Physiology, Growth and Yield of Different Cassava Genotypes Planted in Upland with Dry Environment during High Storage Root Accumulation Stage

Wongnoi, Settawoot; Banterng, Poramate; Vorasoot, Nimitr; Jogloy, Sanun; Theerakulpisut, Piyada

doi:10.3390/agronomy10040576

Cited by 14 publications

(16 citation statements)

References 36 publications

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“…A study (Wongnoi et al., 2020) for the genotypes Kasetsart 50, Rayong 9, Rayong 11, and CMR38‐125‐77 grown under upland field in Thailand indicated that the values for storage root dry weights for the first 6 mo (during rainy season) varied from 5.50 to 13.30 t ha −1 , which are higher than our simulated and observed storage root values. Lower storage root yields obtained from our growing conditions might be due to less amount of rainfall during the dry season.…”

Section: Resultscontrasting

confidence: 76%

“…The desirable performance of the genotype CMR38-125-77 grown under experimental field conditions after rice has also been reported in terms of chlorophyll fluorescence (Fv/Fm and F'v/F'm) and growth rate for leave, stem, storage root, and crop (Sawatraksa et al, 2018;. For the experiments under upland conditions, the genotype CMR38-125-77 also T A B L E 4 Mean dry weight, regression coefficients between dry weight and environmental index, and mean square deviation (MSD) from regression for observed and simulated data for the six environments showed an outstanding performance with respect to physiology (Fv/Fm, F'v/F'm, net photosynthesis, stomatal conductance, water use efficiency, relative water content for leaf, leaf area index, and specific leaf area), biomass, and yield (Janket et al, 2018;Phoncharoen et al, 2019aPhoncharoen et al, , 2019bPhosaengsri et al, 2019;Wongnoi et al, 2020). Therefore, the genotype CMR38-125-77 is not only a suitable genetic resource for production under upland condition, but our study also demonstrates that it is an appropriate genotype for production under paddy field conditions in Thailand.…”

Section: Stability Analysismentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Resultscontrasting

confidence: 76%

Section: Stability Analysismentioning

confidence: 99%

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

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“…Most nutrient elements of the crops planted in the PRS had the maximum increase rate in accumulation of nutrients during the mid-to late growth stages (3-9 MAP) and slowly increased after 9 MAP in all tested genotypes (Table 3), except for N, K and Ca in all genotypes, as they were high during the early to mid-growth stages (1-6 MAP). The maximum increase rate in the accumulation of N occurred for a second time during the late growth stage (9)(10)(11)(12). No statistically significant difference between planting dates and among growth stages were observed in the accumulation rate of S for both planting dates (Table 3).…”

Section: Total Nutrient Uptake Nutrient Uptake By Storage Roots and T...mentioning

confidence: 99%

“…In Thailand, cassava is planted mainly in the ERS, accounting for about 70% of the total planted areas, and planting cassava in the PRS accounts for about 30% [3]. Significant differences among planting dates in crop development, production of biomass and yields as well as the physiological traits of cassava have been observed since they are different in photoperiod, temperatures, solar radiation, and amount and distribution of rainfall [4][5][6][7][8][9]. In addition, a changing climate has the potential to alter the soil abiotic processes, thereby changing the nutrient availability in the soil and, thus, directly affecting the plant growth [10].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Macro-Nutrient Uptake by Cassava in a Tropical Savanna Climate

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Matching fertilization with crop needs is important for maximizing yields and reducing fertilizer losses. Seasonal variation in nutrient uptake dynamics is poorly understood and thus, the ability to optimize fertilization strategies is limited. This study aims to investigate the effects of planting dates on macronutrient uptake dynamics in cassava genotypes with full irrigation. The performance of cassava genotypes, i.e., CMR38-125-77, Kasetsart 50 and Rayong 11, were evaluated in the early rainy (ERS) and post rainy seasons (PRS) for two years using a randomized complete block design with four replicates. The plants were harvested at 1, 3, 6, 9 and 12 months. Planting dates had significant effects on the accumulation of dry matter and storage roots as well as nutrient uptakes and partitioning. On average, the total nutrient uptake per plant to produce 2831–3279 g of biomass with 1244–1810 g of storage roots in the ERS varied among cassava genotypes, ranging from 21.1–24.3 g N, 5.1–5.9 g P, 26.5–29.5 g K, 14.1–22.2 g Ca, 6.1–7.6 g Mg and 2.0–2.3 g S. The total nutrient uptake per plant to produce 3353–3824 g of biomass with 1604–2253 g of storage roots in the PRS ranged from 27.1–32.4 g N, 5.2–6.0 g P, 29.1–31.3 g K, 11.9–20.3 g Ca, 7.3–9.9 g Mg and 1.2–1.5 g S. In the ERS, the majority of the total nutrient uptake occurred at the early growth stages, whereas in the PRS, this occurred at the mid- to late growth stages. At final harvest, the percentages of nutrient removal by the storage roots for ERS were 24.7–36.0% N, 26.0–32.3% P, 43.4–51.5% K, 12.4–17.6% Ca, 22.2–31.5% Mg and 27.2–31.5% S, whereas in the PRS the percentages were 30.4–44.4% N, 33.3–41.6% P, 44.7–57.3% K, 12.0–15.1% Ca, 20.2–28.1% Mg and 12.0–25.4% S. CMR38-125-77 exhibited satisfactory performance in nutrient uptake, nutrient use efficiency and storage roots yield across the planting dates. The evidence obtained from this study would greatly facilitate more efficient adoption of precision agriculture in cassava production by applying recommended fertilizers, e.g., rates, kinds and timings, according to crop demand in each growing season in Thailand and for choosing superior cassava genotypes.

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“…The preferable performances for the genotype CMR38-125-77 under growing conditions of upper paddy fields during the off-season of rice have been reported in terms of chlorophyll fluorescence [41] and based on crop growth rate [42]. Wongnoi et al [43] also reported that the genotypes CMR38-125-77 had good physiology and total crop dry weight. Therefore, it is clear that the CMR38-125-77 genotype was the superior genotype for biomass production (Table 3).…”

Section: Performances Of the Four Cassava Genotypesmentioning

confidence: 99%

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

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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.

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Physiology, Growth and Yield of Different Cassava Genotypes Planted in Upland with Dry Environment during High Storage Root Accumulation Stage

Cited by 14 publications

References 36 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

Quantitative Evaluation of Macro-Nutrient Uptake by Cassava in a Tropical Savanna Climate

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

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