Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.

To effectively manage soil fertility, knowledge is needed of how a crop uses nutrients from fertilizer applied to the soil. Soil quality is a combination of biological, chemical and physical properties and is hard to assess directly because of collective and multiple functional effects. In this pape...

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Main Authors: Hungyen Chen, Junko Yamagishi, Hirohisa Kishino
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0112785
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spelling doaj-4c1c3895bcd74fd0a74ab796c6f72aac2021-03-03T20:11:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01911e11278510.1371/journal.pone.0112785Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.Hungyen ChenJunko YamagishiHirohisa KishinoTo effectively manage soil fertility, knowledge is needed of how a crop uses nutrients from fertilizer applied to the soil. Soil quality is a combination of biological, chemical and physical properties and is hard to assess directly because of collective and multiple functional effects. In this paper, we focus on the application of these concepts to agriculture. We define the baseline fertility of soil as the level of fertility that a crop can acquire for growth from the soil. With this strict definition, we propose a new crop yield-fertility model that enables quantification of the process of improving baseline fertility and the effects of treatments solely from the time series of crop yields. The model was modified from Michaelis-Menten kinetics and measured the additional effects of the treatments given the baseline fertility. Using more than 30 years of experimental data, we used the Bayesian framework to estimate the improvements in baseline fertility and the effects of fertilizer and farmyard manure (FYM) on maize (Zea mays), barley (Hordeum vulgare), and soybean (Glycine max) yields. Fertilizer contributed the most to the barley yield and FYM contributed the most to the soybean yield among the three crops. The baseline fertility of the subsurface soil was very low for maize and barley prior to fertilization. In contrast, the baseline fertility in this soil approximated half-saturated fertility for the soybean crop. The long-term soil fertility was increased by adding FYM, but the effect of FYM addition was reduced by the addition of fertilizer. Our results provide evidence that long-term soil fertility under continuous farming was maintained, or increased, by the application of natural nutrients compared with the application of synthetic fertilizer.https://doi.org/10.1371/journal.pone.0112785
collection DOAJ
language English
format Article
sources DOAJ
author Hungyen Chen
Junko Yamagishi
Hirohisa Kishino
spellingShingle Hungyen Chen
Junko Yamagishi
Hirohisa Kishino
Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
PLoS ONE
author_facet Hungyen Chen
Junko Yamagishi
Hirohisa Kishino
author_sort Hungyen Chen
title Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
title_short Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
title_full Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
title_fullStr Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
title_full_unstemmed Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
title_sort bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
description To effectively manage soil fertility, knowledge is needed of how a crop uses nutrients from fertilizer applied to the soil. Soil quality is a combination of biological, chemical and physical properties and is hard to assess directly because of collective and multiple functional effects. In this paper, we focus on the application of these concepts to agriculture. We define the baseline fertility of soil as the level of fertility that a crop can acquire for growth from the soil. With this strict definition, we propose a new crop yield-fertility model that enables quantification of the process of improving baseline fertility and the effects of treatments solely from the time series of crop yields. The model was modified from Michaelis-Menten kinetics and measured the additional effects of the treatments given the baseline fertility. Using more than 30 years of experimental data, we used the Bayesian framework to estimate the improvements in baseline fertility and the effects of fertilizer and farmyard manure (FYM) on maize (Zea mays), barley (Hordeum vulgare), and soybean (Glycine max) yields. Fertilizer contributed the most to the barley yield and FYM contributed the most to the soybean yield among the three crops. The baseline fertility of the subsurface soil was very low for maize and barley prior to fertilization. In contrast, the baseline fertility in this soil approximated half-saturated fertility for the soybean crop. The long-term soil fertility was increased by adding FYM, but the effect of FYM addition was reduced by the addition of fertilizer. Our results provide evidence that long-term soil fertility under continuous farming was maintained, or increased, by the application of natural nutrients compared with the application of synthetic fertilizer.
url https://doi.org/10.1371/journal.pone.0112785
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