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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #381457
Research Project: Resilient Management Systems and Decision Support Tools to Optimize Agricultural Production and Watershed Responses from Field to National Scale

Location: Grassland Soil and Water Research Laboratory

Title: Assessing the impacts of projected climate changes on maize (Zea mays) productivity using crop models and climate scenario simulation

Author
item YANG, YUAN - Shanxi Agriculture University
item Menefee, Dorothy
item SONG, CUI - Middle Tennessee State University
item RAJAN, NITHYA - Texas A&M University

Submitted to: Crop and Pasture Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/3/2021
Publication Date: 12/2/2021
Citation: Yang, Y., Menefee, D.S., Song, C., Rajan, N. 2021. Assessing the impacts of projected climate changes on maize (Zea mays) productivity using crop models and climate scenario simulation. Crop and Pasture Science. 72(12):969-984. https://doi.org/10.1071/CP21279.
DOI: https://doi.org/10.1071/CP21279

Interpretive Summary: Maize yield was simulated using APSIM and Global Climate Models. The models were calibrated using 2 years of field data in Texas. Climate change scenario models were used to predict the impact of future climate change on Texas maize yield. While there was a wide range of potential outcomes, most suggest maize yield will be at least 75% of historical yield.

Technical Abstract: Investigating the agronomic responses of dryland maize (Zea mays L.) systems under global change could provide important insights in designing climate-resilient cropping systems. In this study, we integrated APSIM with Representative Concentration Pathways 8.5 and 20 Global Climate Models to systematically (i) calibrate APSIM based on large-field two-year study conducted in the East-Central Texas; (ii) evaluate the impacts of climate change on maize productivity and risks; (iii) investigate the variations in growth stage lengths. Results indicated that APSIM simulated grain yield, biomass production, precipitation productivity (PP) and developmental stage transition agreed well with observation (R2 > 0.84, P < 0.01, < 5-day deviation). Compared to baseline data, the median predicted grain yield varied from -2307 to +737 kg ha-1, and mean PP improved 9.2%–36.5% under future projection, implying high potential water productivity. Grain production should at least maintain the standard of 75% of historical seasons in most cases, but also incur greater climate risk for higher threshold (50% of baseline production). Finally, future scenarios projected shortened days (4–13 d) for reaching key stages. The results advocate adoption of management practices and cultivar selection extending growing season, enhancing soil water storage in the future to achieve better sustainability.