Balkovič et al. (2018). Impacts and Uncertainties of +2°C of Climate Change and Soil Degradation on European Crop Calorie Supply

Impacts and Uncertainties of +2°C of Climate Change and Soil Degradation on

European Crop Calorie Supply

Juraj Balkovič

1,2

, Rastislav Skalský

1,3

, Christian Folberth

1

, Nikolay Khabarov

1

, Erwin

Schmid

4

, Mikuláš Madaras

5

, Michael Obersteiner

1

, and Marijn van der Velde

6

1

International Institute for Applied Systems Analysis, Ecosystem Services and Management

Program, Schlossplatz 1, A-2361 Laxenburg, Austria, balkovic@iiasa.ac.at, skalsky@iiasa.ac.at,

folberth@iiasa.ac.at, khabarov@iiasa.ac.at, oberstei@iiasa.ac.at

2

Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava,

Ilkovičova 6, 842 15 Bratislava, Slovak Republic, balkovic@fns.uniba.sk

3

National Agricultural and Food Centre, Soil Science and Conservation Research Institute,

Gagarinova 10, 827 13 Bratislava, Slovak Republic, r.skalsky@vupop.sk

4

Institute for Sustainable Economic Development, University of Natural Resource and Life

Sciences, Vienna, Feistmantelstrasse 4, 1180 Vienna, Austria, erwin.schmid@boku.ac.at

5

Crop Research Institute, Drnovská 507/73, 161 06 Prague, Czech Republic, madaras@vurv.cz

6

European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy,

marijn.van-der-velde@ec.europa.eu

Corresponding author: Juraj Balkovič (balkovic@iiasa.ac.at)

Key Points:



Quantification of the biophysical consequences up to +2°C of global warming on calorie

supply from main crops in Europe



Mostly positive change in calorie supply was estimated, with uncertainty due to crop

management intensification, temperature, and CO

2

effect



Soil degradation could undermine climate-related benefits in Eastern and North-Eastern

Europe

This article has been accepted for publication and undergone full peer review but has not
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may lead to differences between this version and the Version of Record. Please cite this
article as doi: 10.1

002

/

2017EF000629

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Abstract

Even if global warming is kept below +2°C, European agriculture will be significantly impacted.

Soil degradation may amplify these impacts substantially and thus hamper crop production

further. We quantify biophysical consequences and bracket uncertainty of +2°C warming on

calories supply from ten major crops and vulnerability to soil degradation in Europe using crop

modelling. The Environmental Policy Integrated Climate (EPIC) model together with regional

climate projections from the European branch of the Coordinated Regional Downscaling

Experiment (EURO-CORDEX) were used for this purpose. A robustly positive calorie yield

change was estimated for the EU Member States except for some regions in Southern and South-

Eastern Europe. The mean impacts range from +30 Gcal ha

–1

in the north, through +25 and +20

Gcal ha

–1

in Western and Eastern Europe, respectively, to +10 Gcal ha

–1

in the south if soil

degradation and heat impacts are not accounted for. Elevated CO

2

and increased temperature are

the dominant drivers of the simulated yield changes in high-input agricultural systems. The

growth stimulus due to elevated CO

2

may offset potentially negative yield impacts of

temperature increase by +2°C in most of Europe. Soil degradation causes a calorie vulnerability

ranging from 0 to 80 Gcal ha

–1

due to insufficient compensation for nutrient depletion and this

might undermine climate benefits in many regions, if not prevented by adaptation measures,

especially in Eastern and North-Eastern Europe. Uncertainties due to future potentials for crop

intensification are about two to fifty times higher than climate change impacts.

1 Introduction

Climate change and soil degradation are among the major threats to agriculture and food security

(Lal et al., 2007; Meersmans et al., 2016; Tilman et al., 2002) and recognized as such by farmers

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across Europe (Olesen et al., 2011). Following the Paris climate agreement, 193 nations have

committed to keep the global average warming well below 2°C relative to preindustrial

temperatures and pursue efforts to limit the temperature increase to 1.5°C. Recent climate

projections indicate that Europe will warm at a faster rate than the global average, and a +2°C of

global warming will bring a significantly changed climate to Europe (Vautard et al., 2014). For

example, Northern and Eastern Europe in winter and Southern Europe in summer will likely

experience warming up to +3°C, with an increase in winter precipitation in Central Europe and

an overall increase of precipitation over Northern Europe. The trends and amplified weather

variability characterizing this future climate will bring challenges to Europe, one of the largest

producers of agricultural goods.

To inform mitigation efforts, consequences of holding global warming below +2°C should be

quantified. For example, keeping globing warming at a low level may lower future damages in

the Mediterranean region, but it may constrain potential benefits from warming in the north

(Müller et al., 2015). On one hand, increased temperature will accelerate crop development and

shorten crop growth periods, thus potentially reducing crop yields in the productive temperate

and Mediterranean Europe (Asseng et al., 2015; Lobell & Field, 2007; Supit et al., 2010). More

intense heat waves and droughts, together with heavy rains, hail, floods, pests and diseases, will

further hamper crop productivity. On the other hand, carbon dioxide (CO

2

) fertilization has been

recognized to compensate part of the negative effects and possibly even offset productivity

losses, although the magnitudes of these effects are uncertain and subject to debate (Ewert et al.,

2007; Kimball, 2016; Leakey et al., 2009; Long et al., 2006). Since experimental data to estimate

physiological impacts of changing climate are generally lacking or difficult to collect and

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evaluate at large scales (Asseng et al., 2013, 2015), crop models are increasingly used to support

climate change impact assessments (Asseng et al., 2015; Rosenzweig et al., 2014).

An increasing number of studies describe climate change impacts on crops, but the impacts of

soil degradation have received much less attention, even though combatting continuing

degradation is of utmost importance (Bindraban et al., 2012; Montanarella, 2015). Soil with a

lowered capability to store and release water and nutrients can provide only little relief to crops

coping with impacts from climate change. Indeed, healthy soils can help crops to cope better

with increased climate variability (Tubiello et al., 2007). Continuous cropping can deplete soil

fertility due to inadequate replacement of nutrients harvested with produce or lost through

leaching, erosion, and atmospheric emissions. Most of the processes responsible for soil

degradation, including soil organic matter mineralization and erosion, are enhanced by higher

temperature and more intense precipitation. It has been estimated that almost 40% of the total

agricultural land in Europe is prone to soil degradation at a moderate or higher level of severity

(Lal, 2008).

Interactions and transfer of uncertainty through the climate to crop model chain introduce a

concern for climate change impact studies (Asseng et al., 2013). The uncertainty encompasses,

inter alia, inherent uncertainty in climate projections (Challinor et al., 2009), crop response to

increased temperature and elevated atmospheric CO

2

concentration (Asseng et al., 2015;

Kimball, 2016; Long et al., 2006), soil heterogeneity (Folberth, Skalský, et al., 2016), and model

complexity and parameterization (Challinor et al., 2009). Advances in crop management (e.g.

fertilization and irrigation, technological innovations and breeding) together with expansion of

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cropland (Ewert et al., 2005; Jaggard et al., 2010; Smith et al., 2010) contribute additional

uncertainty for future projections. Having this in mind, quantification of uncertainties is therefore

an important endeavour in large-scale impact studies such as ours.

In this study we quantify biophysical impact of global warming as high as +2°C on calorie

supply from ten major crops and its vulnerability to soil degradation in the European Union (EU)

Member States. To our knowledge, such large-scale analysis is still lacking despite a growing

number of impact studies in the agricultural sector. An ensemble of the most recent regionally-

downscaled climate change projections from the European branch of the Coordinated Regional

Downscaling Experiment (EURO-CORDEX) is used, together with the Environmental Policy

Integrated Climate (EPIC) crop model, to capture inherent uncertainties due to climate

simulation. The uncertainties due to EPIC crop modelling are quantified via an uncertainty

analysis with varying model assumptions and parameters.

First, we estimate, at a regional scale, the ranges and robustness of biophysical impacts on the

combined crop-derived calorie yield. Second, we disentangle the vulnerability to soil degradation

in terms of soil loss, depletion of plant nutrients and soil organic carbon since these are among

the major threats to crop production, especially in regions with less developed agricultural

systems. Finally, we bracket projected calorie yield uncertainties related to 1) future crop

intensification possibilities, 2) increased temperature and elevated CO

2

effects, and 3)

uncertainty in soil degradation modelling.

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2 Data and Methods

2.1 Gridded crop model

A gridded pan-European EPIC model was used in this study (Balkovič et al., 2013). The model

was built by coupling the field-scale model EPIC v. 0810 (Izaurralde et al., 2006, 2012;

Williams, 1995) with large-scale data on environmental conditions and crop management

practices in Europe (Table 1).

In EPIC, potential crop growth is calculated daily from intercepted photosynthetically active

radiation using the energy-to-biomass conversion approach modified for vapour pressure deficit

and atmospheric CO

2

concentration effect (Monteith, 1977; Stockle et al., 1992). The potential

daily increase is adjusted to an actual biomass increase if the potential demand for water or

nutrients exceeded actually available supply, or the temperature goes beyond the optimal range.

The root growth is constrained by soil strength or aluminium toxicity. Plant phenological

development, including leaf growth, plant nutrients concentration, partitioning of biomass among

roots and shoots as well as yield formation are defined by heat units (in °C) accumulated over the

growing season. Since soils are storing, cycling and providing nutrients and water for crops in

EPIC, dynamic soil processes ranging from soil hydrology to organic matter and nutrient cycles

are simulated (Izaurralde et al., 2006, 2012; Williams, 1995). EPIC allows simulations with a

static soil profile, when all soil properties except for plant-available nutrients and water are re-

initialized at the beginning of each year, or with a dynamic soil profile when soil properties are

continuously simulated over time. EPIC’s processes relevant for assessment of climate change

impact on crops were summarized in the supplementary information of Folberth, Skalský, et al.

(2016) based on the aforementioned references.

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The bio-physical EPIC v.0810 model was coupled with ArcGIS and a gridded modelling

framework was built by integrating EU-wide geospatial data on soils, terrain, land cover,

watersheds, administrative units and regionalized crop management practices (Table 1) with 1-

km grid. Redundant grids were clustered into simulation units (SimU) upon which the model was

run. The model was designed to simulate regional crop yields as driven by gridded inputs on

weather, site and soil properties, crop management scenarios and atmospheric CO

2

concentration,

and was extensively evaluated by Balkovič et al. (2013).

Ten major European crops, namely winter wheat, winter rye, spring barley, grain maize, rice,

winter rapeseed, soy bean, sunflower, sugar beet and potatoes, were simulated for 25 Member

States of the EU. We excluded Malta, Cyprus and Croatia from the analysis due to lack of input

data. Regional differences in crop varieties characterized by different growth period lengths

follow the climatic stratification in the EU as described by Balkovič et al. (2013). Planting and

harvesting dates were scheduled automatically based on heat unit accumulation, while potential

heat unit requirements (PHU) were constant during the course of the simulation. Automatic

harvest was scheduled at 110% (cereals and potatoes) and 115% (grain maize, rapeseed,

sunflower and soy bean) of the PHU to enable flexible harvesting and to take post-maturity

drying of crops on the field into account. All crops were simulated in mono-crop rotations on all

the available cropland. Crop rotations and present-day harvested areas were not included since

this information is generally lacking at the target resolution and this pattern may change in the

future. Rice was simulated only in Bulgaria, France, Greece, Hungary, Italy, Portugal, Romania

and Spain. Although EPIC’s ability to simulate historical yields was extensively validated for

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some crops by Balkovič et al. (2013), we extended this comparison to reported yields to include

all crops from this study in the Supporting Information (Text S1, Figure S9, and Table S4).

Parameters used to describe crop’s growth characteristics and their response to increased

atmospheric CO

2

are summarized in Table S5.

2.2 Climate data

A regional ensemble of bias-corrected EURO-CORDEX climate change simulations (Jacob et

al., 2014) developed in the Quantifying Projected Impacts Under 2°C Warming project

(IMPACT2C, http://impact2c.hzg.de) was integrated in the crop modelling framework to have a

robust projection of future warming in Europe. In our study, the projections meet the +2°C

threshold when their driving global climate models reach the +2°C threshold globally. This

threshold is reached when the 30-year running mean temperature calculated from the base period

1971–2000, plus the observed pre-industrial warming before this period, exceeds the +2°C

threshold (Vautard et al., 2014). Accordingly, 30-year periods around the year when the +2°C

warming was reached and the base period of 1971–2000 were evaluated throughout the paper. A

total of five moderate emission RCP 4.5 projections were used (Table 1) since most of the low

emission RCP2.6 simulations in EURO-CORDEX do not reach +2°C at all and the high

emission RCP 8.5 scenarios are not compatible with the +2°C mitigation target. Given the

selected RCP 4.5 projections and periods when the +2°C threshold is reached, atmospheric CO

2

concentration increases from about 360 ppm around the year 2000 up to concentration of 470 to

580 ppm at the +2°C threshold.

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2.3 Crop management scenarios

A total of three crop management scenarios were designed to analyse biophysical impacts of

+2°C on calorie yields (Table 2).

(1)

The business-as-usual (BAU) scenario represents fertilization and irrigation practices

around the year 2000, and serves as a baseline for future projection in this study.

Fertilization intensity was estimated by computing fertilizer balances for sub-national

statistical regions (NUTS2). Crop-specific annual nitrogen (N) and phosphorus (P)

fertilizer application rates were estimated from NUTS2 livestock numbers and excretion

coefficients as well as commercial fertilizer consumption from EUROSTAT

(http://ec.europa.eu/eurostat). Fertilizer demands at NUTS2 level were calculated using

crop and forage yields and acreages from EUROSTAT as well as nutrient uptake

coefficients (Balkovič et al., 2013). Annual P and N fertilizer amounts comprise both

mineral and organic fertilizers expressed in kg of mineral N and P equivalent per hectare

and crop. Phosphorus was applied as a rigid amount together with tillage operation before

sowing, while N amount was used as an upper application limit for automatic fertilization

splitting in EPIC. Single N applications were triggered automatically based on crop

requirements. As a rule, 80% of the amount applied in the previous season was applied at

the beginning of the following season, while next applications were triggered using the

same 80% rule always when crop requirements exceeded soil supply, until the annual

limit was reached. As a results, N fertilizer is commonly split in two or three applications.

Irrigation extent as well as area share of irrigated crops were taken from the European

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Irrigation Map (Wriedt et al., 2009). Since spatial data for crop-specific water application

volumes are lacking, we assume that irrigated crops are supplied with ample water

quantities to eliminate water stress (Folberth, Skalský, et al., 2016; Rosenzweig et al.,

2014). Irrigation water was supplied to refill soil water content to its field capacity each

time when crop stress occurred, while the upper annual limit was set to 1000 mm crop

–1

(Balkovič et al., 2013, 2014). Rice was simulated only under full irrigation, but without

excessive water applications typical for paddy cultivation. The BAU calorie yield was

calculated as an average of rainfed and irrigated yields weighted by crop-specific share of

irrigated and rainfed cropland (Section 2.4).

(2)

The scenario P1 is designed to estimate the potential calorie yields assuming

unconstrained intensification of the existing rainfed and irrigated production systems.

Existing irrigation facilities identified by the EIM (Table 1) were allocated to all crops to

fully compensate for water deficit as described above. Phosphorus fertilizer was supplied

automatically in sufficient quantity to entirely avoid P stress (Gerik et al., 2013). The

maximum annual application rate of 250 kg N ha

–1

crop

–1

was used to eliminate nitrogen

stress on all cropland. To estimate N fertilization needed to achieve the P1 potential yield,

the N application rate was distributed automatically by smaller quantities when crop

nitrogen stress occurred, resulting in N applications lower or equal 250 kg ha

–1

crop

–1

.

The final calorie yield was calculated while the fraction of area equipped for irrigation

was used as a weight treating crops equally. Neither pest nor disease effects were

considered.

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(3)

The P2 scenario assumes sufficient fertilization and irrigation in every SimU of European

cropland, irrespective of existing irrigation infrastructure. This scenario provides calorie

yield estimates close to the biophysical potential. Similar to the above scenarios, all crops

except for rice were simulated on all the available cropland to account for possible shifts

in future harvested areas. Pests and diseases were not considered.

All crop management scenarios were simulated with constant soil profiles (

con

), where all soil

variables other than readily available nutrients (nitrogen and phosphorus) and soil water content

were re-initialized each year and soil erosion was not considered. These simulations allow the

assessment of climate change impacts independently from soil degradation. Besides simulations

with constant soil profiles, dynamic soil profiles (

dyn

) together with the BAU crop management

scenario were used to account for future vulnerability to soil degradation (see Section 2.5). The

dyn

soil profile scenario includes water erosion processes and transient simulation of main soil

variables, including soil organic carbon and nitrogen, nutrient and carbon loss with erosion, bulk

density, and soil depth. More details can be found in Text S3 in the Supporting Information.

All simulations were carried out assuming conventional tillage, consisting of two cultivation

operations and moldboard ploughing prior to sowing and an offset disking after harvesting of

cereals. Two row cultivations during the growing season were assumed for maize and one

ridging operation for potatoes. We assume that 20% of crop residues are removed in case of

cereals (excluding maize), while no residues are harvested for other crops (Köble, 2014).

Aboveground crop residues were recycled by a 15 cm deep plough with high mixing efficiency.

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2.4 Spatial aggregation of outputs and impact metrics

2.4.1 Aggregation of model outputs

The simulations were carried out from 1971 till the end of the respective +2°C periods (see Table

1) with all climate projections and crop management scenarios. Model calculations were

preceded by a 50-year spin-up simulation with repeated historic weather to equilibrate initial soil

properties with respect to the BAU management specifications and local climate. Gridded crop

yields (

y

) were converted from dry to fresh matter and subsequently calculated as weighted

average of rainfed and irrigated production with Eq (1). The

y

values were summed across all

crops and converted to the combined calorie yield (

Y

) by Eq (2). Conversion factors for dry-to-

fresh matter yield as well as calorie content per unit of fresh matter yield (

g

) are summarized in

Table S1. The calorie yields were regionalized for each year as an arithmetic average of all

cropland belonging to either a NUTS2 or a country region.

𝑦

𝑡,𝑝,𝑠,𝑐

= (1 − 𝑓

𝑐,𝑝

) ∙ 𝑦(𝑟)

𝑡,𝑝,𝑠,𝑐

+ 𝑓

𝑐,𝑝

∙ 𝑦(𝑖)

𝑡,𝑝,𝑠,𝑐

(1)

𝑌

𝑡,𝑝,𝑠

= ∑

𝑔

𝑐

∙ 𝑦

𝑡,𝑝,𝑠,𝑐

𝑘

𝑐=1

(2)

where

y

t,p,s,c

is crop fresh matter yield (in t ha

-1

) of crop

c

, in grid

p

, climate scenario

s

, and the

t

-

th year,

y(r), y(i)

stand for yield simulated under rainfed and irrigated conditions, respectively,

f

c,p

is the fraction of irrigated area of the

c

-th crop in the

p

-th grid,

Y

t,p,s

is the aggregated calorie

yield expressed as a sum of all simulated crops (in Gcal ha

–1

) in grid

p

, climate scenario

s

, and

year

t

,

k

is the number of crops and

g

c

denotes calorie content per unit yield of the

c

-th crop.

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2.4.2 Impact metrics

Unless stated otherwise, all biophysical impacts are expressed as absolute or relative yield

change between the reference period 1971–2000 with BAU crop management (baseline) and the

respective estimates for the +2°C period. Differences between mean yield values were

statistically evaluated using the two-tailed paired

t

-test where appropriate, while linear

regressions were tested by the

F

-test. All statistical analyses and plotting were done in R (R Core

Team, 2016). In Section 3.2, the sums of daily temperature and precipitation over the growing

seasons of all crops were calculated to support the analysis therein.

The impact robustness (

R

) across climate projections was assessed as a fraction of EPIC

realizations with a positive change in calorie yield with respect to the total number of realizations

calculated for NUTS2 regions. Therefore, the robustness accounts for SimU-level agreement in

climate projections aggregated at NUTS2 level. In this study we assume that the impact is highly

robust when

R

is above 0.8 (Knutti & Sedláček, 2012), meaning that more than 80% of model

realizations in the NUTS2 region agree on a positive sign of impact when the reference

management scenario is considered.

2.5 Vulnerability to soil degradation

Soil degradation is the decline in soil quality caused by degradation processes such as water and

wind erosion, loss of soil organic matter, salinization, acidification, contamination, sealing or

compaction. In this study we address impacts of water erosion and adverse changes in soil

organic matter and nutrient dynamics, including soil organic matter mineralization, nutrient

leaching and loss from export through harvested products, while other degradation processes are

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not accounted for. More details can be found in Text S3 in the Supporting Information. Yield

vulnerability (

Vs

) is quantified as a fraction of projected calorie yield that may be lost due to

harmful effects of soil degradation. Business-as-usual fertilization and irrigation determine the

capacity to stabilize yields on degraded soils.

The impact of changing soil properties on crop yields was calculated by comparing crop yield

impacts simulated with constant soil profile (

con

) relative to the simulations with dynamic soil

profile (

dyn

), both in the BAU scenario (Eq 3). The dynamic soil mode allows accounting for

changing soil properties under a certain management over time (Basso et al., 2015) and it is used

to estimate impacts of climate change in interactions with soil degradation. The coupled soil

organic carbon and nitrogen routine (Izaurralde et al., 2006, 2012) that was parameterized for

high-input agricultural systems in Central Europe by the authors of this study and the small-

watershed Modified Universal Soil Loss Equation erosion method (Williams, 1995) were used

herein. Vulnerability to soil degradation (in Gcal ha

–1

) was calculated for each grid (

p

) and

climate change scenario (

s

) by Eq (3).

𝑉𝑠

𝑝,𝑠

= 𝐼(𝑐𝑜𝑛)

𝑝,𝑠

− 𝐼(𝑑𝑦𝑛)

𝑝,𝑠

,

(3)

where

I

is 30-year mean yield impact (in Gcal ha

–1

) of +2° warming relative to the historic period

(1971–2000) calculated in the respective soil-handling and climate scenarios. Despite the

importance of conservation agriculture for soil protection, conservation practices were not

included in our large scale analysis. Possible implications are tested in the uncertainty analysis

(Section 2.6.1).

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2.6 Evaluation of uncertainties in the modelling approach

To account for the potentially large uncertainty in simulating future climate change impacts with

EPIC we performed an extensive uncertainty analysis for the impact estimates presented herein

with respect to the dimensions of temperature and CO

2

effects, soil degradation modelling, and

management intensification.

2.6.1 Evaluation of uncertainties in modelling temperature, CO

2

, and soil degradation

effects

We performed a detailed uncertainty analysis (UA) for Belgium, Lithuania, Portugal and

Slovakia aiming to quantify uncertainty in yield simulations due to synergic interactions of

increased temperature, atmospheric CO

2

and soil degradation effects across (1) different climatic

regions with contrasting changes in temperature and precipitation patterns, and (2) regions with

different intensity of nutrient inputs.

Since soil input data, tillage practices and parameterization of underlying bio-geochemical

processes represent an important source of uncertainty in EPIC (particularly in the

dyn

scenario),

we randomized a range of tillage and residue management variables, such as the number and

intensity of soil cultivations and crop residue return rates, to account for diversity in cultivation

practices. In addition, we assumed that a fraction of BAU N fertilization comes as farmyard

manure to account for additional carbon inputs with organic fertilization. Finally, we vary the

most sensitive parameters and soil initial values which drive soil organic matter and erosion

dynamics in EPIC to address parameterization and input uncertainties. The parameter ranges

used in the UA (PARM in Table 3) follow recommendations of EPIC developers (Gerik et al.,

2013), while the default values from Table 3 were used in our impact analysis. A detailed

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description of parameters and variables can be found in Gerik et al. (2013). The subset of

parameter values used to imitate soil conservation practices is also summarized in Table 3. The

Latin Hypercube method (McKay, 1992) was used to initiate a total of 3000 parameter and

variable combinations for each simulated unit. For example, more than 28 million of EPIC

simulations were performed for Portugal (3000 parameter combinations × 4790 SimUs × 2 CO

2

scenarios).

The KNMI climate projection (Table 1) was used for the UA, being close to the ensemble

median in terms of temperature change rate. All simulations described above were performed

with constant (360 ppm) and transient atmospheric CO

2

concentration (from 325 ppm in 1971 up

to 700 ppm in 2100). The impact of temperature was analysed by grouping all yield data

simulated for the +2°C period into



T intervals separated by 0.5 °C steps, where



T is relative to

the mean annual temperature from 1971 to 2000. A more generic modelling experiment was

carried out to provide an extended insight into the uncertainty due to incrementally increasing

temperature and CO

2

. Two contrasting crops, namely C

3

wheat and C

4

maize, were used for this

purpose. More detailed description is in Text S2 in the Supporting Information.

2.6.2 Evaluation of uncertainties due to crop intensification

Crop management scenarios with ample nutrient and water supply (P1 and P2) were evaluated

relative to the BAU scenario to bracket projected yield uncertainties related to the range of future

intensification levels. The uncertainty range is expressed as a yield difference between the

baseline BAU projection and the respective high-input scenarios under +2°C. Crop

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intensification options other than more intensive irrigation and fertilization are not accounted for

herein.

3 Results

3.1 Crop calorie yield at the +2°C threshold

At the national level, a positive calorie yield change was estimated for all EU Member States at

the +2°C warming threshold, albeit with different robustness across climate projections and

variability at sub-national level. Figure 1 demonstrates yield ranges estimated from the full

ensemble of climate projections under the BAU scenario and transient atmospheric CO

2

concentration. Ensemble mean +2°C yield impacts relative to the baseline (1971–2000, BAU),

and its 5

th

to 95

th

range of yield projections and robustness in the ensemble, are aggregated at

sub-national level (NUTS2 regions) in Figure 2. Mean calorie yield changes for individual

ensemble members are presented in Table S3 in the Supporting Information.

The most positive relative impact with robustness above 90% was simulated over Northern

Europe, with a lower impact magnitude in the Baltic countries. The aggregated calorie yield is

expected to increase by more than 17% (20–34 Gcal ha

–1

) in Denmark, Ireland, U.K. and

Finland, and by 12% (15 Gcal ha

–1

) in the Baltics. The climate projection-related yield impact

uncertainty ranges from +6% to +20% in regions of the Baltic countries, and from +7% to +30%

in other regions in the north (Figure 2). Yield changes and impact robustness of individual crops

are summarized in Figure S1 and Table S2.

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At the national level, an increase by 17–38 Gcal ha

–1

was estimated for Western European

countries, i.e. countries which currently belong to the most productive in Europe. The lowest but

still robustly estimated positive impact was ~6% as projected for south-western France (Figure

2). There is a large yield impact range projected in France, ranging from –2% to +40% in the

south-western regions. In general, the ranges in yield projections are larger in regions with cooler

montane climate. Maize and other summer crop yields except for sugar beet would increase the

most, especially in higher altitudes. Owing to its long growing season, sugar beet tends to be

negatively though not robustly affected in various parts of Western Europe (Figure S1, Table

S2).

In the countries of the Balkan Peninsula, at the national average, a small overall increase of 6 to

13 Gcal ha

–1

was calculated, while for the other Eastern European countries a moderate gain of

17 to 27 Gcal ha

–1

was estimated. The climate change impacts are largely uncertain across

EURO-CORDEX scenarios and the yield change projections are therefore insufficiently robust

in many NUTS2 regions in Romania and Bulgaria (Figure 2). The projected yields range from –

10% to +25%, and from –5% to +20% in regions of Bulgaria and Romania, respectively. Despite

the positive overall impact demonstrated in Figure 1, all crops experienced a decrease in yields in

at least some simulated climate change scenarios in these regions (Figure S1).

Only a small positive impact of less than 8% (2–18 Gcal ha

–1

) was estimated over the

Mediterranean countries when the BAU crop management scenario is considered. The highest

calorie increase was projected for Italy and Slovenia, owing to relatively high share of cooler

montane regions. Large bars in Figure 1 reveal high yield variability due to heterogeneous site

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conditions and projected weather variability. A substantial disagreement among climate

projections makes the overall yield impact estimates insufficiently robust and largely uncertain in

many regions of Portugal, Spain and Greece, where the sub-national calorie yield changes ranged

between strongly negative of –20 % and positive of up to 35% (Figure 2).

3.2 Climate effects on calorie yields

We carried out a detailed analysis to quantify the contribution of elevated CO

2

and projected

seasonal temperature and precipitation patterns to the regional calorie yield changes summarized

in Section 3.1. Irrigated BAU simulations were used to address irrigated and high-rainfall

cropland, while rainfed simulations from the areas dominated by water stress (water stress in

>50% days of the growing period) were used to disentangle the impacts of precipitation on water

limited production (Figure 3).

Elevated CO

2

and seasonal temperature are two dominant climatic factors determining yield

changes in Northern Europe. A robust increase in seasonal temperature sum (10–40%) resulted

in calorie yield changes ranging from –8% in Lithuania to +7% in Ireland when CO

2

was fixed at

360 ppm, whilst a consistently positive calorie yield change of +13% to +25% was simulated

with elevated CO

2

(circles in Figure 3a). The positive impact is composed of (1) a strong

fertilization effect of elevated CO

2

on winter crops, which would be affected rather negatively if

CO

2

remained constant (Text S2, also Figure S2 and S10), and (2) more favourable temperature

conditions for summer and root crops, allowing them to be more viable on cropland farther

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north. For example, the mean sunflower yield achieved in Europe around the year 2000 (about 2

t ha

–1

) would be exceeded on more than 50% of existing cropland, except for the far north areas

of Finland (data not shown). Changes in precipitation alone do not affect our yield projections

since crops are not critically limited by water deficit. However, >10% increase in rainfall during

the long growing seasons affected the yields negatively via reduced solar radiation, leading to the

weak response of yields to increased temperature in the north (

r

2

< 0.4 in most countries in

Figure 3a). It is also important to note that detrimental effects of increased precipitation such as

hampering field operations or damage due to heavy rains are not addressed in this analysis.

Owing to an increase in seasonal temperature by ~13%, the irrigated and high-rainfall calorie

yields would increase by 1–3% and 19–23% with fixed and elevated CO

2

, respectively, in

Western Europe, but only by 13% in the latter case in France. Importantly, water-limited yields

in southern France decreased by 5% when CO

2

effect is not considered. Yet, elevated CO

2

has

the potential to offset the negative effects, leading to an overall increase by 4% (Figure 3b). In

general, maize and other summer crops would benefit from alleviated low temperature

limitations in the wetter and cooler parts of Western Europe, while winter crops and tubers will

benefit from elevated CO

2

. Winter cereals would be affected negatively without elevated CO

2

fertilization (Text S2, also Figure S2 and S10).

A positive effect of increased temperature under elevated CO

2

was simulated also for irrigated

and high-rainfall crops in Eastern Europe (

r

2

> 0.6 in all countries except for Poland in Figure

3a). A ~13% temperature increase in the growing period, together with elevated CO

2

of up to

470 –580 ppm at +2°C, stimulated the calorie yields by about 17–21% in Czechia, Poland and

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Slovakia, and by 9–12% in Hungary, Bulgaria and Romania. The impact is much weaker at fixed

CO

2

levels though: from –1% to +4% on the country average, while a positive yield response is

in most cases visible only at higher warming rates simulated for the montane climate (Figure 3a).

In general, the yield change is dominated by higher productivity of summer crops and tubers

under elevated CO

2

(Figure S2). A benefit from CO

2

fertilization could more or less offset the

losses in winter cereal yields expected due to shortening of the growing period (Text S2 in the

Supporting Information). Water-limited calories production in the driest Pannonian lowlands of

Hungary, Bulgaria, and especially Romania, is sensitive to the altered precipitation patterns

(Figure 3b). For example, in Romania, a 10% increase in growing season precipitation may lead

to 8% gain in crop calories productivity despite the increase in daily rainfall amounts (Figure

S12). Dryland areas would also significantly benefit from elevated CO

2

, potentially offsetting the

yield losses corresponding to a precipitation decrease by ~5% in Bulgaria and Romania dryland.

Projected changes in seasonal temperature and precipitation alone resulted in calories change

from –6% to +1%, but from +4% to +13% when combined with elevated CO

2

effects (Figure

3b).

In Southern Europe, calories from well-watered crops demonstrate a significant linear

relationship with changes in seasonal temperature (Figure 3a). A 10–15% temperature raise

stimulated calorie yields by 5–14% when supplemented by an additional 100 to 200 ppm of

atmospheric CO

2

compared to the baseline, while the impact was negative to slightly positive (–

4% to +2%) under the present-day CO

2

. Water-limited calorie yields are significantly sensitive to

precipitation changes together with elevated CO

2

in Spain, Italy, Greece and Portugal (Figure 3b,

F

-test

P

< 0.001 for all regressions). In dryland areas, calorie yields would be impacted mostly

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negatively at the present-day CO

2

levels. Yet, elevated CO

2

may offset these losses due to

fertilization effect on C

3

crops, and increased crop water use efficiency (Figure S2, also Text S2

and Figure S10). The estimates in Figure 3b indicate that elevated CO

2

may potentially offset

calorie losses corresponding to a precipitation decrease by ~10% in the Mediterranean dryland.

3.3 Vulnerability to soil degradation

Eastern and some Northern EU countries will be especially vulnerable if crops continued to be

managed with BAU input intensity, since the current inputs don’t provide the capacity to

overcompensate for future degradation-induced nutrient losses. In Eastern Europe apart from

Czechia, the vulnerability ranged between 21 and 32 Gcal ha

–1

in Romania and Hungary (Figure

4), respectively, while the highest relative loss (>20%) was estimated in parts of Bulgaria,

Romania, Hungary and Slovakia (Figure S4). The uncertainty range across individual climate

projection is presented in Figure 4. With current fertilization at 40–50% of the intensity needed

for crops growing without nutrient stress (Figure S3a), nutrient inputs would not sufficiently

compensate for weakening soil fertility under +2°C. An even higher vulnerability of > 40 Gcal

ha

–1

was simulated for the Baltic states with soil carbon loss due to warmer climate (Figure S5)

amplifying insufficient nutrient supply under the BAU scenario

(

fertilization

<

40% of the

intensity under P1). The fraction of calories in Eastern European and the Baltic countries

vulnerable to losses due to soil degradation is in most cases higher than the calorie yield gain due

to climate change (Figure 1). Moreover, in Bulgaria and Romania, caloric yield vulnerability is

over 40% higher when irrigated systems alone are considered (Figure 4). Therefore, soil

degradation may negatively outweigh positive impacts of +2°C when not prevented by

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adaptation measures. Western European states are generally less vulnerable as they have more

capacity to overcompensate soil degradation effects via sufficient fertilizer supply. Current N-

fertilization intensities are at 60 to 140% of the quantity required for unstressed plant growth,

offsetting harmful effects of nutrient losses on degraded soils. Consequently yield vulnerability

only seldom exceeded 10% (Figure S4). In Southern Europe, rainfed systems would be little to

moderately susceptible to soil degradation since the yield would be predominantly controlled by

water deficit during the growing season (e.g. most of Spain and Greece).

Due to low level of fertilization, yield vulnerability at +2°C is largely driven by decreasing soil

organic matter in the Baltic states and Eastern Europe (Figure S5). For example, simulated

carbon stock decreased by 15–20% in the Baltics and by 8–14% in eastern countries when

KNMI projection is considered. Country-level median erosion rates calculated from all cropland

were below 1 t ha

–1

in all EU states, except for Ireland, Slovenia and Luxembourg, leading to an

annual carbon loss ranging between 6 kg ha

–1

in Denmark and 120 kg ha

–1

in Slovenia (Table

S6). Losses of more than 100 t ha

–1

in extreme years were simulated especially in the

Mediterranean countries (see 99

th

percentiles in Table S6). However, erosion contributed only

little to the national yield vulnerability under +2°C. As demonstrated in Figure S6, turning off

water erosion in the scenario with dynamic soil profile resulted in calorie yield impacts similar to

the BAU-

dyn

scenario in Figure 4, since sever erosion affected only a relatively small fraction of

the production area. Soils affected by severe erosion of more than 20 t ha

–1

a

–1

in more than 5%

of croplands were simulated especially in the Mediterranean region (Table S6).

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3.4 Uncertainty due to modelling temperature, CO

2

, and soil degradation responses

A considerable range of yields was estimated by the UA, while our BAU-

con

and BAU-

dyn

simulations (mean values in green and blue colour, respectively) are located roughly in the mid-

range of the respective soil handling scenarios (

con

and

dyn

in red and grey colours,

respectively). Figure 5a demonstrates yield uncertainty induced by 1) varied model parameters

and inputs, 2) atmospheric CO

2

effect, and 3) spatial heterogeneity in natural conditions since all

grids are plotted here.

A decrease in crop calories simulated for Lithuania in Section 3.3 is robust across the whole

range of UA-

dyn

. The mean country-level yield declined between –30 and –65 Gcal ha

–1

relative

to the historic period. This is mainly due to decreasing root and winter crop productivity under

the degradation

dyn

scenario, while summer crops were positively impacted (Figure S7). A

similar though more uncertain effect of +10 to –60 Gcal ha

–1

was estimated for Slovakia, owing

to very heterogeneous terrain and soils. Uncertainty in soil degradation vulnerability, which is

the difference between UA-

con

and UA-

dyn

in the +2°C period relative to the respective historic

period, is substantial for all analysed countries, but the UA-mean values are in a good agreement

with the BAU projections used in Section 3.3 (green and blue lines in Figure 5a).

Importantly, at the scale of our study, avoiding conservation practices from the soil degradation

scenario used in the vulnerability analysis (Section 3.3) does not undermine conclusions driven

for the +2°C period as the differences between BAU-

dyn

and conservation UA-

dyn

scenarios are

marginal there (Figure 5a, “Till effect” panel). In general, historical yields under emulated

conservation practices are on average 85 to 95% of conventional yields, and in Belgium it

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remains similar also in the future. The relative loss is higher in cooler regions of Lithuania and

parts of Slovakia, whilst almost equal though less variable calorie yields are estimated in

Portugal. Yields in the conventional scenario (blue line) decline faster compared to the

conservation scenario (purple line) in Slovakia and Lithuania, suggesting that soil conservation

slows down the decline in yields by mitigating soil degradation effects.

In the

con

scenario, elevated CO

2

stimulated calories by 40 Gcal ha

–1

in Belgium, where crops

largely benefit from direct CO

2

effect due to ample fertilization and high precipitation. This is

not the case in Slovakia, Portugal and Lithuania though, where simulated yields are to a great

extent limited by nutrient or water deficiency, and CO

2

-induced yield gain is lower (~20 Gcal ha

–

1

) under no degradation, and there is no effect in the degradation scenario (Figure 5a, “CO

2

effect” panel). With atmospheric CO

2

fixed at 360 ppm, rising temperature would have a

negative effect on simulated mean calorie yield in all analysed countries except for a small

stimulus of mild warming up to 1.5°C in Slovakia (dashed boxes in Figure 5b). The temperature-

related impact uncertainty is considerably large due to site heterogeneity, particularly in Portugal

and Slovakia: from –80 to +50 Gcal ha

–1

and –45 to +35 Gcal ha

–1

, respectively, when +2°C of

regional warming is considered. Elevated CO

2

(490 to 605 ppm in the +2°C period) significantly

increased calorie yields in all countries and warming intervals (



T) compared to constant CO

2

scenario (paired

t

-test

P

<0.001). Importantly, the growth increment due to CO

2

elevated by ~200

ppm offset temperature increase of +2°C in Belgium, Lithuania and Portugal, and even a higher

warming in Slovakia (Figure 5b). The cooler regions in higher altitudes demonstrate larger yield

increases along the temperature gradient (data not shown), contributing largely to the overall

yield uncertainty in Figure 5b.

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In summary, elevated CO

2

overcompensated the negative impacts of local +2°C warming across

the whole range of UA in Lithuania and to a lesser extent in other analysed countries under the

no-degradation scenario (C+CO

2

versus C–CO

2

in Figure 5c). Soil degradation (D+CO

2

)

robustly decreased the CO

2

-induced yield gain in Belgium, or even reverse the impact sign in

most cases in Lithuania, Slovakia and Portugal. Unlike in Belgium, the contribution of elevated

CO

2

is only marginal under the degradation scenario in Lithuania, Slovakia and Portugal where

yields are dominated by nutrient limitations, and CO

2

cannot compensate for the combined effect

of warming and degradation (D+CO

2

and D–CO

2

in Figure 5c). In the degradation scenario,

conservation practices (Dc in Figure 5c) reduced the country-mean yield losses, especially in

Lithuania, and reduced UA ranges in all analysed countries under both CO

2

scenarios.

3.5 Bracketing crop intensification uncertainties

Extrapolation of BAU crop fertilization and irrigation management towards the time period of

the +2°C threshold represents a considerable source of uncertainty. In this analysis, we used the

calorie yields theoretically achievable in high-input agricultural systems under scenarios P1 and

P2 to bracket this uncertainty. Mean ranges between the BAU projections and the respective

high-input system yields are summarized in Table 4. In general, the yield differences between

high-input scenario P1 and BAU are about two (e.g. Belgium and Netherlands) to fifty (Portugal)

times higher than the projected climate impacts presented in Section 3.1, indicating that crop

intensification has the potential to greatly boost the benefits from climate change in most of

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Europe as well as outweigh possible negative effect in the south (not taking into account

resource constraints).

The production uncertainty ranges between 30 and 85 Gcal ha

–1

in most of Northern and Western

European countries, with the largest relative values in the Baltics. Particularly large ranges were

estimated in France due to irrigation intensification (P1) and expansion (P2) adaptations: almost

60% in the latter scenario. However, compensation for water stress in order to approach the P1

productivity on currently irrigated cropland would require about 100 mm (10–200 mm) of net

irrigation annually when averaged across all winter and spring crops (except for rice), which is

about two times more than water demand simulated for the present days under BAU

management (Figure S8b).

A considerable uncertainty along the crop intensification gradient is estimated also in Eastern

Europe: 47–78 Gcal ha

–1

and 70–182 Gcal ha

–1

for scenarios P1 and P2, respectively.

Approaching the P1 and P2 yields would require a substantial increase in fertilizer inputs

particularly in Romania, Bulgaria and Hungary compared to today (Figure S8a). In addition,

future irrigation capacity contributes significantly to the overall uncertainty in the Pannonian

basin. A much smaller yield difference between BAU and P1 (60–80 Gcal ha

–1

) compared to P2

(130–180 Gcal ha

–1

) points to a large uncertainty due to possible expansion of irrigated cropland.

However, high net irrigation water requirement of 140–200 mm a

–1

crop

–1

on average, and more

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than 250 mm a

–1

crop

–1

in some regions (Figure S8b) would render irrigation expansion

challenging.

Possible expansion and intensification of irrigated cropland represents a large projection

uncertainty also in Southern Europe, mainly in Spain, Greece, Portugal and Italy. Many regions

demonstrate only a limited response to management intensification if currently irrigated cropland

is not expanded under P1: 60 Gcal ha

–1

in Spain and Greece, and about 80–100 Gcal ha

–1

in Italy,

Portugal and Slovenia. With expansion of irrigated cropland under P2 though, the calorie

production under +2°C may exceed 300 Gcal ha

–1

, corresponding to a substantial production

uncertainty of more than 200 Gcal ha

–1

in many regions (Table 4). However, approaching this

hypothetical productivity would mean a net irrigation requirement of more than 250 mm a

–1

crop

–1

(160 mm in Italy and 60 mm in Slovenia) on almost all available cropland (Figure S8b).

4 Discussion

Business-as-usual crop management with transient CO

2

effects and without soil degradation are

standard assumptions in large-scale crop yield impact projections (Elliott et al., 2014; Müller et

al., 2015; Rosenzweig et al., 2014). In summary, our BAU-

con

simulations with transient

atmospheric CO

2

agree with general expectations of:

(1)

a positive effect of warming and elevated CO

2

in Northern Europe and expansion of summer

crops on existing cropland further north (cf. Audsley et al., 2006; Eckersten et al., 2001;

Hildén et al., 2005; Knox et al., 2016; Olesen et al., 2007; Tuck et al., 2006),

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(2)

CO

2

-induced growth stimulus offsetting a decline in crop productivity (especially cereals)

due to shortened growing periods for most of Europe (cf. Asseng et al., 2015; Lobell &

Field, 2007; Supit et al., 2010)

,

and

(3)

spatially variable and insufficiently robust impacts in Southern and South-Eastern Europe,

with a productivity decrease in the most southern and driest areas but an increase in cooler

regions (cf. Audsley et al., 2006; Giannakopoulos et al., 2005; Müller et al., 2015; Olesen et

al., 2007; Santos et al., 2002).

However, many important aspects of future crop production may remain unnoticed with these

“standard” projections, including soil degradation (Louwagie et al., 2011) or future advances in

agriculture (Ewert et al., 2005), which we have addressed herein. There are also other caveats

concerning the reliability of large-scale assessments such as ours, which are discussed in the

following sections.

4.1 Temperature and elevated CO

2

effect

Our simulations suggest that direct fertilization effect of rising CO

2

has the potential to

overcompensate negative effects of increased temperature in irrigated and high-rainfall systems

as well as in some water-limited systems in EU (Section 3.2). The overcompensation is

sufficiently robust across the whole range of uncertainty analysis for high-input systems in

Western Europe (Belgium), but it can be undermined by synergic interactions of soil degradation

and insufficient fertilization (e.g. Lithuania in Section 3.4). The reliability of our projections thus

critically depends on EPIC’s ability to simulate crop yields response to increased temperature

and elevated CO

2

. Long et al. (2006) raised concern that biophysical models including EPIC may

overestimate (by ~50%) the direct CO

2

fertilization effects, which was later disputed (Ewert et

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al., 2007; Kimball, 2016; Tubiello et al., 2007). Figure S10 in the Supporting Information

demonstrates, in a generic way, how the most important crops (C

3

wheat and C

4

maize) respond

to temperature increase and elevated CO

2

in our pan-EU EPIC, allowing comparison to

published outcomes. In summary, our results agree fairly well with temperature and elevated

CO

2

effects experienced in field experiments and field-scale modelling:

(1)

A negative response of wheat yields to increasing temperature at the current atmospheric

CO

2

concentration in most European environments simulated by EPIC is in a good

agreement with results obtained from 30 different wheat field models described by Asseng

et al. (2015), and such response has also been evidenced in field experiments (Ottman et al.,

2012). A positive effect on rainfed yields in arid conditions (Southern Europe in Figure

S10), where accelerated crop growth may prevent plant water and temperature stress later in

the season, was also documented by Asseng et al. (2013).

(2)

A 10–20% yield gain stimulated by +200 ppm of CO

2

at present temperature as simulated by

EPIC (+0°C bin in Figure S10a,b) is in accordance with wheat experimental data (Ainsworth

& Long, 2004; Kimball, 2016; O’Leary et al., 2015). Roughly similar average gains under

+200 ppm of CO

2

were also reported for other C

3

crops, including rice and barley, soy bean,

potatoes and sugar beet by Kimball (2016).

(3)

A higher response to elevated CO

2

under rainfed compared to irrigated conditions can be

attributed to improved water use-efficiency as has been reported also from field experiments

(e.g. Kimball, 2016). A combined effect of CO

2

fertilization and reduced transpiration may

partly off-set negative impacts on C

3

crops in dry regions, although the full advantage from

elevated CO

2

can only be realized when irrigated (e.g. Dono et al., 2016).

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(4)

In accordance with the under-laying experimental data used to parameterize EPIC (Kimball,

1983), simulated maize yields demonstrate ~10% increase due to CO

2

concentration

elevated from 360 to 550 ppm in both rainfed and irrigated conditions. However, recent

analyses indicate that maize may have only marginal or no yield response to elevated CO

2

under ample water supply, while more substantial response of about 18% has been reported

for water-limited environments (Kimball, 2016). This overestimation in maize response to

elevated CO

2

may contribute to cancelling out the negative effects of warming, especially in

Southern Europe (Figure S10c,d).

4.2 Soil degradation concept

Future soil degradation and its impacts on crop production is unknown and uncertain by nature

since it will depend on intensity of hazardous processes, exposure, vulnerability, and cropland

management. In this study we quantify vulnerability (Oppenheimer et al., 2014) as a

“predisposition” of calorie yields to be adversely affected by soil degradation. From a variety of

degradation processes (e.g. Louwagie et al., 2011) we address the decline in fertility due to

nutrient and soil organic matter depletion and soil erosion. Other degradation processes, such as

soil structure decline, salinization or acidification, are not represented here (see also Text S3). As

expected, fertilization is the dominant factor controlling yield vulnerability in this study since it

determines the capacity to offset the degradation-induced nutrient losses. Fertilization intensity,

which is a fraction of BAU fertilization over fertilization needed for crop yields without nutrient

stress (scenario P1), explains more than 50% of variability in the

Vs

values (Figure S3a). On the

contrary, irrigation intensity has only a small effect on yield vulnerability (Figure S3b),

suggesting that the soil’s ability to supply water is affected less at our scale of analysis. Changes

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in hydro-physical soil properties (wilting point and field water capacity) as simulated by pan-EU

EPIC are of inferior importance compared to the changes in soil organic matter pool (Figure

S11). More detailed description and discussion with this respect is provided in Text S3 in the

Supporting Information. It should be noted that our concept does not account for processes of

organic carbon and nutrient redistribution, which contribute additional uncertainty on soil

vulnerability (e.g. Paustian et al., 2016).

Intensive fertilization may indeed overcome limitations due to soil degradation by SOC loss so

that crop yields are not affected (Holland, 2004), but it may also lead to environmental pollution

(Sutton, 2011) and adverse trends in soil health hampering future production (Squire et al.,

2015). Therefore, soil conservation has been recognized as a prominent adaptation strategy

when coping with soil degradation (Bindraban et al., 2012; Montanarella, 2015). Given the

diversity of practices in conservation agriculture and a lack of consolidated data in Europe (e.g.

Merante et al., 2017), conservation practices are not explicitly included in out vulnerability

assessment in Section 3.3. We examine possible implications of avoiding conservation practices

for our analysis in Section 3.4 by analysing the calorie yields simulated under assumptions of 1)

low intensity of soil disturbance, 2) high crop residue return, 3) mulching, 4) manure

fertilization, 5) and high erosion control (see Table 3), i.e. measures known to enhance soil

quality (Lal, 2004). First, the effects of conservation practices generated by pan-EU EPIC are in

general accordance with expectations: our historical yields under conservation practices are on

average 85 to 95% of conventional yields (de Ponti et al., 2012; Soane et al., 2012), and the

relative loss is higher in cooler regions (Lithuania), whilst almost equal though less variable

calorie yields are estimated in Portugal. Indeed, conservation practices are particularly

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appropriate in semi-arid regions where they allow for better water management and erosion

control (Pittelkow et al., 2015; Soane et al., 2012). Second, avoiding conservation practices does

not undermine the robustness of our vulnerability analysis (see Section 3.3).

Many studies identified water erosion among major soil threats with a negative impact on crop

production, both in terms of affected area and impact intensity (Holland, 2004; Panagos et al.,

2015). However, given the scale and design of our study, erosion contributed only little to the

national calorie yield vulnerability. With ~3.5 t ha

–1

we slightly underestimated the average

annual erosion rate in EU croplands compared to 5 t ha

–1

estimated by Panagos et al. (2015)

when the corresponding time periods are considered. Besides, we estimated that approximately

9% of EU croplands is currently affected by soil loss of more than 5 t ha

–1

a

–1

(data not shown),

while Panagos et al. (2015) reported ~13%. Therefore, we assume that the soil erosion effects on

yields may be underestimated in our study. It is worth noting that only deep soils were

considered in this study (see Text S3), which may have contributed to the underestimation of soil

loss impacts on crop yields.

4.3 Crop intensification implications

Large yield potentials (scenario P1 and P2, Section 3.5) suggest that possible future

intensification of fertilization and irrigation may represent a substantial uncertainty for projected

calorie yields at +2°C (Table 4). Looking back, we learn that crop intensification was

undoubtedly among main drivers of yield increase in the past. For example, wheat yields in

Europe have nearly tripled since 1960 because of intensification and improved crop management

(Ewert et al., 2007). However, regardless of the untapped future potential, major changes

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towards more intensive fertilization are foreseen only by farmers in the northernmost regions

and, to a lesser extent, in continental and Pannonia zones (Olesen et al., 2011). In fact, more

intensive fertilization is not a policy option for Central and Western European croplands because

of serious pollution of ground and surface waters in many watersheds and aquifers. Besides,

assuming that a maximum ~80% of the yield potential can be utilized by farmers (van Ittersum et

al., 2013; Lobell et al., 2009), the BAU fertilization already provides sufficient supply to

capitalize from +2°C in most of Western and Northern Europe, except for the Baltics. Improved

fertilizer use efficiency in particular of organic fertilizers together with other technological

developments and breeding are likely options in those high-intensity regions in order to comply

with the EU environmental policies (Levers et al., 2016). In Eastern and South-Eastern Europe,

though, increased fertilizer application is likely required to take the advantage of the +2°C

warming. Increasing trends in fertilizer application rates are already reality in some countries,

such as Czechia or Poland (Levers et al., 2016; Sutton, 2011). Future development will strongly

depend on economic and political boundary conditions, including agricultural and environmental

policies in the EU.

Irrigation availability will doubtlessly determine future yields in dry zones of Southern and

Pannonian Europe (Section 3.5). Expansion of irrigation on rainfed cropland under scenario P2

and intensification of the existing irrigation systems under scenario P1 bracket large projection

uncertainties in these regions (Table 4). However, even when targeting at 80% of the potential

productivity, the net irrigation requirement would about double on currently irrigated cropland

compared to today, and approximately the same quantity would be needed on vast areas of

currently rainfed cropland in case of irrigation expansion (Figure S8b). Out of the options

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evaluated here, water availability will therefore be the key factor for future intensification.

Probably due to concerns about future water scarcity (Elliott et al., 2014; Lehner et al., 2006)

large irrigation expansion for food crops is not expected by farmers in very dry zones (Olesen et

al., 2011).

Besides intensification of nutrient and water managements, other adaptation options, policies and

market-driven changes represent additional drivers and uncertainties of future calorie yields that

are not addressed in this study.

4.4 General limitations of the modelling approach

Despite efforts to quantify and bracket uncertainties, limitations remain in our large-scale study

especially with respect to insufficiently captured heterogeneity in crop management practices,

including distribution of crop varieties, cultivation practices, fertilization and irrigation allocation

to individual crops (Balkovič et al., 2013, 2014; Wriedt et al., 2009), under-performing

calibration with respect to climate change (Xiong et al., 2016), insufficiently captured soil

heterogeneity (Folberth, Skalský, et al., 2016), uncertainty of crop yield aggregations (Porwollik

et al., 2016), and may lack relevance at small scales (van Ittersum et al., 2013).

There are also limits concerning the bio-physical models’ ability to represent extreme weather

events. Increased frequency and intensity of extreme heat, drought or heavy rains will

doubtlessly undermine future yield production. For example, heat can cause water stress by

increased atmospheric water demand and depletion of soil water as well as it can directly damage

plant tissues, impair flowering, trigger oxidative stress or lower net photosynthesis rates

Accepted

Article

(Schauberger et al., 2017). Lacking representation of heat shocks in bio-physical crop models

could lead to an overestimation of positive impacts. More intensive rainfall (Figure S12) can also

hamper future yields. For the +2°C of global warming, Vautard et al. (2014) projected a robust

increase in heavy precipitation events everywhere except Southern Europe in summer, with

amplitudes in the range 0–20%. However, EPIC, as other crop models, fails to capture the

negative impacts of heavy rain and extremely wet conditions. Long-term impacts presented in

this study should be less sensitive to such models’ deficiencies with respect to the extreme

weather events though.

Although elevated CO

2

increases the total protein content in crop yield, it reduces its

concentration and thus negatively affects nutritional value of food (Haddad et al., 2016; Myers et

al., 2014; Wieser et al., 2008). This aspect is not accounted for in our analysis, but should be kept

in mind.

Currently, the use of multiple crop models has become the norm to characterize the uncertainty

in climate impacts on crops (Asseng et al., 2015; Müller et al., 2016; Rosenzweig et al., 2014).

At the same time though, the wide range and limited comparability of regional outputs, even

among models with similar biophysical algorithms, raise some concern (Folberth, Elliott, et al.,

2016). Given the limitations on both sides, the skills of pan-European EPIC against the multi-

model approach should be explored.

Accepted

Article

5 Conclusions

Assuming current crop management practices and increasing CO

2

concentrations, a robustly

positive calorie yield change of 5 to 20% under future +2°C scenarios was simulated for the EU

except for some NUTS2 regions in Bulgaria, Romania, Portugal, Spain, Greece and Italy (Figure

2). Owing to inherent uncertainty in EURO-CORDEX projections, the impact results are largely

uncertain in these regions (form –10% up to 30% at 5

th

to 95

th

percentiles), and they are well

below the acceptable threshold for robustness.

The positive impact is mostly stimulated by 1) CO

2

fertilization effect, and 2) improved growing

season temperatures for summer crops in Northern Europe and in higher altitudes. The

projections suggest that 100 to 200 ppm more CO

2

in the atmosphere under +2°C compared to

the baseline will overcompensate otherwise mostly negative, or only a slightly positive, effects

of warming in temperature limited (high-rainfall and irrigated) systems as well as in some water-

limited environments in Europe (Figure 3). There are some caveats concerning the fertilization

effect of elevated CO

2

. For example, the impacts on temperature-limited systems would be

considerably smaller, but still mostly positive, when only ~50% efficiency of CO

2

fertilization is

considered (roughly halfway between the circles in Figure 3a): about 10% or less in most

countries of Western, Northern and Eastern Europe. The impacts on water-limited systems of

Southern and South-Eastern Europe will be even more uncertain, varying between slightly

negative and positive. A possible overestimation in maize response to elevated CO

2

in EPIC may

contribute to lessening out the negative effects of warming, especially in Southern Europe.

Accepted

Article

Soil degradation in terms of SOM decrease could be a serious threat for European agriculture

under +2°C warming. Potential yield losses of more than 20% in some Eastern European and

Baltic regions may undermine the positive impact of elevated CO

2

and warming if soil nitrogen

status degradation is not prevented. At a country level, soil erosion contributed only little to the

calorie yield vulnerability since severe erosion affected only a small fraction of cropland area.

Nutrient status is more undermined by organic matter mineralization, nutrient leaching and loss

from nitrogen export through harvested products. Agricultural systems with currently insufficient

fertilization are especially vulnerable since they don’t have the capacity to 1) overcompensate for

losses due to nutrient depletion, and 2) benefit from rising CO

2

and warming. In contrast,

fertilization surplus in some Western European countries provides sufficient capacity to cope

with soil degradation. It should be noted that in spite of the robust response to fertilization

intensity (Figure S3a), the vulnerability analysis is burdened by a considerable uncertainty due to

modelling of soil processes and crop management practices as quantified in Section 3.4.

Nevertheless, this study is a pioneering attempt to address yield vulnerability to future soil

degradation.

The highest uncertainty range is related to future intensification options. The uncertainty

bracketed by scenarios P1 and P2 is about two to fifty times higher than the projected impacts

due to climatic changes. More intensive fertilization and irrigation provide the potential to

overcompensate the synergic effects of warming and soil degradation, while still increasing the

calorie yield significantly.

Accepted

Article

Acknowledgments

J.B., R.S., C.F., N.K. and M.O. acknowledge support from EU FP7 project IMPACT2C (grant

no. 282746) and the European Research Council Synergy grant IMBALANCE-P (grant no.

ERC-2013-SynG-610028). M.M acknowledges support from the Ministry of Agriculture of the

Czech Republic (projects no. RO0416 and QJ1610547). Model input data were obtained from

sources listed in Table 1. Crop management data needed to reproduce our simulations and data

underlying the figures are available at

http://pure.iiasa.ac.at/15104/ (DOI: 10.22022/ESM/02-

2018.15104). We used the EPIC v.0810 model version available from https://epicapex.tamu.edu.

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Accepted

Article

Table 1.

List of input data sets included in the gridded pan-European EPIC model

Data set

Description

Spatial
resolution

Temporal
resolution

Source

Climate
change

Bias-corrected data
from EURO-CORDEX
database, including
daily minimum and
maximum temperature,
shortwave solar
radiation, precipitation
and relative humidity.

0.11 arc-

deg

Daily, all

projections

from 1971

to 2100

The Quantifying Projected Impacts Under 2°C
Warming (IMPACT2C) Project
(http://impact2c.hzg.de)

Projection name
(regional/global
climate model):

+2°C
interval:

EURO-CORDEX data:

CSC-REMO/MPI-
ESM-LR (RCP 4.5)

2050–

2079

http://www.euro-cordex.net

SMHI-RCA4/EC-
EARTH (RCP 4.5)

2042–

2071

KNMI-
RACMO22E/EC-
EARTH (RCP 4.5)

2042–

2071

SMHI-
RCA4/HadGEM2-ES
(RCP 4.5)

2023–

2052

IPSL-
WRF331F/IPSL-
CM5A-MR

(RCP

4.5)

2028–

2057

Terrain

Shuttle Radar
Topographic Mission
Data (SRTM)

3’’

N/A

Werner (2001)

Global 30 Arc Second
Elevation Data
(GTOPO)

30’’

N/A

http://eros.usgs.gov

Soil

European Soil Bureau
Database (version 2.0)

1 km

N/A

https://esdac.jrc.ec.europa.eu

Database of Hydraulic
Properties of European
Soils

N/A

Wösten et al. (1999)

Map of organic carbon
content in the topsoil

1 km

N/A

Lugato et al. (2014)

Land cover

Combined CORINE
2000 and PELCOM
land cover map

1 km

N/A

Joint Research Centre

Admin. units Geographic Information

System of the European
Commission (GISCO)

NUTS2

sub-

national

regions

N/A

http://ec.europa.eu/eurostat/web/gisco
http://ec.europa.eu/eurostat/web/nuts/overview

Watersheds

European River
Catchment Database,
version 2

www.eea.europa.eu

Management Crop sowing dates

50 km

around

2000

Balkovič et al. (2013)

Regional N and P

NUTS2

around

Balkovič et al. (2013)

Accepted

Article

fertilization rates
(mineral + organic)

regions

2000

Statistics on crop yields

NUTS2

regions

1996-2007 EUROSTAT

European Irrigation
Map (EIM)

1 ha

around

2000

Wriedt et al. (2009)

Table 2.

Fertilization and irrigation scenarios to simulate different levels of crop calorie yields

Scenario

Irrigated cropland
area

Max. irrigation volume per
crop (mm a

—1

)

N per crop
(kg ha

–1

a

–1

)

P per crop
(kg ha

–1

a

–1

)

Presently
rainfed

Presently
irrigated

BAU

crop-specific

0

1000*

BAU

P1

all equipped cropland

0

1000*

Max. 250

Automatic

P2

all cropland

1000*

Max. 250

Automatic

*

The upper limit of irrigation water supply (simulated irrigation water volume is less or equals 1000 mm

a

–1

)

Table 3

List of EPIC input variables and parameters used in the uncertainty analysis; the default

values were used in the impact assessment, while the ranges in brackets were used in the

uncertainty analysis

EPIC variable / parameter

Selected default value and
range

Values used to imitate
soil conservation

Farm yard manure (% of BAU N fertilizer)

0 (20, 40)

40

Number of tillage operations per crop

1,2,3,4,5*

1,2*

Soil mixing by tillage (fraction)

0.5 (0.1–0.9)

< 0.3

Tillage depth (mm)

150 (10–400)

< 100

Erosion control factor (0-1 fraction)

0.5 (0–0.7)

< 0.2

Initial SOC content scaling factor (multiplier)

1 (0.5–1.5)

(0.5–1.5)

Stable humus fraction (fraction)

0.5 (0.3–0.7)

(0.3–0.7)

Soil strength constraint on root growth
(PARM2)

1.2 (1–2)

(1–2)

Soil evaporation coefficient (PARM12)

2 (1.5–2.5)

(1.5–2.5)

Microbial decay rate coefficient (PARM20)

0.8 (0.3–1.5)

(0.3–1.5)

Biological mixing depth (PARM24)

0.3 (0.1–0.5)

(0.1–0.5)

Water stress weighting coefficient (PARM35)

0.5 (0–1)

(0–1)

Slow humus transformation rate (PARM47)

0.000548 (0.0003–0.0009)

(0.0003–0.0009)

Passive humus transformation rate (PARM48)

0.000012 (0.0000072–0.00002)

(0.0000072–0.00002)

Tillage effect on residue decay rate (PARM52)

10 (5–15)

(5–15)

* crop-specific number

Accepted

Article

Table 4.

Mean absolute (in Gcal ha

–1

) and relative (in %) difference between the BAU

calorie

yields projections relative to the potential yields achievable in high-input systems (in Gcal ha

–1

,

and % relative to BAU), assuming present-day distribution of rainfed and irrigated cropland

(scenario P1), and calorie yields unlimited by water and nutrient stress on all available cropland

(scenario P2). All differences are statistically significant (the paired

t

-test

P

< 0.001).

Country

Scenario P1

Scenario P2

Gcal ha

–1

%

Gcal ha

–1

%

Northern

Denmark

60

28

64

30

Europe

Estonia

62

46

63

47

Finland

33

26

40

32

Ireland

57

26

62

28

Lithuania

61

44

67

49

Latvia

73

52

76

54

Sweden

56

40

62

44

U.K.

66

33

83

41

Western

Austria

52

28

82

44

Europe

Belgium

51

23

64

29

Germany

41

21

64

32

France

73

34

127

59

Luxembourg

44

22

68

34

Netherlands

44

18

60

25

Eastern

Bulgaria

62

47

182

138

Europe

Czechia

47

26

83

46

Hungary

78

47

131

79

Poland

58

32

70

38

Romania

65

44

144

96

Slovakia

73

46

103

65

Southern

Spain

60

49

244

199

Europe

Greece

55

49

216

192

Italy

98

58

196

117

Portugal

99

73

238

174

Slovenia

76

38

84

42

Accepted

Article

Figure 1

Crop calorie yield (in Gcal ha

–1

) simulated for the historic and the +2°C period with the

BAU scenario (blue and red crossbars, respectively); mean and 5

th

to 95

th

percentile ranges are

plotted. Numbers below crossbars represent mean yield change in Gcal ha

–1

and % relative to the

historic baseline. All changes are statistically significant (the paired

t

-test

P

<0.001) – more

details in Table S3 in the Supporting Information.

Figure 2

Calorie yield impact of a +2°C global warming on crop calorie yield (ensemble mean,

5

th

and 95

th

percentiles) and robustness of the positive impact in the ensemble simulations.

Figure 3.

(a)

Calorie yield response to changes in the growing season (GS) temperature sums in

temperature-limited production systems, and (b) calorie yield response to changes in the GS

precipitation sums in water-limited production systems. Data points represent ensemble-mean

impacts (in %) or transient CO

2

(orange, blue) and fixed CO

2

levels (grey); circle markers

demonstrate respective mean changes at the national level. The differences between simulations

with transient and fixed CO

2

are statistically significant in all the plots (the paired

t

-test

P

<

0.001). Prefix letters denote the geographic region (e.g. N = Northern European countries).

Figure 4

Crop calorie yield impact distribution under scenario with (BAU-

dyn

, orange) and

without (BAU-

con

, blue) soil degradation (mean values are portrayed as lines in the respective

colours on the top). Vulnerability to soil degradation (

Vs

in Gcal ha

–1

and %) are denoted by

asterisk where statistically significant at

P

<0.001. The black lines indicate mean calorie yields

estimated for irrigated systems with (dotted) and without (solid) soil degradation under the BAU

Accepted

Article

scenario. Calorie yield impacts calculated for individual climatic projections are portrayed as

thin lines in the respective colours.

Figure 5

(a)

Uncertainty range of simulated crop calorie yield under different soil degradation

and atmospheric CO

2

assumptions calculated for Belgium, Lithuania, Portugal and Slovakia

under the KNMI climate ensemble member. Grey and red shadings represent the yield

uncertainty ranges calculated for the

dyn

and

con

soil handling scenarios, respectively (black and

red dashed lines demonstrate the UA average); blue and green lines represent the mean yields

calculated under the

dyn

and

con

scenarios, respectively, used for the

Vs

analysis in Section 3.3.

The vertical black lines portray the corresponding +2°C period in KNMI. (b) the UA range of

yield change (in Gcal ha

–1

) relative to the historical average (1971–2000) simulated for different

regional warming levels occurring within the +2°C of global warming period. (c) the UA yield

change range (in Gcal ha

–1

) relative to the historical period simulated with no-degradation (C)

and degradation (D) scenarios, with constant (–) and transient (+) atmospheric CO

2

, and with soil

conservation practices (Dc) in the degradation scenario.

Accepted

Article

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