Measurement of Within-Field Spatial Variability for Evaluating Soil Degradation

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 12136

Special Issue Editors


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Guest Editor
Department of Agricultural, Food and Forest Sciences, University of Palermo, 90128 Palermo, Italy
Interests: precision agriculture; global navigation satellite systems for agricultural machines; geo-referenced measurement and mapping of soil compaction; remote sensing; renewable energy in agriculture

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Guest Editor
Precision Soil and Crop Engineering (Precision Scoring), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium
Interests: proximal soil sensing; soil and water management; soil dynamics; tillage; traction; compaction; mechanical weeding; soil remediation and management and precision agriculture
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Agricultural, Food and Forest Sciences, University of Palermo, 90128 Palermo, Italy
Interests: precision agriculture; global navigation satellite systems for agricultural machines; geo-referenced measurement and mapping of soil compaction

Special Issue Information

Dear Colleagues,

The within-field spatial variability is the variation of the soil and/or crop parameters of a field.

Soil parameters include: structure; cone penetrometer resistance, index of soil compaction; shear strength; draft force; depth of cultivated layer; texture; pH; nutrient, water and organic matter contents; microflora; microfauna; weeds; parasites.

Crop parameters include: plant biomass; vegetative vigour; yield; production quality.

Precision agriculture takes into account the within-field spatial variability, so that its cycle is constituted by three phases: geo-referenced measurement of field parameters; mapping and interpretation of geo-referenced data; application of spatially variable rates of crop inputs.

The correct field management according to the principles of precision agriculture also requires the real-time geo-referenced measurement of crop and soil parameters, by means of specific sensors.

The implementation of intensive agriculture has led to use heavy machines having high working capacity and requiring high draft force. The traffic of agricultural machines, having a higher weight, causes: higher pressure on the soil and, therefore, soil compaction; reduction of porosity to less than 10%; creation of obstacles to air, water and nutrient movements, as well as root penetration; increase of soil density, even at 0-0.50 m depth, where the elongated pores distributed along lines parallel to the field plane are prevalent on those distributed normally to the field plane itself, that are relevant for water drainage.

Repeated passes of agricultural machines on a field can cause pans, having a low permeability to water and nutrients and a high resistance to root penetration.

Plant root diameter, elongation and morphology are negatively affected by soil compaction, that, therefore, can reduce crop yield.

Besides soil compaction, other negative effects are caused by the excessive pressure of agricultural machines on soils: destruction of soil structure; change of the balances of soil water, air and nutrients; destruction of vegetation cover and plants roots.

In order to prevent the negative effects caused by the traffic of agricultural machines, it is needed to minimise the pressure on the contact area between their propulsion organs and the soil: tracked tractors or wheeled ones equipped with low-pressure or twinned or triple tyres can be used.

Another solution to the problem of soil compaction is Controlled Traffic Farming (CTF), where agricultural machines must follow the same trajectories.

A further solution to the above problem is the geo-referenced measurement of soil cone penetrometer resistance, in order to produce soil compaction maps and, therefore, plan spatially variable depth soil tillage.

Soil degradation includes not only soil compaction but also hydrogeological instability, comprising natural events accelerated and, therefore, converted into natural disasters by human activities: surface erosion; landslides; floods; water stagnation. Surface erosion, caused by water, is the transport of soil mass from the top to the bottom of a slope. The first step of this type of hydrogeological instability is soil erosion.

In order to prevent or minimise soil erosion, soil contour ploughing is needed but the mouldboard plough must let the soil slice rotate upslope, in order to compensate for soil erosion, moving it downslope.

Another option for preventing or minimising soil erosion can be the implementation of conservative soil tillage techniques (e.g. minimum tillage), by means of implements different from mouldboard plough (e.g. subsoiler, rotary tiller and chisel plough).

Therefore, the geo-referenced measurement and mapping of within-field spatial variability (i.e. both soil and crop parameters) is needed for evaluating soil degradation, including compaction and erosion, as well as the other negative effects of the excessive pressure of agricultural machines on soils.

We are pleased to invite you to submit your works to this Special Issue, which aims at examining soil degradation, that is compaction and erosion, as well as the other negative effects of the excessive pressure of agricultural machines on soils, from different points of view, including those outside our research expertise.

We welcome contributions reporting novel results at a regional or local scale, as long as they consider the usefulness and interest for an international audience.

This Special Issue aims at collecting outstanding results on the geo-referenced measurement and mapping of within-field spatial variability (i.e. both soil and crop parameters), needed for evaluating soil degradation, including compaction and erosion, as well as the other negative effects of the excessive pressure of agricultural machines on soils.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Proximal real-time sensors for the geo-referenced measurement of within-field soil and crop parameters.
  2. Techniques for the remote sensing of within-field soil and crop parameters from unmanned aerial vehicles (UAVs), aircrafts and satellites.
  3. Methods for the 2D and 3D mapping of soil and crop parameters.
  4. Methods for evaluating soil compaction and erosion, as well as the other negative effects of human activities on soils.
  5. Proposals of solutions for minimising soil compaction and erosion, as well as the other negative effects of human activities on soils.

We look forward to receiving your contributions.

Prof. Dr. Antonio Comparetti
Prof. Dr. Abdul M. Mouazen
Dr. Santo Orlando
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soil and crop parameters
  • soil compaction
  • soil erosion
  • hydrogeological instability
  • human activities
  • best practices
  • agricultural mechanization
  • conservative soil tillage
  • precision agriculture
  • proximal and remote sensing

Published Papers (7 papers)

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Research

17 pages, 2993 KiB  
Article
Digital Mapping of Soil Organic Carbon Using Machine Learning Algorithms in the Upper Brahmaputra Valley of Northeastern India
by Amit Kumar, Pravash Chandra Moharana, Roomesh Kumar Jena, Sandeep Kumar Malyan, Gulshan Kumar Sharma, Ram Kishor Fagodiya, Aftab Ahmad Shabnam, Dharmendra Kumar Jigyasu, Kasthala Mary Vijaya Kumari and Subramanian Gandhi Doss
Land 2023, 12(10), 1841; https://doi.org/10.3390/land12101841 - 27 Sep 2023
Viewed by 1170
Abstract
Soil Organic Carbon (SOC) is a crucial indicator of ecosystem health and soil quality. Machine learning (ML) models that predict soil quality based on environmental parameters are becoming more prevalent. However, studies have yet to examine how well each ML technique performs when [...] Read more.
Soil Organic Carbon (SOC) is a crucial indicator of ecosystem health and soil quality. Machine learning (ML) models that predict soil quality based on environmental parameters are becoming more prevalent. However, studies have yet to examine how well each ML technique performs when predicting and mapping SOC, particularly at high spatial resolutions. Model predictors include topographic variables generated from SRTM DEM; vegetation and soil indices derived from Landsat satellite images predict SOC for the Lakhimpur district of the upper Brahmaputra Valley of Assam, India. Four ML models, Random Forest (RF), Cubist, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), were utilized to predict SOC for the top layer of soil (0–15 cm) at a 30 m resolution. The results showed that the descriptive statistics of the calibration and validation sets were close enough to the total set data and calibration dataset, representing the complete samples. The measured SOC content varied from 0.10 to 1.85%. The RF model’s performance was optimal in the calibration and validation sets (R2c = 0.966, RMSEc = 0.159%, R2v = 0.418, RMSEv = 0.377%). The SVM model, on the other hand, had the next-lowest accuracy, explaining 47% of the variation (R2c = 0.471, RMSEc = 0.293, R2v = 0.081, RMSEv = 0.452), while the Cubist model fared the poorest in both the calibration and validation sets. The most-critical variable in the RF model for predicting SOC was elevation, followed by MAT and MRVBF. The essential variables for the Cubist model were slope, TRI, MAT, and Band4. AP and LS were the most-essential factors in the XGBoost and SVM models. The predicted OC ranged from 0.44 to 1.35%, 0.031 to 1.61%, 0.035 to 1.71%, and 0.47 to 1.36% in the RF, Cubist, XGBoost, and SVM models, respectively. Compared with different ML models, RF was optimal (high accuracy and low uncertainty) for predicting SOC in the investigated region. According to the present modeling results, SOC may be determined simply and accurately. In general, the high-resolution maps might be helpful for decision-makers, stakeholders, and applicants in sericultural management practices towards precision sericulture. Full article
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15 pages, 2581 KiB  
Article
Determining Attribute—Response Relationships of Soils under Different Land Uses: A Case Study
by Cristian Vasilică Secu, Dan Cristian Lesenciuc, Ionuț Vasiliniuc, Gabi Zaldea, Ancuța Nechita and Lulu Cătălin Alexandru
Land 2023, 12(9), 1750; https://doi.org/10.3390/land12091750 - 8 Sep 2023
Viewed by 578
Abstract
Soil researchers are interested in a gaining better understanding of the soil system state by analyzing its properties and their dynamics in time as well as in relation to land use change. Tilled, abandoned, and forest soils were assessed regarding attribute–response relationships for [...] Read more.
Soil researchers are interested in a gaining better understanding of the soil system state by analyzing its properties and their dynamics in time as well as in relation to land use change. Tilled, abandoned, and forest soils were assessed regarding attribute–response relationships for the bulk density (BD), total porosity (TP), volumetric moisture (θv), and penetration resistance (PR) with the use of the interquartile ratio (IRI) integrated into a resilience formula and Shannon entropy indices. The IRI results differentiated soil properties according to agrotechnics (wheel track vs. between wheels) and the state of the system (tilled vs. abandoned vineyard). Entropy (En) indicated a high level of uncertainty for PR. The linear regression applied to the pairs of BD-TP, TP-θv, and PR-θv showed better results for the IRI weight (IRIweight) compared to the entropy weight (Enweight) for the soil between the wheels. The soil of the abandoned vineyard showed a faster tendency toward resilience that was more pronounced in the tilled wheel tracks than in the area between the wheels. The IRI can thus be an alternative to entropy in the evaluation of the response of some soil properties according to their use. When integrated into a resilience formula, the IRI can estimate the dynamics of soil properties for abandoned land compared to reference soil. Full article
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14 pages, 2876 KiB  
Article
Spatial Distribution of the Fertility Parameters in Sericulture Soil: A Case Study of Dimapur District, Nagaland
by Dharmendra Kumar Jigyasu, Amit Kumar, Aftab Ahmad Shabnam, Gulshan Kumar Sharma, Roomesh Kumar Jena, Bachaspati Das, Vinodakumar Somashing Naik, Siddique Ali Ahmed and Kasthala Mary Vijaya Kumari
Land 2023, 12(5), 956; https://doi.org/10.3390/land12050956 - 25 Apr 2023
Cited by 1 | Viewed by 2082
Abstract
Dimapur (Nagaland, India) is dominated by undifferentiated hillside slopes and alluvial plains. The Muga and Eri silk industries are important cultural and economic activities for the inhabitants of Dimapur. Profitable silk production requires adequate quality and quantity of healthy leaves and is highly [...] Read more.
Dimapur (Nagaland, India) is dominated by undifferentiated hillside slopes and alluvial plains. The Muga and Eri silk industries are important cultural and economic activities for the inhabitants of Dimapur. Profitable silk production requires adequate quality and quantity of healthy leaves and is highly dependent on the soil fertility of the region. Keeping this in view, the present study was carried out as a first attempt to prepare a geographic information system (GIS) map for Muga and Eri soils in Dimapur, Nagaland. A total 65 surface (0–15 cm) soil samples from Muga farms and 79 surface soil samples from Eri farms were collected and analysed for soil pH, organic carbon content and availability of macro- and micronutrients. Soils of both Muga and Eri farms were found to be extremely (<3.05) to moderately (5.09–6.84) acidic. Soils of Muga and Eri farms were found to have low to high organic carbon content (from 0.24 to 1.98%), low to high available nitrogen content (179.8–612.5 kg ha−1) and medium available phosphorus content (2.68–154.6 kg ha−1). The sulphur availability index was 0.26 and 11.81 for Muga and Eri host plant farms, respectively. The multi-macronutrient map revealed that 46.95% of the district’s total geographical area (TGA) showed deficiencies in one or more macronutrients (high priority zone). Thus, these regions need urgent attention in terms of nutrient management decisions in order to reduce the declining trend of soil fertility and achieve sustainable sericulture production. Full article
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19 pages, 11033 KiB  
Article
Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale
by Mohamed A. E. AbdelRahman, Mohamed R. Metwalli, Maofang Gao, Francesco Toscano, Costanza Fiorentino, Antonio Scopa and Paola D’Antonio
Land 2023, 12(4), 855; https://doi.org/10.3390/land12040855 - 10 Apr 2023
Cited by 4 | Viewed by 2346
Abstract
In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses [...] Read more.
In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation’s indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis. Full article
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8 pages, 2644 KiB  
Communication
A New Approach to Monitor Soil Microbial Driven C/N Ratio in Temperate Evergreen Coniferous Forests Managed via Sentinel-2 Spectral Imagery
by Lizardo Reyna, Jarosław Lasota, Lizardo Reyna-Bowen, Lenin Vera-Montenegro, Emil Cristhian Vega-Ponce, Maria Luisa Izaguirre-Mayoral and Ewa Błońska
Land 2023, 12(2), 284; https://doi.org/10.3390/land12020284 - 19 Jan 2023
Viewed by 1633
Abstract
Forests are key ecosystems for climate change mitigation, playing a pivotal role in C and N land sequestering and storage. However, the sustainable management of forests is challenging for foresters who need continuous and reliable information on the status of soil conditions. Yet, [...] Read more.
Forests are key ecosystems for climate change mitigation, playing a pivotal role in C and N land sequestering and storage. However, the sustainable management of forests is challenging for foresters who need continuous and reliable information on the status of soil conditions. Yet, the monitoring of soils in temperate evergreen forests, via satellite data, is jeopardized by the year round prevailing heavily dense canopy. In this study, the Sentinel-2 spectral imagery derived normalized difference vegetation index (NDVI), proved to be a reliable tool to determine the C/N ratio in two managed pine-dominated forests, in southern Poland. Results showed a strong negative correlation between NDVI values and the on-site C/N ratios measured at the upper soil horizons in 100 and 99 randomly distributed sampling points across the Kup (r2 = −0.8019) and Koniecpol (r2 = −0.7281) forests. This indicates the feasibility of using the NDVI to predict the microbial driven soil C/N ratio in evergreen forests, and to foresee alterations in the vegetation patterns elicited by microbial hindering soil abiotic or biotic factors. Spatial/temporal variations in C/N ratio also provide information on C and N soil dynamics and land ecosystem function in a changing climate. Full article
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16 pages, 5973 KiB  
Article
Reduction in Water Erosion and Soil Loss on Steep Land Managed by Controlled Traffic Farming
by Miroslav Macák, Jana Galambošová, František Kumhála, Marek Barát, Milan Kroulík, Karol Šinka, Petr Novák, Vladimír Rataj and Paula A. Misiewicz
Land 2023, 12(1), 239; https://doi.org/10.3390/land12010239 - 12 Jan 2023
Cited by 2 | Viewed by 1516
Abstract
Controlled traffic farming (CTF) is used to confine soil compaction to the least possible area of the field, thereby achieving economic and environmental benefits. In the context of climate change, soil erosion is one of the most discussed topics, and there is a [...] Read more.
Controlled traffic farming (CTF) is used to confine soil compaction to the least possible area of the field, thereby achieving economic and environmental benefits. In the context of climate change, soil erosion is one of the most discussed topics, and there is a research gap in understanding the effects of CTF on soil erosion in Central Europe. The aim of this work was to show the potential of CTF to reduce water erosion, in terms of water runoff and soil loss on steep land. A 16 ha experimental field with a CTF technology implemented since 2009 at the Slovak University of Agriculture was used in this research. Three traffic intensity locations were selected and watered using a rainfall simulator. The results showed that the soil which had not been wheeled for 12 years had the lowest water runoff: its intensity after 20 min of simulated rain was 10 times lower compared to the multiple traffic treatment. The soil loss, expressed as the total soil sediments collected after 35 min, in the no traffic area was lower by 70%, compared to the soil with one-pass treatment and only 25% of the loss in the multiple traffic areas. These results show that CTF can significantly reduce soil loss through water runoff on steep land. Full article
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19 pages, 6500 KiB  
Article
Study on the Scale Effect of Spatial Variation in Soil Salinity Based on Geostatistics: A Case Study of Yingdaya River Irrigation Area
by Li Lu, Sheng Li, Rong Wu and Deyou Shen
Land 2022, 11(10), 1697; https://doi.org/10.3390/land11101697 - 30 Sep 2022
Cited by 4 | Viewed by 1569
Abstract
Soil salinization seriously restricts the development of agricultural economies in arid and semi-arid areas. Mastering the spatial variability characteristics of multi-scale soil salt in irrigated areas is of great significance for the improvement and utilization of saline soil and agricultural production. The middle [...] Read more.
Soil salinization seriously restricts the development of agricultural economies in arid and semi-arid areas. Mastering the spatial variability characteristics of multi-scale soil salt in irrigated areas is of great significance for the improvement and utilization of saline soil and agricultural production. The middle and lower reaches of the Yingdaya River were selected as the study area, and the irrigation area was divided into three scales: the L scale (irrigation area), the M scale (township level) and the S scale (village level). A total of 131 data sets were obtained through field investigations and sampling, and the spatial variability characteristics and scale effects of the soil salt in multi-scale irrigated areas were analyzed using classical statistics, geostatistics and nested model methods. The results showed that the average soil salinities at the L, M and S scales were 1.664%, 0.263% and 0.217%, respectively, and the coefficients of variation were 2.564, 1.312 and 0.866, respectively. The soil salinities at different scales exhibited moderate spatial correlation and anisotropic characteristics, through which, the maximum variation directions for L and M were 113° and 139°, respectively, and the maximum variation direction of the S scale was 86°. The spatial distribution of the soil salinity is affected by the scale effect, but the accuracy of spatial estimations can be effectively improved by using a multi-scale nested model for interpolation. The high-value areas of soil salt in the irrigation areas were distributed in the southeastern regions of the study area, and weakened in small areas around the high-value areas. The influence of each influencing factor on the soil salinization at different scales also differed. Except for the slope, the correlations between other influencing factors and the soil salt content gradually decreased with decreases in the scale. This study provides a concise summary of the spatial variation analysis of soil characteristic variables, and also provides a scientific basis for the formulation and implementation of salinization control programs. Full article
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