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A Three-State Pecan-Almond Project: Help from Physiological Models, Remote Sensing, & Ground-Based PowerPoint Presentation
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A Three-State Pecan-Almond Project: Help from Physiological Models, Remote Sensing, & Ground-Based

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A Three-State Pecan-Almond Project: Help from Physiological Models, Remote Sensing, & Ground-Based

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  1. A Three-State Pecan-Almond Project: Help from Physiological Models, Remote Sensing, & Ground-Based Measurements Vince Gutschick, Global Change Consulting Consortium, Inc. Ted Sammis, Plant & Environmental Science, NMSU Junming Wang, Plant & Environmental Science, NMSU Manoj Shukla, Plant & Environmental Science, NMSU Rolston St. Hilaire, Plant & Environmental Science, NMSU

  2. Challenges • Water shortages  deficit irrigation - what schedule is best? • General resource management, including N • Crafting plans and management tools • Optimal deficit irrigation – guidance from models <-> experiments • Develop monitoring, particularly ET - large areas, near-real time • Validate monitoring methods • Develop simple management plan – distill the knowledge • Validate the management plan • Deliver practical tools • NMSU part: • Focus on pecans • Development of framework applicable to other nut crops

  3. First three elements • Optimal deficit irrigation • Maximal retention of yield and yield capacity • Zillion risky expts.? No. Use models: • To develop hypotheses • Then to guide experimental design and interpretation • Monitoring – cover large areas, in near-real time • Satellite estimates of ET by energy balance • Validate monitoring • Eddy covariance, SWB, and physiological stress measures (optical…)

  4. Three more elements • Develop a simple management plan • Distill the response of yield to fraction of normal • water use (ET) – that is, yield as Y(E/E0) • Validate optimal management results • Deliver practical tools • Monitoring of stress indicators, not just end yield • Using simple, mostly automated tools • Simpler is better - experience of DSSs, and • even simpler tools (nomograms,…) • Novel satellite estimates of ET in near-real time • Easily obtained ground data

  5. Highlight: satellite estimates of ET by energy balance - a large-scale, rapid tool for monitoring stress and water use • Modification of Surface Energy Balance Land (SEBAL)  RSET • Key problem avoided: low accuracy of surface temperature • Including atmospheric effects, view angle (air mass) effects • Remaining difficulty – disparity of aerodynamic resistance for • soil & canopy(2 sources) • Some clues for future • Even “as is” -for ag areas with good cover, not a big problem • Automation a challenge • Finding and processing scenes • Locating hot and cold spots • Including correction for differences in elevation, θ (VPT)

  6. Overall scheme for using • satellite, • weather, and • ground data

  7. Comparison of measured and remote sensing calculated ET for a Pecan orchard at Las Cruces, NM.

  8. Highlight: modelling plant responses to stress, for yield optimization Where do we want to end up? Whole-season water use and yield  Leafout (canopy leaf area, as a function of E/E0)  Nutfill (canopy photosynthesis, as a function of E/E0  Concurrent information: PS partitioning, leaf N dynamics • What we do know? • What have physiological models given us over the years? • Decision support systems Erect leaf varieties …… • Great detail needed in models  great body of knowledge • E.g., Ball-Berry, Farquhar et al., micromet, light interception… interception, LA phenology, Vcmax(stress), gs(stress - Tardieu…) • Specific to pecans • Our previous models • Gas-exchange and stress data of David Johnson

  9. What we don't know well enough & therefore need to measure • Seasonal patterns of stomatal control and WUE • What’s the unstressed Ball-Berry slope? • Does it really double from pre-monsoon to monsoon? • Evidence: gain in water-use rates • (Basis in ecology under natural conditions?)

  10. How does the Ball-Berry slope respond to root or leaf water potential? How much do we need to cut it to reduce E to 0.5 E0? How does WUE change under stress? 2. Seasonal patterns of photosynthetic capacity (Vc,max25) and relation to leaf N content (linear? intercept = ??)

  11. Optimality • Distill the more detailed physiological and developmental • models of: • Leaf area development – to a simple function of fraction of • unstressed ET (E/E0) • Basically, reset leaf area to a smaller fraction of normal, • reducing future ET demand • Canopy photosynthesis – to a similarly simple function of E/E0) • See a gain in water-use efficiency that makes the cut in • season-total photosynthesis less than the cut in water use • Find the combination of cuts in E/E0 in both stages that • leaves the greatest nut yield, for a given total water use • (a numerical solution)

  12. Data needs for studies of stress responses and optimization • - under several stress levels (treatments and interplant/ • microsite variation) • Leaf gas exchange • To eludicate the stomatal control program • Aerial environment (2 fundamental parameters) • Water stress (3rd fundamental parameter) • To estimate photosynthetic capacity (Vc,max25) and its • relation to leaf N and light integral on the leaf •  Concurrent measurements of leaf N and PAR levels •  Determining seasonal trends in both • Water stress quantification – soil water balance and • soil moisture release curve • Measurements of growth, carbohydrate reserves, and nut yield

  13. Pecan model irrigation subroutine

  14. Growth portion of model