# EXPLORING GXEXM SYNERGIES IN WORLD-WIDE WHEAT PRODUCTION AND THE OPPORTUNITIES FOR INTERNATIONAL COLLABORATION

EDITED BY : Brian L. Beres, Jerry Lee Hatfield, Henning Kage and James Robert Hunt PUBLISHED IN : Frontiers in Plant Science and Frontiers in Agronomy

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ISSN 1664-8714 ISBN 978-2-88966-220-3 DOI 10.3389/978-2-88966-220-3

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# EXPLORING GXEXM SYNERGIES IN WORLD-WIDE WHEAT PRODUCTION AND THE OPPORTUNITIES FOR INTERNATIONAL COLLABORATION

Topic Editors: Brian L. Beres, Agriculture and Agri-Food Canada, Canada Jerry Lee Hatfield, Agricultural Research Service, United States Department of Agriculture, United States Henning Kage, University of Kiel, Germany James Robert Hunt, La Trobe University, Australia

Citation: Beres, B. L., Hatfield, J. L., Kage, H., Hunt, J. R., eds. (2020). Exploring GxExM Synergies in World-Wide Wheat Production and the Opportunities for International Collaboration. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88966-220-3

# Table of Contents

### *05 Toward a Better Understanding of Genotype × Environment × Management Interactions—A Global Wheat Initiative Agronomic Research Strategy*

Brian L. Beres, Jerry L. Hatfield, John A. Kirkegaard, Sanford D. Eigenbrode, William L. Pan, Romulo P. Lollato, James R. Hunt, Sheri Strydhorst, Kenton Porker, Drew Lyon, Joel Ransom and Jochum Wiersma

Till Rose and Henning Kage

*39 Physiological Basis of Genotypic Response to Management in Dryland Wheat*

Amanda de Oliveira Silva, Gustavo A. Slafer, Allan K. Fritz and Romulo P. Lollato

Graham R.S. Collier, Dean M. Spaner, Robert J. Graf and Brian L. Beres

Allan S. Peake, Kerry L. Bell, R.A. Fischer, Matt Gardner, Bianca T. Das, Nick Poole and Michael Mumford

David J. Cann, William F. Schillinger, James R. Hunt, Kenton D. Porker and Felicity A. J. Harris

*194 Interactions of Spring Cereal Genotypic Attributes and Recovery of Grain Yield After Defoliation*

Lindsay W. Bell, John A. Kirkegaard, Lihua Tian, Sally Morris and John Lawrence

Claudio O. Stöckle and Armen R. Kemanian

*231 Evaluation of G × E × M Interactions to Increase Harvest Index and Yield of Early Sown Wheat*

Kenton Porker, Michael Straight and James Robert Hunt

*245 Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects*

Malcolm John Hawkesford and Andrew B. Riche

# Toward a Better Understanding of Genotype × Environment × Management Interactions—A Global Wheat Initiative Agronomic Research Strategy

Brian L. Beres1 \*, Jerry L. Hatfield2 , John A. Kirkegaard3 , Sanford D. Eigenbrode4 , William L. Pan5 , Romulo P. Lollato6 , James R. Hunt7 , Sheri Strydhorst8 , Kenton Porker9 , Drew Lyon5 , Joel Ransom10 and Jochum Wiersma11

### Edited by:

Hans-Peter Kaul, University of Natural Resources and Life Sciences Vienna, Austria

### Reviewed by:

Anna-Maria Botha-Oberholster, Stellenbosch University, South Africa Eric Ober, National Institute of Agricultural Botany (NIAB), United Kingdom

> \*Correspondence: Brian L. Beres<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Crop and Product Physiology, a section of the journal Frontiers in Plant Science

Received: 13 December 2019 Accepted: 22 May 2020 Published: 16 June 2020

### Citation:

Beres BL, Hatfield JL, Kirkegaard JA, Eigenbrode SD, Pan WL, Lollato RP, Hunt JR, Strydhorst S, Porker K, Lyon D, Ransom J and Wiersma J (2020) Toward a Better Understanding of Genotype × Environment × Management Interactions—A Global Wheat Initiative Agronomic Research Strategy. Front. Plant Sci. 11:828. doi: 10.3389/fpls.2020.00828 1 Lethbridge Research and Development Centre, Prairie Boreal Plain Region, Science and Technology Branch, Agriculture and Agri-Food Canada, Lethbridge, AB, Canada, 2 USDA-ARS, National Laboratory for Agriculture and the Environment, Ames, IA, United States, 3 Commonwealth Scientific and Industrial Research Organisation Agriculture and Food, Canberra, ACT, Australia, 4 Department of Entomology, Plant Pathology and Nematology, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID, United States, 5 Department of Crop and Soil Sciences, College of Agricultural, Human, and Natural Resource Sciences, Washington State University, Pullman, WA, United States, 6 Department of Agronomy, College of Agriculture, Kansas State University, Manhattan, KS, United States, 7 Department of Animal, Plant and Soil Sciences, La Trobe University, Melbourne, VIC, Australia, 8 Cropping Systems Section, Livestock and Crops Research Branch, Primary Agriculture Division, Alberta Agriculture and Forestry, Barrhead, AB, Canada, 9 Crop Sciences, Agronomy Group, South Australia Research and Development Institute, Urrbrae, SA, Australia, 10 Department of Plant Sciences, North Dakota State University, Fargo, ND, United States, 11 Department of Agronomy and Plant Genetics, University of Minnesota Crookston, Crookston, MN, United States

The Wheat Initiative (WI) and the WI Expert Working Group (EWG) for Agronomy (www.wheatinitiative.org) were formed with a collective goal to "coordinate global wheat research efforts to increase wheat production, quality, and sustainability to advance food security and safety under changing climate conditions." The Agronomy EWG is responsive to the WI's research need, "A knowledge exchange strategy to ensure uptake of innovations on farm and to update scientists on changing field realities." The Agronomy EWG aims to consolidate global expertise for agronomy with a focus on wheat production systems. The overarching approach is to develop and adopt a systems-agronomy framework relevant to any wheat production system. It first establishes the scale of current yield gaps, identifies defensible benchmarks, and takes a holistic approach to understand and overcome exploitable yield gaps to complement genetic increases in potential yield. New opportunities to increase productivity will be sought by exploiting future Genotype × Environment × Management synergies in different wheat systems. To identify research gaps and opportunities for collaboration among different wheat producing regions, the EWG compiled a comprehensive database of currently funded wheat agronomy research (n = 782) in

**5**

countries representing a large proportion of the wheat grown in the world. The yield gap analysis and research database positions the EWG to influence priorities for wheat agronomy research in member countries that would facilitate collaborations, minimize duplication, and maximize the global impact on wheat production systems. This paper outlines a vision for a global WI agronomic research strategy and discusses activities to date. The focus of the WI-EWG is to transform the agronomic research approach in wheat cropping systems, which will be applicable to other crop species.

Keywords: wheat, Wheat Initiative, agronomy, Expert Working Group, Genotype × Environment × Management

### INTRODUCTION

Genetic improvements in yield continue in the world's staple crops (Li et al., 2018), but to realize the potential of these improvements in farmer's fields to meet global demands will require improved agronomic practices (Fischer and Connor, 2018). The yield gap between potential and farm yields for major crops is substantial. For example, farm yields in rice, wheat, and maize are just 80% of potential yields under irrigated conditions, and 50% or less under rainfed conditions (Lobell et al., 2009). Potential yield is defined here as the yield of the best adapted cultivar with current best practice agronomic management ensuring the absence of manageable abiotic and biotic stresses (Fischer, 2015). Potential yields are constrained in many climates by water limitations, but additional constraints are within the capacity of farmers to mitigate. Economic yield is the yield attained by farmers given the prevailing weather, but inputs and practices are applied at the economic optimum (maximizing margin), which may not necessarily coincide with the levels that produce a maximum yield. This remains at approximately 75–85% of potential yield or water limited potential yield (Zhang et al., 2019). The difference between economic yield and farm yield is the exploitable yield gap. Recent research also suggests that the yield gap for the crop sequence is even larger than for individual crops due to inefficiencies in the system as a whole (Hochman et al., 2014). Comprehensive efforts to improve food security must couple genetic increases in potential yield with agronomic approaches to reduce exploitable yield gaps in all major cropping systems. Global intervention to improve agronomy can also increase the resilience of agriculture and agriculture-based livelihoods by increasing and stabilizing the returns to producers and ensuring the capacity of these systems to provide ecosystem services (Food and Agriculture Organization of the United Nations, 2016). Agronomic approaches to achieve these ends can fall under the concept of Agroecology: The discipline that provides the basic ecological principles for how to study, design, and manage agroecosystems that are both productive and natural resource conserving, and that are culturally sensitive, socially just, and economically viable (Altieri and Nicholls, 1995).

Wheat is second to rice as a source of calories in developing countries and first as a source of protein (Braun et al., 2010). It is grown on more land area than any other crop. Wheat currently provides 20% of the daily protein and of food calories for 4.5 billion people (Shewry and Hey, 2015). Although estimates of the demand for food by 2050 vary (Hunter et al., 2017), the challenge to increase production by 30–50% is still a major endeavor requiring a global response. Many studies indicate that a warming climate has a general negative effect on yield of staple crops like wheat (Porter et al., 2014). Under projected temperature increases of 2◦C above late 20th century levels for the period 2030–2049, models predict wheat yield reductions up to 25% in many areas without modifications of existing cropping practices. However, in some regions, increases in average yield are anticipated due to extended growing seasons and elevated CO2. Global temperature increases of approximately 4◦C or more would lead to further declines in wheat yield, which when combined with projections of increasing food demand, poses a large risk to regional and global food security.

Wheat production is challenged not only by changes in climate and the extreme growing conditions that could accompany predictions for climate change, but also by changing disease and insect pressures (Figueroa et al., 2018) and management (Zhang et al., 2019). Wheat breeding programs, both public and private, have and will continue to develop new varieties with higher yield potential in the current production conditions and improved resistance to current economic disease and pest problems. While breeding resistance to pest and disease problems is generally thought of as the most cost effective and sustainable approach to combat economic losses, shifts in climate and the resulting changes to weather patterns, cropping system responses, and pest and pathogen populations indicate that breeding targets and goals are likely to shift faster and more frequently than ever before. An integrated deployment of genetics and management options becomes essential in situations where breeders cannot provide genetic solutions in a timely manner or where the frequency of pest and disease outbreaks are too infrequent and variable to allow breeders to incorporate genetic resistance effectively. Furthermore, breeders and government agencies that conduct variety performance evaluations in many of the world's wheat growing regions test new wheat varieties under relatively constant and often conservative management regimes. The rationale for this approach is quite simple; application of management inputs masks differences in responses to biotic and abiotic stressors among varieties. This means that wheat variety performance evaluations in many regions do not actually measure the attainable yield, but rather the actual yield. This approach potentially biases results in favor of more disease or pest resistant varieties rather than those

with the highest genetic yield potential. While logical and defendable, it is a poor reflection of the genetic potential of individual varieties when a cost-effective management input, such a single application of a fungicide, can drastically change the performance and thus ranking of the variety. In many regions, this means that agronomists or producers themselves are left to develop management practices ad hoc to exploit the available genotype × management interactions.

Many of the challenges of increasing climate variability, increasing world population, and its resultant impact on food demand and global food security can be addressed by improvements in wheat genetics and agronomy; however, this requires a globally concerted R&D effort. The problem is that these efforts are often fragmented across the globe, conducted in silos, and often lack cross-disciplinary approaches. The recognition of these challenges and issues gave rise to the Wheat Initiative (WI) in 20101 . The WI presently has 17 countries as members, two international research centers (International Wheat and Maize Center and International Center for Agricultural Research in Dry Areas), and a number of private sector corporations largely interested in the genetic improvement of wheat (Wheat Initiative, 2020b). There are four themes for research priorities in the WI's Strategic Research Agenda and two cross-cutting themes that relate to enabling technologies and knowledge sharing and education. Themes 1 and 2 encompass aspects of breeding new wheat cultivars that have increased yield and increased resistance to biotic and abiotic stressors. Theme 3 addresses protecting the environment and improving the sustainability of wheat production systems. Theme 4 relates mainly to ensuring the quality and safety of wheat.

Ten Expert Working Groups (EWGs) were established within the WI focused on technical issues, predominantly genetic improvement and crop protection, but also including wheat germplasm conservation, phenotyping, nitrogen use efficiency, and wheat quality (Wheat Initiative, 2020a). In 2016, the WI established a new EWG in Agronomy2 , recognizing that crop management is an essential complement to genetic improvement in order to achieve the potential economic yields of new and improved wheat cultivars in farmer's fields. The Agronomy EWG contributes primarily to Theme 3, but also ensures that varieties developed under Themes 1 and 2 approach their potential yield in the hands of farmers. The EWG views itself as a discovery group linking research priorities and research inventories from each country to help improve the efficiency of the global agronomic research efforts in wheat, and to establish synergies among the various agronomy networks around the world. In this policy paper, we describe the vision, aims, and ongoing efforts of the Agronomy EWG within the WI since its establishment in 2016. The EWG acknowledges that certain challenges require an international approach to make significant progress via new collaborative partnerships, more efficient use of research funds, and effective networks to share and communicate new knowledge to farmers who grow wheat in both developed and developing countries. The WI

1www.wheatinitiative.org

welcomes new members and the EWGs are open for interested parties to join.

### VISION FOR AN AGRONOMIC RESEARCH STRATEGY WITHIN THE WI

The Agronomy EWG will be guided by the principles inherent to sustainable intensification (SI) as called for by the FAO (Hunter et al., 2017) to meet projected increases in demand for food with projected increases in global population. The definition of SI varies, but the Agronomy EWG defines SI as increased agricultural production without adverse environmental impact and without the conversion of additional non-agricultural lands (Hunter et al., 2017). This necessitates increasing farm yields on existing crops lands. While breeders develop new wheat varieties with higher potential yield and resistance to abiotic and biotic stresses, agronomists must design and help implement cropping systems that allow the potential to be realized. These wheat cropping systems must also maintain or improve soil, water, and air quality, and ensure profitability and economic security for farmers. Crucially, those countries that import large quantities of wheat but with potential to increase their domestic wheat production (e.g., countries in Africa, Middle East, and SE Asia) will require agronomists working closely with farmers to develop strategies to adapt new technologies to local conditions more effectively than in the past. By 2030, the Agronomy EWG seeks to promote a framework to improve the effectiveness of global agronomic efforts in wheat-based systems to enhance farm profitability, increase environmental resilience, and ensure an adequate supply of food and feed for the value-added and processing industries.

To accomplish this vision, the focus will be on four priorities with specific actions and outcomes:

### Priority 1—Development of Sustainable Wheat Cropping Systems

Wheat production occurs within a range of different systems worldwide that span the intensive irrigated rice-wheat systems of the Indo-Gangetic Plain, subsidized high input and high yielding systems of northern Europe and China, to the semiarid broad-acre systems of Australia and North America. Despite the differences in specific details of these systems, most share common challenges including lack of crop diversity, rundown in soil fertility, decreasing terms of trade for growers, increasing risk from climate change, increasing public scrutiny over environmental concerns of soil degradation, N leaching, and chemical usage. Significant research into farming systems innovations such as better integration of legumes and oilseeds, no-till and controlled traffic systems, dual-purpose cropping systems, opportunity cropping to replace summer fallow, and integrated pest, disease and weed management strategies are in progress in many regions of the world. This multi-year systems research provides the broader agronomic framework into which novel wheat genotypes and agronomy must be integrated, and also provide the only way in which to monitor the longer term ecosystem impacts of wheat farming systems such as soil

2https://www.wheatinitiative.org/wheat-agronomy

degradation, off-site impacts, and greenhouse gas emissions to inform investigations. As specific new technologies emerge in wheat agronomy (e.g., new autonomous digitally enabled machinery, novel soil microbial amendments, or fertilizer formulations), it will be important to understand how this technology interacts with different systems of production and various growing environments to ensure most effective impact on wheat production systems. The impact of new practices and innovations will be, in part, measured by influences on yield gaps wherever adopted. Yield gaps vary widely depending on country, weather conditions, and soil types; and closing the exploitable gap would add significantly to world wheat production without expansion of current agricultural land. To be useful as a learning and measurement tool, yield gap data need to be developed using a standardized approach that is replicable in different growing regions and accounts for the biophysical limits for production as imposed by weather conditions. Yield gap analysis based on using FAO yield data for wheat collected over time allows the rate of progress in wheat yield to be measured in different countries (Hatfield and Beres, 2019). The ability to calculate yield gaps at a resolution close to the farm level may be possible through the Global Yield Gap Atlas (GYGA) project3 , which provides a worldview and country differences of actual yields of wheat relative to potential yield and adjusted for weather. The GYGA project also estimates yield gaps at small zones with similar soils and weather, which has utility not only at the local or farm level, but can be upscaled to regional and country levels to contribute to the development of policies and prioritization of research and development funds; and finally to help develop principles for regional cropping system maximizing wheat productivity.

It is recognized that wheat varieties are developed to be adapted to specific growing regions, but wheat production system development is likely to share commonality across growing regions. Partnership in these situations, resulting in large internationally coordinated projects, is a powerful tool to understand the impact and best use of these technologies. The challenge is to have enough understanding of the diverse elements that impact wheat production in order that the best combination of sustainable practices can be employed profitably, so that farmers have the incentive to continue to innovate. The EWG would initially select one or more of the following actions as a pilot to build international partnerships. Workshops held in conjunction with other agronomy scientific conferences would be the primary method to gain active participation and encourage more members from areas currently underrepresented (e.g., Asia, Africa, South America) to join the EWG. Some of the potential collaborative areas are large disciplines that would require further discussion to identify projects with international significance.

### Actions

• Perform a meta-analysis of research on global wheat production systems.

### Anticipated Outcomes

## Priority 2—Improved Management of Wheat Biotic and Abiotic Threats to Sustainable Production

Several agronomy-related issues are common across current wheat systems including the alignment of crop life cycle to changing seasonal patterns; biocide resistance in weeds, fungal pathogens, and insect pests; increasing yields while reducing biocide use due to product withdrawals or social acceptance; provision of sufficient N to achieve potential yields while minimizing environmental damage; new production and product quality possibilities afforded by hybrid, perennial, and geneedited cultivars of wheat. These themes of research all require linkages between groups (including EWGs) that address singlecomponent issues (e.g., nutrient use efficiency) for effective impact. The Agronomy EWG uses a holistic approach to improve wheat production systems by focusing on the integration of relevant discipline-specific expertise (Hunt et al., 2019). The approach acknowledges the need to capture effective synergies between innovations emerging from discipline-specific research in order to have the greatest impact on production, socioeconomic, and ecological outcomes.

### Actions

3http://www.yieldgap.org

• Development of N management strategies and evaluation of novel products to improve NUE.

### Anticipated Outcomes

fpls-11-00828 June 15, 2020 Time: 18:3 # 5

## Priority 3—Tools to Support Improved Management Systems for Wheat

Historically, concomitant advancements in breeding and agronomy translated into yield improvements for wheat at the farm level, though the individual contribution of each varied by region (Bell et al., 1995; Nalley et al., 2008; Lollato et al., 2020). Technologies developed to support efforts related to plant breeding more consistently resulted in deployment of tools when compared to agronomy, perhaps because the end user of the tool is the breeder rather than the producer. Fischer and Connor (2018) provide many examples of such technologies, including molecular markers resulting in genomic selection (e.g., Bernardo, 2016), the development of high throughput phenotyping tools (e.g., Araus and Cairns, 2014; Reynolds et al., 2020), and the use of dynamic crop simulation models to inform breeding programs of traits of interest (Chenu et al., 2009; Sciarresi et al., 2019). Despite the potential for improved agronomy through deployment of tools to improve in-season management decisions by producers, these have been scarce.

A few successful examples of tools impacting agronomic on-farm decisions can be cited. The EPIPRE is one of the first interactive decision support systems that incorporated mesoscale weather data and in-field observations to guide producers when to apply fungicides to control foliar pathogens in winter wheat (Zadoks, 1981). A decade later the epidemiological underpinnings of EPIPRE were adapted to the HRSW production of Minnesota and North Dakota4 . This led to the Fusarium Head Blight Prediction Center that is available to wheat producers across 22 states in the United States5 . Another example is, Yield Prophet R , an internet service that uses a dynamic crop simulation model to inform Australian growers about seasonal yield prospects and potential effects of management practices on yield and profit so that inseason management adjustments were data-driven (Hochman et al., 2009). Tools have also been developed to improve nitrogen management for winter wheat and other crops using remote sensing technologies in the Great Plains region of the United States. These efforts started by estimating the crop's yield potential using canopy reflectance (Raun et al., 2001), followed by the development of a commercial GreenSeekerTM sensor (Solie et al., 2002), the development of response indices (Mullen et al., 2003), including soil- and seasonal-specific conditions on the crop's yield potential (Raun et al., 2008). A more recent example includes Canopeo (Patrignani and Ochsner, 2015), an easy-to-use smartphone tool that quantifies fractional green canopy cover and can improve the management of irrigation (Libardi et al., 2019) and wheat grazing in dual-purpose systems (Butchee and Edwards, 2013). While these are successful examples, deployment on-farm is often challenging due to a perception by farm managers or their advisors that adopting new technologies is either too costly or operationally prohibitive (Hochman et al., 2009). Involving growers or other users into the process in a participatory way from the outset would help to overcome issues around onfarm adoption.

The combination of the wealth of agronomic research, the availability of tools to deliver interactive decision support systems, and the nearly ubiquitous access to cellular data networks globally, means that the potential to develop and deploy decision support systems is grossly underutilized. For instance, research papers have exploited the variation in productive capacity for wheat across a field and integrated it with growing season assessments of crop growth for improved nitrogen management (e.g., Schwalbert et al., 2019). Likewise, weed and disease monitoring with remote sensing is an emerging area of crop management (e.g., Franke and Menz, 2007; Cruppe et al., 2017; Pott et al., 2019). Farmers have variable rate seed and fertilizer equipment and often access local weather stations. They also have access to unmanned aerial vehicles (UAVs) that can be used to remotely sense crop growth, the onset of disease and pest stresses, and patterns of water or nutrient stress (Malveaux et al., 2014), though this technology is still at the early stages and opinions on its potential for full integration into on-farm use vary (Freeman and Freeland, 2015). Likewise, no-cost satellite imagery is available to individual producers to help them understand the crop's yield potential and adjust on-farm decisions accordingly (e.g., Schwalbert et al., 2018, 2020). Precision agriculture has advanced rapidly over the last 20 years (Chlingaryan et al., 2018); however, having more ground-truthing studies is needed to better understand the potential and ROI of proprietary tools around "what works where."

### Actions

4https://www.ag.ndsu.edu/cropdisease/small-grain-disease-forecasting-modelhomepage

5http://wheatscab.psu.edu

### Anticipated Outcomes

fpls-11-00828 June 15, 2020 Time: 18:3 # 6

## Priority 4—Improved Knowledge Mobilization and Sharing

Success improving agronomic practices to enhance wheat productivity and sustainability depends upon designing these practices for compatibility with current technology, cultures, and other regional conditions. Producers face a plethora of risks of which agronomic risks are but one. Consequently, producers often choose risk avoidance strategies even when a scientific body of evidence indicates that particular practices are needed. Success will require farmer consultations and input at the on-set of research projects, rather than after the research is completed. This typically will entail transdisciplinary engagement with farmers and educators in formulating these approaches (Eigenbrode et al., 2018). Using participatory on-farm research networks such as the University of Nebraska On Farm Research Network6 , or grower groups such as in Australia's national water-use efficiency Initiative (Kirkegaard et al., 2014), allows not only for validation of small plot research but also identifies early-adopters of new technologies or management tactics who, in turn, can serve as multipliers locally once results have shown to be positive. It will also require effective communication for dissemination to ensure correct implementation and documentation of adoption. A key element to successful adoption is for researchers to recognize and be sensitive to local or regional socio-economic barriers to adoption.

### Actions

### Anticipated Outcomes

• Increased global linkages and knowledge sharing among producers, agronomists, innovators, scientists, producer organizations, private companies, and governments.

• To better understand how effective knowledge mobilization and sharing systems can be adapted to meet regional needs in a digital world.

### IMMEDIATE GOALS OF THE AGRONOMY EWG OF THE WI

## Goal 1: Develop Agronomic Research Priorities That More Broadly Reflect International Requirements for Collaboration

We have taken the first, essential steps toward the development of agronomic research priorities. These included joint meetings of agronomists from different member countries to share information about current projects, and an ongoing effort to establish a database or research inventory of agronomy projects. Beyond the projects led by the agronomists participating in the meetings, we also asked for the participating agronomists to contact colleagues and funding bodies nearby their geography to compile information specifically on wheat that were either funded at the time of data collection (i.e., 2017–18), had recently been terminated, or had received confirmation of future funding. We collected information from 782 research projects originating from Australia (15% of total projects in the database), Canada (30%), China (10%), Spain (1%), United States (42%), and CIMMYT (2%) (**Table 1**). While 106 projects did not report start or end dates, the start date of the remaining projects ranged from 1999 to 2020; with termination dates ranging from 2015 to 2022. Average project duration (weighted by the number of projects in each duration category) was 4.2 years, ranging from 1 to 20 years (**Figure 1**). About 30% of the projects were funded for periods of 3 years or less, and the majority (c.a., 67%) of the projects were funded for a period of 4–6 years. The pilot database in its present form is a starting point and will be added to the Wheat Vivo database of the WI and potentially expanded with the inclusion of other key countries that produce wheat (e.g., Argentina, Uruguay, Black Sea Region) and developing countries that might wish to expand wheat production (e.g., North Africa, Sub Sahara, and South Africa). The pilot database has been used by the EWG to analyze ongoing work with collaborative opportunities and perhaps more importantly where there are gaps in knowledge that require a broad approach. While there has been no discussion within the EWG, the database could be further developed into a centralized repository for agronomic data particularly if the EWG is successful in launching internationally coordinated studies to address common research priorities. Data driven agricultural innovation [Genetics, Environment, Management, Socioeconomics (GEMS)], an international effort led by the University of Minnesota (GEMS, 2020) integrates special and temporally distributed genomic, environmental, management, and socio-economic data into a single platform. The interpretation of big data will require a much more diverse expertise than agronomy alone, but offers solutions to real world challenges.

6https://cropwatch.unl.edu/on-farm-research

TABLE 1 | Number of research projects currently funded by Wheat Initiative (WI) Strategic Research Agenda theme and country (or institution).

The "Other" category included projects that were either transdisciplinary (e.g., interaction between fertility management and weed control), that focused on non-growing season management (e.g., fallow, stubble, or break-crops), or that involved crop modeling and weather-related crop stresses.

This information is particularly important as longer term funding allows for better sampling of environments, development of probabilistic response curves to given management practices, and for a more representative measurement of sustainability indicators. While single-year funded projects might not allow for development of these indicators and many times consisted of industry-sponsored research protocols; single-year projects can also be extremely relevant in cases where a rapid outcome from agriculture research is needed in response to emerging or unanticipated threats. The EWG will work to grow that database and update it regularly, which will be particularly important as the Agronomy EWG expands its membership.

Initial analysis of the wheat agronomy research inventory showed there is a considerable body of work underway in general agronomy and crop management (30% of the projects in the database), followed by increasing wheat yield potential

(24%), and controlling wheat diseases and pests (18%) (**Table 1**). Other notable projects focused on nutrient use efficiency (7%), ensuring the supply of high quality and safe wheat products (7%), and improving wheat tolerance to abiotic stresses (3%). Only around 5% of the projects were either transdisciplinary, focused on non-growing season management, or involved crop modeling and weather-related crop stresses, and were grouped in the "Other" category. Projects focused on knowledge exchange and education (5%), and enabling technologies and shared resources (2%) completed the reported activities. Much of this work produces regionally specific results with few projects linked to national or international collaborations. However, even though the impacts of the work are often regional and specific to the area in which each project is conducted, many of the constraints they address are universal or at least experienced in some other production systems around the world. While the specifics of agronomy may be context dependent, the principles and approaches of transformational agronomic research can be universal (Hunt et al., 2019). The EWG will work to improve global collaborations addressing common constraints or opportunities and promoting the principles of transformational agronomy. The EWG could play an important role in establishing a central repository for agronomy protocols similar to that put in place by CSIRO in Australia (Nictora and McIntosh, 2011) that would support more standardized procedures as has been evident [e.g., Global Yield Gap Atlas (2020)], and in sharing SI approaches through collaboration and partnerships.

The draft agronomic research strategy developed by the EWG has research priority areas that have been formulated to address the overall need to increase production but balanced to protect the environment. The approach is intended to bring together research, knowledge sharing, and funding capacity to focus on a common vision of SI and outcomes to move the industry forward. An integrated crop management strategy is suggested as the priorities for research, technology sharing, and capacity building are shared while respecting the specific mandates of all contributors. The strategy encompasses

crops, soils, environment, climate change, production, and economics. The central assumption is that wheat is not grown in isolation but in systems that include other field crops, the crop/animal interface and the socio-economic context of different production environments. The strategy integrates systems research to: (1) enhance sustainability, both economic and environmental; (2) find more effective methods for longterm crop production, which support and preserve soil, water, air and economic viability of agriculture; (3) enhance economic return through a more efficient conversion of inputs, natural or manmade, to economic product; capturing and holding more components of the system (e.g., carbon credits, biodiversity) and to reduce movement of nutrients beyond the agricultural system (environmental risk); and (4) implement systematic approaches to manage disease, weeds, and insects that are significant threats to crops and the crop/livestock interface that impact value-chains.

### Goal 2: Assessment System for Wheat Sustainability (e.g., an Index); Global Assessment of Wheat Production Systems for Sustainability

A successful global network of sustainable wheat agronomic research will require a common framework of metrics to enable comparative work and sharing of findings, and as a basis to assess the global success of its efforts to reduce yield gaps. The goal would not be to develop a new sustainability index but to understand the work that has been done in many countries and adopting a common approach to measuring sustainability and how SI can be achieved. Adoption of common understanding of definitions and metrics for SI will enable coordinated efforts and serve to amplify the potential impact of the technological advances in wheat production supported by the other EWGs within the WI. A workshop with full participation of the EWG is needed to develop the working definitions for the sustainability of wheat production systems to meet the criteria of SI.

## Goal 3: Country-Specific EWGs for Agronomy Established; Ideas for International Collaborations Developed

To maximize effectiveness, the Agronomy EWG needs nodes for implementation at the country level that bring in key players such as producer groups or associations, private industry, and others such as federal and provincial governments. Thus, performance and success of the Agronomy EWG will also be measurable in terms of level of involvement of key players and development of influential partnerships. The number and types of collaborative partnerships to address the challenges identified by the Wheat Initiative will be key performance indicators. The representational structure that has developed within the Agronomy EWG with members from different regions and countries can be extended to maximize involvement. Agronomy EWG members chosen for their strong credentials, achievements, and influence in wheat agronomy in their countries and regions is needed.

## WHEAT INITIATIVE AGRONOMISTS COMMUNITY IN THE AMERICAN SOCIETY OF AGRONOMY

In 2017, the Agronomy EWG applied to the American Society of Agronomy to consider the formation of a WI Agronomists Community within its Global Section. The main aim of this Community was to consolidate global expertise for agronomy with a focus on wheat production systems. The approach developed and adopted a "systems agronomy framework" relevant to any wheat production system in the world. Such an approach first establishes the scale of current yield gaps identifying physiologically defensible benchmarks, and then takes a holistic approach to understand and overcome exploitable yield gaps. Finally, new opportunities to increase current and potential yield would be sought by capturing future G × E × M synergies identified in different systems. It will be important to have Agronomy EWG in participating countries to feed into the WI as this will allow flexibility for each distinct country and their funding systems.

Supporting aims include:

The aim of this Community and the Agronomy EWG is to bring together experts from a broad range of disciplines (vs. a silo approach) who would all contribute to the enhancement of the wheat phase as part of a systems agronomy approach that will meet the global challenges facing wheat growers and end-users today and well into the future. Thus, we encourage members from all supporting disciplines, industry colleagues, policy-makers, and funding stakeholders to join this Community and the WI's Agronomy EWG.

## APPLICABILITY OF THE FRAMEWORK FOR OTHER CROPS

Although the Agronomy EWG within the WI is focused on wheat and wheat-centered systems, its vision and framework as described above is applicable to many other crops grown on a global scale. Advances in genetics and crop breeding can only be realized when deployed in agronomic production

systems suited to the natural and social resources of a region and in systems designed with resilience to local biotic and abiotic stresses. We are not aware of initiatives of similar scope for other staple or key nutritional crops. We hope the Agronomy WI will serve as a model and thereby serve more broadly to enhance food security and agricultural sustainability worldwide.

### AUTHOR CONTRIBUTIONS

BB prepared the original manuscript with input, revisions, and editorial contributions provided by all co-authors. JK, JRH, SE,

### REFERENCES

DL, RL, JW, and SS provided significant contributions in the subsequent revision.

### ACKNOWLEDGMENTS

The authors would like to thank the WI's Secretariat, Science Board and Institutions' Coordination Committee for providing resources to enable the formation and development of the Agronomy EWG. The authors also sincerely thank Dr. Stephen Morgan Jones and Dr. George Clayton for facilitating the Agronomy EWG's efforts around the establishment of research priorities.

to increase wheat yield with nitrogen. Agron. J. 95, 347–351. doi: 10.2134/ agronj2003.0347

satellite imagery. Biosyst. Eng. 171, 179–192. doi: 10.1016/j.biosystemseng.2018. 04.020

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Beres, Hatfield, Kirkegaard, Eigenbrode, Pan, Lollato, Hunt, Strydhorst, Porker, Lyon, Ransom and Wiersma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Yield Gaps in Wheat: Path to Enhancing Productivity

### *Jerry L. Hatfield1\* and Brian L. Beres2*

1 National Laboratory for Agriculture and the Environment, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States, 2 Lethbridge Research and Development Centre, Lethbridge, Agriculture and Agri-Food Canada, Lethbridge, AB, Canada

Wheat production is required to supply food for the world's population, and increases in production will be necessary to feed the expanding population. Estimates show that production must increase by 1 billion metric tons to meet this demand. One method to meet future demand is to increase wheat yields by reducing the gap between actual and potential yields. Potential yields represent an optimum set of conditions, and a more realistic metric would be to compare actual yields with attainable yields, where these yields represent years in the record where there is no obvious limitation. This study was conducted to evaluate the yield trends, attainable yields, and yield gaps for the 10 largest wheat producing countries in the world and more localized yield statistics at the state or county level. These data were assembled from available government sources. Attainable yield was determined using an upper quantile analysis to define the upper frontier of yields over the period of record and yield gaps calculated as the difference between attainable yield and actual yield for each year and expressed as a percentage of the attainable yield. In all countries, attainable yield increase over time was larger than the yield trend indicating the technological advances in genetics and agronomic practices were increasing attainable yield. Yield gaps have not shown a decrease over time and reflect that weather during the growing season remains the primary limitation to production. Yield gap closure will require that local producers adopt practices that increase their climate resilience in wheat production systems.

### Edited by:

Hans-Peter Kaul, University of Natural Resources and Life Sciences Vienna, Austria

### Reviewed by:

Qiang Yu, Northwest A&F University, China Neil Huth, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia

### \*Correspondence:

Jerry L. Hatfield<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Crop and Product Physiology, a section of the journal Frontiers in Plant Science

Received: 26 June 2019 Accepted: 14 November 2019 Published: 06 December 2019

### Citation:

Hatfield JL and Beres BL (2019) Yield Gaps in Wheat: Path to Enhancing Productivity. Front. Plant Sci. 10:1603. doi: 10.3389/fpls.2019.01603

Keywords: yield, wheat production, yield gap, weather, gap analyses

## INTRODUCTION

Productivity of agricultural commodities throughout the world must increase in order to supply the food needs of the expanding population. Alexandros and Bruinsma (2012) estimated that by 2050, an additional 1 billion metric tons per year of cereals would be needed to meet the demand, which would require an increase in production from 2.1 to 3.0 billion metric tons. This requires that we either increase yield of crops through closing the yield gap between the potential and actual yields or by increasing the potential yield of crops. Evans and Fischer (1999) introduced the concept of potential yield in crops and the value of considering potential yield in evaluating progress of crop management programs. Most would argue that decreasing the yield gap is more achievable than increasing the potential yield. If we assume that the potential yield can be described as

$$Y\_p = \text{Cx } \text{St} \propto \text{e}\_i \propto \text{e}\_c \propto \text{e}\_p \tag{1}$$

where Yp is the potential yield, St is the incident solar radiation, C is the fraction of photosynthetically active radiation in total solar radiation, εi the interception efficiency, εc the conversion efficiency of solar radiation into photosynthetic products, and εp the efficiency of the conversion of stored carbon into harvestable products. Long et al. (2006) conducted an in-depth analysis on these terms and concluded that εi and εp are near the theoretical maximum for agronomic crops while there is improvement in εc possible and potentially increase Yp by 50%. This relationship was first described by Monteith (1977) to evaluate the efficiency of light capture by crops and understanding how light is efficiently captured by agronomic crops will pay dividends in increasing crop productivity. We can describe the yield gap as

$$Y\_{\mathcal{g}} = Y\_{\mathcal{p}} - Y\_a \mathcal{w} \tag{2}$$

where Yg is the yield gap and Ya the actual yield. There have been several studies on the yield gap for a variety of crops that has led to the development of a global yield gap atlas (www.yieldgap. org) based on the application of the use of Yp and Ya as described by van Ittersum et al. (2013). Guilpart et al. (2017) provide a methodology for estimating Yg at the cropping system level. The approach has demonstrated the utility of being able to quantify yield gaps for different crops and climates.

In their analysis of yield gaps for a variety of crops, Fischer et al. (2014) provided an in-depth assessment of the trends in yields for different crops in their megaclimatic regions and proposed that Yp would represent yields with no limitations of water or nutrients of the best-adapted varieties. Evans and Fischer (1999) proposed this definition to provide a standard for comparison among experiments. Fischer et al. (2014) defined farm yield (Yaf) as the crop yield reported at the field, district, regional, or national average, and attainable yield (Yat) is the yield achieved under economically optimal practices with minimal limitations due to the weather during the growing season. Fischer et al. (2014) proposed yield gaps (Yg) should be expressed as a percentage of Yat because this metric would have more impact in evaluating the limitations to production rather than Yp.

In their analysis of wheat (*Triticum aestivum* L.) yields and Yg, Fischer et al. (2014) found that increasing Yp is an important factor in increasing Ya and that increases in Ya are a result of improved agronomic practices and would require implementation of multiple practices. They proposed that increases in Yp are associated with increased grain number, harvest index, grain weight, and total dry matter. Yield gaps in wheat are closing slowly because of the adoption of agronomic practices that enhance Ya (Fischer et al., 2014). Yield gaps have been the focus of several studies on a range of agronomic crops. Lobell et al. (2009) evaluated Yg and found in irrigated wheat, rice (*Oryza sativa* L.), and maize (*Zea mays* L.) that yields were near 80% of the Yp, and that weather was the major constraint on productivity variation during the growing season. Nuemann et al. (2010) evaluated yield gaps of global grain production and suggested that closing the yield gap would require a detailed understanding of the specific limitations for each region. Mueller et al. (2012) found nutrient and water management were key to closing the yield gap because yield variability was affected by fertilizer use, irrigation, and climate. This conclusion was also supported by Sinclair and Rufty (2012) where they observed crop yield increases were more closely associated with nitrogen and water than plant genetics. Ray et al. (2015) observed that climate variation explained one-third of the global crop yield variability and in some areas of the world over 60% of the yield variability could be attributed to climate variation. Grassini et al. (2013) cautioned that historical yield trends needed to be evaluated to determine their trends and potential plateaus. They reported that wheat yields since 1960 in northern Europe, e.g., France, the Netherlands, and United Kingdom, had plateaued while increases were still evident in Australia, China, and India. In developing countries, George (2014) argued that crop yields have not increased in proportion to the advances in agronomic practices and there is much potential in productivity to be realized with adoption of improved practices.

Using yield gap analysis based on Yat from country-level data in the Midwestern US, Hatfield et al. (2017) showed that Yg on maize and soybean (*Gylcine max* L. Merr.) was related to July maximum temperatures, August minimum temperatures, and July–August total precipitation. Each of these climatic variables has a direct relationship to the physiological efficiency of these crops. However, in Great Plains wheat production, Hatfield and Dold (2018) found that Yg was closely related to precipitation during the grain-filling stage and that temperatures were not a consistent limiting factor because wheat in these areas was not exposed to temperatures above the upper range for development. Analysis of the factors causing yield gaps can provide a valuable tool for assessing the limitations to productivity and improved management strategies to increase Yaf. The objective of this study is to evaluate the yield gaps in the major wheat growing regions of the world using readily available data and to determine the limitations to productivity using yield gap analysis. The study serves as the foundation to develop strategies to decrease the yield gap for the major yield producing areas.

## METHODOLOGY

Data on wheat yields were collected from various sources to represent a range of scales. The primary data source was Food and Agriculture Organization Statistics (FAOSTAT) (www.fao. org/faostat accessed 3-June-2019) for the 10 highest-producing countries in the world. These countries are shown in **Table 1** with the area harvested and production for 2017. Data from 1961 through 2017 were extracted for the area harvested, yield, and total production. For the United States, state-level data were extracted for the top three wheat producing states, Kansas, North Dakota, and Washington, from the National Agricultural Statistics Service (nass.usda.gov, accessed June 3, 2019) for the period from 1950 through 2018. A more in-depth analysis was conducted for the top three producing counties in Kansas, Mitchell, Saline, and Sumner, with the data extracted from the NASS site for the period from 1950 through 2007. The area harvested and total production for these states and counties are shown in **Table 2**.

TABLE 1 | Area of wheat production and annual production in 2017 and the average yield gap from 1960 to 2017 for the top 10 wheat producing countries.

Data extracted from FAOSTAT (www.fao.org/faostat).

TABLE 2 | Area harvested and total production in 2018 for the three top producing states in the United States and the average yield gap from 1950 through 2018 and the area harvested and total production for the top three producing counties in Kansas and the average yield gap from 1950 through 2007.

Data extracted from the National Agricultural Statistics Service (www.nass.usda.gov). Standard errors are shown in parentheses.

Analysis of the data was conducted across all yield records with the same approach. The process used quantile regression at the 95th percentile (PROC QUANTREG in SAS, SAS for Windows v 9.3, SAS Institute, Cary, NC). This procedure was followed to find the attainable yield (Yat) for all of the observed yield data for a country, state, or county. Use of this approach to obtain boundary lines was described by Webb (1972) and Cade and Noon (2003). This method was used by Egli and Hatfield (2014a, 2014b) and uses a statistical method to select the years at the upper frontier of the record across the observed period of record to determine Yat for different counties in the Midwest. These Yat yields are assumed to represent the years in which weather was not a limitation to production. The yield gap is calculated as

$$Y\_{\mathcal{X}} = \left(Y\_{\mathcal{at}} - Y\_{\mathcal{a}}\right) / Y\_{\mathcal{at}} \tag{3}$$

where Yat is the attainable yield obtained from the quantile statistical analysis. Equation 3 provides the fraction yield gap. For each year, Yat and Yg are computed and an average of the Yg computed across the total record. Data are presented to show the yield trend and Yat and the temporal trend of Yg for the 10 countries with the largest production since 1960. To obtain the yield trend line in the Ya values, we used linear regression through the observed data with (PROC REG from SAS).

### RESULTS AND DISCUSSION

### Yield Trends and Attainable Yield

In this analysis, we focused on two metrics from the yield record, the Yat and Ya values. The assumption made was that the slope of the Yat line would represent the technology increase for a given country, while the slope of Ya would represent the ability of the country-level yields to increase given the combination of technology and weather within the growing season.

Wheat yields in all countries have shown a continual increase since 1960. There is variation among the countries for both the slopes in the Yat and Ya values (**Table 3**). The differences between the two values for a given country reveal that technological advances have increased more than the yield trend line. If technology was the only factor contributing to the yield trend, then the expectation would be for the slopes of the lines to be similar; however, the slopes were significantly different (ρ < 0.01) using simple T-test. The standard error of the estimates was computed for each regression analysis and was quite small for all of the countries we evaluated. The Yg values are computed on an individual year, and although there may be differences in the production area within a given country, this would be negated by the estimated Yat and Ya observations within a given year because these values are from the same land area. Yield gap analysis at the country level could be independent of the production region unless there was a major shift in the production region from good to poorer soils or from rainfed to primarily irrigated areas. To evaluate this, we found no significant relationship between the area under production and Yg for a given year across any of the countries. The assumption would be that trends in country-level yields would be reflective of the technology adoption within the country.

There are differences among countries. China wheat yields have shown an increase in yield; however, the slope of Yat is 99.8 kg ha−1 year−1 while Ya is at 88.3 kg ha−1 year−1. China has demonstrated the most consistent increase in wheat productivity since 1960, suggesting that wheat production has benefited

Standard errors are shown in parentheses.

Hatfield and Beres World Wheat Yield Gaps

from adoption of technology across the country (**Figure 1A**). If we compare the yield trends in Australia (**Figure 1B**), Canada (**Figure 1C**), and the United States (**Figure 1D**), these national scale yields show variability among the years with the Yat trends exhibiting a larger increase than Ya. In the top wheat producing countries, the Yat increase was greater than the Ya trend (**Table 1**), with France and Germany showing the largest values in Ya and Yat. These two countries have used a combination of advanced technology in managing the crop and have a climate that is ideally suited to wheat production because of the combinations of temperature and precipitation during the growing season. Technology adoption was suggested by Fischer et al. (2014) as being a significant factor in closing the yield gap.

### Yield Gaps

Each country has shown an increase in Ya; however, the Yg has remained relatively constant over the years. The average Yg for the period from 1961 through 2017 for the 10 top producing countries showed a range from 0.0 in Germany to 0.24 in Australia and Canada (**Table 3**). These are the average values calculated from the annual Yg values. There is variation in Yg across the years for all countries with no significant trend in closing the gap between actual and attainable wheat yields, e.g., China (**Figure 2A**), Australia (**Figure 2B**), Canada (**Figure 2C**), and the United States (**Figure 2D**). China is showing a decrease in the variation in Yg in the last decade (**Figure 2A**); however, this has not impacted the overall Yg trend. Australia exhibits the largest Yg values, often exceeding 0.5, and there has been no change in the Yg values since 1960 (**Figure 2B**). This can be attributed to the large variation in the meteorological conditions during the growing season related to the El Nino Southern Oscillation (ENSO) Index and years with large negative Southern Oscillation Index (SOI) values have the largest Yg values and large positive values showed the smallest Yg values. However, the scatter among all of the years showed the overall Yg record was not significantly correlated with SOI values because other factors contributed to the inability of the wheat crop to achieve its potential. Canada has the same average Yg value as Australia and has recorded Yg values near 0.5; however, the past 4 years have shown very small Yg values compared to the early record because of more favorable weather, e.g., slightly warmer temperatures and above-normal precipitation during the grain-filling period for the wheat producing regions. Yield gap values in the United States average 0.12 and show a trend toward decreasing Yg values, but this trend is not significant. These observations of Yg in these countries represent the range of yield gaps in wheat producing countries in

FIGURE 1 | Yield trends and attainable yield for China (A), Australia (B), Canada (C), and United States (D) from 1960 through 2017. Data from FAOSTAT (www. fao.org/faostat).

FIGURE 2 | Yield gap trends from 1960 to 2017 for China (A), Australia (B), Canada (C), and United States (D) using the data obtained from FAO (www.fao.org/faostat).

the world. It is important to realize that national scale yields are comprised of many environments and soils and reflect the largescale impacts of technology and the weather, and it is not possible to produce any assessment other than general trends.

### State- and County-Level Yields and Yield Gaps

To address the question of trends in more regional scale observations we extracted state-level data for the top three wheat producing states in the United States and computed their Yat and Yg values. The yield trends for Kansas show an increase in Ya over time with no decrease in the Yg values (**Figure 3**). We found the same results for North Dakota and Washington with increasing yields but no decrease in the Yg values over time. To further refine the scale to the county level, the top three wheat producing counties in Kansas were selected, and they also showed the same pattern as the Kansas aggregate data. There were differences among the three counties in their average Yg values (**Table 2**), with Mitchell county showing the largest Yg average. The same results were observed by Hatfield and Dold (2018) when they examined Kansas, Oklahoma, and North Dakota wheat production and found Yg values were related to the precipitation amounts during the grain-filling period. For these three counties, one weather event in 2007, a late spring freeze during heading was responsible for large Yg values pf 0.64 (Mitchell), 0.76 (Saline), and 0.71 (Sumner).

Saskatchewan is the top wheat producing province in Canada and shows the same trends as the whole country of Canada

(**Figure 4**). There is a difference in Canadian wheat production with the growth of spring wheat varieties in the western provinces. The attainable yields exhibit a larger slope than the observed yield trends revealing that weather limitations on yields reduce the effect of technology. As we change the scale of the observed yield trends, there will be greater differences in the variation around the trend line because of the more local effects of weather and soil variations and their interactions. For example, within countylevel yields, the impact of a drought or freeze could be quite large; however, at the statewide or country level, these effects may not be seen because the effects would not be evident across the large area.

### Yield Gap Trends

There is no discernable trend in the Yg values across any scale we examined in this study. The Yat values exhibit a larger increase than the Ya trends, indicating that technology (agronomic and genetic) has increased the attainable yield and the potential yield; however, we are not closing the yield gap. The fact that the Yg values have not decreased would suggest that weather remains the dominant factor limiting production around the world because the adoption of technology has provided for substantial yield increases across all countries. Since weather is the dominant effect on Yg, the challenge will be to determine how climate resilient a cropping system can be for a given region. This will require changes in the management practices as proposed by Hatfield and Walthall (2015) where they discussed the role of the genetics × environment × management (GxExM) concept in providing a framework for increasing productivity. The scale of yield data assembled from national, province, state, or county level provides

### REFERENCES

an indication of the potential progress toward reducing the Yg at a large scale. However, this scale doesn't provide potential options for a producer to increase their productivity and reduce the yield gap. Evaluation of specific factors and potential management options for producers will have to be evaluated at a scale that represents the actual growing conditions. The analyses in this study were focused on the country-scale assessments to determine our progress toward decreasing the yield gap.

### CONCLUSIONS

The concept of yield gaps provides a framework for assessing the trends in yield for all crops. Across the top wheat producing countries of the world, there are differences in the progress for increasing yield. In France and Germany, the yield increase is near 100 kg ha−1 year−1, while in Australia, it is 15 kg ha−1 year−1, which can be attributed to a large difference in the variation in the climate between these two regions. There is also a major difference in the magnitude of the Yg values between these two areas. Yields are more stable in the northern Europe environments compared to the Australian continent, also reflective of the weather variation among growing seasons. Evaluating smaller-scale yields, e.g., county, reveals that weather within the growing season is the dominant factor affecting yield gaps (Lobell et al, 2009). Technological advances have increased the attainable yields at a greater level than the yield trends, indicating that to close the yield gap, wheat producers will have to adopt practices at the local scale that will allow the technology improvements to be realized. These are local decisions made by individual producers; however, efforts to demonstrate how soil and agronomic practices that increase productivity could reduce the yield variation among years will pay dividends in closing the yield gap in wheat.

### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

### AUTHOR CONTRIBUTIONS

JH prepared the data analysis and JH and BB jointly prepared the paper.

### FUNDING

This research is supported from project 5030-11610-005-00D.

Egli, D. B., and Hatfield, J. L. (2014a). Yield gaps and yield relationships in central U.S. *Soybean Prod. Syst. Agron. J.* 106, 560–566. doi: 10.2134/agronj2013.0364

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Hatfield and Beres. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities

*Till Rose\* and Henning Kage*

Institute of Crop Science and Plant Breeding, Agronomy and Crop Science, Christian-Albrechts-University, Kiel, Germany

Wheat yields in many of the main producing European countries stagnate since about 20 years. Hence, it is of high interest, to analyze breeding progress in terms of yield and how associated traits changed. Therefore, a set of 42 cultivars (released between 1966 and 2012) was selected and yield as well as functional traits defined by the Monteith and Moss equation were evaluated under three levels of management intensity. The Monteith Moss equation thereby calculates grain yield as the product of incident photosynthetically active radiation, fraction of intercepted radiation, radiation use efficiency, and harvest index. The field trial was performed in a high yielding environment in Northern Germany in two seasons (2016–2017 and 2017–2018) with very contrasting rainfall rates. The three differing managements were: intensive (high N + pesticides), semi-intensive (high N − pesticides), and extensive (low N − pesticides). The results indicate that the stagnation of wheat yields in Central-Europe is not caused by a diminishing effect of breeding on yield potential. This equally applies to suboptimal growing conditions like extensified crop management and restricted water supply. Nearly all functional sub-traits showed a parallel progress but coefficients of determination of relationships between traits and year of variety release are decreasing along the hierarchy of yield formation. One exception is radiation interception which did not show a stable linear increase during breeding history. In recent years, biomass is getting more important in comparison to harvest index. Values of harvest index are slowly approaching theoretical maxima and correlations with grain yield are decreasing.

Keywords: wheat, breeding progress, harvest index, biomass, radiation interception, radiation use efficiency, high-throughput phenotyping, uav

## INTRODUCTION

Breeding progress of common wheat (*Triticum aestivum* L.) recently gained much attention because after at least half a century of continuing increase of farm level wheat yields (Calderini and Slafer, 1998) this process stagnates in many of the main producing countries, including France, the United Kingdom, and Germany (Lin and Huybers, 2012). These countries achieve above-average yields and therefore have major importance for worldwide wheat supply. An analysis of the breeding progress

### Edited by:

Hans-Peter Kaul, University of Natural Resources and Life Sciences Vienna, Austria

### Reviewed by:

Roberto Tuberosa, University of Bologna, Italy Antonio Costa De Oliveira, Universidade Federal de Pelotas, Brazil

> \*Correspondence: Till Rose<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Crop and Product Physiology, a section of the journal Frontiers in Plant Science

Received: 01 July 2019 Accepted: 31 October 2019 Published: 06 December 2019

### Citation:

Rose T and Kage H (2019) The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities. Front. Plant Sci. 10:1521. doi: 10.3389/fpls.2019.01521

1 **22** of the past can help to get back on track for a much-needed increase of wheat yields.

A deeper understanding if yield formation was analyzed using the equation (1) (Monteith and Moss, 1977):

$$GY = \sum\_{s \text{overlap}}^{harsst} \left[ R\_{\text{PAR}} \times RI \times RUE \right]\_{\text{BIO}} \times HI,\tag{1}$$

which calculates grain yield (*GY*) as the product of incident photosynthetically active radiation (*R*PAR), fraction of intercepted radiation (*RI*), radiation use efficiency (*RUE*), and harvest index (*HI*). This equation can be aggregated to the term: *GY* is the product of above-ground biomass (*BIO*) and *HI*.

The amount of incident radiation is determined by the location, its seasonal weather, and the dates of sowing and harvest. The intercepted fraction of this radiation is defined by the speed of development, the final size, the longevity, and the architecture of the canopy (Long et al., 2006). A high speed of canopy development is labeled "early-vigor". Under Central-European growing conditions losses until canopy closure mainly occur during March and April, when the green area index (*GAI*) of the canopy is not sufficient for near total interception and in parallel usable incoming radiation occurs (Rose et al., 2017). A fast canopy development in this early phase can increase biomass production.

The longevity of the canopy mainly depends on its senescence dynamics. When canopies reach their final size the subsequent phase of maximal photosynthesis is rapidly replaced by the phase of senescence. Here, physiological integrity is maintained in the beginning but complete self-destruction finally terminates all growth processes (Thomas and Smart, 1993). Like the subtle process of canopy formation, its senescence is a well-programmed sequence and after the period of carbon assimilation, leaves now contribute to the grain yield by the remobilization of their nutrients (Wu et al., 2012). The visible symptom of leaf senescence is the loss of chlorophyll and genotypes which express delayed chlorophyll catabolism are named "stay-green" (Thomas and Ougham, 2014).  In contrast, unfavorably delayed leaf senescence might result in a low nitrogen use efficiency as well as a low grain protein content (Wu et al., 2012). A tradeoff between a longer maintenance of leaf chlorophyll and a less efficient remobilization of nitrogen has been shown by Gaju et al. (2011). Therefore, Thomas and Ougham (2014) define the ideotype to "comprise late initiation of canopy senescence, to maximize C capture, followed by fast and complete mobilization of N and other nutrients".

The onset of senescence is both, part of the development process of a plant and inducible by numerous external factors (temperature, drought, nutrient supply, pathogen attack) (Buchanan-Wollaston, 1997). The developmental senescence is a consequence of phenology and induced by internal signals such as phytohormones. Stress-induced senescence in contrast is triggered by external signals (Derkx et al., 2012).  So, a "stay-green" phenotype can represent the phenological component per se but also just reflects the ability to cope with the aforementioned external factors (Richards, 2000; Rebetzke et al., 2016).

The amount of intercepted radiation is multiplied by the radiation use efficiency to get the final biomass, the measure for biological performance. This transformation efficiency includes all forms of stress (drought, pathogens, etc.) but is also affected by crop architecture (Zhu et al., 2010) and light saturated photosynthetic rates (Gaju et al., 2016). The increase of radiation use efficiency in a historical breeding set was not related to photosynthesis but shifts in canopy-level traits (Sadras et al., 2012).

The final biomass is multiplied with harvest index to get the grain yield, the measure for agronomical performance. In contrast to the preceding steps of yield formation which can be improved by the optimization of manifold biological processes, here, the quite simple parameter—fraction of biomass that is part of the harvested organs—can considerably change the final grain yield. The upper border of this fraction is restricted by an increased risk of lodging (Berry et al., 2007) and eventually occurring negative interactions with the processes of biomass production. Educated guesses for this threshold are: 0.62 (Austin, 1980), 0.64 (Foulkes et al., 2010), and 0.66 (Shearman et al., 2005).

For an adequate representation of environmental variables which influence complex traits, uncontrolled field trials are still the only adequate facility. Aerial platforms—especially unmanned drones—are an attracting platform for the use in these large-scale open field trials. Equipped with different sensors, they perform high throughput phenotyping with a high spatial resolution (Haghighattalab et al., 2016; Pauli et al., 2016; Araus et al., 2018; Condorelli et al., 2018). Additionally, in comparison to ground based methods, airborne methods often reach a higher precision because they can represent the whole plot (statistical selection can be applied to all pixels) and confounding environmental effects (temperature, sun angle etc.) are reduced through the short measurement time (Tattaris et al., 2016). This can result in higher repeatabilities for a trait like the NDVI (Condorelli et al., 2018).

The progress in the development of small unmanned drones, in combination with calibrated spectral sensors for the prediction of whole season green area indices, now allows to measure all components of the yield equation by Monteith and Moss (1977) in large field trials.

The aim of this study is to analyze the functional pathways historical breeding used to improve yield potential of winter wheat in Central-Europe. To enlighten the black box of the preceding breeding success can help to open new perspectives for the contribution of breeding to a much-needed increase of wheat yields. The analysis is complemented by suboptimal crop management intensities and experimental years with contrasting water supply which might become more relevant due to environmental concerns and a changing climate.

### MATERIAL AND METHODS

### Field Trials

The field trials were conducted during two consecutive years (growing seasons 2016–2017 and 2017–2018). Sowing date was from September 20 to September 22, 2016 and on October 17, 2017. Harvest date was from August 8 to August 15, 2017 and on July 27, 2018 (**Table 1**). The experiment comprised two factors: crop management and winter wheat cultivar, and the design was laid out as a split-plot with three replications in which the factor crop management was nested within replication and the factor cultivar was nested within crop management.

The factor cultivar includes 42 levels. The set of cultivars is chosen to represent the German breeding progress in the period from 1966 to 2012 (for more details see **Table 2**). The factor crop management includes three levels: intensive, semiintensive, and extensive. The intensive treatment received mineral fertilizer at a total N supply rate of 220 kg N ha−1 (fertilization adjusted for soil mineral nitrogen, Nmin) as well as full intensity of fungicides, insecticides, and growth regulators, representing standard wheat production systems in Central-Europe. The semi-intensive treatment received mineral fertilizer at the same level as well as growth regulators, but no fungicides and insecticides, representing a scenario with no chemical plant protection. The extensive treatment received mineral fertilizer at a total N supply rate of 110 kg N ha−1 (fertilization adjusted for Nmin), no fungicides and insecticides, and no growth regulators in the first season (due to problems with lodging growth regulators were applied in the second season), representing a scenario of comprehensive extensification in crop production.

To prevent patchy effects from weed cover, chemical control measures were applied across all crop management treatments (including extensive). Nutrients other than N were applied consistent across all crop managements according to requirements determined individually in each year.

TABLE 1 | Main agronomical and phenological dates during both growing seasons.

Values in parentheses indicate the range of the analyzed set.

TABLE 2 | Detailed information about the set of analyzed cultivars.

Year of cultivar release, breeder, and baking quality group. German baking quality groups are: E (elite, premium quality bread wheat), A (bread wheat), B (milling wheat), and C (feed quality).

To avoid neighboring effects between plots (caused by differing plant heights) and to allow separate harvest of early and late maturing cultivars, those were grouped by expected plant height and maturation time in four incomplete sub-blocks (early/short, early/high, late/short, late/high) which were randomized within crop management. Those sub-blocks were omitted in the second season because it has shown that differing maturation time is not a problem (all sub-blocks were harvested in parallel) and plant height is quite similar when growth regulators are applied.

The experiments were part of the project BRIWECS (breeding innovations wheat for resilient cropping systems). An overview analysis for context can be seen in Voss-Fels et al. (2019).

## Site, Soil, and Weather

The field trials were conducted at the Hohenschulen Experimental Farm (northern Germany, 54°18′51.2″ N 9°59′28.8″ E, 30 m a.s.l). The soil is characterized as a pseudogleyic sandy loam (Luvisol: 170 g kg−1 clay, pH 6.7, 13 g kg−1 Corg, 1.1 g kg−1 Norg in 0–30 cm). The climate of northern Germany is humid temperate with a long-term mean annual temperature at the location of 8.8°C and mean annual precipitation of 751 mm, whereof 396 mm occur during the main growing season (March–August).

Daily weather was recorded at a station nearby the field trials. In 2016–2017 temperatures below average occurred from the beginning of October to the mid of February with the exception of a warm period during December. A cold period appeared again from mid of April to the mid of May. After a period with low precipitation from November to March, the season was characterized by high precipitation from March until harvest. Levels of global radiation were on average.

In the season 2017–2018 temperatures above average occurred almost during the whole season with the exception of a cold period in February and March. After a period with altering phases of below and above precipitation until the end of April, a long period of very low precipitation until harvest followed. Levels of global radiation were above average in May and July.

In summary, the seasons mainly differed regarding the amount of precipitation after anthesis (**Table 3**) and the phenological development during grain filling as a result of air temperatures and drought (**Table 1**). Weather in comparison to climate during both growing seasons is shown in more detail in **Figure S1**.

### Measurements and Calculations

### Grain Yield, Biomass, Harvest Index, and Phenology

All plots were harvested by combine between August 8 and August 15, 2017 and on July 27, 2018, respectively. Grain yield was standardized to 100% dry matter. In addition, harvest by hand was performed at 0.5 m along the row in the first season (corresponds to 0.06 m2 ) and 1 m along the row in the second season (corresponds to 0.12 m2 ) to ascertain the harvest index (ratio of grain dry matter to total dry matter). Harvest by hand took place 6–13 days before combine harvest in 2017 and 4 days before combine harvest in 2018.

Small samples, like the hand harvest, are adequate to measure ratios between fractions but not absolute values. These are appreciable affected by variation inside a plot. As a consequence,

TABLE 3 | The Amount and distribution of precipitation as well as effective PA-radiation during the growing seasons.

we calculated biomass as the ratio of grain yield (combine) and harvest index (hand harvest) to minimize sampling errors.

Lodging and/or damage by game animals occurred in the 2017 season. Plots were visually classified and damaged ones were excluded from the analysis (12%). In the season 2018 no disturbance occurred.

For the detailed analysis of drought, the variables grain yield drop (*GY drop*) and biomass drop (*BIO drop*) are introduced. These are calculated as the difference between the stressed season (2018) and the unstressed season (2017). Consequently, the higher the effect of drought the lower the value (usually more negative).

Phenological stages ear emergence and hard dough were visually classified in the intensive crop management by frequent observations in the relevant periods. These stages refer to the states 59 and 87, respectively, in the BBCH-scale (Lancashire et al., 1991). For statistical analysis both traits were transformed to growing degree days (base temperature 0°C) to improve comparison between both seasons.

### Radiation Interception and Radiation Use Efficiency

The amount of intercepted radiation can be described following Beer–Lambert law (Monsi and Saeki, 1953):

$$I = I\_o \times \left(1 - e^{-k \times GMI}\right) \tag{2}$$

where *I*0 is the incoming radiation, *k* the extinction coefficient and *GAI* the one-sided area of all green plant material per ground area. To focus on interception of productive radiation, the incoming radiation was weighted by a temperature weighting factor as a function of the daily mean temperature. The function ranges between 0 and 1 and is trapezoidal with transition points at 2.5°C, 9.5°C, 20°C, and 35°C. As a consequence, *I* represents the intercepted effective radiation. The extinction coefficient *k* is ascertained to be 0.7 (unpublished own measurements) for the whole genotype set. Differences regarding *k* might occur but are assumed to be of minor importance for radiation interception (sensitivity of *k* to differing leaf angles is quite low around 45° sun angle, where most radiation occurs at the latitude of the location). Values of *GAI* were determined for every single plot with a high measurement frequency (at least biweekly during the main growing phase, in total 11,538 data points, **Table 4**) to provide suitable interpolated values for every single day. Calculation of total incoming effective radiation and total intercepted effective radiation starts with the mean day of sowing and ends with the mean day of the phenological stage hard dough

TABLE 4 | Date and method of GAI measurements.

in each season. This represents the period of land use by the analyzed crop. The ratio of total intercepted effective radiation to total effective radiation is hereafter mentioned as fraction of intercepted radiation (*RI*). Radiation use efficiency (*RUE*) was calculated as the ratio of final above-ground biomass to the sum of intercepted effective radiation so the parameter describes the potential *RUE* (when temperature is optimal throughout the whole season).

### **Green Area Index**

Measurements of *GAI* were conducted by two different methods: ortho images and reflection measurements. In the beginning of the first season *GAI* values were tracked using the ortho image method, all subsequent dates were measured using the reflection measurement method. In total, 13 measurement dates exist in the season 2016–2017 and 16 measurement dates exist in the season 2017–2018.

For the calculation of radiation interception, daily values of *GAI* are necessary. For interpolation, locally weighted scatterplot smoothing (LOESS) (Cleveland, 1979) was applied as proposed by Magney et al. (2016) instead of more functional growth curves (e.g. sigmoidal) because events like initiating drought could not be represented adequately by quite rigid growth curves. As LOESS algorithm, the homonymous function in base *R* (R Core Team, 2018) was used. The smoothing parameter α was set to 0.5 in the season 2016–2017 and 0.45 in the season 2017–2018.

Ortho images are RGB photos taken approximately 1.5 m above the ground with a view direction perpendicular to the grounds surface. All pixels are classified in plant as well as ground pixels and the ratio from plant pixels to the total number of pixels (ground cover) closely correlates with the *GAI*. Ortho-images were not taken in every plot because of restricted throughput using this ground-based method. On December 28, 2016 one image for every cultivar and on March 10, 2017 one image for every cultivar management combination was taken.

The RGB images were cropped to reduce angular effects at the border area and each pixel was classified into the groups plant and ground by a support vector machine with linear kernel using the R-package *e107* (Meyer et al., 2017) streamlined by the R-package *caret* (Kuhn, 2017). The algorithm was trained by manually classified pixels (16,629 in total, 99 different images), in doing so 80% of the images were used as training set and 20% set aside as test set. Chosen predictor variables are: red, green, blue, mean red value of the whole image, mean green value of the whole image, and mean blue value of the whole image. Predictors are centered and scaled. The tuning parameter *cost* was set to 0.04 using the largest value in a grid search with 10-fold cross validation. The trained support vector machine is reliably able to differentiate between plant and soil pixels, reaching a sensitivity of 0.88 and a specificity of 0.90 in the test set. Ground cover was calculated for each image as the ratio of plant pixels to total pixels.

Ground cover values were transformed into *GAI* values using the equation 3,

$$GAI = -\frac{\log\left(T\right)}{k},\tag{3}$$

whereof *T* (transmission) corresponds to 1 − ground cover and *k* (extinction coefficient) is assumed to be 0.75 on 28 December 2016 and 0.65 on 10 March 2017 to account for the erecting of leaves during development.

Reflection measurements were conducted with the Sequoia camera (Parrot), a multispectral sensor which records simultaneously images in four wavebands: green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm). Red edge has a bandwidth of 10 nm, all other wavebands have a bandwidth of 40 nm. The Sequoia camera has an incoming light sensor and therefore provides fractional reflection values regarding the incoming radiation. On each measurement date, images of a grayscale target were made for radiometric calibration.

The eBee Plus (senseFly) served as carrier system. It is a lightweight fixed-wing drone operated with the flight manager eMotion 3 (senseFly). The chosen resolution was 8 × 8 cm pixel−1 and a high degree of overlap (80% in both directions) was applied which resulted in adequate raw material for postprocessing with the photogrammetry software Pix4Dmapper (Pix4D SA., Switzerland). On days with fast-moving clouds, a manual screening of the images was conducted prior postprocessing to exclude those images containing both, regions with cloud shadow and full sunlight. The results were four orthogonal reflection maps, one for every waveband. With a RTK-enabled eBee, it is possible to include RINEX-files (Receiver Independent Exchange Format) in the post-flight-processing in eMotion 3. If this function was not available, the reflectance maps were georeferenced manually using the Georeferencer Plugin in *QGIS* (QGIS Development Team, 2019). The extraction of the reflectance data of the sampling spots was undertaken in *R* (R Core Team, 2018) using the package *sf* (Pebesma, 2018), whereby all pixels of a plot were summarized as median.

Reflection values were transformed into *GAI* values using the vegetation index VIQUO (Bukowiecki et al., submitted manuscript). The linear model reaches a MAE of 0.44 m2 m−2 in an independent data set and showed to be stable during the whole growing season (applicability during senescence is demonstrated).

### Statistical Analysis

All data processing and statistical analysis were conducted in the statistical environment *R* (R Core Team, 2018), the package *ggplot2* was used for visualizations (Wickham, 2016).

A linear mixed model was used for the analysis of variance of the relationship of cultivar, crop management as well as year with all functional traits (*GY*, *BIO*, *HI*, *RI*, *RUE*):

$$\begin{aligned} P\_{ijklm} &= \mathcal{ox} + \mathcal{g}\_i + t\_j + \mathcal{y}\_k + \left(\mathcal{gt}\right)\_{ij} + \left(\mathcal{yt}\right)\_{kj} \\ &+ \left(\mathcal{yg}\right)\_{kl} + \left(\mathcal{yt}\mathbf{g}\right)\_{kjl} + B\_l + \left(BT\right)\_{lj} + \left(\mathbf{BTS}\right)\_{ljm} + \mathcal{z}\_{ijklm}, \end{aligned} \tag{4}$$

where *Pijklm* is the phenotype of the *i*th cultivar, the *j*th crop management, the *k*th year, the *l*th block and the *m*th sub-block, *μ* is the general mean, *gi* is the fixed effect of the *i*th cultivar, *tj* is the fixed effect of the *j*th crop management, *yk* is the fixed effect of the *k*th year, (*gt*)*ij* is the fixed interaction of the *i*th cultivar in the *j*th crop management, (*yt*)*kj* is the fixed interaction of the *j*th crop management in the *k*th year, (*yg*)*ki* is the fixed interaction of the *i*th crop management in the *k*th year, (*ytg*)*kji* is the fixed interaction of the *i*th cultivar in the *j*th crop management and the *k*th year, *Bl* is the random effect of the *l*th block, (*BT*)*lj* is the random interaction of the *j*th crop management in the *l*th block, (*BTS*)*ljm* is the random interaction of the *m*th sub-block in the *j*th crop management and the *l*th block, and *ε*ijklm is the error term.

The model (and all subsequently described models with random components) was implemented with the package *lme4* (Douglas et al., 2015). Following Type II Wald chisquare test was conducted with the package *car* (John and Sanford, 2011).

For the estimation of variance components for each trait, a fully randomized model was used:

$$\begin{aligned} P\_{ijklu} &= \mathcal{ex} + G\_l + T\_j + Y\_k + \left( GT\right)\_{ij} + \left( GY\right)\_{ik} + \left( TY\right)\_{jk} \\ &+ \left( GCY\right)\_{ijk} + \left( YB\right)\_{kl} + \left( YBT\right)\_{klj} + \left( YBTS\right)\_{kljm} + \mathcal{e}\_{ijklu}, \end{aligned} \tag{5}$$

where *Pijklm* is the phenotype of the *i*th cultivar, the *j*th crop management, the *k*th year, the *l*th block and the *m*th sub-block, *μ* is the general mean, *Gi* is the random effect of the *i*th cultivar, *Tj* is the random effect of the *j*th crop management, *Yk* is the random effect of the *k*th year, (*GT*)*ij* is the random interaction of the *i*th cultivar in the *jt*h crop management, (*GY*)*ik* is the random interaction of the *i*th cultivar in the *k*th year, (*TY*)*jk* is the random interaction of the *j*th crop management in the *k*th year, (*GTY*)*ijk* is the random interaction of the *i*th cultivar in the *j*th crop management and the *k*th year, (*YB*)*kl* is the random interaction of the *l*th block in the *k*th year, (*YBT*)*klj* is the random interaction of the *j*th crop management in the *l*th block and the *k*th year, (*YBTS*)*kljm* is the random interaction of the *m*th sub-block in the *j*th crop management and the *l*th block and the *k*th year, and *ε*ijklm is the error term. The variances of (*YB*), (*YBT*) and (*YBTS*) are summed up to the component design.

Adjusted means for every cultivar in every year were calculated prior to the analysis of relationships between traits and year of variety release as well as the stepwise analysis of the contribution of traits to the yield formation to reduce the influence of soil properties and terrain. Because the experimental design (blocks, sub-blocks) did not ameliorate sufficiently, we added a continuous variable to the model which represents the effects of soil and terrain. The variable is calculated by the Papadakismethod (type PAP-8) described by Gezan et al. (2010). The final model for the calculation of adjusted means is:

$$P\_{ijkl} = \mu + \mathcal{g}\_i + t\_j + \left(\text{gt}\right)\_{ij} + soil + B\_k + \left(TB\right)\_{jk} + \left(STB\right)\_{jk} + \mathcal{e}\_{jkl}, \quad \text{(6)}$$

where *Pijkl* is the phenotype of the *i*th cultivar, the *j*th crop management, the *k*th block and the *l*th sub-block, *μ* is the general mean, *gi* is the fixed effect of the *i*th cultivar, *tj* is the fixed effect of the *j*th crop management, (*gt*)*ij* is the fixed interaction of the *i*th cultivar in the *j*th crop management, *soil* is the continuous variable accounting for soil properties and terrain, *Bk* is the random effect of the *k*th block, (*TB*)jk is the random interaction of the *j*th treatment in the *k*th block, (*STB*)ijk is the random interaction of the *l*th sub-block in the *j*th treatment and the *k*th block, and *ε*ijkl is the error term.

Correlations between traits are calculated with the function *corr* in base *R* (R Core Team, 2018). The package *ggcorrplot* (Alboukadel, 2018) was used for visualizations. For the study of temporal changes, the same analysis is conducted with a 25-years sliding window over year of variety release creating multiple subsets of the dataset. Their results are assigned to the center of the considered period. The results of the first and final ten years are deleted to ensure a sufficient large subset.

The path analysis shows standardized beta coefficients (centered by mean and scaled by standard deviation) for relationships with inherent causal relationship (*GY* = *BIO* × *HI* and *BIO* = *RI* × *RUE*) and Pearson's correlation coefficients for intercorrelations (*BIO* with *HI* and *RI* with *RUE*). The package *ggraph* is used for the visualization (Pedersen, 2019).

### RESULTS

### Grain Yield, Biomass, and Harvest Index

Grain yield (*GY*) ranged between 408 and 900 g m−2 over all cultivars, crop managements, and experimental years. Median values of crop managements raised from 553 g m−2 (extensive), over 653 g m−2 (semi-intensive) to 722 g m−2 (intensive). Median values of experimental years dropped from 663 g m−2 in the growing season 2016–2017 to 618 g m−2 in the growing season 2017–2018 (**Figure 1**).

There was a significant main effect of cultivar on *GY*, a significant main effect of crop management, and a significant main effect of experimental year. Additionally, there was a significant interaction effect between cultivar and crop management on *GY*, a significant interaction effect between cultivar and experimental year, and a significant interaction effect between cultivar, crop management, and experimental year. The interaction effect between crop management and experimental year was not significant (**Table 5**). The variance of *GY* was mainly explained by crop management, followed by cultivar. Experimental year is of exceptional low importance, almost only occurring in interaction with other predictors (**Figure 5**).

The absolute drop of *GY* due to drought (*GY drop*) was higher for high *BIO* genotypes in the unstressed season 2017 (r = −0.48), correlations of *GY drop* and the subtraits of *BIO* as well as *GY* itself were consequently strong, too (*RUE:* r = −0.47; *RI:* r = −0.3; *GY:* r = −0.38). Genotypes with later ear emergence were stronger affected by drought, but correlations were very moderate (r = −0.11). *HI* was uncorrelated to *GY drop* (**Figure S4**).

Final biomass (*BIO*) ranged between 935 and 1707 g m−2 over all cultivars, crop managements, and experimental years. Median values of crop managements raised from 1131 g m−2 (extensive), over 1322 g m−2 (semi-intensive) to 1393 g m−2 (intensive). Median values of experimental years dropped from 1422 g m−2 in the 2016–2017 season to 1211 g m−2 in the 2017–2018 season (**Figure 1**).

There was a significant main effect of cultivar on *BIO*, a significant main effect of crop management, and a significant

main effect of experimental year. Additionally, there was a significant interaction effect between cultivar and experimental year. The interaction effects between cultivar and crop management, between experimental year and crop management, and between experimental year, crop management, and cultivar were not significant (**Table 5**). The variance of *BIO* was nearly equally explained by crop management and experimental year, whereas cultivar had a quite low contribution (**Figure 5**).

The harvest index (*HI*) ranged between 0.38 and 0.57 over all cultivars, crop managements, and experimental years. Median values of crop managements were nearly unchanged from 0.49 (extensive), over 0.49 (semi-intensive) to 0.51 (intensive). Median values of experimental years showed an increase from 0.47 in the 2016–2017 season to 0.52 in the 2017–2018 season (**Figure 1**).

There was a significant main effect of cultivar on *HI*, a significant main effect of crop management, and a significant main effect of experimental year. Additionally, there was a significant interaction effect between cultivar and experimental year and a significant interaction effect between crop management and experimental year. The interaction effects between cultivar and crop management and between experimental year, crop management, and cultivar were not significant (**Table 5**). The variance of *HI* was mainly explained by experimental year, followed by cultivar. Crop management had a comparatively low contribution and the sum of design and residual term is exceptionally low, indicating a low influence by soil differences and/or low measurement errors (**Figure 5**).

## Radiation Interception and Radiation Use Efficiency

Measured values of *GAI* showed a feasible course during both seasons and the applied interpolation method was an adequate compromise between smoothing and sufficient representation of the original data (**Figure 2**). The MAE between interpolated and original data was 0.1 m2 m−2 in the season 2016–2017 and 0.08 m2 m−2 in the season 2017–2018, respectively.

After late sowing in the season 2017–2018, values of *GAI* were much lower in early spring but the development of canopies caught up due to a rapid development in May and maximum *GAI* values were quite similar in both experimental years. In 2016–2017 the mean values of the whole cultivar set reached as its maximum peak value: 4.35 m2 m−2 (intensive), 4.43 m2 m−2 (semi-intensive), and 3.23 m2 m−2 (extensive). In the season 2017–2018 they reached: 4.38 m2 m−2 (intensive), 4.41 m2 m−2 (semi-intensive), and 3.47 m2 m−2 (extensive). In late May 2018, drought became severe and *GAI* values dropped rapidly followed by a fast senescence and early ripening (**Figure 2**).

Canopies nearly reached total radiation interception when *GAI* reached its maximum, this was particular the case when abundant nitrogen is supplied (**Figure 3**). In the season 2016– 2017 the mean value of the fraction of absorbed radiation over the whole cultivar set reached as its maximum peak value: 95% (intensive), 95% (semi-intensive), and 89% (extensive). In the season 2017–2018 they reached: 95% (intensive), 95% (semiintensive), and 91% (extensive).

### TABLE 5 | Results of ANOVAs for all functional traits.

The fraction of intercepted radiation (*RI*) ranged between 0.51 and 0.72 over all cultivars, crop managements, and experimental years. Median values of crop managements raised from 0.59 (extensive), over 0.66 (semi-intensive) to 0.67 (intensive). Median values of experimental years dropped from 0.67 in the growing season 2016–2017 to 0.62 in the growing season 2017– 2018 (**Figure 4**).

There was a significant main effect of cultivar on *RI*, a significant main effect of crop management, and a significant main effect of experimental year. Additionally, there was a significant interaction effect between cultivar and crop management, a significant interaction effect between cultivar and experimental year, and a significant interaction effect between experimental year, crop management, and cultivar. The interaction effect between crop management and experimental year was not significant (**Table 5**). The variance of *RI* was mainly explained by crop management, followed by experimental year. Cultivar was of low importance, and interaction components are exceptionally low (**Figure 5**).

The radiation use efficiency (*RUE*) ranged between 1.68 and 2.33 g MJ−1 over all cultivars, crop managements, and experimental years. Median values of crop managements raised from 1.89 (extensive), over 1.96 (semi-intensive) to 2.03 g MJ−1 (intensive). Median values of experimental years dropped from 2.06 g MJ−1 in the growing season 2016–2017 to 1.90 g MJ−1 in the growing season 2017–2018 (**Figure 4**).

There was a significant main effect of cultivar on *RUE*, a significant main effect of crop management, and a significant main effect of experimental year. Additionally, there was a significant interaction effect between cultivar and experimental year. The interaction effect between cultivar and crop management, between crop management and experimental year, and between cultivar, crop management, and experimental year were not significant (**Table 5**). The variance of *RUE* was mainly explained by experimental year, followed by crop management and cultivar. The

FIGURE 2 | Comparison of GAI courses in the season 2016/2017 and 2017/2018. Lines and points represent the mean value of different managements, the pale ribbon indicates the range between the .05 quantile and .95 quantile.

FIGURE 3 | Seasonal course of the amount of intercepted effective radiation in the cultivar set and the incoming effective radiation (yellow area). Both seasons are segmented in 10 equidistant periods and their mean values are presented. Box-whisker-plots are dodged around the mean value (diamond shape), statistical outliers are excluded.

sum of design and residual term was exceptionally high, indicating a high influence by soil differences and/or high measurement errors, maybe as a result of blown up errors because the calculation is based upon biomass and intercepted radiation (**Figure 5**).

## Breeding Progress

To evaluate the breeding progress related to functional traits, relationships between traits and year of variety release were analyzed. The linear regression models of all traits within year and crop management showed positive slopes and the vast majority of them were significant. Non-significant slopes mainly occurred within the trait *RI* (**Table 6**). The scatterplot of this trait indicates that here, progress and regress alternated during breeding history (**Figure S2**). Progress of the highest-order trait *GY* as the mean of both years was 3.4 g m−2 y−1 in the intensive crop management, was slightly lower in the extensive management with 3.2 g m−2 y−1, and was highest in the semi-intensive management

TABLE 6 | Relationship between functional traits and year of variety release.

of intercepted radiation; RUE, radiation use efficiency.

R2, P value, and slope of the linear model trait explained by year of release are shown for every trait, year, and management combination.  *GY* progress as the mean of all managements within years dropped from 3.8 g m−2 y−1 in the season 2016–2017 to 3.2 g m−2 y−1 in the season 2017–2018. Relationships between ear emergence and year of variety release were not significant.

The coefficient of determination was highest for the trait *GY*, with an R2 value of 0.53 as the mean of all management year combinations and decreased over *HI* (R2 : 0.48), *BIO* (R2 : 0.29), and *RUE* (R2 : 0.26) to *RI* (R2 : 0.10) (**Table 6**).

This decline of the coefficient of determination—going down from grain yield to the sub- and sub-sub-traits—is reflected in a qualitative view on the cultivar set. The straight increase of grain yield was accompanied by a quite vague increase of its functional sub-traits and similar yields can be reached by quite different pathways (**Figure 6**).

FIGURE 6 | Combination of functional traits in the cultivar set. Mean values of the intensive crop management over both experimental years are shown. Values in parentheses specify the year of release.

Besides the analysis of temporal changes of functional traits, an analysis of their importance to the formation of their higherorder traits (*GY* = *BIO* × *HI*, *BIO* = *RI* × *RUE*) is of interest. Even a trait without any temporal trend can be of high importance for the explanation of differences between cultivars.

Standardized beta coefficients of the linear model *GY* = *BIO* × *HI* were higher for *BIO* in the growing season 2016–2017, in the growing season 2017–2018 *HI* and *BIO* were roughly of equal importance, except in the extensive crop management where the relationship was nearly unchanged. Both traits were nearly uncorrelated in the intensive crop management during the first season but moderate to strong correlated in all other management year combinations (**Figure 7**).

Standardized beta coefficients of the linear model *BIO* = *RI* × *RUE* were higher for *RUE* in all management year combinations and the coefficient of *RI* was especially low in the semi-intensive management during the first season, indicating that infections primarily affected radiation use efficiency. Semi-intensive and intensive crop management behaved quite similar in the second season which reflects the low infection pressure in the second season. Coefficient of *RI* was comparatively high in the extensive crop management. Both traits were moderately correlated in the 2017 season and only weak correlated in the 2018 season (**Figure 7**).

A broader view of correlations between all functional traits showed that no negative intercorrelations existed. Except the correlation between *HI* and *BIO* (as well as *RI* and *RUE*) in the intensive crop management and the non-drought experimental year, these positive correlations were surprisingly high. In the season 2017, later ear emergence was moderately positive correlated to *RI* and *BIO* and moderately negative correlated to *HI*. In the season 2018, ear emergence was nearly uncorrelated to all other traits (**Figure S3**).

The preceding static view is complemented with a dynamic view in the intensive crop management. The importance of *BIO* outpaces the importance of *HI* around the year 1990 (**Figure 8**, left). On the contrary, the distance between *RUE* and *RI* diminished in the 90s. Interestingly, correlation between both traits increased appreciable at the same time (**Figure 8**, right).

## DISCUSSION

## Grain Yield, Harvest Index, and Biomass

In contrast to the yield stagnation observed in the agricultural practices (Lin and Huybers, 2012), *GY*, as the mean value of all management year combinations, increased with 3.5 g m−2 y−1 between 1966 and 2012 and the data clearly showed that this progress continues. Yield progress was usually higher when growing conditions were less optimal like it has been shown by Voss-Fels et al. (2019). The extensive crop management in the experimental year 2018 (drought) with an increase of 2.4 g m−2 y−1 was an exception. Less mineralization might have resulted in very low N supply which could have been a resource limitation for higher yields (**Table 6**). All growing conditions included, *GY* of more recent cultivars was always higher than that of older ones. This absolute yield under diverse growing conditions—unlike

FIGURE 7 | Path analysis of functional traits during breeding history, grouped by growing seasons and crop management. Implicitly causal relationships show standardized beta coefficients (one headed arrow) other relationships show Pearson's correlation coefficient (two headed arrow).

between grain yield (GY) and harvest index (HI) as well as biomass (BIO). Right: Correlation between biomass (BIO) and radiation interception (RI) as well as radiation use efficiency (RUE). The component intercorr. is the correlation between explanatory variables.

some definitions of yield stability—is the most relevant measure regarding food security (Snowdon et al., 2019).

*GY* is mainly influenced by crop management, followed by cultivar and experimental year (mainly occurring in interaction with crop management) (**Figure 5**). The effect of drought in 2018 might be underestimated due to both suboptimal crop managements. Here, the effects are biased due to differing infection pressure and the uniform application of growth regulators in the second experimental year that shifted the *HI* of the extensive crop management considerably upwards (**Figure 1**). The yield drop of 10% in the intensive crop management represents best the observed effect of drought. Notably, the biomass drop of 16% was partly compensated by an increase in *HI* (**Figure 1**).

High biomass genotypes in the unstressed season (2017) were stronger affected by drought, reflecting the tight coupling of biomass production and transpiration. On the contrary, *HI* is a resource neutral trait and was uncorrelated to *GY drop*. Earlier ear emergence had a small positive influence on the effect of drought (**Figure S4**).

*HI* showed, with a mean R2 of 0.48 of all relationships to year of variety release, the most directional development during breeding history of all subtraits (**Table 6**). This might have two reasons: it is the most considered subtrait by breeders and it is resource neutral. Additionally, until it eventually reaches some physiological boundaries (Berry et al., 2007), it might have very little negative interactions with other subtraits.

Maximum values of *HI* reached 0.55 (**Figure 6**), a value slightly higher than previously reported ones: 0.46 (Brancourt-Hulmel et al., 2003), 0.50 (Schittenhelm et al., 2019), 0.52 (Rose et al., 2017), and 0.53 (Shearman et al., 2005). They slowly approach the theoretical thresholds, hypothesized by Austin (1980) (0.62), Foulkes et al. (2010) (0.64), and Shearman et al. (2005) (0.66).

The contribution of *BIO* and *HI* to the breeding progress of *GY* is a frequently researched question.

We observed a slightly higher correlation between *GY* and *BIO* than with *HI*. The contribution of *HI* was increased in the second experimental year with drought (**Figure 7**) and was variable in the breeding history (**Figure 8**). This dependency on environment and cultivar set corresponds to the variability of results in the literature. The contribution of *HI* decreased in recent breeding history (**Figure 8**) and latest cultivars consistently reach values above 0.5 in the intensive crop management (**Figure S2**). It seems like best cultivars recently converge to theoretical maximum values and potential for improvements are slowly diminishing but the trait should not be regarded as settled. *HI* and *BIO* were nearly uncorrelated in the intensive crop management during the first season but moderately to highly correlated in all other management year combinations (**Figure 7**), indicating that all types of stress are mainly affecting grain filling and consequently both traits in parallel.

*HI* was moderately negatively correlated to ear emergence (**Figure S3**), so early flowering was one component of high *HI* values, but a big part of variation was independent of it.

### Radiation Interception and Radiation Use Efficiency

The challenge of an adequate mapping of *GAI* values during the whole season was implemented with a multispectral approach proposed by Bukowiecki et al. (submitted manuscript). The expansion of the application of spectral reflection measurements to the phase of senescence with vegetation indices, like the NDVI, has been shown before (Lopes and Reynolds, 2012). We extended this approach with a more functional perspective going from the measurement of *GAI* to the calculation of *RI*. The resulting courses during senescence (**Figure 2**) are in agreement with multiple authors who interpolated this phase by some logistic-alike model with an accelerating and decelerating phase (Pepler et al., 2005; Christopher et al., 2008; Vijayalakshmi et al., 2010; Christopher et al., 2014; Christopher et al., 2016; Luo et al., 2018).

Due to nearly complete interception during full canopy development, differences in the cultivar set regarding *RI* mainly occurred in early and late season (**Figure 3**). These phases correspond to the key words "early vigor" and "stay green." High genetic variability for these traits has been shown before: "early vigor" (Turner and Nicolas, 1998; Rebetzke et al., 2001; Rebetzke et al., 2004; Maydup et al., 2013; Zhang et al., 2014), "stay-green" (Joshi et al., 2007). In comparison, "stay green" was of much higher importance because here, cultivar differences coincide with high incoming radiation. Both traits differentiated more in the season 2017–2018. This might be linked to late establishment intensified by high incoming radiation in April and May ("early vigor") (Moore and Rebetzke, 2015) and drought ("stay green") (Idso et al., 1980; Christopher et al., 2008; Graziani et al., 2014; Thomas and Ougham, 2014; Christopher et al., 2016; Christopher et al., 2018). Under reduced nitrogen supply (extensive crop management), appreciable differences of *RI* do occur in midseason, too (**Figure 3**). Here, maximum *GAI* values are on a level where genotypic differences regarding *GAI* do result in noticeable differences for radiation interception (exponential term of Beer-Lambert law). Consequently, the importance of radiation interception for the explanation of final biomass is highest in the extensive crop management (**Figure 7**).

Like Kitonyo et al. (2017) have shown for delayed senescence, we did not detect a linear trend of radiation interception in most management year combinations with the year of variety release (**Table 6**). It seems like progress and regress alternated during breeding history regarding this trait (**Figure S2**).

We were not able to differentiate between cosmetic and functional "stay-greens" which is in general difficult in large field trials (Rebetzke et al., 2016) but a multitude of authors have shown a linking between chlorophyll content and net photosynthetic rate during senescence (Luo et al., 2006; Waters et al., 2009; Derkx et al., 2012; Naruoka et al., 2012). This indicates that the functional type of "stay green" is rather the rule than the exception in wheat. Nonetheless, occurring non parallel progression of *GAI* and photosynthesis might result in low values of *RUE*, as discussed later.

*RUE* ranged between 1.68 and 2.33 g MJ−1 over all cultivars, crop managements, and experimental years and was highly affected by all occurring forms of stress—drought, nitrogen deficiency, and pests. The effect of drought is reflected in the drop of −0.15 g MJ−1 from the experimental year 2017 (non-drought) to the year 2018 (drought), the effect of nitrogen deficiency in the drop of −0.1 g MJ−1 from the semi-intensive to the extensive crop management in the experimental year 2018 (very low infection pressure), and the effect of pests in the drop of −0.1 g MJ−1 from the intensive to the semi-intensive crop management in the experimental year 2017 (high infection pressure). In comparison to management and experimental year, the variance component of cultivars is low but a quite high and significant interaction between cultivar and experimental year occurred (**Figure 5**).

The observed values are lower than reported ranges by Shearman et al. (2005) between 2.33 to 2.64 g MJ−1, Sadras et al. (2012) between 1.54 to 2.68 g MJ−1 and the range from 2.29 to 2.57 g MJ−1 we observed in a preceding trial (Rose et al., 2017). The lower end of the range is highly depending on the minimum year of release of the cultivar set and the amount of occurring stresses, but the upper end should be on a similar level.

The calculation of *RUE* requires an adequate description of radiation interception, so most authors (and the mentioned studies) confine themselves to the pre-anthesis phase. Due to a new calibration of a multispectral sensor (Bukowiecki et al., submitted manuscript) we were able to expand the calculation until harvest. We hypothesize that the *RUE* during senescence is reduced due to the degradation of rubisco and assume that this is the reason for comparably low values.

*RUE* showed a significant linear increase during breeding history except in the intensive crop management during the season 2016–2017 (non-drought) (**Table 6**). Slopes range between 0.0023 and 0.0061 g MJ−1 per year and are much lower than reported values by Sadras et al. (2012), 0.012 g MJ−1 per year. In their Mediterranean environment, evapotranspiration is much higher than rain during the growing season. These results indicate that historical trends of *RUE* should not only be seen in the context of photosynthesis but in the context of the possibilities of genotypes to cope with adverse growing conditions (drought, nitrogen deficiency, pests). Additionally, Sadras et al. (2012) showed the link of *RUE* to canopy traits instead of photosynthesis per se. Here, a better light distribution leads to an increased canopy photosynthesis (Zhu et al., 2010).

In comparison, *RUE* is of higher importance for the explanation of *BIO* than *RI* but nearly equal levels are reached in the extensive crop management (**Figure 7**). A more detailed analysis of temporal changes in the intensive crop management showed that *RI* was clearly less important for most of the breeding history but became nearly equally important during the nineties (**Figure 8**). Both traits are positively correlated which might express that beside the phenological component of "stay green" it also just reflects the ability to cope with the external factors: drought, nitrogen depletion, and fungal infections (Richards, 2000; Rebetzke et al., 2016). Additionally, results of Zhang et al. (2006) suggest, that prolonged retention of high chlorophyll concentrations can be in certain cases an indirect indicator for higher levels of rubisco in previous phases, instead of the direct cause of increased light absorption. A lower intercorrelation in the experimental year 2018, with occurring drought, might reflect a negative relationship between water use (high radiation interception) and water supply in a later phase (low radiation use efficiency). *RI* was moderately correlated to ear emergence (**Figure S3**), so later flowering was one component of high *RI* values, but a big part of variation was independent of it.

## Breeding Progress—Past and Future

Our results show that the stagnation of wheat yields in Central-Europe, is not accompanied by an ending of breeding progress. The coefficient of determination of the linear relationships with year of variety release was highest for the trait *GY*, with an R2 value of 0.53 as the mean of all management year combinations and decreased over *HI* (R2 : 0.48), *BIO* (R2 : 0.29), and *RUE* (R2 : 0.26) to *RI* (R2 : 0.10). This reflects the main procedure in breeding—a selection for higher *GY*. The functional background is usually unknown and progress in *GY* is often accompanied by regress in some of its sub-traits (**Figure 6**). The combination of best performers in the sub-traits: *HI*, *RUE*, and *RI* for crosses is an appealing approach but negative relationships between these traits should exist. Conversely, all traits in the analyzed cultivar set are positively correlated (**Figure S3**) but it must be kept in mind that nearly 50 years of indirect parallel selection for better *HI*, *RUE*, and *RI* constitute the genetic material. Phenotyping of lower level functional traits (*RI*, *RUE*, *HI*) in combination with genotyping would help to detect QTLs which might be assignable to some quite stable trait (e.g. leaf angle, specific leaf area, initiation of senescence). This might help to understand the complex interaction of genotype and environment.

Time of ear emergence was nearly unchanged during the breeding period (**Table 6**) and showed little variation (**Table 1**) which reflects quite strict conceptions of optimal flowering time by local farmers and the consideration of this requirement by breeders.