*2.2. Disease Assessment*

A slight modification (i.e., we integrated the percentage of diseased plants in each plot; D1) of the widely used [36–38] equation proposed by Saari and Prescott [39] was adopted to estimate disease severity (DS) during the phenological stages of tillering, stem elongation and milk development:

$$\text{DS } (\%) = (\text{D1}/100) \times (\text{D2}/\Theta) \times (\text{D3}/\Theta) \times 100 \tag{1}$$

where D1 is the percentage of diseased plants in each plot, D2 is the height of infection (i.e., 1 = the lowest leaf; 2 = the second leaf from base; 3–4 = the second leaf up to below the middle of the plant; 5 = up to the middle of the plant; 6–8 = from the center of the plant to below the flag leaf; and 9 = up to the flag leaf) and D3 is the extent of leaf area affected by disease (i.e., 1 = 10% coverage to 9 = 90% coverage).

The area under disease progress curve (AUDPC) was calculated by following the formula given by Shaner and Finney [40]:

$$ALIDPC = \sum\_{i=-1}^{n-1} \left[ \left| \left( \mathbf{y}\_i + \mathbf{y}\_{(i+1)} \right) / / 2 \right| \times \left( t\_{(i+1)} - t\_i \right) \right] \tag{2}$$

where *Yi* = the disease level at time *ti*, (*<sup>t</sup>*(*i*+1) – *ti*) is the interval between two consecutive assessments and n is the total number of assessments.

Barley varieties were naturally infected by both diseases. The pathogens were further identified in the lab [4].

#### *2.3. Yield and Malt Character Measurements*

At maturity, grain yield estimation was based on an area of 1 m<sup>2</sup> per plot. The grain size was determined by size fractionation using a Sortimat (Pfeuffer GmbH, Kitzingen, Germany) machine, according to the 3.11.1 Analytica EBC "Sieving Test for Barley" method (Analytica EBC, 1998). The nitrogen content was determined by the Kjeldhal method, and the protein content was calculated by multiplying the N content by a factor of 6.25, as described by Vahamidis et al. [41].

#### *2.4. Spatial Statistical Analysis*

Using the geographical coordinates of the experimental plots, ArcGIS 10 was used to explore the spatial associations, based on autocorrelation indices, of the disease severity among the experimental plots during the different developmental stages. Global autocorrelation indices, such as Moran's I, assess the overall pattern of the data and sometimes fail to examine patterns at a more local scale [42]. Thus, aiming at deepening our knowledge on spatial associations, local autocorrelation indices were used to compare local to global conditions. In this framework, hotspot analysis was used to identify statistically significant clusters of high values (hotspots) and low values (cold spots) using the Getis–Ord Gi statistic. Anselin Local Moran's I was used to identify spatial clusters with attribute values similar in magnitude and specify spatial outliers.

In order to further explore the relationship between crop residues and disease severity, the distance from the crop residues of the previous season (2014/2015) to the location of the experimental plots of the investigated growing season (2015/2016) were calculated (concerning Zhana, it was the only cultivar that was infected by *Rhynchosporium secalis*, and Grace was the cultivar with the highest infection by *Pyrenophora teres* f. *teres*).
