Pyrenophora teres and Rhynchosporium secalis infections in malt barley as influenced by genotype, spatial and temporal effects and nitrogen fertilization

Net form net blotch (NFNB) and barley leaf scald are among the most important barley diseases worldwide and particularly in Greece. Their occurrence in malt barley can exert a significant negative effect on malt barley grain yield and quality. An experimental trial across two growing seasons was implemented in Greece in order i) to assess the epidemiology of NFNB and leaf scald in a barley disease free area when the initial inoculation of field occurs through infected seeds, and ii) to further explore the relationship among nitrogen rate, grain yield, quality variables (i.e. grain protein content and grain size) and disease severity and epidemiology. It was demonstrated that both NFNB and leaf scald can be carried over from one season to the next on infected seed under Mediterranean conditions. However, disease severity was more pronounced after barley tillering phase when soil had been successfully inoculated first. When nitrogen rate and genotype were the main sources of variation the epidemiology assessment was implemented with hotspot and Anselin Local Moran’s I analysis. It was found that the location of hotspots was modified during growing season. Soil and plant variables were assessed for the explanation of this variability. According to commonality analysis the effect of distance from the locations with the highest disease infections was a better predictor of disease severity (for both diseases) compared to nitrogen rate during pre-anthesis period. However, disease severity after anthesis was best explained by nitrogen rate only for the most susceptible cultivars to NFNB. The effect of disease infections on yield, grain size and grain protein content varied in relation to genotype, pathogen and stage of crop development. The importance of crop residues on the evolution of both diseases was also highlighted.

quality. An experimental trial across two growing seasons was implemented in Greece 26 in order i) to assess the epidemiology of NFNB and leaf scald in a barley disease free 27 area when the initial inoculation of field occurs through infected seeds, and ii) to 28 further explore the relationship among nitrogen rate, grain yield, quality variables (i.e. 29 grain protein content and grain size) and disease severity and epidemiology. It was 30 demonstrated that both NFNB and leaf scald can be carried over from one season to 31 the next on infected seed under Mediterranean conditions. However, disease severity 32 was more pronounced after barley tillering phase when soil had been successfully 33 inoculated first. When nitrogen rate and genotype were the main sources of variation 34 the epidemiology assessment was implemented with hotspot and Anselin Local 35 Moran's I analysis. It was found that the location of hotspots was modified during 36 growing season. Soil and plant variables were assessed for the explanation of this 37 variability. According to commonality analysis the effect of distance from the Introduction 50 Barley (Hordeum vulgare L) is one of the leading cereal crops of the world and it is 51 clearly number two in Europe in terms of cultivated acreage, next to bread wheat 52 (Triticum aestivum L.) [1]. According to Meussdoerffer and Zarnkow [2], barley is 53 the major source for brewing malts, which constitute the single most important raw 54 material for beer production. Pyrenophora teres f. teres an ascomycete that causes the Nitrogen fertilizer rate plays a major role in malt barley by affecting to a great extent 73 the final yields, grain protein content (that has to be maintained below a threshold of 74 11.5-12.0% depending on brewing industry), as well as the susceptibility to leaf 75 diseases. More nitrogen can increase the yield of malt barley [18][19][20][21], but can also 76 exert an adverse effect on quality by increasing grain protein content [14,[22][23][24]. In 77 addition, high nitrogen rates can also increase the susceptibility of barley to leaf 78 diseases [13,[25][26][27][28]. Therefore, understanding the relationship among nitrogen rate, 79 grain yield, quality variables and leaf disease infections can be very useful to further 80 raising yield and to maintain the quality at a level that meets the requirements of malt 81 industry.

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In this study we aimed, i) to estimate the epidemiology of NFNB and leaf scald in a 83 barley disease free area when the initial inoculation of the field occurs through 84 infected seeds, and ii) to further explore the relationship among nitrogen rate, grain 85 yield, quality variables (i.e. grain protein content and grain size) and disease severity 86 and epidemiology .  conductivity (Ec) 0.29 mmhos cm -1 , total N (Kjeldahl) 0.105%, available P (Olsen) 106 52.84 ppm and 452 ppm exchangeable K.

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In Exp1 the treatments consisted of 5 five malt barley cultivars as stated above. The 108 experimental design was a randomized complete block design with 9 replications (in 109 order to have a better spatial distribution of the selected genotypes) per genotype.   leaf; 9=up to flag leaf) and D3 is the extent of leaf area affected by disease (i.e.

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The area under disease progress curve (AUDPC) was calculated by following the spatially. Hotspot analysis uses the Getis-Ord local statistic given as: Where x j is the disease severity value for experimental plot j, w i,j is the spatial weight 188 between experimental plot i and j, n is the total number of experimental plots and  Local Moran's I is given as: Where x i is an attribute for feature I, is the mean of the corresponding attribute, w i,j 219 is the spatial weight between feature I and j, and:

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NFNB occurred at all developmental stages and in both experiments, whereas leaf 251 scald was consistently observed after the onset of stem elongation phase (Fig 2).

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Although disease severity tended to be higher in Exp1 (disease dispersal from infected with leaf scald. In general, infections by NFNB were more severe compared to those 257 by leaf scald, during all tested developmental phases of malt barley (Fig 2).  Table 1). The only variable that was significantly affected by the rate of 273 applied nitrogen was grain protein content (Table 1). With the exception of Zhana (i.e.

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it was the only cultivar that was infected with Rhynchosporium secalis) an increased 275 disease severity generally resulted in higher grain protein content. However, it was 276 recorded a genotypic variation among the studied cultivars concerning their response 277 to increased disease severity (Fig 5).  Grain yield was significantly affected by cultivar and by the interaction cultivar x 291 nitrogen (Table 1), and varied from 0.84 to 4.26 t ha −1 . Grace and Traveler were the 292 only cultivars that presented significant relationships between grain yield and disease 293 severity (Fig 6). In particular, Traveler recorded a marginal statistically significant 294 negative relationship between grain yield and disease severity, only for the period of 295 tillering (Fig 6). Concerning Grace, grain yield showed a negative significant direct 296 relationship to disease severity for the period of grain filling (milk development) and 297 on the contrary, presented a moderate positive association to disease severity for the 298 period of tillering phase (Fig 6). The proportion of maltable grain size fraction (% grains > 2.2 mm), as well as disease 306 severity during stem elongation and grain filling phases were not significantly 307 affected by the rate of applied nitrogen (Table 1). A negative, but not significant, 308 association was recorded between the proportion of maltable grain size fraction and 309 disease severity for all the studied cultivars (Fig 5).

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The area under disease progress curve (AUDPC) 312 The area under disease progress curve (AUDPC) in Exp2 was not significantly 313 affected either by nitrogen rate or the interaction cultivar x nitrogen (Table 1). 314 However, the analysis of variance for AUDPC indicated that a significant degree of Grace presented the highest values in Exp1 and Exp2, respectively (Fig 3).  nitrogen rate of 100 and 140 kg/ha, respectively (Fig 7). A further investigation 342 revealed that the distance of Traveler experimental plots from the previous season 343 crop residues (i.e. the sites with Grace) explained 34% of the variation in disease 344 severity (Fig 8). RGT Planet with nitrogen rate of 100 kg/ha, was also marked as a 345 hotspot, but less intense since it presents a lower z score (Fig 7). It is reminded that   Planet in the western side) determined with Getis-Ord G* statistic (Fig 7).

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Two Grace plots with 140 kg N /ha were identified as hot spots of highest z scores 373 during milk development and followed by RGT Planet without nitrogen application.

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The Local Moran's I spatial analysis again identified two Zhana plots (i.e. nitrogen 375 rate 0 and 100 kg/ha) as spatial outliers, since they presented low disease severity in a 376 neighborhood of high values (Fig 7).  (Table 2). Examining the 389 unique effects, it was found, that for the period of stem elongation phase, the distance 390 from the nearest hotspot (m) was the best predictor of disease severity for all the study 391 cultivars, uniquely explaining from 16.8 to 45.5 of its variation. This amount of 392 variance represented from 38.76 to 97.65% of the R 2 effect (Table 2). On the contrary, 393 during the onset of grain filling phase the variation in disease severity was best 394 explained by either the nitrogen rate (i.e. Traveler and Grace) or the distance from the 395 nearest hotspot (m) (i.e. RGT Planet and Zhana) ( Table 2).  severity and grain yield when the main source of variation was nitrogen rate (Fig 6). while the survival and further growth of tillers and spikelets is largely determined 421 from stem elongation onwards. Accordingly, our results showed that the highest 422 disease severity, which was recorded in Traveler during tillering phase (Fig 2), 423 exerted a more pronounced negative effect on grain yield (Fig 6). In addition, the 424 higher disease severity in Grace compared to the rest of the studied cultivars during 425 the onset of grain filling phase (Fig 2), led to a significant reduction in grain yield, showed that disease severity for both pathogens tended to increase from anthesis 438 onwards by increasing the rate of applied nitrogen (Fig 4) less dilution of the protein in the grain.

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The epidemiology assessment of both diseases, when nitrogen rate and genotype were 456 the main sources of variation, was implemented with hotspot and Anselin Local 457 Moran's I analysis. The location of hotspots was modified during the growing season 458 (Fig 7). This can be explained either by the soil heterogeneity or by the spatial    On the other hand, Zhana was the only cultivar which was not infected by NFNB 482 during neither seasons (i.e. it was infected only by Rhynchosporium secalis). 483 However, it was found that the distance of Zhana experimental plots from the 484 previous season crop residues (i.e. the sites with Zhana) explained 51% of the 16 485 variation in disease severity (Fig 8). This result is also supported by the Anselin Local 486 Moran's I spatial statistical analysis. Zhana was considered an outlier due to lower 487 disease severity values although surrounded by plots with high values from stem 488 elongation onwards (Fig 7). 489 The late occurrence of Rhynchosporium secalis symptoms on Zhana compared to 490 NFNB (Fig 2) during both experiments, can be possibly attributed to its specific life   However, disease severity was more pronounced after barley tillering phase when soil 507 had been successfully enriched first with the pathogen propagules. When both plant 508 pathogens were present in soil residues, it was shown that the effect of the distance of 509 cultivars from hotspots (i.e. the locations with the highest disease infections) was a 510 better predictor of disease severity (for both diseases) compared to nitrogen rate 511 during the pre-anthesis period. However, after anthesis disease severity was best 512 explained by nitrogen rate concerning the most susceptible cultivars to NFNB. In