CO2 fluxes separtition to evaluate agroecosystem functional heterogeneity under within-field soil bulk density variability

Abstract

The article presents an algorithmic approach for separation of integral CO2 fluxes, allowing for a quantitative assessment of the contribution of field areas with different topsoil bulk densities to the overall carbon balance. The methodology is based on integrating eddy covariance data, dynamic footprint modeling, and digital soil mapping. The experiment was conducted in 2023 on spring barley crops under conditions of pronounced soil bulk density heterogeneity (1.45–1.80 g cm⁻³). The developed approach enabled the partitioning of the total net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration (Reco) components, specific to the identified zones with contrasting densities. It was established that under the dry conditions of June 2023, overcompacted areas (>1.65 gcm⁻³) were characterized by higher values of gross primary production (up to 14 μmolm⁻2s⁻1) and water use efficiency compared to less compacted areas (<1.65 gcm⁻³), where these values did not exceed 10 μmol Cm⁻2s⁻1. In the second half of the growing season, during the ripening stage and with an increase in precipitation in August, an inversion was observed: GPP values in the overcompacted areas were 2.8 μmol C m⁻2s⁻1, and Reco intensity was 2.1 μmol C m⁻² s⁻¹, which is half the values recorded in areas with lower bulk density (4.2 μmolm⁻2s⁻1). The proposed approach made it possible to quantitatively capture the within-field variability of carbon balance components without using spatial averaging.

References

1.    Благовещенский Ю.Н., Самсонова В.П. Моделирование влияния пространственной изменчивости почвенных свойств на урожайность сельскохозяйственных культур (в масштабе угодья) // Агрохимия. 2007. № 8. С. 76–82.
2.    Брыкина А.Д., Морев Д.В., Потапова В.А. и др. Экологическая оценка распределения цинка в растениях ячменя в разные фазы роста в условиях локально-загрязненного участка с нарушенными дерново-подзолистыми почвами // АгроЭкоИнфо. 2024. № 6(66). С. 1–9. https://doi.org/10.51419/202146655
3.    Карелин Д.В., Азовский А.И., Куманяев А.С. и др. Значение пространственного и временного масштаба при анализе факторов эмиссии СО2 из почвы в лесах Валдайской возвышенности // Лесоведение. 2019. № 1. C. 29–37. https://doi.org/10.1134/S0024114819010078
4.    Кузнецова И.В. О некоторых критериях оценки физических свойств почв // Почвоведение. 1979. № 3. С. 81–88.
5.    Кузнецова И.В., Уткаева В.Ф., Бондарев А.Г. Оценка изменения физических свойств пахотных дерново-подзолистых суглинистых почв нечерноземной зоны России в зависимости от характера антропогенного воздействия // Почвоведение. 2009. № 2. С. 152–162.
6.    Кутузова Н.Д., Куст Г.С., Розов С.Ю. и др. Влияние пространственной неоднородности почвенных свойств на рост и урожайность сои // Почвоведение. 2015. № 1. С. 95–95. https://doi.org/10.7868/S0032180X15010116
7.    Курганова И.Н., Лопес де Гереню В.О., Мякшина Т.Н. и др. Температурная чувствительность дыхания почв луговых ценозов в зоне умеренно-континентального климата: анализ данных 25-летнего мониторинга // Почвоведение. 2023. № 9. С. 1059–1076. https://doi.org/10.31857/S0032180X23600476
8.    Полевой определитель почв России. Российская акад. с.-х. наук, Гос. науч. учреждение Почвенный ин-т им. В. В. Докучаева, О-во почвоведов им. В. В. Докучаева. М., 2008. 182 с.
9.    Савин И.Ю. Пространственная адаптация систем земледелия к пестроте полей // Вестник Российского университета дружбы народов. Сер.: Агрономия и животноводство. 2021. Т. 16, № 4. C. 362–369. https://doi.org/10.22363/2312-797X-2021-16-4-362-369
10.    Шишов Л.Л., Тонконогов В.Д., Лебедева И.И. и др. Классификация и диагностика почв России. Смоленск, 2004. 342 с., с дополнениями 2008.
11.    Blagodatsky S., Smith P. Soil physics meets soil biology: towards better mechanistic prediction of greenhouse gas emissions from soil // Soil Biology and Biochemistry. 2012. Vol. 47. P. 78–92. https://doi.org/10.1016/j.soilbio.2011.12.015
12.    Borchard N., Schirrmann M., von Hebel C. et al.Spatio-temporal drivers of soil and ecosystem carbon fluxes at field scale in an upland grassland in Germany // Agriculture, Ecosystems & Environment. 2015. Vol. 211. P. 84–93. https://doi.org/10.1016/j.agee.2015.05.008
13.    Buzylev A.V. Changes in the soil-boniter index and agrophysical properties of urbanized soils of agroecosystems as a result of flattening with turmushezem // Journal of Advanced Research in Natural Science. 2024. № 21. P. 40–43. https://doi.org/10.26160/2572-4347-2024-21-40-43
14.    Chen X., Yang J., Liang A. et al. Effects of soil compaction and tillage practices on carbon dioxide efflux in Northeast China: evidence from an incubation study // Polish Journal of Environmental Studies. 2016. Vol. 25, № 4. P. 1463–1473. https://doi.org/10.15244/pjoes/62344
15.    da Cunha J.M., Campos M.C.C., Gaio D.C. et al. Spatial variability of soil respiration in Archaeological Dark Earth areas in the Amazon // Catena. 2018.Vol. 162, № 3. P. 148–156. https://doi.org/10.1016/j.catena.2017.12.001
16.    Emanuel R. E., Riveros-Iregui D.A., McGlynn B.L. et al. On the spatial heterogeneity of net ecosystem productivity in complex landscapes // Ecosphere. 2011. Vol. 2, № 7. P. 1–13. https://doi.org/https://doi.org/10.1890/ES11-00074.1
17.    Guarrera S., Vanella D., Consoli S. et al.Analysis of small-scale soil CO2 fluxes in an orange orchard under irrigation and soil conservative practices // Heliyon. 2024. Vol. 10, № 9. 16 p.https://doi.org/10.1016/j.heliyon.2024.e30543
18.    Gui W., You Y., Yang F. et al. Soil Bulk Density and Matric Potential Regulate Soil CO2 Emissions by Altering Pore Characteristics and Water Content // Land. 2023. № 12. P.1646. https://doi.org/10.3390/land12091646
19.    Hernandez Rodriguez L.C., Goodwell A.E., Kumar P. Inside the flux footprint: the role of organized land cover heterogeneity on the dynamics of observed land–atmosphere exchange fluxes // Frontiers in Water. 2023. Vol. 5, Аrticle 1097423.https://doi.org/10.3389/frwa.2023.1033973
20.    IUSS Working Group WRB. World Reference Base for Soil Resources. International soil classification system for naming soils and creating legends for soil maps. 4th edition. International Union of Soil Sciences (IUSS), Vienna, Austria, 2022. 236 p.
21.    Kljun N., Calanca P., Rotach M.W. et al.A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP) // Geoscientific Model Development. 2015. Vol. 8, № 11. P. 3695–3713. https://doi.org/10.5194/gmd-8-3695-2015
22.    Kroes J., Supit I., van Dam J. et al. Impact of capillary rise and recirculation on simulated crop yields // Hydrol. Earth Syst. Sci. 2018. Vol. 22. P. 2937–2952. https://doi.org/10.5194/hess-22-2937-2018
23.    Lasslop G., Reichstein M., Papale D. et al.Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation // Global Change Biology. 2010. Vol. 16, № 1. P. 187–208. https://doi.org/10.1111/j.1365-2486.2009.02041.x
24.    Marcolla B., Migliavacca M., Rödenbeck C. et al. Patterns and trends of the dominant environmental controls of net biome productivity // Biogeosciences. 2020. Vol. 16, № 8. P. 2365–2379. https://doi.org/10.5194/bg-17-2365-2020
25.    Mbarki Y., Gumiere S.J., Celicourt P. et al. Study of the effect of the compaction level on the hydrodynamic properties of loamy sand soil in an agricultural context // Front. Water. https://doi.org/10.3389/frwa.2023.1255495
26.    Morev D., Potapova V., Yaroslavtsev A. Agroecological assessment of spatial variability of carbon content in the conditions of disturbed sod-podzolic soils // 3rd International Conference on Research of Agricultural and Food Technologies (I-CRAFT-2023): Research of Agricultural and Food Technologies, Adana, Turkey, 04–06 октября 2023 года. Vol. 85. Les Ulis: EDP Sciences – Web of Conferences, 2024. P. 01063. https://doi.org/10.1051/bioconf/20248501063
27.    Nicolini G., Fratini G., Avilov V. et al. Performance of eddy-covariance measurements in fetch-limited applications // Theoretical and АppliedСlimatology. 2017. Vol. 127, № 3. P. 829–840.
28.    Nychka D., Furrer R., Paige J. et al. fields: Tools for spatial data. R package version 14.1. https://doi.org/10.5065/D6W957CT
29.    Pandey B.K., Bennett M.J. Uncovering root compaction response mechanisms: new insights and opportunities // Journal of Experimental Botany. 2024. Vol. 75, № 2. P. 578–583. https://doi.org/10.1093/jxb/erad389
30.    Ran Y., Li X., Sun R. et al. Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale // Agricultural and Forest Meteorology. 2016. Vol. 230. P. 114–127.
31.    Wutzler T., Lucas-Moffat A., Migliavacca M. et al.Basic and extensible post-processing of eddy covariance flux data with REddyProc // Biogeosciences. 2018. Vol. 15, № 16. P. 5015–5030. https://doi.org/10.5194/bg-2018-56
32.    Yang P., Reijneveld A., Lerink P. et al. Within-field spatial variations in subsoil bulk density related to crop yield and potential CO₂ and N₂O emissions // Catena. 2022. Vol. 213, Аrticle106156. P. 1-11. https://doi.org/10.1016/j.catena.2022.106156
33.    Zhu X., Peng W., Xie Q. et al. Effects of Soil Compaction Stress Combined with Drought on Soil Pore Structure, Root System Development, and Maize Growth in Early Stage // Plants. 2024. Vol. 13,№ 22. Paper 3185. Published 2024 Nov 13. DOI:10.3390/plants13223185