Comparing Dendrometers, Sap Flow and Soil Water Sensors in a 3-year Persimmon Irrigation Trial Introduction – methods for irrigation control Method Basics Advantages Disadvantages Crop factor tables Relative to potential ET Quantitative Inaccurate Simple to use Cumulative errors – no feedback Soil water sensors Volumetric water content Popular and commercial Large spatial variability or Soil water potential Relatively easy and accurate When but not how much Stem and Leaf Water Measure exposed or Supported by the literature Slow potential covered leaves in pressure Precise and reliable Requires skilled manpower chamber No automation currently possible Dendrometer – MDS, Daily course of stem Allows automization Still being developed MXTD, MNTD, DG contraction and growth Non destructive Requires calibration of control treatment Stem sap flow Use of heat to follow xylem Quantitative Requires skilled manpower sap flux Allows automation for installation, operation and analysis Introduction – methods for irrigation control Method Basics Advantages Disadvantages Crop factor tables Relative to potential ET Quantitative Inaccurate Simple to use Cumulative errors – no feedback Soil water sensors Volumetric water content Popular and commercial Large spatial variability or Soil water potential Relatively easy and accurate When but not how much Stem and Leaf Water Measure exposed or Supported by the literature Slow potential covered leaves in pressure Precise and reliable Requires skilled manpower chamber No automation currently possible Dendrometer – MDS, Daily course of stem Allows automization Still being developed MXTD, MNTD, DG contraction and growth Non destructive Requires calibration of control treatment Stem sap flow Use of heat to follow xylem Quantitative Requires skilled manpower sap flux Allows automation for installation, operation and analysis Introduction – methods for irrigation control Method Basics Advantages Disadvantages Crop factor tables Relative to potential ET Quantitative Inaccurate Simple to use Cumulative errors – no feedback Soil water sensors Volumetric water content Popular and commercial Large spatial variability or Soil water potential Relatively easy and accurate When but not how much Stem and Leaf Water Measure exposed or Supported by the literature Slow potential covered leaves in pressure Precise and reliable Requires skilled manpower chamber No automation currently possible Dendrometer – MDS, Daily course of stem Allows automization Still being developed MXTD, MNTD, DG contraction and growth Non destructive Requires calibration of control treatment Stem sap flow Use of heat to follow xylem Quantitative Requires skilled manpower sap flux Allows automation for installation, operation and analysis Introduction – methods for irrigation control Method Basics Advantages Disadvantages Crop factor tables Relative to potential ET Quantitative Inaccurate Simple to use Cumulative errors – no feedback Soil water sensors Volumetric water content Popular and commercial Large spatial variability or Soil water potential Relatively easy and accurate When but not how much Stem and Leaf Water Measure exposed or Supported by the literature Slow potential covered leaves in pressure Precise and reliable Requires skilled manpower chamber No automation currently possible Dendrometer – MDS, Daily course of stem Allows automization Still being developed MXTD, MNTD, DG contraction and growth Non destructive Requires calibration of control treatment Stem sap flow Use of heat to follow xylem Quantitative Requires skilled manpower sap flux Allows automation for installation, operation and analysis Introduction – methods for irrigation control Method Basics Advantages Disadvantages Crop factor tables Relative to potential ET Quantitative Inaccurate Simple to use Cumulative errors – no feedback Soil water sensors Volumetric water content Popular and commercial Large spatial variability or Soil water potential Relatively easy and accurate When but not how much Stem and Leaf Water Measure exposed or Supported by the literature Slow potential covered leaves in pressure Precise and reliable Requires skilled manpower chamber No automation currently possible Dendrometer – MDS, Daily course of stem Allows automization Still being developed MXTD, MNTD, DG contraction and growth Non destructive Requires calibration of control treatment Stem sap flow Use of heat to follow xylem Quantitative Requires skilled manpower sap flux Allows automation for installation, operation and analysis Irrigation control of the future? • Future requirements – Hi Tech system – Sensors measure soil and plant water status – Communications with central management system Our current focus – Control algorithm consisting of: • Algorithm to analyze sensor output based on models and/or empirical results • Decision support system – Economic requirements • Relatively cheap sensors A system that integrates different sensors • Phyto-monitor – The pioneers in this approach are Phytech. For various reasons the systems weren’t widely implemented in Israel. But the concept is correct. Our research was part of a Persimmon irrigation experiment in the Hefer valley • Persimmon (Diospyros kaki L) – originally from the central mountains of China and Japan. Transferred to western agriculture about 150 years ago. • Currently 2100 Ha in Israel – 50% for export. Fourth largest export fruit. • No previous basic research on irrigation. Irrigation based on data from other orchard crops. • FAO tables don’t include Persimmon. Research objectives • To study Persimmon response to irrigation levels, along with behavior of soil, plant and weather sensors. • On the technical side, study integration of soil, climate and weather sensors for a system for future automatic irrigation control. • Evaluation of sensor aspects and requirements for the future. Experimental layout Met station Irrigation Head Tensiometers Granier + Sap Flow Dendrometers Dendrometers Treatments Irrigation factors Irrigation factor 1.2 Four irrigation levels relative to regional seasonal recommendations (700 mm) 1 0.8 0.6 0.4 0.2 Nov Oct Sept Aug July June May April 0 Season Treatment Factor* FactorETo mm appl Factor* FactorETo Factor* - relative to regional recommended irrigation mm appl Factor* FactorETo mm appl Measurements and sensors • Meteorological station – Tair, relative humidity, Solar radiation, wind speed • Tensiometers – 24 electronic. 3 depths (30, 60, 90). • Sap flow sensors – 32 sensors – 8 trees per treatment • 12 LVDT dendrometers • Heat pulse multi-depth sap flow (8 for 25 days in July) Measurements and sensors Plant parameters: • Stem circumference, Plant area index • Natural litter fall • Fruit growth – 500 fruits every 2 weeks Yield and Fruit quality • Harvest – yield weight, reject ratio, fruit number and size. • Check fruit quality post harvest in storage simulation Measurements and sensors Physiological parameters • Leaf conductance and photosynthesis (LI6400), Stem water potential (bi-weekly) • Diurnal courses – stem water potential, leaf conductance and photosynthesis Soil parameters: • Soil samples for mineral and salinity • Calibration of TDP sap flow sensors on lysimeters Results - Yield Yield 2007 - 2009 120 a b 2008 y = 49.48x + 47.89 R0.95 = ² 100 Yield Function 2007-2009 120 100 80 80 60 40 kg/tree kg/tree 2009 y = 91.03x + 14.74 R² = 0.98 60 40 2007 y = 83.39x + 17.34 R² = 0.97 20 R² = 0.94 20 Tx/ET0 = 0.5 Tx/ET0 = 1.0 0 0 0.0 0.2 0.4 0.6 0.8 Tx/ET0 2007 2008 1.0 1.2 1.4 0 200 400 600 800 1000 Irrigation (mm) 2009 •Yield increased with irrigation level •The marginal yield increase was small at higher levels 1200 1400 Results - Yield ‫היקף פרי ממוצע‬ ‫מספר פירות כפונקציה של מנת השקיה‬ • Fruit circumference and fruit number increased with irrigation • For irrigation factor below 0.5 number of rejected increased Results – sap flow Daily Curve DOY 212-227 0.35 0.8 0.3 0.7 T mm/hour 0.5 0.2 0.4 0.15 0.3 0.1 E0 mm/hour 0.6 0.25 0.2 0.05 0.1 0 0.0 0 2 4 6 8 T1 10 T2 12 Hour T3 14 T4 16 18 20 22 24 ET0 • Significant differences between treatments • The level curve at mid-day compared to PET indicates ‘isohydric’ plant control of transpiration. Results – sap flow Seasonal course of crop factor (sap flow relative to Eto) 0.8 Seasonal Kcb Factor (T/ETo) 0.7 0.6 Kcb 0.5 0.4 0.3 0.2 0.1 0.0 T1 T2 T3 T4 These courses are a basis for building seasonal crop factor tables Results – water status indicators Mid-day stem water potential Canopy resistance vs. VPD Canopy conductance vs. VPD a b KPa • Significant differences between treatments in canopy conductance • Slopes of the resistance curves are related to irrigation treatment (proportional to hydraulic resistance (?)) • Shape of the curves indicates strong control of water use by stomata Results – water status indicators Mid-day stem water potential Season 2008 SWP 0.70 0.80 SWP (-MPa) 0.90 1.00 1.10 1.20 1.30 1.40 1T 2T 3T 4T • Significant differences between treatments in most of the seasson • In June indications of water deficit and later abundance. This led us to revise the irrigation tables. Results – water status indicators Mid-day stem water potential • Good agreement between SWP and yield • Correspondence between irrigation level and SWP Results – water status indicators Relationship between leaf conductance and stem and leaf water potential gs Vs. SWP and LWP b Leaf conductance a Stem water potential MPa- Leaf water potential MPa - • Leaf conductance is significantly related to stem water potential. • Leaf water potential similar, but significance level lower. Results – water status indicators Annual course of ‘maximum daily stem shrinkage’ (MDS) 3 Day Average MDS, 2008 500 400 micron Maximum daily shrinkage (MDS) 600 300 200 100 0 MDS1 MDS2 MDS3 MDS4 • Differences in MDS between treatments were significant during most of the season Potential Evaporation Vapor pressure deficit Stem water potential Maximum daily shrinkage (MDS) Results – water status indicators Vapor pressure deficit Potential Evaporation Results – water status indicators SWP Vs. MDS 0 -0.4 Mid Day SWP (Mpa) Stem water potential ‫פוטנציאל מים בגזע‬ -0.2 -0.6 y = -1.38x - 0.64 R² = 0.80 -0.8 -1 -1.2 -1.4 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 MDS (mm) Maximum daily shrinkage • A highly significant relationship between the two, allowing prediction of stem water potential from MDS • The relationship does not change during the season • No severe water stress, which might mess up the relationship Results – water status indicators Signal Intensity 3.0 2.5 20 Signal/Noise Ratio Signal Intensity Signal / Noise ratio 25 2.0 1.5 1.0 0.5 15 10 5 0 0.0 180 200 End June 220 DOY 240 260 280 Beginning of October SWP MDS 180 200 220 240 End June Tensiometers • Signal strengths: MDS > Tensiometers > SWP • Signal to noise ratio: SWP > Tensiometers > MDS 260 280 Beginning of October Results – water status indicators ‫הפרש בין הטיפולים‬ Summary and Conclusions Yield • Vegetative and reproductive parameters of persimmon responded significantly to irrigation levels • Influence of irrigation level on yield started early in fruit development: flower induction, differentiation and fruit set. Fruit growth was influenced later on. Water status indicators • SWP differences apparent all along. Differences significant for extreme treatments. • Stem shrinkage (MDS)- significant differences between treatments. • Significant relationship between MDS and Stem water potential with high correlation coefficient. • Sap flow differences between treatments significant. Can be a water status indicator. Conclusions • Signal strengths: MDS > Tensiometers > SWP • Signal to noise ratio: SWP > Tensiometers > MDS • Number of sensors required to sense treatment differences: Fruit size > MDS > Tensiometers > Sap flow > SWP 116 > 18 reps > 11 reps > 7 reps > 4.1 leaves • We found disagreement between the irrigation table and crop water use during the season. Early in the season irrigation was not enough and late in the season excessive. • A new irrigation table is recommend for persimmon, based on Kcb, and estimations of Ke, Kleaching Ks and a correction factor for irrigation efficiency and non-uniformity of the irrigation system. Thanks • To all participants – – – – Tal Kanety Avraham Grava Ami Gips and Eldad Sokolovski, Shaham Hugo Lemcoff, Ron Zeligman, Amos Naor, Yossi Tanny, Asher Bar-Tal, Uri Dicken, Yehezkel Cohen, … – Irrigation and Persimmon crew – Gv’aot Hahoresh – Giv’at Haim • Chief Scientist of the Ministry of Agriculture (funding) Thanks for your attention Questions? ‫תוצאות – טבלת השקיה‬ Kc Yearly Irrigation Table 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 April 3 1 2 May 3 1 2 June 3 1 2 3 July Corrected 1 2 August Old 3 1 2 3 September 1 2 3 October 1 2 3 November ‫מבוא‬ ‫‪SPAC- Soil Plant Atmosphere Continuum‬‬ ‫אטמוספירה‬ ‫‪ - Kleaf‬מוליכות הידראולית של עלה‬ ‫‪ - Ksoil‬מוליכות הידראולית של קרקע‬ ‫‪ - Kroot‬מוליכות הידראולית של שורש‬ ‫‪ - Kstem‬מוליכות הידראולית של גזע‬ ‫‪ - Kplant‬מוליכות הידראולית של צמח שלם‬ ‫‪- Ψstem‬פוטנציאל מים בגזע‬ ‫‪ - Ψsoil‬פוטנציאל מים בקרקע‬ ‫‪ - Ψxylem‬פוטנציאל מים עצה‬ ‫‪ - Ψleaf‬פוטנציאל מים בעלה‬ ‫‪ - gs‬מוליכות פיוניות‬ ‫‪ - gb‬מוליכות שכבת הגבול‬ ‫‪ -E‬טרנספירציה‬ ‫‪ - VPD‬גרעון לחץ אדי מים עלה‪-‬אויר‬ ‫צמח‬ ‫קרקע‬ ‫)‪)Sack and Holbrook, 2006‬‬ ‫מבוא ‪ -‬שיטות לבקרת השקיה‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫שיטת האצבע‬ ‫טבלאות מקדמי השקיה ביחס להתאדות‬ ‫פוטנציאלית‬ ‫חיישני קרקע‪ -‬תכולת רטיבות‪ ,‬פוטנציאל מים‬ ‫מדדי צמח‪-‬‬ ‫פוטנציאל מים בעלים (‪)LWP‬‬ ‫פוטנציאל מים בגזע (‪)SWP‬‬ ‫התכווצות גזע יומית (‪)MDS‬‬ ‫חיישני זרימת מים בגזע‪-‬‬ ‫• שיטת ‪Cohen, ( ,)Huber,1932( Heat Pulse‬‬ ‫‪)1994‬‬ ‫• דיסיפצית חום (‪)Granier 1985‬‬ ‫חישה מרחוק‬ ‫מבוא ‪ -‬שיטות לבקרת השקיה‬ ‫מדדי צמח‬ ‫תא לחץ‬ ‫• שיטה המקובלת מאוד בספרות‬ ‫• מדויק ואמין‬ ‫• בדיקה איטית‬ ‫• דורשת כוח אדם מיומן – יקר‬ ‫• לא ניתן לאוטומציה‬ ‫שיטות בקרת השקיה‬ ‫דנדרומטריה‬ ‫• מדידת מהלכים יומיים של‬ ‫התכווצות והתרחבות גזע‬ ‫• מודד שינויים באוגר המים של‬ ‫הרקמה הצמחית‬ ‫• מדדים שבשימוש‪:‬‬ ‫• התכווצות גזע יומית ‪MDS -‬‬ ‫• גידול יומי – ‪DG,MXTD, MNTD‬‬ ‫• חיישן המאפשר אוטומציה‬ ‫• בדיקה לא הרסנית‬ ‫• נמצא עדיין בשלב ההוכחה‬ ‫• דורש כיול מול ביקורת‬ ‫)‪(Goldhamer and Fereres, 2001‬‬ ‫שיטות למדידת זרימת מים בעץ‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫שימוש בחום על מנת לעקוב אחר‬ ‫זרימת מים בעצה‪.‬‬ ‫גוף חימום וחיישן טמפרטורה‬ ‫מוחדרים לתוך הגזע\גבעול‬ ‫מדידת קצב התפשטות החום בגזע‬ ‫פתרון נוסחה מתמטית ע"מ לקבל‬ ‫קצב זרימת המים לפי‪:‬‬ ‫– מודל תיאורטי‪ /‬נוסחה אמפירית‬ ‫– קבועים מדודים (כגון צפיפות העץ‬ ‫ומוליכות חום בהעדר תנועת‬ ‫מים)‬ ‫– מקדם כיול‬ ‫הכפלת קצב‪/‬שטף זרימת המים‬ ‫בשטח חתך הגזע\גבעול הפעיל‬ ‫בהובלת מים נותנת את כמות המים‬ ‫העוברת בגזע\גבעול‬ ‫שיטות למדידת זרימת מים בעץ‬ ‫‪Heat Pulse‬‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫הגוף חימום מופעל לזמן קצר‬ ‫מייצר פולס חום חזק‬ ‫שישה חיישנים הממוקמים עד‬ ‫עומק של ‪ 5‬ס"מ‬ ‫מאפשר מדידת התפלגות קצב‬ ‫הזרימה בעומקים שונים‬ ‫מדויק בזרימה מהירה‬ ‫• רגיש מאוד למרחק בין הגוף‬ ‫חימום לחיישן‬ ‫• החיישנים יקרים ועדינים‬ ‫שיטות למדידת זרימת מים בעץ‬ ‫דיסיפצית חום ‪Granier‬‬ ‫• שני חיישני טמפ' מוחדרים לגזע‬ ‫כאשר העליון מלופף בגוף חימום‬ ‫המקבל זרם חימום קבוע‬ ‫• הפרש הטמפרטורות בין‬ ‫החיישנים קשור לקצב זרימת‬ ‫המים‬ ‫• זול יותר ממערכת פולס חום‬ ‫• הנוסחה אמפירית ואין פונקציה‬ ‫תיאורטית‬ ‫• מיצוע של כל אורך החיישן‬ ‫• בכדי למדוד עומקים שונים‬ ‫דרושים מספר חיישנים‬ ‫שיטת ‪ -Heat Pulse‬רקע תאורטי‬ ‫)‪(Marshall, 1958‬‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬ ‫‪V = (x2 - 4 k Tm) 0.5 / Tm‬‬ ‫‪ - V‬מהירות הזרימה של החום‬ ‫‪ - X‬מרחק בין גוף חימום לחיישן‬ ‫)‪k - thermal diffusivity of wood (mm2 s-1‬‬ ‫‪ - Tm‬זמן ממתן הפולס עד לשיא חישת חימום בחיישן‬ ‫ניתן לפתור כאשר אין זרימה ‪:‬‬ ‫)‪k = x2/(4Tm‬‬ ‫• את זה ניתן למדוד בלילה כאשר אין זרימה‬ ‫)‪V = Ji c/(ici‬‬ ‫‪ - Ji‬מהירות זרימת המים בעץ‬ ‫‪ –c and ci‬חום סגולי של העץ והמים בעצה‬ ‫‪ -  and i‬צפיפות העץ והמים בעצה‬ ‫שיטות למדידת זרימת מים בעץ‬ ‫דיסיפצית חום ‪ -Granier‬רקע תאורטי‬ ‫‪Fd = 0.04284 [(Tmax - T)/ T]1.231‬‬ ‫כאשר‪:‬‬ ‫• ‪ -Fd‬שטף המים ליחידת שטח )‪(kg cm-2 h-1‬‬ ‫• ‪ -T‬הפרש טמפרטורות בין הטרמוקופל המחומם לזה שלא‬ ‫מחומם‬ ‫• ‪ -Tmax‬הפרש טמפרטורה מקסימלי בין הטרמוקופלים‬ ‫שמתקבל כשאין זרימה‬ ‫(‪( Granier, 1985, 1987‬‬ ‫תוצאות – זרימת מים בגזע‬ ‫‪VPD‬‬ ‫‪DS‬‬ ‫‪ET0‬‬ ‫‪SR‬‬ ‫‪SF4‬‬ ‫‪SF1‬‬ ‫תוצאות – זרימת מים בגזע‬ Sap Flow Radial Distribution 80 70 60 cm/hour 50 40 30 20 10 0 0 1 2 3 Depth (cm) 4 5 ‫התפלגות הרכב החלקיקים בפרופיל הקרקע‬ ‫‪%‬‬ ‫‪90‬‬ ‫‪80‬‬ ‫‪70‬‬ ‫‪60‬‬ ‫‪50‬‬ ‫‪40‬‬ ‫‪30‬‬ ‫‪20‬‬ ‫‪10‬‬ ‫‪0‬‬ ‫‪0‬‬ ‫‪10‬‬ ‫‪20‬‬ ‫‪30‬‬ ‫‪50‬‬ ‫‪60‬‬ ‫)‪Depth (cm‬‬ ‫‪40‬‬ ‫‪70‬‬ ‫‪80‬‬ ‫‪90‬‬ ‫חול‬ ‫סילט‬ ‫חרסית‬ ‫‪100‬‬