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Characterizing patterns of water deficit and nitrogen stress in maize growing regions of the tropics

By: White, J.W | Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), Mexico DF (Mexico).
Contributor(s): Edmeades, G.O.|Banziger, M.|Mickelson, H.R.|Peña-Valdivia, C.B [eds.] | Elings, A [coaut.].
Material type: materialTypeLabelBookAnalytics: Show analyticsPublisher: Mexico, DF (Mexico) CIMMYT : 1997ISBN: 968-6923-93-4.Subject(s): Drought stress AGROVOC | Nitrogen | Plant physiology | Plant production | Tropical zones | Water depletion | CIMMYT | Zea mays AGROVOCDDC classification: 633.153 Summary: Water and nitrogen deficits frequently reduce maize production in tropical regions, but it is difficult to assign precise values to their impact or to subdivide regions experiencing water deficit into well-defined categories. Nonetheless, there are clear benefits to obtaining such information. Assessments of impact are crucial to priority setting, while classification of patterns of water deficit can improve targeting of research both in crop improvement and crop management. Simple classifications using subjective evaluations or analyses of monthly weather data are useful, but there is strong demand for more quantitative approaches. Process-based crop growth models show much promise for integrating effects of diverse environmental and agronomic factors, but such models are very data intensive. This paper examines three facets of use of models for characterizing patterns of water deficit and nitrogen stress for maize growing regions of the tropics. The water deficit index (WDI) of the CERES-Maize model was found to show promise as an indicator of seasonal variation in water deficit. Similarly, the nitrogen stress index (NSI) of the model appears useful for nitrogen deficit. The model predicted that water deficits show much stronger seasonal variation than nitrogen stress and that varying N-fertilization levels has relatively little impact on variation in WDI. Thus, WDI shows strong spatial and temporal variation, while NSI shows mainly spatial variation. The daily weather data required by simulation models are seldom available for large numbers of sites, so researchers have developed "weather generators" that create simulated weather data based on monthly means, which are easily interpolated over geographic regions. Although the two generators examined, WGEN and SIMMETEO, provided weather data that gave mean yields similar to those obtained from observed weather data, they did not capture expected associations between date of onset of growing seasons and total seasonal precipitation. Thus, data from weather generators may not be suitable for lese with models simulating detailed mechanisms of adaptation to water deficit.Collection: CIMMYT Publications Collection
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Conference proceedings CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

CIMMYT Publications Collection 633.153 EDM (Browse shelf) 1 Available A624179
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Water and nitrogen deficits frequently reduce maize production in tropical regions, but it is difficult to assign precise values to their impact or to subdivide regions experiencing water deficit into well-defined categories. Nonetheless, there are clear benefits to obtaining such information. Assessments of impact are crucial to priority setting, while classification of patterns of water deficit can improve targeting of research both in crop improvement and crop management. Simple classifications using subjective evaluations or analyses of monthly weather data are useful, but there is strong demand for more quantitative approaches. Process-based crop growth models show much promise for integrating effects of diverse environmental and agronomic factors, but such models are very data intensive. This paper examines three facets of use of models for characterizing patterns of water deficit and nitrogen stress for maize growing regions of the tropics. The water deficit index (WDI) of the CERES-Maize model was found to show promise as an indicator of seasonal variation in water deficit. Similarly, the nitrogen stress index (NSI) of the model appears useful for nitrogen deficit. The model predicted that water deficits show much stronger seasonal variation than nitrogen stress and that varying N-fertilization levels has relatively little impact on variation in WDI. Thus, WDI shows strong spatial and temporal variation, while NSI shows mainly spatial variation. The daily weather data required by simulation models are seldom available for large numbers of sites, so researchers have developed "weather generators" that create simulated weather data based on monthly means, which are easily interpolated over geographic regions. Although the two generators examined, WGEN and SIMMETEO, provided weather data that gave mean yields similar to those obtained from observed weather data, they did not capture expected associations between date of onset of growing seasons and total seasonal precipitation. Thus, data from weather generators may not be suitable for lese with models simulating detailed mechanisms of adaptation to water deficit.

English

9801|AGRIS 9702|anterior|R97-98PROCE|FINAL9798

Jose Juan Caballero

CIMMYT Publications Collection

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