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Os transgênicos e os organismos geneticamente modificados são aqueles que tiveram os seus materiais genéticos alterados pelo homem, através
da transferência de um ou mais genes de uma espécie para outra ou da
mesma espécie.

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A soja é a cultura mais importante no estado do Mato Grosso do Sul, é o quinto maior produtor do grão no país, chegando a produzir na safra de  20/21 chegou a 13,305 milhões de toneladas do grão, representando um aumento de 17,8% em relação a safra de 19/20.

O rendimento da safra depende de uma interação entre a genética, o meio ambiente e os fatores de manejo da safra (EMBRAPA, 2020).
Para a obtenção de altos rendimentos, faz-se necessário a utilização de tecnologias, como melhoramento genético, cultivares adaptados às condições climáticas da região, resistência a pragas e doenças, manejo correto do plantio a colheita, e utilização de fertilizantes, como o nitrogênio.

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O estudo da adaptabilidade, estabilidade e extração de nutrientes é necessário esta ligado com o rendimento de uma cultura  que depende da interação entre fatores genéticos e ambientais, neste sentido, pretendemos estudar a interação entre cultivares transgênicas e não transgênicas com o ambiente, realizando quatro ensaios de competição de cultivares, sendo dois em Ponta Porã e dois em Maracaju, no estado de Mato Grosso do Sul.

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Este estudo tem como objetivo analisar o efeito da interação cultivar-ambiente em cultivares comerciais de soja geneticamente modificado (GM) e não-GM sob diferentes doses de nitrogênio.

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A característica estudada foi a produtividade de grãos, sendo utilizados os métodos de adaptabilidade e estabilidade, bem como a técnica de estratificação ambiental. Houve resposta diferencial entre as cultivares oriundas de tecnologias transgênicas e não transgênicas. Cultivares transgênicas e não transgênicas, em sua maioria, foram mais adaptadas aos ambientes favoráveis e desfavoráveis, respectivamente. Houve similaridade entre ambientes oriundos de locais, épocas de semeadura e doses de N, apontando para redução de ambientes em futuros trabalhos de melhoramento.

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Crop yield depends on interaction between genetic and environmental factors, making it essential to study adaptability, stability and environmental stratification in order to mitigate the effects of this interaction. Four experiments were conducted to assess competition between corn cultivars in the 2018/19 growing season, two in Paraíso do Tocantins and two in Palmas, with sowing performed on November 5, 2018 and January 15, 2019. Cultivar-environment interaction was analyzed in genetically modified (GM) and non-GM commercial corn cultivars in the Vale do Araguaia region of Tocantins state (TO), Brazil, A randomized block design was used for all the experiments, in 3 × 12 factorial scheme, with three doses of nitrogen fertilizer as topdressing (50, 100 and 150 kg of N ha⁻¹) and 12 commercial cultivars (six non-GM, 1CHD, 2CV, 3CV, 4CV, 5CTH, 6CDH and six GM, 7GTH, 8GTH, 9GSH, 10GSH, 11GSH, 12GSH. For statistical analysis, the N dose in each experiment represented a different environment. The characteristic studied was grain yield, using the adaptability and stability methods as well as environmental stratification. Different responses were observed between the GM and non-GM cultivars. Most of the GM and non-GM cultivars were better adapted to favorable and unfavorable environments, respectively. All the environments exhibited similar behavior regardless of location, sowing time and the N dose used, demonstrating that fewer environments can be used in future breeding research.

Key words:
Zea mays L.; cultivar x environment interaction; grain yield

O rendimento da cultura depende da interação entre os fatores genéticos e ambientais, tornando-se necessário estudo de adaptabilidade, estabilidade e estratificação ambiental visando atenuar os efeitos desta interação. Neste sentido, visando estudar a interação cultivar x ambiente em cultivares comerciais de milho transgênico e não transgênico na região do Vale do Araguaia TO, foram realizados quatro ensaios de competição de cultivares implantados na safra 2018/19, sendo dois em Paraíso do Tocantins e dois em Palmas, com semeaduras realizadas em 05/11/2018 e 15/01/2019. O delineamento experimental, em cada ensaio, foi de blocos casualizados com 36 tratamentos, que foram dispostos em esquema fatorial 3 × 12, representados por três doses de adubação nitrogenada em cobertura (50, 100 e 150 kg de N ha⁻¹) e por 12 cultivares comerciais, (seis transgênicos: 1CHD, 2CV, 3CV, 4CV, 5CTH, 6CDH e seis não transgênicos: 7GTH, 8GTH, 9GSH, 10GSH, 11GSH, 12GSH). Na análise estatística, cada dose de N em cada ensaio representou um ambiente distinto. A característica estudada foi a produtividade de grãos, sendo utilizados os métodos de adaptabilidade e estabilidade, bem como a técnica de estratificação ambiental. Houve resposta diferencial entre as cultivares oriundas de tecnologias transgênicas e não transgênicas. Cultivares transgênicas e não transgênicas, em sua maioria, foram mais adaptadas aos ambientes favoráveis. e desfavoráveis, respectivamente. Houve similaridade entre ambientes oriundos de locais, épocas de semeadura e doses de N, apontando para redução de ambientes em futuros trabalhos de melhoramento.

Palavras-chave:
Zea mays L.; interação cultivar x ambiente; rendimento de grãos

Crop yield depends on an interaction between genetics, the environment and crop management factors (). Thus, using technologies such as plant breeding, hybrids adapted to regional climate conditions, resistance to disease and pests, correct planting and harvesting strategies and fertilizers such as nitrogen, is essential in obtaining high yields.

Although the literature addresses cultivar-environment interaction, this study aims to analyze the effect of cultivar-environment interaction in commercial genetically modified (GM) and non-GM corn cultivars under different doses of nitrogen (topdressing) in the Vale do Araguaia region of Tocantins state (TO), Brazil, in the 2018/2019 growing season.

Four experiments were conducted to assess competition between cultivars in the 2018/19 growing season in Tocantins, two in Paraíso do Tocantins (10° 267 S; 48° 887 W and 411 m a.s.l.) and two in Palmas (10° 175 S; 48° 358 W and 220 m a.s.l.), with sowing performed on November 5, 2018 and January 15, 2019.

A randomized block design was used for all the experiments. The treatments were arranged in a 3 × 12 factorial scheme, with three doses of nitrogen fertilizer as topdressing at low (50 kg ha⁻¹), medium (100 kg ha⁻¹) and high N levels (150 kg ha⁻¹), to achieve minimum and maximum crop yield (), and 12 commercial cultivars (six GM and six non-GM). The cultivars were purchased from specialized stores in the area where the experiments were conducted, and coded from 1 to 6 (non-GM) and 7 to 12 (GM), as follows: 1.CDH (AG1051, non-GM double-cross hybrid), 2.CV (Cativerde, non-GM variety), 3.CV (Al Alvaré, non-GM variety), 4.CV (AL Bandeirante, non-GM variety), 5.CTH (BM3063, non-GM triple-cross hybrid), 6.CDH (Órion, non-GM double-cross hybrid), 7.GTH (BM3063, GM triple-cross hybrid), 8.GTH (2B655PW, GM triple-cross hybrid), 9.GSH (2M95VIP3, GM single-cross hybrid), 10.GSH (30S31VYH, GM single-cross hybrid), 11.GSH (P3862H, GM single-cross hybrid), 12.GSH (AG709, GM single-cross hybrid).

Soil analysis was performed to determine its chemical characteristics in the experimental areas. Analysis of the chemical characteristics of the soil in Palmas showed the following results: 5.9 pH CaCl ₂; 6.8 mg dm⁻³ of P Melich; 39 mg dm⁻³ of K; 1.2 cmol c dm⁻³ of Ca; 0.5 cmol c dm⁻³ of Mg; 1.0% OM; 54% base saturation and CEC of 3.4 cmol c dm⁻³. The results in Paraíso do Tocantins were 6.1 pH CaCl ₂; 14.1 mg dm⁻³ of P; 54.2 mg dm⁻³ of K; 3.6 cmol c dm⁻³ of Ca; 0.8 cmol c dm⁻³ of Mg; 1.6% OM; 76.4% base saturation and CEC of 5.94 cmol c dm⁻³.

Temperature and rainfall data were obtained weekly at the experimental sites ( Figure 1).

The experimental plot contained four 5-m-long rows spaced 0.80 m apart. Ears were collected from the two center rows and those within the 0.50 m at either end were discarded.

The soil was harrowed twice, followed by furrow opening. Based on the results of soil chemical analysis, liming was not performed. The basal dressing consisted of 400 kg ha⁻¹ of 4-14-8 NPK formulation. Planting was performed manually and the desired population established after 10 days.

Nitrogen topdressing was applied manually alongside the furrow when plants were at vegetative growth stage V4, using doses of 50, 100 and 150 kg ha⁻¹ of N with ammonium sulfate (21% N and 22% S) as the source. The doses used for high and low N environments corresponded to the lowest and highest expected grain yields for corn ().

Stage V4 is when the plant displays four fully developed leaves and the number of grains and yield potential is established. A single application of N at this growth stage is the most beneficial (; ).

Pest, disease and weed control were performed as described by ).

The grains collected in each experiment were identified according to the N dose and cultivar used, then weighed and converted into kg ha⁻¹ after correction for a 13% moisture content.

For statistical analysis, the N dose in each experiment represented a different environment. As such, combinations of the three N doses and four experiments produced 12 environments, presented in Table 1.

The yield data for each environment were submitted to individual analysis of variance (ANOVA) followed by pooled ANOVA of all the environments, using a seven-fold higher residual mean square for the latter. Next, adaptability, stability and environmental stratification were analyzed.

The methods used for adaptability and stability were in line with those described by ) and ), modified by ).

According to ), genotypes with a regression coefficient of one (β1 = 1) exhibit general or broad adaptability; β1 > 1 specific adaptability to favorable environments and β1 < 1 specific adaptability to unfavorable environments. An ideal genotype has a yield higher than the overall mean, regression coefficient equal to one (β1 = 1) and predictable behavior (σ²d = 0). Based on this methodology, stability is estimated by deviation from the regression (σ²d) or the coefficient of determination (R ²), which exhibit an inverse correlation, that is, stable genotypes have high R ² values and low σ²d values.

Complex and straightforward cultivar-environment interactions were estimated as described by ) and, finally, Pearson's correlation between environment pairs was determined.

As shown in Table 2, analysis of variance demonstrated a significant effect for cultivars, environments and cultivar-environment interaction, the last being primarily complex (CI) ( Table 3) with weak correlations (R) between environments ( Table 3). This indicates that cultivar behavior differed as a function of the environment, thereby justifying adaptability, stability and environmental stratification analysis.

The coefficient of variation (CV%) was 18.2, indicating adequate precision in the field experiments (). Similar results were reported by ) (CV% 16.86) and ) (CV% 19.51), who studied the adaptability and stability of corn cultivars in South Central Tocantins state and Patos de Minas in Minas Gerais state, respectively.

Analysis of the environmental indices ( Table 4) according to ) demonstrated that environments four, five and six (Palmas, sowing on 05/11/2018 and seven, eight and nine (Paraíso, sowing on 15/01/2019) were classified as favorable for exhibiting a higher mean value than the overall mean for the environments (positive environmental index). All the environments exhibited uniform rainfall distribution throughout the root development period, ensuring better use of fertilizer and improved root development.

By contrast, environments one, two and three (Paraíso, sowing on 05/11/2018) and 10, 11 and 12 (Palmas, sowing on 15/01/2019) obtained a lower mean value than the overall mean and were therefore considered unfavorable (negative environmental index), probably because the large volume of rainfall in the early stage of the crop cycle ( Figure 1) compromised initial plant development, which, combined with the likely leaching of basal and topdressing, had a negative effect on final yield. Additionally, low rainfall was recorded in Paraiso between 20/12/2018 and 10/01/2019, when plants were in the stage at which their yield potential is defined. ) found that unfavorable environments may result from an inadequate climate or limited use of technology and resources.

According to ), water stress can affect internode length, contributing to lowering the sugar storage capacity of stems and resulting in thinner stems, smaller plants and a smaller leaf area, which can compromise development. Additionally, soil flooding can hamper growth or even kill the plant in a matter of days.

In this respect, for each location and sowing time, the N topdressing doses used (50, 100 and 150 kg of N ha⁻¹) did not alter the classification of environments as favorable or unfavorable, which occurred predominantly as a function of the different rainfall indices at each location and sowing time.

Classifying environments is important because cultivars may respond differently to different environments, hampering cultivar selection and recommendation (; ; ; ). As such, estimating the magnitude and nature of cultivar x environment interaction is highly relevant, since these estimates make it possible to assess the real impact of selection and ensure a high degree of reliability in genotype recommendation for a specific location or group of environments (; ).

The adaptability and stability parameters for average yield according to the methods of ) and ) - modified by ), are presented in Table 5.

Based on the ) method modified by Carneiro (1988), cultivars 1 and 5 also exhibited a low Pi Ufav value, indicating adaptable/stable behavior in unfavorable environments.

In studies by ) and ), cultivars 1 and 6 and cultivar 1, respectively, were also classified as adapted to unfavorable environments and nonresponsive to increased N.

As reported by ), the environmental adaptability of cultivars is influenced by factors such as soil, location, growing season, sowing time, management practices and technologies used, and indicates their ability to make the best use of environmental stimuli ().

In general, most of the cultivars adapted to these environments were non-GM, which may be because these crops are designed for environments with fewer resources and less investment in technology, making them potentially more tolerant of adverse conditions.

Non-GM cultivar 4 and GM cultivars 7, 9, 10 and 12 obtained regression coefficients greater than one (β1 > 1), demonstrating adaptation to favorable environments that typically receive higher investments in technology. The favorable environments in the present study were not exposed to adverse weather conditions. Of these cultivars, only the mean values of 7 and 12 were higher than the overall mean.

The ) method modified by ) also revealed low Pi Fav values for cultivars 7 and 10. In favorable environments, most of the cultivars were the result of greater investment in biotechnology (GM), which likely occurred because these environments are not affected by unstable climates, thereby allowing them to express their genetic potential.

The fact that different cultivars were found to exhibit general adaptability can be explained by the different approaches to the data in each methodology, demonstrating the potential of each cultivar.

The environment groups obtained using the ) method are shown in Table 6.

Six groups of similar environments were obtained, with group 1 containing 11 environments and only environment 9 excluded. As such, group 1 consisted of different N doses, sowing times and locations.

The remaining groups all contained environment 9 (Palmas, 15/01/2019) combined with either environment 2 (Group II), 11 (Group III), 12 (Group IV), 5 (Group V) or 10 (Group 6). Of these, environments 10, 11 and 12 are in the same location (Palmas, sowing 15/01/2019), but received different N doses.

Thus, in general, the climate factors (rainfall and temperature) and N doses used (50, 100 and 150 kg ha⁻¹) at different sowing times and locations did not cause significant changes in the environments. In order to optimize human and financial resources in breeding programs, the number of environments can be reduced from two growing seasons to one and two locations to one, with less nitrogen fertilization (50 kg de N ha⁻¹). This finding is corroborated by ), who evaluated the adaptability and stability of corn genotypes, and by ) in research with sunflowers.

1. In general, genetically modified (GM) cultivars were better adapted to favorable environments.

2. Most of the non-GM cultivars were adapted to unfavorable environments.

3. All the environments exhibited similar behavior regardless of location, seeding time and the N dose used, demonstrating that fewer environments can be used in future breeding research.

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Adaptability, stability and environmental stratification of genetically and nongenetically modified corn in the Cerrado

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Crop yield depends on interaction between genetic and environmental factors, making it essential to study adaptability, stability and environmental stratification in order to mitigate the effects of this interaction. Four experiments were conducted to assess competition between corn cultivars in the 2018/19 growing season, two in Paraíso do Tocantins and two in Palmas, with sowing performed on November 5, 2018 and January 15, 2019.

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Em geral, a maioria das cultivares que foram adaptadas a esses ambientes não eram GM, pois essas culturas são projetadas para ambientes com menos recursos e menos investimento em tecnologia, tornando-as potencialmente mais tolerantes a condições adversas.
A analise da variância demonstrou um efeito significativo para interação cultivar-ambiente. As cultivares diferem em função do ambiente, justificando assim a analise adaptabilidade, estabilidade e estratificação ambiental.

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Quatro experimentos serão conduzidos para avaliar a competição entre cultivares na safra 22/23 em Mato Grosso do Sul, dois em Ponta Porã e dois em Maracaju.

Um delineamento de blocos ao acaso será usado para todos os experimentos. Os tratamentos serão arranjados em esquema fatorial 3x4 com três doses de fertilizante nitrogenado em níveis baixos (15 kg ha⁻¹), médios (20 kg ha⁻¹) e altos de N (25 kg ha⁻¹), para atingir rendimento mínimo e máximo da colheita, e 4 cultivares (dois GM e dois não GM).

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As cultivares serão adquiridas em lojas especializadas na área onde os experimentos serão conduzidos e codificados em 1 e 2 (não GM) e 3 e 4 (GM).

Será realizada análise de solo para determinar as características químicas e físicas nas áreas experimentais.