Calibration and validation of APSIM–Maize, DSSAT CERES–Maize and AquaCrop models for Ethiopian tropical environments

Feleke, Hirut Getachew; Savage, Michael J.; Tesfaye, Kindie

doi:10.1080/02571862.2020.1837271

Cited by 19 publications

(27 citation statements)

References 77 publications

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“…The APSIM-Maize and DSSAT-CERES-Maize models simulated accurately days after planting to anthesis (RMSE = 1.73-4.09) and maturity (RMSE = 1.66-5.36). It was observed by [72] that the DSSAT-CERES-Maize model overestimated DAP to maturity for late-maturing cultivars. The results reported are comparable to this study (Tables 6 and 7) [72].…”

Section: Phenologymentioning

confidence: 99%

“…It was observed by [72] that the DSSAT-CERES-Maize model overestimated DAP to maturity for late-maturing cultivars. The results reported are comparable to this study (Tables 6 and 7) [72]. APSIMmaize has been used to simulate the optimal sowing windows for maize [73].…”

Section: Phenologymentioning

confidence: 99%

“…The model simulated accurately anthesis (DAP) and maturity (DAP), grain yield and biomass for maize cultivars. A multi-model ensemble of the APSIM, DSSAT-CERES and Aquacrop models have been used to simulate maize growth and yield under rainfed condition in Ethiopia [72]. The APSIM-Maize and DSSAT-CERES-Maize models simulated accurately days after planting to anthesis (RMSE = 1.73-4.09) and maturity (RMSE = 1.66-5.36).…”

Section: Phenologymentioning

confidence: 99%

See 2 more Smart Citations

Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

2021

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Crop model calibration and validation is vital for establishing their credibility and ability in simulating crop growth and yield. A split–split plot design field experiment was carried out with sowing dates (SD1, SD2 and SD3); maize cultivars (ZMS606, PHB30G19 and PHB30B50) and nitrogen fertilizer rates (N1, N2 and N3) as the main plot, subplot and sub-subplot with three replicates, respectively. The experiment was carried out at Mount Makulu Central Research Station, Chilanga, Zambia in the 2016/2017 season. The study objective was to calibrate and validate APSIM-Maize and DSSAT-CERES-Maize models in simulating phenology, mLAI, soil water content, aboveground biomass and grain yield under rainfed and irrigated conditions. Days after planting to anthesis (APSIM-Maize, anthesis (DAP) RMSE = 1.91 days; DSSAT-CERES-Maize, anthesis (DAP) RMSE = 2.89 days) and maturity (APSIM-Maize, maturity (DAP) RMSE = 3.35 days; DSSAT-CERES-Maize, maturity (DAP) RMSE = 3.13 days) were adequately simulated, with RMSEn being <5%. The grain yield RMSE was 1.38 t ha−1 (APSIM-Maize) and 0.84 t ha−1 (DSSAT-CERES-Maize). The APSIM- and-DSSAT-CERES-Maize models accurately simulated the grain yield, grain number m−2, soil water content (soil layers 1–8, RMSEn ≤ 20%), biomass and grain yield, with RMSEn ≤ 30% under rainfed condition. Model validation showed acceptable performances under the irrigated condition. The models can be used in identifying management options provided climate and soil physiochemical properties are available.

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Section: Phenologymentioning

confidence: 99%

Section: Phenologymentioning

confidence: 99%

Section: Phenologymentioning

confidence: 99%

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Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

2021

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“…The DSSAT modelling system includes the Crop Environment Resource Synthesis (CERES-Maize) model used for simulating maize (Zea mays L.) growth and yield under various environmental conditions. The model has been used to address a range of applications, such as evaluating strategies to cope with limited weather and soil conditions , evaluating climate change impacts on crop production (Babel & Turyatunga, 2015;Jiang et al, 2021;Lin et al, 2015), optimizing agronomic practices for increased production (Mubeen et al, 2016), and assessing the performance of various cultivars and their suitability in different environmental conditions (Chisanga et al, 2021c;Feleke et al, 2021). Researchers have tested the model under various production environments such as the Tropical, the Mediterranean, and Temperate regions (Ahmad et al, 2021;Arefi et al, 2017;Banterng et al, 2010;Getachew et al, 2021;Kothari et al, 2019;Vilayvong et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…To address these uncertainties, techniques such as assimilation with external data sources have been suggested to constraint the model simulations over time (Joshi et al, 2019;Li et al, 2015). In addition, application of model ensembles has been adopted to characterize biophysical processes across agricultural landscapes (Chisanga et al, 2021b;Feleke et al, 2021). Another requirement for the DSSAT-CERES-Maize model is that investment in significant amount of experimental resources is necessary to account for the variability in various seasons (Hoogenboom et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Kipkulei

Bellingrath-Kimura

Lana

et al. 2022

Int. J. Plant Prod.

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Maize production in low-yielding regions is influenced by climate variability, poor soil fertility, suboptimal agronomic practices, and biotic influences, among other limitations. Therefore, the assessment of yields to various management practices is, among others, critical for advancing site-specific measures for production enhancement. In this study, we conducted a multiseason calibration and evaluation of the DSSAT–CERES-Maize model to assess the maize yield response of two common cultivars grown in Trans Nzoia County in Kenya under various agricultural strategies, such as sowing dates, nitrogen fertilization, and water management. We then applied the Mann–Kendall (MK), and Sen’s Slope Estimator (SSE) tests to establish the yield trends and magnitudes of the different strategies. The evaluated model simulated long-term yields (1984–2021) and characterized production under various weather regimes. The model performed well in simulating the growth and development of the two cultivars, as indicated by the model evaluation results. The RMSE for yield was 333 and 239 kg ha−1 for H614 and KH600-23A, respectively, representing a relative error (RRMSE) of 8.1 and 5.1%. The management strategies assessment demonstrated significant feedback on sowing dates, nitrogen fertilization, and cultivars on maize yield. The sowing date conducted in mid-February under fertilization of 100 kg of nitrogen per hectare proved to be the best strategy for enhancing grain yields in the region. Under the optimum sowing dates and fertilization rate, the average yield for cultivar KH600-23A was 7.1% higher than that for H614. The MK and SSE tests revealed a significant (p < 0.05) modest downwards trend in the yield of the H614 cultivar compared to the KH600-23A. The eastern part of Trans Nzoia County demonstrated a consistent downwards trend for the vital yield enhancement strategies. Medium to high nitrogen levels revealed positive yield trends for more extensive coverage of the study area. Based on the results, we recommend the adoption of the KH600-23A cultivar which showed stability in yields under optimum nitrogen levels. Furthermore, we recommend measures that improve soil quality and structure in the western and northern parts, given the negative model response on maize yield in these areas. Knowledge of yield enhancement strategies and their spatial responses is of utmost importance for precision agricultural initiatives and optimization of maize production in Trans Nzoia County.

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Simulating crop yield using the DSSAT v4.7-CROPGRO-soyabean model with gridded weather and soil data

Singh

Gohain

2023

Model. Earth Syst. Environ.

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Calibration and validation of APSIM–Maize, DSSAT CERES–Maize and AquaCrop models for Ethiopian tropical environments

Cited by 19 publications

References 77 publications

Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Simulating crop yield using the DSSAT v4.7-CROPGRO-soyabean model with gridded weather and soil data

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