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pmid:33380773

Global Gridded Nitrogen Indicators: Influence of Crop Maps.

K Kaltenegger · W Winiwarter

Global biogeochemical cycles · 2020 doi:10.1029/2020GB006634

Paper

complete

Experiment

partial

Data

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Analysis

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2026-11-01T15:41:21Z
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title
Global Gridded Nitrogen Indicators: Influence of Crop Maps.
doi
10.1029/2020GB006634
pmid
33380773
authors array · 2 items
[0]
K Kaltenegger
[1]
W Winiwarter
journal
Global biogeochemical cycles
year
2020
abstract
Displaying Nitrogen (N) indicators on a global grid poses unique opportunities to quantify environmental impacts from N application in different world regions under a variety of conditions. Such calculations require the use of maps showing the geo-spatial distribution of crop production. Although there are several crop maps in the scientific literature to choose from, the consequences of this choice for the calculation of N indicators still need to be evaluated. In this study we analyze the differences in results for N Use Efficiency (NUE) and N surplus calculated on the global scale using two different crop maps (SPAM and M3). For our calculations we used publicly available statistical and literature data combined with each crop map and carefully traced the origins of the differences in the results. Our results showed that the regions most affected by discrepancies caused by differences in crop maps (yields and physical area) are Central Asia and the Russian Federation, Australia and Oceania, and North Africa. However, we also found that the inclusion or exclusion of grass crops influences the results, as does the aggregation of crops to categories. Considering all these differences, we note that M3 seems to provide the more plausible results for the calculation of N indicators. Our analysis not only highlights the importance of determining the critical parameters for N indicator calculation, but also allows key parameters connected with N use and overuse to be identified on the global scale.
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The data that support the findings of this study are openly available in the supporting information and in the IIASA data repository (DARE) (at https://doi.org/10.22022/air/10‐2020.109).
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sections[28] "Data Availability Statement" paragraph 2
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The data that support the findings of this study are openly available in the supporting information and in the IIASA data repository (DARE) (at https://doi.org/10.22022/air/10‐2020.109).
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Ratios Between M3 and SPAM of Different Influences on NUE and N Surplus Results and Shares of Grass Crops in Harvested Area
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PMC7757259
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17
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N Surplus at Soil Surface Level
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Table 1
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Ratios Between M3 and SPAM of Different Influences on NUE and N Surplus Results and Shares of Grass Crops in Harvested Area
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[0]
Region
[1]
SPAM/M3 cropland ratio
[2]
SPAM/M3 manure N ratio
[3]
SPAM/M3 N deposition ratio
[4]
SPAM/M3 harvested area ratio
[5]
SPAM/M3 IFA fertilizer ratio
[6]
SPAM/M3 BNF ratio
[7]
SPAM/M3 production ratio
[8]
SPAM/M3 N harvest ratio
[9]
% grass crops in total harvested area
[1] array · 10 items
[0]
Australia and Oceania
[1]
0.92
[2]
0.92
[3]
0.95
[4]
1
[5]
1.01
[6]
0.94
[7]
0.99
[8]
1.09
[9]
0.05
[2] array · 10 items
[0]
Caribbean
[1]
0.67
[2]
1.02
[3]
0.97
[4]
0.98
[5]
0.95
[6]
0.99
[7]
0.73
[8]
0.87
[9]
0
[3] array · 10 items
[0]
Central Africa
[1]
0.79
[2]
1.07
[3]
0.79
[4]
0.93
[5]
0.92
[6]
0.96
[7]
0.97
[8]
0.92
[9]
0
[4] array · 10 items
[0]
Central America
[1]
0.71
[2]
0.85
[3]
0.71
[4]
0.95
[5]
1.01
[6]
0.94
[7]
0.94
[8]
0.88
[9]
0.09
[5] array · 10 items
[0]
Central Asia and Russian Federation
[1]
0.49
[2]
0.82
[3]
0.46
[4]
1.06
[5]
1
[6]
0.95
[7]
0.99
[8]
1.27
[9]
0.28
[6] array · 10 items
[0]
East Africa
[1]
0.77
[2]
1.01
[3]
0.71
[4]
1.04
[5]
1.02
[6]
0.92
[7]
1.06
[8]
1.01
[9]
0
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[0]
Eastern and South Eastern Europe
[1]
0.76
[2]
0.98
[3]
0.78
[4]
0.93
[5]
1.03
[6]
0.8
[7]
0.79
[8]
1.03
[9]
0.15
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[0]
Eastern Asia
[1]
1.14
[2]
0.99
[3]
1.23
[4]
1
[5]
1
[6]
0.95
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0.99
[8]
0.99
[9]
0.02
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[0]
North Africa
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0.77
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0.87
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[5]
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0.83
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0.97
[8]
1.15
[9]
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Northern America
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0.7
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0.65
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1
[6]
0.98
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0.88
[8]
0.97
[9]
0.2
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1
[5]
1
[6]
1
[7]
0.97
[8]
0.93
[9]
0.04
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1
[3]
0.94
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0.99
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1
[6]
0.99
[7]
1.13
[8]
1.16
[9]
0
[13] array · 10 items
[0]
Southern Africa
[1]
0.43
[2]
0.75
[3]
0.43
[4]
1.01
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1
[6]
1.04
[7]
1.05
[8]
1
[9]
0.19
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Southern Asia
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1
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1
[7]
0.95
[8]
0.89
[9]
0
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Western Asia
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0.7
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0.93
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0.77
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0.97
[5]
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[6]
0.93
[7]
0.97
[8]
1.05
[9]
0.05
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[0]
Western Industrial Europe
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0.69
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0.78
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0.7
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0.7
[8]
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[9]
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Country NUEs Based on the Respective Crop Map and Grass and Forage Crop Shares in Total Harvested Area (Grass and Forage Crop Shares Only Hold for M3)
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Country Results
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Table 2
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Country NUEs Based on the Respective Crop Map and Grass and Forage Crop Shares in Total Harvested Area (Grass and Forage Crop Shares Only Hold for M3)
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[0]
Country (ISO code)
[1]
Region
[2]
NUE SPAM IFA
[3]
NUE M3 IFA no grass
[4]
Grass crops (%)
[5]
Forage crops (%)
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[0]
ARG
[1]
South America
[2]
77%
[3]
87%
[4]
12%
[5]
4%
[2] array · 6 items
[0]
AUS
[1]
Australia and Oceania
[2]
71%
[3]
64%
[4]
5%
[5]
0%
[3] array · 6 items
[0]
BGR
[1]
Eastern and South Eastern Europe
[2]
49%
[3]
57%
[4]
7%
[5]
2%
[4] array · 6 items
[0]
BLZ
[1]
Central America
[2]
49%
[3]
52%
[4]
0%
[5]
0%
[5] array · 6 items
[0]
BRA
[1]
South America
[2]
63%
[3]
65%
[4]
0%
[5]
0%
[6] array · 6 items
[0]
CAN
[1]
Northern America
[2]
66%
[3]
61%
[4]
22%
[5]
1%
[7] array · 6 items
[0]
CHN
[1]
Eastern Asia
[2]
32%
[3]
33%
[4]
1%
[5]
1%
[8] array · 6 items
[0]
COG
[1]
Central Africa
[2]
49%
[3]
46%
[4]
0%
[5]
0%
[9] array · 6 items
[0]
CUB
[1]
Caribbean
[2]
30%
[3]
26%
[4]
0%
[5]
0%
[10] array · 6 items
[0]
CYP
[1]
Western Asia
[2]
23%
[3]
11%
[4]
60%
[5]
33%
[11] array · 6 items
[0]
DEU
[1]
Western Industrial Europe
[2]
81%
[3]
71%
[4]
3%
[5]
27%
[12] array · 6 items
[0]
EGY
[1]
North Africa
[2]
37%
[3]
34%
[4]
17%
[5]
0%
[13] array · 6 items
[0]
ESP
[1]
Western Industrial Europe
[2]
47%
[3]
34%
[4]
7%
[5]
2%
[14] array · 6 items
[0]
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[1]
Western Industrial Europe
[2]
63%
[3]
49%
[4]
31%
[5]
1%
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[0]
FRA
[1]
Western Industrial Europe
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77%
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77%
[4]
12%
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12%
[16] array · 6 items
[0]
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Western Industrial Europe
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[1]
West Africa
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76%
[3]
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[5]
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[0]
IDN
[1]
Southeastern Asia
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38%
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37%
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0%
[5]
0%
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[0]
IND
[1]
Southern Asia
[2]
29%
[3]
30%
[4]
4%
[5]
4%
[20] array · 6 items
[0]
IRL
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Western Industrial Europe
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72%
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38%
[4]
40%
[5]
2%
[21] array · 6 items
[0]
ITA
[1]
Western Industrial Europe
[2]
51%
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43%
[4]
18%
[5]
6%
[22] array · 6 items
[0]
KEN
[1]
East Africa
[2]
45%
[3]
45%
[4]
0%
[5]
0%
[23] array · 6 items
[0]
MEX
[1]
Central America
[2]
39%
[3]
41%
[4]
4%
[5]
2%
[24] array · 6 items
[0]
MYS
[1]
Southeastern Asia
[2]
44%
[3]
45%
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0%
[5]
0%
[25] array · 6 items
[0]
NZL
[1]
Australia and Oceania
[2]
182%
[3]
160%
[4]
18%
[5]
36%
[26] array · 6 items
[0]
PRT
[1]
Western Industrial Europe
[2]
27%
[3]
25%
[4]
28%
[5]
25%
[27] array · 6 items
[0]
RUS
[1]
Central Asia and Russian Federation
[2]
91%
[3]
56%
[4]
33%
[5]
7%
[28] array · 6 items
[0]
SWE
[1]
Western Industrial Europe
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72%
[3]
57%
[4]
42%
[5]
0%
[29] array · 6 items
[0]
TUR
[1]
Western Asia
[2]
56%
[3]
54%
[4]
2%
[5]
0%
[30] array · 6 items
[0]
USA
[1]
Northern America
[2]
63%
[3]
62%
[4]
20%
[5]
2%
[31] array · 6 items
[0]
ZAF
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Southern Africa
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56%
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51%
[4]
21%
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0%
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Summary of Main Discrepancies and Causes Found Between SPAM‐ and M3‐Based NUE Calculations
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Key Findings
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Summary of Main Discrepancies and Causes Found Between SPAM‐ and M3‐Based NUE Calculations
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Central Asia and Russian Federation
[1]
Crop Map & Grass Crops
[2]
Yield difference in “Wheat” (SPAM/M3: 1.3) and “Other Cereals” (SPAM/M3: 1.7) that are increased when N harvest is calculated (SPAM/M3: 1.3 & 1.9)
[2] array · 1 item
[0]
More N input in M3 due to larger cropland area (grass crops)—M3: 4.4 Tg N, SPAM: 3.7 Tg N
[3] array · 3 items
[0]
North Africa
[1]
Crop Map/Crop Categorization
[2]
More N input in M3‐based NUE due to larger cropland area—M3: 2.6 Tg N, SPAM: 2.2 Tg
[4] array · 1 item
[0]
SPAM inclusion of olives in “Other Oilseeds” leads to higher SPAM‐based N harvest—M3: 75 kt N, SPAM: 202 kt N
[5] array · 3 items
[0]
Australia and Oceania
[1]
Crop Map
[2]
Yield difference in “Wheat” and “Other Cereals”
[6] array · 1 item
[0]
More N input in M3 due to higher production of “Residuals” leading to more BNF (M3: 72 kt N, SPAM: 60 kt N). More N input in M3 due to larger cropland area (grass crops)
[7] array · 3 items
[0]
Southern Spain
[1]
Crop Categorization
[2]
SPAM inclusion of olives in “Other Oilseeds” leads to higher SPAM‐based N harvest
[8] array · 3 items
[0]
India
[1]
Crop Distribution
[2]
Sub‐regional crop distribution differences between SPAM and M3
[9] array · 3 items
[0]
North America, Ireland, Russia
[1]
Crop/Grass Distribution
[2]
Many areas with only M3 cropland
[10] array · 3 items
[0]
Central Africa, Indonesia, Madagascar, Papua New Guinea
[1]
Cropland Allocation
[2]
M3 and SPAM allocate cropland to very different areas
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Complete file 
{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33380773", "kind": "science-record", "revision": 3, "content_hash": "sha256:06d9aedda5d3b4133b471cb39f9787dd3a249885dbea12900db4dd07d9fdb5a1", "created_at": "2026-11-01T15:41:21Z", "updated_at": "2026-11-01T18:54:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Global Gridded Nitrogen Indicators: Influence of Crop Maps.", "doi": "10.1029/2020GB006634", "pmid": "33380773", "authors": ["K Kaltenegger", "W Winiwarter"], "journal": "Global biogeochemical cycles", "year": 2020, "abstract": "Displaying Nitrogen (N) indicators on a global grid poses unique opportunities to quantify environmental impacts from N application in different world regions under a variety of conditions. Such calculations require the use of maps showing the geo-spatial distribution of crop production. Although there are several crop maps in the scientific literature to choose from, the consequences of this choice for the calculation of N indicators still need to be evaluated. In this study we analyze the differences in results for N Use Efficiency (NUE) and N surplus calculated on the global scale using two different crop maps (SPAM and M3). For our calculations we used publicly available statistical and literature data combined with each crop map and carefully traced the origins of the differences in the results. Our results showed that the regions most affected by discrepancies caused by differences in crop maps (yields and physical area) are Central Asia and the Russian Federation, Australia and Oceania, and North Africa. However, we also found that the inclusion or exclusion of grass crops influences the results, as does the aggregation of crops to categories. Considering all these differences, we note that M3 seems to provide the more plausible results for the calculation of N indicators. 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Shares of Grass Crops in Harvested Area", "structure": {"pmcid": "PMC7757259", "section_index": 17, "section_title": "N Surplus at Soil Surface Level", "row_count": 18, "column_count": 10}, "table": {"label": "Table 1", "caption": "Ratios Between M3 and SPAM of Different Influences on NUE and N Surplus Results and Shares of Grass Crops in Harvested Area", "rows": [["Region", "SPAM/M3 cropland ratio", "SPAM/M3 manure N ratio", "SPAM/M3 N deposition ratio", "SPAM/M3 harvested area ratio", "SPAM/M3 IFA fertilizer ratio", "SPAM/M3 BNF ratio", "SPAM/M3 production ratio", "SPAM/M3 N harvest ratio", "% grass crops in total harvested area"], ["Australia and Oceania", "0.92", "0.92", "0.95", "1", "1.01", "0.94", "0.99", "1.09", "0.05"], ["Caribbean", "0.67", "1.02", "0.97", "0.98", "0.95", "0.99", "0.73", "0.87", "0"], ["Central Africa", "0.79", "1.07", "0.79", "0.93", "0.92", "0.96", "0.97", "0.92", "0"], ["Central America", "0.71", "0.85", "0.71", "0.95", "1.01", "0.94", "0.94", "0.88", 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["Western Asia", "0.7", "0.93", "0.77", "0.97", "0.99", "0.93", "0.97", "1.05", "0.05"], ["Western Industrial Europe", "0.69", "0.91", "0.78", "0.89", "0.98", "0.7", "0.7", "1.03", "0.13"]], "row_count": 18, "column_count": 10, "numeric_cells": 153, "header_rows": 1, "locator": "section[17]/table-wrap[1]#gbc21061-tbl-0001"}}, {"id": "asset:table:pmid:33380773:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:edf31f1ecb5803367620a6eac34854beb4c30db0e0c767e2ea558b01a0f526f7", "bytes": 2043, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[19]/table-wrap[1]#gbc21061-tbl-0002", "version": null, "note": "Country NUEs Based on the Respective Crop Map and Grass and Forage Crop Shares in Total Harvested Area (Grass and Forage Crop Shares Only Hold for M3)", "structure": {"pmcid": "PMC7757259", "section_index": 19, "section_title": "Country Results", "row_count": 32, "column_count": 6}, "table": {"label": "Table 2", "caption": "Country NUEs Based on the Respective Crop Map and Grass and Forage Crop Shares in Total Harvested Area (Grass and Forage Crop Shares Only Hold for M3)", "rows": [["Country (ISO code)", "Region", "NUE SPAM IFA", "NUE M3 IFA no grass", "Grass crops (%)", "Forage crops (%)"], ["ARG", "South America", "77%", "87%", "12%", "4%"], ["AUS", "Australia and Oceania", "71%", "64%", "5%", "0%"], ["BGR", "Eastern and South Eastern Europe", "49%", "57%", "7%", "2%"], ["BLZ", "Central America", "49%", "52%", "0%", "0%"], ["BRA", "South America", "63%", "65%", "0%", "0%"], ["CAN", "Northern America", "66%", "61%", "22%", "1%"], ["CHN", "Eastern Asia", "32%", "33%", "1%", "1%"], ["COG", "Central Africa", "49%", "46%", "0%", "0%"], ["CUB", "Caribbean", "30%", "26%", "0%", "0%"], ["CYP", "Western Asia", "23%", "11%", "60%", "33%"], ["DEU", "Western Industrial Europe", "81%", "71%", "3%", "27%"], ["EGY", "North Africa", "37%", "34%", "17%", "0%"], ["ESP", "Western Industrial Europe", "47%", "34%", "7%", "2%"], ["FIN", "Western Industrial Europe", "63%", "49%", "31%", "1%"], ["FRA", "Western Industrial Europe", "77%", "77%", "12%", "12%"], ["GBR", "Western Industrial Europe", "81%", "73%", "21%", "3%"], ["GHA", "West Africa", "76%", "78%", "0%", "0%"], ["IDN", "Southeastern Asia", "38%", "37%", "0%", "0%"], ["IND", "Southern Asia", "29%", "30%", "4%", "4%"], ["IRL", "Western Industrial Europe", "72%", "38%", "40%", "2%"], ["ITA", "Western Industrial Europe", "51%", "43%", "18%", "6%"], ["KEN", "East Africa", "45%", "45%", "0%", "0%"], ["MEX", "Central America", "39%", "41%", "4%", "2%"], ["MYS", "Southeastern Asia", "44%", "45%", "0%", "0%"], ["NZL", "Australia and Oceania", "182%", "160%", "18%", "36%"], ["PRT", "Western Industrial Europe", "27%", "25%", "28%", "25%"], ["RUS", "Central Asia and Russian Federation", "91%", "56%", "33%", "7%"], ["SWE", "Western Industrial Europe", "72%", "57%", "42%", "0%"], ["TUR", "Western Asia", "56%", "54%", "2%", "0%"], ["USA", "Northern America", "63%", "62%", "20%", "2%"], ["ZAF", "Southern Africa", "56%", "51%", "21%", "0%"]], "row_count": 32, "column_count": 6, "numeric_cells": 124, "header_rows": 1, "locator": "section[19]/table-wrap[1]#gbc21061-tbl-0002"}}, {"id": "asset:table:pmid:33380773:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:80efaf6da93a6428b1e2c33eb21090372972939f51ef7d9eff1f37113fa61f0a", "bytes": 1597, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[21]/table-wrap[1]#gbc21061-tbl-0003", "version": null, "note": "Summary of Main Discrepancies and Causes Found Between SPAM‐ and M3‐Based NUE Calculations", "structure": {"pmcid": "PMC7757259", "section_index": 21, "section_title": "Key Findings", "row_count": 11, "column_count": 3}, "table": {"label": "Table 3", "caption": "Summary of Main 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