Adaptability and Stability Study of Selected Sweet Sorghum

Research ArticleAdaptability and Stability Study of Selected SweetSorghum Genotypes for Ethanol Production under DifferentEnvironments Using AMMI Analysis and GGE Biplots

Justice Kipkorir Rono1 Erick Kimutai Cheruiyot2

Jacktone Odongo Othira1 Virginia Wanjiku Njuguna2 Joseph Kinyoro Macharia2

James Owuoche2 Moses Oyier2 and Alex Machio Kange2

1Department of Biochemistry and Molecular Biology Egerton University PO Box 536-20115 Egerton Kenya2Department of Crops Horticulture and Soil Egerton University PO Box 536-20115 Egerton Kenya

Correspondence should be addressed to Justice Kipkorir Rono ronojustice8gmailcom

Received 6 June 2016 Revised 24 August 2016 Accepted 8 September 2016

Academic Editor Tadashi Takamizo

Copyright copy 2016 Justice Kipkorir Rono et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The genotype and environment interaction influences the selection criteria of sorghum (Sorghum bicolor) genotypes Eight sweetsorghum genotypes were evaluated at five different locations in two growing seasons of 2014 The aim was to determine theinteraction between genotype and environment on cane juice and ethanol yield and to identify best genotypes for bioethanolproduction in Kenya The experiments were conducted in a randomized complete block design replicated three times Sorghumcanes were harvested at hard dough stage of grain development and passed through rollers to obtain juice that was then fermentedto obtain ethanol Cane juice and ethanol yield was analyzed using the additive main effect and multiplication interaction model(AMMI) and genotype plus genotype by environment (GGE) biplot The combined analysis of variance of cane and juice yield ofsorghum genotypes showed that sweet sorghum genotypes were significantly (119875 lt 005) affected by environments (E) genotypes(G) and genotype by environment interaction (GEI) GGE biplot showed high yielding genotypes EUSS10 ACFC00312 SS14and EUSS11 for cane yield EUSS10 EUSS11 and SS14 for juice yield and EUSS10 SS04 SS14 and ACFC00312 for ethanol yieldGenotype SS14 showed high general adaptability for cane juice and ethanol yield

1 Introduction

Sweet sorghum is gaining popularity for ethanol produc-tion due to its high sugar level in their stem juice It iswidely grown for food feed and fuel in semiarid tropics ofAsia Africa America and Australia [1] due to its droughttolerance Drought is regarded as important abiotic stresscausing yield instability and food insecurity [2] Droughtcan be mitigated through irrigation as one of the availableoptions however developing countries find it challengingdue to huge capital investment The introduction of drought-tolerant crops such as sorghum in the arid and semiaridlands (ASALs) remains the most desirable alternative Sweetsorghum accumulates high amount of fermentable sugarsin the stem Uses of sweet sorghum include brewing for

both industrial and local products and baking and homeconsumption as food Sorghum is a multipurpose crop whichcan be adopted in semiarid parts of the country to help in theeradication of poverty through the supply of grain for foodand sale of the stem to distilleries for ethanol production

Studies of adaptability and stability provide informationabout the behaviour of each genotype under different envi-ronmental conditions The phenotypic performance of eachgenotype is influenced by abiotic and biotic factors somegenotypes may perform well in one environment but fail inseveral others [3]These factors include rainfall temperaturesoil fertility light pests and diseases that vary across loca-tions and significantly influence yield ability of crop varietiesThese factors make it difficult to establish the superiority ofcultivar across diverse environments [4] A major drawback

Hindawi Publishing Corporatione Scientific World JournalVolume 2016 Article ID 4060857 14 pageshttpdxdoiorg10115520164060857

2 The Scientific World Journal

Table 1 Description of soil components of testing locations

County site lowastlowastAEZ pH Soil type Nitrogen () Phosphorous (ppm)Siaya Masumbi lowastLM1 44 Clay loam 011 98Siaya Sinyanya LM3 54 Sandy clay loam 017 88Busia Mundika I LM2 44 Sandy clay loam 009 64Busia Mundika II LM2 44 Sandy clay loam 009 64Kisumu Sagam LM1 58 Sandy clay loam 012 85Kisumu Nyahera LM3 60 Sandy clay loam 015 55lowastLower midland zone lowastlowastAgroecological zone

in the selection of genotypes with high yielding capacity indifferent environments is genotype by environment interac-tion New genotypes must be stable for yields and shouldbe stable across environments or suited to target regions[5] Yield is controlled by the complex polygenic system andstrongly varies depending on environmental conditions [6]Stability analysis is an important step in developing cultivarsfor a wide range of environments or for a specific location[7] Genotype by environment interaction has to be studiedfor yields which are cane juice and ethanol in our case asthey are considered the most important economic traits [8]

The genotype environment and the genotype by envi-ronment interactions impact crop performance Genotype byenvironment interaction (GEI) complicates breeding testingand selection of superior genotypes [9] The GEI changes therankings of genotypes in various environments an increasein GEI diminishes the correlation between genotypic andphenotypic qualities making it hard to distinguish superiorgenotype across environments [10]Theperformance stabilityconcept is therefore important in analyzing GEI in order torecommend genotypes to test environments Additive maineffects and multiplicative interactions (AMMI) analysis isused to determine stability of genotypes across locationsusing the principal component axis (PCA) scores and AMMIstability values (ASV) while genotype plus genotype by envi-ronment (GGE) analysis is effective method which is basedon principal component analysis to fully explore multienvi-ronment trials [11] Average environment coordinates (AEC)of GGE biplot separates entries with below-average meansfrom thosewith above-averagemeans [12] Stability of variouscrops has been studied by applying AMMI and GGE biplotssuccessfully in soybean (Glycine max L Meril) [13] sweetpotatoes (Ipomoea batatas) [8] pepper (Capsicum annuum)[6] finger millet (Eleusine coracana) [14] wheat (Triticumaestivum) [15] grain sorghum [16] and rice (Oryza sativa)[17] GGE and AMMI analysis were applied to determinestability and adaptability of eight sorghum genotypes grownin five different ecological zones

2 Materials and Methods

21 Site Description Sweet sorghum field experiments werecarried out in Kisumu Siaya and Busia Counties ofKenya The specific sites were Sinyanya (00∘06101584068510158401015840S034∘08101584066010158401015840E) at 1168m above sea level (ASL) Masumbi(00∘01101584073010158401015840N 034∘21101584087410158401015840E) at 1370m ASL both in SiayaCounty Mundika (00∘24101584056610158401015840S 034∘07101584093110158401015840E) at 1222m

05

101520253035

Masumbi Sinyanya Sagam Nyahera

Agroecological environments

MundikaII

MundikaI

4 m

onth

sM

ean

daily

tem

pera

ture

(∘C)

Figure 1 Mean daily temperature during sorghum growing period

ASL in Busia Nyahera (00∘0021015840527810158401015840S 034∘391015840035910158401015840E) at1387m ASL and Sagam (00∘031015840208610158401015840N 034∘321015840310610158401015840E) at1216m ASL both in Kisumu County

All sites fell within the same agroecological zone lowermidland (LM) the difference in yield was due to differencein sub agroecological zones as depicted in Table 1 Theenvironments in lower midland zones 1 2 and 3 receiveannual average rainfall of 1800ndash2000 1550ndash1800 and 1200ndash1420mm respectively [18] In general the soil in these areaswas sandy clay loam acidic or slightly acidic (pH = 44ndash60)and was poor in nitrogen and phosphorous Sinyanya wascharacterized by highmeanmaximum temperature (Figure 1)and lower precipitation (Figure 2) The thermal zone 1 (LM1)in Kenya records mean daily temperature and altitude rangeof 222ndash210∘C and 1200ndash1440m ASL respectively The meandaily temperature and altitude range are 222ndash214∘C and1200ndash1350m ASL respectively for LM2 and 227ndash210∘C and140ndash1500m ASL for LM3 [19] The lower midland zones 12 and 3 are regarded as sugarcane marginal sugarcane andcotton zones respectively [20]

22 Experimental Design Eight sweet sorghum genotypeswere grown in a randomized complete block design (RCBD)The genotypes were EUSS10 EUSS11 and EUSS17 as candi-dates with the controls being ACFC00321 SS04 SS14 SS21and SS17 Sowing was done on 18th March in Sinyanya andMasumbi and 19th March 2014 in Mundika for first seasonSowing in the second season was done on 13 September 2014for both Mundika and Sagam while Nyahera was planted on24 September 2014 Genotypes were sown in plots measuring4 times 25m in RCBD with three replications Each plot

The Scientific World Journal 3

0

200

400

600

800

1000

Masumbi

Rainfall

Sinyanya Sagam Nyahera

Tota

l rai

nfal

l (m

m) (

4 m

onth

s)


MundikaII

MundikaI

Figure 2 Cumulative rainfall during sorghum growing period

consisted of four rows of sorghum at a spacing of 60 cm bydrill and the blockswere separated by 15mpath Triple super-phosphate fertilizer was applied uniformly to all plots at a rateof 172 kg per ha before sowing Control of weeds was donemanually using hoes three weeks after seedling emergenceand sorghum were thinned to a spacing of 10 cm within therow And then calcium ammonium nitrate (25N) was top-dressed at the rate of 20 kgNha Birds guarding was effectedsoon after the panicles formed to prevent grains damage

23 Data Collection Emergence was observed in all plotstwo weeks after planting and stand counts were determinedfor all sorghum experimental units Days to 50 headingwere determined by calculating a number of days fromsowing to when 50 of the sorghum heads in each plot hadformed panicle At dough stage of grain plant height of eachgenotype was determined and their panicles were harvestedThree randomly selected plants from each genotype in allreplicates were used for recording plant height Plant heightwas measured from base of the stem to tip of panicle and dataaveraged across three plants

Harvesting took place on 9 July 2014 at the three sitesMasumbi Mundika and Sinyanya for the first season whileSagam and Mundika were harvested on 12 December 2014and Nyahera on 29 December 2014 for the second seasonEight different genotypes were harvested at hard dough stageof grain taking plants in two inner rows of each plot Theleaves were stripped off by hand from harvested stalk andpanicles removed using secateurs The harvested stalks wereweighed with a weighing balance to get fresh cane weightand then transported to the laboratory for juice extractionJuice from the stalk was extracted in one roller crusher (FuanLiyuan China type YC 80B-4) and strained through a sieveinto a juice containerThe volume of juice was measured andBrix () was taken using hand refractometer

24 Ethanol Analysis Juice was sampled from each plottaking 100mL for fermentation Yeast (15) Saccharomycescerevisiae was added to juice and fermentation processcarried out at 35∘C for four days and then distilled to obtainethanol whose volume was determined Refractometer (RFM3330 code 25-330 Bellinghant Stanley limited) was used to

determine the concentration of ethanol The refractive indexof distillate was taken and then compared with that of astandard curve Absolute ethanol was mixed with distilledwater to give the concentration of 0 5 10 15 20 25 and 30ethanol whose refractive index was taken to obtain standardcurve showing the relationship between the refractive indexand percent of ethanol in the distillate

25 Statistical Analysis Statistical computations were carriedout using Genstat software version 151 (VSN Internationallimited 2012) for AMMI and GGE biplot analysis Thegraphic representation of genotypes and environments byAMMI analysis results from a model of main additive effectsand multiplicative interaction [21] This model is expressedmathematically by

119884GEr = 120583 + 120572G + 120573E +sum119899

120582119899120574G119899120575E119899 + 120588GE + 120576GEr (1)

where 119884GEr is mean yield of genotype G in the environmentE for replication r 120583 is grand mean 120572G is deviation of thegenotype G from the grand mean 120573E is deviation of theenvironment E from the grand mean 120582

119899is singular value for

the interaction principal component axis (IPCA) 119899 120574G119899 is thePCA score of a genotype for PCA axis 119899 120575E119899 is the environ-mental PCA score for PCA axis 119899 120588GE is AMMI residual and120576GE is the error term when the experiment is replicated

AMMI stability value (ASV) was calculated by

ASV = radic[SSIPCA1SSIPCA2

(IPCA1score)]2

+ (IPCA2score)2 (2)

AMMI analysis was used to determine the stability ofgenotypes across locations using principal component axis(PCA) scores and ASV Genotypes having the least ASVwere considered as widely adapted genotypes SimilarlyIPCA2 scores close to zero revealed more stable genotypeswhile large values indicated more responsive and less stablegenotypes GGE biplot analysis was used to visualize therelationship between testers and entries and to determineldquowhich-won-whererdquo portionGGEbiplot also reveals stabilityof genotypes genotypes located near the biplot origin areconsidered as widely adapted genotypes while genotypeslocated far are considered as being specifically adapted

3 Results and Discussion

31 Effect of Environment on Maturity Plant Height andBiomass of Selected Sweet Sorghum The effects of genotypewere significant on days to 50 heading with SS21 beingearly maturing genotype across environments (Table 2)Genotypes SS14 EUSS10 and EUSS11 took long to matureacross environments The time difference between early andlate maturing genotypes was more than two weeks acrossenvironments except inMasumbi andMundika (2nd season)Generally genotypes matured earlier during the secondseason compared to the first season From the study it wasobserved that the least number of days to reach 50 headingwas about eight and a half weeks (61 days)


Table 2 Influence of environment on maturity (days to 50 heading) of sweet sorghum genotypes

Genotypes

EnvironmentsMasumbi (LM1) Mundika (LM2) Sinyanya (LM3) Mundika (LM2) Nyahera (LM3) Sagam (LM1)

Number of days to 50 headingSeason 1 (MarchndashJuly) Season 2 (SeptndashDec)

SS04 7933abc 8000b 8033ab 6933bcd 7300ab 7633abcd

SS14 8700a 8500a 8500ab 7600a 7400ab 8200ab

SS21 7300c 7167c 6600c 6633d 6133c 6600d

SS17 7633bc 7933b 8033ab 6730cd 7133b 7267bcd

EUSS17 7967abc 8033b 7800b 7233abc 7533ab 7833abc

EUSS10 8433ab 8633a 8633a 7400ab 7667a 8500a

EUSS11 8033abc 8133b 8033ab 7267abc 7500ab 7700abc

ACFC00312 8000abc 7900b 8200ab 7067abcd 7300ab 7000cd

LSD005

845 281 799 561 423 1054Means followed by the same letter do not differ at the same column at 5 LSD

Table 3 Plant height of sweet sorghum genotypes across environments

Genotypes


Plant height (cm)Season 1 (MarchndashJuly) Season 2 (SeptndashDec)

SS04 18117ab 21517ab 10733a 20344abc 17522ab 19767a

SS14 17667ab 19417c 1110a 20944ab 16244b 21733a

SS21 14033c 19933c 8533a 17856bc 17567ab 19500a

SS17 19033ab 14350d 10800a 17522c 17378ab 21800a

EUSS17 18450ab 20567bc 10800a 19611abc 16678b 20833a

EUSS10 20633a 22667a 10567a 21989a 18144ab 23267a

EUSS11 16700bc 19133c 9100a 18211bc 16756b 22333a

ACFC00312 17283abc 20467bc 8167a 20500abc 18944a 21067a

LSD005


Plant height differed among sweet sorghum genotypesand across locations The tallest and shortest plant heightwere recorded by genotypes in Sagam and Sinyanya respec-tively (Table 3) Genotype SS21 was the shortest during thefirst and the second season SS04 EUSS10 and ACFC00312grew taller consistently across environments In Nyahera theresults indicate plant height was similar for all genotypesranging from 162 to 181 cm except for ACFC00312 (189 cm)though the difference among ACFC00312 EUSS10 SS21SS04 and SS17 was not significant Similarly in Masumbiall genotypes were similar in height except for SS21 thatwas recorded to be 66 cm shorter than EUSS10 though thedifference among ACFC00312 EUSS11 and SS21 was notsignificant

Genotypes varied with environments for cane yield(Table 4) Among the genotypes SS21 gave the lowest yieldacross environments during the first season Since all thegenotypes except SS04 showed their highest cane yield inSagam the environment of Sagam seems to favor betterperformance of the genotypes EUSS10 showed the highestcane inMasumbiMundika IMundika II and Sagam thoughits cane yield was at medium level in Sinyanya Thus it was

suggested that EUSS10 was more suited for lower midlandzones 1 and 2 whose rainfall and temperatures range between804 and 846mmand203 and 290∘C respectivelyGenotypesthat took long to mature grew taller and recorded high caneyield showing a positive relationship between plant heightand cane yieldThesemorphological characters together withstalk diameter and number of internodes per stalk have beenreported to affect final yield in sugarcane [22 23] Hencetall sweet sorghum genotypes should be selected tomaximizecane yield

32 Influence of Environment on Juice Yield Juice yielddiffered significantly among the sweet sorghum genotypesthere were high and low performers (Table 5) Among thegenotypes EUSS10 gave the highest juice yields in MasumbiMundika I Mundika II and Sagam All genotypes recordedthe highest juice yield in Sagam except SS04 and ACFC00312 Though genotypes performed differently across environ-ments LM1 agroecological zones (Masumbi and Sagam)favored better performance In Sinyanya SS14 recorded aboutseven times juice yield recorded by SS21 Similarly EUSS10recorded juice yield about 32 times that recorded by SS21


Table 4 Cane yield (tha) of eight sweet sorghum genotypes across environments

Genotypes


Cane yield (tha)Season 1 (MarchndashJuly) Season 2 (SeptndashDec)

SS04 2639ab 2847ab 1389ab 1597b 2222a 1944b

SS14 2431ab 2082cd 1669a 2083b 1665ab 3264ab

SS21 972b 1320e 833b 1672b 2014a 2847ab

SS17 2778ab 1528de 1389ab 1656b 1597ab 3333ab

EUSS17 2500ab 1806cde 1667a 1663b 1015b 3332ab

EUSS10 3194a 3081a 1250ab 2997a 1181b 4444a

EUSS11 1875ab 2292bc 1389ab 2361ab 2153a 4028a

ACFC00312 2361ab 2431bc 1111ab 2569ab 2153a 3472ab

LSD005


Table 5 Juice yield (lha) of eight sweet sorghum genotypes across environments

Genotypes


Juice yield (lha)Season 1 (MarchndashJuly) Season 2 (SeptndashDec)

SS04 8044ab 6304bc 3567ab 4225bc 4518a 4011c

SS14 7014ab 5090cd 5061a 5588bc 3649ab 8611abc

SS21 1938b 2850e 761c 4281bc 4000ab 4763c

SS17 7086ab 3472de 3310ab 2188c 3311ab 8442abc

EUSS17 7057ab 3907de 4617ab 4063bc 2332b 8135abc

EUSS10 9615a 9051a 3861ab 10111a 3056ab 11146a

EUSS11 4867ab 4849cde 3411ab 6344b 4646a 10647ab

ACFC00312 5990ab 7364ab 2406bc 5674bc 5028a 6269bc

LSD005


in Mundika during the first season Among the genotypesSS21 and EUSS17 gave relatively low juice yield in Masumbiand Nyahera respectively Genotypes responded differentlyto the varied environments during seasons one and two Thegenotypes performed better in Sagam and Masumbi (LM1)due to high total rainfall experienced during the growthperiod

33 Influence of Environment onBrix andEthanol GenotypesEUSS11 recorded consistent high Brix values across environ-ments with the controls SS04 SS14 and ACFC00312 exceptin Sagam (Table 6) Among the genotypes EUSS10 had theleast total soluble solids (Brix) across environments except inMundika during season 1 Genotypes recorded similar Brixin Mundika during the 2nd season ranging from 12 to 157Though SS21 performed poorly in terms of morphologicalcharacters it was the best for Brix inMundika during seasonsI and II and in Sagam A high Brix value shown by genotypesin Sinyanya is attributed to higher temperatures experiencedat that site John and Seebaluck [24] reported that sugarcanerequires higher solar radiation during initial growth stage

and during ripening in order to accumulate more sucrose atripening

Genotypes varied within environments for ethanol yield(Table 7) Genotypes performed similarly during the 2ndseason with SS17 EUSS10 and SS04 recording the lowestvolume of ethanol per hectare in Mundika Nyahera andSagam respectively In Nyahera EUSS10 and EUSS17 hadethanol yield that was lower than that produced by othergenotypes by about 47ndash51 During the season I ethanolyield of the two controls SS21 and SS17 was the lowestin Masumbi and Sinyanya and in Mundika respectivelyThe maximum ethanol yield among the genotypes acrossenvironments was recorded by EUSS11 (838 lha) in SagamPerformance of EUSS17 EUSS10 and EUSS11 was comparableto the best controls SS04 SS14 and ACFC00312 in most ofthe tested environments

The test locations vary in latitude rainfall soil typesand temperature The three environments with high yieldingpotential Masumbi Mundika and Sagam are characterizedby high bimodal rainfall patterns as compared to lowestyielding environments Sinyanya andNyahera LM1 and LM2


Table 6 Brix () of sweet sorghum genotypes across environments

Genotypes


Brix ()Season 1 (MarchndashJuly) Season 2 (SeptndashDec)

SS04 183a 180a 190a 150a 167ab 137abc

SS14 167ab 148abc 210a 130a 170ab 133abc

SS21 140cd 183a 157bc 157a 123c 170a

SS17 163b 117c 183ab 150a 153b 153ab

EUSS17 157bc 130c 187ab 150a 150b 150ab

EUSS10 123d 133bc 150c 120a 93d 107c

EUSS11 170ab 153abc 187ab 150a 177a 130bc

ACFC00312 170ab 148abc 183ab 153a 163ab 170a

LSD005


Table 7 Ethanol yield (lha) of sweet sorghum genotypes across environments

Genotypes


Ethanol yield (lha)Season 1 (MarchndashJuly) Season 2 (SeptndashDec)

SS04 5390ab 2473bc 2762abc 3586ab 3502a 3254b

SS14 4699ab 1701c 3711a 4020ab 2123ab 6291ab

SS21 1298c 1297c 1170d 3517ab 1998ab 4509ab

SS17 4748ab 1128c 1744cd 2048b 1940ab 6766ab

EUSS17 4728ab 1198c 3174ab 3693ab 1475b 7222ab

EUSS10 6442a 4173a 1774cd 5685a 1378b 5730ab

EUSS11 3261ab 1549c 2445abcd 5743a 3373ab 8381a

ACFC00312 4013ab 3363ab 2244bcd 5003a 3772a 6987ab

LSD005


agroecological zones can be utilized for commercial produc-tion of sweet sorghum Genotypes showed satisfactory yieldsin the most favorable environments (LM1) such as Sagamand Masumbi the reason being the ability of genotypes torespond advantageously to a higher amount of rainfall in LM1compared to LM3 agroecological zones High temperaturesand low precipitation are some of contributing factors to poorperformance in LM3 AEZ The slightly better performanceof genotypes in Mundika during season one compared tothe second season could be due to the difference for rainfallduring early growth stages of sorghum plants Since thefluctuation of ethanol yield of SS14 was smaller than othergenotypes it was suggested that its ethanol yield would besuperior to other genotypes in unfavorable environments

34 AMMI Stability Values Analysis The combined analysisof variance of cane and juice yield of sorghum genotypesshowed that sweet sorghum genotypes were affected byenvironments (E) genotypes (G) and genotype by envi-ronment interaction (GEI) (Table 8) However assessmentof genotype by environment interaction on ethanol yieldstability indicated that GIE was not present for ethanol

yield indicating that genotypes did not respond differentlyto varying environmental conditions G E and GEI effectsaccounted for 86 369 and 194 respectively for cane yieldtotal sum of squares 168 243 and 222 respectively forjuice yield total sum of squares and 71 384 and 185respectively for ethanol yield total sum of squares (Table 8)It is important to note that environment contributed largelyto variation in yields

A large sum of squares shows that environments werediverse influencing yields differently which was in harmonywith the findings of Reddy et al [12] in sweet sorghumproduction Traits such as green biomass plant height stemdiameter juice extractability and stem sugar content aremajor contributors of sweet sorghumrsquos economic importancefor biofuel production [25 26] However variability existsin morphological characters of sweet sorghum among geno-types and across locations Identification of adaptable stableand high yielding genotypes under different environmentalconditions prior to release has been reported by Lule et al[14] to be the first and foremost steps for plant breedingEnvironment expresses most of the total yield variationwhile genotype and genotype by environment interactions


Table8Ad

ditiv

emaineffectsandmultip

licativeinteractio

nanalysisof

varia

ncefor

canejuiceand

ethano

lyield

oftheg

enotypes

acrossenvironm

ents

Source

ofvaria

tion

DF

Cane

yield

Juicey

ield

Ethano

lyield

SSMS

Explained(

)SS

MS

Explained(

)SS

MS

Explained(

)To

tal

143

2100813

14691

mdash1306

609520

9137130

mdash7724869

54020

mdashTreatm

ents

4744

6829

9507lowastlowastlowast

6471

827867592

17614204

6336

4944

624


6401

Genotypes

78799

1257lowastlowast

856

220070892


1684

548971

78424lowast

711

Environm

ents

527115


3691

317801649


2432

2963587


3836

Block

12114

84957

mdash70966844

5913904

mdash313774

26148

mdashInteraction

3598910

2826lowast

1944

289995052

8285573lowast

2219

143206

640

916n

s1854

IPCA

111

13783

1253lowast

853

107425316

9765938lowast

822

638384

58035lowast

826

IPCA

29

8550

950lowast

647

91016710

10112968lowast

697

391863

43540n

s507

Resid

uals

159345

623

mdash91553026

6103535

mdash401819

26788

mdashError

844225

503

mdash40

7775083

4854465

mdash2466

471

29363

mdashlowast

119875lt005lowastlowast

119875lt001lowastlowastlowast

119875lt0001nsno

nsignificantDFdegreeso

ffreedom

SSsum

ofsquareM

Smeansquare


are less effective [27] The soilrsquos constituents such as moisturecontent mineral availability and pH that is an integralpart of environment cause large annual variation in yieldperformance of a crop GEI can be reduced by identifyinggenotypes that are most stable [28]

The first interaction principal component (IPCA 1) andthe second (IPCA 2) accounted for 853 and 647 respec-tively of the canersquos IPCA sum squares The IPCA1 accountedfor 822 and 826 of juice and ethanol yield interaction sumof squares respectively while IPCA2 accounted for 697 and507 (Table 8) The first two principal component axes weresignificant and thus best explain interaction sum of squaresand were used in cane and juice yield analysis HoweverAMMI model 1 can be used when only one principal com-ponent axis is significant to explain the interaction betweengenotype and environment [21] as for ethanol yield in ourcase

Environments and genotypes with least ASV scores areconsidered as they are the most stable Accordingly geno-types SS14 SS17 and ACFC00312 had a general adaptationfor cane yield while SS14 was the most stable for juiceand ethanol yield (Table 9) On the other hand SS04 wasmost unstable for cane juice and ethanol yield Similarlyenvironments were classified using ASV as stable for caneyield (Sinyanya Mundika seasons 1 and II) juice yield(MasumbiMundika season I and Sinyanya) and ethanol yield(Mundika season II and Sinyanya) Nyahera and Sagam werethe least stable for cane yield while Sagam was unstable forboth juice and ethanol yield (Table 10)

Furthermore the IPCA2 scores of genotypes in AMMIanalysis indicate stability of genotypes across locations highIPCA2 scores (either negative or positive) are unstable whilethose with low scores are stable [11] Table 9 showed thatgenotypes ACFC00312 SS17 and SS14 for cane yield SS14SS04 and EUSS11 for juice yield and EUSS10 EUSS11 andSS04 for ethanol yield were the most stable genotypes asthey had low IPCA2 scores The most unstable genotypeswere SS04 and SS21 for cane yield SS17 and EUSS10 for juiceyield and SS21 SS17 and EUSS17 for ethanol yield Stablegenotypes follow genes that affect the trait in question andtheir expression relative to the environment being similar toaverage cultivar while unstable genotypes have genes that arechallenged differently by a different environment [29] Datain Table 10 further revealed that Masumbi had the highestIPCA2 score for both cane and ethanol whileMundika seasonII had highest IPCA2 score for juice yield hence they werethe most interactive environments Sinyanya Nyahera andMundika season II were the least interactive for cane juiceand ethanol yield respectively

35 GGEBiplot Analysis Genotypes or environments locatedon the right-hand side of the midpoint of the axis (IPCA1)have higher yields than those on the left-hand side [29] Inthis study genotypes EUSS10 ACFC00312 SS14 and EUSS11for cane yield (Figure 3) EUSS10 EUSS11 and SS14 for juiceyield (Figure 4) and EUSS10 SS04 SS14 and ACFC00312for ethanol yield (Figure 5) were generally high yielding asthey were placed on right-hand side of midpoint of IPC1 axis(representing grandmean) Similarly Mundika seasons I and

SS17SS14

SS21

ACFC00312

EUSS10

EUSS11

Scatter plot (total 8689)

EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377

Sectors of convex hullGenotype scoresEnvironment scores

Convex hull

Figure 3 The which-won-where view of GGE biplot for cane yield

SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull

Figure 4The which-won-where view of GGE biplot for juice yield

II and Sagam andMasumbi were considered to be superior incane yield (Figure 3) while all sites except Nyahera producedhigh juice yield (Figure 4)However all sites performed aboveaverage in terms of ethanol yield (Figure 5)

The polygon view of GGE biplot for cane yield (Figure 3)indicates the best genotypes(s) for each environment(s) Thegenotypes EUSS10 ACFC00312 and SS14 were found to


Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull

Figure 5 The which-won-where view of GGE biplot for ethanolyield

be promising in Masumbi Sagam and Mundika seasonsI and II (LM1 and LM2) EUSS17 and SS04 were betteradapted to Nyahera (LM3) which is low-performing site Thegenotypes located on the vertex of a polygon are the ones thatgave the highest yield for the environment that fall withinthat quadrant The vertex genotypes were EUSS17 SS04SS21 EUSS10 and EUSS11 for cane yield Genotype EUSS10recorded the highest cane in Masumbi and Mundika duringseasons I and II EUSS11 gave the highest cane in Sagamwhileboth SS04 and EUSS17 were best-performing genotypes inNyahera and Sinyanya The polygon reflects that SS21 is poorcane yielding not suitable for either of the environmentsThe genotypes located on the vertex of a polygon are best orpoorest genotypes in some or all environments except left-bottom quadrant [11]

The GGE biplot for juice yield (Figure 4) indicates thatSS14 and EUSS10 are suitable for cultivation in Mundikaduring seasons I and IIMasumbi Sinyanya and Sagam (LM1LM2 and LM3) while ACFC00312 and SS04 were betteradapted toNyahera (LM3) EUSS10 recorded the highest juicevolume in Sagam Masumbi and Mundika during seasons1 and 2 Genotypes SS21 SS17 and EUSS17 fell into sectorswhere there were no locations These genotypes are poorlyadapted to all environments that were tested Locations in onesector having best-performing genotype can be consideredas megaenvironments for that genotype [30] These resultsare in conformity with the findings of Reddy et al [12] whoobserved high yielding and stable genotypes for cane andjuice yield

Biplots were divided into four sectors in Figure 5 geno-typeswhich fall in same sector aswith environment are said tobe adapted to those locations In the present study genotypes

EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377

VectorsEnvironment scoresGenotype scores

Figure 6The biplot showing relationship between testers andmegaenvironments for cane yield

EUSS10 and SS14were adapted toMasumbiMundika seasonsI and II and Sinyanya (LM1 LM2 and LM3) EUS11ACFC00312 and EUSS17 were suitable for cultivation inNyahera and Sagam (LM1 and LM3) Furthermore Figure 5displays ldquowhich-won-whererdquo feature of biplots EUSS11 hadthe highest ethanol yield in Sagam SS21 and SS17 were poorperformers for ethanol yield and were not suitable for testedenvironments

In Figure 3 genotypes SS14 and SS17 for cane yield hadthe shortest vector from origin whereas in Figures 4 and 5SS14 for both juice and ethanol yield was closer to the originthan SS17 Moreover SS14 genotype had IPCA1 gt 0 andis therefore regarded as stable and high yielding GenotypeEUSS10 had the highest IPCA1 score and was located close toIPC2 axis for both juice and ethanol yield indicating that itis high yielding genotype but specifically adapted Dynamicas opposed to static stability is preferred by breeders andagronomist in order to have genotypes that could producemore yields when optimal agronomic inputs and favorableenvironmental conditions are provided [31] Therefore SS14can be chosen forwider adaptability andEUSS10 for favorableenvironments Genotype ACFC00312 had medium stabilityfor cane and ethanol yield across environments

Figures 6 7 and 8 give vector view of GGE biplot of canejuice and ethanol respectively in which environments areconnected with biplot origin via lines They also show therelationship among genotypes This view of biplot aids in theunderstanding of interrelationship among environmentsTheGGE biplot was applied by Rao et al [32] to explain the inter-relationship among the environments and the seasons Thecosine of the angle between the vectors of two environmentsapproximates the correlation coefficient between them


EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901


Figure 7The biplot showing relationship between testers andmegaenvironments for juice yield

Environments with a small angle between them are highlypositively correlated and they provide similar informationon genotypes Present investigations showed that Masumbiand Mundika for cane juice and ethanol yield (Figures6 7 and 8) and Nyahera and Sinyanya for ethanol yield(Figure 8) were considered to be similar as they had smallangle between them In contrast genotypes EUSS10 andSS21 SS04 and EUSS11 were located in opposing quadrantsfor cane juice and ethanol yields therefore the anglesbetween them were larger and are considered as dissimilargenotypes Similarly Nyahera and Sagam were dissimilarfor both cane and juice yield Sinyanya and Nyahera liedclosest to the origin and therefore contributed the least toGEI for cane juice and ethanol yield while Sagam made thehighest contribution From this study it is evident that low-performing genotypes are stable and have wider adaptabilitywhereas high-performing genotypes are less stable

A study by Abubakar and Bubuche [33] in Nigeriafound out that genotype by environment interaction had asignificant influence on sorghum plant height Differences inplant height can result in changes in cane yield across envi-ronments therefore genotypes adapted to specific locationshave to be selected Biomass yield and plant height have beenfound to be major contributors to economic yields in sweetsorghum [34] Furthermore ANOVA revealed there was asignificant effect due to genotype by environment interactionThis indicates that genotypes performed differently at eachsite which is expected due to differences in soil compositionrainfall and temperature Ideal cultivars and environmentsare those having large PC1 scores (high mean yield) andsmall PC2 scores (high stability) [35] Based on thisMundikaseason I and Masumbi were found to be ideal environments

EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390


Figure 8The biplot showing relationship between testers andmegaenvironments for ethanol yield

whereas SS14 was ideal genotype for ethanol productionGenotype EUSS10was thewinning genotype for ethanol yieldin Masumbi and Mundika both in seasons one and two andin Sinyanya and therefore suitable for those sites

4 Conclusion and Recommendations

Cane yield was found to be highly correlated with plantheight Environmental effects as well as GEI had strongeffect on yield of sweet sorghum genotypes The significantGEI for cane and juice yield observed from analysis ofvariance in this study shows that sweet sorghum genotypesrespond differently when grown in different environmentalconditionThe results from this project indicate that SS14 wasmost stable and best genotype across environments whereasEUSS10 has excellent potential for ethanol in areas withhigh yield potential The best-performing genotypes wereEUSS10 ACFC00312 and SS04 while average performerswere EUSS11 EUSS17 and SS14 The genotypes SS21 andSS17 were poor performers for ethanol yield located outsidelimits of any environments It is evident that performanceof sweet sorghum is attributed to both genetic make-up andenvironment

Competing Interests

The authors have not declared any conflict of interests

Acknowledgments

World Bank supported this work financially through KenyaAgricultural Productivity Programme (KAPP) J K Rono


acknowledges ICRISAT for provision of seed materials andthanks the technical staff in the Biochemistry Departmentwho formed part of an MS thesis submitted to GraduateSchool Egerton University by the first author

References

[1] A A Kumar B V Reddy H C Sharma et al ldquoRecent advancesin sorghum genetic enhancement research at ICRISATrdquo Amer-ican Journal of Plant Sciences vol 2 no 4 pp 589ndash600 2011

[2] H M Abdalla and Y A Gamar ldquoClimate change selection ofsorghum genotype with wide adaptation AG-17 for rain-fedareas of Sudanrdquo International Journal of AgriScience vol 1 no3 pp 144ndash155 2011

[3] M Fentie A Assefa and K Beleke ldquoAmmi analysis of yieldperformance and stability of finger millet genotypes acrossdifferent environmentsrdquoWorld Journal of Agricultural Sciencesvol 9 no 3 pp 231ndash237 2013

[4] M Aslam A N Khan M S Mirza I Khan Mi and N UllahldquoEvaluation of different soybean genotypes for stability in yieldperformance across diverse environmentsrdquo Pakistan Journal ofAgricultural Resource vol 14 pp 297ndash303 1993

[5] F F Mendes L J M Guimaraes J C Souza et al ldquoAdaptabilityand stability of maize varieties using mixed model methodol-ogyrdquo Crop Breeding and Applied Biotechnology vol 12 pp 111ndash117 2012

[6] N Panayotov and D Dimova ldquoAssessment of yield and yieldstability of new perspective pepper breeding lines with conicalshaperdquo Genetika vol 46 no 1 pp 19ndash26 2014

[7] B K Joshi ldquoYield stability of Tartary buckwheat genotypes inNapalrdquo Fagopyrum vol 21 pp 1ndash5 2004

[8] S A M Moussa H A A El-Aal and N I A El-Fadl ldquoStabilitystudy of sweet potato yield and its component characters underdifferent environments by joint regression analysisrdquo Journal ofHorticultural Sciences and Ornamental Plants vol 3 pp 43ndash542011

[9] I Romagosa G Borras-Gelonch G Slafer and F van EeuwijkldquoGenotype by environment interaction and adaptationrdquo inSustainable Food Production pp 846ndash870 Springer New YorkNY USA 2013

[10] W Yan and M S Kang GGE Biplot Analysis A Graphical Toolfor Breeders Geneticists and Agronomists CRC Press 2002

[11] G H Hagos and F Abay ldquoAMMI and GGE biplot analysis ofbreadwheat genotypes in the northern part of Ethiopiardquo Journalof Plant Breeding and Genetics vol 1 no 1 pp 12ndash18 2013

[12] P S Reddy B V S Reddy and P S Rao ldquoGenotype by sowingdate interaction effects on sugar yield components in sweetsorghum (Sorghum bicolor L moench)rdquo SABRAO Journal ofBreeding and Genetics vol 46 no 2 pp 305ndash312 2014

[13] U N Ikeogu and G E Nwofia ldquoYield parameters and stabilityof soybean as influenced by phosphorus fertilizer rates intwo ultisolsrdquo International Journal of Advanced Research inAgriculture vol 1 pp 20ndash28 2013

[14] D LuleM Fetene S DeVilliers andK Tesfaye ldquoAdditiveMainEffects and Multiplicative Interactions (AMMI) and genotypeby environment interaction (GGE) biplot analyses aid selectionof high yielding and adapted finger millet varietiesrdquo Journal ofApplied Biosciences vol 76 no 1 pp 6291ndash6303 2014

[15] T Ayalneh T Letta and M Alninasa ldquoAssessment of stabilityadaptability and yield performance of bread wheat (Triticum

aestivum L) cultivars in South Eastern Ethiopiardquo Plant Breedingand Seed Science vol 67 no 1 pp 3ndash11 2014

[16] S P Patil M R Manjare S R Kamdi A M Dethe and MB Ingle ldquoStability analysis in sorghum (Sorghum bicolor LMoench)rdquo International Journal of Plant Science vol 2 pp 70ndash75 2007

[17] M R Islam M M Man H Khatun et al ldquoAMMI analysisof yield performance and stability of rice genotypes acrossdifferent HOAR areasrdquo Eco-Friendly Agriculture Journal vol 7pp 20ndash24 2014

[18] R Jaetzold H Schmidt B Hornetz and C Shisanya ldquoFarmmanagement handbook of Kenya vol II part A natural condi-tions and farm management information (West Kenya NyanzaProvince)rdquo 2009

[19] R Jaetzold H Schmidt B Hornetz and C Shisanya FarmManagementHandbook of KenyaVol II Natural Conditions andFarm Management Ministry of Agriculture Siaya and Kisumucounties Kenya 2nd edition 2005

[20] R Jatzold and H Kutsch ldquoAgro-ecological zones of the tropicswith a sample from Kenyardquo Der Tropenlandwirt-Journal ofAgriculture in the Tropics and Subtropics vol 83 no 1 pp 15ndash341982

[21] W Gebremedhin M Firew and B Tesfye ldquoStability analysisof food barley genotypes in Northern Ethiopiardquo African CropScience Journal vol 22 pp 145ndash154 2014

[22] M Singh and H Singh ldquoCertain correlation studies in sugar-canerdquo in Proceedings of the Biennial Conference of SugarcaneResearch and Development Workers vol 2 pp 70ndash78 1954

[23] R N Panhwar H K Keerio M A Khan et al ldquoRealtionshipbetween yield and yield contributing traits in sugarcane (Sac-charum officinarum L)rdquo Journal of Applied Sciences vol 3 no2 pp 97ndash99 2003

[24] F X John and V Seebaluck Bioenergy for Sustainable Develop-ment and International Competitiveness The Role of Sugar Canein Africa Routledge New York NY USA 2013

[25] A R Almodares R Taheri and S Adeli ldquoStalk yield andcarbohydrate composition of sweet sorghum (Sorghum bicolorL Moench) cultivars and lines at different growth stagesrdquoJournal of Malaysian Applied Biology vol 37 pp 31ndash36 2008

[26] S C Murray A Sharma W L Rooney et al ldquoGeneticimprovement of sorghum as a biofuel feedstock I QTL for stemsugar and grain nonstructural carbohydratesrdquoCrop Science vol48 no 6 pp 2165ndash2179 2008

[27] S M M Mortazavian H R Nikkhah F A Hassani M Sharif-al-Hosseini M Taheri and M Mahlooji ldquoGGE biplot andAMMI analysis of yield performance of barley genotypes acrossdifferent environments in Iranrdquo Journal of Agricultural Scienceand Technology vol 16 no 3 pp 609ndash622 2014

[28] S A Eberhart and W A Russell ldquoStability parameters forcomparing varietiesrdquoCrop Science vol 6 no 1 pp 36ndash40 1966

[29] J M Ngeve and J C Bouwkamp ldquoComparison of statisticalmethods to asses yield stability in sweet potatordquo Journal ofAmerican Society Horticultural Sciences vol 118 pp 304ndash3101993

[30] W Gebre and H Mohammed ldquoStudy on adaptability andstability of drought tolerant maize varieties in drought proneAreas of South Omo Zone SNNPRSrdquo International Journal ofResearch in Agriculture and Forestry vol 2 no 7 pp 9ndash13 2015

[31] D S Djurovic M R Madic N R Bokan V I Stevovic DD Tomic and S T Tanaskovic ldquoStability parameters for grainyield and its component traits inmaize hybrids of different FAO


maturity groupsrdquo Journal of Central European Agriculture vol15 no 4 pp 199ndash212 2014

[32] P S Rao P S Reddy A B H I S H E K Rathore B V Reddyand S A N J E E V Panwar ldquoApplication GGE biplot andAMMI model to evaluate sweet sorghum (Sorghum bicolor)hybrids for genotype x environment interaction and seasonaladaptationrdquo Indian Journal of Agricultural Sciences vol 81 no5 pp 438ndash444 2011

[33] L Abubakar and T S Bubuche ldquoGenotype environment inter-action on biomass production in sorghum (Sorghum bicolorL Moench) in North-Western Nigeriardquo African Journal ofAgricultural Research vol 8 no 35 pp 4460ndash4465 2013

[34] D M Bahadure S Marker A V Umakanth Prabhakar J VPatil and G J Synren ldquoAssessment of genetic diversity forbiomass related traits in sweet sorghum (Sorghum bicolor LMoench)rdquo Journal of Agriculture and Veterinary Science vol 7pp 32ndash34 2014

[35] E Frashadfar H Safari and B Jamshidi ldquoGGE biplot analysisof adaptability inwheat substitution linesrdquo International Journalof Agriculture and Crop Sciences vol 4 no 13 pp 877ndash881 2012

Submit your manuscripts athttpwwwhindawicom

Nutrition and Metabolism

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Food ScienceInternational Journal of

Agronomy


International Journal of



Microbiology

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

AgricultureAdvances in


PsycheHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BiodiversityInternational Journal of


ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GenomicsInternational Journal of


Plant GenomicsInternational Journal of


Biotechnology Research International


Forestry ResearchInternational Journal of


Journal of BotanyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EcologyInternational Journal of


Veterinary Medicine International


Cell BiologyInternational Journal of


Evolutionary BiologyInternational Journal of



Table 1 Description of soil components of testing locations

County site lowastlowastAEZ pH Soil type Nitrogen () Phosphorous (ppm)Siaya Masumbi lowastLM1 44 Clay loam 011 98Siaya Sinyanya LM3 54 Sandy clay loam 017 88Busia Mundika I LM2 44 Sandy clay loam 009 64Busia Mundika II LM2 44 Sandy clay loam 009 64Kisumu Sagam LM1 58 Sandy clay loam 012 85Kisumu Nyahera LM3 60 Sandy clay loam 015 55lowastLower midland zone lowastlowastAgroecological zone

in the selection of genotypes with high yielding capacity indifferent environments is genotype by environment interac-tion New genotypes must be stable for yields and shouldbe stable across environments or suited to target regions[5] Yield is controlled by the complex polygenic system andstrongly varies depending on environmental conditions [6]Stability analysis is an important step in developing cultivarsfor a wide range of environments or for a specific location[7] Genotype by environment interaction has to be studiedfor yields which are cane juice and ethanol in our case asthey are considered the most important economic traits [8]

The genotype environment and the genotype by envi-ronment interactions impact crop performance Genotype byenvironment interaction (GEI) complicates breeding testingand selection of superior genotypes [9] The GEI changes therankings of genotypes in various environments an increasein GEI diminishes the correlation between genotypic andphenotypic qualities making it hard to distinguish superiorgenotype across environments [10]Theperformance stabilityconcept is therefore important in analyzing GEI in order torecommend genotypes to test environments Additive maineffects and multiplicative interactions (AMMI) analysis isused to determine stability of genotypes across locationsusing the principal component axis (PCA) scores and AMMIstability values (ASV) while genotype plus genotype by envi-ronment (GGE) analysis is effective method which is basedon principal component analysis to fully explore multienvi-ronment trials [11] Average environment coordinates (AEC)of GGE biplot separates entries with below-average meansfrom thosewith above-averagemeans [12] Stability of variouscrops has been studied by applying AMMI and GGE biplotssuccessfully in soybean (Glycine max L Meril) [13] sweetpotatoes (Ipomoea batatas) [8] pepper (Capsicum annuum)[6] finger millet (Eleusine coracana) [14] wheat (Triticumaestivum) [15] grain sorghum [16] and rice (Oryza sativa)[17] GGE and AMMI analysis were applied to determinestability and adaptability of eight sorghum genotypes grownin five different ecological zones

2 Materials and Methods

21 Site Description Sweet sorghum field experiments werecarried out in Kisumu Siaya and Busia Counties ofKenya The specific sites were Sinyanya (00∘06101584068510158401015840S034∘08101584066010158401015840E) at 1168m above sea level (ASL) Masumbi(00∘01101584073010158401015840N 034∘21101584087410158401015840E) at 1370m ASL both in SiayaCounty Mundika (00∘24101584056610158401015840S 034∘07101584093110158401015840E) at 1222m

05

101520253035

Masumbi Sinyanya Sagam Nyahera


MundikaII

MundikaI

4 m

onth

sM

ean

daily

tem

pera

ture

(∘C)

Figure 1 Mean daily temperature during sorghum growing period

ASL in Busia Nyahera (00∘0021015840527810158401015840S 034∘391015840035910158401015840E) at1387m ASL and Sagam (00∘031015840208610158401015840N 034∘321015840310610158401015840E) at1216m ASL both in Kisumu County

All sites fell within the same agroecological zone lowermidland (LM) the difference in yield was due to differencein sub agroecological zones as depicted in Table 1 Theenvironments in lower midland zones 1 2 and 3 receiveannual average rainfall of 1800ndash2000 1550ndash1800 and 1200ndash1420mm respectively [18] In general the soil in these areaswas sandy clay loam acidic or slightly acidic (pH = 44ndash60)and was poor in nitrogen and phosphorous Sinyanya wascharacterized by highmeanmaximum temperature (Figure 1)and lower precipitation (Figure 2) The thermal zone 1 (LM1)in Kenya records mean daily temperature and altitude rangeof 222ndash210∘C and 1200ndash1440m ASL respectively The meandaily temperature and altitude range are 222ndash214∘C and1200ndash1350m ASL respectively for LM2 and 227ndash210∘C and140ndash1500m ASL for LM3 [19] The lower midland zones 12 and 3 are regarded as sugarcane marginal sugarcane andcotton zones respectively [20]

22 Experimental Design Eight sweet sorghum genotypeswere grown in a randomized complete block design (RCBD)The genotypes were EUSS10 EUSS11 and EUSS17 as candi-dates with the controls being ACFC00321 SS04 SS14 SS21and SS17 Sowing was done on 18th March in Sinyanya andMasumbi and 19th March 2014 in Mundika for first seasonSowing in the second season was done on 13 September 2014for both Mundika and Sagam while Nyahera was planted on24 September 2014 Genotypes were sown in plots measuring4 times 25m in RCBD with three replications Each plot


0

200

400

600

800

1000

Masumbi

Rainfall


Tota

l rai

nfal

l (m

m) (

4 m

onth

s)


MundikaII

MundikaI








119884GEr = 120583 + 120572G + 120573E +sum119899

120582119899120574G119899120575E119899 + 120588GE + 120576GEr (1)






(IPCA1score)]2

+ (IPCA2score)2 (2)






Genotypes




SS14 8700a 8500a 8500ab 7600a 7400ab 8200ab

SS21 7300c 7167c 6600c 6633d 6133c 6600d

SS17 7633bc 7933b 8033ab 6730cd 7133b 7267bcd


EUSS10 8433ab 8633a 8633a 7400ab 7667a 8500a



LSD005



Genotypes



SS04 18117ab 21517ab 10733a 20344abc 17522ab 19767a

SS14 17667ab 19417c 1110a 20944ab 16244b 21733a

SS21 14033c 19933c 8533a 17856bc 17567ab 19500a

SS17 19033ab 14350d 10800a 17522c 17378ab 21800a

EUSS17 18450ab 20567bc 10800a 19611abc 16678b 20833a

EUSS10 20633a 22667a 10567a 21989a 18144ab 23267a

EUSS11 16700bc 19133c 9100a 18211bc 16756b 22333a


LSD005








Genotypes



SS04 2639ab 2847ab 1389ab 1597b 2222a 1944b

SS14 2431ab 2082cd 1669a 2083b 1665ab 3264ab

SS21 972b 1320e 833b 1672b 2014a 2847ab

SS17 2778ab 1528de 1389ab 1656b 1597ab 3333ab

EUSS17 2500ab 1806cde 1667a 1663b 1015b 3332ab

EUSS10 3194a 3081a 1250ab 2997a 1181b 4444a

EUSS11 1875ab 2292bc 1389ab 2361ab 2153a 4028a


LSD005



Genotypes



SS04 8044ab 6304bc 3567ab 4225bc 4518a 4011c

SS14 7014ab 5090cd 5061a 5588bc 3649ab 8611abc

SS21 1938b 2850e 761c 4281bc 4000ab 4763c

SS17 7086ab 3472de 3310ab 2188c 3311ab 8442abc


EUSS10 9615a 9051a 3861ab 10111a 3056ab 11146a



LSD005









Genotypes



SS04 183a 180a 190a 150a 167ab 137abc

SS14 167ab 148abc 210a 130a 170ab 133abc

SS21 140cd 183a 157bc 157a 123c 170a

SS17 163b 117c 183ab 150a 153b 153ab

EUSS17 157bc 130c 187ab 150a 150b 150ab

EUSS10 123d 133bc 150c 120a 93d 107c



LSD005



Genotypes



SS04 5390ab 2473bc 2762abc 3586ab 3502a 3254b

SS14 4699ab 1701c 3711a 4020ab 2123ab 6291ab

SS21 1298c 1297c 1170d 3517ab 1998ab 4509ab

SS17 4748ab 1128c 1744cd 2048b 1940ab 6766ab

EUSS17 4728ab 1198c 3174ab 3693ab 1475b 7222ab

EUSS10 6442a 4173a 1774cd 5685a 1378b 5730ab



LSD005







Table8Ad

ditiv


licativeinteractio

nanalysisof

varia

ncefor

canejuiceand

ethano

lyield

oftheg

enotypes

acrossenvironm

ents

Source

ofvaria

tion

DF

Cane

yield

Juicey

ield

Ethano

lyield

SSMS

Explained(

)SS

MS

Explained(

)SS

MS

Explained(

)To

tal

143

2100813

14691

mdash1306

609520

9137130

mdash7724869

54020

mdashTreatm

ents

4744

6829


6471

827867592

17614204

6336

4944

624


6401

Genotypes

78799

1257lowastlowast

856

220070892


1684

548971

78424lowast

711

Environm

ents

527115


3691

317801649


2432

2963587


3836

Block

12114

84957

mdash70966844

5913904

mdash313774

26148

mdashInteraction

3598910

2826lowast

1944

289995052

8285573lowast

2219

143206

640

916n

s1854

IPCA

111

13783

1253lowast

853

107425316

9765938lowast

822

638384

58035lowast

826

IPCA

29

8550

950lowast

647

91016710

10112968lowast

697

391863

43540n

s507

Resid

uals

159345

623

mdash91553026

6103535

mdash401819

26788

mdashError

844225

503

mdash40

7775083

4854465

mdash2466

471

29363

mdashlowast



119875lt0001nsno


ffreedom

SSsum

ofsquareM

Smeansquare







SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377


Convex hull


SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull





Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014

























0

200

400

600

800

1000

Masumbi

Rainfall


Tota

l rai

nfal

l (m

m) (

4 m

onth

s)


MundikaII

MundikaI








119884GEr = 120583 + 120572G + 120573E +sum119899

120582119899120574G119899120575E119899 + 120588GE + 120576GEr (1)






(IPCA1score)]2

+ (IPCA2score)2 (2)






Genotypes




SS14 8700a 8500a 8500ab 7600a 7400ab 8200ab

SS21 7300c 7167c 6600c 6633d 6133c 6600d

SS17 7633bc 7933b 8033ab 6730cd 7133b 7267bcd


EUSS10 8433ab 8633a 8633a 7400ab 7667a 8500a



LSD005



Genotypes



SS04 18117ab 21517ab 10733a 20344abc 17522ab 19767a

SS14 17667ab 19417c 1110a 20944ab 16244b 21733a

SS21 14033c 19933c 8533a 17856bc 17567ab 19500a

SS17 19033ab 14350d 10800a 17522c 17378ab 21800a

EUSS17 18450ab 20567bc 10800a 19611abc 16678b 20833a

EUSS10 20633a 22667a 10567a 21989a 18144ab 23267a

EUSS11 16700bc 19133c 9100a 18211bc 16756b 22333a


LSD005








Genotypes



SS04 2639ab 2847ab 1389ab 1597b 2222a 1944b

SS14 2431ab 2082cd 1669a 2083b 1665ab 3264ab

SS21 972b 1320e 833b 1672b 2014a 2847ab

SS17 2778ab 1528de 1389ab 1656b 1597ab 3333ab

EUSS17 2500ab 1806cde 1667a 1663b 1015b 3332ab

EUSS10 3194a 3081a 1250ab 2997a 1181b 4444a

EUSS11 1875ab 2292bc 1389ab 2361ab 2153a 4028a


LSD005



Genotypes



SS04 8044ab 6304bc 3567ab 4225bc 4518a 4011c

SS14 7014ab 5090cd 5061a 5588bc 3649ab 8611abc

SS21 1938b 2850e 761c 4281bc 4000ab 4763c

SS17 7086ab 3472de 3310ab 2188c 3311ab 8442abc


EUSS10 9615a 9051a 3861ab 10111a 3056ab 11146a



LSD005









Genotypes



SS04 183a 180a 190a 150a 167ab 137abc

SS14 167ab 148abc 210a 130a 170ab 133abc

SS21 140cd 183a 157bc 157a 123c 170a

SS17 163b 117c 183ab 150a 153b 153ab

EUSS17 157bc 130c 187ab 150a 150b 150ab

EUSS10 123d 133bc 150c 120a 93d 107c



LSD005



Genotypes



SS04 5390ab 2473bc 2762abc 3586ab 3502a 3254b

SS14 4699ab 1701c 3711a 4020ab 2123ab 6291ab

SS21 1298c 1297c 1170d 3517ab 1998ab 4509ab

SS17 4748ab 1128c 1744cd 2048b 1940ab 6766ab

EUSS17 4728ab 1198c 3174ab 3693ab 1475b 7222ab

EUSS10 6442a 4173a 1774cd 5685a 1378b 5730ab



LSD005







Table8Ad

ditiv


licativeinteractio

nanalysisof

varia

ncefor

canejuiceand

ethano

lyield

oftheg

enotypes

acrossenvironm

ents

Source

ofvaria

tion

DF

Cane

yield

Juicey

ield

Ethano

lyield

SSMS

Explained(

)SS

MS

Explained(

)SS

MS

Explained(

)To

tal

143

2100813

14691

mdash1306

609520

9137130

mdash7724869

54020

mdashTreatm

ents

4744

6829


6471

827867592

17614204

6336

4944

624


6401

Genotypes

78799

1257lowastlowast

856

220070892


1684

548971

78424lowast

711

Environm

ents

527115


3691

317801649


2432

2963587


3836

Block

12114

84957

mdash70966844

5913904

mdash313774

26148

mdashInteraction

3598910

2826lowast

1944

289995052

8285573lowast

2219

143206

640

916n

s1854

IPCA

111

13783

1253lowast

853

107425316

9765938lowast

822

638384

58035lowast

826

IPCA

29

8550

950lowast

647

91016710

10112968lowast

697

391863

43540n

s507

Resid

uals

159345

623

mdash91553026

6103535

mdash401819

26788

mdashError

844225

503

mdash40

7775083

4854465

mdash2466

471

29363

mdashlowast



119875lt0001nsno


ffreedom

SSsum

ofsquareM

Smeansquare







SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377


Convex hull


SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull





Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014


























Genotypes




SS14 8700a 8500a 8500ab 7600a 7400ab 8200ab

SS21 7300c 7167c 6600c 6633d 6133c 6600d

SS17 7633bc 7933b 8033ab 6730cd 7133b 7267bcd


EUSS10 8433ab 8633a 8633a 7400ab 7667a 8500a



LSD005



Genotypes



SS04 18117ab 21517ab 10733a 20344abc 17522ab 19767a

SS14 17667ab 19417c 1110a 20944ab 16244b 21733a

SS21 14033c 19933c 8533a 17856bc 17567ab 19500a

SS17 19033ab 14350d 10800a 17522c 17378ab 21800a

EUSS17 18450ab 20567bc 10800a 19611abc 16678b 20833a

EUSS10 20633a 22667a 10567a 21989a 18144ab 23267a

EUSS11 16700bc 19133c 9100a 18211bc 16756b 22333a


LSD005








Genotypes



SS04 2639ab 2847ab 1389ab 1597b 2222a 1944b

SS14 2431ab 2082cd 1669a 2083b 1665ab 3264ab

SS21 972b 1320e 833b 1672b 2014a 2847ab

SS17 2778ab 1528de 1389ab 1656b 1597ab 3333ab

EUSS17 2500ab 1806cde 1667a 1663b 1015b 3332ab

EUSS10 3194a 3081a 1250ab 2997a 1181b 4444a

EUSS11 1875ab 2292bc 1389ab 2361ab 2153a 4028a


LSD005



Genotypes



SS04 8044ab 6304bc 3567ab 4225bc 4518a 4011c

SS14 7014ab 5090cd 5061a 5588bc 3649ab 8611abc

SS21 1938b 2850e 761c 4281bc 4000ab 4763c

SS17 7086ab 3472de 3310ab 2188c 3311ab 8442abc


EUSS10 9615a 9051a 3861ab 10111a 3056ab 11146a



LSD005









Genotypes



SS04 183a 180a 190a 150a 167ab 137abc

SS14 167ab 148abc 210a 130a 170ab 133abc

SS21 140cd 183a 157bc 157a 123c 170a

SS17 163b 117c 183ab 150a 153b 153ab

EUSS17 157bc 130c 187ab 150a 150b 150ab

EUSS10 123d 133bc 150c 120a 93d 107c



LSD005



Genotypes



SS04 5390ab 2473bc 2762abc 3586ab 3502a 3254b

SS14 4699ab 1701c 3711a 4020ab 2123ab 6291ab

SS21 1298c 1297c 1170d 3517ab 1998ab 4509ab

SS17 4748ab 1128c 1744cd 2048b 1940ab 6766ab

EUSS17 4728ab 1198c 3174ab 3693ab 1475b 7222ab

EUSS10 6442a 4173a 1774cd 5685a 1378b 5730ab



LSD005







Table8Ad

ditiv


licativeinteractio

nanalysisof

varia

ncefor

canejuiceand

ethano

lyield

oftheg

enotypes

acrossenvironm

ents

Source

ofvaria

tion

DF

Cane

yield

Juicey

ield

Ethano

lyield

SSMS

Explained(

)SS

MS

Explained(

)SS

MS

Explained(

)To

tal

143

2100813

14691

mdash1306

609520

9137130

mdash7724869

54020

mdashTreatm

ents

4744

6829


6471

827867592

17614204

6336

4944

624


6401

Genotypes

78799

1257lowastlowast

856

220070892


1684

548971

78424lowast

711

Environm

ents

527115


3691

317801649


2432

2963587


3836

Block

12114

84957

mdash70966844

5913904

mdash313774

26148

mdashInteraction

3598910

2826lowast

1944

289995052

8285573lowast

2219

143206

640

916n

s1854

IPCA

111

13783

1253lowast

853

107425316

9765938lowast

822

638384

58035lowast

826

IPCA

29

8550

950lowast

647

91016710

10112968lowast

697

391863

43540n

s507

Resid

uals

159345

623

mdash91553026

6103535

mdash401819

26788

mdashError

844225

503

mdash40

7775083

4854465

mdash2466

471

29363

mdashlowast



119875lt0001nsno


ffreedom

SSsum

ofsquareM

Smeansquare







SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377


Convex hull


SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull





Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014


























Genotypes



SS04 2639ab 2847ab 1389ab 1597b 2222a 1944b

SS14 2431ab 2082cd 1669a 2083b 1665ab 3264ab

SS21 972b 1320e 833b 1672b 2014a 2847ab

SS17 2778ab 1528de 1389ab 1656b 1597ab 3333ab

EUSS17 2500ab 1806cde 1667a 1663b 1015b 3332ab

EUSS10 3194a 3081a 1250ab 2997a 1181b 4444a

EUSS11 1875ab 2292bc 1389ab 2361ab 2153a 4028a


LSD005



Genotypes



SS04 8044ab 6304bc 3567ab 4225bc 4518a 4011c

SS14 7014ab 5090cd 5061a 5588bc 3649ab 8611abc

SS21 1938b 2850e 761c 4281bc 4000ab 4763c

SS17 7086ab 3472de 3310ab 2188c 3311ab 8442abc


EUSS10 9615a 9051a 3861ab 10111a 3056ab 11146a



LSD005









Genotypes



SS04 183a 180a 190a 150a 167ab 137abc

SS14 167ab 148abc 210a 130a 170ab 133abc

SS21 140cd 183a 157bc 157a 123c 170a

SS17 163b 117c 183ab 150a 153b 153ab

EUSS17 157bc 130c 187ab 150a 150b 150ab

EUSS10 123d 133bc 150c 120a 93d 107c



LSD005



Genotypes



SS04 5390ab 2473bc 2762abc 3586ab 3502a 3254b

SS14 4699ab 1701c 3711a 4020ab 2123ab 6291ab

SS21 1298c 1297c 1170d 3517ab 1998ab 4509ab

SS17 4748ab 1128c 1744cd 2048b 1940ab 6766ab

EUSS17 4728ab 1198c 3174ab 3693ab 1475b 7222ab

EUSS10 6442a 4173a 1774cd 5685a 1378b 5730ab



LSD005







Table8Ad

ditiv


licativeinteractio

nanalysisof

varia

ncefor

canejuiceand

ethano

lyield

oftheg

enotypes

acrossenvironm

ents

Source

ofvaria

tion

DF

Cane

yield

Juicey

ield

Ethano

lyield

SSMS

Explained(

)SS

MS

Explained(

)SS

MS

Explained(

)To

tal

143

2100813

14691

mdash1306

609520

9137130

mdash7724869

54020

mdashTreatm

ents

4744

6829


6471

827867592

17614204

6336

4944

624


6401

Genotypes

78799

1257lowastlowast

856

220070892


1684

548971

78424lowast

711

Environm

ents

527115


3691

317801649


2432

2963587


3836

Block

12114

84957

mdash70966844

5913904

mdash313774

26148

mdashInteraction

3598910

2826lowast

1944

289995052

8285573lowast

2219

143206

640

916n

s1854

IPCA

111

13783

1253lowast

853

107425316

9765938lowast

822

638384

58035lowast

826

IPCA

29

8550

950lowast

647

91016710

10112968lowast

697

391863

43540n

s507

Resid

uals

159345

623

mdash91553026

6103535

mdash401819

26788

mdashError

844225

503

mdash40

7775083

4854465

mdash2466

471

29363

mdashlowast



119875lt0001nsno


ffreedom

SSsum

ofsquareM

Smeansquare







SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377


Convex hull


SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull





Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014


























Genotypes



SS04 183a 180a 190a 150a 167ab 137abc

SS14 167ab 148abc 210a 130a 170ab 133abc

SS21 140cd 183a 157bc 157a 123c 170a

SS17 163b 117c 183ab 150a 153b 153ab

EUSS17 157bc 130c 187ab 150a 150b 150ab

EUSS10 123d 133bc 150c 120a 93d 107c



LSD005



Genotypes



SS04 5390ab 2473bc 2762abc 3586ab 3502a 3254b

SS14 4699ab 1701c 3711a 4020ab 2123ab 6291ab

SS21 1298c 1297c 1170d 3517ab 1998ab 4509ab

SS17 4748ab 1128c 1744cd 2048b 1940ab 6766ab

EUSS17 4728ab 1198c 3174ab 3693ab 1475b 7222ab

EUSS10 6442a 4173a 1774cd 5685a 1378b 5730ab



LSD005







Table8Ad

ditiv


licativeinteractio

nanalysisof

varia

ncefor

canejuiceand

ethano

lyield

oftheg

enotypes

acrossenvironm

ents

Source

ofvaria

tion

DF

Cane

yield

Juicey

ield

Ethano

lyield

SSMS

Explained(

)SS

MS

Explained(

)SS

MS

Explained(

)To

tal

143

2100813

14691

mdash1306

609520

9137130

mdash7724869

54020

mdashTreatm

ents

4744

6829


6471

827867592

17614204

6336

4944

624


6401

Genotypes

78799

1257lowastlowast

856

220070892


1684

548971

78424lowast

711

Environm

ents

527115


3691

317801649


2432

2963587


3836

Block

12114

84957

mdash70966844

5913904

mdash313774

26148

mdashInteraction

3598910

2826lowast

1944

289995052

8285573lowast

2219

143206

640

916n

s1854

IPCA

111

13783

1253lowast

853

107425316

9765938lowast

822

638384

58035lowast

826

IPCA

29

8550

950lowast

647

91016710

10112968lowast

697

391863

43540n

s507

Resid

uals

159345

623

mdash91553026

6103535

mdash401819

26788

mdashError

844225

503

mdash40

7775083

4854465

mdash2466

471

29363

mdashlowast



119875lt0001nsno


ffreedom

SSsum

ofsquareM

Smeansquare







SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377


Convex hull


SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull





Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014

























Table8Ad

ditiv


licativeinteractio

nanalysisof

varia

ncefor

canejuiceand

ethano

lyield

oftheg

enotypes

acrossenvironm

ents

Source

ofvaria

tion

DF

Cane

yield

Juicey

ield

Ethano

lyield

SSMS

Explained(

)SS

MS

Explained(

)SS

MS

Explained(

)To

tal

143

2100813

14691

mdash1306

609520

9137130

mdash7724869

54020

mdashTreatm

ents

4744

6829


6471

827867592

17614204

6336

4944

624


6401

Genotypes

78799

1257lowastlowast

856

220070892


1684

548971

78424lowast

711

Environm

ents

527115


3691

317801649


2432

2963587


3836

Block

12114

84957

mdash70966844

5913904

mdash313774

26148

mdashInteraction

3598910

2826lowast

1944

289995052

8285573lowast

2219

143206

640

916n

s1854

IPCA

111

13783

1253lowast

853

107425316

9765938lowast

822

638384

58035lowast

826

IPCA

29

8550

950lowast

647

91016710

10112968lowast

697

391863

43540n

s507

Resid

uals

159345

623

mdash91553026

6103535

mdash401819

26788

mdashError

844225

503

mdash40

7775083

4854465

mdash2466

471

29363

mdashlowast



119875lt0001nsno


ffreedom

SSsum

ofsquareM

Smeansquare







SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377


Convex hull


SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull





Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014






























SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika II

Mundika I

Nyahera

Sagam

Sinyanya

Masumbi

PC2

23

12

PC1 6377


Convex hull


SS17 SS14SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika I

Masumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 5901

PC2

20

91


Convex hull





Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014

























Table9Th

efirsttwoIPCA

scores

andASV

forg

enotypes

Genotype

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

SS04

2106

minus310

207

458

5112

minus5024

minus1857

6214

3495

minus1318

376

2180

SS14

2199

minus006

032

033

5835

1111

minus1330

1868

3758

019

minus464

465

SS21

1610

minus117

minus256

299

3099

minus1765

2703

3412

2298

297

1012

1122

SS17

2047

030

070

080

4635

1465

minus3873

4243

3062

174

minus92

897

0EU

SS17

2001

087

107

157

5018

1768

minus2910

3581

3582

382

minus90

21096

EUSS10

2691

290

102

396

7807

2514

3407

4517

4197

minus1019

minus10

41664

EUSS11

2350

039

minus19

4201

5794

3020

1818

4001

4125

1231

369

2040

ACFC

00312

2350

minus013

minus068

017

5455

minus3088

2044

4179

4231

234

641

746


Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014

























Table10A

MMIstabilityvalues

ofcanejuiceand

ethano

lyield

for8

sweetsorghum

geno

typese

valuated

indifferent

environm

ents

Environm

ents

Cane

yield

Juicey

ield

Ethano

lyield

Mean(tha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Mean(lha)

PCA1

PCA2

ASV

Masum

bi2344

049

296

303

6451

minus683

minus3633

3721

4322

minus1181minus1114

2224

Mun

dika

I2173

minus058

119

141

5361

minus2619

2227

3809

2110

minus77

8553

1383

Mun

dika

II2075

096

minus098

160

5309

601

4853

4904

4162

166

166

318

Nyahera

1753

minus304

minus18

7442

3817

minus3646

045

4304

2445

154

864

899

Sagam

3333

300

minus17

2432

7753

6233

163

7358

6142

1600

minus711

2702

Sinyanya

1337

minus084

042

118

3374

114

minus3653

3656

2378

039

minus401

406


SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014

























SS17SS14

SS21

ACFC00312

EUSS10

EUSS11


EUSS17

SS04

Mundika IMasumbi

Mundika II

Nyahera

Sagam

Sinyanya

PC1 3390

PC2

31

49


Convex hull





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

23

12

PC1 6377







EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014

























EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17SS21


Mundika II

Mundika IMasumbi

Nyahera

Sagam

Sinyanya

PC2

20

91

PC1 5901





EUSS10

ACFC00312

EUSS11

EUSS17

SS04

SS14SS17

SS21


Mundika II

Mundika I

Masumbi

Nyahera

Sagam

SinyanyaPC2

31

49

PC1 3390






Competing Interests


Acknowledgments




References









































Journal of




Agronomy





Microbiology




Volume 2014


























References









































Journal of




Agronomy





Microbiology




Volume 2014
































Journal of




Agronomy





Microbiology




Volume 2014


























Journal of




Agronomy





Microbiology




Volume 2014
























Documents

Adaptability and Stability Study of Selected Sweet Sorghum