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Are you ready to meet four new and reliable travel companions, ready to travel the streets with you? All you have to do is visit our store to discover which alloy wheels are more appropriate for your own style. All the streets in the world are waiting for you, and you can face them with a whole new feeling. Seven titles in-a-row with OZ! Browse the. Perfect for SUVs. The d-statistic was used because it gives a single index of model performance, which covers bias and variability; it also indicates prediction better than R 2.
A low value for nRMSE expressed in percent is desired to define a good fit. The d statistic has values between zero and one, with one being the best fit. The modeling efficiency, EF [ 29 ] was employed to test modeling efficiency Eq 4. Where n is the number of observations, S i is the simulated data, m i is the measured data, and is the mean of the measured data. The values of GSPs generated using data from calibration experiments and breeder evaluation data are shown in Table 4.
The highest degree days from emergence to end of Juvenile stage P1 was recorded for OBA 9 in both experimental and breeder data. For number of days from silking to end of physiological maturity P5 , the highest values were recorded for Seedco white in both the experimental and breeder data.
The variety Seedco white produced the largest number of maximum possible kernels G2 for experimental data while OBA 9 had the highest values for breeder data.
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The value of G3 kernel filling rate ranged between 6. Evaluation of CERES-Maize for grain yield, number of days to anthesis, number of days to physiological maturity and plant height and using both calibration experiments and breeder evaluation is shown in Fig 2 for two varieties. Calibration of number of days to anthesis, and plant height, were more accurate when experimental data were used compared with breeder data for both varieties.
Calibration of both variables using experimental data resulted in d-index values in the range of 0. For the breeder data however, d-index values ranged from 0. Days to anthesis was calibrated with higher accuracy than plant height for all varieties. Number of leaves per plant and plant height were measured for the experimental data at different time intervals. The simulated values for both plant height and number of leaves were accurate at all sampling periods Fig 3. Biomass and LAI were measured at juvenile stage, at anthesis, and at physiological maturity for the calibration data only.
Fig 4 shows the result of simulation of above-ground biomass and LAI for Sammaz 32 across the trial locations. Good agreements were found between simulated and observed variables for all other varieties. Biomass was simulated with higher accuracy than LAI across all locations. Calibration of both variables had the lowest accuracy at Dambatta. Agreements between observed and simulated LAI were closer for the earliest measurement juvenile stage , followed by measurement at anthesis, and physiological maturity in all locations except at Samaru where the reverse was observed.
For biomass however, measurement at physiological maturity produced the closest agreements between observed and simulated values, while measurement at anthesis produced the lowest agreement between observed and simulated variables. Yield and yield attributes were well calibrated for all varieties in both calibration and breeder datasets.
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Table 5 shows the result of comparisons between observed and simulated mean grain yields of all varieties across different locations. Calibration of grain yield using experimental data was more accurate, as evidenced by low percentage prediction deviations 3. For the breeder data however, prediction deviations of up to Negative prediction deviation which indicate under simulation was only observed in one location BGD 13 for the breeder evaluation data, while in all instances positive prediction deviations were observed.
Grain and tissue nitrogen, as well as grain yield, at harvest were simulated using independent datasets from trials conducted at BUK during the rainy seasons between and Simulations were done using GSPs generated from both experimental and breeder data. Table 6 shows the comparison between observed and simulated grain yields with accompanying model statistics for the two datasets taking SAMMAZ 32 and EE-White as examples. Grain yield was well simulated for both varieties using both datasets, although better fits were observed for GSPs from the calibration data.
Tables 7 and 8 shows comparisons of simulated grain and stover nitrogen using GSPs generated from calibration and breeder evaluation experiments. Better agreements between observed and simulated grain and stover Nitrogen were observed at high Nitrogen and 60 Kg N for both calibration and breeder evaluation experiments. At zero nitrogen application however, the agreements between observed and simulated values where low as evidenced by higher RMSE and lower d-index values.
Calibrated GSPs from the on-station experiments and breeder evaluation experiments were similar to GSPs reported for related varieties in West and Southern Africa [ 30 — 32 ] with respect to yield and yield attributes. For growth and phenology however, data from our experiments produced better calibration of growth and phenology than earlier reported experiments in the Nigerian Savannas.
For calibration using both experimental and breeder data, we set the values of P2 to 0. Recent publications by Lamsal et al. However, while more detailed testing for this phenomenon might be useful future work, our goal was to assess the degree of alignment between model predictions and observations given different sources of calibration data and this has been shown to be adequate.
This high percentage shows that phenological events like number of days to flowering and number of days to maturity were not accurately measured in the breeder experiments due to small sample sizes and because they are not the traits of interest in the breeding program. This is evidenced for example by the under-simulation of days to flowering by 2. The implication of poor phenology measurements is seen by a slight over-estimation of yield and yield attributes thereby confirming assertions made by Kumudini et al.
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Calibration of the GSPs using the on-station experiment datasets produced better model fits than the breeder evaluation data as expected. The closeness of fit observed for the on-station data could be attributed to better experimental sites soils with higher fertility and better moisture retention , better crop management timely weeding, fertilizer application etc. This is evidenced by the breeder data having higher experimental errors for all measured variables when compared to the evaluation experiments.
The evaluation experiments were also done on larger plot sizes and no missing plants were recorded at harvest, while in the breeder data smaller plots were used and there were no considerations for missing plants during yield calculations. In addition, for the experimental datasets more plant-related variables were measured compared to the breeder evaluation experiment data where only grain yield, days to flowering, plant height and days to physiological maturity were measured. For the breeder evaluation experiment, the closeness between observed and simulated plant heights was low.
This could be attributed to the fact that most breeder trials are conducted under water limited conditions, thus rainfall variability may affect crop performance and data quality. Although the model can properly simulate water stress, no stress was observed in any of the breeder evaluation sites and years. Grain yield and days to anthesis were simulated more accurately than plant height for the breeder evaluation experiment. This can be attributed to the high number of datasets used 7 locations and 2 seasons. Anothai et al.