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
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
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…)
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
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)
Overall scheme for using
• satellite, • weather, and
• ground data
01/14/04
10/06/03
06/28/03
03/20/03
09/01/02
05/24/02
12/10/02
Time (day)
04/23/04
Observation
Model
9
8
7
6
5
4
3
2
1
0
02/13/02
ET (mm/day)
Comparison of measured and
remote sensing calculated ET for a
Pecan orchard at Las Cruces, NM.
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…
•
Specific to pecans
• Our previous models
• Gas-exchange and stress data of David Johnson
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?)
26 June 03 Leyendecker
23 Aug 03 Leyendecker
0.3
0.25
gs = 5.6296 IBB + 0.0182
0.2
gs = 11.74 IBB - 0.031
R2 = 0.8977
0.25
2
R = 0.8668
0.2
gs
gs
0.15
0.15
0.1
0.1
0.05
0.05
0
-0.005
0
0.005
0.01
0.015
IBB
0.02
0.025
0.03
0.035
0
0
0.005
0.01
0.015
IBB
0.02
0.025
0.03
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?
Change mBB; b=0.005
2
100
1.5
80
28/0.3/1000
20/0.3/1000
1
28/0.5/1000
28/0.3/600
28/0.3/1000
60
E/E0
WUE/WUE0
Changing mBB; bBB=0.02
20/0.3/1000
28/0.5/1000
40
28/0.3/600
0.5
20
0
0
0.2
0.4
0.6
E/E0
0.8
1
0
0
2
4
6
8
mBB
2. Seasonal patterns of photosynthetic capacity (Vc,max25)
and relation to leaf N content (linear? intercept = ??)
10
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)
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
Pecan model irrigation subroutine
Growth portion of model