Legacy effects of temporary grassland in annual crop rotation on soil ecosystem services

Hoeffner, Kevin; Beylich, Anneke; Chabbi, Abad; Cluzeau, Daniel; Dascălu, Dumitrița; Graefe, Ulfert; Guzmán, Gema; Hallaire, Vincent; Hanisch, Jörg; Landa, Blanca B.; Linsler, Deborah; Menasseri, Safya; Öpik, Maarja; Potthoff, Martin; Şandor, Mignon; Scheu, Stefan; Schmelz, Rüdiger M.; Engell, Ilka; Schrader, Stefan; Vahter, Tanel; Banse, Martin; Nicolaï, Annegret; Plaas, Elke; Runge, Tania; Roslin, Tomas; Decau, Marie-Laure; Sepp, Siim‐Kaarel; Arias-Giraldo, Luis F.; Busnot, Sylvain; Roucaute, Marc; Pérès, Guénola

doi:10.1016/j.scitotenv.2021.146140

Cited by 24 publications

(15 citation statements)

References 119 publications

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“…(2021), whereas a ring insertion depth of 2 cm was reported by Hoeffner et al. (2021). The automated single‐ring infiltrometer by Di Prima (2015) maintains a small (i.e., approximately less than 0.5 cm) water head on the confined soil surface.…”

Section: Introductionmentioning

confidence: 88%

“…For example, Lassabatere et al (2006) inserted the ring to a depth of about 1 cm, and the initial ponded depth of water was 0.2-0.8 cm, depending on the run. A ring inserted 1 cm into the soil and an initial ponded depth of water of nearly 1 cm were reported by Xu et al (2012) and Lozano-Baez et al (2021), whereas a ring insertion depth of 2 cm was reported by Hoeffner et al (2021). The automated single-ring infiltrometer by Di Prima (2015) maintains a small (i.e., approximately less than 0.5 cm) water head on the confined soil surface.…”

Section: Introductionmentioning

confidence: 99%

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Correspondence between theory and practice of a Beerkan infiltration experiment

Bagarello

Dohnal

Iovino

et al. 2022

Vadose Zone Journal

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The Beerkan infiltration experiment is carried out by inserting the ring a short depth into the soil and establishing a positive head of water on the infiltration surface for at least a part of the run. Nevertheless, the data are analyzed by assuming a fully unconfined infiltration process (ring insertion depth, d = 0 cm) and a null ponded depth of water (H = 0 cm). The influence of ring insertion and ponded water on an infiltration process of 2 h sampled every minute was tested in this numerical investigation. Five soils varying from sand to silt loam, three ring radii (5-15 cm), and the Beerkan-specific range of values for both d and H (between 0 and 1 cm) were considered. The differences between the theoretical (d = H = 0 cm) and the practical (d = H = 1 cm) setups varied from −10.4 to +8.6% for the mean infiltration rate and from −10.2 to +8.3% for the final cumulative infiltration. These differences were small, and they decreased in absolute value by considering a soil-dependent ring radius. In particular, nearly negligible differences were detected using a small ring in coarse-textured soils and a large ring in fine-textured soils. In the coarser soils, inserting the ring and establishing a ponded depth of water did not alter the estimated coefficients of the two-parameter infiltration model appreciably with the cumulative linearization method, because these coefficients differed between the theoretical and practical setups by no more than 9.2%. In fine soils, linearization could not be possible regardless of the considered setup, or it was the use of d = H = 1 cm instead of d = H = 0 cm that impeded a convincing linearization of the data. In conclusion, the good correspondence, in many circumstances, between the theoretical and the practical Beerkan infiltration experiment reinforced the interest in this simple experiment as a practical means to collect infiltration data in the field.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Correspondence between theory and practice of a Beerkan infiltration experiment

Bagarello

Dohnal

Iovino

et al. 2022

Vadose Zone Journal

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show abstract

“…It has resulted in shortages in grasslands taxonomy and the efficient functioning of ecosystem services [ 12 ]. Grasslands’ changing diversity and composition impact ecosystem services like precipitation and temperature controls, freshwater supply, erosion control, and soil formation [ 13 – 15 ]. They can likely result in biodiversity loss [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Mapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data

Adagbasa

Mukwada

2022

PLoS ONE

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Vegetation species succession and composition are significant factors determining the rate of ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and effective natural resource management and biodiversity. The succession and composition of grasslands ecosystems worldwide have significantly been affected by accelerated environmental changes due to natural and anthropogenic activities. Therefore, understanding spatial data on the succession of grassland vegetation species and communities through mapping and monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This study used a random forest machine learning classifier on the Google Earth Engine platform to classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation species succession. The results indicate that ASTER MI has the least accuracy of 72%, Landsat 7 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also shows that other species had replaced four dominant grass species totaling about 49 km2 throughout the study.

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“…It has resulted in shortages in grasslands taxonomy and efficient functioning of ecosystem services (12). Grasslands' changing diversity and composition impact ecosystem services like precipitation and temperature controls, freshwater supply, erosion control, and soil formation (13)(14)(15). They can likely result in biodiversity loss (16).…”

Section: Introductionmentioning

confidence: 99%

Mapping Vegetation Species Succession in a Mountainous Grassland ecosystem using Landsat and Sentinel-2 data

Adagbasa

Mukwada

2021

Preprint

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Vegetation species succession and composition are significant factors determining the rate of ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and effective natural resource management and biodiversity. The succession and composition of grasslands ecosystems worldwide have significantly been affected by accelerated environmental changes due to natural and anthropogenic activities. Therefore, understanding spatial data on the succession of grassland vegetation species and communities through mapping and monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This study used a random forest machine learning classifier on the Google Earth Engine platform to classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation species succession. The results indicate that ASTER IM has the least accuracy of 72%, Landsat 7 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also shows that other species had replaced four dominant grass species totaling about 49 km 2 throughout the study.

show abstract

Legacy effects of temporary grassland in annual crop rotation on soil ecosystem services

Cited by 24 publications

References 119 publications

Correspondence between theory and practice of a Beerkan infiltration experiment

Correspondence between theory and practice of a Beerkan infiltration experiment

Mapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data

Mapping Vegetation Species Succession in a Mountainous Grassland ecosystem using Landsat and Sentinel-2 data

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