Aspects of Precision Agriculture

doi:10.1016/S0065-2113(08)60513-1

Advances in Agronomy

Volume 67, 1999, Pages 1-85

https://doi.org/10.1016/S0065-2113(08)60513-1 Get rights and content

Precision agriculture is the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality. Success in precision agriculture is related to how well it can be applied to assess, manage, and evaluate the space-time continuum in crop production. This theme is used here to assess the current and potential capabilities of precision agriculture. Precision agriculture is technology enabled. It is through the integration of specific technologies that the potential is created to assess and manage variability at levels of detail never before obtainable and, when done correctly, at levels of quality never before achieved. The agronomic feasibility of precision agriculture has been intuitive, depending largely on the application of traditional management recommendations at finer scales, although new approaches are appearing. The agronomic success of precision agriculture has been limited and inconsistent although quite convincing in some cases, such as N management in sugar beet (Beta vulgaris L.). Our analysis suggests prospects for current precision management increase as the degree of spatial dependence increases, but the degree of difficulty in achieving precision management increases with temporal variance. Thus, management parameters with high spatial dependence and low temporal variance (e.g., liming, P, and K) will be more easily managed precisely than those with large temporal variance (e.g., mobile insects). The potential for economic, environmental, and social benefits of precision agriculture is complex and largely unrealized because the space-time continuum of crop production has not been adequately addressed.

References (316)

P. Goovaerts
Geostatistics in soil science: State-of-the-art and perspectives
Geoderma
(1999)
R.J. Haggar et al.
A prototype hand-held patch sprayer for killing weeds, activated by spectral differences in crop weed canopies
J. Agric. Eng. Res.
(1983)
F. Adams
“Soil Acidity and Liming,” 2nd ed
(1984)
R.R. Allmaras et al.
Agricultural technology and adoption of conservation practices
American Society of Agricultural Engineers (ASAE) (1991) “Automated Agriculture for the 21 st Century. Proceedings of...
N.W. Anderson et al.
Application equipment for site-specific management
J. Asgard
Soil cultivation in darkness reduced weed emergence
Acta Hon.
(1994)
H. Auernhammer
Global position systems in agriculture [Special issue]
Computers Electronics Agric.
(1949)
H. Auernhammer, T. Muhr, (1991). GPS in a basic rule for environment protection in agriculture. In “Automated...
A. Bahri et al.
Metering characteristics accompanying rate changes necessary for precision farming

Y.M. Bae et al.

Determination of spatially variable yield maps

ASAE

(1987)

K. Ballal et al.

Nozzle selection and replacement based on nozzle wear analysis

V. Barnett et al.

Predicting wheat yields: The search for valid and precise models

R.I. Barnhisel et al.

Agronomic benefits of varying corn seed populations: A central Kentucky study

L.L. Bashford

“External Flute Seed Metering Evaluation Related to Site Specific Farming,”

(1993)

L.L. Bashford et al.

Variability in volume metering devices

M.F. Baumgardener et al.

Effects of organic matter on the multispectral properties of soils

Indiana Acad. Sci.

(1970)

BauschW.C. et al.

Assessment of plant nitrogen in irrigated corn

P.H.X. Beckett et al.

Soil variability: A review

Soils Fertilizers

(1971)

BellJ.C. et al.

Calibration and validation of a soil-landscape model for predicting soil drainage class

Soil Sci. Soc. Am. J.

(1992)

BellJ.C. et al.

Soil drainage class probability mapping using a soil-landscape model

Soil Sci. Soc. Am. J.

(1994)

BellJ.C. et al.

Soil-terrain modeling for site-specific management

H.M. Benedict et al.

Nondestructive method for estimating chlorophyll content of leaves

Science

(1961)

J.K. Berry

“Beyond Mapping: Concepts, Algorithms, and Issues in GIS.”

(1993)

J.K. Berry

“Spatial Reasoning for Effective GIS.”

(1995)

BIOS

“Precision Agriculture 1997.”

(1997)

S.J. Birrell et al.

Nutrient mapping implications of short-range variability

C.A. Black

“Soil Fertility Evaluation and Control.”

(1993)

A.M. Blackmer et al.

Remote sensing to identify spatial patterns in optimal rates of nitrogen fertilization

R.L. Blevins et al.

Conservation tillage for erosion control and soil quality

B.R. Bock et al.

Predicting N fertilizer needs for corn in humid regions: Summary and future directions

M. Boehlje

Information: What is the public role?

(1994)

BorgeltS.C. et al.

Spatially variable liming rates: A method for determination

Trans. Am. Soc. Agric. Eng.

(1994)

J. Bouma

Precision agriculture: Introduction to the spatial and temporal variability of environmental quality

R.B. Brown et al.

Prescription maps for spatially variable herbicide application in no-till corn

Trans. Am. Soc. Agric. Eng.

(1995)

R.B. Brown et al.

Herbicide application control using GIS weed maps

(1990)

BrubakerS.C. et al.

Soil properties associated with landscape position

Soil Sci. Soc. Am. J.

(1993)

D.G. Bullock et al.

Does variable rate seeding of corn pay?

Agron. J.

(1998)

L.G. Bundy et al.

Predicting N fertilizer needs for corn in humid regions: Advances in the upper Midwest

P.A. Burrough

“Principles of Geographic Information Systems for Land Resource Assessment.”

(1986)

M.D. Cahn et al.

Spatial analysis of soil fertility for site-specific crop management

Soil Sci. Soc. Am. J.

(1994)

C.R. Camp et al.

Center pivot irrigation system for site-specific water and nutrient management

(1994)

C.R. Camp et al.

Center pivot irrigation system for site-specific water and nutrient management, ASAE

(1996)

P.M. Carr et al.

Farming soils, not fields: A strategy for increasing fertilizer profitability

J. Prod. Agric.

(1991)

A. Cattanach et al.

Grid soil testing and variable rate fertilizer application effects on sugarbeet yield and quality

J. Chaplin et al.

Measuring the spatial performance of chemical applicators

D.R. Christensen et al.

Effect of lime applied to high pH soils on yield of sugarbeet, corn, soybean, and navy bean

“The 1997 Research Report of the Saginaw Valley Bean & Beet Research Farm and Related Bean-Beet Research,”

(1998)

A.J. Ciha

Slope position and grain yield of soft white winter wheat

Agron. J.

(1984)

R.L. Clark

A comparison of rapid GPS techniques for topographic mapping

R.L. Clark et al.

Variable rate application technology: An overview

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Aspects of Precision Agriculture

Geoderma

J. Agric. Eng. Res.

“Soil Acidity and Liming,” 2nd ed

Agricultural technology and adoption of conservation practices

Application equipment for site-specific management

Soil cultivation in darkness reduced weed emergence

Acta Hon.

Global position systems in agriculture [Special issue]

Computers Electronics Agric.

Metering characteristics accompanying rate changes necessary for precision farming

Determination of spatially variable yield maps

ASAE

Nozzle selection and replacement based on nozzle wear analysis

Predicting wheat yields: The search for valid and precise models

Agronomic benefits of varying corn seed populations: A central Kentucky study

“External Flute Seed Metering Evaluation Related to Site Specific Farming,”

Variability in volume metering devices

Effects of organic matter on the multispectral properties of soils

Indiana Acad. Sci.

Assessment of plant nitrogen in irrigated corn

Soil variability: A review

Soils Fertilizers

Calibration and validation of a soil-landscape model for predicting soil drainage class

Soil Sci. Soc. Am. J.

Soil drainage class probability mapping using a soil-landscape model

Soil Sci. Soc. Am. J.

Soil-terrain modeling for site-specific management

Nondestructive method for estimating chlorophyll content of leaves

Science

“Beyond Mapping: Concepts, Algorithms, and Issues in GIS.”

“Spatial Reasoning for Effective GIS.”

“Precision Agriculture 1997.”

Nutrient mapping implications of short-range variability

“Soil Fertility Evaluation and Control.”

Remote sensing to identify spatial patterns in optimal rates of nitrogen fertilization

Conservation tillage for erosion control and soil quality

Predicting N fertilizer needs for corn in humid regions: Summary and future directions

Information: What is the public role?

Spatially variable liming rates: A method for determination

Trans. Am. Soc. Agric. Eng.

Precision agriculture: Introduction to the spatial and temporal variability of environmental quality

Prescription maps for spatially variable herbicide application in no-till corn

Trans. Am. Soc. Agric. Eng.

Herbicide application control using GIS weed maps

Soil properties associated with landscape position

Soil Sci. Soc. Am. J.

Does variable rate seeding of corn pay?

Agron. J.

Predicting N fertilizer needs for corn in humid regions: Advances in the upper Midwest

“Principles of Geographic Information Systems for Land Resource Assessment.”

Spatial analysis of soil fertility for site-specific crop management

Soil Sci. Soc. Am. J.

Center pivot irrigation system for site-specific water and nutrient management

Center pivot irrigation system for site-specific water and nutrient management, ASAE

Farming soils, not fields: A strategy for increasing fertilizer profitability

J. Prod. Agric.

Grid soil testing and variable rate fertilizer application effects on sugarbeet yield and quality

Measuring the spatial performance of chemical applicators

Effect of lime applied to high pH soils on yield of sugarbeet, corn, soybean, and navy bean

“The 1997 Research Report of the Saginaw Valley Bean & Beet Research Farm and Related Bean-Beet Research,”

Slope position and grain yield of soft white winter wheat

Agron. J.

A comparison of rapid GPS techniques for topographic mapping

Variable rate application technology: An overview