Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions
Mario Gutiérrez-Rodríguez A , Matthew Paul Reynolds B C , José Alberto Escalante-Estrada A and María Teresa Rodríguez-González AA Colegio de Postgraduados, Carretera México-Texcoco Km. 36.5, 56230, Montecillo, México.
B International Maize and Wheat Improvement Center (CIMMYT), Apartado Postal 6-641, 06600 México, D.F. México.
C Corresponding author. Email: m.reynolds@cgiar.org
Australian Journal of Agricultural Research 55(11) 1139-1147 https://doi.org/10.1071/AR04214
Submitted: 1 June 2004 Accepted: 20 September 2004 Published: 26 November 2004
Abstract
Spectral reflectance (SR) indices [NDVI (R900 – R680/R900 + R680); GNDVI (R780 – R550/R780 + R550); and water index, WI (R900/R970)]; and 6 chlorophyll indices (R740/R720, NDI = R750 – R705/R750 + R705, R780 – R710/R780 – R680, R850 – R710/R850 – R680, mND = R750 – R705/R750 + R705 – 2R445, and mSR = R750 – R445/R705 – R445) were measured with a FieldSpec spectroradiometer (Analytical Spectral Devices, Boulder, CO) on bread wheat (Triticum aestivum L.) genotypes adapted to irrigated and drought conditions to establish their relationship with yield in field-grown plots. Bread wheat genotypes from the International Maize and Wheat Improvement Center (CIMMYT) were used for this study in 3 experiments: 8 genotypes in a trial representing historical progress in yield potential, and 3 pairs of near-isolines for Lr19, both of which were grown under well-watered conditions; and the third experiment included 20 drought tolerant advanced genotypes grown under moisture stress. These were grown during the 2000 and 2001 spring cycles in a temperate, high radiation environment in Obregón, NW México. The 9 SR indices were determined during grain filling along with canopy temperature depression (CTD), flag leaf photosynthetic rate, and chlorophyll estimates using a SPAD meter. The relationship of SR indices with grain yield and biomass fitted best with a linear model. NDVI and GNDVI showed positive relationships with grain yield and biomass under well-irrigated conditions (r = 0.35–0.92), whereas NDVI showed a stronger association with yield under drought conditions (r = 0.54). The 6 chlorophyll indices showed significant association with yield and biomass of wheat genotypes grown under well-irrigated conditions (r = 0.39–0.90). The association between chlorophyll indices and chlorophyll estimates was correlated (r = 0.38–0.92), as was the case for photosynthetic rate (r = 0.36–0.75). WI showed a significant relationship with grain yield in wheat genotypes grown under drought stress conditions (r = 0.60) as well as with grain yield and biomass under well-irrigated conditions (r = 0.52–0.91). The relationship between WI and CTD was significant (P ≤ 0.05) in both environments (r = 0.44–0.84). In conclusion, the SR showed potential for identifying higher-yielding genotypes in a breeding program under dry or irrigated conditions, as well as for estimating some physiological parameters.
Additional keywords: normalised vegetation difference index, green normalised vegetation difference index, chlorophyll, water index.
Aparicio N,
Villegas D,
Araus JL,
Casadesus J, Royo C
(2002) Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science 42, 1547–1555.
Aparicio N,
Villegas D,
Casadesus J,
Araus JL, Royo C
(2000) Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal 92, 83–91.
Araus JL, Casadesus J, Bort J
(2001) Recent tools for the screening of physiological traits determining yield. ‘Application of physiology in wheat breeding’. (Eds MP Reynolds, JI Ortiz-Monasterio, A McNab)
pp. 59–77. (CIMMYT: México, DF)
Babar MA, Reynolds MP, Klatt AR, van Ginkel M, Raun WR
(2003) Using spectral reflectance as a selection tool for yield and biomass in spring wheat. ‘Arnel R. Hallauer International Symposium on Plant Breeding 17–22 August 2003’. CIMMYT book of abstracts. (CIMMYT: México, DF)
Blackmer TM,
Schepers JS, Varvel GE
(1994) Light reflectance compared with other nitrogen stress measurements in corn leaves. Agronomy Journal 86, 934–938.
Das DK,
Mishra KK, Kalra N
(1993) Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices. International Journal of Remote Sensing 14, 3081–3092.
Datt B
(1999) Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing 20, 2741–2759.
| Crossref | GoogleScholarGoogle Scholar |
Field CB, Gamon JA, Peñuelas J
(1994) Remote sensing of terrestrial photosynthesis. ‘Ecophysiology of photosynthesis. Ecological studies’. (Eds D Schulze, MM Cadwell, A McNab)
pp. 511–528. (Spring-Verlag: Berlin, Germany)
Fischer RA,
Rees D,
Sayre KD,
Lu ZM,
Condon AG, Larqué-Saavedra A
(1998) Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Science 38, 1467–1475.
Gamon JA,
Field CB,
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.
Gitelson AA,
Kaufman YJ, Merzlyak MN
(1996) Use of green channel in remote sensing of global vegetation from EOS-MODros. Inf. Serv. Remote Sensing of Environment 58, 289–298.
| Crossref | GoogleScholarGoogle Scholar |
Gitelson AA, Merzylac 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.
Han S,
Hendrickson LL, Ni B
(2002) Comparison of satellite and aerial imagery for detecting leaf chlorophyll content in corn. Transactions of the American Society of Agricultural Engineers 45, 1229–1236.
Hume IH, McVicar TR, Roderick ML
(2002) On the optical properties of leaves in the visible and near infra-red under beam and diffuse radiance. Cooperative Research Centre for Catchment Hydrology, Technical Report 02/3, p. 57.
Idso SB,
Reginate RJ,
Hatfield JL, Pinter PJ
(1981) Measuring yield reducing plant water potential depression in wheat by infrared thermometry. Irrigation Science 2, 205–212.
| Crossref |
Jackson RD, Pinter PJ
(1986) Spectral response of architecturally different wheat canopies. Remote Sensing of Environment 20, 43–56.
| Crossref | GoogleScholarGoogle Scholar |
Le Maire G,
Francois C, Dufrene E
(2004) Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 89, 1–28.
| Crossref | GoogleScholarGoogle Scholar |
Ma BL,
Dwyer LM,
Costa C,
Cober ER, Morrison MJ
(2001) Early prediction of soybean yield from canopy reflectance measurements. Agronomy Journal 93, 1227–1234.
Osborne SL,
Schepers JS,
Francis DD, Schlemmer MR
(2002) Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen and water-stressed corn. Crop Science 42, 165–171.
| PubMed |
Peñuelas J
(1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3, 151–156.
| Crossref | GoogleScholarGoogle Scholar |
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.
Peñuelas J,
Isla R,
Filella I, Araus JL
(1997) Visible and near-infrared reflectance assessment of salinity effects on barley. Crop Science 37, 198–202.
Quarmby NA,
Milnes M,
Hindle TL, Silleos N
(1993) The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing 14, 199–210.
Raun WR,
Solie JB,
Johnson GV,
Stone ML,
Lukina EV,
Thomason WE, Schepers JS
(2001) In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93, 131–138.
Reynolds MP,
Calderini DF,
Condon AG, Rajaram S
(2001) Physiological basis of yield gains in wheat associated with the LR19 translocation from A. elongatum. Euphytica 119, 139–141.
| Crossref | GoogleScholarGoogle Scholar |
Reynolds MP,
Rajaram S, Sayre KD
(1999) Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Science 39, 1611–1621.
Roderick ML,
Berry SL,
Saunders AR, Nobel IR
(1999) On the relationship between composition, morphology and function of leaves. Functional Ecology 13, 696–710.
| Crossref | GoogleScholarGoogle Scholar |
Royo C,
Aparicio N,
Villegas D,
Casadesus J,
Monneveux P, Araus JL
(2003) Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. International Journal of Remote Sensing 24, 4403–4419.
| Crossref | GoogleScholarGoogle Scholar |
Rudorff BFT, Batista GT
(1990) Spectral response of wheat and its relationship to agronomic variables in the tropical region. Remote Sensing of Environment 31, 53–63.
| Crossref | GoogleScholarGoogle Scholar |
SAS Institute (1985).
Serrano L,
Filella I, Peñuelas J
(2000) Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science 40, 723–731.
Shanahana JF,
Schepersa JS,
Francisa DD,
Varvela GE,
Wilhelma WW,
Tringea JM,
Schlemmera MR, Majorb DJ
(2001) Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal 93, 583–589.
Shibayama M,
Wiegand CL, Richardson AJ
(1986) Diurnal patterns of bi-directional vegetation indices for wheat canopies. International Journal of Remote Sensing 7, 233–246.
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 |
Slafer GA, Satorre EH
(1999) An introduction to the physiological-ecological analysis of wheat yield. ‘Wheat, ecology and physiology of yield determination’. (Eds EH Satorre, GA Slafer, A McNab)
pp. 1–12. (Food Products Press: New York)
Smith WK,
Vogelmann TC,
DeLucia EH,
Bell DT, Shepherd KA
(1997) Leaf form and photosynthesis. Bioscience 47, 785–793.
Tumbo SD,
Wagner DG, Heinemann PH
(2002) Hyperspectral-based neural network for predicting chlorophyll status in corn. Transactions of the American Society of Agricultural Engineers 45, 825–832.
Vogelman JE,
Rock BN, Moss DM
(1993) Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14, 1563–1575.
Wiegand CL, Richardson AJ
(1990) Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield: II. Results. Agronomy Journal 82, 630–636.
Wiegand CL,
Richardson AJ,
Escobar DE, Gerbermann AH
(1991) Vegetation indices in crop assessments. Remote Sensing of Environment 35, 105–119.
| Crossref | GoogleScholarGoogle Scholar |
Willmott CJ
(1982) Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63, 1309–1313.
| Crossref | GoogleScholarGoogle Scholar |
