This README file was generated on 2025-03-07 by Erik Nelson.

GENERAL INFORMATION

1. Title of Dataset: Reversing the Great Degradation of Nature through Economic Development.
2. Author Information
   Name: Erik Nelson
   Institution: Bowdoin College
   Address: Brunswick, ME USA
   Email: enelson2@bowdoin.edu

Some datasets in this project give country-level statistics on land use, agricultural production, crop yield, kilocalorie consumption, agricultural trade volumes, and population for the years 1961 through 2018.

Other datasets in this project give statistics on land use, agricultural production, crop yield, kilocalorie consumption, and population in the United States from the mid-19th century through 2018.

Other datasets in this project give future projections of country-level crop yields, kilocalorie consumption, kilocalorie trade, population, and gross domestic product per capita.

Other datasets in this project give satellite-derived country-level statistics on land use transitions for the years 1992 through 2018.

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain
2. Links to publications that cite or use the data:

Polasky, Stephen, Erik Nelson, David Tilman, James Gerber, Justin Andrew Johnson, Erwin Corong, Forest Isbell, Jason Hill, and Craig Packer. (2025). Reversing the great degradation of nature through economic development.

1. Recommended citation for this dataset:

Polasky, Stephen, Erik Nelson, David Tilman, James Gerber, Justin Andrew Johnson, Erwin Corong, Forest Isbell, Jason Hill, and Craig Packer. (2025). Reversing the great degradation of nature through economic development. Dryad Digital Repository. https://doi.org/10.5061/dryad.59zw3r2df

DATA & FILE OVERVIEW

The best way to understand how the data is used on our project is to list the datasets that are used to create each figure or table in our paper.

Figure 1 uses,

A) Production_Crops_E_All_Data_(Normalized).csv
B) Ivoire.csv
C) Inputs_LandUse_E_All_Data_(Normalized).csv
D) IvoireInput.csv
E) Population_E_All_Data_(Normalized).csv
F) popIvoire.csv
G) CalorieStats.csv

Figure 2 uses,

A) Population_E_All_Data_(Normalized).csv
B) CalorieStats.csv
C) AreaHarvested.csv
D) KcalProduction.csv

Table 1 uses,

A) NewPanelwoGDP.csv

Figures 3 and 4 use,

A) NoCCCrops23NoZeroes.csv
B) RCP45Crops23NoZeroes.csv
C) HarvHaCroplanHaRatio.csv
D) kcalconspercperyr.csv

SM Figure 6 uses,

A) USHistoryByRegionUpdateDec192020.csv
B) USAbandonByStateUpdateDec192020.csv
C) USYieldeUpdateFeb22021.csv
D) USPopulation.csv
E) USkcals.csv
F) USkcalsSmoothed.csv

SM Figure 7 uses,

A) baretobare.csv
B) baretoclosedforest.csv
C) baretoclosedtoopenforest.csv
D) baretocropland.csv
E) baretograssland.csv
F) baretomosiaccrop.csv
G) baretoopenforest.csv
H) baretootherforest.csv
I) baretoshrubland.csv
J) baretosparseveg.csv
K) baretourban.csv
L) baretowetland.csv
M) closedforesttobare.csv
N) closedforesttoclosedforest.csv
O) closedforesttoclosedtoopenforest.csv
P) closedforesttocropland.csv
Q) closedforesttograssland.csv
R) closedforesttomosiaccrop.csv
S) closedforesttoopenforest.csv
T) closedforesttootherforest.csv
U) closedforesttoshrubland.csv
V) closedforesttosparseveg.csv
W) closedforesttourban.csv
X) closedforesttowetland.csv
Y) closedtoopenforesttobare.csv
Z) closedtoopenforesttoclosedforest.csv
AA) closedtoopenforesttoclosedtoopenforest.csv
BB) closedtoopenforesttocropland.csv
CC) closedtoopenforesttograssland.csv
DD) closedtoopenforesttomosiaccrop.csv
EE) closedtoopenforesttoopenforest.csv
FF) closedtoopenforesttootherforest.csv
GG) closedtoopenforesttoshrubland.csv
HH) closedtoopenforesttosparseveg.csv
II) closedtoopenforesttourban.csv
JJ) closedtoopenforesttowetland.csv
KK) croplandtobare.csv
LL) croplandtoclosedforest.csv
MM) croplandtoclosedtoopenforest.csv
NN) croplandtocropland.csv
OO) croplandtograssland.csv
PP) croplandtomosiaccrop.csv
QQ) croplandtoopenforest.csv
RR) croplandtootherforest.csv
SS) croplandtoshrubland.csv
TT) croplandtosparseveg.csv
UU) croplandtourban.csv
VV) croplandtowetland.csv
WW) grasslandtobare.csv
XX) grasslandtoclosedforest.csv
YY) grasslandtoclosedtoopenforest.csv
ZZ) grasslandtocropland.csv
AAA) grasslandtograssland.csv
BBB) grasslandtomosiaccrop.csv
CCC) grasslandtoopenforest.csv
DDD) grasslandtootherforest.csv
EEE) grasslandtoshrubland.csv
FFF) grasslandtosparseveg.csv
GGG) grasslandtourban.csv
HHH) grasslandtowetland.csv
III) mosiaccroptobare.csv
JJJ) mosiaccroptoclosedforest.csv
KKK) mosiaccroptoclosedtoopenforest.csv
LLL) mosiaccroptocropland.csv
MMM) mosiaccroptograssland.csv
NNN) mosiaccroptomosiaccrop.csv
OOO) mosiaccroptoopenforest.csv
PPP) mosiaccroptootherforest.csv
QQQ) mosiaccroptoshrubland.csv
RRR) mosiaccroptosparseveg.csv
SSS) mosiaccroptourban.csv
TTT) mosiaccroptowetland.csv
UUU) openforesttobare.csv
VVV) openforesttoclosedforest.csv
WWW) openforesttoclosedtoopenforest.csv
XXX) openforesttocropland.csv
YYY) openforesttograssland.csv
ZZZ) openforesttomosiaccrop.csv
AAAA) openforesttoopenforest.csv
BBBB) openforesttootherforest.csv
CCCC) openforesttoshrubland.csv
DDDD) openforesttosparseveg.csv
EEEE) openforesttourban.csv
FFFF) openforesttowetland.csv
GGGG) otherforesttobare.csv
HHHH) otherforesttoclosedforest.csv
IIII) otherforesttoclosedtoopenforest.csv
JJJJ) otherforesttocropland.csv
KKKK) otherforesttograssland.csv
LLLL) otherforesttomosiaccrop.csv
MMMM) otherforesttoopenforest.csv
NNNN) otherforesttootherforest.csv
OOOO) otherforesttoshrubland.csv
PPPP) otherforesttosparseveg.csv
QQQQ) otherforesttourban.csv
RRRR) otherforesttowetland.csv
SSSS) shrublandtobare.csv
TTTT) shrublandtoclosedforest.csv
UUUU) shrublandtoclosedtoopenforest.csv
VVVV) shrublandtocropland.csv
WWWW) shrublandtograssland.csv
XXXX) shrublandtomosiaccrop.csv
YYYY) shrublandtoopenforest.csv
ZZZZ) shrublandtootherforest.csv
AAAAA) shrublandtoshrubland.csv
BBBBB) shrublandtosparseveg.csv
CCCCC) shrublandtourban.csv
DDDDD) shrublandtowetland.csv
EEEEE) sparsevegtobare.csv
FFFFF) sparsevegtoclosedforest.csv
GGGGG) sparsevegtoclosedtoopenforest.csv
HHHHH) sparsevegtocropland.csv
IIIII) sparsevegtograssland.csv
JJJJJ) sparsevegtomosiaccrop.csv
KKKKK) sparsevegtoopenforest.csv
LLLLL) sparsevegtootherforest.csv
MMMMM) sparsevegtoshrubland.csv
NNNNN) sparsevegtosparseveg.csv
OOOOO) sparsevegtourban.csv
PPPPP) sparsevegtowetland.csv
QQQQQ) urbantobare.csv
RRRRR) urbantoclosedforest.csv
SSSSS) urbantoclosedtoopenforest.csv
TTTTT) urbantocropland.csv
UUUUU) urbantograssland.csv
VVVVV) urbantomosiaccrop.csv
WWWWW) urbantoopenforest.csv
XXXXX) urbantootherforest.csv
YYYYY) urbantoshrubland.csv
ZZZZZ) urbantosparseveg.csv
AAAAAA) urbantourban.csv
BBBBBB) urbantowetland.csv
CCCCCC) wetlandtobare.csv
DDDDDD) wetlandtoclosedforest.csv
EEEEEE) wetlandtoclosedtoopenforest.csv
FFFFFF) wetlandtocropland.csv
GGGGGG) wetlandtograssland.csv
HHHHHH) wetlandtomosiaccrop.csv
IIIIII) wetlandtoopenforest.csv
JJJJJJ) wetlandtootherforest.csv
KKKKKK) wetlandtoshrubland.csv
LLLLLL) wetlandtosparseveg.csv
MMMMMM) wetlandtourban.csv
NNNNNN) wetlandtowetland.csv
OOOOOO) countrygroupsjune142023.csv

SM Figure 8 uses,

A) Production_Crops_E_All_Data_(Normalized).csv
B) Inputs_LandUse_E_All_Data_(Normalized).csv
C) Population_E_All_Data_(Normalized).csv
D) CalorieStats.csv

CODE/SOFTWARE

The best way to understand how R script is used in our project is to list the scripts that are used to create each figure or table in our paper.

Figure 1 was created with Figure1TopPanel.R and Figure1BottomPanel.R.

Figure 2 was created with Figure2.R.

Table 1 was created with Table1.R

Figures 3 and 4 was created with SSP1_NoCCYesGTAP.R and SSP1_RCP45YesGTAP.R

SM Figure 6 was created with SMFigure6.R

SM Figure 7 was created with SMFigure7.R

SM Figure 8 was created with SMFigure8.R

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DATA-SPECIFIC INFORMATION FOR: Production_Crops_E_All_Data_(Normalized).csv.
Source: FAOSTAT. Production_Crops_Livestock_E_All_Data_(Normalized). Updated December 22, 2020. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip.

See https://www.fao.org/faostat/en/#data/QCL for metadata

1. Number of variables: 11
2. Number of cases/rows: 2493838
3. Variable List:

Area.Code: Country code.
Area: Country name.
Item.Code:
Element.Code:
Element: e.g., Area harvested; production
Year.Code:
Year:
Unit: e.g., Ha; tonnes
Value:
Flag:

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: We moved Cote d’Ivoire data from this dataset into its own .csv file called Ivoire.csv. We did this because R had difficulty reading the accents in the country name.

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DATA-SPECIFIC INFORMATION FOR: Inputs_LandUse_E_All_Data_(Normalized).csv
Source: FAOSTAT. Inputs_LandUse_E_All_Data_(Normalized). Updated September 10, 2020. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Inputs_LandUse_E_All_Data_(Normalized).zip.

See https://www.fao.org/faostat/en/#data/RL for metadata

1. Number of variables: 11
2. Number of cases/rows: 192046
3. Variable List:

Area.Code: Country code.
Area: Country name.
Item.Code:
Element.Code:
Element: e.g., Area
Year.Code:
Year:
Unit: e.g., 1000 Ha
Value:
Flag:

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: We moved Cote d’Ivoire data from this dataset into its own .csv file called IvoireInput.csv. We did this because R had difficulty reading the accents in the country name.

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DATA-SPECIFIC INFORMATION FOR: Population_E_All_Data_(Normalized).csv
Source: FAOSTAT. Population_E_All_Data_(Normalized). Updated October 21, 2019. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Population_E_All_Data_(Normalized).zip.

See https://www.fao.org/faostat/en/#data/OA for metadata

1. Number of variables: 12
2. Number of cases/rows: 14590
3. Variable List:

Area.Code: Country code.
Area: Country name.
Item.Code:
Element.Code:
Element: e.g., Total Population - Both sexes
Year.Code:
Year:
Unit: e.g., 1000 persons
Value:
Flag:
Note:

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: We moved Cote d’Ivoire data from this dataset into its own .csv file called popIvoire.csv. We did this because R had difficulty reading the accents in the country name.

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DATA-SPECIFIC INFORMATION FOR: CalorieStats.csv
Sources:FAOSTAT. Production_Crops_Livestock_E_All_Data_(Normalized). Updated December 22, 2020. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip.
FAOSTAT. Trade_Crops_Livestock_E_All_Data_(Normalized). Updated December 20, 2020. Accessed on December 24, 2020. http://fenixservices.fao.org/faostat/static/bulkdownloads/Trade_Crops_Livestock_E_All_Data_(Normalized).zip
FAOSTAT. Food Balance Sheets. Updated December 12, 2000. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/FoodBalanceSheets_E_All_Data_(Normalized).zip.

Reports kilocalories consumed (i.e., produced minus exported plus imported) in each county in each year. Kilocalories consumed in a country in a year was generated by merging FAO crop production statistics with kilocalorie per crop tonne statistics from the FAO Food Balance Sheets and then adjusting for imports and exports from FAO Trade_Crops_Livestock_E_All_Data_(Normalized).csv.

See https://www.fao.org/faostat/en/#data/QCL; https://www.fao.org/faostat/en/#data/TCL; and https://www.fao.org/faostat/en/#data/FBS for metadata

1. Number of variables: 59
2. Number of cases/rows: 259
3. Variable List:

Area: Country name.
X1961 - X2018: Kilocalorie consumption in a country in the column’s given year

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: AreaHarvested.csv
Source: FAOSTAT. Production_Crops_Livestock_E_All_Data_(Normalized). Updated December 21, 2021. Accessed on January 3, 2022. https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip.

See https://www.fao.org/faostat/en/#data/QCL for metadata

1. Number of variables: 3
2. Number of cases/rows: 11045
3. Variable List:

Area: Country name.
Year:
AreaHarv: Ha

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: KcalProduction.csv
Sources:FAOSTAT. Production_Crops_Livestock_E_All_Data_(Normalized). Updated December 22, 2020. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip.
FAOSTAT. Food Balance Sheets. Updated December 12, 2000. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/FoodBalanceSheets_E_All_Data_(Normalized).zip.

Reports kilocalories produced in each country each year. This was generated by merging FAO crop production statistics with kilocalorie per crop tonne statistics from the FAO Food Balance Sheets.

See https://www.fao.org/faostat/en/#data/QCL; https://www.fao.org/faostat/en/#data/TCL.

1. Number of variables: 59
2. Number of cases/rows: 259
3. Variable List:

Area: Country name.
X1961 - X2018: Kilocalorie production in a country in the column’s given year

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: NewPanelwoGDP.csv
Sources: The various FAOSTAT datasets used to form other .csv sheets in this project.

1. Number of variables: 22
2. Number of cases/rows: 8526
3. Variable List:

Area: Country name.
Code: Country code.
Year:
Population: Persons.
Cropland: Ha.
Yield: Mg of cereal yield per ha.
Not Suitable: Land area in sq km not suitable for rainfed crops.
Low: Land area in sq km with low suitability for rainfed crops.
Medium: Land area in sq km with medium suitability for rainfed crops.
High: Land area in sq km with high suitability for rainfed crops.
ImpValue: Value of agricultural product imports (1000s of nominal USD).
ExpValue: Value of agricultural product exports (1000s of nominal USD).
PPI: US Producer Price Index.
kcalscons: Kilocalories consumed.
LowC: Indicator variable; = 1 if low income country.
EscapeLow: Indicator variable; = 1 if escaped low income country.
LowMid: Indicator variable; = 1 if low middle income country.
EscapedLowMid: Indicator variable; = 1 if escaped low middle income country.
UpMid: Indicator variable; = 1 if upper middle or escaped upper middle income country.
HighC: Indicator variable; = 1 if high income country.
China: Indicator variable; = 1 if China.
India: Indicator variable; = 1 if India.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: NoCCCrops23NoZeroes.csv
Sources: Various.

1. Number of variables: 37
2. Number of cases/rows: 146
3. Variable List:

code: Country code.
name: Country name.
EconDev: Indicator variable; = 1 if a country initially (in 2018) had a GDP per capita of less than $8,100 (2010 USD).
EscapeLow: Indicator variable; = 1 if escaped low income country.
LowMid: Indicator variable; = 1 if low middle income country.
EscapedLowMid: Indicator variable; = 1 if escaped low middle income country.
UpMid: Indicator variable; = 1 if upper middle or escaped upper middle income country.
High: Indicator variable; = 1 if high income country.
Cat: = 1 if a low income country; = 2 if an escaped low income country; = 3 if a lower middle income country; = 4 if an escaped lower middle income country; = 5 if an upper middle or escaped upper middle income country; = 6 if a high income country; = 7 if China; = 8 if India.
PopSD2018: Population in 2018 as assumed in Economic Development and Equitable Development scenarios.
PopSD2050: Population in 2050 as assumed in Economic Development and Equitable Development scenarios.
PopSD2100: Population in 2100 as assumed in Economic Development and Equitable Development scenarios.
PopRef2018: Population in 2018 as assumed in Business-as-usual and Limited Consumption scenarios.
PopRef2050: Population in 2050 as assumed in Business-as-usual and Limited Consumption scenarios.
PopRef2100: Population in 2100 as assumed in Business-as-usual and Limited Consumption scenarios.
kconscapdayPopRef50: kcal consumption per capita per day in 2050 as assumed in Business-as-usual and Limited Consumption scenarios.
kconscapdayPopRef00: kcal consumption per capita per day in 2100 as assumed in Business-as-usual and Limited Consumption scenarios.
kconscapdaySD50: kcal consumption per capita per day in 2050 as assumed in Economic Development and Equitable Development scenarios.
kconscapdaySD00: kcal consumption per capita per day in 2100 as assumed in Economic Development and Equitable Development scenarios.
twentythreecropkcals18: Output of agricultural production across 23 modeled crops in 2018 (kcals).
allcropkcals18: Output of agricultural production across all crops in 2018 (kcals).
twentythreecropkcals18smooth:
twentythreecropkcals50: Modeled output of agricultural production across 23 modeled crops in 2050 (kcals) assuming 2018 harvested area.
twentythreecropkcals00: Modeled output of agricultural production across 23 modeled crops in 2100 (kcals) assuming 2018 harvested area.
twentythreecroparea18: Harvested area in 2018 (ha) across 23 modeled crops.
allcroparea18: Harvested area in 2018 (ha) across all crops according to the March 24, 2023 version of the FAOSTAT product Production_Crops_Livestock_E_All_Data_(Normalized).
twentythreecropkcalceiling18: Modeled output of agricultural production across 23 modeled crops in 2018 if yield ceilings were reached (kcals) assuming 2018 harvested area.
twentythreecropkcalceiling50: Modeled output of agricultural production across 23 modeled crops in 2050 if yield ceilings were reached (kcals) assuming 2018 harvested area.
twentythreecropkcalceiling00: Modeled output of agricultural production across 23 modeled crops in 2100 if yield ceilings were reached (kcals) assuming 2018 harvested area.
NIBAU2050: Modeled net imports to country in 2050 (kcals) assumed in Business-as-usual scenario.
NIBAU2100: Modeled net imports to country in 2100 (kcals) assumed in Business-as-usual scenario.
NIED2050: Modeled net imports to country in 2050 (kcals) assumed in Economic Development scenario.
NIED2100: Modeled net imports to country in 2100 (kcals) assumed in Economic Development scenario.
NIRC2050: Modeled net imports to country in 2050 (kcals) assumed in Limited Consumption scenario.
NIRC2100: Modeled net imports to country in 2100 (kcals) assumed in Limited Consumption scenario.
NISD2050: Modeled net imports to country in 2050 (kcals) assumed in Equitable Development scenario.
NISD2100: Modeled net imports to country in 2100 (kcals) assumed in Equitable Development scenario.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: RCP45Crops23NoZeroes.csv.
Sources: Various

1. Number of variables: 37
2. Number of cases/rows: 146
3. Variable List:

code: Country code.
name: Country name.
EconDev: Indicator variable; = 1 if a country initially (in 2018) had a GDP per capita of less than $8,100 (2010 USD).
EscapeLow: Indicator variable; = 1 if escaped low income country.
LowMid: Indicator variable; = 1 if low middle income country.
EscapedLowMid: Indicator variable; = 1 if escaped low middle income country.
UpMid: Indicator variable; = 1 if upper middle or escaped upper middle income country.
High: Indicator variable; = 1 if high income country.
Cat: = 1 if a low income country; = 2 if an escaped low income country; = 3 if a lower middle income country; = 4 if an escaped lower middle income country; = 5 if an upper middle or escaped upper middle income country; = 6 if a high income country; = 7 if China; = 8 if India.
PopSD2018: Population in 2018 as assumed in Economic Development and Equitable Development scenarios.
PopSD2050: Population in 2050 as assumed in Economic Development and Equitable Development scenarios.
PopSD2100: Population in 2100 as assumed in Economic Development and Equitable Development scenarios.
PopRef2018: Population in 2018 as assumed in Business-as-usual and Limited Consumption scenarios.
PopRef2050: Population in 2050 as assumed in Business-as-usual and Limited Consumption scenarios.
PopRef2100: Population in 2100 as assumed in Business-as-usual and Limited Consumption scenarios.
kconscapdayPopRef50: kcal consumption per capita per day in 2050 as assumed in Business-as-usual and Limited Consumption scenarios.
kconscapdayPopRef00: kcal consumption per capita per day in 2100 as assumed in Business-as-usual and Limited Consumption scenarios.
kconscapdaySD50: kcal consumption per capita per day in 2050 as assumed in Economic Development and Equitable Development scenarios.
kconscapdaySD00: kcal consumption per capita per day in 2100 as assumed in Economic Development and Equitable Development scenarios.
twentythreecropkcals18: Output of agricultural production across 23 modeled crops in 2018 (kcals).
allcropkcals18: Output of agricultural production across all crops in 2018 (kcals).
twentythreecropkcals18smooth:
twentythreecropkcals50: Modeled output of agricultural production across 23 modeled crops in 2050 (kcals) assuming 2018 harvested area.
twentythreecropkcals00: Modeled output of agricultural production across 23 modeled crops in 2100 (kcals) assuming 2018 harvested area.
twentythreecroparea18: Harvested area in 2018 (ha) across 23 modeled crops.
allcroparea18: Harvested area in 2018 (ha) across all crops according to the March 24, 2023 version of the FAOSTAT product Production_Crops_Livestock_E_All_Data_(Normalized).
twentythreecropkcalceiling18: Modeled output of agricultural production across 23 modeled crops in 2018 if yield ceilings were reached (kcals) assuming 2018 harvested area.
twentythreecropkcalceiling50: Modeled output of agricultural production across 23 modeled crops in 2050 if yield ceilings were reached (kcals) assuming 2018 harvested area.
twentythreecropkcalceiling00: Modeled output of agricultural production across 23 modeled crops in 2100 if yield ceilings were reached (kcals) assuming 2018 harvested area.
NIBAU2050: Modeled net imports to country in 2050 (kcals) assumed in Business-as-usual scenario.
NIBAU2100: Modeled net imports to country in 2100 (kcals) assumed in Business-as-usual scenario.
NIED2050: Modeled net imports to country in 2050 (kcals) assumed in Economic Development scenario.
NIED2100: Modeled net imports to country in 2100 (kcals) assumed in Economic Development scenario.
NIRC2050: Modeled net imports to country in 2050 (kcals) assumed in Limited Consumption scenario.
NIRC2100: Modeled net imports to country in 2100 (kcals) assumed in Limited Consumption scenario.
NISD2050: Modeled net imports to country in 2050 (kcals) assumed in Equitable Development scenario.
NISD2100: Modeled net imports to country in 2100 (kcals) assumed in Equitable Development scenario.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: HarvHaCroplanHaRatio.csv
Sources: FAOSTAT. Inputs_LandUse_E_All_Data_(Normalized). Updated September 10, 2020. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Inputs_LandUse_E_All_Data_(Normalized).zip.
FAOSTAT. Production_Crops_Livestock_E_All_Data_(Normalized). Updated December 22, 2020. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip.

See https://www.fao.org/faostat/en/#data/RL; See https://www.fao.org/faostat/en/#data/QCL for metadata

1. Number of variables: 10
2. Number of cases/rows: 156
3. Variable List:

Country: Country code.
CroplandHa: Cropland in ha.
HarvHa: Harvested area in ha.
Low: Indicator variable; = 1 if low income country.
EscapeLow: Indicator variable; = 1 if escaped low income country.
LowMid: Indicator variable; = 1 if low middle income country.
EscapedLowMid: Indicator variable; = 1 if escaped low middle income country.
UpMid: Indicator variable; = 1 if upper middle or escaped upper middle income country.
High: Indicator variable; = 1 if high income country.
Cat: = 1 if a low income country; = 2 if an escaped low income country; = 3 if a lower middle income country; = 4 if an escaped lower middle income country; = 5 if an upper middle or escaped upper middle income country; = 6 if a high income country; = 7 if China; = 8 if India.

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DATA-SPECIFIC INFORMATION FOR: kcalconspercperyr.csv.
Sources:FAOSTAT. Production_Crops_Lvetock_E_All_Data_(Normalized). Updated December 22, 2020. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip.
FAOSTAT. Trade_Crops_Livestock_E_All_Data_(Normalized). Updated December 20, 2020. Accessed on December 24, 2020. http://fenixservices.fao.org/faostat/static/bulkdownloads/Trade_Crops_Livestock_E_All_Data_(Normalized).zip
FAOSTAT. Food Balance Sheets. Updated December 12, 2000. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/FoodBalanceSheets_E_All_Data_(Normalized).zip.
FAOSTAT. Population_E_All_Data_(Normalized). Updated October 21, 2019. Accessed on December 24, 2020. https://fenixservices.fao.org/faostat/static/bulkdownloads/Population_E_All_Data_(Normalized).zip.

See https://www.fao.org/faostat/en/#data/OA for metadata

Reports kilocalories consumed (i.e., produced minus exported plus imported) per capita in 2018. Kilocalories consumed in a country in 2018 was generated by merging FAO crop production statistics with kilocalorie per crop tonne statistics from the FAO Food Balance Sheets and then adjusting for imports and exports from FAO Trade_Crops_Livestock_E_All_Data_(Normalized).csv. Finally, kcals consumed in a country in 2018 is divided by that country’s 2018 population.

See https://www.fao.org/faostat/en/#data/QCL; https://www.fao.org/faostat/en/#data/TCL; https://www.fao.org/faostat/en/#data/FBS for metadata; https://www.fao.org/faostat/en/#data/OA.

1. Number of variables: 2
2. Number of cases/rows: 156
3. Variable List:

code: Country code.
kconspcappyr: Kilocalorie consumption per capita in 2018.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: USHistoryByRegionUpdateDec192020.csv.
Source: U.S. Department of Agriculture, National Agricultural Statistics Service. 2021. Quick Stats. Agriculture Survey Data, 1866 – 2020. Accessed on Feb.1, 2021. https://quickstats.nass.usda.gov/

1. Number of variables: 4
2. Number of cases/rows: 155
3. Variable List:

Year: Year.
East: Acres of cropland in the Western region of the US. Crops included in this total are maize, wheat, barley, oats, rye, sorghum, soybeans, potatoes, and cotton.
Midwest: Acres of cropland in the Midwest region of the US. Crops included in this total are maize, wheat, barley, oats, rye, sorghum, soybeans, potatoes, and cotton.
West: Acres of cropland in the West region of the US. Crops included in this total are maize, wheat, barley, oats, rye, sorghum, soybeans, potatoes, and cotton.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: USAbandonByStateUpdateDec192020.csv.
Source: U.S. Department of Agriculture, National Agricultural Statistics Service. 2021. Quick Stats. Agriculture Survey Data, 1866 – 2020. Accessed on Feb.1, 2021. https://quickstats.nass.usda.gov/

1. Number of variables: 2
2. Number of cases/rows: 155
3. Variable List:

Year: Year.
Abandon: Acres of abandoned cropland in the US in the given year. This total includes land used for maize, wheat, barley, oats, rye, sorghum, soybeans, potatoes, and cotton.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: USYieldeUpdateFeb22021.csv
Source: U.S. Department of Agriculture, National Agricultural Statistics Service. 2021. Quick Stats. Agriculture Survey Data, 1866 – 2020. Accessed on Feb.1, 2021. https://quickstats.nass.usda.gov/

1. Number of variables: 2
2. Number of cases/rows: 155
3. Variable List:

Year: Year.
Cereals..Yield: Mg per ha. Crops included in this statistic are barley, maize, oats, sorghum, rice, rye, and wheat.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: USPopulation.csv
Source: Texas State Library. United States and Texas Populations 1850-2017. Updated January 8, 2020. Accessed on Feb.1, 2021. https://www.tsl.texas.gov/ref/abouttx/census.html.

1. Number of variables: 2
2. Number of cases/rows: 155
3. Variable List:

YEAR: Year.
Population: Persons.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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DATA-SPECIFIC INFORMATION FOR: USkcals.csv

1. Number of variables: 3
2. Number of cases/rows: 155
3. Variable List:

Years: Year.
kcals: kilocalories consumed in the US in a given year.
pop: Persons.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: Data does not begin until 1961. ‘kcals’ comes from CalorieStats.csv. pop comes from Population_E_All_Data_(Normalized).csv

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DATA-SPECIFIC INFORMATION FOR: USkcalsSmoothed.csv

1. Number of variables: 5
2. Number of cases/rows: 155
3. Variable List:

Years: Year.
kcals: kilocalories consumed in the US in a given year.
pop: Persons.
kcalpc: kcals / pop.
kcalpcsmooth: 3-year running average of kcalpc.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: Data does not begin until 1961. First 3 columns are from USkcals.csv

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DATA-SPECIFIC INFORMATION FOR: SM Figure 7 .csv files with the name «land cover 1»to«land cover 2».csv.
Source: ESA CCI Land Cover and the EC C3S Land Cover Project. Land cover classification gridded maps from 1992 to present derived from satellite observations. Version v2.0.7cds. Published on October 24, 2019. Accessed on February 1, 2020.

Each of the .csv files with the name «land cover 1»to«land cover 2».csv indicates the number of square kilometers that transitioned from «land cover 1» to «land cover 2» by country (rows) over a one year period (columns). For example, the column ‘x20002001’ in «land cover 1»to«land cover 2».csv indicates the number of square kilometers that transitioned from «land cover 1» to «land cover 2» by country (rows) between 2000 and 2001.

We used simplified land cover codes. Below we list each land cover code tracked by the ESA CCI Land Cover and the EC C3S Land Cover Project and how we aggregated these land cover codes into more general categories. First we list the land cover category we use and then the specific land cover codes in each broader land cover category.

Cropland
[10] = ‘Cropland, rainfed’
[11] = ‘Cropland, rainfed, herbaceous cover’
[12] = ‘Cropland, rainfed, tree or shrub cover’
[20] = ‘Cropland, irrigated or post-flooding’

Mosiac crop
[30] = ‘Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)’
[40] = ‘Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)’

Open Forest
[62] = ‘Tree cover, broadleaved, deciduous, open (15-40%)’
[72] = ‘Tree cover, needleleaved, evergreen, open (15-40%)’
[82] = ‘Tree cover, needleleaved, deciduous, open (15-40%)’

Closed to Open Forest
[50] = ‘Tree cover, broadleaved, evergreen, closed to open (>15%)’
[60] = ‘Tree cover, broadleaved, deciduous, closed to open (>15%)’
[70] = ‘Tree cover, needleleaved, evergreen, closed to open (>15%)’
[71] = ‘Tree cover, needleleaved, evergreen, closed to open (>15%)’
[80] = ‘Tree cover, needleleaved, deciduous, closed to open (>15%)’

Closed Forest
[61] = ‘Tree cover, broadleaved, deciduous, closed (>40%)’
[81] = ‘Tree cover, needleleaved, deciduous, closed (>40%)’

Other Forest
[90] = ‘Tree cover, mixed leaf type (broadleaved and needleleaved)’
[100] = ‘Mosaic tree and shrub (>50%) / herbaceous cover (<50%)’
[160] = ‘Tree cover, flooded, fresh or brakish water’
[170] = ‘Tree cover, flooded, saline water’

Grassland
[110] = ‘Mosaic herbaceous cover (>50%) / tree and shrub (<50%)’
[130] = ‘Grassland’

Shrubland
[120] = ‘Shrubland’
[121] = ‘Evergreen shrubland’
[122] = ‘Deciduous shrubland ‘

Sparse vegetation
[140] = ‘Lichens and mosses’
[150] = ‘Sparse vegetation (tree, shrub, herbaceous cover) (<15%)’
[151] = ‘Sparse tree (<15%)’
[152] = ‘Sparse shrub (<15%)’
[153] = ‘Sparse herbaceous cover (<15%)’

Wetland
[180] = ‘Shrub or herbaceous cover, flooded, fresh/saline/brakish water’

Bare areas
[200] = ‘Bare areas’
[201] = ‘Consolidated bare areas’
[202] = ‘Unconsolidated bare areas’

Urban
[190] = ‘Urban areas’

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DATA-SPECIFIC INFORMATION FOR: countrygroupsjune142023.csv
Source: World Bank Country and Lending Groups. http://databank.worldbank.org/data/download/site-content/OGHIST.xls

1. Number of variables: 9
2. Number of cases/rows: 167
3. Variable List:

Country: Country code.
Low: Indicator variable; = 1 if low income country.
EscapeLow: Indicator variable; = 1 if escaped low income country.
LowMid: Indicator variable; = 1 if low middle income country.
EscapedLowMid: Indicator variable; = 1 if escaped low middle income country.
UpMid: Indicator variable; = 1 if upper middle or escaped upper middle income country.
High: Indicator variable; = 1 if high income country.
China: Indicator variable; = 1 if China.
India: Indicator variable; = 1 if India.

1. Missing data codes: NA
2. Specialized formats or other abbreviations used: None

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Version changes

2025-03-07: The first version of this dataset was published on 2023-07-21. The 2023-07-21 version of the dataset was part of a submission to the journal Nature. Our submission was rejected. As part of the rejection we were given substantial comments by reviewers. The reviewers asked us to change how we calculated future crop yields. Therefore, this submission reflects our updated yield calculations. A change in our future yield values also meant we had to re-run our crop trade model. The updated yield values and updated trade flows are recorded in the new CSV files “NoCCCrops23NoZeroes.csv” and “RCP45Crops23NoZeroes.csv.” These new CSV files replace the CSV files “NoCLimateChangeV6.csv” and “SSP1_RCP45V6.csv” from our 2023-07-21 dataset. All other changes we have made to the dataset are cosmetic.