Management zones in agriculture acording to the soil and landscape variables

Delalibera, Hevandro Colonhese; Neto, Pedro Henrique Weirich; Nagata, Noemi

doi:10.1590/s0100-69162012000600021

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…Axis II differentiated the conventional tillage with the highest values of soil density, grassland and slash-and-burn agriculture. Delalibera et al (2012) also succeeded in separating the management areas using multivariate statistical analysis in their assessment of chemical and physical attributes of soils.…”

Section: Resultsmentioning

confidence: 99%

Impact of Management on the Physical Attributes of a Dystrophic Yellow Latosol

Fárias¹,

Feitosa²,

Rodrigues³

et al. 2017

JAS

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Soil use and management systems aim to create conditions that are favorable to crop growth. The hypothesis is that areas subject to intensive use of agricultural machinery and animal trampling tend to have a soil structure that is altered by aggregate fragmentation, which causes soil compaction and consequently decreases the soil's physical and hydraulic properties. The aim of this study was to assess and compare the physical and hydraulic parameters of a dystrophic yellow latosol in an area of Cerrado in the municipality of Chapadinha, Maranhão, Brazil under different use and management systems. The following five use and management systems were studied with five replicates: native forest (control), slash-and-burn agriculture, grassland, no-till crop production and conventional tillage. Data analysis was performed using a completely randomized experimental design. The soil's density, macroporosity, microporosity, total porosity, hydraulic conductivity, infiltration, water retention curve, penetration resistance and Soil quality assessment index (S index) were assessed for all management systems. The soil use and management systems were found to have a significant effect on the penetration resistance and the water infiltration rate. The native forest and slash-and-burn agriculture areas provided the highest soil water infiltration rates and the lowest soil penetration resistance. A multivariate analysis identified the variables associated with each soil use and management system. The slash-and-burn agriculture area had the highest S index, which means it provided soil of the best physical quality.

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Section: Resultsmentioning

confidence: 99%

Impact of Management on the Physical Attributes of a Dystrophic Yellow Latosol

Fárias¹,

Feitosa²,

Rodrigues³

et al. 2017

JAS

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“…Understanding the role of several soil properties together, and their interactions, may help to explain the cause of variation in soil productivity as defined by site‐specific management zones. Management zones are needed when variation in soil characteristics that affect crop production like texture, soil fertility, acidity and so on is widespread (Delalibera et al ., ; Moshia et al ., ; Krishna, ). Following classification of the landscape into SMUs or management zones, critical levels and ranges of soil properties were used for management decisions.…”

Section: Discussionmentioning

confidence: 98%

Soil–landscape variability: mapping and building detail information for soil management

Iticha

Takele

2018

Soil Use and Management

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“…Several types of information have been used to delineate management zones, such as: terrain attributes, electrical conductivity, soil resistance to penetration, chemical soil attributes, crop indices like NDVI (Normalized Difference Vegetation Index ), and crop yield maps (FRAISSE et al, 2001;JIANG et al, 2011;ROSALEN et al, 2011;DELALIBERA et al, 2012). Among these types of information, some studies in Brazil (MOLIN, 2002;AMADO et al, 2007) have defined management zones based on yield maps from data collected over time.…”

Section: Introductionmentioning

confidence: 99%

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

Rodrigues

Corá

2015

Eng. Agríc.

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Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy cmeans clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.KEYWORDS: management zone analyst, precision agriculture, soil pH, tropical soils, Zea mays L. ALGORITMO DE AGRUPAMENTO 'FUZZY' PARA DEFINIÇÃO DE ZONAS DE MANEJO COM BASE NA VARIABILIDADE ESPAÇO-TEMPORAL DOS ATRIBUTOS DO SOLO E DA PRODUTIVIDADE DE MILHORESUMO: O agrupamento de dados de solo e de plantas pode ser utilizado para definir zonas de manejo. O objetivo do estudo foi identificar zonas de manejo usando o algorit mo de grupamento fuzzy c-means com base na variabilidade espacial e temporal dos atributos do solo e da produtividade de milho (PM). A área de estudo (18 por 250 m) está localizada em Jaboticabal-SP. A produtividade de milho foi medida em 100 células amostrais de 4,5 x 10 m, dispostas em quatro transectos (25 pontos por transecto), em cinco anos agrícolas, no período de 2001 a 2010. O procedimento MIXED do SAS foi utilizado para identificar quais variáveis mais influenciaram a variabilidade espacial da PM ao longo dos cinco anos de estudo. A saturação por bases (V) foi a variável que melhor se relacionou com a PM; portanto, modelos de semivariogramas foram ajustados aos dados destas variáveis e, posteriormente, interpolados pelo método da krigagem. O software Management Zone Analyst foi utilizado para realizar o agrupamento com o algor itmo fuzzy c-means. O número ótimo de zonas de manejo alterara-se ao longo do tempo, assim como o grau de similaridade espacial entre as zonas de manejo de V e da PM. Portanto, é de fundamental importância levar em consideração a variabilidade temporal da produtividade das culturas e dos atributos do solo para obter zonas de manejo com maior acurácia.

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Management zones in agriculture acording to the soil and landscape variables

Cited by 8 publications

References 7 publications

Impact of Management on the Physical Attributes of a Dystrophic Yellow Latosol

Impact of Management on the Physical Attributes of a Dystrophic Yellow Latosol

Soil–landscape variability: mapping and building detail information for soil management

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

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