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

Abstract

Most 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.

Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

“…DSSAT yields were found to be closer to be observed yields as compared to AquaCrop yields at all locations with the variation in <0.3% to 12%. This overestimation, although high, is close to the calibration period (0.6%–11%) and is in agreement with several studies that have adopted similar approach for calibrating the models (Klein et al 2012; Bao et al 2017; Adnan et al 2019) There are several factors that are not accounted for in the models used and could have attributed toward the overestimation of crop yield. These factors include, but are not limited to, several biotic and abiotic stresses (Garibay et al 2019) as well as pedo‐climatic conditions (Brilli et al 2017).…”

“…This method presented in our study of using multienvironment crop variety trial data to estimate the cultivar coefficients of crop is a good alternative when detailed crop growth data from during the season are not available. Availability of breeder/variety trial datasets from different locations and over a range of crop management scenarios like planting dates when combined with a systematic approach can prove helpful in calibrating and using crop models in data scarce environments (Adnan et al 2019). We found that a large number (multiple years and multiple locations) of variety trials that have only end of season yield data can be successfully used to derive cultivar information to use in two different crop models.…”

Modeling Planting-Date Effects on Intermediate-Maturing Maize in Contrasting Environments in the Nigerian Savanna: An Application of DSSAT Model

“…The average d-index values for measured parameters were above 0.90 for each variety at both locations. Adnan et al [41] evaluated CERES-Maize model with many varieties and different maturity groups in northern Nigeria and reported average d-index value for grain yield as 0.99 from experimental data and 0.96 from breeder data. Adnan et al [3] also reported a d-index of 0.82 for shoot dry matter in northern Nigeria.…”

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

“…The major physiological processes (photosynthesis, respiration, accumulation, and partitioning of assimilates) in the CERES-Maize model are governed by six genetic coefficients. For the present calibration, GSPs of four already calibrated varieties (two for SS and two for NGS) were collected from Adnan et al (2019). Required crop genetic inputs for CERES-Maize are given in Table 1 and they describe the growth, phenology, and yield characteristics according to varietal differences.…”

“…The authors also suggested that the use of the CERES-Maize model may show limitations due to the inaccessibility of soil and weather data, but most importantly due to lack of detailed crop data for calibrating the genotype-specific parameters (GSPs) of different varieties. The study opined that upon the development of high yielding varieties with upright leaf orientation and greater response to applied N. Having initially calibrated GSPs of 26 modern maize varieties reported to be tolerant to high sowing density (Adnan et al, 2019), the current research was conducted with the following objectives: (i) calibrate CERES-Maize model using data collected from researcher managed experiments conducted in farmers' fields with varying management conditions in two contrasting environments; (ii) evaluate the ability of the model to simulate the effect of elevated sowing density on different maize varieties used in Nigeria and sub-Saharan Africa; (iii) use the calibrated and validated model in making recommendations for optimum sowing density and N fertilizer application of maize in two contrasting environments; and (iv) determine the economic profitability of different management scenarios of maize in the SS and NGS.…”