2018
DOI: 10.1016/j.fcr.2017.09.035
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Soybean response to nitrogen application across the United States: A synthesis-analysis

Abstract: A B S T R A C TThe effects of supplemental nitrogen (N) on soybean [Glycine max (L.) Merr.] seed yield have been the focus of much research over the past four decades. However, most experiments were region-specific and focused on the effect of a single N-related management choice, thus resulting in a limited inference space. Here, we composited data from individual experiments conducted across the US that examined the effect of N fertilization on soybean yield. The combined database included 207 environments (… Show more

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Cited by 101 publications
(86 citation 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: 94%
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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: 94%
“…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%
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“…The main drivers of crop response to gypsum were identified by using the conditional inference regression tree procedure in the software Jmp 13 (SAS Institute, 2016). This method is widely recommended for meta-analyses when some level of the variables considered was not used in a random manner in the same trial (Mourtzinis et al, 2018). In addition, the data to be processed need not exhibit a normal distribution, and the results are scarcely influenced by the presence of outliers or multicollinearity (Tittonel Shepherd, Vanlauwe, & Giller, 2008).…”
Section: Conditional Inference Regression Tree Analysismentioning
confidence: 99%