TY - JOUR
T1 - Growth phase-specific evaporative demand and nighttime temperatures determine Maize (Zea Mays L.) yield deviations as revealed from a long-term field experiment
AU - Singh, Arshdeep
AU - Kukal, Meetpal S.
AU - Shapiro, Charles A.
AU - Snow, Daniel D.
AU - Irmak, Suat
AU - Iqbal, Javed
N1 - Funding Information:
We gratefully acknowledge Dr. Daniel Walters (deceased), who contributed to the design and management of the long-term tillage experiment at Haskell Agricultural Laboratory near Concord, NE. We also acknowledge Mr. Mike Mainz (research technologist), who performed most of the fieldwork for years. We also appreciate the constructive comments and insights from crop physiology and nighttime temperature perspective given by Dr. Puneet Paul.
Publisher Copyright:
© 2021
PY - 2021/10/15
Y1 - 2021/10/15
N2 - 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.
AB - 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.
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U2 - 10.1016/j.agrformet.2021.108543
DO - 10.1016/j.agrformet.2021.108543
M3 - Article
AN - SCOPUS:85111064692
SN - 0168-1923
VL - 308-309
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 108543
ER -