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? Water shortages  deficit irrigation - what schedule is best? General resource management, including N 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. 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…) First three elements

4. Develop a simple management plan Distill the response of yield to fraction of normal water use (ET) – that is, yield as Y(E/E 0 ) 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 Three more elements

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 1. 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 (V c,max 25 ) 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 (3 rd 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

14. Pecan model irrigation subroutine

15. Growth portion of model