Growth phase-specific evaporative demand and nighttime temperatures determine Maize (Zea Mays L.) yield deviations as revealed from a long-term field experiment

Arshdeep Singh, Meetpal S. Kukal, Charles A. Shapiro, Daniel D. Snow, Suat Irmak, Javed Iqbal

Research output: Contribution to journalArticlepeer-review

Abstract

Weather impacts on crop productivity have emerged as a significant research area, and agroecosystems globally have been evaluated for these impacts. However, only a limited body of research relies on experimental determination of field-scale impacts, relative to coarser-scale estimations. Experimental frameworks can control confounding factors that potentially undesirably affect coarser-scale assessments. Here, we employ a long-term (31-year) experimental trial to investigate weather-related signatures on rainfed maize (Zea Mays L.) yield in the western Corn Belt, at seasonal and sub-seasonal scales. Among all the weather indices evaluated [(maximum, minimum, and average air temperatures (Tmax, Tmin, Tavg), precipitation (Pcp), incoming shortwave radiation (Rs), wind speed (u2), relative humidity (RH), vapor pressure deficit (VPD), alfalfa-reference evapotranspiration (ETr), and aridity index (AI)], ETr accounted for the most (⁓50%) yield variance, followed by Tavg, Tmax, VPD, Rs, Tmin, Pcp, u2, RH, and AI. Weather indices most effectively accounted for yield variance during the late reproductive (R) phase, followed by early R phase, late vegetative (V) phase and lastly, early V phase. While seasonal ETr was responsible for highest yield sensitivity (-0.69 s.d. s.d.−1), sub-seasonal weather indices were also identified for highest yield sensitivity (u2, Tavg, ETr, Tmax during early V, late V, early R, and late R, respectively). Among all possible combinations, a multiple linear regression model consisting of ETr and Tmin best predicted yield deviations (R2=0.64) at the seasonal scale, while a sub seasonal model consisting of early V ETr, early R ETr, and late R Tmin performed the best (R2=0.66). Both models were validated for five independent counties to predict USDA estimates of rainfed maize yield deviations. Recent advances in mechanistic understanding of nighttime warming (increasing Tmin) and evaporative demand impacts on crop performance concur with our experimental inference of importance of ETr and Tmin for rainfed maize yields.

Original languageEnglish (US)
Article number108543
JournalAgricultural and Forest Meteorology
Volume308-309
DOIs
StatePublished - Oct 15 2021

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Forestry
  • Agronomy and Crop Science
  • Atmospheric Science

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