Spectral assessments of wheat plants grown in pots and containers under saline conditions
Harald Hackl A , Bodo Mistele A , Yuncai Hu A and Urs Schmidhalter A B
+ Author Affiliations
- Author Affiliations
A Chair of Plant Nutrition, Technische Universität München, Emil-Ramann-Straße 2, D-85350 Freising-Weihenstephan, Germany.
B Corresponding author. Email: schmidhalter@wzw.tum.de
Functional Plant Biology 40(4) 409-424 https://doi.org/10.1071/FP12208
Submitted: 14 July 2012 Accepted: 6 December 2012 Published: 14 February 2013
Abstract
Spectral measurements allow fast nondestructive assessment of plant traits under controlled greenhouse and close-to-field conditions. Field crop stands differ from pot-grown plants, which may affect the ability to assess stress-related traits by nondestructive high-throughput measurements. This study analysed the potential to detect salt stress-related traits of spring wheat (Triticum aestivum L.) cultivars grown in pots or in a close-to-field container platform. In two experiments, selected spectral indices assessed by active and passive spectral sensing were related to the fresh weight of the aboveground biomass, the water content of the aboveground biomass, the leaf water potential and the relative leaf water content of two cultivars with different salt tolerance. The traits were better ascertained by spectral sensing of container-grown plants compared with pot-grown plants. This may be due to a decreased match between the sensors’ footprint and the plant area of the pot-grown plants, which was further characterised by enhanced senescence of lower leaves. The reflectance ratio R760 : R670, the normalised difference vegetation index and the reflectance ratio R780 : R550 spectral indices were the best indices and were significantly related to the fresh weight, the water content of the aboveground biomass and the water potential of the youngest fully developed leaf. Passive sensors delivered similar relationships to active sensors. Across all treatments, both cultivars were successfully differentiated using either destructively or nondestructively assessed parameters. Although spectral sensors provide fast and qualitatively good assessments of the traits of salt-stressed plants, further research is required to describe the potential and limitations of spectral sensing.
Additional keywords: greenhouse, growth environment, phenotyping, reflectance, remote sensing, spectral index.
References
Arvidsson S, Pérez-Rodríguez P, Mueller-Roeber B (2011) A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytologist 191, 895–907.| A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects.Crossref | GoogleScholarGoogle Scholar |
Berger B, Parent B, Tester M (2010) High-throughput shoot imaging to study drought responses. Journal of Experimental Botany 61, 3519–3528.
| High-throughput shoot imaging to study drought responses.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtVert7vO&md5=b3bd4d7847e4ebf53f7f19d87b936eabCAS |
Claudio HC, Cheng Y, Fuentes DA, Gamon JA, Luo H, Oechel W, Qiu H-L, Rahman AF, Sims DA (2006) Monitoring drought effects in vegetation water content and fluxes in chaparral with the 970 nm water band index. Remote Sensing of Environment 103, 304–311.
| Monitoring drought effects in vegetation water content and fluxes in chaparral with the 970 nm water band index.Crossref | GoogleScholarGoogle Scholar |
El-Hendawy SE, Hu Y, Yakout GM, Awad AM, Hafiz SE, Schmidhalter U (2005) Evaluating salt tolerance of wheat genotypes using multiple parameters. European Journal of Agronomy 22, 243–253.
| Evaluating salt tolerance of wheat genotypes using multiple parameters.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXhtlSkurc%3D&md5=83e3a64ca55fed7f345c14a2acf262f6CAS |
Elmetwalli AMH, Tyler AN, Hunter PD, Salt CA (2012) Detecting and distinguishing moisture- and salinity-induced stress in wheat and maize through in situ spectroradiometry measurements. Remote Sensing Letters 3, 363–372.
| Detecting and distinguishing moisture- and salinity-induced stress in wheat and maize through in situ spectroradiometry measurements.Crossref | GoogleScholarGoogle Scholar |
Elsayed S, Mistele B, Schmidhalter U (2011) Can changes in leaf water potential be assessed spectrally? Functional Plant Biology 38, 523–533.
Erdle K, Mistele B, Schmidhalter U (2011) Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research 124, 74–84.
| Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars.Crossref | GoogleScholarGoogle Scholar |
Food and Agriculture Organization (2008) FAO land and plant nutrition management service. Available at: http://www.fao.org/ag/agl/agll/spush [Verified December 2012].
Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16, 635–644.
| Phenomics – technologies to relieve the phenotyping bottleneck.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFOhu7%2FJ&md5=099c62d008e03f9befe0d41b0e830cecCAS |
Gao X, Huete AR, Ni W, Miura T (2000) Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment 74, 609–620.
| Optical-biophysical relationships of vegetation spectra without background contamination.Crossref | GoogleScholarGoogle Scholar |
Golzarian MR, Frick RA, Rajendran K, Berger B, Roy S, Tester M, Lun DS (2011) Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7, 2
| Accurate inference of shoot biomass from high-throughput images of cereal plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXisFSrtb4%3D&md5=a8e459da5cd39d34e16bc4444efa496bCAS |
Graeff S, Claupein W (2007) Identification and discrimination of water stress in wheat leaves (Triticum aestivum L.) by means of reflectance measurements. Irrigation Science 26, 61–70.
| Identification and discrimination of water stress in wheat leaves (Triticum aestivum L.) by means of reflectance measurements.Crossref | GoogleScholarGoogle Scholar |
Gutierrez M, Reynolds MP, Klatt AR (2010) Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes. Journal of Experimental Botany 61, 3291–3303.
| Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXptVymsL4%3D&md5=c486de3285ffb35b7740012c32bb419aCAS |
Guyot G, Baret F, Major DJ (1988) High spectral resolution: determination of spectral shifts between the red and the near infrared. International Archives of Photogrammetry and Remote Sensing 11, 750–760.
Hackl H, Baresel JP, Mistele B, Hu Y, Schmidhalter U (2012) A comparison of plant temperatures as measured by thermal imaging and infrared thermometry. Journal Agronomy & Crop Science 198, 415–429.
| A comparison of plant temperatures as measured by thermal imaging and infrared thermometry.Crossref | GoogleScholarGoogle Scholar |
Holland-Scientific (2008) ‘Crop circle ACS-470 user’s guide’ (Holland Scientific: Lincoln)
Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.
| A soil-adjusted vegetation index (SAVI).Crossref | GoogleScholarGoogle Scholar |
Kipp S, Mistele B, Schmidhalter U (2012) Active sensor performance – dependence on measuring height, device temperature and light intensity. In ‘Proceedings of the 11th international conference on precision agriculture, 15–18 July, 2012, Indianapolis, Indiana USA’. (CD-ROM) (The International Society of Precision Agriculture: Monticello, IL)
Leone AP, Menenti M, Sorrentino G (2001) Reflectance spectrometry to study crop response to soil salinity. Italian Journal of Agronomy 4, 75–85.
Leone AP, Menenti M, Buondonno A, Letizia A, Maffei C, Sorrentino G (2007) A field experiment on spectrometry of crop response to soil salinity. Agricultural Water Management 89, 39–48.
| A field experiment on spectrometry of crop response to soil salinity.Crossref | GoogleScholarGoogle Scholar |
Liebler J, Sticksel E, Maidl FX (2001) Field spectroscopic measurements to characterise nitrogen status and dry matter production of winter wheat. In ‘Proceedings of the third European conference on precision agriculture, Montpellier’, 18–20 June 2001 (Eds S Blackmore, G Grenier) pp. 935–940. (Agro Montpellier: Montpellier)
Major DJ, Baumeister R, Touré A, Zhao S (2003) Methods of measuring and characterizing the effects of stresses on leaf and canopy signatures. In ‘Digital Imaging and Spectral Techniques: Applications to Precision Agriculture and Crop Physiology’. ASA Special Publication 66. pp. 81–93. (American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America: Madison, WI)
Mistele B, Schmidhalter U (2008) Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Research 106, 94–103.
| Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic.Crossref | GoogleScholarGoogle Scholar |
Mistele B, Schmidhalter U (2010) Tractor-based quadrilated spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agronomy Journal 102, 499–506.
| Tractor-based quadrilated spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXltVWquro%3D&md5=b65f4eae93213f674709f3382b2f2d2fCAS |
Mistele B, Gutser R, Schmidhalter U (2004) Validation of field-scaled spectral measurements of the nitrogen status in winter wheat. In ‘Seventh international conference on precision agriculture and other precision resources management, Minneapolis, Minnesota, USA’ (Ed D Mulla), pp. 1187–1195.
Munns R (2005) Genes and salt tolerance: bringing them together. New Phytologist 167, 645–663.
| Genes and salt tolerance: bringing them together.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXhtVGisbfP&md5=a2d23c6d5faa06931cb8a05ae3e289bcCAS |
NTech Industries (2007) ‘GreenSeeker RT 100 datasheet’. (NTech Industries: Ukiah)
Passioura JB (2006) The perils of pot experiments. Functional Plant Biology 33, 1075–1079.
| The perils of pot experiments.Crossref | GoogleScholarGoogle Scholar |
Passioura JB (2010) Scaling up: the essence of effective agricultural research. Functional Plant Biology 37, 585–591.
| Scaling up: the essence of effective agricultural research.Crossref | GoogleScholarGoogle Scholar |
Pearson RL, Miller LD (1972) Remote mapping of standing crop biomass for estimating of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado. In ‘Eighth international symposium on remote sensing of environment’. pp. 1357–1381 (Environmental Research Institute of Michigan: Ann Arbor)
Peñuelas J, Inoue Y (1999) Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica 36, 355–360.
| Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves.Crossref | GoogleScholarGoogle Scholar |
Peñuelas J, Filella I, Biel C, Serrano L, Savé 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.
| The reflectance at the 950–970 nm region as an indicator of plant water status.Crossref | GoogleScholarGoogle Scholar |
Peñuelas J, Filella I, Gamon J (1995) Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist 131, 291–296.
| Assessment of photosynthetic radiation-use efficiency with spectral reflectance.Crossref | GoogleScholarGoogle Scholar |
Peñuelas J, Piñol J, Ogaya R, Filella I (1997a) Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing 18, 2869–2875.
| Estimation of plant water concentration by the reflectance water index WI (R900/R970).Crossref | GoogleScholarGoogle Scholar |
Peñuelas J, Isla R, Filella I, Araus JL (1997b) Visible and near-infrared reflectance assessment of salinity effects on barley. Crop Science 37, 198–202.
| Visible and near-infrared reflectance assessment of salinity effects on barley.Crossref | GoogleScholarGoogle Scholar |
Poorter H, Bühler J, van Dusschoten D, Climent J, Postma JA (2012) Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. Functional Plant Biology
| Pot size matters: a meta-analysis of the effects of rooting volume on plant growth.Crossref | GoogleScholarGoogle Scholar |
Poss JA, Russell WB, Grieve CM (2006) Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared. Journal of Environmental Quality 35, 1060–1071.
| Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xns1Cksbc%3D&md5=b5c31e42fa304ed0153815c7b8ba773fCAS |
Rajendran K, Tester M, Roy SJ (2009) Quantifying the three main components of salinity tolerance in cereals. Plant, Cell & Environment 32, 237–249.
| Quantifying the three main components of salinity tolerance in cereals.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjsVKlsL4%3D&md5=5aee99dfee35684acf7ae3dd02051e93CAS |
Rud R, Shoshany M, Alchanatis V (2011) Spectral indicators for salinity effects in crops: a comparison of a new green indigo ratio with existing indices. Remote Sensing Letters 2, 289–298.
| Spectral indicators for salinity effects in crops: a comparison of a new green indigo ratio with existing indices.Crossref | GoogleScholarGoogle Scholar |
Schmidhalter U, Oertli JJ (1991) Germination and seedling growth of carrots under salinity and moisture stress. Plant and Soil 132, 243–251.
Schmidhalter U, Burucs Z, Camp KH (1998) Sensitivity of root and leaf water status in maize (Zea mays L.) subjected to mild soil dryness. Australian Journal of Plant Physiology 25, 307–316.
| Sensitivity of root and leaf water status in maize (Zea mays L.) subjected to mild soil dryness.Crossref | GoogleScholarGoogle Scholar |
Sembiring H, Raun WR, Johnson GV, Stone ML, Solie JB, Phillips SB (1998) Detection of nitrogen and phosphorus nutrient status in winter wheat using spectral radiance. Journal of Plant Nutrition 21, 1207–1233.
| Detection of nitrogen and phosphorus nutrient status in winter wheat using spectral radiance.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXjvV2ktr4%3D&md5=9b9a685d65f8a1ef3cf0d6fa3ff2c528CAS |
Takebe M, Yoneyama T, Inada K, Murakami T (1990) Spectral reflectance ratio of rice canopy for estimating crop nitrogen status. Plant and Soil 122, 295–297.
| Spectral reflectance ratio of rice canopy for estimating crop nitrogen status.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3cXkt1amtbk%3D&md5=a8bf711ad97c8e5209c50dc9f78e4516CAS |
Tavakkoli E, Rengasamy P, McDonald GK (2010) The response of barley to salinity stress differs between hydroponics and soil systems. Functional Plant Biology 37, 621–633.
| The response of barley to salinity stress differs between hydroponics and soil systems.Crossref | GoogleScholarGoogle Scholar |
Thoren D, Schmidhalter U (2009) Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence. European Journal of Agronomy 30, 238–242.
| Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhvFGnsb0%3D&md5=0fa0cd6ae4185312d75834510f2a81ffCAS |
Turhan H, Genc L, Smith SE, Bostanci YB, Turkmen OS (2008) Assessment of the effect of salinity on the early growth stage of the common sunflower (Sanay cultivar) using spectral discrimination techniques. African Journal of Biotechnology 7, 750–756.
Wang D, Wilson C, Shannon MC (2002) Interpretation of salinity and irrigation effects on soybean canopy reflectance in visible and near-infrared spectrum domain. International Journal of Remote Sensing 23, 811–824.
| Interpretation of salinity and irrigation effects on soybean canopy reflectance in visible and near-infrared spectrum domain.Crossref | GoogleScholarGoogle Scholar |
Winterhalter L, Mistele B, Jampatong S, Schmidhalter U (2011a) High throughput sensing of aerial biomass and above-ground nitrogen uptake in the vegetative stage of well-watered and drought stressed tropical maize hybrids. Crop Science 51, 479–489.
| High throughput sensing of aerial biomass and above-ground nitrogen uptake in the vegetative stage of well-watered and drought stressed tropical maize hybrids.Crossref | GoogleScholarGoogle Scholar |
Winterhalter L, Mistele B, Jampatong S, Schmidhalter U (2011b) High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. European Journal of Agronomy 35, 22–32.
| High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage.Crossref | GoogleScholarGoogle Scholar |
Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Research 14, 415–421.
| A decimal code for the growth stages of cereals.Crossref | GoogleScholarGoogle Scholar |
