Impact of reduced and conventional tillage with mulch management on yield and agronomic traits of spring wheat

Istraživan je utjecaj različitih sustava obrade tla i korištenja slame kao malča te njihove interakcije na prinos i agronomska svojstva jare pšenice. Pokus je postavljen u sjeverozapadnoj Hrvatskoj na fluvioslu u humidnim uvjetima 2021. godine s tri sustava obrade tla (glavni faktor): konvencionalna obrada s oranjem do 20 cm u jesen i tanjuranjem u proljeće (CT), primjena kombiniranog oruđa do 15 cm u proljeće (MT), podrivanje na dubinu 35-40 cm u jesen te rahljenje kombiniranim oruđem do 15 cm u proljeće (RT). Svaki sustav obrade podijeljen je na parcele sa i bez slame kao podfaktorom pokusa. Obrada tla imala je značajan učinak (p<0,05) samo za hektolitarsku masu, dok je značajan učinak korištenja malča zabilježen za masu 1000 zrna i sedimentacijsku vrijednost. Interakcija obrade i slame pokazala je značajan učinak na sva istraživana svojstva (prinos, masa 1000 zrna, hektolitarska masa, sadržaj proteina, sadržaj vlažnog lijepka, sedimentacijska vrijednost). Prema dobivenim rezultatima može se zaključiti da se plitkom obradom (MT) ostvaruje zadovoljavajući prinos u odnosu na konvencionalnu obradu, ali je za optimizaciju kemijskih karakteristika prinosa jare pšenice potrebno testirati i druge agrotehničke mjere.This paper presents the influence of different tillage systems and the use of straw as mulch and their interaction on yield and yield components (physical and chemical) of spring wheat. The experiment was established in northwestern Croatia on alluvial soils under humid conditions with three tillage systems (main factor): conventional tillage with plowing up to 20 cm in autumn and disc harrowing in spring (CT), use of multitiller up to 15 cm in spring (MT); subsoiling to a depth of 35-40 cm in autumn and loosening with multitiller up to 15 cm in spring (RT). Each tillage system has subplots with and without straw. The tillage factor had a significant effect (p < 0.05) only on hectolitre mass, while a significant effect of mulch use was observed on 1000-kernel weight and sedimentation value. The interaction of these factors had a significant effect on all the components studied (yield, 1000-kernel weight, hectolitre mass, protein content, wet gluten content, sedimentation value). From the short-term results obtained, it can be concluded that shallow tillage (MT) gives satisfactory yield compared to conventional tillage, but further optimization of the agrotechnical system is required to improve the chemical properties

Genomic selection for wheat grain quality traits

Pšenica (Triticum aestivum L.) je jedna od najvažnijih biljnih vrsta za proizvodnju hrane u svijetu te najvažniji izvor proteina i energije u ljudskoj prehrani. Kakvoća pšenice određuje se na osnovi mnogobrojnih svojstava, a jedan od najvažnijih čimbenika koji utječe na reologiju tijesta je sadržaj glutena i njegova kakvoća. Reološki profil tijesta može se odrediti miksografom koji se zbog male količine brašna potrebne za analizu pokazao prikladnim za korištenje u oplemenjivanju pšenice, osobito u ranim generacijama kada velike količine zrna nisu dostupne. Budući da većina svojstava kakvoće pšenice pokazuje složene obrasce nasljeđivanja, oplemenjivanje na kakvoću, a posebno na pekarsku kakvoću, jedan je od najzahtjevnijih izazova u oplemenjivanju pšenice. Ciljevi ovog istraživanja bili su procijeniti utjecaj interakcije genotip × okoliš i mogućnost korištenja genomske selekcije za svojstva kakvoće zrna pšenice kako bi se postigla učinkovitija selekcija za navedena svojstva te smanjili potencijalni troškovi genotipizacije i fenotipizacije u oplemenjivačkom procesu. U istraživanju su korištene dvije biparentalne (RIL) populacije pšenice dobivene križanjem roditeljskih sorti Bezostaya-1 × Klara (BK) i Monika × Golubica (MG). Poljski pokusi su provedeni na dvije lokacije u Hrvatskoj tijekom tri godine. U svakom od okoliša određene su vrijednosti sadržaja proteina (GPC), sadržaja vlažnog glutena (WGC) i hektolitarske mase (TW). Reologija tijesta analizirana je pomoću miksografa, a četiri varijable su odabrane za daljnju statističku analizu (MPT, MTW, MTI, MPH). Interakcija genotip × okoliš analizirana je pomoću AMMI modela. RR-BLUP model korišten je kako bi se utvrdila potreba za optimizacijom trenažne populacije na osnovi fenotipske varijance, te ispitao utjecaj veličine trenažne populacije i gustoće biljega na točnost predviđanja genomske selekcije, a analize su provedene za svih sedam svojstava u obje populacije. Za utvrđivanje utjecaja veličine trenažne populacije na točnost predviđanja korištene su tri različite veličine trenažne populacije. Kako bi se utvrdio utjecaj gustoće biljega, genomska selekcija za sva svojstva provedena je korištenjem cijelog skupa biljega te polovice skupa biljega. Učinkovitost RR-BLUP modela uspoređena je s učinkovitošću sedam drugih modela za predviđanje svojstava kakvoće. Analiza interakcije genotip × okoliš pokazala je određene zajedničke obrasce za dvije promatrane populacije. Za GPC, WGC i TW dominantan izvor fenotipske varijacije bio je okoliš. Na MPT i MTW dominantan utjecaj imala je interakcija genotip × okoliš u BK i genotip u MG populaciji, dok je na MTI i MPH dominantan učinak imao okoliš u BK i interakcija genotip × okoliš u MG populaciji. Općenito, utjecaj interakcije genotip × okoliš imao je važniju ulogu za svojstva miksografa u odnosu na ostala promatrana svojstva. Analizom AMMI2 biplota utvrđeni su neki široko prilagođeni RIL-ovi. Za sva svojstva utvrđene su uglavnom visoke vrijednosti heritabilnosti. Smanjenje veličine trenažne populacije imalo je negativan učinak na dobivenu točnost predviđanja genomske selekcije za sva promatrana svojstva u obje populacije. Dobiveni rezultati nisu podržali optimizaciju trenažne populacije na temelju fenotipske varijance. Također je primijećeno da točnost predviđanja može značajno varirati između okoliša. Kada se usporedi utjecaj različitih gustoća biljega na sposobnost predviđanja svojstava kakvoće unutar MG populacije, vrijednosti točnosti predviđanja dobivene korištenjem veće gustoće biljega bile su više u svim slučajevima. Za većinu kombinacija svojstvo-okoliš model elastične mreže je rezultirao najnižim vrijednostima točnosti predviđanja. Iako se RR-BLUP nije pokazao najuspješnijim modelom u svim slučajevima, nije uočena značajna prednost korištenja bilo kojeg drugog modela. Točnosti predviđanja dobivene u sklopu ovog istraživanja podržavaju primjenu genomske selekcije za oplemenjivanje pšenice na kakvoću, uključujući i oplemenjivanje na neka svojstva dobivena miksograf uređajem.Wheat (Triticum aestivum L.) is one of the most important crops for food production in the world. The importance of wheat emphasizes the fact that wheat products are the most important source of dietary proteins and energy supply for humans. Therefore, achieving suitable wheat quality is of great importance. Wheat quality is determined by a large number of traits and under the strong environmental influence. One of the most important factors affecting dough rheology is gluten content and its strength. Gluten is the most abundant wheat protein, and by its structure, gluten is a complex network of monomeric gliadins and polymeric subunits of glutenin. Among gluten components, high molecular weight glutenin subunits have the greatest impact on dough quality. Different instruments can be used to perform rheological tests, which are necessary to assess wheat baking quality more accurately. Mixograph is a dough mixer that creates a dough rheological profile, providing general information about dough mixing, its behaviour during development, and the strength of the dough. Due to the small amount of flour required, mixograph has shown to be highly suitable for use in wheat breeding, particularly in early generations when availability of grain and flour is still limited. Since the majority of quality traits have complex inheritance patterns, breeding for improved baking quality is one of the most demanding objectives in wheat breeding. Taking that into account together with often costly and time-consuming phenotyping, predictability of wheat baking quality may be very challenging. The ability to develop a genotype that exhibits both improved performance and high stability of the quality traits is critical to the success of wheat quality improvement. One of the major challenges in plant breeding, in this context, is the occurrence of genotype-by-environment interaction, since its presence makes selecting widely adapted genotypes difficult. The AMMI model is one of the most commonly used methods for the analysis of genotype-by-environment interaction. However, the extensive development of high-throughput genotyping in the last decade has enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection is one of the recently developed methods that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. Genomic selection aims to capture total additive genetic variance based on the sum of the effects of a large number of genetic markers, encompassing all QTLs that contribute to trait variability. In genomic selection, genotypic and phenotypic information of the training population is used to train a model and estimate the marker effects. Obtained data is then applied to the breeding (validation) population of non-phenotyped candidates to estimate their genomic-estimated breeding values (GEBV). The effectiveness of genomic selection is determined by the obtained prediction accuracy, which is affected by a variety of molecular, genetic, and phenotypic factors, as well as the parameters of the selected statistical model. The correct adjustment of factors that can affect prediction accuracy, such as population structure, size of training population, the relatedness of training and validation population, marker density, etc., is the first step toward successful implementation of genomic selection in practical breeding programs. Different prediction models have been developed to solve the problem of high-dimensional datasets occurring in genomic selection. These models differ primarily in their assumptions about the distribution and variance of marker effects, i.e., how marker effects contribute to the trait. Given the often challenging phenotyping for wheat quality traits, and especially for baking quality traits, the use of classical breeding methods can be costly and time-consuming. Determining the optimal model and parameters of genomic selection would enable the use of molecular markers in the pre-selection process for grain quality traits and the optimization of classical wheat breeding methods. This research aimed to assess the impact of genotype-by-environment interaction and optimize genomic selection for grain quality traits using biparental wheat populations, in order to reduce the potential costs of x genotyping and phenotyping in the breeding process and suggest optimal strategies based on genomic selection for more efficient development of new lines. Two biparental populations of winter wheat were used in this study. The BK population was derived from the Bezostaya-1 × Klara cross and the MG population from the Monika × Golubica cross. In the BK combination the parental genotypes differed in all high molecular weight glutenin subunits, while the parental genotypes used in the MG combination did not differ in any of the high molecular weight glutenin subunits. The BK and MG populations consisted of 145 and 175 genotypes, respectively, including parental genotypes. Field trials were conducted for three consecutive years (2009 – 2011) at two locations in Croatia (Osijek and Slavonski Brod), i.e., in six different environments. In each environment the field trial was set up according to a row-column design. Genotyping of both populations was done using Diversity Arrays Technology. After marker filtering the final dataset used for genomic selection contained 1087 and 2231 SNP markers for BK and MG population, respectively. Grain protein content, wet gluten content, test weight, and dough rheology were assessed in each environment. Dough rheology was investigated using a mixograph and four variables were selected for further statistical analysis (MPT, MTW, MTI, and MPH). Genotype-by-environment patterns for the quality and mixograph traits were studied using the AMMI model. The dissection of genotype-by-environment patterns was visualized by a modified version of the AMMI2 biplot, which adds the main effects to the standard AMMI2 biplot using a colour scale. In the first phase of the genomic selection analysis the need for optimization of the training population based on phenotypic variance was assessed using both biparental populations. Additionally, the influence of the training population size and marker density on the prediction accuracy was investigated. For that purpose, three different sizes of training population were used for both BK and MG populations, and two different marker densities for the MG population. The first phase was conducted using only the RR-BLUP model. In the second phase of the genomic selection, the performance of seven different genomic selection models was compared with the performance of the RR-BLUP model. Models included were elastic net, four Bayesian models (BayesA, BayesB, BayesC, and BayesLASSO), random forest, and reproducing kernel Hilbert spaces. This part of the analysis was performed only within the MG population. Results revealed some positive as well as negative transgressive segregants in both populations for all quality traits although being generally more prevalent in the BK population. This may suggest the dispersion of the alleles with positive (increasor) and negative (decreasor) effects between parental genotypes in both crosses. The environment was the dominant source of variation for grain protein content, wet gluten content, and test weight, accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation was trait- and population-dependent. Overall, genotype-by-environment interaction was shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which MG124 is the most interesting, being the prevalent “winner” for grain protein content and wet gluten content, but also the “winner” for non-correlated trait test weight in environment SB10. The broad-sense heritability across environments was high for all traits, except for MPT in the BK population, the heritability of which was 0.45. Although repeatability varied considerably among environments, it was high for most of the trait-environment combinations, with a value above 0.7. These results suggest that heritability itself should not represent a limitation in achieving good prediction accuracy. The results of genomic selection analysis showed that the size of the training population plays an important role in achieving higher prediction accuracies, while marker density does not represent a major limitation. Additionally, the results of the present study did not support the optimization of the training population based on phenotypic variance as a tool to increase prediction accuracy. The performance of eight prediction models was compared and among them, elastic net showed xi the lowest prediction accuracy for all traits. Bayesian models provided slightly higher prediction accuracy than the RR-BLUP model. However, this may be considered negligible considering the time required to perform an analysis. Although RR-BLUP was not the best performing model in all cases, no advantage of using any other model studied in this research was observed. Furthermore, strong differences among environments in terms of the prediction accuracy were observed. For example, the prediction accuracy for TW within the MG population was moderate in one environment, while being low in all other environments. Comparing these results to the results of a genotype-by-environment analysis it is noticeable that environments that are characterized by unusually high or low values for prediction accuracy compared to the rest of the environments tend to be those that produce the greatest genotype-by-environment interaction. This suggests that less predictive environments should be excluded from the dataset used to train the prediction model in order to achieve higher prediction accuracies. The prediction accuracies obtained in this study support implementation of genomic selection in wheat breeding for end-use quality, including some mixograph traits

Genomic selection for wheat grain quality traits

Pšenica (Triticum aestivum L.) je jedna od najvažnijih biljnih vrsta za proizvodnju hrane u svijetu te najvažniji izvor proteina i energije u ljudskoj prehrani. Kakvoća pšenice određuje se na osnovi mnogobrojnih svojstava, a jedan od najvažnijih čimbenika koji utječe na reologiju tijesta je sadržaj glutena i njegova kakvoća. Reološki profil tijesta može se odrediti miksografom koji se zbog male količine brašna potrebne za analizu pokazao prikladnim za korištenje u oplemenjivanju pšenice, osobito u ranim generacijama kada velike količine zrna nisu dostupne. Budući da većina svojstava kakvoće pšenice pokazuje složene obrasce nasljeđivanja, oplemenjivanje na kakvoću, a posebno na pekarsku kakvoću, jedan je od najzahtjevnijih izazova u oplemenjivanju pšenice. Ciljevi ovog istraživanja bili su procijeniti utjecaj interakcije genotip × okoliš i mogućnost korištenja genomske selekcije za svojstva kakvoće zrna pšenice kako bi se postigla učinkovitija selekcija za navedena svojstva te smanjili potencijalni troškovi genotipizacije i fenotipizacije u oplemenjivačkom procesu. U istraživanju su korištene dvije biparentalne (RIL) populacije pšenice dobivene križanjem roditeljskih sorti Bezostaya-1 × Klara (BK) i Monika × Golubica (MG). Poljski pokusi su provedeni na dvije lokacije u Hrvatskoj tijekom tri godine. U svakom od okoliša određene su vrijednosti sadržaja proteina (GPC), sadržaja vlažnog glutena (WGC) i hektolitarske mase (TW). Reologija tijesta analizirana je pomoću miksografa, a četiri varijable su odabrane za daljnju statističku analizu (MPT, MTW, MTI, MPH). Interakcija genotip × okoliš analizirana je pomoću AMMI modela. RR-BLUP model korišten je kako bi se utvrdila potreba za optimizacijom trenažne populacije na osnovi fenotipske varijance, te ispitao utjecaj veličine trenažne populacije i gustoće biljega na točnost predviđanja genomske selekcije, a analize su provedene za svih sedam svojstava u obje populacije. Za utvrđivanje utjecaja veličine trenažne populacije na točnost predviđanja korištene su tri različite veličine trenažne populacije. Kako bi se utvrdio utjecaj gustoće biljega, genomska selekcija za sva svojstva provedena je korištenjem cijelog skupa biljega te polovice skupa biljega. Učinkovitost RR-BLUP modela uspoređena je s učinkovitošću sedam drugih modela za predviđanje svojstava kakvoće. Analiza interakcije genotip × okoliš pokazala je određene zajedničke obrasce za dvije promatrane populacije. Za GPC, WGC i TW dominantan izvor fenotipske varijacije bio je okoliš. Na MPT i MTW dominantan utjecaj imala je interakcija genotip × okoliš u BK i genotip u MG populaciji, dok je na MTI i MPH dominantan učinak imao okoliš u BK i interakcija genotip × okoliš u MG populaciji. Općenito, utjecaj interakcije genotip × okoliš imao je važniju ulogu za svojstva miksografa u odnosu na ostala promatrana svojstva. Analizom AMMI2 biplota utvrđeni su neki široko prilagođeni RIL-ovi. Za sva svojstva utvrđene su uglavnom visoke vrijednosti heritabilnosti. Smanjenje veličine trenažne populacije imalo je negativan učinak na dobivenu točnost predviđanja genomske selekcije za sva promatrana svojstva u obje populacije. Dobiveni rezultati nisu podržali optimizaciju trenažne populacije na temelju fenotipske varijance. Također je primijećeno da točnost predviđanja može značajno varirati između okoliša. Kada se usporedi utjecaj različitih gustoća biljega na sposobnost predviđanja svojstava kakvoće unutar MG populacije, vrijednosti točnosti predviđanja dobivene korištenjem veće gustoće biljega bile su više u svim slučajevima. Za većinu kombinacija svojstvo-okoliš model elastične mreže je rezultirao najnižim vrijednostima točnosti predviđanja. Iako se RR-BLUP nije pokazao najuspješnijim modelom u svim slučajevima, nije uočena značajna prednost korištenja bilo kojeg drugog modela. Točnosti predviđanja dobivene u sklopu ovog istraživanja podržavaju primjenu genomske selekcije za oplemenjivanje pšenice na kakvoću, uključujući i oplemenjivanje na neka svojstva dobivena miksograf uređajem.Wheat (Triticum aestivum L.) is one of the most important crops for food production in the world. The importance of wheat emphasizes the fact that wheat products are the most important source of dietary proteins and energy supply for humans. Therefore, achieving suitable wheat quality is of great importance. Wheat quality is determined by a large number of traits and under the strong environmental influence. One of the most important factors affecting dough rheology is gluten content and its strength. Gluten is the most abundant wheat protein, and by its structure, gluten is a complex network of monomeric gliadins and polymeric subunits of glutenin. Among gluten components, high molecular weight glutenin subunits have the greatest impact on dough quality. Different instruments can be used to perform rheological tests, which are necessary to assess wheat baking quality more accurately. Mixograph is a dough mixer that creates a dough rheological profile, providing general information about dough mixing, its behaviour during development, and the strength of the dough. Due to the small amount of flour required, mixograph has shown to be highly suitable for use in wheat breeding, particularly in early generations when availability of grain and flour is still limited. Since the majority of quality traits have complex inheritance patterns, breeding for improved baking quality is one of the most demanding objectives in wheat breeding. Taking that into account together with often costly and time-consuming phenotyping, predictability of wheat baking quality may be very challenging. The ability to develop a genotype that exhibits both improved performance and high stability of the quality traits is critical to the success of wheat quality improvement. One of the major challenges in plant breeding, in this context, is the occurrence of genotype-by-environment interaction, since its presence makes selecting widely adapted genotypes difficult. The AMMI model is one of the most commonly used methods for the analysis of genotype-by-environment interaction. However, the extensive development of high-throughput genotyping in the last decade has enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection is one of the recently developed methods that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. Genomic selection aims to capture total additive genetic variance based on the sum of the effects of a large number of genetic markers, encompassing all QTLs that contribute to trait variability. In genomic selection, genotypic and phenotypic information of the training population is used to train a model and estimate the marker effects. Obtained data is then applied to the breeding (validation) population of non-phenotyped candidates to estimate their genomic-estimated breeding values (GEBV). The effectiveness of genomic selection is determined by the obtained prediction accuracy, which is affected by a variety of molecular, genetic, and phenotypic factors, as well as the parameters of the selected statistical model. The correct adjustment of factors that can affect prediction accuracy, such as population structure, size of training population, the relatedness of training and validation population, marker density, etc., is the first step toward successful implementation of genomic selection in practical breeding programs. Different prediction models have been developed to solve the problem of high-dimensional datasets occurring in genomic selection. These models differ primarily in their assumptions about the distribution and variance of marker effects, i.e., how marker effects contribute to the trait. Given the often challenging phenotyping for wheat quality traits, and especially for baking quality traits, the use of classical breeding methods can be costly and time-consuming. Determining the optimal model and parameters of genomic selection would enable the use of molecular markers in the pre-selection process for grain quality traits and the optimization of classical wheat breeding methods. This research aimed to assess the impact of genotype-by-environment interaction and optimize genomic selection for grain quality traits using biparental wheat populations, in order to reduce the potential costs of x genotyping and phenotyping in the breeding process and suggest optimal strategies based on genomic selection for more efficient development of new lines. Two biparental populations of winter wheat were used in this study. The BK population was derived from the Bezostaya-1 × Klara cross and the MG population from the Monika × Golubica cross. In the BK combination the parental genotypes differed in all high molecular weight glutenin subunits, while the parental genotypes used in the MG combination did not differ in any of the high molecular weight glutenin subunits. The BK and MG populations consisted of 145 and 175 genotypes, respectively, including parental genotypes. Field trials were conducted for three consecutive years (2009 – 2011) at two locations in Croatia (Osijek and Slavonski Brod), i.e., in six different environments. In each environment the field trial was set up according to a row-column design. Genotyping of both populations was done using Diversity Arrays Technology. After marker filtering the final dataset used for genomic selection contained 1087 and 2231 SNP markers for BK and MG population, respectively. Grain protein content, wet gluten content, test weight, and dough rheology were assessed in each environment. Dough rheology was investigated using a mixograph and four variables were selected for further statistical analysis (MPT, MTW, MTI, and MPH). Genotype-by-environment patterns for the quality and mixograph traits were studied using the AMMI model. The dissection of genotype-by-environment patterns was visualized by a modified version of the AMMI2 biplot, which adds the main effects to the standard AMMI2 biplot using a colour scale. In the first phase of the genomic selection analysis the need for optimization of the training population based on phenotypic variance was assessed using both biparental populations. Additionally, the influence of the training population size and marker density on the prediction accuracy was investigated. For that purpose, three different sizes of training population were used for both BK and MG populations, and two different marker densities for the MG population. The first phase was conducted using only the RR-BLUP model. In the second phase of the genomic selection, the performance of seven different genomic selection models was compared with the performance of the RR-BLUP model. Models included were elastic net, four Bayesian models (BayesA, BayesB, BayesC, and BayesLASSO), random forest, and reproducing kernel Hilbert spaces. This part of the analysis was performed only within the MG population. Results revealed some positive as well as negative transgressive segregants in both populations for all quality traits although being generally more prevalent in the BK population. This may suggest the dispersion of the alleles with positive (increasor) and negative (decreasor) effects between parental genotypes in both crosses. The environment was the dominant source of variation for grain protein content, wet gluten content, and test weight, accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation was trait- and population-dependent. Overall, genotype-by-environment interaction was shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which MG124 is the most interesting, being the prevalent “winner” for grain protein content and wet gluten content, but also the “winner” for non-correlated trait test weight in environment SB10. The broad-sense heritability across environments was high for all traits, except for MPT in the BK population, the heritability of which was 0.45. Although repeatability varied considerably among environments, it was high for most of the trait-environment combinations, with a value above 0.7. These results suggest that heritability itself should not represent a limitation in achieving good prediction accuracy. The results of genomic selection analysis showed that the size of the training population plays an important role in achieving higher prediction accuracies, while marker density does not represent a major limitation. Additionally, the results of the present study did not support the optimization of the training population based on phenotypic variance as a tool to increase prediction accuracy. The performance of eight prediction models was compared and among them, elastic net showed xi the lowest prediction accuracy for all traits. Bayesian models provided slightly higher prediction accuracy than the RR-BLUP model. However, this may be considered negligible considering the time required to perform an analysis. Although RR-BLUP was not the best performing model in all cases, no advantage of using any other model studied in this research was observed. Furthermore, strong differences among environments in terms of the prediction accuracy were observed. For example, the prediction accuracy for TW within the MG population was moderate in one environment, while being low in all other environments. Comparing these results to the results of a genotype-by-environment analysis it is noticeable that environments that are characterized by unusually high or low values for prediction accuracy compared to the rest of the environments tend to be those that produce the greatest genotype-by-environment interaction. This suggests that less predictive environments should be excluded from the dataset used to train the prediction model in order to achieve higher prediction accuracies. The prediction accuracies obtained in this study support implementation of genomic selection in wheat breeding for end-use quality, including some mixograph traits

EFFECT OF EARTHWORM COELOMIC FLUID ON GROWTH OF PHYTOPATHOGENIC FUNGI FUSARIUM OXYSPORUM

Uloga gujavica i mikroorganizama u funkcioniranju ekosustava tla je dobro poznata, kao i postojanje povezanosti i interakcija između gujavica i gljivica u tlu. Međutim, do sada nije istražen utjecaj celomske tekućine i celomocita, imunoloških stanica gujavica, na rast gljivica u tlu. Naime, prilikom negativnog podražaja gujavice u okoliš mogu ispustiti sluz i celomsku tekućinu s celomocitima koja potencijalno može značajno utjecati na rast gljivica. Stoga je glavni cilj ovog rada bio istražiti učinak celomske tekućine gujavica na rast fitopatogenih gljivica. Ekstrakti celomske tekućine dviju različitih vrsta gujavica korišteni su kako bi se utvrdilo njihovo djelovanje na rast fitopatogene gljivice Fusarium oxysporum, iznimno važne vrste u poljoprivrednoj proizvodnji.The role of earthworms and microorganisms in the soil ecosystem functioning is well known, as well as existence of interactions between earthworms and fungi in the soil. However, the impact of coelomic fluid and coelomocytes, immune cells of earthworms, on the growth of the fungi has not been investigated so far. Namely, in case of negative stimuli earthworms can discard mucus and coelomic fluid containing coelomocytes which can potentially have a significant impact on the growth of fungi. Therefore, the main aim of this thesis was to investigate the effects of earthworm coelomic fluid on the growth of phytopathogenic fungi. Coelomic fluid extracts of two different earthworm species was used to determine the effects on the growth of phytopathogenic fungi Fusarium oxysporum, important in agricultural production

ROLE OF ADIPOKINETIC HORMONES IN STRESS ELICITED BYINSECTICIDES: TRIBOLIUM CASTANEUM MODEL

Adipokinetički hormoni (AKH) su skupina neuropeptida prisutnih kod kukaca koji se pojačano luče u uvjetima stresa kako bi potakli odgovarajuće obrambene reakcije te održali homeostazu organizma. Primarno su uključeni u kontrolu metabolizma, al itakođer aktiviraju i antioksidativni sustav organizma. U ovom radu kao modelni organizam korištena je vrsta Tribolium castaneum, a kao stresor dva insekticida(pirimifos-metil i deltametrin). Kukci su izlagani samo insekticidima te insekticidima u kotretmanu s AKH. Nakon tretmana, mjeren je mortalitet, brzina metabolizma kukaca kao i aktivnost biomarkera oksidativnog stresa. Kako bi se istražila uloga AKH u stresnim uvjetima, pripremljen je i soj AKH-deficijentnih kukaca pomoću RNAi tehnologije te je isti podvrgnut jednakim tretmanima kao i normalni soj.Adipokinetic hormones (AKHs) are insect neuropeptides which major function is to stimulate immune response and to maintain homeostasis in living organism. (AKHs) are primary included in metabolism control, but they also activate insect's antioxidative system. In the present study, Tribolium castaneum (red flour beetle) was used as a model species, and two insecticides as stressors (pirimiphos-methyl and deltamethrin).Insects were exposed to insecticides alone and to insecticides in combination with AKH.Mortality, metabolism rate, and activity of oxidative stress biomarkers were measuredafter the treatment. AKH-deficient beetles were prepared using RNAi technology in order to investigate role of AKHs under stress conditions. They were exposed to the same conditions as normal beetle strain

Genomic selection for wheat grain quality traits

Pšenica (Triticum aestivum L.) je jedna od najvažnijih biljnih vrsta za proizvodnju hrane u svijetu te najvažniji izvor proteina i energije u ljudskoj prehrani. Kakvoća pšenice određuje se na osnovi mnogobrojnih svojstava, a jedan od najvažnijih čimbenika koji utječe na reologiju tijesta je sadržaj glutena i njegova kakvoća. Reološki profil tijesta može se odrediti miksografom koji se zbog male količine brašna potrebne za analizu pokazao prikladnim za korištenje u oplemenjivanju pšenice, osobito u ranim generacijama kada velike količine zrna nisu dostupne. Budući da većina svojstava kakvoće pšenice pokazuje složene obrasce nasljeđivanja, oplemenjivanje na kakvoću, a posebno na pekarsku kakvoću, jedan je od najzahtjevnijih izazova u oplemenjivanju pšenice. Ciljevi ovog istraživanja bili su procijeniti utjecaj interakcije genotip × okoliš i mogućnost korištenja genomske selekcije za svojstva kakvoće zrna pšenice kako bi se postigla učinkovitija selekcija za navedena svojstva te smanjili potencijalni troškovi genotipizacije i fenotipizacije u oplemenjivačkom procesu. U istraživanju su korištene dvije biparentalne (RIL) populacije pšenice dobivene križanjem roditeljskih sorti Bezostaya-1 × Klara (BK) i Monika × Golubica (MG). Poljski pokusi su provedeni na dvije lokacije u Hrvatskoj tijekom tri godine. U svakom od okoliša određene su vrijednosti sadržaja proteina (GPC), sadržaja vlažnog glutena (WGC) i hektolitarske mase (TW). Reologija tijesta analizirana je pomoću miksografa, a četiri varijable su odabrane za daljnju statističku analizu (MPT, MTW, MTI, MPH). Interakcija genotip × okoliš analizirana je pomoću AMMI modela. RR-BLUP model korišten je kako bi se utvrdila potreba za optimizacijom trenažne populacije na osnovi fenotipske varijance, te ispitao utjecaj veličine trenažne populacije i gustoće biljega na točnost predviđanja genomske selekcije, a analize su provedene za svih sedam svojstava u obje populacije. Za utvrđivanje utjecaja veličine trenažne populacije na točnost predviđanja korištene su tri različite veličine trenažne populacije. Kako bi se utvrdio utjecaj gustoće biljega, genomska selekcija za sva svojstva provedena je korištenjem cijelog skupa biljega te polovice skupa biljega. Učinkovitost RR-BLUP modela uspoređena je s učinkovitošću sedam drugih modela za predviđanje svojstava kakvoće. Analiza interakcije genotip × okoliš pokazala je određene zajedničke obrasce za dvije promatrane populacije. Za GPC, WGC i TW dominantan izvor fenotipske varijacije bio je okoliš. Na MPT i MTW dominantan utjecaj imala je interakcija genotip × okoliš u BK i genotip u MG populaciji, dok je na MTI i MPH dominantan učinak imao okoliš u BK i interakcija genotip × okoliš u MG populaciji. Općenito, utjecaj interakcije genotip × okoliš imao je važniju ulogu za svojstva miksografa u odnosu na ostala promatrana svojstva. Analizom AMMI2 biplota utvrđeni su neki široko prilagođeni RIL-ovi. Za sva svojstva utvrđene su uglavnom visoke vrijednosti heritabilnosti. Smanjenje veličine trenažne populacije imalo je negativan učinak na dobivenu točnost predviđanja genomske selekcije za sva promatrana svojstva u obje populacije. Dobiveni rezultati nisu podržali optimizaciju trenažne populacije na temelju fenotipske varijance. Također je primijećeno da točnost predviđanja može značajno varirati između okoliša. Kada se usporedi utjecaj različitih gustoća biljega na sposobnost predviđanja svojstava kakvoće unutar MG populacije, vrijednosti točnosti predviđanja dobivene korištenjem veće gustoće biljega bile su više u svim slučajevima. Za većinu kombinacija svojstvo-okoliš model elastične mreže je rezultirao najnižim vrijednostima točnosti predviđanja. Iako se RR-BLUP nije pokazao najuspješnijim modelom u svim slučajevima, nije uočena značajna prednost korištenja bilo kojeg drugog modela. Točnosti predviđanja dobivene u sklopu ovog istraživanja podržavaju primjenu genomske selekcije za oplemenjivanje pšenice na kakvoću, uključujući i oplemenjivanje na neka svojstva dobivena miksograf uređajem.Wheat (Triticum aestivum L.) is one of the most important crops for food production in the world. The importance of wheat emphasizes the fact that wheat products are the most important source of dietary proteins and energy supply for humans. Therefore, achieving suitable wheat quality is of great importance. Wheat quality is determined by a large number of traits and under the strong environmental influence. One of the most important factors affecting dough rheology is gluten content and its strength. Gluten is the most abundant wheat protein, and by its structure, gluten is a complex network of monomeric gliadins and polymeric subunits of glutenin. Among gluten components, high molecular weight glutenin subunits have the greatest impact on dough quality. Different instruments can be used to perform rheological tests, which are necessary to assess wheat baking quality more accurately. Mixograph is a dough mixer that creates a dough rheological profile, providing general information about dough mixing, its behaviour during development, and the strength of the dough. Due to the small amount of flour required, mixograph has shown to be highly suitable for use in wheat breeding, particularly in early generations when availability of grain and flour is still limited. Since the majority of quality traits have complex inheritance patterns, breeding for improved baking quality is one of the most demanding objectives in wheat breeding. Taking that into account together with often costly and time-consuming phenotyping, predictability of wheat baking quality may be very challenging. The ability to develop a genotype that exhibits both improved performance and high stability of the quality traits is critical to the success of wheat quality improvement. One of the major challenges in plant breeding, in this context, is the occurrence of genotype-by-environment interaction, since its presence makes selecting widely adapted genotypes difficult. The AMMI model is one of the most commonly used methods for the analysis of genotype-by-environment interaction. However, the extensive development of high-throughput genotyping in the last decade has enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection is one of the recently developed methods that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. Genomic selection aims to capture total additive genetic variance based on the sum of the effects of a large number of genetic markers, encompassing all QTLs that contribute to trait variability. In genomic selection, genotypic and phenotypic information of the training population is used to train a model and estimate the marker effects. Obtained data is then applied to the breeding (validation) population of non-phenotyped candidates to estimate their genomic-estimated breeding values (GEBV). The effectiveness of genomic selection is determined by the obtained prediction accuracy, which is affected by a variety of molecular, genetic, and phenotypic factors, as well as the parameters of the selected statistical model. The correct adjustment of factors that can affect prediction accuracy, such as population structure, size of training population, the relatedness of training and validation population, marker density, etc., is the first step toward successful implementation of genomic selection in practical breeding programs. Different prediction models have been developed to solve the problem of high-dimensional datasets occurring in genomic selection. These models differ primarily in their assumptions about the distribution and variance of marker effects, i.e., how marker effects contribute to the trait. Given the often challenging phenotyping for wheat quality traits, and especially for baking quality traits, the use of classical breeding methods can be costly and time-consuming. Determining the optimal model and parameters of genomic selection would enable the use of molecular markers in the pre-selection process for grain quality traits and the optimization of classical wheat breeding methods. This research aimed to assess the impact of genotype-by-environment interaction and optimize genomic selection for grain quality traits using biparental wheat populations, in order to reduce the potential costs of x genotyping and phenotyping in the breeding process and suggest optimal strategies based on genomic selection for more efficient development of new lines. Two biparental populations of winter wheat were used in this study. The BK population was derived from the Bezostaya-1 × Klara cross and the MG population from the Monika × Golubica cross. In the BK combination the parental genotypes differed in all high molecular weight glutenin subunits, while the parental genotypes used in the MG combination did not differ in any of the high molecular weight glutenin subunits. The BK and MG populations consisted of 145 and 175 genotypes, respectively, including parental genotypes. Field trials were conducted for three consecutive years (2009 – 2011) at two locations in Croatia (Osijek and Slavonski Brod), i.e., in six different environments. In each environment the field trial was set up according to a row-column design. Genotyping of both populations was done using Diversity Arrays Technology. After marker filtering the final dataset used for genomic selection contained 1087 and 2231 SNP markers for BK and MG population, respectively. Grain protein content, wet gluten content, test weight, and dough rheology were assessed in each environment. Dough rheology was investigated using a mixograph and four variables were selected for further statistical analysis (MPT, MTW, MTI, and MPH). Genotype-by-environment patterns for the quality and mixograph traits were studied using the AMMI model. The dissection of genotype-by-environment patterns was visualized by a modified version of the AMMI2 biplot, which adds the main effects to the standard AMMI2 biplot using a colour scale. In the first phase of the genomic selection analysis the need for optimization of the training population based on phenotypic variance was assessed using both biparental populations. Additionally, the influence of the training population size and marker density on the prediction accuracy was investigated. For that purpose, three different sizes of training population were used for both BK and MG populations, and two different marker densities for the MG population. The first phase was conducted using only the RR-BLUP model. In the second phase of the genomic selection, the performance of seven different genomic selection models was compared with the performance of the RR-BLUP model. Models included were elastic net, four Bayesian models (BayesA, BayesB, BayesC, and BayesLASSO), random forest, and reproducing kernel Hilbert spaces. This part of the analysis was performed only within the MG population. Results revealed some positive as well as negative transgressive segregants in both populations for all quality traits although being generally more prevalent in the BK population. This may suggest the dispersion of the alleles with positive (increasor) and negative (decreasor) effects between parental genotypes in both crosses. The environment was the dominant source of variation for grain protein content, wet gluten content, and test weight, accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation was trait- and population-dependent. Overall, genotype-by-environment interaction was shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which MG124 is the most interesting, being the prevalent “winner” for grain protein content and wet gluten content, but also the “winner” for non-correlated trait test weight in environment SB10. The broad-sense heritability across environments was high for all traits, except for MPT in the BK population, the heritability of which was 0.45. Although repeatability varied considerably among environments, it was high for most of the trait-environment combinations, with a value above 0.7. These results suggest that heritability itself should not represent a limitation in achieving good prediction accuracy. The results of genomic selection analysis showed that the size of the training population plays an important role in achieving higher prediction accuracies, while marker density does not represent a major limitation. Additionally, the results of the present study did not support the optimization of the training population based on phenotypic variance as a tool to increase prediction accuracy. The performance of eight prediction models was compared and among them, elastic net showed xi the lowest prediction accuracy for all traits. Bayesian models provided slightly higher prediction accuracy than the RR-BLUP model. However, this may be considered negligible considering the time required to perform an analysis. Although RR-BLUP was not the best performing model in all cases, no advantage of using any other model studied in this research was observed. Furthermore, strong differences among environments in terms of the prediction accuracy were observed. For example, the prediction accuracy for TW within the MG population was moderate in one environment, while being low in all other environments. Comparing these results to the results of a genotype-by-environment analysis it is noticeable that environments that are characterized by unusually high or low values for prediction accuracy compared to the rest of the environments tend to be those that produce the greatest genotype-by-environment interaction. This suggests that less predictive environments should be excluded from the dataset used to train the prediction model in order to achieve higher prediction accuracies. The prediction accuracies obtained in this study support implementation of genomic selection in wheat breeding for end-use quality, including some mixograph traits

EFFECT OF EARTHWORM COELOMIC FLUID ON GROWTH OF PHYTOPATHOGENIC FUNGI FUSARIUM OXYSPORUM

Uloga gujavica i mikroorganizama u funkcioniranju ekosustava tla je dobro poznata, kao i postojanje povezanosti i interakcija između gujavica i gljivica u tlu. Međutim, do sada nije istražen utjecaj celomske tekućine i celomocita, imunoloških stanica gujavica, na rast gljivica u tlu. Naime, prilikom negativnog podražaja gujavice u okoliš mogu ispustiti sluz i celomsku tekućinu s celomocitima koja potencijalno može značajno utjecati na rast gljivica. Stoga je glavni cilj ovog rada bio istražiti učinak celomske tekućine gujavica na rast fitopatogenih gljivica. Ekstrakti celomske tekućine dviju različitih vrsta gujavica korišteni su kako bi se utvrdilo njihovo djelovanje na rast fitopatogene gljivice Fusarium oxysporum, iznimno važne vrste u poljoprivrednoj proizvodnji.The role of earthworms and microorganisms in the soil ecosystem functioning is well known, as well as existence of interactions between earthworms and fungi in the soil. However, the impact of coelomic fluid and coelomocytes, immune cells of earthworms, on the growth of the fungi has not been investigated so far. Namely, in case of negative stimuli earthworms can discard mucus and coelomic fluid containing coelomocytes which can potentially have a significant impact on the growth of fungi. Therefore, the main aim of this thesis was to investigate the effects of earthworm coelomic fluid on the growth of phytopathogenic fungi. Coelomic fluid extracts of two different earthworm species was used to determine the effects on the growth of phytopathogenic fungi Fusarium oxysporum, important in agricultural production

An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits

Evaluation of Genomic Selection Methods for Wheat Quality Traits in Biparental Populations Indicates Inclination towards Parsimonious Solutions

Breeding for end-use quality traits is often challenging since their assessment requires larger quantities of grain and flour samples, which are usually not available early in the breeding process. Using the mixograph as a fast and effective method of evaluating dough quality together with genomic selection (GS) can help in pre-selecting high-performing progenies earlier in the breeding process and achieve a higher gain per unit of time and cost. In the present study, the potential of GS to predict seven end-use quality traits, including mixograph traits, in two biparental wheat populations was investigated. Field trials with both populations were conducted at two locations in Croatia (Osijek and Slavonski Brod) over three years. Results showed that the size of the training population (TP) plays an important role in achieving higher prediction accuracies, while marker density is not a major limitation. Additionally, results of the present study did not support the optimization of TP based on phenotypic variance as a tool to increase prediction accuracy. The performance of eight prediction models was compared and among them elastic net showed the lowest prediction accuracy for all traits. Bayesian models provided slightly higher prediction accuracy than the ridge regression best linear unbiased prediction (RR-BLUP) model, which is negligible considering the time required to perform an analysis. Although RR-BLUP was not the best performing model in all cases, no advantage of using any other model studied here was observed. Furthermore, strong differences between environments in terms of the prediction accuracy achieved were observed, suggesting that environments that are less predictive should be removed from the dataset used to train the prediction model. The prediction accuracies obtained in this study support implementation of GS in wheat breeding for end-use quality, including some mixograph traits