Abstract
Farmers consider many variables in making agricultural decisions. The farmer must often choose his inputs for the production function before variables such as weather are known. The uncertainties associated with weather can however be mitigated with the use of decision support tools. As the specific relationships of weather, disease development and crop maturity are determined through research, the value of weather-based decision support tools becomes more evident. The State Climate Office of North Carolina (SCO) at North Carolina State University, USA has successfully developed several agricultural decision support systems. These include climate-based decision support tools for peanuts, strawberries, cucurbits, turfgrass and tobacco. All are built in collaboration with agricultural scientists who have expertise in each respective crop. Real-time and historical data from the North Carolina Environment and Climate Observing Network (ECONet) in conjunction with high-resolution numerical models support the availability of the tools.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cu RM, Phipps PM (1993) Development of a pathogen growth response model for the Virginia peanut leaf spot advisory program. Phytopathology 83:195–201
Doran JC, Fast JD, Barnard JC, Laskin A, Desyaterik Y, Gilles MK, Hopkins RJ (2008) Applications of lagrangian dispersion modeling to the analysis of changes in the specific absorption of elemental carbon. Atmos Chem Phys 8:1377–1389
Fast JD, Easter RC (2006) A lagrangian particle dispersion model compatible with WRF. 7th WRF Users¹ Workshop, NCAR, Boulder, 19–22 June, p 6.2
Grell GA, Dudhia J, Stauffer DR (1995) A description of the fifth generation penn state/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 122 pp
Holmes GJ, Main CE, Keever TZ (1998) Forecasting long-distance movement of cucurbit downy mildew. In: Proceedings cucurbitaceae 1998, Pacific Grove, pp 186–188
Langston DB, Phipps PM, Stipes RJ (2002) An algorithm for predicting outbreaks of sclerotinia blight of peanut and improving the timing of fungicide Sprays. Plant Dis 86:118–126
Nusbaum CJ (1944) The seasonal spread and development of cucurbit downy mildew in the Atlantic coastal states. Plant Dis 28:82–85
Nusbaum CJ (1948) A summary of cucurbit downy mildew reports from Atlantic coastal states in 1947. Plant Dis 32:44–48
Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang XY, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp
Thomas CE (1996) Downy mildew. In: Zitter TA, Hopkins DL, Thomas CE (eds) Compendium of cucurbit diseases. APS Press, St. Paul, pp 25–27
Acknowledgements
The work mentioned herein is supported, in part, by the North Carolina Agricultural Research Service; Office of Extension, Engagement and Economic Development at NC State University; and the Center for Turfgrass Environmental Research and Education at NC State University. The authors would also like to acknowledge our collaborators: Dr. Barbara Shew, Department of Plant Pathology, NC State University; Dr. Gina Fernandez and Dr. Barclay Poling, Department of Horticulture, NC State University; Dr. George Kennedy and Dr. Hannah J. Burrack, Department of Entomology, NC State University; and Dr. Charles Peacock, Department of Crop Science, NC State University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Brooks, M.S., Sims, A.P., Frazier, A.N., Boyles, R.P., Syed, A., Raman, S. (2011). Climate-Based Decision Support Tools for Agriculture. In: Attri, S., Rathore, L., Sivakumar, M., Dash, S. (eds) Challenges and Opportunities in Agrometeorology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19360-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-19360-6_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19359-0
Online ISBN: 978-3-642-19360-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)