Expanding the area under cultivation and increasing the productivity are two solutions to fulfil the high cassava demand. The objective was to determine the quantum efficiency of photochemistry, relative water content (RWC), growth and biomass of four cassava genotypes grown under rain‐fed upper paddy field conditions. The four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11 and CMR38–125–77) were evaluated under three environments in rain‐fed upper paddy field conditions in the Khon Kaen province, Thailand, between 2015 and 2017. The experiment used a randomized complete block design (RCBD) with four replications. Data recorded were soil characteristics prior to planting, soil moisture, leaf RWC, chlorophyll fluorescence under natural light (Fv′/Fm′) and artificial dark (Fv/Fm) conditions, total dry weight (TDW), storage root dry weight (SRDW), starch yield and weather data during crop duration. The results from combined analysis indicated that the growing environment had a greater effect than genotypes for RWC (except for RWC at 90 days after planting (DAP), Fv′/Fm′, Fv/Fm, TDW (except for TDW at 150 DAP), SRDW (except for SRDW at 150 DAP) and starch yield. There were significant interactions between genotypes and environments for TDW, SRDW and starch yield. CMR38–125–77 was the superior genotype for growing under rain‐fed upper paddy field conditions when compared to the other three cassava genotypes. This study also showed the possibility of using Fv′/Fm′ and Fv/Fm as criteria to improve the efficiency for cassava varietal selection.
Cassava (Manihot esculenta Crantz) grown in upper paddy fields after rice (Oryza sativa L.) could increase cassava yield and improve land use efficiency. The objective of this study was to evaluate growth, yield, and starch content of four cassava genotypes grown after the harvest of rice in upper paddy fields in Thailand. Four cassava genotypes—cultivars Kasetsart 50, Rayong 9, and Rayong 11—and line CMR38‐125‐77 were evaluated during the 2015–2016 and 2016–2017 growing seasons for five environments. Each experiment had a randomized complete block design with four replications. Soil characteristics were assessed prior to planting, and crop growth and development and local weather conditions were recorded during the experiments. Environment affected all traits, but genotype and genotype × environment effects were nonsignificant for starch content at 180 d after planting (DAP) and for leaf growth rate (LGR) at 120 to 180 DAP. Average storage root dry weights across five environments were 4301, 3933, 3128, and 5824 kg ha−1 for Kasetsart 50, Rayong 9, Rayong 11, and CMR38‐125‐77, respectively. CMR38‐125‐77 was the most stable genotype for storage root fresh and dry weights, and total dry weight. Physiological determinants of both storage root yield and total biomass of cassava were crop growth rate (CGR) and net assimilation rate (NAR) for 30 to 90 DAP and LGR, stem growth rate (SGR), CGR, and NAR for 120 to 180 DAP, as well as storage root growth rate (SRGR) for 90 to 180 DAP. Leaf growth rate during 120 to 180 DAP is likely to be a new criterion for supporting cassava varietal selection. This investigation showed the feasibility of growing cassava between paddy rice seasons.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.