Agricultural Research Management Information System

Home
Research Summary
Search
About Us
- ARMIS
- Brochure
Contact Us
Report
User Request
Data Input
Help
- Operation Manual
  - PDF
  - Video
- Program Area & Commodity
We have reached 37600 number of research entries at this moment.

- Logout

Research Detail

Home
Research
Detail

Genotype and Environment Interaction of Sweetpotato Varieties

S. SULTANA
Scientific Officer (Plant Breeding), Tuber Crops Research Centre, Bangladesh Agricultural Research Institute (BARI), Gazipur-1701

H. C. MOHANTA
Principle Scientific Officer, Tuber Crops Research Centre, BARI, Gazipur-1701,

Z. ALAM
Scientific Officer, Tuber Crops Research Centre, BARI, Gazipur-1701,

S. NAZNIN4
Scientific Officer (Plant Breeding), Tuber Crops Research Centre, Bangladesh Agricultural Research Institute (BARI), Gazipur-1701

S. BEGUM
Scientific Officer, Plant Breeding Division, BARI, Gazipur-1701, Bangladesh.

Abstract/ Executive Summary:

The article presents results of additive main effect and multiplicative interaction (AMMI) and genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data of 15 sweet potato varieties released from Bangladesh Agricultural Research Institute conducted during 2015–2018. The objective of this study was to determine the effects of genotype, environment and their interaction on tuber yield and to identify stable sweet potato genotypes over the years. The experimental layout was a randomized complete block design with three replications at Gazipur location. Combined analysis of variance (ANOVA) indicated that the main effects due to genotypes, environments and genotype by environment interaction were highly significant. The contribution of genotypes, environments and genotype by environment interaction to the total variation in tuber yield was about 60.16, 10.72 and 12.82%, respectively. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 100% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 71.5% and 28.5% of variability, respectively. The study results identified BARI Mistialu- 5, BARI Mistialu- 14 and BARI Mistialu- 15 as the closest to the “ideal” genotype in terms of yield potential and stability. Varieties ‘BARI Mistialu- 8, BARI Mistialu- 11 and BARI Mistialu- 12’ were also selected as superior genotypes. BARI Mistialu- 3 and BARI Mistialu- 13 was comparatively low yielder but was stable over the environment. Among them BARI Mistialu-12, BARI Mistialu-14 and BARI Mistialu-15 are rich in nutrient content while BARI Mistialu-8 and BARI Mistialu-11 are the best with dry matter content and organoleptic taste. Environments representing in 1st and 3rd year with comparatively short vectors had a low discriminating power and environment in 2nd year was characterized by a high discriminating power.

Keywords: Sweet potato, Genotype x environment interaction, Biplot, Genotype

Location: Tuber Crops Research Centre (TCRC), BARI, Gazipur

Start Date: 00-00-2015

End Date: 00-00-2018

Program Area: Variety and Species

Commodity: Sweet potato

Objectives:

The objective of this study was assessment of variations in yield performance of sweet potato varieties across years based on the AMMI and GGE biplot and identification of the most valuable genotypes.

Methodology:

The experiment was conducted at Tuber Crops Research Centre (TCRC) in Gazipur during 2015-18. Fifteen (15) varieties of BARI released sweet potato were included in the study. Vines were planted on 18th, 15th and 22nd of November in three consecutive years during 2015-2017 in the field having plot size 3.0 x 3.0 m. The crop was fertilized with 250-280 kg/ha urea, 140-170 kg/ha TSP, 230-260 kg/ha MOP, 60-80 kg/ha gypsum, 10-12 kg/ha zinc sulphate, 90120 kg/ha magnesium sulphate, 6-8 kg/ha boric acid and 10,000 kg/ha cowdung. All cowdung, TSP, gypsum, zinc sulphate, boric acid and half urea & MP should be applied during final land preparation. Rest of the urea and MP should be applied after 35-40 DAP in furrow system. Harvesting time were 17th , 20th and 25th March of 2016, 2017 and 2018 accordingly. Both qualitative and quantitative data were collected in the study. The qualitative data included predominant skin colour, predominant flesh colour, sweetness, fibreness and texure etc. Sweetness were evaluated by organoleptic taste, where ≤ 3= slightly sweet, 4-6= moderately sweet, 6≤ =highly sweet. In case of fibreness, ≤ 3= high fibre content, 4-6= moderate fibre content, 6≤ = low fibre content and texure marked as ≤ 3= highly moist, 4-6= moderately dry, 6≤ =highly dry. The quantitative data included root dry matter content, expressed as percentage of root dry weight (g) to fresh root weight (g). Samples (100–200 g) were taken from roots of representative plants in a plot and the roots were cut into smaller pieces and oven dried at 70 °C for 72 h. Fresh root yield, expressed as harvested fresh roots weight of 9 m2 converted to ton per hectare. Data were processed and analyzed using Cropstat 7.2 program and PB Tools 1.4. The β-carotene content was estimated according to the method of the Association of Official Analytical Chemists (AOAC,1980) in Post Harvest Technology Division of BARI . In to a conical flask containing 50ml of 95% ethanol,10g of the macerated sample was placed and maintained at 70-80⁰C in a water bath for 20 minutes with periodic shaking. The supernatant was decanted, allowed to cool and its volume was recorded as initial volume. The ethanol concentration of the mixture was brought to 85% by adding 15 ml of distilled water and it was further cooled in a container of ice water for about 5 minutes. The mixture was transferred in to a separating funnel and 25 ml of petroleum ether (pet-ether) was added and the cooled ethanol was poured over it. The funnel was swirled gently to obtain a homogenous mixture and it was later allowed to stand until two separate layers were obtained. The bottom layer was run off into a beaker while the top layer was collected in to a 250ml conical flask. The bottom layer was transferred in to the funnel and re-extracted with 10 ml pet-ether for 5-6 times until the extract became fairly yellow. The entire pet-ether was collected in to 250 ml conical flask and transferred in to separating funnel for re-extraction with 50 ml of 80% ethanol. The final extract was measured and poured in to sample bottles for further analysis. The absorbance of the extracts was measured using a spectrophotometer (model 22UV/VIS) at a wavelength of 451 nm. Samples of each extract were placed in cuvettes and readings were taken when the figure in the display window became steady. The operation was repeated 5-6 times for each sample and average readings were recorded. The concentration of β- carotene was calculated using Bear-Lamberts Law, which states that the absorbance (A) is proportional to the concentration(C) of the pigment.

Source of Information: Bangladesh J. Agril. Res. 44(3): 501-512, September 2019

Article Link (Online Journal):

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

In the current study, BARI Mistialu- 8, BARI Mistialu- 11 and BARI Mistialu- 12 were selected as superior genotypes. BARI Mistialu- 5, BARI Mistialu- 14 and BARI Mistialu- 15 were found stable for marketable tuber yield. The study also revealed that sweet potato yield was highly influenced by the differences among genotypic effects, followed by genotype by environment interaction (G × E) variable and cultivation environments. This study also clearly demonstrated that the GGE biplot model was effective for the determination of the magnitude and pattern of G × E effect and visualizing the yield potential and stability of sweet potato genotypes as well as discriminating ability and representativeness of the test environments. Considering the seasonal variations, such stable genotypes were regarded to be climate smart and could be used as parents in a breeding programme. However, there is need to increase the number of sites to better reveal the difference among genotypes.

Knowledge Management: Journal