Performance of the CSM–MANIHOT–Cassava model for simulating planting date response of cassava genotypes

Phoncharoen, Phanupong; Banterng, Poramate; Moreno-Cadena, Patricia; Vorasoot, Nimitr; Jogloy, Sanun; Theerakulpisut, Piyada; Hoogenboom, Gerrit

doi:10.1016/j.fcr.2021.108073

Cited by 10 publications

(25 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new values for NODWT were 15.0 for Kasetsart 50, 14.0 for Rayong 9, and 8.0 for both Rayong 11 and CMR38-125-77. These values were lower than the values of genetic coefficients from Phoncharoen et al (2021). The results from the model calibration showed good to excellent agreement for simulating total and storage root dry weights for all three environments with the d values ranging from .80 to .99, except for storage root dry weight for the genotype Kasetsart 50 (d = .77) for Non Daeng and for the genotype CMR38-125-77 (d = .72) for Kham Thao.…”

Section: Model Calibration and Evaluationmentioning

confidence: 83%

“…The genotype CMR38-125-77 had the highest values for both simulated and observed total dry weights with weather data representing the experimental conditions and with the long-term (1988-2018) historical weather data. There were also no significant differences between the regres- Phoncharoen et al (2021).…”

Section: Stability Analysismentioning

confidence: 85%

“…The inputs required for the CSM-MANIHOT-Cassava model in the DSSAT v4.7.5 include crop management, soil properties, daily weather conditions, and genetic coefficients (Hoogenboom et al, 2012;Moreno-Cadena et al, 2020;Tsuji et al, 1994). The genetic coefficients for the four cassava genotypes were obtained from Phoncharoen et al (2021). These coefficients were derived from the experiment designed specifically at Khon Kaen University with different planting dates.…”

Section: Model Calibration Evaluation and Applicationmentioning

confidence: 99%

“…The values of NODWT were adjusted for all four genotypes in order to get better calibration results for upper paddy field conditions with water limitation (Table 3). The growth of four cassava genotypes was first simulated for the three environments with genetic coefficients from Phoncharoen et al (2021). The values of NODWT for all four genotypes were adjusted manually until reciving a good agreement between observed and simulated values for total, aboveground, and storage root dry weights.…”

Section: Model Calibration and Evaluationmentioning

confidence: 99%

“…This model was improved from the CSM-CROPSIM-Cassava model, which was calibrated for application in Thailand (Keawmuangmoon & Jintrawet, 2014;Kumsueb & Jintrawet, 2020). The cassava module simulates growth and yield for various cassava genotypes under different management practices and for many environmental conditions, including upland conditions (Kaweewong et al, 2013;Phoncharoen et al, 2021). However, until now there has not been an evaluation of the CSM-MANIHOT-Cassava model for its ability to simulate growth and yield under upper paddy fields after rice.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Performance of a model in simulating growth and stability for cassava grown after rice

et al. 2021

Self Cite

View full text Add to dashboard Cite

Selecting the appropriate genotype for growing cassava (Manihot esculenta Crantz) after rice (Oryza sativa L.) can help increase the supply of cassava and improve land use efficiency. However, conducting the selection requires data from many years of multi-environment yield trials. A systems analysis approach using crop models can support this process. This study aimed to evaluate a potential application of the Cropping System Model (CSM)-MANIHOT-Cassava in determining genotype stability across different upper paddy field conditions when planted following rice. Four cassava genotypes, that is, Kasetsart 50, Rayong 9, Rayong 11, and CMR38-125-77, were evaluated for the six different environments in Thailand from the 2015 through 2018 growing seasons. The data required for the model were recorded, including biomass and yield. The cultivar coefficients of the CSM-MANIHOT-Cassava model for the four genotypes were calibrated and evaluated with the experimental data. The model was then run for historical weather data from 1988 to 2018 for the six environments for production under rain-fed conditions in upper paddy fields following rice. The overall results showed that the model was able to simulate biomass and yield of the four cassava genotypes quite well when compared to the experimental data. The model identified the same stable genotypes as presented in the actual trials. The genotype CMR38-125-77 was found to be a stable genotype and had the highest mean performance for both the actual yield trials and the simulation study.Therefore, the CSM-MANIHOT-Cassava model could be used to help identify the favorable genotype for planting in various paddy field conditions after rice harvest. INTRODUCTIONCassava (Manihot esculenta Crantz) is recognized as one of the most important food, feed, and energy crops across the Abbreviations: ASEAN, Association of the Southeast Asian Nations; CSM, Cropping System Model; DAP, days after planting; DSSAT, Decision Support System for Agrotechnology Transfer; MSD, mean square deviation; NODWT, node weight of the first stem of the shoot before forking; RCBD, randomized complete block design.

show abstract

Section: Model Calibration and Evaluationmentioning

confidence: 83%

Section: Stability Analysismentioning

confidence: 85%

Section: Model Calibration Evaluation and Applicationmentioning

confidence: 99%

Section: Model Calibration and Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Performance of a model in simulating growth and stability for cassava grown after rice

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

Evaluation of DSSAT‐MANIHOT‐Cassava model to determine potential irrigation benefits for cassava in Jamaica

et al. 2021

Self Cite

View full text Add to dashboard Cite

Cassava (Manihot esculenta Crantz) is an important food crop, especially in developing countries, because of its resilience and ability to grow in conditions generally inhospitable for other crops. However, tropical crops like cassava are not as frequently modeled compared with crops from temperate locations. The objective of this research was to calibrate the CSM-MANIHOT-Cassava model of the Decision Support System for Agrotechnology Transfer, DSSAT beta v4.8 and use the model to evaluate the potential benefits of irrigation on yield. We established two field trials with two water treatments (rainfed and irrigated) and four cultivars that had not been studied previously. We simulated in-season biomass and end-of-season yield, evaluating the model performance with different statistical measures. There was good agreement between simulated and measured values; the best results showed a deviation of 9.7%, normalized RMSE of 18%, and d-index of 0.98 for biomass, with corresponding values of 11, 24, and 0.98, respectively, for yield. Good simulations of yield correlated with accurate simulations for leaf area index and harvest index. The varieties showed differential responses to irrigation, suggesting that there are diverse levels of drought tolerance even within the same environmental conditions.

show abstract

Crop model determined mega-environments for cassava yield trials on paddy fields following rice

Sawatraksa

Banterng

Jogloy

et al. 2023

Heliyon

View full text Add to dashboard Cite

Performance of the CSM–MANIHOT–Cassava model for simulating planting date response of cassava genotypes

Cited by 10 publications

References 46 publications

Performance of a model in simulating growth and stability for cassava grown after rice

Performance of a model in simulating growth and stability for cassava grown after rice

Evaluation of DSSAT‐MANIHOT‐Cassava model to determine potential irrigation benefits for cassava in Jamaica

Crop model determined mega-environments for cassava yield trials on paddy fields following rice

Contact Info

Product

Resources

About