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

Domain: chemistry
Published: 2026-03-04
Editorial outcome: major_revision

Abstract
Studies 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 versus maintenance breeding that aims to keep cultivars adapted to the evolving biophysical environment, including pests, diseases, and climate. Here we show an overall wheat yield improvement of 73 kg ha-1 y-1 based on direct comparison of modern cultivars against older ‘check’ cultivars using data from multi-environment trials from Argentina, France, United Kingdom, and United States. However, almost half of this improvement (33 kg ha-1 y-1) is attributable to maintenance breeding needed to counteract the yield erosion of older cultivars, and the other portion (40 kg ha-1 y-1) can be associated with 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.

Introduction
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 controlled1,2. 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 potential3. 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 grain4,5. Continuous gains in yield potential through genetic improvement is crucial to avoid yield plateaus and reduce land requirements for adequate crop production6–9.

Field studies quantifying genetic yield improvement often rely on side-by-side comparison of sets of historical cultivars in today’s environment10–19. In these studies, the slope of the linear regression between measured grain yields versus each cultivar’s year of release is assumed to represent the annual rate of yield potential gain due to genetic improvement. Other approaches to quantify genetic yield improvement consist of comparison of new cultivars against check cultivars during several years20 or analysis of large long-term variety multi-environment trials (MET) using mixed linear models21,22. A common assumption of the first two approaches (i.e., side-by-side testing of historical series and comparison between new and check cultivars) is that the yield of the check cultivars does not erode over time or that the yield erosion can be fully avoided with check replacement and/or management so that the measured genetic gain can be fully attributable to higher yield potential in newer cultivars23–25. This assumption can be criticized because it ignores the yield decline that occurs over time when cultivars lose adaptation to the evolving climate, atmospheric composition, soil properties, crop management practices, pest and disease pressure, hereafter referred to as a ‘yield erosion’7,26–31. Retrospective analysis of MET datasets allows counteracting this drawback as each cultivar was tested at their peak of use31. 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) yield erosion of old cultivars, or (iii) a combination of both (Fig. 1). Both breeding to counteract yield erosion by maintaining cultivars adapted to the changing abiotic and biotic environments (maintenance breeding) or improving yield potential in new cultivars are useful and needed. However, many studies of this kind on major food crops have assumed that yield differences between old and new cultivars are solely due to genetic gain in yield potential without assessing the contribution from maintenance breeding.

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 area6,32–34. 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 trend in farmer’s fields35–37. But what if existing estimations of genetic gains in yield potential are wrong? Here, we distinguish maintenance breeding from gains in yield potential using wheat as a case study, which 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 ca. 20% of human calories and protein38.

Results
Yield trends over time
We assembled a database (post-2000) that consisted of multi-environmental trials (METs) in Argentina, United States (US), United Kingdom (UK), and France (Supplementary Fig. 1) evaluating 849 cultivars across 17 locations, and including 10 check cultivars that were released no more than five years prior to being included in a trial (to avoid missing any early and quick yield decay) and were tested thereafter against more recently released cultivars for a minimum of 10 years to capture any measurable yield decay over time. In addition, because high-yielding wheat stands have lush leaf canopies that are conducive to development of fungal diseases39,40, yield loss from these diseases was minimized by prophylactic, in-season applications of foliar fungicide across all cultivars in every trial. Most trials included separate treatments with and without fungicide across all cultivars (Supplementary Tables 1 and 2). Overall, our database included 13,003 cultivar×site×fungicide combinations x year combinations.

Average yield of top yielding cultivars and check cultivars oscillated over time due to inter-annual variation in weather conditions (Fig. 2). However, we could not detect any long-term climatic trend based on trends in simulated yields using process-based crop models and local measured weather (Supplementary Fig. 2). On the other hand, two distinct features were apparent from the observed trends in measured yields (Figs. 2 and 3). First, the yield difference between top yielding cultivars and check cultivars was negligible at the beginning of the time series (141 kg ha−1), 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 check cultivars, based on fungicide-treated crops, increased over time at an overall rate of +73 kg ha−1 y−1, highlighting the contribution of breeding to crop yield improvement.

Distinguishing gains in yield potential versus maintenance breeding
At question is how much of the overall yield difference 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 evaluated the overall yield trends of top cultivars and check cultivars in crops treated with foliar fungicide (Figs. 2 and 3). We found that maintenance breeding and yield potential gain have contributed similarly, given the observed rates of -33 kg ha−1 y−1 for checks and +40 kg ha−1 y−1 in top cultivars. Thus, ignoring the contribution of maintenance breeding, as done in previous studies, can lead to a substantial overestimation in yield potential gain. Additionally, untreated check cultivars showed larger yield reductions (-64 kg ha−1 y−1) than treated ones over time, suggesting an increased susceptibility to foliar diseases over time and, thus, a larger yet masked yield erosion.

We further investigated yield trends using the entire database (13,003 yield entries combining site, cultivar, year, and fungicide application) with a mixed-effects model analysis (Table 1). There was a substantial effect of fungicide in all four countries, with an average yield response of +1285 kg ha−1, which is equivalent to 20% of the yield of the untreated crop. Thus, we evaluated yield trends for treated and untreated crops separately and found that genetic gains in treated crops ranged from +32 to 62 kg ha−1 y−1 (0.5–0.9% y−1) across countries. On the other hand, a negative non-genetic trend, which is associated with yield erosion and counteracted by maintenance breeding, was apparent in all cases, except for fungicide-treated crops in UK, ranging from -33 kg ha−1 y−1 (US) to -137 kg ha−1 y−1 (France) in treated crops and from -72 kg ha−1 y−1 (US) to -229 kg ha−1 y−1 (UK) in untreated crops. These results were consistent with the trends shown in Figs. 2 and 3.

Methods
Data from variety trials
We retrieved yield data from four METs in Argentina, France, UK, and US (Supplementary Fig. 1, Supplementary Tables 1 and 2). These trials assess the agronomic value of crops, including yield and diseases resistance in different environmental conditions. Collectively, the four countries where METs were located include 30 Mha and produce 129 Mtons of wheat, accounting for 14% and 17% of wheat global area and production, respectively (average from 2019–2021)38. We only included trial that complied with the following criteria: (i) replicated multi-year 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 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.

Following the above criteria, we compiled data from 17 replicated trial locations (Supplementary Fig. 1, Supplementary Table 1 and 2). All trials were rainfed and most of them have a treatment without fungicide and another that received at least one in-season foliar fungicide application. Inclusion of fungicide-treated and untreated crops allowed us to assess changes in crop susceptibility to diseases over time. There was a total of ten check cultivars across the 17 trial locations, which corresponded to cultivars that were widely grown across millions of hectares globally soon after their time of release. Overall, our database included 13,003 cultivar×site×fungicide×year combinations across four countries. On average, 31 cultivars were tested every year in each trial, ranging from 16 to 55 across trial-year observations. Our database size is reasonable and comparable to previous studies assessing genetic gains22–25,48.

Seed was regenerated every year for the check cultivars. Plot size ranged between 7 and 15 m2 (Argentina: 1.5×5 m; US: 1.5×10 m; UK: 1.65×9 m; France: 7–10 m2), 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% (US), 29% (Europe) and 45% (Argentina) higher than average farmer yields during the same period38. Likewise, except for US, average yields of the top performers were ±15% of the simulated yield potential in each region.

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 as reported by local experts. 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 an approach helped us discard experimental and/or underperforming cultivars. We assumed that the average yield of the 10 top yielding cultivars in 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.

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 closest location to each trial available at the Global Yield Gap Atlas (www.yieldgap.org). Water-limited yield potential is determined by solar radiation, temperature, CO2 concentration, precipitation, and soil properties influencing the water balance1. Briefly, the water-limited yield potential was estimated using process-based crop models including CERES-Wheat (Argentina), SSM (US), and WOFOST (France and UK)52–54 that were specifically validated to be used in the target sites55–58. These previous studies have shown good agreement between simulated and measured yields in experiments conducted across wide range of biophysical environments where nutrient limitations and yield-reducing factors were effectively removed. 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 trends in simulated yield are solely attributable to weather variation during the specific time frame of each experiment (Supplementary Fig. 2). Since there were no statistically significant long-term trends in simulated yield potential (P > 0.363), we analyzed the yield data without any normalization by weather or simulated yields.

Available phenotypic yield data for the statistical analysis consisted of either variety means (UK dataset and 11 out of 130 trial-location-year combinations in Argentina) or raw data per plot (datasets from France and US and 119 out of 130 trials location-year combinations in Argentina) per individual trial. For trials for which raw data were available, weighted variety means were calculated using best linear unbiased estimates (BLUEs) derived from linear mixed models:

yin = μ + Gi + Rn + εin (1)

where yin is the phenotypic observation of genotype i in replicate n; μ is the trial mean; Gi is the fixed additive effect of the genotype i; Rn is the random effect of the replicate n and is ~N(0, σ2r); and εin is a random residual term attributable to the effect of the within-trial experimental error and is μ~N(0, σ2e). Models were fitted using REML59, as implemented in the “asreml” package60 for R61. Four datasets consisting of either variety means per trial (UK), variety BLUEs per trial (France and US), or both (Argentina), were used to (i) test the effect of the fungicide treatment on grain yield; (ii) estimate overall, genetic, and non-genetic linear trends for grain yield over the years covered by the trials; and (iii) compute the BLUEs of the individual variety effects per calendar year for fungicide-treated and untreated crops. We used the following linear mixed models to analyze the effect of fungicide in the four METs:

yijkm = μ + Fj + Yk + Ekm + Gi + (GY)ik + εijkm (2)

where yijkm is the phenotypic observation of genotype i in trial m, conducted in year k under the fungicide treatment j; μ is the overall mean; Fj is the fixed additive effect of the fungicide treatment j; Yk is the random additive effect of the year k and is ~N(0, σ2y); Ekm is the random effect of the trial m nested within year k and is ~N(0, σ2e); Gi is the random additive effect of the genotype i and is ~N(0, σ2g); (GY)ik is the random effect of the interaction between genotype i and year k and is ~N(0, σ2gy); and εijkm is a random residual term attributable to the combined effect of the within-trial experimental error and the genotype × trial (nested within year) interaction and is ~N(0, σ2e). The BLUEs of fungicide effects were computed from this analysis.

With the aim of estimating unbiased genetic and non-genetic progresses for grain yield23,24 over the years comprised by the MET datasets, our model incorporated a fixed genetic trend (β) with respect to the year of first testing of genotype i (ri) and a fixed non-genetic trend (γ) respect to the trial year k (tk) scaled to the first year (2002 in Argentina; 2004 in UK, 2000 in US; and 2001 in France) as follows:

yikm = μ + γtk + Yk + Ekm + βri + Gi + εikm (3)

where Yk and Ekm are the random environmental deviations from the non-genetic trend due to the effects of the year and trial nested within year, respectively; Gi is the random genotypic deviation from the genetic trend, and εijkm is a random residual term attributable to the combined effect of the within-trial experimental error and the genotype × year and genotype × trial (nested within year) interactions. All random terms in model (3) were assumed to follow a normal distribution. This linear mixed model was used to analyze breakouts from the MET datasets, stratified by the presence or absence of fungicide treatments. In a complementary analysis, the term βri was removed from the model to estimate the overall, i.e., genetic + non-genetic, yield trend for the undivided MET dataset. Finally, we computed the BLUEs of the individual variety effects per trial-year. To do so, we used the following linear mixed model for the analysis of the fungicide-treated and untreated crops in the four MET datasets:

yikm = μ + Gi + Yk + Ekm + (GY)ik + εikm (4)

The BLUEs of the variety × year effects were computed from this analysis.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This was a nice paper to read and I enjoyed dissecting it! There is clearly value in the paper, which draws on methods for\ncalculating yield improvement. It reiterates important points about the implications of comparing old check cultivars with new\nvarieties, and how this can inflate genetic gain calculations. This is of course an important point. However, I think the method\nis very simplified and neglects to acknowledge that in many genetic gain calculations, it is not a simple comparison between\nnew cultivars and one old check, but rather can involve e.g. multi-environment trails, amongst other methods cited in this\nwork. There is a lack of acknowledgment of the influence of just comparing new cultivars with just one check, as has been\ndone here. This is not a new point and the importance of using multiple checks in these calculations is discussed in detail in\nRaymond et al (2023) https://doi.org/10.1016/j.fcr.2023.109086 and Piepho and Laidig (2024)\nhttps://onlinelibrary.wiley.com/doi/10.1111/pbr.13240. It does seem there is significant literature lacking on this topic in the\npaper.\nIn terms of the paper’s significance, the authors suggest it is important for breeding efforts, but I would like to see this point\nexplored more in the discussion, in terms of exactly what this means for breeding, or whether its more about how yield\nimprovement is calculated? Also, what are the implications of the results for future yield projections?\nThe methods they used are quite simple, in comparing average yields of old check varieties with the average across new\nvarieties, and the averaging over 3 years to remove some interannual variability seems reasonable. The authors have\nchosen not to do more extensive modelling to account for the different contributors to yield variability, but do manage in\nsome ways to account for longer term simulated climate and weather effects. A lot of weight for these effects is on the model\nsimulations performing very well – I would be interested to hear how accurate the model simulations area. You mention\nusing water-limited yield potential from the Global Yield Gap Atlas. A brief statement clarifying its accuracy in representing\nobserved yields across diverse environments would strengthen the methodology.\nIt is good to see the inclusion of fungicide treatment and check cultivars. But I am curious firstly why so few checks were\nincluded, as there are many to choose from, but also why there is no untreated variety trial yields for Cordiale and Claire (this\nis available and has been used in other studies, perhaps for different sites in the UK). Given it was obviously not possible for\nthe authors to use the untreated data, why were these checks still used? The trend in treated variety trial yields over time for\nClaire would be great to investigate further, through initially check the untreated trial yields. I am also missing a discussion\non the influence of using these specific checks has had on the results.\nPerhaps an acknowledgement of some of the assumptions made, or other factors not accounted for in their calculations,\ncould be included in the discussion. For example, changes in agronomic factors, such as reductions in nitrogen fertiliser, are\nnot discussed, and longer term effects seen as climate effects could be linked to CO2 fertilisation or changing soil fertility.\nThe source of the data is quite clear and mostly the methods are (except where specified in the manuscript).\nPlease find attached detailed comments.", "reviewer_2": "This paper addresses the important problem of assessing long-term genetic trend in crop breeding. The authors propose a\nmethod that they claim allows separating out effects of maintenance breeding and gains in yield potential.\nThe method the authors use is based on a lot of ad hoc assumptions, and there is no discernible underlying statistical\nmodel. The authors use moving averaged and simple linear regressions in several places, but this does not amount to an\nexplicit statement of an overall statistical model.\nThe authors assume that the average yield of the ten top yielding cultivars in each year represents the yield potential. This\nad hoc definition violates their own definition in line 49 as “yield of a well-adapted cultivar when grown without water and\nnutrient limitations and with weeds, diseases, and insect pests effectively controlled.” Under any real circumstances, there\nare always nutrient limitations. In addition, observed yield is subject to substantial experimental error, so assuming that\nobserved yield = potential yield is quite heroic.\nTwo assumptions seem particularly intriguing. (i) In line 292: “For one case with positive yield trends in checks (Claire), we\nassumed that all genetic yield improvement corresponded to yield potential gain.” No justification is given for this\nassumption. Also, making such an assumption based on a single check and using this to assess trend would seem to make\nthe approach quite sensitive to misspecifications. (ii) In line 294: “Conversely, for two cases with negative trends in top\ncultivars (…) we assumed that all genetic yield improvement corresponded to maintenance breeding.” Again, this seems a\nvery ad hoc assumption, and no justification is given.\nThe authors seek to address the problem of yield decay of check cultivars. A paper that has a lot to say on this problem is\nthis:\nRaymond et al. (2023)\nhttps://doi.org/10.1016/j.fcr.2023.109086", "reviewer_3": "The manuscript titled ‘Maintenance breeding and breeding for yield potential equally contribute to genetic improvement in\nwheat yield’ by Andrade et al. has the goal to disentangle and independently estimate the genetic gains due to what the\nauthors call maintenance breeding or breeding to offset genetic erosion due to adaptation to a changing biophysical\nenvironment (i.e. changes in pests, diseases, and climate change), from contributions to an increase in yield\npotential or the genetic gains associated to carbon assimilation and partitioning.\nThe idea behind finding a way to independently assess maintenance from yield potential improvements is enticing and the\nauthors make a good argument for the need and interest in estimating them independently. The authors are on the right track\non how to estimate those, but the manuscript could be improved to provide stronger inference. There are some limitations\nin the study that compromise the inference, including that the dataset is small and limited, the estimation of the yield potential\nneeds further explanation, and the actual statistical methods used seems under-powered.\nDataset limitation. The main limitation of the dataset is its size. There are very few genotypes used in each one of the sites\nwith around 23-47 genotypes tested each year, there are few checks, and even less sites. These are all quite small numbers\nfor the broad inference that is discussed in the manuscript. Furthermore, the dataset has an important confounding effect of\nsite and check-genotype. For example, based on Figure 2 and Supplementary Table S2, genotype Baguette 10, SY200, and\nClaire were evaluated in a single site. There are 17 site-check combinations and 15 total sites. This creates an inference\nproblem; can the results be interpreted beyond the check-site combinations? Would different conclusions have been\nreached if the check had been tested in multiple sites? The check genotype Claire, for example, is one of the checks that\nshows a negative grain yield erosion, or to say it in another way, grain yield of Claire increases over time, and actual yields\nare well over the estimated yield potential for the site. How can this be interpreted biologically? Had Claire been evaluated\nin multiple sites; would the authors have gotten similar results? How can a yield above the yield potential be explained?\nMaybe using larger datasets where check-genotypes are evaluated in multiple environments and compared to larger\ngenotypes could solve these limitations. Breeding-type of dataset might be more appropriate for such a study as large\ndatasets are generated.\nYield potential estimations. The manuscript does not describe in great detail the methodology used for yield potential. The\nissue discussed above with the check genotype Claire, where actual yields were well above the yield potential seems like a\nproblem. There seems to be a year with a yield potential close to zero where actual yields were quite high (i.e. 12t, Figure 2).\nHow can those results be explained? In a broader and more conceptual way, the authors went into great length to explain\nthat there was a need to discriminate between maintenance breeding from gains from yield potential. They go further to\ndescribe the yield potential as an environment where genotypes are ‘grown without water and nutrient limitations and with\nweeds, diseases, and insect pests effectively controlled’ (line 49-50). The manuscript uses a simulated water-limited yield\npotential as a benchmark, using the Global Yield Gap Atlas, that has the issues described above with Claire. In any case,\nthe study does not seem to be posed to compare a true yield potential given that the trials are rainfed and have other\nlimitations beyond water (i.e. management in cultivar evaluation trials is standard, but not optimal for every genotype. The\ntiming of disease management or fertilization as well as planting density and other management practices may prevent a\ngenotype from achieving its potential), and the water-limited yield potential is used as a benchmark. How were these\nconcerns addressed in the current study? The fungicide vs no fungicide application comparison goes in the right direction of\naddressing these, but does not yield the expected results. How can the results of environment degradation be explained\nthen?\nStatistical analysis and power. After having a historical dataset, where the same check genotype is used in all subsequent\ntrials for a given site, the authors report a large year-to-year variability. This variability is quite common in these types of\nexperiments, creating genotype by year interactions. The authors then use the average of the first three years in the series\nand compare them to the last three years in the series (see Table 1 and Figure 3, materials and methods lines 287-291).\nThis seems an odd choice for such an analysis. Why not use a regression over years if the goal is to show the trend effects?\nIt is clear that with the level of year-to-year variability, finding significant differences will be a challenge for any statistical\nanalysis, but a regression will show a true trend compared to the arbitrary 3-years mean comparison choice. Furthermore, no\nstatistical significance is reported for this part of the manuscript. Are those effects significantly different from zero? Finally,\nwhen a regression is used for trends, a regression on a three-year moving average is used to smooth it out (Figure 4,\nmaterials and methods lines 302-308). However, x values are no longer independent, and the p-values are therefore no\nlonger valid. At least not in a frequentist analysis, but the methods are not described in detail. Why not use the regression on\nthe original data? In a way it is also surprising that an alpha-value of 0.1 is used to claim significance in the cases where a\ntest was conducted. Given that less than 70g per year is expected, the study seems quite under-powered to be able to reach\nthese conclusions. Larger datasets will be needed to find significant differences of that magnitude when year-to-year\nvariations are orders of magnitude different.\nThe discussion about the maintenance breeding vs yield potential is at times overly simplistic. Most of the papers described\nin the manuscript are not attempting to disentangle gains associated to yield potential from maintenance breeding\nimprovements, and although they may have lost an opportunity, it does not mean they are wrong when claiming the total\ngenetic gains. The authors should check all their references to this topic and make their statements unambiguous. For\nexample, the title correctly states that both maintenance breeding as well as improvements in yield potential are both\ncontributors to genetic gain. For example lines 134-135 and lines 204-210 seem to misrepresent other studies as not all\nstudies claim to report yield potential improvements, but mere grain yield gains.\nFurther characterization of the dataset would be useful. For example, genotypes with large within trial variability were\nremoved. Did specific genotypes always produce these issues, or were those local problems due to single plot outliers?\nWhat was the heritability of the trials? Further characterization of the top yields would also prove useful to the readers. Are\nthere significant differences between top yielders and the checks? Could the top yielders be further characterized, including\ntheir distribution and/or range?\nFinally, the authors rightly bring the concern over the tension between management and breeding efforts, and the usefulness\nof a methodology such as this one to create awareness. We agree that the concept is needed and well described in this\nmanuscript, but a larger dataset and improved statistical analysis will be needed. Additionally, the tension between\nmanagement and breeding can be bridged with further studies on the genotype by environment by management.\nMinor comments:\nThe legend on Table 1 could be further explained so that it would be self-explanatory.\nSupplementary Table 4 (line 206) was missing from the review materials.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Responses to Reviewers’ Comments (and our responses in red)\nWe thank the three reviewers for their useful comments about our article and the NCOMMS\neditors for reconsidering a revised version of our MS. We have constructively addressed the\nprevious comments in the revised version. Our point-by-point responses are shown below.\nComments from Reviewer #1:\nThis was a nice paper to read and I enjoyed dissecting it! There is clearly value in the paper,\nwhich draws on methods for calculating yield improvement. It reiterates important points about\nthe implications of comparing old check cultivars with new varieties, and how this can inflate\ngenetic gain calculations. This is of course an important point.\nResponse: Thanks.\nHowever, I think the method is very simplified and neglects to acknowledge that in many\ngenetic gain calculations, it is not a simple comparison between new cultivars and one old\ncheck, but rather can involve e.g. multi-environment trails, amongst other methods cited in this\nwork. There is a lack of acknowledgment of the influence of just comparing new cultivars with\njust one check, as has been done here. This is not a new point and the importance of using\nmultiple checks in these calculations is discussed in detail in Raymond et al (2023)\nhttps://doi.org/10.1016/j.fcr.2023.109086 and Piepho and Laidig (2024)\nhttps://onlinelibrary.wiley.com/doi/10.1111/pbr.13240. It does seem there is significant\nliterature lacking on this topic in the paper.\nResponse: This is well taken. Please, note that we do not use the check cultivars to estimate\ngenetic gains. Indeed, genetic gain and yield erosion are independently calculated. On the one\nhand, trends in average yield of the top yielding cultivar were used to derive gains in yield\npotential. On the other hand, trends in the yield of check cultivars over time were used to\nderive the contribution of maintenance breeding. In both cases, yields were adjusted for\nclimatic trends using crop modeling. Based on the comments made by Reviewer #1 (and other\nreviewers), we have re-analyzed the dataset and grouped check cultivars by country. Following\nthis approach, we tracked trends in average yield for a set of check cultivars per country, making\nthe analysis more robust and including multiple checks instead of a single one. Consequently,\nwe have added a new Figure 3, substantially revised Table 1, and modified the associated text.\nWe appreciate this comment as it helped us improve the original analysis.\nComment made on MS file: Whilst very important, this is by no means a novel conclusion.\nIndeed, this has been discussed and investigated in some depth by Raymond et al. (2023)\nhttps://doi.org/10.1016/j.fcr.2023.109086. The results in the abstract “Specifically, we observed\nan increasing yield difference between fungicide treated and untreated variety trials as varieties\nage, driven by both a breakdown in disease resistance and a previously unobserved long-term\n1\nincrease in yield as varieties age in treated trials. This shows that yields of long-term check\nvarieties cannot be assumed to be stable over time. We found that genetic gain estimates were\nhighly sensitive to the long-term check varieties chosen, whilst the inclusion of multiple checks\ndecreased the standard error of the estimate” It may be worth reading this paper and seeing\nfigures e.g. Figure 5. They go onto explore how variety age should be included in calculations\nand also the sensitivity of genetic yield improvement to individual checks chosen. This is also\nexplored in Piepho and Laidig (2024) who draw on this need for multiple cultivars for these\nassessments https://onlinelibrary.wiley.com/doi/10.1111/pbr.13240\nI think its important to include some references to the literature which does already\nacknowledge the decline in check yields over time.\nResponse: Please, note that the original MS already contained references to the body of\nresearch generated by Drs Piepho, Laidig, Mackay and colleagues. Based on this comment, we\nhave rephrased the text to explicitly recognize the contribution of these previous articles in\ndocumenting the yield erosion in untreated crops (L. 200-204 of revised MS). Note that our MS\nis based on cultivars that were treated with fungicide, showing that yield erosion cannot be\nsolely attributed to loss of disease resistance and thus advancing previous knowledge.\nL. 67. Comment made on MS file: Can you point me to explicit examples where this is assumed?\nAs I understand it, the mixed model approaches you mention account for this by including\nmultiple checks which are replaced regularly and overlap so allow for the interannual variability\nassociated with weather and other interaction effects to be accounted for (for example as in\nMackay et al 2011). They do not focus just on comparing one ageing check variety with new\ncultivars, which naturally introduces this issue of comparing checks with declined yields with\nnew higher yield checks, introducing the risk of genetic gain inflation.\nResponse: Based on this comments, we have carefully revised the literature again. The analysis\nmentioned here is a mixed model linear regression (by Piepho et al., 2014 and implemented\npreviously by Mackay et al., 2011 and later by Laidig et al., 2021), which, in turn, admittedly\nassumes that check cultivars do not erode over time or that the yield erosion can be fully\nmasked with management, quoting: “It is important to realize that the ageing effect due to\ndisease breakdown confounds estimates of long-term genetic and non-genetic trend, as can be\nseen from Eq. (8). This problem cannot be fully resolved, unless one is prepared to make some\nstrong assumptions. As was demonstrated in this paper, some progress can be made if two\nagronomic intensities are tested, one of which (I2) is believed to mask the ageing effect.”\nFurthermore, new studies from Piepho´s group (Laidig et al 2021) recognize that there may be\nyield erosion even applying fungicides: “Not only increasing sensitivity or even loss of resistance\nmight be responsible for ageing effects, but also other factors might be involved like changing\ntreatment intensities, altered management practices, climatic changes, or decrease in\n2\natmospheric nitrogen and sulfur dioxide deposition”. Based on this comment, we have\nrephrased original text to make clear that these previous articles have documented the yield\nerosion in wheat cultivars that were not treated with fungicide (L. 200-204 of revised MS).\nL.77. Comment made on MS file: I am really missing a clear definition of maintenance breeding.\nResponse: We rephrased the text to make the definition clear (L. 78-79 of revised MS).\nIn terms of the paper’s significance, the authors suggest it is important for breeding efforts, but\nI would like to see this point explored more in the discussion, in terms of exactly what this\nmeans for breeding, or whether its more about how yield improvement is calculated? Also,\nwhat are the implications of the results for future yield projections?\nResponse: Following this suggestion, we elaborate further on breeding and other broader\nimplications, including yield projections (L 189-191, L 211-213 and L 235-237 of revised MS).\nFigure 1. Comment made on MS file: Nice clear figure for showing the difference between\nthem. Why are the final 3 dots bigger? Is that because that is what you use to compare with the\ncheck yields?\nResponse: Based on this comment, we made symbols all same size in Fig 1 of the revised MS.\nThe methods they used are quite simple, in comparing average yields of old check varieties with\nthe average across new varieties, and the averaging over 3 years to remove some interannual\nvariability seems reasonable.\nResponse: See previous response on this issue. Based on the comments made by Reviewer #1\n(and other reviewers), we have re-analyzed the dataset and grouped check cultivars by country.\nFollowing this approach, we tracked trends in average yield for a set of check cultivars per\ncountry, making the analysis more robust and including multiple checks instead of a single one.\nWe have added a new Figure 3, substantially revised Table 1, and modified the associated text.\nDiscussion, second paragraph: Comment made on MS file: L. 198. But in this study you do not\naccount for check cultivar replacement and the fact that when people account for genetic gain\nthey have multiple long term checks in the study which are replace regularly. Instead you just\nrely on one cultivar for each calculation. Piepho and Laidig (2024) also draw on this need for\nmultiple cultivars for these assessments https://doi.org/10.1111/pbr.13240\nResponse: See our previous response on this issue. The main purpose of using multiple checks\nin previous studies was to account for non-genetic factors, mainly climate. Replacing these long-\nterm checks regularly will not prevent the yield erosion, even when fungicides are applied, as\nshown in our study. Finally, note that the revised analysis includes a set of check cultivars per\ncountry (not just a single one) and that climate effects are accounted by using crop modeling.\ncontributors to yield variability, but do manage in some ways to account for longer term\nsimulated climate and weather effects. A lot of weight for these effects is on the model\nsimulations performing very well – I would be interested to hear how accurate the model\nsimulations area. You mention using water-limited yield potential from the Global Yield Gap Atlas.\nA brief statement clarifying its accuracy in representing observed yields across diverse\nenvironments would strengthen the methodology.\nResponse: Thanks for the comments. Based on this comment, we have elaborated further on\nthe crop model evaluation (L. 300-316 of revised MS).\nIt is good to see the inclusion of fungicide treatment and check cultivars. But I am curious firstly\nwhy so few checks were included, as there are many to choose from, but also why there is no\nuntreated variety trial yields for Cordiale and Claire (this is available and has been used in other\nstudies, perhaps for different sites in the UK). Given it was obviously not possible for the\nauthors to use the untreated data, why were these checks still used? The trend in treated variety\ntrial yields over time for Claire would be great to investigate further, through initially\ncheck the untreated trial yields. I am also missing a discussion on the influence of using these\nspecific checks has had on the results.\nResponse: We could not find more data from untreated cultivars that meet our criteria of\nminimum number of years. Besides that, we focused on treated cultivars as our main goal was\nto evaluate yield erosion in treated check cultivars – note that the topic of yield erosion in\nuntreated crops have been addressed in previous studies by Drs Piepho, Laidig, Mackay and\ncolleagues. Based on this comment, we performed further data search and added data from\nthree extra sites in UK and two new checks (see Fig 2 in the revised MS). Additionally, we\nelaborated on the influence of choice of check as requested (L 245-248 of revised MS).\nPerhaps an acknowledgement of some of the assumptions made, or other factors not\naccounted for in their calculations, could be included in the discussion. For example, changes in\nagronomic factors, such as reductions in nitrogen fertiliser, are not discussed, and longer term\neffects seen as climate effects could be linked to CO2 fertilisation or changing soil fertility.\nThe source of the data is quite clear and mostly the methods are (except where specified in the\nmanuscript).\nResponse: Thanks.\nComment on discussion: This is a very strong conclusion and I think ignores the efforts from\nother studies to include multiple checks in the calculation of genetic gain to avoid the\noverestimation of genetic gain\nResponse: see previous response. The new analysis includes a set of checks per country.\nResponse: See previous responses. Minor edits were addressed directly in the revised MS.\nComments from Reviewer #2:\nThis paper addresses the important problem of assessing long-term genetic trend in crop\nbreeding. The authors propose a method that they claim allows separating out effects of\nmaintenance breeding and gains in yield potential.\nThe method the authors use is based on a lot of ad hoc assumptions, and there is no discernible\nunderlying statistical model. The authors use moving averaged and simple linear regressions in\nseveral places, but this does not amount to an explicit statement of an overall statistical model.\nResponse: Based on the comments made by Reviewer #2 (and other reviewers), we have re-\nanalyzed the dataset and grouped check cultivars by country, making the analysis more robust\nand including multiple checks instead of a single one. We have added a new Figure 3, revised\nTable 1, and modified associated text accordingly.\nThe authors assume that the average yield of the ten top yielding cultivars in each year\nrepresents the yield potential. This ad hoc definition violates their own definition in line 49 as\n“yield of a well-adapted cultivar when grown without water and nutrient limitations and with\nweeds, diseases, and insect pests effectively controlled.” Under any real circumstances, there\nare always nutrient limitations. In addition, observed yield is subject to substantial experimental\nerror, so assuming that observed yield = potential yield is quite heroic.\nResponse: Valid point. We note that, except for experiments in the USA, the yield of top-\nperforming cultivars are within 15% of the simulated yield potential and follow its inter-annual\nvariation. More pragmatically, we do not see a better way to test our hypothesis as data from\nvariety trials that were explicitly managed to achieve yield potential do not exist. Based on this\ncomment, we elaborated on this limitation in the Discussion section (L. 231-239 of revised MS).\nTwo assumptions seem particularly intriguing. (i) In line 292: “For one case with positive yield\ntrends in checks (Claire), we assumed that all genetic yield improvement corresponded to yield\npotential gain.” No justification is given for this assumption. Also, making such an assumption\nbased on a single check and using this to assess trend would seem to make the approach quite\nsensitive to misspecifications. (ii) In line 294: “Conversely, for two cases with negative trends in\ntop cultivars (…) we assumed that all genetic yield improvement corresponded to maintenance\nbreeding.” Again, this seems a very ad hoc assumption, and no justification is given.\nResponse: We admit that the text explaining the assumption was confusing. Briefly, if the yield\ntrend of top cultivars does not increase over time, it suggests a lack of genetic gain. In such\ncases, and in absence of any climate trend, we assume that the yield difference between top\n5\nand check cultivars can be fully attributed to the yield erosion of the check cultivars. In our new\nanalysis, this is only applicable to UK. Based on this comment, we rephrased the text in the\ncaption to Table 1 caption and in the M&M section (L 329-330 of revised MS).\nThe authors seek to address the problem of yield decay of check cultivars. A paper that has a lot\nto say on this problem is this:\nRaymond et al. (2023)\nhttps://doi.org/10.1016/j.fcr.2023.109086\nResponse: See our previous response to Reviewer #1 regarding previous literature. The paper in\nquestion addressed the yield decay on untreated crops, while our focus is on crops that received\nfoliar fungicide. Based on this comment, we rephrased the text to recognize the contribution of\nprevious articles in documenting the yield erosion in untreated crops (L. 200-204 of revised MS).\nComments from Reviewer #3:\nThe manuscript titled ‘Maintenance breeding and breeding for yield potential equally contribute\nto genetic improvement in wheat yield’ by Andrade et al. has the goal to disentangle and\nindependently estimate the genetic gains due to what the authors call maintenance breeding or\nbreeding to offset genetic erosion due to adaptation to a changing biophysical environment (i.e.\nchanges in pests, diseases, and climate change), from contributions to an increase in yield\npotential or the genetic gains associated to carbon assimilation and partitioning.\nThe idea behind finding a way to independently assess maintenance from yield potential\nimprovements is enticing and the authors make a good argument for the need and interest in\nestimating them independently. The authors are on the right track on how to estimate those,\nbut the manuscript could be improved to provide stronger inference. There are some limitations\nin the study that compromise the inference, including that the dataset is small and limited, the\nestimation of the yield potential needs further explanation, and the actual statistical methods\nused seems under-powered.\nResponse: Thanks for the comments. Please find below our detailed responses.\nDataset limitation. The main limitation of the dataset is its size. There are very few genotypes\nused in each one of the sites with around 23-47 genotypes tested each year, there are few\nchecks, and even less sites. These are all quite small numbers for the broad inference that is\ndiscussed in the manuscript. Furthermore, the dataset has an important confounding effect of\nsite and check-genotype. For example, based on Figure 2 and Supplementary Table S2, genotype\nBaguette 10, SY200, and Claire were evaluated in a single site. There are 17 site-check\ncombinations and 15 total sites. This creates an inference problem; can the results be\ninterpreted beyond the check-site combinations? Would different conclusions have been\nreached if the check had been tested in multiple sites? The check genotype Claire, for example,\n6\nis one of the checks that shows a negative grain yield erosion, or to say it in another way, grain\nyield of Claire increases over time, and actual yields are well over the estimated yield potential\nfor the site. How can this be interpreted biologically? Had Claire been evaluated in multiple\nsites; would the authors have gotten similar results? How can a yield above the yield potential\nbe explained? Maybe using larger datasets where check-genotypes are evaluated in multiple\nenvironments and compared to larger genotypes could solve these limitations. Breeding-type of\ndataset might be more appropriate for such a study as large datasets are generated.\nResponse: In response to this comment we have added data from another five extra long-term\nexperiments (one cultivar in Argentina x two fungicide combinations and three cultivar x site in\nUK). Our database now includes 37 long-term experiments (cultivar × site × fungicide\ncombinations), each spanning 10+ years and evaluating 25–41 cultivars per year, across four\ncountries. Reviewer #3 may perceive a limitation in the number of check cultivars (originally\neight, now ten), but these were widely grown cultivars representing elite germplasm across\nmillions of hectares globally. Just for comparison, the recent study cited by reviewers #1 and #2\n(Raymond et al., 2023) included six trials spanning 10 years each in one country. To summarize,\nwe believe our database size is reasonable and comparable to similar previous studies. In\nresponse to this comment, we added text in the M&M section (L 273-277 of revised MS).\nYield potential estimations. The manuscript does not describe in great detail the methodology\nused for yield potential. The issue discussed above with the check genotype Claire, where actual\nyields were well above the yield potential seems like a problem. There seems to be a year with a\nyield potential close to zero where actual yields were quite high (i.e. 12t, Figure 2). How can\nthose results be explained? In a broader and more conceptual way, the authors went into great\nlength to explain that there was a need to discriminate between maintenance breeding from\ngains from yield potential. They go further to describe the yield potential as an environment\nwhere genotypes are ‘grown without water and nutrient limitations and with weeds, diseases,\nand insect pests effectively controlled’ (line 49-50). The manuscript uses a simulated water-\nlimited yield potential as a benchmark, using the Global Yield Gap Atlas, that has the issues\ndescribed above with Claire. In any case, the study does not seem to be posed to compare a\ntrue yield potential given that the trials are rainfed and have other limitations beyond water (i.e.\nmanagement in cultivar evaluation trials is standard, but not optimal for every genotype. The\ntiming of disease management or fertilization as well as planting density and other\nmanagement practices may prevent a genotype from achieving its potential), and the water-\nlimited yield potential is used as a benchmark. How were these concerns addressed in the\ncurrent study?\nadded key references (L 300-316 of revised MS). Additionally, we discussed uncertainties and\nlimitations of crop models and experimental data in the discussion (L 226-250 of revised MS).\nThe fungicide vs no fungicide application comparison goes in the right direction of addressing\nthese, but does not yield the expected results. How can the results of environment degradation\nbe explained then?\nResponse: Yield erosion is an inclusive term that account for the yield decline that occurs over\ntime when cultivars lose adaption to the evolving climate, atmospheric composition, soil\nproperties, crop management practices, and pest and disease pressure, as reported in the\npublished literature (see cited refs in the MS). While we cannot conclusively determine the\nexact cause(s) for wheat (and not the goal of our study), our MS makes a novel contribution by\ndemonstrating that it exists, even in the case of treated crops. We added a short sentence to\nthe discussion listing possible drivers for yield erosion (L 179-182 of revised MS).\nStatistical analysis and power. After having a historical dataset, where the same check genotype\nis used in all subsequent trials for a given site, the authors report a large year-to-year variability.\nThis variability is quite common in these types of experiments, creating genotype by year\ninteractions. The authors then use the average of the first three years in the series and compare\nthem to the last three years in the series (see Table 1 and Figure 3, materials and methods lines\n287-291). This seems an odd choice for such an analysis. Why not use a regression over years if\nthe goal is to show the trend effects? It is clear that with the level of year-to-year variability,\nfinding significant differences will be a challenge for any statistical analysis, but a regression will\nshow a true trend compared to the arbitrary 3-years mean comparison choice. Furthermore, no\nstatistical significance is reported for this part of the manuscript. Are those effects significantly\ndifferent from zero? Finally, when a regression is used for trends, a regression on a three-year\nmoving average is used to smooth it out (Figure 4, materials and methods lines 302-308).\nHowever, x values are no longer independent, and the p-values are therefore no longer valid. At\nleast not in a frequentist analysis, but the methods are not described in detail. Why not use the\nregression on the original data? In a way it is also surprising that an alpha-value of 0.1 is used to\nclaim significance in the cases where a test was conducted. Given that less than 70g per year is\nexpected, the study seems quite under-powered to be able to reach these conclusions. Larger\ndatasets will be needed to find significant differences of that magnitude when year-to-year\nvariations are orders of magnitude different.\nResponse: Valid point. Based on this comment, we dropped the analysis based on a 3-year\nmoving average and instead run regression analysis with the annual observations. We have\nadded a new Figure 3, substantially revised Table 1, and modified the associated text. We also\nincluded the SEM for each data observation across site-cultivar combinations every year.\nMost of the papers described in the manuscript are not attempting to disentangle gains\nassociated to yield potential from maintenance breeding improvements, and although they may\nhave lost an opportunity, it does not mean they are wrong when claiming the total\ngenetic gains. The authors should check all their references to this topic and make their statements\nunambiguous. For example, the title correctly states that both maintenance breeding as well as\nimprovements in yield potential are both contributors to genetic gain. For example lines 134-\n135 and lines 204-210 seem to misrepresent other studies as not all studies claim to report\nyield potential improvements, but mere grain yield gains.\nFurther characterization of the dataset would be useful. For example, genotypes with large\nwithin trial variability were removed. Did specific genotypes always produce these issues, or\nwere those local problems due to single plot outliers? What was the heritability of the trials?\nFurther characterization of the top yields would also prove useful to the readers. Are there\nsignificant differences between top yielders and the checks? Could the top yielders be further\ncharacterized, including their distribution and/or range?\nResponse: These suggestions are well taken. However, they do not align with the specific goals\nof our study so no change was made in the revised MS in response to this comment.\nFinally, the authors rightly bring the concern over the tension between management and\nbreeding efforts, and the usefulness of a methodology such as this one to create awareness. We\nagree that the concept is needed and well described in this manuscript, but a larger dataset and\nimproved statistical analysis will be needed. Additionally, the tension between management and\nbreeding can be bridged with further studies on the genotype by environment by management.\nResponse: Based on this comment, we have modified the statistical analysis (see previous\nresponses on this issue) and included two new check cultivars (from Argentina and UK).\nMinor comments:\nThe legend on Table 1 could be further explained so that it would be self-explanatory.\nResponse: Table was revised as suggested (see revised MS).\nSupplementary Table 4 (line 206) was missing from the review materials.\nResponse: Supplementary Table S4 does not exist so we remove the reference to the table.\nComments from Reviewer #4:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: OK. See our previous responses.\n9"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript shows significant improvement and demonstrates that the authors took the reviewer comments\nseriously. Key changes—such as clarifying the definition of maintenance breeding, expanding the dataset to include\nadditional trial sites, and strengthening the discussion—have all contributed to a clearer and more impactful paper. The\ninclusion of a limitations section have improved transparency and robustness. That said, a few aspects still require attention.\nThe discussion is more convincing overall, particularly due to new additions such as line 189, which helps clarify the\nbroader implications of the findings. However, the sentence in lines 212–214 is overly long, somewhat redundant with\nearlier material, and would benefit from being paraphrased or shortened for readability. The explanation of key terms is now\nstronger, and the authors’ effort to distinguish their contribution from previous work in greater clarity is appreciated.\nThere remain some limitations, especially concerning the country-level conclusions. In France, for example, only one check\ncultivar was used (Apache), and its yield trend was not statistically significant. This undermines the strength of conclusions\nfor that country. The authors could acknowledge this explicitly and note that additional checks would improve reliability.\nSimilarly, questions about the US data persist: simulated yields there appear much higher than observed values, which\nraises concerns about the model being used to calculate yield potential - is there a potential explanation for this?\nMinor edits:\nL171: “grater” → greater\nL172: Clarify which year shows high initial susceptibility for Apache, as it is not visually clear in Figure 3\nL280: Add UK plot sizes for completeness\nGiven not all cultivars have untreated yield data, and that this data isn't used in the maintenance breeding calculation, I am\nstill wondering if its necessary to include this information at all, but it does give a comparison to treated where the data does\nexist and probably is worth still including. Perhaps a justification of this inclusion to avoid questions like my own is useful.\nRegarding the robustness of the analysis and conclusions, the authors have taken steps to strengthen the statistical\nfoundation. This improves the reliability of trend estimates and addresses prior concerns about statistical power.\nOverall, I think the work provides a useful contribution to the literature on genetic gain estimation by introducing a method\nthat distinguishes maintenance breeding from yield potential gains. This builds meaningfully on past studies, particularly by\nfocusing on fungicide-treated crops across multiple countries. The conclusions are generally well-supported, though they\nwould benefit from more cautious interpretation in cases like France.\n############## Request for feedback on reviewer 3 responses ###################\nThanks for the opportunity to read the revised manuscript again. Reviewer 3 makes some very strong recommendations in\ntheir initial review which are in part addressed.\nThe authors do increase the size of the dataset and it is good to see more than one site for Claire being used. However by\nadding more checks, they also add another check with just one site. Having an additional site for all those with 1 would\nincrease the robustness and enable a better understanding of the influence of site on the result. That being said, the\nconclusion is the same with the addition of the new checks, so this is positive.\nThe addition of information of how the Global Yield Gap Altas was used is well received.\nThey still use an alpha value of 0.1 in the regression analysis for check cultivars in Table 1 to assess significance, which\nReviewer 3 recommended against. What are the implications of none of the check cultivar trends are deemed not significant\naccording to a smaller alpha value?\nThe comment regarding the discussion, something that would help with understanding data distribution in general is a table\nin supplementary on the cultivars - how many per year for each site, to see where there may be gaps in data and if some\nyears have very little etc. This could help with completing the picture in terms of what data there is in this study, beyond just\nthe check cultivars.", "reviewer_2": "I do not see any substantial changes in the paper that would address my main concern: There is no explicit statement of a\nmodel that is assumed for the raw data. For example, if the response variable is a cultivar-year mean, I would expect to see a\nmodel being fitted that has effects for both cultivars and years, as is the case, e.g., in Mackay et al. (2011), and in papers that\nfollowed. Also, the normalization the authors perform, involves ratios, rather than differences. Hence, the implicit assumption\nhere is that effects are multiplicative rather than additive. But further down the line, an additive model of the form mX + b is\nfitted. Hence, it is unclear what the model would be that the authors imply for the raw cultivar x year means. I believe it would\nbe important to be able to state such a model."}, "author_response": "Comments from Reviewer #1:\nThe revised manuscript shows significant improvement and demonstrates that the\nauthors took the reviewer comments seriously. Key changes—such as clarifying the\ndefinition of maintenance breeding, expanding the dataset to include additional trial\nsites, and strengthening the discussion—have all contributed to a clearer and more\nimpactful paper. The inclusion of a limitations section have improved transparency and\nrobustness. That said, a few aspects still require attention.\nResponse: Thanks. See our detailed responses below.\nThe discussion is more convincing overall, particularly due to new additions such as line\n189, which helps clarify the broader implications of the findings. However, the sentence\nin lines 212–214 is overly long, somewhat redundant with earlier material, and would\nbenefit from being paraphrased or shortened for readability.\nResponse: Sentence was shortened as requested (L 208-212 of revised MS).\nThe explanation of key terms is now stronger, and the authors’ effort to distinguish their\ncontribution from previous work in greater clarity is appreciated.\nResponse: Thanks.\nThere remain some limitations, especially concerning the country-level conclusions. In\nFrance, for example, only one check cultivar was used (Apache), and its yield trend was\nnot statistically significant. This undermines the strength of conclusions for that country.\nThe authors could acknowledge this explicitly and note that additional checks would\nimprove reliability.\nResponse: We agree. We elaborated on this issue as suggested (L. 250-254 of revised\nMS).\nSimilarly, questions about the US data persist: simulated yields there appear much\nhigher than observed values, which raises concerns about the model being used to\ncalculate yield potential - is there a potential explanation for this?\nResponse: We used the SSM model, which has been calibrated for winter wheat in the\nU.S. southern Great Plains using measured data from trials that explicitly aimed to\nattain maximum possible yield in the area (Lollato and Edwards, Crop Science 2015).\nThe model calibration and validation have been published in Lollato et al. (Field Crops\nResearch, 2017 – see Figure 2) and Couedel et al. (Nature Food, 2024 - see Extended\nFig. 2). The model has shown good ability to accurately reproduce the yield potential\nunder optimal management.\nWe acknowledge the difference between simulated yield potential and observed yields\nfrom check cultivars in our U.S. dataset. This gap likely reflects that variety´s trial\nmanagement is likely not optimized to achieve yield potential. One reason is that the\nweather in the U.S. southern Great Plains is highly unpredictable during the wheat\ngrowing season, leading to large inter-annual variation in yield potential (as shown in\nFigure 2). This climate risk makes investment in inputs such as fertilizers economically\nrisky (Patrignani et al., 2014). Consequently, management tends to be relatively\nconservative and not really optimized to achieve yield potential, leading to ‘yield gaps’\nbetween measured and simulated yields as documented in several publications (de\nOliveira Silva et al, 2020; Giordano et al. Field Crops Res., 2025; Lollato & Edwards,\nCrop Sci., 2015; Lollato et al., Field Crops Res., 2017; Patrignani et al., Agron. J.,\n2014). In response to reviewer’s comment, we have added text to explain the “gap”\nbetween simulated and measured yields in the US trials and provided references to\nsupport it (L 239-241 of revised MS).\nMinor edits:\nL171: “grater” → greater\nResponse: Greater was replaced by “larger” (L 170 of revised MS).\nL172: Clarify which year shows high initial susceptibility for Apache, as it is not visually\nclear in Figure 3\nResponse: Text was rephrased as requested (L 171-174 of revised MS)\nL280: Add UK plot sizes for completeness\nResponse: Added as requested (L. 289 of revised MS).\nGiven not all cultivars have untreated yield data, and that this data isn't used in the\nmaintenance breeding calculation, I am still wondering if its necessary to include this\ninformation at all, but it does give a comparison to treated where the data does exist\nand probably is worth still including. Perhaps a justification of this inclusion to avoid\nquestions like my own is useful.\nResponse: The reason to include the untreated plots was to assess how increased\nsusceptibility to diseases could further increase the yield erosion. Based on this\ncomment, we elaborated on the rationale to include fungicide-untreated crops (L. 279-\n280 & L 169-171 of revised MS).\nRegarding the robustness of the analysis and conclusions, the authors have taken steps\nto strengthen the statistical foundation. This improves the reliability of trend estimates\nand addresses prior concerns about statistical power.\nResponse: Thanks.\nOverall, I think the work provides a useful contribution to the literature on genetic gain\nestimation by introducing a method that distinguishes maintenance breeding from yield\npotential gains. This builds meaningfully on past studies, particularly by focusing on\nfungicide-treated crops across multiple countries. The conclusions are generally well-supported, though they\nwould benefit from more cautious interpretation in cases like\nFrance.\nResponse: Thanks.\n############## Request for feedback on reviewer 3 responses\n###################\nThanks for the opportunity to read the revised manuscript again. Reviewer 3 makes\nsome very strong recommendations in their initial review which are in part addressed.\nThe authors do increase the size of the dataset and it is good to see more than one site\nfor Claire being used. However by adding more checks, they also add another check\nwith just one site. Having an additional site for all those with 1 would increase the\nrobustness and enable a better understanding of the influence of site on the result. That\nbeing said, the conclusion is the same with the addition of the new checks, so this is\npositive.\nResponse: Thanks. We would like to keep the check with a single site given the\nrequest to include a larger dataset in the previous round of review. As Reviewer #1\nindicates, the conclusions will remain the same one way or another.\nThe addition of information of how the Global Yield Gap Altas was used is well received.\nResponse: Thanks\nThey still use an alpha value of 0.1 in the regression analysis for check cultivars in Table\n1 to assess significance, which Reviewer 3 recommended against. What are the\nimplications of none of the check cultivar trends are deemed not significant according to\na smaller alpha value?\nResponse: In Table 1 and Figure 3, we present three levels of significance (P<0.1,\nP<0.05, P<0.01) to provide readers with complete information for their own\ninterpretation. While we acknowledge former Reviewer 3 concern about the alpha = 0.1\nthreshold, we retained this level because it is still commonly used in agricultural field\nstudies where variability is high and helps identify trends that may be meaningful even if\nnot meeting stricter statistical criteria.\nThe comment regarding the discussion, something that would help with understanding\ndata distribution in general is a table in supplementary on the cultivars - how many per\nyear for each site, to see where there may be gaps in data and if some years have very\nlittle etc. This could help with completing the picture in terms of what data there is in this\nstudy, beyond just the check cultivars.\nResponse: Average number of cultivars besides the check is already indicated in\nSupplementary Table S1. The number of cultivars tested every year varies veried little\nover time. We added a comment on this in response to this comment (L 284-285 of\nrevised MS).\nComments from Reviewer #2:\nI do not see any substantial changes in the paper that would address my main concern:\nThere is no explicit statement of a model that is assumed for the raw data. For example,\nif the response variable is a cultivar-year mean, I would expect to see a model being\nfitted that has effects for both cultivars and years, as is the case, e.g., in Mackay et al.\n(2011), and in papers that followed. Also, the normalization the authors perform,\ninvolves ratios, rather than differences. Hence, the implicit assumption here is that\neffects are multiplicative rather than additive. But further down the line, an additive\nmodel of the form mX + b is fitted. Hence, it is unclear what the model would be that the\nauthors imply for the raw cultivar x year means. I believe it would be important to be\nable to state such a model.\nResponse: This is well taken. Please, note that our approach is an alternative to the\none used by Mackay et al (2011) as we are using normalized yields rather than raw\nyield data. Briefly, we used a two-step approach in which the raw yields were first\nnormalized by the climatic yield potential and the resulting normalized yields were\nanalyzed using linear regression models. The justification for using this approach is that\nthe statistical model used by Mackay et al (2011) and other studies cannot distinguish\nbetween temporal trends in climate and yield erosion as we discussed in the original MS\n(L. 201-204 of revised MS). Based on this comment, we have elaborated on the\njustification to follow this approach and include a step-by-step explanation of the\nnormalization of yields to account for climate trends and the subsequent model used to\nanalyze the normalized data, including the associated equations (L. 326-356 of revised\nMS). We believe it is clear now how the raw data was normalized to avoid any climate\ntrends and subsequently analyzed using linear regression to assess changes in yield\npotential and yield erosion as per the goals of our study. We are grateful to Reviewer #2\nfor this comment as this addition have helped make the methodology clear and easy to\nunderstand by potential readers of the article."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I am satisfied with how the authors have responded to the review comments, I think the amendments in response to the\nvarious reviewers have improved the paper and also highlighted the limitations of the method used more clearly.", "reviewer_2": "This revision still does not address my major concern that there is no overall statistical model to describe genetic and non-\ngenetic trends."}, "author_response": "Comments from Reviewer #1:\nI am satisfied with how the authors have responded to the review comments, I think the\namendments in response to the various reviewers have improved the paper and also\nhighlighted the limitations of the method used more clearly.\nResponse: Thanks for the support for publication.\nComments from Reviewer #2:\nThis revision still does not address my major concern that there is no overall statistical model to\ndescribe genetic and non-genetic trends.\nResponse: We appreciate the suggestion from Reviewer #2 about the statistical analysis. In\nresponse to this comment, we have included a new analysis based on a mixed-effect model to\nseparate genetic from non-genetic effects. Results from the analysis are similar and consistent\nwith our previous findings based on yield anomalies and simple linear regression. A detailed\ndescription of the mixed effects model has been added to the Methods sections (L 367-388 of\nrevised MS), results are shown in the new Supplementary Table S3, and text in the Results\nsection has been extended to report key finding (L 124-126 & 154-157 of revised MS). We thank\nagain Reviewer #2 for the valuable suggestion, which helped strengthen the statistical rigor of\nthe manuscript."}, {"round": 4, "reviewer_comments": {"reviewer_2": "The authors have now conducted a mixed model analysis, which I think is the right way to go. However, that analysis is\npresented just as an after-thought, as it were, whereas the original analysis, which I had critizised for various reasons,\nremains as the core approach.\nBoth the original approach, which makes some ad-hoc corrections to the observed data, as well as the mixed model\nanalysis added in the current version, do not fully account for the structure of the data. The full structure of the data\ncomprises the factors year, site and variety. References 28-32 show how a full random-effects model can be set up to\naccount for that structure.\nLet me re-iterate a further issue with the original regression analysis presented in this paper. It uses ratios to make\ncorrections/adjustments. A litmus test for proper statistical modelling is this: can we write down a model for the observed raw\ndata that the corrections we are making implies? The core regression models do not pass that test, in my view. Also, if\ncorrections are made using ratios, the implicit assumption is that effects are multiplicative rather than additive. By contrast,\nthe mixed model analysis presented now implicitly assumes additive effects. If the authors believe that effects are\nmultiplicative, a very simple device is to logarithmically transform the data, because this turns products into sums,\njustifying the assumption of additive effects on the log-scale.\nFurther notes:\nX in lines 357-359 does not seem to be defined.\nLine 372: The equation lacks regression coefficients in front of T_j and Ywa_ijk.\nLine 379: What is the rationale for using Ywa_ijk in the regression? This may seem all to obvious to the authors, but readers\nmay appreciate some detail."}, "author_response": "Comments from Reviewer #2:\nThe authors have now conducted a mixed model analysis, which I think is the right way\nto go. However, that analysis is presented just as an after-thought, as it were, whereas\nthe original analysis, which I had critizised for various reasons, remains as the core\napproach.\nResponse: This is well taken. As requested, we have discarded the original analysis and\nincorporated the mixed model analysis using the entire database as the main analysis\nof our paper (Table 1, L. 149-159, and L 309-359 in the revised MS).\nBoth the original approach, which makes some ad-hoc corrections to the observed data,\nas well as the mixed model analysis added in the current version, do not fully account\nfor the structure of the data. The full structure of the data comprises the factors year,\nsite and variety. References 28-32 show how a full random-effects model can be set up\nto account for that structure.\nResponse: See previous response. Based on this comment, we have discarded the ad-\nhoc corrections and, instead, used BLUEs for the analysis and performed a mixed\nmodel analysis using the entire database considering its structure, as requested (Table\n1, L. 149-159, and L 309-359 in the revised MS).\nLet me re-iterate a further issue with the original regression analysis presented in this\npaper. It uses ratios to make corrections/adjustments. A litmus test for proper statistical\nmodelling is this: can we write down a model for the observed raw data that the\ncorrections we are making implies? The core regression models do not pass that test, in\nmy view. Also, if corrections are made using ratios, the implicit assumption is that effects\nare multiplicative rather than additive. By contrast, the mixed model analysis presented\nnow implicitly assumes additive effects. If the authors believe that effects are\nmultiplicative, a very simple device is to logarithmically transform the data, because this\nturns products into sums, justifying the assumption of additive effects on the log-scale.\nResponse: Based on this comment, and considering that there was no long-term trend\nin simulated yields, we have discarded any normalization by climate and simulated\nyields. We explained this in the revised MS (L. 109-110 and Supplementary Figure S1 of\nrevised MS). Instead, we used a mixed model linear analysis using the entire database\nas requested (Table 1, L. 149-159, and L 309-359 in the revised MS).\nFurther notes:\nX in lines 357-359 does not seem to be defined.\nResponse: Based on this comment, we have clearly defined the terms of our models (L\n309-359 in the revised MS).\nResponse: Based on this comment, we have clearly defined the terms of our models (L\n309-359 in the revised MS).\nLine 379: What is the rationale for using Ywa_ijk in the regression? This may seem all to\nobvious to the authors, but readers may appreciate some detail.\nResponse: This comment does not apply any longer as we are not normalizing yields by\nthe simulated yields in the new analysis."}, {"round": 5, "reviewer_comments": {"reviewer_2": ".\n(Remarks on code availability)\nThis revision looks like a major step ahead. I am pleased to see that the authors now fit a full mixed model that incorporates\ngenetic and nongenetic trend as fixed regression terms.\nMy only comment at this point is that in (3) a random year main effect Y_k is needed. The variance for this effect may be still\nbe large, despite the fixed effect gamma*t_k for non-genetic trend, because random year-to-year deviations from the\ntrendline are usually substantial."}, "author_response": "Comments from Reviewer #2:\nThis revision looks like a major step ahead. I am pleased to see that the authors now fit\na full mixed model that incorporates genetic and nongenetic trend as fixed regression\nterms.\nMy only comment at this point is that in (3) a random year main effect Y_k is needed.\nThe variance for this effect may be still be large, despite the fixed effect gamma*t_k for\nnon-genetic trend, because random year-to-year deviations from the trendline are\nusually substantial.\nAnswer: Thanks for the positive feedback. We agree with Reviewer #2 about the need\nto add a random year main effect (Y_k) to the model. As requested, we have added that\nterm, updated the results, and made changes in the text accordingly (L 344-354 of\nrevised MS & Table 1)."}, {"round": 6, "reviewer_comments": {"reviewer_2": "This revision is fine.\n(Remarks on code availability)"}, "author_response": "Comments from Reviewers:\nReviewer #2 (Remarks to the Author):\nThis revision is fine.\nAnswer: Thanks."}]

Decision letter