Simulating water lateral inflow and its contribution to spatial variations of rainfed wheat yields

https://doi.org/10.1016/j.eja.2022.126515Get rights and content
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Highlights

  • Lateral inflows (LIF) in fields of undulating topography were investigated.

  • LIF impact on rainfed wheat yields was simulated over a 30-year period.

  • Simulated wheat yields varied (within field) an average of 16% due to LIF occurrence.

  • The net yield response to LIF in downslope areas averaged 383 kg grain yield (GY) ha-1.

  • LIF impact on yield was mostly dependent on the year precipitation conditions.

Abstract

Spatial variations of crop yields are commonly observed in typical rainfed systems worldwide. It is accepted that such variations are likely to be associated, among other factors, with water spatial variations due to lateral water flows occurring in fields with undulating topography. However, some of the main processes governing water spatial distribution such as lateral flow are not entirely considered by the most commonly adopted crop simulation models. This brings uncertainty to the process of yield simulation at field-scale, especially under water-limited conditions. Although it is expected that lateral water movement determines spatial variations of crop yields, it is still unclear what is the net contribution of lateral water inflows (LIF) to spatial variations of rainfed yields in fields of undulating topography. In this sense, by combining field experimentation, simulation models (HYDRUS-1D and AquaCrop), and the use of artificial neural networks, we assessed the occurrence and magnitude of LIF, and their impact on wheat yields in Cordoba, Spain, over a 30-year period. Seasonal precipitation varied over 30 years from 212.8 to 759.5 mm, and cumulative LIF ranged from 30 to 125 mm. The ratio of seasonal cumulative LIF divided by seasonal precipitation varied from 10.7% to 38.9% over the 30 years. The net contribution of LIF to spatial variations of rainfed potential yields showed to be relevant but highly irregular among years. Despite the inter-annual variability, typical of Mediterranean conditions, the occurrence of LIF caused simulated wheat yields to vary + 16% from up to downslope areas of the field. The net yield responses to LIF, in downslope areas were on average 383 kg grain yield (GY) ha−1, and the LIF marginal water productivity reached 24.6 ( ± 13.2) kg GY ha−1 mm−1 in years of maximum responsiveness. Decision makers are encouraged to take water spatial variations into account when adjusting management to different potential yielding zones within the same field. However, this process is expected to benefit from further advances in in-season weather forecasting that should be coupled with a methodological approach such as the one presented here.

Abbreviations

ANN
artificial neural network
CC
canopy cover, expressed in %
CP
capacitance probe
CUM. LIF
season cumulative lateral inflow (LIF), expressed in mm
CUM. P
season cumulative precipitation, expressed in mm
DEM
digital elevation model (raster)
FAI
flow accumulation index (the absolute number of upslope cells flowing to each assigned cell of the DEM raster)
GY
grain yield, expressed in Mg (dry mass) ha−1
LIF
lateral inflow, expressed in mm
LIF. MWP
LIF marginal water productivity (expressed in kg GY ha−1 mm−1)
NFAI
normalized flow accumulation index
NP
neutron probe
NYRLIF
net yield response to LIF, expressed in Mg GY ha−1)
P
daily precipitation, expressed in mm day−1
KSAT
saturated hydraulic conductivity, expressed in mm day−1
PF. LIF
post-flowering LIF (the fraction of CUM. LIF taking place at post-flowering stages), expressed in %
Relative. T
mean relative crop transpiration (estimated as the season average of daily crop actual transpiration divided by potential transpiration), expressed in %
SWC
soil water content, expressed in mm

Keywords

Crop modeling
Spatial modeling
Water-balance
Lateral flows
HYDRUS
AquaCrop
Machine Learning
Artificial Neural Network

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