Maize Yield and Planting Date Relationship: A Synthesis-Analysis for US High-Yielding Contest-Winner and Field Research Data

Long, Nguyen Viet; Assefa, Yared; Schwalbert, Raíssa; Ciampitti, Ignacio A.

doi:10.3389/fpls.2017.02106

Cited by 44 publications

(30 citation statements)

References 31 publications

(50 reference statements)

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“…First, we fitted a threelevel unconditional (no predictors) hierarchical nested linear model to assess the random effects associated with field (Level 1) nested within region (Level 2) nested within year (Level 3) on the intercept (i.e., wheat yield). Similar approaches have been previously used in agricultural sciences (Long et al, 2017;Mourtzinis et al, 2018a). Similar approaches have been previously used in agricultural sciences (Long et al, 2017;Mourtzinis et al, 2018a).…”

Section: Multilevel Modelingmentioning

confidence: 92%

“…Although yield contest data have previously been explored for yield-management relationships (Long et al, 2017), these observations may be questionable (Sinclair and Cassman, 2004). Although yield contest data have previously been explored for yield-management relationships (Long et al, 2017), these observations may be questionable (Sinclair and Cassman, 2004).…”

Section: Grain Yield Data and Data Quality Controlmentioning

confidence: 99%

“…Grain yield (Y a ) was measured by harvesting a minimum field size of two contiguous hectares under the supervision of a third-party verifier, who oversaw harvesting, weighing, moisture testing, field size measurements, and yield calculation. Although yield contest data have previously been explored for yield-management relationships (Long et al, 2017), these observations may be questionable (Sinclair and Cassman, 2004). Thus, we compared the relationship between grain yield and crop evapotranspiration (ET c ) simulated for each field-year with previously reported boundary functions for wheat water productivity (French and Schultz, 1984;Zwart and Bastiaanssen, 2004;Sadras and Angus, 2006;Patrignani et al, 2014;Lollato et al, 2017).…”

Section: Grain Yield Data and Data Quality Controlmentioning

confidence: 99%

“…At this step, we used the covariance parameter estimates to compute the intraclass correlation coefficient (ICC) and quantify how much of the total variability in grain yield was accounted for by year and region nested within year, using the ICC of the residuals as an indication of the field-level effect (Bell et al, 2013). Similar approaches have been previously used in agricultural sciences (Long et al, 2017;Mourtzinis et al, 2018a). Next, we gradually estimated more complex models checking for model improvement after each step using the Bayesian information criterion (BIC).…”

Section: Multilevel Modelingmentioning

confidence: 99%

“…Linear and nonlinear regression were performed for continuous variables either across the entire dataset or as quantile regression (Koenker and Bassett, 1978) to explore the relationship between maximum yields (quantile = 0.99) and different management factors. These approaches have been largely used in the literature analyzing similar on-farm data (e.g., Grassini et al, 2015;Long et al, 2017;Rattalino Edreira et al, 2017).…”

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 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.

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Section: Multilevel Modelingmentioning

confidence: 92%

Section: Grain Yield Data and Data Quality Controlmentioning

confidence: 99%

Section: Grain Yield Data and Data Quality Controlmentioning

confidence: 99%

Section: Multilevel Modelingmentioning

confidence: 99%

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2019

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Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

et al. 2020

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The Agricultural Production Systems sIMulator (APSIM) was used to evaluate two alternative approaches for extending the cover crop growing window into corn (Zea mays L.) and soybean (Glycine max L.) crop rotations in Nebraska, USA. We evaluated how: (i) shifting corn planting dates (mid-April to early-June) and (ii) altering comparative relative maturity (CRM) corn hybrids (80 to 115 days) influence cover crop biomass and corn yields over a 30-year period. The APSIM model was tested using experimental data and was then used to simulate a range of cover crop planting and termination scenarios. Our results showed no significant yield differences within the same corn relative maturity when planted on April 20 and May 13 but that yield declined when planted in June. During a six week fall cover crop planting window (September 15-October 31), every day before October 31 that the cover crop was planted resulted in additional 62 kg ha −1 of biomass. We also simulated a one month spring termination window (April 1-April 30) and, every day delay in cover crop termination resulted in per day additional 35 kg ha −1 of biomass. Cover crop biomass accrual was highly dependent on weather, where for identical fall planting dates, a warm wet season accrued approximately four times more biomass than a cool dry season. Although we found significant yield differences between early, medium and late season CRMs, earlier fall cover crop planting associated with either earlier spring corn planting or planting an early to medium season variety leads to tenfold greater cover biomass. Delayed corn planting by mid-May had no yield penalty relative to April planting, and could facilitate four-fold greater cover crop biomass (cover crop terminated April 30 instead of April 1). Our results demonstrate that earlier cover crop planting in fall or later cover crop termination in spring can result in significantly more biomass which can be balanced with yield goals.

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Production practices that maximize soybean yield: What we have learned from the North Carolina soybean yield contest

Vann

Drake-Stowe²,

Buol

et al. 2021

Agronomy Journal

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Agronomic production practices associated with high-yielding soybean (Glycine max) in North Carolina can be used to inform production recommendations across the Southeast USA. 877 individual entries submitted from 2002 to 2019 into the North Carolina Soybean Yield Contest (SYC) were analyzed with the objectives to describe the production practices associated with high-yielding soybeans in North Carolina and to identify management strategies for increasing soybean yield in the Southeast USA region. From 2002 to 2019, SYC entries averaged 4,379 kg ha -1 . The three most important management practices influencing soybean yield were maturity group (MG), foliar fungicide use, and planting date.Using a MG 4 or earlier variety provided a 1,199 kg ha -1 yield advantage across all entries.When MG≤4 was used, foliar fungicide use provided a 754 kg ha -1 yield protection and when MG>4 was used fungicide use provided a 640 kg ha -1 yield protection. Planting dates earlier than May 12 generally provided more yield benefit when earlier maturing varieties were used. Herbicide and insecticide use, irrigation, fungicidal and inoculant seed treatments, tillage, and row spacing were less important predictors of soybean yield. Soybean producers can implement several of these identified management strategies without additional economic investment in an effort to increase soybean yield and profitability in the Southeast USA region.

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Maize Yield and Planting Date Relationship: A Synthesis-Analysis for US High-Yielding Contest-Winner and Field Research Data

Cited by 44 publications

References 31 publications

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

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

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

Production practices that maximize soybean yield: What we have learned from the North Carolina soybean yield contest

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