A mixture neural methodology for computing rice consumptive water requirements in Fada N'Gourma Region, Eastern Burkina Faso

Seydou Traore, Yu Min Wang, Chun E. Kan, Tienfuan Kerh, Jan-Mou Leu

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Crop consumptive water requirement (Crop-ET) is a key variable for developing management plans to optimize the efficiency of water use for crop production particularly in semiarid zone. In Burkina Faso, the unfavorable climatic conditions characterized by the low and unevenly distribution of rainfall have pushed water resources management to the forefront of the crop production issue. Crop-ET is extremely required in rainwater effective management for mitigating the impact of water deficit on the crops. Basically, Crop-ET determination involves reference evapotranspiration (ETo) and crop coefficient (Kc) which required complete climatic data and specific site crop information, respectively. ETo estimation with the recommended FAO56 Penman-Monteith (PM) equation is limited in Burkina Faso due to the numerous meteorological data required which are not always available in many production sites. In such circumstances, research to compute directly Crop-ET as an alternative to the two-step approach of calculating ETo and determining site specific Kc, seems desirable. Therefore, this study aims to evaluate the performance of a mixture principal component analysis neural network (PCANN) model for computing rice Crop-ET directly from temperatures data in Fada N'Gourma region located in Eastern Burkina Faso, Africa. From the statistical results, rice Crop-ET can be successfully computed by using PCANN methodology, when only temperatures data are available in this African semiarid environment. Thus, in poor data situation, Crop-ET direct computation can be rapidly addressed through PCANN model for agricultural water management in African semiarid regions.

Original languageEnglish
Pages (from-to)165-173
Number of pages9
JournalPaddy and Water Environment
Volume8
Issue number2
DOIs
Publication statusPublished - 2010 Jun 1

Fingerprint

computer techniques
Burkina Faso
water requirement
Crops
rice
crop
methodology
crops
Water
water
Evapotranspiration
neural networks
evapotranspiration
principal component analysis
Principal component analysis
water management
crop production
Neural networks
rain
crop coefficient

All Science Journal Classification (ASJC) codes

  • Agronomy and Crop Science
  • Environmental Engineering
  • Water Science and Technology

Cite this

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title = "A mixture neural methodology for computing rice consumptive water requirements in Fada N'Gourma Region, Eastern Burkina Faso",
abstract = "Crop consumptive water requirement (Crop-ET) is a key variable for developing management plans to optimize the efficiency of water use for crop production particularly in semiarid zone. In Burkina Faso, the unfavorable climatic conditions characterized by the low and unevenly distribution of rainfall have pushed water resources management to the forefront of the crop production issue. Crop-ET is extremely required in rainwater effective management for mitigating the impact of water deficit on the crops. Basically, Crop-ET determination involves reference evapotranspiration (ETo) and crop coefficient (Kc) which required complete climatic data and specific site crop information, respectively. ETo estimation with the recommended FAO56 Penman-Monteith (PM) equation is limited in Burkina Faso due to the numerous meteorological data required which are not always available in many production sites. In such circumstances, research to compute directly Crop-ET as an alternative to the two-step approach of calculating ETo and determining site specific Kc, seems desirable. Therefore, this study aims to evaluate the performance of a mixture principal component analysis neural network (PCANN) model for computing rice Crop-ET directly from temperatures data in Fada N'Gourma region located in Eastern Burkina Faso, Africa. From the statistical results, rice Crop-ET can be successfully computed by using PCANN methodology, when only temperatures data are available in this African semiarid environment. Thus, in poor data situation, Crop-ET direct computation can be rapidly addressed through PCANN model for agricultural water management in African semiarid regions.",
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A mixture neural methodology for computing rice consumptive water requirements in Fada N'Gourma Region, Eastern Burkina Faso. / Traore, Seydou; Wang, Yu Min; Kan, Chun E.; Kerh, Tienfuan; Leu, Jan-Mou.

In: Paddy and Water Environment, Vol. 8, No. 2, 01.06.2010, p. 165-173.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A mixture neural methodology for computing rice consumptive water requirements in Fada N'Gourma Region, Eastern Burkina Faso

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AU - Wang, Yu Min

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AU - Kerh, Tienfuan

AU - Leu, Jan-Mou

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