 27 countries of the European Union covering the period 2000-2017. The data were obtained from the databases of World Bank and Eurostat namely the Global Financial Development Database, The Standardized World Income Inequality Database by Solt (2019) and the World Development Indicators. Different indicators were chosen representing the development of banking system and stock markets, economic growth, economic inequality, innovation, fiscal policy, physical and human capital, and trade openness. Finally, diagnostic tools are used inspecting the imputations that are created. Keywords: Multiple imputation, Amelia II, Economic data, Financial development, Inequality.

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