# Evidence for increasing global wheat yield potential

Jose Rafael Guarin, Pierre Martre, Frank Ewert, Heidi Webber, Sibylle Dueri, Daniel Calderini, Matthew Reynolds, Gemma Molero, Daniel Miralles, Guillermo Garcia, et al.

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

## To cite this version:

Jose Rafael Guarin, Pierre Martre, Frank Ewert, Heidi Webber, Sibylle Dueri, et al. Evidence for increasing global wheat yield potential. Environmental Research Letters, 2022, 17 (12), pp.124045. 10.1088/1748-9326/aca77c hal-03946522

HAL Id: hal-03946522

https://hal.inrae.fr/hal-03946522

Submitted on 19 Jan 2023

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_2.png" /></figure>
LETTER • OPEN ACCESS

# Evidence for increasing global wheat yield potential

To cite this article: Jose Rafael Guarín *et al* 2022 *Environ. Res. Lett.* **17** 124045

View the [article online](#) for updates and enhancements.

You may also like

- *LOCALIZATION AND BROADBAND FOLLOW-UP OF THE GRAVITATIONAL-WAVE TRANSIENT GW150914*
B. P. Abbott, R. Abbott, T. D. Abbott et al.

- *A new calibration method for charm jet identification validated with proton-proton collision events at s = 13 TeV*
The CMS collaboration, Armen Tumasyan, Wolfgang Adam et al.
ENVIRONMENTAL RESEARCH
LETTERS

<figure><img src="image_3.png" /></figure>

CrossMark

OPEN ACCESS

RECEIVED
13 June 2022

REVISED
10 October 2022

ACCEPTED FOR PUBLICATION
30 November 2022

PUBLISHED
12 December 2022

Original content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.

Any further distribution
of this work must
maintain attribution to
the author(s) and the title
of the work, journal
citation and DOI.

<figure><img src="image_4.png" /></figure>

LETTER

Evidence for increasing global wheat yield potential

Jose Rafael Guarin<sup>1,2,3,*</sup> (ID), Pierre Martre<sup>4</sup>, Frank Ewert<sup>5,6</sup>, Heidi Webber<sup>5,6</sup>, Sibylle Dueri<sup>4</sup>, Daniel Calderini<sup>7</sup>, Matthew Reynolds<sup>8</sup>, Gemma Molero<sup>9</sup>, Daniel Miralles<sup>10</sup>, Guillermo Garcia<sup>10</sup>, Gustavo Slafer<sup>11</sup>, Francesco Giunta<sup>12</sup>, Diego N L Pequeno<sup>8</sup>, Tommaso Stella<sup>5,6</sup>, Mukhtar Ahmed<sup>13,14 ID</sup>, Phillip D Alderman<sup>15</sup>, Bruno Basso<sup>16,17 ID</sup>, Andres G Berger<sup>18</sup>, Marco Bindi<sup>19</sup>, Gennady Bracho-Mujica<sup>20</sup>, Davide Cammarano<sup>21</sup>, Yi Chen<sup>22</sup>, Benjamin Dumont<sup>23</sup>, Ehsan Eyshi Rezaei<sup>6 ID</sup>, Elias Fereres<sup>24</sup>, Roberto Ferrise<sup>19</sup>, Thomas Gaiser<sup>5 ID</sup>, Yujing Gao<sup>1</sup>, Margarita Garcia-Vila<sup>24 ID</sup>, Sebastian Gayler<sup>25 ID</sup>, Zvi Hochman<sup>26 ID</sup>, Gerrit Hoogenboom<sup>1,27</sup>, Leslie A Hunt<sup>28</sup>, Kurt C Kersebaum<sup>6,20,29</sup>, Claas Nendel<sup>6,29,30 ID</sup>, Jørgen E Olesen<sup>31</sup>, Taru Palosuo<sup>32</sup>, Eckart Priesack<sup>33</sup>, Johannes W M Pullens<sup>31</sup>, Alfredo Rodríguez<sup>34,35</sup>, Reimund P Rötter<sup>20,36</sup>, Margarita Ruiz Ramos<sup>34</sup>, Mikhail A Semenov<sup>37</sup>, Nimai Senapati<sup>37 ID</sup>, Stefan Siebert<sup>38 ID</sup>, Amit Kumar Srivastava<sup>5</sup>, Claudio Stöckle<sup>39</sup>, Iwan Supit<sup>40</sup>, Fulu Tao<sup>22,32</sup>, Peter Thorburn<sup>26</sup>, Enli Wang<sup>41</sup>, Tobias Karl David Weber<sup>25,42</sup>, Liujun Xiao<sup>43,44</sup>, Zhao Zhang<sup>45 ID</sup>, Chuang Zhao<sup>46 ID</sup>, Jin Zhao<sup>46,31</sup>, Zhigan Zhao<sup>41,47</sup>, Yan Zhu<sup>44</sup> and Senthold Asseng<sup>48</sup>

1 Former affiliation: Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, United States of America

2 Current affiliation: Center for Climate Systems Research, Columbia University, New York, NY, United States of America

3 Current affiliation: NASA Goddard Institute for Space Studies, New York, NY, United States of America

4 LEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier SupAgro, Montpellier, France

5 Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany

6 Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany

7 Institute of Plant Production and Protection, Austral University of Chile, Valdivia, Chile

8 International Maize and Wheat Improvement Center (CIMMYT), Mexico DF, Mexico

9 KWS, Lille, France

10 Department of Plant Production, University of Buenos Aires, IFEVA-CONICET, Buenos Aires, Argentina

11 Department of Crop and Forest Sciences, University of Lleida—AGROTECNIO-CERCA Center, Lleida, Spain; and ICREA, Catalonian Institution for Research and Advanced Studies, Barcelona, Spain

12 Department of Agricultural Sciences, University of Sassari, Sassari, Italy

13 Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

14 Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden

15 Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, United States of America

16 Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, United States of America

17 W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI, United States of America

18 National Institute of Agricultural Research (INIA), Colonia, Uruguay

19 Department of AGRICulture, food, environment and forestry (DAGRI), Department of Agri-food Production and Environmental Sciences (DISPAA), University of Florence, Florence, Italy

20 Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany

21 Department of Agronomy, Purdue University, West Lafayette, IN, United States of America

22 Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, People's Republic of China

23 Department Terra & AgroBioChem, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium

24 IAS-CSIC DAUCO, University of Cordoba, Cordoba, Spain

25 Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany

26 CSIRO Agriculture and Food, Brisbane, Queensland, Australia

27 Institute for Sustainable Food Systems, University of Florida, Gainesville, FL, United States of America

28 Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada

29 Global Change Research Institute Academy of Sciences of the Czech Republic, Brno, Czech Republic

30 Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

31 Department of Agroecology, Aarhus University, Tjele, Denmark

32 Natural Resources Institute Finland (Luke), Helsinki, Finland

33 Institute of Biochemical Plant Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany

34 CEIGRAM, Technic University of Madrid, Madrid, Spain

35 Department of Economic Analysis and Finances, University of Castilla-La Mancha, Toledo, Spain

36 Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany

37 Rothamsted Research, Harpenden AL5 2JQ, United Kingdom

38 Department of Crop Sciences, University of Göttingen, Göttingen, Germany

39 Biological Systems Engineering, Washington State University, Pullman, WA, United States of America
40 Water & Food and Water Systems & Global Change Group, Wageningen University, Wageningen, The Netherlands

41 CSIRO Agriculture and Food, Canberra, Australian Capital Territory, Australia

42 Current affiliation: Department of Soil Science, Faculty of Organic Soil Sciences, University of Kassel, Kassel, Germany

43 College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China

44 National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, People's Republic of China

45 State Key Laboratory for Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, People's Republic of China

46 College of Resources and Environmental Sciences, China Agricultural University, Beijing, People's Republic of China

47 Department of Agronomy and Biotechnology, China Agricultural University, Beijing, People's Republic of China

48 Department of Life Science Engineering, Digital Agriculture, Technical University of Munich, Freising, Germany

* Author to whom any correspondence should be addressed.

E-mail: j.guarin@columbia.edu

**Keywords:** yield increase, radiation use efficiency, wheat potential yield, crop model ensemble, global food security

Supplementary material for this article is available [online](#)

## Abstract

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

## 1. Introduction

The global demand for food is continuously increasing and agricultural production must follow to ensure future global food security [1–5]. Wheat (*Triticum aestivum* L.) is one of the most important crops contributing to global food security, providing approximately 20% of calories and protein in the human diet [6]. Although global wheat production has continued to increase over recent decades [7], the rate of yield improvement has stagnated or declined in certain regional wheat-producing areas (e.g. Eastern Europe, Central India, and Western Australia) [8] and projected yield gains fall below the predicted future grain demand [3, 9, 10]. Grain production increases between 35%–56% (30%–62% when accounting for climate change) are needed to meet the projected global food demand by 2050 (relative to 2010 food demand) [11], while endeavoring to improve environmental sustainability and land preservation [2, 12]. Breeding new cultivars to further increase yield potential in high-yielding environments [13] is one approach to address the food security challenge [14], with the advantage that yields in poorer growing conditions could still increase [6, 15]. Investment in yield improvements and intensification can also mitigate greenhouse gas emissions [16]. Improving

physiological traits that have a profound influence on the growth and yield of wheat, such as radiation use efficiency (RUE, biomass accumulation per unit of radiation intercepted by crop canopies), is of paramount importance to meeting the future grain demand [17, 18]. Estimates from the literature show that the RUE of current wheat could be increased by up to 50% under favorable crop-growing conditions [6, 19]. Doubled haploid (DH) crosses with modified traits such as RUE, light extinction coefficient, fruiting efficiency, grain filling duration, and grain size can increase yield potential compared to conventional wheat cultivars [15, 20, 21].

Global wheat yield gap analyses examine the difference between the potential yield (yield of well-adapted wheat cultivars grown with sufficient water and nutrients and without abiotic and biotic stress) and the average actual yield reported by farmers under conventional management practices [22]. Quantifying the yield potential provides a benchmark for future crop production and a guide for the sustainable intensification of agriculture. However, global yield potential analyses have not yet considered the improved genetic yield potential from advancements in physiological traits which would quantify the extent of potential genetic improvements. Expanding strategic genetic crosses
across different target environments can result in sig-
nificant yield gains [23], but crop management must
be adjusted accordingly so that other resources do
not become limiting, e.g. N fertilization [24].

Our aim was to explore the upper limits of cur-
rent wheat genetic yield potential based on field
measurements of selectively bred high-yielding wheat
cultivars including the highest wheat yield repor-
ted in the literature, 16.6 t ha⁻¹ dry weight for a
high-yielding DH line [15, 20]. From these data, we
first evaluated if a multi-model ensemble of state-
of-the-art wheat crop models, which has consist-
ently shown higher accuracy than any individual crop
model [25–27], could reproduce the observed growth
from improved physiological traits, and then we sim-
ulated the potential yield of improved wheat under
prevailing temperatures at 34 global locations. We
also determined a simple quantitative relationship
between wheat production and cumulative incom-
ing solar radiation implicitly involving both radiation
interception and RUE to estimate the current max-
imum potential wheat yield based on incoming solar
radiation (see section 4). This relationship is hence-
forth referred to as the efficiency of incoming radi-
ation capture and use (ERCU). Both the multi-model
ensemble simulations with improved traits and the
upper limit of the ERCU relationship were extrapol-
ated to the global scale based on current wheat grow-
ing areas to estimate global potential wheat produc-
tion and yield according to local radiation levels.

## 2. Results and discussion

**2.1. Crop model ensemble evaluation**

Crop simulation models are robust computational
tools that can simulate wheat growth, including
potential yields of an area, by representing physiolo-
gical traits mathematically within the models. Crop
models incorporate the complexity of crop–soil–
atmosphere interactions, the dynamics of phenology
and growth (through accumulated thermal time), the
dynamics of light interception, and the interactions of
photosynthesis and carbohydrate distribution within
a plant, at daily intervals while accounting for the
spatial and temporal variability of different growth
environments [28]. Models can therefore be adapted
to simulate the growth and production of new wheat
cultivars by altering the components to represent the
improved physiological traits. Since existing wheat
crop models have never before incorporated the trait
advancements reported from the DH wheat field
experiments (table 1), model testing was necessary
to evaluate whether a wheat crop model ensemble
could reproduce the production of the high-yielding
traits of the DH line. Because crop model simulations
contain implicit uncertainties from model input,
structure, and parameters [29], the state-of-the-art
approach in crop model applications is through
multi-model ensembles which have been consistently

shown in recent years to have a significant improved
accuracy in projection compared to any single model
applications [27]. Twenty-nine models using mod-
ified cultivar traits were calibrated and independ-
ently validated using data from field experiments
under non-stress conditions (i.e. irrigated and fer-
tilated plots without biotic stress) at Buenos Aires
(Angentina), Ciudad Obregon (Mexico), and Valdivia
(Chile) observed by Bustos et al [15] and Garcia et al
[20]. The calibrated multi-model ensemble median of
wheat crop models efficiently reproduced the highest
observed yield and biomass ever reported (r² = 0.91
and 0.66; RMSE = 177 g m⁻² and 499 g m⁻², respect-
ively) from the measured yield-enhancing physiolo-
gical traits, particularly increased RUE, in the three
different environments (figures 1 and S1–S3). The
observed yield response of the best-yielding DH lines
at the sites in Argentina and Chile increased by 38%
and 32%, respectively, compared to the check cultivar
Bacanora, due to the enhanced traits, with the highest
yields being recorded at the Chile site. Indeed, the
models were able to reproduce most of the observed
maximum yields at the Chile site (figure 1(f)), but
were less accurate in simulating some of the max-
imum total biomass observations (figure 1(e)). The
observed yield increases at the Argentina and Chile
sites were higher than those at the warmer Mexico site
(21%). The multi-model ensemble simulations over-
estimated biomass for one year at Mexico (figure 1(e),
treatment CO 2009), which may have been influenced
by assessing biomass from observed yield compom-
ents and/or by discrepancies related to the edge effects
from gap areas (spacing) within the raised-bed fields
at that site [30, 31], unlike the more common homo-
genous fields simulated in the models.

**2.2. Efficiency of incoming radiation capture and use (ERCU)**

To determine the ERCU, we considered how much
wheat dry mass accumulates in relation to the entire
resource of incident light during the growing sea-
son, i.e. cumulative incoming solar radiation from
sowing to physiological maturity (figure 2), which is
reminiscent of the widely used wheat yield to water
use relationship described by French and Schultz
[32]. Based on the observed biomass, we determ-
ined that the upper limit of potential above-ground
biomass is 1.57 g m⁻² of additional dry matter for
each additional MJ m⁻² of incoming solar radiation
(figure 2). When combined with the 0.45 harvest
index of the highest yielding DH line, 0.71 g m⁻²
grain yield per MJ m⁻² of incoming solar radiation is
conceivable (figure 3). Wheat production below the
ERCU potential threshold is limited by unimproved
physiological traits such as RUE and canopy archi-
tecture (affecting the light extinction coefficient), the
proportion of diffuse radiation, biomass allocation,
and/or stress factors such as nutrient deficiencies,
weeds, pests, and diseases. For example, improved
<figure><img src="image_5.png" /></figure>

**Figure 1.** Crop model performance and evaluation of wheat production from high-yielding traits. Box plots of simulated (a), (d) duration from sowing to maturity, (b), (e) final above-ground biomass, and (c), (f) yield of 29 wheat crop models for (a), (b), (c) ten treatments used for model calibration with cv. Bacanora, and for (d), (e), (f) five treatments used for model calibration with cv. Bacanora (white boxes) and five treatments used for model evaluation using the best-yielding DH line (gray boxes) at Valdivia, Chile (VA), Buenos Aires, Argentina (BA), and Ciudad Obregon, Mexico (CO). Field experiment data for the cv. Bacanora at Valdivia, Chile in 2010 (VA 2010) was not available, and therefore excluded from the model calibration which may explain the underestimation of the DH line biomass in 2010 (e). Box plots show the 90th percentile, 75th percentile, median, 25th percentile, and 10th percentile (horizontal lines from top to bottom) and outliers (open circles) of simulations based on the multi-model ensemble. RMSE (units same as y-axis) and $r^2$ (coefficient of determination) are shown for the multi-model ensemble median. For the evaluation (d), (e), (f), the five calibrated treatments with cv. Bacanora are shown for comparison only and were not used in the statistical analysis. Biomass and yield values shown are with 0% moisture content.

light interception by the DH line later in the season in 2010 at Valdivia has a strong positive effect on the observed above-ground biomass compared to Bacanora in 2012 (figure 2). Potential above-ground biomass production during early crop development can only be suggested (dashed line in figure 2) because of limited early season biomass data and the difficulty in estimating early leaf expansion and light interception. We estimate that 516 MJ m⁻² of cumulative incoming solar radiation is wasted when crop dry matter fails to accumulate because there is insufficient leaf area to maximize the interception of incident radiation. Therefore, a fast and early establishment of the crop, where ‘early vigor’ traits combine with favorable growing conditions, is important for a high ERCU. As a rule of thumb, the potential yield based on the ERCU with improved physiological traits can be derived as 0.7 g per MJ of incoming solar radiation after subtracting 365 MJ m⁻² from the cumulative incoming solar radiation of a cropping season for any location across the globe (figure 3).

Location and time of sowing have a strong influence on crop growth and the ERCU because the potential to intercept light depends on plant

architecture, which changes with the developmental stage (driven by thermal time), temperature, water availability, soil constraints, and other factors. To examine and account for these interactions, we used a multi-model ensemble median with and without the modified traits (table 1) to simulate the potential yields at 34 high-rainfall or irrigated locations around the globe from 1980 to 2010 (figure S4). Average yield increased by 37 ± 20% across all 34 locations from the baseline simulated yield of check cultivars to the simulated yield with modified traits (figure 3, compare solid to open diamonds). When comparing the ERCU potential yield encompassing the modified traits and a 0.45 harvest index to the multi-model ensemble baseline simulated yield, a 106 ± 43% average yield increase across all locations was observed (figure 3, compare black potential line with open diamonds). The ERCU relationship provides a quick and simple potential genetic yield estimation, similar to the application of the French and Schultz [32] wheat yield to water use relationship, but is only applicable for growth under non-stress conditions.

The high ERCU, biomass, and yields observed at the Chile site were driven by a combination of
<figure><img src="image_6.png" /></figure>

**Figure 2.** Relationship between wheat biomass potential and cumulative incoming solar radiation. Observed cumulative total above-ground biomass was plotted against observed cumulative incoming solar radiation of cv. Bacanora (open symbols) and the DH line (solid symbols) from sowing to maturity for different growing seasons (corresponding years are the same color) at Valdivia, Chile (VA, circles), Buenos Aires, Argentina (BA, squares), and Ciudad Obregon, Mexico (CO, triangles). Solid colored lines indicate cumulative growth. Solid black line indicates the upper threshold of the production potential of cumulative incoming solar radiation calculated using the best pre- and post-anthesis crop response, i.e. average of the Valdivia 2012 cv. Bacanora first two measurements and average of the high-yielding Valdivia 2010 DH line final two measurements, respectively. The slope of the line indicates the Efficiency of incoming Radiation Capture and Use (ERCU, g MJ⁻¹). Dashed black line indicates the suggested production potential for early crop development. Biomass of three older wheat cultivars from Yunusa *et al* [58] are included to show how the ERCU has been improved in newer wheat cultivars. Error bars indicate the standard deviation of replications for available measured data. All biomass values are with 0% moisture content.

the improved RUE of the DH line (table 1) and the highest incoming solar radiation per day throughout the growing season of the locations studied under yield potential conditions (figure S5). Notably, the highest DH yield resulted from a below-average harvest index of 0.45 (for treatment VA 2010) while the observed average harvest index was 0.49 across the Argentina, Chile, and Mexico sites. The yield gap between the ERCU potential yield and the simulated yields increases at locations with higher cumulative incoming solar radiation due to longer growing seasons, slower accumulation of thermal time, and less incoming solar radiation per day (figure S5).

## 2.3. Potential global wheat production

To determine the upper limit of global wheat production, the multi-model ensemble simulations at the 34 high-rainfall or irrigated locations were extrapolated to the global scale using the yield gap between the baseline simulations without modified traits and the simulations with modified traits (figure 3). This upscaling resulted in a potential global wheat production and yield gain of 280 ± 145 Mt yr⁻¹ and 1.3 ± 0.7 t ha⁻¹ yr⁻¹, respectively. This is a 37% increase from the 2020 global wheat production and yield [7] without expanding the current wheat

cropping area, assuming high-rainfall or irrigated field conditions (figure 4). This simulated yield increase agrees well with the observed yield response from the modified DH traits at the Argentina and Chile sites as expected. When extrapolating the yield gap between the multi-model ensemble baseline simulations and the ERCU upper limit to the global scale, the increase from the 2020 global wheat production and yield is 108%. The simulated multi-model ensemble potential yield increase is lower than the ERCU potential yield increase because the model simulations account for the changes in the light interception from the crop developmental stage, water availability, temperature, soil constraints, and other limiting factors. Breeding wheat cultivars with improved physiological traits such as RUE is a costly, time-consuming, and arduous process [6], but these global production estimates show that current wheat production has the potential to meet the necessary yield gains for future grain demand [11] through improved breeding strategies and without the need for additional cropping area.

It is important to note that the potential productivity thresholds assume that optimization of each trait is possible and that there are no trade-offs, but this may not be applicable everywhere [33, 34].
<figure><img src="image_7.png" /></figure>

**Figure 3.** Relationship between wheat yield potential and cumulative incoming solar radiation. Observed yield was plotted against observed cumulative incoming solar radiation for cv. Bacanora (open symbols) and the DH line (solid symbols) in different growing seasons (corresponding years are the same color) at Valdivia, Chile (VA, circles), Buenos Aires, Argentina (BA, squares), and Ciudad Obregon, Mexico (CO, triangles). Solid black line indicates the upper threshold of yield potential of cumulative incoming solar radiation calculated using the biomass potential limit (see figure 2 and section 4) with the harvest index of the highest yielding DH line, 0.45. Dashed black line indicates suggested yield potential during early crop development. Simulated yields are of multi-model ensemble median of 26 wheat crop models with (solid diamonds) and without (open diamonds) modified cultivar traits (table 1) from 1980–2010 for 34 high-rainfall or irrigated AgMIP locations chosen from around the globe. Detailed incoming solar radiation at each location is shown in figure S5. Error bars indicate the 75th and 25th percentiles of the cumulative incoming solar radiation and yields simulated by the multi-model ensemble. All yields shown are with 0% moisture content.

There was no correlation ($r^2$ = 0.02) between the observed RUE and harvest index of the DH lines, which suggests that it may be possible to improve both RUE (source) and harvest index (sink) simultaneously (figure S6). Multi-location breeding trials have shown that strategic source-sink crosses can result in significant yield gains across different target environments [23]. Temperature is a key factor affecting the ERCU because the relationship of temperature with solar radiation is a linear determinant of yield potential [35]. We consider the current temperature conditions in each region when applying the ERCU, but this could be further impacted by climate change in the future [36–38]. Climate change may increase genotype-environment interactions which could create challenges in realizing the same level of genetic gains from adapted cultivars in new environments [39]. Additionally, wheat productivity could be affected by fluctuations in incoming solar radiation between sun and shade [40] or source-sink imbalances [41], and there are still risks of suboptimal yields due to year-to-year variability resulting from extreme climate events and pests and disease [42]. Here, we do not address the economic constraints [43] of the material inputs

(irrigation, fertilizer, biocides) required to maintain and protect higher production levels. For example, the higher-yielding crops may not require more water as was found with the energy balance calculation for maize yields [44], but additional research is needed to determine if this is also applicable to C3 crops. The higher-yielding crops may require more fertilizer [45] or more efficient fertilizer use, which would likely increase N₂O emissions unless further advancements in synthetic N production are established, or advancements in crop N utilization efficiency are achieved as observed in Australia [46]. However, yield potential and N response have been shown to be independent of one another [47, 48], so higher yields with lower N fertilizer rates may be achievable [49, 50]. The nutritional content from large yields should be examined as grain quality may decrease with high yields and under climate change [37].

The simulated potential global production increase of 37% is a feasible scenario, as similar production increases have already been achieved in Chile and Argentina, although high-yielding crops may require higher inputs to support them [36, 45]. To prevent overestimation of the global potential yield in water-limiting areas, we assumed that the current
a

<figure><img src="image_8.png" /></figure>

b

<figure><img src="image_9.png" /><figcaption>**Figure 4.** Potential global yield and production under different high-yielding scenarios. Bar plots show global wheat (a) average yield and (b) production based on the latest reported statistics for 2020 [7] and the results of simulations aggregated from a multi-model ensemble median of 26 wheat crop models for 34 high-rainfall or irrigated AgMIP locations around the globe using modified cultivar traits (table 1), and the potential based on yield gaps determined from the efficiency of incoming radiation capture and use (ERCU) relationship (see figure 3). Total global production is the sum of global low-rainfall production and global high-rainfall or irrigated production. Global yields were calculated by dividing the total global production (including the unimproved yields in all low-rainfall regions, approximately 30% of the global wheat area [26, 51]) by the total global wheat area harvested [7]. Error bars for the simulated production with modified traits scenario indicate standard deviation from annual and model variation. Error bars for ERCU potential production with modified traits scenario indicate standard deviation from yield gaps between the ERCU upper limit and baseline simulated scenario. All production and yields shown are with a commercial moisture content of 13% [7].</figcaption></figure>

production from low-rainfall areas (30% of the total 2020 global production [37, 51]) for all scenarios will not change because low-rainfall production is unlikely to benefit from the improved trait scenarios [41]. However, this is a risk-averse assumption, and it may be possible that low-rainfall areas could partially benefit from the modified trait scenarios which would further increase the potential production. It is also possible that breeding advancements increase the average wheat harvest index of 0.45 in the future [52, 53]. A harvest index of 0.61 has already been

observed in modern elite winter wheat varieties [52], and the DH lines used in this study had an average harvest index of 0.49 across the three locations. While these genetic yield gains are promising, the global implementation of genetically improved cultivars is a laborious process. Providing a new commercially available wheat cultivar takes approximately 8–12 years [54], but an improved exchange of big data between breeding programs can accelerate the production of DH lines [55]. The combination of these modified traits and potential increases in
harvest index are possible (figure S6), but this would
still require multiple breeding cycles before cultivar
release.

In regard to the simplified ERCU relationship,
there is some uncertainty remaining in relating wheat
productivity to incoming solar radiation because
only a portion of the incident radiation is intercep-
ted for photosynthesis depending on the crop can-
opy structure and irradiance received by leaves [56].
However, intercepted radiation can be approximated
from incoming solar radiation because about 50%
of incoming solar radiation is photosynthetic active
radiation (PAR), and roughly 85%-90% of PAR is
intercepted in the period when most biomass accu-
mulation occurs, i.e. when leaf area index is above
three for wheat, depending on canopy structure and
angle of leaves [17, 57]. Studies using the high-
yielding DH line in Argentina, Chile, and Mexico
estimated the RUE for total above-ground biomass
as ~2.1 g MJ⁻¹ intercepted solar radiation [15, 20],
which is well above the average of 1.4 g MJ⁻¹ inter-
cepted solar radiation observed elsewhere for wheat
[17, 58, 59]. The simulated cumulative intercepted
solar radiation of the multi-model ensemble median
(calculated by doubling the simulated cumulative
intercepted PAR) suggests 66% and 71% of the cumu-
lative incoming solar radiation was intercepted across
the 34 locations in the simulations without and with
modified traits, respectively. Assuming 71% of the
incoming solar radiation was intercepted, the RUE
becomes 2.2 g MJ⁻¹ intercepted solar radiation, sim-
ilar to the previous RUE using the high-yielding
DH line [15]. Caution is advised when approxim-
ating intercepted solar radiation from the ERCU
relationship because of seasonal variation in incom-
ing solar radiation and the uncertainty in estimat-
ing light interception by leaves [56]. It is recom-
mended only when the radiation interception rate
is known. Intercepted solar radiation or intercep-
ted PAR provides an accurate representation of crop
dry matter accumulation [17, 60], but the availabil-
ity of such data is often limited to local in situ meas-
urements. The novel ERCU relationship is based on
observed data and reveals the current upper limit
of potential wheat yield gaps at the global scale for
the development and improvement of global wheat
adaptation strategies.

3. Conclusion

For the first time, a multi-model ensemble of exist-
ing wheat crop models has been shown to incorporate
and reproduce the physiological trait advancements
reported from recent high-yielding DH field experi-
ments in different environments through modifica-
tion of parameters within the models. Additionally,
existing relationships between incoming solar radi-
ation and wheat production have never before incor-
porated the trait advancements recently reported

from DH wheat field experiments or used a solar
radiation-wheat production relationship to estim-
ate the upper limit of global genetic yield potential.
These findings suggest that if wheat with improved
physiological traits is grown across all high-rainfall
and irrigated wheat-producing regions in the world,
a potential increase of 37% of global wheat produc-
tion and yield is feasible, as already achieved at sites
in Argentina and Chile. There is also basis for doub-
ling global wheat production and yield when only
limited by solar radiation, although this is an elusive
and immense challenge at the global scale. The 37%
increase of global wheat production would meet the
lower estimate of the projected future grain demand
in 2050, without expanding the current agricultural
cropping area. To achieve this, breeding strategies
would need to optimize physiological traits with min-
imal trade-offs, while additional crop management
inputs may be required to maintain such high yields.

The newly developed ERCU relationship can be
applied as a simplistic upper benchmark, rather like
the yield-water use function is routinely used in low-
yielding rainfed agriculture [32], but with readily
accessible, near real-time global incoming solar radi-
ation data anywhere in the world (https://power.larc.
nasa.gov). Farmers, scientists, and policymakers can
use the ERCU relationship to benchmark local and
regional yield improvements against observed high
yields, provide a yardstick for sustainable intensific-
ation and potential yield gap analysis, and develop
breeding adaptation strategies.

**4. Materials and methods**

**4.1. Crop model ensemble evaluation**

To determine if crop simulation models could repro-
duce the observed yield gains from measured high-
yielding traits, we first evaluated the performance of
29 process-based wheat crop models (table S1) in
simulating the observed yield potential of two spring
wheat cultivars for multiple growing seasons at three
different high-yielding locations with negligible water
or nitrogen stress and full control of weeds, pests,
and diseases. We used the maximum available agricul-
tural model intercomparison and improvement pro-
ject (AgMIP) wheat crop models in this ensemble
since multi-model ensemble accuracy improves with
the inclusion of each additional model [25, 61–63].
The check cultivar Bacanora was compared to a DH
line resulting from a cross between cv. Bacanora and
cv. Weebil with improved RUE, light extinction coef-
ficient, grain filling rate, and potential grain size
and slightly decreased fruiting efficiency and grain
filling duration (table 1). These physiological traits
were calculated from previous field experimental data
observed at Buenos Aires (Argentina), Ciudad Obre-
gon (Mexico), and Valdivia (Chile) by Bustos *et al*
[15] and Garcia *et al* [20]. These locations had fertile
soils and used best management practices to prevent
<table><caption>**Table 1.** Description and calculated percent change between the best-yielding DH line and the check cultivar (cv. Bacanora) of the traits provided to the crop modeling groups. Values are calculated from the observed field experiment data [15, 20, 21]. DM, dry matter; GS, growth stage; PAR, photosynthetically active radiation. The cultivar parameters used by each model are provided in table S2.</caption><thead><tr><th>Type</th><th>Trait</th><th>Units</th><th>Calculation</th><th>DH line(% change vs. cv. Bacanora)</th></tr></thead><tbody><tr><td rowspan="2">Source</td><td>Radiation use efficiency (RUE)</td><td>g MJ<sup>-1</sup></td><td>Slope of above-ground biomass DM (GS10 to GS89) vs. cumulative intercepted PAR</td><td>34</td></tr><tr><td>Light extinction coefficient at GS31 (K)</td><td>m<sup>2</sup> (at ground) m<sup>-2</sup> (of leaf)</td><td>Exponential coefficient of cumulative PAR (pre-anthesis) vs. leaf area index (LAI)</td><td>10</td></tr><tr><td rowspan="4">Sink</td><td>Fruiting efficiency (FEspike)</td><td>grain g<sup>-1</sup> DM</td><td>Grain number divided by DM of anthesis spike</td><td>-5</td></tr><tr><td>Potential grain filling duration (GFD)</td><td>°Cd</td><td>Thermal time (base temperature 0 °C) between anthesis and physiological maturity</td><td>-4</td></tr><tr><td>Potential grain filling rate (GFR)</td><td>mg DM°Cd<sup>-1</sup></td><td>Grain DM divided by thermal time (base temperature 0 °C) between anthesis and physiological maturity under potential growth conditions</td><td>21</td></tr><tr><td>Potential grain size (GWpot)</td><td>mg DM grain<sup>-1</sup></td><td>Average single grain DM under potential growth conditions</td><td>16</td></tr></tbody></table>

yield limitation, e.g. N was applied ad libitum. The modified traits were selected specifically to increase photosynthesis and yield potential under non-stress conditions. The effects on grain quality or disease resistance were not examined. The RUE and light extinction coefficient were calculated using the mean percent change between Bacanora and the two best-yielding DH lines from Chile only because of limited measurements at Argentina and Mexico. The fruiting efficiency, grain filling duration, grain filling rate, and potential grain size were calculated using the mean percent change between the best-yielding DH line and Bacanora from each of the three locations.

The crop modeling experiment was conducted in two steps, model calibration followed by a 'blind' evaluation. In step 1, detailed crop growth data [15, 20] were provided to the modeling groups for five seasons at Valdivia, Chile (2008–2009, 2009–2010, 2012–2013, 2013–2014, and 2014–2015), one season at Buenos Aires, Argentina (2009–2010), and four seasons at Ciudad Obregon, Mexico (2009–2010, 1990–1991, 2015–2016 and 2016–2017) to give a total of ten site-year-treatment combinations with which to calibrate the models for the check cultivar Bacanora. The data at Ciudad Obregon, Mexico in 1990–1991 were obtained from a previous AgMIP study [26, 64]. Values for each season consisted of the mean of three replicated plots where the wheat crops were grown with ample N supply, full irrigation, and agronomic practices to reach potential yield for the local soil and weather conditions. All other crop protection factors including weed, disease and pest control, and potassium, phosphate, and sulphur fertilizers, were applied at levels to prevent limitations on yield. Total initial soil mineral N (NO₃ and NH₄) content was assumed to be equal to 140 kg N ha⁻¹ to ensure negligible N stress. The temperature and solar radiation data were provided from a weather station located <2 km from the experimental field and the rainfall, wind speed, and relative humidity data were obtained using the NASA POWER database (https://power.larc.nasa.gov) [65, 66]. In step 2, a 'blind' simulation was conducted for the best-yielding DH line at each location using the same initial growing conditions from the calibration, without providing the modelers with observed data. Only the calculated trait percent changes (table 1) and instructions describing how to modify the calibrated cv. Bacanora traits for the high-yielding DH line were provided to simulate growth for three seasons at Valdivia, Chile (2008–2009, 2009–2010, and 2010–2011), one season at Buenos Aires, Argentina (2009–2010), and one season at Ciudad Obregon, Mexico (2009–2010). The model cultivar parameters that correspond to the physiological traits for each of the 29 models are provided in table S2. Not all models were able to modify all cultivar parameters (table S2).

## 4.2. Determination of ERCU

The ERCU relationship was developed by following the definition of yield potential from Reynolds et al [6]:

$$ \text{Yield potential} = \text{Light interception} * \text{RUE} * \text{Harvest index} \quad (1) $$

and the approaches of French and Schultz [32] in deriving the relationship between wheat production and water use, and of Bugbee and Salisbury [67] in calculating the potentially achievable yield
from the photosynthetic photon flux in a controlled environment. The ERCU encompasses both RUE and radiation interception and is defined as:

$$ ERCU = \text{Light interception} * \text{RUE} \quad (2) $$

calculated from the observed above-ground biomass per unit of incoming solar radiation (figure 2). The ERCU is similar to net primary production (NPP), a common metric in terrestrial ecosystem and forestry modeling calculated as photosynthetic CO₂ fixation without autotrophic respiration [68], but NPP is the amount of carbon per unit of surface area per unit of time (gC m⁻² d⁻¹) while the ERCU is the rate of above-ground dry matter per unit of incoming solar radiation (g MJ⁻¹). The ERCU uses total incoming solar radiation and RUE which are both standard inputs in most crop models. Total incoming solar radiation for each location and growing season was obtained from local weather stations and/or the NASA POWER database (https://power.larc.nasa.gov) [65]. The production potential threshold was determined from the linear fit between the best pre- and post-anthesis crop response from the high-yielding dataset. This was calculated using the average of the first two measurements of cv. Bacanora in Valdivia 2012 and the average of the final two measurements of the high-yielding DH line in Valdivia 2010 (figure 2). The biomass potential threshold was then multiplied by the harvest index of the highest yielding DH line, 0.45, to obtain the yield potential threshold (figure 3):

$$ \text{Yield potential} = \text{ERCU} * \text{Harvest index} \quad (3) $$

### 4.3. Crop model ensemble global simulations and extrapolation

After confirming that the 29-model ensemble median accurately reproduced the observed effects of the high-yielding traits (figure 1), 26 models of the ensemble (table S1) participated in simulating potential wheat production with and without the modified traits (table 1) from 1980 to 2010 at 34 high-rainfall or irrigated global locations including Buenos Aires, Ciudad Obregon, and Valdivia. As a check, steps 1 and 2 were reanalyzed with the 26-model ensemble to confirm that the ensemble median was still able to reproduce the observed high yield and biomass from the yield-enhancing traits ($r^2 = 0.92$ and 0.64, respectively) (figure S7). 30 of the 34 high-rainfall or irrigated locations were from previous AgMIP studies [26, 37] with the addition of Buenos Aires (Argentina), Valdivia (Chile), Rots (France), and Leeston (New Zealand) (all locations shown in figures S4 and S5). As in the previous AgMIP studies, treatments were simulated with ample N supply, irrigation (if applicable), and with proper agronomic

practices (e.g. typical sowing dates) for the local soil and weather conditions of each location. This ensured that the crop growth interactions with temperature, water, and soil dynamics corresponded to each location. All other crop management operations including weed, disease and pest control, and fertilizers, were applied at levels to prevent limitations on yield. Atmospheric CO₂ concentration of 360 ppm was assumed for all years, so impacts from climate change or elevated atmospheric CO₂ concentrations were not taken into account. For each location, region-specific check cultivars and cultivar parameters from a previous AgMIP study [26] were simulated with and without the modified traits (table 1) to emulate the crop response under the different global environmental and seasonal conditions such as temperature, rainfall, and solar radiation. Cultivar parameters for the individual crop models are shown in Asseng et al [26], and the various temperature functions in wheat crop models are presented in Wang et al [69]. Winter or spring wheat cultivars were chosen based on the available field experiment data and the dominant type of that region. Winter wheat cultivars have a higher yield potential than spring wheat cultivars because of the longer growing season caused by vernalization [70], so the benefits to spring wheat yield potential, especially through a driving trait such as RUE, would also likely benefit winter wheat yield potential [6, 19]. Additionally, similar to the high genetic yield gains in spring wheat observed at Chile by Bustos et al [15], a recent study also conducted at Chile observed high genetic yield gains in winter wheat grain yield, reaching up to 20.5 t ha⁻¹ [71]. For each crop model, the cumulative seasonal incoming solar radiation for each location was calculated based on the simulated growing season. The multi-model ensemble median was then used to calculate the percent change between the simulated baseline yield without modified traits and the potential yield with modified traits at the 34 global high-yielding environments (figure 3, i.e. percent change between solid and open diamonds). We focus on the multi-model ensemble median because it has been shown to provide the best estimates when considering all variables [27].

To extrapolate the results to the global level, we upscaled both the crop model simulations from the 34 high-yielding locations and the ERCU upper limit to the global level using the same ‘bottom-up’ approach as in previous AgMIP studies [26, 37]. This approach used stratified sampling [72], a guided sampling method with several points per wheat mega-region [73]. The simulated results of each location were weighted by the FAO production that each location represented [26] (see supplementary methods). This ‘bottom-up’ stratified sampling approach has shown similar impact results as in
the 'top-down' aggregation of simulated grid cells [74, 75] and reduced uncertainty when estimating yield potential and yield gaps compared to 'top-down' gridded frameworks [76]. The percent change of production was calculated between the simulated baseline scenario without modified traits and the simulated scenario with modified traits (figure 4). The percent change of production for the ERCU upper limit was calculated between the simulated baseline scenario without modified traits and the ERCU upper yield potential with modified traits. The weighted FAO production was multiplied by the percent change of production to obtain the additional production at each of the 34 locations for both scenarios. This additional production at each location was summed to calculate the total additional global production for both scenarios. The total production of the 34 high-rainfall or irrigated locations represented approximately 70% of global wheat production [37, 51], so 30% of the 2020 global average production was added to account for low-rainfall production (figure 4(b)), assumed to be constant for all scenarios because low-rainfall production is unlikely to benefit from the improved trait scenarios [41]. Global yields (figure 4(a)) were calculated by dividing the total global production scenarios in figure 4(b) by the total global wheat area harvested [7].

## Data availability statement

The global agricultural data that support the findings of this study are publicly available at the Food and Agriculture Organization (FAO) FAOSTAT database (www.fao.org/faostat/) and the global incoming solar radiation data are publicly available at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) database (https://power.larc.nasa.gov). Individual crop models and their documentation are listed in table S1.

The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.7910/DVN/VKWKUP. Data will be available from 01 November 2022.

## Acknowledgments

This study was a part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4. The study was supported by the International Wheat Yield Partnership (IWYP, Grant IWYP115) and the International Maize and Wheat Improvement Center (CIMMYT). Experiments carried out in Valdivia (Chile) were funded by IWYP, CIMMYT (Mexico), and the Chilean Technical and Scientific Research Council (CONICYT) by FONDECYT Project 1141048. The experimental work conducted at Valdivia by Dr Jaime Herrera

(UACh) is appreciated. F T was supported by the National Natural Science Foundation of China (Project Nos. 31761143006 and 41571493). K C K was supported by the Ministry of Education, Youth and Sports of Czech Republic through SustEs (CZ.02.1.01/0.0/0.0/16_019/000797). Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) through Designing Future Wheat (BB/P016855/1) and Achieving Sustainable Agricultural Systems (NE/N018125/1).

## Author contributions

J R G, S A, P M, F E, and H W conceived the study. J R G and S A co-wrote the manuscript and produced the figures. J R G, P M, and S D analyzed the crop model data. D C, M R, G M, D M, and G G conducted the observed field experiments and contributed to editing the manuscript. G S, F G, D P, and T S contributed to editing the manuscript. Authors from M A to Y Z performed the crop model simulations and are listed in alphabetical order. J R G and S A wrote the Supplementary Information.

## Declaration of Interests

The authors declare that they have no competing interests.

## ORCID iDs

Jose Rafael Guarín https://orcid.org/0000-0002-3167-4329

Mukhtar Ahmed https://orcid.org/0000-0002-7223-5541

Bruno Basso https://orcid.org/0000-0003-2090-4616

Ehsan Eyshi Rezaei https://orcid.org/0000-0003-2603-8034

Thomas Gaiser https://orcid.org/0000-0002-5820-2364

Margarita Garcia-Vila https://orcid.org/0000-0001-5737-4669

Sebastian Gayler https://orcid.org/0000-0002-8577-6486

Zvi Hochman https://orcid.org/0000-0002-6217-5231

Claas Nendel https://orcid.org/0000-0001-7608-9097

Nimai Senapati https://orcid.org/0000-0002-0238-6694

Stefan Siebert https://orcid.org/0000-0002-9998-0672

Zhao Zhang https://orcid.org/0000-0002-5697-8011

Chuang Zhao https://orcid.org/0000-0002-9535-8181
References

[1] Godfray H C J, Beddington J R, Crute I R, Haddad L, Lawrence D, Muir J F, Pretty J, Robinson S, Thomas S M and Toulmin C 2010 Food security: the challenge of feeding 9 billion people *Science* **327** 812–8

[2] Tilman D, Balzer C, Hill J and Befort B L 2011 Global food demand and the sustainable intensification of agriculture *Proc. Natl Acad. Sci. USA* **108** 20260–4

[3] Ray D K, Mueller N D, West P C and Foley J A 2013 Yield trends are insufficient to double global crop production by 2050 *PLoS One* **8** e8

[4] Hunter M C, Smith R G, Schipanski M E, Atwood L W and Mortensen D A 2017 Agriculture in 2050: recalibrating targets for sustainable intensification *Bioscience* **67** 385–90

[5] Gerten D et al 2020 Feeding ten billion people is possible within four terrestrial planetary boundaries *Nat. Sustain.* **3** 200

[6] Reynolds M, Foulkes J, Furbank R, Griffiths S, King J, Murchie E, Parry M and Slafer G 2012 Achieving yield gains in wheat *Plant Cell Environ.* **35** 1799–823

[7] Food and Agricultural Organization of the United Nations 2022 FAOSTAT Statistics Database (FAO) (available at: www.fao.org/faostat/en/#data/QCL) (Accessed 27 September 2022)

[8] Ray D K, Ramankutty N, Mueller N D, West P C and Foley J A 2012 Recent patterns of crop yield growth and stagnation *Nat. Commun.* **3** 7

[9] Crespo-Herrera L A, Crossa J, Huerta-Espino J, Autrique E, Mondal S, Velu G, Vargas M, Braun H J and Singh R P 2017 Genetic yield gains in CIMMYT’s international elite spring wheat yield trials by modeling the genotype x environment interaction *Crop Sci.* **57** 789–801

[10] Crespo-Herrera L A, Crossa J, Huerta-Espino J, Vargas M, Mondal S, Velu G, Payne T S, Braun H and Singh R P 2018 Genetic gains for grain yield in CIMMYT’s semi-arid wheat yield trials grown in suboptimal environments *Crop Sci.* **58** 1890–8

[11] van Dijk M, Morley T, Rau M L and Saghai Y 2021 A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050 *Nat. Food* **2** 494

[12] Foley J A et al 2011 Solutions for a cultivated planet *Nature* **478** 337–42

[13] Evans L T and Fischer R A 1999 Yield potential: its definition, measurement, and significance *Crop Sci.* **39** 1544–51

[14] Reynolds M et al 2021 Addressing research bottlenecks to crop productivity *Trends Plant Sci.* **26** 607–30

[15] Bustos D V, Hasan A K, Reynolds M P and Calderini D F 2013 Combining high grain number and weight through a DH-population to improve grain yield potential of wheat in high-yielding environments *Field Crops Res.* **145** 106–15

[16] Burney J A, Davis S J and Lobell D B 2010 Greenhouse gas mitigation by agricultural intensification *Proc. Natl Acad. Sci. USA* **107** 12052–7

[17] Sinclair T R and Muchow R C 1999 Radiation use efficiency *Adv. Agron.* **65** 215–65

[18] Wu A, Hammer G L, Doherty A, von Caemmerer S and Farquhar G D 2019 Quantifying impacts of enhancing photosynthesis on crop yield *Nat. Plants* **5** 380–8

[19] Long S P, Marshall-Colon A and Zhu X G 2015 Meeting the global food demand of the future by engineering crop photosynthesis and yield potential *Cell* **161** 56–66

[20] Garcia G A, Hasan A K, Puhl L E, Reynolds M P, Calderini D F and Miralles D J 2013 Grain yield potential strategies in an elite wheat double-haploid population grown in contrasting environments *Crop Sci.* **53** 2577–87

[21] Garcia G A, Serrago R A, Gonzalez F G, Slafer G A, Reynolds M P and Miralles D J 2014 Wheat grain number: identification of favourable physiological traits in an elite doubled-haploid population *Field Crops Res.* **168** 126–34

[22] van Ittersum M K, Cassman K G, Grassini P, Wolf J, Tittonell P and Hochman Z 2013 Yield gap analysis with local to global relevance-A review *Field Crops Res.* **143** 4–17

[23] Reynolds M P et al 2017 Strategic crossing of biomass and harvest index-source and sink-achieves genetic gains in wheat *Euphytica* **213** 23

[24] Brisson N, Gate P, Gouache D, Charmet G, Oury F X and Huard F 2010 Why are wheat yields stagnating in Europe? A comprehensive data analysis for France *Field Crops Res.* **119** 201–12

[25] Asseng S et al 2013 Uncertainty in simulating wheat yields under climate change *Nat. Clim. Change* **3** 827–32

[26] Asseng S et al 2015 Rising temperatures reduce global wheat production *Nat. Clim. Change* **5** 143–7

[27] Martre P et al 2015 Multimodel ensembles of wheat growth: many models are better than one *Glob. Change Biol.* **21** 911–25

[28] Guarín J R and Asseng S 2017 Wheat crop modelling to improve yields *Achieving Sustainable Cultivation of Wheat Volume 2: Cultivation Techniques* vol 2 ed P Langridge (Cambridge, UK: Burleigh Dodds Science Publishing) p 36

[29] Chapagain R, Remenyi T A, Harris R M B, Mohammed C L, Huth N, Wallach D, Rezaei E E and Ojeda J J 2022 Decomposing crop model uncertainty: a systematic review *Field Crops Res.* **279** 12

[30] Sayre K D, Rajaram S and Fischer R A 1997 Yield potential progress in short bread wheats in northwest Mexico *Crop Sci.* **37** 36–42

[31] Fischer R A, Ramos O H M, Monasterio I O and Sayre K D 2019 Yield response to plant density, row spacing and raised beds in low latitude spring wheat with ample soil resources: an update *Field Crops Res.* **232** 95–105

[32] French R J and Schultz J E 1984 Water use efficiency of wheat in a Mediterranean-type environment. 1. The relation between yield, water use and climate *Aust. J. Agric. Res.* **35** 743–64

[33] Quintero A, Molero G, Reynolds M P and Calderini D F 2018 Trade-off between grain weight and grain number in wheat depends on GxE interaction: a case study of an elite CIMMYT panel (CIMCOG) *Eur. J. Agron.* **92** 17–29

[34] Molero G, Joynson R, Pinera-Chavez F J, Gardiner L J, Rivera-Amado C and Hall A 2019 Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential *Plant Biotechnol. J.* **17** 1276–88

[35] Fischer R A 1985 Number of kernels in wheat crops and the influence of solar-radiation and temperature *J. Agric. Sci.* **105** 447–61

[36] Trnka M et al 2019 Mitigation efforts will not fully alleviate the increase in water scarcity occurrence probability in wheat-producing areas *Sci. Adv.* **5** 11

[37] Asseng S et al 2019 Climate change impact and adaptation for wheat protein *Glob. Change Biol.* **25** 155–73

[38] Wang X H et al 2020 Emergent constraint on crop yield response to warmer temperature from field experiments *Nat. Sustain.* **3** 908–16

[39] Xiong W et al 2021 Increased ranking change in wheat breeding under climate change *Nat. Plants* **7** 1207

[40] Taylor S H and Long S P 2017 Slow induction of photosynthesis on shade to sun transitions in wheat may cost at least 21% of productivity *Phil. Trans. R. Soc. B* **372** 1730

[41] Asseng S, Kassie B T, Labra M H, Amador C and Calderini D F 2017 Simulating the impact of source-sink manipulations in wheat *Field Crops Res.* **202** 47–56

[42] Deutsch C A, Tewksbury J J, Tigchelaar M, Battisti D S, Merrill S C, Huey R B and Naylor R L 2018 Increase in crop losses to insect pests in a warming climate *Science* **361** 916–9

[43] Rosa L, Chiarelli D D, Rulli M C, Dell'Angelo J and D'Odorico P 2020 Global agricultural economic water scarcity *Sci. Adv.* **6** 10

[44] Basso B and Ritchie J T 2018 Evapotranspiration in high-yielding maize and under increased vapor pressure deficit in the US Midwest *Agric. Environ. Lett.* **3** 6
[45] Schils R *et al* 2018 Cereal yield gaps across Europe *Eur. J. Agron.* **101** 109–20

[46] Alhabbar Z, Yang R C, Juhasz A, Xin H, She M Y, Anwar M, Sultana N, Diepeveen D, Ma W and Islam S 2018 NAM gene allelic composition and its relation to grain-filling duration and nitrogen utilisation efficiency of Australian wheat *PLoS One* **13** 17

[47] Raun W R, Solie J B and Stone M L 2011 Independence of yield potential and crop nitrogen response *Precis. Agric.* **12** 508–18

[48] Walsh O S, Marshall J, Nambi E, Shafian S, Jayawardena D, Jackson C, Lamichhane R, Owusu Ansah E and McClintick-Chess J 2022 Spring wheat yield and grain quality response to nitrogen rate *Agron. J.* **114** 2562–72

[49] Zorb C, Ludewig U and Hawkesford M J 2018 Perspective on wheat yield and quality with reduced nitrogen supply *Trends Plant Sci.* **23** 1029–37

[50] van Grinsven H J M *et al* 2022 Establishing long-term nitrogen response of global cereals to assess sustainable fertilizer rates *Nat. Food* **3** 122–32

[51] Reynolds M and Braun H (ed) 2013 *Proc. of the 3rd Int. Workshop of the Wheat Yield Consortium* (Ciudad Obregon: International Maize and Wheat Improvement Center (CIMMYT) Mexico)

[52] Dai J *et al* 2016 Harvest index and straw yield of five classes of wheat *Biomass Bioenergy* **85** 223–7

[53] Foulkes M J, Slafer G A, Davies W J, Berry P M, Sylvester-Bradley R, Martre P, Calderini D F, Griffiths S and Reynolds M P 2011 Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance *J. Exp. Bot.* **62** 469–86

[54] Fischer R A T and Edmeades G O 2010 Breeding and cereal yield progress *Crop Sci.* **50** S85–S98

[55] Zhao Y S *et al* 2021 Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat *Sci. Adv.* **7** 13

[56] Monteith J L 1977 Climate and efficiency of crop production in Britain *Phil. Trans. R. Soc.* B **281** 277–94

[57] Kiniry J R, Jones C A, Otoole J C, Blanchet R, Cabelguenne M and Spanel D A 1989 Radiation-use efficiency in biomass accumulation prior to grain-filling for 5 grain-crop species *Field Crops Res.* **20** 51–64

[58] Yunusa I A M, Siddique K H M, Belford R K and Karimi M M 1993 Effect of canopy structure on efficiency of radiation interception and use in spring wheat cultivars during the pre-anthesis period in a Mediterranean-type environment *Field Crops Res.* **35** 113–22

[59] Olesen J E, Jorgensen L N and Mortensen J V 2000 Irrigation strategy, nitrogen application and fungicide control in winter wheat on a sandy soil. II. Radiation interception and conversion *J. Agric. Sci.* **134** 13–23

[60] Della Nave F N, Ojeda J J, Irisarri J G N, Pembleton K, Oyarzabal M and Calibrating O M 2022 APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions *Agric. Syst.* **201** 14

[61] Bassu S *et al* 2014 How do various maize crop models vary in their responses to climate change factors? *Glob. Change Biol.* **20** 2301–20

[62] Li T *et al* 2015 Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions *Glob. Change Biol.* **21** 1328–41

[63] Wallach D *et al* 2018 Multimodel ensembles improve predictions of crop-environment-management interactions *Glob. Change Biol.* **24** 5072–83

[64] Martre P, Reynolds M P, Asseng S, Ewert F, Alderman P D and Cammarano D 2017 The international heat stress genotype experiment for modeling wheat response to heat: field experiments and AgMIP-wheat multi-model simulations *Open Data J. Agric. Res.* **3** 5

[65] Kratz D P, Stackhouse P W, Gupta S K, Wilber A C, Sawaengphokhai P and McGarragh G R 2014 The fast longwave and shortwave flux (FLASHFlux) data product: single-scanner footprint fluxes *J. Appl. Meteorol. Clim.* **53** 1059–79

[66] White J W, Hoogenboom G, Wilkens P W, Stackhouse P W and Hoel J M 2011 Evaluation of satellite-based, modeled-derived daily solar radiation data for the continental United States *Agron. J.* **103** 1242–51

[67] Bugbee B G and Salisbury F B 1989 Current and potential productivity of wheat for a controlled environment life support system *Adv. Space Res.* **9** 5–15

[68] Woodward F I 2007 Global primary production *Curr. Biol.* **17** R269–73

[69] Wang E, Martre P, Zhao Z G, Ewert F, Maiorano A and Rotter R P 2017 The uncertainty of crop yield projections is reduced by improved temperature response functions *Nat. Plants* **3** 11

[70] Vocke G and Ali M 2013 U.S. wheat production practices, costs, and yields: variations across regions *Economic Information Bulletin* p 262124 (United States Department of Agriculture, Economic Research Service)

[71] Del Pozo A, Jobet C, Matus I, Mendez-Espinoza A M, Garriga M, Castillo D and Elazab A 2022 Genetic yield gains and changes in morphophysiological-related traits of winter wheat in Southern Chilean high-yielding environments *Front. Plant Sci.* **12** 20

[72] van Bussel L G J *et al* 2016 Spatial sampling of weather data for regional crop yield simulations *Agric. For. Meteorol.* **220** 101–15

[73] Gbegbelegbe S *et al* 2017 Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars *Field Crops Res.* **202** 122–35

[74] Liu B *et al* 2016 Similar estimates of temperature impacts on global wheat yield by three independent methods *Nat. Clim. Change* **6** 1130–8

[75] Zhao G *et al* 2016 Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops *Environ. Model. Softw.* **80** 100–12

[76] Edreira J I R, Andrade J F, Cassman K G, van Ittersum M K, van Loon M P and Grassini P 2021 Spatial frameworks for robust estimation of yield gaps *Nat. Food* **2** 773–9