Original Research Article
Genotype-by-environment interaction analysis of nutritional composition in newly-developed sweetpotato clones

https://doi.org/10.1016/j.jfca.2020.103426Get rights and content

Highlights

  • Nutrient composition of 24 newly-developed sweetpotato clones were profiled.

  • Genotype, environment and their interactions were estimated using AMMI.

  • Genotype × environment interaction influenced nutritional composition.

  • Clones Resisto × PIPI-2, Resisto × Temesgen-23 and Resisto × Ogansagen-23 possessed excellent nutritional attributes.

  • The identified clones are recommended for direct production to circumvent macro- and micro-nutrient deficiencies in Ethiopia.

Abstract

Development of sweetpotato genotypes with enhanced nutritional composition is key to improve food and nutrition security and for industrial applications. The objective of this study was to determine the effect of genotype-by-environment interaction (GEI) on nutritional composition of sweetpotato clones to identify and select promising clones for large-scale production. Root dry matter content (RDMC), protein (Prot), β-carotene (β-car), iron (Fe), Zinc (Zn), starch (Star), sucrose (Suc), fructose (Fruc) and glucose (Gluc) contents were evaluated among 24 newly developed sweetpotato clones and one check variety across six diverse environments in southern Ethiopia using a 5 × 5 simple lattice design. Data was subjected to additive main effects and multiplicative interaction (AMMI) to estimate genotype (G), environment (E) and GEI effects. According to Gollob’s F-test and FR-test, four interactions principal component axis (IPCA) were significant (p ≤ 0.001) in the AMMI model for most traits, except glucose content where three and two IPCA’s were identified as significant by both tests. As a result, AMMI-4 was diagnosed for all traits, except glucose content where AMMI-2 was recommended. AMMI revealed significant (p ≤ 0.001) G, E and GEI effects on the studied nutritional traits. G explained 54 and 93 % of total variation for DRMC and β-car, respectively indicating these traits were less influenced by GEI effects. Larger E effects were observed for Prot (65 %), Fe (55 %) and Zn (64 %) while larger GEI effects were observed for Star (58 %), Fruc (77 %), Suc (50 %) and Gluc (56 %). Some of the newly-developed clones such as G5 (Ukrewe × Ogansagan-5), G6 (Resisto × Ejumula-7), G12 (Resisto × Temesgen-12), G13 (Resisto × Temesgen-14) and G22 (Ejumula × PIPI-19) with high RDMC (> 30 %), Prot (> 7 %), β-car (> 12 mg 100 g−1), Fe (> 2 mg 100 g−1), Zn (> 1 mg 100 g−1), Star (> 60 %), Fruc (> 4 %), Suc (>10 %) and Gluc (> 5 %) contents were selected for further evaluations for large-scale production, industrial use and and use in breeding programmes.

Introduction

Sweetpotato (Ipomoea batatas [L.] Lam), 2n = 6x = 90) is an important root crop serving for food, feed and development of industrial products (FAOSTAT, 2017). It is a source of vitamins (e.g. A and C), fiber, macro-nutrients such as calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K) and micro-nutrients such as iron (Fe) and zinc (Zn) (Low et al., 2007; Tumwegamire et al., 2011; Tomlins et al., 2012; Laurie et al., 2015a). It is estimated that approximately 125 g of cooked orange-fleshed sweetpotato (OFSP) storage root can provide over 10 % Zn, Fe, Mg and Vitamin C for 4–8-year-old children (Laurie et al., 2015a). Consumption of OFSP can also contribute to nearly 100 % vitamin A of the recommended dietary allowance (RDA), 27 % Mg, 15 % Zn and 11 % Fe for 4–8 year-old children (Laurie et al., 2012). Sweetpotato storage root has high starch content which readily converts into sugars including fructose, sucrose and glucose (Tumwegamire et al., 2011; George et al., 2015; Gurmu et al., 2017a) useful in industrial processing for production of raw materials (e.g., ethanol or butanol; bioplastics) (Ziska et al., 2009). Sweetpotato storage root contains variable protein level ranging between 0.6–9.5% (Woolfe, 1992; Tumwegamire et al., 2011; Magwaza et al., 2016). Generally, the nutritional composition of sweetpotato makes it a crop of choice for combating nutrient deficiencies especially vitamin A, iron and zinc deficiencies (Ezzatti et al., 2002) and improving food and nutrition security in many regions including sub-Saharan Africa (SSA) (Fuglie, 2007).

Ethiopia is the third largest sweetpotato producer in SSA after Tanzania and Nigeria with estimated total production of 2.0 million tons (FAOSTAT, 2017). Average yields in the country are approximately 8 tons/ha (FAOSTAT, 2017) compared to attainable yield levels of > 20 tons/ha−1 in other sub-Saharan countries such as Mozambique, Rwanda, Uganda, South Africa and Tanzania using improved sweetpotato genotypes (Shumbusha et al., 2014; Laurie et al., 2015a,b; Andrade et al., 2016; Mwanga et al., 2016; Ngailo et al., 2019). Cultivation of unimproved varieties which are susceptible to biotic (i.e. weevils and sweetpotato virus disease) and abiotic (i.e. drought and heat) stress factors are major causes of low sweetpotato yields in sub-Saharan African countries including Ethiopia (Gurmu et al., 2015a; Ngailo et al., 2016; Rukundo et al., 2017; van Vugt and Franke, 2018; Kagimbo et al., 2019). There are genetically diverse sweetpotato genotypes adapted to local growing conditions in SSA. Therefore, it is useful to identify and select suitable parental lines possessing desirable traits such as nutritional composition for strategic crosses and cultivar development (Shiferaw et al., 2016).

Breeding efforts to develop sweetpotato genotypes possessing desirable nutritional attributes such as high dry-matter content, β-carotene and other nutritional traits (i.e. Fe, Zn) resulted in the selection of some promising 24 sweetpotato clones in Ethiopia (Gurmu, 2015). The candidate genotypes require continuous selection for nutritional composition and subsequent stability analysis to recommend suitable cultivars for production and processing (Gurmu et al., 2017b). Nutrient composition in sweetpotato is highly influenced by GEI necessitating further varietal selection and recommendation across diverse environments (Laurie et al., 2012; Andrade et al., 2016; Tumwegamire et al., 2016). GEI causes variable genotypic response across growing environments, limiting identification and selection of suitable genotypes (Rukundo et al., 2017; Gurmu et al., 2017b). GEI analysis has proven useful in sweetpotato improvement programmes and aided in evaluation and recommendation of high-yielding genotypes with key agronomic traits (Mwanga et al., 2016; Shumbusha et al., 2014; Gurmu et al., 2017b). GEI effects can be assessed using the additive main effects and multiplicative interaction (AMMI) model which quantifies G, E and GEI effects (Gauch, 2006; Yan et al., 2007; Gauch et al., 2008) and aids in designing appropriate breeding strategies for cultivar development. AMMI can also identify stable genotypes for key agronomic traits, and suitable environments for production or mega-environments for future variety evaluation purposes (Hassani et al., 2018). AMMI model diagnosis is recommended for complex dataset to identify the most predictively accurate member of a model family for gaining accuracy and delineating mega-environments (Gauch, 2006, 2013; Gauch et al., 2008).

In sweetpotato, analysis of GEI effects has been extensively studied but mostly limited to root yield, root dry matter and β-carotene contents (Chiona, 2009; Kivuva et al., 2015; Laurie et al., 2015b; Kathabwalika et al., 2016; Gurmu et al., 2017b). There is limited GEI analysis on nutritional components such as starch, protein, sugar contents and micro-and-macro nutrients (Grüneberg et al., 2005; Tumwegamire et al., 2011, 2016; Laurie et al., 2012; George et al., 2015; Andrade et al., 2016) limiting selection and recommendation of suitable genotypes possessing desirable nutritional traits for food, feed and industrial uses. Analysis of nutritional composition of major food and nutrition security crops such as sweetpotato is useful to develop food-based strategies to combat macro –and micronutrient deficiencies (Durazzo and Lucarini, 2018, 2019). There is a lack of substantive data on sweetpotato nutrient compositions, most particular for genotypes developed in SSA (Tumwegamire et al., 2011). In light of the above background, the objective of this study was to determine GEI effects on nutritional composition (i.e. RDMC, protein content, β-carotene, Fe, Zn, starch, fructose, sucrose and glucose) among newly-developed sweetpotato clones in Ethiopia to select promising clones for further evaluations and recommendation for release.

Section snippets

Study sites and plant materials

The study was conducted during 2014 growing season across six diverse environments in Ethiopia. Description of test environments is provided in Table 1. Twenty-four newly-developed sweetpotato candidate clones and one check variety were used for the study (Table 2). The experimental clones were selected from families developed using a half-diallel mating design involving genetically diverse sweetpotato parents (Gurmu, 2015). The selected clones have varied storage root flesh colour, storage

AMMI model diagnosis for assessed nutritional traits

AMMI model analysis and accuracy gain (i.e. accurate for predicting the true mean values) were performed following the procedures of Ebdon and Gauch (2002a) and Ebdon and Gauch (2002b) and Gauch (2013). According to Gauch (2013) model diagnosis is useful to determine the best AMMI model for a given dataset, based on statistical and practical considerations. The FR-test (Cornelius 1993) and Gollob’s F-test (Gollob, 1968) were used to assess model diagnosis and to identify significant IPCAs in

Model diagnosis predictive accuracy for assessed nutritional traits

The FR-test was calculated at the 0.01 level of significance using AMMISOFT and diagnosed AMMI-4 model family for all the assessed nutritional traits, while AMMI-2 was diagnosed for glucose content (Table 3). Further, results of FR-tests (AMMISOFT) were similar to Gollob’s F-test (subjected to Genstat analysis). Both tests identified four significant IPCA’s for RDMC, protein content, β-carotene, Fe, Zn, starch, fructose and sucrose contents (Table 3). The only differences between results of the

Discussion

Developing sweetpotato genotypes with enhanced nutritional composition is key for human nutrition and industrial processing. The objective of this study was to determine GEI on nutritional composition (i.e. RDMC, protein content, β-carotene, starch, sucrose, Zn, Fe, fructose, sucrose and glucose) of sweetpotato clones to identify and select promising clones for recommendation and release. AMMI analysis of variance indicated highly significant (p ≤ 0.001) effects of G, E and GEI for the assessed

Conclusions

In conclusion, the current study determined nutritional composition of newly-developed sweetpotato clones in six growing environments in Ethiopia. The study identified the following clones: Ukrewe × Ogansagan-5, Resisto × Ejumula-7, Resisto × Temesgen-12, Resisto × Temesgen-14 and Ejumula × PIPI-19. These are recommended for direct production in Ethiopia or similar environments in sub-Saharan Africa. Further, the identified clones are useful genetic resources for future sweetpotato improvement

Author statement

All authors agree with the contents of the manuscript and its submission to Journal of Food Composition and Analysis. Moreover, all listed authors contributed to the work and agree to be in the author list. No part of this research has been published in any form elsewhere.

Acknowledgements

The Southern Agricultural Research Institute (SARI) in Ethiopia is acknowledged for provision of facilities during execution of the field experiments. We appreciate the financial support received from the Alliance for a Green Revolution in Africa (AGRA) through the African Centre for Crop Improvement (ACCI), University of KwaZulu-Natal (South Africa) and the International Foundation for Science (IFS)for the study. Prof. Hugh Gauch Jr. (Cornell University, USA) is thanked for assistance with

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