scholarly journals Soil Quality and Evaluation of Spatial Variability in a Semi-Arid Ecosystem in a Region of the Southeastern Iberian Peninsula (Spain)

Land ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 5
Author(s):  
Fernando Santos-Francés ◽  
Antonio Martínez-Graña ◽  
Carmelo Ávila-Zarza ◽  
Marco Criado ◽  
Yolanda Sánchez-Sánchez
Keyword(s):  
Organic Carbon ◽  
Soil Properties ◽  
Soil Quality ◽  
Water Retention ◽  
Mediterranean Ecosystem ◽  
Arid Ecosystem ◽  
Scoring Functions ◽  
Data Set ◽  
Preliminary Estimation ◽  
Semi Arid

In the last two decades, as the importance of soil has been recognized as a key component of any ecosystem, there has been an increased global demand to establish criteria for determining soil quality and to develop quantitative indices that can be used to classify and compare that quality in different places. The preliminary estimation of the attributes involved in soil quality was made taking into account the opinion of the experts and our own experience in a semi-arid ecosystem. In this study, 16 soil properties have been selected as potential indicators of soil quality, in a region between Campo de Montiel and Sierra de Alcaraz (Spain): sand and clay percentage, pH, electrical conductivity (EC), soil organic carbon (OC), extractables bases of change (Na, K, Ca and Mg), cationic exchange capacity (CEC), carbonate calcium equivalent (CCE), bulk density (BD), water retention at 33 kPa field capacity and 1500 kPa permanent wither point (GWC33 kPa and GWC1500 kPa), coefficient of linear extensibility (COLE) and factor of soil erodibility (K). The main objective has been to develop an adequate index to characterize the quality of the soils in a semi-arid Mediterranean ecosystem. The preliminary estimation of the attributes involved in soil quality was made considering the opinion of the experts and our own experience in semi-arid ecosystems. Two indicator selection approaches have been used to develop the Soil Quality Index (SQI) (total data set -TDS- and minimum data set -MDS-), scoring functions (linear -L- and nonlinear -NL-) and methods (additive -A-, additive weighted -W- and Nemoro -N-. The quality indices have been calculated, considering the properties of the soil control section (between 0 and 100 cm depth), using 185 samples, belonging to horizons A, B and C of 51 soil profiles. The results have shown that the election of the soil properties, both of the topsoil and subsoil, is an important help in establishing a good relationship between quality, soil functions and agricultural management. The Kriging method has been used to determinate the spatial distribution of the soil quality grades. The indices that best reflect the state of soil quality are the TDS-L-W and TDS-L-A should go as sub-indices, as they are the most accurate indices and provide the most consistent results. These indices are especially indicated when carrying out detailed or semi-detailed studies. However, the MDS-L-W and MDS-L-A should go as sub-indices, which use only a limited number of indicators, are best for large-scale studies. The indicators with the greatest influence on soil quality for different land uses and those developed on different rocks, using linear scoring functions, are the following: (Clay), (GWC1500 kPa) and (Ca). These results can also be expressed as follows: the best soils in this region are deep soils, with a clay texture, with high water retention and a neutral or slightly basic pH. However, the indicators with the greatest influence on soil quality, using nonlinear scoring functions, are: (OC Stock), (Ca) and (CaCO3). In other words, the most important indicator is the organic carbon content, which is not logical in the case of a region in which the soils have an excessively low SOC content (0.86%).

scholarly journals Physical Soil Quality of Semi - Arid Savanna as Influenced by Acacia senegalensis in Desert Research Experimental Plot Yobe State University Damaturu, Northern Nigeria

2021 ◽  
pp. 7-17
Author(s):  
Hamza Haruna ◽  
Galal H.G. Hussein ◽  
Mohammed B
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Bulk Density ◽  
Soil Quality ◽  
Minimum Data Set ◽  
Organic Carbon Content ◽  
Data Set ◽  
Minimum Data ◽  
Lower Bulk Density ◽  
Semi Arid

Soil is a living and dynamic natural reservoir and source of plant nutrients that play numerous key roles in terrestrial ecosystems. This study investigated the impact of three adjacent land use systems (Acacia senegalensis plantation (ACP), pilostigma raticulatum plantation (PRP) and Ground nut field (GNF) on selected soil physical quality indicators in a Northern Nigeria semi- arid Savanna. Minimum data set for assessing soil quality (Prime quality agricultural land) in this study include bulk density, organic carbon content, total nitrogen, carbon stock, available phosphorus and pH values obtained from DRMCC research field. Mean values of the data set were arranged and scored to obtain totals among the minimum data set (MDS). Soil quality is considered a key element for evaluating the sustainability of land management practices. Data generated were analyzed using ANOVA and significant means were determined using Duncan multiple range test (DMRT). ACP had significantly higher organic carbon content (9.37 gkg-1) and lower bulk density (2.16 gkg-1) than pilostigma and GNF respectively. The lower bulk density (ρb) and high organic carbon in ACP might be due to high leaf shading by acacia while the lower bulk density in ground nut field aided by trampling induced compaction resulted in its high relative field capacity (RFC), permanent wilting point (PWP) and micro-p ore spaces (PMIC) tillage in ground nut field created loose soil in the plough layer (<20 cm) which turn out to its low bulk density (ρb). Acacia plantation contained highest total nitrogen value (1.23 gkg-1); perhaps resulting Acacia leaf litter is known to have a high decomposition rate. Pilostigma plantation contained (1.22 gkg-1) nitrogen, while the least nitrogen content was obtained under ground nut field. On scoring the land use types and depth against the minimum data set, the least total was that under acacia plantation, followed by pilostigma plantation then ground nut field. Therefore, soils under acacia plantation were ranked best quality (SQ1) for cultivation purposes at 0-10 cm, followed by pilostigma land use type that were ranked SQ2. Ground nut field soils were ranked least (SQ6) in quality for use in crop production at depth of 10-20 cm.

CROPPING SYSTEM EFFECTS ON SOIL QUALITY FOR THREE AGRO-ECOSYSTEMS IN INDIA

2013 ◽  
Vol 50 (3) ◽  
pp. 321-342 ◽  
Author(s):  
NISHANT K. SINHA ◽  
USHA KIRAN CHOPRA ◽  
ANIL KUMAR SINGH
Keyword(s):  
Soil Quality ◽  
Crop Production ◽  
Cropping Systems ◽  
Soil Layer ◽  
Cropping System ◽  
Scoring Functions ◽  
Soil Quality Index ◽  
Data Set ◽  
Non Linear ◽  
Semi Arid

SUMMARYSoil quality integrates the effects of soil physical, chemical and biological attributes. Some of them are dynamic in nature and behave differentially in various agro-ecosystems (AESs) and are quantified in terms of a soil quality index (SQI). An attempt has been made in this paper to develop an SQI based on a minimum data set (MDS), which could be used to evaluate the sustainability of the crop production in three varying AESs in India, namely sub-humid, semi-arid and arid. Thirteen indicators were utilized to develop the SQI from the properties measured from the surface soil layer (0–15 cm). Each indicator of the MDS was transformed into a dimensionless score based on scoring functions (linear and non-linear) and integrated into four SQIs. The weighted non-linear index (WNLI) was identified as the most sensitive for all the AESs and was recommended as an index for future assessments. Based on this index, the quantification of soil quality under several cropping systems was carried out for sub-humid, semi-arid and arid AESs and the most suitable cropping system was identified. WLNI was positively and significantly correlated (R2= 0.79,p< 0.01) with wheat equivalent yield for all the cropping systems. This clearly indicated that the index may be used satisfactorily for quantifying soil quality.

Volumetric soil quality modelling with machine learning in a diverse agricultural landscape in Andalusia, Spain

2020 ◽  
Author(s):  
Tobias Rentschler ◽  
Martin Bartelheim ◽  
Marta Díaz-Zorita Bonilla ◽  
Philipp Gries ◽  
Thomas Scholten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Properties ◽  
Soil Quality ◽  
Three Dimensional ◽  
Soil Mapping ◽  
Soil Profiles ◽  
Soil Functions ◽  
Depth Functions

&lt;p&gt;Soils and soil functions are recognized as a key resource for human well-being throughout time. In an agricultural and forestry perspective, soil functions contribute to food and timber production. Other soil functions are related to freshwater security and energy provisioning. In general, the capacity of a soil to function within specific boundaries is summarised as soil quality. Knowledge about the spatial distribution of soil quality is crucial for sustainable land use and the protection of soils and their functions. This spatial knowledge can be obtained with accurate and efficient machine-learning-based soil mapping approaches, which allow the estimation of the soil quality at distinct locations. However, the vertical distribution of soil properties is usually neglected when assessing soil quality at distinct locations. To overcome such limitations, the depth function of soil properties needs to be incorporated in the modelling. This is not only important to get a better estimation of the overall soil quality throughout the rooting zone, but also to identify factors that limit plant growth, such as strong acidity or alkalinity, and the water holding capacity. Thus, the objective of this study was to model and map the soil quality indicators pH, soil organic carbon, sand, silt and clay content as a volumetric entity. The study area is located in southern Spain in the Province of Seville at the Guadalquivir river. It covers 1,000&amp;#160;km&lt;sup&gt;2&lt;/sup&gt; of farmland, citrus and olive plantations, pastures and wood pasture (Dehesa) in the Sierra Morena mountain range, at the Guadalquivir flood plain and tertiary terraces. Soil samples were taken at 130 soil profiles in five depths (or less at shallow soils). The profiles were randomly stratified depending on slope position and land cover. We used a subset of 99 samples from representative soil profiles to assess the overall 513 samples with FT-IR spectroscopy and machine learning methods to model equal-area spline, polynomial and exponential depth functions for each soil quality indicator at each of the 130 profiles. These depth functions were modelled and predicted spatially with a comprehensive set of environmental covariates from remote sensing data, multi-scale terrain analysis and geological maps. By solving the spatially predicted depth functions with a vertical resolution of 5&amp;#160;cm, we obtained a volumetric, i.e. three-dimensional, map of pH, soil organic carbon content and soil texture. Preliminary results are promising for volumetric soil mapping and the estimation of soil quality and limiting factors in three-dimensional space.&lt;/p&gt;

scholarly journals Variation of soil organic carbon, stable isotopes, and soil quality indicators across an erosion–deposition catena in a historical Spanish olive orchard

SOIL ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 179-194
Author(s):  
José A. Gómez ◽  
Gema Guzmán ◽  
Arsenio Toloza ◽  
Christian Resch ◽  
Roberto García-Ruíz ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Properties ◽  
Soil Quality ◽  
Available Phosphorus ◽  
Δ13c And Δ15n ◽  
Olive Orchard ◽  
Erosion Area ◽  
Deposition Processes ◽  
Δ15n And Δ13c

Abstract. This study compares the distribution of bulk soil organic carbon (SOC), its fractions (unprotected and physically, chemically, and biochemically protected), available phosphorus (Pavail), organic nitrogen (Norg), and stable isotope (δ15N and δ13C) signatures at four soil depths (0–10, 10–20, 20–30, and 30–40 cm) between a nearby open forest reference area and a historical olive orchard (established in 1856) located in southern Spain. In addition, these soil properties, as well as water stable aggregates (Wsagg), were contrasted at eroding and deposition areas within the olive orchard, previously determined using 137Cs. SOC stock in the olive orchard (about 40 t C ha−1) was only 25 % of that in the forested area (about 160 t C ha−1) in the upper 40 cm of soil, and the reduction was especially severe in the unprotected organic carbon. The reference and the orchard soils also showed significant differences in the δ13C and δ15N signals, likely due to the different vegetation composition and N dynamics in both areas. Soil properties along a catena, from erosion to deposition areas within the old olive orchard, showed large differences. Soil Corg, Pavail and Norg content, and δ15N at the deposition were significantly higher than those of the erosion area, defining two distinct areas with a different soil quality status. These overall results indicate that the proper understanding of Corg content and soil quality in olive orchards requires the consideration of the spatial variability induced by erosion–deposition processes for a convenient appraisal at the farm scale.

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