Factors Constraining the Adoption of Soil Organic Carbon Enhancing Technologies Among Small-Scale Farmers in Ethiopia

2021 ◽  
Author(s):  
Wilson Nguru ◽  
Charles KK Gachene ◽  
Cecilia M. Onyango ◽  
Stanley Karanja Ng’ang’a ◽  
Evan H. Girvetz
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Small Scale ◽  
Small Scale Farmers

scholarly journals Factors constraining the adoption of Soil Organic Carbon Enhancing Technologies among small-scale farmers in Ethiopia

Heliyon ◽  
2021 ◽  
pp. e08497
Author(s):  
Wilson M. Nguru ◽  
Charles KK. Gachene ◽  
Cecilia M. Onyango ◽  
Stanley K. Ng’ang’a ◽  
Evan H. Girvetz
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Small Scale ◽  
Small Scale Farmers

Small-scale spatial patterns of soil organic carbon and nitrogen stocks in permafrost-affected soils of northern Siberia

Geoderma ◽  
2018 ◽  
Vol 329 ◽  
pp. 91-107 ◽  
Author(s):  
Alevtina Evgrafova ◽  
Tilman René de la Haye ◽  
Ina Haase ◽  
Olga Shibistova ◽  
Georg Guggenberger ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Patterns ◽  
Small Scale ◽  
Northern Siberia ◽  
Carbon And Nitrogen ◽  
Carbon And Nitrogen Stocks

scholarly journals High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

2018 ◽  
Vol 15 (6) ◽  
pp. 1663-1682 ◽  
Author(s):  
Matthias B. Siewert
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Support Vector ◽  
Small Scale ◽  
Permafrost Degradation ◽  
Discontinuous Permafrost

Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1×1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to be 8.3 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to be 7.7 ± 6.2 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions  >  30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

scholarly journals High-resolution digital mapping of soil organic carbon in permafrost terrain using machine-learning: A case study in a sub-Arctic peatland environment

2017 ◽  
Author(s):  
Matthias B. Siewert
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Support Vector ◽  
Small Scale ◽  
Permafrost Degradation ◽  
Discontinuous Permafrost

Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost affected soils are key components in the global carbon cycle. I quantify SOC stocks in a sub-arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest approach performed best and was used to predict SOC for several depth increments at a spatial resolution of 2 ×2 m. A high-resolution (1 × 1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to 7.9 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to 7.0 ± 6.3 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape SOC storage. A surprising large number of small scale wetland areas are mapped forming very local hot-spots of SOC storage. The results show that robust SOC predictions are possible with the available methods and very high-resolution remote sensing data. Strong environmental gradients associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scale the main factor limiting robust, high-resolution SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Absiko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic in wetland areas with permafrost related landforms. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

Soil organic carbon and nitrogen losses due to soil erosion and cropping in a sloping terrace landscape

Soil Research ◽  
2015 ◽  
Vol 53 (1) ◽  
pp. 87 ◽  
Author(s):  
J. H. Zhang ◽  
Y. Wang ◽  
F. C. Li
Keyword(s):  
Soil Erosion ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Surface Soil ◽  
Soil Horizon ◽  
Small Scale ◽  
Cultivated Land ◽  
N Losses ◽  
Uncultivated Land ◽  
Cultivated Soils

Effects of soil erosion and cropping on soil organic carbon (SOC) stocks need to be addressed to better understand the processes of SOC loss following the conversion of natural ecosystems to agriculture. The aims of the present study were to: (1) understand the mechanism of SOC and total nitrogen (TN) losses in a small-scale agricultural landscape with sloping terraces; and (2) quantitatively assess vertical changes in SOC and TN of soil profiles at specific landscape positions and the lateral distribution of SOC and TN in areas with different soil erosion and deposition rates. Soil samples from cultivated land were collected at 5-m intervals along toposequences in different parts of hilly areas of the Sichuan Basin, China; uncultivated land was used as a reference for 137Cs, SOC and TN. The profile shape of SOC and total N depth distribution was markedly different between cultivated and uncultivated soils, with differences in descriptive coefficients of 2.1–3.4- and 2.0–3.2-fold for a, 1.2–2.2- and 1.0–1.8-fold for b, respectively, in the equation y = –aln(x) + b, where y is the depth SOC or TN concentration and x is the depth from the soil surface. SOC and TN concentrations in the surface soil horizon were significantly higher on uncultivated land (17.5 g kg–1) than on cultivated land (7.06–9.81 g kg–1). In particular, the 0–5 cm surface layer of uncultivated soils had 1.3-, 1.7-, and 2.3-fold higher SOC concentrations than that of the depositional, weak erosional and strong erosional areas, respectively, in cultivated soils. However, there were no significant differences in SOC and TN concentrations in subsoil layers between cultivated and uncultivated lands, suggesting that cropping is one of the factors causing SOC and N losses. SOC and TN inventories exhibited an increasing trend from the upper to toe proportions of the cultivated toposequences. In all the cultivated soils, SOC and TN concentrations of the surface soil horizon and inventories of SOC and TN were closely associated with 137Cs inventories (P < 0.001, P < 0.01, P < 0.0001 and P < 0.0001, respectively), suggesting that soil erosion has an important impact on SOC and TN dynamics in the cultivated landscape. The results of this study suggest that soil erosion and cropping result in SOC and N losses, and that soil erosion contributes to marked variations in SOC and N distribution along the slope transect within individual sloping terraces, as well as in the entire landscape.

scholarly journals Detecting small-scale spatial heterogeneity and temporal dynamics of soil organic carbon (SOC) stocks: a comparison between automatic chamber-derived C budgets and repeated soil inventories

2017 ◽  
Vol 14 (4) ◽  
pp. 1003-1019 ◽  
Author(s):  
Mathias Hoffmann ◽  
Nicole Jurisch ◽  
Juana Garcia Alba ◽  
Elisa Albiac Borraz ◽  
Marten Schmidt ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Heterogeneity ◽  
Temporal Dynamics ◽  
Small Scale ◽  
Short Term ◽  
C Cycle ◽  
Spatial Differences ◽  
Northeastern Germany ◽  
Soc Stocks

Abstract. Carbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (ΔSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10–30 m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in ΔSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil–plant–atmosphere system may help to obtain temporal ΔSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of ΔSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modeled aboveground biomass development (NPPshoot) were used. To verify our method, results were compared with ΔSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Tendencies and magnitude of ΔSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual ΔSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of ΔSOC.

scholarly journals Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4357
Author(s):  
Hongyang Li ◽  
Shengyao Jia ◽  
Zichun Le
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Zhejiang Province ◽  
Small Scale ◽  
Calibration Model ◽  
Target Area ◽  
Good Prediction ◽  
Spiked Models

Near-infrared (NIR) spectroscopy is widely used to predict soil organic carbon (SOC) because it is rapid and accurate under proper calibration. However, the prediction accuracy of the calibration model may be greatly reduced if the soil characteristics of some new target areas are different from the existing soil spectral library (SSL), which greatly limits the application potential of the technology. We attempted to solve the problem by building a large-scale SSL or using the spiking method. A total of 983 soil samples were collected from Zhejiang Province, and three SSLs were built according to geographic scope, representing the provincial, municipal, and district scales. The partial least squares (PLS) algorithm was applied to establish the calibration models based on the three SSLs, and the models were used to predict the SOC of two target areas in Zhejiang Province. The results show that the prediction accuracy of each model was relatively poor regardless of the scale of the SSL (residual predictive deviation (RPD) < 2.5). Then, the Kennard-Stone (KS) algorithm was applied to select 5 or 10 spiking samples from each target area. According to different SSLs and numbers of spiking samples, different spiked models were established by the PLS. The results show that the predictive ability of each model was improved by the spiking method, and the improvement effect was inversely proportional to the scale of the SSL. The spiked models built by combining the district scale SSL and a few spiking samples achieved good prediction of the SOC of two target areas (RPD = 2.72 and 3.13). Therefore, it is possible to accurately measure the SOC of new target areas by building a small-scale SSL with a few spiking samples.

Spatial heterogeneity of soil organic carbon and total nitrogen at small scale in subalpine meadow andPicea meyeriforest in Luya Mountain

2013 ◽  
Vol 33 (24) ◽  
Author(s):  
武小钢 WU Xiaogang ◽  
郭晋平 GUO Jinping ◽  
田旭平 TIAN Xuping ◽  
杨秀云 YANG Xiuyun
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Heterogeneity ◽  
Total Nitrogen ◽  
Small Scale ◽  
Subalpine Meadow

scholarly journals Detecting small-scale spatial heterogeneity and temporal dynamics of soil organic carbon (SOC) stocks: a comparison between automatic chamber-derived C budgets and repeated soil inventories

2016 ◽  
Author(s):  
Mathias Hoffmann ◽  
Nicole Jurisch ◽  
Juana Garcia Alba ◽  
Elisa Albiac Borraz ◽  
Marten Schmidt ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Heterogeneity ◽  
Temporal Dynamics ◽  
Small Scale ◽  
Ground Moraine ◽  
Short Term ◽  
Ne Germany ◽  
C Cycle ◽  
Soc Stocks

Abstract. Carbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (∆SOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial and temporal changes in SOC stocks, particularly pronounced on arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in ∆SOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant-atmosphere system may help to obtain temporal ∆SOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal as well as small-scale spatial dynamics of ΔSOC. Therefore, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modeled aboveground biomass development (NPPshoot) was used. To verify our method, results were compared with ΔSOC observed by soil resampling. AC measurements were performed from 2010 to 2014 under a silage maize/winter fodder rye/sorghum-Sudan grass hybrid/alfalfa crop rotation at a colluvial depression located in the hummocky ground moraine landscape of NE Germany. Widespread in large areas of the formerly glaciated Northern Hemisphere, this depression type is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity in soil properties, such as SOC and nitrogen (Nt). After monitoring the initial stage during 2010, soil erosion was experimentally simulated by incorporating topsoil material from an eroded midslope soil into the plough layer of the colluvial depression. SOC stocks were quantified before and after soil manipulation and at the end of the study period. AC-based ∆SOC values corresponded well with the tendencies and magnitude of the results observed in the repeated soil inventory. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual ΔSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial and short-term temporal dynamics of ∆SOC.

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