Energia na Agricultura, Botucatu, v. 36, n. 3, p. 335-347, julho-setembro, 2021

335

ISSN 2359-6562 (ONLINE) 2359-6562 (CD-ROM)

VARIABILITY OF SOYBEAN AND CORN YIELD AND SOIL TEXTURE IN THE

GENERATION OF MANAGEMENT ZONES

DÁRIO ALEXANDRE SCHWAMBACH¹, JORGE WILSON CORTEZ2, DIANDRA PINTO

DELLA FLORA3, HERMANO JOSÉ RIBEIRO HENRIQUES4 E LUCAS DE OLIVEIRA DONAIRE5

1 Eng. Agr., Mestre, Egresso do Mestrado em Engenharia Agrícola da Faculdade de Ciências Agrárias, Universidade Federal da Grande Dourados, Rodovia Dourados-Itahum, km 12, Cidade Universitária, 79804-970, Dourados, MS, Brasil, e-mail: dario_schwambach@yahoo.com .

2 Eng. Agr., Prof. Dr. da Faculdade de Ciências Agrárias, Universidade Federal da Grande Dourados, Rodovia Dourados-Itahum, km 12, Cidade Universitária, 79804-970, Dourados, MS, Brasil, e-mail: jorgecortez@ufgd.edu.br .

3 Eng. Agr., Doutorando em Agronomia na Faculdade de Ciências Agrárias, Universidade Federal da Grande Dourados, Rodovia Dourados-Itahum, km 12, Cidade Universitária, 79804-970, Dourados, MS, Brasil, e-mail: diandradellaflora@gmail.com .

4 Eng. Agr., Doutor, Egresso do Doutorado em Agronomia da Faculdade de Ciências Agrárias, Universidade Federal da Grande Dourados, Rodovia Dourados-Itahum, km 12, Cidade Universitária, 79804-970, Dourados, MS, Brasil, e-mail: hermano.henriques.hh@gmail.com .

5

Eng. Agícola, Mestre, Egresso do Mestrado em Engenharia Agrícola da Faculdade de Ciências Agrárias, Universidade Federal da Grande Dourados, Rodovia Dourados-Itahum, km 12, Cidade Universitária, 79804-970, Dourados, MS, Brasil, e-mail: lucas.donaire@outlook.com .

ABSTRACT: The spatial representation of soil texture and crop yield allows the formulation of solid and efficient management strategies. Thus, this study aimed to evaluate the spatial variability of soybean/corn yield and soil texture in the generation of management zones. The study was carried out in an area of 130 ha with summer soybean and off-season corn succession. A sample grid was generated with 64 points in a regular grid and 13 random points, totaling 77 points, one for every 1.7 ha. Soil sample collection was performed manually, and soybean (2017/2018 and 2018/2019) and corn (2018 and 2019) yield data were collected from the harvester’s yield monitor.

Texture and yield data were subjected to descriptive statistics and spatialization. Soybean and corn yield variabilities were influenced by soil texture and climate. Corn and soybean yields presented higher values in regions with more clay under water deficit conditions, whereas soybean yield was higher in regions with more sand under normal conditions. Management zones can be made from this isolated attribute in areas with differing clay content.

Keywords: precision agriculture, harvest maps, particle size.

VARIABILIDADE DA PRODUTIVIDADE DE SOJA E MILHO E DA TEXTURA DO

SOLO NA GERAÇÃO DE ZONA DE MANEJO

RESUMO: A representação espacial da textura do solo e produtividade das culturas permitem formular estratégias de manejo sólidas e eficientes. Assim, objetivou-se avaliar a variabilidade espacial da produtividade de soja/milho e a textura do solo na geração de zonas de manejo. O

trabalho foi realizado em área de 130 ha com sucessão soja verão e milho safrinha. Foi gerado grade amostral, com 64 pontos em malha regular e mais 13 pontos aleatórios, totalizando 77 pontos, um a cada 1,7 ha. A coleta das amostras de solo foi realizada de forma manual e os dados de produtividade de soja (2017/2018 e 2018/2019) e milho (2018 e 2019) foram coletados do monitor de produtividade da colhedora. Os dados de textura e produtividade foram submetidos à estatística descritiva e a espacialização. A variabilidade das produtividades de soja e de milho foi influenciada pela textura do solo e pelo clima. Em condições de déficit hídrico, as produtividades de milho e soja apresentaram maiores valores nas regiões com mais argila, já quando condições normais a produtividade de soja foi maior nas regiões com mais areia. Em áreas com teor de argila discrepantes podem ser feitas zonas de manejo a partir desse atributo isolado.

Recebido em 18/08/2021 e aprovado para publicação em 19/09/2021

DOI: http://dx.doi.org/10.17224/EnergAgric.2021v36n3p335-347

336

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) Palavras-chaves: agricultura de precisão, mapas de colheita, granulometria.

1 INTRODUCTION

the variability of these characteristics and crop yield.

With increase in the world population,

Management Zones (MZ) are included

there is also a great demand for food. The in the larger context of PA. For Molin, Amaral current agriculture to seeks to innovate in e Colaço et al. (2015) as management zones or

machines and technologies that allow using differentiated management unit (UGDs) can be

resources rationally. What promotes increase defined as regions that contain minimal spatial in food production, in a sustainable way, wtih variability and temporal consistency and that the use of agricultural inputs at varying rates.

describe the response potential of the area.

The concept of Precision Agriculture

According to Kuiawski et al. (2017), is an (PA) is associated with the use of new

economically viable option

that

can be

technologies that allow for the management of

delineated using altitude and vegetation

spatial and temporal variability, maximizing indices, showing differences in management

profit and minimizing environmental damage, zones regarding phosphorus, clay, and silt at and having greater control over the causes of the end of the process.

variation associated with higher amounts of In this context, the knowledge and

information

(TSCHIEDEL;

FERREIRA,

spatial representation of yield over the years 2002). According to Cortez et al. (2018a) and

allow for the formulation of more solid and Cortez et al. (2018b), the special variability efficient management strategies, such as the observed by geostatistical methods allows

division of the property into areas that present defining specific regions in an area, which higher homogeneity, creating differentiated

enable or require specific local management.

management units or management zones to

Buss et al. (2019) suggest that

PA

adopt the most adequate crop management for

implementation requires prior knowledge

each soil condition. Thus, this study aimed to about the spatial variability of crops, soil evaluate the spatial variability of soybean and attributes, and the generation of management corn yield

during

two

growing seasons

zones.

associated with soil texture in the generation The concept of PA uses variable rate

of management zones.

application, spatial variability of yield, and other factors (CAMICIA et al., 2018). In the 2 MATERIAL AND METHODS

PA context, yield maps describe the spatial variability of the study area and depend on the The study was carried out on a

sampling arrangement and density (GUEDES

commercial farm with approximately 130 ha, et al., 2016).

located in the municipality of Ponta Porã, MS, Among the factors considered in the

Brazil, which is located at latitude 22°22′58″

adoption of PA techniques, the spatial of soil S, longitude 55°10′30″ W, and an average

texture from the asjustment of models that altitude of 440 m (Figure 1). The climate is describe the spatial dependence, has been the humid tropical with a dry winter. The property subject

of

numerous

studies

in

the

is cultivated in the rainfed system and adopts determination of soil physical and chemical the no-tillage system in straw with a soybean soil

attributes

in

usampled

locations

and

off-season

corn

succession

for

(OLIVEIRA et al., 2015). However, there are approximately 20 years.

still few studies on the relationship between

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) 337

Figure 1. Altitude data of the experimental area Source: Author (2020).

The soil in the area is classified as a kg−1 of clay, silt, and total sand, respectively.

medium-textured Oxisol (Latossolo Vermelho

Table 1 shows the chemical attributes in the distrófico,

Brazilian

Soil

Classification

0.0–0.20 m layer with their mean values in the System), with a gently wavy relief (SANTOS

area.

et al., 2018) and means of 343, 84, and 573 g

Table 1. Mean contents of chemical attributes at a depth of 0.00–0.20 m.

Chemical attributes

pH

5.77

OM

g dm-3

36.00

P

mg dm-3

12.30

Ca2+

5.09

Mg2+

1.17

K+

0.18

SB

cmolc dm-3

6.44

Al3+

0

Al3++H+

2.08

CEC

8.52

m

0

Ca

59.74

Mg

%

13.73

K

2.11

V

75.59

Cation exchange capacity at pH 7.0 (CEC), sum of bases (SB), base saturation (V), aluminum saturation (m), and mean calcium (Ca), magnesium (Mg), and potassium saturation (K) relative to the total CEC. Source: Author (2020).

The meteorological data of maximum

Western Agriculture, Dourados, MS, Brazil,

and minimum temperature (Figure 2) were

while the precipitation data (Figure 2) were

collected at the weather station of Embrapa collected in the study area.

338

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) Figure 2. Monthly meteorological data for 2017, 2018, and 2019, with volumes accumulated in the period of soybean and corn cultivation

Source: Author (2020) and Embrapa Western Agriculture (2020).

A

sampling grid with 64 points

grid had 77 sampling points at the density of distributed regularly, with one point every 2.0

one sample per 1.7 ha. Baio et al. (2021) used ha (143.61 × 143.61 m) plus 13 sampling

10% additional points in the grid at a distance points (the equivalent of approximately 20%

smaller than the sampling grid to improve the of the points from the original sampling grid), estimation of the semivariogram on the

randomly allocated according to the spatial microscale. Molin, Amaral e Colaço et al.

variability of the observed yield in the corn (2015) suggested the use of 30% additional and soybean harvest maps for 2018 and 2019,

points in the sampling grid. Therefore, we was generated for the collection of soil data decided to use 20% of the additional points in (Figure 2). Therefore, the adopted sampling this study.

Figure 3. Regular grid with original and additional sampling points for collecting soil samples Source: Author (2020).

Soil samples were collected manually

1 ½″ x 18 mm. The composite samples were in 2019. To remove the soil was used a Stihl®

collected at a depth of 0.00–0.20 m, with 10

BT 45 gasoline drill with an Irwin® Mathieson

subsamples within a radius of 5 m from the

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) 339

sampling point to be characterized.

et al., 1994). All texture data were estimated Yield data were collected over two

scaled to a 10 m spatial resolution and

years by harvesters equipped with an impact interpolated by ordinary kriging with QGIS

plate mass sensor, monitor, and GNSS receiver

software.

capable of collecting data every 1 second. The The yield data were subjected to

data obtained the mapping system were

interpolation by the inverse distance weighting filtered using an electronic spreadsheet to (IDW) using the value 2 as a weight, after they eliminate

discrepant data (outliers). The

had gone through the cleaning process. All criteria adopted for data elimination were as data the yields were estimated to a 10 m

follows: data containing positioning error

spatial resolution with QGIS software. The

and/or null geographic coordinates, data with yields values were classified and grouped into zero grain mass moisture, data collected with a five classes.

minimum working width of the platform less The management zones were generated

than 95% of the working width for soybean after analyzing the data collected annually and and 75% for corn for the 2018 growing season

temporally, being classified as a function of and 100% for the 2019 corn growing season the clay content according to Santos et al.

(adapted from MENEGATTI; MOLIN, 2004).

(2018), in which: medium texture, with

Considering as the yield data mass is large material with clay content between 150 and

generated by the mapping system, the values 350 g kg−1; clayey texture, material with clay identified as outliers were removed.

content between 350 and 600 g kg−1; and very

Soil texture and crop yield data were

clayey texture, material with clay content subjected to descriptive statistics. The Ryan-higher than 600 g kg−1.

Joiner test at the 5% probability was used to verify the fit of the normal distribution.

3 RESULTS AND DISCUSSION

The geostatistical analysis of the soil

texture data was performed using experimental

3.1 Granulometry analysis

semivariograms

(exponential, gaussian

or

spherical) and the model adjustment was

The

spatial

variability

for

soil

performed based on the lowest sum of squared

granulometry attributes (Table 2) has a high residuals (SSR) and the best coefficient of range. For clay the values were with minimum

determination (r2). The following parameters of 180.40 g kg−1 and maximum values of

were defined: nugget effect (Co), contribution 580.40 g kg−1, showing the variability of this (C), sill (Co+C), and range (a). Data were

attribute. However, marked variations were

cross-validated to validate the model. The observed for silt and sand. The relative

spatial dependence index (SDI) was calculated

dispersion of the coefficient of variation (CV) by the equation SDI=[C0/(C0+C1)]*100. The

data for the analyzed attributes was higher for degree of spatial dependence (DSD) was

silt despite being classified as medium (CV

classified based on SDI as strong, for SDI ≤

between 15 and 60%), according to Warrick 25%; moderate, for SDI between 25 and 75%;

and Nielsen (1980).

and weak, for SDI > 75% (CAMBARDELLA

340

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) Table 2. Descriptive statistics of soil particle size at a depth of 0.0–0.2 m at the study area in Ponta Porã, MS, Brazil.

Parameter

Clay

Silt

Sand

------------ g kg−1 ------------

Mean/plot

343.50

84.61

572.70

SD

94.80

45.37

115.20

Variance

8984.70

2058.12

13281.80

Minimum

180.40

19.80

277.00

Maximum

580.40

309.60

750.00

Range

400.00

289.80

473.00

CV

27.61

53.62

20.12

Sk

0.48

2.82

−0.31

K

−0.36

12.23

−0.46

RJ

0.99ns

0.86*

0.99ns

SD: standard deviation; CV (%): coefficient of variation; Sk: skewness; K: kurtosis; RJ: Ryan-Joiner test, where (*) significant at levels of p < 0.05 and (ns) non-significant distribution. The hypothesis for normal distribution is rejected when it is significant. Source: Author (2020).

The attribute silt did not follow a

to 1.0, measuring the reliability of the adopted normal frequency distribution (Table 2), which models.

was confirmed by the skewness coefficient

Range (A) values (Table 3) showed

shifted to the right (Sk > 0) and kurtosis with a that the use of a regular grid (original grid) leptokurtic distribution (Ck > 0), confirmed by with a density of one sampling point for every the Ryan-Joiner test (RJ). However, the

2.06 hectares, associated with random points attributes clay and total sand presented values (additional points) at a density of 20% of the close to zero for the skewness and kurtosis regular grid, allocated according to soybean coefficients so that the frequency distribution and second crop corn yield variability for a tends to normality, which can be verified by period of two years, was efficient to identify the result of the RJ test.

the spatial dependence of soil texture attributes A

strong spatial

dependence

was

in the study area, all of which were classified observed for the attributes sand, clay, and silt as strong. Molin, Amaral e Colaço et al.

(Table 3), with an exponential semivariogram (2015) state that adding points to the regular model more suitable for the three fractions.

grid improves the quality of investigation and The cross-validation test showed that all analysis of spatial dependence.

estimators have a regression coefficient close Table 3. Parameters for the semivariogram adjustment regarding soil texture.

Attribute

Model

Co

Co+C

A (m)

SSR

SDI

DSD

r2

Clay

Exponential

10.00 11.050.00

621.00

7.69

0.09

Strong

0.93

Silt

Exponential 118.00 2.389.00

300.00

2.73

4.94

Strong

0.52

Total

Exponential

10.00 17.990.00

606.00

1.74

0.06

Strong

0.94

sand

Co: nugget effect; Co+C: sill; A: range; SSR: sum of squared residuals; SDI: spatial dependence index; DSD: degree of spatial dependence; r2: determination coefficient. Source: Author (2020).

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) 341

Clay, silt, and total sand spatialization

lower sand contents (315 to 396 g kg−1). This (Figure 4) indicate high variability in the area.

fact was already expected and also confirmed Moreover, the behavior of clay and sand are by Lima et al. (2014) who found an inverse antagonistic, that is, regions where clay

spatial distribution of the clay and total sand contents were higher (441 to 518 g kg−1) had fractions.

Figure 4. Variability maps of clay (A), silt (B), and total sand (C) contents Source: Author (2020).

Soil texture characteristics may vary

3.2 Grain yield

due to the relief, which can affect crop yield.

Thus, clay content decreases as altitude

Soybean and corn yield data (Table 4)

decreases (Figure 1). Kuiawski et al. (2017) showed high variability, indicated by the data stated that there are several associations of the range. The relative dispersion of the data, soil-plant-atmosphere relationship that affect according to Warrick and Nielsen (1980), was

yield and the terrain altimetry associated with classified as low (CV lower than 15%) for the

vegetation indices shows greater agreement soybean and medium (CV between 15 and

with the yield

zones of the soybean crop.

60%) for corn in both growing seasons.

Cortez, Anghinoni e Arcoverde et al. (2020) Skewness values were negative and kurtosis

observed that one of the factors that affect values were close to three for the two soybean yield is soil compaction and machinery traffic.

and corn growing seasons, characterizing the non-normal distribution of data, attested by the RJ normality test (Table 4).

342

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) Table 4. Descriptive statistics of soybean (2017/2018 and 2018/2019) and corn (2018 and 2019) yield at the study area in Ponta Porã, MS, Brazil.

Soybean

Corn

Parameter

2017/18

2018/19

2018

2019

--------------------- kg ha−1 --------------------

Mean/plot

4,013.10

3,458.01

2,823.70

5,085.57

SD

428.98

319.33

800.37

912.02

Variance

183920.71

101972.95

640585.00

831784.64

Minimum

2,506.30

2,428.00

351.10

2,103.00

Maximum

5,517.60

4,486.00

5,289.60

8,019.30

Range

3,011.30

2,058.00

4,938.50

5,916.30

CV

10.69

9.23

28.34

17.93

Sk

−0.14

−0.30

−0.55

−0.40

K

3.87

3.43

3.36

3.60

RJ

1.00*

1.00*

0.99*

0.99*

SD: standard deviation; CV (%): coefficient of variation; Sk: skewness; K: kurtosis; RJ: Ryan-Joiner test, where (*) significant at levels of p < 0.05 and (ns) non-significant distribution. The hypothesis for normal distribution is rejected when it is significant. Source: Author (2020).

The higher ranges observed in both

Soybean yield variation in the study

growing seasons for corn yield showed the

area (Figure 5) indicates an antagonistic

“elasticity” of C4 plants relative to C3 plants behavior in regions of high and low yield in when compared to the range of soybean yield the 2017/2018 and 2018/2019

growing

data. According to Santi et al. (2013), this seasons, associated with the rainfall regime behavior is due to the higher productive

and clay contents.

Rainfall

volume and

potential of corn compared to other crops, such distribution in both growing seasons (Figure as soybean.

2), 558 and 486 mm, respectively, during the The yield achieved in the 2019 corn

phenological stages R1 to R7, the time of growing season was higher than in 2018

highest water demand, associated with clay (Table 4). It can be explained by the climate and sand contents (Figure 4), justify this conditions (Figure 2). Because

as the

behavior. The occurrence of deficits during the accumulated rainfall in the region during the reproductive phase of the soybean crop leads crop cycle (from sowing to harvest) was 585

to a higher reduction in yield (Nunes et al., mm in 2019, a volume 29.4% higher for the 2016).

period compared to 2018.

Figure 5. Soybean yield map for the 2017/2018 (A) and 2018/2019 (B) growing seasons Source: Author (2020).

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) 343

Proper management allows obtaining

The 2018 second crop corn yield map

best soybean yields in sandy soils in the failed in the process of recording it on a flash Cerrado region, which is in line with the yield drive by the harvester at the harvest time found in the 2017/2018 growing season

(Figure 6A). This failure proved to be

(Figure 5A). Soybean yield for the 2018/2019

irreversible, leaving part of the field without growing season (Figure 5B) reflects the

information. Corn yield (Figure 6) showed

regional climate instability effects, with a

similar behavior to soybean yield (Figure 5).

lower yield in the area with the highest amount The red regions in the corn harvest map of sand. Buss et al. (2019) studying a spatial represent lower yields, corroborating with

variability of soybean yield and soil physical regions with the lowest amount of clay in the attributes, observed that soybean yield was soil. This behavior can be explained by the soil inversely related to coarse and fine sand water storage capacity. Soils with more clay content in the 0-0.20 m layer.

influence

water

retention

in

the

soil

(CARDUCCI et al., 2011).

Figure 6. Corn yield map second crop for the 2018 (A) and 2019 (B) Source: Author (2020).

Precipitation

volume

(Figure

2)

height and yield. This fact justified the

accumulated in the 2018 growing season

behavior of the low yield region, located in the throughout the corn cycle (from sowing to plot where the soil has the highest total sand harvest) in the region was 133 mm lower (452

content (Figure 4), that is, the soil with higher mm – 585 mm) compared to the 2019 growing

macroporosity

and

possibly

less

water

season. The occurrence of two droughts, the retention capacity.

first in the period from 04/04 to 11/05 (lasting In general, the regions with the highest

37 days) and the second from 06/06 to 08/02

corn yields are located in areas with the (57 days), coinciding with the beginning of the highest clay content. On the other hand, the definition

of

the

productive

potential,

highest soybean yield depends on the water flowering, and grain filling, affected the corn regime, associated with soil clay content.

crop development and yield. Caetano and

However, Corassa et al. (2018) found that a Casaroli (2017) observed that water deficit low crop yield is associated with high clay causes a reduction in sugarcane yield. Souza et content in Oxisols under no-tillage systems in al. (2020) found that the water deficit imposed the Rio Grande do Sul region and that organic

on the cowpea crop favored a lower yield.

matter was the indicator of high yields. Thus, Rocha et al.

(2021) found that the

the literature presents contradictory results characteristics of corn most affected by water regarding the reasons that affect high yields, deficit were male flowering, plant and ear requiring further research on the subject.

344

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) 3.3 Management zones

(Figure 7). In this sense, medium clay (150 to 350 g kg−1) and clayey regions (350 to

One of the simplest ways to make

600 g kg−1) were distinguished in the area and management zones (MZ) is through the

the yield maps (Figures 5 and 6) correlate and spatialization of clay, using the classification express the responses as a function of years proposed by Santos et al. (2018), which

with or without rainfall.

allowed us to simplify the spatial variability Figure 7. Map of management zones considering the clay content in the plot, representing the medium (yellow) and clayey textures (green)

Source: Author (2020).

The use of clay as a delimiter of

(aiming to increase of organic matter and management zones was efficient in plots that consequently of cation exchange capacity in showed marked variations in clay content

the more sandy regions) and also the plant (spatial variation), in which the effect of this population to be sown in each MZ. According

granulometry variation can be observed in the

to Vian et al. (2016), in a study of the spatial yield maps throughout the growing seasons

variability of corn yield, state that obtaining (temporal variation). Leal et al. (2015) state high grain yields is conditioned by the final that clay is one of the effective estimators in plant

population,

with

uniform

spatial

estimating second crop corn yield when using distribution of plants in the area.

neural networks as techniques. Therefore, clay content becomes a reliable component to

4 CONCLUSIONS

define management zones.

Comparing

the

maps

of

corn

The attributes clay, silt, and total sand

productivity with the map of management

have strong spatial dependence and one point zones, it can be seen that the regions with the every two hectares, added to 20% of random highest productivity are related to the MZ with points, are efficient for identifying spatial the highest clay content. For soybean yield, dependence.

this behavior was not observed. Considering The spatial variability of soybean and

the use of the MZ map as a management tool corn yields is influenced by soil texture.

to be adopted in the corn and soybean crop, Corn and soybean yield under water

some decisions could be based on the spatial deficit conditions showed higher values in distribution of the clay content of the area, regions with higher clay content. On the

including the establishment of cover plants contrary, soybean yield without water deficit

Schwambach et al./ Variability of soybean.../v36n3p335-347 (2021) 345

was higher in regions with more sand.

Areas with contrasting clay content

values allow the generation of management zones using this attribute.

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