DOI: 10.5061/dryad.171q4
title: Data from: Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis

description: Quantitative resistance is likely to be more durable than major gene
 resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier
 studies hypothesized that resistance affecting the degree of host damage,
 as measured by the percentage of leaf area covered by STB lesions, is
 distinct from resistance that affects pathogen reproduction, as measured
 by the density of pycnidia produced within lesions. We tested this
 hypothesis using a collection of 335 elite European winter wheat cultivars
 that was naturally infected by a diverse population of Zymoseptoria
 tritici in a replicated ﬁeld experiment. We used automated image analysis
 (AIA) of 21420 scanned wheat leaves to obtain quantitative measures of
 conditional STB intesity that were precise, objective, and reproducible.
 These measures allowed us to explicitly separate resistance affecting host
 damage from resistance affecting pathogen reproduction, enabling us to
 confirm that these resistance traits are largely independent. The cultivar
 rankings based on host damage were different from the rankings based on
 pathogen reproduction, indicating that the two forms of resistance should
 be considered separately in breeding programs aiming to increase STB
 resistance. We hypothesize that these different forms of resistance are
 under separate genetic control, enabling them to be recombined to form new
 cultivars that are highly resistant to STB. We found a significant
 correlation between rankings based on automated image analysis and
 rankings based on traditional visual scoring, suggesting that image
 analysis can complement conventional measurements of STB resistance, based
 largely on host damage, while enabling a much more precise measure of
 pathogen reproduction. We showed that measures of pathogen reproduction
 early in the growing season were the best predictors of host damage late
 in the growing season, illustrating the importance of breeding for
 resistance that reduces pathogen reproduction in order to minimize yield
 losses caused by STB. These data can already be used by breeding programs
 to choose wheat cultivars that are broadly resistant to naturally diverse
 Z. tritici populations according to the different classes of resistance.
description: Data on quantitative
 resistance of wheat to Septoria tritici blotchData consists of 10
 columns, first row shows column names. Each subsequent row corresponds to
 data from an individual leaf. Column 1 - leaf index, column 2 - leaf
 label, column 3 - leaf area in mm2, column 4 - area covered by necrotic
 tissue in mm2, column 5 - percentage of leaf area covered by lesions
 (PLACL), column 6 - number of pycnidia on the leaf, column 7 - mean area
 of pycnidia on the leaf in mm2, column 8 - number of pycnidia per cm2
 leaf, column 9 - number of pycnidia per cm2 lesion, column 10 - pycnidia
 grey value. Leaf label in column 2 uniquely identifies each leaf in the
 collection. It consists of three parts divided by underscore symbols
 "_". First part describes the time point of collection
 ("c1" - collection t1, 25 May 2016; "c3" - collection
 t2, 4 July, 2016). Second part is the sowing number that uniquely
 identifies the small wheat plot planted with a specific wheat cultivar.
 Third part is the index of a leaf within a specific plot. For example,
 leaf with the label "c1_sn133_7" comes from collection t1,
 sowing number 133, leaf index 7.Output_c1_c3.csvCorrespondence between sowing numbers and cultivar namesColumn 1 - cultivar name, column 2 - corresponding sowing number. Sowing numbers in the range 1-360 belong to Lot 4 (replicate 1), sowing numbers in the range 433-792 belong to Lot 3 (replicate 2).cultivars_sowingnumbers.csvVisual disease scores and values of the area under disease progress curve (AUDPC)Column 1 - cultivar name; column 2 - genotype ID number; columns 3--6 - visual scores of STB severity, averaged over two replicates (assumed to be 0 at day 0, first scoring assigned to day 14); column 7 - AUDPC calculated from the visual scoring; column 8 - normalized AUDPC (divided by average AUDPC: 103.5).2018_Karisto_et_al_Cultivar_rankings_AUDPC_data.xlsx