scholarly journals Implications of Bioenergy Cropping for Soil: Remote Sensing Identification of Silage Maize Cultivation and Risk Assessment Concerning Soil Erosion and Compaction

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 128
Author(s):  
Thorsten Ruf ◽  
Mario Gilcher ◽  
Thomas Udelhoven ◽  
Christoph Emmerling
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Cropping Systems ◽  
Arable Land ◽  
Soil Health ◽  
Spatially Explicit ◽  
Mountain Range ◽  
Silage Maize ◽  
Maize Cultivation ◽  
Spatially Explicit Data

Energy transition strategies in Germany have led to an expansion of energy crop cultivation in landscape, with silage maize as most valuable feedstock. The changes in the traditional cropping systems, with increasing shares of maize, raised concerns about the sustainability of agricultural feedstock production regarding threats to soil health. However, spatially explicit data about silage maize cultivation are missing; thus, implications for soil cannot be estimated in a precise way. With this study, we firstly aimed to track the fields cultivated with maize based on remote sensing data. Secondly, available soil data were target-specifically processed to determine the site-specific vulnerability of the soils for erosion and compaction. The generated, spatially-explicit data served as basis for a differentiated analysis of the development of the agricultural biogas sector, associated maize cultivation and its implications for soil health. In the study area, located in a low mountain range region in Western Germany, the number and capacity of biogas producing units increased by 25 installations and 10,163 kW from 2009 to 2016. The remote sensing-based classification approach showed that the maize cultivation area was expanded by 16% from 7305 to 8447 hectares. Thus, maize cultivation accounted for about 20% of the arable land use; however, with distinct local differences. Significant shares of about 30% of the maize cultivation was done on fields that show at least high potentials for soil erosion exceeding 25 t soil ha−1 a−1. Furthermore, about 10% of the maize cultivation was done on fields that pedogenetically show an elevated risk for soil compaction. In order to reach more sustainable cultivation systems of feedstock for anaerobic digestion, changes in cultivated crops and management strategies are urgently required, particularly against first signs of climate change. The presented approach can regionally be modified in order to develop site-adapted, sustainable bioenergy cropping systems.

Assessment and Monitoring of Soil Erosion Risk and Land Degradation in Arable Land Combining Remote Sensing Methodologies and RUSLE Factors

2021 ◽  
pp. 704-716
Author(s):  
Biagio Tucci ◽  
Gabriele Nolè ◽  
Antonio Lanorte ◽  
Valentina Santarsiero ◽  
Giuseppe Cillis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Land Degradation ◽  
Arable Land ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Assessment And Monitoring

scholarly journals Remote Sensing Based Binary Classification of Maize. Dealing with Residual Autocorrelation in Sparse Sample Situations

2019 ◽  
Vol 11 (18) ◽  
pp. 2172 ◽  
Author(s):  
Mario Gilcher ◽  
Thorsten Ruf ◽  
Christoph Emmerling ◽  
Thomas Udelhoven
Keyword(s):  
Spatial Autocorrelation ◽  
Binary Classification ◽  
Soil Health ◽  
Spatial Relationship ◽  
Error Rates ◽  
Support Vector ◽  
Silage Maize ◽  
Maize Cultivation ◽  
Gaussian Blur ◽  
Prediction Approach

In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research, especially on soil health, where risk factors like erosion and compaction depend on variables that are specific to every site, and hard to generalize for larger administrative aggregates. The focus of this study is to apply established classification algorithms to estimate maize abundance for each independent pixel, while at the same time accounting for their spatial relationship. Therefore, two ways to incorporate spatial autocorrelation of neighboring pixels are combined with three different classification models. The performance of each of these modeling approaches is analyzed and discussed. Finally, one prediction approach is applied to the imagery, and the overall predicted acreage is compared to publicly available data. We were able to show that Support Vector Machine (SVM) classification and Random Forests (RF) were able to distinguish maize pixels reliably, with kappa values well above 0.9 in most cases. The Generalized Linear Model (GLM) performed substantially worse. Furthermore, Regression Kriging (RK) as an approach to integrate spatial autocorrelation into the prediction model is not suitable in use cases with millions of sparsely clustered training pixels. Gaussian Blur is able to improve predictions slightly in these cases, but it is possible that this is only because it smoothes out impurities of the reference data. The overall prediction with RF classification combined with Gaussian Blur performed well, with out of bag error rates of 0.5% in 2009 and 1.3% in 2016. Despite the low error rates, there is a discrepancy between the predicted acreage and the official records, which is 20% in 2009 and 27% in 2016.

scholarly journals Impact of Land Cover Change on Soil Erosion Hazard in Northern Jordan Using Remote Sensing and GIS

2013 ◽  
Vol 19 ◽  
pp. 912-921 ◽  
Author(s):  
M.Minwer Alkharabsheh ◽  
T.K. Alexandridis ◽  
G. Bilas ◽  
N. Misopolinos ◽  
N. Silleos
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Land Cover ◽  
Land Cover Change ◽  
Remote Sensing And Gis ◽  
Erosion Hazard ◽  
Northern Jordan

Mapping soil erosion–prone sites through GIS and remote sensing for the Tifnout Askaoun watershed, southern Morocco

2021 ◽  
Vol 14 (9) ◽  
Author(s):  
Abdellaali Tairi ◽  
Ahmed Elmouden ◽  
Lhoussaine Bouchaou ◽  
Mohamed Aboulouafa
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Gis And Remote Sensing

scholarly journals Drone-Based Remote Sensing for Research on Wind Erosion in Drylands: Possible Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 283
Author(s):  
Junzhe Zhang ◽  
Wei Guo ◽  
Bo Zhou ◽  
Gregory S. Okin
Keyword(s):  
Remote Sensing ◽  
Wind Erosion ◽  
Field Measurements ◽  
Spatially Explicit ◽  
Surface Model ◽  
High Resolution Imagery ◽  
Soil Elevation ◽  
3D Photogrammetry ◽  
Using Data ◽  
Sample Set

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.

scholarly journals Camelina, an ancient oilseed crop actively contributing to the rural renaissance in Europe. A review

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Federica Zanetti ◽  
Barbara Alberghini ◽  
Ana Marjanović Jeromela ◽  
Nada Grahovac ◽  
Dragana Rajković ◽  
...  
Keyword(s):  
Cropping Systems ◽  
Critical Evaluation ◽  
Soil Health ◽  
Farming Systems ◽  
Crop Productivity ◽  
Oilseed Crop ◽  
Crop Diversification ◽  
Pests And Diseases ◽  
European Farming ◽  
European Agriculture

AbstractPromoting crop diversification in European agriculture is a key pillar of the agroecological transition. Diversifying crops generally enhances crop productivity, quality, soil health and fertility, and resilience to pests and diseases and reduces environmental stresses. Moreover, crop diversification provides an alternative means of enhancing farmers’ income. Camelina (Camelina sativa (L.) Crantz) reemerged in the background of European agriculture approximately three decades ago, when the first studies on this ancient native oilseed species were published. Since then, a considerable number of studies on this species has been carried out in Europe. The main interest in camelina is related to its (1) broad environmental adaptability, (2) low-input requirements, (3) resistance to multiple pests and diseases, and (4) multiple uses in food, feed, and biobased applications. The present article is a comprehensive and critical review of research carried out in Europe (compared with the rest of the world) on camelina in the last three decades, including genetics and breeding, agronomy and cropping systems, and end-uses, with the aim of making camelina an attractive new candidate crop for European farming systems. Furthermore, a critical evaluation of what is still missing to scale camelina up from a promising oilseed to a commonly cultivated crop in Europe is also provided (1) to motivate scientists to promote their studies and (2) to show farmers and end-users the real potential of this interesting species.

The effect of rainfall intensity on soil erosion and particulate phosphorus transfer from arable soils

1999 ◽  
Vol 39 (12) ◽  
pp. 41-45 ◽  
Author(s):  
A. I. Fraser ◽  
T. R. Harrod ◽  
P. M. Haygarth
Keyword(s):  
Soil Erosion ◽  
Suspended Sediment ◽  
Rainfall Intensity ◽  
Overland Flow ◽  
Agricultural Soils ◽  
Arable Land ◽  
Significant Proportion ◽  
Particulate Phosphorus ◽  
Arable Soils ◽  
Flow Monitoring

Soil erosion, in the form of transported suspended sediment in overland flow, is often associated with high rates of particulate phosphorus (PP) (total P>0.45 μm) transfer from land to watercourses. Particulate P may provide a long-term source of P for aquatic biota. Twenty-two sites for winter overland flow monitoring were selected in south-west England within fields ranging from 0.2–3.8 ha on conventionally-managed arable land. Fields were situated on highly porous, light textured soils, lacking impermeable horizons and often overlying major aquifers. Long arable use and modern cultivation methods result in these soils capping under rain impact. Overland flow was observed when rainfall intensity approached the modest rate of 0.8 mm hr−1 on land at or near to field capacity. Low intensity rainfall (<2 mm hr−1) produced mean suspended sediment losses of 14 kg ha−1 hr−1, with associated PP transfer rates of 16 g ha−1 hr−1. In high intensity rainfall (>9 mm hr−1) mean PP losses of 319 g ha−1 hr−1 leaving the field were observed. As might be expected, there was a good relationship between PP and suspended sediment transfer in overland flow leaving the sites. The capacity of light soils to cap when in arable use, combined with heavy or prolonged rainfall, resulted in substantial discharges, soil erosion and associated PP transfer. Storms with heavy rain, typically of only a few hours duration, were characterised by considerable losses of PP. Such events, with return periods of once or twice a winter, may account for a significant proportion of total annual P transfer from agricultural soils under arable crops. However, contributions from less intense rain with much longer duration (around 100 hours per winter in many arable districts of the UK) are also demonstrated here.

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