Weather-Driven Crop Models

Transcription

1 Weather-Driven Crop Models James W. Jones Agricultural & Biological Engineering University of Florida

2 Weather-Driven Crop Models Rationale Crop Model Concepts Effects of Weather Variables on Growth and Development Processes Soil Water Availability Examples of Climate Effects on Crop Yield Overview of Capabilities, Limitations

3 Rationale Spatial & Temporal Variability of Weather Observations from Experiments Pertain to Local Conditions Experiments are Costly (Financial & Human Resources) Experiments may not be Possible Trial & Error approach to agricultural research is inadequate Information needed for decision making Gap between information needed and that created by disciplinary research Need to Integrate Scientific Understanding from Different Disciplines for Application Need Tools for Prediction of Agricultural Responses to Climate, Soil, Management for Decision Making

4 Rationale Complexity of Crop Responses to Climate, Soil, Management plus Temporal and Spatial Variability Living Systems, many Biological Processes Non-linearity Complex Interactions Time Varying Responses Systems Approach Integration of knowledge is essential

5 Terminology System Collection of Components Boundary Separates System Components from its Environment Model Mathematical Representation of System (Components & their Interactions) State Variables Measures of System that Change over Time Environmental Variables (inputs) system dynamics do not affect them Simulation Solving a Model, predicting system behavior over time

6 Systems Approach Research Problem Solving Research Model Development Model Control/ Management/ Decision Support Increased Understanding Prediction Test Predictions Application/ Analysis

7 Incorporating Crop Models into Traditional Agronomic Research Q uestion, Problem Hypotheses Computer Experiments Experiments Analysis Conclusions, Decisions Recom mendations

8 Hierarchy is Important in Biological/Ecological/Agricultural Systems WORLD REGIONS FARMING AREAS CROP ECOSYSTEMS CORN, PASTURE,... ELEMENTS INDIVIDUAL PLANTS,... COMPONENTS LEAVES, STEMS, ROOTS,... MICRO-COMPONENTS STOMATA, BIO-CHEM. PATHWAYS... M A N A G E M E N T / R E S E A R C H

9 Forrestor Diagrams; Developing and Communicating Conceptual Model of System General Equations for Level i Dynamics: a. Level c. Source b. Rate d. Auxillary Variable dx i /dt = Σ j I i,j - Σ k O i,k where x i = level of i th variable, dx i /dt = rate of change in the level of the i th variable, I i,j = rate of flow into level i, from source j, O i,k = rate of flow out of level i, to source k. e. Pathway for Material Flow f. Pathway for Information Flow

10 Simple Example (A) i(t) H 1 V 1 f 1,2(t) A 1 H 2 V 2 O(t) A 2 Two tanks, water flow between them

11 Forrestor Diagram, Two-tank System (B) SOURCE i(t) Functional Equations: dv 1 /dt = i(t) - f 1,2 (t) dv 2 /dt = f 1,2 (t) - O(t) V 1 V 2 f 1,2 (t) A 1 A 2 C 1 C 2 g g Final Equations: dv 1 /dt= i(t) - C 1 (2gV 1 /A 1 ) ½ dv 2 /dt = C 1 (2gV 1 /A 1 ) ½ -C 2 (2gV 2 /A 2 ) ½ O(t) SINK

12 Simulation Results Constant i(t) for 5 hours, specific values of coefficients TANK 1 C 1, C 2 = 0.5 A 1, A 2 = 15 VOLUME, m^3 10 TANK HOURS

13 Forrestor Diagram for Insect Population Model SOURCE i(t) N 1 MORT. u 1,2 SINK N 2 MORT. u 2,3 SINK N 3 MORT. u 3,4 SINK N 4 MORT. O(t) SINK SINK

14 BIOLOGICAL, BIOPHYSICAL MODEL EXAMPLES Crop Growth Soil Organic Matter Microbial Growth Insect Populations Predator-Prey Populations Bio Reactors Fish Growth and Reproduction Heart Function Animal Temperature Regulation Eutrophication Processes Chemical Transport in Soil, Water Food Chain Livestock growth Plant and Animal Genetics

15 Crop Simulation Models Based on understanding of plants, soil, weather, management interactions o Vegetative and reproductive development o photosynthesis, respiration, growth o Root water uptake, stress effects on growth processes Predict growth, yield, timing (Outputs) Require information (Inputs) o Field, Soil characteristics o Weather (daily) o Cultivar characteristics o Management Can be used to perform what-if experiments

16 Concepts Components Modeled (Plant, Soil Water, etc.) Mathematical Descriptions of Processes Flows & Transformations; Mass, Energy Balances Homogenous Area Dynamic, Deterministic Integrate Components

17 1 2 Production situation potential attainable Crop Model Concepts Yield increasing measures defining factors: CO2 radiation temperature crop characteristics -physiology, phenology -canopy architecture limiting factors: a: water b: nutrients -nitrogen -phosphorous 3 actual Yield protecting measures reducing factors: Weeds pests diseases pollutants ,000 20,000 Production level (kg ha -1 ) Source: World Food Production: Biophysical Factors of Agricultural Production, 1992.

18 Crop Model Processes Development (Affected mostly by temperature and daylength) Dry Matter Increase or Growth (Affected mostly by light energy interception, influenced by stresses due to water, nutrients)

19 photosynthesis parameters light (I 0 ) temperature (T) (potential) assimilation (P g ) intercepted light leaf area (L ) development rate (r m r(t)) partitioning (f c (N )) canopy dry matter (W c ) assimilates (A p ) development stage (N) growth (dw/dt ) root dry matter (W r ) conversion efficiency (E) maintenance respiration (R m W)

20 A Simple Crop Development Model Development rate: Rate of Change of N = k(t t ) or, dn t /dt = k(t t ) Solving the model N t+1 = N t + k(t t )

21 T01 = 28 oc (Vegetative) 26 oc (Early and Late reproductive) T02 = 35 oc (Vegetative) 30 oc (Early reproductive) 34 oc (Late reproductive) Vegetative Early Reproductive Late Reproductive Relative Effect on Dev. Rat TB = 7 oc (Vegetative) 6 oc (Early reproductive) -48 oc (Late repro ductive ) Temperature (oc) TM = 45.0 o C (a ll) Effect of temperature on development rates (Soybean)

22 Crop Development Vegetative Growth Period Reproductive Growth Period Harvest Maturity Plant Emerge 1st Flower 1st Seed Phys. Maturity Time Vegetative Development is mainly affected by Temperature such as appearance of leaves on main stem) Reproductive Development is affected by temperature and daylength (such as duration of seed growth phase) Sensitivity to stresses varies considerably with stage of growth

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24 Duration of Soybean Growing Season Maturity date for Stonewall 170 Simulated (days after planting) Observed (days after planting)

25 Dry Matter Growth Rate: Photosynthesis and Respiration Growth Rate = CVF * Gross Photosynthesis - Respiration or dw/dt = CVF * ((30/44) * A - MC * W) dw/dt = Plant Growth Rate, g m -2 s -1 CVF = Conversion Efficiency, g tissue (g glucose) -1 30/44 = Converts CO 2 into Glucose, g glucose (g CO 2 ) -1 A = Gross Photosynthesis, g [CO 2 ] m -2 s -1 MC = Maintenance Respiration Coefficient, s -1 W = Plant Tissue Mass, g m -2

26 Conversion Factor (CVF) 1/CVF= f leaf / f stem / f root / f storage /Co CVF= Conversion factor (g product g -1 glucose) f = Fraction of each organ in the increase in total dry matter (3f=1) C o = Conversion factor of storage organ (g product g -1 glucose) For example, Co is 0.67 for maize, 0.78 for potato, 0.46 for soybean, and 0.40 for peanut.

27 CO 2 Flow Diagram for Potential Production Crop Model Light Temperature Photosynthesis Rate Intercepted Light Leaf Area Development Rate Assimilate Assimilate Growth Respiration Rate Partitioning Shoot Shoot Dry Dry Matter Matter Development Development Stage Stage Maintenance Respiration Rate Conversion Efficiency Root Dry Root Dry Matter Matter CO 2 Source: World Food Production: Biophysical Factors of Agricultural Production, 1992.

28 Daily Weather Effects: Temperature Vegetative and reproductive development, duration of crop season Leaf area expansion Photosynthesis Dry matter partitioning, grain vs. vegetative growth Potential evapotranspiration, water stress Soil organic matter transformations, nutrient availability

29 Daily Weather Effects: CO 2 Photosynthesis Potential evapotranspiration & crop water use Daylength Reproductive development Dry matter partitioning

30 Daily Weather Effects: Solar Radiation Photosynthesis Potential evapotranspiration, thus actual evapotranspiration and plant water stress Temperature in soil & canopy (microclimate) 1 Humidity, Wind 1 Evapotranspiration Temperature in soil & canopy 1 In some weather-driven crop models

31 1.2 Bragg 1.0 Williams Altona 0.8 Relative Rate PPSEN 0.2 CSDVAR CLDVAR Day Length (hr) Relative sensitivity of three soybean cultivars to daylength

32 T01 = 28 oc (Vegetative) 26 oc (Early and Late reproductive) T02 = 35 oc (Vegetative) 30 oc (Early reproductive) 34 oc (Late reproductive) Vegetative Early Reproductive Late Reproductive Relative Effect on Dev. Rat TB = 7 oc (Vegetative) 6 oc (Early reproductive) -48 oc (Late repro ductive ) Temperature (oc) TM = 45.0 o C (a ll) Effect of temperature on development rates (Soybean)

33 TEMP=25 C TEMP=15 C TEMP=20 C 0.7 Relative Effect Photosynthetically Active Radiation (PAR) Effects of Light and Temperature on Specific Leaf Area

34 Temperature effect on canopy photosynthesis Relative Rate Temperature (oc)

35 oc 26.5 oc Fraction set oc 40.0 oc Temperature (oc) Relative effect of temperature on pod setting using quadratic curve

36 Relative Rate Average Daily Temperature (oc) Effect of Temperature on Seed Potential Growth Rate

37 60 50 CANOPY PG (g/m2-d) PAR (E/m2-d) Photosynthetically Active Radiation effects on Canopy Photosynthesis

38 Relative Rate CO2 Concentration (vpm) CO 2 effects on canopy photosynthesis

39 1.50 C-3 Crops C-4 Crops Reference point: 330 vpm CO Relative Effect CO2 Concentration (vpm) Effects of CO2 concentration on transpiration rates

40 Plant Transpiration Dynamics of Water Availability Rainfall Soil Evaporation Run-on Soil Surface Runoff Infiltration Roots Drainage Leaching

41 Potential ET vs. Time: Effects of Solar Radiation

42 Extractable Soil Water vs. Time: Effect of Solar Radiation

43 Canopy and Seed Mass vs. Time: Effects of Solar Radiation

44 Growth Stage vs. Time Effects of Temperature

45 Canopy and Seed Growth: Effects of Temperature Change

46 Potential ET vs Time: Effects of Temperature Change

47 Extractable Soil Water vs. Time: Effects of Rainfall Changes

48 Actual ET vs. Time: Effects of Rainfall Changes

49 Canopy and Seed Growth vs. Time: Effects of Rainfall Changes

50 Dry Weight (kg/ha) Simulated and Measured Soybean G ai nesville, FL 1978 Yield Day of Year Total Crop - IRRIGATED Grain - IRRIGATED Total Crop - NOT IRRIGATED Grain - NOT IRRIGATED

51 Observed vs. simulated yields, Georgia yield trials ( ) 1996) Observed Yield (kg/h y = x R 2 = Simulated Yield (kg/ha)

52 Relationship between seasonal average max temperature and soybean yield; Observed (Georgia yield trials) and Simulated Observed Yields Simulated Yields Yield (kg/ha) Max Temp Average (C) Yield (kg/ha) Max Temp Average (C)

53 Relationship between seasonal rainfall and soybean yield; Observed (Georgia yield trials) and Simulated Observed Yield vs. Rainfall (mm/d) Simulated Yield vs. Rainfall (mm/d) Yield (kg/ha) Yield (kg/ha) Rainfall (mm/d) Rainfall (mm/d)

54 Georgia Variety Trials Observed Data Variety Yield (kg/ha) Hutcheso n= x Bryan = x site Yield Average (kg/ha) Hutcheson Bryan

55 Georgia Variety Trial Soybean Crop Model Predictions Variet y Yield (kg/ha) Hutcheson = x Bryan = x sit e Yield Average (kg/ha) Hutcheson Bryan

56 Staying Current: A New Modular Version of DSSAT Cropping System Models

57 Limitations and Challenges Many factors not included in Crop Models Need to evaluate for local conditions, range of conditions Input requirements Complexity Stability, documentation..gradually overcoming these limitations

58 Future Prospects Broader applications, particularly for issues related to weather effects Crop Model Improvements, more factors, more testing Technology for more easily providing inputs, such as weather and soil data for specific sites Wide participation of agricultural researchers Model-based Decision Support Systems are in early stage of development; more efforts anticipated

59 Thank You