Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

Lombardo, Luigi; Saia, Sergio; Schillaci, Calogero; Martin, P.; Huser, Raphaël

doi:10.1016/j.geoderma.2017.12.011

Cited by 53 publications

(27 citation statements)

References 63 publications

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“…Data were aggregated into a stepwise regression model with bidirectional elimination, where TN, P, and K constituted the three dependent variables. The pedological parameters previously listed were utilized as predictors, following the FAO Global Soil Partnership, which fosters the exploration of links between socio-economic and environmental variables utilizing regressions in the context of the Digital Soil Mapping Initiative (Meeting in Teheran, January 2018) (Lombardo et al 2018).…”

Section: Computation Of the Soil Management Ability At Household Levelmentioning

confidence: 99%

Traditional knowledge affects soil management ability of smallholder farmers in marginal areas

Occelli

Mantino

Ragaglini

et al. 2021

Agron. Sustain. Dev.

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Soil fertility is key to sustainable intensification of agriculture and food security in sub-Saharan Africa. However, when soil nutrients are not adequately managed, smallholder farming practices slowly erode soils to almost inert systems. This case study contributes to the understanding of such failures in marginal areas. We integrate agronomic and social sciences approaches to explore links between smallholder households’ farming knowledge and soil fertility in an ethnopedological perspective. We interview 280 smallholder households in two areas of the Ethiopian highlands, while collecting measures of 11 soil parameters at their main field. By analyzing soil compositions at tested households, we identify a novel measure of soil management ability, which provides an effective empirical characterization of the soil managing capacity of a household. Regression analysis is used to evaluate the effects of household knowledge on the soil management ability derived from laboratory analysis. Results highlight the complexity of knowledge transmission in low-input remote areas. We are able to disentangle a home learning and a social learning dimension of the household knowledge and appraise how they can result in virtuous and vicious cycles of soil management ability. We show that higher soil management ability is associated with farmers relying to a great extent on farming knowledge acquired within the household, as a result of practices slowly elaborated over the years. Conversely, lower soil management ability is linked to households valuing substantially farming knowledge acquired through neighbors and social gatherings. The present study is the first to formulate the concept of soil management ability and to investigate the effects of the presence and the types of farming knowledge on the soil management ability of smallholder farmers in remote areas. We show that farming knowledge has a primary role on soil fertility and we advise its consideration in agricultural development policies.

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Section: Computation Of the Soil Management Ability At Household Levelmentioning

confidence: 99%

Traditional knowledge affects soil management ability of smallholder farmers in marginal areas

Occelli

Mantino

Ragaglini

et al. 2021

Agron. Sustain. Dev.

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“…Possible reasons for the unsatisfactory results included (i) the irregular time-span of the landslide inventories, ranging from 1 to 23 years, (ii) the highly variable Table 2: Morphometric (M), lithological (L), and bedding-related (structural, S) explanatory variables (covariates) used in the study for space-time landslide predictive modelling in the Collazzone area, Umbria, Central Italy (see Figure 1). References: 1, http://www.umbriageo.regione.umbria.it/pagina/distribuzione-carta-tecnicaregionale-vettoriale-1; 2, Zevenbergen and Thorne (1987); 3, Lombardo et al (2018b); 4, Heerdegen and Beran (1982); 5, Böhner and Selige (2006); 6, Beven and Kirkby (1979); 7, Guzzetti et al (2006a). 19411941-195419541954-197719771977-198519851985Snow 1998 May 2004 19411941-195419541954-197719771977-198519851985Snow 1998 May 2004 To aggregate the data over space, we looked into the inventories and realized that some of the landslides have a large areal extent at certain times, leading the instability to affect several SUs simultaneously.…”

Section: Pre-processingmentioning

confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

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Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a novel Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km 2 area into 889 slope units (SUs). In each SU, we computed the percentage of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model, and 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to a "traditional" landslide susceptibility model. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In partic...

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“…From the Shuttle Radar Topography Mission 90 m (Jarvis et al, ) digital elevation model we derived the following morphometric covariates: (i) Elevation ; (ii) Slope (Zevenbergen & Thorne, ); (iii) Eastness and Northness (i.e., the sine and cosine of the Aspect, respectively, Lombardo, Saia, et al, ); (iv) Planar and Profile Curvatures (Heerdegen & Beran, ); (v) Relative Slope Position (Böhner & Selige, ); (vi) Topographic Wetness Index (Beven & Kirkby, ); and (vii) Landform Classes (Weiss, ). We also computed the Euclidean distances from each pixel to the nearest fault line, stream, and geological boundary (or lithological contact) to produce Distance to Faults , Distance to Streams , and Distance to Geoboundaries .…”

Section: Data Set Creationmentioning

confidence: 99%

Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

et al. 2019

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We investigate earthquake-induced landslides using a geostatistical model featuring a latent spatial effect (LSE). The LSE represents the spatially structured residuals in the data, which remain after adjusting for covariate effects. To determine whether the LSE captures the residual signal from a given trigger, we test the LSE in reproducing the pattern of seismic shaking from the distribution of seismically induced landslides, without prior knowledge of the earthquake being included in the model. We assessed the landslide intensity, that is, the expected number of landslides per mapping unit, for the area in which landslides triggered by the Wenchuan and Lushan earthquakes overlap. We examined this area to test our method on landslide inventories located in near and far fields of the earthquake. We generated three models for both earthquakes: (i) seismic parameters only (proxy for the trigger); (ii) the LSE only; and (iii) both seismic parameters and the LSE. The three configurations share the same morphometric covariates. This allowed us to study the LSE pattern and assess whether it approximated the seismic effects. Our results show that the LSE reproduced the shaking patterns for both earthquakes. In addition, the models including the LSE perform better than conventional models featuring seismic parameters only. Due to computational limitations we carried out a detailed analysis for a relatively small area (2,112 km 2 ), using a data set with higher spatial resolution. Results were consistent with those of a subsequent analysis for a larger area (14,648 km 2 ) using coarser-resolution data.

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Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

Cited by 53 publications

References 63 publications

Traditional knowledge affects soil management ability of smallholder farmers in marginal areas

Traditional knowledge affects soil management ability of smallholder farmers in marginal areas

Space-Time Landslide Predictive Modelling

Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

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