Plant arrangement on soybean performance

Agronomic performance of soybean with indeterminate growth habit in different plant arrangements
Daniel Augusto Silveira1, Beatriz Braga Silveira2, Cássio Egídio Cavenaghi Prete2, Carlos André Bahry3 and Maicon Nardino4
1Syngenta Proteção de Cultivos Ltda, Londrina, PR, Brasil.
2Universidade Estadual de Londrina, Londrina, PR, Brasil.
3Universidade Tecnológica Federal do Paraná, Dois Vizinhos, PR, Brasil.
4Universidade Federal de Viçosa, Viçosa, MG, Brasil.

Abstract
Plant arrangements are a practice that has remained constant for decades, with little research about soybeans, with contrasting rainfall conditions. The purpose of this work was to evaluate the agronomic performance of indeterminate soybean with variation for the plant arrangements, populations and in cultivation of crops. The research was conducted in Arapongas-PR with an experimental design of randomized blocks, organized in a 6x3x2 factorial scheme, with four replications. The variation factors were six populations (100, 200, 300, 400, 500 and 600 thousand plants ha-1), three plant arrangements (0.25 cm, 0.50 cm and 0.25×0.50 cm in paired-rows) and two cultivation of crops. The statistical analysis sought to evaluate the interactions and break down the simple effects and the main effects. Row spacing influenced the variables grain yield, height of the first pod insertion, plant height and leaf area index. Populations with up to 300 thousand plants reached the highest levels of grain yield. The plant arrangement for the paired-rows is superior to traditional spacing, with 4,732 kg ha-1 for the spacing 0.25×0.50 m, 3,817 kg ha-1 for the spacing 0.50 m and 3,628 kg ha-1 for the spacing 0.25 m.

Highlighted Conclusions
1. The agronomic performance of the CA7442 RR1 soybean cultivar, with undetermined habit, is influenced by the spatial arrangement, by the plant population.
2. There is a tendency to reduce grain yield in higher populations under water stress conditions.
3. The arrangement of paired-rows in relation to single-rows revealed grain yield 20% higher for harvests with normal precipitation


Communications in Plant Sciences | 2021 | vol.11 | p.009-021
DOI: 
10.26814/cps2021002 | Article code: cps2021002
Keywords: Glycine max L., Paired-rows, Plant population, Spatial arrangement

Correspondence to: Maicon Nardino <nardino@ufv.br>

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Submission on October 25, 2020 | First Publication on May 06, 2021 | Open Access
Authors declared no conflict of interest
Article licensed under a Creative Commons Attribution-NonCommercial 4.0 International


Agronomic performance of soybean with indeterminate growth habit in different plant arrangements

INTRODUCTION

Soybean is one of the most relevant crops in the world and in Brazil, and its evolution occurred through the expansion of areas requiring numerous technological advances, however, in the last ten years, mean productivity has stabilized around 3 ton ha-1. Conab’s estimates for the 2020/21 harvest indicate a prospect of an increase of 2 million tons, motivated by prices in Brazil, which corresponds to 7.1%, with estimates of a 4.4% increase in productivity and 2.5% in area (Conab 2020). Conab’s estimate is that Brazil will continue to be the second largest soybean producer in the world with 133,673 million tons.

The spatial plant arrangement can directly interfere in the speed of closing of row spacing (Shaw and Weber 1967, Heiffig et al. 2006), in the production of dry mass because depending on the morphological characteristics of the plant, it can produce a greater amount of leaves, increasing the photosynthetic leaf area (Cox and Cherney 2011), in the soy architecture itself (more branched plants) (Cox et al. 2010), in the severity of diseases (microclimate formation) (Lima et al. 2012) and in the component most important production, in other words, in grain yield (Rambo et al. 2003, Rambo 2004, Bruin and Pedersen 2008, Hanna et al. 2008). These impacts are due to the intraspecific competition that directly affects the capture of environmental resources such as water, light, nutrients and CO2.

The use of spacings in paired-rows has been widely studied in the culture of corn, where great responses are observed. Paired or double rows have been adopted in order to increase productivity, through better light interception and, consequently, increasing the photosynthetic activity of the crop (Novacek et al. 2013).

In soybean cultivation, the adoption of double rows can lead to a reduction in plant size without interfering in crop productivity, in this work Duarte et al. (2016) identified that, for the ANTA 82 variety, there was a response to the population increase regardless of the arrangement adopted. The main reason for the change in the plant arrangement, by reducing the distance rows, is to reduce the time for 95% of the incident solar radiation to be intercepted by the crop, and then, the amount of light captured per unit area and time is increased (Board et al. 1992).

In the double row planting system, the planting population is adjusted, aiming at the row spacing, freeing up spaces for light penetration. This arrangement modality can be considered as an agronomic practice that can increase grain yield with low cost of adoption. Higher yields can be obtained under the arrangement with two rows compared to planting in single spacings (Bruns 2011, Güllüllüoglu et al. 2016).

Another factor that interferes with the interception of solar radiation is what we call the leaf area index (LAI) that represents the relationship between the area of foliage and the surface of the soil it occupies and is variable according to plant species, climate, seasons and plant development stage (Heiffig et al. 2006). The increase in the LAI, up to a critical value, also increases the interception of light and, consequently, liquid photosynthesis. Shibles and Weber (1965) also referred to the height of the LAI at the end of fruiting and the beginning of soy granulation, in addition to having observed two peaks in photosynthetic activity: the first at the time of full flowering of soy, to meet fertilization; the second, when granulation starts, when the presence of photoassimilates is necessary for the development of pods and grains inside.

The determination of vegetation indexes can also be carried out by means of spectral combinations of green coverage area. Many vegetation indexes are adopted to determine the relations of leaf area and soil cover areas, the Normalized Difference Vegetation Index (NDVI) can be used for several purposes, from satellite monitoring (Sellers et al. 1994) , even in the evaluation of the spectral behavior of different soybean genotypes in different field conditions (Crusciol et al. 2013).

According to this scenario, the present study aimed to evaluate the agronomic performance of the CA 7442 RR1 soybean cultivar of indeterminate growth habit, in six plant populations, three spatial arrangements and two agricultural crops.

MATERIAL AND METHODS

Experimental material. The tests were conducted in two cultivation of crops, with experiment 1 in the 2013/14 crop and experiment 2 in the 2015/16 crop. Both experiments were conducted at Fazenda Gaúcha, located in the city of Arapongas – PR, with geographic coordinates 23o 29’ 4’’ S and 51o 25’ 40’’ O, altitude of 831 meters above sea level.

Characterization of the place and management of the crop. The farm soil was identified as Latossolo Vermelho Distroférrico (Santos et al. 2006) and according to the analysis carried out it had the following elements and quantities before the implementation of the experiments: pH in CaCl2: 5.23; C (g dm-3): 33.29; P (mg dm-3), Mehlich 1: 18.49; K+ (cmolc dm-3), Mehlich-1:  0.78; Ca+2 (cmolc dm-3), KCl 1N: 5.31; Mg+2 (cmolc dm-3), KCl 1N:  2.22; Al+3 (cmolc dm-3), KCl 1N:  0.00; H++Al+3 (cmolc dm-3), SMP:  3.75; SB (cmolc dm-3): 5.81; V – Base saturation index (%): 55,01; M – Saturation index by Al+3 (%): 0,00; CTC (cmolc dm-3): 10.55.

The predecessor crop for soybeans was black oats (Avena strigosa L.). The fertilization in the sowing of black oats was adopted aiming at the cultivation system for the subsequent culture, with fertilization of 400 kg ha1 of the fertilizer formulated 10-15-15. The sowing of soybeans for both harvests was the system of direct seeding, carried out in October of each year. The seed treatment used was carried out with Avicta 500FS products; Cruiser 250 FS and Maxim Advanced in doses of 70, 200 and 100 mL per 100 kg of seeds.

Climate conditions – characterization. The data of precipitation, maximum and minimum temperature of the first (2013/2014) and second crop (2015/2016) are shown in Figure 1.

cps2021002_fig1

Figure 1. (A) Results of meteorological data for experiment one (1st experiment) in the period from 09/20/2013 to 03/20/2014. Sowing on October 30, 2013 – (B) Results of meteorological data for experiment two (2nd experiment) for the region of Arapongas-PR in the period from 09/20/2015 to 03/20/2016. Sowing on October 23, 2015. Source: IAPAR.

Experiment design and treatment. Initially, a survey of the strange characteristics that were active or that could influence the results of the treatment effects was carried out, which, therefore, must be controlled partially or totally so that the results of the expression of the effects of the treatment design are reliable. The experiments were installed in a complete randomized block design, in a 3×6×2 factorial scheme with four replications. The factors studied were: Factor A – three spatial arrangements of plants: 0.25 m, 50 cm and the combination 0.25 x 0.50 m; Factor B – six soybean plant populations (100, 200, 300, 400, 500 and 600 thousand plants ha-1); Factor C – analysis of the two cultivation of crops, conducted in 2013/2014 and 2015/2016.

Cultivar CA7442 RR1 was used as for the study plant material (Table 1). The variables analyzed were described in the Chart 1. The experimental unit consisted of 16 sowing rows, five meters long. For the first level of factor A (0.25 cm arrangement), it was necessary to carry out two sowing in the same area with GPS equipment from the TRIMBOW model, model US-17 and autopilot.

Table 1. Description of the soybean cultivar used in the cultivation tests of 2013/2014 and 2015/2016. Londrina, 2020.

Cultivar

CA 7442 RR*

Cycle

Precocious

Maturation group

5.7

Growth habit

Indeterminate

Thousand seed mass Sieve 5.5

145 grams

Hight plant

0.66m

Flower

White

Pubescence

Light brown

Hilum

Black

Lodging

Resistant

Source: *Syngenta (2017).

Chart 1. Description of the variables evaluated in the experiments.

Obs

Variable

Unit

Description

1

PH

cm

It was measured by measuring from the base of the plant to the end, performed on 10 plot plants.

2

HIP

cm

Measured by the measure of insertion of the first pod to the upper end of the plant

3

ACM

Notes

Bedded plants were those with a slope greater than 45º in relation to the soil surface. The lodging of soybean plants was determined when the plants were in stage R7. The evaluation was made by means of visual analysis, with scores given to the plots based on a scale of 1 (0% of bedded plants in the plot) to 9 (100% of bedded plants in the plot).

4

LAI

m² m-2

The determination of the leaf area index was carried out through the process of extracting the pixels referring to the green areas of the plants. For this purpose, three plants were used per plot, defoliated and photographed on a clean flat surface with a white background (BRILHADOR, 2013).

5

NDVI

 

Normalized Difference Vegetation Index: the GreenSeeker equipment by Trimble – Hand Crop Sensor was used, in the vegetative stage R5.3 through manual reading in the field.

6

NPP

unit

It was determined by counting the pods present in the entire plant, to measure this variable, one meter of row was sampled, taken at random inside each plot, after the plants reached the R8 stage.

7

NGP

unit

It was carried out based on the ratio of the number of grains per plant in each plot to the number of pods.

8

GY

kg ha-1

It was obtained from mechanized harvesting and weighing of the grains from the useful area of each experimental plot automatically with an experimental harvester from the brand Almaco SPC-40. After harvest, the results were corrected for humidity of 13%.

The estimate of maximum technical efficiency (MET) was obtained by to equation the follow: MET = -[(b1)/(2b2)]). The quadratic regression equation was adjusted. The software used in all analyzes was the Statistical Analysis System (SAS 2013).

Statistical analysis. The experimental data obtained were subjected to homogeneity of variance tests (Levene test), error linearity, model additivity and normality of errors (Shapiro-Wilks test). After meeting the assumptions of the analysis of variation (ANOVA), the data were subjected to analysis of variance considering the three-factor mathematical model adopted in the experimental plan. In the presence of significance for interaction, the procedure adopted was to unfold the simple effects of the factors. In the absence of significance for interaction, the main effects of the factors were separated separately, the qualitative ones being compared by means test and the quantitative factor via linear regression (population) considering the probability of 5% of error.

RESULTS AND DISCUSSION

According to the Figure 1, the meteorological conditions that occurred in Experiment 2 are characteristic of the rain regime called “El Niño”. The pluviometric indexes were the highest recorded for the region of Arapongas-PR. In this sense, it can be said that these conditions contrast with the conditions of Experiment 1, where there was a seasonality of rainfall distribution, mainly during the grain filling period. The temperature conditions in the 2015/2016 harvest were within ideal levels for soybean culture, which contrasts with the temperatures recorded in experiment 1, which revealed temperatures above 30º C during most of the grain filling period.

In general, the 1st experiment showed periods with maximum temperatures above 30º C in most of the plant development and absence of rain during grain filling, which promoted strong impacts on the soy granulation. The 2nd experiment was carried out under the meteorological conditions characteristic of the rain regime called “El Niño”, where the rainfall indexes were the highest ever recorded for the region of Arapongas-PR.

The results of measurement of the plant lodging variable were not analyzed statistically, due to the non-occurrence of bedded plots in both agricultural crops.

Analysis of variance – ANOVA. The results of the analysis of variance (ANOVA) for the variables plant height (PH), height of insertion of the first pod (HIP), leaf area index (LAI), normalized difference vegetation index (NDVI), grain yield (GY), number of pods per plant (NPP) and number of grains per pod (NGP) are described in Table 2. ANOVA revealed significant effects for the triple exp×arrangement×pop interaction for the variable LAI, NDVI, GY, NPP and NGP. It revealed significance for the double interactions exp×pop, exp×arrangement and pop×arrangement in the variable HIP.

Table 2. Summary of analysis of variance (ANOVA) for the characteristics of height of insertion of the first pod (HIP), plant height (PH), leaf area index (LAI), normalized difference vegetation index (NDVI), grains yield (GY), number of pods per plant (NPP) and number of grains per pod (NGP) evaluated in the 2013/2014 and 15/16 harvests.

Variation source

GL

Mean square

 

AIV

ALP

LAI

NDVI

GY

NVP

NGP

Block/Exp

6

7.5ns

36ns

3.26*

19.27*

257639.4**

47.34ns

0.08ns

Exp

1

3803.8ns

38272.4**

21.67*

2.00ns

79882183.4**

724.9*

9.3**

Population

5

196.6**

537**

33.39**

125.19**

1069870**

15387.4**

0.36*

Arrangement(A)

2

31.4**

91.4*

3.41*

9.63 ns

5635461.1**

233.8ns

0.03ns

Exp*Pop

5

58.7**

30.6ns

5.72*

16.59*

1387982.9**

2830.0**

1.26**

Exp*A

2

14.8**

3.4ns

1.58ns

42.84*

4948015.9**

454.7*

0.01ns

Pop*A

10

19.6**

24.1ns

2.00ns

2.10 ns

735115.2**

137.1ns

0.05ns

Exp*Pop*A

10

13.5ns

18.0ns

2.77*

13.39*

815481.6**

278.91*

0.32*

Error

102

7.4

20.4

1.26

6.31

314664.5

80.5

0.09

Mean

 

14.2

77.8

4.15

82.70

3348.2

46.73

2.07

CV%

 

19.1

5.8

27.09

3.04

16.8

19.19

15.05

                   

*Significant for the F test at 5% probability. ns not significant, * and ** significant at the level of 5% and 1%, respectively, by the F test.

The variable HIP showed significance of pop×arrangement, indicating that depending on the arrangement of plants a specific population that may have a better performance. Barni et al. (1995), Gaudêncio et al. (1990) and Peixoto et al. (2000) reported that soy tolerates a wide variation in the plant population, where greater changes are observed in morphology than in grain yield. The results showed that the 2013/2014 crop had a higher HIP compared to the 2015/2016 crop for all populations studied (Table 3).

Table 3. Split between the interaction between the population × 2013/14 harvest and 2015/16 harvest, population × arrangement and arrangement × cultivation of crop for the variable height of insertion of the first pod (HIP).

Population#

Harvest 2013/14

Harvest 2015/16

100000

13.1 A

7.08 B

200000

15.29 A

7.67 B

300000

17.21 A

9.08 B

400000

22.59 A

9.92 B

500000

23.97 A

9.67 B

600000

23.03 A

10.92 B

Population

Row arrangement

0.25

0.50

0.25×0.50

100000*

9.6ª

11.02ª

10.87ª

200000

11.35ª

11.65ª

11.43ª

300000

12.00A

13.16ª

14.27ª

400000

15.11B

18.11ª

15.53AB

500000

17.71ª

16.80ª

15.96ª

600000

13.82C

16.68B

20.38ª

Arrangements

Harvest 2013/2014

Harvest 2015/2016

0.25**

17.78Ba

8.75aB

0.50

19.89aA

9.25aB

0.25 × 0.50

20.32aA

9.16aB

             

 # Means followed by the same letters on the line belong to the same group of means  by the Tukey test, at a level of 5% probability. * Means followed by the same letters on the line do not differ from the plant arrangements by the Tukey test, at the level of 5% probability. ** Means followed by uppercase letters in the row and lowercase letters in the column do not differ from cultivation of crops and plant arrangements by the Tukey test at a 5% probability level.

The results of the comparison of means of interaction between population × arrangement for variable HIP revealed that the populations of 100 to 300 thousand plants ha-1  are statistically equal. For a population of 400 thousand plants-1 the largest HIP was in the 0.50 arrangement, not statistically differing from 0.25 × 0.50, the smallest HIP in this population was in the 0.25 arrangement. Considering the population of 600 thousand plants-1 the highest HIP was in the 0.25×0.50 arrangement, with the lowest HIP in the 0.0.25 m arrangement (Table 3).

In the analysis of the HIP response as a function of the different populations for each plant arrangement in the first growing season, there was a tendency to increase the HIP for the 0.25 and 0.50 arrangements up to the population of 500 thousand plants ha-1, after there was a tendency to reduce HIP. In contrast, for the 0.25 × 0.50 arrangement, there is a tendency to increase the HIP from 500 to 600 thousand plants ha-1. Regarding the 2nd cultivation season, there was a linear trend of increasing HIP with an increase in the plant population, however the means were considerably lower than the 1st cultivation season (Figure 2).

cps2021002_fig2

Figure 2. Effect of the interaction of plant population factors × between rows in the variable height of the first pod (HIP) in the 2013/2014 harvest: y (Exp1A1) = -0.67x3 + 6.43x2 – 15.10x + 22.72 R² = 0.88, y (Exp1A2) = -0.43x3 + 4.05x2 – 8.22x + 19.00 R² = 0.90, y (Exp1A3) = 0.08x3 – 0.47x2 + 2.63x + 12.09 R² = 0.96 and for the 2015/16 harvest: y (Exp2A1) = 0.01x3 – 0.18x2 + 1.62x + 5.08 R² = 0.99, y (Exp2A2) = -0, 07x3 + 0.7619x2 – 1.41x + 8.25 R² = 0.81, y (Exp2A3) = 0.11x3 – 1.28x2 + 4.88x + 3.08 R² = 0.90. Regression equations for plant population interaction × row arrangement of the plant height variable (PH) in the 2013/2014 harvest: y (Exp1A1) = -0.31x3 + 2.52x2 – 2.45x + 86.56 R² = 0, 68, y (Exp1A2) = -0.07x3 – 0.15x2 + 6.71x + 77.28 R² = 1, y (Exp1A3) = -0.29x3 + 2.85x2 – 5.44x + 93.73 R² = 0.76 and for 2015/16 harvest: y (Exp2A1) = -0.22x3 + 1.98x2 – 2.25x + 54.75 R² = 0.93, y (Exp2A2) = 0.21x3 – 2.47x2 + 11.52x + 42.25 R² = 0.98, y (Exp2A3) = 0.46x3 – 5.17x2 + 18.68x + 41.33 R² = 0.94.

The lowest HIP values were observed in the 2015/16 crop in the spacing where the plants were distributed equidistant, provided by the smallest populations with means values of the order of 7.5 cm. The results obtained in the 2013/14 harvest, the lowest values were in the order of 14 cm. The effect of the lower insertion heights of the first pod brings complications in the cultural treatments, as well as hinders the mechanical harvesting, which can cause significant losses in the field and even the inability to adopt the cultivar.

Some cultivars express these characteristics in a very peculiar way, for example, Gewehr et al. (2014) studied the behavior of five soybean varieties, BMX Turbo RR, BMX Força RR, BMX Potencia RR, BRS 246 RR and Fundacep 59 RR, and observed that, according to the population increase, the HIP also obtained the highest values, being that the Fundacep 59 variety had the highest HIP values. Studies in different rows spacing corroborated these results, and the increase in the population of plants in cross-sowing or in conventional sowing generally influences the plants height, thus reflecting in the HIP, with some varieties stabilizing their growth in a given population. This increase in plant height is directly linked to the shading that plants undergo in high populations, in which there is a competition for adjacent plants for light (Lima et al. 2012).

This characteristic could be inherent to the cultivar, where in some cases, plants with a high arrangement and populations can exhibit the behavior of raising the height of the first pod (Urben Filho and Souza 1993). In studies by Gewehr et al. (2014) it was found that, regardless of the type of growth (determined or undetermined), the plants showed interaction between the population and the rows spacing for the HIP, with higher means in larger populations. The higher HIP values ​​can be considered an important characteristic, because the higher the HIP, the smaller the loss of grains in the harvest, since the higher HIP facilitates the entry of the harvester cutter bar from the harvesters, thus avoiding the loss in the harvest. According to Heiffig et al. (2006), the height of insertion of the first pod must be from 0.10 to 0.15 m to obtain a harvest with a minimum of losses by the cutter bar. Thus, the means observed in the two sowing systems meet the ideal heights reported by the author. Chioderoli et al. (2012) and Pereira Júnior et al. (2010) observed soybean AIV of 0.14 and 0.15 m, respectively, and stated that these values ​​are within the normal standards for cutting height in mechanized harvesting, in which they obtained lower losses during the harvesting process.

In the analysis of the plant height (PH) of the plant arrangement, there were significant differences, the plant arrangement 0.25×0.50 was higher and it differed statistically from the other plant arrangements for the character PH. When comparing the means of the cultivation of crops, it can be seen that the 2013/2014 harvest was higher than the 2015/2016 harvest for PH, a result similar to the HIP, it is worth noting that the differences between the means are greater than 33 cm in height (Table 4).

Table 4. Comparison of means of the variable plant height (PH) between the different row arrangements and cultivation of crops.

Row arrangements

PH

Harvest

PH

 
 

0.25 #

76.98 b

 

2013/2014##

94.07 a

 

0.50

76.97 b

 

2015/2016

61.47 b

 

0.25 × 0.50

79.36 a

       

# Means followed by the same letters in the column do not differ the plant height in the different plant arrangements and or ## the cultivation of crops.

Regarding the PH response (Figure 2) due to the different populations studied, initially there was a tendency to increase PH, up to the population of 500 thousand plants ha-1, after a slight tendency to reduce plant height for the 1st harvest of cultivation. In the 2nd crop season, the PH were considerably lower, the 0.25 arrangement showed the same trend as the 1st crop, whereas the other arrangements showed a slight response to increase the PH in populations above 500 thousand plants ha-1.

However, for both experiments, the plant height was higher in larger populations. The results corroborate with Mattioni et al. (2008), in which the authors worked with organic soybean cultivation with different populations and also observed that the plants height (Gewehr et al. 2014) studying the influence of the plant population on plant height, there was no difference between the means of PH for the different populations studied, but the variable height of insertion of the first pod shows an increasing trend as the population increases. In this study, the values observed for the PH variable did not respond to the variable in relation to the different rows spacings, nor in relation to the interactions rows spacing and different plant populations. However, in the tests performed in different spacing when used 0.20 m, obtained the highest plant height in the evaluation of 45 days after emergence.

Interaction in an experiment × arrangement × plant population. The significance for the triple interaction between experiment×arrangement×plant population indicates that the dependence on arrangement, population and crop yield. Barni et al. (1995), Gaudêncio et al. (1990) and Peixoto et al. (2000) reported that soybean management, as an arrangement and management, directly influence yield components, such as number of pods per plant, number of grains per pod, grain mass and grain yield.

The breakdown of the LAI variable is shown in Table 5. Regarding the comparison of the arrangements within each population and year, there were no differences for the 1st year between the arrangements in the populations of 100, 300 and 600 thousand plants ha-1. The 0.25 plant arrangement has the highest LAI, considering the populations of 400 and 500 thousand plants ha-1.

Table 5. Decomposition of the triple interaction of the experiment factor to the variable leaf area index (LAI).

Population

           Arrangement

LAI

Exp 1

Exp 2

100000

0.25 m

2.07aA

 

2.32aA

 

0.5 m

1.85aA

 

2.10aA

 

0.25×0.50 m

1.90aA

 

2.48aA

 

200000

0.25 m

2.80abA

 

3.87aA

 

0.5 m

4.02aA

 

4.50aA

 

0.25×0.50 m

2.27bB

 

3.97aA

 

300000

0.25 m

4.57aA

 

4.87aA

 

0.5 m

3.87aA

 

4.70aA

 

0.25×0.50 m

4.62aA

 

4.45aA

 

400000

0.25 m

5.80aA

 

5.65aA

 

0.5 m

3.75bA

 

4.07bA

 

0.25×0.50 m

4.47abA

 

3.60bA

 

500000

0.25 m

5.95aA

 

4.47bB

 

0.5 m

3.25bB

 

6.00aA

 

0.25×0.50 m

3.95bA

 

5.12abA

 

600000

0.25 m

4.60aB

 

7.07abA

 

0.5 m

4.57aA

 

5.72bA

 

0.25×0.50 m

3.87aB

 

8.0aA

 

* Means followed by uppercase letters in the row and lowercase letters in the column do not differ statistically at 5% probability of error.

For the 2nd experiment, the differences occurred only between the arrangements for populations above 300 thousand plants ha-1. The 0.25 arrangement is superior in the population of 400 thousand plants ha-1 in 500 thousand plants ha-1 the arrangement 0.5 is superior and in the population of 600 thousand plants ha-1 the arrangement of paired-rows 0, 25×0.5 has a higher LAI.

When comparing the 1st and 2nd harvests in the 0.25×0.50 arrangement and the population of 200 thousand plants ha-1 the 2nd year revealed a higher LAI, in the same sense for 500 thousand plants ha-1 for 0.5 m arrangement and 600 thousand plants ha-1 for 0.25 and 0.25×0.5 arrangements. The 1st crop showed the highest LAI in the population of 500 thousand plants ha-1 in the 0.25 m arrangement.

Regarding the response of the variable leaf area index, in the first harvest (Figure 3) the populations of 400 and 500 thousand plants showed a maximum in the 0.25 and 0.25×0.50 arrangements, after showing a tendency to reduce the LAI, and in the 0.50 arrangement showed a tendency to increase the LAI from the population of 500 thousand plants. The results of the 2015/16 crop show a response contrary to the 1st crop, where the 0.25 and 0.25×0.50 arrangements tended to increase the LAI from 500 thousand plants, whereas the 0.50 arrangement showed a tendency to reduce the LAI after this same population.

cps2021002_fig3

Figure 3. Effect of the interaction of plant arrangements, populations and 2013/2014 crop (E1) E1A1: y = -0.30x2 + 2.74x-0.69 R² = 0.91, E1A2: y = 0.16x3-1.85x2 + 6.20x-2.4 R² = 0.97, E1A3: y = -0.25x2 + 2.1x-0.17 R² = 0.84 and 2015/2016 (E2) E2A1: y = 0.15x3 – 1.66x2 + 6.01x – 2.33 R² = 0.89, E2A2: y = -0.12 x4 + 1.82x3-9.31x2 + 19.51x-9.68 R² = 0.95, E2A3: y = 0.21x3-2.06x2 + 6.33x – 2.06 R² = 0.98 for the variable leaf area index (LAI).

Regarding the dismemberment of the triple interaction for variable NDVI, there were significant differences for the 1st and 2nd harvest in the population of 100 thousand plants, where the largest NDVI was for the 0.25 m arrangement, compared with 0.5 in the 1st harvest. In contrast, in the 2nd harvest, this same arrangement had a lower NDVI. In the 2nd harvest, there were significant differences for the population of 400 thousand plants with superiority for the 0.25 arrangements and paired-rows in relation to 0.50 meters (Table 6).

Table 6. Decomposition of the triple interaction and comparison of means for the NDVI variable.

Population

          Arrangement

NDVI

 

 

Exp 1

Exp 2

100000

0.25 m

80.75aA

 

76.50bB

 

0.5 m

73.50bB

 

81.75aA

 

0.25×0.50 m

76.75abB

 

81.00aA

 

200000

0.25 m

84.25aA

 

81.00aA

 

0.5 m

81.25aA

 

83.25aA

 

0.25×0.50 m

81.00aA

 

82.25aA

 

300000

0.25 m

84.75aA

 

83.25aA

 

0.5 m

83.25aA

 

82.75aA

 

0.25×0.50 m

83.25aA

 

83.75aA

 

400000

0.25 m

84.75aA

 

83.00abA

 

0.5 m

84.00aA

 

79.75bB

 

0.25×0.50 m

84.50aA

 

83.50aA

 

500000

0.25 m

85.25aA

 

83.50aA

 

0.5 m

84.25aA

 

84.00aA

 

0.25×0.50 m

84.50aA

 

84.25aA

 

600000

0.25 m

86.00aA

 

84.00aA

 

0.5 m

84.25aA

 

84.50aA

 

0.25×0.50 m

84.25aA

 

84.50aA

 

* Means followed by uppercase letters in the row and lowercase letters in the column do not differ statistically at 5% probability of error.

When comparing the NDVI variable in the different harvests, the 1st crop had the highest NDVI in the population of 100 thousand for arrangement 0.25, whereas for the other arrangements, the 2nd crop had the highest NDVI. Another significance revealed was for the population of 400 thousand plants, where in the 0.50 arrangement, the 1st crop was greater than the 2nd crop.

For the regression equations in the 1st harvest, NDVI had an increasing behavior up to the populations of 500 thousand plants in the three studied plant arrangements. In the analysis of the 2nd harvest, the 0.50 arrangements and paired-rows revealed a positive linear behavior, as the plant population increases, reaching a maximum in the population of 500 thousand plants. In contrast, in the 0.25 arrangement, there was a quadratic response, initially increasing, tending to reduce the population of 600 thousand plants (Figure 4).

cps2021002_fig4

Figure 4. Effect of the interaction of plant, population and 2013/2014 crop arrangements (E1) E1A1: y = 0.83x + 81.36 R² = 0.72, E1A2: y = -0.817×2 + 7.53x + 67.77 R² = 0.93, E1A3: y = -0.58×2 + 5.46x + 72.1 R² = 0.99 and 2015/2016 (E2) E2A1: y = -0.48×2 + 4.65x + 72.9 R² = 0.93, E2A2: y = 81.36, E2A3: y = 0.66x + 80.88 R² = 0.86 for the NDVI variable.

As a way of demonstrating the relationship between the LAI and the NDVI, it is shown in Figure 5 that the higher the LAI, the greater the NDVI tends to be for both cultivation of crops. This demonstrates the linear and positive relationship between LAI and NDVI.

cps2021002_fig5

Figure 5. Relationship between the LAI x NDVI for the 1st crop of E1 crop: y = 2.01x + 75.10 R² = 0.54 and the 2nd cultivation of crop (E2): y = 0.91x + 78.37 R² = 0.48.

The results of the Split of the means of the triple population x arrangement x cultivation of crop for the variables grain yield (GY), number of pods per plant (NPP) and number of grains per pod (NGP) are shown in Table 7.

Table 7. Decomposition of the triple interaction of the Nested experiment with the double interaction Population x Arrangement for the GY grain yield variables; mass of a thousand grains MTG; number of grains per NGP plant; number of pods per NPP plant.

Population

Arrangement

GY

 

NVP

 

NGV

Exp 1

Exp 2

 

Exp 1

Exp 2

 

Exp 1

Exp 2

100000

0.25 m

2501 aB

3858 bA

 

140 aB

62 bA

 

1.65 bB

2.75 aA

0.5 m

2337 a B

3663 bA

 

121 bB

77 aA

 

2.12 aB

2.37 abA

0.25×0.50 m

2558 aB

5060 aA

 

94 cB

73 abA

 

2.45 aB

2.05 bA

200000

0.25 m

2526 aB

3534 bA

 

61 a B

55 aA

 

2.25 aB

1.82 aA

0.5 m

2487 aB

3244 bA

 

57 aB

48 aA

 

2.15 aB

1.92 aA

0.25×0.50 m

2456 aB

4483 aA

 

56 aB

56 aA

 

2.30 aB

2.07 aA

300000

0.25 m

2633 aB

3684 bA

 

36 a B

41 aA

 

2.52 aB

1.77 aA

0.5 m

2623 aB

4167 bA

 

36 aB

44 aA

 

2.60 aB

1.75 aA

0.25×0.50 m

2700 aB

5604 aA

 

35 aB

37 aA

 

2.47 aB

2.05 aA

400000

0.25 m

2916 aB

4452 abA

 

38 aB

38 a A

 

2.32 aB

1.50 aA

0.5 m

2364 aB

3639 bA

 

28 aB

37 aA

 

2.50 aB

1.57 aA

0.25×0.50 m

2700 aB

5316 aA

 

27 aB

37 aA

 

2.35 aB

1.57 aA

500000

0.25 m

2838 aB

3357 bA

 

28 aB

33 a A

 

2.57 abB

1.42 aA

0.5 m

2403 aB

4310 aA

 

27 aB

31 aA

 

2.17 bB

1.72 aA

0.25×0.50 m

2718 aB

4059 abA

 

25 aB

36 aA

 

2.62 aB

1.60 aA

600000

0.25 m

2871 aB

2879 b A

 

22 aB

33 aA

 

2.50 aB

1.60 a A

0.5 m

2449 aB

3881 aA

 

24 aB

31 aA

 

2.22 abB

1.50 aA

0.25×0.50 m

2780 aB

3869 aA

 

26 aB

32 aA

 

2.07 bB

1.62­ aA

* Means followed by uppercase letters in the line and lowercase letters in the column do not differ statistically at 5% probability of error.

With regard to the deployment of the GY triple interaction, there is a superiority of the means of the second crop, compared to the means of the first crop, for all studied populations and plant arrangements. The results obtained for this source of variation are possibly related to the precipitation data, which are shown in Figure 1, where low levels of precipitation are recorded in the first crop, and higher and higher levels in the second crop. Precipitation among all meteorological elements is crucial for obtaining greater grain yield, as water is directly associated with the growth and development phases of the plant, as well as with the development of reproductive structures and with the filling of grains. So, the stress due to water deficiency in any of these stages is likely to compromise the grain yield components and grain yield, specifically.

In the analysis of the comparison of means of experiment 1, for the same population, we noticed the absence of significant effects for comparing arrangements in the same population in all experiment 1 (Table 7). It is noteworthy that the mean productivity of all sources of variation was below 3 tons, that is to say close to the national mean, a characteristic that is not commonly observed in tests conducted in the region, in which the responses to grain yield are considerably higher.

The presence of water stress in the 2013/2014 harvest (Figure 1) reveals that the effects were considerably pronounceable, which neutralized the expression of significance of the other sources of variation present in the experiment (population and plant arrangement). Biologically, stress is considered a significant deviation from the ideal conditions in which plants are grown, preventing them from fully expressing their genetic potential for growth, development and reproduction, the changes and responses induced, at all functional levels of the organism, at first, they are reversible (elastic deformation), but can become permanent (plastic deformation). Abiotic stresses can trigger a series of responses in plants from changes in gene expression and cellular metabolism. The duration, severity and frequency, with which a stress is imposed, as well as the affected organs and tissues, stage of development and the genotype, also influence the response of plants to stress. Consequently, a different combination of conditions can cause different responses of plants to the same type of stress. The definition of drought is given as a multidimensional phenomenon, including not only water deficiency in the soil, but also that of the atmosphere, which, in turn, is fundamentally determined by relative humidity and air temperature.

The occurrence of abiotic stress, as mentioned above, compromises all the physiological mechanisms of growth and development of any crop, as plants under water stress conditions demand a greater amount of water to cool internal mechanisms, due to the greater stomatal opening, which can be highlighted here that water stress is generally associated with high temperatures, as well as increasing losses due to the reduction of transpiration, since species with C3, mechanisms, such as soybeans, are less efficient in the use of carbon molecules captured during photosynthesis.

Plants tend to adapt to water deficit conditions, but this is a very complex mechanism, involving morphological, physiological, biochemical and molecular changes. The leaf area and stomatal conductance (degree of stoma opening) are the main factors that determine transpiratory rates and, therefore, their decrease allows the water potential to increase or remain within limits that allow the maintenance of plant development. These factors are also the main determinants of carbon accumulated by plants. A reduced leaf area can lead to less interception of light radiation, thus contributing to the reduction of photosynthetic rates. The reduction in stomatal conductance leads to less CO2 inflow into the chloroplasts, causing reductions in photosynthetic rates and contributing to less biomass accumulation by the plant. Thus, stomatal conductance is the main aggravating factor of the photosynthetic process in conditions of water deficiency, another aggravating factor is the reduction of perspiration, which provides an increase in leaf temperature (with possible reflexes in the increase of maintenance respiration and photorespiration) by reducing stomatal conductance. Mechanisms that can overcome these problems are the development of cultivars with CO2, concentrating mechanisms, for example that found in C4. Another strategy would be to increase the specificity of the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (rubisco) to CO2, thus reducing losses related to photorespiration in C3 plants. The increase in mesophilic conductance is directly associated with increases in photosynthetic rates, without the need for greater stomatal conductance. Another strategy involves increasing the specific leaf mass, since this increase represents a greater amount of photosynthetic machinery per unit leaf area (Taiz et al. 2017).

Regarding the second crop, significant differences are observed between the different plant arrangements for the same population. For the population of 100 thousand plants, the 0.25×0.50 m arrangement has a mean GY higher than the 0.25 and 0.50 m arrangements, it is important to note that such superiority is above 1,200 kg ha-1. In all studied populations, the 0.25×0.50 arrangement revealed means equal to or higher than the other arrangements in all studied plant populations.

In the analysis of the grain yield response by the regression analysis (Figure 6) considering the opening of the interaction of experiment 2 for the 0.25 and 0.25×0.50 arrangements, it demonstrated a high superiority of the 0.25×0.50 in the arrangement means in the populations of 300 and 400 thousand plants ha-1. The greater magnitude of means of the 0.25 arrangement is revealed in the population of 400 thousand plants ha-1. The estimate of maximum technical efficiency obtained was 271 mil plants ha-1 and 285 mil plants ha-1 for the variable grain yield (Figure 6).

cps2021002_fig6

Figure 6. Decomposition of the triple interaction of the nested experiment factor with the double interaction Population x Arrangement for the grain yield variables. y (Exp2A3) = -135.27x2 + 732.08x + 4221.4 R² = 0.60, y (Exp2A1) = -102.63x2 + 585.28x + 3135.6 R² = 0.51 and for the variable number of pods per plant. y (Exp1A1) = -3.39x3 + 43.17x2 – 180x + 278.95 R² = 0.99, y (Exp1A2) = -2.25x3 + 30.54x2 – 136.26x + 228.17 R² = 0.99, y (Exp1A3) = -0.82x3 + 13.52x2 – 72.53x + 153.5 R² = 1, y (Exp2A1) = 0.19x3 – 0.76x2 – 8.67x + 71.43 R² = 0.97, y (Exp2A2) = -0.76x3 + 10.36x2 – 49.04x + 115.03 R² = 0.97, y (Exp2A3) = -0.62x3 + 8.95x2 – 44.23x + 109.95 R² = 0.97 and for the variable number of grains per pod (NGV) y (Exp1A1) = – 0.0086x + 3.34 R² = 0.00, y (Exp1A2) = -0.2421x + 4.26 R² = 0.82 , y (Exp1A3) = 0.68x + 0.18 R² = 0.97, y (Exp2A1) = -0.044 + 1.74 R² = 0.10, y (Exp2A2) = 0.021x + 1.53 R² = 0.12, y (Exp2A3) = 0.037x + 1.53 R² = 0.16.

Peixoto et al. (2000) reported that the greatest response in grain yield is verified for the variation in the rows spacing of the plant, with a tendency for higher yields in the smallest spacing. The lower response of soy to the population is due to its ability to compensate for the use of space between plants. Pires et al. (2000), worked with the estimate of the soybean yield potential, considering two spacing of 20 and 40 cm and two plant populations 30 and 40 plants m-2, concluded by the grain yield estimates that regardless of the number of plants the 20 cm spacing provides greater yield potential, especially at the beginning of grain filling, which leads to high yield if soil and weather conditions are favorable.

Regarding the variable number of pods per plant (NPP), experiment 1 had a higher NPP than experiment 2 (Table 7). Such differences were observed in the population of 100 thousand plants ha-1, the other treatments did not reveal significant effects. In the aforementioned population, for the first crop the arrangement with the highest NPP was 0.25, in the second growing season it was 0.50, but not different from the 0.25×0.50 arrangement. Ludwig et al. (2010) also evaluated the effect of the combination of different sowing densities and the types of habit, undetermined and determined growth on the reduction of the number of pods per plant and concluded that the largest populations had the lowest amount of pods per plant, data that corroborate with this job. The data in the present study are also in agreement with Peixoto et al. (2000), who describes that one of the components of the plant that contributes to the greater tolerance to variation in the population is the number of pods per plant that varies inversely with the increase or decrease in the plant population.

In the regression analysis for the NPP variable (Figure 6), populations between 300 and 600 thousand plants per hectare point to differences at very small levels. However, analyzing the results of the 0.50 arrangement for the two cultivation of crops in a more judicious way, we can thoroughly identify that with an increase in plant density in the row (population increase), which is greater in this arrangement, there is a sharper decline in the number of pods per plant.

These responses, according to Pires et al. (2000) indicate that, for spacing, what controls the production or retention of vegetables is the competition between plants, since the increase in competition, by the density of plants in the row, decreases the number of vegetables. According to the author, this may reinforce the hypothesis of nutritional limitation exemplified by Heitholt et al. (1986). The author argues that in the initial stages of formation of the reproductive structures, R2 e R5, the reduction of the population did not allow to compensate for the number of vegetables m-2. After the start of filling, there is an increase in competition between plants for factors directly related to grain yield, reducing the individual productive capacity of plants under larger populations, tending to approach yields between populations.

The variable number of grains per pod is characterized as one of the most stable production components, among the others, however this study revealed through the data obtained the triple interaction for this variable. The decomposition of the triple interaction revealed that this variable suffered interaction only for experiment 1 and the arrangements with single-rows of 0.50 m and the paired 0.25×0.50 m. The NGP variable also showed differences between experiments 1 and 2.

Experiment 1 showed a significant positive response for the different row spacing arrangements and for the interactions between the arrangements and the plant populations analyzed. The arrangement with double rows showed a lower number of grains per pod than the other spacing, however, experiment 2 was not able to demonstrate these responses for this variable. Procópio et al (2013) using the cross-planting system in soybean cultivar in a cultivar of indeterminate growth habit observed that this planting system showed a reduction in the number of grains per pod in the studied populations, but did not affect the final grain yield.

In the regression analysis for the variable NGP, the 0.25 m and paired-rows 0.25 x 0.50 m arrangements of experiment 1 increased the NGP, as the plant population increased (Figure 6). The other interactions reduced NGP with an increase in plant population. However, the 0.25 and 0.50 m arrangements in experiment 2 revealed lower NGP, compared to the other interactions between experiment x arrangement, for the respective growing season. Tourino et al. (2002) in their study with two spacing (0.45 and 0.60) and five sowing densities (10, 13, 16, 19, and 22 plants m-2) did not obtain differences for the number of grains per pod.

The results of the 2013/2014 harvest were significantly lower for grain yield and the main yield components of the 2015/2016 crop, however these results allow for a contrast of scenarios (water deficiency and excessive precipitation) for the analysis of plant arrangements and also for the populations used in the present work. It is interesting to note that the field results of a third crop, possibly will be directed towards the greater agronomic performance of arrangements with smaller spacing, as well as with the use of larger plant populations.

In conclusion, the performance of cultivar CA7442 RR1 revealed that the grain yield, number of pods per plant and number of grains per pod is dependent on the arrangement, plant population and agricultural crop. Under water stress conditions (2013/2014 harvest), there is no difference between the arrangements, but there is a tendency to reduce grain yield in larger populations. The grain yield obtained by cultivar CA7442 RR1, due to the interaction between year and the plants arrangement, showed superiority for the paired-rows in relation to the spacing with single-rows with 4,732 kg ha-1 for the spacing 0.25×0.50 m, 3,817 kg ha-1 for the 0.50 m and 3,628 kg ha-1 for the 0.25 m, in the second year of cultivation, indicating a new proposal for rows spacing for the current models.

Acknowledgments

The authors would like to thank CNPq, CAPES, for financial assistance and scholarships.

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