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

Abstract

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.

Capability of cassava model to determine biomass of two branching types at different plant spacings

“…The overall model evaluation showed that the CSM-MANIHOT-Cassava model provided acceptable results in simulating the response of cultivars at certain plant spacings, indicating the potential of this model to help generate crop data for determining suitable plant spacing to increase land use efficiency. Other studies have demonstrated the ability of the CSM-MANIHOT-Cassava model to identify desirable cassava cultivars in terms of total crop biomass at harvest for different growing dates in particular upland conditions (Phoncharoen et al, 2021a, b) and in selecting a stable cassava cultivar based on the final crop biomass and storage root yield for different upper paddy fields during the rice off-season (Sawatraksa et al, 2021) for a tropical wet or savanna climate in Thailand. Therefore, the CSM-MANIHOT-Cassava model could be used to support decision making to improve crop productivity in Thailand and in other similar production areas.…”

“…The CSM-MANIHOT-Cassava model allows researchers to quantify the impact of different growing environments on growth and yield, including plant spacing. The model has been used to evaluate the responses of cassava to certain rates of nitrogen fertilizer for some soil series (Kaweewong et al, 2013), different planting dates in a specific location (Phoncharoen et al, 2021b) and different upper paddy fields following rice (Sawatraksa et al, 2021). Validating the CSM-MANIHOT-Cassava model for the simulation of the growth and yield at different plant spacings is still pending.…”

Identifying Suitable Genotypes for Different Cassava Production Environments—A Modeling Approach

“…The model can be used to determine crop management for improving cassava productivity, and it has been used to determine the nitrogen requirements for cassava in Thailand [25]. It has also been evaluated on the basis of the growth and yield of cassava grown under upland conditions in Thailand for different planting dates [26] and for cassava grown in paddy fields following rice [27]. Although there are studies that have shown the potential of the CSM-MANIHOT-Cassava model, reports on the potential of this model to help identify suitable cassava genotypes and appropriate growing environment is limited.…”