Variety Testing Theory and Practice. Brian Diers University of Illinois

Transcription

1 Variety Testing Theory and Practice Brian Diers University of Illinois

2 Outline Why variety testing is important Site selection and experimental design Data collection Traits, how to score Data analysis IP protection Soybean diseases Plant breeding

3 University of Illinois Morrow Plots Oldest Experimental Field in the USA "The wealth of Illinois is in her soil, and her strength lies in its intelligent development." -Andrew Sloan Draper, President, University of Illinois,

4 Reasons for yield and agronomic testing Testing of experimental lines in breeding programs to identify those that should be advanced and released. Testing released varieties to identify those best adapted to field environments. Testing agronomic methods and inputs to identify ways to increase profitability.

5 S e e d Y i e l d ( b u / a c ) Y e a r S e e d Y i e l d ( k g / h a ) Yield testing is key to crop improvement 5 0 U S A S o y b e a n Y i e l d 4 0 Akaike Information Criterion (AIC) indicates that the two segment model most probable

6 Seed Yield kg/ha Yields in Africa are equal to the USA in the 1930 s USA Average Africa Year

7

8 University of Illinois Variety Testing 13 locations grouped into 5 regions. Location Conventiona l Trials Liberty Trials Roundup Trials Entries Excel PDF Excel PDF Excel PDF Region 1: Erie, Mt. Morris & DeKalb Region 2: Monmouth, Goodfield & Dwight Region 3: Perry, New Berlin & Urbana Region 4: Belleville & St. Peter Region 5: Elkville & Harrisburg Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF Excel PDF

9 2016 Soybean Test Results Region 2: Roundup Resistant 2 yr 3 yr Regional Results Monmouth Goodfield Dwight Avg Avg Regional Yield Maturity Lodging Height Yield Yield Yield Yield Yield Protein Oil COMPANY NAME ST 1 bu/a Date in bu/a bu/a bu/a bu/a 13% Roundup Resistant Early (MG ) Asgrow AG28X7 ACC / Asgrow AG30X6 ACC / Channel 2617R2X ACC / Channel 2817R2X ACC / Channel 3116R2X ACC / Dyna-Gro S31RY86 ACC / Great Lakes 3055NRX AST / Hisoy HS 26X60 CC / Hisoy HS 27X60 CC / Hisoy HS 28A42 ACC / Hisoy HS 28X50 ACC / Hisoy HS 29X60 CC / Hisoy HS 31X60 CC / Monier M2766RX RAN / Monier M2837R2 RAN / Monier M2947R2 RAN / Monier M3016RX RAN / Munson 8284R2Y INTS / Munson 8306R2Y INTS / Munson 9286RR2X INTS / Munson 9316RR2X INTS / Nutech 7279 GIA / Nutech 7307 GIA / Pfister 29R25 CC / Pfister 30R205 CC / PowerPlus 28H5 EEGI / PowerPlus 31W7 PRSLD / Renk RS276NX CMXO / Renk RS306NX CMXO / Renk RS316NR2 CMXO / Renk RS317NX CMXO / Roeschley 2957CRR2 CMXV / Steyer 3110XR SS / Stone 2RX2627 ACC / Stone 2RX2827 ACC / Stone 2RX3116 ACC / Sun Praire SP31RX6 ACC / AVERAGE L.S.D. 25% LEVEL COEFF. OF VAR. (%)

10 Setting up variety tests Sites need to be predictive of farmer s fields. Field sites should be productive. Results from unproductive fields often do not differentiate varieties as all varieties will have similar poor performance. Fields should be as uniform as possible. Uniform for soil type and fertility As little slope as possible. Consistent lighting (no shade)

11 Need to use an appropriate experimental design Experimental design makes it possible to know if differences observed are meaningful. In experiments we: Randomize Gives all treatments an equal chance of receiving a treatment. Assures unbiased estimates of treatment means and experimental error. Replicate Makes it possible to estimate an experimental error and a more precise measure of treatment effects.

12 0.5 m Plot size Plots in the experiment will be 5 meters x 2 meters 4 rows wide 5 meters long 1 meter between ranges 0.5 meter row spacing 5 cm between plants 1 meter alley between ranges of plots Harvest middle 2 rows 5 m 1 m

13 1 m 0.5 m 5 m 1 m

14 Arrange the field so replications are as square as possible Each range of plots is 6 m (5 m plot and 1 m alley). Individual plots are 2 m wide. Assuming 30 varieties, arrange replications 10 plots wide (20 meters) and 3 ranges deep (18 meters). Surround field with fill plots Rep 1 Rep Rep

15 Arrange reps so field differences are across reps Maximize the differences in slope, soil type and fertility across reps. Differences between reps is removed by the rep effect in the analysis Differences within reps will be part of the error effect which reduces the ability to show genotypes are significantly different. Rep 1 Rep 2 Rep 3 Slope

16 Data collection Need to consider what traits are important. Just because you can measure or count it, doesn t mean that you need to score it. What is the focus of the project? Identify soybean varieties that are adapted and high yielding in Malawi. Need to identify what traits need to be measured to determine this. Seeds/pod? Pods/plant? Biomass? Weight of nodules? Focus on measure a few traits well instead of many poorly.

17 Data collection Plant emergence Flower color during flowering Pubescence color at maturity Flowering date Maturity date Plant lodging Plant height Seed yield Plant shattering 100 seed weight

18 Plant emergence Why score? Determine plant stands to identify if seed vigor influences yield When score? After emergence is completed Count the number of plants in the two middle rows

19 Flower color Why score? Quality control When score? At flowering Flowers are either purple or white in soybean varieties (plots can be mixed).

20 Pubescence color Why score? Quality control When score? At maturity Score as grey or tawny (plants can be light tawny but this is difficult to score) (plots can be mixed)

21 Date of R1 (First open flower) Why score? Determine adaptation When score? At flowering Score the date when 50% of the plants in a plot have at least 1 open flower Score the plots about every three days Need to push the canopy over to see flowers

22 Date of R8 (Pod maturity) Why score? Determine adaptation When score? At maturity Score the date when 85% of the pods in a plot have turned to their mature color Score the plots about every three days Be sure to focus on pod color, not stem color

23 Maturity scoring can be obscured by green stem disorder Normal maturity Green stem disorder

24 Lodging Why score? Measure the average of how upright the plants are in the plot When score? At maturity Score plots on a 1-5 scale (use 0.5 increments) Score when the plots are mature

25 Lodging What scores would you give these plots?

26 Plant height Why score? Measure adaptability of varieties When score? At maturity Measure the height of plants (in cm) from the soil surface to the top node of plants Score when the plots are rated mature cm

27 Seed yield Why score? Yield is critical for variety selection When score? After maturity and before shattering Harvest the middle 2 rows of the 4-row plots Thresh and winnow the grain Measure the weight of grain at least 5 days after harvest from each plot

28 Plant shattering Why score? Farmers need to know how much varieties shatter. When score? 2 weeks after maturity Rate each plot on a 1 to 5 scale with 1=no shattering and 5=100% of the pods shattered

29 100 seed weight Why score? Seed size is important to growers and processors When score? After seeds are dry Count and weigh 100 seed from each plot

30 Data analysis Analyses will be done across environments Collaborators will submit data from each plot to project organizers (Diers, Chigeza, Klauser). The data from each environment and across environments will be subjected to analysis of variance so statistics such as least significant differences can be calculated. GxE analyses will be conducted to identify environments that varieties are similarly adapted. Data will be placed in a database that is being developed by the Syngenta Foundation

31 2016 Soybean Test Results Region 2: Roundup Resistant 2 yr 3 yr Regional Results Monmouth Goodfield Dwight Avg Avg Regional Yield Maturity Lodging Height Yield Yield Yield Yield Yield Protein Oil COMPANY NAME ST 1 bu/a Date in bu/a bu/a bu/a bu/a 13% Roundup Resistant Early (MG ) Asgrow AG28X7 ACC / Asgrow AG30X6 ACC / Channel 2617R2X ACC / Channel 2817R2X ACC / Channel 3116R2X ACC / Dyna-Gro S31RY86 ACC / Great Lakes 3055NRX AST / Hisoy HS 26X60 CC / Hisoy HS 27X60 CC / Hisoy HS 28A42 ACC / Hisoy HS 28X50 ACC / Hisoy HS 29X60 CC / Hisoy HS 31X60 CC / Monier M2766RX RAN / Monier M2837R2 RAN / Monier M2947R2 RAN / Monier M3016RX RAN / Munson 8284R2Y INTS / Munson 8306R2Y INTS / Munson 9286RR2X INTS / Munson 9316RR2X INTS / Nutech 7279 GIA / Nutech 7307 GIA / Pfister 29R25 CC / Pfister 30R205 CC / PowerPlus 28H5 EEGI / PowerPlus 31W7 PRSLD / Renk RS276NX CMXO / Renk RS306NX CMXO / Renk RS316NR2 CMXO / Renk RS317NX CMXO / Roeschley 2957CRR2 CMXV / Steyer 3110XR SS / Stone 2RX2627 ACC / Stone 2RX2827 ACC / Stone 2RX3116 ACC / Sun Praire SP31RX6 ACC / AVERAGE L.S.D. 25% LEVEL COEFF. OF VAR. (%)

32 IP protection It is critical to protect the intellectual property of varieties in the tests. Breeders have large investments in variety development that should not be compromised. As an inbred crop, soybean can easily be stolen and propagated. If this happens, soybean developers may not bring new germplasm into Malawi.

33 IP protection Variety names will be coded in the field and names will not be provided until the test is completed. Talk about the importance of IP protection at field days. Have attendees at field days sign an agreement that they will respect IP and not steal seed.

34 Questions?

35 Soybean breeding Chicago University of Illinois

36 H a r v e s t e d A r e a ( m i l l i o n a c o r h a ) P r o d u c t i o n ( m i l l i o n t o r b u ) a c h a Soybean Area and Production in U S A S o y b e a n the A r eusa a a n d P r o d u c t i o n t b u H a r v e s t e d A r e a > < P r o d u c t i o n Y e a r USDA National Agricultural Statistics Service

37 S e e d Y i e l d ( b u / a c ) S e e d Y i e l d ( k g / h a ) On-Farm Soybean Yield Gains USA Linear 23 kg ha -1 year -1 U S A S o y b e a n Y i e l d Y e a r

38 S e e d Y i e l d ( b u / a c ) S e e d Y i e l d ( k g / h a ) On-Farm Soybean Yield Gains USA Pre-breakpoint 21 kg ha -1 year -1 Post-breakpoint U S A S o y b e29 a n kg Yha i e -1 l dyear Akaike Information Criterion (AIC) indicates that the two segment model most probable Y e a r

39 Genetic Gain Evaluation How much of the yield gain in the USA is the result in improved genetics? Yield increases are the result of improved genetics, agronomics, environmental changes, and their interactions. How have soybean plants been altered to achieve greater yields? S e e d Y i e l d ( b u / a c ) U S A S o y b e a n Y i e l d Y e a r S e e d Y i e l d ( k g / h a )

40 B Soybean Maturity Groups II III IV (1000 bu = tons)

41 Genetic Gain Study Collected sets of MG II, III and IV soybean cultivars from the 1920 s to present day. Included modern commercial cultivars from Syngenta, Monsanto and Pioneer. In cultivars grown: 15 MG II locations 13 MG III locations 14 MG IV locations

42 Genetic Gain Study Collected sets of MG II, III and IV soybean cultivars from the 1920 s to present day. Included modern commercial cultivars from Syngenta, Monsanto and Pioneer. In cultivars grown: 15 MG II locations 13 MG III locations 14 MG IV locations

43 Genetic Gain Study Collected sets of MG II, III and IV soybean cultivars from the 1920 s to present day. Included modern commercial cultivars from Syngenta, Monsanto and Pioneer. In cultivars grown: 15 MG II locations 13 MG III locations 14 MG IV locations

44 Genetic Gain Study Collected sets of MG II, III and IV soybean cultivars from the 1920 s to present day. Included modern commercial cultivars from Syngenta, Monsanto and Pioneer. In cultivars grown: 15 MG II locations 13 MG III locations 14 MG IV locations

45 S e e d Y i e l d ( b u / a c ) Soybean Genetic Yield Improvement Y e a r S e e d Y i e l d ( k g / h a ) 7 0 MG II 23 kg ha -1 year -1 Post-breakpoint 31 kg ha -1 year -1 MG III 23 kg ha -1 year -1 Post-breakpoint 29 kg ha -1 year -1 MG S oiv y 19 b e kg a n hag -1 eyear n e -1 t ipost-breakpoint c Y i e l d I m p r o26 v kg e mhae -1 nyear t -1 M G I I M G I I I M G I V

46 S e e d Y i e l d ( b u / a c ) Soybean Genetic Yield Improvement Y e a r S e e d Y i e l d ( k g / h a ) 7 0 MG II 23 kg ha -1 year -1 Post-breakpoint 31 kg ha -1 year -1 MG III 23 kg ha -1 year -1 Post-breakpoint 29 kg ha -1 year -1 MG S oiv y 19 b e kg a n hag -1 eyear n e -1 t ipost-breakpoint c Y i e l d I m p r o26 v kg e mhae -1 nyear t -1 M G I I M G I I I M G I V Are the current yield increases consistent with breeding efforts?

47 S e e d Y i e l d ( b u / a c ) S e e d Y i e l d ( k g / h a ) S e e d Y i e l d ( b u / a c ) S e e d Y i e l d ( k g / h a ) Soybean Genetic Yield Improvement On-farm improvement 23 kg ha -1 yr -1 Genetic improvement MG II 23 kg ha -1 yr -1, MG III 23 kg ha -1 yr -1, MG IV 19 kg ha -1 yr -1 U S A S o y b e a n Y i e l d S o y b e a n G e n e t i c Y i e l d I m p r o v e m e n t M G I I M G I I I M G I V Y e a r Y e a r

48 Y i e l d ( b u / a c ) Y i e l d ( k g / h a ) On-Farm Soybean Yield Gains MG II & III 27 kg ha -1 year -1 / MG II, III & IV 25 kg ha -1 year -1 / USA 23 kg ha -1 year -1 / MG IV 21 kg ha -1 year -1 S o y b e a n Y i e l d T r e n d s : U S A & B y M G ) M G I I & I I I M G I I, I I I, I V U S A M G I V Y e a r

49 Y i e l d ( b u / a c ) Y i e l d ( k g / h a ) On-Farm Soybean Yield Gains MG II & III 27 kg ha -1 year -1 / MG II, III & IV 25 kg ha -1 year -1 / USA 23 kg ha -1 year -1 / MG IV 21 kg ha -1 year -1 S o y b e a n Y i e l d T r e n d s : U S A & B y M G ) Approximately 2/3 of yield increases the result of genetic improvement M G I I & I I I M G I I, I I I, I V U S A M G I V Y e a r

50 S e e d O i l & P r o t e i n ( g k g - 1 ) Changes in Seed Protein and Oil Across MG Protein -0.2 g kg -1 yr -1 / Oil 0.1 g kg -1 yr Protein M G I I M G I I I M G I V Oil Y e a r o f C u l t i v a r R e l e a s e

51 Vegetative Growth Duration (Days from V1 to R1) with Early and Late Planting New cultivars have a shorter vegetative period because they flower earlier than old cultivars Vegetative Growth Duration (Days) MGII - May PD; y = (±0.01)x MGII - June PD; y = (±0.01)x MGIII - May PD; y = (±0.01)x MGIII - June PD; y = (±0.01)x Year of Cultivar Release

52 Reproductive Growth Duration (Days from R1 to R7) with Early and Late Planting New cultivars have a longer reproductive period than old cultivars. Reproductive Growth Duration (Days) MGII - May PD; y = (±0.04)x-82.2 MGII - June PD; y = 0.058(±0.03)x-43.9 MGIII - May PD; y = 0.197(±0.03)x MGIII - June PD; y = (±0.02)x Year of Cultivar Release

53 Cultivar Yield Stability Analysis Yield stability (Finlay and Wilkinson, 1963) calculated for each cultivar by regressing cultivar yield on environment yields. These stability coefficients were then regressed on the year of release. Cult A b = 1 Cult B b = 0 Environment Mean Yield

54 Stability Coefficient Stability Analysis Yield stability (Finlay and Wilkinson, 1963) calculated for each cultivar by regressing cultivar yield on environment yields. These stability coefficients were then regressed on the year of release. No change in stability across years Year of Release

55 Stability Coefficient Stability Analysis Yield stability (Finlay and Wilkinson, 1963) calculated for each cultivar by regressing cultivar yield on environment yields. These stability coefficients were then regressed on the year of release. New cultivars less stable Year of Release

56 Y I e l d S t a b i l i t y C o e f f i c i e n t Stability Analysis M G I I 1. 2 M G I I I M G I V Y e a r o f C u l t i v a r R e l e a s e

57 Cultivar Yield Why Are New Cultivars Less Stable? Compared performance of 6 newest and 6 oldest cultivars across a range of environments New cultivars greater yielding in both poor and good environments New cultivars Old cultivars Environment Mean Yield

58 Cultivar Yield Why Are New Cultivars Less Stable? Compared performance of 6 newest and 6 oldest cultivars across a range of environments New cultivars greater yielding only in good environments New cultivars Old cultivars Environment Mean Yield

59 C u l t i v a r Y i e l d ( k g h a - 1 ) New Cultivars Outperformed Old Cultivars at High and Low Yield Environments M G I V New cultivars Old cultivars S i t e - Y e a r M e a n Y i e l d ( k g h a - 1 )

60 C u l t i v a r Y i e l d ( k g h a - 1 ) Yield of New and Old Cultivars at Each Environment M G I I M G I I I M G I V New cultivars Old cultivars S i t e - Y e a r M e a n Y i e l d ( k g h a - 1 )

61 Correlations Between Year of Release and Disease Resistance - Glen Hartman Foliar Ratings (R6) Urbana 2010 Urbana 2011 Arthur 2011 Bacterial diseases -0.82*** *** Brown spot -0.58*** NS -0.65*** Downy mildew 0.58*** NS NS Insect feeding 0.75*** NS NS Root and stem ratings (R8) Anthracnose -0.69*** NT NS Cercospora blight -0.45** NT NS Charcoal rot -0.38* NT NS Pod and stem blight -0.65*** NT NS Root health -0.74*** NT -0.41**

62 Soybean Breeding Most of the yield improvements of varieties are the result of traditional breeding. Make crosses Select the best lines Develop experimental lines Slow and steady wins the race.

63 Modeling Yield Lisa Ainsworth USDA-ARS

64 Changes in Parameters Over Time Interception Conversion Harv. index Koester, R.P. et al. J. Experimental Botany. doi: /jxb/eru187.

65 Breeding conclusions Soybean yields are increasing the USA Increases in soybean production needed Much of the yield increases that have occurred is through the genetic improvement of varieties Yield improvement occurred together with changes in protein and oil concentration and greater light interception efficiency, conversion and harvest index