Abstract
As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten maize-producing counties. A cropping system model, CERES-Maize of Decision Support System for Agrotechnology Transfer (DSSAT) was calibrated using observed maize yield. To account for the spatial variability of soils, the gSSURGO soil database was used. The model was run for a baseline and future climate change scenarios under two Representative Concentration Pathways (RCP4.5 and RCP8.5) to assess the impact of future climate change on rainfed maize yield. The simulation results showed that maize yield was impacted by spatial soil variability, and that using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county. The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss. Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively. The results indicate the uncertainties of growing maize in our study region under the changing climate, emphasizing the need for developing strategies to sustain maize production in the region.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
Spatial soil variability affected simulated maize yield.
Zea maysNot reportedSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“maize yield was impacted by spatial soil variability”
Claim · model-written summary
Spatially distributed soils simulated maize yield better than using the dominant county soil.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county”
t0“Maize”
Claim · model-written summary
Higher temperature and lower growing-season precipitation projected greater maize yield loss.
- Treatment
- increased temperature and lower precipitation patternse0
- Outcome
- yield losse0
- Direction
- highere0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss”
t0“Maize”
Claim · model-written summary
At century end, RCP4.5 projected 28% higher maize yield loss.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively”
t0“Maize”
Claim · model-written summary
At century end, RCP8.5 projected 45% higher maize yield loss.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively”
t0“Maize”