Early prediction of wheat grain yield production from root-zone soil water content at heading using Crop RS-Met

David Helman*, Itamar M. Lensky, David J. Bonfil

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Wheat production in drylands is determined greatly by the available water at the critical growth stages. In dry years, farmers usually face the dilemma of whether to harvest at an early stage for hay or silage, with reduced profit, or leave the crop for grain production with the risk of a major economic loss. Thus, an early prediction of potential wheat grain yield production is essential for agricultural decision making, particularly in water-limited areas. Here, we test whether using a proximal-based biophysical model of actual evapotranspiration (water use) and root-zone soil water content (SWC) – Crop RS-Met – may assist in providing early grain yield predictions in dryland wheat fields. Crop RS-Met was examined in eight experimental fields comprising a variety of spring wheat (Triticum aestivum L.) cultivars exposed to different treatments and amounts of water supply (185 mm - 450 mm). Crop RS-Met was first validated against SWC measurements at the root-zone profile. Then, modeled SWC at heading (SWCHeading) was regressed against end-of-season grain yields (GYEOS), which ranged from 1.30 tons ha−1 to 7.12 tons ha−1, for a total of 56 treatment blocks in 4 seasonal years (2014–2017). Results show that Crop RS-Met accurately reproduce seasonal changes in SWC with an average R2 of 0.89 ± 0.05 and RMSE and bias of 0.014 ± 0.004 m3 m−3 and -0.002 ± 0.004 m3 m−3, respectively. Modeled SWCHeading showed high and significant positive linear relationship with GYEOS (GYEOS[tons ha-1] = 0.080×SWCHeading[mm] - 5.387; R2 = 0.90; P < 0.001; N=56). Moreover, Crop RS-Met showed to be capable of accurately predicting GYEOS even in cases where water supply and grain yield had adverse relationships. Aggregating results to the field-scale level and classifying fields per water supply conditions resulted in an even stronger linear relationship (R2 = 0.94; P < 0.001; N=9). We conclude that Crop RS-Met may be used to predict GYEOS at heading in dryland fields for possible use by farmers in decision making at critical wheat growth stages.

Original languageAmerican English
Pages (from-to)11-23
Number of pages13
JournalField Crops Research
Volume232
DOIs
StatePublished - 15 Feb 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.

Keywords

  • Crop RS-Met
  • Grain yield
  • NDVI
  • Soil water content (SWC)
  • Wheat

Fingerprint

Dive into the research topics of 'Early prediction of wheat grain yield production from root-zone soil water content at heading using Crop RS-Met'. Together they form a unique fingerprint.

Cite this