2 systems analysis in agriculture
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TRANSCRIPT
RESEARCH PROGRAMS ON
Climate Change,Agriculture andFood Security
Integrated Systemsfor the HumidTropics
Roots, Tubersand Bananas
Yield Gap Analysis and Crop Modeling WorkshopNairobi, Kenya
SYSTEMS ANALYSIS IN AGRICULTURE
International Potato CenterSub-program: Production Systems and Environment
SYSTEMS ANALYSIS IN AGRICULTURE
1. Collection of elements2. Connected3. Forming a unit
A particular attribute of most agricultural systems is their complexity. Therefore, when studying complex systems we should follow Albert Einstein’s rule: Make things as simple as possible, BUT NOT SIMPLER THAN THAT
Mathematics is used to synthesize and understand the behavior of a system:
• Reductionist knowledge of the parts of a system (known as mathematical models)
• Mean of articulating our ideas and formalizing them in an abstract way
Stephen W. HawkingTheoretical PhysicistCambridge University
Methodology
Define objectives
Analysis of the system
Synthesis
Verification
Validation
Sensitivity analyses
Scenario analyses
Documentation
y = 1.0657x - 195.55
R2 = 0.9925
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Observados
Sim
ula
do
s
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Y
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Methodology
Define objectives
Analysis of the system
Synthesis
Verification
Validation
Sensitivity analyses
Scenario analyses
Documentation
Defining ObjectivesProblem to be Addressed
Defining Effective Measurements
Analysis of the SystemDetermine Components of the System
Defining model Variables
SynthesisDefining working hypotheses
Abstraction of components
Developing the Mathematical Algorithm
Programming
Define objectives
Analysis of the system
Synthesis
Verification
Validation
Sensitivity analyses
Scenario analyses
Documentation
BiomassDay t-1
NPPDay t
IrradianceDay t hour h
RespirationDay t
GPPDay t
Linear Regression (Observed vs. Simulated).
y = 1.0657x - 195.55
R2 = 0.9925
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5000
2000 2500 3000 3500 4000 4500 5000
Observados
Sim
ula
do
s
Ho (1) : o = 0 Ho (2) : 1 = 1
Ha (1) : o 0 Ha (2) : 1 1Residual Analysis (Observed vs.
Simulated).
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Observaciones
Res
idu
ales
(y-
ye)
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Observaciones
Res
idu
ales
(y-
ye)
ei = y
i – ye
i
Define objectives
Analysis of the system
Synthesis
Verification
Validation
Sensitivity analyses
Scenario analyses
Documentation
Running the model to generate desired information
Find estimated values of input and state variables that maximize (or minimize) ouput variables
What Happens if
Define objectives
Analysis of the system
Synthesis
Verification
Validation
Sensitivity analyses
Scenario analyses
Documentation
Basic concepts required to model systems
dynamics
Soils
Climate
Germplasm
CO2
Weeds
Crop Traits
Diseases
Radiation
Temperature
Water
Pests
Nutrients
Potential yield (Yp)
Attainable yield
Actual yield (Ya)
Dry Matter Yield, Mg/Ha
Defining factors
Limiting factors
Reducing factors
Hierarchy of Yield Drivers and Associated Yield Levels
Modified by R. Quiroz from Penning de Vries & Rabbinge, 1995
Yield increasing measures
Yield protecting measures
Pro
du
ctio
n S
itu
atio
n
Growth and development
Growth. The increase of weight or volume of the total plant or various plant organs.
Development. The passing through consecutive phenological phases. Characterized by the order and rate of appearance of vegetative and reproductive plant organs.
Let us say we put a single bacteria in a culture that divides itself every half minute; in 15 min there will be 45
Most living organism present growth patterns similar to this figure. That is, it follows an exponential increase in number or weight.
Let’s assume we have a culture that divides itself every unit of time (t). If we record the weight and we say that the first cell had a weight w0, then when divided into two the weight is 2w0, son on and so forth, we will have:
The shape of the growth response, as a function of time, might be generically described by an exponential function:
W(t) = w0 *e k*t
Time, t Weight, w
1 w0
2 2w0
3 3w0
4 4w0
5 5w0
dw/dt = k* W0 *Exp (k*t)
The growth rate at any time is:
We can calculate now the relative growth rate (RGR), defined as the rate of growth divided by the weight:
dw/dt k* W0 *Exp (k*t)
W (t) W0 *Exp (k*t)==RGR
RGR = k
Now we have a little problem, plants and other biological systems do not grow indefinitely; as the organisms get bigger, their growth rate slows until it reaches its mature size, when RGR becomes zero
Therefore we need to modify our equation for RGR. There are different ways and we will use an arbitrary but convenient way
dw/dt
W ==RGR* k (1 – g*W)
Where: g=1/Wmax
Putting this in words, when W is close to W0 RGR is close to k but as W approaches Wmax RGR also approaches zero
*(1 – g*W)
BiomassDay t-1
GPPDay t
NPPDay t
IrradianceDay t
RespirationDay t
Now, let us say we have a plant growing without restriction (water, climate, pest control, etc.)
W (t)= W0 *e k*t
Where: W(t) – weight at any time t W0 – weight at t=0 k – growth constant
Conceptual representation of a horizontal surface at the top of the
canopy
GB R NIR
A. Effect of temperature on the metabolic reaction rate
Reaction Rate%
B. Effect of soil temperature on the emergency rate of potato plants
Optimal t°
Temperature ( °C )
Em
erg
en
cy
Ra
te
Temperature ( °C )
C. Effect of temperature on photosynthesis andrespiration in potato
Respiration/photosynthesis rates(gCO2 cm -2 hoja min -1
Total photosynthesis
Net
photosynthesis
Respiration
Air temperature ( °C )
D. Relationship between total dry matter and intercepted solar energy under different environmental conditions
Cu
mm
ula
tiv
eD
M (
gc
m-2
)
Cold weather + waterB = 2.0
Warm weather + waterB = 1.2
Warm weather w/o waterB = 0.8
Intercepted solar radiation
A. Effect of temperature on the metabolic reaction rate
Reaction Rate%
B. Effect of soil temperature on the emergency rate of potato plants
Optimal t°
Temperature ( °C )
Em
erg
en
cy
Ra
te
Temperature ( °C )
C. Effect of temperature on photosynthesis andrespiration in potato
Respiration/photosynthesis rates(gCO2 cm -2 hoja min -1
Total photosynthesis
Net
photosynthesis
Respiration
Air temperature ( °C )
D. Relationship between total dry matter and intercepted solar energy under different environmental conditions
Cu
mm
ula
tiv
eD
M (
gc
m-2
)
Cold weather + waterB = 2.0
Warm weather + waterB = 1.2
Warm weather w/o waterB = 0.8
Intercepted solar radiation
A. Effect of temperature on the metabolic reaction rate
Reaction Rate%
B. Effect of soil temperature on the emergency rate of potato plants
Optimal t°
Temperature ( °C )
Em
erg
en
cy
Ra
te
Temperature ( °C )
C. Effect of temperature on photosynthesis andrespiration in potato
Respiration/photosynthesis rates(gCO2 cm -2 hoja min -1
Total photosynthesis
Net
photosynthesis
Respiration
Air temperature ( °C )
D. Relationship between total dry matter and intercepted solar energy under different environmental conditions
Cu
mm
ula
tiv
eD
M (
gc
m-2
)
Cold weather + waterB = 2.0
Warm weather + waterB = 1.2
Warm weather w/o waterB = 0.8
Intercepted solar radiation
A. Effect of temperature on the metabolic reaction rate
Reaction Rate%
B. Effect of soil temperature on the emergency rate of potato plants
Optimal t°
Temperature ( °C )
Em
erg
en
cy
Ra
te
Temperature ( °C )
C. Effect of temperature on photosynthesis andrespiration in potato
Respiration/photosynthesis rates(gCO2 cm -2 hoja min -1
Total photosynthesis
Net
photosynthesis
Respiration
Air temperature ( °C )
D. Relationship between total dry matter and intercepted solar energy under different environmental conditions
Cu
mm
ula
tiv
eD
M (
gc
m-2
)
Cold weather + waterB = 2.0
Warm weather + waterB = 1.2
Warm weather w/o waterB = 0.8
Intercepted solar radiation
Thermal time and growth
Growth and development of crops are strongly dependent on temperature.
Each species requires a specific temperature range for development to occur. They are named cardinal temperatures:
• Base temperature, Tb• Optimum temperature, To• Maximum temperature, Tm
Thermal time are commonly calculated as a Growing Degree Days (GDDs), Growing Degree Units (GDUs), or heat units (HUs). Different methods exist for calculating heat units.
Growing degree days calculation
Classical approach
ET Effective temperatureADD Acummulated degree days
ET = TX-Tb
Where Tx Mean temperature
Tx < To, ET = TX (1-((Tx-To)/(To-Tb))2
Alternative Approach
Tx > To, ET = TX (-((Tx-Tm)/(Tm-To))2 Tm
To
Tb
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Temperature
Effec
tive
tem
pera
ture
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Temperature
Effec
tive
tem
pera
ture
Potato phenology
Phase 0 between planting and emergence
Phase 1 between emergence and tuber initiation
Phase 2 between tuber initiation and the moment when 90% of assimilates are partitioned to the tubers
Phase 3 until the end of crop growth
Potato phenologyPatacamaya, La Paz
17°16' S 68°55' W 3800 m.a.s.l.
0%
20%
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GDD
Can
opy
Cov
er
Luk'y Waycha Alpha Ajanhuiri Gendarme
Phase 1 350 - 450 GDD
Phase 2 800 – 1000 GGD
Phase 3 1200 – 1400 GDD
SOLANUM Conceptual framework
Roots Stems Leaves
GC LAI
Light Reflectance
PhotosyntheticApparatus
Tubers
Light
Interception
Kg DM.ha¨¹.d ¨¹
Light
LUE( )DMPAR—
T
Dry matter accumulation equation
The growth model, based on light interception and utilization as proposed by Spitters (1987, 1990) and Kooman (1995), was used to simulate the daily dry matter accumulation, through the following general equation: Wt = flint*PAR*LUE Where:
• Wt Growth rate at day t (g DM.m-2.d-1) • flint Fraction of PAR intercepted by the foliage• PAR Photosynthetically active radiation (MJ.m-2.d-1)• LUE Light utilization efficiency (g DM.MJ-1 PAR)
The main growth processes
Light interceptionLight use efficiencyTuber partitioning
Model parameters
Fraction of light intercepted (FLINT)
Growth phase:FLINT = (MCC * N * f0 * exp (R0*t)) / (N *f0 * exp(R0*T) + 1 – N *f0).
P1 maximum canopy cover, MCC P2 initial light interception capacity, f0 (m2 pl-1) P3 initial relative crop growth rate R0 (ºCd-1)
Senescence phase:Ft = 0.5 – (t - t0.5) / d.
P4 duration of leaves senescence, d (ºCd), P5 time when light interception was reduced to 50%, t0.5 (ºCd).
Fraction of light intercepted (FLINT)
0.0
0.2
0.4
0.6
0.8
1.0
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Thermal time
Canopy cover
f0
R0
MCC
Radiation use efficiencyP6 light use efficiency, RUE (gr MJ-1)
Partitioning harvest index functionHI=M/(1+(t_ac/A)b) P7 asymptotic harvest index, MP8 initial slope of the harvest index curve, b (ºCd-1), P9 thermal time at the initial harvest index curve, A (ºCd)
Tuber dry matterP10 tuber dry matter content (DMcont)
Model parameters
Radiation use efficiency - RUE
y = 5.552xR² = 0.933
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To
tal d
ry m
att
er
(g
r. m-2
)
Intercepted PAR (MJ.m-2)
Asymptotic harvest index
0.0
0.2
0.4
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0.8
1.0
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Thermal time
Tuberization index
A
M
b
The soil - plant - atmosphere system
Atmosphere
Plant
Soil
CO2
TemperatureRadiation
Rainfall
PhotosynthesisRespiration
PhotorespirationTranspiration
Dry matter
WaterNutrients
Evaporation
Farmerpractices
Model parameterization“Minimum data set”
What to measure? When to measure?How to measure?
Atmosphere
Plant
Planting dateEmergence dateHarvest dateCanopy cover/LAI/VIDry matter by plant organDry matter content of tubers
Solar radiation Temperature
What to measure for estimating potential production?
When to measure?
Daily meteorological data
Periodic crop growth measurements Weekly 10 days15 days
How to measure?
Meteorological data and equipment
• Minimum and maximum air temperature
• Solar incoming radiation• Rainfall• Reference -
evapotranspiration• Soil temperature
Leaf area data acquisition
Determining leaf area index (LAI) from NDVI
Where:
NIR: Near Infrarred
R: Red
NDVI =NIR - R
NIR + R
Relationship between LAI and NDVI
data
simulated
Canopy cover data acquisition
Grid method
Post-processingSegmented image method
Canopy cover data acquisition
Measuring dry matter
Leaves Stems Tubers
Roots
Parameter calculation example
La Molina, Peru
Latitude 12º 04’39” SLongitude 76º 56’53” W
Altitude 280 m.a.s.l.
June - November 2006
Thanks