Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

Mohammad Kazemi Garajeh; Keyvan Hassangholizadeh; Amir Reza Bakhshi Lomer; Amin Ranjbari; Ladan Ebadi; Mostafa Sadeghnejad

doi:10.1038/s41598-023-42367-9

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

Sci Rep. 2023 Sep 12;13(1):15054. doi: 10.1038/s41598-023-42367-9.

Authors

Mohammad Kazemi Garajeh¹, Keyvan Hassangholizadeh², Amir Reza Bakhshi Lomer³, Amin Ranjbari², Ladan Ebadi⁴, Mostafa Sadeghnejad⁵

Affiliations

¹ Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, 00138, Rome, Italy. kazemi20.0432@gmail.com.
² Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran.
³ Department of Geography, Birkbeck, University of London, London, WC1E 7HX, UK.
⁴ Department of Surveying Engineering, Faculty of Engineering, Golestan University, Aliabad Katoul, Iran.
⁵ Department of Geography and Geospatial Sciences, Kansas State University, 920 N17th Street, Manhattan, KS, USA.

Abstract

To the best of our knowledge, the impacts of crop residue cover (CRC) on agricultural productivity and soil fertility have not been studied by previous researchers. In this regard, this study aims to apply an integrated approach of remote sensing and geospatial analysis to detect CRC and monitor the effects of CRC on agricultural productivity, as well as soil chemical and physical characteristics. To achieve this, a series of Landsat images and 275 ground control points (GCPs) collected from the study areas for the years 2013, 2015, and 2021 were used. A convolutional neural network (CNN), a class of artificial neural network has commonly applied to analyze visual imagery, was employed in this study for CRC detection in two classes (Not-CRC and CRC) for the years 2013, 2015, and 2021. To assess the effects of CRC, the Normalized Difference Vegetation Index (NDVI) was applied to Landsat image series for the years 2015 (22 images), 2019 (20 images), and 2022 (23 images). Furthermore, this study evaluates the impacts of CRC on soil fertility based on collected field observation data. The results show a high performance (Accuracy of > 0.95) of the CNN for CRC detection and mapping. The findings also reveal positive effects of CRC on agricultural productivity, indicating an increase in vegetation density by about 0.1909 and 0.1377 for study areas 1 and 2, respectively, from 2015 to 2022. The results also indicate an increase in soil chemical and physical characteristics, including EC, PH, Na, Mg, HCO₃, K, silt, sand, and clay from 2015 to 2022, based on physical examination. In general, the findings underscore that the value of an integrated approach of remote sensing and geospatial analysis for detecting CRC and monitoring its impacts on agricultural productivity and soil fertility. This research can offer valuable insight to researchers and decision-makers in the field of soil science, land management and agriculture.