28Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide 29 the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect 30 of most modelling exercises because it requires expensive and time-consuming field experiments. 31GSPs could also be estimated using multi-year and multi locational data from breeder evaluation 32 experiments. This research was set up with the following objectives: i) to determine GSPs of 10 33 newly released maize varieties for the Nigerian Savannas using data from both calibration 34 experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the 35 accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-36 Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different 37 experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. 38 Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were 39 evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 40 120kg N ha -1 ). For the model calibration using experimental data, calculated model efficiency (EF) 41 values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. 42 Calibration of time-series data produced nRMSE below 7% while all prediction deviations were 43 below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges 44 were lower. Prediction deviations were below 17% of the means for all measured variables. Model 45 evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high 46 EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen 47 contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from 48 detailed experiments. If unavailable, data from breeder experimental trials collected from many 49 locations and planting dates can be used with lower but acceptable accuracy.
In this study, the CERES-Maize model was calibrated and evaluated using data from 60 farmers’ fields across Sudan (SS) and Northern Guinea (NGS) Savannas of Nigeria in 2016 and 2017 rainy seasons. The trials consisted of 10 maize varieties sown at three different sowing densities (2.6, 5.3, and 6.6 plants m−2) across farmers’ field with contrasting agronomic and nutrient management histories. Model predictions in both years and locations were close to observed data for both calibration and evaluation exercises as evidenced by low normalized root mean square error (RMSE) (≤15%), high modified d-index (> 0.6), and high model efficiency (>0.45) values for the phenology, growth, and yield data across all varieties and agro-ecologies. In both years and locations and for both calibration and evaluation exercises, very good agreements were found between observed and model-simulated grain yields, number of days to physiological maturity, above-ground biomass, and harvest index. Two separate scenario analyses were conducted using the long-term (26 years) weather records for Bunkure (representing the SS) and Zaria (representing the NGS). The early and extra-early varieties were used in the SS while the intermediate and late varieties were used in the NGS. The result of the scenario analyses showed that early and extra-early varieties grown in the SS responds to increased sowing density up to 8.8 plants m−2 when the recommended rate of N fertilizers (90 kg N ha−1) was applied. In the NGS, yield responses were observed up to a density of 6.6 plants m−2 with the application of 120 kg N ha−1 for the intermediate and late varieties. The highest mean monetary returns to land (US$1336.1 ha−1) were simulated for scenarios with 8.8 plants m−2 and 90 kg N ha−1, while the highest return to labor (US$957.7 ha−1) was simulated for scenarios with 6.6 plants m−2 and 90 Kg N ha−1 in the SS. In the NGS, monetary return per hectare was highest with a planting density of 6.6 plants m−2 with the application of 120 kg N, while the return to labor was highest for sowing density of 5.3 plants m−2 at the same N fertilizer application rates. The results of the long-term simulations predicted increases in yield and economic returns to land and labor by increasing sowing densities in the maize belts of Nigeria without applying N fertilizers above the recommended rates.
Selection of appropriate sowing density is an important yield enhancing management decision in maize (Zea mays L.) production particularly in rainfed conditions. This study aimed at evaluating the optimum stand densities (OSDs) of 10 recently released maize varieties under different crop management decisions and environments. Ten maize varieties of varying characteristics were planted in the Northern Guinea Savanna of Nigeria across 30 farmer's fields in the rainy seasons of 2016 and 2017 under three stand densities: 2.6, 5.3, and 6.6 plants m−2. Grain yield and yield components were greatest under the high density in both years across all locations. The intermediate maturing varieties produced higher grain yields compared to the early and late maturing varieties in both years and locations. The environmental indices from the Factor Analytic Model showed 20% of the fields were optimal, 28.3% moderate, 31.7% poor, and 20% were very poor environments. Increasing planting density did not significantly affect the grain yield of the varieties in very poor environments. A linear increase in grain yield was observed in moderate and optimum environments with every increase in stand density for all varieties except Sammaz 32, however, optimum planting densities could not be reached for all the varieties. Therefore, tropical maize varieties should be planted under specific densities that account for environmental and management conditions to maximize yield.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.