Abstract
There are many statistics to assess cultivar yield stability: some deal with genotype × environment interaction (GEI) patterns ( b i , and interaction principal component scores), some with GEI noise ( s 2 di ), some with total GEI sum of squares ( ), and others with rank changes [ S (1)]. Our objective was to assess the repeatability in soybean [ Glycine max (L.) Merr.] of different yield stability statistics. We evaluated nine statistics derived from additive main effects and mnitiplicative interaction (AMMI) analyses, regression coefficients from analyses using either yield‐based or plant height‐based environmental indices, as well as , S (1). Yield data were from Arkansas trials of maturity group V cultivars from 1987 to 1994. Stability analyses were warranted because GEI was significant, partitioning of GEl sum of squares by AMMI or regression techniques was generally significant, and all stability statistics found significant differences among the cultivars. The AMMI‐derived stability statistics produced information on cultivar stability that was similar to that produced by s 2 di and . The repeatability of cultivar rankings for s 2 di , , S (1), and most AMMI statistics, estimated in different single‐ or two‐year periods were low, indicating that these statistics would not be useful to breeders or growers selecting for stability. The repeatability of two regression coeffacient statistics and one AMMI‐derived statistic were moderate, particularly when estimated over environments from 2 yr. Each of the three statistics relates to a different concept of stability and may be useful to growers and breeders attempting to select cultivars with predictable yield across environments.
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Claims 8
Claim · model-written summary
Genotype-by-environment interaction was significant.
- Taxon
- Soybean
Glycine maxt0 - Outcome
- GEIe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“GEI was significant”
t0“Soybean”
Claim · model-written summary
AMMI or regression partitions of GEI sums of squares were generally significant.
- Taxon
- SoybeanGlycine maxt0
- Outcome
- partitioning of GEl sum of squarese0
- Comparator
- AMMI or regression techniquese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“partitioning of GEl sum of squares by AMMI or regression techniques was generally significant”
t0“Soybean”
Claim · model-written summary
All stability statistics detected significant cultivar differences.
- Taxon
- SoybeanGlycine maxt0
- Outcome
- significant differences among the cultivarse0
- Comparator
- stability statisticse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“all stability statistics found significant differences among the cultivars”
t0“Soybean”
Claim · model-written summary
AMMI-derived statistics gave cultivar stability information similar to the named comparator statistics.
- Taxon
- SoybeanGlycine maxt0
- Outcome
- information on cultivar stabilitye0
- Comparator
- s 2 die0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The AMMI‐derived stability statistics produced information on cultivar stability that was similar to that produced by s 2 di and .”
t0“Soybean”
Claim · model-written summary
Repeatability of cultivar rankings for S (1) and most AMMI statistics was low.
- Taxon
- SoybeanGlycine maxt0
- Outcome
- repeatability of cultivar rankingse0
- Direction
- lowe0
- Comparator
- S (1)e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The repeatability of cultivar rankings for s 2 di , , S (1), and most AMMI statistics, estimated in different single‐ or two‐year periods were low”
t0“Soybean”
Claim · model-written summary
Two regression coefficient statistics had moderate repeatability, especially over two years of environments.
- Taxon
- SoybeanGlycine maxt0
- Outcome
- repeatabilitye0
- Direction
- moderatee0
- Qualifier
- environments from 2 yre0
- Comparator
- two regression coeffacient statisticse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The repeatability of two regression coeffacient statistics and one AMMI‐derived statistic were moderate, particularly when estimated over environments from 2 yr”
t0“Soybean”
Claim · model-written summary
One AMMI-derived statistic had moderate repeatability, especially over two years of environments.
- Taxon
- SoybeanGlycine maxt0
- Outcome
- repeatabilitye0
- Direction
- moderatee0
- Comparator
- one AMMI‐derived statistice0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The repeatability of two regression coeffacient statistics and one AMMI‐derived statistic were moderate, particularly when estimated over environments from 2 yr”
t0“Soybean”
Claim · model-written summary
The three moderately repeatable statistics may help select cultivars with predictable yield.
- Taxon
- SoybeanGlycine maxt0
- Outcome
- select cultivars with predictable yield across environmentse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Each of the three statistics relates to a different concept of stability and may be useful to growers and breeders attempting to select cultivars with predictable yield across environments”
t0“Soybean”