Trends in mean performance and stability of winter wheat and winter rye yields in a long-term series of variety trials

Hadasch, Steffen; Laidig, Friedrich; Macholdt, Janna; Bönecke, Eric; Piepho, Hans‐Peter

doi:10.1016/j.fcr.2020.107792

Cited by 24 publications

(26 citation statements)

References 39 publications

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“…The model allowed temporal yield variability to be determined independently of the yield level, as recommended by Shukla [20]. Yield variability can be incorrectly interpreted if there is a systematic dependency in the variation from the mean [11]; no such dependencies were found in this analysis.…”

Section: Discussionmentioning

confidence: 96%

“…Long-term field experiments (LTEs), which record yield data for crops grown on the same soil, under similar management, and over a period of more than 20 years [8] are ideal for such analyses. While these properties make LTEs ideal for quantifying temporal variation they are only now beginning to be used more extensively to assess yield variability [9,10], but analyses of the effects of agronomic management practices (e.g., impact of cultivar choice [11], sowing conditions [12] and fertilization [13]) for winter rye are still rare. The LTEs also play an important role in the study of changes in soil properties that cannot be observed in a short time, e.g., soil organic carbon, pH and phosphorus contents [7].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

et al. 2021

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The anticipated increases in environmental variability associated with climate change may lead to enhanced abiotic plant stresses (e.g., heat stress, drought stress, etc.) resulting in greater inter-annual yield fluctuations and higher crop production risk. While there has been increasing attention to adaptation measures, there is little evidence available on how to change agronomic management strategies to maintain stable yields in winter rye production systems in Poland. This study uses rye yields from the unique Skierniewice Long-term experiment (Poland) to examine for the first-time the long-term effects of different nutrient regimes on crop yield stability from 1966 to 2015. Yields from six combinations of mineral fertilizers and lime (CaNPK, NPK, CaPK, CaNK, CaNP, Ca), with and without additional manure, were used to estimate the temporal yield variability of winter rye. A novel statistical approach based on a mixed model approach with REML (restricted maximum likelihood) stability parameter estimation was used. The results showed that the use of additional manure in ‘sub-optimal’ mineral fertilizer treatments, such as Ca and CaPK (without mineral N), reduced the temporal yield variability of rye. In contrast, additional organic input led to more variable rye yields in already ‘optimal’ treatments including mineral N (CaNPK and NPK), compared to those with no additional manure. Winter rye given CaNPK and NPK, without additional organic manure demonstrated high yield and low temporal yield variability. In contrast, yields of treatments with no mineral N (Ca and CaPK) and no additional manure supply were low and unstable. In addition, it was found that increasing soil organic carbon resulted in larger, more stable yields. These findings highlight the importance of ensuring rye crops receive sufficient fertilizer to maintain crop production levels and yield stability, especially in dry years. They also demonstrate the importance of avoiding the excessive use of organic manures when fertilizer inputs are sufficient to meet crop demand. Overall, the study provides novel insights about how to maintain grain yields and minimize temporal yield variation of rye in arable cropping systems, which will become increasingly important in a changing climate in Poland and in other temperate climate areas. This study also highlights the importance of soil organic carbon for improving the climate resilience of winter rye, while simultaneously meeting the demand for more sustainable management of the soil.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

et al. 2021

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“…Moreover, with a large number of covariates, covariate selection will be beneficial. R-square (R 2 ) for mixed models (Piepho 2019) is an option for covariate selection, as demonstrated in Hadasch et al (2020). The best approach to accommodating a larger number of covariates certainly deserves further research.…”

Section: Discussionmentioning

confidence: 99%

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

2021

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Key message We propose the utilisation of environmental covariates in random coefficient models to predict the genotype performances in new locations. Abstract Multi-environment trials (MET) are conducted to assess the performance of a set of genotypes in a target population of environments. From a grower’s perspective, MET results must provide high accuracy and precision for predictions of genotype performance in new locations, i.e. the grower’s locations, which hardly ever coincide with the locations at which the trials were conducted. Linear mixed modelling can provide predictions for new locations. Moreover, the precision of the predictions is of primary concern and should be assessed. Besides, the precision can be improved when auxiliary information is available to characterize the targeted locations. Thus, in this study, we demonstrate the benefit of using environmental information (covariates) for predicting genotype performance in some new locations for Swedish winter wheat official trials. Swedish MET locations can be stratified into zones, allowing borrowing information between zones when best linear unbiased prediction (BLUP) is used. To account for correlations between zones, as well as for intercepts and slopes for the regression on covariates, we fitted random coefficient (RC) models. The results showed that the RC model with appropriate covariate scaling and model for covariate terms improved the precision of predictions of genotypic performance for new locations. The prediction accuracy of the RC model was competitive compared to the model without covariates. The RC model reduced the standard errors of predictions for individual genotypes and standard errors of predictions of genotype differences in new locations by 30–38% and 12–40%, respectively.

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“…Further, as another way to consider the entire distribution of yield values in relation to stability, we explore an approach often used in hydrology to assess probabilities and return periods of extreme values (Loaiciga and Leipnik 1999) using the Rengen grassland LTE (data set 3). This approach is based on the Gumbel distribution (Eugster et al 2010;Gumbel 1958). For each time series of yield values, this simply determines the frequency f with which the yield is lower than or equal to a given reference yield value Y r .…”

Section: Example: Interpreting Stability From the Entire Yield Distrimentioning

confidence: 99%

“…Therefore, this tremendous resource could be exploited more effectively in the future. In addition, methods for analysis of long-term data series from field experiments can also be applied to sets of variety trials, which are not measuring cumulative effects on the same field but ensure long-term comparability of measurements through other rigorous rules (Hadasch et al 2020;Laidig et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Methods of yield stability analysis in long-term field experiments. A review

Reckling

Ahrends

Chen

et al. 2021

Agron. Sustain. Dev.

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In the face of a changing climate, yield stability is becoming increasingly important for farmers and breeders. Long-term field experiments (LTEs) generate data sets that allow the quantification of stability for different agronomic treatments. However, there are no commonly accepted guidelines for assessing yield stability in LTEs. The large diversity of options impedes comparability of results and reduces confidence in conclusions. Here, we review and provide guidance for the most commonly encountered methodological issues when analysing yield stability in LTEs. The major points we recommend and discuss in individual sections are the following: researchers should (1) make data quality and methodological approaches in the analysis of yield stability from LTEs as transparent as possible; (2) test for and deal with outliers; (3) investigate and include, if present, potentially confounding factors in the statistical model; (4) explore the need for detrending of yield data; (5) account for temporal autocorrelation if necessary; (6) make explicit choice for the stability measures and consider the correlation between some of the measures; (7) consider and account for dependence of stability measures on the mean yield; (8) explore temporal trends of stability; and (9) report standard errors and statistical inference of stability measures where possible. For these issues, we discuss the pros and cons of the various methodological approaches and provide solutions and examples for illustration. We conclude to make ample use of linking up data sets, and to publish data, so that different approaches can be compared by other authors and, finally, consider the impacts of the choice of methods on the results when interpreting results of yield stability analyses. Consistent use of the suggested guidelines and recommendations may provide a basis for robust analyses of yield stability in LTEs and to subsequently design stable cropping systems that are better adapted to a changing climate.

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Trends in mean performance and stability of winter wheat and winter rye yields in a long-term series of variety trials

Cited by 24 publications

References 39 publications

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

Methods of yield stability analysis in long-term field experiments. A review

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