Corn Yield Response to Excessive Soil Water Conditions

Hardjoamidjojo, S.; Skaggs, R. W.; Schwab, G. O.

doi:10.13031/2013.33641

Cited by 34 publications

(31 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The same concept could be used to measure the degree of stress imposed on plants under excessive soil-water conditions. Ravelo et al (1982) and Hardjoamidjojo et al (1982) used this concept to characterize the drainage requirements of crops.…”

Section: Stress-day-index Modelmentioning

confidence: 99%

“…Ravelo (1982) used the SEW^Q as the stress-day factor, SD, in the above equation. SD in the above equation was also used later as SEW^Q by Hardjoamidjojo et al (1982) and Kanwar et al (1988). Hardjoaitiidjojo et al (1982) attempted to determine if yield affected by excessive soil-water conditions can be related directly to a stress-day index based on SEW^Q as a stress-day factor.…”

Section: =1mentioning

confidence: 99%

“…Some studies have sought a relationship between crop yield and water stress. For instance, Sieben (1964) introduced the SEW^Q concept, and Hardjoamidjojo et al (1982) incorporated an approximate method based on the stress-day-index (SDI) concept (Hiler, 1969). The SDI approach is based on CS and SD factors to calculate the SDI values.…”

Section: Summary Of Review Of Literaturementioning

confidence: 99%

“…Additionally, Hardjoamidjojo et al (1982) developed the relation between relative yield and SDI. No study has been conducted to determine the reliability of the concept.…”

Section: General Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Monitoring high water table effects on corn growth and water quality in growth chambers and field lysimeters

Ahmad

View full text Add to dashboard Cite

Section: Stress-day-index Modelmentioning

confidence: 99%

Section: =1mentioning

confidence: 99%

Section: Summary Of Review Of Literaturementioning

confidence: 99%

“…Additionally, Hardjoamidjojo et al (1982) developed the relation between relative yield and SDI. No study has been conducted to determine the reliability of the concept.…”

Section: General Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring high water table effects on corn growth and water quality in growth chambers and field lysimeters

Ahmad

View full text Add to dashboard Cite

“…A produtividade relativa em resposta somente ao excesso de água no solo, YRW, é calculada com base no índice diá-rio de estresse e na soma dos excessos diários de água a profundidades menores que X cm, SEW X (Hardjoamidjojo et al, 1982;Evans & Fausey, 1999;Borges Júnior, 2004). Na análise financeira, o critério utilizado é o valor presente líquido por unidade de área (VPL, R$ ha -1 -Figura 1).…”

Section: Os Termos Indicados Na Figura 4 Sãounclassified

Modelo computacional para suporte à decisão em áreas irrigadas. Parte I: Desenvolvimento e análise de sensibilidade

Júnior

Ferreira²,

Hedden-Dunkhorst

et al. 2008

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

RESUMOEste trabalho se refere ao desenvolvimento de um modelo computacional para suporte à tomada de decisão, quanto ao planejamento e manejo de projetos de irrigação e/ou drenagem. O modelo computacional, denominado MCID, é aplicá-vel em nível de unidade de produção, gerando informações sobre como diferentes práticas de manejo da irrigação e configurações do sistema de drenagem afetam a produtividade e o retorno financeiro. Essas informações podem ser empregadas em estudos de otimização de padrão de cultivo em nível de propriedade agrícola, em relação ao retorno financeiro e ao uso da água, associados à análise de risco com base em simulações. O balanço hídrico e de sais na zona radicular e as estimativas da profundidade do lençol freático e vazão nos drenos, são conduzidos em base diária. A análise de sensibilidade indicou que os parâmetros de entrada que mais influenciaram o requerimento de irrigação totalizado para o ciclo, foram espaçamento entre drenos, porosidade drenável, número da curva, condutividade hidráulica horizontal do solo saturado, profundidade da camada impermeável e os parâmetros n e α do modelo de van GenuchtenMualem. Palavras-chave: irrigação, drenagem, manejo solo-água-planta-salinidade, análise financeiraComputer model for decision support on irrigated areas Part I: Development and sensitivity analysis ABSTRACT This paper refers to the development of a decision support model for planning and managing irrigation and/or drainage schemes. The computer model, called MCID, is applicable to a production unit level, generating information on how different irrigation management practices and drainage designs affect crop yield and financial return. This information may be applied in studies of crop patterns at farm level, taking into consideration financial return and water use, associated to risk analysis based on simulations. The water and salt balance in the root zone, as well as the water table depth and drain discharge predictions, are carried out on a daily basis. The sensitivity analysis pointed out that the most influential parameters on the seasonal irrigation requirement were: drain spacings, drainable porosity, curve number, horizontal soil saturated hydraulic conductivity, impermeable layer depth and the van Genuchten-Mualem parameters n and α.

show abstract

Water-in-Plants Bibliography

Pospíšilová¹,

Solárová²

1983

Water-in-Plants Bibliography

View full text Add to dashboard Cite

Corn Yield Response to Excessive Soil Water Conditions

Cited by 34 publications

References 0 publications

Monitoring high water table effects on corn growth and water quality in growth chambers and field lysimeters

Monitoring high water table effects on corn growth and water quality in growth chambers and field lysimeters

Modelo computacional para suporte à decisão em áreas irrigadas. Parte I: Desenvolvimento e análise de sensibilidade

Water-in-Plants Bibliography

Contact Info

Product

Resources

About