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This article in AJ

  1. Vol. 93 No. 4, p. 773-782
     
    Received: Dec 17, 1999
    Published: July, 2001


    * Corresponding author(s): jcavero@eead.csic.es
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doi:10.2134/agronj2001.934773x

Simulation of Maize Grain Yield Variability within a Surface-Irrigated Field

  1. Jose Cavero *a,
  2. Enrique Playána,
  3. Nery Zapataa and
  4. Jose M. Facib
  1. a Dep. Genética y Producción Vegetal, Estación Experimental de Aula Dei (CSIC), Apdo. 202, 50080 Zaragoza, Spain
    b Unidad de Suelos y Riegos, Servicio de Investigación Agroalimentaria (DGA), Apdo. 727, 50080 Zaragoza, Spain

Abstract

Spatial variability of crop yield within a surface-irrigated field is related to spatial variability of available water due to nonuniform irrigation and soil characteristics, among other factors (e.g., soil fertility). The infiltrated depth at each location within the field can be estimated by measurements of opportunity time and infiltration rate or simulated with irrigation models. We investigated the use of the crop growth model EPICphase to simulate the spatial variability of maize (Zea mays L.) grain yield within a level basin using estimated or simulated (with the irrigation model B2D) infiltrated depth. The relevance of the spatial variability of infiltration rate, opportunity time, and soil surface elevation in the simulation of grain yield spatial variability was also investigated. The measured maize grain yields at 73 locations within the level basin, ranging from 3.16 to 11.54 t ha−1 (SD = 1.79 t ha−1), were used for comparison. Estimated infiltrated depth considering uniform infiltration rate resulted in poor simulation of the spatial variability of grain yield [SD = 0.59 t ha−1, root mean square error (RMSE) = 1.98 t ha−1]. Simulated infiltrated depth with the irrigation model considering uniform infiltration rate and soil surface elevation resulted in grain yield simulations with lower variability than measured (SD = 0.64 t ha−1, RMSE = 1.58 t ha−1). Introducing both sources of spatial variability in the irrigation model resulted in the best simulation of grain yield spatial variability (SD = 1.68 t ha−1, RMSE = 1.16 t ha−1; regression of calculated vs. measured yields: slope = 0.74, r 2 = 0.56).

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Copyright © 2001. American Society of AgronomyPublished in Agron. J.93:773–782.