Characterizing soybean vigor and productivity using multiple crop canopy sensor readings

Field Crops Research

Volumn 216, Issue , 2018, Pages 22-31

  Miller, Joshua J ^a   Schepers, James S ^a   Shapiro, Charles A ^a   Arneson, Nicholas J ^a   Eskridge, Kent M ^a   Oliveira, Maxwel C ^a   Giesler, Loren J ^a  

^a UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

Area under the reflectance progress curve (AURPC); Management zones; Normalized difference red edge (NDRE) index; Reflectance; Soybean

Indexed keywords

AGRICULTURAL MANAGEMENT; CANOPY; CANOPY REFLECTANCE; CROP PRODUCTION; CROP YIELD; INDEX METHOD; MANAGEMENT PRACTICE; PROBABILITY; PRODUCTIVITY; SENSOR; SOYBEAN; SPATIAL ANALYSIS; VIGOR;

GLYCINE MAX; TRITICUM AESTIVUM; ZEA MAYS;

EID: 85033488922     PISSN: 03784290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fcr.2017.11.006     Document Type: Article

References (41)

1. Barreto, H., Edmeades, G., Chapman, S., Crossa, J., et al. The alpha lattice design in plant breeding and agronomy: generation and analysis. Edmeades, G.O., (eds.) Developing Drought- and Low N-tolerant Maize, El Batán, Mexico, 1996 (pp. 544).
2. Board, J.E., Maka, V., Price, R., Knight, D., Baur, M.E., Development of vegetation indices for identifying insect infestations in soybean. Agron. J. 99 (2007), 650–656, 10.2134/agronj2006.0155.
3. Eitel, J.U., Keefe, R.F., Long, D.S., Davis, A.S., Vierling, L.A., Active ground optical remote sensing for improved monitoring of seedling stress in nurseries. Sensors 10 (2010), 2843–2850, 10.3390/s100402843.
4. Fehr, W.R., Caviness, C.E., Stages of Soybean Development, Special Report., 1977, Iowa State University, Cooperative Extension Service, Ames, IA.
5. Fleming, K., Westfall, D., Wiens, D., Brodahl, M., Evaluating farmer defined management zone maps for variable rate fertilizer application. Precis. Agric. 2 (2000), 201–215.
6. Gaspar, A.P., Marburger, D.A., Mourtzinis, S., Conley, S.P., Soybean seed yield response to multiple seed treatment components across diverse environments. Agron. J. 106 (2014), 1955–1962, 10.2134/agronj14.0277.
7. Gitelson, A., Merzlyak, M.N., Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B 22 (1994), 247–252, 10.1016/1011-1344(93)06963-4.
8. Gitelson, A.A., Merzlyak, M.N., Lichtenthaler, H.K., Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. J. Plant Physiol. 148 (1996), 501–508, 10.1016/S0176-1617(96)80285-9.
9. Gitelson, A.A., Viña, A., Verma, S.B., Rundquist, D.C., Arkebauer, T.J., Keydan, G., Leavitt, B., Ciganda, V., Burba, G.G., Suyker, A.E., Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res. Atmos., 2006, 111, 10.1029/2005JD006017.
10. Hatfield, J.L., Prueger, J.H., Kustas, W.P., Remote sensing of dryland crops. Ustin, S.L., (eds.) Remote Sensing for Natural Resource Management and Environmental Monitoring: Manual for Remote Sensing, 2004, John Wiley, Hoboken, NJ, 531–568.
11. Hatfield, J., Gitelson, A.A., Schepers, J.S., Walthall, C., Application of spectral remote sensing for agronomic decisions. Agron. J. 100 (2008), S-117–S-131.
12. Heatherly, L.G., Elmore, R.W., Managing inputs for peak production. Boerma, H.R., Specht, J.E., (eds.) Soybeans: Improvement, Production, and Uses, third edition, 2004, ASA-CSSA-SSSA, Madison, WI, 451–536.
13. Holland, K.H., Schepers, J.S., Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agron. J. 102 (2010), 1415–1424, 10.2134/agronj2010.0015.
14. Holland, K.H., Lamb, D.W., Schepers, J.S., Radiometry of proximal active optical sensors (AOS) for agricultural sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5 (2012), 1793–1802, 10.1109/JSTARS.2012.2198049.
15. Inman, D., Khosla, R., Reich, R., Westfall, D., Normalized difference vegetation index and soil color-based management zones in irrigated maize. Agron. J. 100 (2008), 60–66, 10.2134/agrojnl2007.0020.
16. Kandel, Y.R., Mueller, D.S., Hart, C.E., Bestor, N.R.C., Bradley, C.A., Ames, K.A., Giesler, L.J., Wise, K.A., Analyses of yield and economic response from foliar fungicide and insecticide applications to soybean in the North Central United States. Plant Health Progress, 2016, 17, 10.1094/PHP-RS-16-0038.
17. Keyser, H.H., Li, F., Potential for increasing biological nitrogen fixation in soybean. Plant Soil 141 (1992), 119–135, 10.1007/BF00011313.
18. Kumar, R., Silva, L., Light ray tracing through a leaf cross section. Appl. Opt. 12 (1973), 2950–2954, 10.1364/AO.12.002950.
19. Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., Liu, Y., Liu, B., Ustin, S.L., Chen, X., Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Res. 157 (2014), 111–123, 10.1016/j.fcr.2013.12.018.
20. Lichtenthaler, H.K., Buschmann, C., Chlorophylls and Carotenoids: Measurement and Characterization by UV–vis Spectroscopy, Current Protocols in Food Analytical Chemistry. 2001, John Wiley and Sons, New York, NY, F4.3.1–F4.3.8.
21. Luedders, V.D., Genetic improvement in yield of soybeans. Crop Sci. 17 (1977), 971–972.
22. Ma, B., Dwyer, L.M., Costa, C., Cober, E.R., Morrison, M.J., Early prediction of soybean yield from canopy reflectance measurements. Agron. J. 93 (2001), 1227–1234, 10.2134/agronj2001.1227.
23. Medlin, C.R., Shaw, D.R., Gerard, P.D., LaMastus, F.E., Using remote sensing to detect weed infestations in Glycine max. Weed Sci. 48 (2000), 393–398.
24. Miao, Y., Mulla, D.J., Batchelor, W.D., Paz, J.O., Robert, P.C., Wiebers, M., Evaluating management zone optimal nitrogen rates with a crop growth model. Agron. J. 98 (2006), 545–553, 10.2134/agronj2005.0153.
25. Mourtzinis, S., Rowntree, S.C., Suhre, J.J., Weidenbenner, N.H., Wilson, E.W., Davis, V.M., Naeve, S.L., Casteel, S.N., Diers, B.W., Esker, P.D., The use of reflectance data for in-season soybean yield prediction. Agron. J. 106 (2014), 1159–1168, 10.2134/agronj13.0577.
26. Nutter, F., Tylka, G., Guan, J., Moreira, A., Marett, C., Rosburg, T., Basart, J., Chong, C., Use of remote sensing to detect soybean cyst nematode-induced plant stress. J. Nematol., 34, 2002, 222.
27. Pinter, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S., Upchurch, D.R., Remote sensing for crop management. Photogr. Eng. Remote Sens. 69 (2003), 647–664.
28. R. Development Core Team, R: A Language and Environment for Statistical Computing. 2008, R Foundation for Statistical Computing, Vienna, Austria.
29. Raun, W., Solie, J., Stone, M., Martin, K., Freeman, K., Mullen, R., Zhang, H., Schepers, J., Johnson, G., Optical sensor-based algorithm for crop nitrogen fertilization. Commun. Soil Sci. Plant Anal. 36 (2005), 2759–2781, 10.1080/00103620500303988.
30. Rincker, K., Nelson, R., Specht, J., Sleper, D., Cary, T., Cianzio, S.R., Casteel, S., Conley, S., Chen, P., Davis, V., Genetic improvement of US soybean in maturity groups II, III, and IV. Crop Sci. 54 (2014), 1419–1432.
31. Rowntree, S.C., Suhre, J.J., Weidenbenner, N.H., Wilson, E.W., Davis, V.M., Naeve, S.L., Casteel, S.N., Diers, B.W., Esker, P.D., Specht, J.E., Conley, S.P., Genetic gain × management interactions in soybean: i. planting date. Crop Sci. 53 (2013), 1128–1138, 10.2135/cropsci2012.03.0157.
32. S.A.S. Institute, The SAS System for Windows, Release 9.4. 2016, SAS Institute, Cary, N.C.
33. Schepers, A.R., Shanahan, J.F., Liebig, M.A., Schepers, J.S., Johnson, S.H., Luchiari, A., Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agron. J. 96 (2004), 195–203, 10.2134/agronj2004.0195.
34. Shaner, G., Finney, R., The effect of nitrogen fertilization on the expression of slow-mildewing resistance in Knox wheat. Phytopathology 67 (1977), 1051–1056, 10.1094/Phyto-67-1051.
35. Solari, F., Shanahan, J., Ferguson, R., Schepers, J., Gitelson, A., Active sensor reflectance measurements of corn nitrogen status and yield potential. Agron. J. 100 (2008), 571–579, 10.2134/agronj2007.0244.
36. Specht, J., Hume, D., Kumudini, S., Soybean yield potential—a genetic and physiological perspective. Crop Sci. 39 (1999), 1560–1570, 10.2135/cropsci1999.3961560x.
37. Specht, J.E., Diers, B.W., Nelson, R.L., Francisco, J., de Toledo, F., Torrion, J.A., Grassini, P., Soybean. Smith, B.D. Stephen, Specht, James, Carver, Brett, (eds.) Yield Gains in Major US Field Crops, 2014, ACSESS-Alliance of Crop, Soil, and Environmental Science Societies, 311–356.
38. Swoboda, C., Pedersen, P., Effect of fungicide on soybean growth and yield. Agron. J. 101 (2009), 352–356, 10.2134/agronj2008.0150.
39. USDA-NASS, 2015 Agricultural Chemical Use Survey: Soybeans. NASS Highlights No. 2016-4. 2016, NASS, Washington, DC (accessed 20.08.16) https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Chemical_Use/2015_Cotton_Oats_Soybeans_Wheat_Highlights/ChemUseHighlights_Soybeans_2015.pdf.
40. Voldeng, H.D., Cober, E.R., Hume, D.J., Gillard, C., Morrison, M.J., Fifty-eight years of genetic improvement of short-season soybean cultivars in Canada. Crop Sci. 37 (1997), 428–431, 10.2135/cropsci1997.0011183X003700020020x.
41. Zhang, M., Hendley, P., Drost, D., O'Neill, M., Ustin, S., Corn and soybean yield indicators using remotely sensed vegetation index. Precis. Agric., 1999, 1475–1481.