scholarly journals Regional estimation of net anthropogenic nitrogen inputs (NANI) and the relationship with socio-economic factors

2020 ◽  
Author(s):  
Haolin Xu ◽  
Weimin Xing ◽  
Peiling Yang ◽  
Chang Ao
Keyword(s):  
Unit Area ◽  
Hubei Province ◽  
Economic Factors ◽  
Fertilizer Use ◽  
Nitrogen Inputs ◽  
N Input ◽  
Input Source ◽  
Anthropogenic Nitrogen ◽  
N Pollution ◽  
N Fertilizer Use

Abstract Background: The accurate evaluation of net anthropogenic nitrogen inputs (NANI) is very important for making countermeasures to control N pollution. The N inputs of Hubei has a crucial impact on the eco-environment of the downstream Yangtze Basin. Our objective was to estimate the NANI of Hubei province and access the relationships between the components of NANI and socio-economic indices for controlling N pollution in the Yangtze River basin. Methods: The spatiotemporal distribution and the main components of NANI at city scale in Hubei province from 2008-2018 were discussed by the NANI model with ArcGIS 10.6. The relationships between the components of NANI and 6 economic factors, including gross industrial output value per unit area (GIOV), gross agricultural output value per unit area (GAOV), grain yield per unit area (GY), fertilizer consumption density (FCD), population density(PD) and, cultivated land area per unit area(CLA), was estimated using a Pearson analysis. Results: NANI in Hubei tended to increase from 14782.62 kg/(km2∙a) in 2008 to 16700.32 kg/(km2∙a) in 2012, and then fell to 13630.40 kg/(km2∙a) in 2018. NANI was higher in center and east than in west and southeast of Hubei province. N fertilizer use (Nfer), which accounted for 61.27% of NANI, was the largest N input source, followed by net N import in food＆feed (Nim), atmospheric N deposition (Ndep), N fixation (Nfix), and seeding N (Nsee). Pearson correlation analysis showed that FCD was the primary factor responsible for NANI in Hubei province (r=0.956), followed by GAOV (r=0.606) and CLA (r=0.527). The most direct driving factors of Ndep, Nfer, Nsee and Nim were GIOV (r=0.466), FCD (r=0.979), CLA (r=0.813) and GAOV (r=0.745), respectively. All factors had a significant negative impact on Nfix. Conclusions: The NANI decline strategy is to control the fertilizer application amount, as well as improving agricultural construction. Also, it’s necessary to eliminate some backward technology as well as high pollution industries, and support the development of ecological industries, which is beneficial to reduce the risk of N pollution.Highlight:(1) The calculation method of N import in food was improved by distinguishing the diet structure of urban population and rural population.(2) NANI was higher in plain areas and smaller in the mountain areas. (3) NANI increased first and then decreased from 2008 to 2018 in Hubei.(4) N fertilizer use was the largest N input source and fertilizer consumption was the primary factor to NANI.

Assessing Nitrogen Legacies in Western Europe

2021 ◽  
Author(s):  
Andreas Musolff ◽  
Sophie Ehrhardt ◽  
Rémi Dupas ◽  
Rohini Kumar ◽  
Pia Ebeling ◽  
...  
Keyword(s):  
Time Series ◽  
Agricultural Land ◽  
Time Lags ◽  
Water Safety ◽  
Biological Fixation ◽  
Nitrogen Inputs ◽  
Wide Range ◽  
N Input ◽  
Nitrate Concentrations ◽  
Input Time

<p>Intensive agricultural land use have introduced vast quantities of nutrients such as reactive nitrogen (N) to soils and subsequently to groundwater and surface waters. High nitrate concentrations are still a pressing issue for drinking water safety and aquatic ecosystem health e.g. in Europe, although fertilizer inputs have been significantly lowered in the last decades. This is partly due to a slow response of riverine nitrate concentrations to changes in nitrogen inputs attributed to N legacies in catchments. N can be stored organically bound as a biogeochemical legacy in soils or can be slowly transported as nitrate in groundwater forming a hydrologic legacy. Legacy can thus lead to a net retention of N in catchments and to substantial time lags in the response to input changes. Here, we systematically explore legacy effects over a wide range of catchment in the Western European countries France and Germany. We are making use of long observational time series of nitrate concentration in 238 catchments covering 40% of the total area of France and Germany. We apply a Weighted Regression on Time, Discharge, and Season (WRTDS) to derive continuous daily flow-normalized concentrations and loads. The temporal pattern of concentration and loads at the catchment outlet is compared to the N input time series evolving from agricultural N surplus, atmospheric deposition and biological fixation. We found that on long-term catchments retain on average 72% of the N input. Time lags between input and output were successfully explained by a lognormal transport time distribution. The modes of these distributions were found to be rather short with a median mode of 5.4 years across all catchments. Based on this data-driven assessment only the fate of N in the catchments is hard to assess as denitrification in soil and groundwater can lead to similar observations as the storage of N in legacies. Focusing on the mobile part of N that is exported by catchments, we estimate that a substantial amount of N is still stored in the subsurface that will be released in the coming years. We therefore analyzed how catchment nitrate export will evolve under the scenario of a total cut down, reduced or constant future N inputs. We report the expected timescale of reaction to implemented measures to help tackling this pressing water quality problem.</p>

scholarly journals Increasing anthropogenic nitrogen inputs and riverine DIN exports from the Changjiang River basin under changing human pressures

2010 ◽  
Vol 24 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Weijin Yan ◽  
Emilio Mayorga ◽  
Xinyan Li ◽  
Sybil P. Seitzinger ◽  
A. F. Bouwman
Keyword(s):  
River Basin ◽  
Changjiang River ◽  
The Changjiang River ◽  
Nitrogen Inputs ◽  
Anthropogenic Nitrogen

Net anthropogenic nitrogen inputs to watersheds and riverine N export to coastal waters: a brief overview

2012 ◽  
Vol 4 (2) ◽  
pp. 203-211 ◽  
Author(s):  
Dennis P Swaney ◽  
Bongghi Hong ◽  
Chaopu Ti ◽  
Robert W Howarth ◽  
Christoph Humborg
Keyword(s):  
Coastal Waters ◽  
Nitrogen Inputs ◽  
Anthropogenic Nitrogen

ADDING VALUE TO FIELD-BASED AGRONOMIC RESEARCH THROUGH CLIMATE RISK ASSESSMENT: A CASE STUDY OF MAIZE PRODUCTION IN KITALE, KENYA

2011 ◽  
Vol 47 (2) ◽  
pp. 317-338 ◽  
Author(s):  
P. N. DIXIT ◽  
P. J. M. COOPER ◽  
J. DIMES ◽  
K. P. RAO
Keyword(s):  
Weed Control ◽  
N Fertilizer ◽  
Sub Saharan Africa ◽  
Climate Risk ◽  
Weather Data ◽  
Rates Of Return ◽  
Maize Production ◽  
Fertilizer Use ◽  
N Fertilizer Use

SUMMARYIn sub-Saharan Africa (SSA), rainfed agriculture is the dominant source of food production. Over the past 50 years much agronomic crop research has been undertaken, and the results of such work are used in formulating recommendations for farmers. However, since rainfall is highly variable across seasons the outcomes of such research will depend upon the rainfall characteristics of the seasons during which the work was undertaken. A major constraint that is faced by such research is the length of time for which studies could be continued, typically ranging between three and five years. This begs the question as to what extent the research was able to ‘sample’ the natural longer-term season-to-season rainfall variability. Without knowledge of the full implications of weather variability on the performance of innovations being recommended, farmers cannot be properly advised about the possible weather-induced risks that they may face over time. To overcome this constraint, crop growth simulation models such as the Agricultural Production Systems Simulator (APSIM) can be used as an integral part of field-based agronomic studies. When driven by long-term daily weather data (30+ years), such models can provide weather-induced risk estimates for a wide range of crop, soil and water management innovations for the major rainfed crops of SSA. Where access to long-term weather data is not possible, weather generators such as MarkSim can be used. This study demonstrates the value of such tools in climate risk analyses and assesses the value of the outputs in the context of a high potential maize production area in Kenya. MarkSim generated weather data is shown to provide a satisfactory approximation of recorded weather data at hand, and the output of 50 years of APSIM simulations demonstrate maize yield responses to plant population, weed control and nitrogen (N) fertilizer use that correspond well with results reported in the literature. Weather-induced risk is shown to have important effects on the rates of return ($ per $ invested) to N-fertilizer use which, across seasons and rates of N-application, ranged from 1.1 to 6.2. Similarly, rates of return to weed control and to planting at contrasting populations were also affected by seasonal variations in weather, but were always so high as to not constitute a risk for small-scale farmers. An analysis investigating the relative importance of temperature, radiation and water availability in contributing to weather-induced risk at different maize growth stages corresponded well with crop physiological studies reported in the literature.

scholarly journals MAXIMIZING WATER AND N FERTILIZER USE EFFICIENCIES UNDER MAIZE CROP AT NORTH DELTA

2012 ◽  
Vol 3 (1) ◽  
pp. 27-39
Author(s):  
H. A. Sonbol ◽  
Z. M. El-Sirafy ◽  
E. A. E. Gazia ◽  
H. A. Shams El-Din ◽  
Sahar H. Rashed
Keyword(s):  
N Fertilizer ◽  
Maize Crop ◽  
Fertilizer Use ◽  
N Fertilizer Use
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