Weather variables
This is the first study to evaluate the rainfall distribution pattern created by gridded weather database (GWD) considering its impact on soil condition in crop modeling. These patterns of GWD rainfall are of critical importance, prone to generate mistaken attainable yields. There are studies related to GWD rainfall estimation uncertainties in amount and distribution, such as Ruane et al. (2015), Xavier et al. (2016) and Mourtzinis et al. (2017). Battisti et al. (2019) showed that Daily Gridded (DG) underestimated rainfall by 21.17 mm cycle-1 in the soybean crop, they also found good agreements between DG and MWD for Tmax and Tmin, reporting an r of 0.92 and RMSE of 0.72 for Tmax, and r of 0.87 and RMSE of 1.05 for Tmin.
The RMSE observed in DG rainfall data (Fig. 3) agrees with the ones reported by Xavier et al. (2016) for Amazon and Tocantins river basins, with values of 13.17 and 10.54, respectively. Lower ME values from DG agrees with previous statements, given that the dataset has a lower underestimation of total rainfall than NASA/POWER (NP). Both GWD has a good RS, agreement with MWD, DG has a slightly better agreement, showing R², RMSE, and d of 0.65, 3.09, and 0.9, respectively, against 0.52, 4.09, and 0.84 from NP.
Simulated yield and phenology
Battisti et al. (2019) found a better performance at simulating attainable (Ya) and potential (Yp) yield using DG datasets for simulating soybean crop modeling. In our study, we found that DG has a better performance when simulating Yp and Ya than NP (Table 2, Fig. 4 and 5). Since potential yield depends only on maximum (Tmax) and minimum (Tmin) air temperature, as well as solar radiation (Sr), DG also showed better agreement than NP. Because Ya has rainfall as input, the most uncertain variable, it is expected a reduction in its agreement to MWD ones.
The ORYZA (v3) crop model phenology is defined by air temperature and photoperiod (Bouman et al. 2001). Errors in GWD-based simulations for these variables are low enough not to cause high-magnitude errors for this estimation (Fig. 2 and 5). In this case the phenology is the same for all soil conditions, since drought don’t change rice phenology in the simulation.
Weather variables play an important role in growth and crop yield (Sridevi and Chellamuthu 2015). Tropical rice has an optimal temperature range between 25 and 35°C. High temperatures, above 35°C (Hussain et al. 2019) for most cultivars or 36.6°C for the studied cultivar (Heinemann et al. 2015), have a negative impact on growth and pollination, leading to spikelet sterility. Likewise, low temperature, below 25°C, may cause delays in phenological stages (Hussain et al. 2019). Both situations decrease productivity.
Soil water availability effect on attainable yield
In our study, it is observed that GWD-based simulations have a closer result to Yp, meanwhile GWD overestimates Ya, when compared to MWD-based simulations (Fig. 4). GWD overestimates Ya as a consequence of misestimating the daily rainfall amount and distribution (Fig. 1 and 3) (Wart et al. 2013; Mourtzinis et al. 2015; Xavier et al. 2016).
In general, this overestimation took place mainly in the distribution range of 0.1 to 12 mm, which accounts for around of 70% of the times observed (Fig. 3). On the other hand, extreme rainfall events (> 50 mm d-1) are underestimated by GWD (Fig. 1, 2 and 3). Therefore, gridded weather data ends up simulating an irrigation-sheet-like rainfall pattern in the range of 0.1 to 12mm, an overestimation by GWD based rainfall. This rainfall pattern is incompatible with measured weather data, resulting in increased productivity for all soils. As a consequence of soil characteristics, its overestimations increase as soil textures become coarser. It is possible to visualize this difference in the cumulative evapotranspiration (ETCUM) (Fig. 6).
Both regression lines from GWD and MWD showed that in both soil conditions, GWD overestimates MWD ETCUM, with higher overestimations in sandy soil condition (Fig. 4e and f). It is noticeable that cumulative evapotranspiration (ETCUM) from GWD tends to have increased overestimation in sandy soils, even though the accumulated rain is similar (Fig. 6e, f, i, and j). The violinplot (Fig. 4i and j) shows MWD ETCUM values ranging at lower values than GWD ones. The opposite pattern is seen in accumulated drainage (DRAIN) (Fig. 6k and l). In this regard, GWD DRAIN values are ranging in values lower than MWD ones, showing that irrigation-sheet-like rain pattern generated by GWD reduces total drainage, a consequence of an underestimation of high-precipitation events and an overestimation of precipitation values in the range of 0.1 to 12 mm.
According to Bouman et al. (2001), when drought occurs in the ORYZA (v3) crop model, solar radiation absorption is negatively affected due to leaf rolling, reduced leaf expansion rate, and changed assimilate partitioning. Hence the reduced PARCUM in sandy soil conditions. Li et al. (2017) stated that photosynthesis for non-drought-tolerant cultivars linearly decreases as the soil water content decreases, the yield for drought-tolerant cultivars would be mildly impacted in a mild drought.
It is observed that DG and NP-based crop simulations overestimation YP in 3.5% and 13.7%, respectively. Meanwhile, for Ya in clay soil condition, there was 9.6% and 18.4% yield overestimation, with 67% and 75% of total events overestimating MWD, for DG and NP respectively. The widest differences between GWD and MWD-based simulations are observed for Ya in sandy soil condition, where there are 21.3% and 29.1% yield overestimation, with 84% and 85% of total events overestimating MWD for DG and NP datasets. These overestimation percentages are based on the mean errors and the average yield (Table 2, Fig. 4 and 7).
In Yp conditions, NP-based simulations overestimate MWD-based simulations in a wider productivity range, from 2.5 to 6.5 Mg ha-1 (Fig. 7). NP overestimates MWD in about 20% of the simulations before DG, overestimating MWD ones in the range of 2.5 to 6.5 Mg ha-1, whereas DG overestimates in the range of 3 to 5 Mg ha-1 Fig. 7a. In Ya clay soil condition, both GWD overestimate MWD around the same range, from 2 to 4 Mg ha-1 Fig. 7b. In Ya sandy soil condition as in occurred before, both GWD overestimate in about the same range of conditions, ranging from 1 to about 2.5 Mg ha-1 Fig. 7c. It is noteworthy to point out that the graph curve is further away in relation to the last case (Fig. 7a, b, and c). Overestimation worsens as soil water storage capacity decreases.
In clay soil condition, only two DG GWD municipalities show higher values of attainable yield than MWD (Paracatu and Rio Branco), NP did not show any underestimation at clay soil condition, whereas both GWD did not show underestimation at sandy soil conditions (Table 2), which are less prone to happen. We also divided the 32 years’ time series in two in order to assess the temporal impact on crop model (Table 3), its possible to notice that DG overestimate even more for all conditions in the second part of time series, in this period DG used more rain gauge in interpolations than the first period assessed (Xavier et al. 2016). NP overestimated more in the second period for Yp condition and underestimated for Ya in sandy soil condition (Table 3).
Similar yield trends are observed for all simulated conditions along 32 seasons (Fig. 7d, e, and f). Soybean simulations by Battisti et al. (2019), based on GWD and MWD, also found the same trends along sowing years in simulating attainable yield. These similar systematic errors may indicate model sensitivity on capturing climate effects on yield variability (Pirtiojja et al. 2015).
Ya was overestimated by the model running the GWD, similar to what has been found in other studies. Battisti et al. (2019), simulations of soybean yield with DG dataset, found the ME for Yp and Ya of 69 kg ha-1 and 178 kg ha-1, respectively. It was concluded that the DG data estimates soybean yield within acceptable error boundaries. However, this study used a soil with 0.162 cm3 cm-3 soil water storage, classified as high soil water storage (Jensen and Allen 2016), and set the root growth to hit maximum values of 120 cm, possibly smoothing the errors related to GWD precipitation rain pattern. Van Wart et al. (2015) found a ME for Ya of about 10% on maize, wheat, and rice simulations in several countries using the NP dataset. There is no study that analyzed the interaction between GWD rain pattern and soil water storage.
Limitations and future work
Although current findings point out an acceptable correspondence between GWD and MWD on crop model for rice, several limitations must be considered. GWD are known to be less accurate in regions where MWD are less available or do not exist (Wart et al. 2013; Xavier et al. 2016). These uncertainties increase as virtual weather stations get further away from measured weather stations (Van Wart et al. 2015). Second, GWD does not estimate daily MWD rainfall distribution with accuracy. Therefore, we suggest caution when using GWD as an input in crop models, especially in sandy soils, since they tend to have lower water storage, which is prone to worse attainable yield overestimation in upland rice. We assume this may happen in other crops as well, but there are no studies supporting this hypothesis. Third, studies comparing crop performance among different regions with distinct soil profiles may generate misleading results. Lastly, according to Li et al. (2017), ORYZA (v3) crop model has coupled nitrogen uptake with water uptake, hence affecting nitrogen studies with ORYZA (v3) that relies on GWD.