Geographical approaches for integrated pest management integrated pest management of arthropods in forestry forestry and row crops

Precision Crop Protection - The Challenge and Use of Heterogeneity

Volumn , Issue , 2010, Pages 183-202

  Willers, Jeffrey L ^a   Riggins, John J ^b  

^a USDA ARS Genetics and Precision Agriculture Unit   (United States)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 82855180326     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-90-481-9277-9_12     Document Type: Chapter

References (51)

1. Bugayevskiy LM, Snyder JP (1995) Map projections. A reference manual. Taylor and Francis, Philadelphia.
2. Campbell PKE, Rock BN, Martin ME et al (2004) Detection of initial damage in Norway spruce canopies using hyperspectral airborne data. Int J Remote Sens 25:5557-5584.
3. Campenella R (2000) Testing components toward a remote-sensing-based decision support system for cotton production. Photogramm Eng Remote Sens 66:1219-1227.
4. Carter GA (1993) Responses of leaf spectral reflectance to plant stress. Am J Bot 80:239-243.
5. Carter GA, Miller RL (1994) Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sens Environ 50:295-302.
6. Cibula WG, Carter GA (1992) Identification of a far-red reflectance response to ectomycorrhizae in slash pine. Int J Remote Sens 13:925-932.
7. Corwin DL, Lesch SM (2003) Application of soil electrical conductivity to precision agriculture: Theory, principles, and guidelines. Agron J 95:455-471.
8. D'Agostino RB, Stephens MA (1986) Goodness-of-fit techniques. Marcel Dekker, New York.
9. de Smith MJ, Goodchild MF, Longley PA (2007) Geospatial analysis. A comprehensive guide to principles, techniques and software tools. Matador, Leicester.
10. Dupont JK, Campanella R, Seal MR et al (2000) Spatially variable insecticide applications through remote sensing. In Duggar P, Richter D (eds) Proceedings of the Beltwide Cotton Conferences, vol 2. National Cotton Council, Memphis.
11. Gotway CA, Bullock DG, Pierce FJ et al (1997) Experimental design issues and statistical evaluation techniques for site-specific management. In Pierce FJ, Sadler EJ (eds) The state of site-specific management for agriculture. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison.
12. Hoque E, Hutzler PJS, Hiendl H (1992) Reflectance, colour, and histological features as parameters for the early assessment of forest damages. Can J Remote Sens 18:104-110.
13. Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley-Interscience, Wiley, New York.
14. Jackson RD, Huete AR (1991) Interpreting vegetation indices. Prev Vet Med 11:185-200.
15. Jähne B (1997) Image processing for scientific applications. CRC Press, Boca Raton.
16. Jensen JR (2005) Introductory digital image processing: A remote sensing perspective, 3rd edn. Prentice-Hall, Upper Saddle River.
17. Jensen JR (2007) Remote sensing of the environment: An earth resource perspective, 2nd edn. Prentice-Hall, Upper Saddle River.
18. Kasischke ES, Goetz S, Hansen MC et al (2004) Temperate and boreal forests in remote sensing for natural resource management and environmental monitoring. In Ustin SL (ed) Manual of remote sensing, 3rd edn. Wiley, Hoboken.
19. Kerr JT, Southwood TRE, Cihlar J (2001) Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. Proc Natl Acad Sci 98:11365-11370.
20. Liebhold AM, Rossi RE, Kemp WP (1993) Geostatistics and geographic information systems in insect ecology. Annu Rev Entomol 38:303-327.
21. Littell RC, Milliken GA, Stroup WW et al (2006) SAS ®system for mixed models, 2nd edn. SAS Institute Inc., Cary.
22. Long JS (1997) Regression models for categorical and limited dependent variables. Sage Publications, Thousand Oaks.
23. Lu D (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27:1297-1328.
24. McCarter KS, Burris E, Milliken GA et al (2007) Specifications of a prototype software system for developing variable-rate treatment prescriptions for use in precision agriculture. In: Boyer JE (ed) Proceedings of the 19th annual Kansas State University conference on applied statistics in agriculture, Manhattan, Kansas, 27 April-1 May 1 2007.
25. McKinion JM, Jenkins JN, Willers JL, Zusmanis A (2009) Spatially variable insecticide applications for early season control of cotton insect pests. Comput Electron Agric 67:71-79.
26. Milliken GA, Johnson DE (2002) Analysis of messy data. Analysis of covariance, vol. 3. Chapman & Hall/CRC Press, New York.
27. Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens Environ 61:319-346.
28. Parresol BR (1999) Assessing tree and stand biomass: A review with examples and critical comparisons. For Sci 45:573-593.
29. Pinter PJ, Hatfield JL, Scheppers JS, Barnes EM, Moran MS, Daughtry CST (2003) Remote sensing for crop management. Photogramm Eng Remote Sens 69:647-664.
30. Pontius J, Hallett R, Martin M (2005a) Assessing hemlock decline using visible and near-infrared spectroscopy: indices comparison and algorithm development. Appl Spectrosc 59:836-843.
31. Pontius J, Hallett R, Martin M (2005b) Using AVIRIS to assess hemlock abundance and early decline in the Catskills, New York. Remote Sens Environ 97:163-173.
32. Pouncey R, Swanson K, Hart K (eds) (1999) ERDAS field guide, 5th edn. ERDAS, Atlanta.
33. Richards JA, Jia X (1999) Remote sensing digital image analysis. An introduction, 3rd edn. Springer, Berlin.
34. Riggins JJ, Tullis JA, Stephen FM (2009) Per-segment aboveground forest biomass estimation using LIDAR-derived height percentile statistics. GIScience Remote Sens 46:232-248.
35. Riley JR (1989) Remote sensing in entomology. Ann Rev Entomol 34:247-271.
36. Sampson PH, Zarco-Tejada PJ, Mohammed GH, Miller JR, Noland TL (2003) Hyperspectral remote sensing of forest condition: estimating chlorophyll content in tolerant hardwoods. For Sci 49:381-391.
37. SAS Institute Inc (2008) SAS/ETS R- 9.2 User's guide. Chapter 10. The COUNTREG Procedure. SAS Institute Inc, Cary.
38. Seelan SK, Laguette S, Sasady GM, Seielstad GA (2003) Remote sensing applications for precision agriculture: A learning community approach. Remote Sens Environ 88:157-169.
39. Shaw DR, Willers JL (2006) Improving pest management with remote sensing. Outlooks Pest Manag 17:197-201.
40. Smith HA, McSorley R (2000) Intercropping and pest management: A review of major concepts. Am Entomol 46:154-161.
41. Stefansson V (1947) Great adventures and explorations. The Dial Press, New York.
42. Stern VM, Smith RF, van den Bosch R, Hagen KS (1959) The integrated control concept. Hilgardia 9:81-101.
43. Strahler A (1980) The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sens Environ 10:135-163.
44. Thompson SK (1992) Sampling. Wiley-Interscience, New York.
45. White JC, Coops NC, Hilker T et al (2007) Detecting mountain pine beetle red attack damage with EO-1 HYPERION moisture indices. Int J Remote Sens 28:2111-2121.
46. Willers JL, Jenkins JN, Ladner WL et al (2005) Site-specific approaches to cotton insect control. Sampling and remote sensing analysis techniques. Prec Agric 6:431-452.
47. Willers JL, Jenkins JN, McKinion JM et al (2009a) Methods of analysis for georeferenced sample counts of tarnished plant bugs in cotton. Prec Agric 10:189-212.
48. Willers JL, McKinion JM, Jenkins JN (2006) Remote sensing, sampling and simulation applications in analyses of insect dispersion and abundance in cotton. In Aguirre-Bravo C, Pellicane PJ, Burns DP, Draggan S (eds) Monitoring science and technology symposium: unifying knowledge for sustainability in the western hemisphere. USDA Forest Service Proceedings RMRS-P-42CD. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins. http://www.fs.fed.us/rm/pubs/rmrs-p042.html. Accessed 9 Dec 2009.
49. Willers JL, Milliken GA, Jenkins JN et al (2008) Defining the experimental unit for the design and analysis of site-specific experiments in commercial cotton fields. Agric Syst 96:237-249.
50. Willers JL, Seal MR, Luttrell RG (1999) Remote sensing, line-intercept sampling for tarnished plant bugs (Heteroptera: Miridae) in Mid-South cotton. J Cotton Sci 3:160-170.
51. Willers JL, Wu J, Jenkins JN (2009b) Categorical likelihood method for combining NDVI and elevation information for cotton precision agricultural applications. Proceedings of the 5th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Groton, 28-30 July 2009.