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Bayesian Inference of Baseline Fertility and Treatment Effects via a Crop Yield-Fertility Model

机译:通过作物产量-生育力模型对基线生育力和处理效果的贝叶斯推断

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摘要

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.
机译:为了有效地管理土壤肥力,需要了解作物如何利用肥料施用到土壤中的养分。土壤质量是生物学,化学和物理性质的结合,由于集体和多重功能效应而难以直接评估。在本文中,我们重点介绍这些概念在农业中的应用。我们将土壤的基准肥力定义为作物可以从土壤中获取生长所需的肥力水平。有了这个严格的定义,我们提出了一个新的农作物产量-生育力模型,该模型可以仅根据农作物产量的时间序列来量化提高基准生育力的过程和治疗效果。该模型根据Michaelis-Menten动力学进行了修改,并根据基线生育力测量了治疗的其他效果。使用30多年的实验数据,我们使用贝叶斯框架来估计基线肥力的改善以及化肥和农家肥(FYM)对玉米(Zea mays),大麦(Hordeum vulgare)和大豆(Glycine max)的影响。 )的收益。在三种作物中,肥料对大麦产量的贡献最大,而FYM对大豆产量的贡献最大。施肥前,玉米和大麦的地下土壤基线肥力很低。相反,该土壤的基线肥力约为大豆作物的半饱和肥力。添加FYM可以增加土壤的长期肥力,但是肥料的加入会降低FYM的影响。我们的结果提供了证据,与合成肥料相比,通过施用天然养分可以维持或提高连续耕作下的长期土壤肥力。

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