High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage

The high throughput determination of the water status of maize (Zea mays L.) in precision agriculture presents numerous benefits, but also shows the potential for improvement. On the former count, the differentiation of maize hybrids could be used in screening drought tolerance in plant breeding, whereas, on the latter count, the monitoring of plant water status by non-destructive high-throughput sensing carried out on GPS based vehicles could enable the fast evaluation of various traits over a large area, improving the management decisions of farmers. The aim of this study was to assess the ability to measure the canopy water mass (CWM; amount of water in kg m−2) of several tropical maize hybrids using high throughput sensing. Experimental field trials were conducted in Thailand (National Corn and Sorghum Research Center) in the years 2007–2009, where seven hand sown tropical high yield hybrids were analyzed under four furrow irrigation treatments. High throughput canopy reflectance and thermal radiance measurements, as well as biomass samplings were done on a regular basis until flowering. Both a large number of spectral indices from literature and newly developed for this study were validated. Selected spectral indices and IR-temperature were highly correlated with CWM and able to show the different drought stress levels. Several indices showed global coefficients of determination of over 0.70 and it was possible to differentiate and classify the hybrids into three consistent groups (above, below, or average performance) under control and stress environments. The results of this study show that it is indeed possible to both detect CWM and discriminate between groups of hybrids using non-destructive high throughput phenotyping, and that this technology presents a potentially useful application for breeding in the future.

[1]  Mark E. Cooper,et al.  Improving drought tolerance in maize: a view from industry , 2004 .

[2]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[3]  A. Gitelson,et al.  Application of Spectral Remote Sensing for Agronomic Decisions , 2008 .

[4]  Guiping Yu,et al.  A proposal for universal formulas for estimating leaf water status of herbaceous and woody plants based on spectral reflectance properties , 2000, Plant and Soil.

[5]  Yuncai Hu,et al.  Drought and salinity: A comparison of their effects on mineral nutrition of plants , 2005 .

[6]  Jiyul Chang,et al.  Characterizing Water and Nitrogen Stress in Corn Using Remote Sensing , 2006 .

[7]  Wilhelm Claupein,et al.  Identification and discrimination of water stress in wheat leaves (Triticum aestivum L.) by means of reflectance measurements , 2007, Irrigation Science.

[8]  J. Passioura.  The drought environment: physical, biological and agricultural perspectives. , 2006, Journal of experimental botany.

[9]  M. Bänziger,et al.  Nitrogen uptake and utilization in contrasting nitrogen efficient tropical maize hybrids , 2007 .

[10]  L. Stodieck,et al.  Extraneous variables and their influence on reflectance-based measurements of leaf water content , 2008, Irrigation Science.

[11]  D. Major,et al.  Methods of measuring and characterizing the effects of stresses on leaf and canopy signatures. , 2003 .

[12]  R. Richards,et al.  Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment , 2010 .

[13]  M. Chaves,et al.  Drought effects and water use efficiency: improving crop production in dry environments , 2010 .

[14]  C. Giardino,et al.  Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling , 2008 .

[15]  Urs Schmidhalter,et al.  Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence , 2009 .

[16]  John A. Gamon,et al.  Monitoring drought effects on vegetation water content and fluxes in chaparral with the 970 nm water band index , 2006 .

[17]  William J. Emery,et al.  Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants , 2008 .

[18]  B. Mistele,et al.  Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic , 2008 .

[19]  Daniel Rodriguez,et al.  Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts , 2006 .

[20]  L. Grant.  Diffuse and specular characteristics of leaf reflectance , 1987 .

[21]  U. Schmidhalter.  Sensing soil and plant properties by non-destructive measurements , 2005 .

[22]  B. Mistele,et al.  High‐Throughput Sensing of Aerial Biomass and Above‐Ground Nitrogen Uptake in the Vegetative Stage of Well‐Watered and Drought Stressed Tropical Maize Hybrids , 2011 .

[23]  Wenjiang Huang,et al.  Estimating winter wheat plant water content using red edge parameters , 2004 .

[24]  José Luis Araus,et al.  Relationship between Growth Traits and Spectral Vegetation Indices in Durum Wheat , 2002 .

[25]  M. Bänziger,et al.  Selection Improves Drought Tolerance in Tropical Maize Populations: I. Gains in Biomass, Grain Yield, and Harvest Index , 1999 .

[26]  U. Schmidhalter,et al.  Application and testing of a crop scanning instrument – field experiments with reduced crop width, tall maize plants and monitoring of cereal yield , 2001 .

[27]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[28]  Abraham Blum,et al.  Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress , 2009 .

[29]  Jeffrey W. White,et al.  Genomics and the physiologist: bridging the gap between genes and crop response , 2004 .

[30]  J. Maranville,et al.  Deficit irrigation and nitrogen effects on maize in a Sahelian environment: I. Grain yield and yield components , 2000 .

[31]  Yoshio Inoue,et al.  Non-destructive Estimation of Water Status of Intact Crop Leaves Based on Spectral Reflectance Measurements , 1993 .

[32]  James S. Schepers,et al.  Measuring Chlorophyll Content in Corn Leaves with Differing Nitrogen Levels and Relative Water Content , 2019 .

[33]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[34]  C. Grieve,et al.  Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared. , 2006, Journal of environmental quality.

[35]  William J. Emery,et al.  Plant water parameters and the remote sensing R1300/R1450 leaf water index: controlled condition dynamics during the development of water deficit stress , 2009, Irrigation Science.

[36]  Wenjiang Huang,et al.  Estimating winter wheat plant water content using red edge width , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[37]  S. R. Evett,et al.  Spatial and Temporal Analysis of Crop Conditions Using Multiple Canopy Temperature Maps Created with Center-Pivot-Mounted Infrared Thermometers , 2007 .

[38]  G. Hoogenboom,et al.  Impact of Water Stress on Maize Grown Off‐Season in a Subtropical Environment , 2007 .

[39]  E. J. Milton,et al.  Processing of High Spectral Resolution Reflectance Data for the Retrieval of Canopy Water Content Information , 1998 .

[40]  Simon D. Jones,et al.  Remote sensing of nitrogen and water stress in wheat , 2007 .

[41]  J. Schepers,et al.  Use of Spectral Radiance to Estimate In-Season Biomass and Grain Yield in Nitrogen- and Water-Stressed Corn. , 2002, Crop science.

[42]  W. Bausch,et al.  Potential Use of Nitrogen Reflectance Index to estimate Plant Parameters and Yield of Maize , 2003 .

[43]  W. D. Branch.  First 100 Years – Inheritance of Testa Color in Peanut ( Arachis hypogaea L.) , 2011 .

[44]  K. Camp.  Transpiration efficiency of tropical maize (Zea mays L.) , 1996 .