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Climate change has drawn the attention not only of scientists but of politicians and societies worldwide. The aim of this paper is to present a method for selecting research studies on climate change, waste management and the role of microbes in the recycling of organic matter in soil that analyze the role of organic agriculture as the main connection between agricultural losses and climate change. VOSviewer version 1.6.18 free software tool was used in this study in order to achieve the bibliometric and mapping approach for studies on the effects of climate change in terms of soil recycled organic matter and microbe interaction. Scopus database (accessed 29 September 2022) indexed a total of 1,245,809 bibliographic items classified into paradigms. The presented documents were downloaded from Scopus as graph-based maps and as distance-based maps in order to reflect the strength of the relation between the items. Climate change includes changes in soil and soil microorganisms as affected by natural climate variations and local weather, which have beneficial or negative effects on soil organic matter. From the examination of the selected papers, it was concluded that climate change and changing precipitation patterns are having an impact on microorganisms, particularly bacterial groups, and thus ecosystem function. [ABSTRACT FROM AUTHOR]

Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed. [ABSTRACT FROM AUTHOR]