Ertragsstabilität im Ökolandbau: Wo steht die Forschung?

Despite the importance of yield stability in organic agriculture, little quantitative information is currently available on the factors limiting stability or on optimal approaches for improving it. Research so far indicates that organic systems are not always more stable than conventional systems; which system is more stable is likely to depend on the spatial and temporal scale of stability and on the measure of stability used. We show that opportunities for quantifying yield stability in organic farming lie in the targeted coordination of existing data networks within the organic community in order to increase yield stability on farms and beyond

Treatment comparisons in agricultural field trials accounting for spatial correlation

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The classical analysis model for agricultural field trials is based on the principles of experimental design – randomization, replication and blocking – and it assumes independent residual effects. Accounting for any existent spatial correlation as an add-on component may be beneficial, but it requires selection of a suitable spatial model and modification of classical tests of treatment contrasts. Using a sugar beet trial laid out in complete blocks for illustration, it is shown that tests obtained with different modifications yield diverging results. Simulations were performed to decide whether different test modifications lead to valid statistical inferences. For the spherical, power and Gaussian models, each with six different values of the range parameter and without a nugget effect, the suitability of the following modifications was studied: a generalization of the Satterthwaite method (1941), the method of Kenward and Roger (1997), and the first-order corrected method described by Kenward and Roger (2009). A second-order method described by Kenward and Roger (2009) is also discussed and detailed results are provided as Supplemental Material (available at: http://journals.cambridge.org/AGS). Simulations were done for experiments with 10 or 30 treatments in complete and incomplete block designs. Model selection was performed using the corrected Akaike information criterion and likelihood-ratio tests. When simulation and analysis models were identical, at least one of the modifications for the t-test guaranteed control of the nominal Type I error rate in most cases. When the first-order method of Kenward and Roger was used, control of the t-test Type I error rate was poor for 10 treatments but on average very good for 30 treatments, when considering the best-fitting models for a given simulation setting. Results were not satisfactory for the F-test. The more pronounced the spatial correlation, the more substantial was the gain in power compared to classical analysis. For experiments with 20 treatments or more, the recommendation is to select the best-fitting model and then use the first-order method for t-tests. For F-tests, a randomization-based model with independent error effects should be used.Peer Reviewe

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

UID/MAT/00297/2019 UIDB/00297/2020 SFRH/BSAB/105935/2014 SFRH/BSAB/142919/2018 Project PT/A13/17-DE/57339863 Grant PI 377/18-1 Grant OG 83/1-1 & OG 83/1-2BACKGROUND: Genomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets. RESULTS: The robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data. CONCLUSIONS: The proposed robust approach enhances the predictive accuracy of heritability and genomic prediction by minimizing the deleterious effects of outliers for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.publishersversionpublishe

Measuring late blight attack of potato foliage in field trials: optimal resource allocation in assessment trials

Ein empirischer Datensatz wurde analysiert, um Empfehlungen zur optimalen Faktorallokation in einem Feld­versuchssystem zur Erfassung des Krautfäulebefalls bei Kartoffel zu geben. Der Datensatz stammt von einem Experiment mit 854 Genotypen, drei Jahren, zwei Wiederholungen pro Jahr und 16 bis 18 Boniturterminen pro Jahr. AUDPC (area under disease progress curve) Werte wurden auf der Basis von Prozent Krautbefall berechnet. Zur Etablierung der Krautfäule im Bestand wurde künstliche Inokulation verwendet. Drei Prüfjahre, zwei Wiederholungen pro Jahr, und drei Boniturtermine werden als ausreichend empfohlen. Diese Ergebnisse gehen von der Voraussetzung aus, dass die Qualität der Daten unabhängig ist von der Häufigkeit der Datenerhebung. Diese Voraussetzung wird kritisch diskutiert.An empirical data set was analysed in order to give recommendations on the optimal resource allocation in a field testing system to measure late blight attack in potato. The data set was derived from an experiment comprising 854 genotypes, three years, two replicates per year, and 16 to 18 scoring dates per year. AUDPC (area under disease progress curve) values were calcu­lated based on percentage of attacked haulm. Artificial inoculation was used to establish late blight in the testing field. Three testing years, two replicates per year, and three scoring dates per year are recommended to be sufficient. The results are based on the assumption, that the quality of data is independent of the frequency of data collection. This assumption is critically discussed

Mitscherlich's slide method and what it has to do with geostatistics

Die Mitscherlich-Anlage ist eine Versuchsanlage, bei der die Versuchsglieder in systematischer Reihenfolge in einer Reihe von Parzellen angeordnet sind. Diese Anordnung erlaubt es, mittels gleitender vollständiger Blöcke gleitende Mittelwerte über die gesamte Versuchsfläche zu berechnen. Diese Mittelwerte erfassen den räumlichen Trend und können zu einer Korrektur der Beobachtungswerte herangezogen werden. Dem Verfahren, das als Gleitmethode bekannt ist, wurde bei seiner Entwicklung allerdings kein explizites statistisches Modell zugrunde gelegt, so dass die Frage der zu bevorzugenden Auswertung solcher systematischen Versuchsanlagen offen ist. Des Weiteren stellen sich Fragen nach der relativen Vorzüglichkeit solcher Versuchsanlagen gegenüber randomisierten Versuchsanlagen und nach der besten Analysemethode. Diese Fragen sind insofern von aktueller Bedeutung, als es noch heute genutzte Langzeitver­suche gibt, welche nach einem systematischen Design ange­legt wurden. In diesem Beitrag gehen wir diesen Fragen nach, indem wir dem für die Gleitmethode vor­geschlagenen Auswertungsalgorithmus ein lineares gemischtes Modell zugrunde legen, welches eine lineare geostatistische Kovarianzstruktur impliziert. Wir betrach­ten dabei auch eine zweidimensionale Erweiterung des Mitscherlich-Verfahrens, welches von von Boguslawski unter der Bezeichnung „Ertragsflächenmethode“ vorgeschlagen wurde. Unsere Ergebnisse zeigen, dass ein systematisches Design unter dem hier vorgeschlagenen Modell tatsächlich optimal ist. Hieraus sollte allerdings nicht geschlossen werden, dass ein systematisches Design randomisierten Versuchsplänen vorzuziehen ist, denn es ist nicht zu erwarten, dass dieses spezielle Modell grundsätzlich besser an gegebene Daten passt als andere Vertreter aus der großen Klasse der geostatistischen Modelle. Die Vorteile einer Randomisation von Versuchen werden in der Schlussfolgerung betont.The Mitscherlich design entails a systematic arrangement of the treatments in a single row of plots. This arrange­ment allows forming “sliding” complete blocks across the whole design, for which moving averages can be computed. These means capture spatial trend and may be employed for correcting the observed data. The method, which is known as “slide method”, does not have an underlying statistical model, meaning that the choice of best method for analysis remains an open question for these systematic designs. Furthermore, there is the question of the relative merit of a systematic design compared to randomized designs and that concerning the best method of analysis. These questions are of current interest because several long-term experiments laid out accord­ing to a systematic design are still in use today. In this contribution, we pursue these questions based on a newly proposed linear mixed model that can be thought of as underlying the slide method and which implies a linear geostatistical covariance structure. We also consider a two-dimensional extension of the Mitscherlich method, which von Boguslawski proposed under the term “response surface method”. Our results show that a systematic arrangement is indeed optimal under the assumed model. This should not be taken to imply, however, that a systematic design is generally to be preferred to randomized designs, because it is not to be expected that the model proposed here provides the best fit among the large class of geostatistical models to any given dataset. The advantages of a randomized experimental design are emphasized in the conclusion

Phenotypic Selection in Ornamental Breeding: It's Better to Have the BLUPs Than to Have the BLUEs

Plant breeders always face the challenge to select the best individuals. Selection methods are required that maximize selection gain based on available data. When several crosses have been made, the BLUP procedure achieves this by combining phenotypic data with information on pedigree relationships via an index, known as family-index selection. The index, estimated based on the intra-class correlation coefficient, exploits the relationship among individuals within a family relative to other families in the population. An intra-class correlation coefficient of one indicates that the individual performance can be fully explained based on the family background, whereas an intra-class correlation coefficient of zero indicates the performance of individuals is independent of the family background. In the case the intra-class correlation coefficient is one, family-index selection is considered. In the case the intra-class correlation coefficient is zero, individual selection is considered. The main difference between individual and family-index selection lies in the adjustment in estimating the individual's effect depending on the intra-class correlation coefficient afforded by the latter. Two examples serve to illustrate the application of the BLUP method. The efficiency of individual and family-index selection was evaluated in terms of the heritability obtained from linear mixed models implementing the selection methods by suitably defining the treatment factor as the sum of individual and family effect. Family-index selection was found to be at least as efficient as individual selection in Dianthus caryophyllus L., except for flower size in standard carnation and vase life in mini carnation for which traits family-index selection outperformed individual selection. Family-index selection was superior to individual selection in Pelargonium zonale in cases when the heritability was low. Hence, the pedigree-based BLUP procedure can enhance selection efficiency in production-related traits in P. zonale or shelf-life related in D. caryophyllus L