# Achieving High Crop Yields with Low Nitrogen Emissions in Global Agricultural Input Intensification

Wenfeng Liu, Hong Yang, Christian Folberth, Christoph Müller, Philippe Ciais, Karim Abbaspour, Rainer Schulin

► To cite this version:

Wenfeng Liu, Hong Yang, Christian Folberth, Christoph Müller, Philippe Ciais, et al. Achieving High Crop Yields with Low Nitrogen Emissions in Global Agricultural Input Intensification. Environmental Science and Technology, 2018, 52 (23), pp.13782-13791. 10.1021/acs.est.8b03610. hal-02975845

HAL Id: hal-02975845

https://hal.science/hal-02975845v1

Submitted on 29 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Achieving high crop yields with low nitrogen emissions in global
agricultural input intensification

Wenfeng Liu¹,²*, Hong Yang¹,³, Christian Folberth⁴, Christoph Müller⁵, Philippe Ciais², Karim

C. Abbaspour¹, Rainer Schulin⁶

¹Eawag, Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstrasse 133,
CH-8600 Duebendorf, Switzerland.

²Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ,
Université Paris-Saclay, F-91191 Gif-sur-Yvette, France.

³Department of Environmental Sciences, MGU, University of Basel, Petersplatz 1, CH-4003
Basel, Switzerland.

⁴Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria.

⁵Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.

⁶ETH Zürich, Institute of Terrestrial Ecosystems, Universitätstr. 16, CH-8092 Zürich,
Switzerland.

<footnote>*Corresponding author. Email: wenfeng.liu@eawag.ch & wfliu2012@gmail.com</footnote>
1 Abstract

Increasing demand for food is driving a worldwide trend of agricultural input intensification.

However, there is no comprehensive knowledge about the interrelations between potential yield
gains and environmental trade-offs that would enable the identification of regions where
input-driven intensification could achieve higher yields, yet with minimal environmental
impacts. We explore ways of enhancing global yields, while avoiding significant nitrogen (N)
emissions (N<sub>e</sub>) by exploring a range of N and irrigation management scenarios. The simulated
responses of yields and N<sub>e</sub> to increased N inputs (N<sub>in</sub>) and irrigation show high spatial
variations due to differences in current agricultural inputs and agro-climatic conditions.

Nitrogen use efficiency (NUE) of yield gains is negatively correlated with incremental N<sub>e</sub> due to N<sub>in</sub> additions. Avoiding further intensification in regions where high fractions of climatic yield potentials, ≥80%, are already achieved is key to maintain good NUE. Depending on the intensification scenarios, relative increases in N<sub>e</sub> could be reduced by 0.3–29.6% of the baseline N<sub>e</sub> with this intensification strategy as compared to indiscriminate further intensification, at the cost of a loss of yield increases by 0.2–16.7% of the baseline yields. In addition, irrigation water requirements and N<sub>in</sub> would dramatically decrease by considering this intensification strategy.

<figure><img src="image_2.png" /></figure>

TOC Art
19

# 1. Introduction

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

With a continuously growing global population, shifts to more animal-based diets, and possibly increasing competition in agricultural land use between food and biofuel crops, food production has to increase substantially¹⁻³. It was estimated that global food production needs to be doubled by 2050 if no changes occur on the demand side⁴. There are in principle two different strategies to increasing food production: expansion of croplands and intensification⁵.

For the first option, however, suitable land resources are very limited and their conversion to cropland is increasingly constrained by other land-use purposes. In many places, croplands are actually shrinking due to encroachment by urban development⁶,⁷. The option of cropland expansion is only feasible in underdeveloped areas and often at high environmental and climate change costs such as greenhouse gas emissions and biodiversity loss from forest clearing⁸. For the majority of countries and regions, intensification of crop production on existing croplands is the only way to meet the increasing food demand. Concerning limited resources and severe environmental impacts, modern agriculture is facing tremendous challenges⁹. A major task faced by scientists, farmers, and policy makers is to find ways to increase global food production while keeping the environmental costs at a tolerable level¹⁰⁻¹³.

Since the Green Revolution in the 1960s, crop yields have been increased, in particular through the breeding of more productive crop varieties¹⁴ and the intensification of land management with increasing fertilizer inputs and irrigation¹⁵. However, input intensification has also put substantial pressure on the environment due to unbalanced input-output agricultural systems.

The mismatch between inputs and outputs not only decreased resource use efficiency¹⁶⁻¹⁹, but
also caused serious environmental problems<sup>20-24</sup>. In particular, increasing inputs of nitrogen (N) fertilizers have led to high N emissions to the environment<sup>25,26</sup>, causing severe eutrophication and drinking water quality deterioration<sup>27</sup>, as well as air pollution by N<sub>2</sub>O and NH<sub>3</sub> emissions<sup>28</sup>. Hence, there is an ever more pressing need to develop pathways towards input intensification without further compromising environmental health and quality<sup>4,11,29,30</sup>.

Given that the yields of major crops have recently been stagnated or even decreased in many regions of the world, where high agricultural inputs were applied<sup>31-34</sup>, it seems that the benefits of additional inputs, particularly fertilizers, are saturating in these regions, suggesting that further input intensification will not help to increase yields. However, the scope for improved efficiency varies among different cropping systems<sup>35</sup>. It is essential to identify regions where a high yield return can still be obtained at low environmental impacts and to explore how input intensification can be achieved in these regions most efficiently. Previous studies have shown a high potential to further increase crop production through agricultural intensification, and concluded that this is key to increasing global food production to meet the demand<sup>36-41</sup>.

However, these studies have been limited in several aspects, including: a) focusing on one type of input only, either N<sup>37</sup> or irrigation<sup>36</sup>; b) no explicit consideration of environmental impacts<sup>39,40</sup>; and c) simply using N surpluses to indicate environmental impacts of N pollution without explicitly quantifying different forms of N emissions<sup>41</sup>.

Here we comprehensively address the agricultural challenges elaborated above by using the global agronomic model PEPIC<sup>42</sup>, Python-based Environmental Policy Integrated Climate (EPIC), to explore the benefits and trade-offs of further global agricultural input intensification
in terms of increased crop yields and associated N emissions (N<sub>e</sub>). We used food production units (FPUs) as a spatial unit. The FPUs are based on river basins and economic regions as introduced by Cai and Rosegrant<sup>43</sup> and modified by Kummu et al.<sup>44</sup> Focusing on N inputs (N<sub>in</sub>) and irrigation water (Irr), we considered four input intensification scenarios representing low, high and max levels of intensification (two scenarios for the low level) in addition to a baseline scenario with no additional intensification. Three major cereal crops (maize, rice, and wheat) were included in the analysis. In addition to looking into the differences in yields (ΔY), differences in N<sub>e</sub> (ΔN<sub>e</sub>), and differences in input requirements (ΔN<sub>in</sub> and ΔIrr) between the intensification and the baseline scenarios, we explored the relationships between ΔY and ΔN<sub>e</sub> in response to ΔN<sub>in</sub> to identify the priority regions for intensification. We also assessed the N use efficiency (NUE) of yield gains as ΔY/ΔN<sub>in</sub> and N emission intensity of new inputs as ΔN<sub>e</sub>/ΔN<sub>in</sub>.

## 2. Materials and methods

### 2.1. Simulation model and input data

The PEPIC model<sup>42</sup> was used to simulate crop growth at a daily time step and the associated nutrient dynamics globally at a spatial resolution of 30 arc minutes (about 50 km at the equator). PEPIC has been used for investigating global crop-water relations of maize<sup>42</sup> and assessing global N and phosphorus (P) emissions from the cultivation of major crops<sup>12,26</sup>. In addition, it also shows comparable performance in representing yield variability at country level along with 13 other global crop models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP)<sup>45,46</sup>. Inputs for PEPIC include longitude, latitude, elevation,
slope, soil properties (e.g. layer depth, pH, bulk density, organic carbon content), climate data
(precipitation, temperature, solar radiation, relative humidity, wind speed), land cover, and crop
management information. Land cover data are derived from the MIRCA2000 datasets, which
provide rainfed and irrigated cultivation areas for 26 crops⁴⁷. As for management data, planting
date, harvesting date, fertilizer, and irrigation are required. Planting and harvesting dates were
obtained from the Center for Sustainability and the Global Environment (SAGE)⁴⁸.
Crop-specific fertilizer and manure application data for the baseline simulation (including N
and P) were downloaded from the EarthStat dataset (http://www.earthstat.org/), which were
based on Mueller et al.⁴⁹ and West et al.⁵⁰ Information on model performance and other input
data is provided in the Supporting Information.

## 2.2. Management practices and intensification scenarios

In this study, we considered four intensification scenarios—N<sub>25</sub>, N<sub>25</sub>Irr<sub>10</sub>, N<sub>50</sub>Irr<sub>30</sub> and N<sub>max</sub>Irr<sub>max</sub>—in addition to the baseline scenario (Table 1). A sufficient amount of P was applied automatically in each scenario to eliminate the effects of P deficit on plant growth following Folberth et al.<sup>51</sup> N<sub>in</sub> in the baseline (N<sub>in-base</sub>) was determined by the minimum value between actual N<sub>in</sub> (N<sub>in-a</sub>) and automatic N<sub>in</sub> (N<sub>in-auto</sub>), where N application is based on crop N requirements, for the baseline yield simulation. Here, N<sub>in-a</sub> was based on the EarthStat dataset, while N<sub>in-auto</sub> was estimated by PEPIC with automatic N fertilization without N limitation at a trigger value of 10% N stress<sup>52</sup> under the baseline irrigation cropland condition based on the MIRCA2000 dataset. Such setting excluded the impacts of over N application on N<sub>e</sub> in some regions<sup>26,53</sup>, since this study focused on the effects of input additions. The automatic N
fertilization used here can determine the proper amount of N<sub>in</sub> at right time based on soil N concentration and crop N requirements<sup>54</sup>. It deserves to note that the automatic N fertilization may lead to optimistic bias on NUE simulated and that actual management practices are often less targeted (e.g. timing). Irrigation was applied automatically without water limitation for the irrigated cultivation at a trigger value of 10% water stress<sup>55</sup>. Increased N<sub>in</sub> and conversion of rainfed to irrigated cropland relative to the baseline were considered as intensification scenarios in this study. For N<sub>in</sub>, we first used PEPIC to determine the maximum N<sub>in</sub> (N<sub>in-max</sub>) by using the automatic fertilization schedule setting different N<sub>in</sub> caps considering all croplands with full irrigation. We found that when N<sub>in</sub> cap was set to 400 kg N per ha, maximum crop yields (Ymax) could be achieved for the three crops (Table S1). Therefore, actual N<sub>in</sub> estimated from this condition was used as maximum N<sub>in</sub>. Here, Ymax was estimated by forcing the model with sufficient water and N fertilizer without changing the response curves of yields to N and water. This definition of Ymax is consistent with Lassaletta et al.<sup>18,56</sup>, Mueller et al.<sup>57</sup>, and Mogollón et al.<sup>58</sup>, but different from the definition of van Ittersum et al.<sup>59</sup>, that also considered changes in the response curves. Combination of this fertilizer and full irrigation was applied in the N<sub>max</sub>Irr<sub>max</sub> scenario. Then, the difference between N<sub>in-max</sub> and N<sub>in-base</sub>, i.e. ΔN<sub>in-max</sub>, was calculated for each grid cell.

Insert Table 1 here

In N<sub>25</sub>, N<sub>in</sub> was increased by 25% of ΔN<sub>in-max</sub> relative to the baseline, while in N<sub>25</sub>Irr<sub>10</sub>, 10% of the rainfed cropland was converted to irrigated land in addition to a 25% of ΔN<sub>in-max</sub> increase in N<sub>in</sub>. In N<sub>50</sub>Irr<sub>30</sub>, 30% of the rainfed land was converted to irrigated land in addition to a 50% of
ΔN<sub>in-max</sub> increase in N<sub>in</sub>. These levels of intensification were based on Mueller et al.<sup>41</sup>, who found that jointly increasing N<sub>in</sub> by 30% and irrigated lands by 25% would reach productivity levels that represent 75% of the attainable yields of the year 2000. Therefore, we considered an increasing intensification level with an ascending order of baseline, N<sub>25</sub>, N<sub>25Irr10</sub>, N<sub>50Irr30</sub>, and N<sub>maxIrrmax</sub>. Although global constant percentage values of ΔN<sub>in-max</sub> and rainfed land were used here to increase N<sub>in</sub> and irrigation, the actual increases in N<sub>in</sub> and irrigation were different due to the large differences in N<sub>in</sub> and rainfed land area under the baseline condition (Figs. S1 and S2) and the input requirements under the N<sub>maxIrrmax</sub> scenario. It should be noted that it may not be practical to set the high- and/or max-level input intensification scenarios everywhere across the world. This holds particularly for many poor countries in Africa, as they are currently in lack of infrastructure and/or enough water and fertilizer to reach such intensified agricultural input requirements. However, the scenarios considered here reflect general mechanisms and challenges under the agricultural intensification. Thus, they provide useful information on exploring sustainable pathways towards future agricultural development. They can also be used in land use socio-economic models to prioritize agricultural subsidies and better agricultural practices for increasing N<sub>in</sub> or N recycling in regions where yields are severely limited by this nutrient.

## 2.3. Definition of target variables and data analysis

Four model outputs were considered in the analysis, i.e. irrigation water (Irr [mm]), N inputs (N<sub>in</sub> [kg N per ha]), yields (Y [t per ha]), and N emissions (N<sub>e</sub> [kg N per ha]). In this study, N<sub>e</sub> refers to total N emissions, including N emissions to the aquatic and atmospheric environments,
estimated by Eq. 2 in the Supporting Information. Crop growth was simulated separately under rainfed and irrigated conditions. Therefore, there are two response curves of yields and N<sub>e</sub> to N<sub>in</sub> for irrigated and rainfed cultivations. The combined outputs of each variable were calculated using the area-weighted average of irrigated and rainfed outputs based on the MIRCA2000 dataset of crop-specific fractions of irrigated and rainfed land for each crop in each grid cell<sup>47</sup>. The combined results were aggregated to FPUs, continental, and global levels.

Subsequently, the differences of each variable between the intensification scenarios and the baseline were calculated, i.e. differences in Irr (ΔIrr), differences in N<sub>in</sub> (ΔN<sub>in</sub>), differences in Y (ΔY), and differences in N<sub>e</sub> (ΔN<sub>e</sub>). For FPUs with ΔY < 0.05 t per ha, outputs under the intensification scenarios were treated as the same values of the baseline to exclude possible errors due to minor responses. After this treatment, ΔIrr, ΔN<sub>in</sub>, ΔY, and ΔN<sub>e</sub> were re-evaluated.

The NUE of yield gains, defined as ratio ΔY/ΔN<sub>in</sub>, and N emission intensity of new inputs, defined as ratio ΔN<sub>e</sub>/ΔN<sub>in</sub>, were calculated to explore the responses of yields and N<sub>e</sub> to N<sub>in</sub> additions under different intensification scenarios.

We constructed frontier lines of cumulative ΔY to cumulative ΔN<sub>e</sub> by intensifying the FPUs one by one. We first focused on the FPUs with low N emission intensity of new inputs and high NUE of yield gains and then on the FPUs with high N emission intensity of new inputs and low NUE of yield gains. It means that we logically intensified the FPUs with an ascending order of N emission intensity of new inputs and a descending order of NUE of yield gains. In addition to intensifying all FPUs to high/max levels, we investigated the impacts of stopping intensification in FPUs where a target yield, defined by a given fraction of Ymax, is achieved.
Different fractions (70%, 75%, 80%, 85%, 90%, and 95%) of Ymax were tested. If a given yield target was already achieved in an FPU under scenarios of baseline, N<sub>25</sub>, N<sub>25</sub>Irr<sub>10</sub>, N<sub>50</sub>Irr<sub>30</sub> scenarios, we stopped further intensification in that FPU.

## 3. Results

### 3.1. Yield benefits

The simulated responses of crop yields to increased N<sub>in</sub> and irrigation showed high spatial variations, with very large effects in some regions, but only minor ones in other areas, mainly reflecting current limitations (Fig. 1). These benefits also showed different spatial patterns among the three crops. Areas with high ΔY for maize and rice were found to be concentrated mainly in the southern parts of Africa and South America, where the baseline yields were quite low and differences in yields between the N<sub>max</sub>Irr<sub>max</sub> and baseline scenarios were very high (Fig. S3). In addition to these regions, parts of East Europe and the Middle East also showed large ΔY for wheat. Wheat presented smaller yield improvements compared to maize and rice. For other regions, particularly China and India, only small ΔY were predicted for the three crops. Globally, the average ΔY in the four intensification scenarios ranged between 10 and 50% of baseline yields for maize, 7 and 19% for rice, and 16 and 71% for wheat (Table 2). Yields responded mainly to increased N<sub>in</sub> and less to intensified irrigation. Increased N<sub>in</sub> were found to substantially increase irrigation water use efficiency (defined as yield per unit of applied irrigation water), while the effects of irrigation additions on NUE (defined as yield per unit of applied N<sub>in</sub>) were quite small at the continental level (Tables S2 and S3). Meanwhile, to achieve high yields, significant increases in the inputs of these resources were required (Table 2), with
187 $\Delta$Irr between 0 and 250% of baseline irrigation water and $\Delta$N<sub>in</sub> between 12 and 138% of baseline N<sub>in</sub>, depending on scenarios and crops.

189 Insert Fig. 1 here

190 Insert Table 2 here

191 **3.2. Nitrogen emissions**

192 The simulations predicted that yield benefits from N<sub>in</sub> and irrigation intensification would result in substantial $\Delta$N<sub>e</sub> around the world, with increases varying between 9 and 59% of the baseline N<sub>e</sub> for maize, 7 and 39% for rice, and from 12 to 64% for wheat among the four scenarios (Table 2). Increases in N<sub>e</sub> was particularly high in Africa, Oceania, and South America for maize and rice, and in Africa, Europe, Oceania, and South America for wheat (Fig. 2 and Fig. S4). The overall geographical distribution patterns were similar to those for the respective $\Delta$Y, but there were also some differences. Large $\Delta$N<sub>e</sub> were predicted in Southeast Asia for all three crops and in the eastern parts of the USA for wheat, while yields were not expected to increase significantly in these regions in response to N<sub>in</sub> and irrigation additions. While increasing N<sub>in</sub> always increased N<sub>e</sub>, increased irrigation could reduce N<sub>e</sub> in some regions relative to the baseline scenario. That is because the plants that grow better under irrigation cultivation take up more N.

204 Insert Fig. 2 here

205 **3.3. Relationship between yield increases and nitrogen emissions**

206 The NUE of yield gains, expressed in terms of the ratio $\Delta$Y/$\Delta$N<sub>in</sub>, and the N emission intensity of new inputs, expressed as the ratio $\Delta$N<sub>e</sub>/$\Delta$N<sub>in</sub>, present opposite spatial distribution patterns at
the FPUs level (Figs. S5 and S6). The Southeast Asia and the northern parts of South America showed high N emission intensity of new inputs for maize and rice. The NUE of yield gains showed clear negative linear relationships to the N emission intensity of new inputs for all the three crops, especially for maize and rice under all four intensification scenarios, and for wheat under the $N_{max}Irr_{max}$ scenario (Fig. 3). The position of individual FPU along the relationships between NUE of yield gains and N emission intensity of new inputs was closely related to the magnitude of $N_{in}$ additions. In contrast, the increases in irrigation land had little effect on the balance between relative benefits on yield vs. $N_e$, as these relationships showed little differences between the scenarios without and with irrigation addition, for instance between $N_{25}$ and $N_{25Irr_{10}}$. The negative relationships shown in Fig. 3 indicate that there is a win-win situation with higher yields and low $N_e$ if further input intensification concentrates on regions where additional inputs have the highest NUE of yield gains, as these are also the regions with the smallest additional $N_e$.

Insert Fig. 3 here

In most FPUs with a high N emission intensify of new inputs and a low NUE of yield gains (Figs. S5 and S6), yields under each intensification scenario have already obtained a high fraction (e.g. 80%) of $Y_{max}$ under the former scenario considering an order of baseline, $N_{25}$, $N_{25Irr_{10}}$, $N_{50Irr_{30}}$ and $N_{max}Irr_{max}$ (Fig. 3). This means that significant $N_e$ can be avoided by stopping further increases in $N_{in}$ and irrigation applications in such FPUs. This condition was found to be especially common for rice cultivation, where about 79% of the croplands presently under rice cultivation already reaches 80% of $Y_{max}$ in the baseline scenario (Table S4). For
229 wheat and maize, the respective percentages were 35% and 26%. The regions showing low
230 yield gap were mainly located in East Asia, central Europe and the eastern USA for maize,
231 eastern and southeastern Asia for rice, and China and India for wheat (Fig. S7). In the $N_{50}\text{Irr}_{30}$
232 scenario, the fraction of croplands cannot achieve 80% of the $Y_{\text{max}}$ is still big for maize and
233 wheat.

234 **3.4. Frontier lines for intensifying croplands on a global scale**

235 Based on the negative relationship between NUE of yield gains and N emission intensity of
236 new inputs as shown in Fig. 3, we constructed the distributions of cumulative $\Delta Y$ against
237 cumulative $\Delta N_e$ (Fig. 4), sorting the FPUs by an ascending order in N emission intensity of new
238 inputs and meanwhile a descending order in NUE of yield gains. Starting with FPUs low in $\Delta N_e$
239 and high in $\Delta Y$ which are the best regions for intensification, these lines decrease in slope as $\Delta Y$
240 decreases, while $\Delta N_e$ increases. This is particularly notable for scenarios $N_{50}\text{Irr}_{30}$ and $N_{\text{max}}\text{Irr}_{\text{max}}$.
241 We observed that initial increases in yields can be achieved by lower-input systems at less
242 environmental burden (e.g. yellow line steeper than red line for rice and initially for wheat and
243 maize). Furthermore, the frontier lines show a large difference between the $N_{\text{max}}\text{Irr}_{\text{max}}$ and the
244 other scenarios, indicating the high N pollution versus small yield benefits of further
245 intensifying crop production when the level of intensification is already high.

246 Insert Fig. 4 here

247 An important strategy for limiting $\Delta N_e$ with minimal compromises on yield increases is to
248 avoid further intensification in FPUs, where high fractions of $Y_{\text{max}}$ have been achieved, which
249 we here define as a threshold of 80% (Fig. 4). With this condition, simulated $\Delta N_e$ were reduced
from 6.4–40.4 kg N per ha to 6.2–20.2 kg N per ha (this reduction representing 0.3–29.6% of the baseline N<sub>e</sub>) for maize; from 5.4–28.7 kg N per ha to 3.7–10.7 kg N per ha (the reduction representing 2.2–24.3% of the baseline N<sub>e</sub>) for rice; and from 5.1–27.7 kg N per ha to 4.9–18.1 kg N per ha (the reduction representing 0.4–22.3% of the baseline N<sub>e</sub>) for wheat, depending on the scenarios. Expected ΔY were reduced by only 0.0–0.9 t per ha (corresponding to 0.2–16.7% of the baseline yields) for maize; 0.1–0.4 t per ha (corresponding to 1.8–8.8% of the baseline yields) for rice; and 0.0–0.3 t per ha (corresponding to 0.2–9.8% of the baseline yields) for wheat (Tables 2 and 3). Furthermore, the results also show that much less N and irrigation water resources are required to achieve the same increases in yield with a strategy in which further intensification is limited to cropland where the yield gap is still comparatively large than with indiscriminate further intensification. In addition to restricting intensification to FPUs with yield levels of <80% Ymax, we performed the analogous analysis setting the restriction fractions at 70%, 75%, 85%, 90%, and 95% of Ymax (Fig. S8). Similar trends for ΔY and ΔN<sub>e</sub> were found for these fractions as with the fraction of 80%. However, the trade-offs between Y achievements and increases in N<sub>e</sub> varied by using the different yield target levels.

Insert Table 3 here

## 4. Discussion

To detail the different responses of yields and N<sub>e</sub> to N<sub>in</sub> and water additions, the response curves of yields and N<sub>e</sub> to N<sub>in</sub> under irrigated and rainfed cultivations are displayed for eight continental-climate regions in Fig. 5. The climate classification is based on the Köppen-Geiger climate map<sup>60</sup>. We observed very different responses in different regions, consistent with the
EPIC simulations from Balkovič et al.⁶¹ The differences between the irrigated and rainfed cultivations are particularly high in the arid regions, where the irrigation water requirements are generally high. These response curves from PEPIC highlight the large spatial variability of yield benefits and N<sub>e</sub> trade-offs under intensification scenarios on a global scale. Comparison of the PEPIC response curves with those from other crop models would help to estimate the uncertainty on our results arising from the use of a single model.

Insert Fig. 5 here

The high spatial heterogeneity of the responses of yields and N<sub>e</sub> to increased N<sub>in</sub> and irrigation inputs is mainly explained by differences in these inputs between under the baseline scenario and under the N<sub>max</sub>Irr<sub>max</sub> scenario. Regions with a high level of baseline N<sub>in</sub> (Fig. S1) generally respond with low NUE of yield gains but high N emission intensity of new inputs to further intensification. This is in agreement with previous studies. When N<sub>in</sub> is already high, the yield benefits of additional N<sub>in</sub> are low<sup>18</sup>, while the potential of N<sub>e</sub> tends to be high<sup>14</sup>. Maximum N requirements for achieving Ymax presented high variation (Fig. S1) and were mainly determined by climate condition, particularly the potential heat units (Fig. S9), as both of them show quite similar spatial distribution. Such patterns of maximum N<sub>in</sub> also partially explain the heterogeneity of yield responses to N<sub>in</sub>. Compared to N<sub>in</sub>, most regions showed relatively low ΔY in response to more irrigation in all three crops, with significant variations across regions (Fig. S10), which is true as most cultivation regions of these three crops were not found limited by irrigation<sup>49</sup>. Especially, yield response is more sensitive to N<sub>in</sub> additions than to irrigation in Africa and South America, where the baseline N<sub>in</sub> was generally very low. The different
292 responses of yields to irrigation additions are mainly due to different fractions of irrigated lands
293 to total cultivated lands (Fig. S2) and the differences in irrigation requirements under different
294 scenarios, e.g. the baseline management as shown in Fig. S11. A low irrigation requirement
295 indicates that growing season precipitation is generally sufficient for crop growth (Fig. S12),
296 and so there are low benefits from increased irrigation. On the other hand, there is only limited
297 opportunity to further increasing yields by taking additional land under irrigation where a large
298 fraction of the cropland is already under irrigation. On land where yields are already close to
299 what can be achieved with the maximum N and irrigation water inputs, further increasing the
300 intensity can produce only minor benefits, while the environmental impacts and the
301 consumption of resources become more significant. Such regions need to be identified and
302 excluded from further intensification.

303 Based on our simulations, it was not possible to double the production of the three crops on the
304 current cropland area even if there was no limit on N fertilizer application and irrigation,
305 especially in rice production (Table 2). Similar findings were reported by Mueller et al.⁴⁹, who
306 estimated production potentials close to 100% of maximum attainable yields through nutrients
307 and irrigation management. Therefore, other measures need to be considered to further increase
308 yields, such as breeding more productive varieties⁶², change of cropping intensities⁶³, and better
309 allocation on a global scale of crops to cultivated land⁴⁰, among others. Combining these
310 measures with better N and irrigation management can be expected to further increase Ymax
311 and strengthen the responses of yields to N<sub>in</sub> and irrigation in currently high yield regions, as
312 elaborated in Mogollón et al.⁵⁸ Besides, for a better agricultural development, extensification,
313 which focuses on reducing external inputs, could also be considered in the already intensified
314 regions to alleviate environmental impacts with minor yield reduction⁶⁴. Nevertheless, here we
315 focused on the three major food crops. It is worth noting that other types of crops (particularly
316 fruits and vegetable) may hold the potential to double the production⁶⁵. For instance, apple
317 yields in China could increase from 16.5 t per ha to 37 t per ha through better management⁶⁶.

318 Hence, a comprehensive assessment including different management options and incorporating
319 more crops should be conducted in further research.

320 Agricultural inputs cannot be increased without limit and they are also associated with
321 significant environmental impacts. While there is sufficient N in the atmosphere for practically
322 unlimited industrial production of mineral N fertilizer, the energy required for it is likely to set
323 an upper limit. Already about 2% of the world's energy use is for the production of reactive N⁶⁷.

324 Moreover, there is still a long way to go for many developing countries, mainly in Africa, to
325 afford and distribute enough nutrients for their croplands. In comparison to N fertilizer,
326 freshwater is a much more limited resource⁶⁸ and its spatiotemporal distribution is already very
327 uneven for natural reasons⁶⁹. That is why we set lower levels of irrigated land expansion. While
328 we found that by expanding the irrigated areas, maize and wheat yields could be increased
329 substantially in the western USA and western and central Asia (Fig. S10), the available water
330 resources there set a rather low upper limit to this option⁷⁰. Further expansion of irrigation
331 agriculture hence bears the risk of worsening this problem. Therefore, it will be vital to increase
332 the efficiency of irrigation⁷¹,⁷² and fertilizer¹⁹ applications. As shown here, one strategy to
333 achieve this is to concentrate intensification efforts in regions with currently low yields and
high yield potentials, while avoiding further intensification in regions where yields are already very high. This strategy could simultaneously conserve agricultural resources and also substantially reduce additional N pollution.

Mueller et al.⁴¹ also explored trade-offs between excess N (differences between N<sub>in</sub> and N in crop harvest) and crop production using frontier lines. The difference between our results and their study is that we used frontier lines to explore the benefits of avoiding further ‘useless’ intensification in regions where yields are already close to their maximum levels, i.e. 70–95% of Ymax (Fig. 4 and Fig. S8). The ΔN<sub>e</sub> may be reduced if setting a low yield target. But then the increases in yields will also be lower. Thus, it is important to find a level at which these effects are in a desirable balance. However, setting such a target level is subject to local and regional policies, social and economic trade-offs, access to N fertilizers, capacity to improve agronomical practices, as well as consideration of regional environmental vulnerabilities. Due to significant variations of agricultural performance among different countries in terms of trade-offs between yield achievements and environmental impacts because of different natural conditions (e.g. climate and soil) and agronomic practices (e.g. different NUE), much more efforts should be made to determine a reasonable yield target level on local scale⁵⁷,⁷³. Due to enormous disparities in capacity and socioeconomic conditions among different countries, it may not be possible to intensify agricultural inputs to such a reasonable yield target level in the short term in economically weak regions, e.g. many poor countries in Africa. Therefore, in addition to setting a critical target level, it is also important to make sure that the high yield improvements without significant environmental costs could be practically achieved for these
regions. Answering this question requires detailed socio-economic information and complex economic models to address benefits and trade-offs between agricultural intensification and food trade-dependence⁵³,⁷³. This is beyond the focus of the current study, but it certainly deserves an in-depth investigation.

Our previous analysis showed that the PEPIC model realistically captured large-scale $N_e$ due to $N$ inputs, but with high uncertainties from model parameters²⁶. In this study, we document the variable responses of yields and $N_e$ to $N_{in}$ and water additions in different regions across the world. More detailed follow up studies to significantly reduce uncertainties should focus on evaluating the PEPIC response against data from field experiments with nutrient and water treatments⁷⁴,⁷⁵, and on comparing PEPIC response curves with other crop models, such as planned in AgMIP⁷⁶. We did not consider the impacts of climate change and adaptation strategies⁷⁷, which are also important factors affecting future food security, although their uncertainties are also high⁵⁵,⁷⁸,⁷⁹. As the purpose of this study is to investigate the yield potentials of input intensification, associated environmental trade-offs, and their regional differences, we only considered four intensification scenarios (representing low, high, and maximum levels) in order to reduce the computation time. A full range of $N$ and irrigation intensification scenarios would help to identify the optimal intensification level. Overcoming the limitations specified above was beyond the scope of this study and will be subject to future research.

**Supporting Information:** Details on model description and model performance. Details on nitrogen inputs, crop yield responses, nitrogen emission responses, and percentage of potential
yields achieved under difference scenarios. Details on irrigated land, potential heat units,
irrigation water requirements, and growing season precipitation under baseline. Details on
yield-nitrogen responses, water use efficiency, nitrogen use efficiency at the continental level.

**Author contributions:** W.L. and H.Y. designed research. W.L. performed research and analysed data. All authors participated in the interpretation of results and the writing and editing process.

**Acknowledgments:** This study was supported by funding from the Swiss Federal Institute of Aquatic Science and Technology (Eawag) and the World Food System Center at ETH Zürich, Switzerland. W.L. acknowledges the support received from the Early Postdoctoral Mobility Fellowship awarded by Swiss National Science Foundation (P2EZP2_175096). C.F. was supported by European Research Council Synergy grant ERC-2013-SynG-610028 Imbalance-P. C.M. acknowledges financial support from the MACMIT project (01LN1317A) funded through the German Federal Ministry of Education and Research (BMBF).

**Competing interests:** The authors declare that they have no competing financial interests.

## References

1. Bodirsky, B. L.; Popp, A.; Lotze-Campen, H.; Dietrich, J. P.; Rolinski, S.; Weindl, I.; Schmitz, C.; Muller, C.; Bonsch, M.; Humpenoder, F.; Biewald, A.; Stevanovic, M., Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution.
*Nat. Commun.* **2014**, *5*, 3858.

2. Bodirsky, B. L.; Rolinski, S.; Biewald, A.; Weindl, I.; Popp, A.; Lotze-Campen, H., Global food demand scenarios for the 21st century. *PLoS One* **2015**, *10*, (11), e0139201.
397 3. Lotze-Campen, H.; von Lampe, M.; Kyle, P.; Fujimori, S.; Havlik, P.; van Meijl, H.; Hasegawa, T.; Popp, A.; Schmitz, C.; Tabeau, A.; Valin, H.; Willenbockel, D.; Wise, M., Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. *Agr. Econ.* **2014**, *45*, (1), 103-116.

401 4. Tilman, D.; Balzer, C.; Hill, J.; Befort, B. L., Global food demand and the sustainable intensification of agriculture. *Proc. Natl. Acad. Sci. U.S.A.* **2011**, *108*, (50), 20260-20264.

403 5. Matson, P. A.; Parton, W. J.; Power, A. G.; Swift, M. J., Agricultural intensification and ecosystem properties. *Science* **1997**, *277*, (5325), 504-509.

405 6. Deng, J. S.; Qiu, L. F.; Wang, K.; Yang, H.; Shi, Y. A. Y., An integrated analysis of urbanization-triggered cropland loss trajectory and implications for sustainable land management. *Cities* **2011**, *28*, (2), 127-137.

408 7. Lambin, E. F.; Meyfroidt, P., Global land use change, economic globalization, and the looming land scarcity. *Proc. Natl. Acad. Sci. U.S.A.* **2011**, *108*, (9), 3465-3472.

410 8. Foley, J. A.; Ramankutty, N.; Brauman, K. A.; Cassidy, E. S.; Gerber, J. S.; Johnston, M.; Mueller, N. D.; O'Connell, C.; Ray, D. K.; West, P. C.; Balzer, C.; Bennett, E. M.; Carpenter, S. R.; Hill, J.; Monfreda, C.; Polasky, S.; Rockstrom, J.; Sheehan, J.; Siebert, S.; Tilman, D.; Zaks, D. P. M., Solutions for a cultivated planet. *Nature* **2011**, *478*, (7369), 337-342.

414 9. Godfray, H. C. J.; Beddington, J. R.; Crute, I. R.; Haddad, L.; Lawrence, D.; Muir, J. F.; Pretty, J.; Robinson, S.; Thomas, S. M.; Toulmin, C., Food Security: The challenge of feeding 9 billion people. *Science* **2010**, *327*, (5967), 812-818.

417 10. Foley, J. A.; DeFries, R.; Asner, G. P.; Barford, C.; Bonan, G.; Carpenter, S. R.; Chapin, F.
418 S.; Coe, M. T.; Daily, G. C.; Gibbs, H. K.; Helkowski, J. H.; Holloway, T.; Howard, E. A.;
419 Kucharik, C. J.; Monfreda, C.; Patz, J. A.; Prentice, I. C.; Ramankutty, N.; Snyder, P. K.,
420 Global consequences of land use. *Science* **2005**, *309*, (5734), 570-574.

421 11. Godfray, H. C. J.; Garnett, T., Food security and sustainable intensification. *Phil. Trans.*
422 *R. Soc. B* **2014**, *369*, (1639), 20120273.

423 12. Liu, W.; Yang, H.; Ciais, P.; Stamm, C.; Zhao, X.; Williams, J. R.; Abbaspour, K. C.;
424 Schulin, R., Integrative crop-soil-management modeling to assess global phosphorus losses
425 from major crop cultivations. *Global Biogeochem. Cycles* **2018**, *32*, (7), 1074-1086.

426 13. Sayer, J.; Cassman, K. G., Agricultural innovation to protect the environment. *Proc. Natl.
427 Acad. Sci. U.S.A.* **2013**, *110*, (21), 8345-8348.

428 14. Bodirsky, B. L.; Muller, C., Robust relationship between yields and nitrogen inputs
429 indicates three ways to reduce nitrogen pollution. *Environ. Res. Lett.* **2014**, *9*, (11), 111005.

430 15. Tilman, D.; Fargione, J.; Wolff, B.; D'Antonio, C.; Dobson, A.; Howarth, R.; Schindler,
431 D.; Schlesinger, W. H.; Simberloff, D.; Swackhamer, D., Forecasting agriculturally driven
432 global environmental change. *Science* **2001**, *292*, (5515), 281-284.

433 16. Conant, R. T.; Berdanier, A. B.; Grace, P. R., Patterns and trends in nitrogen use and
434 nitrogen recovery efficiency in world agriculture. *Global Biogeochem. Cycles* **2013**, *27*, (2),
435 558-566.

436 17. Cui, Z. L.; Wang, G. L.; Yue, S. C.; Wu, L.; Zhang, W. F.; Zhang, F. S.; Chen, X. P.,
437 Closing the N-use efficiency gap to achieve food and environmental security. *Environ. Sci.
438 Technol.* **2014**, *48*, (10), 5780-5787.
18. Lassaletta, L.; Billen, G.; Grizzetti, B.; Anglade, J.; Garnier, J., 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. *Environ. Res. Lett.* **2014**, 9, (10), 105011.

19. Zhang, X.; Davidson, E. A.; Mauzerall, D. L.; Searchinger, T. D.; Dumas, P.; Shen, Y., Managing nitrogen for sustainable development. *Nature* **2015**, *528*, (7580), 51-59.

20. Clark, C. M.; Tilman, D., Loss of plant species after chronic low-level nitrogen deposition to prairie grasslands. *Nature* **2008**, *451*, (7179), 712-715.

21. Liu, J.; You, L.; Amini, M.; Obersteiner, M.; Herrero, M.; Zehnder, A. J.; Yang, H., A high-resolution assessment on global nitrogen flows in cropland. *Proc. Natl. Acad. Sci. U.S.A.* **2010**, *107*, (17), 8035-40.

22. Liu, X.; Zhang, Y.; Han, W.; Tang, A.; Shen, J.; Cui, Z.; Vitousek, P.; Erisman, J. W.; Goulding, K.; Christie, P.; Fangmeier, A.; Zhang, F., Enhanced nitrogen deposition over China. *Nature* **2013**, *494*, (7438), 459-62.

23. Schlesinger, W. H., On the fate of anthropogenic nitrogen. *Proc. Natl. Acad. Sci. U.S.A.* **2009**, *106*, (1), 203-208.

24. Steffen, W.; Richardson, K.; Rockstrom, J.; Cornell, S. E.; Fetzer, I.; Bennett, E. M.; Biggs, R.; Carpenter, S. R.; de Vries, W.; de Wit, C. A.; Folke, C.; Gerten, D.; Heinke, J.; Mace, G. M.; Persson, L. M.; Ramanathan, V.; Reyers, B.; Sorlin, S., Planetary boundaries: Guiding human development on a changing planet. *Science* **2015**, *347*, (6223), 1259855.

25. Babbin, A. R.; Ward, B. B., Controls on nitrogen loss processes in Chesapeake Bay sediments. *Environ. Sci. Technol.* **2013**, *47*, (9), 4189-96.
26. Liu, W.; Yang, H.; Liu, J.; Azevedo, L. B.; Wang, X.; Xu, Z.; Abbaspour, K. C.; Schulin, R., Global assessment of nitrogen losses and trade-offs with yields from major crop cultivations. *Sci. Total Environ.* **2016**, *572*, 526-537.

27. Liu, W.; Antonelli, M.; Liu, X.; Yang, H., Towards improvement of grey water footprint assessment: With an illustration for global maize cultivation. *J. Cleaner Prod.* **2017**, *147*, 1-9.

28. Cui, Z. L.; Yue, S. C.; Wang, G. L.; Zhang, F. S.; Chen, X. P., In-season root-zone N management for mitigating greenhouse gas emission and reactive N losses in intensive wheat production. *Environ. Sci. Technol.* **2013**, *47*, (11), 6015-6022.

29. Garnett, T.; Appleby, M. C.; Balmford, A.; Bateman, I. J.; Benton, T. G.; Bloomer, P.; Burlingame, B.; Dawkins, M.; Dolan, L.; Fraser, D.; Herrero, M.; Hoffmann, I.; Smith, P.; Thornton, P. K.; Toulmin, C.; Vermeulen, S. J.; Godfray, H. C., Agriculture. Sustainable intensification in agriculture: premises and policies. *Science* **2013**, *341*, (6141), 33-4.

30. Pretty, J., Agricultural sustainability: concepts, principles and evidence. *Phil. Trans. R. Soc. B* **2008**, *363*, (1491), 447-465.

31. Iizumi, T.; Yokozawa, M.; Sakurai, G.; Travasso, M. I.; Romanernkov, V.; Oettli, P.; Newby, T.; Ishigooka, Y.; Furuya, J., Historical changes in global yields: major cereal and legume crops from 1982 to 2006. *Global Ecol. Biogeogr.* **2014**, *23*, (3), 346-357.

32. Ray, D. K.; Mueller, N. D.; West, P. C.; Foley, J. A., Yield trends are insufficient to double global crop production by 2050. *PLoS One* **2013**, *8*, (6), e66428.

33. Ray, D. K.; Ramankutty, N.; Mueller, N. D.; West, P. C.; Foley, J. A., Recent patterns of crop yield growth and stagnation. *Nat. Commun.* **2012**, *3*, 5989.
34. Wei, X.; Zhang, Z.; Shi, P. J.; Wang, P.; Chen, Y.; Song, X.; Tao, F. L., Is yield increase sufficient to achieve food security in China? *PLoS One* **2015**, *10*, (2), e0116430.

35. Carberry, P. S.; Liang, W. L.; Twomlow, S.; Holzworth, D. P.; Dimes, J. P.; McClelland, T.; Huth, N. I.; Chen, F.; Hochman, Z.; Keating, B. A., Scope for improved eco-efficiency varies among diverse cropping systems. *Proc. Natl. Acad. Sci. U.S.A.* **2013**, *110*, (21), 8381-8386.

36. Brauman, K. A.; Siebert, S.; Foley, J. A., Improvements in crop water productivity increase water sustainability and food security—a global analysis. *Environ Res Lett* **2013**, *8*, (2), 024030.

37. Chen, X.; Cui, Z.; Fan, M.; Vitousek, P.; Zhao, M.; Ma, W.; Wang, Z.; Zhang, W.; Yan, X.; Yang, J.; Deng, X.; Gao, Q.; Zhang, Q.; Guo, S.; Ren, J.; Li, S.; Ye, Y.; Wang, Z.; Huang, J.; Tang, Q.; Sun, Y.; Peng, X.; Zhang, J.; He, M.; Zhu, Y.; Xue, J.; Wang, G.; Wu, L.; An, N.; Wu, L.; Ma, L.; Zhang, W.; Zhang, F., Producing more grain with lower environmental costs. *Nature* **2014**, *514*, (7523), 486-9.

38. Chen, X. P.; Cui, Z. L.; Vitousek, P. M.; Cassman, K. G.; Matson, P. A.; Bai, J. S.; Meng, Q. F.; Hou, P.; Yue, S. C.; Romheld, V.; Zhang, F. S., Integrated soil-crop system management for food security. *Proc. Natl. Acad. Sci. U.S.A.* **2011**, *108*, (16), 6399-6404.

39. Johnston, M.; Licker, R.; Foley, J.; Holloway, T.; Mueller, N. D.; Barford, C.; Kucharik, C., Closing the gap: global potential for increasing biofuel production through agricultural intensification. *Environ. Res. Lett.* **2011**, *6*, (3), 034028.

40. Mauser, W.; Klepper, G.; Zabel, F.; Delzeit, R.; Hank, T.; Putzenlechner, B.; Calzadilla,
502 A., Global biomass production potentials exceed expected future demand without the need for cropland expansion. *Nat. Commun.* **2015**, *6*, 8946.

504 41. Mueller, N. D.; West, P. C.; Gerber, J. S.; MacDonald, G. K.; Polasky, S.; Foley, J. A., A tradeoff frontier for global nitrogen use and cereal production. *Environ Res Lett* **2014**, *9*, (5), 054002.

507 42. Liu, W. F.; Yang, H.; Folberth, C.; Wang, X. Y.; Luo, Q. Y.; Schulin, R., Global investigation of impacts of PET methods on simulating crop-water relations for maize. *Agr. Forest Meteorol.* **2016**, *221*, 164-175.

510 43. Cai, X. M.; Rosegrant, M. W., Global water demand and supply projections part - 1. A modeling approach. *Water Int.* **2002**, *27*, (2), 159-169.

512 44. Kummu, M.; Ward, P. J.; de Moel, H.; Varis, O., Is physical water scarcity a new phenomenon? Global assessment of water shortage over the last two millennia. *Environ. Res. Lett.* **2010**, *5*, (3), 034006.

515 45. Müller, C.; Elliott, J.; Chryssanthacopoulos, J.; Arneth, A.; Balkovic, J.; Ciais, P.; Deryng, D.; Folberth, C.; Glotter, M.; Hoek, S.; Iizumi, T.; Izaurralde, R. C.; Jones, C.; Khabarov, N.; Lawrence, P.; Liu, W.; Olin, S.; Pugh, T. A. M.; Ray, D.; Reddy, A.; Rosenzweig, C.; Ruane, A. C.; Sakurai, G.; Schmid, E.; Skalsky, R.; Song, C. X.; Wang, X.; de Wit, A.; Yang, H., Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. *Geosci. Model Dev.* **2017**, *10*, 1403-1422.

521 46. Porwollik, V.; Müller, C.; Elliott, J.; Chryssanthacopoulos, J.; Iizumi, T.; Ray, D. K.; Ruane, A. C.; Arneth, A.; Balkovič, J.; Ciais, P.; Deryng, D.; Folberth, C.; Izaurralde, R. C.;
Jones, C. D.; Khabarov, N.; Lawrence, P. J.; Liu, W.; Pugh, T. A. M.; Reddy, A.; Sakurai, G.; Schmid, E.; Wang, X.; de Wit, A.; Wu, X., Spatial and temporal uncertainty of crop yield aggregations. *Eur. J. Agron.* **2017**, *88*, 10-21.

47. Portmann, F. T.; Siebert, S.; Doll, P., MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. *Global Biogeochem. Cycles* **2010**, *24*.

48. Sacks, W. J.; Deryng, D.; Foley, J. A.; Ramankutty, N., Crop planting dates: an analysis of global patterns. *Global Ecol. Biogeogr.* **2010**, *19*, (5), 607-620.

49. Mueller, N. D.; Gerber, J. S.; Johnston, M.; Ray, D. K.; Ramankutty, N.; Foley, J. A., Closing yield gaps through nutrient and water management. *Nature* **2012**, *490*, (7419), 254-257.

50. West, P. C.; Gerber, J. S.; Engstrom, P. M.; Mueller, N. D.; Brauman, K. A.; Carlson, K. M.; Cassidy, E. S.; Johnston, M.; MacDonald, G. K.; Ray, D. K.; Siebert, S., Leverage points for improving global food security and the environment. *Science* **2014**, *345*, (6194), 325-328.

51. Folberth, C.; Yang, H.; Gaiser, T.; Liu, J.; Wang, X.; Williams, J.; Schulin, R., Effects of ecological and conventional agricultural intensification practices on maize yields in sub-Saharan Africa under potential climate change. *Environ. Res. Lett.* **2014**, *9*, (4), 044004.

52. Balkovič, J.; van der Velde, M.; Skalský, R.; Xiong, W.; Folberth, C.; Khabarov, N.; Smirnov, A.; Mueller, N. D.; Obersteiner, M., Global wheat production potentials and management flexibility under the representative concentration pathways. *Global Planet. Change* **2014**, *122*, 107-121.
544 53. Liu, W.; Yang, H.; Liu, Y.; Kummu, M.; Hoekstra, A. Y.; Liu, J.; Schulin, R., Water resources conservation and nitrogen pollution reduction under global food trade and agricultural intensification. *Sci. Total Environ.* **2018**, *633*, 1591-1601.

547 54. Williams, J. R., The EPIC model. In *Computer Models of Watershed hydrology*, Singh, V. P., Ed. Water Resources Publications: Highlands Ranch, Colo, 1995.

549 55. Rosenzweig, C.; Elliott, J.; Deryng, D.; Ruane, A. C.; Muller, C.; Arneth, A.; Boote, K. J.; Folberth, C.; Glotter, M.; Khabarov, N.; Neumann, K.; Piontek, F.; Pugh, T. A. M.; Schmid, E.; Stehfest, E.; Yang, H.; Jones, J. W., Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. *Proc. Natl. Acad. Sci. U.S.A.* **2014**, *111*, (9), 3268-3273.

554 56. Lassaletta, L.; Billen, G.; Garnier, J.; Bouwman, L.; Velazquez, E.; Mueller, N. D.; Gerber, J. S., Nitrogen use in the global food system: past trends and future trajectories of agronomic performance, pollution, trade, and dietary demand. *Environ. Res. Lett.* **2016**, *11*, (9).

558 57. Mueller, N. D.; Lassaletta, L.; Runck, B. C.; Billen, G.; Garnier, J.; Gerber, J. S., Declining spatial efficiency of global cropland nitrogen allocation. *Global Biogeochem. Cycles* **2017**, *31*, (2), 245-257.

561 58. Mogollón, J. M.; Lassaletta, L.; Beusen, A. H. W.; van Grinsven, H. J. M.; Westhoek, H.; Bouwman, A. F., Assessing future reactive nitrogen inputs into global croplands based on the shared socioeconomic pathways. *Environ. Res. Lett.* **2018**, *13*, (4).

564 59. van Ittersum, M. K.; Cassman, K. G.; Grassini, P.; Wolf, J.; Tittonell, P.; Hochman, Z.,
565 Yield gap analysis with local to global relevance—A review. *Field Crop Res.* **2013**, *143*, 4-17.

566 60. Peel, M. C.; Finlayson, B. L.; McMahon, T. A., Updated world map of the
567 Koppen-Geiger climate classification. *Hydrol. Earth Syst. Sci.* **2007**, *11*, (5), 1633-1644.

568 61. Balkovič, J.; van der Velde, M.; Schmid, E.; Skalský, R.; Khabarov, N.; Obersteiner, M.;
569 Sturmer, B.; Xiong, W., Pan-European crop modelling with EPIC: Implementation, up-scaling
570 and regional crop yield validation. *Agr. Syst.* **2013**, *120*, 61-75.

571 62. Khush, G. S., Green revolution: the way forward. *Nat. Rev. Genet.* **2001**, *2*, (10),
572 815-822.

573 63. Challinor, A. J.; Parkes, B.; Ramirez-Villegas, J., Crop yield response to climate change
574 varies with cropping intensity. *Global Change Biol.* **2015**, *21*, (4), 1679-1688.

575 64. van Grinsven, H. J. M.; Erisman, J. W.; de Vries, W.; Westhoek, H., Potential of
576 extensification of European agriculture for a more sustainable food system, focusing on
577 nitrogen. *Environ. Res. Lett.* **2015**, *10*, (2), 025002.

578 65. Ebert, A., Potential of underutilized traditional vegetables and legume crops to contribute
579 to food and nutritional security, income and more sustainable production systems.
580 *Sustainability* **2014**, *6*, (1), 319-335.

581 66. Wang, N.; Wolf, J.; Zhang, F., Towards sustainable intensification of apple production in
582 China — Yield gaps and nutrient use efficiency in apple farming systems. *Journal of
583 Integrative Agriculture* **2016**, *15*, (4), 716-725.

584 67. Sutton, M., A.; Bleeker, A.; Howard, C. M.; Bekunda, M.; Grizzetti, B.; de Vries, W.; van
585 Grinsven, H. J., M.; Abrol, Y. P.; Adhya, T. K.; Billen, G.; Davidson, E. A.; Datta, A.; Diaz, R.;
Erisman, J. W.; Liu, X., J.; Oenema, O.; Palm, C.; Raghuram, N.; Reis, S.; Scholz, R. W.;

Sims, T.; Westhoek, H.; Zhang, F. S., *Our Nutrient World: the challenge to produce more food and energy with less pollution*. Centre for Ecology and Hydrology (CEH): 2013.

68. Liu, W.; Antonelli, M.; Kummu, M.; Zhao, X.; Wu, P.; Liu, J.; Zhuo, L.; Yang, H.,
Savings and losses of global water resources in food-related virtual water trade. *WIREs Water* **2018**, e1320.

69. Oki, T.; Kanae, S., Global hydrological cycles and world water resources. *Science* **2006**, *313*, (5790), 1068-1072.

70. Elliott, J.; Deryng, D.; Mueller, C.; Frieler, K.; Konzmann, M.; Gerten, D.; Glotter, M.;
Florke, M.; Wada, Y.; Best, N.; Eisner, S.; Fekete, B. M.; Folberth, C.; Foster, I.; Gosling, S.

N.; Haddeland, I.; Khabarov, N.; Ludwig, F.; Masaki, Y.; Olin, S.; Rosenzweig, C.; Ruane, A.

C.; Satoh, Y.; Schmid, E.; Stacke, T.; Tang, Q. H.; Wisser, D., Constraints and potentials of
future irrigation water availability on agricultural production under climate change. *Proc. Natl.
Acad. Sci. U.S.A.* **2014**, *111*, (9), 3239-3244.

71. Jagermeyr, J.; Gerten, D.; Heinke, J.; Schaphoff, S.; Kummu, M.; Lucht, W., Water
savings potentials of irrigation systems: global simulation of processes and linkages. *Hydrol.
Earth Syst. Sci.* **2015**, *19*, (7), 3073-3091.

72. Jagermeyr, J.; Gerten, D.; Schaphoff, S.; Heinke, J.; Lucht, W.; Rockstrom, J., Integrated
crop water management might sustainably halve the global food gap. *Environ. Res. Lett.* **2016**,
*11*, (2), 025002.

73. Zhang, X., BIOGEOCHEMISTRY A plan for efficient use of nitrogen fertilizers. *Nature*
607 **2017**, 543, (7645), 322-323.

608 74. Li, Z.; Xu, M.; Zhang, H.; Zhang, W.; Gao, J., Grain yield trends of different food crops under long-term fertilization in China. *Scientia Agricultura Sinica* **2009**, *42*, (7), 2407-2414.

610 75. Stewart, W. M.; Dibb, D. W.; Johnston, A. E.; Smyth, T. J., The contribution of commercial fertilizer nutrients to food production. *Agron. J.* **2005**, *97*, (1), 1-6.

612 76. Elliott, J.; Müller, C.; Deryng, D.; Chryssanthacopoulos, J.; Boote, K.; Büchner, M.; Foster, I.; Glotter, M.; Heinke, J.; Iizumi, T., The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1. 0). *Geosci. Model Dev.* **2015**, *8*, (2), 261-277.

616 77. Sinha, E.; Michalak, A. M.; Balaji, V., Eutrophication will increase during the 21st century as a result of precipitation changes. *Science* **2017**, *357*, (6349), 405-408.

618 78. Pugh, T. A.; Muller, C.; Elliott, J.; Deryng, D.; Folberth, C.; Olin, S.; Schmid, E.; Arneth, A., Climate analogues suggest limited potential for intensification of production on current croplands under climate change. *Nat. Commun.* **2016**, *7*, 12608.

621 79. Ray, D. K.; Gerber, J. S.; MacDonald, G. K.; West, P. C., Climate variation explains a third of global crop yield variability. *Nat. Commun.* **2015**, *6*, 5989.
<figure><img src="image_3.png" /><figcaption>Figure 1. Differences in crops yields [t per ha] between N<sub>25</sub> and baseline (a, c, e), as well as between N<sub>50</sub>Irr<sub>30</sub> and baseline (b, d, f). Note: eight continent-climate regions in panel b are used to detail the responses of yields and nitrogen emissions to nitrogen inputs under irrigated and rainfed cultivations. Maps of different food production units were obtained from Kummu et al.<sup>44</sup> AF: Africa, AS: Asia, EU: Europe, NA: North America, OC: Oceania, SA: South America.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2. Differences in nitrogen (N) emissions [kg N per ha] between N<sub>25</sub> and baseline (a, c, e), as well as between N<sub>50</sub>Irr<sub>30</sub> and baseline (b, d, f). Maps of different food production units were obtained from Kummu et al.<sup>44</sup></figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3. Relationships between nitrogen (N) use efficiency (NUE) of yield gains [kg grain per kg N] and N emission intensity of new inputs [-] under different intensification scenarios at the food production units (FPUs) level. Ymax is estimated yields by PEPIC under the $N_{max}Irr_{max}$ scenario. The FPUs with the smallest areas (for a total of 2% of global total cropland areas of each crop) are not shown. Size represents cropland area for each FPU. Colors represent the fractions to which Ymax have been achieved. For a given intensification scenario, the colors show the situation of its previous scenario with an intensification order of baseline, $N_{25}$, $N_{25}Irr_{10}$, $N_{50}Irr_{30}$ and $N_{max}Irr_{max}$ scenarios. That is to say, for instance, colors in $N_{25}$ give the achieved Ymax fractions of the baseline scenario. Equations represent the linear relationship between NUE of yield gains and N emission intensity of new inputs (points with negative N emission intensity of new inputs are not included for regression analysis). $R^2$ is the coefficient of determination of equation.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>(c) rice w/o 80% Ymax achieved</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(d) rice w/ 80% Ymax achieved</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(e) wheat w/o 80% Ymax achieved</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(f) wheat w/ 80% Ymax achieved</figcaption></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

Figure 4. Percentage increases in global average crop yields and nitrogen (N) emissions under different intensification scenarios without (w/o) considering 80% of maximum yields (Ymax) achieved (a, c, e) and with (w/) considering 80% of Ymax achieved (b, d, f). Considering 80% of Ymax achieved means that the food production units (FPUs) with 80% of Ymax achieved under a given intensification scenario adopt the first scenario of baseline, N<sub>25</sub>, N<sub>25</sub>Irr<sub>10</sub> and N<sub>50</sub>Irr<sub>30</sub>, which has already achieved 80% of Ymax. Points in each curve are derived by intensifying FPUs one-by-one with an ascending order of N emission intensity of new inputs and then a descending order of N use efficiency of yield gains on the basis of Fig. 3. For better visualization, every 10 points between the first and last points are plotted for each curve.
Note: lines for N<sub>max</sub>Irr<sub>max</sub> are not always highest because different sorting sequences are used for different scenarios.
<figure><img src="image_12.png" /><figcaption>(a) AF Arid</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(b) EU Temperate</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(i) AF Arid</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(j) EU Temperate</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>(c) OC Arid</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(d) SA Tropical</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(k) OC Arid</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(l) SA Tropical</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(e) AS Arid</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(f) AS Cold</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(m) AS Arid</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(n) AS Cold</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>(g) NA Arid-Cold</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>(h) NA Temperate</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>(o) NA Arid-Cold</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(p) NA Temperate</figcaption></figure>

Figure 5. Response curves of crop yields (a-h) and nitrogen (N) emissions (i-p) to N inputs (N<sub>in</sub>) under irrigated (IR, solid lines) and rainfed (RF, dashed lines) cultivations in different continental-climate regions. Irr<sub>m</sub>: irrigation water requirements of maize; Irr<sub>r</sub>: irrigation water requirements of rice; Irr<sub>w</sub>: irrigation water requirements of wheat. A map of the continental-climate regions can be found in Fig. 1b. AF: Africa, AS: Asia, EU: Europe, NA: North America, OC: Oceania, SA: South America. The response curves are derived from simulations with automatic N fertilization by setting different N<sub>in</sub> caps: 25, 50, ... 400, 450, 500, and 1000 kg N ha<sup>-1</sup> yr<sup>-1</sup>. Actual N<sub>in</sub> is lower than N<sub>in</sub> cap when a high-level cap is used. Rainfed cultivation has lower maximum N<sub>in</sub> than irrigated cultivation.
<table><caption>Table 1. Description of intensification scenarios in terms of increasing nitrogen (N) inputs and irrigation (Irr) areas.</caption><thead><tr><th>Scenario</th><th>N inputs</th><th>Irrigation areas</th></tr></thead><tbody><tr><td>baseline</td><td>N<sub>in-base</sub></td><td>A<sub>ir</sub></td></tr><tr><td>N<sub>25</sub></td><td>N<sub>in-base</sub> + 0.25×ΔN<sub>in-max</sub></td><td>A<sub>ir</sub></td></tr><tr><td>N<sub>25</sub>Irr<sub>10</sub></td><td>N<sub>in-base</sub> + 0.25×ΔN<sub>in-max</sub></td><td>A<sub>ir</sub> + 0.10×A<sub>rf</sub></td></tr><tr><td>N<sub>50</sub>Irr<sub>30</sub></td><td>N<sub>in-base</sub> + 0.50×ΔN<sub>in-max</sub></td><td>A<sub>ir</sub> + 0.30×A<sub>rf</sub></td></tr><tr><td>N<sub>max</sub>Irr<sub>max</sub></td><td>N<sub>in-max</sub></td><td>Full irrigation</td></tr></tbody></table>

$N_{in-base} = \min(N_{in-a}, N_{in-auto})$; $N_{in-a}$: actual N inputs based on the EarthStat dataset; $N_{in-auto}$: optimal N inputs derived from PEPIC simulations considering baseline irrigation land condition based on the MIRCA2000 dataset; $N_{in-max}$: optimal N inputs derived from PEPIC simulations considering croplands with full irrigation; $\Delta N_{in-max} = N_{in-max} - N_{in-base}$; $A_{ir}$ and $A_{rf}$: baseline areas for irrigation and rainfed cultivations.

<table><caption>Table 2. Global average irrigation water (Irr) [mm], nitrogen inputs (N<sub>in</sub>) [kg N per ha], crop yields (Y) [t per ha], and nitrogen emissions (N<sub>e</sub>) [kg N per ha] under baseline and different intensification scenarios by intensifying all croplands.</caption><thead><tr><th rowspan="2"></th><th colspan="4">maize</th><th colspan="4">rice</th><th colspan="4">wheat</th></tr><tr><th>Irr</th><th>N<sub>in</sub></th><th>Y</th><th>N<sub>e</sub></th><th>Irr</th><th>N<sub>in</sub></th><th>Y</th><th>N<sub>e</sub></th><th>Irr</th><th>N<sub>in</sub></th><th>Y</th><th>N<sub>e</sub></th></tr></thead><tbody><tr><td>baseline</td><td>50.2</td><td>93.2</td><td>5.5</td><td>68.3</td><td>41.3</td><td>86.8</td><td>4.5</td><td>74.2</td><td>44.7</td><td>70.7</td><td>2.7</td><td>43.4</td></tr><tr><td>N<sub>25</sub></td><td>50.2</td><td>111.0</td><td>6.0</td><td>74.7</td><td>41.3</td><td>97.5</td><td>4.8</td><td>79.5</td><td>44.7</td><td>92.1</td><td>3.1</td><td>48.5</td></tr><tr><td>N<sub>25</sub>Irr<sub>10</sub></td><td>62.0</td><td>111.5</td><td>6.1</td><td>73.9</td><td>42.1</td><td>97.5</td><td>4.8</td><td>79.3</td><td>55.4</td><td>92.3</td><td>3.2</td><td>48.0</td></tr><tr><td>N<sub>50</sub>Irr<sub>30</sub></td><td>86.8</td><td>132.8</td><td>6.8</td><td>81.0</td><td>43.9</td><td>108.9</td><td>5.1</td><td>85.4</td><td>78.0</td><td>114.2</td><td>3.6</td><td>52.7</td></tr><tr><td>N<sub>max</sub>Irr<sub>max</sub></td><td>173.0</td><td>193.0</td><td>8.2</td><td>108.6</td><td>50.1</td><td>136.0</td><td>5.4</td><td>102.9</td><td>156.7</td><td>168.2</td><td>4.6</td><td>71.1</td></tr></tbody></table>

<table><caption>Table 3. Global average irrigation water (Irr) [mm], nitrogen inputs (N<sub>in</sub>) [kg N per ha], crop yields (Y) [t per ha], and nitrogen emissions (N<sub>e</sub>) [kg N per ha] under different intensification scenarios by avoiding further intensifying croplands with 80% of maximum Y achieved.</caption><thead><tr><th rowspan="2"></th><th colspan="4">maize</th><th colspan="4">rice</th><th colspan="4">wheat</th></tr><tr><th>Irr</th><th>N<sub>in</sub></th><th>Y</th><th>N<sub>e</sub></th><th>Irr</th><th>N<sub>in</sub></th><th>Y</th><th>N<sub>e</sub></th><th>Irr</th><th>N<sub>in</sub></th><th>Y</th><th>N<sub>e</sub></th></tr></thead><tbody><tr><td>N<sub>25</sub></td><td>50.2</td><td>110.6</td><td>6.0</td><td>74.5</td><td>41.3</td><td>94.4</td><td>4.7</td><td>77.9</td><td>44.7</td><td>91.7</td><td>3.1</td><td>48.3</td></tr><tr><td>N<sub>25</sub>Irr<sub>10</sub></td><td>61.5</td><td>111.1</td><td>6.1</td><td>73.9</td><td>41.8</td><td>94.4</td><td>4.7</td><td>77.9</td><td>54.9</td><td>91.8</td><td>3.2</td><td>48.0</td></tr><tr><td>N<sub>50</sub>Irr<sub>30</sub></td><td>79.7</td><td>129.0</td><td>6.7</td><td>80.0</td><td>42.7</td><td>100.2</td><td>4.9</td><td>81.1</td><td>75.2</td><td>112.0</td><td>3.6</td><td>52.7</td></tr><tr><td>N<sub>max</sub>Irr<sub>max</sub></td><td>117.6</td><td>150.7</td><td>7.3</td><td>88.4</td><td>44.8</td><td>106.8</td><td>5.0</td><td>84.9</td><td>131.0</td><td>146.5</td><td>4.3</td><td>61.5</td></tr></tbody></table>