U.S. winter wheat yield loss attributed to compound hot-dry-windy events

Zhao, Haidong; Zhang, Lina; Kirkham, M.B.; Welch, Stephen M.; Nielsen‐Gammon, John W.; Bai, Guihua; Luo, Jiebo; Andresen, Daniel; Rice, Charles W.; Wan, Nenghan; Lollato, Romulo P.; Dian-feng, Zheng; Gowda, Prasanna H.; Lin, Xiaomao

doi:10.1038/s41467-022-34947-6

Cited by 28 publications

(15 citation statements)

References 55 publications

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“…Also, multiple limitations may occur at the same time. As an example of this phenomenon, Zhao et al. (2022) attributed considerable losses of winter wheat to the co-occurring extreme events of high temperatures, dryness and hard winds compared to events in which a single limiting factor occurred.…”

Section: Discussionmentioning

confidence: 99%

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

et al. 2023

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IntroductionDynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies.MethodsHere we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values.ResultsWe demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation.DiscussionA combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

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Section: Discussionmentioning

confidence: 99%

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

et al. 2023

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“…Recent efforts evaluated probabilistic patterns of drought (Sciarresi et al, 2019) and of the combination of drought and thermal regime stresses (Couëdel et al, 2021;Lollato et al, 2017Lollato et al, , 2020Zhao et al, 2022) for winter wheat in the US Great Plains, overwhelmingly concluding that grain yield of winter wheat in this region is often impacted by heat and drought stresses. The current research expands on these previous environmental characterizations to explore functional relationships between weather experienced in the growing season and grain yield and quality traits.…”

Section: Environmentmentioning

confidence: 99%

Intensive management simultaneously reduces yield gaps and improves milling and baking properties of bread wheat

et al. 2023

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Genotype (G), environment (E), management (M), and their interactions determine grain yield, biochemical composition, and quality of wheat. However, we are not aware of studies evaluating whether intensive management can simultaneously reduce yield gaps and improve wheat milling and baking attributes, which were our main objectives. A factorial experiment of five genotypes × four management intensities (farmer practice, high-input, high-input minus nitrogen, and high-input minus fungicide) was conducted in six Kansas (United States) environments resulting from the combination of four locations and three seasons. Grain yield and 13 parameters characterizing the grain (test weight [TWT], thousand kernel weight [TKW], grain protein [GP]), flour (e.g., ash, protein), dough (e.g., water absorption [WA],dough stability [DS]), and baking qualities (loaf volume [LV]) were measured. For most variables, the primary contributor of the variance was E while M and G were secondary and tertiary contributors, except for LV in which G was a larger moderator. High input practices increased yield (4206 kg ha −1 ) compared to farmer practice (3347 kg ha −1 ) suggesting a yield gap of 859 kg ha −1 . Management intensities receiving foliar fungicides improved grain properties related to milling characteristics (i.e., TKW and TWT), while management intensities receiving more nitrogen improved baking characteristics (e.g., WA and DS). Across sources of variation, yield response to improved management associated positively with the responses of TKW, TWT, and in some cases, GP, and WA. Our results showed the possibility of simultaneously improving grain yield and selected milling and baking attributes of bread wheat genotypes through intensive management.Abbreviations: DDT, dough development time (min); DS, dough stability (min); FA, flour ash (%); FNV, falling number (s); FST, farinograph stability (min); FY, flour yield (%); G, genotype; GP, grain protein (%); GY, grain yield (kg ha −1 ); HWW, hard winter wheat; LV, loaf volume (mL); MTI, mixing tolerance index (BU); TKW, thousand kernel weight (g); TWT, test weight (kg hl −1 ); WA, water absorption.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In part, because wheat stubble provides excellent soil cover to minimize wind and water erosion and improve soil water storage in the central Great Plains, contributing to soil water conservation needed for successive crops (Schlegel et al., 2023). However, a recent study reported that the frequency of hot‐dry‐windy weather events has increased in Kansas and surrounding regions and is attributed to significant yield reduction (Zaho et al., 2022). Schlegel et al.…”

Section: Introductionmentioning

confidence: 99%

Historic winter wheat yield, production, and economic value trends in Kansas, the “Wheat State”

Holman,

Obour,

O'Brien

et al. 2024

Crop Science

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Year‐to‐year weather variability and technologies (fertilizer, irrigation, herbicides, conservation tillage, varieties, mechanization, and others) are among factors that affect winter wheat (Triticum aestivum L.) yields in water‐limited cropping systems. The objective of these analyses was to quantify winter wheat yield trends, variability, relationship with weather, yield gap, and value in the “Wheat State”, Kansas, USA. Two data sources were used for this study, i.e., annual winter wheat yield reported for selected counties from United States Departments of Agriculture (USDA) and the Kansas State University wheat variety performance trial (KWVPT) near Colby, Garden City, Hays, and Tribune, Kansas. Results of the study showed a decline in harvest area and inflation adjusted market value per area of winter wheat beginning in the 1930's. However, both the survey and research data showed continuous increase in winter wheat yield at rates of 3 to 180 kg ha−1 yr−1 that varied by irrigation, location, data source, or time range considered, except for a few years where yield stagnated or declined. Despite increased productivity, results indicated an increase in annual winter wheat yield variation over time. A positive correlation was observed between non‐irrigated wheat yield and all months of precipitation except for January, February, and June. October through December precipitation provided the best positive correlation with non‐irrigated wheat yield. Wheat yields negatively correlated with high temperature in months that coincided with wheat flowering and maturity. The yield gap between actual and potential wheat yield was estimated at 15 to 55%. The decreasing revenue per land area may be part of the reasons for decreasing wheat planting acreage over time. We concluded producers need to increase adoption of latest technology (varieties with drought and heat tolerance) and management to obtain yield potential. Research should investigate methods to increase the stability of state‐wide wheat production and financial return.This article is protected by copyright. All rights reserved

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U.S. winter wheat yield loss attributed to compound hot-dry-windy events

Cited by 28 publications

References 55 publications

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

Intensive management simultaneously reduces yield gaps and improves milling and baking properties of bread wheat

Historic winter wheat yield, production, and economic value trends in Kansas, the “Wheat State”

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