Adalibieke et al. (2023). Global crop-specific nitrogen fertilization dataset in 1961–2020

www.nature.com/scientificdata

Global crop-specific nitrogen

fertilization dataset in 1961–2020

Wulahati Adalibieke

1,3

, Xiaoqing

Cui

1,3

, Hongwei

Cai

1

, Liangzhi

You

2

& Feng

Zhou

1

✉

Nitrogen (N) is an important nutrient for crop growth. However, the overuse of N fertilizers has led
to a series of devastating global environmental issues. Recent studies show that multiple datasets
have been created for agricultural N fertilizer application with varied temporal or spatial resolutions,
nevertheless, how to synchronize and use these datasets becomes problematic due to the inconsistent
temporal coverages, spatial resolutions, and crop-specific allocations. Here we reconstructed a
comprehensive dataset for crop-specific N fertilization at 5-arc-min resolution (~10 km by 10 km)
during 1961–2020, including N application rate, types, and placements. The N fertilization data was
segmented by 21 crop groups, 13 fertilizer types, and 2 fertilization placements. Comparison analysis
showed that our dataset is aligned with previous estimates. Our spatiotemporal N fertilization dataset
could be used for the land surface models to quantify the effects of agricultural N fertilization practices
on food security, climate change, and environmental sustainability.

Background & Summary

Nitrogen (N) is one of the most essential nutrients for crop production. Since the development of the

Haber-Bosch process, the consumption of synthetic N fertilizers has increased exponentially to meet the food

demand of growing human population

1

,

2

. However, excess N fertilizers are escaping into the environment, via

a cascade of processes through atmospheric, aquatic, marine and terrestrial pools

3

. For example, agricultural

N pollutants due to emitting, run-offs and leaching could result in severe air pollution (i.e., fine particle matter

and aerosols)

, global climate change (i.e., ozone depletion in the stratosphere)

, eutrophication

and compro-

mised water quality in surface and groundwater aquatic ecosystems

, biodiversity loss

as well as acidification

in terrestrial ecosystems

13

. In order to assess the effect of N fertilization, which was defined as N application

rate, types (including synthetic N fertilizers, manure and crop residues) and placement, on the environment

at different scales (especially for a globally large scale), diverse datasets of agricultural N fertilization practices

segmented by various temporal and spatial resolutions are urgently needed.

N fertilization has been considered as an important input in multiple models, e.g., the Organising Carbon

and Hydrology In Dynamic Ecosystems model (ORCHIDE-Crop)

and PKU-NRUNOFF model

14

require

crop-specific N inputs, the Soil & Water Assessment Tool (SWAT)

15

and the Magnitude And Seasonality of

Agricultural Emissions model for NH

3

(MASAGE_NH3 models)

16

require fertilizer types as input data besides.

More importantly, the DeNitrification-DeComposition model (DNDC)

17

18

and the flux upscaling model for N

2

O

emissions

19

simultaneously need crop-specific N inputs, fertilizer types, and application placement as input data.

Generally, the country-level N fertilizer applied to agriculture could be obtained from the Food and Agriculture

Organization of the United Nations (FAO)

and the International Fertilizer Association (IFA)

, which have

been widely used in global and national research. However, detailed geospatial distributions, which are usually

required in many process-based models, are unavailable from these international statistics. Crop-specific N

fertilizer application rate at a resolution of 5 arc-min was firstly generated by Mueller

et al

22

, whereas this study

only focused on the rates for the year 2000. The related dataset was further expanded to a period of 1961–2013 at

a relatively lower resolution of 0.5 degree

×

0.5 degree and segmented by NH

4

+

and NO

3

−

forming N fertilizers

Moreover, Wang

et al

.

extended the temporal period of N application rate data into the year 1961–2014, but

only into two categories (i.e., upland crops and paddy rice) through aggregating administrative-unit statistic

data. Meanwhile, a 5-arc-min gridded global dataset pertaining to global manure N production and application

in cropland during 1860–2015 was reconstructed by Zhang

et al

25

. Historic anthropogenic N inputs to terrestrial

1

institute of carbon neutrality, Laboratory for earth Surface Processes, college of Urban and environmental

Sciences, Peking University, Beijing, 100871, China.

2

international food Policy Research institute (ifPRi),

Washington, DC20005, USA.

3

These authors contributed equally: Wulahati Adalibieke, Xiaoqing Cui.

✉

e-mail:

zhouf@pku.edu.cn

DATA DeSCRipTOR

OpeN

2

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

biosphere with 5 arc-min during 1960–2019 were also reconstructed, including annual rates of synthetic N

fertilizer, manure application, and atmospheric N deposition on cropland, pasture, and rangeland

26

. It is noted

that most current research and datasets mainly focus on N fertilization inputs instead of N fertilizer types and

N fertilization placement. Moreover, those datasets did not show long-term and high-resolution patterns of

crop-specific N fertilization (rate, type, placement) and were not at uniform spatial resolutions or with consistent

allocation algorithms, making it impossible to use them together.

To provide input data for the land surface models for assessing the effects of agricultural N fertilization

practices on food security, climate change and ecosystem health, we developed a new global crop-specific N fer-

tilization dataset. This newly developed N fertilization dataset covers not only more than one single fertilization

rate but also crop-specific N application rate, N fertilizer types, as well as N application placement at a spatial

resolution of 5 arc-min in the past six decades. This dataset was developed based on multiple statistical datasets

(e.g., the FAOSTAT

, the IFASTAT

, and the EUROSTAT

27

), empirical estimates as well as gridded dataset

(e.g., gridded harvested area from the EARTHSTAT

, gridded N application for Wang

et al

and Zhan

et al

and gridded climate data from Climatic Research Unit (CRU)

30

). This dataset also combines previous datasets at

different spatial and temporal scales to reconstruct the important components of N fertilization (i.e., rate, type

and placement) by crop types. The uncertainties of crop-specific N application rate were estimated and further

comparison between our new N fertilization dataset and existing N fertilization datasets were also analyzed. The

limitations that users should be aware and the future research priorities to build a more accurate and compre-

hensive N fertilization dataset were also discussed in the “Usage Notes”.

Methods

A comprehensive crop-specific N fertilization dataset was reconstructed across the global agricultural soils,

including the N application applied to twenty-one crops groups with different fertilizer types and application

methods. The N fertilization was defined as N application of synthetic N fertilizers, crop residues and manure to

cropland in this study. The N application amount can be quantified as below:

Namount

Harea

Nrate

FerRatio

Placement

(1)

cro f p y g

cro y g

f y g

cro f p y g

, , , ,

, ,

, , , ,

=

×

where

Namount

cro,f,p,y,g

(kg N) is N application amount of fertilizer type

f

applied by placement

p

to crop

cro

in

grid

g

in the year

y

.

Harea

cro,y,g

(ha) is harvested area of crop

cro

in grid

g

in the year

y

.

Nrate

cro,y,g

(kg N ha

−

1

) is N

application rate (N application amount per harvested area) of crop

cro

in grid

g

in the year

y

.

FerRatio

f,y,g

(%) is

the percentage of fertilizer

f

inputs to total N application amount in grid

g

in the year

y

.

Placement

cro,f,p,y,g

(%) is

the percentage of surface placement

p

(or deep placement) inputs to total inputs of fertilizer

f

applied to crop

cro

in grid

g

in the year

y

. It’s noteworthy that crops were aggregated into 21 crop groups according to Zhan

et al

.

The detailed methods for generating each individual item were described as below, respectively. The workflow of

methods is presented in Fig.

1

. Main variables used in equations see Table

1

. Main data used for developing this

new crop-specific N fertilization dataset are listed in Table

2

.

Crop-specific harvested areas.

The gridded crop-specific harvested area (ha) for the year 1961–

2020 (

Harea

cro,y,g

) was integrated from gridded harvested area in the year 2000 from the EARTHSTAT

and

country-level statistics of crop-specific harvested area during the period of 1961–2020 from the FAOSTAT

. The

historical areas of arable land and permanent crops (here referred to cropland areas), were also included from the

History Database of the Global Environment (HYDE version 3.2)

to identify the spatial distributions of crop-

land and to map the statistics of harvested areas onto a grid.

The gridded harvested area from the EARTHSTAT

is relatively detailed and covers a wide range of crop

types, which was developed based on agricultural census and survey information of 175 crops from the 22,050

political units reasonably obtainable for 206 countries during 1997–2003. Thus, we developed crop-specific

gridded harvested area based on this dataset. First, the harvested area of 136 crops (accounting for 92.9% of

total harvested area) from the EARTHSTAT

were integrated into 21 crop groups and administrative units (i.e.,

county/district, a lower level subdivision than state or province) according to Zhan

et al

and political bound-

aries from the GADM

32

, respectively. Note that, arable forage crops (such as green maize, alfalfa) or temporary

and permanent grasslands are not included in this study. Details for crop information see Table

3

Then, the national harvested area from the FAOSTAT

was then used to extend the harvested area from

the EARTHSTAT

(as a reference map for the distribution of harvested area) in the year 2000 to the period of

1961–2020. The harvested area can be extended by the following equation:

=

×

Harea

HareaM

HareaFAO

(2)

cro y i

cro

i

cro y j

cro

j

, ,

,2000,

, ,

,2000,

where,

Harea

cro,y,i

is harvested area of crop

cro

in the county/district

i

in the year

y

.

HareaM

cro,2000,i

is harvested

area in the county/district

i

around the year 2000 from the EARTHSTAT

HareaFAO

cro,y,j

is harvested area of

crop

cro

in the country

j

from the FAOSTAT

. Here, we assumed that the annual changes within each county/

district (

i

) of country (

j

) were consistent with the annual changes in that respective country. The harvested area

of the countries, which experienced political disintegration, was distributed to individual newly-formed coun-

tries using the ratio derived from the harvested area in the first year after disintegration.

Thirdly, the crop-specific harvested area for each county/district during the period of 1961–2020 (

Harea

cro,y,i

)

was mapped onto a grid by spatially disaggregating it using the dynamic cropland distribution from the

HYDE3.2

. The HYDE3.2

provided distribution and area croplands for a long time series (annual time-step

after the year 2000 to 2017 and a decadal time-step during 1700–2000). The mapping of the gridded crop-specific

3

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

harvested area was in proportion to the cropland area of each grid in each county/district, which can be calcu-

lated as below:

=











×

=

×

=

Harea

Carea

if cro

rice

Harea

Carea

if cro

rest of crops

(3)

cro y g

cro y i

rice y g

rice y i

cro y i

upland y g

upland y i

, ,

where,

Harea

cro,y,g

is the harvested area of crop

cro

in grid

g

.

Carea

rice,y,g

and

Carea

upland,y,g

are cropland area of rice

and upland for grid

g

from the HYDE3.2

, respectively.

Carea

rice,y,i

and

Carea

upland,y,i

are total calculated cropland

area of rice and upland for county/district

i

from the HYDE3.2

. Due to the cropland area from the HYDE3.2

was available only up to the year 2017 and at decadal time-step before the 1990s, we assumed that the cropland

area after the year 2017 was the same as that in 2017 and the cropland area before the year 2000 was constant

in each decade. Lastly, to be comparable with that from the FAOSTAT

, the disaggregated gridded dataset was

matched by the total crop-specific harvested area of each country from the FAOSTAT

Crop-specific N application rate.

The gridded crop-specific N application rate (including synthetic N

fertilizers (SN), crop residues (CR) and manure (MA)) for the year 1961–2020 (

Nrate

cro,y,g

) was derived from the

gridded N application amount in 1961–2014 developed by Wang

et al

.

, the country level N consumption for

Fig. 1

Workflow for developing the crop-specific N application dataset.

4

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

agriculture from the FAOSTAT

(including SN, CR, and MA), the country-level crop-specific fertilizer consump-

tion from FUBC

and the N application rate for 21 crop groups from Zhan

et al

.

Due to its high spatial and temporal resolution, the gridded N application amount in 1961–2014 provided by

Wang

et al

.

was used as a reference dataset to develop the crop-specific N application rate. Firstly, the gridded

N application amount (divided into SN, CR, and MA) from Wang

et al

was integrated into the GADM admin-

istrative units

32

such as county, district or equivalent that is a lower level subdivision than state or province.

Then, SN, MA, and CR application amount from the FAOSTAT

spanning the period from 1961–2020 were

utilized to extend and calibrate the above aggregated N application amount. Since the N application amount

from the FAOSTAT

includes both the cropland use and the other agricultural uses (e.g., pasture), we separated

fertilizer use applied to croplands for each county/district based on the country-scale crop-wise proportion

34

, by

assuming the same proportion of cropland use within a certain country. The calculation was described as below:

=

×

≤ ≤

NamountW

NamountFAO

if

y

2015

2020

(4)

f y i

f

i

f y j

f

j

, ,

,2014,

, ,

,2014

Where

NamountW

f,y,i

is N application amount of fertilizer

f

(SN, MA or CR) for county/district

i

in the year

y

(from 2015 to 2020).

NamountW

f,2014,i

is N application amount of fertilizer

f

(SN, MA or CR) developed by Wang

et al

.

for county/district

i

in 2014.

NamountFAO

f,y,j

and

NamountFAO

f,2014,j

is application amount of fertilizer

f

(SN, MA or CR) from FAOSTAT

for country

j

in the year

y

and 2014.

Note that, total N application amount from Wang

et al

.

was on average 12% lower than the FAOSTAT

that removed the pasture synthetic-fertilizer application, due to the reduced amount of SN application. Thus, we

corrected SN application amount from Wang

et al

to the FAOSTAT

. CR and MA application amount were

consistent with Wang

et al

.

. Then, the total N application amount (

Namount

f,y,i

) of this dataset was obtained

and the calculation was described as below:

Namount

NamountW

if f

CR or MA

NamountW

NamountFAO

NamountW

if f

SN

(5)

f y i

SN y j

f y j

, ,

=











=

×

=

Variables

Definition

Sources

Units

cro

Crop groups defined in this study

Zhan

et al

—

cro2

Crop groups defined in FUBC

FUBC

—

f

Fertilizer types

Developed by authors

—

p

Placements

Developed by authors

—

y

Year

Developed by authors

—

g

Grid

Developed by authors

—

i

County/district

Developed by authors

—

j

Country

Developed by authors

—

m

Country or region

Developed by authors

—

Namount

cro,f,p,y,g

N application amount

Developed by authors

kg N

Nrate

cro,y,g

Total N application rate

Developed by authors

kg N ha

−

1

Harea

cro,y,g

Harvested area

Developed by authors

ha

FerRatio

f,y,g

The percentage of fertilizer

f

inputs to total N application amount

Developed by authors

%

Placement

cro,f,p,y,g

The percentage of surface placement (or deep placement) inputs to total

application of fertilizer

f

Developed by authors

%

HareaM

cro,2000,i

Harvested area around 2000 from the EARTHSTAT

EARTHSTAT

ha

HareaFAO

cro,y,j

Harvested area from the FAOSTAT

FAOSTAT

ha

Carea

upland,y,g

Cropland area for upland crops from the HYDE3.2

HYDE3.2

ha

Carea

paddy rice,y,g

Cropland area for paddy rice from the HYDE3.2

HYDE3.2

ha

NamountFAO

f,y,i

N application amount from the FAOSTAT

FAOSTAT

kg N

NamountW

f,y,i

N application amount from Wang

et al

.

Wang

et al

.

kg N

NrateZ

cro,f,2000,i

N application rate in the year 2000 from Zhan

et al

.

Zhan

et al

kg N ha

−

1

NconFUBC

cro2,SN,y0,j

SN fertilizer consumption from FUBC

FUBC

kg N ha

−

1

FerIFA

f,y,m

N application amount of fertilizer

f

from the IFASTAT

IFASTAT

kg N

FerIFA

total,y,m

N application amount of total synthetic fertilizers from the IFASTAT

IFASTAT

kg N

Placement

cro,f,deep,y,g

The percentage of deep placement N inputs to total N inputs

Developed by authors

%

Placement

cro,f,surface,y,g

The percentage of surface placement N inputs to total N inputs

Developed by authors

%

NoTill

cro,y,g

Non-tillage ratio

Developed by authors

%

Table 1.

Main variables used in methods. Note: - means dimensionless.

5

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

Then, since gridded crop-specific N application rate of SN, MA, and CR from Zhan

et al

.

were only avail-

able in 2000, we assumed that the N application rate changed at the same ratio for all crops across all the time

during 1961–2020 according to Zhang

et al

.

35

. We used the same calculation as Zhang

et al

.

35

to calculate the N

application rate of fertilizer

f

(SN, MA, and CR) applied to crop

cro

for county/district

i

in the year

y

(

Nrate

cro,f,y,i

).

=

×

∑

×

Nrate

NrateZ

Namount

NrateZ

Harea

(6)

cro f y i

cro f

i

f y i

cro

cro f

i

cro y i

, , ,

, ,2000,

, ,

, ,2000,

, ,

where

Nrate

cro,f,y,i

is N application rate of fertilizer

f

(SN, MA, CR) applied to crop

cro

for county/district

i

in the

year

y

.

NrateZ

cro,f,2000,i

is N application rate of fertilizer

f

(SN, MA, CR) applied to crop

cro

for county/district

i

in the

year 2000 from Zhan

et al

.

Namount

f,y,i

is N application amount of each county/district obtained from Eq. (

).

NamountW

f,y,i

and

Harea

cro,y,g

was obtained above.

Lastly, to minimize the uncertainties of crop-specific N application rate, we calibrated crop-specific N appli-

cation rate after the year 2003 using country-level N synthetic fertilizer consumption reported by the Fertilizer

Use By Crop (FUBC)

for the years 2006, 2007, 2010, 2014 and 2018. Thus, the above-derived crop-specific syn-

thetic N application rate (

Nrate

cro,SN,y0,i

) multiplied with the crop-specific harvested area (

Harea

cro,y0,i

) was used

to achieve crop-specific N application amount, and then synthetic N crop-specific amount of each crop group

belongs to crop group

cro2

was summed up by county/district

i

and corrected with the country-level N synthetic

fertilizer consumption by crop group

cro2

from the FUBC

. The calculation was described below:

Nrate

NconFUBC

Nrate

Harea

if

y

if

y

if

y

if

y

if y

y

2003

2006, 0

2006

2007

2008, 0

2007

2009

2012, 0

2010

2013

2016, 0

2014

2017, 0

2018

(7)

cro f y i

cro SN y j

cro i

cro SN y i

cro y i

, , ,

2,

, 0,

,

, 0,

, 0

=

×

∑

×















≤ ≤

=

≤ ≤

=

≤ ≤

=

≤ ≤

=

≥

=

where

NconFUBC

cro2,SN,y0,j

is SN fertilizer consumption from FUBC

to the 11 crop groups in the year 2006,

2007, 2010, 2014, 2018.

Finally, the county-level crop-specific N application rate (divided into SN, CR, and MA) summed up to

generate the total N application rate and then was resampled into 5 arc-min resolution assuming that N appli-

cation rate of each grid in a county/district was equal to the N application rate of this county/district, thus the

crop-specific N application rate in each grid (

Nrate

cro,y,g

) was obtained.

N fertilizer types.

The gridded percentage of different N fertilizer type inputs to total N application amount

for the year 1961–2020 (

FerRatio

f,y,g

) was integrated from country- and continent-scale annual fertilizer consump-

tion surveys provided by the IFASTAT

and N application amount (

Namount

f,y,i

) developed above. We sorted

N fertilizers into 13 types, include anhydrous ammonia (AA), ammonium nitrate (AN), ammonium sulphate

Data name

Year

Spatial resolution

Reference

Harvested area for 175 crops

average of 1997–2003

5 min by 5 min

EARTHSTAT

Harvested area for 175 crops

1961–2020

Country level

FAOSTAT

Gridded cropland area

1960–2017

5 min by 5 min

HYDE3.2

Nitrogen application rate for 21 crop groups

2000

5 min by 5 min

Zhan

et al

Nitrogen application to cropland

1961–2014

5 min by 5 min

Wang

et al

.

Nitrogen application amount

2014–2020

Country level

FAOSTAT

Crop-specific nitrogen application amount

2006, 2007, 2010, 2014, 2018

Country or region

FUBC

Fertilizer inputs for different fertilizer types

1961, 1973–2020

Country or region

IFASTAT

National GDP

1961–2020

Country level

Word Bank

37

Population

1961–2020

Country level

FAOSTAT

Cropland area

1961–2020

Country level

FAOSTAT

No-tillage area of China

1980–2017

Province level

CAMI

43

No-tillage area by USA

1989–2008

State level

CTIC

44

No-tillage area for 75 countries

1989–2018

Country level

FAOSTAT,EUROSTAT

27

Harvested area of irrigated crops and rainfed crops 2005,2010

5 min by 5 min

SPAM2005,SPAM2010

45

46

Field size

2015

1 km by 1 km

Lesiv

et al

.

47

Income level

1987–2018

Country level

Word Bank

37

Water erosion

2001,2013

5 min by 5 min

Borrelli

et al

48

Potential evapotranspiration and precipitation

1961–2020

0.5° by 0.5°

CRU

30

Table 2.

Main data used for developing this new N fertilization dataset.

6

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

(AS), calcium ammonium nitrate (CAN), nitrogen solutions (NS), other N straight (ONS), Urea, ammonium

phosphate (AP), N K compounds (NK), N P K compounds (NPK), other NP (ONP), crop residues (CR) and

manure (MA).

Consumption of each synthetic fertilizer type was available from both the IFASTAT

and the FAOSTAT

However, about 5.87% and 59.3% of the values from the IFASTAT

and the FAOSTAT

, respectively, was miss-

ing. Hence, we chose to apply fertilizer consumption data from the IFASTAT due to the lesser missing val-

ues. Consumption of SN fertilizers for 1962–1972 from the IFASTAT is missing for all countries and regions.

Moreover, the data is also missing from countries that have experienced political disintegration, especially in the

years following it. As for data from the FAOSTAT

, missing data spread all over the period 1961–2020, espe-

cially for the developing countries. The missing data from the IFASTAT

was filled by the multiple data imputa-

tion method (e.g., “Amelia”) for time-series cross-section datasets proposed by King

et al

.

36

, which were used by

Nishina

et al

to fill the missing fertilizer consumption data from the FAOSTAT. Covariates (e.g., the national

GDP (from the World Bank

37

), population (from the FAOSTAT

), and cropland area (from the FAOSTAT

))

were chosen to apply the “Amelia” algorithm for imputation of the missing values as Nishina

et al

. R package

“Amelia” was used for statistical imputation and settings, which is ideal in handling national-based time-series

and cross-sectional data with advanced statistical imputation protocol

. More details see Nishina

et al

.

Then, the calculation for the percentage of each N fertilizer type inputs to total N application amount can be

sorted into two classifications. For crop residues and manure, the percentage can be directly obtained with the

ratio of crop residue and manure inputs amount and total N application amount. For the remaining 11 synthetic

fertilizers, the percentage was calculated through multiplying the consumption proportion of each synthetic

fertilizer to the total synthetic N fertilizer from the IFASTAT by the proportion of synthetic fertilizer inputs to

total N application amount obtained above.

The percentage of each fertilizer consumption can be calculated as following equations:

=











=

×

FerRatio

Ninputs

f

CR MA

FerIFA

Ninputs

f belongs to SN

,

(8)

f y i

total y i

f y m

total y m

SN y i

total y i

, ,

Crop name in this study Crop name in the EARTHSTAT

Crop name in the FUBC

Rice

rice

Rice

Maize

maize

Maize

Wheat

wheat

Wheat

Barley

barley

Other cereals

Sorghum

sorghum

Millet

millet

Rye

rye

Sugarbeet

sugarbeet

Sugar crops

Sugarcane

sugarcane

Oil palm

oilpalm

Oil palm

Sunflower

sunflower

Other oil crops

Rapeseed

rapeseed

Groundnut

groundnut

Potato

potato

Other crops

Sweetpotato

sweetpotato

Cassava

cassava

Cotton

cotton

Cotton

Soybean

soybean

Soybean

Vegetables

artichoke,asparagus,cabbage,carrot,cauliflower,chilleetc,cucumberetc,eggplant,garlic,greenbean,greenbroadbean,

greencorn,greenonion,greenpea,lettuce,melonetc,mushroom,okra,onion,pumpkinetc,

spinach,stringbean,tomato,vegetablenes,watermelon

Vegetables and fruits

Fruits

apple,apricot,avocado,banana,berrynes,blueberry,carob,cashewapple,cherry,citrusnes,cranberry,currant,

date,fig,fruitnes,gooseberry,grape,grapefruitetc,kiwi,lemonlime,mango,orange,papaya,peachetc,pear,persimmon,

pineapple,plantain,plum,quince,rasberry,sourcherry,stonefruitnes,strawberry,tangetc,tropicalnes

Other crops

abaca,agave,almond,aniseetc,areca,bambara,bean,brazil,broadbean,buckwheat,canaryseed,cashew,castor,cerealnes,

chestnut,chickpea,chicory,cinnamon,clove,cocoa,coconut,coffee,cowpea,fibrenes,flax,fonio,ginger,gums,hazelnut,

hemp,hempseed,hop,jute,jutelikefiber,kapokfiber,kapokseed,karite,kolanut,lentil,linseed,lupin,mate,melonseed,

mixedgrain,mustard,nutmeg,nutnes,oats,oilseednes,olive,pea,pepper,peppermint,pigeonpea,pimento,pistachio,

poppy,pulsenes,pyrethrum,quinoa,ramie,rootnes,rubber,safflower,sesame,sisal,spicenes,sugarnes,taro,tea,tobacco,

triticale,tung,vanilla,vetch,walnut,yam,yautia

Other crops

Table 3.

Crop Designations.

7

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

where,

FerRatio

f,y,i

is the percentage of fertilizer

f

inputs to total N application amount in the county/district

i

in the year

y

.

Namount

f,y,i

,

Namount

SN,y,i

, Namount

total,y,i

was generated above, which represents N application

amount of fertilizer

f

(MA or CR), SN and all fertilizers for county/district

i

in the year

y

, respectively.

FerIFA

f,y,co

and

FerIFA

total,y,co

is the consumption amount of synthetic fertilizer

f

and

total

synthetic fertilizer in the country

or region

m

in the year

y

from the IFASTAT

, respectively.

Lastly, the percentages of each fertilizer were resampled into 5 arc-min resolution to obtain the percentages

of each fertilizer in each grid (

FerRatio

f,y,g

).

N application placement.

The gridded percentage of different N fertilizer types applied with deep

placement (or surface placement) inputs to total input applied to different crop (

Placement

cro,f,deep,y,g

and

Placement

cro,f,surface,y,g

) for the year 1961–2020 was quantified based on fertilizer type, tillage practice, and

base-topdressing application ratio, as referred to Zhan

et al

.

. and can be calculated by the following equations.

=











=

. ×

=

−

=

Placement

f

NoTill

f

Other fetilizer

NoTill

f

CR MA

1

AA, AN

0 7

1

,

(9)

cro f deep y g

cro y g

, ,

= −

Placement

1

(10)

cro f surface y g

cro f deep y g

, ,

where,

Placement

cro,f,deep,y,g

and

Placement

cro,f,surface,y,g

is the percentage of fertilizer

f

applied with deep placement

and surface placement inputs to total inputs applied to crop

cro

in grid

g

in the year

y

, respectively.

NoTill

cro,y,g

is

non-tillage ratio for crop

cro

in grid

g

in the year

y

. 0.7 is base-topdressing application ratio from Nishina

et al

.

Anhydrous ammonia and N solutions, commonly injected, were placed deep or totally infiltrated into soils

38

All manure and crop residues were assumed to be applied before sowing or transplanting, thus the incorporation

of manure and crop residues was linearly correlated to the tillage fraction

39

. For the rest synthetic fertilizer, if it

was applied as base fertilizers, the proportion of incorporation or deep application should be positively related

to the fraction of non-tillage (zero tillage or direct drilling during planting) in high-income countries. Because

in developed countries, the non-tillage was mainly achieved through mechanized seeding and fertilization prac-

tices

40

. However, when the synthetic fertilizer was applied during the top-dressing process if applicable, we

assumed that broadcasting techniques were applied

42

. It is noteworthy that the gridded non-tillage fraction for

each crop during the period of 1961–2020 (

NoTill

cro,y,g

) was specially developed, including the obtain of national

or sub-national non-tillage area and its downscaling to grid and crop.

We firstly calculated the national non-tillage fraction during the period of 1961–2020 with the regression

model provided by Zhan

et al

.

, which fitted the relationship between the cropland area per capita of the

rural population (i.e., a proxy of the spatial variation of labor availability for tillage activity) and country-level

non-tillage fractions (46 countries from the FAOSTAT

and EUROSTAT

27

databases, as well as China

(2000

−

2016)

43

and the United States of America (1989

−

2008)

44

). National cropland area per capita of rural

population for prediction can be obtained from the FAOSTAT

. Then the national non-tillage area can be calcu-

lated through multiplying the above-mentioned fraction and the cropland area from the HYDE3.2

. Specially,

data of non-tillage area by province (or state) for China (2000–2016)

43

and the USA (1989–2008)

44

were publicly

available and can be used directly; for the rest year, the sub-national non-tillage area was obtained by downscal-

ing the above predicted national non-tillage area.

Then, the national non-tillage area was downscaled to grid and crop following the methods by Porwollik

et al

.

with the gridded and country-scale datasets (including the harvested area of irrigated crops and rain-fed crops

from the SPAM2005 and the SPAM2010

,

field size

, income (World Bank

), water erosion

, and aridity

(calculated by potential evapotranspiration and precipitation from the CRU TS V4.06

30

)). Firstly, potential

non-tillage area was allocated based on crop type, field size and local income. The potential non-tillage area was

assumed that derived from the cropland of 11 rain-fed annual crops (Soybean, Wheat, Maize, Barley, Rapeseed,

Sunflower, Sorghum, Cotton, Milled, Groundnut, Vegetables) in grid cells reporting dominant large field size in

low-income countries and all field sizes in high-income countries

Secondly, a logit model combined four predictors (including crop mix, field size, water erosion, and aridity)

was developed to determine the probability of non-tillage occurrence per grid cell as well as the spatial distri-

bution of country-level non-tillage area. Crop mix was defined as the ratio of the area sum of 11 non-tillage

suitable crop types over the sum of total cropland area per grid cell, thus we assumed an increasing probability

for non-tillage area occurrence in grid cells with an increasing cultivated area share of non-tillage suitable crop

types. Farm size is highly positively correlated with the share of non-tillage area on arable land, with the coeffi-

cient of determination r

2

=

0.66 (

p

<

0.001, slope

=

0.116)

, thus non-tillage was thus assumed highly probable

for cropland in grid cells with large fields (here serving as a spatial proxy for large farm size and mechaniza-

tion). Besides, non-tillage was considered to be suitable for arable production under arid conditions, due to

less aeration, more stable pores, and soil aggregates compared to soils managed with conventional tillage

Moreover, erosion levels as allocation criteria, non-tillage was regarded as suitable for crop production in areas

with elevated erosion levels to protect the soil surface

,

. The probability of no-tillage in a grid cell (

NTA

g

)

was derived through the following equation:

8

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

=

+

−∑

−

=

NTA

k vx

xmid

1

exp(

(

)

(11)

g

n

1

4

where

n

represents the input datasets of water erosion, aridity, crop mix (the ratio of sum of harvested area of

no-tillage suitable crop types over the sum of total harvested area per grid cell), and field size (proxy for farm

size),

k

n

refers to the slope value,

xmid

n

to the central points of each of the logit curves, and

vx

n

to grid cell values

of the referring input dataset. Values of

k

n

and xmid

n

are directly derived from Porwollik

et al

.

Thirdly, the abovementioned projected national or sub-national non-tillage area was downscaled to all grid

cells suitable for non-tillage in this country based on the gridded probability. According to the non-tillage prob-

ability, the grid cell in this country was sorted in decreasing order, which was further selected as non-tillage until

the added potential non-tillage area (calculated as grid area multiplied by the probability) reached the projected

country-level non-tillage area threshold. Lastly, crop-specific non-tillage area per grid (

NoTill

cro,y,g

) was attained

by disaggregating the non-tillage area of per grid cell according to the proportion of harvested area occupied by

each crop in this grid.

Uncertainty analysis.

A Monte Carlo simulation method was utilized to estimate the uncertainty for N

application rate. To generate a proper estimate interval, here we mainly accounted for the uncertainties related

to the assumption that the crop-specific N application rate changed at the same ratio for all crop types across all

the time during 1961–2020. Thus, to estimate the uncertainty around the mean changing ratio of N application

rate by crop and by region, we derived standard deviation, coefficient variations (CV) of each crop group for

24 regions or countries from 5-year (i.e. 2006, 2007, 2010, 2014, 2018) crop-specific fertilizer use reported by the

Fertilizer Use By Crop (FUBC)

. Then, we run 1,000 iterations for each crop by randomly varying the annual

change ratio by coefficient variations with normal distributions, so that the 95% prediction interval as well as

standard deviation could be constructed. Undoubtedly, if there is a large inter-annual variation in N application

rate for a particular crop in a region, the uncertainty associated with the estimated N application rate will be

correspondingly high.

Data Records

The gridded N application data by crops, fertilizer, and placement from 1961 to 2020 and the gridded harvested

area (1961–2020) are available at National Tibetan Plateau/Third Pole Environment Data Center

55

. The dataset

extend from 180°E to 180°W longitude and 90°S to 90°N latitude at a resolution of 5 arc-min for the temporal

period 1961–2020 in standard WGS84 coordinate system. The dataset is provided in Hierarchical Data Format

(HDF5) format, which can be read by many tools (i.e., IDL, MATLAB).

The gridded N application data by crops, fertilizer, and placement are stored in the file “N_rate_{‘Crop

name’}_1961–2020.h5”, while {‘Crop name’} represents each crop group listed in Table

. The file contains 26

records named by {‘Fertilizer type name’_‘Placement name’}, while {‘Fertilizer type name’_‘Placement name’}

represents each fertilizer type with two kinds of fertilization placements listed in Table

. In addition, to

Fig. 2

Spatial pattern of crop-specific N application rate (kg N per hectare of harvested area) applied to

cropland in the 1960s and 2010s.

9

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

calculate the N application amount by crop, the gridded harvested area was summarized in the file “Harvested_

area_1961–2020.h5”, containing 21 crop groups named by {‘Crop name’}. The unit of N application rate was kg

N per hectare of harvested area and unit of the harvested area was hectare per 5-arc-min grid cell.

Crop-specific N application.

Crop-specific N application in the 1960s (average from 1961 to 1970) and

2010s (average from 2011 to 2020) were shown in Fig.

2

. The average N application rate applied to cropland was

44.6 kg N ha

−

1

yr

−

1

in the 1960s, mainly in Europe and USA. Average N application rate for maize (49.2 kg N ha

−

1

yr

−

1

), wheat (61.8 kg N ha

−

1

yr

−

1

), vegetables and fruits (67.7 kg N ha

−

1

yr

−

1

) were relatively higher than for other

crops. In the 2010s, average N application rate increased to 100.9 kg N ha

−

1

yr

−

1

, and the hotspots also shifted

from Europe and the USA to China. The N application rate between 1961 and 2020 for rice, vegetables and fruits

showed the highest growth rate, increasing by 237.6%, 175.6%, and 252.3%, respectively, compared to wheat

(152.2%), maize (141.3%), other cereals (86.8%), and rest of crops (34.1%).

N fertilizer types.

The spatial pattern of the N application for each fertilizer was illustrated in Fig.

3

. In

the1960s, the N application rate of manure and crop residues was about 26.1 kg N ha

−

1

yr

−

1

, and the hotspots

Crop name

Fertilizer type name

Placement name

Barley, Cassava, Cotton, Fruits, Groundnut, Maize, Millet, Oilpalm, Potato,

Rapeseed, Rye, Rice, Sorghum, Soybean, Sugarbeet, Sugarcane, Sunflower,

Sweetpotato, Vegetables, Wheat, Other crops

AA, AN, AS, CAN, NS, ONS, Urea, AP, NK,

NPK, ONP, CR, MA

Surface, Deep

Table 4.

List of data record names in the HDF5 files. Note: AA, AN, AS, CAN, NS, ONS, AP, NK, NPK, ONP,

CR, MA refers to anhydrous ammonia, ammonium nitrate, ammonium sulphate, calcium ammonium nitrate,

nitrogen solutions, other N straight, ammonium phosphate, N K compounds, N P K compounds, other NP, crop

residues and manure, respectively.

Fig. 3

Spatial pattern of N application rate for different fertilizers (kg N per hectare of harvested area) applied to

cropland. Fertilizers include Urea, nitrate fertilizers (ammonium nitrate (AN) and calcium ammonium nitrate

(CAN)), compound fertilizers (ammonium phosphate (AP), N K compounds (NK), N P K compounds (NPK),

other NP (ONP)), AA&NS (anhydrous ammonia (AA) and N solutions (NS)), other synthetic fertilizers (other

N straight (ONS) and ammonium sulphate (AS)) and MA&CR (manure (MA) and crop residues (CR)).

10

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

were mainly in USA, Europe and south America. Hotspots of synthetic fertilizers varied depending on the ferti-

lizer types. For urea, nitrate fertilizers and compound fertilizers, the hotspots were mainly distributed in Eastern

Europe, while for other synthetic fertilizers were mainly in Southeastern China and Middle Europe. Anhydrous

ammonia and N solutions were mainly applied in Middle USA. Both the N application rate and spatial pattern of

different N-consumed fertilizers changed significantly after five decades, e.g., the contributions of synthetic fer-

tilizers grew from 41.5% to 64.4%. Urea as the most important synthetic-N fertilizers, with an average N applica-

tion rate more than 100 kg N ha

−

1

yr

−

1

, shared 31.0% of global N fertilizer consumption in 2010s and were mainly

distributed in China, India and Pakistan. The hotspots of compound fertilizers and manure and crop residues also

expanded to China, while the hotspots of nitrate fertilizers, other synthetic fertilizers, anhydrous ammonia and

N solutions didn’t change much.

N application placement.

Crop-specific N application rate by deep placement in the 1960s and 2010s were

displayed in Fig.

. The average N application rate applied by deep placement enhanced from 28.0 kg N ha

−

1

yr

−

1

in the 1960s to 41.3 kg N ha

−

1

yr

−

1

in the 2010s, while the hotspots were mainly in southern USA, southeast of

Fig. 4

Spatial pattern of crop-specific N application rate (kg N per hectare of harvested area) applied by deep

placement in the 1960s and 2010s.

Fig. 5

Uncertainties of global N application rate (kg N ha

−

1

) for 8 crop groups. The grey color area in each panel

shows the 95% confidence interval of N application rate estimates by Monte Carlo simulations, whereas the

average value is presented by a black line.

11

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

South America, northwestern Europe and Australia during 1961–2020. N application rate by deep placement for

most of crops has increased (64.7% for wheat, 64.4% for rice, 68.9% for maize, 46.9% for other cereals, 63.6% for

vegetables and fruits and 132.9% for oil crops) during 1961–2020.

Fig. 6

Spatial pattern of uncertainties related to N application rate (kg N ha

−

1

) for 8 crop groups in 2010s.

Fig. 7

Relative differences between crop-specific N application rate in this dataset with data from the first to

fifth FUBC report for the year during 1978–2001. The box plots show the range of relative differences, whereas

numbers besides the box plots represents sample size of country-year.

12

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

Technical Validation

To check the spatiotemporal consistency and plausibility between the newly developed global crop-specific N

fertilization dataset and previous datasets, we did a comprehensive comparison analysis, including the uncer-

tainty analysis, direct validation analysis against various available data, etc.

Fig. 8

A comparison of N application rate between data reported by the United States Department of

Agriculture (USDA) and this dataset.

Fig. 9

A comparison of N application rate between data reported by Cost and Income of Chinese Farm Produce

(NDRCC) and this dataset.

13

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

Firstly, we utilized Monte Carlo simulations to quantify the uncertainties of crop-specific N application rate

derived from the assumption, that the N application rate changed at the same ratio for all crops all the time.

Fig. 10

Comparison of country-level consumption per harvested area (kg N ha

−

1

) of different fertilizer types

during 1962–1972. Panel (a–c) represent comparison per ha of harvested area of urea, ammonium nitrate and

ammonium sulphate filled by Amelia approach against official data from the FAOSTAT

, respectively. Panel

(d) represents comparisons of sum of fertilizer consumption per ha of harvested area filled by Amelia approach

against total fertilizer consumption from the IFASTAT

. Each point represents the fertilizer consumption of a

specific country in a particular year.

Fig. 11

Comparison for the percentage of deep placement in China during 1980–2017 between this study and

Adalibieke

et al

.

42

14

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

The 95% confidence intervals of crop-specific N application rate by crop, regions, and years can be gained, and

are shown in Figs.

. Globally, uncertainties of wheat, rice, maize, sugar crops and vegetables&fruits at 95%

confidence interval accounted for

±

27.9%,

±

31.5%,

±

24.4%,

±

25.1%,

±

35.0% of average N application rate

during 1961–2020, respectively (Fig.

). Uncertainties of other cereals, oil crops, and rest of crops were larger

(

±

42.0%,

±

50.8% and

±

80.0%), however, the three together only accounting for 34.9% of the total N applica-

tion amount (Fig.

). In addition, the spatial patterns of uncertainties related to the crop-specific N application

rate were also generated (Fig.

). Spatial heterogeneity of uncertainties was striking, regardless of crop types.

For the vast majority of regions, the standard deviation (SD) of crop-specific N application rate were within

acceptable limits, but in specific regions and for specific crops, the SD of the N application rate exceeded 20 kg N

ha

−

1

yr

−

1

(Fig.

). However, it is worth noting that N application rate tended to be higher in these regions and

crops, such as China, Russia, and Australia for wheat; China and South Asia for rice; and the USA, China, and

South America for maize. As a result, the coefficient of variation (CV) for these regions is relatively low, usually

below 20%, despite the high standard deviation. Nevertheless, regions with both high SD and high CV, such as

vegetables and fruits in USA, Russia and South America; maize in India and Southwest America; sugar crops in

China, may have relatively large uncertainties, and caution should be exercised when using the N application

rate of these crop groups in these regions. Despite the large uncertainties in certain regions and crop groups,

it is important to note that the regions with large uncertainties (

>

20 kg N ha

−

1

) and large CV (

>

25%) only

contributed 8.9% of the total N application amount due to limited crop cultivation in these regions. This means

that although we made very ambitious assumptions, the result looks reasonable due to the relatively low overall

uncertainty.

Crop-specific N application rate from 2003 onwards was adjusted to match the crop-specific N fertilizer use

from the FUBC

report, which likely reduced uncertainties after 2000. As for the year before 2000, country-level

fertilizer use data from the first to fifth FUBC report for the years 1978–2001 were extracted to validate the data.

To determine the differences in the N application rate from this dataset with respect to the FUBC

report, we

calculated the country-wise relative difference

Dif

(%) as equation below:

=

−

×

Dif

Nrate

Nrate FUBC

Nrate

_

100

(12)

cro y j

, ,

Where

Nrate

cro,y,j

is N application rate of crop

cro

in the country

j

in the year

y

.

Nrate_FUBC

cro,y,j

is corresponding

N application rate from FUBC

The mean and SD for

Dif

of 8 crops groups and 9 countries or regions were presented here (Fig.

7

). In most

of the regions except for South Asia, the mean

Dif

between our estimates and the FUBC report was contained in

the range of

±

50%. Large values of the mean

Dif

were observed for some crops, such as vegetables and fruits in

China (

Dif

=

50.4%), other crops in Canada (

Dif

=

70.9%), sugar crops in Africa (

Dif

=

53.0%), rice in Europe

(

Dif

=

−

63.0%). N application rate for South Asia in this study might be underestimated due to most of the

mean

Dif

for 8 crop groups lower than 0.

Finally, we evaluated the reasonability of our results on a regional scale, with the historical N application sta-

tistics of USA and China from the United States Department of Agriculture (USDA)

56

and the Cost and Income

of Chinese Farm Produce (NDRCC 2003, 2022

57

58

), respectively. The N application rate for wheat in this study

was relatively close to the USDA record (Fig.

8

). However, for the USA, the N application rate for maize was

32.8% lower than the USDA record during 1964–2018, whereas those for cotton and soybean were 24.1% and

35.3% higher during 1964–2000, respectively. For China, our estimates for wheat, rice, maize, and potatoes are

generally in agreement with the NDRCC record, with differences within the range of

±

20% (Fig.

9

). The esti-

mated N application rate for vegetables and fruits were close to the records from the NDRCC before the year

2000, but after that, it was significantly lower (

−

13.8% and

−

36.4%). In general, the narrow differences between

this study and regional statistic reflects that our crop-specific N application rate is relatively reasonable in regions.

In addition, we verified the reliability of the Amelia methods in the imputation of missing data by using

national N fertilizer use by different types from the FAOSTAT

. Though, there were missing data from the

FAOSTAT

, we still found 858 records of 69 countries during 1962–1972 overlapped the data filled by the

Amelia approach. We directly compared the consumption of urea, ammonium nitrate and ammonium sulphate

filled by Amelia method with those records from the FAOSTAT

. The scatter plots showed that the consump-

tions of these three N fertilizers per harvested area (kg N ha

−

1

) were relatively close to the FAOSTAT

(Fig.

10

).

However, there are also some estimates that are not accurate, such as urea for New Zealand, ammonium nitrate

for Germany, Italy and Norway. Although the consumption of different N fertilizer types is missing for a few

years from the IFASTAT

, the IFASTAT

provides the total N fertilizer consumption amount for these years.

Thus, we also calculated the summed-up N fertilizer consumption rate of different N fertilizer types filled by

the Amelia methods and directly compared it with the IFASTAT

. It was confirmed that the total N fertilizer

consumption rate filled by Amelia method in this study was very close to that from the IFASTAT

, except for

Norway and Nepal.

Lastly, although the data about fertilization placement was scarce, the comparison for regional scale

(i.e., China) demonstrated that the values obtained by our estimation methods were consistent with that obtained

by Adalibieke

et al

.

42

(Fig .

11

). It was noteworthy that the temporal trends about the percentage of N fertilization

with the deep placement were close between these two studies, except in the year after 2000. Possible explanation

is that the calculation of fertilization placement related to tillage fraction and fertilizer type in this study ignored

some specific issues (e.g., the implementation of “Agricultural Cost-saving and Efficiency-increasing Program”,

which encouraged the implement of deep fertilization machine to enhance fertilizer use efficiency and save

agricultural cost for field crops), while the survey data can reflect more about the reality.

15

Scientific

Data

|

(2023) 10:617

|

www.nature.com/scientificdata

www.nature.com/scientificdata/

Overall, despite the inevitable uncertainties in our dataset, the analysis indicated that our data was aligned

reasonably well with other existing multiple data sources or estimates provided by previous studies (including

the FUBC

report, USDA

, NDRCC

, FAOSTAT

and so on). Needless to say, uncertainties related to this data-

set can be easily reduced in the future as new underlying datasets become available or existing datasets are updated.

Usage Notes

Our dataset provides a gridded estimate of the global crop-specific N application by fertilizer types and place-

ments during the period of 1961–2020. Moreover, N application amount of each crop groups, NH

4

+

and NO

3

−

application can also be obtained from this dataset. For example, N application amount could be calculated by

N application rate multiplied by harvested area. NH

4

+

and NO

3

−

application in synthetic N fertilizer could be

calculated by N application of each fertilizer multiplied by composition of N types (i.e., NH

4

+

or NO

3

−

).

However, there were several limitations that users should be aware when using this dataset. First, the his-

torical cropland spatial and temporal distribution patterns provided by the HYDE3.2

are inconsistent with

the one from satellite-derived land use (e.g. China and India

59

,

60

) or national census (e.g. USA

61

) at regional

scale. Therefore, the downscaling of crop-specific harvested area from cropland area might be biased, so did

the calculation of the N application amount. Second, no significant interannual difference was assumed to the

relative proportion of N application rate by each crop. The crop-specific N application rate was determined by

the spatial patterns of N application amount over the study period and the relative proportion among crops,

which is against the reality and survey data from FUBC

. We acknowledged the existence of this assumption

and assessed the uncertainty quantitatively, however, we believe that uncertainty associated with this assumption

will be reduced as new relevant data available. Third, this study aimed to provide a dataset about N fertiliza-

tion practices by crop, however, we cannot obtain the data for the proportion of different types of N fertilizers

applied by crop for such a long-term period from the current statistic data. Fourth, due to the lack of mechanical

sowing and fertilization data, we assumed that fertilizers in non-tillage regions with high-income were deeply

applied through fertilizer spreaders. While in reality, mechanical sowing and fertilization is more achievable

than the conservation tillage (i.e., non-tillage), which means that cropland with tillage practice may be equipped

with fertilizer spreaders and thus the percentages of N fertilization with deep placement are underestimated

40

In addition, in our study, the non-tillage area was downscaled by crop type and the relationship between field

size and non-tillage practices adoption, whereas the temporal coverage of field size was only available in the

year 2015, assuming it constant during 1961–2020

. Thus, the non-tillage area in our study could be under-

estimated, since the field size has been increasingly associated with the economic growth and mechanization

level

62

. Furthermore, the mechanical fertilization with deep placement in this study was also potentially under-

estimated. Furthermore, based on the analysis of uncertainties, the use of the N application rate for other cere-

als, oil crops, and rest of crops should be cautious, as well as the N application rate before the year 2000, the

consumption of urea for New Zealand, and ammonium nitrate for Germany, Italy, and Norway. Lastly, although

not quantified in this paper, we must acknowledge that other uncertainties arise from the use of multiple data

sources and methodological choices, which can be considered in future studies.

Nevertheless, our study extended the crop-specific N fertilization for a long period of time and also provided

a comprehensive dataset about N fertilization including N application rate, N fertilizer types and N fertilization

placement simultaneously at uniform spatiotemporal resolution. In addition, we revealed a uniform approach

using public and available datasets and methodologies to reconstruct N fertilization dataset over a relative long

period of time. Such dataset can also be easily further updated when new underlying datasets and the update of

existing datasets become available in the future.

Future directions of this study could focus on the conducting of finer scale and crop-specific surveys and

reporting data on agricultural N fertilization practices. For example, the proportions of N fertilizers applied

with different types varied by crops and geospatial distribution. The current survey data on different types of N

fertilizer consumption from the IFASTAT

and FAOSTAT

are available at country-level and without dividing

by detailed crop type. In comparison, the IFASTAT

https://doi.org/10.1038/ngeo325

provided years of country-level N consumption data by

crop types but not by fertilizer types. Therefore, continuous survey data of crop-specific N fertilizer consump-

tion with different types at sub-national scales are necessary and critical for further improvement of our dataset.

Moreover, application frequency and application timing are also crucial parts of agricultural N fertilization

practices, suggesting that including the development of fertilizer application frequency and timing datasets

could also be beneficial to improve our dataset. Last but not least, we can take advantage of satellite data and use

machine learning and/or other comprehensive data fusion methods to improve the accuracy and precision of

agricultural N fertilization practices

63

.

Code availability

Codes used in MATLAB 2020a and RStudio to deal with the dataset and plot the figures are available at available

at National Tibetan Plateau/Third Pole Environment Data Center

55

.

Received: 20 December 2022; Accepted: 31 August 2023;

Published: xx xx xxxx

References

1. Erisman, J. W., Sutton, M. A., Galloway, J., Klimont, Z. & Winiwarter, W. How a century of ammonia synthesis changed the world.

Nature Geoscience

1, 636–639,

(2008).

2. Lu, C. & Tian, H. Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: shifted hot spots

and nutrient imbalance.

Earth System Science Data

9, 181–192,

https://doi.org/10.5194/essd-9-181-2017

(2017).

16

Scientific

Data

|

(2023) 10:617

|

https://doi.org/10.1016/j.envpol.2003.08.042

www.nature.com/scientificdata

www.nature.com/scientificdata/

3. Galloway, J. N., Bleeker, A. & Erisman, J. W. in

Annual Review of Environment and Resources, Vol 46, 2021 Vol. 46 Annual Review of

Environment and Resources

(eds Gadgil, A. & Tomich, T. P.) 255–288 (2021).

4. Schlesinger, W. H. & Bernhardt, E. S.

Biogeochemistry: an analysis of global change

. (Academic press, 2020).

5. Erisman, J. W. & Schaap, M. The need for ammonia abatement with respect to secondary PM reductions in Europe.

Environmental

Pollution

129, 159–163,

(2004).

6. Wu, X.

et al

. ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe.

Geoscientific Model Development

9, 857–873,

https://doi.org/10.5194/gmd-9-857-2016

(2016).

7. Clark, M. A.

et al

. Global food system emissions could preclude achieving the 1.5 degrees and 2 degrees C climate change targets.

Science

370, 705–708,

https://doi.org/10.1126/science.aba7357

(2020).

8. Hauglustaine, D. A., Balkanski, Y. & Schulz, M. A global model simulation of present and future nitrate aerosols and their direct

radiative forcing of climate.

Atmospheric Chemistry and Physics

14, 11031–11063,

https://doi.org/10.5194/acp-14-11031-2014

(2014).

9. Elser, J. J.

et al

. Shifts in Lake N:P Stoichiometry and Nutrient Limitation Driven by Atmospheric Nitrogen Deposition.

Science

326,

835–837,

https://doi.org/10.1126/science.1176199

(2009).

10. Erisman, J. W.

et al

. Consequences of human modification of the global nitrogen cycle.

Philosophical Transactions of the Royal Society

B-Biological Sciences

368,

https://doi.org/10.1098/rstb.2013.0116

(2013).

11. Fowler, D.

et al

. The global nitrogen cycle in the twenty-first century.

Philosophical Transactions of the Royal Society B-Biological

Sciences

368,

https://doi.org/10.1098/rstb.2013.0164

(2013).

12. Eskelinen, A., Harpole, W. S., Jessen, M. T., Virtanen, R. & Hautier, Y. Light competition drives herbivore and nutrient effects on

plant diversity.

Nature

611, 301–305,

https://doi.org/10.1038/s41586-022-05383-9

https://doi.org/10.1016/j.agee.2016.07.001

13. Guo, J. H.

et al

. Significant Acidification in Major Chinese Croplands.

Science

327, 1008–1010,

https://doi.org/10.1126/

science.1182570

(2010).

14. Hou, X.

et al

. Spatial patterns of nitrogen runoff from Chinese paddy fields.

Agriculture Ecosystems & Environment

231, 246–254,

(2016).

15. Gassman, P. W., Reyes, M. R., Green, C. H. & Arnold, J. G. The soil and water assessment tool: Historical development, applications,

and future research directions.

Transactions of the Asabe

50, 1211–1250 (2007).

16. Paulot, F.

et al

. Ammonia emissions in the United States, European Union, and China derived by high-resolution inversion of

ammonium wet deposition data: Interpretation with a new agricultural emissions inventory (MASAGE_NH3.

Journal of Geophysical

Research-Atmospheres

119, 4343–4364,

https://doi.org/10.1002/2013jd021130

(2014).

17. Li, C. S., Frolking, S. & Frolking, T. A. A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and

sensitivity.

Journal of Geophysical Research-Atmospheres

97, 9759–9776,

https://doi.org/10.1029/92jd00509

(1992).

18. Li, C. S., Frolking, S. & Frolking, T. A. A model of nitrous oxide evolution from soil driven by rainfall events: 2. Model applications.

Journal of Geophysical Research-Atmospheres

97, 9777–9783,

https://doi.org/10.1029/92jd00510

(1992).

19. Cui, X.

et al

. Global mapping of crop-specific emission factors highlights hotspots of nitrous oxide mitigation.

Nature Food

2,

886–893,

https://doi.org/10.1038/s43016-021-00384-9

(2021).

20. FAOSTAT Data:Food and Agriculture Organization

https://www.fao.org/faostat/en/#home

https://doi.org/10.5194/essd-9-149-2017

21. IFASTAT Data:International Fertilizer Association

https://www.ifastat.org

(2023).

22. Mueller, N. D.

et al

. Closing yield gaps through nutrient and water management.

Nature

490, 254–257,

https://doi.org/10.1038/

nature11420

(2012).

23. Nishina, K., Ito, A., Hanasaki, N. & Hayashi, S. Reconstruction of spatially detailed global map of NHNH

4

+

and NO

3

−

application

in synthetic nitrogen fertilizer.

Earth System Science Data

9, 149–162,

(2017).

24. Wang, Q.

et al

. Data-driven estimates of global nitrous oxide emissions from croplands.

National Science Review

7, 441–452,

https://

doi.org/10.1093/nsr/nwz087

(2020).

25. Zhang, B.

et al

. Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global

dataset for Earth system modeling.

Earth System Science Data

9, 667–678,

https://doi.org/10.5194/essd-9-667-2017

(2017).

26. Tian, H.

et al

. History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset

from 1860 to 2019.

Earth System Science Data

14, 4551–4568,

https://doi.org/10.5194/essd-14-4551-2022

https://ec.europa.eu/eurostat/statistics-explained/images/0/0f/Tilled_arable_area_by_tillage_practice%2C_EU-

27. EUROSTAT

27%2C_IS%2C_NO%2C_CH%2C_ME_and_HR%2C_2010.png

https://doi.org/10.1029/2007gb002947

28. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types,

and net primary production in the year 2000.

Global Biogeochemical Cycles

22,

(2008).

29. Zhan, X.

et al

. Improved Estimates of Ammonia Emissions from Global Croplands.

Environmental Science & Technology

55,

1329–1338,

https://doi.org/10.1021/acs.est.0c05149

(2021).

30. Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.

Scientific Data

7,

https://doi.org/10.1038/s41597-020-0453-3

(2020).

31. Goldewijk, K. K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene - HYDE 3.2.

Earth

System Science Data

9, 927–953,

https://doi.org/10.5194/essd-9-927-2017

(2017).

32. GADM. Database of Global Administrative Areas

https://gadm.org/download_world.html

https://doi.org/10.1038/s41597-022-01592-z

33. Ludemann, C. I., Gruere, A., Heffer, P. & Dobermann, A. Global data on fertilizer use by crop and by country.

Scientific Data

9,

https://doi.org/10.1088/1748-

34. Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J. & Garnier, J. 50 year trends in nitrogen use efficiency of world cropping systems:

the relationship between yield and nitrogen input to cropland.

Environmental Research Letters

9,

9326/9/10/105011

(2014).

35. Zhang, X.

et al

. Managing nitrogen for sustainable development.

Nature

528, 51–59,

https://doi.org/10.1038/nature15743

(2015).

36. King, G., Honaker, J., Joseph, A. & Scheve, K. Analyzing incomplete political science data: An alternative algorithm for multiple

imputation.

American Political Science Review

95, 49–69,

https://doi.org/10.1017/s0003055401000235

(2001).

37. World Bank: World Development indicators- historical classification by income

https://datahelpdesk.worldbank.org/

knowledgebase/articles/906519-world-bank-country-and-lending-groups

https://doi.org/10.1029/2000gb001389

38. Bouwman, A. F., Boumans, L. J. M. & Batjes, N. H. Estimation of global NH3 volatilization loss from synthetic fertilizers and animal

manure applied to arable lands and grasslands.

Global Biogeochemical Cycles

16,

(2002).

39. Lutz, F., Stoorvogel, J. J. & Muller, C. Options to model the effects of tillage on N2O emissions at the global scale.

Ecological Modelling

392, 212–225,

https://doi.org/10.1016/j.ecolmodel.2018.11.015

(2019).

40. Jaleta, M., Baudron, F., Krivokapic-Skoko, B. & Erenstein, O. Agricultural mechanization and reduced tillage: antagonism or

synergy?

International Journal of Agricultural Sustainability

17, 219–230,

https://doi.org/10.1080/14735903.2019.1613742

(2019).

41. Porwollik, V., Rolinski, S., Heinke, J. & Mueller, C. Generating a rule-based global gridded tillage dataset.

Earth System Science Data

11, 823–843,

https://doi.org/10.5194/essd-11-823-2019

(2019).

42. Adalibieke, W.

et al

. Decoupling between ammonia emission and crop production in China due to policy interventions.

Global

Change Biology

27, 5877–5888,

https://doi.org/10.1111/gcb.15847

(2021).

43. CAMI:China Agricultural Machinery Industry Yearbook for China (Statistics of Agricultural Mechanization from 2000 to 2017)

https://data.cnki.net/v3/trade/Yearbook/Single/N2022030117?zcode

=

Z032

17

Scientific

Data

|

(2023) 10:617

|

https://doi.org/10.1038/s41467-017-02142-7

www.nature.com/scientificdata

www.nature.com/scientificdata/

44. CTIC: The Conservation Technology Information Center data for USA (National Crop Residue Management Survey form 1989 to

2008).

45. International Food Policy Research, I.

MapSPAM

(ed Institute International Food Policy Research) (Harvard Dataverse, 2019).

46. International Food Policy Research, I. & International Institute for Applied Systems, A. (eds Institute International Food Policy

Research & Analysis International Institute for Applied Systems) (Harvard Dataverse, 2016).

47. Lesiv, M.

et al

. Estimating the global distribution of field size using crowdsourcing.

Global Change Biology

25, 174–186,

https://doi.

org/10.1111/gcb.14492

(2019).

48. Borrelli, P.

et al

. An assessment of the global impact of 21st century land use change on soil erosion.

Nature Communications

8,

(2017).

49. Giller, K. E.

et al

. Beyond conservation agriculture.

Frontiers in Plant Science

6,

https://doi.org/10.3389/fpls.2015.00870

(2015).

50. Kassam, A., Friedrich, T., Shaxson, F. & Pretty, J. The spread of Conservation Agriculture: justification, sustainability and uptake.

International Journal of Agricultural Sustainability

7, 292–320,

https://doi.org/10.3763/ijas.2009.0477

(2009).

51. Pittelkow, C. M.

et al

. When does no-till yield more? A global meta-analysis.

Field Crops Research

183, 156–168,

https://doi.

org/10.1016/j.fcr.2015.07.020

(2015).

52. Basso, B., Ritchie, J., Grace, P. & Sartori, L. Simulating tillage impacts on soil biophysical proprerties using the SALUS model. 3, 1–10

(2006).

53. Govaerts, B.

et al

. Conservation agriculture and soil carbon sequestration: between myth and farmer reality.

Critical Reviews in Plant

Science

28, 97–122 (2009).

54. Schmitz, P. M., Mal, P. & Hesse, J. W.

The importance of conservation tillage as a contribution to sustainable agriculture: A special case

of soil erosion

. (Inst. für Agribusiness, 2015).

55. Wulahati, A., Cui, X., Cai, H., You, L., Zhou, F. Global crop-specific nitrogen fertilization dataset in 1961-2020.

National Tibetan

Plateau/Third Pole Environment Data Center

https://doi.org/10.11888/Terre.tpdc.300446

(2023).

56. USDA: United States Department of Agriculture

https://www.ers.usda.gov/data-products/fertilizer-use-and-price.aspx

data.stats.gov.cn/easyquery.htm?cn

57. NDRCC (National Development and Reform Commission of China). Statistics of cost and income of Chinese farm produce since

the establishment of the nation from 1950–1997 (2023).

58. NDRCC (National Development and Reform Commission of China). Statistics of cost and income of Chinese farm produce.

https://

=

C01

https://doi.org/10.1029/2009gb003687

59. Liu, M. & Tian, H. China’s land cover and land use change from 1700 to 2005: Estimations from high-resolution satellite data and

historical archives.

Global Biogeochemical Cycles

24,

(2010).

60. Tian, H., Banger, K., Bo, T. & Dadhwal, V. K. History of land use in India during 1880-2010: Large-scale land transformations

reconstructed from satellite data and historical archives.

Global and Planetary Change

121, 78–88,

https://doi.org/10.1016/j.

gloplacha.2014.07.005

(2014).

61. Yu, Z. & Lu, C. Historical cropland expansion and abandonment in the continental US during 1850 to 2016.

Global Ecology and

Biogeography

27, 322–333,

https://doi.org/10.1111/geb.12697

(2018).

62. Wu, Y.

et al

. Policy distortions, farm size, and the overuse of agricultural chemicals in China.

Proceedings of the National Academy of

Sciences of the United States of America

115, 7010–7015,

https://doi.org/10.1073/pnas.1806645115

(2018).

63. You, L. & Sun, Z. Mapping global cropping system: Challenges, opportunities, and future perspectives.

Crop and Environment

1,

68–73,

https://doi.org/10.1016/j.crope.2022.03.006