Correlations between soil organic carbon, land use and soil type in Serbia

Manojlovic, S.; Djordjevic, R.; Nešić, Milan; Predic, M Tihomir

doi:10.2298/zmspn2038009v

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…In recent years, the accuracy of farmland SOC digital mapping has been remarkably improved by combining farmland planting and management systems, including cropping system, crop type, multiple cropping index, stubble index, and distance to irrigation canals [5,[11][12][13][14][15]. However, existing studies only address typical cropping systems in certain regions, where the systems are relatively straightforward [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Ou,

Wu,

Yan

et al. 2024

Environ Sci Eur

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Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing soil quality and achieving precision agriculture. The cropping system is an important factor that affects the soil carbon cycle in farmlands, and different agricultural managements under different cropping systems lead to spatial heterogeneity of SOC. However, current research often ignores differences in the main controlling factors of SOC under different cropping systems, especially when the cropping pattern is complex, which is not conducive to farmland zoning management. This study aims to (i) obtain the spatial distribution map of six cropping systems by using multi-phase HJ-CCD satellite images; (ii) explore the stratified heterogeneous relationship between SOC and environmental variables under different cropping systems by using the Cubist model; and (iii) predict the spatial map of SOC. The Xiantao, Tianmen, and Qianjiang cities, which are the core agricultural areas of the Jianghan Plain, were selected as the study area. Results showed that the SOC content in rice–wheat rotation was the highest among the six cropping systems. The Cubist model outperformed random forest, ordinary kriging, and multiple linear regression in SOC mapping. The results of the Cubist model showed that cropping system, climate, soil attributes, and vegetation index were important influencing factors of SOC in farmlands. The main controlling factors of SOC under different cropping systems were different. Specifically, summer crop types had a greater influence on spatial variations in SOC than winter crops. Paddy–upland rotation was more affected by river distance and NDVI, while upland–upland rotation was more affected by irrigation-related factors. This work highlights the differentiated main controlling factors of SOC under different cropping systems and provides data support for farmland zoning management. The Cubist model can improve the prediction accuracy of SOC under complex cropping systems.

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Section: Introductionmentioning

confidence: 99%

Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Ou,

Wu,

Yan

et al. 2024

Environ Sci Eur

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Soil organic carbon stocks in relation to the altitude in Serbia

Manojlović¹,

Đorđević²,

Savić³

et al. 2021

Zemljište i biljka

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The research was conducted to determine the soil organic carbon (SOC) stocks in relation to the altitude in Serbia. The database included a total of 1,140 soil profiles. SOC were investigated at different the altitudes (0-200 m, 200-500 m, 500-1,000 m, 1,000 - 2.000 m) and at two soil depths (0-30 cm and 0-100 cm). Statistical correlation was done for five regions where locations were grouped according to the spatial distribution. The results showed that the highest mean values of SOC were measured on the terrain that includes mountains with the altitudes of 1,000-2,000 m and covers an area of 11.5% of the territory of Serbia. The lowest obtained result is related to the lowland areas with the largest number of locations. Greater variability in the results of SOC stocks were found at the higher altitudes and the greatest on the low mountains of 500-1,000 m altitude. There is a medium to strong statistical dependence of the altitude with the SOC stocks at two soil depths (0-30 cm and 0-100 cm). The result indicate that the relationship between SOC stocks and altitude varies between the regions. This study shows that altitude is an important factor affecting SOC stocks.

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Correlations between soil organic carbon, land use and soil type in Serbia

Cited by 2 publications

References 5 publications

Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Soil organic carbon stocks in relation to the altitude in Serbia

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