Agronomic Practices for Reducing Wheat Yield Gaps: A Quantitative Appraisal of Progressive Producers

By R. Lollato, D. R. Diaz, E. DeWolf, M. Knapp, D. Peterson, A. Fritz. Crop science, 2019

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There is limited information on agronomic practices affecting wheat (Triticum aestivum L.) yield in intensively managed dryland systems despite the opportunity to narrow the existing yield gap (YG). We used a unique database of 100 intensively managed field-years entered in the Kansas Wheat Yield Contest during the 2010 to 2017 harvest seasons to (i) quantify the YG, (ii) describe wheat management, and (iii) identify management opportunities and weather patterns associated with yield. We simulated wheat water-limited yield (Yw) using Simple Simulation Modeling–Wheat (SSM-Wheat) model for each field-year to estimate YG as the difference between Yw and actual yield (Ya) and used 11 statistical approaches to test the association of management practices and weather variables with Ya. Wheat Ya averaged 5.5 Mg ha −1, and simulated Yw averaged 6.4 Mg ha −1, resulting in a YG of 0.9 Mg ha−1 (15% of Yw). High-yielding fields had lower maximum and minimum temperatures and greater cumulative solar radiation and precipitation during grain fill. Varieties susceptible to fungal diseases responded to foliar fungicide (0.8–1.4 Mg ha−1), whereas resistant varieties did not. Seeding rate was negatively associated with Ya, as yield quantile 0.99 was 7.5 Mg ha −1 and decreased by 2.7 Mg ha−1 for every 100-seed m−2 increase in seeding rate above 305 seeds m−2. In-furrow P fertilizer, previous crop, tillage practice, and N timing were also associated with Ya. We conclude that fields entered in yield contests have closed the exploitable YG, and there are opportunities to improve Ya through improved management in regions with stagnant wheat yield. R.P. Lollato, D.A. Ruiz Diaz, M. Knapp, D.E. Peterson, and A.K. Fritz, Dep. of Agronomy, Kansas State Univ., Manhattan, KS 66506; E. DeWolf, Dep. of Plant Pathology, Kansas State Univ., Manhattan, KS 66506. Received 13 Apr. 2018. Accepted 7 Oct. 2018. *Corresponding author (lollato@ksu.edu). Assigned to Associate Editor Qingwu Xue. Abbreviations: BIC, Bayesian information criterion; DOY, day of year; ETc, crop evapotranspiration; ETo, reference crop evapotranspiration; ICC, intraclass correlation coefficient; LAR, least angle regression; LASSO, least squared shrinkage operator; RS, incident solar radiation; SSM-Wheat, Simple Simulation Modeling–Wheat; TKW, 1000-kernel weight; Tmax, maximum temperature; Tmin, minimum temperature; Ya, actual yield; YG, yield gap; Yw, water-limited yield. Published in Crop Sci. 59:333–350 (2019). doi: 10.2135/cropsci2018.04.0249 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published December 20, 2018

Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/30462a838ddd209dac897394801b088fbeb43a28

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