Options for calibrating ceres-maize genotype specific parameters under data-scarce environments

Open Access JournalMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha-1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88–0.94 and coefficient of determination (d-index) between 0.93–0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58–0.88) and d-index (0.56–0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy

The influence of phase-modulation on femtosecond time-resolved coherent Raman spectroscopy

The influence of phase-modulation on femtosecond time-resolved coherent Raman scattering is investigated theoretically and experimentally. The coherent Raman signal taken as a function of the spectral position shows unexpected temporal oscillations close to time zero. A theoretical analysis of the coherent Raman scattering process indicates that the femtosecond light pulses are amplitude and phase modulated. The pulses are asymmetric in time with more slowly decaying trailing wings. The phase of the pulse amplitude contains quadratic and higher-order contributions

Optimizing sowing density-based management decisions with different nitrogen rates on smallholder maize farms in northern Nigeria

Open Access Article; Published online: 18 Jan 2021In 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

Optimum stand density of tropical maize varieties: an on-farm evaluation of grain yield responses in the Nigerian Savanna

Open Access Journal; Published online: 24 Mar 2022Selection 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

CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria

Open Access Article; Published online: 11 May 2020When properly calibrated and evaluated, dynamic crop simulation models can provide insights into the different components of genotype by environment interactions (GEIs). Modelled outputs could be used to complement data from multi-environment trials. Field experiments were conducted in the rainy and dry seasons of 2015 and 2016 across four locations in maize growing regions of Northern Nigeria using 16 maize varieties planted under near-optimal conditions of moisture and soil nitrogen. The CERES-Maize model was calibrated using data from three locations and two seasons (rainy and dry) and evaluated using data from one location and two seasons all in 2015. Observed data from the four locations and two seasons in 2016 was used to create eight different environments. Two profile pits were dug in each location and were used separately in the simulations for each environment to provide replicated data for stability analysis in a combined ANOVA. The effects of the environment, genotype and GEI were highly significant (p = 0.001) for both observed and simulated grain yields. The environment explained 67 % and 64 % of the variations in observed and simulated grain yields respectively. The variance component of GEI (13 % for observed and 15 % for simulated) were lower but still considerable when compared to that of genotypes (19 % for observed and 21 % for simulated). From the stability analysis of the observed and simulated grain yields using six different stability models, three models (ASV, Ecovalence, and Sigma) ranked Ife Hybrid as the most stable variety. The slope of the regression (bi) model ranked Sammaz 11 as the most stable variety, while the Shukla model ranked Sammaz 28 as the most stable variety. Long-term seasonal analysis with the CERES-Maize model revealed that early and intermediate maturing varieties produce high yields in both wet and dry savannas, early and extra-early varieties produce high yields only in the dry savannas, while late maturing varieties produce high yields only in the wet savannas. When properly calibrated and evaluated, the CERES-Maize model can be used to generate data for GEI and stability studies of maize genotype in the absence of observed field data