Abstract
Crop simulation models can be used to identify appropriate genotypes and growing environments for improving cassava yield. The aim of this study was to determine the best genotypes for different cassava production environments using the cropping system model (CSM)–MANIHOT–Cassava. Data from cassava experiments that were conducted from 2009–2011 and 2014–2015 at Khon Kaen, Thailand, were used to evaluate the model. Simulations were then conducted for different scenarios using four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11, and CMR38–125–77), twelve planting dates (at monthly intervals starting in January and ending in December), and ten locations in Thailand under fully irrigated and rainfed conditions using 30 years of historical weather data. Model evaluation with the experimental data for total biomass and storage root yield indicated that the model classified well for relative productivity among different planting dates. The model indicated that growing cassava under irrigated conditions generally produced higher biomass and storage root yield than under rainfed conditions. The cassava genotype CMR38–125–77 was identified for high biomass, while the genotype Rayong 9 was identified as a good genetic resource for high yield. The December planting date resulted in the highest biomass for all locations, while the February planting date produced the highest storage root yield for almost all locations. The results from this study suggest that the CSM–MANIHOT–Cassava model can assist in determining suitable genotypes for different cassava production environments for Thailand, and that this approach could be applicable to other cassava growing areas.
Highlights
Cassava (Manihot esculenta Crantz) is a commercial crop commonly used for human consumption, animal feed, and industrial products [1]
Potential applications of the Decision Support System for Agrotechnology Transfer (DSSAT) crop models for agricultural research have been reported for several approaches, for example, defining the suitable genotypes for peanut based on multienvironment yield trials [13,14,15], forecasting maize yield for the off-season in a subtropical environment [16], determining optimum management strategies for soybean [17], wheat [18], and rice [19], evaluating the impact of climate variability on wheat grain yield [20], examining the El Niño–Southern Oscillation effect on cotton yields at different planting dates and spatial aggregation levels [21], and estimating seasonal fragrant rice production in Thailand [22]
This study showed the potential of the cropping system model (CSM)–MANIHOT–Cassava model to identify appropriate genotypes and growing environments in northeast Thailand
Summary
Cassava (Manihot esculenta Crantz) is a commercial crop commonly used for human consumption, animal feed, and industrial products [1]. Potential applications of the DSSAT crop models for agricultural research have been reported for several approaches, for example, defining the suitable genotypes for peanut based on multienvironment yield trials [13,14,15], forecasting maize yield for the off-season in a subtropical environment [16], determining optimum management strategies for soybean [17], wheat [18], and rice [19], evaluating the impact of climate variability on wheat grain yield [20], examining the El Niño–Southern Oscillation effect on cotton yields at different planting dates and spatial aggregation levels [21], and estimating seasonal fragrant rice production in Thailand [22]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
