GUMCAS: a model describing the growth of cassava (Manihot esculenta L. Crantz)

Matthews, Robin; Hunt, L. A.

doi:10.1016/0378-4290(94)90054-x

Cited by 90 publications

(86 citation statements)

References 24 publications

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“…O início da acumulação de amido ocorre com aproximadamente 21 folhas visíveis na haste principal, na 'Fepagro RS 13', o que acontece, aproximadamente, entre 70 e 80 dias após o plantio (Schons et al, 2007). Esse estágio é importante, pois marca o início da translocação dos fotoassimilados para os órgãos de reserva da espécie, o que modifica a relação fonte/dreno na planta (Matthews & Hunt, 1994), e é nesse período que a ocorrência de deficit hídrico é mais prejudicial (Alves, 2002).…”

Section: Methodsunclassified

Transpiração e crescimento foliar de plantas de mandioca em resposta ao deficit hídrico no solo

Lago

Streck

Bisognin

et al. 2011

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi determinar a transpiração e o crescimento foliar de duas cultivares de mandioca, em resposta ao conteúdo de água disponível no solo representado pela fração de água transpirável no solo (FATS). Foram realizados dois experimentos, em vasos de 8 L, com as cultivares Fécula Branca e Fepagro RS 13. O plantio foi feito em 11/9/2009 e 8/9/2010, em delineamento experimental inteiramente casualizado. A água disponível, a transpiração e o crescimento foliar foram medidos diariamente, em cada experimento, durante o período de imposição da deficiência hídrica. A FATS crítica, em que a transpiração começa a ser reduzida, foi de 0,45 para 'Fécula Branca' e 0,50 para 'Fepagro RS 13'. A redução do crescimento foliar começou quando a FATS atingiu 0,51 para 'Fécula Branca' e 0,49 para 'Fepagro RS 13'. A FATS crítica para transpiração e crescimento foliar difere em condições de atmosfera com baixa e com alta demanda evaporativa do ar, conforme a cultivar de mandioca utilizada.Termos para indexação: Manihot esculenta, deficit hídrico, fechamento estomático, fração de água transpirável, umidade crítica no solo. Transpiration and leaf growth of cassava plants as a response to soil water deficitAbstract -The objective of this work was to determine the transpiration and leaf growth of two cassava cultivars, as a response to the available soil water content represented by the fraction of transpirable soil water (FTSW). Two experiments were carried out in 8-L pots with the cassava cultivars Fécula Branca and Fepagro RS 13. Planting was done on 9/11/2009 and on 9/8/2010, in a completely randomized design. The available soil water, the transpiration and the leaf growth were measured on a daily basis, in each experiment, during the period of soil drying. The threshold FTSW, at which the transpiration begins to reduce, was 0.45 for 'Fécula Branca' and 0.50 for 'Fepagro RS 13'. Reduction of leaf growth started at 0.51 FTSW for 'Fécula Branca' and at 0.49 for 'Fepagro RS 13'. Threshold FTSW for transpiration and for leaf growth differs in atmosphere conditions with high and low atmospheric demand, depending on the utilized cassava cultivar.

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Section: Methodsunclassified

Transpiração e crescimento foliar de plantas de mandioca em resposta ao deficit hídrico no solo

Lago

Streck

Bisognin

et al. 2011

Pesq. agropec. bras.

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“…160 Table S10 shows the parameters that were modified or introduced in the 162 SIMPLACE<LINTUL2 CC HEAT> model. 163 (Table S11) The hourly temperature is derived from the minimum and the maximum daily temperature 229 using the cosine function described in Matthews and Hunt (1994).…”

Section: S14 Fasset 61mentioning

confidence: 99%

Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

Maiorano

Martre

Asseng

et al. 2017

Field Crops Research

133

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61To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles 62 (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce 63 the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models 64 needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-65 parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and 66 senescence, biomass growth, and grain number and size using detailed field experimental data from the 67 USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model 68 improvement were then evaluated with independent field experiments from a CIMMYT world-wide field 69 trial network (evaluation data set). Model improvements decreased the variation (10 th to 90 th model 70 ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration 71 data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures 72 >24°C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME 73 uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of 74 variation observed in field experiments and used as a benchmark can be reached with half the number of 75 models in the MME. Improving crop models is therefore important to increase the certainty of model-76 based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. 77 78

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“…Based on the village climate data, we calculated degree days (DD) during the maize growing months from May to September. Following Matthews and Hunt [22], hourly temperature ( ) is expressed by a cosinusoidal function assuming the maximum temperature occurs at 14:00,…”

Section: Datamentioning

confidence: 99%

Extreme Weather Impacts on Maize Yield: The Case of Shanxi Province in China

et al. 2016

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Extreme weather can have negative impacts on crop production. In this study, we statistically estimate the impacts of dry days, heat waves, and cold days on maize yield based on household survey data from 1993 to 2011 in ten villages of Shanxi province, China. Our results show that dry days, heat waves, and cold days have negative effects on maize yield, although these effects are marginal if these extreme events do not increase dramatically. Specifically, a one percent increase in extreme-heat-degree-days and consecutive-dry-days results in a maize yield declines of 0.2% and 0.07%, respectively. Maize yield also is reduced by 0.3% for cold days occurring during the growing season from May to September. However, these extreme events can increase dramatically in a warmer world and result in considerable reduction in maize yields. If all the historical temperatures in the villages are shifted up by 2 degrees Celsius, total impacts of these extreme events would lead to a reduction of maize yield by over 30 percent. The impacts may be underestimated since we did not exclude the offset effect of adaptation measures adopted by farmers to combat these extreme events.

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GUMCAS: a model describing the growth of cassava (Manihot esculenta L. Crantz)

Cited by 90 publications

References 24 publications

Transpiração e crescimento foliar de plantas de mandioca em resposta ao deficit hídrico no solo

Transpiração e crescimento foliar de plantas de mandioca em resposta ao deficit hídrico no solo

Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

Extreme Weather Impacts on Maize Yield: The Case of Shanxi Province in China

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