Microsoft Word - 24-Agra_46995


1871 
Bioscience Journal  Original Article 

Biosci. J., Uberlândia, v. 35, n. 6, p. 1871-1877, Nov./Dec. 2019 
http://dx.doi.org/10.14393/BJ-v35n6a2019-46995 

SOIL ATTRIBUTES AND WEED SEEDBANK SPATIAL CORRELATION 
 

CORRELAÇÃO ESPACIAL ENTRE ATRIBUTOS FÍSICOS E QUÍMICOS DO SOLO E 
O BANCO DE SEMENTES DE PLANTAS INFESTANTES 

 
Ígor Araujo Menezes de AVILA1; Sandro Manuel Carmelino HURTADO2;  

Gabriel Camargo de JEZUS3; Gustavo Costa SILVA1; Matheus Mendes REZENDE1 
1. Graduando em Agronomia, Instituto de Ciências Agrárias – ICIAG, Universidade Federal de Uberlândia - UFU, Uberlândia, MG, 

Brasil. igoramnz@gmail.com@gmail.com; 2. Professor, Doutor, Instituto de Ciências Agrárias – ICIAG, UFU, Uberlândia, MG, Brasil; 
3. Graduando em Agronomia, Instituto Federal do Triangulo Mineiro - IFTM, Uberlândia, MG, Brasil. 

 
ABSTRACT: The crop yield potential is affected by the crop-weed competition and their control 

create a dependence on herbicide use who brings, as consequence, soil impacts. Knowing the weed’s spatial 
distribution on the field is a feasible alternative for improving the crop yield. The goal of this paper is the 
identification of the spatial variability on physical and chemical attributes of soil as well as the weed’s 
seedbank so that, when correlated, may find standards to help on field management. The experiment was 
conducted on Uberlandia Federal University premises at soybean no-till area. Using georeferenced soil 
samples, were analyzed the physical and chemical attributes as well as the weed’s seedbank. The weed 
population on controlled environment was quantify, sorting out broadleaf and grassy weeds species. The 
obtained data were analyzed by descriptive statistic and geostatistics for a semivariogram modeling, 
interpolation by the kriging methodology and the spatial variability maps achievement. The average value, 
coefficient of variation (CV%), asymmetry, kurtosis coefficient and the significant linear correlations interfered 
on data spatial variability which we concluded by the spatial dependences on the attributes that had a linear 
correlation between them. The semivariograms presented varied range between 202 to 752 meters. Using the 
maps, verified two different regions for the broadleaf and grassy weeds seedbank. For both situations there was 
influence by the soil attributes on infestation level, which makes it possible to target the herbicide management 
reducing costs and the environmental impact. From the analyzed data we conclude that there is a spatial 
dependence for the physical and chemical soil attributes and their spatial distribution explains the weed 
seedbank spatial variability. 
 

KEYWORDS: Precision Agriculture. Soil fertility. Spatial variability. 
 
INTRODUCTION  

 
The Brazil is recognized by its agricultural 

potential, where the soybean (Glycine max L.) 
stands out with productive levels close to 120 
million tons per year (CONAB, 2018). However, the 
soybean genetic potential may be reduced by biotic 
and abiotic factors as crop-weed competition 
(VASCONCELOS et al., 2012), which changes the 
crop morphophysiology (LAMEGO et al., 2005), 
besides the additional costs for the weed control. 
Silva et al. (2009) related a dry mass accumulation 
reduction and a leaf area index modification. 
Formigheiri et al. (2018) showed a soybean seeds 
vigor and germination index reduction by studying 
the allelopathic influence of Ambrosia 
artemisiifolia.  

According to IMEA (2018), the chemical 
weed control represented 4% of the total production 
cost per hectare in 2017. For this reason, the 
watercourses contamination by agrochemicals it is a 
constant concern to ecosystems (URI et al., 1998; 

HOLLAND, 2004), where the residual effect 
achieves the main role on soil environment impact. 
On silty loam fields, Silva et al., (2011) observed 
phytotoxicity and crop biomass reduction because 
the residual effect of herbicides managed right 
before the crop planting.  

According to Barroso el al., (2004), the 
negative impact reduction is obtained targeting the 
management at the specific areas under attack by 
weeds as well as by the herbicide application rates 
adjustment. Here it is necessary the knowledge on 
the weed’s spatial distribution by identifying they 
canopy or seedbank (MONQUERO et al., 2008; 
IZQUIERDO et al., 2009). The qualitative and 
quantitative estimative is obtained by the direct 
germination of the soil samples or by the physical 
extraction of the seeds, associated to variability tests 
(LUSCHEI et al., 1998; MONQUERO; 
CHRISTOFFOLETTI, 2005). 

The soil is subjected to chemical 
applications and shows heterogeneity and spatial 
distribution on its physical and chemical attributes 

Received: 15/02/19 
Accepted: 10/10/19 



1872 
Soil attributes...  AVILA, I. A. M. et al. 

Biosci. J., Uberlândia, v. 35, n. 6, p. 1871-1877, Nov./Dec. 2019 
http://dx.doi.org/10.14393/BJ-v35n6a2019-46995 

(VIEIRA, 2000). It receives influence from the 
management system and its use (SOARES et al., 
2011) also interferes on crop development, as 
reported by Shiratsuchi et al., (2005), on the high 
correlation between pH, Ca, V%, m% and the 
weed’s seedbank.  

To recognize the soil attributes and weed 
seedbank spatial variability (CLAY et al., 1999; 
JURADO-EXPOSITÓRIO, 2003; SCHAFFRATH 
et al., 2007; CHIBA et al., 2010) it is a feasible 
alternative for reduce the production costs targeting 
the applications right on the observed parches as 
well as reducing the necessity on producing new 
maps because the temporal and spatial infestation 
stability (BARROSO et al., 2006; LONGCHAMPS 
et al., 2012).  

The goal of this study was to identify the 
spatial variability on physical and chemical 
attributes of soil, as well as the weed seedbank so 
that, when correlated, we may find standards that 
could help on field management. 

 
MATERIAL AND METHODS  

 
The experiment was conducted on 

experimental farm Capim Branco (18º53’S e 
48º20’W), placed in Uberlandia/MG which belongs 
to Federal University of Uberlandia. The 
experimental field, which is 25 hectares wide, has 
the soil classified as a Red Latosol (EMBRAPA, 
2006), soft wavy relief and Aw climate, according 
to Koppen classification. This study area is destined 
to no-till soybean growing since 2012 in succession 
planting with corn or sorghum.  

The chemical (TEIXEIRA et al., 2017) and 
physical (CAMARGO et al., 2009) soil attributes 
evaluation was realized using 0 to 0,2m deep 
georeferenced soil samples according to a dense and 
systemically randomized sampling grid two 
sampling points per hectare. These sampling points 
were about 75m away. For each of the 50 grid 
points were randomly sampled 10 sub-samples with 
the aim of composing a composite sample.   
 The weed seedbank determination was 
obtained by sampling 3 sub-samples to compose a 
composite sample on the same 50 points, being 
them also 0 to 0,2m deep. In order to better 
represent the weed seedbank, the soil samples were 
not sifted. After sampling, the soil samples were 
placed on 1,43dm3 bulk styrofoam trays under 
controlled environment. The moisture was 
monitored by daily irrigation aiming to offer the 
necessary conditions to seed’s germination and 
emergency. Evaluations have done twice a week for 
a period of 14 weeks (MONQUEIRO; 

CHRISTFFOLETI, 2005), sorting out broadleaf (B) 
and grassy (G) weeds species.  
 The data were analyzed descriptively to 
obtain the average, minimum and maximum values 
as well as the coefficient of variation, asymmetry 
and kurtosis coefficient using the Sisvar software 
(FERREIRA, 2008) and Statistica software 
(STATSOFT, 2004). The geostatistical analysis, 
realized with the GS+ software (ROBERTSON, 
1998), allowed the experimental semivariograms 
modelling (VIEIRA, 2000) and the interpolation by 
the kriging methodology for the spatial distribution 
maps obtainment. 
 
RESULTS 

 
The grassy weeds seedbank predominates 

over broadleaf weeds seedbank (Table 1). The 
grassy (8.2) and broadleaf (5.6) averages, away 
from the maximum value found for each evaluation 
(47 and 55, respectively) allow us to infer a possible 
spatial dependence presence for both groups of 
weeds (Table 1). The information can even be 
testified by the high CV% values (WARRICK; 
NIELSEN, 1980) and by asymmetry and kurtosis 
coefficient found for both groups (Table 1) which 
characterize counting data, where the average does 
not adequately represent the sample population 
(CHIBA et al., 2010). In practical ways, this 
behavior may indicate parches existence 
(SHIRATSUCHI et al., 2005) independently of the 
preparation system (SHAFFRATH et al., 2007). For 
the attributes Ca, Mg and sand, the CV% was 
considered medium (12<CV%<62) or near the 
average (V%). For the grassy weeds data, the 
correlation with the soil attributes under analysis 
allowed us the testification of significant and 
negative values for the attribute sand (r=-0,32). A 
significate correlation was also obtained between 
broadleaf weeds and the attribute Ca (r=0,39), Mg 
(r=0,51) and base saturation (r=0,38). 

The present data refers to the attributes 
where we observed a significant correlation. The 
spatial dependence was verified for the attributes 
with a linear correlation presence, except by the V% 
(Table 2). For these attributes the semivariograms 
were better modeled to exponential and gaussian 
models, whereas, the parameter range showed 
values between 202 and 752m. From the data, we 
can infer that there is a more homogenous 
distribution on broadleaf seedbank when compared 
to grassy, which is reflected in the presence of 
bigger and lower incident parches or spots for the 
first group. The GDE (CAMBARDELLA et al., 



1873 
Soil attributes...  AVILA, I. A. M. et al. 

Biosci. J., Uberlândia, v. 35, n. 6, p. 1871-1877, Nov./Dec. 2019 
http://dx.doi.org/10.14393/BJ-v35n6a2019-46995 

1994), classified from moderated to strong, 
evidences a good data collection. 

 

 
Table 1. Weeds seedbank and physical-chemical soil attributes data descriptive statistic and geostatistics 

analysis. Uberlandia, 2018. 

1V(%):base saturation; 2EPP: pure nugget effect; 3Co: nugget effect; 
4C0+C: baseline; 

5a: practical range (m); 6GDE: degree of spatial 
dependence (Co/Co+C)*100; r²: determination coefficient. 
 
 From the interpolated maps (Figure 1, 2 and 
3), there is an aggregate behavior (SHIRATSUCHI 
et al., 2005; SCHAFFRATH et al., 2007), testifying 
the information obtained by the descriptive statistics 

(Table 1). The grassy seedbank and the sand maps 
testified the negative correlation between them 
(Figure 1). 
 

 
 
 

Figure 1. Interpolated maps by kriging methodology for the attributes A: grassy weeds (plants/tray) and B: 
sand (g/kg).  

 
On field management, this information 

lights up and alert for the flat rate applications, 
considering, sometimes, the highest dose of the 
package insert recommendation. According to the 
data, lower doses can be applied on sandy areas as 
the southern region of the analyzed field (Figure 

1B), inasmuch as these areas are prone to increased 
leaching indexes and herbicides availability 
(PROCÓPIO et al., 2001). Beyond the product 
saving possibility, there is a possible contaminations 
reduction, mainly in irrigated fields (FIRMINO et 
al., 2008). 

Attribute Minimum Maximum Average CV% 
Coefficient of 

Asymmetry Kurtosis 
Grassy weeds 
(plants / tray) 

0.00 47.00 8.20 103.48 2.25 7.79 

Broadleaf weeds 
(plants / tray) 

0.00 55.00 5.66 144.18 4.84 28.08 

Calcium (cmolc/dm³) 1.90 5.00 3.32 18.56 0.11 0.55 
Magnesium (cmolc/dm³) 0.40 1.70 0.82 26.83 1.35 1.30 
V(%)1 37.95 67.31 51.60 11.67 0.14 0.37 
Sand (g/kg) 347.00 710.00 531.92 17.52 0.24 -0.95 
Attribute  Model C0

3 C0+C
4 a (m)5 GDE6 r² 

Grassy weeds 
(plants / tray) 

Gaussian 0.10 55.92 202 high 0.71 

Broadleaf weeds 
(plants / tray) 

Gaussian 37.40 124.00 752 medium 0.74 

Calcium (cmolc/dm³) Exponencial 0.28 0.59 559 medium 0.31 
Magnesium (cmolc/dm³) Exponencial 0.04 0.11 243 medium 0.50 
V(%)1 EPP2 37.94 - - - - 
sand (g/kg) Gaussian 10.00 9130.00 663 high 0.99 



1874 
Soil attributes...  AVILA, I. A. M. et al. 

Biosci. J., Uberlândia, v. 35, n. 6, p. 1871-1877, Nov./Dec. 2019 
http://dx.doi.org/10.14393/BJ-v35n6a2019-46995 

 

Figura 2.  Interpolated maps by kriging methodology for the attributes A: broadleaf weeds (plants/tray) 
and B: magnesium (cmolc/dm3). 

 
 
 
 
 
 
 
 
 

Figura 3. Interpolated maps by kriging methodology for the attributes A: calcium (cmolc/dm3) and interpolated 
map by the inverse square distance for B: base saturation (%).  

 
According to the maps, the broadleaf 

seedbank parches spatial distribution may be 
justified by the soil fertility attributes distribution 
Ca, Mg % V% (Figure 2A, 2B, 3A & 3B), with 
linear correlations of r=0,39; r=0,52 & r=0,38, 
respectively. For both situations it is evidenced that 
the physical and chemical soil attributes contribute 
to the broadleaf and grassy weeds highest 
infestation levels. 

Considering the large information amount 
available to this field, it becomes possible the 
definition of homogeneous zones or differentiated 

management units (CLAY et al., 1999; MOLIN et 
al., 2015), which allows the decision-making on 
herbicide doses management aiming for the costs 
and environment impacts reduction.  

 
CONCLUSION 

 
There is a spatial dependence for the 

physical and chemical soil attributes and their 
spatial distribution explains the weed seedbank 
spatial variability. 

 
 

RESUMO: O potencial produtivo das culturas é afetado pela competição por plantas infestantes e o 
seu controle cria dependência no uso de herbicidas, com consequente impacto ao solo. Conhecer o 
comportamento espacial das espécies infestantes é alternativa viável no aumento do lucro das lavouras. O 



1875 
Soil attributes...  AVILA, I. A. M. et al. 

Biosci. J., Uberlândia, v. 35, n. 6, p. 1871-1877, Nov./Dec. 2019 
http://dx.doi.org/10.14393/BJ-v35n6a2019-46995 

presente trabalho teve por objetivo identificar o comportamento espacial nos atributos físicos e químicos do 
solo e no banco de sementes de plantas infestantes, de maneira que, ao correlacioná-los possa-se encontrar 
padrões que auxiliem no manejo das áreas agrícolas. O estudo foi realizado em área pertencente à Universidade 
Federal de Uberlândia-MG em área cultivada com soja em plantio direto. A partir de amostragens 
georreferenciadas foi realizada avaliação química e física do solo e determinação do banco de sementes de 
plantas infestantes. Foi mensurado o número de plantas infestantes em ambiente controlado, separando as 
espécies germinadas em Folha Estreita e Folha Larga. Os dados foram avaliados pela estatística descritiva e 
geoestatística, para ajuste de semivariogramas, interpolação por krigagem e obtenção de mapas de distribuição 
espacial. Os valores de média, CV%, assimetria, curtose, e as correlações lineares significativas inferiram no 
comportamento espacial dos dados, comportamento verificado pela dependência espacial observada para os 
atributos com correlação linear significativa entre eles. Os semivariogramas apresentaram alcance variando 
entre 202 m e 752 m. A partir dos mapas verifica-se duas regiões distintas para o banco de plantas infestantes 
de folha estreita e larga. Para ambas situações houve contribuição dos atributos do solo nos níveis de 
infestação, o que possibilita o direcionamento no ajuste de doses herbicidas para redução de custos e impacto 
ambiental. A partir dos dados conclui-se que há comportamento espacial para atributos químicos e físicos de 
solo, e a sua distribuição espacial explica o comportamento espacial do banco de sementes de plantas 
infestantes. 
 

PALAVRAS-CHAVE: Agricultura de precisão. Fertilidade do solo. Plantas daninhas. 
 
 
REFERENCES 
 
BARROSO, J., FERNÀNDEZ‐QUINTANILLA, C., RUIZ, D., HERNAIZ, P., REW, L.J. Spatial stability of 
Avena sterilis ssp. ludoviciana populations under annual applications of low rates of imazamethabenz. Weed 
Research, v. 44, n. 3, p. 178-186, 2004. https://doi.org/10.1111/j.1365-3180.2004.00389.x 
 
BARROSO, J., NAVARRETE, L., SÁNCHEZ DEL ARCO, M. J., FERNANDEZ‐QUINTANILLA, C., 
LUTMAN, P. J. W., PERRY, N. H., HULL, R. I. Dispersal of Avena fatua and Avena sterilis patches by 
natural dissemination, soil tillage and combine harvesters. Weed Research, v. 46, n. 2, p. 118-128, 2006. 
https://doi.org/10.1111/j.1365-3180.2006.00500.x 
 
CAMARGO, O.A., MONIZ, A.C., JORGE, J.A., VALADARES, J.M.A.S. Métodos de Análise Química, 
Mineralógica e Física de Solos do Instituto Agronômico de Campinas. Campinas: Instituto Agronômico, 
2009. 
 
CAMBARDELLA, C. A., MOORMAN, T. B., PARKIN, T. B., KARLEN, D. L., NOVAK, J. M., TURCO, R. 
F., KONOPKA, A. E. Field-scale variability of soil properties in central lowa soils. Soil Science Society of 
America Journal, v. 58, n. 5, p. 1501-1511, 1994. https://doi.org/10.2136/sssaj1994.03615995005800050033x 
 
CHIBA, M.K., GUEDES FILHO, O., VIEIRA, S.R. Variabilidade espacial e temporal de plantas daninhas em 
Latossolo Vermelho argiloso sob semeadura direta. Acta Scientiarum-Agronomy, v. 32, n. 4, p. 735-742, 
2010. https://doi.org/10.4025/actasciagron.v32i4.5445 
 
CLAY, S.A., LEMS, G.J., CLAY, D.E., FORCELLA, F. Sampling weed spatial variability on a fieldwide 
scale. Weed Science, v. 47, n. 6, p. 674-681, 1999. https://doi.org/10.1017/S0043174500091323 
 
CONAB. Companhia Nacional de Abastecimento. Acompanhamento da safra brasileira: grãos. quarto 
levantamento, janeiro, 2019. Disponível em: <https://www.conab.gov.br/info-agro/safras/graos/boletim-da-
safra-de-graos>. Acesso em 21 de janeiro de 2019.  
 
EMBRAPA. EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA. Centro Nacional de Pesquisa de 
Solos. Sistema Brasileiro de Classificação de Solos. Rio de Janeiro: EMBRAPA-SPI, 2006. 
 



1876 
Soil attributes...  AVILA, I. A. M. et al. 

Biosci. J., Uberlândia, v. 35, n. 6, p. 1871-1877, Nov./Dec. 2019 
http://dx.doi.org/10.14393/BJ-v35n6a2019-46995 

FERREIRA, D.F. Sisvar: um programa para análise e ensino de estatística. Revista Symposium, v. 6, n.1, p. 
36-41, 2008. 
 
FIRMINO, L.E., TUFFI SANTOS, L.D., FERREIRA, L.R., FERREIRA, F.A., QUIRINO, A.L.S. Movimento 
do herbicida imazapyr no perfil de solos tropicais. Planta Daninha, v. 26, n. 1, p. 223-230, 2008. 
https://doi.org/10.1590/S0100-83582008000100023 
 
FORMIGHEIRI, F.B., BONOME, L. T. S., BITTENCOURT, H. V. H., LEITE, K., REGINATTO, M., 
GIOVANETTI, L. K. Alelopatia de Ambrosia artemisiifolia na germinação e no crescimento de plântulas de 
milho e soja. Revista de Ciências Agrárias, v. 41, n. 3, p. 729-739, 2018. https://doi.org/10.19084/RCA18074 
 
HOLLAND, J. M. The environmental consequences of adopting conservation tillage in Europe: reviewing the 
evidence. Agriculture, Ecosystems & Environment, v. 103, n. 1, p. 1-25, 2004. 
https://doi.org/10.1016/j.agee.2003.12.018 
 
IMEA. Instituto Mato-grossense de Economia Agropecuaria. Boletim semanal da soja. janeiro, 2019. 
Disponível em: <http://www.imea.com.br/imea-site/relatorios-mercado-detalhe?c=4&s=2>. Acesso em 21 de 
janeiro de 2019. 
 
IZQUIERDO, J., BLANCO-MORENO, J. M., CHAMORRO, L., GONZÁLEZ-ANDÚJAR, J. L., SANS, F. X, 
Spatial distribution of weed diversity within a cereal field. Agronomy for Sustainable Development, v. 29, n. 
3, p. 491-496, 2009. https://doi.org/10.1051/agro/2009009 
 
JURADO-EXPÓSITO, M.; LÓPEZ-GRANADOS, F.; GARCÍA-TORRES, L., GARCÍA-FERRER, A. Multi-
species weed spatial variability and site-specific management maps in cultivated sunflower. Weed Science, v. 
51, n. 3, p. 319-328, 2003. https://doi.org/10.1614/0043-1745(2003)051[0319:MWSVAS]2.0.CO;2 
 
LAMEGO, F. P., FLECK, N. G., BIANCHI, M. A., VIDAL, R.A. Tolerância a interferência de plantas 
competidoras e habilidade de supressão por cultivares de soja – I. Resposta de variáveis de crescimento. Planta 
Daninha, v. 23, n. 3, p. 405-414, 2005. https://doi.org/10.1590/S0100-83582005000300003 
 
LONGCHAMPS, L., PANNETON, B., SIMARD, M. J., LEROUX, G. D. Could weed sensing in corn 
interrows result in efficient weed control? Weed Technol., v. 26, n. 4, p. 649-656, 2012. 
https://doi.org/10.1614/WT-D-12-00030.1 
 
LOGHAVI, M., MACKVANDI, B. B. Development of a target oriented weed control system. Computers and 
Electronics in Agriculture, v. 63, n. 2, p. 112-118, 2008. https://doi.org/10.1016/j.compag.2008.01.020 
 
LUSCHEI, E. C., BUHLER, D. D., DEKKER, J. H. Effect of separating giant foxtail (Setaria faberi) seeds 
from soil using potassium carbonate and centrifugation on viability and germination. Weed Science, v. 46, n. 5, 
p. 545-548, 1998. https://doi.org/10.1017/S0043174500091074 
 
MOLIN, J.P., AMARAL, L.R., COLAÇO, A.F. Agricultura de Precisão. São Paulo: Oficina de Textos, 
2015. 
 
MONQUERO, P. A., CHRISTOFFOLETI, P. J. Banco de sementes de plantas daninhas e herbicidas como 
fator de seleção. Bragantia, v. 64, n. 2, p. 203-209, 2005. https://doi.org/10.1590/S0006-87052005000200006 
 
MONQUERO, P. A., AMARAL, L. R., BINHA, D. P., SILVA, P. V., SILVA, A. C., MARTINS, F. R. A. 
Mapas de infestação de plantas daninhas em diferentes sistemas de colheita da cana-de-açúcar. Planta 
Daninha, v. 26, n. 1, p. 47-55, 2008. https://doi.org/10.1590/S0100-83582008000100005 
 
PROCÓPIO, S. O., SILVA, A. A., SANTOS, J. B., FERREIRA, L. R., MIRANDA, G. V., SIQUEIRA, J. G.  
Efeito da irrigação inicial na profundidade de lixiviação do herbicida s-metolachlor em diferentes tipos de 
solos. Planta Daninha, v. 19, n. 3, p. 409-417, 2001. https://doi.org/10.1590/S0100-83582001000300014 



1877 
Soil attributes...  AVILA, I. A. M. et al. 

Biosci. J., Uberlândia, v. 35, n. 6, p. 1871-1877, Nov./Dec. 2019 
http://dx.doi.org/10.14393/BJ-v35n6a2019-46995 

ROBERTSON, G.P. GS+: Geostatistics for the environmental sciences. Plainwell: Gamma Design Software, 
1998. 
 
TEIXEIRA, P. C., DONAGEMMA, G. K., FONTANA, A., TEIXEIRA, W. G. editores. Manual de métodos 
de análise de solo. Brasília, DF: Embrapa, 2017. 
 
SCHAFFRATH, V.; TORMENA, C. A., GONÇALVES, A. C. A., OLIVEIRA JUNIOR, R. S. Variabilidade 
espacial de plantas daninhas em dois sistemas de manejo de solo. Revista Brasileira de Engenharia Agrícola 
e Ambiental, v. 11, n. 1, p. 53-60, 2007. https://doi.org/10.1590/S1415-43662007000100007 
 
SILVA, A. F., CONCENÇO, G., ASPIAZÚ, I., FERREIRA, E. A., GALON, L., COELHO, A. T. C. P., 
SILVA, A. A., FERREIRA, F. A. Interferência de plantas daninhas em diferentes densidades no crescimento da 
soja. Planta Daninha, v. 27, n. 1, p. 75-84, 2009. https://doi.org/10.1590/S0100-83582009000100011 
 
SILVA, F. M. L., CAVALIERI, S. D., SÃO JOSÉ, A. R., ULLOA, S. M., VELINI, E. D. Atividade residual de 
2,4-D sobre a emergência de soja em solos com texturas distintas. Revista Brasileira de Herbicidas, v. 10, n. 
1, p. 29-36, 2011. https://doi.org/10.7824/rbh.v10i1.85 
 
SHIRATSUCHI, L. S., FONTES, J. R. A., RESENDE, A. V. Correlação da distribuição espacial do banco de 
sementes de plantas daninhas com a fertilidade dos solos. Planta Daninha, v. 23, n. 3, p. 429-436, 2005. 
https://doi.org/10.1590/S0100-83582005000300006 
 
SOARES, M. B. B., FINOTO, E. L., BOLONHEZI, D., CARREGA, W., ALBUQUERQUE, J. A. A., 
PIROTTA, M. Z. Fitossociologia de plantas daninhas sob diferentes sistemas de manejo de solo em áreas de 
reforma de cana crua. Agroambiente, v. 5, n. 3, p. 173-181, 2011. https://doi.org/10.18227/1982-
8470ragro.v5i3.594 
 
STATISTICA. The Small Book Release 7.0. Tulsa: StatSoft Inc., 2004. 
 
URI, N. D., ATWOOD, J. D., SANABRIA, J. The environmental benefits and costs of conservation tillage. 
Science of the Total Environment, v. 216, n. 1/2, p. 13-32, 1998. https://doi.org/10.1016/S0048-
9697(98)00134-X 
 
VASCONCELOS, M. C. C.; SILVA, A. F. A.; LIMA, R. S. Interferência de plantas daninhas sobre plantas 
cultivadas. Agropecuária Cientifica no Semi-Árido, v. 8, n. 1, p. 1-6, 2012. 
 
VIEIRA, S.R. Geoestatística em estudos de variabilidade espacial do solo. In: Novais RF, Alvarez VH, 
Schaefer, CEGR, editores. Tópicos em ciência do solo. Viçosa: SBCS; 2000. p.1-54. 
 
WARRICK, A.W., NIELSEN, D.R. Spatial variability of soil physical properties in the field. In: Hillel D, 
editor. Applications of soil physics. New York: Academic; 1980. p.319-344. https://doi.org/10.1016/B978-0-
12-348580-9.50018-3