IR58 (IRRI, Philippines)

 Experimental  treatment and design

Cultivar IR58 was used in this experiment. Treatments were laid out in four replicates in a split-plot design, with N treatments as the main plots. All experiments were fully irrigated, and were kept as free from weeds, pests, and diseases as possible. Fertilizer-N application rates in the calibration set ranged from 0 to 225 kg N ha–1 with different splits (Table 1).

Table 1. The nitrogen-fertilizer application rate (kg N ha–1) and splits (kg N ha–1) in calibration set. WS indicates the wet season and DS means the dry season, and the number following is the year. The code is used for field experiments as well as for simulation studies.

Cultural practice

Twelve-day-old seedlings grown in trays were transplanted into each subplot.

Data collection

At the major phenological stages and in between, samples were taken from 12–14 hills, and LAI and organ dry weights were measured. Sequential crop samples were taken during the growing season from 14 hills to determine LAI and biomass of green leaves, dead leaves, stems, and panicles. At harvest, yield components were measured, including individual grain weight. The phenological dates of the calibration set were recorded in Table 2 for all treatments.

Table 2. Phenological development of IR58 rice with different fertilizer management practices at the IRRI farm for model calibration.

Simulations and parameterization

Each treatment in Table 1 was one simulation. We simulated crop growth and development for each treatment using actual fertilizer regimes, emergence dates, seedbed durations, transplanting densities, and daily weather data. For each treatment and experiment, the same model parameters were used, except for development rates that were treatment-specific because we know that N level may affect development rates even though this effect is not yet included in the model. The development rates associated with different treatments were derived from the actual phenological development as recorded in Table 2. In general, however, development rates should be quite stable across environmental conditions. In fact, slightly different development rates were used for different treatments in both calibration and validation sets (Table 3) because the algorithms of current ORYZA2000 did not use functions to express the effects of soil-water and nitrogen status on phenological development. For indigenous soil N supply, we used a value of 0.8 kg ha–1 d–1 for the dry season and 0.6 kg ha–1 d–1 for the wet season, calculated from total N uptake divided by growth duration in zero-N plots in the calibration data set.

Table 3. Phenological development rates of IR68 rice under different nitrogen-fertilizer management practices. DVRJ, DVRI, DVRP, and DVRR are the development rates  (°Cd–1) in juvenile, photoperiod-sensitive, panicle development, and reproductive phases, respectively.

Evaluation of simulated results

A standard evaluation method as introduced in Section 2.5 was used to evaluate the model in this experiment. The biomass of crop organs and LAI were the components used for evaluating model performance.

Because we could not retrieve the variations in measured values of means for our data sets, we estimated standard deviations (SD) and coefficients of variance (CV) for measurements of biomass and LAI from recent experiments at IRRI.  These experiments used rice variety Apo under flooded conditions with high-N and zero-N levels, in four replicates, in four wet seasons and four dry seasons from 2001 to 2003 at the IRRI lowland farm (for experimental details, see Castañeda et al 2002). Because measurements in these experiments followed the same protocols as in our data sets, we used these SD and CV values as proxies for experiments with flooded rice (Table 4).

Table 4. Standard deviation (SD, same unit as variable) and coefficient of variation (CV, %) of measured crop growth variables in experiments with flooded rice. Data calculated from six seasons of field experiments at IRRI using variety Apo under flooded conditions with zero-N and high-N inputs (120 kg ha–1 in wet season and 150 kg ha–1 in dry season). N, number of data pairs.

Parameter values

The parameters of phenological development were derived from recorded phenological development of Table 2. The development rates in Table 3 can be used in other experiments with the same rice varieties because development rates should be quite stable across environmental conditions.

Biomass and LAI

Typical examples of comparisons between simulated and measured crop growth variables are given for the wet-season experiment of 1991 in Figure 1 and for the dry-season experiment of 1992 in Figure 2. In both seasons, the dynamics in biomass of leaves, stems, and panicles was simulated quite well at all levels of N ranging from 0 to 225 kg ha–1. In the wet season, simulated LAI values consistently exceeded measured values in the midst of the growing season at all N levels. In the dry season, simulated LAI exceeded measured LAI only at 0 N, whereas good fits were obtained at 180 and 225 kg N ha–1. In the other years, we also got a better fit between simulated and measured LAI at high levels of N than at low levels of N. In individual years or treatments, better results were obtained with treatment-specific lower values of specific leaf area than with the average values used in our simulations.

 Figure 3 compares simulated with measured crop growth variables for all data. For reference, the 1:1 line plus and minus the estimated SD of measured variables is also shown. The best results were obtained for total aboveground biomass where most of the data points fell between the ±SD lines of measured biomass. There is more spread in the data of leaf, stem, and panicle biomass, and more data fell outside the ±SD lines. However, the most spread is observed for LAI, for which more than 75% of the data points were above the +SD line, indicating a consistent overestimation of LAI. Figure 4 gives the simulated and measured yields and final biomass at harvest, together with the 1:1 ± SD lines. All simulated biomass values fell within or close to the 1:1 ± SD lines, whereas about 25% of the simulated yields were below the 1:1 ± SD line.

Table 5 gives the RMSE for each treatment and experiment separately, the goodness-of-fit parameters for the dynamic crop variables of the whole data set, and these parameters for yield and final biomass at harvest of the whole data set. There was some variation in RMSE among treatments and years, but general patterns were consistent. The RMSE of LAI was consistently largest and that of total aboveground biomass consistently smallest. Moreover, except for LAI, the range in RMSE values for each crop variable was small. There was no relationship between RMSE value and total amount of N applied. Using the whole data set, Student’s t-test indicates that simulated crop growth variables differ from measured values except for total and stem biomass (Table 5). Except for LAI, the slopes were close to 1, and the intercepts were fairly small. The higher for yield indicates the general overestimation of simulated values. The relatively low R2 reflects the large spread in the data. The absolute RMSE and the normalized RMSE of LAI simulations were about four times greater than the typical SD and CV values for measured LAI, respectively (Table 4). The normalized RMSE of simulated aboveground biomass was similar to the CVs of measured values. However, the RMSE of simulated biomass of leaves, stems, and panicles was higher than the SD values of the measurements. For end-of-season biomass, all goodness-of-fit parameters indicate a close fit between simulated and measured data (Table 5).

Reference

Castañeda AR, Bouman BAM, Peng S, Visperas RM. 2002. The potential of aerobic rice to reduce water use in water-scarce irrigated lowlands in the tropics. In: Bouman BAM, Hengsdijk H, Hardy B, Bindraban PS, Tuong TP, Ladha JK, editors. Water-wise rice production. Proceedings of the International Workshop on Water-wise Rice Production, 8-11 April, 2002, Los Baños, Philippines. Los Baños (Philippines): International Rice Research Institute. p 165-176.