Active optical sensors in irrigated durum wheat: Nitrogen and water effects

Agronomy Journal

Volumn 109, Issue 3, 2017, Pages 1060-1071

  Bronson, Kevin F ^a   White, Jeffrey W ^b   Conley, Matthew M ^a   Hunsaker, Doug J ^a   Thorp, Kelly R ^a   French, Andrew N ^b   Mackey, Bruce E ^c   Holland, Kyle H ^d  

^a US Arid Land Agricultural Research Station   (United States)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c WESTERN REGIONAL RESEARCH CENTER   (United States)

^d Electrical Engineer and Proprietor   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85019031746     PISSN: 00021962     EISSN: 14350645     Source Type: Journal    
DOI: 10.2134/agronj2016.07.0390     Document Type: Article

References (60)

1. Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration. FAO Irrigation and Drainage Paper 56. Food and Agriculture Organization of the United Nations, Rome.
2. Barnes, E.M., T.R. Clarke, S.E. Richards, P.D. Colaizzi, J. Haberland, M. Kostrzewski et al. 2000. Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multi-spectral data [CD-ROM]. In P.C. Robert et al., editor, Proceedings 5th International Confernece Precision Agriculture, Bloomington, MN. 16–19 July 2000. ASA, CSSA, and SSSA, Madison, WI.
3. Blandino, M., P. Vaccino, and A. Reyneri. 2015. Late-season nitrogen increases improve common and durum wheat quality. Agron. J. 107:680–690. doi:10.2134/agronj14.0405
4. Bronson, K.F., A. Malapati, P.C. Scharf, and R.L. Nichols. 2011. Canopy reflectance-based nitrogen management strategies for subsurface drip irrigated cotton in the Texas High Plains. Agron. J. 103:422–430. doi:10.2134/agronj2010.0161
5. Cabrera-Bosquet, L., G. Molero, A.M. Stellacci, J. Bort, S. Nogues, and J.L. Araus. 2011. NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Res. Commun. 39:147–159. doi:10.1556/CRC.39.2011.1.15
6. Cammarano, D., G. Fitzgerald, B. Basso, G. O’Leary, D. Chen, P. Grace, and C. Fiorentino. 2011. Use of canopy chlorophyll content index (CCCI) for remote estimation of wheat nitrogen content in rainfed environments. Agron. J. 103:1597–1603. doi:10.2134/agronj2011.0124
7. Clay, D.E., T.P. Kharel, C. Reese, D. Beck, C.G. Carlson, S.A. Clay, and G. Reicks. 2012. Winter wheat crop reflectance and nitrogen sufficiency index values are influenced by nitrogen and water stress. Agron. J. 104:1612–1617. doi:10.2134/agronj2012.0216
8. Clay, D.E., K.-I. Kim, J. Chang, S.A. Clay, and K. Dalsted. 2006. Characterizing water and nitrogen stress in corn using remote sensing. Agron. J. 98(3):579–587. doi:10.2134/agronj2005.0204
9. Dash, J., and P.J. Curran. 2004. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 25:5403–5413. doi:10.1080/0143116042000274015
10. Datt, B. 1999. A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using Eucalyptus leaves. J. Plant Physiol. 154:30–36. doi:10.1016/S0176-1617(99)80314-9
11. de Souza, E.G., P.C. Scharf, and K.A. Sudduth. 2010. Sun position and cloud effects on reflectance and vegetative indices of corn. Agron. J. 102:734–744. doi:10.2134/agronj2009.0206
12. Doerge, T.A., R.L. Roth, and B.R. Gardner. 1991. Nitrogen fertilizer management in Arizona. College of Agric., Univ. of Arizona, Tucson.
13. El-Shikha, D.M., P. Waller, D. Hunsaker, T. Clarke, and E. Barnes. 2007. Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agric. Water Manage. 92:183–193. doi:10.1016/j.agwat.2007.05.020
14. Erdle, K., B. Mistele, and U. Schmidhalter. 2011. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 124(1):74–84. doi:10.1016/j.fcr.2011.06.007
15. Er-Raki, S., A. Chehbouni, N. Guemouria, B. Duchemin, J. Ezzahar, and R. Hadria. 2007. Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agric. Water Manage. 87(1):41–54. doi:10.1016/j.agwat.2006.02.004
16. ESRI. 2015. ArcMap 10.3.1. Environ. Systems Res. Inst., Redlands, CA.
17. Fernandez, G.C.J. 1991. Repeated measure analysis of line-source sprinkler experiments. HortScience 26:330–342.
18. Fitzgerald, G., D. Rodriguez, L.K. Christensen, R. Belford, V.O. Sandras, and T.R. Clarke. 2006. Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precis. Agric. 7:233–248. doi:10.1007/s11119-006-9011-z
19. Fitzgerald, G., D. Rodriguez, and G. O’Leary. 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index–The canopy chlorophyll content index (CCCI). Field Crops Res. 116:318–324. doi:10.1016/j.fcr.2010.01.010
20. Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996. Use of green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289–298. doi:10.1016/S0034-4257(96)00072-7
21. Gitelson, A.A., Y.J. Kaufman, R. Stark, and D. Rundquist. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80:76–87. doi:10.1016/S0034-4257(01)00289-9
22. Gitelson, A.A., and M.N. Merzlyak. 1994. Quantitative estimation of chlorophyll using reflectance spectra. J. Photochem. Photobiol. B Biol. 22(3):247–252. doi:10.1016/1011-1344(93)06963-4
23. Gitelson, A.A., A. Vina, V. Ciganda, D.C. Rundquist, and T.J. Arkebauer. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32. doi:10.1029/2005GL022688
24. Glenn, E.P., C.M.U. Neale, D.J. Hunsaker, and P.L. Nagler. 2011. Vegetation index-based crop coefficients to estimate evaporation by remote sensing in agricultural and natural ecosystems. Hydrol. Processes 25(26):4050–4062. doi:10.1002/hyp.8392
25. Hanks, R.J., D.V. Sisson, R.L. Hurst, and K.G. Hubbard. 1980. Statistical analysis of results from irrigation experiments using the line-source sprinkler system. Soil Sci. Soc. Am. J. 44:886–888. doi:10.2136/sssaj1980.03615995004400040048x
26. Hansen, P.M., J.R. Jørgensen, and A. Thomsen. 2002. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. J. Agric. Sci. 139(3):307–318. doi:10.1017/S0021859602002320
27. Hodson, D.P., and J.W. White. 2007. Use of spatial analysis for global characterization of wheat-based production systems. J. Agric. Sci. 145:115–125. doi:10.1017/S0021859607006855
28. Holland, K.H., J.S. Schepers, J.F. Shanahan, and G.L. Horst. 2004. Plant canopy sensor with modulated polychromatic light source [CD-ROM]. In: P.C. Robert et al., editor, Proceedings of 7th International Conference on Precision Agricriculture, Minneapolis, MN. 25–28 July 2004. ASA, CSSA, and SSSA, Madison, WI.
29. Hong, S.-D., J.S. Schepers, D.D. Francis, and M.R. Schlemmer. 2007. Comparison of ground-based remote sensors for evaluation of corn biomass affected by nitrogen stress. Commun. Soil Sci. Plant Anal. 38:2209–2226. doi:10.1080/00103620701549157
30. Hunsaker, D.J., E. Barnes, T.R. Clarke, G.J. Fitzgerald, and P.J. Pinter, Jr. 2005b. Cotton irrigation scheduling using remotely sensed and FAO-56 basal crop coefficients. Trans. ASAE 48(4):1395– 1407. doi:10.13031/2013.19197
31. Hunsaker, D.J., P.J. Pinter, Jr., and B.A. Kimball. 2005a. Wheat basal crop coefficients determined by normalized difference vegetation index. Irrig. Sci. 24:1–14. doi:10.1007/s00271-005-0001-0
32. Idso, S.B., R.D. Jackson, and R.J. Reginato. 1977. Remote sensing for agricultural water management and crop yield prediction. Agric. Water Manage. 1(4):299–310. doi:10.1016/0378-3774(77)90021-X
33. Jackson, R.D., R.J. Reginato, and S.B. Idso. 1977. Wheat canopy temperature: A practical tool for evaluating water requirements. Water Resour. Res. 13:651–656. doi:10.1029/WR013i003p00651
34. Johnson, D.E., U.N. Chaudhuri, and E.T. Kanemasu. 1983. Statistical analysis of line-source sprinkler experiments and other nonrandomized experiments using multivariate methods. Soil Sci. Soc. Am. J. 47:309–312. doi:10.2136/sssaj1983.03615995004700020027x
35. Li, F., Y. Miao, F. Zhang, Z. Cui, R. Li, X. Chen, H. Zhang, J. Schroder, and W.R. Raun. 2009. In-season optical sensing improves nitrogen-use efficiency for winter wheat. Soil Sci. Soc. Am. J. 73:1566–1574. doi:10.2136/sssaj2008.0150
36. Liang, Z., K.F. Bronson, K.R. Thorp, J. Mon, M. Badaruddin, and G. Wang. 2014. Cultivar and N fertilizer rate affect yield and N use efficiency in irrigated durum wheat. Crop Sci. 54:1175–1183. doi:10.2135/cropsci2013.03.0159
37. Littell, R.C., G.A. Miliken, W.W. Stroup, and R.D. Wolfinger. 1996. SAS system for mixed models. SAS Inst., Cary, NC.
38. Long, D.S., J.U.H. Eitel, and D.R. Huggins. 2009. Assessing nitrogen status of dryland wheat using the canopy chlorophyll content index. www.plantmanagementnetwork.org/cm/. Crop Manage. doi:10.1094/CM-2009-1211-01-RS
39. Malhi, S.S., A.M. Johnston, J.J. Schoenau, Z.L. Wang, and C.L. Vera. 2006. Seasonal biomass accumulation and nutrient uptake of wheat, barley and oat on a Black Chernozem soil in Saskatchewan. Can. J. Plant Sci. 86:1005–1014. doi:10.4141/P05-116
40. Mon, J., K.F. Bronson, D.J. Hunsaker, K.R. Thorp, J.W. White, and A.N. French. 2016. Interactive effects of nitrogen fertilization and irrigation on grain yield, canopy temperature, and nitrogen use efficiency in overhead sprinkler-irrigated durum wheat. Field Crops Res. 191:54–65. doi:10.1016/j.fcr.2016.02.011
41. Mullen, R.W., K.W. Freeman, W.R. Raun, G.V. Johnson, M.L. Stone, and J.B. Solie. 2003. Identifying and in-season response index and the potential to increase wheat yield with nitrogen. Agron. J. 95:347–351. doi:10.2134/agronj2003.0347
42. Ottman, M., and T. Thompson. 2006. Fertilizing small grains in Arizona. Coop. Ext. Bull. 1346. Univ. of Arizona, Tucson. http://ari-zona.openrepository.com/arizona/bitstream/10150/147019/1/az1346-2006.pdf (accessed 20 May 2015).
43. Peñuelas, J., J.A. Gamon, A.L. Freedon, J. Merino, and C.B. Field. 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower seeds. Remote Sens. Environ. 48:135–146. doi:10.1016/0034-4257(94)90136-8
44. Piepho, H.P., A. Büshcse, and C. Richter. 2004. A mixed modelling approach for randomized experiments with repeated measures. J. Agron. Crop Sci. 190:230–247. doi:10.1111/j.1439-037X.2004.00097.x
45. Raun, W.R., J.B. Solie, G.V. Johnson, M.L. Stone, R.W. Mullen, K.W. Freeman et al. 2002. Improving nitrogen use-efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94:815–820. doi:10.2134/agronj2002.8150
46. Rouse, J.W., R.H. Haas, J.A. Schell, D.W. Deering, and J.C. Harlan. 1974. Monitoring the vernal advancements and retrogradation of natural vegetation. Final rep. NASA/GSFC, Greenbelt, MD.
47. SAS Institute. 2013. The SAS system for Windows version 9.3. SAS Inst., Cary, NC.
48. SAS Institute. 2016. SAS/STAT 14.2 User’s Guide. SAS Inst., Cary, NC.
49. Shaver, T.M., R. Khosla, and D.G. Westfall. 2010. Evaluation of two ground-based active crop canopy sensors in maize: Growth stage, row spacing, and sensor movement speed. Soil Sci. Soc. Am. J. 74:2101–2108. doi:10.2136/sssaj2009.0421
50. Shiratsuchi, L., R. Ferguson, J. Shanahan, V. Adamchuck, D. Rundquist, D. Marx, and G. Slater. 2011. Water and nitrogen effects on active canopy sensor vegetation indices. Agron. J. 103:1815–1826. doi:10.2134/agronj2011.0199
51. Solari, F., J. Shanahan, R. Ferguson, J. Schepers, and A. Gitelson. 2008. Active sensor reflectance measurements of corn nitrogen status and yield potential. Agron. J. 100:571–579. doi:10.2134/agronj2007.0244
52. Suárez, L., P.J. Zarco-Tejada, G. Sepulcre-Canto, O. Perz-Priego, J.R. Miller, J.C. Jimenez-Munoz, and J. Sobrino. 2008. Assessing canopy PRI for water stress detection with durnal airbone imagery. Remote Sens. Environ. 112:560–575. doi:10.1016/j. rse.2007.05.009
53. Tilling, A.K., G.J. O’Leary, J.G. Ferwerda, S.D. Jones, G.J. Fitzgerald, D. Rodriquez, and R. Belford. 2007. Remote sensing of nitrogen and water stress in wheat. Field Crops Res. 104:77–85. doi:10.1016/j.fcr.2007.03.023
54. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127–150. doi:10.1016/0034-4257(79)90013-0
55. USDA-NAAS. 2016. Small grains 2016 summary. USDA-NAAS, Washington, DC.
56. USDA-NRCS. 2017. Official soil series descriptions. USDA-NRCS. https://www.nrcs.usda.gov/wps/portal/nrcs/detailfull/soils/sur-vey/class/data/?cid=nrcs142p2_053587 (accessed 21 Mar. 2017).
57. Wang, Z.L., J.H. Wang, L.Y. Liu, W.J. Huang, C.J. Zhao, and C.Z. Wang. 2004. Prediction of grain protein content in wheat (Triti-cum aestivum L.) using plant pigment ratio (PPR). Field Crops Res. 90:311–321. doi:10.1016/j.fcr.2004.04.004
58. White, J.W., and M.M. Conley. 2013. A flexible, low-cost cart for proximal sensing. Crop Sci. 53:1646–1649. doi:10.2135/cropsci2013.01.0054
59. Wright, D.L., V.P. Rasmussen, D. Ramsey, and D.J. Baker. 2004. Canopy reflectance estimation of wheat nitrogen content for grain protein management. GIsci. Remote Sens. 41:287–300. doi:10.2747/1548-1603.41.4.287
60. Zadoks, J.C., T.T. Chang, and C.F. Konzak. 1974. A decimal code for the growth stages of cereals. Weed Res. 14:415–421. doi:10.1111/j.1365-3180.1974.tb01084.x