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Future-proof and sustainable healthy diets based on current eating
patterns in the Netherlands
Roline Broekema,1Marcelo Tyszler,1Pieter van ’t Veer,2Frans J Kok,2Agnès Martin,3Anne Lluch,3and Hans TJ Blonk1
1Blonk Consultants, Gouda, Netherlands; 2Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands; and
3Department of Global Public Affairs, Danone Nutricia Research, Palaiseau, France
ABSTRACT
Background: To keep global warming <1.5◦C as recommended
by the Intergovernmental Panel on Climate Change (IPCC), eating
patterns must change. However, future diets should be modeled at a
national level and respect cultural acceptability.
Objectives: We aimed to identify diets among Dutch adults
satisfying nutritional and selected environmental requirements
while deviating minimally from the baseline diet among Dutch
adults.
Methods: We calculated per capita food system greenhouse
gas emission (GHGE) targets derived from the IPCC 1.5-degree
assessment study. Using individual adult dietary intake from the
National Food Consumption Survey in the Netherlands (2007–2010)
to form a baseline, we used quadratic optimization to generate diets
that followed the baseline Dutch diet as closely as possible, while
satisfying nutritional goals and remaining below GHGE targets. We
considered 12 scenarios in which we varied GHGE targets [2050:
1.11 kg of carbon dioxide equivalent (kg CO2-eq) per person per
day (pppd); 2030: 2.04 kg CO2-eq pppd; less strict 2030: 2.5 kg
CO2-eq pppd; no target], modeled eating patterns (food-based dietary
guidelines; exitarian; pescatarian; lacto-ovo-vegetarian; vegan),
and conducted exploratory analyses (food diversity; acceptability;
food chain interdependency).
Results: Optimized solutions for 2030 required major decreases
(<33% of baseline values) in consumption of beef, pork, cheese,
snacks, and butter and increased consumption (>150% of baseline
values) of legumes, sh and shellsh, peanuts, tree nuts, vegetables,
soy foods, and soy drink. Eight food groups were within 33%–150%
of the baseline diet among Dutch adults. The optimized solution
complying to the lowest GHGE target (2050) lacked food diversity,
and the (lacto-ovo) vegetarian and vegan optimized diets were prone
to nutritional inadequacies.
Conclusions: Within Dutch eating habits, satisfying optimization
constraints required a shift away from beef, cheese, butter, and
snacks toward plant-based foods and sh and shellsh, questioning
acceptability. Satisfying 2050 food system GHGE targets will require
research in consumer preferences and breakthrough innovations in
food production and processing. Am J Clin Nutr 2020;112:1338–
1347.
Keywords: sustainability, dietary scenarios, health impact, environ-
mental impact, dietary change
Introduction
Food systems are important contributors to global greenhouse
gas emissions (GHGEs), as well as to land occupation and
degradation, biodiversity loss, nutrient ow disruption, freshwa-
ter depletion, and depletion of fossil fuels (1,2). To meet the
2030 and 2050 GHGE targets of the Paris Agreement or the
Intergovernmental Panel on Climate Change (IPCC) report (3),
transitions are needed in food systems and diets.
A global reference diet that considers the health and envi-
ronmental sustainability aspects of eating patterns was recently
published in the EAT–Lancet report (4). The authors call for
country-specic analyses, using individual consumption data
if possible, while staying in line with their proposed global
reference diet. Recent publications have attempted to dene this
at a national level. An example is the Swiss scenario which
showed that achieving a sustainable diet would entail a greatly
Supported by Danone Nutricia Research (to HTJB). AM and AL from
Danone co-designed the study and provided meaningful revision input to the
manuscript.
Supplemental Tables 1–8 and Supplemental Methods are available from the
“Supplementary data” link in the online posting of the article and from the
same link in the online table of contents at https://academic.oup.com/ajcn/.
Data described in the article, code book, and analytic code will be made
available upon request pending [e.g., application and approval, payment,
other].
Address correspondence to MT (e-mail: mtyszler@gmail.com).
Abbreviations used: FBDG, food-based dietary guideline; GHGE, green-
house gas emission; IPCC, Intergovernmental Panel on Climate Change; kg
CO2-eq, kilograms of carbon dioxide equivalent; LCA, life-cycle assessment;
pppd, per person per day; PRI, population reference intake.
Received December 20, 2019. Accepted for publication July 9, 2020.
First published online August 7, 2020; doi: https://doi.org/10.1093/ajcn/
nqaa217.
1338 Am J Clin Nutr 2020;112:1338–1347. Printed in USA. Copyright
©The Author(s) on behalf of the American Society for Nutrition 2020. This is an Open Access article distributed under the terms of the Creative Commons
Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction
in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Future-proof and sustainable healthy diets (NL) 1339
reduced intake of meat and vegetable oils, and a moderate
reduction in cereals, roots, and sh products, while increasing
the intake of legumes, nuts, seeds, fruits, and vegetables (5).
Another study used nonlinear optimization to design diets for
152 countries that met cultural, environmental, and dietary
constraints (6). However, both studies used aggregated data based
on food balance sheets, which leaves room for relatively much
uncertainty.
In this study, we aimed to identify diets among Dutch adults
that satised nutritional requirements and remained below GHGE
targets but that deviated only minimally from the baseline
diet among Dutch adults. Like previous studies (7,8), we
used quadratic optimization to derive future diets from the
baseline food consumption data, which were based on the most
recently available national food consumption survey. Novel
to this study, we calculated per capita food system GHGE
targets for 2030 and 2050 designed to limit the global average
temperature rise to 1.5◦C. Using projected emission factors
and different scenarios, we optimized diets to comply with
different sets of constraints. We also studied how food-based
dietary guidelines (FBDGs) and eating patterns of exitarians,
pescatarians, (lacto-ovo) vegetarians, and vegans affected the
results. Finally, we considered 3 scenarios focusing on food diver-
sity, acceptability, and interdependency between food production
chains.
Methods
The model
Future diets that were nutritionally adequate and met food
system GHGE targets were generated using a quadratic opti-
mization algorithm are described in detail in the Supplemental
Methods: optimization algorithm. Solutions were found by
minimizing the summation of the quadratic differences in the
consumption amounts (g) of each food item, while satisfying
specic constraints for each scenario. The results of any
optimization satised all nutritional requirements and GHGE
targets, and minimized deviation from the baseline diet among
Dutch adults, according to the metric of the computation
algorithm. Computations were implemented using Optimeal®
3.0, a software package developed by Blonk Consultants in
cooperation with the Netherlands Nutrition Centre (9).
Baseline diet among Dutch adults
Dietary data were obtained from 2 independent 24-h dietary
recalls collected by unannounced phone calls in a 3-y period
within the scope of the Dutch National Food Consumption
Survey (2007–2010) (10), and processed as in a previous study
(7). To gain insight into habitual food consumption, the 24-h
dietary recalls were spread among all days of the week and the
different seasons (10). More details about the study protocol are
presented in the Supplemental Methods: Dutch National Food
Consumption Survey Protocol.
First, the original list of 1599 food items matched to the
Dutch Food Composition Table (11) was reduced by, among
other things, replacing branded food products with their generic
counterparts and raw products with their ready-for-consumption
versions. Product selection was also guided by the availability
of data required for the calculation of GHGEs, coverage within
each food group, and the presence of (lacto-ovo) vegetarian
alternatives. The reduced list had 207 generic food products,
including beverages, covering 77% of consumed weight and 56%
of the energy intake across the 3819 participants of the survey
(males and females aged 7–69 y).
Second, restricted to the reduced list of 207 products, we
modeled a baseline diet using the 2-d average diet of the 699
adults (men and women) aged 31–50 y. Here, we rounded to
0 g the intake of food items representing <1% of the mass
intake of a food group. Rounding food items with very small
amounts to0gproduces a modeled baseline diet that is more
realistic and meaningful from a 1-wk time frame. Finally, we
proportionally adjusted the intake of other food items within
each food group to match the observed mean consumed energy
intake, covering 100% of the original energy intake of the
diet. Supplemental Table 1 provides a comparison between
the modeled diets resulting from the aforementioned process
and the Dutch National Food Consumption Survey (10)average
diets. The overall nutrient composition of the modeled diets
was similar but not identical. We considered, however, that
it was similar enough for the analytical purposes of this
study.
Further details about the process described above are available
in the Supplemental Methods: selection of products for optimiza-
tion and ne tuning of the baseline diet. Supplemental Table 2
provides the full list of products. Supplemental Table 3 shows
the detailed composition of the baseline diet among Dutch adults
as obtained using these steps.
Nutritional data and requirements
Nutrient composition of foods was primarily obtained from
the Dutch Food Composition Table (11), used to register the 24-
h recall answers from the Dutch National Food Consumption
Survey (2007–2010) (10), whereas amino acid composition was
derived from the National Nutrient Database of the USDA (12),
using the matching indicated in Supplemental Table 4.The
combined data set contained data on 61 nutritional properties,
including macronutrients, vitamins, minerals, amino acids, and
dietary ber.
Lower limits of nutritional requirements were derived from the
Dutch dietary reference values and more specically population
reference intake (PRI). If values for PRI were not available,
values for adequate intake were used. Upper limits of require-
ments were based on values for the tolerable upper intake level
(13–21). Table 1 lists these nutritional requirements.
Environmental impact data and food system GHGE targets
For each food item we used life-cycle assessment (LCA)
to calculate GHGEs, fossil energy use, and land occupation
(22,23). We conducted the LCAs as part of a larger research
program, for which references are included in Supplemental
Table 5. The LCAs considered the different countries of origin
of crops for the Netherlands, taking account of agricultural
activities such as application of fertilizers, and emissions from
activities due to transport, processing, packaging, distribution,
retail (e.g., lighting and cooling), cooling at home, food
preparation, and waste treatment. Wastage was accounted for at
all life-cycle stages. All LCAs spanned the full life cycle, from
1340 Broekema et al.
TABLE 1 Daily energy and nutrient requirements for the Dutch diet, and intake amounts of the baseline diet among Dutch adults and 2030 scenario
Property Lower limit Upper limit
Baseline diet among
Dutch adults 2030 scenario
Energy, kcal 2125 2375 242012375
Protein, g 54.5 140.6 97 89
Fat, g 50 100 96.5 89.5
Saturated fat, g — 25 33.6120.3
Polyunsaturated fat, g — 30 19.5 23.8
Linoleic acid, g 5 — 16.4 19.6
α-Linolenic acid, g 2.5 — 2.2512.50
Trans fatty acids, g — 2.5 1.11 0.44
Cholesterol, mg — — 219 152
Carbohydrates, g 225 393.8 256 266
Fiber, g 32 — 21.6132.0
Water, g 2400 3900 3260 3164
Alcohol, g — 15 11 10
DHA and EPA, mg 200 1000 16011000
Retinol activity equivalent, μg 740 3000 751 1001
Thiamin, mg 0.94 — 0.9210.94
Riboavin, mg 1.6 — 1.5511.60
Niacin, mg 15 — 22.2 23.9
Vitamin B-6, mg 1.5 25 1.59 1.58
Folate, μg 300 1000 2591329
Vitamin B-12, μg 2.8 — 4.95 5.59
Vitamin C, mg 75 — 86.7 84.9
Vitamin D, μg 3.3 75 3.37 3.5
Vitamin E, mg 12 300 14.7 12.0
Vitamin K, μg 105 — 143 211
Calcium, mg 955 2500 1170 955
Phosphorus, mg 550 3000 1770 1839
Iron, mg 13.5 25 11.3114.5
Sodium, mg — 2400 279012205
Potassium, mg 3500 — 3850 4022
Magnesium, mg 325 565 383 554
Zinc, mg 8 25 12.6 11
Selenium, μg 70 300 51.9170.0
Copper, mg 0.9 5 1.21 2.19
Iodine, μg 150 600 188 232
Tryptophan, g 0.33 — 1.04 1.01
Threonine, g 1.23 — 3.35 2.97
Isoleucine, g 1.64 — 4.05 3.52
Leucine, g 3.20 — 7.35 6.39
Lysine, g 2.46 — 6.66 4.68
Methionine, g 0.85 — 2.13 1.65
Cysteine, g 0.34 — 1.17 1.24
Valine, g 2.13 — 4.87 4.29
Histidine, g 0.82 — 2.78 2.17
1Value fails to satisfy the requirement.
farm to fork, and the geographical scope of the LCAs is the
Netherlands.
In order to adjust the GHGEs of each food item to forecasted
scenarios for 2030 and 2050, we used a climate impact trend
analysis described in a report entitled “The Menu of Tomorrow”
(24). For each food group, we derived changes in agricultural
production, as well as trends in the supply processes that are
part of the agricultural production system, such as energy and
fertilizer production. These trends were then translated into
implications for each life-cycle stage, resulting in the forecasted
emission factors for 2030 and 2050 (see Supplemental Table 3).
For instance, in cultivation of crops it is expected that nitrogen
use efciency will increase, as it has done in the past in the
Netherlands, leading to increased yield and reduced nitrogen
application by better fertilization and soil management. This is
further explained in the Supplemental Methods: forecasting the
environmental impacts of products.
We considered 3 different GHGE targets, all expressed as
GHGEs per person per day (pppd): the 2030 target of 2.04 kg
of carbon dioxide equivalent (kg CO2-eq) pppd and the 2050
target of 1.11 kg CO2-eq pppd, both derived from the IPCC 1.5-
degree assessment study (3); and a less strict target of 2.5 kg
CO2-eq pppd was also considered. This less strict target might
be realistic should other sectors reduce GHGE impact to a great
extent. The calculation of these targets is explained in detail in
the Supplemental Methods: translating the global climate change
Future-proof and sustainable healthy diets (NL) 1341
targets into personal targets from food. Fossil energy use and land
occupation were measured, but were not used as targets for the
optimization.
Scenarios
We considered 3 types of optimization scenarios: different
food system GHGE targets, different eating patterns, and
exploratory scenarios. Satisfying all nutritional requirements was
part of every scenario.
First, 4 optimization scenarios used different food system
GHGE targets. The 2030 scenario met the 2030 GHGE target
using emission factors forecasted for 2030 whereas the 2050
scenario met the 2050 GHGE target using emission factors
forecasted for 2050. To determine the relative importance of
the GHGE targets, we modeled 2 additional variations of the
2030 scenario: a scenario with no GHGE target (no-GHGE-
target scenario) and a scenario with a less strict GHGE target
(relaxed-GHGE-target scenario). This less strict target might
be realistic if and when other sectors reduce GHGE impact
more than initially agreed upon, allowing more possibilities
for GHGEs from food production. The scenarios for different
eating patterns and for the exploratory analyses were all further
variations of the initial 2030 scenario and used the 2030 GHGE
target.
We then modeled scenarios using 5 eating patterns. Here the
initial scenario was based on FBDGs (FBDGs scenario): to the
2030 scenario we added the dietary requirements derived from the
Dutch Health Council’s dietary guidelines (17). Each of the items
in the Dutch dietary guidelines was translated into computation
requirements as Supplemental Table 6 details. Four specic
eating patterns were modeled by including additional restrictions:
a exitarian diet, with 35 g meat/d [50% of the maximum amount
of meat recommended by the Netherlands Nutrition Centre (15)];
a pescatarian diet, without any meat; a (lacto-ovo) vegetarian diet,
without any meat or sh and shellsh; and a vegan diet, without
any animal products.
Finally, 3 exploratory optimization scenarios considered
food diversity, acceptability, and interdependency between food
production systems. In the diversity scenario, we added the
Wheel of Five (25) requirements to the FBDGs scenario, resulting
in the most complete dietary advice for the Netherlands. The
Netherlands Nutrition Centre’s Wheel of Five recommends that
85% of all calories should come from the following 5 food
groups: nonalcoholic and nonsugar beverages; bread, grains, and
potatoes; sh and shellsh, pulses, meat, eggs, nuts, and dairy;
margarine and cooking fats; and fruits and vegetables. It also
recommends that foods in each food group should be consumed
every day. In the acceptability scenario we kept the dietary
solutions even closer to the baseline diet among Dutch adults by
limiting dietary changes to 33%–150% of the baseline amounts
(g) of each food group. This range was informed by the typical
log normally shaped, and thus asymmetric, distribution of food
group intake (26,27). Finally, in the food chain interdependency
scenario we considered the coproduction of dairy and beef. Here
we xed the amount of dairy other than cheese and butter to the
level of the results of the 2030 scenario, and set the amount of
beef from dairy cows to 1 g beef for every 46 g milk (28–31).
This ratio is based on the production of milk and beef in animal
husbandry of dairy cows.
Sensitivity analysis
The robustness of the 2030 scenario to uncertainties in the
nutritional and environmental data was tested via 1000 Monte
Carlo simulations. We assumed that all nutritional and environ-
mental characteristics represented the average characteristics of
each of the 207 foods in our analysis. In each round of the
simulation, a random realization of the average nutritional and
environmental characteristics was used, and the dietary solution
for the 2030 scenario was recalculated. The variability of the
nutritional (e.g., vitamin C or calcium) and environmental (i.e.,
GHGEs, fossil energy use, and land occupation) characteristics
was approximated using a normal distribution with a relative
SD of 12.5% for all characteristics and foods. The 5th and
95th percentiles of the distribution of the 1000 Monte Carlo
results gave an indication of the robustness of the results of the
2030 scenario to uncertainties in the underlying environmental
sustainability indicators and nutrient composition data.
Results
Table 1 shows the amounts of nutrients for the baseline diet
among Dutch adults and for the 2030 scenario. Supplemental
Table 3 shows the detailed composition of all scenarios,
Supplemental Table 7 shows nutrient values for all scenarios,
and Supplemental Table 8 shows the food group intake for
all scenarios. The baseline diet among Dutch adults that we
calculated did not meet all nutritional requirements, in line with
ndings of the Dutch National Food Consumption Survey report
(10); specically, it was too high in energy (kcal), saturated fat,
and sodium and too low in α-linolenic acid (18:3n–3), ber,
the omega-3 fatty acids DHA (22:6n–3) and EPA (20:5n–3),
thiamin, riboavin, folate, iron, and selenium. The solutions for
all scenarios attempted to correct the nutritional inadequacies in
the baseline diet among Dutch adults.
Table 2 shows the optimized diets for the 4 scenarios in
which we varied food system GHGE target amounts. For each
food group, we have specically highlighted in the text below
consumption differences that were <33% or >150% of the
baseline diet among Dutch adults, in line with the threshold used
in the acceptability scenario. To achieve a nutritionally adequate
diet with no limits on GHGEs (no-GHGE-target scenario), the
main differences with the baseline diet among Dutch adults were
a higher consumption of vegetables, sh and shellsh, legumes,
soy foods, and tree nuts and elimination of butter. This diet had
lower GHGEs than the baseline diet among Dutch adults, slightly
lower land use, but higher fossil energy use. The additional
dietary changes needed to achieve the 2030 GHGE target (2030
scenario) were a higher consumption of peanuts and soy drink
(fortied with vitamin B-12 and calcium), elimination of beef and
snacks, and lower consumption of pork and cheese. In the 2030
scenario, consumption of sh and shellsh, peanuts, and tree nuts
was higher than in the no-GHGE-target scenario. Consumption
of legumes was higher than in the baseline diet, but lower than
in the no-GHGE-target scenario. Intakes of grains and starches,
fruits, dairy other than cheese and butter, unsaturated oils, sugar
and confectionary, cakes, soups and bouillon, and beverages were
within 33%–150% of the baseline diet among Dutch adults. This
diet also had a lower fossil energy use and land occupation than
the baseline diet among Dutch adults. The sensitivity test of
1342 Broekema et al.
TABLE 2 Food intake (g/d) and environmental indicators of the baseline diet among Dutch adults and for 4 scenarios with varying food system GHGE
targets1
Food group
Baseline diet among
Dutch adults
No-GHGE-target
scenario
Relaxed-GHGE-target
scenario
2030 scenario
(sensitivity range2) 2050 scenario
Grains and starches
Rice, wheat, corn, and other 225 268 278 289 (278, 295) 293
Potatoes and cassava 114 112 112 109 (105, 112) 86
Vegetables
Dark green vegetables 51 93 80 65 (58, 77) 145
Red and orange vegetables 50 77 59 38 (33, 46) 33
Other vegetables 45 76 73 70 (62, 80) 153
Fruits
All fruit 116 134 119 99 (94, 105) 16
Dairy foods
Cheese 39 26 19 3 (0, 10) 0
Liquid dairy 371 364 368 363 (348, 376) 128
Butter 6 0 0 0 (0, 0) 0
Protein sources
Beef and lamb 44 37 0 0 (0, 0) 0
Chicken and other poultry 30 26 22 11 (5, 17) 0
Pork 56 24 17 10 (5, 19) 0
Eggs 11 13 15 17 (12, 21) 0
Fish and shellsh 18 44 54 48 (42, 57) 52
Dry beans, lentils, and peas 4 37 30 23 (17, 31) 0
Soy foods 0 5 6 5 (1, 9) 0
Tree nuts 2 20 35 55 (44, 59) 63
Peanuts 14 20 28 36 (30, 40) 17
Added fats
Unsaturated oils 28 16 12 10 (8, 16) 27
Sugars and snacks
Sugar and confectionary 44 36 31 23 (14, 31) 0
Cakes 56 37 37 31 (24, 36) 23
Snacks 9 6 2 0 (0, 0) 0
Other foods
Condiments and sauces 24 12 14 9 (5, 13) 0
Soups and bouillon 63 61 53 36 (29, 42) 0
Beverages
Alcoholic beverages 212 210 200 203 (200, 205) 132
Nonalcoholic beverages 2118 2117 2112 2102 (2097, 2105) 1797
Soy drink39 10 14 19 (15, 23) 96
Greenhouse gas emissions, kg CO2-eq pppd 4.21 3.65 2.5 2.04 1.11
Fossil energy use, MJ pppd 37.2 40.5 32.8 27.8 19.6
Land occupation, m2a pppd 4.65 4.34 3.26 3.11 2.79
1kg CO2-eq, kilograms of carbon dioxide equivalent; MJ, megajoules; m2a, square meters annually; pppd, per person per day.
2The robustness of the 2030 scenario to uncertainties in the nutritional and environmental data was tested via 1000 Monte Carlo simulations.
3Soy drink is a fortied product with vitamin B-12 and calcium.
the 2030 scenario suggested quite robust results at food group
level.
The relaxed-GHGE-target scenario contained more cheese,
chicken, and pork and lower amounts of tree nuts than the
2030 scenario, but still no beef. The diet in the 2050 scenario
was limited to only a few food groups. Compared with the
2030 scenario, consumption of cheese, chicken, pork, eggs,
legumes, soy foods, sugar and confectionary, soups and bouillon,
and condiments and sauces was completely removed, whereas
consumption of total vegetables, tree nuts, and soy drink was
higher.
Table 3 shows the results of the eating pattern scenarios. None
of the optimized eating patterns contained beef. The exitarian
scenario contained the most meat (chicken and pork). In the
(lacto-ovo) vegetarian scenario, egg and tree nut consumption
was greatly increased. The (lacto-ovo) vegetarian and vegan
scenarios did not meet the requirements for DHA and EPA for
which sh and shellsh is a main source. The vegan scenario
only met requirements for vitamin B-12 and calcium through high
consumption of fortied soy drink.
Table 4 shows the results of the exploratory scenarios. In
the diversity scenario, products from cakes, condiments and
sauces, snacks, soups and bouillon, and sugar and confectionary
were a priori limited to 15% of total energy intake. As a
consequence, the optimized diet contained more nonmeat protein
sources and unsaturated oils than the baseline diet among
Dutch adults. In the acceptability scenario, meat consumption
(including beef) remained higher than in the 2030 scenario,
as did consumption of cheese and unsaturated oils, whereas
consumption of legumes, soy foods, and tree nuts was lower. In
Future-proof and sustainable healthy diets (NL) 1343
TABLE 3 Food intake (g/d) and environmental indicators for the FBDGs scenario and for the exitarian, pescatarian, (lacto-ovo) vegetarian, and vegan
scenarios1
Food group
2030 scenario
(sensitivity range2)
FBDGs
scenario
Flexitarian
scenario
Pescatarian
scenario
Lacto-ovo vegetarian
scenario Vegan scenario
Grains and starches
Rice, wheat, corn, and other 289 (278, 295) 268 267 270 267 281
Potatoes and cassava 109 (105, 112) 107 106 108 100 32
Vegetables
Dark green vegetables 65 (58, 77) 77 80 72 66 182
Red and orange vegetables 38 (33, 46) 41 41 51 36 50
Other vegetables 70 (62, 80) 82 80 78 98 181
Fruits
All fruit 99 (94, 105) 200 200 200 200 200
Dairy foods
Cheese 3 (0, 10) 0 0 1 8 0
Liquid dairy 363 (348, 376) 360 354 368 336 0
Butter 0 (0, 0) 0 0 0 0 0
Protein sources
Beef and lamb 0 (0, 0) 0 0 0 0 0
Chicken and other poultry 11 (5, 17) 8 17 0 0 0
Pork 10 (5, 19) 7 18 0 0 0
Eggs 17 (12, 21) 16 13 21 68 0
Fish and shellsh 48 (42, 57) 49 45 53 0 0
Dry beans, lentils, and peas 23 (17, 31) 20 20 20 20 20
Soy foods 5 (1, 9) 1 0 2 0 0
Tree nuts 55 (44, 59) 57 58 54 82 39
Peanuts 36 (30, 40) 35 35 34 25 0
Added fats
Unsaturated oils 10 (8, 16) 12 11 22 24 5
Sugars and snacks
Sugar and confectionary 23 (14, 31) 23 19 22 4 38
Cakes 31 (24, 36) 27 23 30 46 0
Snacks 0 (0, 0) 0 0 0 0 0
Other foods
Condiments and sauces 9 (5, 13) 7 4 8 5 52
Soups and bouillon 36 (29, 42) 29 24 33 31 236
Beverages
Alcoholic beverages 203 (200, 205) 200 200 200 193 163
Nonalcoholic beverages 2102 (2097, 2105) 2099 2097 2100 2081 1953
Soy drink319 (15, 23) 19 20 20 14 532
Greenhouse gas emissions, kg CO2-eq pppd 2.04 2.04 2.04 2.04 2.04 2.04
Fossil energy use, MJ pppd 27.8 28.2 27.7 28.1 26.8 30.03
Land occupation, m2a pppd 3.11 3.11 3.13 3.1 3.58 2.77
1FBDG, food-based dietary guideline; kg CO2-eq, kilograms of carbon dioxide equivalent; MJ, megajoules; m2a, square meters annually; pppd, per
person per day.
2The robustness of the 2030 scenario to uncertainties in the nutritional and environmental data was tested via 1000 Monte Carlo simulations.
3Soy drink is a fortied product with vitamin B-12 and calcium.
the food chain interdependency scenario, where the consumption
of beef was required, the consumption of cheese, pork, and
chicken was lower than in the 2030 scenario.
Discussion
The results of this study suggest that the 2030 food system
GHGE target cannot be achieved by only correcting nutritional
inadequacies, indicating that additional dietary changes are
needed. We do show, however, that it is possible to meet the
2030 and 2050 GHGE targets, but large shifts in diets might be
needed and the feasibility of those changes may be limited. Our
results also suggest that it is possible to achieve climate targets
and nutritional adequacy with specic eating patterns; however,
fortication and/or supplementation will be required. In most of
the optimization scenarios consumption of beef was nil, but our
food chain interdependency scenario achieved the 2030 GHGE
target with limited consumption of beef.
The 33%–150% range that we chose for selecting relevant
changes might have inuenced our results. In the 2030 scenario,
increased consumption of eggs and decreased consumption of
chicken, unsaturated oils, and condiments and sauces were
on the borderline of the 33%–150% range. Some relatively
large changes might be more feasible than relatively smaller
changes. For example, a 100% reduction of butter consumption
corresponds with a small change in grams (from 6 to 0 g/d) but
might be more feasible than a relatively smaller reduction of
chicken and poultry by 65% (from 30 to 11 g/d).
1344 Broekema et al.
TABLE 4 Food intake (g/d) and environmental indicators for the diversity, acceptability, and food-chain-interdependency scenarios1
Food group
2030 scenario
(sensitivity range2)
Diversity
scenario
Acceptability
scenario
Food-chain-
interdependency scenario
(dairy)
Grains and starches
Rice, wheat, corn, and other 289 (278, 295) 299 338 289
Potatoes and cassava 109 (105, 112) 109 93 107
Vegetables
Dark green vegetables 65 (58, 77) 68 77 65
Red and orange vegetables 38 (33, 46) 41 17 35
Other vegetables 70 (62, 80) 91 67 71
Fruits
All fruit 99 (94, 105) 200 76 95
Dairy foods
Cheese 3 (0, 10) 0 13 1
Liquid dairy 363 (348, 376) 368 272 363
Butter 0 (0, 0) 0 2 0
Protein sources
Beef and lamb 0 (0, 0) 0 14 8
Chicken and other poultry 11 (5, 17) 15 46 5
Pork 10 (5, 19) 5 24 7
Eggs 17 (12, 21) 23 17 16
Fish and shellsh 48 (42, 57) 44 27 47
Dry beans, lentils, and peas 23 (17, 31) 21 5 20
Soy foods 5 (1, 9) 0 0 3
Tree nuts 55 (44, 59) 54 3 58
Peanuts 36 (30, 40) 23 21 38
Added fats
Unsaturated oils 10 (8, 16) 44 21 9
Sugars and snacks
Sugar and confectionary 23 (14, 31) 4 32 23
Cakes 31 (24, 36) 1 40 27
Snacks 0 (0, 0) 0 3 0
Other foods
Condiments and sauces 9 (5, 13) 0 8 6
Soups and bouillon 36 (29, 42) 12 21 29
Beverages
Alcoholic beverages 203 (200, 205) 200 166 200
Nonalcoholic beverages 2102 (2097, 2105) 2093 2012 2098
Soy drink319 (15, 23) 22 14 22
Greenhouse gas emissions, kg CO2-eq pppd 2.04 2.04 2.04 2.04
Fossil energy use, MJ pppd 27.8 28.3 25.7 27.1
Land occupation, m2a pppd 3.11 3.33 2.76 3.15
1kg CO2-eq, kilograms of carbon dioxide equivalent; MJ, megajoules; m2a, square meters annually; pppd, per person per day.
2The robustness of the 2030 scenario to uncertainties in the nutritional and environmental data was tested via 1000 Monte Carlo simulations.
3Soy drink is a fortied product with vitamin B-12 and calcium.
Our results suggest that reducing consumption of beef, pork,
poultry, cheese, butter, and snacks and increasing consumption
of legumes, sh and shellsh, peanuts, tree nuts, vegetables,
soy foods, and soy drinks are critical to achieve GHGE targets
while maintaining a healthy eating pattern. Dairy products other
than cheese, grains, and starches can be consumed in amounts
similar to those of the baseline diet among Dutch adults. Higher
fruit consumption is not necessary to either satisfy nutritional
requirements or reach GHGE targets, but does provide improved
health benets according to FBDGs (17).
Our use of quadratic optimization to search for diets that are
close to the baseline diet among Dutch adults and that also satisfy
nutritional and climate change requirements is an approach that
has been used in other contexts where similar food and dietary
data were available (6,24,32). The fact that we chose quadratic
programming to minimize deviations of the modeled diets from
the baseline diet does not guarantee diverse and acceptable diets.
Others have used linear and nonlinear programming (33–35)or
simulated new diets by expert-informed replacement of food
products, e.g., the EAT–Lancet report (4). An analysis of diets
in 4 European Union countries (36) took national food patterns
into account by means of regression analysis of GHGEs and
land occupation for major food groups, followed by expert-
informed isocaloric substitution of main food groups. To obtain
more realistic and acceptable model solutions, models are being
developed that account for intrinsic relations between foods and
consumer preferences in daily menus (E Mertens, A Kuijsten, A
Kanellopoulos, M Dofková, L Mistura, L D’Addezio, A Turrini,
C Dubuisson, S Havard, E Trolle, S Biesbroek, J Bloemhof, J M
Geleijnse, P Veer van ‘t, unpublished results, 2019).
Future-proof and sustainable healthy diets (NL) 1345
Sustainable diets should be affordable, so even though price
was out of scope in our analysis, price should be considered in
the development of acceptable, healthy, sustainable diets (37). A
recent study in the United Kingdom showed that healthy diets that
meet GHGE targets can be created for all income quintiles (38).
Indeed, tailoring changes to income groups is expected to make
dietary changes more achievable. In addition, taste proles and
texture should be considered when proposing healthier and more
sustainable diets (39).
Modeling diets for 2050 was technically feasible, but the
solutions in this scenario lacked food diversity and deviated
greatly from baseline eating patterns. Results showed that
nutritional adequacy for essential sh fatty acids and vitamin
B-12 requires attention. In our results, the risk of vitamin B-12
and calcium deciency was alleviated by fortied soy products,
which points at the potential role of other biomimicry products
such as plant-based meat substitutes and/or supplementation
by pills or powder-based food. Although fortication and
supplementation will increase the total environmental impact
slightly, this is unlikely to cancel out the net benecial effects
of altered dietary habits. Innovations in agriculture and food
technology may lead to an altered food supply, new recipes, and
new eating patterns. Moreover, this article has not considered the
consequences for trade and global shifts in supply and demand
due to shifts in consumer choices. Thus, the time horizon of
2050 is associated with many uncertainties owing to lack of
insight into future environmental footprints, nutritional proper-
ties, and dietary habits. Nevertheless, these results show that
attention to essential nutrients is warranted in the food systems
transition.
Comparability with other studies can be challenging owing
to their use of different data sources, such as food balance
sheets (4,5,40). When food consumption is estimated via food
balance sheets, the conversions from commodity production to
consumer food items are not always properly included. For
instance, parts of cereal production are consumed as alcoholic
beverages or as added ingredients in pastries or sauces. Although
24-h recalls also do not guarantee exact annual consumption,
they are considered a preferred and more precise data collection
method relative to indirect methods (41,42). We therefore believe
that using direct dietary assessments of nal consumer food items
is more rened and more useful for providing dietary advice
than only considering indirect measurements, such as produced
commodities. Studies have been done using consumer food items,
specically for the Netherlands (7,8). However, we calculated
GHGE targets to limit temperature rise due to global warming to
1.5◦C and used projected emissions factors for the food products
for 2030 and 2050.
Our environmental assessment was intended to be as compre-
hensive as possible in order to deliver an accurate estimation
of both environmental impacts and reduction potential, in line
with a recent report by the IPCC (43). Unlike the EAT–Lancet
report, our LCAs included retailing practices and home food
preparation as well as fossil carbon dioxide emissions in addition
to methane and nitrous oxide. We also considered fossil energy
use and land occupation. Although we did not add targets for
these indicators, we did show that the impacts on fossil energy
use and land occupation were reduced in the 2030 scenario by
25% and 33%, respectively. Other environmental metrics, such
as water use or biodiversity losses, could improve our analysis.
Because these metrics are not fully agreed on by experts and there
are no data available for all LCAs, we chose to omit them for now.
The impact on climate change due to direct land use change (e.g.,
deforestation) was not accounted for. The method to calculate
impact due to direct land use change requires data on the past
20 y, whereas in our optimization scenarios we look forward to
2030 and 2050. This means that the required data for the scenarios
are not yet available and because land use change can hardly be
forecasted it was omitted from this study.
Our results show that future diets, complying to GHGE targets
and meeting nutritional constraints, should contain less meat—
especially beef—and more plant-based food products. These
ndings are in line with a report from the Food, Agriculture,
Biodiversity, Land, and Energy (FABLE) consortium which
investigated pathways to sustainable land use and food systems
for 18 countries (44). For countries that are in the same
geographical region as the Netherlands, such as Finland and the
United Kingdom, this report found directions of change toward
2050 similar to those identied in the current study, namely
a decrease in meat consumption (48%–66%) and a signicant
increase in vegetables, legumes, eggs, and sh and shellsh. In
another study in 4 European countries, besides energy intake,
total meat consumption and the proportion of ruminant meat
explained a substantial part of the variation in GHGEs and land
occupation for different diets (36). Based on regression models,
isocaloric substitution by grain products of 5% energy from
meat would decrease GHGEs and land occupation by 10% and
15%, respectively. Although an increase in plant-based products
has been suggested by several studies, the required shifts in the
use of animal-based products might not be homogeneous across
countries or gender (35).
Summarizing, our results suggest that modeling acceptable
country-level diets, that consider both health and sustainability
parameters (45), is challenging. However, using country-level
data might lead to more acceptable diets than using a global
approach. It is possible to meet nutritional requirements and
a strict GHGE target, but it is difcult to achieve diverse
and acceptable diets with current food product availability.
Strong product and process innovation, improving nutritional
proles (e.g., reformulation, fortication, supplementation) and
environmental performance is needed to meet the 2050 GHGE
target in terms of healthy and acceptable diets, taking into
account price, texture, and taste. Current dietary patterns in the
Netherlands are not consistent with the required GHGE targets,
and specic policies may be needed to help people shift to diets
with lower environmental impacts (46).
In conclusion, we used newly calculated per capita food
system GHGE targets for 2030 and 2050 designed to limit
global average temperature rise to 1.5◦C. Forecasting GHGE
factors for food products, we optimized diets to comply to
different sets of constraints, studying how FBDGs and eating
patterns of exitarians, pescatarians, (lacto-ovo) vegetarians, and
vegans affect the results. We have found multiple solutions
for future diets and for different eating patterns, meeting food
system GHGE targets and nutritional needs. We have shown that
more stringent GHGE targets and more stringent eating patterns
reduce the diversity of possible diets, potentially limiting their
acceptability and increasing the risk of inadequate vitamin B-12
and calcium intake. Taken together, the scenarios considered here
suggest some clear shifts will be needed to meet food system
1346 Broekema et al.
GHGE targets and comply to nutritional constraints: less meat
(especially beef), cheese, butter and snacks, and more sh and
shellsh and plant-based products.
More research is needed, considering a wider range of
environmental indicators; additional aspects such as price,
taste, and texture; product reformulation; fortication; and
supplementation. Shedding more light on these aspects will
enable policy makers and advisory associations to decide on
ways to identify acceptable changes and guide the necessary
dietary shifts. Changes should focus not only on various food
consumption patterns, but also on food production.
We thank Corné van Dooren from the Netherlands Nutrition Centre for his
expert judgment on the nutritional requirements and formulating the diversity
scenario. We also thank Sally Hill for improving the use of English in the
manuscript and providing critical comments.
The authors’ responsibilities were as follows—RB: led the calculations of
the study and the analysis; MT: co-developed the optimization algorithm and
led the structuring of the paper; RB and MT: greatly contributed to the writing
of the manuscript; PvV and FJK: contributed primarily on the analysis and
interpretation of the results and writing of the discussion; AM, AL, and HTJB:
co-designed the study; AM and AL: provided meaningful revision input to the
manuscript; HTJB: led the computation of the food system GHGE targets and
LCAs; and all authors: actively took part in the writing, provided feedback,
are accountable for all aspects of the work, and read and approved the nal
manuscript. RB reports indirect fees from Danone Nutricia Research, during
the conduct of the study; and personal fees from Blonk Consultants, outside
the submitted work. MT reports personal fees from Blonk Consultants and
indirect fees from Danone Nutricia Research, during the conduct of the study;
and personal fees from KIT Royal Tropical Institute, outside the submitted
work. PvV reports personal fees from Wageningen University and Research,
outside the submitted work. FJK reports personal fees from Wageningen
University and Research, outside the submitted work; and is a member of
the Board of Danone Institute International. AM reports personal fees from
Danone Nutricia Research, outside the submitted work. AL reports personal
fees from Danone Nutricia Research, outside the submitted work. HTJB
reports indirect fees from Danone Nutricia Research, during the conduct of
the study; and personal fees from Blonk Consultants, outside the submitted
work. In addition, HTJB has a patent for Optimeal Software licensed to
Danone.
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