Sifting and winnowing: Analysis of farmer field data for soybean in the US North-Central region

Mourtzinis, Spyridon; Edreira, Juan I. Rattalino; Grassini, Patricio; Roth, Adam C.; Casteel, Shaun N.; Ciampitti, Ignacio A.; Kandel, Hans; Kyveryga, Peter; Licht, Mark A.; Lindsey, Laura E.; Mueller, Daren S.; Nafziger, Emerson D.; Naeve, Seth L.; Stanley, Jordan D.; Staton, Michael J.; Conley, Shawn P.

doi:10.1016/j.fcr.2018.02.024

Cited by 70 publications

(66 citation statements)

References 33 publications

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“…On the other hand, the lack of yield decrease in low plant populations contradicts published research, which indicates that grain yield response to crop density is represented by parabolic (Holliday, 1960) or asymptotic models (Martin and Field, 1987). However, the lack of yield decrease under low populations resulted from only six field-years, and the contrasting results between surveyed data and controlled experiments deserve further investigation (Mourtzinis et al, 2018b). Our data suggest that parabolic models may not be the most appropriate to represent intensively managed fields, as they fail to account for the interaction of plant density with resource availability.…”

Section: Potential Management Practices To Reduce Wheat Yield Gaps Inmentioning

confidence: 73%

“…We used stepwise, forward selection, backward elimination, least angle regression (LAR), least squared shrinkage operator (LASSO), elastic net, random forest regression, and conditional inference trees to rank the 23 management variables in descending order according to the frequency with which they were identified as significant (Mourtzinis et al, 2018b). These models represent a range of traditional to modern regression methods, and as suggested by Mourtzinis et al (2018b), some have properties that can mitigate data multicollinearity (e.g., LASSO, LAR, and elastic net; Zou and Hastie, 2005;Dormann et al, 2013). These models represent a range of traditional to modern regression methods, and as suggested by Mourtzinis et al (2018b), some have properties that can mitigate data multicollinearity (e.g., LASSO, LAR, and elastic net; Zou and Hastie, 2005;Dormann et al, 2013).…”

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

“…This method is used to evaluate unbalanced data, and its use has recently increased in agricultural sciences (e.g., Tittonell et al, 2008a;Mourtzinis et al, 2018aMourtzinis et al, , 2018b due to properties such as the ability to explore interactions, lack of statistical distribution assumptions, the ability to handle both continuous and categorical variables, and the ability to handle outliers, multicollinearity, and heteroscedasticity (Tittonell et al, 2008a). However, one weakness of this approach is that the analysis of interactions between management effects was limited to hypothesis testing, which can be subjective in nature and depend on the researcher's understanding of the crop's response to input.…”

Section: Conditional Inference Treesmentioning

confidence: 99%

“…The second step was to perform a series of statistical procedures commonly used to analyze unstructured data (Mourtzinis et al, 2018b). We used stepwise, forward selection, backward elimination, least angle regression (LAR), least squared shrinkage operator (LASSO), elastic net, random forest regression, and conditional inference trees to rank the 23 management variables in descending order according to the frequency with which they were identified as significant (Mourtzinis et al, 2018b). If a variable was considered significant across all models and at the multilevel model, then its frequency would sum to nine.…”

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

“…If a variable was considered significant across all models and at the multilevel model, then its frequency would sum to nine. These models represent a range of traditional to modern regression methods, and as suggested by Mourtzinis et al (2018b), some have properties that can mitigate data multicollinearity (e.g., LASSO, LAR, and elastic net; Zou and Hastie, 2005;Dormann et al, 2013). Random forest and conditional inference trees were built using 'randomForest' and 'partykit' packages in R, and the remaining models using PROC GLMSELECT.…”

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

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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: Potential Management Practices To Reduce Wheat Yield Gaps Inmentioning

confidence: 73%

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

Section: Conditional Inference Treesmentioning

confidence: 99%

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

Section: Regression Analyses and Evaluation Of Individual Managementmentioning

confidence: 99%

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

et al. 2019

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Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies

Cooper

Messina

Tang

et al. 2022

Plant Breeding Reviews

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Climate‐risk assessment for winter wheat using long‐term weather data

Lollato

Bavia²,

Perin

et al. 2020

Agronomy Journal

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Temperature and water deficit stresses cause large year-to-year yield variability, and matching crop phenology with periods less prone to stresses can improve yield stability. We used 30 years of daily weather data from 69 stations in the U.S. Great Plains to quantify the risk of water deficit and temperature stresses for winter wheat (Triticum aestivum L.) cultivars differing in maturity, and to evaluate whether the selected variables explained variability in yield and area abandonment. Crop phenology was estimated using a simple temperature-based model based on 282 field observations. A difference between the 15-d running sums of reference evapotranspiration (ET o ) and precipitation greater than 40% of the soil's available water holding capacity (AWHC) determined atmospheric water deficit (AWD). Heat and freeze stresses occurred when maximum temperatures >27 • C and minimum temperatures <0 • C occurred around heading. Probabilities of AWD in the spring was greater in the west and in the south; however, latitudinal AWD gradients dissipated when crop maturity was considered.The day of year (DOY) for last freeze increased from south to north and from east to west; and the DOY for onset of heat stress increased from south to north but did not follow a longitudinal gradient. Early maturing varieties avoided heat and AWD stresses during heading but were more likely to experience freezing conditions. Regional yield decreased and area abandonment increased with early onset of spring AWD and heat stresses. This conceptual framework for evaluating the risk of environmental stresses can be applied to other regions and cropping systems. 2132wileyonlinelibrary.com/journal/agj2 Agronomy Journal. 2020;112:2132-2151.

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Sifting and winnowing: Analysis of farmer field data for soybean in the US North-Central region

Cited by 70 publications

References 33 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

Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies

Climate‐risk assessment for winter wheat using long‐term weather data

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