气候变化影响下俄罗斯列宁格勒地区耕地的农业生态评估

Agronomy Pub Date : 2024-09-17 DOI:10.3390/agronomy14092113
Ekaterina Yu. Chebykina, Evgeny V. Abakumov
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摘要

本文分析了气候特征对农业用地适宜性评级的影响。土壤肥力是影响土地生产力和作物产量的最重要因素之一;它是一个复杂的数值,不仅取决于土壤的农业物理和农业化学特性,还取决于气候等其他自然因素。肥力水平的定量评估有不同的方法。研究的目的是以列宁格勒地区为例,了解活动温度总和与年降水量总和的分布是否对农业用地适宜性等级评估的空间和时间异质性有显著影响。本文对谢苗诺夫-布拉戈维多夫的优质土地估算方法和卡尔曼诺夫的土壤评估方法进行了估算和比较。与传统方法相比,卡尔曼诺夫方法能够更有效地评估土壤的生态指数,因此在本文中得到了强调。研究表明,气候变化可能导致土壤质量的变异性增加,在某些气候情景下可能对农业有利,但与此同时,夏季过高的温度和降水量可能成为限制因素,导致产量下降。评估结果表明,所使用的计算模型可用于预测未来时期的作物产量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agroecological Assessment of Arable Lands in the Leningrad Region of Russia under the Influence of Climate Change
The paper presents an analysis of the influence of climatic characteristics on the rating of land suitability for agricultural use. Soil fertility is one of the most important factors in land productivity and crop capacity; it is a complex value that depends not only on agrophysical and agrochemical soil properties but also on other natural factors, such as climate. There are different methodical approaches for a quantitative assessment of fertility level. The objectives of the research were to understand whether the distributions of active temperature sums and annual precipitation sums have a significant effect on the spatial and temporal heterogeneity of the rating assessment of land suitability for agricultural use in the example of the Leningrad region. The estimation and comparison between Semenov–Blagovidov’s method of quality land estimation and Karmanov’s method of appraisal of soils are given in this article. Karmanov’s method is highlighted in this paper for its ability to assess soil’s ecological indices more effectively than traditional methods. The research suggested that climate change may lead to increased variability in soil quality, with potential benefits for agriculture under certain climate scenarios, but at the same time, excessive temperatures in summer and precipitations might become a limiting factor, pushing down yields. The results of such assessment show that the performed calculation models can be used to forecast crop yields for future periods.
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