Midday Stem Water Potential. Rolston St. Hilaire Plant and Environmental Sciences
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1 Midday Stem Water Potential Rolston St. Hilaire Plant and Environmental Sciences
2 Ways to improve irrigation Irrigation scheduling When to irrigate How much to apply efficiency in pecans Irrigation scheduling depends on the sensitivity of field measurements to water deficit. h6p://
3 Measurements used for detecting water status in pecan orchards Plant-based Soil-based Plant-soil-based monash.edu.au Midday stem water Soil TDR Lysimeters potential (Ψ smd ) ncare.gov.jo Limitations: Small area, extraordinary cost, extraction time, leaf destruction.
4 We tried remote sensing to detect moisture status NASA'sLandsat- 8satellite Handheld portable Spectroradiometer Aerial photo plane Portable Spectroradiometer
5 Remote sensing applications Scale up leaf-level physiological responses to large areas Detect pigment concentration Spectral regions from nm and nm were correlated with pecan water deficit (Johnson 2004). Landsat-5 image Electromagnetic spectrum
6 Objectives The overall goal of this research was to develop an advanced sensing and management technologies tooptimize water resources and drought monitoring of pecan orchards. Initial research objectives: 1 Screen leaf-level physiological changes that occur during cyclic irrigation to determine parameters that best represented changes in moisture status. 2 Assess the impact of water deficit developed during the flood irrigation dry-down cycles on photosynthesis and gas exchange. 3 Establish preliminary values of midday stem water potential where photosynthesis and gas exchange of pecans are affected.
7 Locations of the studies
8 Locations of the studies Mesilla Valley La Mancha Leyendecker
9 Sampling design
10 Sampling design
11 Meteorological instrumentation Net radiometer Temperature & relative humidity Anemometer Pyranometer
12 Treatment T1 Well-watered treatment T2 Moderate treatment T3 Severe treatment Irrigation Irrigation cycle Irrigation
13 Spectral and physiological measurements Irrigation Day 1 Day 3 Day Measurements a) Midday stem water potential b) Remotely sensed surface reflectance data Irrigation
14 Satellite image processing 1) Satellite images were downloaded from United States Geological SurveyGlobal Visualization Viewer
15 Satellite image processing 2) Radiometric correction Improve the accuracy of surface spectralreflectance 3) Geometric correction Place data in their proper position.
16 Vegetation indices Band ratio B5/B7 Normalized Difference Infrared Index (NDII) (ρ NIR ρ SWIR(5) ) /(ρ NIR +ρ SWIR(5) ) Vegetation moisture index I I (VMI--II) ( x ρ B ) + (0.002 x ρ G ) (0.001 x ρ SWIR(2) ) (ρ NIR ρ R ) / (ρ NIR + ρ R ) NDVI
17 Physiological measurements Midday stem water potential Leaf area ratio Relative water content (%) Leaf temperature, transpiration Chlorophyll fluorescence (F v /F m ) Chlorophyll content (SPAD) Photosynthesis, stomatal conductance, vapor pressure deficit.
18 Screen leaf-level physiological variables Midday stem water potential Relative water content Leaf temperature Leaf area ratio Transpiration Photosynthesis Stomatal conductance Vapor pressure deficit Chlorophyll fluorescence Chlorophyll content (SPAD) Midday stem water potential (Ψ smd )
19 Remote sensing data Boxplot analysis of vegetation moisture index at three levels of midday stem water potential (Ψ smd ) at the La Mancha pecan orchard measured in 2012 and 2013.
20 Remote sensing index Boxplot analysis of vegetation moisture index at three levels of midday stem water potential (Ψ smd ) at the Leyendecker pecan orchard measured in 2012 and 2013.
21 Screen leaf-level physiological variables Midday stem water potential (Ψ smd ),and relative water content (RWC) of La Mancha and Leyendecker orchards measured in Different letters indicated a significant difference between irrigation treatments (P < 0.05).
22 Midday stem water potential boxplots of La Mancha and Leyendecker pecan orchards (Mesilla Valley, New Mexico) measured in 2012 and Rectangles represent the 25%, 50% (median), and 75% percentile of the data Outlier Midday stem water potential (MPa) Mean (dotted line) Maximum non-outlier value 75th percentile Median 25th percentile Minimum non-outlier value -1.6 Well watered (n = 60) Water deficit (n = 50) La Mancha (sandy loam soil) Leyendecker (clay loam soil)
23 Screen leaf-level physiological variables Boxplots of midday stem water potential (Ψ smd ) of two southern New Mexico pecan orchards subjected to cyclic flood irrigation (La Mancha and Leyendecker) during the 2011 growing season.
24 Relationship between stomatal conductance and midday stem water potential of trees at La Mancha and Leyendecker, southern New Mexico pecan orchards.
25 Relationship between photosynthesis and midday stem water potential of trees at La Mancha and Leyendecker, southern New Mexico pecan orchards. We used data set within the range -0.9 to -2.0 MPa to derive the mixed model equation.
26 Relationship between photosynthesis (decline) and midday stem water potential of trees at La Mancha and Leyendecker, southern New Mexico pecan orchards A B Photosynthesis % Decline % Decline Photosynthesis [CO 2 ( µ mol. m s )] P< Water deficit Well watered Water deficit Well watered Midday s tem water potential (MP a) Midday s tem water potential (MP a) L a Mancha L eyendecker P<
27 Conclusions Midday stem water potential was the best performing leaf-level physiological response variable for detecting moisture status in pecans. A marked decline in photosynthesis was noticed when midday stem water potential dropped below -0.9 MPa. A 50% reduction in photosynthesis and gas exchange only occurred when midday stem water potential exceeded -1.5 MPa. Data from a handheld spectroradiometer could be used to differentiate between well watered (Ψ smd -0.4 to MPa) and moderate water deficit (Ψ smd -0.9 to -1.5 MPa) trees.
28 Recommendation Maintain pecan orchards at midday stem water potentials that range between to MPa to prevent significant reductions in carbon assimilation and gas exchange.
29 Acknowledgements Personnel Yahia Othman Caiti Steele Dawn Vanleeuwen Richard Heerema Salim Bawazir Max P. Bleiweiss Cameron Radosevich Sanjit Kumar Deb Mohammad Sawaleha Brandon Radosevich Joshua Sherman Mid Ray Clark Leyendecker Research Staff Funding USDA-SCRI, grant no Plant and Environmental Sciences Department
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