2017 Final Report to Ceres Trust for the project:

Value-added Einkorn for Organic Production in the Great Plains Region

submitted by

Abdullah Jaradat USDA North Central Soil Conservation Research Laboratory Morris, MN,

Dipak Santra UNL Panhandle Research Extension Center Scottsbluff, NE,

Steve Zwinger NDSU Carrington Research Extension Center Carrington, ND,

Joel Ransom NDSU Extension Fargo, ND,

Frank Kutka Northern Plains Sustainable Agriculture Society Lamoure, ND

INTRODUCTION:

The purpose of this project was to identify einkorn accessions with desirable traits for performance within organic farming and marketing systems in the Northern Great Plains. Einkorn (Triticum monococcum), a wheat species with hulls like barley, was cultivated thousands of years before the development of modern wheat. However, there is a renewed interest in einkorn because of its apparent salinity and other stress tolerance, its high nutritional quality, and its usually impressive flavor. Finding superior varieties of high value crops, such as einkorn, is a key approach to improve nutritional quality for consumer health and to improve farm profitability and soil health across the Great Plains region. Given the relatively limited experience with einkorn in our region and low number of varieties currently available, extensive evaluation of varieties was required in order to find those that might provide real benefits to both organic farmers and consumers. This report summarizes our study of agronomic traits, nutritional traits, and salinity tolerance of einkorn accessions selected from the collection deposited at the USDA National Small Grains Collection, Aberdeen, Idaho and several available in the specialty seed trade.

Figure 1. Einkorn grown organically in Carrington, North Dakota in 2014.

ACTIVITIES AND OUTCOMES:

Objective 1: Evaluate einkorn germplasm for adaptability to organic management

Replicated field evaluation of einkorn accessions Our initial work in 2014 was a non-replicated screen of 100 accessions and varieties of einkorn at our three research locations (Scottsbluff, Morris, Carrington). Most of these were originally obtained from the USDA National Plant Germplasm System and increased previously as part of other research at the Carrington Research Extension Center. Several others were purchased from specialty seed houses in the USA and Canada that were selling small lots of einkorn for gardeners. After considering the initial data from the non- replicated screen, we identified nineteen accessions that appeared worthy of more intensive evaluation to see if our first impressions would hold up, and if some of the less obvious traits like higher nutritional quality might be present. Given the experience of specialty wheat research at the Carrington Research Extension Center, we chose the variety “TM23” (PI 355523) to be used as a check variety. TM23 has been getting some use by NPSAS Farm Breeding Club members who are increasing the seeds for larger scale production. In a change to our initial plan, we increased the seed of the twenty lines of einkorn at the NDSU Carrington Research Extension Center in 2015. Seeds for field evaluations were sent to the USDA North Central Soil Conservation Research Laboratory in Morris, MN and to the University of Nebraska Panhandle Research and Extension Center in Scottsbluff in 2016 and 2017. Four replicates of each line were planted out in an alpha design at each of these locations each spring. An Alpha design uses incomplete blocks as factors in order to better account for soil and other variation across the experiment. When the effect of these incomplete blocks is insignificant or small compared to the effect of complete replicates with all of the experimental varieties, the Alpha design conveniently reverts to the randomized complete block design (RCBD) with which most agronomists are familiar. Plot sizes were approximately 5’ x 25’ and four replicates were planted at each location each year at appropriate times for spring wheat establishment. In 2016 plugging in the plot planter resulted in mixed stands at the Nebraska location. The drought of 2017 also severely limited growth at Scottsbluff, and yields for most plots were scarcely more than a few bushels per acre. Therefore, the yield data analyzed and presented here were solely from Carrington, ND and Morris, MN. With twenty experimental entries (nineteen accessions and a check variety), four replicates, two locations and two years of evaluations, we ended up with three hundred and twenty total observations. As we did not find large differences between the results of the RCBD and the alpha design, for simplicity we present here the statistical analysis with the RCBD. The analysis of variance (ANOVA) of the RCBD used a simple model with treatments, replicates within environments, environments (site-years), and treatment by environment interaction as factors. For the grain yield data, the overall model had an F value of 13.41 and was significant at the p<0.001 level (Table 1), so we reject the null hypothesis that these factors do not have an influence on einkorn yield. Each of the factors in the model also had F values that were significant at the p>0.01 level given this large data set (Table 1). This suggests that observations were different among the four site-years, among the replicates within each of those environments, among the einkorn accessions, and that performance of these accessions interacted with the environments. This sort of interaction can make it more difficult to identify lines that are clearly superior across the region (broad adaptation), but it can still be possible to identify those that are superior at specific locations if the differences are striking (local adaptation).

Table 1. Results of the analysis of variance (ANOVA) for a study of nineteen einkorn accessions and one experimental check variety conducted in Carrington, ND and Morris, MN in 2016 and 2017. Source DF Sum of Squares Mean Square F Value Pr>F Model 91 101616395.2 1116663.7 13.41 <0.0001 Env 3 86694989.18 28898329.73 347.04 <0.0001 Rep (Env) 12 3260312.31 271692.69 3.26 0.0002 Var 19 3966861.44 208782.18 2.51 0.0007 Env*Var 57 7694232.27 134986.53 1.62 0.0071 Error 228 18985749.0 83270.8 Corrected Total 319 120602144.2 Average yields for the nineteen accessions and check variety were largely similar (Table 2). When the yield data were ranked or standardized to percent of the average replicate yield, there were also significant differences among the einkorn lines (Table 2) and there was also significant interaction between the accessions and the environment. Ranking and other standardization techniques can be used to control or eliminate some environmental variation. These do not provide predictions of likely grain yield, but can give an indication of the likelihood of a variety ranking well or yielding better than average compared to other varieties. However, there was still enough variation in ranks to produce a good deal of noise in the data set and prevent the clear identification of a few superior varieties. The same held true when the data were standardized to replicate averages.

Table 2. Average grain yield of nineteen einkorn accessions and one experimental check variety at Carrington, ND and Morris, MN in 2016 and 2017. Results are shown as kg/ha, ranked within replicates, and percent yield within replicates. An ANOVA was performed on the overall data so least significant differences are shown for those data. The best performer overall and at each location is highlighted in yellow. Those accessions that differ from the best performer using the LSD estimates are highlighted in red.

Overall Carrington, ND Morris, MN Accession Yield Rank % Mean Yield Rank % Mean Yield Rank % Mean 94743 2495 10.8 100.1 1973 11.2 98.3 3018 10.4 101.9 119422 2408 11 95.2 1772 13.3 87.7 3044 8.8 102.8 119423 2513 9.4 102 2119 7.8 105.2 2907 11.1 98.8 167526 2377 13.3 95.3 1892 13.2 94 2861 13.4 96.6 170196 2552 10 102.3 1996 12 99.6 3108 8.1 105 191381 2633 8.1 105.3 2076 8.6 103.4 3191 7.7 107.3 237659 2586 7.9 104 2111 7.7 105 3062 8.3 103 254195 2505 10.8 101.4 2043 11 102 2968 10.5 101 326317 2533 9.5 103.2 2151 8.3 107.5 2916 10.8 99 428149 2702 6.6 108.4 2132 8 106.6 3273 5.4 110.3 428150 2470 10.9 99.4 2026 9.5 100.6 2915 12.3 98.4 428155 2508 9.7 100.7 2027 10.2 100.9 2990 9.3 100.5 428156 2300 15.5 90.7 1684 17.7 83.7 2916 13.4 97.8 428158 2415 11.9 96.6 1843 14.4 92.1 2988 9.4 101.3 428163 2533 8.6 104 2240 5.8 111.6 2827 11.4 96.5 428164 2490 9.6 101.7 2159 6.2 107.6 2821 13.1 95.9 428171 2447 11.8 99.1 1993 12.7 99.7 2902 10.9 98.5 428172 2172 16.2 88.4 1854 14.7 92.4 2490 17.8 84.5 428173 2514 9.6 101 2008 10 100 3020 9.2 102.1 TM23 2503 9.2 101.2 2069 8.8 102.6 2937 9.7 99.8 Average 2483 10.5 100 2008 10.6 100 2958 10.56 100 LSD 0.05 402 7.3 17.5

Accession PI 428149 had the highest yield of grain, the lowest average rank, and the highest percentage of average yields in the overall results (Table 2). Least significant difference tests show that the means of two other accessions (PI 428156 and PI 428172) were significantly lower, while the others were not significantly different. PI 428149 was also ranked the best at Morris, but not at Carrington. Differences in average ranks and percent average yield for each variety between the two locations show the effect of the interaction of environments with varieties. Average yields were also higher at Morris than at Carrington in 2016 and especially 2017 (data not shown). Results from Carrington alone show clearly the shifting of ranks and performance among the einkorn accessions year by year (Tables 3 and 4). TM23 was the top yielder in 2016, but was lower than many of the experimental accessions in 2017. LSD results also show that most accessions did not yield differently from the highest yielder in either year. There was a fairly tight range in heading date and plant height for these lines. However, there was an impressive range in lodging ratings. Lodging ratings help to suggest a smaller

Table 3. Results of the 2017 evaluation of einkorn at Carrington, ND.

number of accessions that are likely to succeed in commercial production. TM23, our experimental check, still looks like a very good variety across the data set given its steady yields and standing ability that are not significantly different from the best performers (Tables 2 and 3). Accession PI 237659 also stood well and tended to be an above average yielder. PI 428150 and PI 428155 stood well and yielded near overall and location averages, while PI 428149 and PI 428173 exhibited average lodging ratings with average to above average yields. Lodging ratings were very different in Carrington in 2016 (Table 4), where a hailstorm caused some damage. PI 428150 was still one of the best yielding and standing lines, although its seedling vigor was low. Only PI 326317 had significantly better seedling vigor than TM23, and five accessions had significantly higher test weights, a potentially useful trait for hulled wheat varieties.

Table 4. Results of the 2016 evaluation of einkorn at Carrington, ND.

Participatory evaluation of einkorn accessions The purpose of this activity was to give growers, researchers and other stakeholders a chance to participate in the selection of einkorn lines being considered for release as new varieties. There are many factors that can be considered when selecting a line or accession for release as a variety. High yield and yield stability are two of the primary criteria that are used in this process. These data are collected from yield trials conducted in a number of environments usually by plant breeders or agronomists (as above). In addition to yield there are a number of characteristics that are preferred by growers in the varieties that they grow. Characters such as lodging resistance, maturity length, plant height, ease of threshing or processing, early season vigor, and taste are valued by growers (and end- users in the case of taste), even when they may not directly impact yield. Therefore, the input of growers and other stakeholders was sought to supplement field performance data being collected from trials being conducted by researchers in order to improve the likelihood that any released material will meet the varied requirements of potential growers and other stakeholders. For participatory evaluations we used the replicated small plot evaluations of the 19 einkorn accessions and the standard check variety, TM23, that had been planted at the USDA Research Center near Morris, Minnesota, near the University of Nebraska’s Panhandle Research and Extension Center in Scottsbluff, Nebraska, and the Carrington Research Extension Center in North Dakota. These were the same small plots that were established to collect agronomic data described above. All locations were grown under organic conditions. As noted above, problems with a planter and the drought of 2017 prevented us from making use of the Nebraska site for this evaluation, although it was still used in field events with farmers to exhibit this wheat species. Plant growth was relatively good at the other two locations in both years so evaluations did take place at Morris, MN and Carrington, ND. However, drought conditions and periods of warmer than normal temperatures earlier in the season reduced plant height and yield potential in 2017. Lodging was not observed to be a problem in any of the plots and therefore was not a criterion used in the evaluation in 2017. There was almost no observed foliar disease development, so difference in disease resistance could not be evaluated either. Instead, plant height, maturity, and yield potential were rated. Visual evaluations were conducted during grain filling in July of both years. All plots were still green and even the earliest maturing accessions were a few weeks away from maturity and harvest. Evaluators were farmers or other stakeholders with an interest in einkorn production and utilization. These participants responded to an invitation to evaluate these materials and were therefore knowledgeable about the potential of the crop, but none had actually grown it commercially. In 2017 four individuals evaluated the plots in Morris, but only the first and second replications were evaluated. At Carrington in 2017, four attendees evaluated all four replications. Prior to evaluating the plots, the participants were told the purpose of the activity and a rating scale was agreed upon. Basically plots were rated on a scale of 1-5, with one being the best. Most evaluators had several lines that were given a number 1 ranking. The frequency of number 1 ratings, was tallied across the various evaluators and in the case of Carrington, across replications. The top five entries (those with the most number 1 scores) are summarized in Tables 5 and 6. Reason given by the evaluators on why lines were not ranked highly included: small spike size, too early or too late, too tall, and lack of uniformity. Since these comments were not consistent across reps and evaluators, they have not been summarized, but indicate characteristics that the evaluators considered important. Yield and yield stability will no doubt be primary criteria used for the release of a new line. Nevertheless, the data collected from this participatory selection process can be used to assist in the final decision of which lines to release. There was only one line, 428156, that was ranked in the top five at both locations in 2017. All of the top five rated lines in Carrington were also given a number one rating by at least one evaluator in Morris, while only three lines of the top five in Morris were rated number one in Carrington.

Table 5. Top ranked einkorn accessions based on three visual characteristics at early grain filling, Carrington, 2017. Accession Times ranked #1 at Carrington Times ranked #1 at Morris 428149 10 2 119422 5 2 94743 4 1 TM23 4 2 428156 3 3

Table 6. Top ranked einkorn accessions based on three visual characteristics at early grain filling, Morris, 2017. Accession Times ranked #1 at Morris Times ranked #1 at Carrington 428155 4 0 428150 4 0 167526 4 1 428156 3 3 428173 3 2

In 2016 five individuals attended the evaluation in Morris, but only one filled out a data form. Thirteen attended the evaluation in Carrington and seven data forms were filled out. While the rating scale was reversed in 2016, it is still clear that there were several lines that scored very well in 2016 (Table 7). PI 428150 and PI 428156 were at the top of the ratings at both locations in 2016, and were also well considered in 2017. However, PI 428156 did not turn out to be a high yielder in the evaluations above.

Table 7. Average overall visual characteristic scores (lodging, height, maturity, yield potential) for einkorn accessions at early grain filling in 2016. All scores were on a 1-5 scale with 5 being the best. Those highlighted in yellow were not significantly different from the best score. Accession Carrington Morris Combined 119422 3.43 3.47 3.45 119423 3.34 2.88 3.11 167526 3.41 3.63 3.52 170196 3.83 3.78 3.81 191381 3.59 3.44 3.51 237659 3.38 3.06 3.22 254195 3.31 3.50 3.40 326317 3.60 2.56 3.08 428149 2.81 3.00 2.90 428150 4.53 3.63 4.08 428155 3.60 3.94 3.77 428156 4.42 4.00 4.21 428158 3.43 3.23 3.33 428163 3.64 3.38 3.51 428164 3.70 3.13 3.41 428171 3.47 3.38 3.42 428172 3.42 3.28 3.35 428173 3.54 3.59 3.57 94743 3.40 3.31 3.36 TM23 3.43 3.19 3.31 Mean 3.56 3.37 3.47 LSD 0.05 0.43 0.54 0.53

Figure 2. Evaluators rating einkorn lines at Carrington in 2017.

Figure 3. Evaluators rating einkorn lines at Morris, MN in 2017.

Figure 4. Evaluation team conferring prior to evaluating einkorn lines at Morris, MN in 2017.

Objective 2: Identify einkorn accessions with salinity tolerance

Growth in saline conditions and sodium exclusion from plant tissues Hydroponic growth chamber studies and tissue analyses of salt tolerance and sodium/potassium were carried out at the North Central Soil Conservation Research Laboratory. Einkorn plants were grown to the four-leaf stage, and mineral content of each tissue sample was determined with inductively coupled plasma mass spectrometry (ICP). Results for the twenty accessions show general, but still variable, tolerance to sodium (Table 8). PI 94743 was the most tolerant of all the experimental lines, but like the other very tolerant lines, was not an especially notable performer in the agronomic evaluations above. The experimental accessions and our check TM23 that had shown good yields did also all show good salinity tolerance. Field testing of salinity tolerance and agronomic performance under organic management could be especially useful.

Table 8. Salinity tolerance ratings for nineteen einkorn accessions and one experimental check. The salinity tolerance index is the proportion of tissue growth under saline conditions to that observed under normal conditions. Higher numbers show increasing salinity tolerance. The potassium to sodium ratios in plant tissues also show higher salinity tolerance when numbers are higher. Accession Sal. Index K/Na Leaf K/Na Root 94743 1 149.9 47.6 119422 0.53 95.5 20.5 119423 0.76 121 19.2 167526 0.5 132 37.6 170196 0.7 146.8 38.1 191381 0.57 138.8 49 237659 0.9 98.5 26.1 254195 0.65 123.7 35 326317 0.63 115.5 36.5 428149 0.43 68 33.1 428150 0.67 99.7 36.1 428155 0.57 121 28 428156 0.73 103.1 25.9 428158 0.65 101.2 33.5 428163 0.83 81.2 31.2 428164 0.77 84.4 29.5 428171 0.37 49.3 30 428172 0.8 80.2 38.2 428173 0.6 74.6 31.1 TM23 0.57 100.1 33.6

Marker study for know sodium tolerance genes We also used polymerase chain reaction (PCR) and primers to search for markers of the Na+ tolerance genes Nax1 and Nax2 (provided to us by Richard James of CSIRO in Australia) to better understand the inheritance of this trait in einkorn. A follow-up PCR screen was run on the original 100 lines to verify the findings and determine if the lines are homozygous or not. Researchers in Australia discovered two genes in durum which originated within einkorn’s (Triticum monococcum) genome, Nax1 and Nax2; these genes confer salt tolerance to the roots and leaves, selectively removing sodium, resulting in very low rates of Na+ accumulation in leaves (Munns et al., 2000; Munns et al., 2012; James et al., 2011; Munns & Filmer, 2007; James et al., 2006). Yields under salt stress increased by 25% or more when one or both of these genes are present (Munns et al., 2012). A marker diagnostic kit for the Nax genes in wheat was developed (Munns & James, 2012) and shared with the NPSAS FBC, with expressed interest in our project’s PCR results. One hundred Einkorn wheat lines were grown in 4-inch pots filled with Sunshine Mix 2 Basic Professional Growing Mix (Westco, Morill, NE, USA) in a greenhouse at the University of Nebraska, Panhandle Research and Extension Center, Scottsbluff. Day length was set to 16 hours with the greenhouse temperature at 270C ± 20C. Leaf tissues of 21 days old plants were used for DNA extraction (Doyle and Doyle, 1987). DNA quality and quantity were checked on 0.8% agarose gel using 0.5XTBE buffer and visualized under UV transillumination. Concentration of DNA in each sample was determined comparing intensity of bands of Lambda/HindIII DNA markers. PCR consisted of 10 ul reaction volume containing 1x reaction buffer, 1 U Taq polymerase, 200 uM dNTPs, 2 mM MgCl2, 5 pmoles primers, 20 ng DNA. PCR cycle for both Nax1 and Nax2 genes was according to report of Munns & James (2012): initial denaturation of 15 min at 950 C; followed by 5 cycles of 1 min at 940 C, 1 min at 580 C, and 1 min at 720 C, and 30 cycles of 30 s at 94oC, 30 s at 58oC and 50 s at 72oC. PCR was done using BioRad S1000 Thermal cycler. PCR amplified products were separated in 2% agarose gel using 0.5XTBE buffer and visualized under UV transillumination after staining with ethidium bromide @0.5 ug/ml. DNA standard markers (>50 bp) were scored manually using gel image and band size was determined considering 25 bp size difference between the bands. The markers for Nax1 and Nax2, xgwm312 and cslinkNax2, respectively, were scored manually using gel images (Fig. 5, 6, 7).

Figure 5. Xgwm312 marker profile of 95 einkorn accessions for Nax1. The number on each lane corresponds to the PI accession numbers in Table 9 below. Lane B is a blank check. M=DNA size marker (NEB 50 bp ladder).

Figure 6. cslinkNax2 marker profile of 95 einkorn accessions for Nax2. The number on each lane corresponds to the PI accession numbers in Table 9 below. Lane B is a blank check. M=DNA size marker (NEB 50 bp ladder).

Figure 7. cslinkNax2 and Xgwm312 marker profile of five einkorn accessions (numbers 96- 100) for Nax2 and Nax1, respectively. Proso millet varieties Huntsman (H), Rise (R), Minsum (Mi) were used as negative checks. B=Blank Check. Mr=NEB 50 bp ladder.

Genotypes for the one hundred accessions were generally the same. We observed that 95 lines had both Nax1 and Nax2 genes, PI 94743 (line 96) has only Nax1, and PI 428152 (line 72) from Bulgaria has only Nax2 (Table 9). Three lines, PI 428175 (line 10) from Turkey, PI 237659 from Kenya (line 81), and PI 428153 (line 82) from United States, do not carry either of these two salt tolerance genes. These are impressive results given that our salinity tolerance studies show variation among tested lines, and there is the potential for other salt tolerance genes to be present. Notably, our most tolerant line PI 94743 had only one salt tolerance gene and PI 237659 had none but still appeared to be fairly salt tolerant. Table 9. Genotypes of 100 Einkorn wheat lines for Nax1 and Nax2 genes based on diagnostic DNA markers Xgwm312 and cslinkNax2 (Munns & James, 2012). Entry Accession COUNTRY Xgwm Nax cslink Nax Nax genotype Number 312 1 Nax2 2 1 191097 Spain 199 + 171 + Nax1Nax2 2 377670 Former Yugoslavia 199 + 171 + Nax1Nax2 3 428155 United Kingdom 199 + 171 + Nax1Nax2 4 352483 Spain 199 + 171 + Nax1Nax2 5 119422 Turkey 199 + 171 + Nax1Nax2 6 10474 Germany 199 + 171 + Nax1Nax2 7 167627 Turkey 199 + 171 + Nax1Nax2 8 266844 United Kingdom 199 + 171 + Nax1Nax2 9 428165 Turkey 199 + 171 + Nax1Nax2 10 428175 Turkey Absent - Absent - nax1nax2 11 191098 Spain 199 + 171 + Nax1Nax2 12 377671 Former Yugoslavia 199 + 171 + Nax1Nax2 13 428156 United Kingdom 199 + 171 + Nax1Nax2 14 355530 Spain 199 + 171 + Nax1Nax2 15 119423 Turkey 199 + 171 + Nax1Nax2 16 13961 United States 199 + 171 + Nax1Nax2 17 167634 Turkey 199 + 171 + Nax1Nax2 18 272535 Hungary 199 + 171 + Nax1Nax2 19 428166 Turkey 199 + 171 + Nax1Nax2 20 428176 Turkey 199 + 171 + Nax1Nax2 21 191146 Spain 199 + 171 + Nax1Nax2 22 418583 Georgia 199 + 171 + Nax1Nax2 23 428157 United Kingdom 199 + 171 + Nax1Nax2 24 355535 Italy 199 + 171 + Nax1Nax2 25 119435 Turkey 199 + 171 + Nax1Nax2 26 13962 United States 199 + 171 + Nax1Nax2 27 168803 United States 199 + 171 + Nax1Nax2 28 277140 Germany 199 + 171 + Nax1Nax2 29 428167 Turkey 199 + 171 + Nax1Nax2 30 503874 South Africa 199 + 171 + Nax1Nax2 31 191381 Ethiopia 199 + 171 + Nax1Nax2 Russian 199 + 171 + Nax1Nax2 32 418587 Federation 33 428158 United Kingdom 199 + 171 + Nax1Nax2 34 355536 Italy 199 + 171 + Nax1Nax2 35 167526 Turkey 199 + 171 + Nax1Nax2 36 13963 United States 199 + 171 + Nax1Nax2 37 168804 United States 199 + 171 + Nax1Nax2 38 290509 Hungary 199 + 171 + Nax1Nax2 39 428168 Turkey 199 + 171 + Nax1Nax2 40 518452 Spain 199 + 171 + Nax1Nax2 41 191383 Ethiopia 199 + 171 + Nax1Nax2 42 428149 Sweden 199 + 171 + Nax1Nax2 43 428159 United Kingdom 199 + 171 + Nax1Nax2 44 377648 Former Yugoslavia 199 + 171 + Nax1Nax2 White 199 + 171 + Nax1Nax2 45 Einkorn United States 46 13964 United States 199 + 171 + Nax1Nax2 47 168805 United States 199 + 171 + Nax1Nax2 48 290511 Hungary 199 + 171 + Nax1Nax2 49 428169 Turkey 199 + 171 + Nax1Nax2 50 538721 Turkey 199 + 171 + Nax1Nax2 51 Stone Age United States 199 + 171 + Nax1Nax2 52 428150 Romania 199 + 171 + Nax1Nax2 53 428160 Turkey 199 + 171 + Nax1Nax2 54 377662 Former Yugoslavia 199 + 171 + Nax1Nax2 55 167589 Turkey 199 + 171 + Nax1Nax2 56 13965 United States 199 + 171 + Nax1Nax2 57 168806 United States 199 + 171 + Nax1Nax2 58 307984 Morocco 199 + 171 + Nax1Nax2 59 428170 Turkey 199 + 171 + Nax1Nax2 60 584654 Italy 199 + 171 + Nax1Nax2 61 221415 Serbia 199 + 171 + Nax1Nax2 62 428151 Italy 199 + 171 + Nax1Nax2 63 428161 Turkey 199 + 171 + Nax1Nax2 64 377666 Former Yugoslavia 199 + 171 + Nax1Nax2 65 167591 Turkey 199 + 171 + Nax1Nax2 66 14520 Canada 199 + 171 + Nax1Nax2 67 170196 Turkey 199 + 171 + Nax1Nax2 68 326317 Azerbaijan 199 + 171 + Nax1Nax2 69 428171 Turkey 199 + 171 + Nax1Nax2 70 591871 Georgia 199 + 171 + Nax1Nax2 71 225164 Greece 199 + 171 + Nax1Nax2 72 428152 Bulgaria Absent - 171 + nax1Nax2 73 428162 Turkey 199 + 171 + Nax1Nax2 74 377667 Former Yugoslavia 199 + 171 + Nax1Nax2 75 167611 Turkey 199 + 171 + Nax1Nax2 76 17653 United States 199 + 171 + Nax1Nax2 77 191094 Spain 199 + 171 + Nax1Nax2 78 330551 United Kingdom 199 + 171 + Nax1Nax2 79 428172 Turkey 199 + 171 + Nax1Nax2 Black 199 + 171 + Nax1Nax2 80 Einkorn Canada 81 237659 Kenya Absent - Absent - nax1nax2 82 428153 United States Absent - Absent - nax1nax2 83 428163 Turkey 199 + 171 + Nax1Nax2 84 377668 Former Yugoslavia 199 + 171 + Nax1Nax2 85 167615 Turkey 199 + 171 + Nax1Nax2 86 94740 Spain 199 + 171 + Nax1Nax2 87 191095 Spain 199 + 171 + Nax1Nax2 88 345133 Serbia 199 + 171 + Nax1Nax2 89 428173 Turkey 199 + 171 + Nax1Nax2 Blu dur 199 + 171 + Nax1Nax2 90 Arcour Canada 91 254195 Turkey 199 + 171 + Nax1Nax2 92 428154 Turkey 199 + 171 + Nax1Nax2 93 428164 Turkey 199 + 171 + Nax1Nax2 94 377669 Former Yugoslavia 199 + 171 + Nax1Nax2 95 167625 Turkey - - - - - Russian 199 + Absent - Nax1nax2 96 94743 Federation 97 191096 Spain 199 + 171 + Nax1Nax2 98 349049 Armenia 199 + 171 + Nax1Nax2 99 428174 Turkey 199 + 171 + Nax1Nax2 100 Alaska Spelt Canada 199 + 171 + Nax1Nax2

Objective 3: Identify einkorn accessions with unique and marketable nutritional qualities At Morris, random seed samples of each accession were digitally photographed with a high-resolution camera to document their color (i.e., RGB and L*, a*, and b*) after dehulling or milling into flour. Mineral content of each grain sample was determined with inductively coupled plasma mass spectrometry (ICP). High-pressure liquid chromatography (HPLC) was used to determine carotenoid content and LECO Combustion Analysis was used to determine N and protein content. These data are combined with other data from the Morris site into three indices estimated for each accession based on (1) agronomic, (2) nutritional and (3) salinity tolerance traits for additional, more intensive considerations of the relationships among all of these traits (See Appendix). In general, our einkorn accessions had very high protein, high carotene values, and were very yellow compared with most wheat varieties (Table 10). Even PI 428149 and PI 428150, the accessions with the lowest protein values, had nearly 19% protein. Given that spring wheat markets look for 14+% crude protein, these numbers suggest that there could be great value in larger, farm-scale trials of einkorn on organic farms to verify the results. NPSAS Farm Breeding Club members have grown TM23 organically at farm scale and recently found 14.2% crude protein. This is very good, and if some other varieties can also provide levels like this or better at similar yields, then some einkorn should be a great fit for organic farms. The carotene levels were much higher than most wheat, around the average reported for einkorn elsewhere (Hidalgo et al., 2006). Whatever accessions we chose to use for larger scale evaluation or commercialization, they all appear to come with high protein and high carotenoid levels that could be useful for baked goods and feeds.

Table 10. Grain quality traits for nineteen einkorn accessions and one experimental check. Carotene is reported as micrograms/gram dry weight. The b* values are on an arbitrary scale representing blue to yellow coloration, with higher numbers indicating more yellow. Accession % Protein Carotene Kernel b* Flour b* 94743 21.31 8.53 16.47 11.57 119422 21.04 8.25 15.53 10.29 119423 20.87 8.33 15.83 10.79 167526 23 8.84 15.5 9.77 170196 21.01 8.5 14.5 10.44 191381 20.44 8.22 16.46 10.31 237659 20.88 8.45 15.18 10.5 254195 20.96 8.37 15.66 10.66 326317 21.05 8.43 15.57 10.44 428149 18.91 7.93 15.48 11.93 428150 18.92 7.53 14.41 10.8 428155 22.69 7.9 15.83 9.89 428156 21.29 8.53 16.12 11.28 428158 20.5 8.17 15.42 10.69 428163 20.08 7.84 15.83 10.83 428164 21.24 8.11 14.75 10.28 428171 21.25 8.24 14.65 10.42 428172 20.04 7.79 13.63 10.37 428173 21 8.52 14.89 10.02 TM23 20.75 8.21 15.45 10.64

Einkorn is known for high mineral content in the grain, and results with our accessions lend support to this view (Table 11). When compared with results for wheat cultivars grown in Sweden (Hussain et al., 2010), TM23 was a little lower for Calcium, but higher for potassium, magnesium, phosphorus, sulfur, copper, iron, manganese, and zinc. Other high yielding accessions like PI 237659, PI 428149, PI 428150, PI 428155, and PI 428173 also had very good mineral content across the board from macro to micronutrients, although there was variation for specific traits. Some accessions were higher yet, especially PI 94743, although this was a somewhat lower yielding accession. Further evaluations of mineral content on einkorn grain grown under organic management at larger scales could be very valuable to fully understanding the full potential of this crop and of the better performing accessions.

Table 11. Mineral content in the grain of nineteen einkorn accessions and one experimental check evaluated with inductively coupled plasma mass spectrometry (ICP). All minerals are reported as mg/kg dry matter. Accession Ca K Mg P S Cu Fe Mn Zn 94743 408.4 5852.8 1812.5 5543 2075 6.5 67.3 59 67.2 119422 310.1 4378 1267.7 4222 1888 5.4 48 40.2 46.9 119423 395.8 5502.5 1856.2 5495 2143 7.6 58.6 52.4 54 167526 403.1 4215.8 1480.1 4439 2176 6.8 57.9 49.1 54.7 170196 289.7 3820.4 1187.7 3701 1944 7.3 49.7 36.1 54.6 191381 267.3 4019.3 1206 3479 1946 5.9 50.4 30.9 48 237659 352.4 5350.8 1606.8 4738 2010 6.9 47.4 34.5 46.9 254195 347.2 4722 1530.5 4616 2024 7.5 56.8 43.1 54.6 326317 322.5 4497.6 1432.5 4405 1987 7.2 55 40.1 53 428149 352.1 5880.5 2048.8 6557 2178 8.4 54.6 64.6 54 428150 333.4 4179.5 1242.6 4456 2016 6.3 55.3 39.6 46.7 428155 395.3 4625 1578 4823 2054 5.4 61.4 40.1 51 428156 407 5086.8 1604.8 5487 1892 8 61.1 49.3 55 428158 365.6 4615 1475.6 4633 2046 7.9 55.7 41.9 53.5 428163 337.3 4672.3 1285.6 3975 2052 5.9 65.2 34.8 44.9 428164 328.4 4017.4 1152.1 3984 1800 6.5 59.3 34.9 45.1 428171 350.7 4912.6 1591 5173 1786 7.5 72.3 48.9 44.7 428172 397.1 4529.7 1404.3 4872 2080 7.8 60.6 43.1 48.5 428173 414.8 4344.2 1298.5 4154 2054 6.3 57.5 39.7 51.8 TM23 354.9 4529.9 1443.7 4508 1994 7.4 57.4 41.4 52.4

Analysis of gliadin and glutenin fraction of seed protein of 100 einkorn accessions There is a strong focus on gluten today with so much gluten intolerance and cases of celiac disease among some consumers. Gluten is the main class of protein storage proteins in wheat seeds, and Gliadins and Glutenins are the two main components of the gluten fraction. Gliadin is a class of proteins present in wheat and several other cereals within the grass genus Triticum. Gliadins are essential for giving bread the ability to rise properly during baking. Certain fractions of Gliadins and Glutenins also appear to be more important for wheat sensitivity, so it is very useful to know the kinds of storage proteins present for both health and baking quality concerns. Gliadin and glutenin protein fractions were extracted following the method of Alvarez et al. (2006) with modifications (Appendix A). Extracted protein fractions were analyzed using denaturing Polyacrylaminde gel electrophoresis (SDS-PAGE). Thirty-two samples completed and the result is presented in Table-1. The table shows presence and absence of proteins which differed among the lines. The remaining 68 samples will be completed within January and the analyzed data will be shared once completed. Gliadin proteins fractions ranged from approximate 75 kDa to 13 kDa. Gliadin protein fractions of the lines differed by only one protein of ~42 kDa (Fig.1). Glutenin proteins fractions ranged from approximate 10 kDa to 75 kDa. Glutenin protein fractions of the lines differed only by two proteins - 45 kDa and 38 kDa (approximately) (Fig.2).

Table 1: Gliadin and glutenin protein analysis of 32 Einkorn wheat lines. Proteins that differed among the lines are scored and presented here. Sample ACNO PLANTID COUNTRY Glia-42 Glu-45 Glu-38 # kDa kDa kDa 1 191097 Escana de Jerez Spain - - + 2 377670 958 Former Yugoslavia - + + 3 428155 G1467 United Kingdom + + + 4 352483 T-1600 Spain + + - 5 119422 1259 Turkey + - + 6 10474 Einkorn Germany - + - 7 167627 3412 Turkey + - - 8 266844 87 United Kingdom + + - 9 428165 G2906 Turkey + + - 10 428175 G2944 Turkey + - + 11 191098 Escana de Martos Spain - + - 12 377671 959 Former Yugoslavia + + - 13 428156 G1471 United Kingdom + + - 14 355530 69Z5.19 Spain + - + 15 119423 1277 Turkey - + + 16 13961 W49-27-1 United States - + + 17 167634 3468 Turkey - + - 18 272535 I-1-3430 Hungary - - + 19 428166 G2907 Turkey + + - 20 428176 G2946 Turkey + + - 21 191146 2259 Spain + - + Iz Populyatcii + - + 22 418583 Zanduri Georgia 23 428157 G1474 United Kingdom + + + 24 355535 Je 24 Italy + + - 25 119435 2592 Turkey - + - 26 13962 W49-64-2 United States + - + 27 168803 United States + - + 28 277140 TRI 4309 Germany + - + 29 428167 G2908 Turkey - - + 30 503874 A 544 South Africa + - - 31 191381 Flavescens Ethiopia + + + Russian - + + 32 418587 WIR 48993 Federation

Mr 22 23 24 25 26 27 28 29 30 31 32 kDa 250 150 100

75

50

42kDa 37

25 20

15

10

Fig.1. Representative picture showing gliadin protein bands. Sample numbers are shown on top. Protein size standard (BioRad Cat. #161-0375). The lines differ for a 42 kDa protein (approximately), marker by black arrow.

kD Mr 22 23 24 25 26 27 28 29 30 31 32 a250 150 100 75

50

45kDa 38kDa 37

25

20

15

10

Fig.2. Representative picture showing glutenin protein bands. Sample numbers are shown on top. Protein size standard (BioRad Cat. #161-0375). The lines differ for 45 kDa and 38 kDa proteins, marker by black arrow.

FURTHER CONSIDERATIONS: A screening procedure was developed and tested at the North Central Soil Conservation Laboratory for characterizing and evaluating the germplasm, and selected accessions were ranked using several multivariate statistical and visualization procedures (Appendix B). Several einkorn accessions were identified with high grain yield potential, good nutritional value, and tolerance to salinity using this procedure. Classification and regression models can be used to identify accessions with single or multiple traits that can be recombined into new genotypes. We will continue to make use of this more objective approach to choosing among the experimental accessions as we add additional data to the overall data set.

CONCLUSIONS: A multi-year, multi-location field experiment [Morris, MN and Carrington, ND], complemented by laboratory studies (using wet chemistry, hydroponics, PCR, gel electrophoresis, high-throughput analytics) in Morris, MN and Scottsbluff, NE, was conducted to select, characterize and evaluate einkorn accessions with high nutritional value and adaptation to climatic and edaphic conditions in the Northern Great Plains, where soil salinity is increasingly impacting wheat growth and yield potential. Grain yield, kernel physical and chemical composition and seed germination and seedling growth under salinity were assessed and the germplasm was classified as to its overall agronomic value, as well as its nutritional and salinity characteristics. Einkorn has been demonstrated to have potential on the Northern Great Plains, and ongoing research is suggested to fully accomplish the objectives of the “Value-added Einkorn for Production in the Northern Plains Region” project. Our ability to differentiate the mean performance of the accessions was limited to some degree by noise in the data and environmental interactions that are common in our region. Since significant differences among many of the accessions could not be detected, it seems most reasonable to go forward with a larger number of the lines that generally yielded well, lodged less, were fairly to very tolerant of salinity, and had high nutritional quality for wheat in general. Given the results of the evaluations and of the modeling efforts to help condense and better relate the data points, we feel confident in choosing a number of accessions (our check variety TM23, PI 355523, and also the experimental accessions PI 237659, PI 428149, PI 428150, PI 428155, and PI 428173) for ongoing research and development and possible commercialization. Further work on specific traits and lines that were exceptional performers for those traits also appears reasonable in order to better understand the genetics of these traits in einkorn and wheat in general.

RECOMMENDATIONS: Seed increase at a research station to produce enough quality seed (as to seed purity, health and uniformity) for further on-farm research and development. The superior accessions should be evaluated further in farmers’ fields using farm-scale implements and management. At least 4 locations, with increasing salinity levels, should be used in this evaluation. A discussion is needed to better understand what visual evaluators saw in some varieties that really did not yield well and why these were scored so high. The salt tolerance of accessions that did not have both or any of the known salt tolerance genes, and the variation for salt tolerance among those that did, especially deserve further study. There may be other salt tolerance genes to discover and use. A research and development program, based on participatory breeding involving wheat breeders, agronomists, cereal technologists, farmers, and consumers, is suggested do develop new genotypes and much improved varieties for the future. The importance of the variation in Gliadin and Glutenin fractions among these einkorn fractions needs to be determined both for bakers and for those concerned about human health. Medium-large scale dehulling equipment, like the Horn Dehuller owned by NPSAS, is needed to process large seed volumes for commercial use and distribution to millers and bakers for evaluation of the superior accessions for end-use products. Quality evaluation of products made from on-farm produced seed (flour, baked goods, feed grains) should be carried out in collaboration with the food and feed industries.

FINANCIAL STATEMENT: Please note the attached spreadsheet that includes the budgeted and actual expenditures for 2017.

REFERENCES: Doyle J.J and J.L. Doyle. 1987. A rapid DNA isolation procedure from small amount of fresh leaf tissue, Phytochem. Bull 19:11-15. Hidalgo A., Brandolini A., Pompei C., Piscozzi R., 2006. Carotenoids and tocols of einkorn wheat (triticum monococcum ssp. monococcum l). Journal Cereal Science 44(2), 182 – 193. Hussain, A., H. Larsson, R. Kuktaite, and E. Johansson. 2010. Mineral composition of organically grown wheat genotypes: contribution to daily minerals intake. Int. J. Environ. Res. Public Health 7:3442-3456. James, R.A., C. Blake, C.S. Byrt, and R. Munns. 2011. Major genes for Na+ exclusion, Nax1 and Nax2 (wheat HKT1;4 and HKT1;5), decrease Na+ accumulation in bread wheat leaves under saline and waterlogged conditions. J. Exp. Botany 62(8): 2939-2947. James, R.A., R.J. Davenport, and R. Munns. 2006. Physiological characterization of two genes for Na+ exclusion in durum wheat, Nax1 and Nax2. Plant Physiology 142:1537- 1547. Munns, R., R.A. James, 2012. Diagnostic kit for salt tolerance Nax genes in wheat. Accessed through personal communication, R.A. James, 9/11/13. Munns, R., R.A. James, B. Xu, A. Athman, S.J. Conn, C. Jordans, C.S. Byrt, R.A. Hare, S.D. Tyerman, M. Tester, D. Plett, and M. Gilliham. 2012. Wheat grain yield on saline soils is improved by an ancestral Na+ transporter gene. Nature Biotechnology 30:360-364. Munns & Filmer, 2007. Paving the Way For Salt Tolerant Wheat. Accessed 9/8/13 at: http://www.csiro.au/en/Outcomes/Food-and-Agriculture/Paving-the-way-for-salt- tolerant-wheat.aspx

ACKNOWLEDGEMENTS: We would like to thank the Ceres Trust for supporting this research and our administrators for having approved this ongoing einkorn research project. We thank the USDA National Plant Germplasm System for having maintained and supplied seeds of these einkorn accessions from around the globe, and Gil Stallknecht (Montana State University) who began research into these specialty wheats on the Northern Great Plains in the 1990s. A large number of USDA-ARS support staff at the North Central Soil Conservation Research Laboratory, and the University of Minnesota-Morris (UMM) students were involved in the design and implementation of field and laboratory experiments of this collaborative project. Thanks are due to Jana Rinke (Chemist) for leading the field and laboratory work and data compilation, Chris Wente, Charles Hennen and Scott Larson for field operations, Jay Hanson for carrying out part of the chemical analyses, and University of Morris students Drake Burri, Keyha Stone, Lindsey Jennifer, Eric King, Michael Medlyn, and Sam Schlagen. We thank Allison Rickey, Vernon Florke, and David Blanke who assisted with all aspects of the field trials at the UNL Panhandle Research and Extension Center. We also thank Dr. Santosh Rajput who collaborated on Nax gene marker genotyping, and Rituraj Khound (UNL) and Dr. Sanjay Gupta (University of Minnesota) who helped with gliadin and glutenin seed protein analyses. Thanks go to Steve Shaubert who offered critical support for field evaluations at the NDSU Carrington Research Extension Center, to Kelly Bjerke and Cassidy Vandehollen who assisted with lab work, and to Myrna Freidt who assisted with putting results from Carrington on the web. NDSU professor Burton Johnson provided critical support for the final statistical analyses of the einkorn yield data for which we are grateful. Thanks also to the growers and other stakeholders who shared of their valuable time and helped in the participatory evaluation process. Their comments and insights were most useful in helping the project identify possible new varieties for future release and dissemination. Finally, we thank NPSAS staff members Edd Goerger, Jonathon Moser, Theresa Podoll, Susan Long, and Jill Wald who helped to manage the project and helped to promote it via field events and print, web, and other media.

OTHER PHOTOS:

Figure 9. Einkorn growing in Morris, MN in 2017.

Figure 10. Einkorn growing under extreme drought stress near Scottsbluff, NE in 2017. APPENDIX A: Extraction and analysis of Gliadin and Glutenin fractions from Einkorn wheat flour

• 15 mg flour • Add 200 ul 70% Ethanol • Vortex for 15 mins • Centrifuge for 14,000 rpm for 5 mins • Take 100ul supernatant (to be used for Gliadin fraction) o Keep the supernatant at 70oC oven to air-dry o Add 25 ul 1X Loading buffer (LB) for SDS-PAGE (composition below) • Pellet to be used for Glutenin o Wash the pellet with 100% acetone for three times o Air-dry it at room temperature o Add 100 ul Tris- buffer (composition below) o Vortex for 5 mins o Centrifuge at 14,000 rpm for 5 mins o Take 50 ul supernatant o Add 12 ul 5X LB to bring the total volume 62 ul Denatuing Polyacrylamide Gel Electrophoresis • Set up gel in unit. Pour 1XTris-glycine buffer for both lower and upper tank buffer. Load 25 ul Gliadin and 40 ul Glutenin to each well • Load 5ul Protein size standard (BioRad) • Run SDS-PAGE gel in BioRad Unit at 100 constant voltage for ~2 hrs when the due reaches to the bottom edge of the gel. Use 1XTris-glycine buffer for both lower and upper tank buffer. • Remove the gel and rinse with water for 5 mins (with gentle shaking) x3. This is to remove SDS • Fix the washed gel in 10% acetic acid for 15 mins • Rinse with water for 15 mins (with gentle shaking) x1 • Stain with Coamassie Blue for 45 mins • De-stain (14% methanol and 10% acetic acid) overnight or more until bands are clearly visible without any significant background. • To score the protein bands, use the gels on white light transilluminator • Take photo for record

Reagents and equipment used: 1. Tris-Buffer for Glutenin extraction (50 mM Tris, pH7.4 + 1 mM DTT or 25ul beta marcaptoethanol/ml. For 10 Tris buffer: 1M Tris, pH 7.4) = 500 ul Beta-mercaptoethanol = 250 ul Water = 9.25 ml 2. Loading buffer (50% glycerol with beta-mercapto ethanol and Bromo Phenol Blue - BPB) For 10 ml 5X LB 100% glycerol = 5 ml Beta ME = 500 ul 0.5% BPB = 250 ul 3. Polyacrylaminde gel: Any KDTM CriterionTM Gel, 12+2 well (BioRad Cat #5677-1123) 4. Gel electrophoresis unit: CriterionTM Cell (BioRad Cat #1656001) 5. 1xTris/Glycine Buffer (dilute 1:10 ratio): 10x Trias/Glycine/SDS (BioRad Cat #161-0732) 6. Power Supply: BioRad Cat# 7. Protein size Standard: Precision Plus ProteinTM KaleidoscopeTM #161-0375 8. EZBlueTM Gel Staining Reagent: Sigma Cat # G1041 9. Detaining solution: Coomassie Brilliant Blue R-250 Destaining Solution (BioRad Cat#1610438)

Reference: Alvarez J.B., A. Moral and L.M. Martin (2006). Polyacrylamid and genetic diversity for the seed storage proteins in Spanish cultivated einkorn wheat (Triticum monococcum L. ssp. monococcum). Genetic Resources and Crop Evolution 53:1061-1067.

Appendix B. Analyses of relationships among characteristics of einkorn accessions studied at the USDA North Central Soil Conservation Research Laboratory in Morris, MN.

Thirty-eight measured or estimated traits per accession are summarized here (Table 1). The accessions were sorted, then classified into 4 groups based on their overall grain yield at Morris (i.e., low yield, Medium yield, High yield, and Very High Yield).

Table 1. Summary Statistics for the einkorn evaluation from Morris, MN.

Basic statistics (Table 2) indicated that most traits expressed typical levels of variation for most agronomic, nutritional and salinity traits (C.V. between 5 and 10%); however, a few traits expressed large (e.g., the nutrients Mg, Mn) or very large (e.g., the micronutrient Cu and most salinity traits). The two-way clustering procedure of traits and accessions (Fig. 1) classified the whole germplasm into four groups, regardless of their grain yield groupings. The accessions PI 94743, followed by PI 428149 had larger values of a large number of traits and each one comprised a single cluster. Three accessions (PIs 167526, 428156, and 119423) were classified into a single cluster; while the remaining 15 accessions formed the last but most heterogeneous cluster; the accession PI 428172 had the smallest values of almost all traits, except flour yellowness (b*). On the other hand, traits formed a large number of clusters and were largely indicative of associations among agronomic, nutritional and salinity stress traits.

Table 2. Basic statistics averaged over 19 einkorn accessions evaluated under field and laboratory conditions in Morris, MN between 2014 and 2017. Descriptive Variable Statistics (EinkornRe port-Morris) Mean Minimum Maximum Variance Std.Dev. Coef.Var. Seedling Vigor 66.95 55.00 85.00 59 7.660 11.44196 Early Vigor/100 72.75 50.00 90.00 72 8.503 11.68810 Plant Height cm 73.25 62.00 82.00 27 5.230 7.14024 Earliness/100 78.25 45.00 100.00 190 13.791 17.62454 1000 kernel weight, g 28.74 25.94 31.11 2 1.498 5.21030 Test Weight 62.05 60.96 65.00 1 0.853 1.37495 GY2014 2918.00 2228.31 3711.32 201594 448.992 15.38699 GY2015 2636.10 1467.70 3237.00 156202 395.224 14.99276 GY2016 2669.95 2362.85 2833.00 16198 127.270 4.76675 GY2017 3228.97 2539.06 3714.00 81866 286.122 8.86110 Mean GY 2863.29 2599.93 3185.00 26086 161.511 5.64076 Agronomic Index 0.82 0.75 0.91 0 0.046 5.54215 Kernel-L* 55.65 53.00 58.83 2 1.331 2.39225 Kernel-b* 15.36 13.63 16.47 1 0.713 4.64169 Kernel L*/b* 3.63 3.36 3.97 0 0.148 4.08978 Flour L* 86.55 85.00 87.53 0 0.518 0.59840 Flour-b* 10.60 9.77 11.93 0 0.527 4.97161 Flour L*/b* 8.20 7.24 8.96 0 0.415 5.06683 Protein 20.86 18.91 23.00 1 0.971 4.65249 Total Carotenoids 8.23 7.53 8.84 0 0.315 3.82031 Ca, mg/kg 356.64 267.29 414.76 1746 41.790 11.71767 K, mg/kg 4687.60 3820.38 5880.50 343870 586.404 12.50970 Mg, mg/kg 1475.22 1152.07 2048.83 56894 238.524 16.16868 P, mg/kg 4663.90 3478.67 6556.55 524534 724.247 15.52878 S, mg/kg 2007.30 1786.15 2178.44 11678 108.067 5.38369 Total Macronutrients, mg/kg 13190.66 10917.22 17016.42 2470835 1571.889 11.91668 Cu, mg/kg 7.42 5.38 18.44 7 2.714 36.56566 Fe, mg/kg 57.57 47.42 72.32 39 6.259 10.87177 Mn, mg/kg 43.19 30.85 64.55 72 8.504 19.69097 Zn, mg/kg 51.35 44.49 67.15 28 5.273 10.26802 Descriptive Variable Statistics (EinkornRe port-Morris) Mean Minimum Maximum Variance Std.Dev. Coef.Var. Total Micronutrients, mg/kg 159.53 135.09 200.00 286 16.904 10.59640 Nutrient Index 0.78 0.64 1.00 0 0.092 11.87725 Salinity Index 150 mM 0.66 0.37 1.00 0 0.155 23.49260 K/Na 4th Leaf 104.22 49.31 149.91 726 26.938 25.84711 K/Na Saline 4th Leaf 9.65 7.34 11.59 1 1.203 12.46085 K/Na Root 32.99 19.15 49.01 56 7.503 22.74557 K/Na Saline Root 7.74 6.46 10.00 1 0.837 10.81593 Salinity Index 0.68 0.57 0.85 0 0.077 11.24690

Fig. 1. Hierarchical clustering of 19 einkorn accessions (and a check) based on all agronomic, nutritional, and salinity stress traits evaluated under field and laboratory conditions in Morris, MN between 2014 and 2017 (Color codes: Green=low; Yellow=Medium; Red=High).

Agronomic traits (Table 3) expressed large variation and almost 50% of their correlation coefficients were significant. The strongest correlation (r=0.64) was found between early growth vigor and earliness of maturity, followed by a strong negative correlation between early growth vigor and 1000 kernel weight (r=-0.49). Seedling vigor was positively correlated with early growth vigor (r=0.33; p<0.05) and with test weight (r=0.30). The 2-way clusters of agronomic traits and einkorn accessions (Fig. 2) indicated the strength of trait association and which accession or group of accessions are characterized by low, medium or high levels of one or more traits. For example, PI 167526 had the largest values of seedling vigor, early growth vigor and earliness in maturity; while, PI 237659 had the smallest values of these traits, but the largest 1000 kernel weight.

Table 3. Basic statistics (Mean and standard deviation) and correlation coefficients between agronomic traits of 19 einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017. Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Means Std.Dev. SeedlingVigor EarlyVigor/100 Plant Height cm Seedling Vigor 66.95000 7.66039 1.000000 0.333507 -0.186208 Early Vigor/100 72.75000 8.50310 0.333507 1.000000 -0.122783 Plant Height cm 73.25000 5.23023 -0.186208 -0.122783 1.000000 Earliness/100 78.25000 13.79121 -0.075600 0.637878 0.312844 TKWT g 28.74150 1.49752 -0.163097 -0.496544 0.046114 Test Weight 62.04650 0.85311 0.301097 0.061254 0.175844

Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Earliness/100 TKWTg Test Wt Seedling Vigor -0.075600 -0.163097 0.301097 Early Vigor/100 0.637878 -0.496544 0.061254 Plant Height cm 0.312844 0.046114 0.175844 Earliness/100 1.000000 -0.388374 0.065435 TKWTg -0.388374 1.000000 -0.157947 Test Weight 0.065435 -0.157947 1.000000

Fig. 2. Two-way clustering of einkorn accessions based on agronomic traits during 2014- 2017 in Morris, MN (Color codes: Green=low; Yellow=Medium; Red=High).

Grain yield (Table 4) during 2014 to 2017, on average, ranged from 2636 (in 2015) to 3229 (in 2017), with an average of 2863 kg/ha over accessions and years. Most correlations between mean estimates were positive, except between GY2014 and GY2016 (r=-0.43). GY variability in 2014 was the largest (S.D. = 449 kg/ha) because it was based on single rows, while the smallest was in 2016 (S.D. = 127 kg/ha). Mean GY, and the agronomic index were positively correlated with GY in all years, except in 2016. The variability in GY during the 2014-2017 and classification of einkorn accessions (Fig. 3) indicated that the genotype x year interaction was large for some accessions, but not for others. A cluster composed of PIs 94743, 428150, 428156 and 170196 expressed the largest GY values during the first 2 years and medium values during the last 2 years. While, for example, PI 428172 expressed a stable but low GY during the whole period.

Table 4. Basic statistics (Mean and standard deviation) and correlation coefficients between grain yield estimates of 19 einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017. Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Means Std.Dev. GY2014 GY2015 GY2016 GY2017 GY2014 2918.000 448.9923 1.000000 0.144169 -0.432479 0.065114 Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Means Std.Dev. GY2014 GY2015 GY2016 GY2017 GY2015 2636.102 395.2244 0.144169 1.000000 -0.054268 -0.292952 GY2016 2669.947 127.2698 -0.432479 -0.054268 1.000000 0.196882 GY2017 3228.971 286.1223 0.065114 -0.292952 0.196882 1.000000 Mean GY 2863.293 161.5115 0.727116 0.571916 -0.049527 0.347816 Agronomic Index 0.822 0.0455 0.694962 0.619813 -0.023904 0.315789

Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) MeanGY AgronomicIndex GY2014 0.727116 0.694962 GY2015 0.571916 0.619813 GY2016 -0.049527 -0.023904 GY2017 0.347816 0.315789 Mean GY 1.000000 0.997826 Agronomic Index 0.997826 1.000000

Fig. 3. Two-way clustering of einkorn accessions based on grain yield during 2014-2017 in Morris, MN (Color codes: Green=low; Yellow=Medium; Red=High).

Data on kernel and flour color spaces (Table 5) indicated that kernel color (L*) was intermediate (on a scale between 0.0. white, to 100, dark), while flour yellowness (b* = 10.59) was less intense than kernel yellowness (b* = 15.35). The L*/b* ratio reflects the strength of yellow pigment in relation to the dark-light color of the grain or the flour. It was negatively and strongly correlated with kernel b* (r = -0.87), and with flour b* (-0.99). Kernel and flour yellowness (b*) estimates were positively, but not significantly correlated (r = 0.29) due to the mixing of several constituents of the kernel after milling. Total carotenoids, total macro- and micronutrients and their index (Table 6) indicated that total carotenoids was significantly correlated with protein (r = 0.64) but not with nutrients; the latter were not strongly correlated with protein. A nutrients index was equally and significantly correlated with both macro- and micronutrients, but not with protein or carotenoids contents. Three accessions (PI 94743, 428155, and PI 191381) had the largest yellow flour color estimates (i.e., large b*); while, PIs 428171, and 428172 had the least yellow flour color spaces (Fig. 4). On the other hand, PI 167526 had the largest estimates of total carotenoids, protein and darkest flour color space. However, it is important to point to the narrow range of both kernel L* (53 to 58.8) and flour L* (85 to 87.5); in relation to kernel b* (13.63 to 16.47) and flour b* (9.77 to 11.93).

Table 5. Basic statistics (Mean and standard deviation) and correlation coefficients between grain and flour color space estimates of 19 einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017 (L*: dark to light color; b*: yellowness of seed or flour as an estimate of yellow pigment; the L*/b* ratio reflects the strength of yellow pigment in relation to the dark-light color of the grain or the flour). Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Means Std.Dev. Kernel-L* Kernel-b* Kernel L*/b* Flour L* Flour-b* Kernel-L* 55.65450 1.331396 1.000000 0.507359 -0.010951 0.361381 -0.099530 Kernel-b* 15.35800 0.712871 0.507359 1.000000 -0.866043 0.015021 0.291688 Kernel L*/b* 3.63063 0.148485 -0.010951 -0.866043 1.000000 0.164399 -0.384112 Flour L* 86.55450 0.517946 0.361381 0.015021 0.164399 1.000000 -0.590848 Flour-b* 10.59600 0.526792 -0.099530 0.291688 -0.384112 -0.590848 1.000000 Flour L*/b* 8.19837 0.415398 0.142701 -0.239358 0.345976 0.658660 -0.992194

Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Flour L*/b* Kernel-L* 0.142701 Kernel-b* -0.239358 Kernel L*/b* 0.345976 Flour L* 0.658660 Flour-b* -0.992194 Flour L*/b* 1.000000

Table 6. Basic statistics (Mean and standard deviation) and correlation coefficients between protein, carotenoids, macronutrients and micronutrients (and a nutrient index; 0.0 to 1.0) in kernels of 19 einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017. Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Means Std.Dev. Protein Total Carotenoids TotalMacroNutrients Protein 20.86 0.971 1.000000 0.638099 -0.138273 Total Carotenoids 8.23 0.315 0.638099 1.000000 0.027179 Total Macronutrients 13190.66 1571.889 -0.138273 0.027179 1.000000 Total Micronutrients 159.53 16.904 0.036228 0.169646 0.811395 Nutrient Index 0.78 0.092 -0.136688 0.028752 0.999981

Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) TotalMicronutrients NutrientIndex Protein 0.036228 -0.136688 Total Carotenoids 0.169646 0.028752 Total Macronutrients 0.811395 0.999981 Total Micronutrients 1.000000 0.815021 Nutrient Index 0.815021 1.000000

Fig. 4. Two-way clustering of einkorn accessions based on grain and flour color space traits evaluated during 2014-2017 in Morris, MN (L*: dark to light color; b*: yellowness of seed or flour as an estimate of yellow pigment; the L*/b* ratio reflects the strength of yellow pigment in relation to the dark-light color of the grain or the flour).

Macro- and micronutrients clustered into three groups, separating Ca, Fe, S, and Zn from the remaining nutrients (Fig. 5). Two accessions (PI 428171 and PI 428163) had large Fe and Zn; while, PI 94743 and PI 428149 had the largest total macro- and micronutrients. The micronutrient Cu was separated from other micronutrients due to a single outlier in the original data file (See Table 1). All accessions were clustered into three groups; the first (PI 428149 to 428171) with largest nutrients contents; the second group had intermediate nutrients contents (PI 428172 – 254195); and a third group with the smallest nutrients contents (PI 428164 – 170196).

Fig. 5. Two-way clustering of einkorn accessions based on macro- and micronutrients in grains produced between 2014 and 2017 in Morris, MN.

Frequency distributions of three indices (agronomic, nutrients and salinity tolerance (Fig. 6), basic statistics (mean and standard deviation; Table 7) and correlation coefficients between indicators of salinity tolerance estimated on seed germination (150 mM) and on the fourth leaf and roots of seedlings grown in saline solution (100 mM) indicated that salinity indices based on seed germination and on seedling growth under salinity were not significantly correlated. However, the former (at seed germination) was significantly and positively correlated with K/Na in the 4th leaf of seedlings grown under salinity (100 mM) (r =0.33); while, the latter (seedling salinity index) was strongly and positively correlated with all K/Na estimates in leaves and roots. There was a clear separation between salinity tolerance at the germination stage from indicators of salinity tolerance at the seedling stage; also, K/Na of roots under salinity was distantly clustered with the remaining salinity indicators (Fig. 7). However, the accession PI 94743 expressed the largest salinity tolerance estimates at both stages and the accession PI 237659 displayed a combination of high salinity index at the germination stage and K/Na salinity at the 4th leaf stage; while the roots of PI 428171 displayed the largest value for K/Na in saline solution. Accessions displayed a wide range of K/Na ratios, whether with or without salinity treatment; however, the overall salinity index was closely associated with K/Na in roots (no salinity). Basic statistics (Mean and standard deviation) and correlation coefficients between three indices calculated on agronomic (0.82), nutrients (0.77), and salinity (0.68) traits (Table 8) indicated that the agronomic index was positively correlated with the other two indices; while nutrients and salinity indices were not. However, when agronomic and salinity indices were combined with total macro- and micronutrients and carotenoids, one accession (PI 94743) displayed large estimates for almost all of these variables; while three others (PI 428156, PI 428171, PI 119423, and 428149) showed reasonably moderate values for all indices and variables (Fig. 8).

Fig. 6. Frequency distribution and relationships between three indices describing agronomic, nutritional and salinity stress traits in 19 einkorn accessions and a check evaluated under field and laboratory conditions in Morris, MN during 2014 to 2017.

Table 7. Basic statistics (Mean and standard deviation) and correlation coefficients between indicators of salinity tolerance estimated on seed germination (150 mM) and on fourth leaf and roots of seedlings grown in saline solution (100 mM) of 19 einkorn accession in Morris, MN. Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Means Std.Dev. Salinity Index 150 mM K/Na 4th Leaf Salinity Index 150 mM 0.6615 0.15540 1.000000 0.325996 K/Na 4th Leaf 104.2198 26.93782 0.325996 1.000000 K/Na Saline 4th Leaf 9.6524 1.20278 0.163458 0.356959 K/Na Root 32.9884 7.50339 0.106537 0.433596 K/Na Saline Root 7.7413 0.83729 -0.130454 -0.080000 Salinity Index 0.6805 0.07654 0.195966 0.729229

Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) K/Na Saline 4th Leaf K/Na Root K/Na Saline Root SalinityIndex Salinity Index 150 mM 0.163458 0.106537 -0.130454 0.195966 K/Na 4th Leaf 0.356959 0.433596 -0.080000 0.729229 K/Na Saline 4th Leaf 1.000000 0.169816 0.150370 0.587655 K/Na Root 0.169816 1.000000 0.182163 0.765392 K/Na Saline Root 0.150370 0.182163 1.000000 0.393518 Salinity Index 0.587655 0.765392 0.393518 1.000000

Fig. 7. Two-way clustering of einkorn accessions based on seed germination (150 mM) and on fourth leaf and roots of seedlings grown in saline solution (100 mM) of 19 einkorn accession in Morris, MN (See Table 7 for details).

Table 8. Basic statistics (Mean and standard deviation) and correlation coefficients between three indices calculated on agronomic, nutrients, and salinity traits in 19 einkorn accessions evaluated under farm and laboratory conditions in Morris, MN between 2014 and 2017.

Correlations (EinkornReport-Morris) Variable Marked correlations are significant at p < .05000 N=20 (Casewise deletion of missing data) Means Std.Dev. AgronomicIndex NutrientIndex SalinityIndex Agronomic Index 0.821800 0.045545 1.000000 0.280984 0.441073 Nutrient Index 0.775813 0.092145 0.280984 1.000000 -0.027001 Salinity Index 0.680500 0.076535 0.441073 -0.027001 1.000000

Fig. 8. Two-way clustering of einkorn accessions based on agronomic index, salinity index and total macro- and micronutrients in kernels of einkorn accessions.

Summary of the analysis of variance for 38 agronomic, nutritional and salinity tolerance traits between four groups of grain yield (low, medium, high, and very high; based on overall grain yield ± standard deviation of 19 einkorn accessions evaluated under field and laboratory conditions in Morris, MN between 2014-29017 are presented in Table 9. Significant differences between “groups” were found for seven variables only. The largest differences were found for mean GY and the agronomic index. However, when subjected to discriminant analysis, the four grain-yield groups (Table 10) were separated on the basis of the agronomic index, total micronutrients, total micronutrients, and salinity index (Table 10A). Multivariate distances were significant between all groups, except between MY and HY (Table 10D). Three groups were 100% correctly classified on the basis of these variables; while, MY was the only group that was not fully correctly classified (Table 10F; Fig. 9).

Table 9. Summary of analysis of variance for 38 agronomic, nutritional and salinity tolerance traits between four groups of grain yield (low, medium, high, and very high; based on overall grain yield ± standard deviation of 19 einkorn accessions evaluated under field and laboratory conditions in Morris, MN between 2014-29017. Analysis of Variance (EinkornReport-Morris) Variable Marked effects are significant at p < .05000 SS df MS SS df MS F Effect Effect Effect Error Error Error Seedling Vigor 294 3 98 821 16 51 1.91175 Early Vigor/100 38 3 13 1335 16 83 0.15309 Plant Height cm 87 3 29 432 16 27 1.07839 Earliness/100 39 3 13 3575 16 223 0.05781 TKWTg 8 3 3 35 16 2 1.25190 Test Wt 4 3 1 10 16 1 3.25282 GY2014 2097445 3 699148 1732842 16 108303 6.45551 GY2015 1149416 3 383139 1818428 16 113652 3.37117 GY2016 95090 3 31697 212664 16 13292 2.38473 GY2017 160463 3 53488 1394991 16 87187 0.61348 Mean GY 424445 3 141482 71188 16 4449 31.79918 Agronomic Index 0 3 0 0 16 0 32.66074 Kernel-L* 2 3 1 32 16 2 0.27113 Kernel-b* 0 3 0 9 16 1 0.23440 Kernel L*/b* 0 3 0 0 16 0 0.45105 Flour L* 0 3 0 5 16 0 0.45852 Flour-b* 1 3 0 4 16 0 1.22776 Flour L*/b* 1 3 0 3 16 0 1.13865 Protein 1 3 0 17 16 1 0.42199 Total Carotenoids 0 3 0 2 16 0 0.94406 Ca 973 3 324 32209 16 2013 0.16104 K 1236825 3 412275 5296702 16 331044 1.24538 Mg 256627 3 85542 824352 16 51522 1.66031 P 1965530 3 655177 8000623 16 500039 1.31025 S 10679 3 3560 211210 16 13201 0.26967 Total Macro Nutrients 9220940 3 3073647 37724934 16 2357808 1.30360 Cu 35 3 12 105 16 7 1.75275 Fe 163 3 54 581 16 36 1.49293 Mn 322 3 107 1052 16 66 1.63241 Zn 101 3 34 427 16 27 1.26562 Total Micronutrients 953 3 318 4476 16 280 1.13587 Analysis of Variance (EinkornReport-Morris) Variable Marked effects are significant at p < .05000 SS df MS SS df MS F Effect Effect Effect Error Error Error Nutrient Index 0 3 0 0 16 0 1.29569 Salinity Index 150 mM 0 3 0 0 16 0 0.10696 K/Na 4th Leaf 1303 3 434 12484 16 780 0.55687 K/Na Saline 4th Leaf 10 3 3 17 16 1 3.28409 K/Na Root 207 3 69 863 16 54 1.27659 K/Na Saline Root 2 3 1 11 16 1 2.21673 Salinity Index 0 3 0 0 16 0 1.42975

Analysis of Variance Variable (EinkornReport-Morris) Marked effects are significant at p < .05000 p SeedlingVigor 0.168391 EarlyVigor/100 0.926171 Plant Height cm 0.386297 Earliness/100 0.981100 TKWTg 0.324075 Test Wt 0.121537 GY2014 0.004527 GY2015 0.044655 GY2016 0.107414 GY2017 0.616102 Mean GY 0.000001 Agronomic Index 0.000000 Kernel-L* 0.845290 Kernel-b* 0.871072 Kernel L*/b* 0.720072 Flour L* 0.715047 Flour-b* 0.332077 Flour L*/b* 0.363414 Protein 0.739783 Total Carotenoids 0.442615 Ca 0.920990 K 0.326217 Mg 0.215393 P 0.305546 S 0.846318 Total Macronutrients 0.307600 Cu 0.196650 Fe 0.254366 Mn 0.221419 Zn 0.319616 Total Micronutrients 0.364438 Nutrient Index 0.310065 Salinity Index 150 mM 0.954800 K/Na 4th Leaf 0.651028 K/Na Saline 4th Leaf 0.048097 K/Na Root 0.316096 Analysis of Variance Variable (EinkornReport-Morris) Marked effects are significant at p < .05000 p K/Na Saline Root 0.053358 Salinity Index 0.270963

Table 10. Discriminant analysis between four grain yield groups (See Table 9, above) based on four traits on 19 einkorn accessions evaluated under filed and laboratory conditions in Morris between 2014 and 2017. A Discriminant Function Analysis Summary (EinkornReport-Morris) Traits No. of vars in model: 4; Grouping: Group (4 grps) Wilks' Lambda: .06768 approx. F (12,34)=5.1080 p< .0001 Wilks' Partial F-remove p-value Toler. 1-Toler. Lambda Lambda (3,13) (R-Sqr.) Agronomic Index 0.394899 0.171395 20.94936 0.000029 0.833833 0.166167 Total Macronutrients 0.126145 0.536556 3.74287 0.038790 0.208319 0.791681 Total Micronutrients 0.103428 0.654406 2.28845 0.126583 0.227968 0.772032 Salinity Index 0.085494 0.791682 1.14025 0.369412 0.748767 0.251233

B Squared Mahalanobis Distances (EinkornReport-Morris) Yield LY MY HY VHY Group LY 0.00000 MY 6.83736 0.00000 HY 9.67220 3.47967 0.00000 VHY 35.63193 21.85293 10.00472 0.00000

C F-values; df = 4,13 (EinkornReport-Morris) Yield LY MY HY VHY Group LY 0 MY 3.33322 0 HY 4.71520 1.41362 0 VHY 21.71321 10.65330 4.877303 0

D p-values (EinkornReport-Morris) Yield LY MY HY VHY Group LY 0 MY 0.043648 0 HY 0.014289 0.284013 0 VHY 0.000012 0.000472 0.012672 0

E Classification Matrix (EinkornReport-Morris) Yield Rows: Observed classifications Group Columns: Predicted classifications Percent LY MY HY VHY Correct p=.30000 p=.20000 p=.20000 p=.30000 LY 100.0000 6 0 0 0 MY 75.0000 0 3 1 0 HY 100.0000 0 0 4 0 E Classification Matrix (EinkornReport-Morris) Yield Rows: Observed classifications Group Columns: Predicted classifications Percent LY MY HY VHY Correct p=.30000 p=.20000 p=.20000 p=.30000 VHY 100.0000 0 0 0 6 Total 95.0000 6 3 5 6

Fig. 9. Discriminant analysis between 19 einkorn accessions and a check based on four traits. Root1: Agronomic traits; 75% and Root 2: Micronutrients and salinity stress; 12.5% of total variation (See Table 10).

Partial Least Squares (PLS) regression models were employed to predict and validate protein content, carotenoids, macronutrients, micronutrients, and salinity index. In each case, the remaining variables were used to predict each of these variables (traits). In addition, each of the agronomic, nutritional and salinity tolerance indices were predicted using only traits involved in defining that particular index. For each PLS model, the data was partitioned into training set (70%) and validation set (30%).

Protein content: Thirty-one traits grouped in five groups explained a wide range of variation in protein content (Table 11). Grain yield was the least (R2=12.9%), while, kernel and flour traits were the most effective (R2=67.4%) in explaining protein variation in these accessions. The remaining groups were almost similar in their R2 values. Most traits had positive coefficients in the PLS models; however, certain traits (e.g., plant height, 1000-kernel weight, most nutrients, and some of the salinity indicators) had negative effects on protein estimates.

Table 11. Partial Least Squares models used to predict protein content in 19 einkorn accessions evaluated under field and laboratory conditions between 2014 and 2017. Dependent Independent variables Coefficients R2 (%) variable Group Variables Protein Agronomic Seedling vigor 0.203 Early vigor 0.159 Plant height -0.342 Earliness 0.036 Test weight 0.063 1000 kernel weight -0.282 49.2 Grain yield GY2014 -0.017 GY2015 0.056 GY2016 -0.203 GY2017 -0.023 12.9 Kernels Carotenoids 0.284 Kernel L* 0.127 Kernel b* 0.125 Kernel L*/b* -0.075 Flour L* 0.200 Flour b* -0.257 Flour L*/b* 0.284 67.4 Nutrients Ca 0.248 K -0.118 Mg -0.012 P -0.125 S -0.073 Cu -0.359 Fe 0.141 Mn -0.043 Zn 0.173 49.6 Salinity Salinity Index (150 mM) -0.131 K/Na 4th leaf 0.762 K/Na 4th leaf (100 mM) -0.276 K/Na Root -0.225 K/Na Root (100 mM) 0.227 45.3

Carotenoids: Groups of traits ranged in their ability to account for variation in carotenoids content; grain yield was the least (R2=19.8); while kernel and flour traits were the most (R2=56.5) effective (Table 12). Obviously, at least in these accessions, the relationship between carotenoids and nutrients was not a strong one (R2=23.9%); on the other hand, agronomic traits (R2=54.6%), and salinity tolerance indicators (46.9%) were more effective than nutrients. Notably, seedling vigor, kernel (but not flour) b*, and Zn, had positive coefficients in predicting total carotenoids.

Table 12. Partial Least Squares models used to predict carotenoids in 19 einkorn accessions evaluated under field and laboratory conditions between 2014 and 2017. Dependent Independent variables Coefficients R2 (%) variable Group Variables Carotenoids Agronomic Seedling vigor 0.246 Early vigor 0.037 Plant height -0.485 Earliness -0.263 Test Weight 0.115 1000 kernel weight -0.065 54.6 Grain yield GY2014 0.155 GY2015 0.194 GY2016 0.243 GY2017 0.276 19.8 Kernels Protein 0.492 Kernel L* -0.254 Kernel b* 0.175 Kernel L*/b* -0.356 Flour L* -0.098 Flour b* -0.039 Flour L*/b* 0.049 56.5 Nutrients Ca 0.129 K 0.042 P -0.029 Mg 0.085 S -0.055 Cu -0.093 Fe -0.066 Mn 0.093 Zn 0.327 23.9 Salinity Salinity Index (150 mM) 0.18 K/Na 4th leaf 0.31 K/Na 4th leaf (100 mM) 0.45 K/Na Root 0.29 K/Na Root (100 mM) 0.38 46.9

Macronutrients: The micronutrients were the best predictors of macronutrients (R2=66.9%), while grain yield was the least (R2=19.5%) (Table 13). However, from a practical point of view, kernel traits, especially kernel b*, flour L*, flour b*, and the carotenoids, had positive coefficients in predicting macronutrients.

Table 13. Partial Least Squares models used to predict macronutrients in 19 einkorn accessions evaluated under field and laboratory conditions between 2014 and 2017. Dependent Independent variables Coefficients R2 (%) variable Group Variables Macronutrients Agronomic Seedling vigor 0.154 Early vigor -0.125 Plant height 0.155 Earliness -0.115 Test weight -0.236 1000 kernel weight 0.118 22.5 Grain yield GY2014 0.053 GY2015 0.337 GY2016 0.339 GY2017 0.421 19.5 Kernels Protein 0.412 Carotenoids 0.098 Kernel L* -0.045 Kernel b* 0.195 Kernel L*/b* -0.135 Flour L* 0.253 Flour b* 0.325 Flour L*/b* -0.229 42.9 Nutrients Cu 0.189 Fe 0.387 Mn 0.461 Zn 0.335 66.9 Salinity Salinity Index (150 mM) 0.192 K/Na 4th leaf -0.168 K/Na 4th leaf (100 mM) 0.302 K/Na Root -0.219 K/Na Root (100 mM) 0.222 28.7

Micronutrients: A similar trend in the ability of different trait groupings in accounting for variation in micronutrients was observed (Table 14). Kernel and flour traits (R2=68.2%), as well as macronutrients had the largest (R2=67.8%), while those of agronomic (R2=23.8) and grain yield (R2=25.6) traits had the smallest R2 values.

Table 14. Partial Least Squares models used to predict micronutrients in 19 einkorn accessions evaluated under field and laboratory conditions between 2014 and 2017. Dependent Independent Coefficients R2 (%) variable variables Group Variables Micronutrients Agronomic Seedling vigor 0.078 Early vigor -0.125 Plant height -0.108 Earliness -0.098 Test weight -0.185 1000 kernel weight 0.017 23.8 Grain yield GY2014 0.201 GY2015 0.165 GY2016 0.155 GY2017 0.109 25.6 Kernels Protein 0.519 Kernel L* -0.118 Kernel b* 0.297 Kernel L*/b* -0.172 Flour L* -0.135 Flour b* 0.238 Flour L*/b* -0.213 68.2 Nutrients Ca 0.175 K 0.207 P 0.231 Mg 0.238 S 0.113 67.8 Salinity Salinity tolerance @ germination (150 mM) -0.139 K/Na 4th leaf -0.044 K/Na 4th leaf (100 mM) 0.138 K/Na Root 0.102 K/Na Root (100 mM) 0.223 22.9

Salinity tolerance: The largest variation in the salinity index (R2=61.6%) was explained by the nutrients; a macronutrient (S) and a micronutrient (Zn) had the largest positive coefficients (Table 15). The next largest R2 was accounted for by kernel traits, including carotenoids (R2=51.4%).

Table 15. Partial Least Squares models used to predict salinity tolerance index in 19 einkorn accessions evaluated under field and laboratory conditions between 2014 and 2017. Dependent Independent Coefficients R2 (%) variable variables Group Variables Salinity Index Agronomic Seedling vigor 0.049 Early vigor -0.145 Plant height -0.155 Earliness -0.229 Test weight 0.027 1000 kernel weight 0.273 43.3 Grain yield GY2014 0.041 GY2015 0.045 GY2016 0.147 GY2017 0.328 27.8 Kernels Protein 0.047 Carotenoids 0.201 Kernel L* -0.021 Kernel b* 0.168 Kernel L*/b* -0.198 Flour L* -0.215 Flour b* 0.068 Flour L*/b* -0.068 51.4 Nutrients Ca -0.281 K -0.044 P -0.232 Mg 0.005 S 0.246 Cu -0.149 Fe -0.154 Mn 0.039 Zn 0.726 61.6

Final Indices: PLS models for each of the agronomic (R2=84.3), nutrients (R2=95.2), and salinity (R2=95.3) indices accounted for most variation (Table 16); while, all traits expressed positive coefficients in these models. Seedling vigor, early plant vigor, GY during 2014 and 2015 had the largest coefficients in predicting the agronomic index. Among the nutrients, K, Mg, P, Cu and Mn, had the largest coefficients. While, all seedling salinity tolerance traits had the largest coefficients in predicting the salinity index.

Table 16. Partial Least Squares models used to predict agronomic, nutrients and salinity indices in 19 einkorn accessions evaluated under field and laboratory conditions between 2014 and 2017. Dependent variable Independent variables Coefficients R2 (%) Agronomic Index Seedling vigor 0.147 Early Vigor 0.245 Plant height 0.096 Earliness 0.112 1000 Kernel weight 0.097 Test Weight 0.035 GY 2014 0.608 GY 2015 0.615 GY 2016 0.053 GY 2017 0.298 84.3 Nutrients Index Protein 0.065 Carotenoids 0.053 Ca 0.081 K 0.259 Mg 0.229 P 0.227 S 0.035 Cu 0.115 Fe 0.042 Mn 0.142 Zn 0.018 95.2 Salinity Index Salinity tolerance @ germination (150 mM) 0.013 K/Na 4th leaf 0.415 K/Na 4th leaf (100 mM) 0.331 K/Na Root 0.483 K/Na Root (100 mM) 0.298 95.3

Finally, we used bivariate regression models between key quality traits to determine the predictability of the traits using others that may be more readily measured or estimated.

Agronomic Index-Protein content: The agronomic index is a poor and unreliable predictor of protein content (Fig. 10). Obviously, four samples can be considered as outliers, thus distorting the relationship between the agronomic index and protein content. Therefore, combining several agronomic traits to predict protein content is not a reasonable or recommended approach. The regression model indicated that neither linear nor quadratic relationships were significant. However, the mean of response for protein was 20.8%, which is larger than the majority estimates of bread and durum wheat species.

Fig. 10. Statistical relationship between agronomic index and protein in seed of einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017.

Agronomic Index-Carotenoids (Fig. 11): The mean response for carotenoids was 8.23. Although the relationship between these traits was not significant; however, variation in the agronomic index explained 18.8% of the variation in total carotenoids (9.3% after adjusting for degrees of freedom); this low value was caused by two outlier samples (one above and one below the regression line).

Fig. 11. Statistical relationship between agronomic index and total carotenoids in seed of einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017.

Agronomic Index-Nutrients Index: Although the majority of samples would fit a linear relationship, a few outliers caused a the small R2 value for this relationship (Fig. 12).

Fig. 12. Statistical relationship between agronomic index and nutrient index in seed of einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017.

Agronomic Index-Salinity Index (Fig. 13): A positive, and relatively larger (compared with previous bivariate models) R2 value for this relationship (0.21; and 0.12, when adjusted) suggests that plants with higher agronomic index (See traits included in its estimation) have better potential for tolerance to salinity.

Fig. 13. Statistical relationship between agronomic index and salinity index in seed of einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017.

Yield Groups-Accessions-Agronomic Index-Salinity Index (Fig. 14): The four-way classification can be used to identify accessions with above-average agronomic and salinity indices, while taking grain yield into consideration. For example, with a mean response of 0.68 for salinity index (Fig. 13), two accessions (PI 1676526, and PI 170196) are candidates for further agronomic and salinity tolerance improvement.

Fig. 14. Four-way classification of einkorn accessions, grain yield groups, salinity index (upper value) and agronomic index (lower value).

Thousand-kernel weight-Salinity Index (Fig. 15): A positive, but non-linear relationship between these two traits indicates that, there should be an optimum kernel weight that confers a reasonably good salinity tolerance. Three accessions (above the regression line) suggest that the 30 mg kernels helped produce seedlings with the highest salinity tolerance.

Fig. 15. Statistical relationship between 1000-kernel weight and salinity index in seed of einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017.

Macronutrients-Micronutrients (Fig. 16): A strong linear relationship between macro- and micronutrients is expected with R2 of >0.60; however, a few accessions were able to combine large (above mean values of both variables) nutrients content. The top two accessions are candidates for further research and improvement of other agronomic and nutritional traits.

Fig. 16. Relationship between total macronutrients and total micronutrients in kernels of einkorn accessions evaluated under field conditions in Morris, MN between 2014 and 2017.

Appendix C. Distribution, normality test and basic statistics for 38 variables in 19 einkorn accessions and a check evaluated under field and laboratory conditions in Morris, MN between 2014 and 2017.