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January - March

Volume 40 Number 1

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KASETSART JOURNAL

NATURAL SCIENCE

The publication of Kasetsart University

VOLUME 40 January - March NUMBER 1

Tillage System and Fertilizer Rate Effects on Sorghum Productivity in the Central Rift Valley of

Oromiya, Ethiopia

Worku Burayu, Sombat Chinawong, Rungsit Suwanketnikom,

Thongchai Mala1 and Sunanta Juntakool 1

Repeatability, Optimal Sample Size of Measurement and Phenotypic Correlations of

Quantitative Traits in Guava

Kriengsak Thaipong and Unaroj Boonprakob 11

Heritability, Heterosis and Correlations of Fruit Characters and Yield in Thai Slicing Melon

(Cucumis melo L. var. conomon Makino)

Chamnan Iathet and Kasem Piluek 20

Seed Development and Maturation of Eryngo (Eryngium foetidum L.)

Boonsong Ekpong and Sutevee Sukprakarn 26

Evaluation for Antibiosis Resistance in Cotton to Helicoverpa armigera Larvae

Praparat Hormchan and Arunee Wongpiyasatid 33

Influence of Biotic and Chemical Plant Inducers on Resistance of Chilli to Anthracnose

Le Thi Kieu Oanh, Vichai Korpraditskul,

Chainarong Rattanakreetakul and Sirikul Wasee 39

Incidence of Cymbidium Mosaic Virus and Odontoglossum Ringspot Virus on

In Vitro Thai Native Orchid Seedlings and Cultivated Orchid Mericlones

Yuphin Khentry, Ampaiwan Paradornuwat,

Sureeya Tantiwiwat, Salak Phansiri and Niphone Thaveechai1 49

Comparative Performances of Holstein-Friesian Cows under Smallholder and Large Scale

Farmers’ Management in Central Rift Valley, Ethiopia

Nega Tolla, Pravee Vijchulata, Pornsri Chairatanayuth

and Suwapong Swsdiphanich 58

Biochemical Properties of Nile Tilapia (Oreochromis niloticus) Hemoglobin

Kriangkrai Thongsarn, Wanchai Worawattanamateekul,

Suriyan Tunkijjanukij, Choosri Sribhen and Apassara Choothesa 69

Screening of Ethiopian Traditional Medicinal Herbs for their Nitrification Inhibiting Ability

Wassie Haile, Thongchai Mala,

Yongyuth Osotsapar and Visoot Verasan 74

Seasonal Characteristics of Wood Formation in the Elite Genetic – Based

Eucalyptus camaldulensis Dehnh.

Teera Veenin, Tadashi Nobuchi, Minoru Fujita

and Somkid Siripatanadilok 83

Development of Catalase Gene Nuclear DNA-Based Marker for Population Genetic Analysis

in Thai Teak (Tectona grandis L.f.)

Jongkon Cheua-ngam and Hugo Volkaert 91

Effects of Na+, K+ and Ca2+ Accumulation on the Expression of Ca2+-ATPase Gene

in Rice KDML

Wunrada Surach, Mingkwan Mingmuang

and Amara Thongpan 99

Molecular Identification of Cycas by Restriction Fragment Length Polymorphism (RFLP)

and Random Amplified Polymorphic DNA (RAPD)

Pattamon Sangin, Amara Thongpan,

Anders J. Lindstrom and Mingkwan Mingmuang

Physiological Study and Alcohol Oxidase Gene(s) of Thermotolerant Methylotrophic

Yeasts Isolated in Thailand

Nantana Srisuk, Savitree Limtong,

Hiroya Yurimoto, Yasuyoshi Sakai and Nobuo Kato

Characterization of Grass Degrading Bacteria Active on β-1,,4-D-glucans from

Bacillus subtilis GN Potential Use for Grass Silage-Making

Jirawan Apiraksakorn, Tonglian Buwjoom

and Sunee Nitisinprasert

Thermal Ageing of Thermoplastic Elastomeric Natural Rubber-Low Density

Polyethylene Blends

Wiwat Suaysom and Wirunya Keawwattana

Morphology and Haemolymph Composition Changes in Red Sternum Mud Crab (Scylla serrata)

Jintana Salaenoi, Anchanee Sangcharoen,

Amara Thongpan and Mingkwan Mingmuang

A Comparison of Rearing Pacific White Shrimp (Liptopenaeus vannamei Boone, )

in Earthen Ponds and in Ponds Lined with Polyethylene

Onanong Prawitwilaikul, Chalor Limsuwan,

Wara Taparhudee and Niti Chuchird

Application of Near Infrared Spectroscopy to Predict Crude Protein in Shrimp Feed

Jirawan Maneerot, Anupun Terdwongworakul,

Warunee Tanaphase and Nunthiya Unprasert

The Effect of Peptidoglycan on Immune Response in Black Tiger Shrimp

(Penaeus monodon Fabricius)

Watchariya Purivirojkul, Nontawith Areechon

and Prapansak Srisapoome

Distribution and Early-life Development of Thai River Sprat Clupeichthys aesarnensis

Wongratana, Larvae, in Pasak Jolasid Reservoir, Lop Buri Province, Thailand

Santi Poungcharean

Gonadal Development and Sex Inversion in Saddleback Anemonefish Amphiprion polymnus

Linnaeus ()

Sukjai Rattanayuvakorn, Pisut Mungkornkarn,

Amara Thongpan and Kannika Chatchavalvanich

In sacco Degradation Characteristics of Crop Residues and Selected Roughages in

Brahman-Thai Native Crossbred Steers

Songsak Chumpawadee, Kritapon Sommart,

Thevin Vongpralub and Virote Pattarajinda

Comparative Efficiency of KU and ISO Plungers in Mixing Composite Bulk Raw Milk

Jigme Wangdi, Pravee Vijchulata,

Pornsri Chairatanayuth and Suwapong Swasdiphanich

Influences of Physicochemical Characteristics of Rice Flour and Cassava Starch on the

Gelation of Calcium-Induced Egg Albumen-Flour Composite

Parichat Hongsprabhas and Kamolwan Israkarn

The Product Design of Puffed Snacks by Using Quality Function Deployment (QFD)

and Reverse Engineering (RE) Techniques

Wiwat Wangcharoen, Tipvanna Ngarmsak and Brian H. Wilkinson

Effect of Coating on Doughnut Cake Preference using R-index

Tunyaporn Sirilert, Anuvat Jangchud, Phaisan Wuttijumnong

and Kamolwan Jangchud

Application of Artificial Neural Networks for Reservoir Inflow Forecasting

Varawoot Vudhivanich, Santi Thongpumnak,

Nimit Cherdchanpipat, Areeya Rittima and Nattaphun Kasempun

Jordan Derivations on Rings

Orapin Wootijiruttikal and Utsanee Leerawat

Geoinformatic Public Domain System Model “ SWAT “ in Thailand

Hansa Vathananukij

Kasetsart J. (Nat. Sci.) 40 : 1 - 10 ()

Tillage System and Fertilizer Rate Effects on Sorghum Productivity

in the Central Rift Valley of Oromiya, Ethiopia

Worku Burayu 1, *, Sombat Chinawong 1 , Rungsit Suwanketnikom 2 ,

Thongchai Mala 1 and Sunanta Juntakool 2

ABSTRACT

Soil moisture and soil nutrient are the most sorghum yield limiting factors in semi-arid Oromiya.

Hence, to identify appropriate crop management system for sorghum productivity, the field experiment

was conducted in cropping season using factorial combination of four levels of tillage systems and

four rates of fertilizer in spilt plot design at two locations. It was found that the stand count and height

of sorghum varied significantly between locations, and lower stand count recorded at Wolenchity (

plant ha -1 ) than at Malkassa ( plant ha -1 ) while greater plant height was obtained at Wolenchity.

Grain yield was significantly affected by location and fertilizer rate. Significantly (P≤) higher grain

yield was obtained at Wolenchity ( kg ha -1 ) than that at Malkassa ( kg ha -1 ). Grain yield

achieved at the highest fertilizer rate of kg N-P 2O 5 ha -1 was significantly (P≤) higher than

that at the current rate of kg N-P 2O 5 ha -1 . The highest grain yield was recorded from tie-ridge plot

but varied with fertilizer rate for each location. Harvest Index (HI) followed the same pattern as the

grain yield. However, significantly (P ≤ ) higher stover and biomass yield of sorghum were obtained

at Malkassa. These findings indicated that applications of fertilizer beyond kg N-P 2O 5 ha -1

could give no significant yield advantage and thus, would not be economically feasible. The tie-ridge

and reduced tillage tied furrow were encouraging but need further investigation to incorporate in sorghum

cropping system.

Key words: harvest index, no-tillage, soil nutrient, soil moisture, tied-ridge

INTRODUCTION

Cereal crops account for over 86% of the

area planted with food crops each year in Ethiopia

(CSA, ). Sorghum (Sorghum bicolor) is one

of the food crops that occupy 20% of the cultivated

land under cereals (CSA, ). It is a staple food

for a significant proportion of the lowland rural

population. Known as the most drought tolerant

crop, sorghum is grown as one of the major

1 Faculty of Agriculture, Kamphaeng Saen, Kasetsart University, Kamphaeng Saen Campus, Nakhonpathom , Thailand.

2 Kasasart University, Bangkean Campus, Bangkok , Thailand;

* Corresponding author, e-mail: workuburayu@manicapital.com

multipurpose cereals in the semi-arid areas. Close

to one million hectares is developed and about

million tones are produced each year (Central

Statistical Authority, CSA, ).

Despite the importance of sorghum crop,

the productivity of sorghum is far below the

genetic potential of the crop; the national mean

yield has been estimated at about t ha -1 on

peasant farms (CSA, ). However, research

results have shown that using improved varieties

Received date : 25/07/05 Accepted date : 13/12/05

2

and management practices a sorghum grain yield

of t ha -1 can be achieved (Kidane et al., ).

Such low yields and the production shortfall of

sorghum cropping system in semi-arid areas are

attributable to several factors. Among these, soil

moisture stress, poor soil fertility, and pests are

the most limiting factors (Kidane and Abuhay,

). Water stress is one of the major causes for

low yields and total crop failure of cereals in the

semi-arid areas of the country and soil fertility is

the next constraint generally faced in such regions

(Kidane et al., ). Hence, moisture stress and

nutrient deficiency is critical in such soils and

regions. Of all nutrients, nitrogen and phosphorus

are the most crop growths and yield limiting

factors in the country (Kidane et al., ). The

study conducted in the central rift valley of

Oromiya also emphasized that the principal

constraints to increase crop production in semiarid

regions were the minimal combination of

technologies for water availability, soil fertility,

and new genetic material (Kidane et al., ).

These insist the necessity of further studies in

combined technologies for soil moisture

conservation and fertilizer requirements for crop

production of this region. Furthermore, the studies

on combined influences of conservation tillage

practices and soil fertility management have been

minimal particularly in the dryland central rift

valley of the country. Hence, the need for in-depth

research on the combination of moisture

conservation techniques and fertility management

is unprecedented. This experiment was, therefore,

initiated to undertake comprehensive studies with

the objectives to identify the appropriate tillage

systems and fertilizer rates for productivity of

sorghum in the semi-arid, central rift valley of

Ethiopia.

MATERIAL AND METHODS

The experiment was initiated in at

Melkassa Agricultural Research Center (MARC)

Kasetsart J. (Nat. Sci.) 40(1)

and Wolenchity research sub-station with the

hypothesis that implementation of various

conservation-tillage systems with different rates

of fertilizer would result in better sorghum crop

yield as compared to the conventional tillage.

MARC and Wolenchity are located in the semiarid

central rift valley of Oromiya. The MARC is

located at 8° 24’N latitude and 39° 12’E longitude

of m above mean sea level and Wolenchity

at 8° 40’N latitude and 39° 26’E longitude of

m above mean sea level. The soil of experimental

sites are loam soil with 41% sand, 37% silt, 22%

clay content and a pH of for MARC, and

sandy loam with 46% sand, 34% silt and 20% clay

content with a pH of for Wolenchity.

The combined effects of tillage systems

and fertilizer rates on sorghum productivity trial

consisted of 16 treatments comprising the factorial

combinations of four levels of tillage management,

i.e., Conventional Tillage (CT), Reduced Tillage

(RT), No-Tillage (NT) and Tie Ridge (TR), and

four levels of fertilizer, i.e., (F 0), (F 1),

(F 2), (F 3) kg N-P 2O 5 ha -1 . The

experiment was laid out in a 4 × 4 spilt plot design

with three replications at two locations. The fourtillage

systems were main plots of 14m × m

(m 2 ), and fertilizer rates placed in sub plots of

14m × m (m 2 ). Pathways of m, m

and 1m were placed between subplots, main plots

and replications, respectively. A row spacing of

m was used. The conventional tillage system

consisted of four plowings with traditional oxen

plow ‘Maresha’ (farmer’s practice) to a depth of

first pass approximately 8 cm and the other two

passes perpendicular to the previous path with a

final one at cm depth prior to planting. In

conventional tillage the first hand weeding was

done days after crop emergence (DAE) and

the second hand weeding was done DAE.

In the no-tillage treatment, no soil disturbance was

made except for seeding and fertilizer application.

In the reduced tillage tied furrow, it was designed

to use the ridger only after one pass with the ox-

plow, then furrow ties were made during planting

at 5 m interval. Both no-tillage and reduced tillage

tied furrow plots were sprayed with glyphosate at

a rate of 3 l ha -1 as preplanting herbicides. In tie

ridge treatments, after three plowings with

traditional ox plow, 35 cm high ridges were

constructed 75 cm apart and cross-tied with soil

bunds across the ridges with tie ridger at about

every 5 m ridge length.

The fertilizer sources were urea (46% N)

and diammonium phosphate (18% N and 46%

P 2O 5). All of the P fertilizer and half of the N

fertilizer were banded 5 cm below and 5 cm away

from the rows as a basal application during

planting. The rest half of the N fertilizer was

applied when crop reached a knee height.

The improved sorghum variety,

Meko-1, an early maturity type ( days to

anthesis) was used and the seeds were placed in

rows and sorghum seedlings were thinned to one

plant per hill 15 days after emergence to ensure

the targeted population.

Data on various crop parameters were

collected throughout the cropping season. Stand

count at harvesting was recorded by counting the

actual numbers of plant in the subplot area and

expressed on hectare basis. Plant height for a

randomly selected six plants (two plants within a

36 m segment of three rows) per sub-plot was

determined. Sorghum heads and stover were

harvested at the base of the lowest grain branch

and at the ground surface level, respectively from

areas of m 2 (6m × m) DAE. Then

sorghum head height was determined, sun-dried

and weighed. Counting grains in duplicates

and weighing them on two decimal electronic

balances, thousand seed weight was determined.

The weights thus obtained were added and

multiplied by two to reach seed fresh weight.

They were then oven-dried at °C and

weighed again to determine moisture contents and

to obtain seed dry weight. Grain yield and

above ground biomass were determined from areas

Kasetsart J. (Nat. Sci.) 40(1) 3

of m 2 . Grain yield was adjusted to %

moisture content. A total above ground biomass,

which included stover and whole panicles, was

used to obtain biomass yield. Harvest index (HI)

values were computed as the ratio of the mass of

grain yield to total biomass (grain + stover).

Soil moisture at depth was

determined gravimetrically for each plot in the

central row in two replications using a core

sampler. Soil water data were recorded at various

growth stages from planting until the physiological

maturity of sorghum crop. Gravimetric water

content was converted to a volumetric basis using

bulk densities of soil core taken from each depth

(Lopez et al., ).

Data were subject to General Linear

Models (GLM) Procedure using SAS Statistical

Software (SAS, ). Duncan’s Multiple Range

Test (DMRT) and Least Significant Differences

(LSD) were used for mean separation at the

or probability levels.

RESULTS AND DISCUSSION

Stand count, plant height, head height and

grain weight

Stand count of sorghum varied

significantly between locations and different

tillage systems but no significant differences

among fertilizer rates could be detected. The data

in Table 1 revealed that an estimated mean stand

count of sorghum at Wolenchity was significantly

lower than that observed at Malkassa. When the

data for different fertilizer rates and both locations

were combined the stand counts from conventional

tillage and the reduced tillage tied furrow were

significantly higher than those obtained from either

no-tillage or tie-ridge tillage systems.

Slight difference was observed in plant

height between locations, among different rates

of fertilizer application and the interaction between

location and fertilizer, and between tillage and

fertilizer rates. Unlike the stand count of sorghum,

4

the greater plant height was obtained at Wolenchity

as compared to that obtained at Malkassa. The

unfertilized plot had significantly lower plant

height those that obtained from fertilized plots

(Table 2).

Almost equal head height was observed

at Wolenchity and Malkassa (Table 3). It was only

reduced tillage that was varying across locations

and had significantly higher head height at MARC

than the corresponding tillage at Wolenchity.

Kasetsart J. (Nat. Sci.) 40(1)

The grain weight of sorghum was

significantly affected by location (P ≤ ), and

significantly higher seed weight was obtained

at Wolenchity as compared to that obtained at

Malkassa (Table 4).

The highest seed weight was observed

on the tied ridge treatment with the highest rate of

fertilizer application at Wolenchity and no-tillage

treatment of the same rate of fertilizer at Malkassa.

Sorghum grain weight reflects the crop growing

Table 1 Influences of tillage system on stand count (plant ha -1 ) with varied locations.

Tillage system Location

Wolenchity Malkassa Mean 1

CT a

RT a

NT b

TR c

Mean B A

1 Means followed by common lowercase letters within a column do not differ significantly at 5% probability level of significance,

and means followed by different uppercase letters within row differ significantly at 5% probability level of significance.

Table 2 Influences of fertilizer on plant height (cm) with varied locations.

Fertilizer rate Location

Wolenchity Malkassa Mean

F 0 b

F 1 a

F 2 a

F 3 a

Mean

1 Means followed by common letters within a column do not differ significantly at 5% probability level of significance.

Table 3 Influences of tillage systems on head height (cm) with varied locations.

Tillage system** Location

Wolenchity MARC Mean

CT AB* AB

RT B A

NT AB AB

TR AB AB

Mean

* Means followed by the same common letters are not significantly different at 5% probability; ** CT = Conventional tillage,

RT = Reduced tillage, NT = No-tillage, TR = Tie ridge

conditions during the grain filling period. The

greater seed weight at Wolenchity than at Malkassa

might be found due to mild water deficit during

grain filling at the latter location.

Grain yield and harvest index of sorghum

Grain yield of sorghum were

significantly affected by locations (Table 5). The

sorghum grain yield obtained at Wolenchity was

Kasetsart J. (Nat. Sci.) 40(1) 5

significantly higher (P

6

at Wolenchity (Figure 3) that led to higher grain

yield. The difference in mean grain yield among

fertilizer rate was highly significant (P

significant yield advantage.

In the study, the highest sorghum grain

yield was recorded due to tie-ridge tillage but

varied with fertilizer rate for each location (

kg ha -1 grain at kg N-P 2O 5 ha -1 for

Wolenchity; and kg ha -1 at kg N-

P 2O 5 ha -1 for Malkassa). The yield obtained due

to tie-ridge and reduced tillage tied furrow was

equal at Malkassa. There were many other results

which validated these findings, as it was evident

from the extensive published data on tillage

affecting crop yield that differed with soil

conditions and environments (Lal, ; Triplett,

; Arnon, ; Dao, ; Radford et al.,

).

Harvest index of sorghum varied

significantly with location (P

8

Stover and aboveground biomass

Contrary to the grain yield and HI,

significantly higher (P

CONCLUSION

The stand count, plant height, grain yield,

HI, stover and biomass yield of sorghum differed

significantly between locations and some among

fertilizer rates but no significant differences among

tillage systems could be detected. Significantly

greater stand count, stover and biomass yield of

sorghum were obtained at Malkassa. Plant height,

grain yield and harvest index of sorghum were

significantly higher at Wolenchity. Grain yield and

HI achieved at the highest fertilizer rate was

significantly (P

10

Radford, B.J., A.J. Dry, L.N. Robertson and B.A.

Thomas. Conservation tillage increases

soil water storage, soil animal population.

J. Soil Water Consv.

SAS, SAS Institute Inc., Cary, NC, USA.

SAS software release Unpublished.

Kasetsart J. (Nat. Sci.) 40(1)

Triplett, G.B. Crop management practices

for surface-tillage systems, pp. In

M.A. Sprague and G.B. Triplett (eds.). The

tillage revolution. Zero-tillage and Surfacetillage

Agriculture. John Wiley, New York.

Kasetsart J. (Nat. Sci.) 40 : 11 - 19 ()

Repeatability, Optimal Sample Size of Measurement and

Phenotypic Correlations of Quantitative Traits in Guava

ABSTRACT

Kriengsak Thaipong and Unaroj Boonprakob*

Five fruits from each of 11 guava genotypes were evaluated in dry and early rainy seasons

under Thailand conditions for fruit weight, flesh thickness, flesh weight, seed cavity (central pulp)

weight, fruit firmness, total soluble solids, titratable acidity, juice acidity, and ascorbic acid to estimate

repeatability (R), phenotypic correlations (r), and to predict the optimal sample size. The repeatability

of the fruit weight, flesh thickness, flesh weight, seed cavity weight, titratable acidity, juice acidity, and

ascorbic acid were relatively high (R ≥ ). The flesh thickness, titratable acidity, juice acidity, and

ascorbic acid were the traits with highest estimates, , , , and , , , in

dry and early rainy seasons, respectively. Based on a threshold of 10% increase in relative efficiency, a

sample of three fruits was sufficient for evaluating guava fruit traits. Most physical traits (fruit weight,

flesh thickness, flesh weight, and seed cavity weight) had weak negative correlations ( ≤ r ≤ –)

with chemical traits (total soluble solids, titratable acidity, and ascorbic acid). Fruit firmness had no

correlation with all other fruit traits. There were strong positive correlations between fruit weight and

flesh thickness (r = ), flesh weight (r = ), and seed cavity weight (r = ). Therefore, fruit

weight could be used as an indirect selection for flesh thickness, flesh weight, and seed cavity weight.

Key words: Psidium guajava L., breeding, quantitative trait analysis, fruit qualities

INTRODUCTION

Guava (Psidium guajava L.) is native to

tropical America and presently found distributing

in several tropical and subtropical regions (Cobley,

) such as India, South Africa, Brazil, Cuba,

Venezuela, New Zealand, the Philippines, Hawaii,

Florida, and California (Yadava, ), Vietnam

(Le et al., ), and Thailand (Tate, ). In

part because it is a highly variable species for many

morphological and horticultural traits, tolerant to

environmental stress such as salinity (Nakasone

and Paull, ), and its fruit has a high nutritional

value; especially ascorbic acid, dietary fibers and

some antioxidant compounds (Jimenez-Escrig et

al., ).

In Thailand, major guava production

areas of nearly 8, ha are located in the Central

and Western parts of the country, especially

Nakhon Pathom, Samut Sakhon, and Ratchaburi

provinces; however, a guava plant can grow and

produce fruits well in most regions in Thailand

throughout the year. Prominent commercial

cultivars are ‘Paen Seethong’, ‘Klom Salee’, and

‘Yen Song’. These white flesh cultivars account

for more than 90% of fresh guava consumption.

Department of Horticulture, Kasetsart University, Kamphaeng Saen campus, Nakhon Pathom , Thailand.

* Corresponding author, email: unaroj.b@manicapital.com

Received date : 22/06/05 Accepted date : 26/12/05

12

At present, new cultivars with high nutritional

value, excellent flavor, tolerant to biotic and abiotic

stresses are increasingly important.

Major fruit qualities are quantitative traits

and the phenotypic expression is complex.

Knowledge of genetic and environmental factors

that influence their phenotypic expressions is

fundamental for a successful breeding program.

The phenotypic variance can be partitioned into

variances within and between individuals when a

trait is repeatedly measured on each individual.

Repeatability is a ratio of the between individual

variance to the phenotypic variance. Repeatability

estimates are useful for making predictions on

progress in measurement, determining an upper

limit of heritability, and predicting future

performance from past records (Becker, ;

Falconer and Mackay, ). Knowledge of the

repeatability of quantitative traits helps in selecting

efficient breeding strategies, including optimal

sample size and evaluation methods. Several fruit

breeding programs such as persimmon (Yamada

et al., ), strawberry (Sacks and Shaw, ),

apricot (Akca and Sen, ), and peach (De

Souza et al., ) used the benefits of

repeatability.

In the present research, the repeatability,

optimal sample size, and phenotypic correlations

of guava fruit traits were estimated to provide

quantitative genetic information for guava

breeding programs.

MATERIALS AND METHODS

Experimental materials

Eleven randomly selected guava clones

consisted of six white flesh dessert types (‘Klom

Salee’, ‘Khoa Um-porn’, ‘Yen Song’, ‘Paen Yak’,

‘Paen Seethong’ and ‘Na Suan’), one pink flesh

dessert type (‘Keynok Daeng’), two maroon flesh

dessert types (‘Daeng Siam’ and ‘Philippines’),

and two pink flesh processing types (‘MCL

S’ and ‘PC ’) from the guava germplasm

Kasetsart J. (Nat. Sci.) 40(1)

collection of the Department of Horticulture,

Kasetsart University, Kamphaeng Saen campus,

Nakhon Pathom, Thailand were used. Guava trees

were randomly planted in an experimental field

(14°01′N lat., 99°58′E lon.) in December , at

a m × m spacing. The environmental

conditions in the dry season (November to

February) and the early rainy season (March to

June) in had daily average max/min air

temperature of /°C and /°C, daily

average max/min RH of 95/50% and 95/58%, total

precipitation of 9 mm and mm, and daily

average saturated light duration of h d -1 and

h d -1 , respectively.

Sampling methods

Fruit thinning by leaving one fruit per

shoot was done in order to minimize the effects of

over-cropping on fruit qualities such as size and

sugar contents. Five fruits were randomly sampled

from the same tree of each genotype in dry and

early rainy season when the trees were 14 and 18

months old, respectively. In general, guava trees

propagated by air-layering or cutting begin to set

fruits in two to three months after planting but most

growers do not allow trees to set fruits until six to

eight months old. The changing in skin color was

used as harvesting indicator. White flesh fruits

were harvested when their skin color changed from

dark green to light green, maroon flesh fruits were

harvested when their skin color changed from dark

maroon to light maroon, and processing types were

harvested when their skin color changed from dark

green to yellow green.

Fruit quality measurements

Five physical fruit traits: fruit weight

(FW), flesh thickness (FLT), flesh weight (FLW),

seed cavity (central pulp) weight (SCW), fruit

firmness (FF), and four chemical fruit traits: total

soluble solids (TSS), titratable acidity (TA), juice

acidity (pH), and ascorbic acid (AA) were

evaluated. FW (g) and SCW (g) were measured

y digital balance (SK, A&D, Japan). FLT

(cm) was measured at equatorial plane with a

caliper. FLW (g) was calculated by subtracting FW

with SCW. FF (Newton; N) was determined on

one side of fruit with fruit hardness tester (N.O.W.,

Japan) using cm diameter probe after cm

skin was sliced off. Extracted juice from a flesh

portion was used for determining the chemical

traits. TSS was measured as °Brix with a

temperature compensated hand refractometer

(ATC-1E, Atago, Japan). TA (%) was determined

by titration with N NaOH and 1%

phenolphthalein as an indicator using a digital

burette (Burette digital III, Brand, Germany). The

pH was determined using pH meter (pHScan 2,

Eutech, Singapore). AA (mg) was estimated with

oxalo-acetic acid solution and titration with 2, 6dichlorophenolindophenol-dye

solution

(A.O.A.C., ).

Statistical analysis

Data from each season was analyzed as

a completely randomized design. An appropriate

statistical model for expressing the phenotypic

value of a trait is P ij = µ + g i + f ij (Becker, ).

Where P ij is the phenotypic value of the j th fruit of

the i th genotype, µ is the overall mean, g i is the

random effect of the i th genotype, and f ij is the

random effect of j th fruit in the i th genotype. The

repeatability of the guava fruit traits was estimated

using one-way analysis of variance procedure

(Becker, ). The formula is written as

2

σ

Repeatability = B

2 2

σB + σE

where

σ2 B is the between genotypic variance and σ2 E is

the within genotypic variance.

with standard error of repeatability

S.E. =

2 2

[ 21 ( − R) ][ 1+ ( k − 1)

R]

kk ( −1)( n−1)

Where k is the number of measurements (fruits)

Kasetsart J. (Nat. Sci.) 40(1) 13

per genotype, n is the number of genotypes, and

R is the repeatability value.

The relative efficiency of measurements

was estimated to obtain the optimal sample size

for evaluating guava fruit traits. The formula is

k

Relative efficiency =

1+ ( k − 1)

R

Where k is the number of measurements (fruits)

and R is the repeatability value.

In this research, optimal sample size was

selected when the relative efficiency increased by

less than 10% with an additional measurement.

The phenotypic correlations among traits were

estimated on a cultivar mean basis from two

seasons using Pearson’s correlation coefficient (r)

analysis.

RESULTS AND DISCUSSION

Variance components

The phenotypic variance (σ 2 P) of guava

fruit traits in the dry and the early rainy seasons

was different (Table 1), indicating that seasonal

environmental conditions influenced the

phenotypic expression of guava fruit qualities. The

combined analysis of variance (ANOVA) over

seasons confirmed that several traits, especially

the chemical traits, were affected by seasons (Table

2). Therefore, it could be concluded that genetic

expressions of chemical traits were highly

sensitive to the changing of seasonal environments

probably temperature and precipitation because

these were clearly different between the two

seasons as previously described in materials and

methods. Rathore () has reported that guava

fruits harvested in spring, rainy and winter seasons

in India had different levels of several chemical

traits with rainy season fruits showing the lowest

levels due to the fruits having the highest moisture

contents. Effects of temperature on chemical

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