Maintenance breeding and breeding for yield potential equally contribute to genetic improvement in wheat yield

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This approach, however, does not allow to distinguish gains in yield potential versus maintenance breeding that aims to keep cultivars adapted to the evolving biophysical environment, including pests, diseases, and climate change. Our analysis of long-term wheat trials from Argentina, Europe, and United States revealed an overall genetic yield improvement of 97 kg ha − 1 y − 1 (1.14% per annum) based on comparison of modern cultivars against older ‘check’ cultivars. However, nearly half of the genetic improvement (46 kg ha − 1 y − 1 ) was attributable to maintenance breeding and the other half (51 kg ha − 1 y − 1 ) to the higher yield potential of modern cultivars. We conclude that comparison of new versus old cultivars under current conditions leads to an overestimation of genetic gains in yield potential. One sentence summary : Crop yield potential gains are lower than reported. Biological sciences/Plant sciences/Plant ecology Earth and environmental sciences/Ecology/Ecophysiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Continued yield gains are essential to ensure adequate food supply while substantially reducing land conversion and greenhouse gas emissions due to expansion of crop production area 1-4 . Clarity on the factors with greatest contribution to yield advance in recent decades is essential for effective prioritization of future research to improve yields. In the world’s most productive cropping systems, increase in yield potential via genetic improvement is the trait that is most widely credited with greatest impact on yield trends in farmer’s fields 5-7 . But what if that is wrong? Yield potential is defined as the yield of a well-adapted cultivar when grown without water and nutrient limitations and with weeds, diseases, and insect pests effectively controlled 8,9 . Under these conditions, yield potential is determined by solar radiation, temperature, and atmospheric carbon dioxide concentration. In the case of rainfed crops, water supply and soil water storage impose another limit to yield potential 10 . Hence, in absence of climatic trends, changes in yield potential over time can be attributed to genetic traits influencing light interception and conversion to biomass as governed by photosynthesis, respiration, water-use efficiency, and biomass partitioning to grain 11,12 . Continuous improvement in yield potential through genetic improvement is crucial to avoid yield plateaus and reduce land requirements for adequate crop production 1,13-15 . Most field studies assessing genetic improvement in yield potential rely on side-by-side comparison of sets of historical cultivars in today’s environment 16-23 . In these studies, the slope of the linear regression between measured grain yield versus each cultivar’s year of release is assumed to represent the annual rate of gain in yield potential due to genetic improvement. This approach can be criticized, however, because it does not account for progressive yield decreases that occur over time when cultivars lose adaption to the evolving climate, atmospheric composition, soil properties, crop management practices, pest and disease pressure, hereafter referred to as a loss in ‘agronomic fitness’ 13,24-29 . Consequently, observed yield differences between new and old cultivars at any point in time can be associated with (i) higher genetically determined yield potential in new cultivars, (ii) loss in agronomic fitness of old cultivars (maintenance breeding), or (iii) a combination of both (Figure 1) . Unfortunately, most studies of this kind on major food crops have assumed that yield differences between old and new cultivars are due to genetic improvement in yield potential without assessing contributions from loss of agronomic fitness in older cultivars. Here we developed a methodology to distinguish maintenance breeding from gains in yield potential using wheat as a case study. Wheat is the most widely grown staple food crop worldwide, with a global harvested area of 217 million hectares, which accounts for about one quarter of global cereal production and ~21% of human calories and protein 30 . We assembled a database (post-2000) of 15 long-term trials in Argentina, United States, and Western Europe ( Supplementary Figure S1 ) that include a set of commercial cultivars tested each year, together with check cultivars that were released no-more than five years prior to being included in a trial and were tested for a minimum of 10 years thereafter against more recently released cultivars (Supplementary Tables 1 and 2) . Because high-yielding wheat stands have lush leaf canopies that are conducive to development of fungal diseases 31,32 , yield loss from these diseases were minimized by a prophylactic, in-season application of foliar fungicide, at least once, across all cultivars in every trial. Most trials also included a separate treatment across cultivars that did not receive fungicide. We assumed the average yield of the ten highest yielding cultivars each year represented the yield potential, and then evaluated yield trends of these highest yielders over time as a measure of changes in yield potential. Similarly, we tracked yield trends of the check cultivars to assess the magnitude of loss in agronomic fitness of older cultivars. Results Average yield of top yielding cultivar and check cultivars oscillated over time due to inter-annual variation in weather conditions ( Fig. 2 ) . However, two distinct features were apparent. First, the yield difference between top yielding cultivars and check cultivars were negligible at the beginning of a time series, indicating that the check cultivars chosen in our study were among the top yielding cultivars at the beginning of the trials. Second, the yield difference between the top yielding cultivars and cultivar checks increased over time, highlighting the contribution of breeding to crop yields. After adjusting yields to account for climate trends using simulated yields, we calculated the overall rate of genetic yield improvement by assessing yield differences between top and check cultivars over time (see Methods) . Following this approach, we determined an overall rate of 97 kg ha − 1 y − 1 , equivalent to 1.14% per annum [p.a.] ( Fig. 3 , Table 1 ). At question is how much of the overall yield improvement can be attributed to increase in yield potential versus maintenance breeding aiming to keep cultivars adapted to the evolving environment. To distinguish between these two components, we followed the same approach as for the overall yield improvement but based on yield trends of top cultivars and check cultivars to estimate annual rates in yield potential gain and yield erosion, respectively. We found that maintenance breeding and yield potential gain have contributed almost equally, with annual rates averaging 46 (0.54% p.a. ) and 51 kg ha − 1 y − 1 (0.60% p.a. ), respectively ( Fig. 3 , Table 1 ) . Thus, ignoring the contribution of maintenance breeding, as done in previous studies, would have led to a two-fold overestimation in yield potential gain. Table 1 Annual rate of yield change in top yielding and check cultivars, overall genetic yield improvement, and its subcomponents (maintenance breeding and yield potential) based on long-term cultivar trials conducted in Argentina, Europe, and United States. Relative rates are also shown (% p.a.). An extended version of this table is shown as Supplementary Table S3. ---- Yield change (kg ha − 1 y − 1 ) a ---- ------------------- Genetic yield improvement (kg ha − 1 y − 1 ) b ----------------------- Check cultivar Check cultivars Top yielding cultivars Overall Maintenance breeding Yield potential Apache c -80 -18 62 (0.59%) 62 (0.59%) nil Baguette 10 -54 112 166 (2.27%) 54 (0.74%) 112 (1.35%) Baguette P.11 -40 85 125 (1.75%) 40 (0.56%) 85 (1.19%) Claire c 135 190 55 (0.40%) nil 55 (0.40%) Cordiale -99 55 154 (1.20%) 99 (0.77%) 55 (0.43%) Duster -62 66 128 (2.79%) 62 (1.35%) 66 (1.44%) Endurance -24 38 62 (1.19%) 24 (0.46%) 38 (0.73%) SY 200 c -84 -57 27 (0.39%) 27 (0.39%) nil Average -39 59 97 (1.14%) 46 (0.54%) 51 (0.60%) a Annual rates of yield change were estimated by comparing the average yield of the top cultivars over the last three years with that of the first three years in each time series, divided by the number of years elapsed, after a weather-based correction was applied based on relative changes in simulated yield between the two periods. The same methodology was followed to estimate the rate of yield change in check cultivars. b Overall yield improvement was estimated by summing up annual yield changes in top cultivars and checks. For one case with positive yield change in checks (Claire), we assumed yield improvement to be fully accounted by increase in yield potential. Conversely, for two cases with negative trends in yield of top cultivars (Apache and SY200), we assumed that yield improvement to be fully accounted by maintenance breeding. Relative rates (% p.a. ) were computed as the quotient between absolute rates and average yield of top yielding cultivars during the last three years of each time series. We further confirmed patterns in yield erosion by assessing trends in 3-year moving average yields for check cultivars ( Fig. 4 ) . The overall trend in check cultivar yield was estimated at -68 kg ha − 1 y − 1 (range from nil to -190 kg ha − 1 y − 1 ) (p 0.1), which means yield erosion was not ubiquitous. Because of the non-linear nature of the yield trend, resulting linear estimates were substantially more negative than those estimated using 3-year averages at the beginning and end of the time series (-68 versus − 39 kg ha − 1 ). Finally, evaluation of six cultivars in sites that included side-by-side treatments with and without in-season foliar fungicide revealed that performance against diseases was primarily determined by the inherent susceptibility of each check cultivar rather than cultivar age ( Supplementary Figure S2 ). For example, some cultivars (Apache and Baguette 10) exhibited large susceptibility to diseases soon after they were released, while others (SY 200, Duster, and Endurance) had relatively low and stable yield response to fungicide application. One cultivar (Baguette P. 11) showed considerably less yield erosion with fungicide treatment than without it, indicating that its yield loss can be partly attributed to greater susceptibility to foliar diseases over time. Discussion Genetic improvement contributes to on-farm yield gains through farmer adoption of new cultivars that have been selected for increased yielding ability, grain quality, disease resistance, winter hardiness, etc . across a wide range of relevant environments 40 , 41 . In that process, new cultivars typically have improved resistance and adaptation to evolving pest populations, management practices, soil properties, and climate, thus avoiding the yield erosion that would occur over time in absence of cultivar replacement 33 , 36 , 42 . Genetic improvement can also contribute to on-farm yield gain through commercial release and farmer adoption of new cultivars with higher yield potential due to improvements in carbon assimilation and partitioning to grain, as has been the case for wheat and rice cultivars released since the onset of Green Revolution in the 1960s, and hybrid maize cultivars over the same time frame 43 – 46 . At issue is how to estimate the relative contributions of ‘maintenance’ breeding to compensate for yield erosion of old cultivars 33 versus improvement in yield potential. This distinction is crucial to guide public and private investment in agricultural research and development more effectively. Our study shows that comparing yields of new versus old cultivars in today’s environment does not provide a reliable estimate of genetic improvement in yield potential of wheat ( Fig. 3 , Table 1 ) . The historical cultivar yield trial approach carries the risk of inflating the estimated return on investment in yield potential increase through genetic improvement because such comparisons are biased against the old cultivars when there is a progressive loss of adaptation to the evolving biophysical environment. This bias could be more pronounced when only a handful of cultivars are meant to represent several decades of breeding 16 – 18 , 21 , 23 , 47 . Previous studies assessing yield gains in long-term trials have admittedly relied on the assumption that the yield erosion can be fully offset via improved agronomic management 28 – 30 , 48 , which does not seem to be the case as our study shows. Indeed, the yield erosion is well known by breeders, who purposely replace check cultivars after a few years of inclusion in the trials 31 . Interestingly, the magnitude of yield erosion was diverse across cultivars, with some of them showing a sharp yield decline after being released while others exhibit little or no yield decline over time ( Fig. 2 , Table 1 ) . We conclude that, with few exceptions 34 , 49 , the extensive literature on yield potential improvement has overestimated gains in yield potential. While this conclusion is sobering, it also highlights the importance of robust ‘maintenance’ breeding programs that combat yield erosion by continuously releasing new cultivars better adapted to the changing biophysical environments of intensive crop production systems 32 , 33 , and the challenge to increase yield potential of the major crops to meet food demand and reduce associated land requirements and negative environmental impacts. If yield potential gain is slower than previously thought, as it seems to be the case for wheat based on the present study and for maize and rice as reported elsewhere 32 , 50 , future yield gains will increasingly depend more on closing the existing gap between on-farm yield and yield potential via improvements in agronomic management. Unfortunately, studies that explicitly seek to measure yield erosion in cultivars of other major upland crops such maize or soybean have not yet been conducted. It would be surprising if there were no yield declines in widely grown cultivars in these other major crops. To better estimate future food production capacity on at national to global scale will require improved estimates of expected crop yield gains due to improvements in agronomic management and genetic gain. Methods Data collection We retrieved yield data from long-term variety trials used to evaluate the performance of new cultivars before being released to the market and widely grown varieties, also known as VCU -Value for Cultivation or Use- trials, located in United States, Western Europe (France and United Kingdom), and Argentina (Supplementary Figure S1; Supplementary Table S1-S2) . These trials assess the agronomic value of crops, including yield and diseases resistance in different environmental conditions. Collectively, the abovementioned countries include 30 M ha and 129 M tons of wheat, accounting for 14% and 17% of wheat global area and production (average from 2019-2021) 37 . We only included trials that complied with the following criteria: (i) replicated field trials including at least 10 years of data, (ii) conducted after 2000 and including semi-dwarf cultivars, (iii) located within main wheat producing regions in each country/region, (iv) included the most widely sown cultivars and (v) included ‘check’ cultivars that remained in the trials over a longer period of time compared with others (>10 years) and were incorporated to the trials within three years after their commercial release. According to the above criteria, we compiled data from 15 long-term replicated trials (Supplementary Figure S1; Supplementary Table S1-S2) . All trials were rainfed and received at least one in-season foliar fungicide application. All trials, except for United Kingdom, also included an untreated fungicide treatment (in addition to the treatment receiving fungicide). Overall, there were a total of eight check cultivars across the 15 trials, which corresponded to cultivars that were widely grown soon after their time of release. Seed was regenerated every year for the check cultivars. Plot size ranged between 7 and 15 m 2 (Argentina 1.5 x 5 m; United States 1.5 x 10 m; France 7-10 m 2 ), which is a common size for variety trials. Crops received recommended management practices regarding sowing date, seeding rate, fertilization, and weed and pest management at each site every year. As a result, high yields were obtained in these experiments, which were, on average, 18% (USA), 29% (Europe) and 45% (Argentina) higher than average farmer yields during the same period 37 . Data analysis We excluded cultivar-year combinations with suspicious values ( e.g. , CV>30% among replicates) and specific years with near crop failure due to drought, severe lodging, and/or frost. A total of 1% cultivar-year observations were eliminated following our quality control. Subsequently, we calculated the average yield of the 10 top-yielding cultivars for each trial-year combination, excluding the checks. Such approach helped us discard experimental and/or underperforming cultivars. We assessed the influence of weather on the yield trends of both the top yielding and check cultivars. To do this, we retrieved the simulated water-limited yield potential associated to the nearby location to each trial available at the Global Yield Gap Atlas (www.yieldgap.org). Water-limited yield potential is determined by solar radiation, temperature, CO 2 concentration, precipitation, and soil properties influencing the water balance (8). Briefly, the water-limited yield potential was estimated using process-based crop models including CERES-Wheat (Argentina), SSM (USA), and WOFOST (France and UK) 51-53 that were specifically validated to be used in the target sites 54-57 . The water-limited yield potential was simulated based on local measured weather, soil, and management practices and assumed no nutrient limitation and complete absence of yield-reducing factors such as weeds, pathogens, and insect pests. For each specific trial, we assessed changes in simulated water-limited yield potential over time for a fixed cultivar and set of management practices so that interannual variation in simulated is solely attributable to weather variation during the specific timeframe of each experiment ( Figure 2 ). To determine the annual rate of change in yield potential and yield erosion in check cultivars, separately, we compared the average yield over the last three years with that of the first three years in each time series ( Table 1 ). The difference was then divided by the number of years elapsed, and a weather-based correction was applied, determined by the relative changes in simulated yields between the two periods. In the case of Baguette P.11, we considered the first four years of the time series due to some missing data in early years at a few sites. For one case with positive yield trends in checks (Claire), we assumed that all genetic yield improvement corresponded to yield potential gain. Conversely, for two cases with negative trends in top cultivars (those against Apache and SY 200 checks), we assumed that all genetic yield improvement corresponded to maintenance breeding ( Supplementary Table S4 ). The overall rate of yield improvement to crop yields was obtained as the difference between top and check cultivar yield change, divided by the number of years of the time series. Relative rates of yield change were calculated as the ratio between the absolute rates and average 3-year yield at the end of the time series. We further investigated trends in yield erosion, with and without in-season foliar fungicides, over time. To do this, we determined the average yield of check cultivars during the first three years. These average values served as the baseline to evaluate subsequent changes in the yield of check cultivars over time ( Figure 2; Supplementary Table S3 ). Subsequently, to reduce the noise associated with inter-annual variation in weather, we used a 3-year moving average that smoothed the progression in yield erosion (checks) over time. Additionally, we estimated the climatic trend from linear regressions adjusted to the 3-year moving average of simulated water-limited yields. We found two cases (sites testing Baguette 10 and Baguette P.11) with a significant negative trend and two cases (sites testing Cordiale and SY 200) with a significant positive trend (p<0.1). Thus, we deducted the relative contribution of weather on yield trends from check and top cultivars for those cases. The effect of climate on yield potential over time was ignored for the remaining cases given that trends were not statistically significant (p>0.1). Finally, we fitted linear regression models to the 3-year moving yield averages of check cultivars and derived their slopes, which represent the rates of yield erosion (Figure 2, Supplementary Table S3) . Declarations Acknowledgement s We thank Drs Jacques Le Gouis (INRAE), Andy Macdonald (Rothamsted Research), and Susannah Bolton (AHDB) for helping retrieve the experimental data from trials in France and United Kingdom. Data availability Experimental trial data are publicly available via the links provided in the Supplementary material section. Data on simulated yield potential are publicly available via the Global Yield Gap Atlas website (www.yieldgap.org). Author contributions P.G. and K.G.C. conceived the project. J.F.A., J.M., S.Y., J.P.M., J.I.R.E., R.P.L., and C.L. provided, compiled, and/or analyzed the data. J.F.A., P.G., K.G.C., and S.Y. wrote the manuscript with input from all authors. Competing interests The authors declare no competing interests. References Cassman, K. G. Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. Proc. Natl. Acad. Sci. USA. 96 , 5952-5959 (1999). Marin, F. R., et al. 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Agronomic practices for reducing wheat yield gaps: a quantitative appraisal of progressive producers. Crop Sci. 59 , 333-350 (2019). Wolf, J. et al. Modeling winter wheat production over Europe with WOFOST – the effect of two new zonations and two newly calibrated model parameter sets. Methods of Introducing System Models into Agricultural Research. Advances in Agricultural Systems Modeling 2: Trans-disciplinary Research, Synthesis, and Applications, eds LR Ahuja, L Ma (ASA-CSSA-SSSA, Madison, WI), pp. 297–326 (2011). Additional Declarations There is NO Competing Interest. Supplementary Files 111.pdf Reporting Summary SupportingInformationforAndradeetal.docx Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3957062","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":403265335,"identity":"9d042c4b-cac4-4503-84f0-18ef80d5dcc7","order_by":0,"name":"Patricio Grassini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYPACCQY29uYDDAwFQPYBorXwHEtgYDAgXgtIV44BcVrM+dc+/Pjjj0UeH0PON+kCAwY5vhsJ+LVYznhuLM3bJlHMxnB2m/QMAwZjSUJaDG4cY5BmbJBIbGPs3SbNY8CQuIEILcw/f/wBamHmeQbSUk9Yy/k2NgkeNqAWNh42kJYEA8J+YWOzBvolsY2Hzdiax0DCcOaZB/i1mPMfY775409d4vz5jx/e5qmwkec7TshhEqgKJPArB2vhP0BY0SgYBaNgFIxwAAA6kz6GkLHMQQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7501-842X","institution":"University of Nebraska-Lincoln","correspondingAuthor":true,"prefix":"","firstName":"Patricio","middleName":"","lastName":"Grassini","suffix":""},{"id":403265336,"identity":"41d76241-c3d9-4a59-b7db-13fbb86dab4d","order_by":1,"name":"Jose Andrade","email":"","orcid":"","institution":"University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"","lastName":"Andrade","suffix":""},{"id":403265337,"identity":"bb0f06c1-2e70-4fd4-beaa-f8a6d61bf36e","order_by":2,"name":"Jianguo Man","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Man","suffix":""},{"id":403265338,"identity":"04dadf35-55a3-4148-88fd-c61ae538b5a4","order_by":3,"name":"Juan Pablo Monzon","email":"","orcid":"https://orcid.org/0000-0001-6992-1842","institution":"University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Pablo","lastName":"Monzon","suffix":""},{"id":403265339,"identity":"c757db40-a0cd-4388-8f40-a2aa72770e44","order_by":4,"name":"Juan Ignacio Rattalino Edreira","email":"","orcid":"","institution":"University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Ignacio Rattalino","lastName":"Edreira","suffix":""},{"id":403265340,"identity":"7c8afefc-5479-4126-9772-7861fb7d948f","order_by":5,"name":"Shen Yuan","email":"","orcid":"https://orcid.org/0000-0003-4786-145X","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shen","middleName":"","lastName":"Yuan","suffix":""},{"id":403265341,"identity":"ff5d9698-65f9-4fba-82ed-1c9f36b68798","order_by":6,"name":"Romulo Lollato","email":"","orcid":"https://orcid.org/0000-0001-8615-0074","institution":"Kansas State University","correspondingAuthor":false,"prefix":"","firstName":"Romulo","middleName":"","lastName":"Lollato","suffix":""},{"id":403265342,"identity":"ff8340bd-0b57-4915-9603-403c46b1ffd0","order_by":7,"name":"Clara Llorens","email":"","orcid":"","institution":"Chacra Experimental Miramar","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"","lastName":"Llorens","suffix":""},{"id":403265343,"identity":"979a3bd0-c3f3-41d6-9880-f90a0d621da5","order_by":8,"name":"Shaobing Peng","email":"","orcid":"https://orcid.org/0000-0003-1696-9409","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shaobing","middleName":"","lastName":"Peng","suffix":""},{"id":403265344,"identity":"9053f83e-b044-4a1d-9b6d-8c9dabc2aff6","order_by":9,"name":"Kenneth Cassman","email":"","orcid":"https://orcid.org/0000-0002-9775-3468","institution":"University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Cassman","suffix":""}],"badges":[],"createdAt":"2024-02-14 22:10:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3957062/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3957062/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-69936-6","type":"published","date":"2026-03-04T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74426113,"identity":"513ba6e1-5a7b-4935-9e38-c7d7eab3e2cf","added_by":"auto","created_at":"2025-01-22 08:06:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrends in yield potential and associated drivers. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eScheme showing how yield differences between new and old cultivars can be attributed to genetic improvement in yield potential (a), loss of agronomic fitness in older cultivars (b), or both (c). This scheme assumes no changes in yield due to climate or agronomic practices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3957062/v1/748da6c17a20a621df56d378.png"},{"id":74424214,"identity":"1715511d-d682-4009-8ec1-377cc3a4af6d","added_by":"auto","created_at":"2025-01-22 07:50:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEvolution of simulated yield potential, yield of top cultivars, and check cultivars with fungicide application. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThe name of the reference check cultivar is indicated within each panel.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eApache (France): 4 sites; Claire and Cordiale (UK): 1 and 2 sites, respectively; Baguette P.11, Baguette 10, SY 200 (Argentina): 4, 1, and 1 site; Duster and Endurance (US): 2 sites each.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3957062/v1/c4aae474eb7f5552f466c192.png"},{"id":74424217,"identity":"9594e9d8-cc8b-43b4-83a6-314dd558cb48","added_by":"auto","created_at":"2025-01-22 07:50:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEstimation of genetic improvement in yield potential as influenced by the yield loss in the check cultivars in treatments receiving foliar fungicide. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eBars represent the apparent rate of improvement in yield potential based on comparison of yield between top yielding and check cultivars at the beginning and end of each time series shown in Figure 2, without accounting for yield loss of the check cultivars. The portion of yield improvement attributable to the yield loss of check cultivars is shown in red. Horizontal dashed lines show the average apparent (black) and real rates (blue) of gain in yield potential due to genetic improvement. For visualization purposes, cultivars were sorted from highest to lowest overall yield improvement. See Methods and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e for detailed description.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3957062/v1/a217e8bef9e1b72821c8b9fd.png"},{"id":74425871,"identity":"9aa6a2a6-dfa2-4e4e-ade2-81b630276de2","added_by":"auto","created_at":"2025-01-22 07:58:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eYield trends for check cultivars in treatments receiving foliar fungicide.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Trends in average yield for check cultivars. Yields are expressed relative to the average yield during the first three years of the time series and a 3-year moving average was calculated to smooth the inter-annual yield variation. Weather influence was previously deducted from Baguette P.11, Baguette 10, Cordiale, SY 200 (see methods and Supplementary Table S3). Each line corresponds to a check. Regression of average yield trend is shown with a discontinuous line.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3957062/v1/b3e529a76e11ad0b7d6497e9.png"},{"id":103972976,"identity":"aed619a1-3e58-4763-9d06-7112450434f6","added_by":"auto","created_at":"2026-03-05 08:05:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1340184,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3957062/v1/539c1a80-ec5c-4fd7-bd21-cf8e7b7edba1.pdf"},{"id":74425872,"identity":"7161ae42-aaa6-4c29-8b2d-65400e10f14b","added_by":"auto","created_at":"2025-01-22 07:58:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1975793,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"111.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3957062/v1/04053aa79584cc3b6ec122fe.pdf"},{"id":74425869,"identity":"a48cfec4-48d2-409d-905a-4227f83a4001","added_by":"auto","created_at":"2025-01-22 07:58:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2113306,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformationforAndradeetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-3957062/v1/052a1748f4a138bc04834bdd.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Maintenance breeding and breeding for yield potential equally contribute to genetic improvement in wheat yield","fulltext":[{"header":"Introduction","content":"\u003cp\u003eContinued yield gains are essential to ensure adequate food supply while substantially reducing land conversion and greenhouse gas emissions due to expansion of crop production area\u003csup\u003e1-4\u003c/sup\u003e. Clarity on the factors with greatest contribution to yield advance in recent decades is essential for effective prioritization of future research to improve yields. In the world\u0026rsquo;s most productive cropping systems, increase in yield potential \u003cem\u003evia\u003c/em\u003e genetic improvement is the trait that is most widely credited with greatest impact on yield trends in farmer\u0026rsquo;s fields\u003csup\u003e5-7\u003c/sup\u003e. But what if that is wrong?\u003c/p\u003e\n\u003cp\u003eYield potential is defined as the yield of a well-adapted cultivar when grown without water and nutrient limitations and with weeds, diseases, and insect pests effectively controlled\u003csup\u003e8,9\u003c/sup\u003e. Under these conditions, yield potential is determined by solar radiation, temperature, and atmospheric carbon dioxide concentration. In the case of rainfed crops, water supply and soil water storage impose another limit to yield potential\u003csup\u003e10\u003c/sup\u003e. Hence, in absence of climatic trends, changes in yield potential over time can be attributed to genetic traits influencing light interception and conversion to biomass as governed by photosynthesis, respiration, water-use efficiency, and biomass partitioning to grain\u003csup\u003e11,12\u003c/sup\u003e. Continuous improvement in yield potential through genetic improvement is crucial to avoid yield plateaus and reduce land requirements for adequate crop production\u003csup\u003e1,13-15\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost field studies assessing genetic improvement in yield potential rely on side-by-side comparison of sets of historical cultivars in today\u0026rsquo;s environment\u003csup\u003e16-23\u003c/sup\u003e. In these studies, the slope of the linear regression between measured grain yield \u003cem\u003eversus\u003c/em\u003e each cultivar\u0026rsquo;s year of release is assumed to represent the annual rate of gain in yield potential due to genetic improvement. This approach can be criticized, however, because it does not account for progressive yield decreases that occur over time when cultivars lose adaption to the evolving climate, atmospheric composition, soil properties, crop management practices, pest and disease pressure, hereafter referred to as a loss in \u0026lsquo;agronomic fitness\u0026rsquo; \u003csup\u003e13,24-29\u003c/sup\u003e. Consequently, observed yield differences between new and old cultivars at any point in time can be associated with (i) higher genetically determined yield potential in new cultivars, (ii) loss in agronomic fitness of old cultivars (maintenance breeding), or (iii) a combination of both \u003cstrong\u003e(Figure 1)\u003c/strong\u003e. Unfortunately, most studies of this kind on major food crops have assumed that yield differences between old and new cultivars are due to genetic improvement in yield potential without assessing contributions from loss of agronomic fitness in older cultivars.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we developed a methodology to distinguish maintenance breeding from gains in yield potential using wheat as a case study. Wheat is the most widely grown staple food crop worldwide, with a global harvested area of 217 million hectares, which accounts for about one quarter of global cereal production and ~21% of human calories and protein\u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;We assembled a database (post-2000) of 15 long-term trials in Argentina, United States, and Western Europe (\u003cstrong\u003eSupplementary Figure S1\u003c/strong\u003e) that include a set of commercial cultivars tested each year, together with check cultivars that were released no-more than five years prior to being included in a trial and were tested for a minimum of 10 years thereafter against more recently released cultivars \u003cstrong\u003e(Supplementary Tables 1 and 2)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBecause high-yielding wheat stands have lush leaf canopies that are conducive to development of fungal diseases\u003csup\u003e31,32\u003c/sup\u003e, yield loss from these diseases were minimized by a prophylactic, in-season application of foliar fungicide, at least once, across all cultivars in every trial. Most trials also included a separate treatment across cultivars that did not receive fungicide. We assumed the average yield of the ten highest yielding cultivars each year represented the yield potential, and then evaluated yield trends of these highest yielders over time as a measure of changes in yield potential. Similarly, we tracked yield trends of the check cultivars to assess the magnitude of loss in agronomic fitness of older cultivars.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAverage yield of top yielding cultivar and check cultivars oscillated over time due to inter-annual variation in weather conditions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. However, two distinct features were apparent. First, the yield difference between top yielding cultivars and check cultivars were negligible at the beginning of a time series, indicating that the check cultivars chosen in our study were among the top yielding cultivars at the beginning of the trials. Second, the yield difference between the top yielding cultivars and cultivar checks increased over time, highlighting the contribution of breeding to crop yields. After adjusting yields to account for climate trends using simulated yields, we calculated the overall rate of genetic yield improvement by assessing yield differences between top and check cultivars over time \u003cb\u003e(see Methods)\u003c/b\u003e. Following this approach, we determined an overall rate of 97 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, equivalent to 1.14% per annum [p.a.] \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAt question is how much of the overall yield improvement can be attributed to increase in yield potential \u003cem\u003eversus\u003c/em\u003e maintenance breeding aiming to keep cultivars adapted to the evolving environment. To distinguish between these two components, we followed the same approach as for the overall yield improvement but based on yield trends of top cultivars and check cultivars to estimate annual rates in yield potential gain and yield erosion, respectively. We found that maintenance breeding and yield potential gain have contributed almost equally, with annual rates averaging 46 (0.54% \u003cem\u003ep.a.\u003c/em\u003e) and 51 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (0.60% \u003cem\u003ep.a.\u003c/em\u003e), respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Thus, ignoring the contribution of maintenance breeding, as done in previous studies, would have led to a two-fold overestimation in yield potential gain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual rate of yield change in top yielding and check cultivars, overall genetic yield improvement, and its subcomponents (maintenance breeding and yield potential) based on long-term cultivar trials conducted in Argentina, Europe, and United States. Relative rates are also shown (% p.a.). An extended version of this table is shown as Supplementary Table S3.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e---- Yield change (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003ea\u003c/sup\u003e ----\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e------------------- Genetic yield improvement (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003eb\u003c/sup\u003e -----------------------\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCheck cultivar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCheck cultivars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTop yielding cultivars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaintenance breeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYield potential\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApache \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (0.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (0.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003enil\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaguette 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (2.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (0.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112 (1.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaguette P.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (1.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (0.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85 (1.19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClaire \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (0.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003enil\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (0.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCordiale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154 (1.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99 (0.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (0.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (2.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (1.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (1.44%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (1.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (0.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (0.73%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSY 200 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003enil\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (1.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (0.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (0.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Annual rates of yield change were estimated by comparing the average yield of the top cultivars over the last three years with that of the first three years in each time series, divided by the number of years elapsed, after a weather-based correction was applied based on relative changes in simulated yield between the two periods. The same methodology was followed to estimate the rate of yield change in check cultivars.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Overall yield improvement was estimated by summing up annual yield changes in top cultivars and checks. For one case with positive yield change in checks (Claire), we assumed yield improvement to be fully accounted by increase in yield potential. Conversely, for two cases with negative trends in yield of top cultivars (Apache and SY200), we assumed that yield improvement to be fully accounted by maintenance breeding. Relative rates (% \u003cem\u003ep.a.\u003c/em\u003e) were computed as the quotient between absolute rates and average yield of top yielding cultivars during the last three years of each time series.\u003c/p\u003e \u003cp\u003eWe further confirmed patterns in yield erosion by assessing trends in 3-year moving average yields for check cultivars \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The overall trend in check cultivar yield was estimated at -68 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (range from nil to -190 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). However, it was notable that yields did not decline in two of the eight check cultivars (p\u0026thinsp;\u0026gt;\u0026thinsp;0.1), which means yield erosion was not ubiquitous. Because of the non-linear nature of the yield trend, resulting linear estimates were substantially more negative than those estimated using 3-year averages at the beginning and end of the time series (-68 \u003cem\u003eversus\u003c/em\u003e \u0026minus;\u0026thinsp;39 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Finally, evaluation of six cultivars in sites that included side-by-side treatments with and without in-season foliar fungicide revealed that performance against diseases was primarily determined by the inherent susceptibility of each check cultivar rather than cultivar age (\u003cb\u003eSupplementary Figure S2\u003c/b\u003e). For example, some cultivars (Apache and Baguette 10) exhibited large susceptibility to diseases soon after they were released, while others (SY 200, Duster, and Endurance) had relatively low and stable yield response to fungicide application. One cultivar (Baguette P. 11) showed considerably less yield erosion with fungicide treatment than without it, indicating that its yield loss can be partly attributed to greater susceptibility to foliar diseases over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGenetic improvement contributes to on-farm yield gains through farmer adoption of new cultivars that have been selected for increased yielding ability, grain quality, disease resistance, winter hardiness, \u003cem\u003eetc\u003c/em\u003e. across a wide range of relevant environments\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In that process, new cultivars typically have improved resistance and adaptation to evolving pest populations, management practices, soil properties, and climate, thus avoiding the yield erosion that would occur over time in absence of cultivar replacement\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Genetic improvement can also contribute to on-farm yield gain through commercial release and farmer adoption of new cultivars with higher yield potential due to improvements in carbon assimilation and partitioning to grain, as has been the case for wheat and rice cultivars released since the onset of Green Revolution in the 1960s, and hybrid maize cultivars over the same time frame\u003csup\u003e\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. At issue is how to estimate the relative contributions of \u0026lsquo;maintenance\u0026rsquo; breeding to compensate for yield erosion of old cultivars\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eversus\u003c/em\u003e improvement in yield potential. This distinction is crucial to guide public and private investment in agricultural research and development more effectively.\u003c/p\u003e \u003cp\u003eOur study shows that comparing yields of new \u003cem\u003eversus\u003c/em\u003e old cultivars in today\u0026rsquo;s environment does \u003cem\u003enot\u003c/em\u003e provide a reliable estimate of genetic improvement in yield potential of wheat \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The historical cultivar yield trial approach carries the risk of inflating the estimated return on investment in yield potential increase through genetic improvement because such comparisons are biased against the old cultivars when there is a progressive loss of adaptation to the evolving biophysical environment. This bias could be more pronounced when only a handful of cultivars are meant to represent several decades of breeding \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Previous studies assessing yield gains in long-term trials have admittedly relied on the assumption that the yield erosion can be fully offset \u003cem\u003evia\u003c/em\u003e improved agronomic management\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, which does not seem to be the case as our study shows. Indeed, the yield erosion is well known by breeders, who purposely replace check cultivars after a few years of inclusion in the trials\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Interestingly, the magnitude of yield erosion was diverse across cultivars, with some of them showing a sharp yield decline after being released while others exhibit little or no yield decline over time \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. We conclude that, with few exceptions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, the extensive literature on yield potential improvement has overestimated gains in yield potential. While this conclusion is sobering, it also highlights the importance of robust \u0026lsquo;maintenance\u0026rsquo; breeding programs that combat yield erosion by continuously releasing new cultivars better adapted to the changing biophysical environments of intensive crop production systems\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and the challenge to increase yield potential of the major crops to meet food demand and reduce associated land requirements and negative environmental impacts.\u003c/p\u003e \u003cp\u003eIf yield potential gain is slower than previously thought, as it seems to be the case for wheat based on the present study and for maize and rice as reported elsewhere\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, future yield gains will increasingly depend more on closing the existing gap between on-farm yield and yield potential \u003cem\u003evia\u003c/em\u003e improvements in agronomic management. Unfortunately, studies that explicitly seek to measure yield erosion in cultivars of other major upland crops such maize or soybean have not yet been conducted. It would be surprising if there were no yield declines in widely grown cultivars in these other major crops. To better estimate future food production capacity on at national to global scale will require improved estimates of expected crop yield gains due to improvements in agronomic management and genetic gain.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrieved yield data from long-term variety trials used to evaluate the performance of new cultivars before being released to the market and widely grown varieties, also known as VCU -Value for Cultivation or Use- trials, located in United States, Western Europe (France and United Kingdom), and Argentina \u003cstrong\u003e(Supplementary Figure S1; Supplementary Table S1-S2)\u003c/strong\u003e. These trials assess the agronomic value of crops, including yield and diseases resistance in different environmental conditions. Collectively, the abovementioned countries include 30 M ha and 129 M tons of wheat, accounting for 14% and 17% of wheat global area and production (average from 2019-2021)\u003csup\u003e37\u003c/sup\u003e. We only included trials that complied with the following criteria: (i) replicated field trials including at least 10 years of data, (ii) conducted after 2000 and including semi-dwarf cultivars, (iii) located within main wheat producing regions in each country/region, (iv) included the most widely sown cultivars and (v) included \u0026lsquo;check\u0026rsquo; cultivars that remained in the trials over a longer period of time compared with others (\u0026gt;10 years) and were incorporated to the trials within three years after their commercial release.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the above criteria, we compiled data from 15 long-term replicated trials \u003cstrong\u003e(Supplementary Figure S1; Supplementary Table S1-S2)\u003c/strong\u003e. All trials were rainfed and received at least one in-season foliar fungicide application. All trials, except for United Kingdom, also included an untreated fungicide treatment (in addition to the treatment receiving fungicide). Overall, there were a total of eight check cultivars across the 15 trials, which corresponded to cultivars that were widely grown soon after their time of release. Seed was regenerated every year for the check cultivars. Plot size ranged between 7 and 15 m\u003csup\u003e2\u003c/sup\u003e (Argentina 1.5 x 5 m; United States 1.5 x 10 m; France 7-10 m\u003csup\u003e2\u003c/sup\u003e), which is a common size for variety trials. Crops received recommended management practices regarding sowing date, seeding rate, fertilization, and weed and pest management at each site every year. As a result, high yields were obtained in these experiments, which were, on average, 18% (USA), 29% (Europe) and 45% (Argentina) higher than average farmer yields during the same period\u003csup\u003e37\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe excluded cultivar-year combinations with suspicious values (\u003cem\u003ee.g.\u003c/em\u003e, CV\u0026gt;30% among replicates) and specific years with near crop failure due to drought, severe lodging, and/or frost. A total of 1% cultivar-year observations were eliminated following our quality control. Subsequently, we calculated the average yield of the 10 top-yielding cultivars for each trial-year combination, excluding the checks. Such approach helped us discard experimental and/or underperforming cultivars.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe assessed the influence of weather on the yield trends of both the top yielding and check cultivars. To do this, we retrieved the simulated water-limited yield potential associated to the nearby location to each trial available at the Global Yield Gap Atlas (www.yieldgap.org). Water-limited yield potential is determined by solar radiation, temperature, CO\u003csub\u003e2\u003c/sub\u003e concentration, precipitation, and soil properties influencing the water balance (8). Briefly, the water-limited yield potential was estimated using process-based crop models including CERES-Wheat (Argentina), SSM (USA), and WOFOST (France and UK)\u003csup\u003e51-53\u003c/sup\u003e that were specifically validated to be used in the target sites\u003csup\u003e54-57\u003c/sup\u003e. The water-limited yield potential was simulated based on local measured weather, soil, and management practices and assumed no nutrient limitation and complete absence of yield-reducing factors such as weeds, pathogens, and insect pests. For each specific trial, we assessed changes in simulated water-limited yield potential over time for a fixed cultivar and set of management practices so that interannual variation in simulated is solely attributable to weather variation during the specific timeframe of each experiment (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine the annual rate of change in yield potential and yield erosion in check cultivars, separately, we compared the average yield over the last three years with that of the first three years in each time series (\u003cstrong\u003eTable 1\u003c/strong\u003e). The difference was then divided by the number of years elapsed, and a weather-based correction was applied, determined by the relative changes in simulated yields between the two periods. In the case of Baguette P.11, we considered the first four years of the time series due to some missing data in early years at a few sites. For one case with positive yield trends in checks (Claire), we assumed that all genetic yield improvement corresponded to yield potential gain. Conversely, for two cases with negative trends in top cultivars (those against Apache and SY 200 checks), we assumed that all genetic yield improvement corresponded to maintenance breeding (\u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). The overall\u003cem\u003e\u0026nbsp;\u003c/em\u003erate of yield improvement to crop yields was obtained as the difference between top and check cultivar yield change, divided by the number of years of the time series. Relative rates of yield change were calculated as the ratio between the absolute rates and average 3-year yield at the end of the time series.\u003c/p\u003e\n\u003cp\u003eWe further investigated trends in yield erosion, with and without in-season foliar fungicides, over time. To do this, we determined the average yield of check cultivars during the first three years. These average values served as the baseline to evaluate subsequent changes in the yield of check cultivars over time (\u003cstrong\u003eFigure 2; Supplementary Table S3\u003c/strong\u003e). Subsequently, to reduce the noise associated with inter-annual variation in weather, we used a 3-year moving average that smoothed the progression in yield erosion (checks) over time. Additionally, we estimated the climatic trend from linear regressions adjusted to the 3-year moving average of simulated water-limited yields. We found two cases (sites testing Baguette 10 and Baguette P.11) with a significant negative trend and two cases (sites testing Cordiale and SY 200) with a significant positive trend (p\u0026lt;0.1). Thus, we deducted the relative contribution of weather on yield trends from check and top cultivars for those cases. The effect of climate on yield potential over time was ignored for the remaining cases given that trends were not statistically significant (p\u0026gt;0.1). Finally, we fitted linear regression models to the 3-year moving yield averages of check cultivars and derived their slopes, which represent the rates of yield erosion \u003cstrong\u003e(Figure 2, Supplementary Table S3)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Drs Jacques Le Gouis (INRAE), Andy Macdonald (Rothamsted Research), and Susannah Bolton (AHDB) for helping retrieve the experimental data from trials in France and United Kingdom.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental trial data are publicly available \u003cem\u003evia\u003c/em\u003e the links provided in the Supplementary material section. Data on simulated yield potential are publicly available \u003cem\u003evia\u003c/em\u003e the Global Yield Gap Atlas website (www.yieldgap.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.G. and K.G.C. conceived the project. J.F.A., J.M., S.Y., J.P.M., J.I.R.E., R.P.L., and C.L. provided, compiled, and/or analyzed the data. J.F.A., P.G., K.G.C., and S.Y. wrote the manuscript with input from all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCassman, K. G. Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. \u003cem\u003eProc. Natl. Acad. 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Advances in Agricultural Systems Modeling 2: Trans-disciplinary Research, Synthesis, and Applications, eds LR Ahuja, L Ma (ASA-CSSA-SSSA, Madison, WI), pp. 297\u0026ndash;326 (2011).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3957062/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3957062/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudies that quantify the contribution of genetic improvement to crop yields typically rely on comparisons of old cultivars grown side-by-side with more recent ones. This approach, however, does not allow to distinguish gains in yield potential \u003cem\u003eversus\u003c/em\u003e maintenance breeding that aims to keep cultivars adapted to the evolving biophysical environment, including pests, diseases, and climate change. Our analysis of long-term wheat trials from Argentina, Europe, and United States revealed an overall genetic yield improvement of 97 kg ha\u003csup\u003e− 1\u003c/sup\u003e y\u003csup\u003e− 1\u003c/sup\u003e (1.14% per annum) based on comparison of modern cultivars against older ‘check’ cultivars. However, nearly half of the genetic improvement (46 kg ha\u003csup\u003e− 1\u003c/sup\u003e y\u003csup\u003e− 1\u003c/sup\u003e) was attributable to maintenance breeding and the other half (51 kg ha\u003csup\u003e− 1\u003c/sup\u003e y\u003csup\u003e− 1\u003c/sup\u003e) to the higher yield potential of modern cultivars. We conclude that comparison of new \u003cem\u003eversus\u003c/em\u003e old cultivars under current conditions leads to an overestimation of genetic gains in yield potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOne sentence summary\u003c/strong\u003e: Crop yield potential gains are lower than reported.\u003c/p\u003e","manuscriptTitle":"Maintenance breeding and breeding for yield potential equally contribute to genetic improvement in wheat yield","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 07:50:35","doi":"10.21203/rs.3.rs-3957062/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"78b38f62-95aa-4577-9e2a-95f10897f456","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42994716,"name":"Biological sciences/Plant sciences/Plant ecology"},{"id":42994717,"name":"Earth and environmental sciences/Ecology/Ecophysiology"}],"tags":[],"updatedAt":"2026-03-05T08:05:15+00:00","versionOfRecord":{"articleIdentity":"rs-3957062","link":"https://doi.org/10.1038/s41467-026-69936-6","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-03-04 05:00:00","publishedOnDateReadable":"March 4th, 2026"},"versionCreatedAt":"2025-01-22 07:50:35","video":"","vorDoi":"10.1038/s41467-026-69936-6","vorDoiUrl":"https://doi.org/10.1038/s41467-026-69936-6","workflowStages":[]},"version":"v1","identity":"rs-3957062","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3957062","identity":"rs-3957062","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}