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

ChallengesChallenges• Water shortages Water shortages deficit irrigation - what schedule is best? 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

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

• 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

Three more elements

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

Comparison of measured and Comparison of measured and remote sensing calculated ET remote sensing calculated ET

for a Pecan orchard at Las for a Pecan orchard at Las Cruces, NM. Cruces, NM.

0123456789

02/1

3/02

05/2

4/02

09/0

1/02

12/1

0/02

03/2

0/03

06/2

8/03

10/0

6/03

01/1

4/04

04/2

3/04

Time (day)

ET

(m

m/d

ay)

ObservationModel

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

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

gs = 5.6296 IBB + 0.0182

R2 = 0.8668

0

0.05

0.1

0.15

0.2

0.25

-0.005 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035

IBB

gs

23 Aug 03 Leyendecker

gs = 11.74 IBB - 0.031

R2 = 0.8977

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.005 0.01 0.015 0.02 0.025 0.03

IBB

gs

Changing mBB; bBB=0.02

0

0.5

1

1.5

2

0 0.2 0.4 0.6 0.8 1

E/E0

WU

E/W

UE

0 28/0.3/1000

20/0.3/1000

28/0.5/1000

28/0.3/600

Change mBB; b=0.005

0

20

40

60

80

100

0 2 4 6 8 10

mBB

E/E

0

28/0.3/1000

20/0.3/1000

28/0.5/1000

28/0.3/600

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 = ??)

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 Pecan model irrigation subroutine subroutine

Growth portion of model Growth portion of model