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Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, FranceTom O. Delmont, Patrick Wincker & Eric PelletierResearch Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOsee, Paris, FranceTom O. Delmont, Juan José Pierella Karlusich, Chris Bowler, Patrick Wincker & Eric PelletierInstitut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005, Paris, FranceJuan José Pierella Karlusich & Chris BowlerGraduate Program in Biophysical Sciences, University of Chicago, Chicago, IL, 60637, USAIva VeseliDepartment of Medicine, University of Chicago, Chicago, IL, 60637, USAJessika Fuessel & A. Murat ErenBay Paul Center, Marine Biological Laboratory, Woods Hole, MA, 02543, USAA. Murat ErenDepartment of Ecology, Environment and Plant Sciences, Stockholm University Stockholm, Stockholm, 10691, SwedenRachel A. Foster More

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The GCBD is stored at figshare23. Below we describe 20 Tables (also see Fig. 3 schematic) that comprise the GCBD: (1) Site_Info_tbl, (2) Sample_Event_tbl, (3) R_Scripts_tbl, (4) Cover_tbl, (5) Bleaching_tbl, (6) Environmental_tbl, (7) Authors_LUT, (8) Bleaching_Level_LUT, (9) City_Town_Name_LUT, (10) Country_Name_LUT, (11) Data_Source_LUT, (12) Ecoregion_Name_LUT, (13) Exposure_LUT, (14) Ocean_Name_LUT, (15) Realm_Name_LUT, (16) State_Island_Province_Name_LUT, (17) Substrate_Type_LUT, (18) Relevant_Papers_tbl, (19) Severity_Code_LUT, and (20) Bleaching_Prevalence_Score_LUT, where LUT stands for look-up table.

1)

Site Information (Site_Info_tbl)
Latitude_Degrees: latitude coordinates in decimal degrees.
Longitude_Degrees: longitude coordinates in decimal degrees.
Ocean_Name: the ocean in which the sampling took place.
Realm_Name: identification of realm as defined by the Marine Ecoregions of the World (MEOW)12.
Ecoregion_Name: identification of the Ecoregions (150) as defined by Veron et al.13.
Country_Name: the country where sampling took place.
State_Island_Province_Name: the state, territory (e.g., Guam) or island group (e.g., Hawaiian Islands) where sampling took place.
City_Town_Name: the region, city, or nearest town, where sampling took place.
Site_Name: the accepted name of the site or the name given by the team that sampled the reef.
Distance_to_Shore: the distance (m) of the sampling site from the nearest land.
Exposure: a site was considered exposed if it had >20 km of fetch, if there were strong seasonal winds, or if the site faced the prevailing winds. Otherwise, the site was considered sheltered or ‘sometimes’. ‘Sometimes’ refers to a few sites with a >20 km fetch through a narrow geographic window, and therefore we considered that the site was potentially exposed during cyclone seasons. We left the category ‘sometimes’ in the database because those sites were not clearly exposed sites, nor were they clearly sheltered sites, and future researchers may be interested in temporary exposure.
Turbidity: kd490 with a 100-km buffer.
Cyclone_Frequency: number of cyclone events from 1964 to 2014.
Comments: comments of any issues with the site or additional information.

2)

Sample Event Information (Sample_Event_tbl)
Site_ID: site ID field from Site_Info_tbl.
Reef_ID: name of reef site that was adopted by sampling group (from ReefCheck).
Quadrat_No: quadrat number (from McClanahan et al.)20.
Date_Day: the date of the sampling event.
Date_Month: the month of sampling event.
Date_Year: the year of sampling event.
Depth: depth (m) of sampling site. Comments: comments of any issue or additional information of sampling event.

3)

R Code (R_Scripts_tbl)
Relevant_Papers_ID: relevant papers ID field from Relevant_Papers_tbl.
Project name: name of project associated with R code.
Paper_Title: title of paper where R code was published.
Code_Name: name of R code file.
Description: description of the R code.
Data_Source: data source ID field from Data_Source_LUT.
R_Code: attachment of R code file.
URL: hyperlink to R code or link to github.

4)

Coral Cover Information (Cover_tbl)
Sample_ID: sampled ID field from Sample_Event_tbl.
Substrate_Type: substrate type ID field from Substrate_LUT.
S1: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment one of transect.
S2: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment two of transect.
S3: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment three of transect.
S4: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment four of transect.
Perc_hardcoral: percent hard coral cover from McClanahan et al.20 data source.
Perc_macroalgae: percent macroalgae cover from McClanahan et al.20 data source.
Average_Ellipse_Transect: calculated percent hard coral cover per 10 m × 1 m transect using ellipse equation.
Average_Ellipse_Site: calculated percent hard coral cover per site using ellipse equation.
Comments: comments of any issue or additional information of sampling event

5)

Bleaching Information (Bleaching_tbl)
Sample_ID: sample ID field from Sample_Event_tbl.
Bleaching_Level: Reef Check data, coral population or coral colony.
S1: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment one of transect.
S2: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment two of transect.
S3: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment three of transect.
S4: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment four of transect.
Percent_Bleaching_RC_Old_Method: old method of determining percent bleaching from Reef_Check.
Severity_Code: coded range of bleaching severity from Donner et al.10.
Percent_Bleached: percent of coral bleaching.
Number_Bleached_colonies: number of bleached corals from McClanahan et al.20 data source.
Bleaching_intensity: from McClanahan et al.20 data source.
Bleaching_Prevalence_Score: coded range of bleaching prevalence from Safaie et al.21.

6)

Environmental Parameter Information (Environmental_tbl)
Sample_ID: sample ID field from Sample_Event_tbl.
ClimSST: CoRTAD. [Climatological Sea-Surface Temperature (SST)] based on weekly SSTs for the study time frame, created using a harmonics approach.
Temperature_ Kelvin: CoRTAD. SST in Kelvin.
Temperature_Mean: CoRTAD. Mean SST in degrees Celsius.
Temperature_Minimum: CoRTAD. Minimum SST in degrees Celsius.
Temperature_Maximum: CoRTAD. Maximum SST in degrees Celsius.
Temperature_Kelvin_Standard_Deviation: CoRTAD. Standard deviation of SST in Kelvin.
Windspeed: CoRTAD. meters per hour.
SSTA: CoRTAD. (Sea-Surface Temperature Anomaly) weekly SST minus weekly climatological SST.
SSTA_Standard_Deviation: CoRTAD. The Standard Deviation of weekly SSTA in degrees Celsius over the entire period.
SSTA_Mean: CoRTAD. The mean SSTA in degrees Celsius over the entire period.
SSTA_Minimum: CoRTAD. The minimum SSTA in degrees Celsius over the entire period.
SSTA_Maximum: CoRTAD. The maximum SSTA in degrees Celsius over the entire period.
SSTA_Frequency: CoRTAD. (Sea Surface Temperature Anomaly Frequency) number of times over the previous 52 weeks that SSTA  >  = 1 degree Celsius.
SSTA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of SSTA Frequency in degrees Celsius over the entire time period of 40 years.
SSTA_FrequencyMax: CoRTAD. The maximum SSTA Frequency in degrees Celsius over the entire time period.
SSTA_FrequencyMean: CoRTAD. The mean SSTA Frequency in degrees Celsius over the entire time period of 40 years.
SSTA_DHW: CoRTAD. (Sea Surface Temperature Degree Heating Weeks) sum of previous 12 weeks when SSTA  >  = 1 degree Celsius.
SSTA_DHW_Standard_Deviation: CoRTAD. The standard deviation SSTA DHW in degrees Celsius over the entire period.
SSTA_DHWMax: CoRTAD. The maximum SSTA DHW in degrees Celsius over the entire time period of 40 years.
SSTA_DHWMean: CoRTAD. The mean SSTA DHW in degrees Celsius over the entire time period of 40 years.
TSA: CoRTAD. (Thermal Stress Anomaly) weekly SSTs minus the maximum of weekly climatological SSTs in degrees Celsius.
TSA_Standard_Deviation: CoRTAD. The standard deviation of TSA in degrees Celsius over the entire time period of 40 years.
TSA_Minimum: CoRTAD. The minimum TSA in degrees Celsius over the entire time period of 40 years.
TSA_Maximum: CoRTAD. The maximum TSA in degrees Celsius over the entire time period of 40 years.
TSA_Mean: CoRTAD. The mean TSA in degrees Celsius over the entire time period of 40 years.
TSA_Frequency: CoRTAD. The number of times over previous 52 weeks that TSA  >  = 1 degree Celsius.
TSA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of frequency of TSA in degrees Celsius over the entire time period of 40 years.
TSA_FrequencyMax: CoRTAD. The maximum TSA frequency in degrees Celsius over the entire time period of 40 years.
TSA_FrequencyMean: CoRTAD. The mean TSA frequency in degrees Celsius over the entire time period of 40 years.
TSA_DHW: CoRTAD. (Thermal Stress Anomaly Degree Heating Weeks) sum of previous 12 weeks when TSA  >  = 1 degree Celsius.
TSA_DHW_Standard_Deviation: CoRTAD. The standard deviation of TSA DHW in degrees Celsius over the entire time period of 40 years.
TSA_DHWMax: CoRTAD. The maximum TSA DHW in degrees Celsius over the entire time period of 40 years.
TSA_DHWMean: CoRTAD. The mean TSA DHW in degrees Celsius over the entire time period of 40 years.

7)

Author Names (Authors_LUT)
Last_Name: author’s last name.
First_Name: author’s first name.
Middle_Initial: author’s middle initial.

8)

Bleaching Level Information (Bleaching_Level_LUT)
Bleaching_Level: Reef Check data, coral population or coral colony.

9)

City, Town Names (City_Town_Name_LUT)
City_Town_Name: the region, city, or town, where sampling took place.

10)

Country names (Country_Name_LUT)
Country_Name: name of the country where sampling took place.

11)

Data Source Information (Data_Source_LUT)
Data_Source: name of source of original data set.
Sample_Method: Description of the sampling methods used to collect the data. If more than one method was used then we stated that an amalgamation of methods were used to collect the data, and the original papers are found in “Relevant_Papers_tbl”, and can be referenced therein.

12)

Ecoregion Names (Ecoregion_Name_LUT)
Ecoregion_Name: name of Ecoregion from Veron et al.13.

13)

Exposure Type (Exposure_LUT)
Exposure_Type: site exposure to fetch.

14)

Ocean Name Information (Ocean_Name_LUT)
Ocean_Name: name of ocean where sampling took place.

15)

Name of Realm (Realm_Name_LUT)
Realm_Name: name of realm as identified by the Marine Ecoregions of the World (MEOW)12.

16)

State, Island, Province Name (State_Island_Province_Name_LUT)
State_Island_Province_Name, Name of the state, territory (e.g. Guam) or island group (e.g. Hawaiian Islands) where sampling took place.

17)

Substrate Type (Substrate_Type_LUT)
Substrate_Type: type of substrate from Reef Check data.

18)

Relevant Publications (Relevant_Papers_tbl)
Data_Source: source associated with publication.
Author_ID: author ID field from Authors_LUT.
Title: title of published work.
Journal_Name: name of publication journal.
Year_Published: year of publication.
Volume: volume number of journal.
Issue: issue number of journal.
Pages: page range of publication.
URL: hyperlink to publication.
DOI: DOI number of publication.
pdf: pdf attachment of publication.

19)

Severity Index Code (Severity_Code_LUT)
Severity_Code: coded range of bleaching severity from Donner et al.10.

20)

Bleaching Prevalence Code (Bleaching_Prevalence_Score_LUT)

Bleaching_Prevalence_Score: coded range of bleaching prevalence from Safaie et al. 21. More

Here we describe methods for the GAEZ+ 2015 Annual Crop Data, and the GAEZ+ 2015 Monthly Cropland Data. The Annual Crop Data was generated first, then the Monthly Cropland Data was calculated based on the Harvest Area results of the Annual Data (Fig. 1).Fig. 1Schematic overview of annual and monthly data production methods. The GAEZ+ 2015 products described in this paper are in dark blue boxes; publicly available data used are in light blue. Dark blue arrows indicate which data are used in each processing step, and grey arrows from steps to data show which steps result in final GAEZ+ 2015 data products. The processing steps listed here are referred to in the Methods section text.Full size imageGAEZ+ 2015 Annual Crop Data MethodsThe GEAZ+ 2015 Annual Crop Data updates the 2010 GAEZ v4 crop harvest area, yield, and production maps6,7 (identified as Theme 5 in ref. 7) using national-scale data on the change in crop harvested area and livestock numbers from 2010 to 2015, based on statistics for 160 crop groups, and cattle and buffalo, from FAOSTAT5.Three datasets were used to produce GAEZ+ 2015 Annual Crop Data:

1.

FAOSTAT crop production domain: annual, country-level data on crop harvested area (H) and crop production (P) for each crop from the FAOSTAT database (Table 1)Table 1 GAEZ and FAOSTAT crop harmonization.Full size table

2.

GAEZ v46,7 gridded global annual harvested area, yield, and production by crop for the 26 FAOSTAT crops and crop categories at 5-minute resolution

3.

Global Administrative Unit Layer (GAUL 2012)13 data. GAUL 2012 reports the fraction of each global 5-minute grid cell that falls within a given country or disputed territory. There are 275 unique global administrative units.

Step 1. Calculate crop changes from 2010 to 2015 by country:
For each country, we extracted the harvested area (H) and crop production (P) for each of the 160 FAOSTAT crop categories, c, from the FAOSTAT database. We averaged three years (2009–2011) of annual national crop harvested area data to represent 2010 national crop harvest area, H2010, and three years (2014–2016) of annual crop harvested area data to represent 2015 national crop harvest area, H2015, then calculated a ratio, rHc, of 2015 to 2010 harvested areas for each crop c in each country, and equivalently, for crop production:$$r{H}_{c}={H}_{2015}/{H}_{2010}$$
(1)
$$r{P}_{c}={P}_{2015}/{P}_{2010}$$
(2)
This results in 160 rH and rP values per country. If harvest area and production values for a particular crop are zero or unreported in the FAOSTAT data, then rHc and rPc are both set to 1.0 (i.e., no change from 2010 to 2015). Three years of data are averaged (2009 – 2011 and 2014 – 2016) to account for missing data for some country/year combinations and to avoid emphasizing reported outliers.
Step 2. Aggregate FAOSTAT-based ratios to the GAEZ crop categories:
We followed the crop aggregation methods of the GAEZ model to aggregate the FAOSTAT crop list (160 unique crops as of 2019) to 26 crops (see Table 1). For each of the 26 GAEZ crop categories, if there is more than one matching FAOSTAT crop (see Table 1) then we applied an area-weighted average (based on FAOSTAT year 2015 harvested area) of the FAOSTAT crops within each country to the rH and rP values for that crop and country. This results in 26 rH and rP values per country. There was one exception to this: the GAEZ_2010 crop category ‘fodder crops’ was an aggregate of 17 FAOSTAT crops (see Table 1) for which harvest area data are no longer reported on FAOSTAT; i.e., GAEZ_2010 had obtained FAOSTAT data on fodder crops circa 2010, but FAOSTAT no longer provides any data on fodder crops for any year. We assumed that the 2010 to 2015 fractional change in fodder crop harvest area in each country was proportional to the change in the FAOSTAT reported national herd sizes for cattle and buffalo livestock data5 for that country, following the same methodology as for crop harvested area change (see Step 2 below). This method assumes a negligible international trade of fodder crops as indicated by bilateral trade matrices available from FAOSTAT.
Step 3. Apply country-level ratios to grid cells:
Calculated country-level ratios were then applied to each grid cell k, using the GAUL_201213 definitions for which grid cells fall within which countries. Some grid cells are split between two or more countries. In this case, all model output variables for the grid cell are divided between the countries based on the fraction of grid cell area falling within the country i:$${H}_{c,2015}^{k}={H}_{c,2010}^{k}{sum }_{i},{f}_{i}^{k}r{H}_{c,i}$$
(3)
$${P}_{c,2015}^{k}={P}_{c,2010}^{k}{sum }_{i},{f}_{i}^{k}r{P}_{c,i}$$
(4)
where ({H}_{c,2015}^{k}) is the year 2015 harvested area (or production) for crop c in grid cell k; ({f}_{i}^{k}) is the fraction of country i in grid cell k, and rHc,i and rPc,i are the ratios for crop c in country i as calculated in Eqs. 1 and 2. This results in 26 H and P values per grid cell. If the sum of all crop harvest areas exceeds 99% of the grid cell area, all crop harvest areas are reduced equally to fit within 99% of the area.
Special Case: Sudan
FAOSTAT data for years before 2011 report data for Sudan, and for South Sudan and Sudan after 2011. To compute the ratios for these grid cells, we split the 2010 data for Sudan into a virtual ‘North’ Sudan and ‘South_Sudan’, using the data for the year 2012, which was reported for both countries. We then used these generated 2010 data and applied the same methodology as described above to calculate changes in harvested areas and production in all grid cells in both countries.
Special Case: Small regions and islands
Forty-nine countries – generally small regions or islands – had no data reported for crop harvested area by FAOSTAT. We assumed that there was no change in crop harvested area for the grid cells in these countries. Note that many may have had zero ha as previously-reported crop area in GAEZ v4. These countries are (the number following each region is the region’s number in ADM0_CODE in the GAUL_2012 data13):Anguilla (9), Aruba (14), Ashmore_and_Cartier_Islands (16), Azores_Islands (74578), Baker_Island (22), Bassas_da_India (25), Bird_Island (32), Bouvet_Island (36), British_Indian_Ocean_Territory (38), Christmas_Island (54), Clipperton_Island (55), Cocos (Keeling)_Islands (56), Europa_Island (80), French_Southern_and_Antarctic_Territories (88), Glorioso_Island (96), Greenland (98), Guernsey (104), Heard_Island_and_McDonald_Islands (109), Howland_Island (112), Isle_of_Man (120), Jarvis_Island (127), Jersey (128), Johnston_Atoll (129), Juan_de_Nova_Island (131), Kingman_Reef (134), Kuril_islands (136), Madeira_Islands (151), Mayotte (161), Midway_Island (164), Navassa_Island (174), Netherlands_Antilles (176), Norfolk_Island (184), Northern_Mariana_Islands (185), Palmyra_Atoll (190), Paracel_Islands (193), Pitcairn (197), Saint_Helena (207), Scarborough_Reef (216), Senkaku_Islands (218), South_Georgia_and_the_South_Sandwich_Islands (228), Spratly_Islands (230), Svalbard_and_Jan_Mayen_Islands (234), Tromelin_Island (247), Turks_and_Caicos_Islands (251), United_States_Virgin_Islands (258), Wake_Island (265), Gibraltar (95), Holy_See (110), Liechtenstein (146).
Special Case: Disputed Areas
Some grid cells in the GAUL_201213 cell-table database are assigned to nine disputed areas, rather than to specific countries. We assumed that there was no change in crop harvested area or production from 2010 to 2015 for grid cells these disputed areas. These areas are (the number following each region is the region’s number of the ADM0_CODE in the GAUL_201213 data):Abyei (102), Aksai_Chin (2), Arunachal_Pradesh (15), China/India (52), Hala’ib_Triangle (40760), Ilemi_Triangle (61013), Jammu_and_Kashmir (40781), Ma’tan_al-Sarra (40762), Falkland_Islands_(Malvinas) (81).
Step 4. Compute 2015 crop yields:
Crop yields were computed for each crop, c, and grid cell, k, as the ratio of crop production to crop harvest area (if harvest area, Hc,k,2015, is zero, then yield, Yc,k,2015, is set to zero):$${Y}_{c,k,2015}={P}_{c,k,2015}/{H}_{c,k,2015}$$
(5)
The resulting gridded global data are:

A.

GAEZ+ 2015 Crop Harvest Area14

B.

GAEZ+ 2015 Crop Yield15

C.

GAEZ+ 2015 Crop Production16

This new data product consists of 156 data files in geotiff format, one rainfed harvested area file and one irrigated harvested area file for each crop harvest area (1000 ha (107 m2) per 5-minute grid cell), crop production (1000 tonnes (106 kg) per 5-minute grid cell), and crop yield (tonnes per ha (10−1 kg m−2) per 5-minute grid cell), for each of the 26 GAEZ crops or crop categories in Table 1.GAEZ+ 2015 monthly cropland area methodsTwo datasets were used to produce monthly cropland area by crop and by irrigated vs rainfed management. These are:

1.

GAEZ+ 2015 Annual Harvested Area14 (as developed above)

2.

MIRCA2000 cropland area4

Step 5. Harmonize the GAEZ+ 2015 and MIRCA2000 crop lists
The MIRCA20004 cropland product provides monthly growing area grids (gridded physical cropland area) for 26 irrigated and rainfed crops and crop categories, as well as cropping calendars that identify the planting month and harvesting month for each crop (via ‘subcrops’ – see below). However, the MIRCA2000 crop list is not the same as the GAEZ+ 2015 crop list; we matched each crop type in the GAEZ+ 2015 crop list to a crop type in the MIRCA2000 crop list to enable the application of MIRCA2000 crop calendars to GAEZ+ 2015 crops (Table 2). Out of the 26 GAEZ+ 2015 crops, 18 had clear 1:1 matching crop categories within MIRCA2000. The remaining 8 crops were matched based on general crop characteristics, i.e., annual vs. perennial, or to unmatched MIRCA2000 cereals.Table 2 List of GAEZ crop categories used in all GAEZ+ 2015 products, as well as the matching between GAEZ+ 2015 crops and MIRCA20004 crop categories for the purposes of producing GAEZ+ 2015 monthly cropland data.Full size tableAn essential component of the MIRCA2000 cropland dataset is the identification of subcrop categories within each crop category to split crops into areas grown in different seasons, or crops with different planting and harvesting dates within the same season. Up to 5 subcrops can be defined to represent such multi-cropping practices. Below, we use the following notation:HG = annual harvested area from the GAEZ+ 2015 product for a given cropHM = annual harvested area calculated from the MIRCA2000 data for a given cropAM,n = cropland area of MIRCA2000 crop, subcrop n, by monthAG,n = cropland area of GAEZ+ 2015 crop, subcrop n, by monthAG = cropland area of GAEZ+ 2015 crop, by month
Step 6. Apply MIRCA2000 monthly crop calendars to GAEZ+ 2015 annual data
To generate the monthly cropland physical area of GAEZ+ 2015 crops, we followed these steps for each GAEZ crop in each grid cell:

1.

For a given GAEZ crop in a given grid cell, is the area reported >0 for the matching MIRCA2000 crop?

a.

If YES, then use the MIRCA2000 data for the grid cell and crop considered.

b.

If NO, then find the closest grid cell with the matching MIRCA2000 crop category, and apply the MIRCA2000 crop rotation from that grid cell to the given crop/grid cell combination for the following steps.

2.

Does the matching MIRCA2000 crop category (Table 1) have more than 1 subcrop?

a.

If NO, then AG = HG for all months of the cropping season, as defined by the MIRCA2000 crop calendar.

b.

If YES, then for each subcrop category n, apply the ratio of AM,n/HM to HG, then sum the subcrop areas within each month such that:

$${A}_{G}=sum _{n}frac{{A}_{M,n}}{{H}_{M}}{H}_{G}$$

3.

For each month and each grid cell, check if the sum of all crops (irrigated and rainfed) is greater than the 99% of area of the grid cell. We assume that at least 1% of land must be retained as non-cropland for agricultural infrastructure such as roads, buildings, irrigation infrastructure, and other landcovers (e.g. rivers, wetlands).

a.

If NO, then no further processing is done.

b.

If YES, then reduce crop area by the excess value based on a removal order (Table 2). Rainfed crops have higher removal order numbers for the excess truncation (starting with 1) before removing irrigated crops, until the cell area is not exceeded. A large removal number (e.g., 20) indicates that the crop’s land is unlikely to be removed. Large priority numbers are given to the staple crops to ensure these important food producing lands are consistent with FAOSTAT country data.

The maximum monthly amount of physical cropland that was removed by step 3 is 711,543 ha, which is 0.05% of total global cropland physical area.The resulting global gridded data from Step 6 are monthly time series of cropland physical area by crop, subcrop, and production system, called GAEZ+_2015 Monthly Cropland Data17. Combining the MIRCA2000 crop calendar and subcrop rotation information with the GAEZ+ 2015 annual data allows for the representation of crop seasonality; e.g., Fig. 2 shows the aggregate monthly cropland physical area for Rice 1 and Rice 2 (two sub-crops of rice) over the northern hemisphere, clearly illustrating the two main rice-growing seasons.Fig. 2Aggregate monthly cropland physical area for Rice 1 and Rice 2 subcrops from monthly GAEZ+ 2015 over the northern hemisphere shows the two main rice-growing seasons. This seasonality is the result of combining GAEZ+ 2015 annual data with the MIRCA20004 crop calendars and subcrop divisions.Full size image More