Estimation of Yield Response Factor for Each Growth Stage under Local Conditions Using AquaCrop-OS

Journal
Water
ISSN
2073-4441
Date Issued
2020
Author(s)
Mathias Kuschel-Otárola
Niels Schütze
Eduardo Holzapfel
Oleksandr Mialyk
Type
Resource Types::text::journal::journal article
Scopus ID
2-s2.0-85084642492
WoS ID
WOS:000539527500157
DOI
URL
Abstract
We propose a methodology to estimate the yield response factor (i.e., the slope of the water-yield function) under local conditions for a given crop, weather, sowing date, and management at each growth stage using AquaCrop-OS. The methodology was applied to three crops (maize, sugar beet, and wheat) and four soil types (clay loam, loam, silty clay loam, and silty loam), considering three levels of bulk density: low, medium, and high. Yields are estimated for different weather and management scenarios using a problem-specific algorithm for optimal irrigation scheduling with limited water supply (GET-OPTIS). Our results show a good agreement between benchmarking (mathematical approach) and benchmark (estimated by AquaCrop-OS) using the Normalised Root Mean Square Error (NRMSE), allowing us to estimate reliable yield response factors ( K y ) under local conditions and to dispose of the typical simple mathematical approach, which estimates the yield reduction as a result of water scarcity at each growth stage.
Cite this document
Kuschel-Otárola, M., Schütze, N., Holzapfel, E., Godoy-Faúndez, A., Mialyk, O., & Rivera, D. (2020). Estimation of yield response factor for each growth stage under local conditions using aquacrop-os. Water, 12(4), 1080. https://doi.org/10.3390/w12041080
Project(s)
Subjects
aquacrop-os

; 

crop yield function

; 

growth stages

; 

yield response factor

; 

beta vulgaris subsp. vulgaris

; 

triticum aestivum

; 

zea mays

; 

crops

; 

grain (agricultural product)

; 

mean square error

; 

scheduling

; 

sugar beets

; 

water supply

; 

limited water supplies

; 

local conditions

; 

management scenarios

; 

mathematical approach

; 

optimal irrigation scheduling

; 

problem-specific algorithms

; 

root mean square errors

; 

silty clay loam

; 

algorithm

; 

benchmarking

; 

error analysis

; 

irrigation system

; 

numerical model

; 

silty clay

; 

sowing date

; 

yield response

; 

estimation