Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-28T16:23:35.823Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  01 June 2017

A. C. C. Bernardi ,
G. M. Bettiol ,
G. G. Mazzuco ,
S. N. Esteves ,
P. P. A. Oliveira  and
J. R. M. Pezzopane
A. C. C. Bernardi*
Affiliation:
Embrapa Pecuária Sudeste Cx.P.339, CEP: 13560-970 São Carlos - SP -Brazil
G. M. Bettiol
Affiliation:
Embrapa Cerrados, Planaltina - DF-Brazil
G. G. Mazzuco
Affiliation:
Gestão e Análise Ambiental – UFSCar. São Carlos - SP –Brazil
S. N. Esteves
Affiliation:
Embrapa Pecuária Sudeste Cx.P.339, CEP: 13560-970 São Carlos - SP -Brazil
P. P. A. Oliveira
Affiliation:
Embrapa Pecuária Sudeste Cx.P.339, CEP: 13560-970 São Carlos - SP -Brazil
J. R. M. Pezzopane
Affiliation:
Embrapa Pecuária Sudeste Cx.P.339, CEP: 13560-970 São Carlos - SP -Brazil
*
†E-mail: alberto.bernardi@embrapa.br
Get access

Abstract

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.

Keywords

soil fertilitypastureUrochloa brizanthavariable rate
Type
Data analysis and Geostatistics
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 590 - 593
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Balbino, LC, Cordeiro, LAM, Silva, VP, Moraes, A, Martínez, GB, Alvarenga, RC, Kichel, NA, Fontaneli, RS, Santos, HP, Franchini, JC and Galerani, PR 2011. Evolução tecnológica e arranjos produtivos de sistemas de integração lavoura pecuária floresta no Brasil. Pesquisa Agropecuária Brasileira 46, i–xii.CrossRefGoogle Scholar
Bernardi, ACC, Bettiol, GM, Ferreira, RP, Santos, KEL, Rabello, LM and Inamasu, RY 2016. Spatial variability of soil properties and yield of a grazed alfalfa pasture in Brazil. Precision Agriculture 1–16.Google Scholar
Bernardi, ACC, Oliveira, PAA, Primavesi, O 2012. Soil fertility of tropical intensively managed forage system for grazing cattle in Brazil. In Soil fertility improvement and integrated nutrient management - a global perspective (ed. Whalen), pp. 37–56. Rijeka, Croatia: Intechopen.Google Scholar
Bernardi, ACC, Rabello, LM, Inamasu, RY, Grego, CR, Andrade, RG 2014. Variabilidade espacial de parâmetros físico-químicas do solo e biofísicos de superfície em cultivo do sorgo. Revista Brasileira de Engenharia Agrícola e Ambiental 18, 623–630.CrossRefGoogle Scholar
Carvalho, JRP, Silveira, PM, Vieira, SR 2002. Geoestatística na determinação da variabilidade espacial de características químicas do solo sob diferentes preparos. Pesquisa Agropecuária Brasileira 37, 1151–1159.CrossRefGoogle Scholar
Cambardella, CA, Moorman, TB, Novak, JM, Parkin, TB, Karlen, DL, Turco, RF and Konopka, AE 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58, 1501–1511.CrossRefGoogle Scholar
Kravchenko, AN 2003. Influence of spatial structure on accuracy of interpolation methods. Soil Science Society of America Journal 67, 1564–1571.CrossRefGoogle Scholar
Robinson, TP and Metternicht, G 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture 50, 97–108.CrossRefGoogle Scholar