Code:food1.sql
Overview
This is sqlite code to analyze the production of food crops and nutrition.
Some key questions it can help to answer:
- How much food could there be (globally per capita) if nothing was wasted and everything was plant-based?
- Which countries produce a surplus?
- Which crops get the best yields?
- Which plant-based diets are the most optimal for feeding the world and preventing deforestation?
Data is mostly from FAO and USDA.
Dependencies (data files as input)
- Code:fao_usda.csv
- File:sr28-collated.csv
- File:fao-crops-primary.csv
- File:API_SP.POP.TOTL_DS2_en_csv_v2_4150863.csv
- File:global-hunger-index-2021.csv
- File:country-and-continent-codes-list-csv_csv.csv
See more notes below the code.
Code
-- This is used in the methodology of various olam.wiki pages
-- Copyright Elie Goldman Smith, 2022 (Creative Commons license)
-- Run this in SQLite3
----------------------
-- Load data from various sources
.mode csv
-- cross-reference: crops (FAO data) with foods (USDA nutrition data):
.import fao_usda.csv fao_usda
-- nutrition (USDA):
.import data/sr28-collated.csv usda
-- crop production (FAO):
.import data/fao-crops-primary.csv fao
-- population of regions (World Bank):
.import data/API_SP.POP.TOTL_DS2_en_csv_v2_4150863.csv pop
-- global hunger index (.com)
.import data/global-hunger-index-2021.csv ghi
-- region codes:
.import data/country-and-continent-codes-list-csv_csv.csv region
.mode list
-- Fix some of the region codes, to match the ISO3 standard
UPDATE pop SET `Country Code`="X01" WHERE `Country Code`="WLD";
-- Replace some empty strings with NULLs, so aggregate functions work
UPDATE fao SET `Value`=NULL WHERE `Value`='';
UPDATE pop SET `2021`=NULL WHERE `2021`='';
UPDATE pop SET `2020`=NULL WHERE `2020`='';
UPDATE pop SET `2019`=NULL WHERE `2019`='';
UPDATE pop SET `2018`=NULL WHERE `2018`='';
UPDATE pop SET `2017`=NULL WHERE `2017`='';
UPDATE pop SET `2016`=NULL WHERE `2016`='';
UPDATE pop SET `2015`=NULL WHERE `2015`='';
UPDATE pop SET `2014`=NULL WHERE `2014`='';
UPDATE pop SET `2013`=NULL WHERE `2013`='';
UPDATE pop SET `2012`=NULL WHERE `2012`='';
UPDATE pop SET `2011`=NULL WHERE `2011`='';
-- Generate a secondary table 'main' for easier querying.
-- It's big, and it intentionally contains some redundancy.
CREATE TEMPORARY TABLE almost_main AS
SELECT
region_name, region_iso3, region_m49, region_population, region_cont, region_ghi,-- these 6 columns are always correlated
crop_name, food_name, food_code, -- these 3 columns are always correlated
production_food_grams_per_day,
production_food_grams_per_day * fat_fraction_of_food AS production_fat_grams_per_day,
production_food_grams_per_day * protein_fraction_of_food AS production_protein_grams_per_day,
production_food_grams_per_day * kalories_per_gram_of_food AS production_kalories_per_day,
yield_food_grams_per_day_per_acre,
yield_food_grams_per_day_per_acre * fat_fraction_of_food AS yield_fat_grams_per_day_per_acre,
yield_food_grams_per_day_per_acre * protein_fraction_of_food AS yield_protein_grams_per_day_per_acre,
yield_food_grams_per_day_per_acre * kalories_per_gram_of_food AS yield_kalories_per_day_per_acre,
farmland_acres
FROM (
SELECT
fao0.`Area Code (ISO3)`AS region_iso3, -- Technically `Area Code (ISO3)` is a "bare column", but it ALWAYS correlates with `Area`, so it's all good.
fao0.`Area` AS region_name,
fao0.`Item` AS crop_name,
usda.`Long_desc` AS food_name,
usda.`NDB_No` AS food_code,
reg0.`Country_Number` AS region_m49,
reg0.`Continent_Name` AS region_cont,
-- population from most recent year available:
COALESCE(pop.`2021`,pop.`2020`,pop.`2019`,pop.`2018`,pop.`2017`,pop.`2016`,pop.`2015`,pop.`2014`,pop.`2013`,pop.`2012`,pop.`2011`)
AS region_population,
-- global hunger index from most recent year available:
ghi.`2021` AS region_ghi,
-- crop production...
-- in tonnes/year:
p.val
-- edible fraction of crop:
* fao_usda.`Multiplier` * 0.01*(100 - usda.`Refuse`)
-- conversion factor: "tonnes/year" to "grams/day":
* 2737.9093
-- result:
AS production_food_grams_per_day,
-- crop yields...
-- in hectograms/year/hectare:
y.val
-- edible fraction of crop:
* fao_usda.`Multiplier` * 0.01*(100 - usda.`Refuse`)
-- conversion factor: "hectograms/year/hectare" to "grams/day/acre":
* 0.11079926
-- result:
AS yield_food_grams_per_day_per_acre,
-- crop land...
-- in hectares:
a.val
-- conversion factor: "hectares" to "acres":
* 2.4710538
-- result:
AS farmland_acres,
-- protein...
-- grams per 100g food:
usda.`Protein (g)`
-- conversion factor:
* 0.01
-- result:
AS protein_fraction_of_food,
-- fat...
-- grams per 100g food:
usda.`Total lipid (fat) (g)`
-- conversion factor:
* 0.01
-- result:
AS fat_fraction_of_food,
-- kalories...
-- per 100g food:
usda.`Energy (kcal)`
-- conversion factor:
* 0.01
-- result:
AS kalories_per_gram_of_food
FROM usda
JOIN fao_usda USING(`NDB_No`)
JOIN (SELECT * FROM fao GROUP BY `Area`,`Item`) fao0 USING(`Item Code (FAO)`) -- Technically `Item Code (FAO)` is a "bare column", but it ALWAYS correlates with `Item`, so it's all good.
LEFT JOIN (SELECT *, AVG(`Value`) AS val FROM fao WHERE `Element`="Production" GROUP BY `Area`,`Item`) p ON fao0.`Area`=p.`Area` AND fao0.`Item`=p.`Item`
LEFT JOIN (SELECT *, AVG(`Value`) AS val FROM fao WHERE `Element`="Yield" GROUP BY `Area`,`Item`) y ON fao0.`Area`=y.`Area` AND fao0.`Item`=y.`Item`
LEFT JOIN (SELECT *, AVG(`Value`) AS val FROM fao WHERE `Element`="Area harvested" GROUP BY `Area`,`Item`) a ON fao0.`Area`=a.`Area` AND fao0.`Item`=a.`Item`
LEFT JOIN (SELECT *,MAX(`Continent_Name`)FROM region GROUP BY `Three_Letter_Country_Code`) reg0 ON fao0.`Area Code (ISO3)`= reg0.`Three_Letter_Country_Code` -- The GROUP BY is in there because otherwise: some country codes will match 2 rows (because of countries that span 2 continents) which would cause double-counting and fuck up the aggregation. Sorry Russia, you don't get both Europe and Asia lol
LEFT JOIN pop ON fao0.`Area Code (ISO3)` = pop.`Country Code` -- for region_population
LEFT JOIN ghi ON fao0.`Area` = ghi.`Country` -- for Global Hunger Index
);
-- Population of the continents
CREATE TEMPORARY TABLE cont_pop AS
SELECT region_cont, SUM(DISTINCT region_population) AS region_cp -- The 'cp' stands for "continent population"
FROM almost_main
GROUP BY region_cont;
-- Join it all together to create 'main'
CREATE TABLE main AS
SELECT * FROM almost_main LEFT JOIN cont_pop USING(region_cont); -- This adds the column 'region_cp' which is fully correlated with all the other columns that start with 'region_'
-- Done, get rid of temporary tables
DROP TABLE cont_pop;
DROP TABLE almost_main;
--- end of mandatory intro code to run ---
------------------------------------------
-- Overview, by country --
SELECT
CHAR(10)||
region_name||CHAR(10)||
PRINTF(" %d kalories/day/person (primary)"||CHAR(10)||
" %d grams protein/day/person (primary)"||CHAR(10)||
" farmland: %.2f acres/person"||CHAR(10),
SUM(production_kalories_per_day) / region_population,
SUM(production_protein_grams_per_day) / region_population,
SUM(farmland_acres) / region_population)||
CASE WHEN region_ghi IS NOT NULL AND region_ghi <> ''
THEN ">hunger index: "||region_ghi||CHAR(10)
ELSE "" END
FROM main GROUP BY region_name HAVING region_population;
----- olam.wiki/Crop_choices -----
--==Best yielding crops for each region==
-- Generates a wiki table (but you still have to add the {| and |} manually).
-- Ranked from best (left) to worst (right).
-- Ranking system: 2000 kalories has the same "value" as 60 grams of protein.
-- .once -e
SELECT
"|'''"||region_name||"'''"||CHAR(10)||
"|.<br />.<br /> kalories/day/acre <br /> g protein/day/acre "||CHAR(10)||
PRINTF("|''Status quo<br />all crops averaged''<br />%d<br />%d",
SUM(production_kalories_per_day )/SUM(farmland_acres),
SUM(production_protein_grams_per_day)/SUM(farmland_acres)
) ||CHAR(10)||
GROUP_CONCAT(
PRINTF("|'''%s'''<br />%d<br />%d",
crop_name, yield_kalories_per_day_per_acre, yield_protein_grams_per_day_per_acre
), CHAR(10)
) ||CHAR(10)||'|-'
FROM (
SELECT * FROM main
ORDER BY yield_kalories_per_day_per_acre/2000 + yield_protein_grams_per_day_per_acre/60 DESC
)
GROUP BY region_name;
--==Scenario: Every country grows mostly its best yielding crops==--
-- Farm land is reallocated in a way that favors high-yielding crops.
-- Each crop's land use will be proportional to the square of its yield (as scored above).
-- Total farmland remains the same.
CREATE TEMPORARY TABLE scored AS
SELECT *, yield_kalories_per_day_per_acre/2000 + yield_protein_grams_per_day_per_acre/60 AS score
FROM main
WHERE region_name <> "World" -- "World" is the only aggregated region in the dataset; leave it out.
AND crop_name <> "Mushrooms and truffles"; -- Mushrooms have exceptionally high yields, but this is because they grow without sun - they get their energy from the [[biomass waste]] of other food crops, generally. If you don't omit mushrooms, they end up being like 80% of the whole food supply. But this is not [[Term:viable|]] because there wouldn't be much biomass waste from other crops to grow the mushrooms in. So mushrooms are removed from here, but are added back below, with the same production/yield/areaharvested as the status quo.
UPDATE scored SET score = score/2 WHERE crop_name="Garlic"; -- Garlic has to be nerfed a bit too, because otherwise it'll recommend a whole pound of garlic per day per person!
CREATE TEMPORARY TABLE scenario1 AS
SELECT *, score*score*ratio AS new_acres
FROM scored JOIN (
SELECT region_name, SUM(farmland_acres)/SUM(score*score) AS ratio
FROM scored GROUP BY region_name
) USING(region_name);
INSERT INTO scenario1 SELECT *, 0 AS score, 0 AS ratio, farmland_acres AS new_acres FROM main WHERE crop_name = "Mushrooms and truffles" AND region_name <> "World"; -- see above for explanation
DROP TABLE scored; -- TABLE scenario1 is ready to be used now...
-- Text report, by country
SELECT PRINTF(
"%s"||CHAR(10)||
" farmland: %.2f acres per person"||CHAR(10)||
" crops per day per person:"||CHAR(10)||
" Status quo : %d kalories, %d grams of protein, %d grams of fat"||CHAR(10)||
" New scenario: %d kalories, %d grams of protein, %d grams of fat"||CHAR(10)||
" Top yielding crop: %s"||CHAR(10),
region_name,
SUM(farmland_acres) /region_population,
SUM(production_kalories_per_day) /region_population,
SUM(production_protein_grams_per_day) /region_population,
SUM(production_fat_grams_per_day) /region_population,
SUM(new_acres*yield_kalories_per_day_per_acre) /region_population,
SUM(new_acres*yield_protein_grams_per_day_per_acre)/region_population,
SUM(new_acres*yield_fat_grams_per_day_per_acre) /region_population
+ MAX(score)*0, -- tells sqlite to use the name of the top-scoring crop
crop_name)
FROM scenario1
GROUP BY region_name;
-- Text report, by continent
SELECT PRINTF(
"%s"||CHAR(10)||
" farmland: %.2f acres per person"||CHAR(10)||
" crops per day per person:"||CHAR(10)||
" Status quo : %d kalories, %d grams of protein, %d grams of fat"||CHAR(10)||
" New scenario: %d kalories, %d grams of protein, %d grams of fat"||CHAR(10),
region_cont,
SUM(farmland_acres) /region_cp, --
SUM(production_kalories_per_day) /region_cp, --
SUM(production_protein_grams_per_day) /region_cp, -- divide by
SUM(production_fat_grams_per_day) /region_cp, -- continent population
SUM(new_acres*yield_kalories_per_day_per_acre) /region_cp, --
SUM(new_acres*yield_protein_grams_per_day_per_acre)/region_cp, --
SUM(new_acres*yield_fat_grams_per_day_per_acre) /region_cp) --
FROM scenario1
GROUP BY region_cont;
-- Text report, world summary
SELECT PRINTF(
"World"||CHAR(10)||
" farmland: %.2f acres per person"||CHAR(10)||
" crops per day per person:"||CHAR(10)||
" Status quo : %d kalories, %d grams of protein, %d grams of fat"||CHAR(10)||
" New scenario: %d kalories, %d grams of protein, %d grams of fat"||CHAR(10),
SUM(farmland_acres) /7.9e9,
SUM(production_kalories_per_day) /7.9e9,
SUM(production_protein_grams_per_day) /7.9e9,-- divide by
SUM(production_fat_grams_per_day) /7.9e9,-- world population
SUM(new_acres*yield_kalories_per_day_per_acre) /7.9e9,
SUM(new_acres*yield_protein_grams_per_day_per_acre)/7.9e9,
SUM(new_acres*yield_fat_grams_per_day_per_acre) /7.9e9)
FROM scenario1;
-- Data for generating maps
-- food-crop-production1-if-crop-choices.png
SELECT region_m49 ||' '|| (SUM(new_acres*yield_kalories_per_day_per_acre)/SUM(farmland_acres))
FROM scenario1
GROUP BY region_m49
HAVING region_m49; -- use the results as input for: ./number-pairs-to-array-file yields-if-crop-choices
-- Also make a similar map of status quo, for comparison
-- food-crop-production1.png
SELECT region_m49 ||' '|| (SUM(production_kalories_per_day)/SUM(farmland_acres))
FROM scenario1
GROUP BY region_m49
HAVING region_m49; -- use the results as input for: ./number-pairs-to-array-file yields
-- Nutrition calculator links
-- Food crops per day per capita
-- For each of these, use the results as input for: make-nutrition0.9-url-from-sql-results.html
SELECT SUM(new_acres*yield_food_grams_per_day_per_acre)/7.9e9 AS f, food_name
FROM scenario1 -- World
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(new_acres*yield_food_grams_per_day_per_acre)/region_cp AS f, food_name
FROM scenario1 WHERE region_cont="Africa"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(new_acres*yield_food_grams_per_day_per_acre)/region_cp AS f, food_name
FROM scenario1 WHERE region_cont="Asia"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(new_acres*yield_food_grams_per_day_per_acre)/region_cp AS f, food_name
FROM scenario1 WHERE region_cont="Europe"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(new_acres*yield_food_grams_per_day_per_acre)/region_cp AS f, food_name
FROM scenario1 WHERE region_cont="North America"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(new_acres*yield_food_grams_per_day_per_acre)/region_cp AS f, food_name
FROM scenario1 WHERE region_cont="Oceania"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(new_acres*yield_food_grams_per_day_per_acre)/region_cp AS f, food_name
FROM scenario1 WHERE region_cont="South America"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
-- Also generate links for the status quo, for comparison:
SELECT SUM(production_food_grams_per_day)/7.9e9 AS f, food_name
FROM main WHERE region_name<>"World" -- summing it ourselves instead of using the pre-summed 'World' rows
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(production_food_grams_per_day)/region_cp AS f, food_name
FROM main WHERE region_cont="Africa"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(production_food_grams_per_day)/region_cp AS f, food_name
FROM main WHERE region_cont="Asia"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(production_food_grams_per_day)/region_cp AS f, food_name
FROM main WHERE region_cont="Europe"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(production_food_grams_per_day)/region_cp AS f, food_name
FROM main WHERE region_cont="North America"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(production_food_grams_per_day)/region_cp AS f, food_name
FROM main WHERE region_cont="Oceania"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
SELECT SUM(production_food_grams_per_day)/region_cp AS f, food_name
FROM main WHERE region_cont="South America"
GROUP BY food_name ORDER BY f DESC LIMIT 70;
-- Done with the temporary table
DROP TABLE scenario1;
-- olam.wiki/Deforestation --
-- Crop land trends --
SELECT PRINTF("%s:"||CHAR(9)||"%s%.1f%%/year",
`Area`,
CASE WHEN y2020 > y2018 THEN "+" ELSE "" END,
100.0 * (y2020 - y2018) / (2020 - 2018) / y2018)
FROM (SELECT SUM(`Value`) AS y2018, `Area` FROM fao WHERE `Element`="Area harvested" AND `Year`="2018" GROUP BY `Area`)
JOIN (SELECT SUM(`Value`) AS y2020, `Area` FROM fao WHERE `Element`="Area harvested" AND `Year`="2020" GROUP BY `Area`) USING (`Area`);
-- olam.wiki/Clothing --
-- General overview of fibre crops --
-- global production, in grams per day per capita
-- Note: some of these aren't fibre crops, it's any crops that aren't in the fao_usda cross-reference table
SELECT AVG(`Value`) -- tonnes/year
* 2737.9093 -- convert to grams/day
/ 7.9e9 -- per capita (divide by world population)
AS g,
`Item`
FROM fao WHERE `Area`="World" AND `Element`="Production" AND (
`Item Code (FAO)` NOT IN (SELECT `Item Code (FAO)` FROM fao_usda)
OR `Item`="Seed cotton")
GROUP BY `Item`
ORDER BY g;
-- keep in mind that an average t-shirt weighs 142 grams.
-- olam.wiki/calc - Data points generated for the calculator --
-- cotton.production, tonnes/year (to be fair, a more accurate number might be found in crops-processed.csv)
SELECT AVG(`Value`*(1-`Multiplier`))
FROM fao JOIN fao_usda USING (`Item Code (FAO)`)
WHERE `Area`="World" AND `Item`="Seed cotton" AND `Element`="Production";
-- cotton.farmland, hectares
SELECT AVG(`Value`) FROM fao
WHERE `Area`="World" AND `Item`="Seed cotton" AND `Element`="Area harvested";
--- MISC ---
-- How much of wheat comes from Ukraine and Russia
SELECT production_food_grams_per_day / 7.9e9 -- per capita, world population
FROM main WHERE crop_name="Wheat" AND region_name="World";
SELECT production_food_grams_per_day / 7.9e9 -- per capita, world population
FROM main WHERE crop_name="Wheat" AND region_name="Ukraine";
SELECT production_food_grams_per_day / 7.9e9 -- per capita, world population
FROM main WHERE crop_name="Wheat" AND region_name="Russian Federation";
------------------------
----- CODE TESTING -----
-- Test out the fao_usda cross-reference table --
SELECT CHAR(10)||
"Crop name: "||`Item`||CHAR(10)||
"Food name: "||`Long_Desc`||CHAR(10)||
"Harvest (as defined) is "||((100-`Refuse`)*`Multiplier`)||"% food."||CHAR(10)
FROM fao
JOIN fao_usda USING(`Item Code (FAO)`)
JOIN usda USING(`NDB_No`)
WHERE `Area`="World" AND `Element`="Production" AND `Year`="2018";
-- Test out the fao_usda cross-reference table, again --
SELECT CHAR(10)||
'FAO "'||`Item`||'"'||CHAR(10)||
' -' ||(100-100*`Multiplier`)||'% ='||CHAR(10)||
'USDA "'||`Long_Desc`||'"'||CHAR(10)||
' -' ||`Refuse`||'% "'||`Ref_desc`||'" ='||CHAR(10)||
'Edible portion.'||CHAR(10)
FROM fao
JOIN fao_usda USING(`Item Code (FAO)`)
JOIN usda USING(`NDB_No`)
WHERE `Area`="World" AND `Element`="Production" AND `Year`="2018";
-- Test that "World" is the only aggregate --
-- Each pair of sums should match - they don't have to match perfectly but should be pretty close
SELECT SUM(production_food_grams_per_day) FROM main WHERE region_name = "World";
SELECT SUM(production_food_grams_per_day) FROM main WHERE region_name <> "World";
SELECT SUM(production_kalories_per_day) FROM main WHERE region_name = "World";
SELECT SUM(production_kalories_per_day) FROM main WHERE region_name <> "World";
SELECT SUM(DISTINCT region_population) FROM main WHERE region_name = "World";
SELECT SUM(DISTINCT region_population) FROM main WHERE region_name <> "World";
-- Percent difference between sums (of production, for each crop):
SELECT crop_name, CAST(((a-b)/b)*100 AS INT)||"%"
FROM (SELECT SUM(production_food_grams_per_day) a, crop_name FROM main WHERE region_name<>"World" GROUP BY crop_name)
JOIN (SELECT SUM(production_food_grams_per_day) b, crop_name FROM main WHERE region_name= "World" GROUP BY crop_name)
USING(crop_name)
ORDER BY crop_name;
-- No two regions have identical population counts. [This fact allows us to use SUM(DISTINCT region_population) for aggregating regions. This was used in the creation of table 'main', to get the population of continents (main.region_cp)]
-- Verify that it's true:
SELECT region_population, region_name FROM main
GROUP BY region_name
ORDER BY CAST(region_population AS INT);
Notes
Data gaps
Some crops, in some regions, lack data on 'yield' and 'area harvested'. This puts them at zero production in the crop-choices scenario. Hopefully this won't throw off the results too much. Maybe the code could be tweaked to handle these NULLs better (maybe similarly to how mushrooms are handled in the code).
On multi-continent countries
- For aggregation to work, they have to be counted in only 1 continent
- Russia is the biggest multi-continent country (both in terms of population and land)
- Most of Russia's population is in Europe, but most of Russia's land is in Asia
- So if we count Russia as Europe, we inflate Europe's total land mass
- But if we count Russia as Asia, we inflate Asia's total population
- Either way, it's a compromise. We settled on Russia=Europe, and implemented it via the MAX(`Continent_Name`) which favors the one further down in the alphabet (Europe in this case). Hopefully this won't inflate Europe's food per capita too much.
About the crop choices scenario
Countries where the new scenario actually has less food
The scenario is designed to give higher-yielding crops more land than lower-yielding crops (but every crop at least gets some land). In most countries, this produces more food than the status quo. But occasionally you will find a country that is already growing even more of its top crops - so the status quo has more food than the scenario. Brazil is one example (their top crop is soybeans - and unfortunately the benefits of high yields are cancelled out by the fact that most soybeans are fed to animals).
Using the crop-choices to prevent deforestation
The scenarios in this code are presented as:
- Same amount of land + Growing different crops = More food
But they could also be presented as
- Using less land + Growing different crops = Same amount of food, without all the deforestation.
You can also opt for something in-between: Using a bit less land and also producing a bit more food.
Which one is best? Depends on whether food is currently abundant or scarce.
The "in between" is probably best for the world, overall. Even modest increases in food supply would be enough to end hunger, and even modest decreases in land usage would be enough to end deforestation.
Importance of imports and exports
In the scenario, some countries grow an extreme amount of certain foods (foods that wouldn't, on their own, make a balanced diet). But worldwide - and even continent-wide, the food supply is quite nutritionally balanced (see nutrition calculator links). In other words, countries would really have to trade with each other.
This would require more shipping as food would be less local. But still, overall carbon emissons would probably be lower, because of all the deforestation prevented (as explained above). Keep in mind that food transport is only a small percent of food's environmental footprint. And it could be smaller still if foods are processed locally and shipped in their more concentrated forms.
Nutrition calculator results
Why do tomatoes come out on top
Tomatoes aren't very calorie-dense or protein-dense, because they have a very high water content. However, as crops, they are still fairly high yielding in terms of calories and protein (not necessarily #1, but up there). So you end up with about a kilogram of tomatoes
What's with garlic being so high-yielding?
I know, right? I had to manually decrease its score so it wouldn't recommend a whole pound of garlic per person per day (or a whole kilogram in Africa). Strange because you wouldn't normally think of garlic as being a useful source of protein or calories. I guess it does have a lot of oils. I don't think it's possible for a person to eat that much garlic no matter how it's processed - too many sulfur compounds. Those pungent compounds might also be reason it's so high-yielding: Not a lot of pests go near it I guess.
Africa
Maybe lack of technology isn't the main reason for Africa's currently low crop yields. Maybe it's just because of not growing the right crops. Grains seem to do quite badly in Africa, but sugarcane and chili peppers do extraordinarily well.
Is this because sugar cane is inherently more suitable for the climate? Or is it because sugarcane farmers have (on average) more access to fertilizer etc for other reasons? This whole analysis assumes that it's mostly the former, and thus suggests that Africa grow more sugarcane and less grain. But we should check with other research.
If we look at history,
- On one hand, it's possible that the grains grown today in Africa were introduced by colonialists who ignorantly thought "these grow well in Europe/Asia, they must grow well in Africa".
- On the other hand, it's possible that colonialists invested more into sugarcane farming than into grain farming, because grains could already be grown in Europe/Asia. Perhaps Africa's grains are mostly grown by people who lack resources, while Africa's sugarcane is mostly grown by corporations who own all the resources? More research is needed here (sorry for the clichet).
On cassava: Interestingly, the scenarios actually put more cassava in Asia than in Africa. But still, some African countries still get very high cassava yields.
URLs generated
| World: | before | after |
| Africa: | before | after |
| Asia: | before | after |
| Europe: | before | after |
| North America: | before | after |
| Oceania: | before | after |
| South America: | before | after |
Take some of these results with a grain of salt
Monoculture vs polyculture
Be careful not to interperet these results as "This one crop is the best, let's just grow that and nothing else!" It could be that some crops depend on each other for their high yields. I recommend growing a mix of top crops, and especially include legumes for nitrogen fixing.
Local variation
It's possible that a country's top-yielding crops wouldn't be top-yielding in other parts of that country, if the soil or climate is different.
Social, economic, and technological factors
Maybe the top-yielding crop just has more resources invested into it - or the people who grow it are richer for historical reasons. In which case, maybe some currently-less-productive crops could be just as good if given a chance.
Why is ranking only based on protein and calories?
It's true that humans need a lot more than just protein and calories. We also need vitamins, minerals, fiber, and omegas (3 and 6). But in a world where farm land is scarce
- Nearly every crop in its natural form contains some vitamins and minerals.
- If there are any missing nutrients, then (worst-case) they can be supplemented/fortified with almost zero land footprint.
- There doesn't seem to be a lot of missing nutrients when all crops are combined (see nutrition calculator links).
- There is almost always an overabundance of fibre in crops. There are parts of plants we don't eat because they are so fiberous.
- Omegas 3 & 6 might be a genuine concern, and we should analyze this too. The next version of the nutrition calculator will track these.