Published February 24, 2015
Research
Gains in Grain Yield of Early Maize Cultivars Developed During Three Breeding Eras under Multiple Environments B. Badu-Apraku,* M. A. B. Fakorede, M. Oyekunle, G. C. Yallou, K. Obeng-Antwi, A. Haruna, I. S. Usman, and R.O. Akinwale
ABSTRACT Maize (Zea mays L.), an important staple crop in West and Central Africa (WCA), has enormous potential to reduce food insecurity in this subregion. Research covering three periods or eras of breeding has been conducted to develop cultivars resistant/tolerant to three maize stress factors: Striga parasitism, drought, and low soil nitrogen. A study was conducted under optimal or natural growing environments at 35 locations in WCA for 2 yr to determine genetic improvement in grain yield of the maize cultivars developed during the three breeding periods: 1988–2000 (period 1), 2001–2006 (period 2), and 2007–2010 (period 3). The average rate of increase in grain yield under optimum growing conditions was 40 kg ha-1 yr-1 with a genetic gain of 1.3% yr-1, which was slightly higher than the gain of 30 kg ha-1 yr-1, an annual genetic gain of 1.2% across 16 stress environments. It was concluded that substantial improvement in the yield potential of early maize under relatively nonstress environmental conditions has been made in this subregion by breeding for stress tolerance during the past three decades. The varieties EV DT-W 2008 STR, 2009 DTE-Y STR Syn, and TZE-W DT C2 STR, all from the latest era of improvement, were identified as the highest yielding and most stable cultivars and should be promoted to contribute to food security in this subregion.
B. Badu-Apraku and M. Oyekunle, International Institute of Tropical Agriculture (UK) Limited, Carolyn House, 26 Dingwall Rd., Croydon, CR9 3EE, UK. M.A.B. Fakorede and R.O. Akinwale, Dep. of Crop Production and Protection, Obafemi Awolowo Univ., Ile-Ife, Nigeria. G.C. Yallou, Institut National de Recherches Agricoles du Bénin, Cotonou, Bénin. K. Obeng-Antwi, CRI-CSIR, Box 3785, Kumasi, Ghana. A. Haruna, CSIR-SARI, Box 52, Tamale, Ghana. I.S. Usman, IAR, Ahmadu Bello Univ., Samaru, Nigeria. Received 23 Nov. 2013. *Corresponding author (
[email protected]). Abbreviations: AMMI, additive main effects and multiplicative interaction; ANOVA, analysis of variance; G × E, genotype × environment; GGE, genotype main effect plus genotype × environment interaction; IITA, International Institute of Tropical Agriculture; IPCA, interaction principal component axes; LSD, least significant difference; NGS, northern Guinea savanna; PC, principal component; SGS, southern Guinea savanna; SS, Sudan savanna; WAP, weeks after planting; WCA, West and Central Africa.
M
aize is widely cultivated in all the agroecological zones of West and Central Africa (WCA). It is especially well adapted to the savanna zones with mono-modal rainfall distribution. The southern Guinea savanna (SGS) and northern Guinea savanna (NGS) are characterized by adequate moisture, relatively low disease pressure, high solar radiation, and low night temperatures, all of which are favorable to maize production. Until the last three decades, maize was a minor crop in the Guinea savanna zones, grown near household compounds, where there was regular application of organic manure, primarily as household refuse. However, the availability of early (90–95 d to maturity) and extraearly (80–85 d to maturity) varieties, and improved agronomic
Published in Crop Sci. 55:527–539 (2015). doi: 10.2135/cropsci2013.11.0783 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. crop science, vol. 55, march– april 2015
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practices have subsequently expanded the boundaries of suitability, and maize has spread to drier areas of the NGS, Sudan savanna (SS), and to a lesser extent, the more humid areas of the dry savanna and the rainforest agro-ecology. Maize is about the only food source available during the hunger period in the savannas of WCA in July when all other food reserves are depleted after the long dry period and the new crop of the normal growing season is not ready for harvest. The wide acceptance of maize as a staple crop is due in part to its versatility, providing a food source early during the hunger period, when it is consumed as green maize as well as grain for making flour for traditional foods for the rest of the year. There is also an increasing demand for maize for industrial uses, including processed food, livestock feed, and malting for beer production. Early and extra-early maturing cultivars have a great prospect to contribute to the fight against food insecurity in the savannas of WCA because they mature more quickly and can be harvested much earlier in the season than the traditional sorghum [Sorghum bicolor (L.) Moench] and millet (Eleusine sp.) crops. Despite the wide adoption of maize as a major staple crop in WCA, its production in the savannas is threatened by several constraints, including recurrent drought aggravated by global climate changes (Curry et al., 1995; Hillel and Rosenzweig, 2002); reduced soil fertility, especially low soil nitrogen and water-holding capacity; and Striga hermonthica (Delile) Benth. parasitism (Bänziger et al., 2000), among others. Production worth billions of US dollars is lost annually due to these constraints. For example, annual yield loss due to drought is about 24 million tons, which is about 17% of a normal year’s production in the developing world (Edmeades et al., 1992). The loss could be higher if drought occurs at the flowering and grain filling periods (Denmead and Shaw, 1960; NeSmith and Ritchie, 1992). Striga infestation was estimated to cause annual yield loss of about US $7 billion in WCA (M’Boob, 1986). In addition, increased population pressure on available land has resulted in an intensification of crop production, reduced fallow periods, and consequently, low soil fertility (Vogt et al., 1991), all of which together have worsened the Striga problem. Annual loss of maize yield due to low-N stress varies from 10 to 50% (Wolfe et al., 1988) in WCA and is caused by several factors, the most common being little or no application of inorganic fertilizer by farmers and rapid mineralization of organic matter in the soil (McCown et al., 1992; Bänziger and Lafitte, 1997). Under field conditions, drought, Striga, and soil nutrient deficiencies can occur simultaneously and the combined effect can be devastating. Drought stress and low soil N aggravate S. hermonthica parasitism on maize (Lagoke et al., 1991; Cechin and Press, 1993; Kim and Adetimirin, 1997). Studies conducted in WCA on the combined effects of the stress factors showed 44 to 53% grain-yield 528
reduction by drought stress, 42 to 65% by Striga infestation, and 40% by low N (Badu-Apraku et al., 2004; 2010). The use of host plant resistance is considered the most economically feasible and sustainable approach for reducing the effects of the three stress factors (DeVries, 2000; Badu-Apraku and Akinwale, 2011). Currently, farmers in the Striga endemic ecologies of SSA are demanding varieties that combine tolerance to the three stress factors (Badu-Apraku et al., 2011b). During the last three decades, the International Institute of Tropical Agriculture (IITA) maize improvement program has devoted considerable effort and resources to developing early-maturing maize varieties with tolerance to drought, low soil N, and resistance to S. hermonthica. The research efforts have covered three breeding eras: 1988–2000 (era 1), 2001–2006 (era 2), and 2007–2010 (era 3) and the strategies used for the development of the cultivars in each era have been described in detail by BaduApraku et al. (2013a, 2013b). Maize breeders in developed countries, especially the United States and Canada, have measured breeding progress by comparing the performance of cultivars developed and released over a long period of time in the same environments (Russell, 1984; Tollenaar, 1989). Similar studies have also been conducted in other crops such as soybean [Glycine max (L.) Merr.] (Tefera et al., 2009; Specht et al., 1999; Voldeng et al., 1997; Kamara et al., 2004), oats (Avena sativa L.) and wheat (Triticum aestivum L.) (Lopes et al., 2012; Xiao et al., 2012). In general the studies led to the conclusion that varieties developed in later breeding eras are higher yielding, display better agronomic traits, and are more responsive to production inputs. The genetic contribution to yield improvement relative to earlier eras ranged from about 60 to 90% in these studies. Very few such studies have been conducted in WCA, one of which was reported by Kamara et al. (2004) who showed a genetic gain of 0.4% per year for late-maturing maize cultivars released from 1970 to 1999 in the Nigerian savannas. Although a holistic evaluation of the gains from the efforts over the three eras is worthwhile now to make a decision on the future breeding strategies to employ in the subregion, only studies on the genetic gain in grain yield for the individual stresses over the three eras have been previously reported (Badu-Apraku et al., 2013a, 2013b). In general, gain in grain yield, ranging from 1.1 to 2.1% yr-1 was made under each stress over the 22 yr of the selection program. No direct comparisons of grain yield potential and other agronomic traits have been made under normal, naturally nonstress growing conditions of several locations in countries of WCA for which the cultivars were being developed. This is particularly important considering that no separate breeding program was put in place for such environments. In addition, since drought, Striga infestation, and low N do not occur singly but occur simultaneously
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under field conditions, it was desirable to compare the genetic gains in grain yield improvement under normal multi-environment trials with those obtained across the multiple stress environments. Therefore, the primary objective of the present study was to evaluate the valueadded genetic yield gain that has been made in breeding multiple stress-tolerant early-maturing maize during the three breeding eras under nonstress, normal growing environments. A secondary objective was to compare the gains under normal environments with those obtained by combined analysis of the response to the multiple stresses (managed drought stress, Striga infestation, low N).
MATERIALS AND METHODS Germplasm Development during the Three Breeding Eras A drought-tolerant population (Pool 16 DT SR) developed through eight cycles of recurrent selection and subsequently converted for resistance to the maize streak virus disease (Badu-Apraku et al., 2012) was the main source population for developing the first generation of drought tolerant early maturing maize cultivars with resistance to the Maize streak virus disease between 1988 and 1993 (Badu-Apraku et al. (2013a, 2013b). Two new broad-based populations of white (TZE-W Pop STR C0) and yellow (TZE-Y Pop ST C0) kernel color were formed by intermating promising local and adapted germplasm followed by introgression of drought tolerance and Striga resistance genes from selected IITA inbred lines as the sources of resistance. The populations were subjected to recurrent selection under artificial Striga infestation without intentional selection for drought tolerance to develop the second generation of Striga-resistant and drought-tolerant early white and yellow maize cultivars (Badu-Apraku et al., 2009). Additional cycles of recurrent selection were conducted during the period 2007–2011 to further increase the frequencies of favorable alleles for tolerance to drought and resistance to Striga in the two populations. Furthermore, two new populations, DTE STR-Y Syn Pop C0 and DTE STR-W Syn Pop C0, were developed in 2008 from selected testcrosses involving drought and Striga resistant yellow and white inbred lines, respectively. The improved cycles of selection of these two populations were the important sources of the third generation of Striga-resistant and drought-tolerant early-maturing white and yellow cultivars. Even though there was no conscious effort to select for tolerance to low N in the germplasm in the IITA maize program, selections for Striga resistance and drought tolerance are normally conducted under low-N conditions. Nonetheless, Badu-Apraku et al. (2009) showed that the yield gain after three cycles of selection in extra-early white and yellow populations for grain yield under artificially Striga-infested and Striga-free environments was more pronounced in the advanced cycles under high N (157 kg ha-1 cycle -1) than low N (144 kg ha-1 cycle -1) in the yellow populations, and higher under low N than high N in the white populations. Furthermore, Badu-Apraku et al. (2010) identified EVDT 97 STRC1, TZE-W DT STR C4, and TZE Comp3 C3 as tolerant to low N even though there was no conscious effort to select for tolerance to low N in the recurrent selection crop science, vol. 55, march– april 2015
for Striga resistance and/or drought tolerance programs through which they were derived. It was therefore hypothesized that some of the cultivars selected for drought tolerance and Striga resistance would also possess tolerance to low N.
Evaluation of Cultivars across Multiple Stress Environments Fifty early-maturing maize cultivars selected for tolerance/ resistance to drought and/or S. hermonthica of three breeding eras Table 1; Badu-Apraku et al. (2013a, 2013b) were evaluated in 2010 and 2011 under normal growing season environmental conditions at Bagauda, Mokwa, Samaru, Saminaka, and Zaria in Nigeria; Nyanpala, Ejura, Fumesua, and Yendi in Ghana; and in Benin Republic at Ina in the SGS and Angaradebougou in the NGS. In addition, the cultivars were evaluated at IleIfe, a location in the forest agro-ecology in 2 yr, and at Ikenne under full season irrigation during the 2009–2010 and 2010– 2011 dry seasons. Description of the test locations is presented in Table 2. The test locations of the normal growing season experiments were Striga-free but were not protected from other naturally occurring stresses except at Ikenne, which was irrigated throughout the growth period using a sprinkler irrigation system, which provided 17 mm of water per week. Additional plantings were made about 2 mo after the first planting at four locations (Ejura, Fumesua, Mokwa, and Abuja) in 2010 and three locations (Ile-Ife, Zaria, and Mokwa) in 2012 for a total of 35 environments under normal growing conditions. A 10 × 5 randomized incomplete block design with three replications was used in each experiment of this study. A plot consisted of two rows, 5 m long, spaced 0.75 m apart with 0.40-m spacing between plants within a row in all trials. Three seeds were planted per hill, and the resulting maize plants were thinned to two per stand about 2 wk after emergence to give a final plant population density of 66,000 plants ha-1. All trials received 60 kg ha-1 N, 60 kg ha-1 P, and 60 kg ha-1 K at planting with an additional 60 kg N ha-1 top-dressed at 4 wk after planting (WAP) and weeds were controlled with herbicides and/ or manually. In all experiments under normal growing season environmental conditions, grain yield was computed based on 80% (800 g grain kg-1 ear weight) shelling percentage and adjusted to 150 g kg-1 moisture content. For the purpose of comparison, the 50 cultivars were evaluated under drought stress in four environments, imposed Striga stress in eight environments, and low soil-N conditions in four environments. The induced drought experiments were conducted during the 2009–2010 and 2010–2011 dry seasons at Ikenne, and under terminal drought stress (natural drought stress during the growing season, especially towards the end of the season) at Samaru, in 2010 and 2011. The induced drought stress at Ikenne was achieved by withdrawing irrigation water from 28 d after planting until maturity so that the maize plants depended on stored water in the soil for growth and development. In this case, flowering and grain filling periods coincided with the managed drought stress with symptoms of stress appearing after about 25 to 30 d of induced drought stress. The 50 cultivars were evaluated in 2010 and 2011 for yield potential and tolerance or resistance to Striga under artificial infestation with S. hermonthica at Mokwa and Abuja, both in the SGS agro-ecological zone of Nigeria. In addition, the cultivars were
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Table 1. Maize cultivars used for the study, their year of breeding, and reaction to multiple stresses. Reaction to stresses
Code
Cultivars
Year of development
Drought
Striga hermonthica
Low N
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
KAMB 88 Pool 16 DT EV DT 94 C2 EV DT 97 STR C1 EV DT-W 99 STR QPM EV DT-W 99 STR C0 AC 94 Pool 16 DT STR TZE-W DT STR C4 TZE-Y DT STR C4 TZE Comp 3 DT C2 F2 TZE COMP 3 × 4 F2 TZE Comp3 DT C1F2 TZE Comp 3 DT C3 AC 90 Pool 16 DT STR EV DT-Y 2000 STR EV DT-W 2000 STR C0 TZE-W DT STR QPM C0 TZE-W Pop × 1368 STR
1988 1994 1997 1999 1999 1994 2004 2004 2006 2000 2004 2001 1990 2000 2000 2004 2000
Tolerant Tolerant Tolerant Tolerant Tolerant Susceptible Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Susceptible Tolerant Susceptible
Susceptible Susceptible Resistant Tolerant Tolerant Susceptible Resistant Resistant Susceptible Susceptible Susceptible Susceptible Tolerant Resistant Tolerant Resistant Susceptible
Susceptible Susceptible Tolerant Tolerant Susceptible Susceptible Tolerant Tolerant Tolerant Tolerant Tolerant Susceptible Tolerant Tolerant Susceptible Tolerant Tolerant
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
TZE-W Pop × 1368 STR QPM TZE-Y DT STR QPM C0 EV DT-Y 2000 STR QPM EV DT-Y 2008 STR EV DT-W 2008 STR 2008 DTMA-W STR 2008 DTMA-Y STR 2009 TZE-W Pop DT STR 2009 TZE-Y Pop DT STR 2009 DTE-W STR Syn 2009 DTE-Y STR Syn TZE-W DT C2 STR TZE-Y DT C2 STR TZE-W DT C1 STR TZE-Y DT C1 STR DTE-Y STR Syn C1 DTE-W STR Syn C1 Syn DTE STR-Y Syn DTE STR-W DT-Y STR Synthetic DT-W STR Synthetic 98 Syn WEC STR C0 2000 Syn WEC BG 97 TZE COMP3 × 4 TZE-Y Pop DT STR C2 TZE-W Pop DT STR C0
2004 2004 2004 2008 2008 2008 2008 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 1998 2000 1997 2002 2001
Susceptible Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Susceptible Tolerant
Susceptible Resistant Resistant Resistant Tolerant Tolerant Resistant Tolerant Tolerant Tolerant Resistant Resistant Tolerant Tolerant Tolerant Resistant Tolerant Tolerant Tolerant Resistant Tolerant Tolerant Tolerant Susceptible Susceptible Susceptible
Susceptible Tolerant Tolerant Tolerant Tolerant Susceptible Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Tolerant Susceptible Tolerant Tolerant Tolerant
44 45 46 47 48 49 50
TZE-Y Pop DT STR C0 2004 TZE-W Pop DT STR C4 2004 TZE-Y Pop DT STR C4 TZE-W Pop DT STR C3 TZE-Y Pop DT STR C3 99 Syn WEC Tillering Early DT
2001 2004 2004 2003 2003 1999 2007
Tolerant Tolerant Tolerant Susceptible Tolerant Susceptible Tolerant
Susceptible Resistant Resistant Susceptible Susceptible Susceptible Tolerant
Susceptible Tolerant Tolerant Tolerant Tolerant Susceptible Tolerant
evaluated in Benin Republic at Ina in the SGS and Angaradebougou in the NGS. The two rows making each plot were infested with seeds of S. hermonthica using the Striga infestation method developed by IITA Maize Program (Kim, 1991; Kim 530
and Winslow, 1991). The Striga seeds used were collected from fields of sorghum at the end of the previous growing season and mixed with finely sieved sand in a 1:99 ratio by weight. About 5000 germinable seeds were used in each hill for infestation.
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SGS, southern Guinea savanna; NGS, northern Guinea savanna; FT, forest-savanna transition zone; SS, Sudan savannah.
Statistical Analysis Two combined analyses of variance (ANOVAs) were done, across stresses and optimal growing environments (well-watered, Striga-free, and high N) for the grain-yield data on plot means with PROC GLM in SAS using a RANDOM statement with the TEST option (SAS Institute, 2001). In each combined ANOVA, genotypes were considered as fixed effects, while test environments, replications, genotype × environment (G × E) interaction, and all other sources of variation were considered as random effects. Means were separated using the least significant difference (LSD). Analysis of variance was also conducted for each environment (location-year combinations) and also across all environments for each of the studies to determine if G × E interaction was significant. Subsequently, the yield data were subjected to the additive main effects and multiplicative interaction (AMMI) analysis to assess relationships among cultivars, environments and G × E. The AMMI model was described by Zobel et al. (1988), Gauch and Zobel (1988) and Crossa (1990). This analysis uses principal component analysis to decompose the multiplicative effects (G × E) into a number of interaction principal component axes (IPCAs). The genotype main effect plus G × E interaction (GGE) biplot software Windows application that fully automates biplot analysis (Yan, 2001a) was used for the AMMI analysis. The AMMI model equation (Sadeghi et al., 2011) is Yger = m + a g + b e + NS n = 1 l n , Vgn h en + r ge + e ger [1]
†
————————————————————————————————————————————— Fertilizer rate (kg/ha) ————————————————————————————————————————————— 60 60 60 60 60 60 60 60 60 60 60 60 45 46 60 60 60 60 60 60 60 60 0 0 0 60 45 14 120 90 120 30 – 90 120 120 90 90 90 90 114 64 – 30 – – – 30 – – – – – – – – 120 – – – 120 – – – – – – – – – – 30 – 30 – – – – – – – – – – P 2O 5 K 2O Nrate (kg/ha) under optimum Nrate (kg/ha) under low N Nrate (kg/ha) under drought Nrate (kg/ha) under Striga
9°58¢N 2°44¢W 358 900 11°32¢N 3°05¢W 297 1000 6°41¢N 1°28¢W 150 1345 7°38¢N 1°37¢E 90 1108 9026¢N 0010¢W 157 1300 9°25¢N 0°58¢E 340 611 12°00¢N 8°22¢E 640 1120 9°5¢N 6°45¢E 264 1000 7°28¢N 4°32¢E 280 1200 12°01¢N 8°19¢E 520 900 9°15¢N 7°20¢E 300 1500 12°00¢N 8°22¢E 640 1120 9°18¢N 5°4¢E 457 1100 6°53¢N 3°42¢E 60 1200 Latitude Longitude Altitude (m asl) Rainfall during growing season (mm)
Ina
IA SGS AN SS FM FT EJ FT YD SGS NY NGS SM NGS SK NGS IF FT BG SS AB SGS ZA NGS MO SGS IK FT† Code Agro ecological zone
Bénin
Fumesua Angaredebou Ejura
Ghana
Yendi Saminaka Samaru Nyanpala Bagauda Abuja Zaria Mokwa Ikenne Location
Nigeria
Ile-Ife
Countries
Table 2. Description of the test locations of early maize cultivars developed during the three breeding eras and evaluated at 14 locations in West Africa, 2010–2011.
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About 2 wk before planting and Striga infestation, ethylene gas was injected into the soil to stimulate suicidal germination of existing Striga seeds in the soil at both sites. Fertilization of all plots in this study at Ina, Angaradebougou, Mokwa, and Abuja was delayed until about 30 d after planting when 30 kg ha-1 N, 30 kg ha-1 P, and 30 kg ha-1 K were applied as 15–15–15 N–P–K. Weeds other than Striga were controlled manually. The 50 cultivars were also evaluated under low N (30 kg ha-1 N) stress at the Teaching and Research Farm, Obafemi Awolowo University, Ile-Ife in the forest agro-ecological zone of Nigeria and Mokwa in 2010 and 2011. The soil at Mokwa is a luvisol (FAO classification) with 0.27, 0.035, and 0.48% organic carbon, organic nitrogen, and phosphorus content, respectively. The soil at Ile-Ife is characterized as fine-loamy, isohyperthemic Plinthustalf (USDA taxonomy). The experimental fields were depleted of N by continuously planting maize and removing the biomass after each harvest. Soil samples were taken each year before planting for all the test environments and the N content was determined at the IITA Soil Laboratory at Ibadan. The total N in the soils was determined by Kjeldahl digestion and colorimetric determination on Technicon AAII Autoanalyser (Bremner and Mulvaney, 1982). Fertilizers were applied to bring the total available N to 30 kg ha-1 when the soil N was less than that. The N fertilizer was applied 2 WAP. Also, single superphosphate (P2O5) and muriate of potash (K 2O) were applied at the rate of 60 kg ha-1. Each of the four low-N trials was kept weedfree with the application of herbicides and by hand weeding. In all stress experiments, harvested ears from each plot were shelled to determine the percentage grain moisture. Grain yield was adjusted to 15% moisture and computed from the shelled grain weight.
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where Yger is the average yield of genotype g in environment e for replicate r, m is the grand mean, a g is the genotype mean deviation (mean minus the grand mean), b e is the environment mean deviation, N is the number of SVD axes retained in the model, l n is the singular value for SVD axis n, zgn is the genotype singular vector value for SVD axis n, h en is the environment singular vector value for SVD axis n, r ge is the AMMI residuals, and e ger is the error term. Furthermore, GGE biplot analysis was used on the mean grain yield adjusted for block effects to obtain information on the yield performance of the cultivars across infestation, drought stress, and optimum growing environments. The analyses were done using GGEbiplot (Yan, 2001a, 2001b). The GGE biplot model equation is Yij − Yj = ʎ1xi1h j1 + ʎ 2 xj2h j2 + S ij
[2]
where Yij is the average yield of genotype i in environment j, Yj is the average yield across all genotypes in environment j, ʎ1 and ʎ 2 are the singular values for principal component (PC)1 and PC2, xi1 and xj2 are the PC1 and PC2 scores for genotype i, h j1 and h j2 are the PC1 and PC2 scores for environment j, S ij is the residual of the model associated with the genotype i in environment j. The data were neither transformed (Transform = 0) nor standardized (Scale = 0), but they were environment centered (Centering = 2). The relationship between cultivar yield and year of breeding (expressed as number of years since 1988) across the contrasting environments (stressed vs. optimal growing environments) was determined using regression analysis. The mean grain yield of the maize cultivars was used as the dependent variable and regressed on the year of breeding as independent variables to obtain regression coefficient (b-value) across stress and optimal growing environments, using SAS. The b-value was then divided by the intercept and multiplied by 100 to obtain the relative genetic gain per year (Badu-Apraku et al., 2009). Also, the relationship between grain yield across research environments was graphically analyzed for each breeding period using scatter diagrams. For this purpose, the mean values of yield across stress environments were regressed on yield in optimal growing environments using simple linear models.
RESULTS ANOVA across Multiple Stress and Optimal Growing Environments The combined ANOVA for grain yield of the 50 maize cultivars for each group of experiments showed highly significant (P < 0.01) mean squares for all sources of variation (Table 3). The environment accounted for the largest proportion of the variation, 55% for the stressed and 66% for the nonstressed environments. In all cases, era accounted for a rather low proportion of the total variation (about 2%) while cultivar and era each accounted for less than 5%. The environment × cultivar/era interaction accounted for much higher proportion of the total variation (9–13%) than the environment × era interaction (≤1%). 532
Table 3. Mean squares (MS) and percentage contributions (%SSQ) of the sources of variation for grain yield of 50 maize cultivars from three breeding eras evaluated under 16 multiple stress and 35 nonstress environments in Benin, Ghana, and Nigeria, 2010 and 2011.† 16 Stress environments Source
df
MS
% SSQ
Environment, E 15 117935952** 55.0 Block (E × Rep) 176 1319862** 7.2 Rep (E) 28 2786291** 2.4 Era 2 34459299** 2.1 Cultivar (Era) 47 3266814** 4.8 E × Cultivar (Era) 704 584284** 12.8 E × Era 30 795538** 0.7 Error 1190 402937 14.9
35 Nonstress environments df
MS
% SSQ
34 228785264** 66.3 412 1134849** 4.0 68 6227585** 3.6 2 133599837** 2.3 47 9897354** 4.0 1597 636002** 8.7 68 903145** 0.5 2914 432607 10.7
** Significant F-test at 0.01 probability level. †
df, degrees of freedom; SSQ, sum of square.
Genetic Gains in Grain Yield Significant increase in the grain yield of the early maize cultivars occurred during the three breeding eras under the conditions of each evaluation environment (Table 4). Mean grain yields under the stress environments were significantly lower than under nonstress environments for each of the eras, but the genetic gain per era was higher for the multistress environments, although it was only better than the optimum environments by 2 percentage points (Table 4). Averaged across all environments in this study, productivity of early maturing maize cultivars in WCA increased from 2512 kg ha-1 during era 1 to 3207 kg ha-1 during era 3, at the rate of 8.9% per era, a total genetic gain of about 27% in the 22-yr period covered by the three eras (Table 4). The annual genetic gain in grain yield was 0.5, 1.52, and 1.6% for the three eras, respectively.
Regression Analyses of Grain Yield in the Evaluation Environments The regression of the mean grain yield of the maize cultivars under nonstress on stress or under stress on nonstress environments revealed a distinct separation of the cultivars of the three breeding eras except for instances in which few cultivars of era 2 produced grain yields below those of era 1 cultivars (Fig. 1A and 1B). In general, the era 3 cultivars showed outstanding performance across both research environments (Fig. 1). The regression analysis also revealed that yield performance under stress environments closely predicted yield under nonstress environments, with a b-value of 0.99 compared with a b-value of 0.7 when yield under stress was predicted from performance under nonstress. An R 2 value of about 70% occurred under both conditions (Fig. 1).
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Table 4. Mean grain yield (kg ha-1) ± SE for three breeding eras, regression parameters and percent genetic gain per era for early maturing maize cultivars evaluated in two groups of environments in Benin, Ghana, and Nigeria, 2010 and 2011. Era Environment Multistress (16 environments) Nonstress (35 environments) Mean
Regression parameters
1 (1988–2000) 15 cultivars
2 (2001–2006) 16 cultivars
3 (2007–2010) 19 cultivars
R2
Intercept
b-value
% Genetic gain era-1
2176 ± 54.20 3398 ± 52.50 2760.25
2286 ± 49.20 3615 ± 46.70 2908.5
2606 ± 49.00 3957 ± 42.30 3206.5
0.93 0.98 0.96
1926.0 3097.7 2512.2
215.0 279.5 223.1
11.16 9.02 8.88
Figure 1. Regression of yield (A) under stress environments on yield under nonstress environments, and (B) under nonstress environments on yield under stress environments.
Grain Yield and Stability of the EarlyMaturing Maize Cultivars The AMMI biplot for grain yield of the 50 early-maturing maize cultivars evaluated under multiple stresses between 2010 and 2011 is presented in Fig. 2, while Fig. 3 represents the biplots for grain yield across optimal growing environments. The AMMI biplot with the genotype and environment main effects for grain yield on the x axis and the IPCA1 scores on the y axis are presented in Fig. 2 and Fig. 3. The horizontal dotted line represents the crop science, vol. 55, march– april 2015
grand mean for grain yield, while the vertical dotted line (y-ordinate) represents the IPCA1 value of zero. Genotypes located close to the horizontal line have small interactions and are considered to be more stable than those further from it. The results of the AMMI biplot analysis of the cultivars evaluated in eight locations across stress environments showed that environments accounted for 68.9% of the total variation in the sum of squares for grain yield, while the G and IPCA1 sources of variation accounted for only 15.2 and 5.2% of the total variation,
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Figure 2. AMMI biplot of grain yield and the first interaction principal component axis (IPCA1) of 50 early maturing maize cultivars evaluated across eight stress environments in West Africa between 2010 and 2011. The vertical line is the grand mean, while the horizontal line represents IPCA1 values of zero. See Tables 1 and 2 for the legends.
Figure 3. AMMI biplot of grain yield and the first interaction principal component axis (IPCA1) of 50 early maturing maize cultivars evaluated across 18 optimal growing environments in West Africa between 2010 and 2011. The vertical line is the grand mean, while the horizontal line represents IPCA1 values of zero. See Tables 1 and 2 for the legends.
respectively. Thus, a total of 88.7% of the treatment sum of squares was captured by the AMMI biplot (Fig. 2). Similarly, across optimal growing conditions, the AMMI biplot analysis of the cultivars evaluated at 18 locations across optimal growing environments showed that environments accounted for 77.6% of the total variation in the 534
sum of squares for grain yield, while the G and IPCA1 sources of variation accounted for only 13.4 and 1.8% of the total variation, respectively. A total of 92.7% of the treatment sum of squares was therefore captured by the AMMI biplot (Fig. 3), indicating that the biplots are effective in explaining both the main effects and providing
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Figure 4. The biplot view showing the ranking of 50 early-maturing maize cultivars evaluated in eight environments based on both the discriminating ability and representativeness of the locations. Principal component (PC)1 and PC2 explained 61.7% of yield variation. See Table 1 for cultivar code.
insight into the G × E across stress and optimal growing environments. The biplots showed large variability among the 50 early maturing cultivars and test locations. Across stress environments, cultivars 14, 27, 47, 35, and 45 were characterized by IPCA1 scores near zero and therefore had small interaction with the environments; they had grain yield above the grand mean, and therefore were the most stable cultivars. In contrast, cultivars 46 and 26 were comparable in terms of grain yield and had above mean grain yield and strong positive interaction with the IPCA1 scores (Fig. 2). Therefore, even though they were among the highest yielding cultivars, they were very unstable across stress environments. The large positive interaction of cultivars 46 and 26 with IPCA1 implied that they were probably adapted to favorable environments. In contrast, cultivars 22, 28, and 29 were comparable in terms of grain yield with above mean grain yield. They had low positive interaction with IPCA1 score and were the highest yielding and most stable cultivars across stress environments (Fig. 3). On the other hand, cultivars 18 and 49 had similar grain yield response, which was below the grand mean and thus were the low-yielding cultivars across stress environments. Under optimal growing environments, however, cultivars 7, 23, 26, 42, and 50 were characterized by IPCA1 scores near zero and therefore had small crop science, vol. 55, march– april 2015
interactions with the environments and grain yield above grand mean and consequently were the most stable cultivars. In contrast, cultivars 33, 30, and 41 had similar grain yield response (above the grand mean) but with strong interaction with IPCA1 score and thus, although they were high-yielding cultivars, were less stable across optimal growing environments. On the other hand, cultivars 1, 13, and 18 had similar grain yield response (below the grand mean) and strong negative interaction with IPCA1 score and thus were the lowest yielding and most unstable cultivars. The negative interactions of cultivars 1, 13, and 18 suggested that these cultivars were probably adapted to low-yield environments. Cultivars 29, 26, and 28 were comparable in terms of grain yield, since they were above the mean grain yield with relatively low interaction with IPCA1 score and thus were the highest yielding and most stable cultivars across optimal growing environments. According to Yan et al. (2000), the ideal genotype should have a high mean yield and high stability. The vector view of the GGE biplot, which shows the ranking of the cultivars based on both the discriminating power and representativeness, identified cultivars 22, 25, 27, 28, 31, and 34 as the closest to the ideal genotype across test environments (Fig. 4).
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DISCUSSION Maize cultivars have differential responses to varying environmental conditions. Therefore, a major challenge facing maize breeders is the selection of superior cultivars for narrow or wide adaptation and the identification of the best testing sites that can be used to identify superior and stable cultivars (Badu-Apraku et al., 2011a). The environment accounted for the largest proportion of total variance in the experiments reported here. In addition to climatic, edaphic, and biotic factors that normally constitute the physical environment, crop microclimate also seems to have played a major role in the experiments. For each group of environments, block and replication sources of variation were statistically significant and, along with the environment and error terms, together accounted for 79.5 and 84.5% of total variation for multistress and optimum evaluation environments, respectively. Era and cultivarwithin-era, which were the primary foci of the study, accounted for a low proportion of the total variation in each group of trials probably because the environmental effects submerged or confounded the era and cultivar effects. Large environmental effects, resulting in large coefficients of variation are very common in maize yield trials conducted in WCA (Badu-Apraku et al., 2011a). This is a big challenge to breeders and biometricians. The presence of significant interaction of environment with breeding eras and cultivar-within-era sources of variation suggest differential responses of the genotypes and the need to identify high-yielding and stable genotypes across environments, as noted by some earlier researchers (Sabaghnia et al., 2008; Moghaddam and Pourdad, 2009). This result confirmed the need for extensive testing of cultivars in multiple environments, including location and years before cultivar recommendations are made in the subregion (Badu-Apraku et al., 2011a). Some meaningful and useful information may be deduced from the study. First, there is a tremendous genetic gain in grain yield of early-maturing cultivars developed from 1988 to 2010. This conclusion becomes more convincing as one considers that it is consistent for both stress and nonstress evaluation environments, although the exact value varies among the environments. Second, genetic gain obtained in our study compare favorably with those reported in some earlier studies (Tollenaar, 1989; Russell, 1984; Kamara et al., 2004). On average across all evaluation environments in the present study, there was 8.88% gain per era, which translates to a total gain of 26.6% for the 22 yr under study, equivalent to an annual gain of about 1.2%. Kamara et al. (2004) reported annual genetic gain of 0.4%, for late-maturing maize cultivars developed from 1970 to 1999 in the West African savannas. Similarly, Russell (1984) reported annual genetic gain of 0.7% for U.S. Corn Belt cultivars developed between the 1930s and the 1980s. Tollenaar 536
(1989) reported 1.7% per year for commercially important maize hybrids in Central Ontario, Canada, from the late 1950s to the late 1980s. Similarly, Badu-Apraku et al. (2013b) reported an average genetic gain in grain yield of 1.7% per era when the 50 cultivars in the present study were evaluated under Striga infestation per se. Badu-Apraku et al. (2013a) reported 1.1% annual genetic gain under drought stress while the average rate of increase in grain yield under optimum growing conditions was 40 kg ha -1 yr-1 with a genetic gain of 1.3% yr-1. Third, genetic improvement of early-maturing maize for stress tolerance in WCA has been quite effective. When evaluated under multiple stress conditions, mean grain yield improved from about 2.2 Mg ha-1 for era 1 cultivars to 2.6 Mg ha-1 for era 3 cultivars, with a gain of 11% per era. The genetic gains of 0.5% observed in grain yield across stress environments for era 1 cultivars is substantially lower than the gains of 1.5% and 1.6% observed for eras 2 and 3 cultivars, respectively. Era 1 cultivars were developed for tolerance to drought and resistance to MSV but were largely susceptible to Striga and low N, which constitute the major constraints in the testing sites of the savannas of WA where the cultivars in the present study were evaluated (Badu-Apraku et al., 2013a, 2013b). The relatively higher genetic yield gain observed for era 2 and 3 cultivars under stress in the present study justified the research efforts and investments expended on recurrent selection for tolerance or resistance to the three stresses (drought, low N, and Striga) for the development of the era 2 and 3 cultivars, resulting in the development of several cultivars with combined resistance/tolerance to the three stresses. These improvements were reflected in the superior performance of the era 3 cultivars compared with those of era 1. Results obtained in the present study, along with those of earlier reports (Kamara et al., 2004; BaduApraku et al., 2013a, 2013b) provide empirical evidence from WCA that validate several genetic principles, including tandem selection, recurrent selection, and pyramiding of favorable genes in cultivars. The results clearly demonstrate that cultivars of later eras possess the traits of the earlier era cultivars plus an additional one or two other traits that confer value-added characteristics that make the latest era cultivars better performing than those of earlier eras under both stress and nonstress environmental conditions. The differences in the observed genetic gain reported in the present study and those reported by Kamara et al. (2004) could be due to the environments under which the cultivars were evaluated, the stress level imposed for the evaluation, the type of material evaluated (open-pollinated versus hybrids; late maturing versus early maturing), methods of development of the cultivars, breeding periods, and number of cultivars involved in the evaluations. In the present study, early maturing open-pollinated cultivars were evaluated under the three major stress factors constraining maize production and productivity in
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the savannas of WCA as well as natural, nonstress maize cultivation environments; compared to the commercial maize hybrids investigated by Tollenaar (1989) and Russell (1984). For example, the mean grain yield reduction obtained across stress environments in the present study ranged from 34 to 37% and is relatively similar to that reported by Badu-Apraku et al. (2004). The differences among cultivars in the level of yield reduction could be due to differences in the levels of resistance/tolerance to the three stresses of the maize cultivars used in the present study (Akaogu et al., 2013). The regression analysis of the mean grain yield of maize cultivars across stress and optimal growing environments clearly separated the maize cultivars into three groups closely corresponding with the three breeding eras. Era 3 cultivars displayed outstanding performance across both stress and optimum growing environments (Fig. 1). This result indicated that substantial progress has been made in breeding cultivars with combined resistance and/or tolerance to drought, Striga and low N during the past three decades. This result corroborates the findings of Badu-Apraku et al. (2013a, b). The strong positive association between the performance of the cultivars across both stress and optimal growing environments suggested that cultivars selected across stress environments may also have superior performance under optimal growing environments and, to a lesser extent, vice versa. The identification of cultivars 18 and 49 as the lowest yielding by the AMMI biplot is expected because the cultivars were developed during era 1 and were highly susceptible to the three stresses used for the evaluations in the present study (Fig. 2). The identification of cultivars, 29, 26, and 28, as the highest yielding and most stable across both stress and optimal growing environments, indicated that they were not only high yielding and the most stable across stress environments but were also the highest yielding and the most stable across optimal growing environments. This result has confirmed the effectiveness of the recurrent selection strategy used in the population improvement program and for the extraction of Striga resistant/tolerant, drought and low-N tolerant open-pollinated cultivars used in the present study. These results are in agreement with the findings of earlier workers (Menkir and Kling, 2007; Badu-Apraku and Akinwale, 2011; Badu-Apraku et al., 2011b, 2013b) who reported that the S1 family recurrent selection is effective in increasing the frequency of favorable alleles in a population as well as improving quantitatively inherited traits in maize populations under stress conditions. Several workers have reported that the use of recurrent selection methods that capitalize on additive gene action in combination with an effective and reliable artificial method of Striga infestation for the screening of segregating families should facilitate the accumulation of resistance/tolerance genes to develop crop science, vol. 55, march– april 2015
germplasm with multigenic resistance/tolerance that could be effective and durable over time (Berner et al., 1995; Menkir and Kling, 2007; Badu-Apraku et al., 2012). Apart from cultivars 28, 29, and 26 possessing genes for resistance/tolerance to Striga, they also have inherent abilities to tolerate drought, low soil N, and MSV. The outstanding performance of these cultivars has been confirmed in several earlier studies (Badu-Apraku et al., 2013a, 2013b). The drought and low N tolerance and Striga resistance of these cultivars are of special interest because drought, low N, and Striga do not occur separately under field conditions in WCA; they occur simultaneously, and the combined effect can be devastating (Cechin and Press, 1993; Kim and Adetimirin, 1997). The superior performance of these cultivars is therefore very interesting and desirable because maize varieties targeted to the drought-prone areas of WCA must also be tolerant to low N and resistant/ tolerant to Striga. These results suggest that the outstanding cultivars from the breeding program have broad adaptation to the growing environments in WCA. These outstanding cultivars should be extensively tested in on-farm trials in WCA and vigorously promoted for adoption with a view to commercialization in the subregion.
Conclusions Substantial progress has been made in breeding cultivars with combined tolerance/resistance to the three stresses Striga, drought, and low N during the past three decades in WCA. The average rate of increase in grain yield was 30 kg ha-1 yr-1 corresponding to 1.2% annual genetic gain across the multiple stresses. The average rate of increase in grain yield under optimum growing conditions was even higher, being 40 kg ha-1 yr-1 with a genetic gain of 1.3% yr-1. Cultivars 2009 TZE-Y Pop DT STR, 2009 DTE-Y STR Syn, and TZE-W DT C2 STR, all from the breeding era 3, were the highest yielding and relatively most stable cultivars across both stress and nonstress environments and should, therefore, be promoted for adoption and commercialization to contribute to food security in WCA. Acknowledgments The authors are grateful to the staff of IITA’s Maize Program in Ibadan for technical support. The financial support of DTMA Project and IITA for this research is also gratefully acknowledged.
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