Framework Farm operators make strategic and tactic decisions based on dynamic climate and market processes. However, they do not access and use all the information enabled by powerful information technologies.

1. Strengthening farm operators’
capacity for climate change
adaptation
Armen R. Kemanian
Associate Professor
The Pennsylvania State University
Agroclimatology Project Directors Meeting
18 December 2016

2. Foreword
It is March 25, 2012 in Central Pennsylvania. March has been unusually warm, and
Jeanne the Farmer is getting ready for planting corn ahead of schedule. The soil
condition is perfect, the planter is loaded with seed, the fertilizer is in the tank. Yet,
Jeanne is pondering if planting corn this early risk exposing the crop to a late frost
under the guise of an eventual bumper crop. Jeanne decides to go ahead and plant
corn, after all she already paid for a long season hybrid. Four weeks later 60% of the
crop is killed by a late frost. Jeanne needs to decide now if it is worth replanting corn,
and if so if a short season hybrid should be used, to avoid late maturation and frost
again, now in the fall. But, are seeds available? She also heard that climate forecasters
predict a strong chance of a drier than usual summer. What does that mean for risk of
drought or corn borer damage? There is also strong competition for machinery, and
getting a good contractor for the planting timely is becoming challenging. There was a
chance of losing that crop to a frost, there were other options - perhaps planting early
only 50% of the area? Decision points, uncertainty. Can science and technology help
Jeanne deal with the havoc brought up by climate variability? Can the losses be
avoided or reduced with a strategy that recognize in advance the existing risks? We
think so.
This project is about improving the capacity of farmers to deal with the uncertainty
of climate change and variability in agricultural production.

3. Team members
Rob Weaver, Project Director
Econometrics and modeling
Armen Kemanian
Biophysical modeling and agronomy
John Tooker
IPM and extension
Charlie White
Biophysical modeling, agronomy and extension
Chris Duffy
Hydrological modeling, databases and visualization

4. Framework
Local CCR
Realization
Events
CC Predictions
& Scenarios
Multiple Time
Scales
Predictions &
Scenarios
Materials &
Supplies
Farm labor
Custom services
Predictions &
Scenarios
Output prices
Input prices
Local
Supply
Realization
Events
Local Price
Realization
Events
Long-term/Medium-term/Pre-season/Intra-season
Multiplenestedtimescaleplanning&action
Farm Options for Adaptation
Farm Actions: Adaptation
Strategic management problem: Multiple climate and market
processes with feedback
Options
InformationQuality

5. • Advanced planning for land use
• Adaptive capacity
– Short-run for field operation choice and timing
– Medium-term for shifts in long-term plans
• Market conditions reflect both realized and
anticipated agroecosystem and climate
conditions
• Market volatility spans quantity and prices
Farm level decision salient features

6. • Markets faced by farm operators are local
• Procurement and sales involves
– Imperfect and dynamic information with respect to
> supply availability and transaction prices
> extent and timing of demand from buyers
– Transactions are bilateral involving
> Search costs for buyers
> Opportunity costs for suppliers
Procurement and sales model

7. • Developed to predict prices from bilateral
transactions
• Provides basis for farm-level scenario analysis
that incorporates salient features of farm-level
procurement and sales settings.
Market level model

8. Provides production and environmental impacts
data for econometrics and integrated models
Model helps managing complex reality
Ernst et al 2016 Field Crops
Research 186, 107-116
Wheatyield,kgha-1
Actual yield
Attainable yield
Climatic Index
Climate, soil, genetics,
management and
economic interactions

9. 1. Water balance (includes irrigation, water potential based)
2. Crop growth (includes nutrient uptake, optimization theory)
3. Model plant communities (competition, polycultures)
4. Carbon and nutrient balance (saturation theory)
5. Apply management practices (tillage, fertilization, …)
Cycles notes
Inputs: database + conditioning of
Meteorological data
Soils data
Management data

10. 𝒘 =
𝐴
𝐸
=
λ 𝒄 𝒂 − Γ∗ − 𝑹 𝒅 𝒌
𝑟𝑔
1
𝐷
𝒘 ≈
λ 𝒄 𝒂 − Γ∗
𝒓 𝒈
1
𝐷
Model fundamentals
Growth is radiation and transpiration limited
When transpiration is not limited,
radiation tends to drive growth,
modulated by other factors.
When transpiration is limited by the
hydraulics of the plant or the supply
from the soil, then stomata close and
growth becomes proportional to
transpiration.
This is a simplification of Cowan’s
optimization theory, the method has
a strong physiological foundation.

11. White, C.M., A.R. Kemanian, and J.P. Kaye. 2014. Biogeosciences 11(23): 6725–6738.
Carbon and nitrogen cycling
Carbon and nutrient cycling follow saturation theory

12. - Model discriminates chemical and organic N fertilization
- Calibrating for top soil gives reasonable subsoil simulations
- Calibrating for the subsoil degrades simulation of topsoil
Kemanian et al 2011 Ecological Modeling 222, 1913–1921
Long term simulation of soil C

13. Corn Rye Soy Wheat Canola Corn Rye Soy Wheat AlfalfaCrop Rotation:
Compost/Fertilizer
0
20
40
60
80
100
1998 1999 2000 2001 2002 2003 2004
SoilNO3
-at5-10cm
(mgN/kg)
Inorganic Fertilizer
Compost
Compost/Fertilizer Compost/Fertilizer Compost/Fertilizer
0
0.1
0.2
0.3
1998 1999 2000 2001 2002 2003 2004
N2ODenitrification
(kgN/ha/day)
Inorganic Fertilizer
Compost
0
0.2
0.4
0.6
0.8
1
1998 1999 2000 2001 2002 2003 2004
NitrateLeaching(kg
N/ha/day)
Inorganic Fertilizer Compost
Simulation of N dynamics

14. 1. Use hydrological model to classify field hydrology and soils
within a watershed (one time, intense)
2. Create representative fields (soil and CN combinations)
3. Combine with algorithm for rotation selection – no market
feedback – no pest simulation (uncoupled)
4. Market feedback (next)
5. Run
6. Display results in decision-friendly format (pending)
Tool operational steps

15. Tools
GIS - Geographic Information System
TIN - Domain Decomposition:
Triangular Irregular Net
FVM - Finite Volume Method
PDE - Partial Differential Equations
PDAE - Differential-Algebraic Equations
C. Duffy, L. Leonard and L. Shu
PIHM

16. Conestoga

17. Conestoga

18. DEM
DEM

19. Forcing data
Climate forcing

20. Land use

21. Conestoga Watershed in Lancaster County, PA
Mesh Cells with > 50% Agricultural Land Use
Inform Cycles

22. Interested in limitations to crop productivity, informed by PIHM
• Interrogated average behavior of mesh cells by month
• Focused on drivers of transpiration
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
0.00 5.00 10.00 15.00 20.00 25.00
AverageTranspirationinAugust(mm/day)
Average August Water Storage (Saturated + Unsaturated) m
Inform Cycles

23. 0
5
10
15
20
25
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00
WaterStorage(Saturated+Unsaturated)m
Average August Infiltration (mm/day)
Interested in limitations to crop productivity, informed by PIHM
• Interrogated average behavior of mesh cells by month
• Focused on drivers of transpiration
Inform Cycles

24. 0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00
AverageAugustTranspiration(mm/day)
Average August Infiltration (mm/day)
Interested in limitations to crop productivity, informed by PIHM
• Interrogated average behavior of mesh cells by month
• Focused on drivers of transpiration
Inform Cycles

25. Using CN to regulate infiltration rate and Cycles crop yield output
y = 0.975x
y = 0.8763x
y = 0.6795x
y = 0.5212x
y = 0.2683x
0
5
10
15
20
25
30
0 5 10 15 20 25 30
CropBiomasswithNewCurveNumber
(Mg/ha)
Crop Biomass with Curve Number = 60 (Mg/ha)
CN = 89
CN = 95
CN = 97
CN = 98
CN = 99
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
0.00 1.00 2.00 3.00 4.00 5.00
AverageAugustTranspiration(mm/day)
Average August Infiltration (mm/day)
Curve Number
99 97 60
Use Cycles

26. Decision Support Tool Example
- Set up price series for winter wheat, maize and soybean
- Establish planting time window for each crop
- Establish soil condition for planting
- Add rotational constrains
Run (for two soils in this case)

27. Adding a biomass crop buffer strip
greatly reduces N near stream
Why not C-PIHM? Computational demand
Maize biomass and soil nitrate concentration in a 5 ha watershed
Simulation of 10 years may take 4 to 6 hours

28. Next steps
1. Use hydrological model to classify field hydrology and soils
within a watershed (one time, intense)
2. Create representative fields (soil and CN combinations)
3. Combine with algorithm for rotation selection – no market
feedback – (add) pest simulation (currently uncoupled)
4. Market feedback (create “farms” with fields)
5. Run
6. Display results in decision-friendly format (pending)
7. Web-based prototype? This is the ultimate goal

29. Thank you!
NIFA award# 2014-68002-21768
(Most likely no time for questions)