Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Bernardi, A. C. de C.; Grego, C. R.; Andrade, Ricardo Guimarães; Rabello, L. M.; Inamasu, Ricardo Yassushi

doi:10.1590/1807-1929/agriambi.v21n8p513-518

Cited by 24 publications

(17 citation statements)

References 15 publications

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“…On the other hand, the mapping of soil properties with higher variability can be less accurate than that of soil properties with lower variability. Trends in the variation of soil attributes obtained in this study are consistent with those observed by Bernardi et al (2014 and2016) for the same soil parameters. The lime recommendation data had shown a pure nugget effect which indicates a weak spatial dependence due the large variability.…”

Section: Resultssupporting

confidence: 89%

Spatial variability of soil fertility in an integrated crop livestock forest system

Bernardi

Bettiol

Mazzuco

et al. 2017

Advances in Animal Biosciences

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Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.

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Section: Resultssupporting

confidence: 89%

Spatial variability of soil fertility in an integrated crop livestock forest system

Bernardi

Bettiol

Mazzuco

et al. 2017

Advances in Animal Biosciences

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“…Most researchers [17,18,[31][32][33], however, use normalized difference vegetation index (NDVI) as an auxiliary variable for the regression process. NDVI has been applied to many different aspects of rangeland ecology, but it has limitations.…”

Section: Introductionmentioning

confidence: 99%

Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

Pham

Kappas

Chương

et al. 2019

IJGI

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Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to evaluate the best environmental variables to estimate soil organic carbon (SOC), total nitrogen (TN), and soil reaction (pH) with a regression kriging (RK) model, and (ii) to compare the accuracy of the ordinary kriging (OK) and RK methods. SOC, TN, and soil pH data were measured at 155 locations within the research area with a sampling grid of 2 km × 2 km for a soil layer from 0 to 30 cm depth. From these samples, 117 were used for interpolation, and the 38 randomly remaining samples were used for evaluating accuracy. The chosen environmental variables are land use type (LUT), topographic wetness index (TWI), and transformed soil adjusted vegetation index (TSAVI). The results indicate that the LUT variable is more effective than TWI and TSAVI for determining TN and pH when using the RK method, with a variance of 7.00% and 18.40%, respectively. In contrast, a combination of the LUT and TWI variables is the best for SOC mapping with the RK method, with a variance of 14.98%. The OK method seemed more accurate than the RK method for SOC mapping by 3.33% and for TN mapping by 10% but the RK method was found more precise than the OK method for soil pH mapping by 1.81%. Further selection of auxiliary variables and higher sampling density should be considered to improve the accuracy of the RK method.

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“…The spectral signature of leaf tissue is correlated with its photosynthetic pigment content, its cell anatomical structure, and its water content. Because VIs are associated with plant biophysical characteristics, they are related to many crop agronomic characteristics, such as leaf area (Viña et al, 2010), plant nutrient and health status (Cammarano et al, 2014;Feng et al, 2017), and soil physical and chemical characteristics (Bernardi et al, 2017). VIs are commonly used for spatial analysis of vegetation development and may therefore also aid in selecting management practices in cultivated areas.…”

Section: Introductionmentioning

confidence: 99%

Delimitation of Homogeneous Zones in Vineyards Using Geostatistics and Multivariate Analysis of Different Vegetation Indices

et al. 2019

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Vegetation indices (VIs) are quantitative measures used to describe the distribution and spatial variability of the vegetation cover of natural or cultivated areas. The aim of this study was to delimit homogeneous zones (HZs) of different VIs using geostatistics and multivariate analysis in order to identify vegetation patterns in Cabernet Franc and Cabernet Sauvignon vineyards. The evaluation was performed in two vineyards in the municipality of Espírito Santo do Pinhal in the state of São Paulo, Brazil. Reflectance (ρ) was measured at three wavelengths of the electromagnetic spectrum (670, 730, and 780 nm) at canopy height in georeferenced points along planting rows using the Crop Circle ACS-430 active sensor. Nine VIs were calculated based on the ratios between the ρ values. Geostatistical data analysis allowed the spatial prediction of VIs by ordinary kriging interpolation. Principal component analysis and fuzzy k-means clustering were applied for HZs delimitation and the optimal number of zones was defined according to cluster validity functions. Despite the variations of the VIs spatial distribution patterns, the multivariate analysis resulted in a representative categorization of the grapevine vegetative vigor and delimitation of HZs for this characteristic. This was validated according to the observed significant differences between VIs.

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Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Cited by 24 publications

References 15 publications

Spatial variability of soil fertility in an integrated crop livestock forest system

Spatial variability of soil fertility in an integrated crop livestock forest system

Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

Delimitation of Homogeneous Zones in Vineyards Using Geostatistics and Multivariate Analysis of Different Vegetation Indices

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