The differences in land use can potentially affect soil quality. In this respect, this study sought to assess the long-term effect of horticulture and arable lands on the soil quality of the clayey Arıklı soils (Entic Chromoxerert). Physical, chemical, biological and fertility indicators of the soil were analyzed to determine the soil quality index (SQI) using the Fuzzy Analytical Hierarchy Process (FAHP) and Geographical Information System (GIS) for a 225 ha area of the Arıklı soil series. The physical quality received the highest weight in the study. The highest weights in physical, chemical, biological and fertility quality were obtained in AWC, pH, OC and P indicators, respectively. SQI in arable lands ranged from 38.60 to 68.91%, with a mean of 51.77%. Compared to arable, SQI in horticulture varied within a narrower range and had a higher mean SQI (59.87%). In this study, 74.2% of the area had a low SQI and 25.8% had medium SQI. Conventional soil tillage and burning of stubble residues in the arable lands had a statistically negative effect on SQI. Compared to arable, in horticulture BD, OC, AWC, MBC, BGEA and K had better scores thus improving SQI. The results concluded that conventional soil tillage practice should be abandoned and reduced or no-till practices should be adopted.
Research Article
Effect of long-term different land use on soil quality in an Entic Chromoxerert
https://doi.org/10.21203/rs.3.rs-2230202/v1
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Soil quality
FAHP
Horticulture
Arable
Sustainability in agricultural production greatly depends on the protection of the soil. Although soilless farming practices exist as an alternative in, the most basic resource in agricultural production is soil. Conventional agricultural practices such as intensive tillage, excessive fertilization and irrigation and pesticide use in order to get higher yields from a unit area cause the irreversible decline of soil quality and consequently the inability of soils to fulfill their functions due to the influence of man and nature (Karlen et al. 1997). Monitoring soil quality will aid producers/farmers in selecting the best agricultural practices that will ensure the sustainability of agricultural production.
Soil quality is the product of anthropogenic interactions on inherent and dynamic soil properties (Karlen et al. 2013). The greatest effect on soil quality in lands with similar inherent properties is the changes in dynamic soil properties caused by different anthropogenic activities such as land use and management practices (Masto et al. 2008; Li et al. 2013; Çelik et al. 2021). Studies shows that land use or management practices change soil quality (Delelegn et al. 2017; Raiesi 2017; da Silva et al. 2022) by affecting dynamic soil properties such as bulk density, aggregate stability, macro and micro nutrients, organic matter and microbial activity (Ayoubi et al. 2014; Obade and Lal 2014; Yu et al. 2018). Therefore, monitoring soil quality is a key component in preventing long-term degradation caused by land use and management practices.
When comparing two areas with low and high agricultural potential, the area with relatively high agricultural potential is more valuable. Monitoring and evaluating soil quality is even more important in lands with high agricultural potential. Çukurova, located within the provincial borders of Adana and Mersin, is one of the lands with the highest agricultural potential in Turkey (Celik et al. 2011). Various agricultural practices have been carried out in the plains over the years. These practices are sometimes in the form of annual crops and sometimes in the form of perennial planting. In recent years, different crops are been grown on the plain, where cotton cultivation was common before. In this change, the shift to horticultural practice has been the subject of attention with tangerines, lemons and oranges. According to the 2002-2021 agricultural data, there was a decline in arable land, but an increase in horticultural crops. The arable land area, which was 4.9 million acres in 2002, decreased to 3.9 million acres in 2021, whereas, fruit production increased from 420,500 acres to 900,310 acres within same period. The state of these three crops in the country agricultural production is 35.89%, 26.18% and 18.31%, respectively (Anonymous 2022).
These changes in land use in the plain, where intensive agricultural activities have been dominant for many years, is considered unsustainable and inefficient in the literature in terms of soil quality. The effect change in land use in production areas over the years on soil quality is the main aim of this study. In this study, the effect of different land use, including horticulture and arable land, on soil quality in the Arıklı series (Entic Chromoxerert) was investigated.
Study area
This study was carried out in a selected area of 225 ha in the Arıklı soil series located in the Çukurova region of southern Turkey, which is one of the most fertile plains used for intensive agricultural production (Celik et al. 2011) (Fig. 1). The study coordination is between 4,081,768 and 4,083,268 N and 709,876–711,376 E (WGS-84, UTM-m, 36 Zone; 9-12 m above sea level).
Fig. 1
Arıklı soil series is the most common series with 24% (56,083 ha) in the region. The soil has a clay texture and slope of less than 1, formed on the alluvial deposits of old river terraces (Dinç et al. 1995), and classified as Entic Chromoxerert (Soil Survey Staff 1999). The prevailing climate of the study area is Mediterranean with the summers hot and dry and the winters rainy and mild. According to the long term (1929–2019) meteorological data (TSMS 2020), mean annual temperature, total annual evapotranspiration and total annual rainfall in the study area are 19.2 °C, 1557 mm and 639 mm, respectively. About 75% of the annual rainfall falls in the winter and spring months. Soil temperature and moisture regimes of the study area are thermic and xeric, respectively.
In the study area, agricultural production based on arable crops has been carried out for long period of time (more than 50 years). Crop production in arable lands at the time of soil sampling was majorly corn (Zea mays L.), soybean (Glycine max. L.), cotton (Gossypium hirsutum L.), melon (Cucumis melo L.) and watermelon (Citrullus lanantus). Soils in the arable lands are conventionally tilled with a moldboard plow (Celik et al. 2011). In addition, residues are removed from the land by burning in most places.
The proliferation of citrus species suitable for cultivation in the plain in recent years, increased the area that were converted to horticulture lands, namely to mandarin (Citrus reticulata L.), grapefruit (Citrus paradisi L.), lemon (Citrus lemon L.) and pomegranate (Punica granatum L.) that have been grown for 17 years on average in the plain with the oldest since 1995 and the youngest since 2015.
Soil sampling and analyses
The study was carried out on a land section of 1.5 km x 1.5 km width within the Arıklı soil series (Fig. 1). This section was divided into square grids of 250 m x 250 m for soil sampling. Soil samples were collected at 49 main points, approximately at the corner point of each grid. In addition, intermediate sampling lines as transect were placed between the corner point of 6 grids in order to more accurately predict changes in soil properties at distances shorter than 250 meters. On these lines, a total of 36 soil samples were collected at 25 m, 75 m and 150 m distances in the south-north and east-west directions, and 6 samples were collected from these points at a distance of 175 m in the north-east, north-west, south-east and south-west directions. In total, 42 intermediate sampling points along a transect added up to 91 soil samples. Soil samples were collected at a depth of 0-20 cm, which is the preferred depth in many studies where the effects of different land use were studied (Wang et al. 2012; Wu et al. 2019). In the study, disturbed and undisturbed soil samples were collected at each point in September 2018. All samples were carefully transported to the laboratory. After the removal of roots and coarse particles, soil samples were prepared for physical, chemical, biological and fertility analysis. All analyses were performed with three soil replicates. The list of soil properties and analytical protocols selected are presented in Table 1.
Table 1
Indicator selection and SQI
SQI is generally determined with four steps: (i) selection of indicators, (ii) scoring of selected indicators, (iii) determination of indicator weights using FAHP, and (iv) integrating the weighted indicator scores into a comparative SQI using Weighted Linear Combination (Gozukara et al. 2022). In this study, physical, chemical, biological and fertility indicators were carefully selected by soil experts. While deciding on the indicators with the opinion of experts, based on the recommendation of the experts, climate, soil properties and land use were considered.
AS, BD, AWC and WFPS used in this study are common physical properties used in soil quality related studies (Cherubin et al. 2016; Budak et al. 2018; Dengiz 2020; Çelik et al. 2021). Blanco Canqui and Ruis (2018) indicated that changes in soil physical properties affect all ecosystem services important to life that soil provides such as food, feed, fiber and fuel production, etc. Therefore, soil physical properties were the indicators considered in assessment of soil quality.
Soil chemical characteristics such as soil pH, EC, SAR, CaCO3, nutrients were preferred by researchers in many studies due to their effect on nutrient availability, root growth and fertility (Karaca et al. 2021; Mahajan et al. 2021; Gozukara et al. 2022; Kılıc et al. 2022). Intensive use of chemical fertilizers, especially nitrogen fertilizers, for agricultural production in the study area (İbrikçi et al. 2015) highlights soil pH as the core soil properties responsible for nutrient availabilities in the area (Zurovec et al. 2021). As excessive and long term chemical fertilization may cause salinity problems in the soil, EC was taken into account as one of the parameters to be monitored. SAR, an important determinant of sodium problem, is also one of the indicators that should be considered in study areas where furrow irrigation methods are used.
OC is an important property affecting soil physical, chemical, biological and fertility properties and processes associated with them. OC improves soil structure (Ozlu and Kumar 2018), enhances microbial activity (Koçak and Cenkseven 2021), soil functions and soil quality (Çelik et al. 2021), and increases crop productivity (Lal 2004) and thus is preferred in many studies as an important indicator of SQI (Raiesi and Kabiri 2016; Liu et al. 2018; Acir et al. 2022). In addition, MBC, BGEA and qCO2, which are significantly related to organic carbon (Bastida et al. 2008), were preferred in soil quality studies as they are properties that respond quickly to change (Bhaduri et al. 2017).
After selection of indicators, standard scoring functions were used to convert them to unitless scores in the second step (Table 2). In the literature, there are three scoring functions, “more is better”, “less is better” and “mid-point is optimum”, according to where the best soil functionality is considered as low, high or medium values or ranges (Liebig et al. 2001). In this study, AS, AWC, OC, MBC, BGEA, K, Fe, Cu, Mn and Zn due to their roles in soil fertility were scored with “more is better” function while BD, EC, SAR, CaCO3 and qCO2 due to their indicators of compaction, salinity and poor quality were scored with “less is better” function. When scoring pH, P and WFPS, “mid-point optimum” function is preferred (Andrews et al. 2004; Wienhold et al. 2009). In the scoring of pH, the most desirable limits for plant growth are between 6.0 and 7.0 (Marschner 2011). In this study, as all pH values are above 7.0, “less is better” function was used for soil pH. Bai et al. (2013) reported that the critical Olsen-P levels for optimum crop yield ranged from 10.9 mg kg-1 to 21.4 mg kg-1. Therefore, P values within the range were scored as 1. “Less is better” function was used for P values above the range and “more is better” function was used for P values below the range. Linn and Doran (1984) stated that WFPS is around 60% for optimum microbial activity. In this study, this value was accepted as the critical value. “Less is better” function was used for WFPS > 60% and “more is better” function was used for WFPS < 60%.
Table 2
Fig. 2
Table 3
In the third step, all indicators were grouped into four sub-criteria: physical, chemical, biological and fertility (Fig. 2). After that, the scored indicators in the groups and groups in the SQI were weighted with FAHP. AHP is one of the multi-purpose decision making methods which considers the opinion of a group or individual as a priority evaluating qualitative and quantitative variables together (Saaty 1980). AHP has been deployed in solving many real life decision making problems (Dağdeviren 2007). However, AHP does not necessarily fully reflect the complexity of the non-numeric nature of the pairwise comparisons due to the single numeric values taken from the 1–9 scale (Pedrycz and Song 2014). AHP is also criticized for its inadequacy to handle situations of uncertainty and indecision (Deng 1999). In order to enhance the effectiveness of AHP in decision making process, the method was integrated with fuzzy logic and FAHP concept emerged. Van Laarhoven and Pedrycz (1983) proposed the first triangular method adopted in FAHP. Chang (1996) further developed it for pairwise comparison of triangular fuzzy numbers (Table 3) and has been used in many studies in recent years (Turan et al. 2020; Kılıc et al. 2022; Kaya et al. 2022). To combine fuzzy logic and AHP, different methods have been proposed in the literature (Buckley 1985; Chang 1996; Deng 1999). In this study, the extent analysis method developed by Chang (1996) was used to integrate both methods. The main component of the equation used in FAHP is given in Eq. (1).
where l ≤ m ≤ u, l and u are the lower and upper values of M respectively, and m is taken as the modal value. l, m, and u are the the triangular fuzzy number. M is the set of the defined elements. if all the triangular fuzzy numbers are equal (l = m = u) it is discounted as a fuzzy number. After determining the relative importance levels of the indicators, the selected indicators were combined into an overall SQI using Eq. (2).
where SQI is the soil quality index, W is the relative importance of the indicator with FAHP and S is the indicator score.
Descriptive statistics and spatial variability of SQI
Descriptive statistics were conducted using the SPSS 21.0 statistical software (SPSS, Inc., Chicago, IL). Spatial variability of SQI was determined using geostatistical techniques. Modeling of experimental semivariogram was performed with GS+ (version 7) (Gamma Design Software). The best fit model for SQI was determined with RSS (Residual Sum of Squares) and r2 values of the semivariogram model. Models with RSS values close to zero and r2 values close to 1.0 were selected as the best fitted model (Yang et al. 2011). The most appropriate parameters (range, sill, nugget, lag number) obtained by semi-variogram models were used to obtain the spatial distribution map of SQI. The kriging map of SQI was created using the geostatistical extension of ArcGIS 10.2. SQI was mapped considering very low (<40%), low (40-55%), medium (55-70%), high (70-85%) and very high (>85%) grades specified by Gugino et al. (2009).
Soil physical, chemical, biological and fertility properties
The physical, chemical, biological and fertility properties of soil assessed in this present study showed variability as result of the dynamic interactions of different land use and human activities such as tillage, fertilization and irrigation, under long-term intensive agricultural production on Arıklı soil series. Descriptive statistics of soil properties used in SQI calculation are given in Table 4. AS in general ranged from 25.54 to 76.22%. BD varied between 1.18 and 1.46 g cm-3, with an average of 1.28 g cm-3 in arable land. However, compared to arable, BD values in horticulture decreased and it ranged from 1.05 to 1.26 g cm-3, with an average of 1.18 g cm-3. The Mean AWC was 10.94% and 12.10% in arable and horticulture lands, respectively. These values are close to the 12% AWC value stated by Hillel (1980) for clay soils. WFPS varied between 0.18 and 0.67 in arable land, 0.26 and 0.84 in horticulture land. The different plants grown in the study area and their different water requirement necessitate their irrigation at different times and with amounts causing wide variation in WFPS.
Table 4
All the soil was alkaline and very calcareous with the range of 7.48–8.16 and 10.02-20.68%, respectively. Soil alkaline character and high CaCO3 content may adversely impact the plant nutrient availability. However, P and K were adequate due to continuous use of phosphorus and potassium fertilizers within the study area (İbrikçi et al. 2015). EC and SAR ranged from 0.41 to 2.51 dS m-1 and from 0.04 to 0.48, which indicate no problem with regard to salinity and sodium.
The soils of the study area under long-term intensive agricultural production had low OC content. OC values in arable land ranged from 0.70 to 1.48%, with a mean of 1.09%. Compared to arable land, OC was higher in horticulture land. OC in horticulture land ranged from 0.63 to 1.68%, with a mean of 1.20%. Especially arable lands are conventionally tilled soil with moldboard plow. In addition, some farmers burn the stubble residues which are potential sources of organic matter (Celik et al. 2011). Burning of crop residues and conventional soil tillage resulted in reduction of organic carbon and other associated biological indicators due to oxidation of organic matter with increasing temperature and aeration. Contrary to the situation in arable land, farmers into horticultural agriculture engage in agricultural production with the support of agricultural engineers who have a high level of knowledge about the relevant crop and soil, advising them on how to engage in long-term sustainable agricultural production. In addition, practices such as conventional tillage and burning of prunings, which reduce the soil organic matter, are avoided in horticulture. Therefore, OC content of horticulture soil increases compared to arable land.
EC, SAR, P, Zn, MBC and BGEA in the arable land and WFPS, EC, SAR, CaCO3, P, K and Zn were not normally distributed. However, all the remaining indicators had normal distribution. In order to determine the variability of soil quality indicators within study area, the data set was tested with the coefficient of variation (CV). The variability of a soil indicator is considered low (CV < 15%), moderate (CV between 15 and 35%) or high (CV > 35%) (Wilding, 1985). In this study, BD, pH, CaCO3, Cu and OC in arable land and BD, pH, CaCO3, K and qCO2 in horticulture land had low CV, whereas EC, SAR, P and Zn in arable land and WFPS, EC, SAR, P, Cu, Zn and BGEA in horticulture land had high CV. In addition, AS, AWC, WFPS, K, Fe, Mn, MBC, BGEA and qCO2 in arable and AS, AWC, Fe, Mn, OC and MBC in horticulture land had moderate CV. Although soils of the study area had the same genetic characteristics, dynamic interactions resulting from different land use, crops grown, frequency and density of tillage and time and amount of irrigation and fertilization caused high variability in soil properties.
Soil quality assessment and spatial variability of SQI
Weight values of soil properties used for SQI were determined with FAHP. Firstly, soil quality indicators were hierarchically grouped into four main criteria (Fig. 2). Pairwise comparisons made by experts for this study were performed using the triangular fuzzy conversion scale in this hierarchical structure (Table 3). The pairwise comparison matrix created from the evaluation made by experts is given in Table 5. Afterwards, indicator weights were obtained according to the FAHP (Table 6).
Table 5
Table 6
Eighteen soil properties were divided into four groups in order to assess SQI (Fig. 2). Physical, biological and chemical indicators of soil can be defined as natural or inherent factors created from soil formation and environmental conditions (Karaca et al. 2021). Therefore, physical, chemical and biological quality had the highest weights with 0.382, 0.297 and 0.225, respectively (Table 6). Fertility, concerning the dynamic components such as macro and micro nutrients had the lowest weight value with 0.096. While physical, chemical and biological weights were found to be more than 10% fertility weight was determined below 10%. Among inherent quality components, soil physical indicators play a key role in soil-plant-water relations, soil conservation, productivity and sustainability (Gümüş et al. 2022). Dexter (2004) also stated that soil physical quality greatly affects biological and chemical properties and processes related with them in the soil. Therefore, soil physical quality received the highest weight for the evaluation of SQI.
The highest weight in physical quality was determined for AWC with a weight value of 0.471 (Table 6). This was followed by BD, AS and WFPS with weight values of 0.277, 0.134 and 0.118, respectively. All weights for physical quality were found to be more than 10%. AWC is one of the most important soil properties for plant growth, carbon management, nutrient cycling and controlling crop yield (Minasny and McBratney 2018). In addition, AWC becomes even more important considering changing climatic conditions anticipated to affect water management. The results of this study are consistent with the results of the studies conducted by Acir (2019), Dengiz (2020) and Karaca et al. (2021) where AWC was determined to have high weights.
Soil pH was determined to have the highest weight in chemical quality with a weight of 0.382 due to its influence in chemical reactions and the nutrients availability for plant growth (Schoenholtz et al. 2000). This was followed by CaCO3 and EC with weight values of 0.297 and 0.225, respectively (Table 6). SAR, which varies in a narrow range and shows no sodium problem in the study area, had the lowest weight in chemical quality with a weight value of 0.096.
The highest weight in biological quality was determined as OC with a weight of 0.332 (Table 6). This was followed by MBC, BGEA and qCO2 with a weight of 0.289, 0.231 and 0.148, respectively. OC affects physical, chemical and biological properties of soil (Dengiz 2020; Gozukara et al. 2022) and is also an important indicator for aggregation and erosion, storing and providing nutrients and overall improvement of soil fertility and soil quality (Karaca et al. 2021). In this respect, OC had a higher weight in soil biological quality compared to other relevant biological indicators. As for the fertility quality, P and K received the highest weights as macronutrients needed by the plant. Compared to macronutrients, micronutrients had lower weights. The lowest weights were determined as Cu and Mn in this study.
SQI was determined by weighing a total of 18 indicators under 4 groups with FAHP and mapped in GIS where the Exponential model was adjudged to be the best at predicting SQI (Table 7). The spatial dependency used to express the extent of spatial changes within the study area indicated strong, moderate or weak degrees of spatial dependence when it is less than 25%, between 25 and 75% and higher than 75%, respectively (Cambardella et al. 1994). The spatial dependence of SQI had a strong degree an indication of an ongoing similarity between samples even over long distances (Kılıc et al. 2022). In addition, the SQI of arable and horticultural land had a normal distribution and low CV in the study area (Table 4).
Table 7
Fig. 3
SQI in arable land ranged from 38.60 to 68.91%, with a mean of 51.77% (Table 4). Compared to arable, SQI in horticulture varied within a narrower range (from 50.21 to 68.18%) and had a higher mean SQI (59.87%). Although SQI in arable land had was very low (8.60%), it did not reflect on the map (Fig. 3). In the study, 74.2% (167 ha) of the area had a low SQI and 25.8% (58 ha) had medium SQI. Farmers in horticultural lands constantly seek expert advice in production, which has been effective in improving SQI. The BD value scored by “less is better” decreased and AWC, OC, MBC, BGEA and K values scored by “more is better” increased in horticulture compared to arable. As a result, these indicators received high scores and also contributed to higher SQI in horticulture by taking the highest weights in their quality groups. However, in arable lands, farmers conventionally till their land with moldboard plow and burn the stubble residues (Celik et al. 2011). This negatively affects the soil's ability to produce functions as stated in the literature is consistent with results from this study. In a long-term soil tillage experiment conducted in a similar soil series in the region, it was stated that the lowest SQI was obtained in treatments the soil with conventional tillage, while higher SQI were obtained in conservation tillage practices (Çelik et al. 2021). One study indicated that cultivated soils had a significantly higher degradation index than soils under other land use types due to higher bulk density and lower aggregate stability (Islam and Weil 2000). In another study, Raiesi (2017) stated that the conversion of native rangeland to cropland caused SQI to deteriorate. Therefore, burning of stubble and conventional tillage with plow should be avoided, which are the main causes of soil degradation in arable lands (reduction in soil quality). For sustainable and better crop yields it is thus necessary, to adopt conservation tillage methods such as reduced tillage, which improve soil quality (Çelik et al. 2021).
Monitoring soil quality is an important factor in determining the degree of deterioration that adversely affects agricultural production. The results showed that differences in land use on the same soil series significantly affect soil quality. Conventional soil tillage and burning of stubble residues in arable lands had a significant negative impact on SQI. On the contrary, SQI improved with the abandonment of these negative practices, especially in the lands that were converted to horticulture in the last 17 years. The main factor responsible for increase in SQI is that farmers for a long time have cultivated their land with technical support of experts in crop production. In this context, the indicators of BD, OC, AWC, MBC, BGEA and K, which made significant contributions to SQI, had better scores in horticultural lands compared to arable lands. Therefore, SQI results revealed that conventional tillage practices negatively affecting SQI in arable lands should be abandoned and that reduced or no-till practices according to soil conditions should be adopted.
Acknowledgements We would like to thank Prof. Dr. Selim Kapur for editing the English in an earlier version of this paper.
Funding This study was supported by the Scientific and Technological Council of Turkey (TUBITAK) (Grant number 118 O 773).
Competing Interests The authors have no relevant financial or non-financial interests to disclose.
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Table 1 Protocol measurements for parameters selected in the study.
|
Indicators |
Units |
Protocol |
Reference |
|
AS |
% |
Wet sieving |
Kemper and Rosenau (1986) |
|
BD |
g cm-3 |
Undisturbed condition |
Blake and Hartge (1986) |
|
AWC |
% |
Calculated from taking difference between field capacity and permanent wilting point |
Klute (1986) |
|
WFPS |
|
Ratio of volumetric soil water content to total soil porosity |
Linn and Doran (1984) |
|
pH |
|
Saturated soil paste |
Rhoades et al. (1999) |
|
EC |
dS m−1 |
Saturated soil paste |
Rhoades et al. (1999) |
|
SAR |
|
Saturated soil paste |
Soil Survey Staff (1996) |
|
CaCO3 |
% |
Scheiber's calcimeter |
Kacar (1994) |
|
OC |
% |
Walkley-Black |
Nelson and Sommers (1982) |
|
MBC |
mg kg-1 |
Substrate-induced respiration |
Anderson and Domsch (1978) |
|
BGEA |
mg PNP kg-1 h-1 |
Colorimetric estimation of the p-nitrophenol |
Tabatabai (1982) |
|
qCO2 |
% |
Calculated as the ratio of MBC to SOC |
Anderson and Domsch (1989) |
|
P |
mg kg-1 |
Sodium bicarbonate Olsen |
Olsen et al. (1954) |
|
K |
mg kg-1 |
Ammonium acetate extraction |
Thomas (1982) |
|
DTPA–Fe, Cu, Mn, Zn |
mg kg-1 |
DTPA extraction |
Lindsay and Norvell (1978) |
AS: Aggregate stability, BD: Bulk density, AWC: Available water content, WFPS: Water-filled pore space, EC: Electrical conductivity, SAR: Sodium adsorption ratio, CaCO3: Lime, OC: Organic carbon, MBC: Microbial biomass carbon, BGEA: Beta-glucosidase enzyme activity, qCO2: Metabolic quotient, P: Available Phosphorus, K: Exchangeable potassium, Fe: Available Iron, Cu: Available Copper, Mn: Available Manganese, Zn: Available Zinc
Table 2 Standard scoring functions and parameters for quantitative soil parameters.
Table 3 Triangular fuzzy conversion scale
|
Numerical value |
Linguistic scales for importance |
Triangular fuzzy numbers |
Triangular fuzzy reciprocal numbers |
|
1 |
Equally important |
(1, 1, 1) |
(1, 1, 1) |
|
2 |
Intermediate |
(1, 2, 3) |
(1/3, 1/2, 1/1) |
|
3 |
Weak |
(2, 3, 4) |
(1/4, 1/3, 1/2) |
|
4 |
Intermediate |
(3, 4, 5) |
(1/5, 1/4, 1/3) |
|
5 |
Strong |
(4, 5, 6) |
(1/6, 1/5, 1/4) |
|
6 |
Intermediate |
(5, 6, 7) |
(1/7, 1/6, 1/5) |
|
7 |
Very strong |
(6, 7, 8) |
(1/8, 1/7, 1/6) |
|
8 |
Intermediate |
(7, 8, 9) |
(1/9, 1/8, 1/7) |
|
9 |
Absolutely important |
(8, 9, 10) |
(1/10, 1/9, 1/8) |
Table 4 Descriptive statistics of physical, chemical and biological properties of soil
|
Min. |
Max. |
Mean |
SD |
CV (%) |
Skewness |
|
|
Arable (N=77) |
||||||
|
AS (%) |
27.82 |
72.11 |
54.46 |
9.96 |
18.28 |
-0.68 |
|
BD (g cm-3) |
1.18 |
1.46 |
1.28 |
0.06 |
4.90 |
0.89 |
|
AWC (Volumetric, %) |
2.29 |
18.58 |
10.94 |
3.59 |
32.85 |
-0.19 |
|
WFPS |
0.18 |
0.67 |
0.37 |
0.11 |
28.28 |
0.30 |
|
pH |
7.48 |
8.16 |
7.89 |
0.10 |
1.31 |
-0.93 |
|
EC (dS m-1) |
0.41 |
2.51 |
0.71 |
0.30 |
42.82 |
3.91 |
|
SAR |
0.04 |
0.48 |
0.17 |
0.07 |
42.83 |
1.88 |
|
CaCO3 (%) |
10.02 |
20.68 |
14.69 |
1.87 |
12.73 |
0.22 |
|
P (mg kg-1) |
2.39 |
28.19 |
10.76 |
4.67 |
43.35 |
1.64 |
|
K (mg kg-1) |
199.40 |
494.00 |
298.51 |
48.06 |
16.10 |
0.99 |
|
Cu (mg kg-1) |
1.74 |
3.33 |
2.34 |
0.31 |
13.12 |
0.35 |
|
Fe (mg kg-1) |
7.59 |
15.57 |
10.89 |
1.79 |
16.47 |
0.37 |
|
Mn (mg kg-1) |
5.71 |
14.27 |
9.32 |
2.26 |
24.21 |
0.42 |
|
Zn (mg kg-1) |
0.25 |
1.26 |
0.54 |
0.19 |
35.59 |
1.56 |
|
OC (%) |
0.70 |
1.48 |
1.09 |
0.16 |
14.70 |
-0.11 |
|
MBC (mg kg-1) |
59.12 |
133.28 |
79.53 |
13.43 |
16.89 |
1.24 |
|
BGEA (mg PNP kg-1 sa-1) |
32.20 |
129.77 |
66.45 |
20.92 |
31.48 |
1.01 |
|
qCO2 (%) |
0.53 |
1.16 |
0.74 |
0.11 |
15.31 |
0.78 |
|
SQI (%) |
38.60 |
68.91 |
51.77 |
5.61 |
10.83 |
0.38 |
|
Horticulture (N=14) |
||||||
|
AS (%) |
25.54 |
76.22 |
50.20 |
16.81 |
33.49 |
0.07 |
|
BD (g cm-3) |
1.05 |
1.26 |
1.18 |
0.06 |
5.34 |
-0.51 |
|
AWC (Volumetric, %) |
3.51 |
17.96 |
12.10 |
3.83 |
31.69 |
-0.66 |
|
WFPS |
0.26 |
0.84 |
0.42 |
0.19 |
44.52 |
1.22 |
|
pH |
7.60 |
8.04 |
7.80 |
0.11 |
1.41 |
0.02 |
|
EC (dS m-1) |
0.57 |
1.89 |
0.95 |
0.44 |
46.61 |
1.36 |
|
SAR |
0.06 |
0.29 |
0.12 |
0.05 |
45.73 |
2.90 |
|
CaCO3 (%) |
12.82 |
20.33 |
15.60 |
1.82 |
11.66 |
1.24 |
|
P (mg kg-1) |
9.03 |
59.82 |
21.44 |
13.97 |
65.18 |
1.99 |
|
K (mg kg-1) |
271.45 |
497.20 |
420.76 |
57.63 |
13.70 |
-1.14 |
|
Cu (mg kg-1) |
1.65 |
7.11 |
3.92 |
1.56 |
39.69 |
0.36 |
|
Fe (mg kg-1) |
5.70 |
11.98 |
9.24 |
1.81 |
19.56 |
-0.17 |
|
Mn (mg kg-1) |
6.92 |
16.40 |
10.31 |
2.72 |
26.42 |
0.92 |
|
Zn (mg kg-1) |
0.33 |
5.51 |
1.40 |
1.37 |
98.06 |
2.50 |
|
OC (%) |
0.63 |
1.68 |
1.20 |
0.32 |
26.29 |
-0.27 |
|
MBC (mg kg-1) |
63.24 |
147.22 |
107.79 |
25.77 |
23.90 |
-0.50 |
|
BGEA (mg PNP kg-1 sa-1) |
53.86 |
312.59 |
202.93 |
94.81 |
46.72 |
-0.50 |
|
qCO2 (%) |
0.65 |
1.13 |
0.91 |
0.14 |
14.97 |
-0.49 |
|
SQI (%) |
50.21 |
68.18 |
59.87 |
6.38 |
10.66 |
-0.51 |
Table 5 Pairwise comparisons for the main, physical, chemical, biological and fertility properties
|
Main |
||||||||
|
|
Physical |
Chemical |
Biological |
Fertility |
||||
|
Physical |
1 |
2 |
2 |
3 |
||||
|
Chemical |
1/2 |
1 |
2 |
2 |
||||
|
Biological |
1/2 |
1/2 |
1 |
2 |
||||
|
Fertility |
1/3 |
1/2 |
1/2 |
1 |
||||
|
Physical |
||||||||
|
AS |
BD |
AWC |
WFPS |
|||||
|
AS |
1 |
2 |
1/2 |
2 |
||||
|
BD |
1/2 |
1 |
1/2 |
2 |
||||
|
AWC |
2 |
2 |
1 |
3 |
||||
|
WFPS |
1/2 |
1/2 |
1/3 |
1 |
||||
|
Chemical |
||||||||
|
pH |
EC |
SAR |
CaCO3 |
|||||
|
pH |
1 |
2 |
3 |
2 |
||||
|
EC |
1/2 |
1 |
2 |
1/2 |
||||
|
SAR |
1/3 |
1/2 |
1 |
1/2 |
||||
|
CaCO3 |
1/2 |
2 |
2 |
1 |
||||
|
Biological |
||||||||
|
OC |
MBC |
BGEA |
qCO2 |
|||||
|
OC |
1 |
2 |
2 |
2 |
||||
|
MBC |
1/2 |
1 |
2 |
2 |
||||
|
BGEA |
1/2 |
1/2 |
1 |
2 |
||||
|
qCO2 |
1/2 |
1/2 |
1/2 |
1 |
||||
|
Fertility |
||||||||
|
P |
K |
Cu |
Fe |
Mn |
Zn |
|||
|
P |
1 |
2 |
3 |
3 |
3 |
3 |
||
|
K |
1/2 |
1 |
3 |
2 |
3 |
2 |
||
|
Cu |
1/3 |
1/3 |
1 |
1/2 |
2 |
1/2 |
||
|
Fe |
1/3 |
1/2 |
2 |
1 |
2 |
2 |
||
|
Mn |
1/3 |
1/3 |
1/2 |
1/2 |
1 |
1/2 |
||
|
Zn |
1/3 |
1/2 |
2 |
1/2 |
2 |
1 |
||
Table 6 Weight values for physical, chemical, biological, fertility and soil quality calculated by the FAHP.
|
Soil Quality |
|||||
|
Physical |
Chemical |
Biological |
Fertility |
Total |
|
|
Indicators |
0.382 |
0.297 |
0.225 |
0.096 |
1.000 |
|
AS |
0.134 |
||||
|
BD |
0.277 |
||||
|
AWC |
0.471 |
||||
|
WFPS |
0.118 |
||||
|
pH |
0.382 |
||||
|
EC |
0.225 |
||||
|
SAR |
0.096 |
||||
|
CaCO3 |
0.297 |
||||
|
OC |
0.332 |
||||
|
MBC |
0.289 |
||||
|
BGEA |
0.231 |
||||
|
qCO2 |
0.148 |
||||
|
P |
0.311 |
||||
|
K |
0.259 |
||||
|
Cu |
0.083 |
||||
|
Fe |
0.186 |
||||
|
Mn |
0.017 |
||||
|
Zn |
0.145 |
||||
|
Total |
1.000 |
1.000 |
1.000 |
1.000 |
|
Table 7 Parameters of semivariogram models calculated for SQI
|
Arıklı |
Model |
Nugget (Co) |
Sill (Co+C) |
Spatial Dependence (%) |
Range (m) |
R2 |
RSS |
|
SQI |
Exponential |
7.9 |
45.02 |
17.55 |
408 |
0.997 |
0.406 |
No competing interests reported.