Assessing the genetic variation of phosphate efficiency in European maize (Zea mays L.)

Why should plant breeders in Central Europe care about phosphate efficiency? Soil phosphorus levels have mostly reached high to very high levels over the last decades in intensively farmed, livestock-rich regions. However, the European Union demands a restructuring of the agricultural production systems through setting ambitious goals envisaged in the Farm to Fork Strategy. By 2030, fertilizer use should be reduced by 20 %, nutrient losses by at least 50 %. As a consequence, farmers have to be even more efficient with crop inputs, among them the globally limited resource of phosphorus fertilizers, while maintaining high yields. Plant breeding means thinking ahead. Therefore, phosphate-efficient varieties should be developed to help farmers meet this challenge and reduce the need for additional fertilizers. One prerequisite to reach this target is that genotypic variation for the relevant traits is available. Moreover, approaches that assist selection by accurate but also time- and resource-efficient prediction of genotypes are highly valuable in breeding. Finally, the choice of the selection environment and suitable trait assessment for the improvement of phosphate efficiency under well-supplied conditions, need to be elaborated. In this dissertation, a diverse set of maize genotypes from ancient landraces to modern hybrids was investigated for phosphate efficiency-related traits under well-supplied P soil conditions. Multi-environmental field trials were conducted in 2019 and 2020. The reaction to different starter fertilizer treatments of the 20 commercially most important maize hybrids grown in Germany was studied. In the hybrid trial, the factor environment had a significant effect on the impact of starter fertilizers. Especially in early developmental stages genotypes showed a different response to the application of starter fertilizers. On the overall very well-supplied soils, we observed no significant genotype-by-starter fertilizer interaction. Nonetheless, we identified hybrids, which maintained high yields also if no starter fertilizer was provided. Thus, it seems that sufficient variation is available to select and breed for phosphate efficiency under reduced fertilizer conditions. Furthermore, the concept of phenomic prediction, based on near-infrared spectra instead of marker data to predict the performance of genotypes, was applied to 400 diverse lines of maize and compared to genomic prediction. For this, we used seed-based near-infrared spectroscopy data to perform phenomic selection in our line material, which comprised doubled haploid lines from landraces and elite lines. We observed that phenomic prediction generally performed comparable to genomic prediction or even better. In particular, the phenomic selection approach holds great potential for predictions among different groups of breeding material as it is less prone to artifacts resulting from population structure. Phenomic selection is therefore deemed a useful and cost-efficient tool to predict complex traits, including phosphorus concentration and grain yield, which together form the basis to determine phosphate efficiency. Lastly, 20 different indicators for phosphate efficiency were calculated, the genetic variation of the different measures present in this unique set of lines was quantified, and recommendations for breeding were derived. Of the different measures for phosphate efficiency reported in literature, Flint landraces demonstrated valuable allelic diversity with regard to phosphate efficiency during the seedling stage. Due to the highly complex genetic architecture of phosphate efficiency-related traits, a combination of genomic and phenotypic selection appears best suited for their improvement in breeding. Taken together, phosphate efficiency, including its definition and meaning, is largely dependent on the available phosphorus in the target environment as well as the farm type, which specifies the harvested produce and thereby the entire phosphorus removal from the field. In conclusion, future maize breeding should work in environments that are similar to the future target environments, meaning reduced fertilizer inputs and eventually lower soil P levels. Our results demonstrate that breeding of varieties, which perform well without starter fertilizers is feasible and meaningful under the well-supplied conditions prevalent in Central Europe. For the improvement of the highly complex trait phosphate efficiency through breeding we recommend to apply genomic and phenomic prediction along with classical phenotypic screening of genotypes and by this making our food systems more resilient towards upcoming challenges in agriculture.Warum sollten sich Pflanzenzüchter:innen in Mitteleuropa mit Phosphateffizienz beschäftigen? Der Phosphorgehalt im Boden ist in den letzten Jahrzehnten vor allem in intensiv bewirtschafteten viehreichen Regionen auf ein hohes bis sehr hohes Niveau angestiegen. Die Europäische Union formuliert jedoch in der Farm to Fork-Strategie ehrgeizige Ziele für eine Umstrukturierung der landwirtschaftlichen Produktionssysteme. Bis 2030 soll der Düngemitteleinsatz um 20 % und die Nährstoffverluste um mindestens 50 % reduziert werden. Das bedeutet, dass die Landwirte noch effizienter mit dem Einsatz von Betriebsmitteln umgehen müssen, insbesondere mit der weltweit begrenzten Ressource von Phosphordüngern, bei gleichzeitig weiterhin hohen Erträgen. Pflanzenzüchtung bedeutet vorausschauend zu denken. Daher sollten phosphateffiziente Sorten entwickelt werden, die den Landwirten helfen, diese Herausforderung zu meistern und den Bedarf an zusätzlichen Düngemitteln zu verringern. Eine Voraussetzung, um dieses Ziel zu erreichen, ist, dass genotypische Variation für die relevanten Merkmale vorhanden ist. Darüber hinaus sind Ansätze, die die Selektion durch eine genaue, aber auch zeit- und ressourceneffiziente Vorhersage von Genotypen unterstützen, in der Züchtung sehr wertvoll. Außerdem müssen die Wahl der Selektionsumwelt und eine geeignete Merkmalserfassung für die Verbesserung der Phosphateffizienz unter gut versorgten Bedingungen näher beleuchtet werden. In dieser Dissertation wurde eine Reihe von Maisgenotypen, von alten Landrassen bis hin zu modernen Hybriden, bezüglich Phosphateffizienz-Merkmalen auf gut mit P versorgten Böden untersucht. In den Jahren 2019 und 2020 wurden mehrortige Feldversuche durchgeführt. Untersucht wurde die Reaktion der 20 kommerziell wichtigsten in Deutschland angebauten Maishybriden auf unterschiedliche Unterfußdüngungen. In dem Hybridversuch hatte der Faktor Umwelt einen signifikanten Einfluss auf die Wirkung von Unterfußdüngern. Insbesondere in frühen Entwicklungsstadien reagierten die Genotypen unterschiedlich auf die Gabe von Unterfußdüngern. Auf den insgesamt sehr gut versorgten Böden beobachteten wir keine signifikante Interaktion zwischen Genotyp und Unterfußdünger. Dennoch konnten wir Hybriden identifizieren, die auch ohne Unterfußdünger hohe Erträge erzielten. Es scheint also genügend Variation vorhanden zu sein, um unter reduzierten Düngebedingungen auf Phosphateffizienz zu selektieren und zu züchten. Darüber hinaus wurde das Konzept der phänomischen Vorhersage, welches auf Nahinfrarotspektren anstelle von Markerdaten zur Vorhersage der Leistung von Genotypen basiert, auf 400 verschiedenen Maislinien angewandt und mit der genomischen Vorhersage verglichen. Hierbei nutzten wir samenbasierter Daten der Nahinfrarotspektroskopie, um phänomische Selektion in unserem Linienmaterial durchzuführen, welches doppelhaploide Linien von Landrassen und Elitelinien enthielt. Wir konnten feststellen, dass die phänomische Vorhersage im Allgemeinen mit der genomischen Vorhersage gleichauf oder sogar besser war. Der phänomische Selektionsansatz hat insbesondere für Vorhersagen zwischen verschiedenen Gruppen von Zuchtmaterial großes Potenzial, da er weniger anfällig für Artefakte ist, die aus der Populationsstruktur resultieren. Die phänomische Selektion hat sich daher als nützliches und kosteneffizientes Instrument zur Vorhersage komplexer Merkmale erwiesen, einschließlich der Phosphorkonzentration und des Kornertrags, welche zusammen die Grundlage für die Bestimmung der Phosphateffizienz bilden. Zuletzt wurden 20 verschiedene Indikatoren für Phosphateffizienz berechnet, die genetische Variation der verschiedenen Messgrößen in dieser spezifischen Zusammensetzung von Linien quantifiziert und Empfehlungen für die Züchtung abgeleitet. Von den verschiedenen in der Literatur beschriebenen Maßen für die Phosphateffizienz zeigten Flint-Landrassen eine wertvolle allelische Diversität in Bezug auf die Phosphateffizienz im Keimlingsstadium. Aufgrund der hochkomplexen genetischen Struktur von Phosphateffizienz-Merkmalen, scheint eine Kombination aus genomischer und phänotypischer Selektion am besten geeignet, um diese züchterisch zu verbessern. Alles in allem hängt die Phosphateffizienz, einschließlich ihrer Definition und Bedeutung, weitgehend vom verfügbaren Phosphor in der angestrebten Umwelt sowie vom Betriebstyp ab, da dieser das Erntegut und damit die Gesamtphosphorabfuhr vom Feld bestimmt. Zusammenfassend lässt sich sagen, dass die zukünftige Maiszüchtung in Umgebungen arbeiten sollte, die den zukünftigen Zielumwelten ähnlich sind, was einen geringeren Düngemitteleinsatz und schließlich einen niedrigeren P-Gehalt im Boden bedeutet. Unsere Ergebnisse zeigen, dass die Züchtung von Sorten, die ohne Unterfußdünger auskommen, unter den in Mitteleuropa vorherrschenden gut versorgten Bedingungen realisierbar und sinnvoll ist. Zur Verbesserung des hochkomplexen Merkmals Phosphateffizienz durch Züchtung empfehlen wir, neben der klassischen phänotypischen Begutachtung von Genotypen auch genomische und phänomische Vorhersagen anzuwenden und damit unsere Nahrungsmittelsysteme widerstandsfähiger gegenüber den kommenden Herausforderungen in der Landwirtschaft zu machen

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe

Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing Crop

Hybrid-enabled line profiling (HELP) is a new integrated breeding strategy for self-fertilizing crops that combines existing and recently identified elements, resulting in a strategy that synergistically exceeds existing breeding concepts. Heterosis in selfing crops is often driven by additive and additive X additive gene action, the molecular basis of which is increasingly being revealed. Unlike nonadditive heterosis, additive forms can be relatively easily fixed in homozygous lines, meaning that their seed can simply be resown to express the same “heterosis.” Crossing diverse, complementary “selfing” parents to create the desired trait or allele line profile requires strict male sterility of the female; this can now be achieved relatively easily through present and emerging chemical, environmental, or genetic techniques. Fairly small amounts of hybrid seed are needed, with no need to scale up seed production, as it is not the hybrid that will be commercialized. After multilocation testing, homozygous lines from only the most superior hybrids, driven mainly by additive effects and additive X additive gene action, are rapidly derived using techniques such as doubled haploids. Multilocation testing and molecular confirmation of target line profiles then identify superior lines for release to farmers. The HELP strategy integrates modern high-throughput versions of existing and new concepts and methodologies into a breeding system strategy that focuses on the most superior crosses, <10% of all crosses. This focus results in significant increases in efficiency and can reverse the edible yield plateauing seen or feared in some of our major selfing food crops

Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations

This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops

Integration of hyperspectral, genomic, and agronomic data for early prediction of biomass yield in hybrid rye (Secale cereale L.)

Currently, the combination of a growing bioenergy demand and the need to diversify the dominant cultivation of energy maize opens a highly attractive scenario for alternative biomass crops. Rye (Secale cereale L.) stands out for its vigorous growth and increased tolerance to abiotic and biotic stressors. In Germany, less than a quarter of the total harvest is used for food production. Consequently, rye arises as a source of renewables with a reduced bioenergy-food tradeoff, emerging biomass as a new breeding objective. However, rye breeding is mainly driven by grain yield while biomass is destructively evaluated in later selection stages by expensive and time-consuming methods. The overall motivation of this research was to investigate the prospects of combining hyperspectral, genomic, and agronomic data for unlocking the potential of hybrid rye as a dual-purpose crop to meet the increasing demand for renewable sources of energy affordably. A specific aim was to predict the biomass yield as precisely as possible at an early selection stage. For this, a panel of 404 elite rye lines was genotyped and evaluated as testcrosses for grain yield and a subset of 274 genotypes additionally for biomass. Field trials were conducted at four locations in Germany in two years (eight environments). Hyperspectral fingerprints consisted of 400 discrete narrow bands (from 410 to 993 nm) and were collected in two points of time after heading for all hybrids in each site by an uncrewed aerial vehicle. In a first study, population parameters were estimated for different agronomic traits and a total of 23 vegetation indices. Dry matter yield showed significant genetic variation and was stronger correlated with plant height (r_g=0.86) than with grain yield (r_g=0.64) and individual vegetation indices (r_g: =<|0.35|). A multiple linear regression model based on plant height, grain yield, and a subset of vegetation indices surpassed the prediction ability for dry matter yield of models based only on agronomic traits by about 6 %. In a second study, whole-spectrum data was used to indirectly estimate dry matter yield. For this, single-kernel models based on hyperspectral reflectance-derived (HBLUP) and genomic (GBLUP) relationship matrices, a multi-kernel model combining both matrices, and a bivariate model fitted also with plant height as a secondary trait, were considered. HBLUP yielded superior predictive power than the models based on vegetation indices previously tested. The phenotypic correlations between individual wavelengths and dry matter yield were generally significant (p < 0.05) but low (r_p: =< |0.29|). Across environments and training set sizes, the bivariate model yielded the highest prediction abilities (0.56 0.75). All models profited from larger training populations. However, if larger training sets cannot be afforded, HBLUP emerged as a promising approach given its higher prediction power on reduced calibration populations compared to the well-established GBLUP. Before its incorporation into prediction models, filtering the hyperspectral data available by the least absolute shrinkage and selection operator (Lasso) was worthwhile to deal with data dimensionally. In a third study, the effects of trait heritability, as well as genetic and environmental relatedness on the prediction ability of GBLUP and HBLUP for biomass-related traits were compared. While the prediction ability of GBLUP (0.14 - 0.28) was largely affected by genetic relatedness and trait heritability, HBLUP was significantly more accurate (0.41 - 0.61) across weakly connected datasets. In this context, dry matter yield could be better predicted (up to 20 %) by a bivariate model. Nevertheless, due to environmental variances, genomic and reflectance-enabled predictions were strongly dependant on a sufficient environmental relationship between data used for model training and validation. In summary, to affordably breed rye as a double-purpose crop to meet the increasing bioenergy demands, the early prediction of biomass across selection cycles is crucial. Hyperspectral imaging has proven to be a suitable tool to select high-yielding biomass genotypes across weakly linked populations. Due to the synergetic effect of combining hyperspectral, genomic, and agronomic traits, higher prediction abilities can be obtained by integrating these data sources into bivariate models.Die Kombination eines wachsenden Bioenergiebedarfs und die Notwendigkeit, den vorherrschenden Anbau von Energiemais zu diversifizieren, eröffnen ein äußerst attraktives Szenario für alternative Biomassekulturen. Roggen (Secale cereale L.) zeichnet sich, durch ein kräftiges vegetatives Wachstum und eine erhöhte Toleranz gegenüber abiotischen und biotischen Stressfaktoren. In Deutschland wird weniger als ein Viertel der gesamten Roggenernte für die Lebensmittelproduktion verwendet. Daher gewinnt Roggen durch einen geringeren Zielkonflikt zwischen Bioenergie- und Lebensmittelnutzung an Bedeutung als Quelle für erneuerbare Energien, wobei Biomasse als neues Züchtungsziel auftaucht. Die Roggenzüchtung konzentriert sich derzeit jedoch hauptsächlich auf den Kornertrag, während die Biomasse in späteren Selektionsstadien durch teure und zeitaufwändige Methoden destruktiv erfasst wird. Die übergeordnete Motivation dieser Arbeit war es, die Aussichten der Kombination von hyperspektralen, genomischen und agronomischen Daten für die Erschließung des Potenzials von Hybridroggen als Zweinutzungspflanze zu untersuchen, um den steigenden Bedarf an erneuerbaren Energiequellen kostengünstig zu decken. Das spezifische Ziel war es, den Biomasseertrag in einem frühen Selektionsstadium so genau wie möglich vorherzusagen. Dazu wurde ein Panel von 404 Elitelinien genotypisiert und als Testkreuzungen für Kornertrag - eine Teilmenge von 274 Genotypen auch für Biomasse-Ertrag ausgewertet. Feldversuche wurden an vier Standorten in zwei Jahren in Deutschland (entspricht acht Umwelten) durchgeführt. Die hyperspektralen Daten (400 diskreten Banden; 410-993 nm) wurden zu zwei Zeitpunkten nach dem Ährenschieben für alle Testkreuzungen an jedem Ort von einer Drohne gesammelt. In einer ersten Studie wurden Populationsparameter für verschiedene agronomische Merkmale und insgesamt 23 Vegetationsindizes geschätzt. Der Trockenmasseertrag zeigte eine signifikante genetische Variation und korrelierte stärker mit der Wuchshöhe (r_g=0.86) als mit dem Kornertrag (r_g=0.64) und den einzelnen Vegetationsindizes (r_g: =<|0.35|). Ein multiples lineares Regressionsmodell, welches auf Wuchshöhe, Kornertrag und den besten Vegetationsindizes basierte, übertraf die Vorhersagefähigkeit für den Trockenmasseertrag von Modellen, die nur auf agronomischen Merkmalen basierten, um etwa 6%. In einer zweiten Studie wurde das ganze Wellenlängenspektrum verwendet, um den Trockenmasseertrag indirekt abzuschätzen. Hierzu wurden Einzelkernmodelle (single-kernel models) basierend auf genomischen (GBLUP) oder hyperspektralen (HBLUP) Beziehungsmatrizen, ein Mehrkernmodell (multi-kernel model), das beide Matrizen kombiniert, sowie ein bivariates Modell, welches auch Wuchshöhe als ein sekundäres Merkmal enthielt, analysiert. HBLUP lieferte eine bessere Vorhersagekraft als die Modelle, die auf Vegetationsindizes basierten. Die phänotypische Korrelationen zwischen einzelnen Wellenlängen und dem Trockenmasseertrag waren im Allgemeinen signifikant (p<0,05), jedoch geringfügig (r_p: =<|0.29|). Über alle Umwelten und Trainingssatzgrößen hinweg ergab das bivariate Modell die höchsten Vorhersagefähigkeiten (0,56-0,75). Alle Modelle profitierten von größeren Trainingspopulationen. Wenn jedoch keine größeren Trainingssätze bereitgestellt werden können, zeigte HBLUP eine höhere Vorhersagefähigkeit als das etablierte GBLUP. Vor der Einbeziehung in Vorhersagemodelle hat sich das Filtern der verfügbaren Hyperspektraldaten durch den least absolute shrinkage and selection operator (Lasso) als notwendig erwiesen, um die Dimensionalität der Daten zu verringern. In einer dritten Studie wurden die Auswirkungen der Heritabilität sowie der Ähnlichkeit innerhalb von Genotypen und Umwelten auf die Vorhersagefähigkeit von GBLUP und HBLUP für biomassebezogene Merkmale verglichen. Während die Vorhersagefähigkeit von GBLUP (0,14-0,28) weitgehend durch genetische Verwandtschaft und die Merkmalsheritabilitäten beeinflusst wurde, war HBLUP in wenig verwandten Datensätzen signifikant genauer (0,41-0,61). In diesem Zusammenhang konnte der Trockenmasseertrag durch ein bivariates Modell bis zu 20% besser vorhergesagt werden. Aufgrund hoher Genotyp-Umwelt-Interaktionen waren genomische und reflexionsbasierte Vorhersagen nur schlecht geeignet, um die Leistung fehlender Umwelten vorherzusagen. Zusammenfassend ist es für eine kostengünstige Züchtung von Roggen als Zweinutzungspflanze zur Deckung des steigenden Bioenergiebedarfs entscheidend, die Biomasse über Selektionszyklen hinweg frühzeitig vorherzusagen. Die hyperspektrale Bildgebung hat sich als geeignetes Instrument zur Auswahl ertragreicher Biomasse-Genotypen auch in wenig verwandten Populationen erwiesen. Dank des synergetischen Effekts der Kombination von hyperspektralen, genomischen und agronomischen Merkmalen können durch die Integration dieser Datenquellen in bivariaten Modelle höhere Vorhersagefähigkeiten erzielt werden

Development and use of introgression populations for the detection of QTL related to important agronomic traits in eggplant

Tesis por compendio[ES] La berenjena (Solanum melongena L.) es uno de los cultivos comerciales de hortalizas solanáceas más importantes que se cultiva ampliamente en Asia y la región del Mediterráneo. A pesar de su importancia económica, la disponibilidad de poblaciones experimentales y herramientas genómicas para el mejoramiento es aún muy limitada en comparación con otros cultivos importantes. Debido a la alteración progresiva del ecosistema global por el cambio climático, las plantas están constantemente expuestas a condiciones ambientales estresantes que impactan negativamente en su productividad. El cuello de botella genético ocurrido durante la domesticación de la berenjena, que limita la disponibilidad de recursos genéticos para su mejoramiento genético, hace que este cultivo sea extremadamente vulnerable al cambio climático, por lo que se requieren nuevas estrategias para reducir su erosión genética. En este contexto, los parientes silvestres de los cultivos (CWRs) han demostrado ser un recurso genético válido para la mejora vegetal, ya que su uso permite ampliar la diversidad genética de los cultivos y, en paralelo, desarrollar variedades mejoradas adaptadas al cambio climático. Para lograr este objetivo, en esta tesis doctoral informamos sobre el desarrollo y la evaluación de materiales avanzados de berenjena obtenidos mediante el uso de parientes silvestres. En el primer capítulo, realizamos una evaluación fenotípica en dos ambientes de un conjunto de 16 ILs de berenjena con introgresión de S. incanum, un pariente silvestre. Se evaluaron diecisiete caracteres agronómicos para comparar el rendimiento de las ILs con el parental recurrente e identificar QTLs para los caracteres investigados. Encontramos diferencias morfológicas significativas entre los parentales, y el híbrido resultó heterótico para los caracteres de vigor. A pesar de que la interacción entre genotipo y ambiente (G x E) resultó significativa para la mayoría de los caracteres, en general las ILs mostraron pocas diferencias fenotípicas con el progenitor receptor, incluso en presencia de grandes fragmentos de introgresión del progenitor silvestre. Se encontraron valores de heredabilidad bajos a moderados para los caracteres agronómicos. En total, detectamos diez QTLs estables, dos de los cuales estaban relacionados con caracteres de planta y cuatro para caracteres de flor y fruto. En general, las introgresiones de S. incanum mejoraron los valores medios de la mayoría de los caracteres de planta y flor, y disminuyeron el de los caracteres de fruto. Para tres QTLs relacionados con la longitud del pedicelo del fruto y con el peso del fruto, encontramos evidencia de sintenia con otros QTLs identificados previamente en poblaciones de berenjena. Siete QTLs eran nuevos, de los cuales cuatro relacionados con la altura de la planta, con la espinosidad del cáliz de la flor y con la longitud del pedicelo del fruto no colocalizaron con ningún QTL previamente identificado en las poblaciones de berenjena, y tres relacionados con el diámetro del tallo, con la longitud del pedúnculo y del estigma, fueron los primeros identificados en berenjena para estos caracteres. En el segundo capítulo, el conjunto de IL de berenjena con introgresiones de S. incanum se evaluó para la forma del fruto en dos ambientes. Específicamente, realizamos un fenotipado detallado de los frutos de los parentales, del híbrido y de las ILs utilizando 32 descriptores morfológicos de la herramienta fenómica Tomato Analyzer. Se encontraron grandes diferencias morfológicas en los frutos de los parentales, y el híbrido presentó valores negativos de heterosis para muchos de los caracteres de forma del fruto, siendo fenotípicamente más cercano al parental S. incanum. Para la mayoría de los descriptores de forma del fruto observamos diferencias significativas entre las ILs y el parental receptor, incluso en presencia de pequeños fragmentos de introgresión del parental silvestre. A pesar de que la contribución del ambiente y la...[CAT] L'albergínia (Solanum melongena L.) és un dels cultius comercials d'hortalisses solanácees més importants que es cultiva àmpliament a Àsia i la regió del Mediterrani. Malgrat la seua importància econòmica, la disponibilitat de poblacions experimentals i eines genòmiques per al millorament és encara molt limitada en comparació amb altres cultius importants. A causa de l'alteració progressiva de l'ecosistema global pel canvi climàtic, les plantes estan constantment exposades a condicions ambientals estressants que impacten negativament en la seua productivitat. El coll de botella genètic ocorregut durant la domesticació de l'albergínia, que limita la disponibilitat de recursos genètics per al seu millorament genètic, fa que aquest cultiu siga extremadament vulnerable al canvi climàtic, per la qual cosa es requereixen noves estratègies per a reduir la seua erosió genètica. En aquest context, els parents silvestres dels cultius (CWRs) han demostrat ser un recurs genètic vàlid per a la millora vegetal, ja que el seu ús permet ampliar la diversitat genètica dels cultius i, en paral·lel, desenvolupar varietats millorades adaptades al canvi climàtic. Per a aconseguir aquest objectiu, en aquesta tesi doctoral presentem el desenvolupament i l'avaluació de materials avançats d'albergínia obtinguts mitjançant l'ús de parents silvestres. En el primer capítol, realitzem una avaluació fenotípica en dos ambients d'un conjunt de 16 IL d'albergínia amb introgresions de S. incanum, un parent silvestre. Es van puntuar dèsset caràcters agronòmics per a avaluar el rendiment de les ILs en comparació amb el parental recurrent i identificar els QTL per als caràcters investigats. Trobarem diferències morfològiques significatives entre els parentals, i l'híbrid va resultar heteròtic per als caràcters de vigor. A pesar que la interacció entre genotip i ambient (G x E) va resultar significativa per a la majoria dels caràcters, en general les ILs van mostrar poques diferències fenotípiques amb el progenitor receptor, fins i tot en presència de grans fragments d'introgresió del progenitor silvestre. Es van trobar valors de heredabilitat baixos a moderats per als caràcters agronòmics. En total, detectarem deu QTL estables, dos dels quals estaven relacionats a caràcters de planta i quatre per a caràcters de flor i fruit. En general, les introgresions de S. incanum van millorar els valors mitjos de la majoria dels caràcters de planta i flor, i van disminuir el dels caràcters de fruit. Per a tres QTL relacionats amb la longitud del pedicel del fruit i amb el pes del fruit, trobem evidència de sintenia amb altres QTLs identificats prèviament en poblacions d'albergínia. Set QTL eren nous, dels quals quatre estaven relacionats amb l'altura de la planta, amb la espinositat del calze de la flor i amb la llargària del pedicel del fruit no van colocalitzar amb cap QTL prèviament identificat en les poblacions d'albergínia, i tres relacionats amb el diàmetre de la tija, amb la llargària del peduncle i de l'estigma, van ser els primers reportats en albergínia per a aquests caràcters. En el segon capítol, el conjunt de IL d'albergínia amb introgresions de S. incanum es va avaluar per a la forma del fruit en dos ambients. Específicament, realitzarem un fenotipado detallat dels fruits dels parentals, de l'híbrid i de les ILs utilitzant 32 descriptors morfològics de l'eina fenómica Tomato Analyzer. Es van trobar grans diferències morfològiques en els fruits dels parentals, i l'híbrid va presentar valors negatius de heterosis per a molts dels caràcters de forma del fruit, sent fenotípicamente més pròxim al parental S. incanum. Per a la majoria dels descriptors de forma del fruit observarem diferències significatives entre les ILs i el parental recipient, fins i tot en presència de xicotets fragments d'introgresió del parental silvestre. A pesar que la contribució de l'ambient i la interacció G × E van ser significatives per a quasi tots els descriptors, trobem que els seus...[EN] Eggplant (Solanum melongena L.) is one of the most important commercial solanaceous vegetable crops grown widely in Asia and Mediterranean region. Despite its economic importance, the availability of experimental populations and genomic tools for breeding is still very limited compared to other major crops. Due to the progressive alteration of global ecosystem by climate change, plants are constantly exposed to stressful environmental conditions that impact negatively on their productivity. The genetic bottleneck occurred during eggplant domestication, which limits the availability of genetic resources for its genetic improvement, makes this crop extremely vulnerable to climate change, and, therefore, new strategies are needed for reducing its genetic erosion. In this context, crop wild relatives (CWRs) have demonstrated to be a valid genetic resources for plant breeding, as their use allows to broaden the genetic diversity of the crop and, in parallel, develop improved varieties adapted to climate change. To achieve this objective, in this doctoral thesis we reported on the development and evaluation of eggplant advanced materials obtained by using crop wild relatives. In the first chapter, we have conducted a phenotypic evaluation in two environments of a set of 16 eggplant ILs with introgression from S. incanum, a close wild relative. Seventeen agronomic traits were scored to test the performance of ILs compared to the recurrent parent and identify QTLs for the investigated traits. We found significant morphological differences between parents, and the hybrid was heterotic for vigour related traits. Although significant genotype x environment interaction (G x E) was detected for most traits, the ILs generally exhibited few phenotypic differences with recipient parent, even in the presence of large introgression fragments from the wild parent. Low to moderate heritability values were found for the agronomic traits. In total, we detected ten stable QTLs, two of which were for plant-related traits and four for both flower- and fruit-related traits. In general, S. incanum introgressions improved the performance of most plant- and flower-related traits and decreased that of fruit-related traits. For three QTLs related to fruit pedicel length and fruit weight, we found evidence of synteny to other QTLs previously reported in eggplant populations. Seven QTLs were new, of which four related to plant height, flower calyx prickles, and fruit pedicel length, did not colocalized with any previous identified QTLs in eggplant populations, and three related to stem diameter, peduncle length, and stigma length, were the first reported in eggplant for these traits. In the second chapter, the set of eggplant ILs with introgression from S. incanum was evaluated for fruit shape in two environments. Specifically, we performed a detailed phenotyping of the fruits of the parents, hybrid, and ILs using 32 morphological descriptors of the phenomics tool Tomato Analyzer. Large differences in fruit morphology were found between ILs parents, and the hybrid exhibited negative values of heterosis for many fruit shape traits, being phenotypically closer to S. incanum parent. For most fruit shape descriptors, we observed significant differences between ILs and recipient parent, even in the presence of small wild donor fragments. Although the contribution of the environment and G × E interaction were significant for almost all descriptors, we found that their effects on fruit shape were relatively low, and the observed variations in fruit shape was mainly genetically regulated. Hierarchical clustering revealed nine clusters of highly correlated traits and six ILs groups. A total of 41 QTLs were mapped. Of these, sixteen associated to Basic Measurement and Fruit Shape Index descriptors were syntenic to other previously reported in several intraspecific and interspecific eggplant populations, while twenty-five QTLs related to Blockiness, Homogeneity....This work was undertaken as part of the initiative “Adapting Agriculture to Climate Change: Collecting, Protecting, and Preparing Crop Wild Relatives”, which is supported by the Government of Norway. The project is managed by the Global Crop Diversity Trust with the Millennium Seed Bank of the Royal Botanic Gardens, Kew and implemented in partnership with national and international gene banks and plant breeding institutes around the world. For further information, see the project website: http://www.cwrdiversity.org/. Funding was also received from Spanish Ministerio de Economía, Industria y Competitividad and Fondo Europeo de Desarrollo Regional (grant AGL2015-64755-R from MINECO/FEDER); from Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional (grant RTI-2018-094592-B-100 from MCIU/AEI/FEDER, UE); from European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 677379 (G2P-SOL project: Linking genetic resources, genomes and phenotypes of Solanaceous crops); and from Vicerrectorado de Investigación, Innovación y Transferencia de la Universitat Politècnica de València (Ayuda a Primeros Proyectos de Investigación; PAID-06-18). Giulio Mangino is grateful to Generalitat Valenciana for a predoctoral grant within the Santiago Grisolía programme (GRISOLIAP/2016/012).Mangino, G. (2022). Development and use of introgression populations for the detection of QTL related to important agronomic traits in eggplant [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/188916Compendi

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Türk Mısır (Zea mays L.) hibridlerinin üşüme stresi toleranslarında fenotipik varyasyonların belirlenmesi

Maize (Zea mays L.) is a tropical crop and chilling temperatures (below 15 ºC) cause growth retardation and yield losses. The development of chilling-tolerant maize varieties is one of the goals of plant breeders growing maize in cool climates. Hybrids are more vigorous than their parents, including being more tolerant to diverse stresses. However, stress screening is a problematic. This study aims to evaluate chilling stress tolerance of Turkish maize hybrids and to determine suitable indicators for selecting the most tolerant hybrid. Nine hybrids were subjected to low night-time temperatures following germination until the third leaf was fully enlarged. Hybrids were evaluated at the morphological, cellular and physiological levels by comparison with control seedlings. The data were subjected to kinematic analysis and statistical tools. The findings showed that all indicators differed significantly among the hybrids. Indicators such as leaf elongation rate, mature cell length and cell production increase our understanding of stress tolerance by establishing connections between phenotype and cellular functions. Shoot fresh and dry weight emerged useful indicators for revealing association between growth and the physiological stress response of seedlings. In conclusion, this study identified beneficial indicators for breeding studies at early seedling screening of maize hybrids exhibiting genetic variation in terms of chilling stress tolerance.Mısır (Zea mays L.) tropikal orjinli bir bitkidir ve düşük sıcaklıklar (15 ᵒC'nin altında) büyüme inhibisyonuna yol açarak verim kayıplarına neden olur. Bu nedenle, üşüme stresine dayanıklı mısır çeşitlerinin geliştirilmesi, serin iklimlerde mısır yetiştirebilmek için mısır ıslahçılarının temel amaçları arasındadır. Hibridler, çeşitli streslere daha toleranslı olduklarından ebeveynlerine göre üstündür. Ancak, stres taramasının yapılması zordur. Bu bağlamda, çalışma, Türk mısır hibritlerinin üşüme stres toleranslarını değerlendirmeyi ve en toleranslı hibrit seçiminde uygun belirteçleri belirlemeyi amaçlamaktadır. Bu doğrultuda dokuz farklı genotipe sahip mısır hibridi, çimlenmelerinin ardından üçüncü yaprakları tamamen olgunlaşıncaya kadar düşük gece sıcaklığına maruz bırakılmıştır. Üşümeye maruz bırakılan hibridler, kontrol şartlarında yetiştirilen fideler ile karşılaştırılarak stres toleransları morfolojik, hücresel ve fizyolojik seviyelerde değerlendirilmiştir. Veriler kinematik analiz ve istatistiksel araçlar ile analiz edilmiştir. Bulgulara göre, tüm stres belirteçleri hibridler arasında önemli derecede farklılık göstermiştir. Yaprak uzama oranı (LER), olgun hücre uzunluğu (MCL) ve hücre üretimi (CP) gibi belirteçler, fenotip ve hücresel fonksiyonlar arasında bağlantı kurmaya olanak sağladığından stres tolerans mekanizmasını anlamamızda faydalı olduğu görülmüştür. Bununla birlikte, taze ve kuru fide ağırlığının (SFW ve SDW) fidelerin büyüme ile fizyolojik stres tepkisi arasındaki ilişkiyi ortaya çıkarmak için yararlı göstergeler olduğu saptanmıştır. Sonuç olarak, bu çalışma, genetik varyasyon sergilediği gözlenen üşüme stresi toleransı geliştirmeyi amaçlayan ıslah çalışmalarında mısırın erken aşamada taranabilmesine olanak sağlayan bir yaklaşım sunmaktadır