Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts
D. Rodriguez A E , G. J. Fitzgerald B , R. Belford C and L. K. Christensen DA Agricultural Production Systems Research Unit (APSRU), Department of Primary Industries and Fisheries, PO Box 102, Toowoomba, Qld 4350, Australia.
B USDA-ARS, U.S. Water Conservation Laboratory, 4331 E. Broadway Rd, Phoenix, AZ 85040, USA.
C Primary Industries Research Victoria, PO Box 260, Horsham, Vic. 3401, Australia.
D Nordic Gene Bank, PO Box 41, SE-23053 Alnarp, Sweden.
E Corresponding author. Email: daniel.rodriguez@dpi.qld.gov.au
Australian Journal of Agricultural Research 57(7) 781-789 https://doi.org/10.1071/AR05361
Submitted: 12 October 2005 Accepted: 17 February 2006 Published: 14 July 2006
Abstract
We tested the capacity of several published multispectral indices to estimate the nitrogen nutrition of wheat canopies grown under different levels of water supply and plant density and derived a simple canopy reflectance index that is greatly independent of those factors. Planar domain geometry was used to account for mixed signals from the canopy and soil when the ground cover was low. A nitrogen stress index was developed, which adjusts shoot %N for plant biomass and area, thereby accounting for environmental conditions that affect growth, such as crop water status. The canopy chlorophyll content index (CCCi) and the modified spectral ratio planar index (mSRPi) could explain 68 and 69% of the observed variability in the nitrogen nutrition of the crop as early as Zadoks 33, irrespective of water status or ground cover. The CCCi was derived from the combination of 3 wavebands 670, 720 and 790 nm, and the mSRPi from 445, 705 and 750 nm, together with broader bands in the NIR and RED. The potential for their spatial application over large fields/paddocks is discussed.
Additional keywords: water stress, plant density, remote sensing, nitrogen stress index.
Acknowledgments
This work was fully funded by the Department of Primary Industries of Victoria, Australia. We greatly appreciate the excellent field work done by Russel Argall. We appreciate the comments made by Dr Graeme Wright and Tom Clarke on early versions of this manuscript. We also thank the personnel at the USA Water Conservation Laboratory for their hard work and dedication.
Angus JF,
van Herwaarden AF,
Fischer RA,
Howe GN, Heenan DP
(1998) The source of mineral nitrogen for cereals in south-eastern Australia. Australian Journal of Agricultural Research 49, 511–522.
| Crossref | GoogleScholarGoogle Scholar |
Bausch WC
(1993) Soil background effects on reflectance-based crop coefficients for corn. Remote Sensing of Environment 46, 213–222.
| Crossref | GoogleScholarGoogle Scholar |
Bausch WC, Diker K
(2001) Innovative remote sensing techniques to increase nitrogen use efficiency of corn. Communications in Soil Science and Plant Analysis 32, 1371–1390.
| Crossref | GoogleScholarGoogle Scholar |
Blackmer TM,
Schepers JS, Varvel GE
(1986) Light reflectance compared with other nitrogen stress measurements in corn leaves. Agronomy Journal 86, 934–938.
Bowman WD
(1989) The relationship between leaf water status, gas exchange and spectral reflectance in cotton leaves. Remote Sensing of Environment 30, 249–255.
| Crossref | GoogleScholarGoogle Scholar |
Chaves MM,
Maroco JP, Pereira JS
(2003) Understanding plant responses to drought—from genes to the whole plant. Functional Plant Biology 30, 239–264.
| Crossref | GoogleScholarGoogle Scholar |
Colwell JD
(1963) The estimation of the phosphorus fertilizer requirements of wheat in southern New South Wales by soil analysis. Australian Journal of Experimental Agriculture and Animal Husbandry 3, 190–197.
| Crossref | GoogleScholarGoogle Scholar |
Cook SE, Bramley RGV
(1998) Precision agriculture—opportunities, benefits and pitfalls of site-specific crop management in Australia. Australian Journal of Experimental Agriculture 38, 753–763.
| Crossref | GoogleScholarGoogle Scholar |
Datt B
(1999) Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing 14, 3081–3092.
Filella L, Peñuelas J
(1994) The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing 15, 1459–1470.
Follett RH,
Follett RF, Halvorson AD
(1992) Use of a chlorophyll meter to evaluate the nitrogen status of dryland winter wheat. Communications in Soil Science and Plant Analysis 23, 687–697.
Gamon JA,
Field CV,
Goulden ML,
Griffin KL,
Hartley AE,
Joel G,
Peñuelas J, Valentini R
(1995) Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications 5, 28–41.
| Crossref | GoogleScholarGoogle Scholar |
Gastal F, Lemaire G
(2002) N uptake and distribution in crops: an agronomical and ecophysiological perspective. Journal of Experimental Botany 53, 789–799.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Gitelson AA, Merzlyak MN
(1994) Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology 143, 286–292.
Greenwood DJ,
Neeteson JJ, Draycott A
(1986) Quantitative relationships for the dependence of growth rate of arable crops on their nitrogen content, dry weight and aerial environment. Plant and Soil 91, 281–301.
| Crossref | GoogleScholarGoogle Scholar |
Huete AR
(1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.
| Crossref | GoogleScholarGoogle Scholar |
McCallum MH,
Peoples MB, Connor DJ
(2000) Contributions of nitrogen by field pea (Pisum sativum L.) in a continuous cropping sequence compared with a lucerne (Medicago sativa L.)-based pasture ley in the Victorian Mallee. Australian Journal of Agricultural Research 51, 13–22.
| Crossref | GoogleScholarGoogle Scholar |
Moran MS,
Pinter PJ,
Clothier BE, Allen SG
(1989) Effect of water stress on the canopy architecture and spectral indices of irrigated alfalfa. Remote Sensing of Environment 29, 251–261.
| Crossref | GoogleScholarGoogle Scholar |
O’Leary GJ, Connor DJ
(1998) A simulation study of wheat crop response to water supply, nitrogen nutrition, stubble retention, and tillage. Australian Journal of Agricultural Research 49, 11–19.
| Crossref | GoogleScholarGoogle Scholar |
Osborne SL,
Schepers JS,
Francis DD, Schlemmer MR
(2002) Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agronomy Journal 94, 1215–1221.
Peñuelas J,
Filella I,
Biel C,
Serrano L, Save R
(1993) The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14, 1887–1905.
Pinter PJ,
Hatfield JL,
Schepers JS,
Barnes EM,
Moran MS,
Daughtry CST, Upchurch DR
(2003) Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing 69, 647–664.
Raun WR,
Solie JB,
Johnson GV,
Stone ML,
Lukina WE,
Thomason WE, Schepers JS
(2001) In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93, 131–138.
Raun WR,
Solie JB,
Stone ML,
Zavodny DL,
Martin KL, Freeman KW
(2005) Automated calibration stamp technology for improved in-season nitrogen fertilisation. Agronomy Journal 97, 338–342.
Richardson AJ, Wiegand CL
(1977) Distinguishing vegetation from soil background information. Photogrammetric Engineering & Remote Sensing 43, 1541–1552.
Rodriguez D,
Sadras VO,
Christensen LK, Belford R
(2005) Spatial assessment of the physiological status of wheat crops as affected by water and nitrogen supply using infrared thermal imagery. Australian Journal of Agricultural Research 56, 983–993.
| Crossref | GoogleScholarGoogle Scholar |
Sims DA, Gamon JA
(2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81, 337–354.
| Crossref | GoogleScholarGoogle Scholar |
Stone RC, Auliciems A
(1992) SOI phase relationships with rainfall in eastern Australia. International Journal of Climatology 12, 625–636.
Thomas JR, Gausman HW
(1977) Leaf reflectance versus leaf chlorophyll and carotenoid concentrations for eight crops. Agronomy Journal 69, 799–802.
Vogelmann TC,
Rock BN, Moss DM
(1993) Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14, 1563–1575.
Wold S,
Albano C,
Dunn WJ,
Esbensen K,
Hellberg S, Johansson E
(1984) Modelling data tables by principal components and PLS: class patterns and quantitative predictive relationships. Analysis 12, 477–485.
Wood CW,
Reeves DW, Himelrick DG
(1993) Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status, and crop yield: A review. Proceedings Agronomy Society of New Zealand 23, 1–9.
Yoder BJ, Pettigrew-Crosby RE
(1995) Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sensing of Environments 53, 199–211.
| Crossref | GoogleScholarGoogle Scholar |