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R. M. Lark - One of the best experts on this subject based on the ideXlab platform.
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Boundary Line models for soil nutrient concentrations and wheat yield in national scale datasets
European Journal of Soil Science, 2020Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.
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Boundary Line models for soil nutrient concentrations and wheat yield in national‐scale datasets
European journal of soil science, 2019Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.
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Boundary Line analysis of the effect of water‐filled pore space on nitrous oxide emission from cores of arable soil
European Journal of Soil Science, 2016Co-Authors: R. M. Lark, Alice E. MilneAbstract:The Boundary Line has been proposed as a model of the effects of a variable on a biological response, when this variable might limit the response in only some of a set of observations. It is proposed that the upper Boundary (in some circumstances the lower Boundary) represents the response function of interest. Boundary-Line analysis is a method for estimating this response function from data. The approach has been used to model the emission of N2O from soil in response to various soil properties. However, the methods that have been used to identify the Boundary are based on somewhat ad hoc partitions of the data. A statistical model that we have presented previously has not been applied to this problem in soil science, and we do so here to represent how the water-filled pore space (WFPS) of the soil affects the rate of N2O emission. We derive a Boundary-Line response that can be shown to be a better model for the data than an unbounded alternative by statistical criteria. Furthermore, the fitted Boundary-response model is consistent with past empirical observations and modelling studies with respect to both the WFPS at which the potential emission rate is largest and the measurement error for the emission rates themselves. We show how the fitted model might be used to interpret data on soil volumetric water content with respect to seasonal changes in potential emissions, and to compare potential emissions between soil series that have contrasting physical properties.
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Estimating a Boundary Line model for a biological response by maximum likelihood
Annals of Applied Biology, 2006Co-Authors: Alice E. Milne, Richard B. Ferguson, R. M. LarkAbstract:The Boundary Line model was proposed to interpret biological data sets, where one variable is a biological response (e.g. crop yield) to an independent variable (e.g. available water content of the soil). The upper (or lower) Boundary on a plot of the dependent variable (ordinate) against the independent variable (abscissa) represents the limiting response of the dependent variable to the independent variable value. Although the concept has been widely used, the methods proposed to define the Boundary Line have been subject to criticism. This is because of their ad hoc nature and lack of theoretical basis. In this article, we present a novel method for fitting the Boundary Line to a set of data. The method uses a censored probability distribution to interpret the data structure. The parameters of the distribution (and hence the Boundary Line parameters) are fitted using maximum likelihood and related confidence intervals deduced. The method is demonstrated using both simulated and real data sets.
B P Marchant - One of the best experts on this subject based on the ideXlab platform.
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Boundary Line models for soil nutrient concentrations and wheat yield in national scale datasets
European Journal of Soil Science, 2020Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.
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Boundary Line models for soil nutrient concentrations and wheat yield in national‐scale datasets
European journal of soil science, 2019Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.
Alice E. Milne - One of the best experts on this subject based on the ideXlab platform.
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Boundary Line analysis of the effect of water‐filled pore space on nitrous oxide emission from cores of arable soil
European Journal of Soil Science, 2016Co-Authors: R. M. Lark, Alice E. MilneAbstract:The Boundary Line has been proposed as a model of the effects of a variable on a biological response, when this variable might limit the response in only some of a set of observations. It is proposed that the upper Boundary (in some circumstances the lower Boundary) represents the response function of interest. Boundary-Line analysis is a method for estimating this response function from data. The approach has been used to model the emission of N2O from soil in response to various soil properties. However, the methods that have been used to identify the Boundary are based on somewhat ad hoc partitions of the data. A statistical model that we have presented previously has not been applied to this problem in soil science, and we do so here to represent how the water-filled pore space (WFPS) of the soil affects the rate of N2O emission. We derive a Boundary-Line response that can be shown to be a better model for the data than an unbounded alternative by statistical criteria. Furthermore, the fitted Boundary-response model is consistent with past empirical observations and modelling studies with respect to both the WFPS at which the potential emission rate is largest and the measurement error for the emission rates themselves. We show how the fitted model might be used to interpret data on soil volumetric water content with respect to seasonal changes in potential emissions, and to compare potential emissions between soil series that have contrasting physical properties.
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Estimating a Boundary Line model for a biological response by maximum likelihood
Annals of Applied Biology, 2006Co-Authors: Alice E. Milne, Richard B. Ferguson, R. M. LarkAbstract:The Boundary Line model was proposed to interpret biological data sets, where one variable is a biological response (e.g. crop yield) to an independent variable (e.g. available water content of the soil). The upper (or lower) Boundary on a plot of the dependent variable (ordinate) against the independent variable (abscissa) represents the limiting response of the dependent variable to the independent variable value. Although the concept has been widely used, the methods proposed to define the Boundary Line have been subject to criticism. This is because of their ad hoc nature and lack of theoretical basis. In this article, we present a novel method for fitting the Boundary Line to a set of data. The method uses a censored probability distribution to interpret the data structure. The parameters of the distribution (and hence the Boundary Line parameters) are fitted using maximum likelihood and related confidence intervals deduced. The method is demonstrated using both simulated and real data sets.
Vincent Gillingham - One of the best experts on this subject based on the ideXlab platform.
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Boundary Line models for soil nutrient concentrations and wheat yield in national scale datasets
European Journal of Soil Science, 2020Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.
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Boundary Line models for soil nutrient concentrations and wheat yield in national‐scale datasets
European journal of soil science, 2019Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.
David Langton - One of the best experts on this subject based on the ideXlab platform.
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Boundary Line models for soil nutrient concentrations and wheat yield in national scale datasets
European Journal of Soil Science, 2020Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.
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Boundary Line models for soil nutrient concentrations and wheat yield in national‐scale datasets
European journal of soil science, 2019Co-Authors: R. M. Lark, Vincent Gillingham, David Langton, B P MarchantAbstract:In Boundary Line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper Boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered Boundary Line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the Boundary Line model was preferable in most cases. The Boundary Line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guideLines. Furthermore, there was evidence that the Boundary Line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. Highlights Boundary Line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that Boundary Line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.