Detecting interactive effects of N fertilization and heat stress on maize productivity by remote sensing techniques

European Journal of Agronomy

Volumn 73, Issue , 2016, Pages 11-24

  Elazab, Abdelhalim ^a   Ordóñez, Raziel A ^b   Savin, Roxana ^b   Slafer, Gustavo A ^b,c   Araus, José Luis ^a  

^a UNIVERSITY OF BARCELONA   (Spain)

^b UNIVERSITY OF LLEIDA   (Spain)

^c INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

Author keywords

Abiotic stresses; Digital RGB images; Multispectral images; Vegetation indices

Indexed keywords

ABOVEGROUND BIOMASS; CHLOROPHYLL; COMPARATIVE STUDY; CROP PRODUCTION; CROP YIELD; DETECTION METHOD; DIGITIZATION; FERTILIZER APPLICATION; FLOWERING; HEAT FLUX; IMAGE ANALYSIS; LEAF MORPHOLOGY; MAIZE; NDVI; NITROGEN; REFLECTANCE; REMOTE SENSING; SPECTRAL ANALYSIS;

ZEA MAYS;

EID: 84949034917     PISSN: 11610301     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eja.2015.11.010     Document Type: Article

References (79)

1. Araus J.L., Casadesús J., Bort J. Recent tools for the screening of physiological traits determining yield. Application of Physiology in Wheat Breeding 2001, 59-77. CIMMYT, Mexico. M.P. Reynolds, J.I. Ortiz-Monasterio, A. McNab (Eds.).
2. Araus J.L., Cairns J.E. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 2014, 19:52-61.
3. Babar M.A., Reynolds M.P., van Ginkel M., Klatt A.R., Raun W.R., Stone M.L. Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 2006, 46:1046-1057.
4. Berry J., Bjorkman O. Photosynthetic response and adaptation to temperature in higher plants. Annu. Rev. Plant Physiol. 1980, 31:491-543.
5. Borrás L., Westgate M.E., Otegui M.E. Control of kernel weight and kernel water relations by post-flowering source-sink ratio in maize. Ann. Bot. 2003, 91:857-867.
6. Cairns J., Sonder K., Zaidi P.H., Verhulst P.N., Mahuku G., Babu R., Nair S.K., Das B., Govaerts B., Vinayan M.T., Rashid Z., Noor J.J., Devi P., san Vicente F., Prasanna B.M. Maize production in a changing climate: impacts, adaptation, and mitigation strategies. Adv. Agron. 2012, 144:1-58.
7. Cairns J., Hellin J., Sonder K., Araus J.L., MacRobert J., Thierfelder C., Prasanna B.M. Adapting maize production to climate change in sub-saharan Africa. Food Secur. 2013, 5:345-360.
8. Casadesús J., Kaya Y., Bort J., Nachit M.M., Araus J.L., Amor S., Ferrazzano G., Maalouf F., Maccaferri M., Martos V., Ouabbou H., Villegas D. Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann. Appl. Biol. 2007, 150:227-236.
9. Cicchino M., Rattalino Edreira J.I., Uribelarrea M., Otegui M.E. Heat stress in field grown maize: response of physiological determinants of grain yield. Crop Sci. 2010, 50:1438-1448.
10. 4 plant, maize, to heat stress. Plant Physiol. 2002, 129:1773-1780.
11. D'Andrea K.E., Otegui M.E., Cirilo A.G. Kernel number determination differsamong maize hybrids in response to nitrogen. Field Crop Res. 2008, 105:228-239.
12. Daughtry C.S.T., Bauer M.E., Crecelius D.W., Hixson M.M. Effects of management practices on reflectance of spring wheat canopies. Agron. J. 1980, 72:1055-1060.
13. Dobrowski S.Z., Pushnik J.C., Zarco-Tejada P.J., Ustin S.L. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sens. Environ. 2005, 97:403-414.
14. Engels C., Marschne H. Plant uptake and utilization of nitrogen. Nitrogen Fertilization and the Environment 1995, 41-83. Marcel Dekker, New York. P.E. Bacon (Ed.).
15. Facchi A., Baroni G., Boschetti M., Gandolfi C. Comparing optical and direct methods for leaf area index determination in a maize crop. J. Agric. Eng. 2010, 41:33-40.
16. Fageria N.K., Baligar V.C., Jones C.A. Growth and Mineral Nutrition of Field Crops 2011, CRC Press, Boca Raton.
17. FAO., 2012. FAO Stat. (accessed 15.06.14.). http://faostat.fao.org/site/567/DesktopDefault.aspx%3FPageID=567#ancor.
18. Farooq M., Bramley H., Palta J.A., Siddique K.H.M. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant Sci. 2011, 30:491-507.
19. Filella I., Serrano L., Serra J., Peñuelas J. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 1995, 35:1400-1405.
20. Frink C.R., Waggoner P.E., Ausubel J.H. Nitrogen fertilizer: Retrospect and prospect. Proc. Natl. Acad. Sci. U. S. A. 1999, 96:1175-1180.
21. Gambín B.L., Borrás L., Otegui M.E. Kernel weight dependence upon plant growth at different grain-filling stages in maize and sorghum. Aust. J. Agric. Res. 2008, 59:280-290.
22. Gates D. Biophysical Ecology 1980, Springer-Verlg, New York.
23. Gitelson A.A., Merzlyak M.N. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 1997, 18:2691-2697.
24. Gitelson A.A., Kaufman Y.J., Stark R., Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80:76-87.
25. Gitelson A.A., Peng Y., Arkebauer T.J., Schepers J. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: implications for remote sensing of primary production. Remote Sens. Environ. 2014, 144:65-72.
26. Hatfield J.L., Gitelson A.A., Schepers J.S., Walthall C.L. Application of spectral remote sensing for agronomic decisions all rights reserved. Agron. J. 2008, 92:117-131.
27. Hawkins E., Fricker T.E., Challinor A.J., Ferro C.A.T., Ho C.K., Osborne T.M. Increasing influence of heat stress on French maize yields from the 1960 to the 2030. Global Change Biol. 2013, 19:937-947.
28. Henik J.J. Utilizing NDVI and remote sensing data to identify spatial variability in plant stress as influenced by management. M. Sc Thesis 2012, Iowa State University, USA.
29. Hodgins R., Van Huystee R.B. Porphyrin metabolism in chill stressed Maize (Zea mays L.). J. Plant Physiol. 1986, 125:325-336.
30. Hoyos-Villegas V., Houx J.H., Singh S.K., Fritschi F.B. Ground-based digital imaging as a tool to assess soybean growth and yield. Crop Sci. 2014, 54:1756-1768.
31. Huete A.R., Jackson R.D., Post D.F. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ. 1985, 17:37-53.
32. Hunt E.R., Cavigelli M., Daughtry C.S.T., McMurtrey J.E., Walthall C. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric. 2005, 6:359-378.
33. Hunt E.R., Doraiswamy P.C., McMurtrey J.E., Daughtry C.S.T., Perry E.M., Akhmedov B. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Obs. Geoinf. 2013, 21:103-112.
34. Kanemasu E.T. Seasonal canopy reflectance patterns of wheat, sorghum, and soybean. Remote Sens. Environ. 1974, 3:43-47.
35. Keane R.E., Reinhardt E.D., Scott J., Gray K., Reardon J. Estimating forest canopy bulk density using six indirect methods. Can J. For. Res. 2005, 35:724-739.
36. Keeling P.L., Greaves J.A. Effects of temperature stress on corn: opportunities for breeding and biotechnology. Proceedings 45th Annual Corn and Sorghum Research Conference. American Seed Trade Association 1990, 29-42. D. Wilkinson (Ed.).
37. Kipp S., Mistele B., Baresel P., Schmidhalter U. High-throughput phenotyping early plant vigour of winter wheat. Eur. J. Agron. 2014, 52:271-278.
38. Knipling E.B. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1970, 1:155-159.
39. Lan Y., Thomson S.J., Huang Y., Hoffmann W.C., Zhang H. Current status and future directions of precision aerial application for site-specific crop management in the USA. Comput. Electron. Agric. 2010, 74:34-38.
40. Leamer R.W., Noriega J.R., Wiegand C.L. Seasonal changes in reflectance of two wheat cultivars. Agron. J 1978, 70:113-118.
41. Ling Q., Huang W., Jarvis P. Use of a SPAD-502 meter to measure leaf chlorophyll concentration in Arabidopsis thaliana. Photosynth. Res. 2011, 107:209-214.
42. Liu K., Wiatrak P. Corn production response to tillage and nitrogen application in dry-land environment. Soil Tillage Res. 2012, 124:138-143.
43. Lobell D.B., Burke M.B., Tebaldi C., Mastrandrea M.D., Falcon W.P., Naylor R.L. Prioritizing climate change adaptation needs for food security in 2030. Science 2008, 319:607-610.
44. Lobell D.B., Schlenker W., Costa-Roberts J. Climate trends and global crop production since 1980. Science 2011, 333:616-620.
45. 4 crops. J. Exp. Bot. 2011, 62:3135-3153.
46. Maddonni G.A., Otegui M.E., Bonhomme R. Grain yield components in maize II. Postsilking growth and kernel weight. Field Crop Res. 1998, 56:257-264.
47. Nguy-Robertson A., Gitelson A.A., Peng Y., Viña A., Arkebauer T., Rundquist D. Green leaf area index estimation in maize and soybean: combining vegetation indices to achieve maximal sensitivity. Agron. J. 2012, 104:1336-1347.
48. Ning P., Li S., Yu P., Zhang Y., Li C. Post-silking accumulation and partitioning of dry matter, nitrogen, phosphorus and potassium in maize varieties differing in leaf longevity. Field Crops Res. 2013, 144:19-27.
49. Ordóñez R.A., Savin R., Cossani C.M., Slafer G.A. Yield response to heat stress as affected by nitrogen availability in maize. Field Crop Res. 2015, 183:184-203.
50. Parry M.A.J., Flexas J., Medrano H. Prospects for crop production under drought: research priorities and future directions. Ann. Appl. Biol. 2005, 147:211-226.
51. Passioura J.B. The perils of pot experiments. Funct. Plant Biol. 2006, 33:1075-1079.
52. Passioura J.B. Scaling up: the essence of effective agricultural research. Funct. Plant Biol. 2010, 37:585-591.
53. Peng Y., Gitelson A.A. Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agric. For. Meteorol. 2011, 151:1267-1276.
54. Prasad P.V.V., Craufurd P.Q., Summerfield R.J. Sensitivity of peanut to timing of heat stress during reproductive development. Crop Sci. 1999, 39:1352-1357.
55. Prasad P.V.V., Staggenborg S.A. Impacts of drought and/or heat stress on physiological, developmental, growth, and yield processes of crop plants. Response of Crops to Limited Water: Understanding and Modeling Water Stress Effects on Plant Growth Processes 2008, 301-356. American Society of Agronomy/Crop Science Society of America/Soil Science Society of America, Madison. L.R. Ajuha, V.R. Reddy, S.A. Saseendran, Q. Yu (Eds.).
56. Rajcan I., Tollenaar M. Source: sink ratio and leaf senescence in maize: I. Dry matter accumulation and partitioning during grain filling. Field Crops Res. 1999, 60:245-253.
57. Rattalino Edreira J.I., Budakli Carpici E., Sammarro D., Otegui M.E. Heat stress effects around flowering on kernel set of temperate and tropical maize hybrids. Field Crops Res. 2011, 123:62-73.
58. Rattalino Edreira J.I., Otegui M.E. Heat stress in temperate and tropical maize hybrids: differences in crop growth, biomass partitioning and reserves use. Field Crops Res. 2012, 130:87-98.
59. Rattalino Edreira J.I., Otegui M.E. Heat stress in temperate and tropical maize hybrids: a novel approach for assessing sources of kernel loss in field conditions. Field Crops Res. 2013, 142:58-67.
60. Raun W.R., Solie J.B., Johnson G.V., Stone M.L., Lukina E.V., Thomason W.E., Schepers J.S. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 2001, 93:131-138.
61. Raun W.R., Solie J.B., Johnson G.V., Stone M.L., Mullen R.W., Freeman K.W., Thomason W.E., Lukina E.V. Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 2002, 94:815-820.
62. Roberts D.C., Brorsen B.W., Solie J.B., Raun W.R. The effect of parameter uncertainty on whole-field nitrogen recommendations from nitrogen-rich strips and ramped strips in winter wheat. Agric. Syst. 2011, 104:307-314.
63. Rorie R.L., Purcell L.C., Karcher D.E., King C.A. The assessment of leaf nitrogen in corn from digital images. Crop Sci. 2011, 51:2174-2180.
64. Sakamoto T., Gitelson A.A., Nguy-Robertson A.L., Arkebauer T.J., Wardlow B.D., Suyker A.E., Verma S.B., Shibayama M. An alternative method using digital cameras for continuous monitoring of crop status. Agric. For. Meteorol. 2012, 154-155:113-126.
65. Sakamoto T., Gitelson A.A., Wardlow B.D., Arkebauer T.J., Shashi B.V., Suyker A.E., Shibayama M. Application of day and night digital photographs for estimating maize biophysical characteristics. Precis. Agric. 2012, 13:285-301.
66. Samuel R.A., Scott W.O., Hoft R.G. Modern Corn Production 1986, A and L publishers Inc., Illinois.
67. Schoper J.B., Lambert R.J., Vasilas B.L., Westgate M.E. Plant factors controlling seed set in maize. Plant Physiol. 1987, 83:121-125.
68. Smith G.M., Milton E.J. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sens. 1999, 20:2653-2662.
69. Soil Survey Staff (SSS) Key to Soil Taxonomy. SMSS Technical Monograph n°6 1990, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. fourth edition.
70. Srivastava H.S., Singh R.P. Nitrogen Nutrition and Plant Growth 1999, Science Publishers Inc., New Hampshire.
71. Suits G.H. The calculation of the directional reflectance of a vegetative canopy. Remote Sens. Environ. 1971, 2:117-125.
72. Svensgaard J., Roitsch T., Christensen S. Development of a mobile multispectral imaging platform for precise field phenotyping. Agron 2014, 4:322-336.
73. Tassawar H., Iftikhar A.K., Zulfigar A. Study on gene action and combining abilities for thermo tolerant abilities of corn (Zea mays L.). Intern. J. Plant Prod. 2007, 1:1-12.
74. Teal R.K., Tubana B., Girma K., Freeman K.W., Arnall D.B., Walsh O., Raun W.R. In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron. J. 2006, 98:1488-1494.
75. Wahid A., Gelani S., Ashraf M., Foolad M.R. Heat tolerance in plants: an overview. Environ. Exp. Bot. 2007, 61:199-223.
76. Warren G., Metternicht G. Agricultural applications of high-resolution digital multispectral imagery. Photogramm. Eng. Remote Sens. 2005, 71:595-602.
77. Weier, J., Herring, D. 2000. Measuring vegetation (NDVI & EVI), Earth Observatory, NASA, USA. (accessed 20.06.14.). http://earthobservatory.nasa.gov/Features/MeasuringVegetation/.
78. Zhang J.H., Wang K., Bailey J.S., Wang R.C. Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere 2006, 16:108-117.
79. Zhou L., He H.L., Sun X.M., Zhang L., Yu G.R., Ren X.L., Wang J.Y., Zhao F.H. Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data. Ecol. Inform. 2013, 18:69-78.