genetic gains to grain yield of maize varieties for small

_____________________________________________________________________________________________________ *Corresponding author: E-mail: [email protected], [email protected];

Journal of Experimental Agriculture International 42(12): 1-12, 2020; Article no.JEAI.64506 ISSN: 2457-0591 (Past name: American Journal of Experimental Agriculture, Past ISSN: 2231-0606)

Genetic Gains to Grain Yield of Maize Varieties for Small Farmers in Brazil

Ana Paula Cândido Gabriel Berilli1, Rafael Nunes de Almeida2*,

Luis Eduardo Gottardo1, Monique Moreira Moulin1, Messias Gonzaga Pereira2, Sávio da Silva Berilli1, Julio Cesar Fiorio Vettorazzi2,

Roberto dos Santos Trindade3 and Robson Ferreira de Almeida1

1Instituto Federal de Educação Ciência e Tecnologia do Espírito Santo, Rodovia BR 482, Km 47 s/n,

Alegre, ES, CEP: 29520-000, Brasil. 2Universidade Estadual do Norte Fluminense Darcy Ribeiro, Centro de Ciências e Tecnologias

Agropecuárias, Avenida Alberto Lamego, 2000 –Parque Califórnia –Campos dos Goytacazes, RJ, CEP: 28013-602, Brasil.

3Empresa Brasileira de Pesquisa e Agropecuária, Embrapa Milho e Sorgo, Rodovia MG 424, Km 45 s/n, Sete Lagoas, MG, CEP: 35701-970, Brasil.

Authors’ contributions

This work was carried out in collaboration among all authors. Authors APCGB, LEG, MMM, MGP,

SSB and RST designed the study, performed the statistical analysis, wrote the protocol, and wrote the first draft of the manuscript. Authors RNA and JCFV managed the analyses of the study. Authors

APCGB and RFA managed the literature searches. All authors read and approved the final manuscript.

Article Information

DOI: 10.9734/JEAI/2020/v42i1230622

Editor(s): (1) Dr. Daniele De Wrachien, State University of Milan, Italy.

Reviewers: (1) R. Ravikesavan, Tamil Nadu Agricultural University, India.

(2) Maria Helena de Aguiar Pereira e Pestana, ISCTE-IUL Lisbon Univerity and Loreate Lisbon Univeristy, Portugal. (3) Suranjana Sarkar, Surendranath College, India.

Complete Peer review History: http://www.sdiarticle4.com/review-history/64506

Received 28 November 2020 Accepted 30 December 2020

Published 31 December 2020

ABSTRACT

The demand for expanding the genetic base in working collections of older maize breeding programs points to the need to pool efforts and reaffirm methodologies for conserving genetic variability that can still be accessed in maize populations. The objective of the work was to select full sib maize progenies and to estimate genetic gains in the first cycle of reciprocal recurrent

Original Research Article

Berilli et al.; JEAI, 42(12): 1-12, 2020; Article no.JEAI.64506

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selection for common maize intended for cultivation in a region characterized by family farm, in Brazil. We evaluated 120 full sib families of maize from crossbreeding between individuals of the Cimmyt and Piranão varieties. Competition trials were conducted at two experimental stations in the state of Espírito Santo, Brazil. A randomized block design was adopted with 2 repetitions, arranged in sets. Different selection indexes were tested in order to enhance gains in productivity and prolificacy. The selection of 40 superior families made it possible to estimate a gain of 0.77 Mg ha-1 in grain productivity for producers in the region. With these results, we discussed the importance of work to improve maize populations for small producers to motivate the conservation of the genetic variability of tropical maize germplasm. Thus, from the results obtained in this study, we show the possibility of investing in technologies aimed at small producers in order to motive the conservation of genetic resources, food sovereignty of producers and, consequently, world security, given the importance of this culture for human feed.

Keywords: Zea mays; sustainability; food sovereignty; reciprocal recurrent selection.

1. INTRODUCTION

Maize belongs to the group of cereals that compose the food base of the world population. Similar to other cultures in this group, the climate changes scenario has been worrying countries around the world [1]. Thus, an eminent objective in the agricultural innovation process is the development of farming technologies that cannot only increase productivity in the short term, but that contribute to mitigating the impacts of climate change [2].

The corn breeding continues to be the main factor responsible for the constant increase in production worldwide. The breeding programs have worked both on the development of cultivars to increase the productivity of areas already traditionally cultivated as well as on the development of cultivars adapted to regions previously not explored by Challinor et al. [1,3].

Thus, knowledge about the genetic variability available and identification of the best strategies for the development of superior cultivars are challenges for breeders in tropical regions with programs at an early stage of development [4-6]. For temperate regions, on the other hand, one of the main challenges has been the expansion of the genetic basis of breeding programs aiming at increasing the possibilities of prospecting genotypes more adapted to shorter cultivation periods [7,8].

The demand of large corn-producing countries for genetic variability, mainly of tropical adaptation germplasm, has directed greater investments in resource conservation by the holding institutions [9]. On the other hand, the problems arising from genetic erosion in the already narrow genetic base of older programs alert tropical breeding programs to the importance of maintaining genetic variability

throughout the cultivar development process [10]. The challenge is to motivate farmers to maintain the crop varieties of broad genetic base. Small farmers, who previously held traditional varieties and cultivated maize populations, have increasingly adhered to the use of commercial cultivars, which are mostly of narrow genetic basis. This process of abandoning traditional varieties is a consequence of a constant process of cultural erosion in rural areas, a direct reflection of the low investment in technologies aimed at small producers [11,12]. As a breeding population method, recurrent selection combines advantages such as increased productivity and maintenance of genetic variability [13]. Populations improved by recurrent selection, in addition to being promising varieties for cultivation, are also sources of germplasm for breeding programs worldwide. The objective of the work was to select full sib maize progenies and to estimate genetic gains in the first cycle of reciprocal recurrent selection for common maize intended for cultivation in the state of Espírito Santo, a region characterized by small farms.

2. MATERIALS AND METHODS In a partnership between researchers from two of its campuses, the Federal Institute of Espírito Santo (IFES) developed the first cycle of reciprocal recurrent selection of its maize breeding program.

2.1 Obtaining Full Sib Families (FS)

The conduct of the first cycle of reciprocal recurrent selection in maize was carried out by


3

obtaining full sib famimies among plants of the Cimmyt and Piranão varieties. The Cimmyt variety belongs to the heterotic Flint group and the Piranão variety to the Dent group. These varieties were pre-improved by Universidade Federal de Viçosa and Universidade Federal do Norte Fluminense Darcy Ribeiro.

Obtainment of families was performed at the Federal Institute of Espírito Santo (IFES) Campus Itapina, in the northwest of the Espírito Santo state. In this stage, the two populations were cropped in alternating lines with a length of 6 meters and spacing between plants of 0.4 x 1.0 meters. About 300 plants from each population were planted. Cultural treatments were conducted according to the recommendations for culture. Each of the plants within the rows was numbered to facilitate the identification of intersections. In each pair of rows, plants that were visibly more vigorous and had at least two ears been selected as parental to obtain the families. To avoid pollen fertilization of unknown individuals, the ears of the selected plants received protection before the emission of the stigma. After identifying the beginning of pollen emission in the selected plants and the respective emission of the stigma in female organs of these plants, the tassels (male organ) were protected with “Kraft” paper bags for 24 hours. After this period, crosses were conducted, with two ears being pollinated in each plant. In the upper ears, self-fertilization was performed to obtain the S1 families and in the lower ears, crosses between the plants of the different populations were conducted to obtain the full sib families (FS). A total of 120 FSs and respective 240 S1 families were obtained.

2.2 FS Families Assessment The evaluation of the agronomic performance of FS families was carried out during the harvest period in the experimental fields of IFES Campus Itapina and IFES Campus Alegre.

The yield trials between FS families were installed in a randomized block design, with two repetitions arranged in sets. Each block was subdivided into 4 sets so that each set contained 30 FS families. A planting line with 16 plants comprised the experimental unit. The spacing used was 0.2 x 1.0 meters, corresponding to the density of 50 thousand plants ha-1.

The plants received fertilization for sowing and coverage. In planting, 30 kg ha-1 of nitrogen (N), 100 kg ha-1 of phosphorus (P) and 60 kg ha-1 of potassium (K) were applied. The cover fertilization was applied 120 kg ha-1 of N, divided into two applications at 30 and 45 days after planting, respectively. In each experimental unit, 12 agronomic traits were evaluated: After flowering - plant height (PH), obtained by the average height (m) of six plants, corresponding to the length of the stem from the ground level until the insertion of the last leaf and; ear insertion height (EH), obtained by the average ear height (m) of six plants, corresponding to the length of the stem from the ground level to the insertion of the upper ear; The day before the harvest - the number of plants suitable for harvest (NP); number of lodging plants (NLP), corresponding to the number of plants with a slope greater than 45º with the vertical. After harvest - number of ears harvested (NE); number of ears attacked by pests (NPAE); prolificacy (PRO), corresponding to the ratio between NE and NP (ears plant-1); mass of ears (EW) harvested without straw (kg); average ear mass (EMW), corresponding to the ratio between EW and NE (kg ear-1); mass (kg) of grains (GW); average mass of 100 grains (GW100), corresponding to mass (g) of 100 healthy grains collected in the sample corresponding to the parcel and; grain yield (GY), corresponding to the grain mass estimate in 1 ha (Mg ha-1).

2.3 Statistical and Biometric Analysis In order to verify the homogeneity of variances between the two environments, the Hartley test (maximum F) was applied at a significance level of 5%. Once the homogeneity of variance between the environments was identified, each of the evaluated traits was subjected to a joint analysis, considering the statistical model:

�� = � + �� + �/��(�) + �/��(��)

+ �/��(�) + ��/��(�) + ��

Where Yijkl is the value observed in the plot of the i-th genotype, evaluated in the j-th environment, allocated in the k-th repetition and hierarchized in the l-th set; µ is the general constant; Ej is the effect of the j-th environment considered as a random effect; R / Ek(j) is the effect of the k-th


4

block (repetition) within the j-th environment; S / REl(kj) is the effect of the l-th set within the k-th block in the j-th environment; F / Si(l) is the random effect of the i-th genotype (family) hierarchized in the l-th set; FE / Sij(l) is the effect of the interaction between the i-th family and the j-th environment in the respective l-th set e; εijkl is the random error associated with the Yijkl observation. Parallel to the analysis of variance, genetic parameters were estimated: Phenotypic variance (σ²f):

�²� =��/�

��

Residual variance (σ²r):

�²� =��

��

Genotypic variance (σ²g):

�²� =��/� − ��

��

Where: MSF/S and MSErro is the mean square for family effects and the mean square for residual effects, respectively; e is the number of environments; r is the number of repetitions. Heritability based on average of families (h²):

ℎ²�� = �²�

�²�

Coefficient of genetic variation (CVg):

�� = 100��²�

��

Genetic variation index (Îv):

�� = ��

��

After identifying significant differences for the genotypes, families were selected.

2.4 Selection of FS Families In order to enhance gains for multiple traits of interest in this initial stage of the breeding program, the use of selection indexes was

adopted as an option. In order to select 40 families (33%), the selection gains were estimated from the general averages of the families in the two environments. In order to avoid selection based on strongly correlated trait, Person's correlation between the means of pairs was estimated. After this analysis, it was decided to adopt for selection: GY, PRO, EMW, GW100, NLP, and NPAE. For these last two traits, the selection was performed in order to reduce the average. Four selection methodologies were compared: direct selection on GY and indirect selection on the other traits; Classic index of Smith and Hazel [14,15]; Mulamba and Mock index [16] and; Willians selection index [17]. For the three indexes used, two weight options were adopted: pre-established economic weights (PE): 1000, 50, 20, 100, 100, for the GY, PRO, GW100, NLP and NPAE, respectively; and also adopting as a weight the coefficients of genotypic variation (CVg) as suggested by Cruz, Carneiro and Regazzi [18].

The analysis of variance was performed using the SAS software; the estimate of gain for the selection index were obtained using the Genes software [19], and correlation estimates and graph construction were conducted performing R software, from the basic packages and the corrplot package [20].

3. RESULTS AND DISCUSSION The first cycle of reciprocal recurrent selection of the IFES maize breeding program was evaluated by 120 FS families in two environments. The environments showed significant differences for all traits evaluated, in which notably in Itapina, the conditions were more favorable for grain production (Table 1).

Significant differences between FS families were observed for the PH, EH, PRO, EMW, GW100 and GY only. One of the factors that contributed to the non-significance of the other traits was the high residual effect due mainly to the discrepancies between environments. However, the main objective in this stage of the program is to obtain gains in GY and PRO, traits which have been detected significant differences between families.


5

Interactions between families and environment (GE/S) were significant for the EH, EMW and GW100. The significance of the interaction can be caused by nutritional and phytosanitary aspects of each environment. The chemical and physical quality of the soil as well as the ability to provide nutrients are factors that directly affect the assimilation of matter in the grains [21,22]. In addition, the greater lodging of plants in Alegre is a critical factor for weight loss of ears and grains [23]. The greater lodging of plants results in deficient nutrition of reproductive organs and increases the risk of attack by pests and diseases that directly reflect in the lower production of grains. These results indicate that the efforts made to mitigate environmental effects are expected to increase even more in the next cycles, mainly aiming at reducing the lodging of plants in the experimental field of Alegre. The no interaction GxE for traits such as PRO and GY can be a facilitating factor for the family selection process aiming at gains in both

environments. This is because the no interaction will not require the identification of more stable and adapted genotypes for both environments. As a direct reflection of the residual variation (σ²) resulting from environmental effects, the highest estimated heritability (h²) was 0.43 for the PRO (Table 2). Since heritability is an essential component in obtaining genetic gains [18], the potential for effective gains was reduced in this first selection cycle. However, it is expected that greater efforts to mitigate environmental effects or increase the number of repetitions may maximize the gains for the next cycles. The highest values of heritability estimated for PRO, EMW and GW100 in relation to the other traits are important for optimization of genetic gains. Together with GY, the gain in prolificacy is necessary for the success of the next cycles. The low prolificacy of most of the parents was the main factor that prevented the obtaining of a larger number of families. The evaluation of an

Table 1. Mean squares of joint analysis of variance for 12 agronomic traits evaluated in 120

families of full maize siblings, in two environments: Alegre and Itapina, in the state of Espírito Santo, Brazil

SV PH EH NP NLP NE NPAE Enviroment (E) 12.79

** 10.14

** 178.85

** 8052.41

** 3146.75

** 10055.85

**

Repetition(R)/E 0.21 ** 0.06 * 18.94 ns 141.37 ** 6.30 ns 21.32 ns Set/BE 0.16

** 0.11

** 39.83

** 277.81

** 121.32

** 97.52

**

Family(G)/S 0.05 ** 0.03

** 8.87

ns 15.20

ns 47.94

ns 40.40

ns

GE/S 0.03 ns 0.02 * 9.79 ns 13.38 ns 49.70 ns 30.75 ns Error 0.03 0.02 7.64 14.03 41.32 33.72 CV(%) 7.10 9.77 13.82 61.95 21.91 77.83 Means Alegre 2.21 1.14 20.60 10.14 26.78 12.04 Itapina 2.54 1.54 19.38 1.95 31.90 2.88 PRO EW EMW GW GW100 GY Enviroment (E) 14.63 ** 472.29 ** 1.2689 ** 448.01 ** 19192.33 ** 2734.82 ** Repetition(R)/E 0.04

ns 1.52

ns 0.0043

ns 0.54

ns 136.74

** 2.01

ns

Set/RE 0.44 ** 1.69 ** 0.0029 ** 0.86 * 75.33 ** 2.63 ** Family(G)/S 0.14

** 0.74

ns 0.0012

* 0.52

ns 16.23

** 2.25

*

GE/S 0.10 ns

0.82 ns

0.0011 * 0.47

ns 14.75

** 1.18

ns

Error 0.08 0.67 0.0008 0.47 10.30 1.66 CV(%) 19.61 33.96 24.52 35.37 11.47 26.16 Means Alegre 1.31 1.41 0.07 0.96 21.66 2.54 Itapina 1.66 3.40 0.17 2.90 34.31 7.31

ns non-significant effects; ** significant effects at the 1% probability level; * significant effects at the 5% probability level; according to test F. PH = plant height (m); EH = ears height; NP = number of plants in the plot; NLP =

number of lodging plants in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears (kg); EMW = average ear weight (kg); GW = grain weight (kg); GW100 = weight

of 100 healthy grains (g); GY = grain yield (Mg ha-1

)


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Table 2. Estimation of genetic parameters for twelve agronomic traits evaluated in 120 full sibling families of corn grown in Alegre and Itapina, Espírito Santo - Brazil

Traits σ

2f σ

2r σ

2g h

2 CVg Îv

PH 0.0125 0.0075 0.0050 0.40 4.59 0.65 EH 0.0075 0.0050 0.0025 0.33 4.24 0.43 NP 2.2175 1.9100 0.3075 0.14 12.40 0.90 NLP 3.8000 3.5075 0.2925 0.08 22.00 0.36 NE 11.9850 10.3300 1.6550 0.14 23.75 1.08 NPAE 10.1000 8.4300 1.6700 0.17 47.31 0.61 PRO 0.0350 0.0200 0.0150 0.43 10.05 0.51 EW 0.1850 0.1675 0.0175 0.09 8.53 0.25 EMW 0.0003 0.0002 0.0001 0.33 2.89 0.12 GW 0.1300 0.1175 0.0125 0.10 8.05 0.23 GW100 4.0575 2.5750 1.4825 0.37 23.02 2.01 GY 0.5625 0.4150 0.1475 0.26 17.31 0.66 PH = plant height; HE = height of ears; NP = number of plants in the plot; NLP = number of plants lodging in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears;

EMW = average ear weight; GW = grain weight; GW100 = weight of 100 healthy grains; GY = grain yield unrepresentative number of individuals may bring risks to the genetic variability of the population. Thus, prioritizing prolific gains at the beginning of the program will be essential for the success of the next cycles.

In general, in this first cycle, one of the options for maximizing gains, given the low values of heritability, could be the increase in selection pressure, however this would not be positive due to the risk of loss of genetic variability to which populations would be submitted [24].

Thus, in order to optimize the selection of families, it was decided to maintain the selection of 40 families with better agronomic performance, aiming mainly to obtain gains in GY and to verify possible gains with the use of selection indexes to maximize gains in simultaneous traits.

Due this correlation (Fig. 1), we chose to consider only the GY, PRO, GW100, NLP and NPAE for the composition of the selection indexes. The NP and NLP did not present significant differences between the families, however, given the need to reduce the loss of plants and ears, mainly in Alegre, they were chosen as components of the indexes, aiming at a negative selection.

In addition, subsequent gains in NPAE reduction are not only important in reducing production costs, they are even more important in the context of family-based agriculture. Since improved populations can be used and maintained, mainly by small farmers, the

reduction in demand for agrochemicals, such as those used to control corn pests, represents a reduction in the food sovereignty risks of this community [25]. The use of selection indexes, as expected [18], showed the possibility of obtaining gains for simultaneous traits of interest. However, the adoption of economic weights proved to be more efficient in obtaining gains in GY (Table 3). When CVg values were used, greater gain was obtained to reduce NPAE, result of the greater magnitude of CVg of this trait in relation to the others (see Table 2).

Although the gains for NPAE reduction are important for the reasons previously described, it was observed that increasing the weights for this trait in this first cycle could considerably reduce the gains in GY. Thus, in order to optimize productivity gains in this initial stage, NPAE should receive greater attention from breeders in subsequent cycles.

Considering the allocation of economic weights, the selection based on the Mulamba and Mock index resulted in the greatest gain for GY among the indexes. However, the direct selection on GY is indirect on the other traits. Comparing the selection using the Mulamba and Mock index with the direct selection, it was observed that although the gains in GY are similar, the index resulted in a 0.12% higher gain in PRO, in addition to small advantages for NP, NLP and NE. Thus, it was decided to use the Mulamba and Mock index as a selection criterion for the 40 corn FS families.

Fig. 1. Linear correlation between agronomic traits evaluated in 120 families of full maize

siblings in of Alegre and IPH = plant height; HE = height of ears; NP = number of plants in the plot;

plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears; EMW = average ear weight; GW = grain weight; GW100 = weight of 100 h

Table 3. Estimation of selection gains (GS) in full sib families of maize for simultaneous trait

for direct and indirect selection (33%) compared to thr

Traits Smith e HazelDirect PE

PH 0.67 0.76 EH 0.58 0.75 NP 0.17 0.22 NLP -0.14 -0.34 NE 1.06 0.71 NPAE 0.36 -2.99 PRO 2.20 1.13 EW 1.37 1.09 EMW 3.78 2.96 GW 1.61 1.17 GW100 1.16 1.31 GY 3.07 2.01

PH = plant height (m); HE = height of ears (m); NP = number of plants in the plot; NLP = number of plants lying in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears (kg); EMW = average ear weight (kg); GW = grain

grain yield (Mg ha-1

). PE = economic weights: 1000, 50, 20, 100, 100, for the GY, PRO, GW100, NBP and NPAE, respectively; CVg = coe

As previously mentioned, enhancing gains in PRO is a key factor in this first stage. This is because in the recurrent selection methodology

Berilli et al.; JEAI, 42(12): 1-12, 2020; Article no.

7

1. Linear correlation between agronomic traits evaluated in 120 families of full maize siblings in of Alegre and Itapina, Espírito Santo, Brazil

PH = plant height; HE = height of ears; NP = number of plants in the plot; NLP = number of plants lying in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears;

EMW = average ear weight; GW = grain weight; GW100 = weight of 100 healthy grains; GY = grain yield

Estimation of selection gains (GS) in full sib families of maize for simultaneous trait for direct and indirect selection (33%) compared to three different selection indexes

GS(%) Smith e Hazel Mulamba e Mock Williams

CVg PE CVg PE 0.08 0.66 0.20 0.55 0.48 0.65 0.59 0.80 -0.03 0.18 0.07 0.27 -0.73 -0.16 -0.49 -0.43 0.30 1.12 0.45 0.92 -4.83 0.17 -5.50 -2.94 0.89 2.32 1.01 1.53 0.29 1.36 0.57 1.22 1.11 3.75 1.68 3.23 0.31 1.60 0.79 1.60 1.08 1.12 1.16 0.87 0.63 3.06 1.38 2.77

(m); HE = height of ears (m); NP = number of plants in the plot; NLP = number of plants lying in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears (kg); EMW = average ear weight (kg); GW = grain weight (kg); GW100 = weight of 100 healthy grains (g); GY =

). PE = economic weights: 1000, 50, 20, 100, 100, for the GY, PRO, GW100, NBP and NPAE, respectively; CVg = coefficient of genotypic variation

As previously mentioned, enhancing gains in PRO is a key factor in this first stage. This is because in the recurrent selection methodology

the stage of obtaining families is carried out simultaneously to obtaining S1 families from the parents, which will be used in the recombination

; Article no.JEAI.64506

1. Linear correlation between agronomic traits evaluated in 120 families of full maize

NLP = number of plants lying in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears;

ealthy grains; GY = grain yield

Estimation of selection gains (GS) in full sib families of maize for simultaneous trait ee different selection indexes

Williams

CVg -0.01 0.32 0.07 -0.48 0.09 -6.06 0.03 0.34 0.90 0.59 1.04 0.80

(m); HE = height of ears (m); NP = number of plants in the plot; NLP = number of plants lying in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears

weight (kg); GW100 = weight of 100 healthy grains (g); GY = ). PE = economic weights: 1000, 50, 20, 100, 100, for the GY, PRO, GW100, NBP and NPAE,

the stage of obtaining families is carried out simultaneously to obtaining S1 families from the

be used in the recombination


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stage. Thus, the non-prolificacy of the plants can compromise the efficiency in the stage of obtaining the progenies.

The differences in the variation of means between selected families and the total of evaluated families demonstrated that even for traits in which the gains were smaller. Such as NP and NLP, more than 50% of the selected families presented more than 20 in the average of NP, and more than 50% had less than six in the mean NLP (Fig. 2).

It was observed that the reduction in the amplitude of the average of the selected families

reduced around 2/3 of the initial amplitude for GY in direct correspondence to the selection of 1/3 of the upper FS families for this trait. In agreement with the estimated degree of correlation, there were also markedly greater reductions in the amplitude of means for NE, EW, EMW and GW.

It is worth noting that even for traits whose analysis of variance did not detect a significant genotypic effect; it was possible to obtain genetic gains. This reinforces the importance of knowing the variability of populations and the advantage of exploring the selection tools currently available to breeders.

Fig. 2. Variation between means for the 120 full sib maize families (red box) and the variation between the 40 selected families (yellow boxes) by the Mulamba and Mock index based on pre-

established economic weights PH = plant height (m); HE = height of ears (m); NP = number of plants in the plot; NLP = number of plants lying in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears (kg); EMW = average ear weight (kg); GW = grain weight (kg); GW100 = weight of 100 healthy grains (g); GY =


)


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The 40 families selected ranged from 5.2 to 6.3 Mg ha

-1 for GY (Table 4). On average, there was

an increase of 0.77 Mg ha-1 in relation to the average of 120 families. Considering an average

price of R$ 30.00 for each 60 kg bag, it is estimated that the gain obtained results in an increase of R$ 385.00 per hectare on average in the region.

Table 4. Averages for agronomic traits in 40 full sib maize families grown in Alegre and Itapina,

Espírito Santo, Brazil

Family PH EH NP NLP NE NPAE PRO EW EME GW GW100 GY 41 2.49 1.41 20.28 4.51 34.05 2.72 1.71 3.17 0.16 2.37 30.84 6.27 102 2.48 1.44 21.48 3.07 33.99 5.64 1.58 3.24 0.15 2.56 27.29 6.14 39 2.44 1.47 18.79 5.46 31.44 6.14 1.67 2.81 0.15 2.29 29.70 6.09 19 2.38 1.30 19.81 5.46 36.46 7.70 1.85 3.09 0.15 2.33 27.46 6.32 107 2.32 1.38 22.45 4.87 37.25 6.02 1.67 3.41 0.15 2.56 30.18 6.04 23 2.35 1.34 20.11 4.52 28.14 6.92 1.39 2.61 0.13 2.55 31.53 6.05 100 2.34 1.36 19.92 7.04 35.05 12.84 1.76 3.25 0.16 2.61 25.46 6.34 90 2.40 1.44 21.37 5.65 30.65 6.17 1.44 3.17 0.15 2.58 30.30 5.95 103 2.32 1.36 21.47 2.21 29.55 3.76 1.39 2.90 0.14 2.52 29.05 5.73 17 2.45 1.44 21.44 5.60 33.22 8.71 1.59 2.98 0.14 2.35 25.33 6.06 84 2.40 1.40 21.36 2.62 28.64 7.77 1.35 3.12 0.15 2.50 29.41 5.86 89 2.33 1.42 19.65 5.51 31.10 6.05 1.58 2.62 0.13 2.32 30.07 5.84 30 2.45 1.40 20.98 8.94 30.52 9.46 1.49 2.92 0.15 2.44 30.61 6.06 85 2.56 1.56 17.68 7.98 28.86 7.48 1.65 2.70 0.15 2.19 30.23 5.87 113 2.33 1.34 21.16 6.32 31.91 6.87 1.52 2.99 0.14 2.29 27.63 5.84 14 2.59 1.51 19.84 8.65 32.48 9.76 1.64 2.86 0.14 2.24 32.10 5.97 76 2.24 1.35 21.60 3.87 34.05 6.63 1.60 3.08 0.15 2.23 29.31 5.56 57 2.46 1.49 20.04 6.93 32.11 5.92 1.62 2.82 0.14 2.12 31.54 5.61 106 2.34 1.32 21.94 3.78 30.35 13.93 1.42 3.04 0.14 2.48 28.24 5.82 10 2.37 1.38 21.56 2.50 31.06 14.30 1.44 3.00 0.14 2.52 30.78 5.65 11 2.51 1.47 18.31 9.24 32.35 8.25 1.76 2.49 0.14 1.89 29.27 5.77 95 2.29 1.31 18.51 2.92 31.20 3.00 1.72 2.35 0.13 1.84 27.49 5.35 32 2.35 1.43 21.36 6.82 33.86 2.66 1.58 2.89 0.13 2.37 28.62 5.50 21 2.53 1.48 18.61 6.46 36.51 10.22 1.98 2.99 0.16 1.92 27.26 5.58 68 2.33 1.32 19.28 8.43 29.24 4.27 1.55 2.44 0.13 1.96 28.81 5.53 51 2.31 1.36 20.91 6.19 30.51 12.55 1.42 3.05 0.14 2.52 29.21 5.66 105 2.44 1.51 20.41 3.17 32.66 3.83 1.61 2.53 0.13 2.07 27.37 5.30 33 2.52 1.47 19.36 5.51 33.56 8.15 1.73 2.59 0.13 1.99 29.57 5.43 58 2.31 1.32 19.26 7.11 27.86 3.11 1.50 2.09 0.11 2.13 25.63 5.48 116 2.47 1.40 19.45 5.52 30.71 9.64 1.59 2.74 0.14 2.03 29.96 5.47 99 2.45 1.46 20.25 3.98 29.15 5.01 1.46 2.59 0.13 2.13 28.29 5.26 112 2.44 1.40 18.90 10.25 31.28 11.36 1.68 2.54 0.13 2.07 29.21 5.56 117 2.42 1.44 20.62 5.47 30.15 7.09 1.52 2.66 0.13 2.20 25.06 5.43 52 2.32 1.35 16.43 6.37 30.15 10.36 1.84 2.31 0.14 1.70 27.53 5.46 118 2.31 1.39 21.17 9.84 32.08 9.80 1.52 2.82 0.13 2.32 26.74 5.55 70 2.54 1.51 21.38 6.85 33.99 9.63 1.60 3.00 0.14 1.92 30.63 5.38 109 2.35 1.45 20.46 4.17 28.33 7.76 1.39 2.59 0.13 2.18 31.60 5.24 38 2.50 1.53 21.06 5.61 27.68 3.33 1.32 2.68 0.13 2.24 26.59 5.22 24 2.45 1.35 21.11 4.05 30.93 11.78 1.48 2.57 0.12 2.16 28.74 5.31 36 2.56 1.53 20.84 6.11 32.89 4.94 1.60 2.57 0.13 2.04 27.95 5.20 Means General 2.41 1.41 20.27 5.74 31.65 7.54 1.58 2.81 0.14 2.24 28.81 5.69 Alegre 2.24 1.25 20.26 9.92 28.11 12.14 1.41 1.65 0.08 1.11 22.67 3.11 Itapina 2.58 1.58 20.27 1.56 35.19 2.94 1.75 3.95 0.20 3.37 34.96 8.27 PH = plant height (m); HE = height of ears (m); NP = number of plants in the plot; NLP = number of plants lying in the plot; NE = number of ears; NPAE = number of ears attacked by pests; PRO = prolificacy; EW = weight of ears (kg); EMW = average ear weight (kg); GW = grain weight (kg); GW100 = weight of 100 healthy grains (g); GY =


)


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According to estimates by the National Supply Company (CONAB), Brazil has an average corn yield estimated at 5.64 Mg ha-1 [26]. Among the 40 families, 20 have productivity higher than the country's average. This information reaffirms how investments in improving maize culture can contribute to increased productivity even in regions composed of small properties.

Beyond the populations that will originate in this first cycle of recurrent selection, there are FS families that can be commercially exploited as interparietal hybrids as a way of making new technologies immediately available to the region. In addition, families like 17, 19 and 23 can be explored as sources of alleles of interest for simultaneous increase in productivity averages in the south and Midwest regions of the state. Although the genetic gains in this first selection cycle have been small compared to the probable genetic potential of the population, the possible economic effects already in this first cycle are considered promising for family farming in the region. It is also worth noting that improving management to control factors such as high plant lodging, pest attack and improving soil fertility conditions in Alegre can contribute to more significant gains. The first cycle of reciprocal recurrent selection in common maize from the Ifes breeding program resulted in a gain of 3.06%, corresponding to an increase of 770 kg of maize per hectare in the Cimmyt and Piranão populations. For the next selection cycles, greater efforts to reduce plant loss in crops in the southern region and to improve chemical soil conditions may lead to higher gains. It is also important to note that in this first cycle, there were no gains in reducing the incidence of pests and diseases, and it is valid to give more attention to these traits in future cycles.

The estimation gains observed in this first selection cycle point out that the improvement of populations is a promising method for increasing corn productivity in the south and central-west of the state of Espírito Santo, Brazil.

It is hoped that the achievement of populations with superior performance will serve to promote small farmers to move to the planting of maize populations in order to join efforts in favor of food sovereignty and the ex situ conservation of tropical maize varieties. In addition, the maintenance of genetic material adapted for agriculture in the region may be a contributing

factor in guaranteeing food security in the face of the imminent threats of the climate change scenario, especially for small farmers [27]. Lastly, we hope that results showed in this work can promote, in the scientific community, the perception about the importance of improve the technology levels on small food systems in parallel with the genetic diversity conservation, being the population breeding methods, a god way for it in maize crop. 4. CONCLUSION The first cycle of recurrent selection resulted in an increase of 3.06% in grain yield and gains in prolificacy besides decreasing the lodging of plants ratio. The study identified full-sib families that can be used with cultivars so that to increase the average grain yield of maize in the region. The use of the Mulamba e Mock selection index was the most efficient to obtain gains in this study.

ACKNOWLEDGEMENTS

The authors would like to thank the Espírito Santo Research Support Foundation - FAPES for funding the research and Federal Institute of Espírito Santo - Ifes for granting the scholarships.

DISCLAIMER

The products used for this research are commonly and predominantly use products in our area of research and country. There is absolutely no conflict of interest between the authors and producers of the products because we do not intend to use these products as an avenue for any litigation but for the advancement of knowledge. Also, the research was not funded by the producing company rather it was funded by personal efforts of the authors.

COMPETING INTERESTS

Authors have declared that no competing interests exist.

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genetic gains to grain yield of maize varieties for small

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