Monitoring Soil Fertility Trends Linked to Arable Land-Use Change in Hungary, 2000–2020: A Systematic Review Integrating Field and Remote Sensing Data

Quantifying the effects of land-use changes on soil fertility is essential for agricultural planning, yet long-term analyses combining field and remote sensing data remain scarce in Hungary. This systematic review followed PRISMA 2020 guidelines to assess arable land fertility trends between 2000 and 2020. A comprehensive search of WoS, Scopus, and Google Scholar identified 202 records, with 106 studies meeting inclusion criteria. Eligibility required empirical soil data collected from Hungarian arable lands. Among these, 17% reported declines in SOC, 13% indicated nutrient depletion, 36% observed stable or lost fertility, and 34% documented improvements. Regarding monitoring methods, 41% relied solely on field sampling, 44% applied GIS or spatial analyses, and 15% incorporated remote sensing indices such as NDVI. Evidence revealed spatial–temporal heterogeneity: fertility declines occurred in intensively cultivated regions, while western Transdanubia showed stability. Trends were linked to land-use intensification and intermittent reductions in agricultural area. Integration of remote sensing indices, such as NDVI, with field observations enhanced detection of spatial and temporal patterns. These findings underscore the need for harmonised monitoring frameworks, precision agriculture tools, and predictive modelling to support sustainable soil management. Identifying fertility-decline zones informs policy aligned with the EU Soil Strategy 2030 and supports Hungary’s agricultural resilience.

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Publication Details

Journal
Agriculture
Published
2026-04-15
DOI
https://doi.org/10.3390/agriculture16080876
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
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article

Monitoring Soil Fertility Trends Linked to Arable Land-Use Change in Hungary, 2000–2020: A Systematic Review Integrating Field and Remote Sensing Data

Magdoline Mustafa Ahmed Osman, András Tamás, Brian Ssemugenze, Péter Ragán et al.
Agriculture
Soil Geostatistics and Mapping
article

Monitoring Soil Fertility Trends Linked to Arable Land-Use Change in Hungary, 2000–2020: A Systematic Review Integrating Field and Remote Sensing Data

Magdoline Mustafa Ahmed Osman, András Tamás, Brian Ssemugenze, Péter Ragán, Ronald Kuunya
article en

Abstract

Quantifying the effects of land-use changes on soil fertility is essential for agricultural planning, yet long-term analyses combining field and remote sensing data remain scarce in Hungary. This systematic review followed PRISMA 2020 guidelines to assess arable land fertility trends between 2000 and 2020. A comprehensive search of WoS, Scopus, and Google Scholar identified 202 records, with 106 studies meeting inclusion criteria. Eligibility required empirical soil data collected from Hungarian arable lands. Among these, 17% reported declines in SOC, 13% indicated nutrient depletion, 36% observed stable or lost fertility, and 34% documented improvements. Regarding monitoring methods, 41% relied solely on field sampling, 44% applied GIS or spatial analyses, and 15% incorporated remote sensing indices such as NDVI. Evidence revealed spatial–temporal heterogeneity: fertility declines occurred in intensively cultivated regions, while western Transdanubia showed stability. Trends were linked to land-use intensification and intermittent reductions in agricultural area. Integration of remote sensing indices, such as NDVI, with field observations enhanced detection of spatial and temporal patterns. These findings underscore the need for harmonised monitoring frameworks, precision agriculture tools, and predictive modelling to support sustainable soil management. Identifying fertility-decline zones informs policy aligned with the EU Soil Strategy 2030 and supports Hungary’s agricultural resilience.

AgricultureVol. 16(8)
University of Debrecen (HU), National Centre for Research (Sudan) (SD)
Zero hunger
Openalex Percentile: Top 22%
Soil Geostatistics and Mapping
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