结合多个数字协变量的机器学习算法对土壤微量营养素的空间预测

IF 2.4 3区农林科学 Q2 SOIL SCIENCE Nutrient Cycling in Agroecosystems Pub Date : 2023-08-13 DOI:10.1007/s10705-023-10303-y

A. Keshavarzi, F. Kaya, L. Başayiğit, Y. Gyasi‐Agyei, J. Rodrigo‐Comino, Andrés Caballero-Calvo

引用次数: 3

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Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates

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来源期刊

Nutrient Cycling in Agroecosystems 农林科学-土壤科学

CiteScore

5.90

自引率

3.20%

发文量

审稿时长

4.5 months

期刊介绍： Nutrient Cycling in Agroecosystems considers manuscripts dealing with all aspects of carbon and nutrient cycling as well as management and examining their effect in ecological, agronomic, environmental and economic terms. Target agroecosystems include field crop, organic agriculture, urban or peri-urban agriculture, horticulture, bioenergy, agroforestry, livestock, pasture, and fallow systems as well as their system components such as plants and the fertility, chemistry, physics or faunal and micro-biology of soils. The scale of observation is the cycles in the soil-plant-animal system on or relevant to a field or watershed level as well as inputs from or losses to the anthroposphere, atmosphere and hydrosphere. Studies should thus consider the wider system in the examination of cycling and fluxes in agroecosystems or their components. These may include typically multi-year field observations, farm gate budgets, watershed studies, life cycle assessments, enterprise and economic analyses, or regional and global modeling. Management objectives may not only include the maximization of food, fiber and fuel production, but also its environmental and economic impact. The results must allow mechanistic conclusions of broad applicability and distinguish itself from empirical results or case studies of merely local or regional importance. If unsure whether a study fits into this scope, please contact the editor with a brief inquiry before manuscript submission.