Crop characteristics

Exercise objective:

       ·   Potential production; the crop characteristics in the crop data file

Suggested reading: 

Chapters 7.3 and 8.2 of the book, ORYZA2000: modelling lowland rice.

Exercise: 

Ex-II.1. Start up the FSEWinRunOnly Shell. Open project C:\COURSE\POTENTIAL\ORYZAWin. Read CONTROL.DAT and verify the following contents:

FILEIT = 'C:\COURSE\POTENTIAL\IR72DS2.T92'   ! Experimental data

FILEI1 = 'C:\COURSE\POTENTIAL\IR72.D92'      ! Crop data

FILEIR = 'C:\COURSE\POTENTIAL\RERUNS.DAT'    ! Rerun file

Read the experimental data file IR72DS2.T92 and verify that the simulation environment and the crop establishment parameters are set to simulate treatment 2 of the experiment given in Box II.1. Run ORYZA2000, fill out column Sim1 in Table II.1 (use the files OP.DAT and RES.DAT), and make some graphs to study the results.

Table II.1.

View ANSWERS from the Tutorial_answer_sheet.pdf file.

Figure II.1. Simulated and observed dry weights (kg ha-1) of total above-ground biomass (WAGT), green leaves (WLVG) and storage organs (WSO); cv. IR72 grown under potential production conditions, IRRI, Los Baños, 1992.

Ex-II.2. Open the crop data file IR72.D92, and verify that this file contains parameter values that define the rice variety IR72.

Most parameters that are given in IR72.D92 are generic for rice and only a few are specific for cv. IR72 (see Chapter 8.3 of the book ORYZA2000: modelling lowland rice for more details). The following exercises illustrate the effect of some parameters that are variety-specific.

The development rate parameters in Section 1 Phenological development parameters determine the phenological development of the crop. Phenological development is characterized by the order and rate of appearance of the various crop organs (roots, leaves, stem, panicle, grains). Some varieties complete development in a relatively short time (short duration), whereas other varieties take (much) more time (long duration). A full explanation of the way in which phenological development is calculated in ORYZA2000 is given in Chapter 3.2.1 of the book ORYZA2000: modelling lowland rice.

Ex-II.3. Open the file RERUNS.DAT and create the following two rerun sets:

* rerun set 1

DVRP  = .000627  ! Development rate in panicle development (°-1)

DVRR  = .001427  ! Development rate in reproductive phase (°-1)

* rerun set 2

DVRP  = .000941  ! Development rate in panicle development (°-1)

DVRR  = .002141  ! Development rate in reproductive phase (°-1)

The first set has parameter values that are 20% lower than the original values, and the second set has parameter values that are 20% higher (check this!). Run ORYZA2000, fill out Sim2 (first rerun) and Sim3 (second rerun) in Table II.1, and make a graph of simulated and observed leaf area index versus time for all three runs. Explain the differences in Sim1, Sim2 and Sim3, and explain the graph.

A:  With a lower development rate, crop duration is longer and biomass production higher (more total light interception => more photosynthesis), (Sim2); with a higher development rate, crop duration is shorter and biomass production lower (Sim3). Crop growth duration is illustrated with the pattern of leaf area index in Figure II.2.

Figure II.2. Simulated and observed leaf area index (-) versus time, for three runs with different development rate parameters.

The relative growth rate (growth rate per unit leaf) of the leaves determines the rate with which leaves grow in area in the early stages of crop growth. The area of leaves determines how much sunlight is intercepted for photosynthesis and, thus determines to a large extent biomass accumulation. A full explanation of leaf area growth calculations in ORYZA2000 is given in Chapter 3.2.9 of the book ORYZA2000: modelling lowland rice.

Ex-II.4. Create a rerun file with two rerun sets:

* rerun set 1

RGRLMX = 0.0100  ! Maximum relative growth rate of leaf area (°-1)

* rerun set 2

RGRLMX = 0.0070  ! Maximum relative growth rate of leaf area (°-1)

The first set has a parameter value that is 0.0015 larger than the original value, and the second set has a parameter value that is 0.0015 smaller (check this!). Run ORYZA2000, fill out Sim4 (first rerun) and Sim5 (second rerun) in Table II.1, and make a graph of (1) simulated and observed leaf area index versus time for all three runs, and (2) simulated and observed total biomass in time for all three runs. Explain the differences in Sim1 (the default run), Sim4 and Sim5, and explain the graphs.

A: With a higher leaf growth rate (Sim4), the leaves grow faster which leads to higher LAI values which leads to more light interception which leads to more biomass (Figure II.3). Since the development rate of the crop is only determined by temperature, and not affected by photosynthesis, the phenology of the crop is not affected.

Following the early stages of crop growth, the thickness of the leaves is an important characteristic determining their surface area. Leaf thickness is defined as the specific leaf area SLA, specifying the surface area of leaves per unit weight (ha kg-1). Leaves of a certain weight have more surface area when their specific leaf area is higher and, therefore, intercept more sunlight and, hence, show higher photosynthesis. The SLA may change over time with development and ageing of the crop. In the crop data file for ORYZA2000, SLA is defined as a function of development stage, DVS.

Figure II.3a. Simulated and observed leaf area index (-) versus time, for three runs with different relative leaf growth rates.

Figure II.3b. Simulated and observed above-ground biomass (kg ha-1) versus time, for three runs with different relative leaf growth rates.

Ex-II.5. Locate in the crop data file the information on specific leaf area, SLA. Notice the two options to give SLA as a function of development stage DVS. First, as a smooth function defined by the function parameters ASLA, BSLA, CSLA, DSLA and SLAMAX. Second, as a look-up table that is daily interpolated by ORYZA2000.

Ex-II.6. Make a rerun file with two sets of reruns on SLA:

* rerun set 1

SWISLA = 'TABLE'   ! Give SLA as a function of DVS in the table SLATB

SLATB = 0.00, 0.0030,

2.50, 0.0030

* rerun set 2

SWISLA = 'TABLE'   ! Give SLA as a function of DVS in the table SLATB

SLATB = 0.00, 0.0020,

2.50, 0.0020

The first line specifies that SLA will be supplied as a function of development stage instead of being defined as a smooth function. The first set has a relatively high SLA and the second set a relatively low SLA throughout the growth period. Notice, that reruns can also be made for so-called array variables or functions! Run ORYZA2000, fill out Sim2 (first rerun) and Sim3 (second rerun) in Table II.2, and make a graph of (1) simulated and observed leaf area index versus time for all three runs, and (2) simulated and observed total biomass versus time for all three runs. Explain the differences in Sim1 (the default run), Sim2 and Sim3, and explain the graphs.

A: With a higher specific leaf area (Sim2), the leaves produce more surface area, which leads to a higher LAI, which leads to more light interception, which leads to more biomass (Figure II.4; positive feedback).

The so-called partitioning parameters define the fractions of the daily produced assimilates transferred to the roots, the stems, the leaves and the storage organs (panicles). Since these fractions change with the development and ageing of the crop, they are defined as functions of development stage and are supplied as tables (arrays) in the crop data file. One table defines the partitioning of assimilates between roots and shoot (= above-ground crop organs), called FSHTB; one the fraction of shoot assimilates that goes to the leaves, FLVTB; one the fraction of shoot assimilates that goes to the stems, FSTTB; and one the fraction of shoot assimilates that goes to the panicles, FSOTB.

Table II.2

View ANSWERS from the Tutorial_answer_sheet.pdf file.

Figure II.4a. Simulated and observed leaf area index (-) versus time, for three runs with different specific leaf areas.

Figure II.4b. Simulated and observed above-ground biomass (kg ha-1) versus time, for three runs with different specific leaf areas.

Ex-II.7. Locate in the crop data file (IR72.D92) the partitioning tables FSHTB, FLVTB, FSTTB and FSOTB. Note that the table FSHTB tells us that, at emergence, 50% of the daily produced assimilates is partitioned to the shoot, at development stage 0.43 (around maximum tillering) 75%, and from flowering to harvest 100%. The complement is partitioned to the roots. Note that the sum of the partitioning fractions for leaves, stems and panicles always equals 1 (no more above-ground assimilates can be partitioned than its total). Early in the crop growth cycle, 60% of all shoot assimilates goes to the leaves, 40% to the stems and 0% to the panicles (which have not been formed yet). At development stage 0.75, which is shortly after panicle initiation, assimilates are being partitioned to the growing panicle inside the shoot, a small proportion of the assimilates goes to the stem that will support the panicle, and no more leaves are formed (no assimilates are partitioned to the leaves). After the panicle has fully emerged at development stage 1.2, the stem also stops growing and all assimilates are transferred to the panicle. Note that, although simulation with ORYZA2000 stops at physiological maturity (DVS=2.), the partitioning tables go up to DVS=2.5 to avoid extrapolation errors on the last day of simulation when simulated DVS can slightly overshoot the value 2.

Ex-II.8. Make a rerun file with two sets of reruns on partitioning factors for the leaves and for the stems:

* rerun set 1

FLVTB  = 0.000, 0.80,

0.500, 0.80,

0.750, 0.40,

1.000, 0.00,

1.200, 0.00,

2.5  , 0.

FSTTB  = 0.000, 0.20,

0.500, 0.20,

0.750, 0.60,

1.000, 0.40,

1.200, 0.00,

2.5  , 0.

* rerun set 2

FLVTB  = 0.000, 0.40,

0.500, 0.40,

0.750, 0.20,

1.000, 0.00,

1.200, 0.00,

2.5  , 0.

FSTTB  = 0.000, 0.60,

0.500, 0.60,

0.750, 0.80,

1.000, 0.40,

1.200, 0.00,

2.5  , 0.

The first set partitions more assimilates early in the season to the leaves, hence less to the stems, and the second set vice versa. Run ORYZA2000, fill out Sim4 (first rerun) and Sim5 (second rerun) in Table II.2, and make a graph of (1) simulated and observed weight of green leaves versus time for all three runs, and (2) simulated and observed leaf area index versus time for all three runs. Explain the differences in Sim1 (the default run), Sim4 and Sim5, and explain the graphs.

A: With more assimilates partitioned to the leaves (Sim4), the leaves grow faster, which leads to a higher LAI, which leads to more light interception, which leads to more biomass (Figure II.5).

Photosynthesis is not only determined by leaf area and the amount of light intercepted, but also by the nitrogen (in chlorophyll) content of the leaves. With more nitrogen, there is more chlorophyll and more photosynthesis per unit leaf area (see Chapter 3.2.2 section Maximum leaf photosynthesis rate of the book ORYZA2000: modelling lowland rice). In the POTENTIAL production mode, ORYZA2000 reads the leaf nitrogen concentration (on area basis) from the crop data file. Leaf nitrogen concentration normally changes over time with development and ageing of the crop and it is defined as a function of development stage. In the nitrogen-limited production mode, ORYZA2000 dynamically calculates leaf nitrogen concentration as a function of supply and demand (Chapter 5 of the book ORYZA2000: modelling lowland rice).

 Figure II.5a. Simulated and observed green leaf weight (kg ha-1) versus time, for three runs with different partitioning tables for leaves and stems.

 Figure II.5b. Simulated and observed leaf area index (-) versus time, for three runs with different partitioning tables for leaves and stems.

Ex-II.9. Locate in the crop data file the nitrogen concentration in the leaves as the table NFLVTB (N fraction in leaves on leaf area basis; g N m-2 leaf). Notice that leaf N concentration is low (0.54) in the very young crop (until development stage 0.16), increases steeply to about 1.5 around tillering and then gradually decreases to a value of 0.8 at maturity.

Ex-II.10. Make a rerun file with two reruns on NFLVTB:

* rerun set 1

NFLVTB = 0.00, 0.54,

0.16, 0.54,

0.33, 1.75,

2.02, 1.00,

2.50, 1.00

* rerun set 2

NFLVTB = 0.00, 0.54,

0.16, 0.54,

0.33, 1.25,

2.02, 0.60,

2.50, 0.60

Run ORYZA2000 and study the output in OP.DAT and produce some graphs. Explain the differences.

The parameters in Sections 1-7 in the crop data file all affect the growth and development of rice in the potential production situation. The parameters in Sections 8-10 affect processes of crop growth only in situations of water and/or nitrogen limitations.

Ex-II.11. Use the rerun option to vary the values of various parameters (scalars or functions) of the crop data file IR72.D92. The rerun option is an ideal tool for sensitivity analysis and to gain understanding of the way in which parameter values affect crop growth and development. Remember that the default run (RUNNUM 1 in OP.DAT, or Run_0) is always the run with the original parameter values, and that these run results are always supplied in the output files.