**Long-Term Effect of Pig Slurry and Mineral Fertilizer Additions on Soil Nutrient Content, Field Pea Grain and Straw Yield under Winter Wheat–Spring Barley–Field Pea Crop Rotation on Cambisol and Luvisol**

**Lukáš Hlisnikovský \*, Ladislav Menšík, Pavel Cerm ˇ ák, Kateˇrina Kˇrížová and Eva Kunzová**

Crop Research Institute, 16106 Prague, Czech Republic<EMAIL_ADDRESS>(L.M<EMAIL_ADDRESS>(P.C<EMAIL_ADDRESS>(K.K<EMAIL_ADDRESS>(E.K.) ˇ **\*** Correspondence<EMAIL_ADDRESS>Tel.: +420-233-022-248

**Abstract:** Different fertilizers have different effects on soil chemistry and crop yields. In this paper, we analyzed how long-term and regular application of mineral fertilizers, pig slurry and their combinations (15 fertilizer treatments totally) affect soil pH, nutrient content and yield of field pea at two sites with different soil (cambisol and luvisol) and climatic conditions. The long-term trials evaluated in this paper were established in 1972 at Pernolec and Kostelec, Czech Republic. Results of the soil analyses (evaluated period) are from the years 2015–2020, covering two sequences of crop rotation (winter wheat–spring barley–field pea). The fertilizer treatments significantly affected the soil reaction; application of mineral fertilizers and their combinations resulted in the lowest pH values. On the other hand, the same treatments provided the highest yields and left the highest pool of nutrients in the soil. Pig slurry can provide the same yields of field pea as mineral NPK fertilizers, without a negative effect on soil reaction. Analyzing the mineral fertilizers only, a reasonable dose of N (according to the linear-plateau model) can range from 73 and 97 kg ha−1 N in Pernolec, according to the weather conditions.

**Keywords:** *Pisum sativum* L.; organic manure; NPK; pH; SOM; macronutrients; nutrient content

#### **1. Introduction**

The nutrient content of the soil is one of the parameters determining its fertility and quality. It is a parameter influenced by a wide range of natural, anthropogenic and interrelated factors such as soil type [1], farming method (conventional, organic farming) [2], crop rotations and fertilization [3–5], microbial activity in the soil [6], or soil organic matter content [7]. The application of fertilizers represents the main way of supplying nutrients to the soil; for the crops grown, fertilizers thus directly affect soil chemical [8–11], physiological [12,13] and biological [14,15] properties and crop growth.

Fertilizers are divided into three categories, namely mineral and organic fertilizers and organic manures. They differ in origin, composition and nutrient content, speed of nutrient release and availability to farmers. Mineral fertilizers are fast-acting and have a precisely defined composition, which makes it easier to adjust the dose of nutrients delivered. On the other hand, they are costly and, if used unwisely, can pose a significant threat to the environment [16,17] or arable products [18]. In particular, the effect of nitrogen mineral fertilizers on soil pH poses a risk of acidification [19–22] and a risk to elements' availability [23]. Manure fertilizers have a low nutrient content and must be applied in large doses (the classic dose of cow farmyard manure is 40 t ha−1 in Czech Republic). The nutrients contained in manure are released gradually, depending on the origin [24] and C:N ratio. Manures with a low C:N ratio (slurries) release nutrients to a greater extent already in the first year of application; on the other hand, manures with a high C:N ratio

**Citation:** Hlisnikovský, L.; Menšík, L.; Cermák, P.; Kˇ ˇ rížová, K.; Kunzová, E. Long-Term Effect of Pig Slurry and Mineral Fertilizer Additions on Soil Nutrient Content, Field Pea Grain and Straw Yield under Winter Wheat–Spring Barley–Field Pea Crop Rotation on Cambisol and Luvisol. *Land* **2022**, *11*, 187. https://doi.org/ 10.3390/land11020187

Academic Editors: Guido Wyseure, Julián Cuevas González, Jean Poesen and Angelinus Franke

Received: 3 December 2021 Accepted: 21 January 2022 Published: 25 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(farmyard manure) release nutrients over a longer period in smaller doses [25]. According to [26], approximately 11% of the organic N is mineralized during the first year from the application of composted manure and around 20% for non-composted manure. In the case of slurries, approximately 40% of the organic N is mineralized at the same time [25]. The application of organic manures is usually associated with positive effects on soil properties [7,8,27–29], but one has to be careful about the doses and dry matter content. In the case of slurry, the dry matter and nutrient content is very important information in order to correctly adjust the applied dose. Ignorance of this information can easily lead to overdosing, which can significantly damage the crops grown or adversely affect the environment via leaching and volatilization of nitrogen and salinization [30–33]. In addition to directly supplied nutrients, the unifying factor for the positive effects of organic manures and the nutrient content, pH value, physical and biological properties of soils, is organic matter. Soil organic carbon and nutrient content are usually higher after application of solid organic manures [34–36], while the benefit of liquid organic manures, such as slurries, is mainly to increase the nutrient content and the effect on soil organic carbon can be neutral (no changes) [37] or positive [38,39] as the liquid manures contain a lower amount of organic carbon than the solid. As in the case of mineral fertilizers, organic manures can also pose a threat to the environment if not applied judiciously [40,41] or because of the presence of pharmaceuticals [42].

One of the major problems of agriculture in Czech Republic is the disruption between crop and livestock production, reduction of cultivated crops in crop rotations and the fact that most of the arable land is rented [43]. Disruption of the balance between crop and livestock production is manifested by a lack of organic manure and reduced input of organic carbon into the soil. Together with the significant dependence of crop production on mineral nitrogen (and the low level of phosphate and potassium fertilizers applied), we then experience soil erosion (lack of organic carbon), lower content of macronutrients (doses of P and K mineral fertilizers) and soil acidification (due to nitrogen fertilizers application) [44,45]. One way to reduce the negative impact of mineral nitrogen fertilizers on the soil while ensuring good soil nutrient supply and crop yields is to apply mineral and manure fertilizers together. Multiple scientific papers have indicated that joint application of mineral fertilizers and manure has a positive effect on both crop yields and reduction of negative impacts of mineral fertilizers on the soil properties [46–50]. Another problem of Czech agriculture is the reduction of crops in crop rotation. Over the years, there has been a change in the proportion of crops grown, mainly in favor of winter rape. Soil-improving crops such as root crops, forage crops and legumes are grown to a lesser extent than in the past [51]. While root crops (potatoes and sugar beet) are considered as soil-improving plants mainly due to the manure applied to them, legumes have a unique ability to fix airborne nitrogen in the soil, due to their symbiosis with rhizobacteria. Field pea (FP) is the most cultivated legume in Czech Republic (79% of all legumes), yet its representation in the crop rotations of Czech Republic is low (1.2%) (average values from 2015 to 2019 [52]). From the point of view of human nutrition and soil care, it is a valuable crop. Thanks to their symbiosis with rhizobia bacteria, legumes and FP cover a large part of their nitrogen needs from the symbiosis (depending on the type of legumes, they cover their nitrogen requirement from the soil from approximately 15-30%) and leave nitrogen in the soil for use by subsequent crops [53,54]. Although FP can use nitrogen from symbiosis with rhizobia bacteria, fertilizer application significantly affects its yield and quality. Foliar application of phosphorus can significantly improve yield and quality parameters of FP, especially on soils with low phosphorus content [55], but even on soils rich in P content, it has a positive effect on FP yields [56]. N fertilization can also increase yield and quality. Early application of N fertilizers is important, as the actual fixation of airborne nitrogen takes place only in the later stages of growth. Depending on soil and climate conditions, optimum N rates can range from 40 to 80 kg ha−1 N and higher doses can provide lower yields as high N doses can reduce bacteria nodule mass [57]. However, under different soil and climate conditions

the response of FP to N fertilization may be different as yields can increase up to the dose of 135 kg ha−1 N [58].

In 1972, long-term experiments were set up at two sites with different soil and climatic conditions to study the effect of the application of organic manure (pig slurries), mineral fertilizers and their combinations on soil chemistry and yields of wheat, barley and peas. The design of this experiment allows us to analyze the long-term effect of different fertilizer combinations on soil properties, which is currently a hot topic due to the dependence of conventional agriculture on mineral nitrogen, the low rates of applied P and K fertilizers and the limited availability of organic manures (slurries). In other words, our experiment can provide answers on how to take better care of the soil with the help of organic manure and how to avoid undesirable effects of mineral nitrogen applied without organic manure (current situation in Czech Republic). Soil types are represented by Cambisol (about 45% of the soil in Czech Republic) and Luvisol (about 13% of the soil in Czech Republic), representing the two most widespread soil types in Czech Republic. The article includes an analysis of the effect of fertilization on pea yields in 2017 and 2020 in Pernolec and the determination of a reasonable dose (using a linear-plateau model) of mineral nitrogen fertilization.

#### **2. Materials and Methods**

#### *2.1. General Information and Sites Description*

The results come from two long-term field trials located at Pernolec and Kostelec, Czech Republic, Central Europe. Both trials were established in 1972. The long-term trials aim to analyze the effect of mineral fertilizers (mineral nitrogen–N, phosphorus–P and potassium–K), pig slurry (three different doses), and their combination on the yield of arable crops. The crop rotation of both trials consists of winter wheat (*Triticum aestivum* L., WW), spring barley (*Hordeum vulgare* L., SB) and field pea (*Pisum sativum* L., FP). At the same time, the effect of long-term fertilizer application on basic soil properties is monitored (pH, the concentration of soil P, K, magnesium–Mg, calcium–Ca, the content of soil organic carbon–Cox, the content of soil total N–Ntot). In this paper, we assessed the period from 2015 to 2020 (six years) to analyze how long-term regular application of mineral fertilizers and organic manures affects soil properties and FP yields (yields from the years 2017 and 2020; 2015–WW, 2016–SB, 2017–FP, 2018–WW, 2019–SB, 2020–FP).

According to Köpper–Geiger climate classification [59], both sites are located in warm summer humid continental climate (Dfb). The basic site description of both localities is shown in Table 1. Detailed weather information can be found in Section 2.3. It should be noted here that our team was not the team that established the experiments in 1972 and we have not been able to find the results of soil analyses from the period of the trial establishment.

**Table 1.** The description of trial sites—Pernolec and Kostelec.

Note: the long-term average precipitation and temperature for Pernolec are based on the data from the years 1977–2014 (37 years) and for Kostelec from the years 1982–2014 (32 years).

#### *2.2. Field Trials Description*

In both long-term trials, the effect of a total of fifteen fertilization treatments with four replications has been running since 1972. The trial consists of sixty plots (15 × 4) arranged in a completely randomized block design. The plot size is 8 m × 5.5 m (44 m2). The fertilization treatments are identical in both trials, but the fertilization rates differ slightly (Tables 2 and 3 show fertilizer treatments and rates applied to FP in Pernolec and Kostelec. Tables S1 and S2 show the fertilizer treatments and rates applied over the whole three-year crop rotation—the sum of nutrients applied to all three crops over the three years). Mineral N was applied in two forms. Ammonium sulfate (AS) was applied in the spring, before the planting. Calcium ammonium nitrate (CAN) was applied during the beginning of stem elongation (BBCH 30). Mineral P was applied as superphosphate and mineral K as potassium sulfate. Both mineral fertilizers and PS were applied in the autumn, before tillering. Mineral fertilizers were spread by hand at both sites. Pig slurries were applied by manual sprayer. The average content of dry matter (DM) ranged from 0.68% (Pernolec) to 1.8% (Kostelec). This is a very low value: the amount of DM in slurry usually ranges from 0.7% to 24% [61] and is significantly affected by the season of the year [62]. Quality slurry is considered to have a dry matter content between 6% and 8%. The average pH and concentrations of N, P, K, Ca and Mg (% of DM) in Pernolec were 7.75, 1.79%, 0.52%, 16.77%, 1.11%, and 0.73%, respectively. In Kostelec, the average pH value of PS was 7.68 and the concentrations of N, P, K, Ca and Mg (% of DM) were 1.95%, 1.53%, 14.53%, 3.84% and 0.98%, respectively. Pig slurries were obtained from the nearest livestock farms that were able to supply manure in time. The FP (cul. Eso) was sown at the beginning of April (one million germinating seeds per ha, approximately 270 kg) and harvested in the first half of August.

**Table 2.** Forms and doses of mineral fertilizers and pig slurry (PS) according to the fertilizer treatments applied to FP in Pernolec.

Note: AS–ammonium sulphate; CAN–calcium ammonium nitrate; N (kg ha−1) in the PS column represents the content of N applied in PS.

#### *2.3. Weather Information*

Weather data (average monthly temperatures and monthly precipitation) were evaluated according to [63], which describes the World Meteorological Organization's recommendations for describing meteorological and climatological conditions of a defined period (text in Czech, tables in English). The weather analysis was based on long-term records. In Pernolec we compared the years 2017 and 2020 with the period from 1977 to 2014 (37 years). In Kostelec, we based our analysis on the period from the years 1982 to 2014 (32 years).

In Pernolec, the year 2017 was evaluated as warm (+0.9 ◦C in comparison with long– term average) and 2020 as very warm (+1.5 ◦C). In terms of precipitation, 2017 was a very dry year (76% of the long–term average), while 2020 was normal (98%). In Kostelec, the year 2017 was evaluated as warm (+0.8 ◦C) and 2020 as very warm (+1.5 ◦C). In terms of precipitation, 2017 was a normal year (109%), while 2020 was a very wet year (135%, Table 1). Detailed weather information for 2017 and 2020 at both sites, including assessments, is provided in Tables S3 and S4.

**Table 3.** Forms and doses of mineral fertilizers and pig slurry (PS) according to the fertilizer treatments applied to FP in Kostelec.

Note: AS–ammonium sulphate; CAN–calcium ammonium nitrate; N (kg ha−1) in the PS column represents the content of N applied in PS.

#### *2.4. Soil Analyses*

Following the harvest of the crops, soil samples were taken using the stainless-steel soil probe sampler. The soil samples were taken from the topsoil layer (0–20 cm). Four samples from each plot were taken. The samples were then mixed and transported to the laboratory, where they were dried and sieved to get fine and dry soil. The soil pH was analyzed potentiometrically using 0.2 mol KCl (inoLab pH 730, WTW, Xylem Analytics, Weilheim, Germany). The concentration of total N (Ntot) was analyzed using sulfuric acid in the heating block (Tecator, Foss Analytics, Hillerød, Denmark), followed by the Kjeldahl method [64]. The concentrations of P, K, Ca and Mg were analyzed using the Mehlich III solution [65], followed by the ICP-OES analysis (Thermo Scientific iCAP 7400 Duo, Thermo Fisher Scientific, Cambridge, UK). The SOC content was analyzed colorimetrically and via oxidimetric titration according to [66,67].

#### *2.5. Data Analyses*

One-way and multivariate analysis of variance (ANOVA, MANOVA) was used to compare the results of pH and soil element concentrations as affected by fertilization treatments and to analyze the effect of weather and fertilization treatments on FB yields (Pernolec locality only). Due to the occurrence of certain problems, we have only the summed FP yield values (average values without repeats) from the Kostelec site. For this reason, it was not possible to perform statistical analysis as in the case of the Pernolec. However, the average FP yield values from the Kostelec were suitable for PCA. FP yields from the Kostelec site are shown in Table S5. In this article, we have analyzed a total of fifteen fertilization treatments. Such a large set makes the interpretation of the results difficult and ambiguous (the results of the post hoc analysis overlap widely). For this reason, we proceeded to group the treatments (Control, PK, NPK, PS, PS+PK, PS + NPK) and calculate separate ANOVA for soil parameters where significant differences between the fertilizer treatments were recorded previously. If statistically significant differences were found, we used Tukey's HSD post hoc analysis to separate treatments. Statistical analyses were performed in Statistica 13.3. (Tibco Software Inc. Palo Alto, CA, USA). The nitrogen use efficiency (NUE) was calculated as ((GYT-GYC)/N rate) where GYT represents grain yield from the particular fertilizer treatment and GYC represents the grain yield from the Control treatment. The NUE was calculated from seven fertilizer treatments (NPK, PS1, PS1+NPK, PS2, PS2+NPK, PS3, and PS3+NPK. To evaluate the relationships between the yields, fertilizer treatments and soil parameters, principal component analysis (PCA) and factor analysis (FA) were used (Statistica 14.0.). MS Excel 2019 was used for weather analyses (Microsoft Corporation, Washington, DC, USA). The linear-plateau model, analyzing the reasonable N dose for FP (calculated from mineral fertilizer treatments), was calculated using R software (R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2020), together with three R packages [68–70].

#### **3. Results**

#### *3.1. Comparison of Localities*

The two sites are statistically significantly different from each other in all observed soil parameters (results from all fertilizer treatments and for the whole period 2015–2020, Table 6). Compared to Kostelec, the soil in Pernolec is characterized by a higher pH value, and lower mean content of available P, K and Ca. In contrast, the average content of Mg, Cox and Ntot is higher in Pernolec.

#### *3.2. The Effect of Fertilizer Treatments on Soil Chemical Properties*

In the following sections the results of the effect of fertilization on pH, nutrient concentration, Cox and Ntot at each site will be presented. A summary description of the relationships between the fertilization treatments and the individual parameters is given in the last section, in which the PCA results are presented.

#### 3.2.1. Soil Reaction

In Pernolec (Cambisol), the soil pH was statistically significantly affected by the fertilizer treatment (d.f. = 14; F = 10.6; p < 0.001). Comparing all 15 treatments, the lowest mean pH value (4.73) was recorded in the NPK E3 treatment. The highest mean pH value (5.93) was recorded in PK treatment (Table 4). Comparing the groups of fertilizers, the lowest mean pH was recorded in NPK treatments (5.16), followed by PS+NPK (5.48), while the highest pH was recorded in PS+PK (5.81) and PK (5.93) treatments (Figure 1a).

In Kostelec (Luvisol), the value of the pH was also significantly affected by the fertilizer treatment (d.f. = 14; F = 4.2; *p* < 0.001). The lowest mean pH value was recorded in PS2 + NPK (5.04) treatments, while the highest was in Control (5.75) and PK (5.67) treatments (Table 5). Comparing the groups of fertilizer treatments, similarly to Pernolec, the lowest mean pH values were recorded in treatments with mineral N–NPK (5.33) and PS+NPK (5.14), while the highest value was recorded in Control (5.75) treatment (Figure 1b).

The results show that the application of NPK, either alone or in combination with PS, results in the lowest pH values. In Kostelec, the pH values for the NPK and PS+NPK treatments were comparable and significantly different from the other treatments. In Pernolec, the effect of NPK was most significant, while the combined application of PS+NPK was comparable to PS, yet lower. The negative effect of ammonium nitrogen on pH is particularly noticeable when compared to the PK treatment (Figure 1).

#### 3.2.2. Phosphorus

The concentration of P in the soil was not affected by the long-term application of slurry and mineral fertilizers in Pernolec (d.f. = 14; F = 0.6; *p* = 0.84). The lowest mean concentration was recorded in Control (58 mg kg−1), and the highest in PS3+PK treatment (111 mg kg−1) (Table 4). A different situation occurred in Kostelec, where differences between fertilizer treatments were significant (d.f. = 14; F = 16.47; *p* < 0.001). As in Pernolec, the lowest concentration was recorded in Control (124 mg kg−1), and the highest in PS3+NPK (262 mg kg−1) treatment (Table 5). Comparing the groups of fertilizers, ANOVA separated three groups of fertilizers according to their effect on soil P concentration in Kostelec (Figure 2a). The lowest mean concentration was recorded in Control (124 mg kg−1), followed by NPK, PK and PS treatments. The combined application of PS+NPK and PS+PK resulted in the highest mean P concentrations, ranging from 229 to 235 mg kg−1 (Figure 2a).

**Table 4.** Soil pH value, the concentration of P, K, Ca and Mg (mg kg−1), the content of organic carbon (Cox, %) and total nitrogen (Nt, %) as affected by the fertilizer treatments (2015–2020) in Pernolec.

Mean values (±SE) followed by the same letter (a vertical comparison of the effect of fertilizer treatment) are not statistically significantly different. Columns without letters (P, Mg, Cox, Nt) represent values without statistically significant differences, where the effect of fertilizer treatments was insignificant.

**Figure 1.** The effect of fertilizer treatments on pH in (**a**) Pernolec and (**b**) Kostelec (2015–2020). Mean values followed by the same letter are not significantly different. Red triangles represent the raw data. The blue lines represent the mean value of the particular treatment, while the red line represents the mean value calculated from all fertilizer treatments.

**Table 5.** Soil pH value, the concentration of P, K, Ca and Mg (mg kg−1), the content of organic carbon (Cox, %) and total nitrogen (Nt, %) as affected by the fertilizer treatments (2015–2020) in Kostelec.

Mean values (±SE) followed by the same letter (a vertical comparison of the effect of fertilizer treatment) are not statistically significantly different. Columns without letters (P, Mg, Cox, Nt) represent values without statistically significant differences, where the effect of fertilizer treatments was insignificant.

**Table 6.** Average values of soil parameters in Pernolec and Kostelec. The values are based on the results of soil analyses of all fertilization treatments and all analyzed years (2015–2020).

Note: F: F statistic; d.f.: degree of freedom; *p:* level of significance. Mean values ± standard error of the mean (SE) followed by the same letter are not significantly different.

#### 3.2.3. Potassium

Of all the analyzed parameters, potassium was the element most affected by the fertilization treatments (d.f. = 14; F = 22.83; *p* < 0.001 for Pernolec and d.f. = 14; F = 19.11; *p* < 0.001 for Kostelec). In Pernolec, the lowest concentration was recorded in NPK E1 treatment (116 mg kg−1), and the highest in PS1+NPK treatment (215 mg kg−1) (Table 4). In Kostelec, the K mean concentration varied from 112 mg kg−1 (NPK E1) to 252 mg kg−1 (PS3+PK) (Table 5). If we compare the fertilizer groups, we find that both localities have a comparable pattern. In Pernolec, application of no fertilizers (Control), NPK and PS resulted in lower K soil concentrations without differences between these treatments, while application of PK, PS+NPK and PS+PK resulted in higher K concentrations (Figure 3a). The situation in Kostelec was similar, with one exception, namely for the PK and PS treatments. The differences between these two treatments were not significant, as in Pernolec (Figure 3b).

#### 3.2.4. Calcium

In Pernolec, the mean Ca soil concentrations varied significantly (d.f. = 14; F = 4.83; *p* < 0.001) between the treatments and ranged from 1113 mg kg−1 (NPK E3) to 1447 mg kg−1 (PS1) (Table 4). Similarly, in Kostelec, the differences between fertilization treatments were significant (d.f. = 14; F = 4.46; *p* < 0.001), and varied from 1213 mg kg−1 (PS2+NPK) to 1549 mg kg−1 (Control) (Table 5). Comparing the fertilizer groups, we find that the

effect of fertilization on soil Ca content is similar in the two sites. The lowest mean Ca concentrations were recorded in NPK and PS+NPK treatments (Figure 4a,b), while application of no fertilizers (Control) and PS resulted in the highest Ca concentrations.

**Figure 2.** The effect of fertilizer treatments on (**a**) soil P and (**b**) Mg concentration in Kostelec (2015–2020). The differences between P and Mg concentrations as affected by fertilizer treatment were insignificant in Pernolec (Table 6). Mean values followed by the same letter are not significantly different. Red triangles represent the raw data. The blue lines represent the mean value of the particular treatment, while the red line represents the mean value calculated from all fertilizer treatments.

**Figure 3.** The effect of fertilizer treatments on soil K concentration in (**a**) Pernolec and (**b**) Kostelec (2015–2020). Mean values followed by the same letter are not significantly different. Red triangles represent the raw data. The blue lines represent the mean value of the particular treatment, while the red line represents the mean value calculated from all fertilizer treatments.

#### 3.2.5. Magnesium

Average soil Mg concentrations in Pernolec were not significantly affected by the fertilization treatments (d.f. = 14; F = 0.57; *p* = 0.88) and ranged from 104 mg kg−1 (NPK E3) to 131 mg kg−1 (PS1) (Table 4). In Kostelec, on the other hand, the long-term application of slurry and mineral fertilizers had a significant effect on the Mg concentration (d.f. = 14; F = 7.10; *p* = 0.001), which varied from 58 mg kg−1 (NPK) to 83 mg kg−1 (PS3) (Table 5). Comparing the groups of fertilizers, the lowest mean concentrations were recorded in NPK

and PK treatments, while the highest concentrations occurred in PS and PS+PK treatments (Figure 2b).

#### 3.2.6. Soil Organic Carbon Content

Long-term and regular application of slurry, mineral fertilizers and their combinations did not significantly affect the soil organic carbon content in either Pernolec (d.f. = 14; F = 0.91; *p* = 0.56) or Kostelec (d.f. = 14; F = 0.77; *p* = 0.70). In Pernolec, the Cox content in the soil varied from 0.88% (PK, PS1) to 1.02% (PS3+NPK) (Table 4). In Kostelec, the Cox ranged from 0.77% (PK) to 0.87% (PS3+NPK) (Table 5).

#### 3.2.7. Total Nitrogen Content

Similar to soil organic carbon, long-term and regular application of manure, mineral fertilizers and their combinations did not significantly affect total soil nitrogen content at either of the two sites (Pernolec: d.f. = 14; F = 0.52; *p* = 0.91; Kostelec: d.f. = 14; F = 0.64; *p* = 0.83). In Pernolec, the Ntot content ranged from 0.11% to 0.13% (Table 4), in Kostelec from 0.10% to 0.11% (Table 5).

#### 3.2.8. Principal Component Analysis (PCA)

Based on the PCA results (Figure 5a), we can classify the fertilizers in Pernolec (cambisol) into four categories according to their effect on yield and soil properties (Figure 5b). (1) The unfertilized treatment (Control) gives lower crop yields and has low P and K concentrations due to no external supply of nutrients. (2) Pig slurry (PS) applied alone, application of mineral P and K (PK), and combination of PS+PK (generally the fertilizers without mineral N): these fertilizers have a positive relationship with pH and Ca and Mg content, and there is no decrease in pH compared to other treatments. On the other hand, the absence of mineral N puts this group at a disadvantage in terms of low grain and straw yields and the soils have a low organic matter content (no organic matter in the PK treatment and low organic matter in the slurry). (3) The third group is represented by PS+NPK treatments. The joint application of PS and mineral NPK represents a kind of golden mean ensuring relatively high grain and straw yields, nutrient and soil organic matter content. However, the presence of the ammonium form of mineral N negatively affects soil pH. (4) The fourth group consists of separately applied mineral fertilizers (NPK, without manure supplement). Mineral fertilizers are clearly closely and positively associated with yield, followed by soil organic carbon and nitrogen. On the other hand, the presence of the ammonium form of

nitrogen, accompanied by the absence of slurry, accentuates the negative effect on pH even more significantly (compared to PS+NPK combinations).

**Figure 5.** Results of the PCA*—*relationships between soil chemical parameters and grain and straw yields as affected by the fertilization treatments: (**a**,**b**) Pernolec, (**c**,**d**) Kostelec. Grain and straw yields are based on the average WW, SB and FP yield from 2015 to 2020.

With a change in soil type (Kostelec, luvisol), we can see a different response to the long-term application of manure and mineral fertilizers on yield and soil properties (Figure 5c). The separation of fertilizers in Kostelec (Figure 5d) is not as clear-cut as in Pernolec, which means that the differences between fertilizer treatments are not as pronounced. As in Pernolec, unfertilized Control is strongly and positively correlated with pH and soil Ca content. On the other hand, treatment without external nutrient inputs (Control) is associated with low grain and straw yields and also with low concentrations of soil P and K (soil depletion). The PK group (mineral P and K fertilizers) has a completely different status than PS (in Pernolec these two fertilizer groups were together in one cluster). PK has a strong negative relationship with soil organic carbon and total nitrogen. This treatment highlights the need for nitrogen, either supplied in mineral form or the form of manure. In contrast, the application of pig slurry (PS) is strongly and positively associated with soil organic carbon content combined with a neutral relationship to both yield and pH. Mineral fertilizers (NPK) occupy a similar position to PS in terms of yield and pH, with the exception that they are closer to higher yields and lower pH. Quite different (compared to Pernolec) is their relationship to soil organic carbon, with which it is moderately and rather

negatively correlated. Similarly to Pernolec, the PS+NPK fertilizer group is dominant. It is associated with high yields, high soil nutrient content, a relatively neutral relationship to soil organic matter and a significantly (strongly) negative relationship to soil pH (stronger negative relationship to pH than in Pernolec).

#### 3.2.9. FP Grain and Straw Yields

As mentioned in Section 2.5, the results of the FP grain and straw yields from Kostelec cannot be statistically analysed. The average grain and straw yields in 2017 and 2020 are shown in Table S5. In 2017, the grain yields varied from 2.8 t ha−1 (PS1, PS1+PK) to 3.7 t ha−1 (PS3+NPK), while in 2020 the grain yields varied from 2.5 t ha−1 (NPK E2) to 3.3 t ha−1 (PS1+PK). Straw yields varied from 2.1 t ha−1 (PS1) to 3.2 t ha−1 (PS3+NPK) in 2017 and from 3.0 t ha−1 (PS1+NPK, NPK E1) to 3.6 t ha−1 (PS2) (Table S5).

According to MANOVA results, the FP grain yields were significantly affected by year (d.f. = 1; F = 71.55; p < 0.001), fertilizer treatment (d.f. = 14; F = 6.76; *p* < 0.001), and their interaction (d.f. = 14; F = 2.33; *p* < 0.01) in Pernolec. The effect of year was dominant (89%), while the effect of fertilizer treatment influenced yields by 8%. If we look at the weather in a particular year, we find that 2017 in Pernolec was marked by drought in May and June. Moreover, 2017 was significantly marked by very high temperatures in June and July (Table S3). These were factors that caused significantly lower yields compared to 2020, which was characterized by both higher precipitation and milder temperatures (Table 1). Straw yields were comparable in 2017 and 2020 as the differences were insignificant in Pernolec (d.f. = 1; F = 0.40; *p* = 0.53), while the effect of the fertilizer treatment was significant (d.f. = 14; F = 4.32; *p* < 0.001). The interaction between the factors of year and treatment was insignificant (d.f. = 14; F = 1.90; *p* < 0.07).

In 2017, the grain yields were significantly affected by the fertilization (d.f. = 14; F = 3.18; *p* < 0.01) and varied from 1.2 t ha−1 (Control) to 2.3 t ha−1 (PS3+PK and NPK E3) (Table 7). Significantly different were Control and PS1+PK against PS3+PK, PS3+NPK and NPK E3. Grain yield slightly increased with increasing nitrogen rate (Figure 6a). According to the linear-plateau model, calculated from the mineral fertilizer treatments (NPK, NPK E1, NPK E2, NPK E3), the FP yield response to different rates of mineral N plateaued at 97 kg ha−1 N, with a corresponding yield of 2.08 t ha−1 (Figure 7, left). Comparing the nitrogen use efficiency (NUE), the highest NUE was recorded in NPK treatment (23.3 kg per 1 kg of N applied), followed by PS1 (5.9 kg), PS1+NPK (4.3 kg), PS2, PS3 and PS3+NPK (3.5 kg), the lowest NUE was recorded in PS2+NPK treatment (3.0 kg per 1 kg of N applied). This calculation shows that mineral fertilizers, compared to organic manures (slurries), supply nutrients very quickly and, even in small quantities can significantly and efficiently promote growth. On the other hand, their effectiveness is offset by their negative effect on the soil environment.

In 2020, the grain yields varied from 1.8 t ha−1 (PS1 and PS1+PK) to 2.8 t ha−1 (NPK E3). As we can see, the response to the fertilization was a little different as the weather conditions changed (Figure 6b, the red line representing a quadratic model). We can see that grain yield slightly increased with increasing N dose, as in 2017. The course of the function indicates the attainment of a local maximum, which, according to the quadratic model, is located at an N rate of 400 kg ha−1. At this rate, the maximal average yield of 2.4 t ha−1 would be achieved, which is actually lower than the yields already obtained with lower inputs (Figure 6b). According to the linear-plateau model, the response of FP yields to different rates of N doses plateaued at 73 kg ha−1, corresponding with the yield 2.71 t ha−1 (Figure 7, right), showing better weather conditions for yield development in 2020. Comparing the NUE, the highest efficiency was again recorded in NPK treatment (10.0 kg per 1 kg N applied), followed by PS2+NPK (3 kg), PS1+NPK (2.6 kg), PS3+NPK (2.4 kg), PS3 (0.4 kg) and PS1 and PS2 (−2.4 and −0.5, respectively), where the efficiency was negative as the mean yield was lower than in the Control treatment.

**Table 7.** The effect of the year (2017, 2020) and fertilizer treatment on FP grain and straw yield (t ha−1) in Pernolec.

Mean values (±SE) followed by the same letter (a vertical comparison of the effect of fertilizer treatment) are not statistically significantly different.

**Figure 6.** FP grain yield (t ha−1) as affected by N dose in Pernolec in (**a**) 2017 and (**b**) 2020. The average yields (blue points) are interleaved with the quadratic function (red line). The equation of the quadratic model is given above the figure.

Comparing the results from both years (Table 7), we find that the highest average yields were obtained with the NPK E3 treatment (2.5 t ha−1). However, lower, but statistically comparable, yields were obtained with the NPK (30 kg mineral N ha−1 with an average yield of 2.1 t ha−1) and PS3 (51 t ha−1 with an average yield of 2.1 t ha−1) treatments. This is a very important finding as PS applied in higher doses can completely replace mineral fertilizers and a negative effect of mineral fertilizers on soil pH can be partially avoided.

**Figure 7.** The response of FP yields to different doses of mineral N fertilizers (NPK, NPK E1, NPK E2, NPK E3 treatments) in 2017 (**left**) and 2020 (**right**). Yields (black dots) are interleaved with the linear-plateau model (blue line). The equation of the model is given above the figure.

Straw yields were significantly affected by fertilization in 2020 (d.f. = 14; F = 3.26; *p* < 0.05), with insignificant differences in 2017 (d.f. = 14; F = 3.07; *p* < 0.05, Tukey's test did not confirm ANOVA as multiple comparison methods generally have lower test power than analysis of variance*-*ANOVA). Straw yield tended to increase with increasing fertilizer rate. The differences between 2017 and 2020 were insignificant. The highest yields obtained were recorded for the PS3+NPK and NPK E3 treatments; however, the PK, PS1 and PS2 treatments also provided statistically comparable yields (Table 7).

#### **4. Discussion**

Long-term and regular application of mineral fertilizers, pig slurry, and their combinations significantly affected soil properties and the effect of fertilizers depends on soil conditions (type) of the site. One of the most important soil properties is the value of the pH. Soil pH is considered to be the dominant factor directly influencing other soil properties such as elements' availability [10,71,72] and abundance and representation of plant and microbial communities [73] and their activity [74]. All macronutrients are best available in neutral to alkaline soils, while in acid soils their availability decreases and the availability of elements such as Fe, Mn, B, Zn and Al increases. Changes in pH thus directly affect the soil's ability to supply nutrients to plants. In our case, the lowest pH values were recorded for the NPK treatments (applied alone or in combination with PS, but only in the NPK treatments with the highest N doses, Tables 4 and 5). The same result was recorded worldwide [11,13,21,72,75] and has been known for a long time [76]. The primary driver of downward pH changes is mineral nitrogen, in its ammonium form, because the conversion of the ammonium form to nitrate in soils releases hydrogen, directly affecting its concentration in the soil environment. This can be particularly evident in the case of PK treatments. As mentioned above, Czech conventional crop production is primarily dependent on mineral nitrogen. Add to this the fact that most of the cultivated land is rented and its owners have no idea or do not care about acidification. This leaves room for acidification to run freely. An interesting survey was carried out in the USA, which also shows that acidification is taking place there and that about half of the farmers were not even aware of it [77]. One way to reduce the negative effects of mineral fertilizers on soil pH is to combine mineral fertilizers and organic manures [78]. Co-application of mineral fertilizers and organic manures is often cited as a sustainable method of fertilization, providing high and stable yields and a healthy state of the soil. The unifying element of this approach is organic matter (together with nutrients) [3,5,7,8,13,29,79,80] added to the soil, beneficially affecting soil chemical, physical and microbiological properties. From this point of view, we can support these results only partially as the combined application of PS+NPK provided better pH values than NPK only in Pernolec (Figure 1a), in contrast to Kostelec (lower

and comparable to NPK treatment, Figure 1b). This may be due to the overall higher soil organic matter content in Pernolec (Table 1) and the very low organic matter content in the slurry, which seems to be behind the non-significant Cox differences between fertilization treatments in both locations (Tables 4 and 5). The DM of pig slurry usually ranges from 0.7% up to 23% [61] and quality slurry has a dry matter content between 6% and 8% in Czech Republic. In our case, the dry matter content of the available and applied pig slurry was very low, which is probably the reason why the soil organic matter content is slightly higher in the high slurry fertilizer treatments, but not statistically significantly higher compared to the other fertilization treatments.

From the point of view of nutrients, the highest concentrations of macronutrients were always connected with PS+PK, NPK and PS+NPK treatments (Figure 5), while nutrient depletion can be found in Control treatment. PS+PK treatment has a close relationship to nutrient content and a moderate relationship to yields (Figure 5), showing that nitrogen is a limiting element in this treatment and its P and K nutrients are not utilized completely. The combination of mineral fertilizers and organic manures provides high yields while leaving a high micronutrient content in the soil (Figure 5). From the point of view of agriculture in Czech Republic, we can expect that acidification problems will intensify, as mineral nitrogen is important for all agricultural crops and significantly affects yields, which is the most monitored parameter. The application of mineral fertilizers at higher doses (NPK E3, PS2+NPK, PS3+NPK treatments) significantly reduced the soil reaction values at both sites (Kostelec and Pernolec) compared to the Control; a more significant decrease was recorded on the luvisol soil type (Kostelec). Similar findings (decrease in pH in treatments fertilized with mineral fertilizers only) are supported by some other studies [81–84]. The negative effect of acidification on the content of available nutrients (Ca, Mg) in the plough soil horizon is shown in Tables 4 and 5 (in the NPK E3, PS2+NPK, PS3+NPK treatments, low Ca and Mg contents were recorded at both sites). For available nutrients P and K, the acidification effect was predominant in the mineral fertilized treatments (NPK E1-3). This is confirmed by the results of the multicriteria PCA evaluation. These results are in agreement with [85,86], which showed a negative effect of acidification on the regime of available nutrients in the soil. Without the addition of other nutrients (PK treatments), there will be a reduction in the content of these nutrients in the soil (as in the case of Control). The combination of mineral fertilizers and organic manures can partially reduce the negative effect of mineral fertilizers on pH (depending on the location and soil and climate conditions), which is good news, but the lack of organic manures due to reduced livestock production in the country plays against the solution to the current problems.

In terms of pea yields, we can clearly see the dependence of yields on nitrogen, with pea yields increasing with increasing nutrient rates, although the differences are not statistically significant between higher doses of fertilizers. The yields are strongly affected by fertilization and by weather conditions. While nutrient utilization is lower in years with poorer weather conditions, nutrient utilization increases in years with normal conditions. This can be seen in the results of the linear-plateau model, which compared nutrient and yield dependence in 2017 and 2020. Based on this model, we can say that under normal weather conditions the optimum nitrogen rate in Pernolec is around 70 kg ha−1. As the variation from normal conditions increases, the nutrient requirement increases as the optimal dose of N raised to 97 kg ha−1 N in 2017. Another important finding is that mineral fertilizers can be completely replaced by PS applied in higher doses (51 t ha−1 in our case). PS has a low C:N ratio, and the mineralization of slurries is rapid, providing a huge amount of available nutrients at the beginning of the season before symbiosis with mycorrhizal bacteria fully develops. Replacing mineral fertilizers with PS can provide comparable yields without a negative effect on soil pH value.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10.3 390/land11020187/s1, Table S1: Forms and doses of mineral fertilizers and pig slurry (PS) according to the fertilizer treatments applied in Pernolec. Cumulative doses for the entire three-year crop rotation. Table S2: Forms and doses of mineral fertilizers and pig slurry (PS) according to the fertilizer treatments

applied in Kostelec. Cumulative doses for the entire three-year crop rotation. Table S3: The longterm mean precipitation (Mean; 1977–2016 for Pernolec; 1982–2016 for Kostelec; mm) and the sum of precipitation (mm) in individual months in 2017 and 2020 in Pernolec and Kostelec. The comparison between long-term mean and actual (2017, 2020) precipitation was done according to [63]. Table S4: The long-term mean temperature (Mean; 1977–2016 for Pernolec; 1982–2016 for Kostelec; ◦C) and the average temperature (◦C) in individual months in 2017 and 2020 in Pernolec and Kostelec. The comparison between the long-term mean and actual (2017, 2020) temperature was done according to [63]. Table S5: The effect of the year (2017, 2020) and fertilizer treatment on FP grain and straw yield (t ha−1) in Kostelec.

**Author Contributions:** Conceptualization, L.H., L.M. and E.K.; methodology, E.K.; software, L.H, L.M. and K.K.; formal analysis, L.H., L.M. and K.K.; investigation, L.H., L.M. and K.K.; resources, E.K. and P.C. writing—original draft preparation, L.H.; writing—review and editing, L.H., L.M. and ˇ E.K.; visualization, L.H. and K.K.; supervision, E.K.; project administration, E.K.; funding acquisition, E.K. and P.C. All authors have read and agreed to the published version of the manuscript. ˇ

**Funding:** This research was funded by the Ministry of Agriculture of the Czech Republic, grant number RO0418, Czech National Agency for Agricultural Research, grant number QK1810010, QK21020155 and QK21010124, and EU project H2020 677407—Soil care for profitable and sustainable crop production in Europe.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to the non-use of experimental animals or human subjects.

**Informed Consent Statement:** No human trials were conducted during the study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank the staff at the experimental station in Pernolec, Pavel Beneš and Vera Folejtárová, and the staff of the experiment in Kostelec nad Orlicí, Petr Iviˇcic, Vlatimil Basaˇr and Jaroslav Málek.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Perceived Causes and Solutions to Soil Degradation in the UK and Norway**

**Niki Rust 1, Ole Erik Lunder 2, Sara Iversen 3, Steven Vella 4, Elizabeth A. Oughton 1, Tor Arvid Breland 2, Jayne H. Glass 5, Carly M. Maynard 5, Rob McMorran 5 and Mark S. Reed 6,\***

**Abstract:** Soil quality is declining in many parts of the world, with implications for the productivity, resilience and sustainability of agri-food systems. Research suggests multiple causes of soil degradation with no single solution and a divided stakeholder opinion on how to manage this problem. However, creating socially acceptable and effective policies to halt soil degradation requires engagement with a diverse range of stakeholders who possess different and complementary knowledge, experiences and perspectives. To understand how British and Norwegian agricultural stakeholders perceived the causes of and solutions to soil degradation, we used Q-methodology with 114 respondents, including farmers, scientists and agricultural advisers. For the UK, respondents thought the causes were due to loss of soil structure, soil erosion, compaction and loss of organic matter; the perceived solutions were to develop more collaborative research between researchers and farmers, invest in training, improve trust between farmers and regulatory agencies, and reduce soil compaction. In Norway, respondents thought soils were degrading due to soil erosion, monocultures and loss of soil structure; they believed the solutions were to reduce compaction, increase rotation and invest in agricultural training. There was an overarching theme related to industrialised agriculture being responsible for declining soil quality in both countries. We highlight potential areas for land use policy development in Norway and the UK, including multi-actor approaches that may improve the social acceptance of these policies. This study also illustrates how Q-methodology may be used to co-produce stakeholder-driven policy options to address land degradation.

**Keywords:** conservation agriculture; deliberative democracy; q-methodology; regenerative agriculture; soil conservation; sustainable land management

### **1. Introduction**

"Countries can withstand coups d'état, wars and conflict, even leaving the EU, but no country can withstand the loss of its soil and fertility". (Rt Hon Michael Gove, former Secretary of State for the Environment, speaking at the British parliamentary launch of the 'Sustainable Soils Alliance', October 2017).

The ground beneath our feet is not only a substrate upon which we traverse this earth but is also a vital component of our natural capital. Soils are the foundation of terrestrial

**Citation:** Rust, N.; Lunder, O.E.; Iversen, S.; Vella, S.; Oughton, E.A.; Breland, T.A.; Glass, J.H.; Maynard, C.M.; McMorran, R.; Reed, M.S. Perceived Causes and Solutions to Soil Degradation in the UK and Norway. *Land* **2022**, *11*, 131. https:// doi.org/10.3390/land11010131

Academic Editors: Guido Wyseure, Julián Cuevas González, Jean Poesen and Christine Fürst

Received: 24 November 2021 Accepted: 13 January 2022 Published: 14 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

food production, supporting directly or indirectly 95% of our food production [1]. Along with providing a substrate to grow our food, soils also confer other essential ecosystem services, such as water storage and filtration, nutrient cycling, biodiversity and carbon storage [2]. However, demand for food, increasing human populations and the effects of climate change are placing unprecedented pressures on soil. Over the last 70 years, the supply of global per capita food calories increased by about one-third, with the use of irrigation water roughly doubling and use of inorganic nitrogen fertiliser increasing nearly nine-fold [3]. At the same time, climate change has led to faster rates of warming on land than the global mean and altered precipitation patterns, which have contributed to altered growing seasons and regional crop yield reductions [4]. With rising human populations, coupled with increased individual wealth, it is expected that food demand will grow by as much as 70% by 2050; an estimated 46% of that demand needs to come from increasing food production [5]. This increase in food productivity must be achieved whilst significantly reducing greenhouse gas emissions from agriculture, if warming is to be restricted "well below" 2 ◦C, as proposed in the Paris Agreement [6]. How this is achieved without negatively impacting soils any further remains a challenge.

Soil quality 1 in many parts of the world is declining due to a combination of physical, chemical and biological degradation coupled with socio-economic drivers, reducing the soil's ability to undertake these important ecosystem functions [7]. Globally, 20–30 gigatons of soil are lost each year due to water erosion [7] and climate change is projected to increase erosion from water and reduce levels of soil organic carbon, especially in drylands [4]. There is thus an urgent need to develop and encourage widespread adoption of effective and profitable sustainable soil management practices [8,9]. This is articulated in Sustainable Development Goal 15, which aims to "protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss" [10], and its Land Degradation Neutrality target which aims to counterbalance expected losses with measures to achieve equivalent gains within the same type of land [11].

There are many competing methods to deal with agricultural soil degradation at different governance scales: from multilateral policies such as the United Nations Convention to Combat Desertification (UNCCD) and the proposed EU Soils Directive, to national and sub-national policies and measures designed to incentivise and regulate the management of soils. These policies typically seek changes in management at farm and field scales, for example through the adoption of soil-improving cropping systems and other sustainable land management technologies and approaches (e.g., WOCAT [12]). The lack of scalable policy options was cited in the UNCCD's Global Land Outlook [9] as a key barrier to more sustainable land management, but there are no easy solutions given the different social, cultural, economic, environmental and technological contexts in which policies and practices need to operate [8]. Again, the attractiveness and appropriateness of different options for policy and practices differ based on the subjective experience and contrasting knowledge and values of the people the policies are meant to serve.

Policies and practices that can tackle the multiple causes of declining soil quality are urgently needed and stakeholder engagement in the policy formulation process is crucial for this complex issue, given the subjective and value-laden nature of both the causes of and solutions to the challenge. Effectively representing diverse stakeholder perspectives in decision-making processes can lead to better informed, more durable, and flexible outcomes across a wide range of contexts (De Vente et al., 2016). Policies created through deliberative democracy can align better with social and cultural norms, resulting in increased trust and ownership of problems and solutions; together, this can lead to decisions that are more likely to be accepted and implemented, helping to achieve environmental goals more effectively [13,14].

As interest has grown in participatory approaches to policy making and other forms of deliberative democracy, methods have been sought to represent and integrate the range of perspectives, values and beliefs held by citizens in the policy-making process [15]. The application of Q-methodology to the co-production of policy options with stakeholders has been used by Rust [16] and Addams and Proops [17] as a form of deliberative democracy. These studies had the normative goal of representing more diverse perspectives in the policy-making process. They also had a pragmatic goal of improving the quality of decisions or range of policy options based on more comprehensive information inputs and/or improving the acceptability of policies based on deeper insights into the way publics conceptualise environmental issues.

In this study, we used Q-methodology to understand a wide range of stakeholder perspectives that could inform the design of socially acceptable options in agricultural soil management policy and practice. To address the lack of scalable policy options noted above by the UNCCD, this was done in the UK and Norway which are two countries experiencing similar types of soil degradation that are broadly representative of soil quality issues and climatic variation across northern temperate regions of Europe. The countries have contrasting agricultural policy environments, with Norway not being a part of the EU and the UK in the process of leaving the EU when the research was conducted. The countries also have quite different models of social democracy in land governance [18], which provides an opportunity to consider how the different land tenure regimes influence policy formulation and application. Both countries are interested in the co-development of policies to increase food production whilst reducing the environmental impacts of agriculture. By understanding where agricultural stakeholders agreed and disagreed over causes and solutions to declining soil quality in each country, we sought to highlight potential scalable options for land use policy development. This study also illustrates how Q-methodology may be used to co-produce stakeholder-driven policy options to address land degradation.

#### **2. Materials and Methods**

*2.1. Study Sites*

2.1.1. UK

Like much of the rest of Europe, the UK has a long history of unsustainable soil management practices, leading to a loss of soil structure and fertility. About 25% of the total land area of the UK is suitable for arable cropping, with an average farm size of 81 hectares [19]. Currently, soil erosion exceeds the rate of soil formation in many areas in the UK, with around 17% of arable land showing signs of erosion, although as much as 40% may be at risk of further degradation [20]. The cost of soil erosion to the UK has been estimated at £45 million a year, including £9 million in lost production [21]. UK soil is being lost at a rate ten times that which it is created [2], with dramatic economic implications.

A comparison of soil nutrient balances from the year 2000 to 2019 shows a 24% decrease for nitrogen and a 46% decrease for phosphate (in kg per hectare) [19]. Soil erosion, compaction and loss of organic matter are thought to cost arable farmers an average of £5584 per year [22] and English water companies spend £21 million a year on addressing soil erosion [23]. Improving soil management in the UK is therefore not only an environmental but also an economic imperative. Soil quality decline in the UK is more pronounced in arable regions due to the highly intensive practices used, such as monocropping, use of heavy machinery, overuse of chemical inputs and a lack of integration of organic material [21,24].

#### 2.1.2. Norway

Only 3.1% of the total land area in Norway is suitable for arable cropping, with an average farm size of 23.9 hectares in 2016; cereals can only be grown on one third of this area due to limiting natural conditions [25]. Although agricultural policies in Norway advocate multifunctional agriculture [26], regional agricultural specialisation, known as "kanaliseringspolitikken", was introduced in the after-war period, which led to increased agricultural production by incentivising cereal production in lower-lying areas [27]. In the last two decades, the total Norwegian cereal yield has declined due to a reduction in the area used for cropping [28]. Despite this decline, the Norwegian government has set a target of increasing food production by 20% by 2030 from 2010 levels, to meet projected population growth in Norway [29]. Three counties (Akershus, Østfold, and Hedmark) in southeastern Norway produce 60% of the country's cereal; however, soil organic matter (SOM) content has declined in the region, with an average loss of 1% of SOM a year from 1991 to 2001, which is not sustainable [30]. The underpinning governance and institutions (both formal and informal) are strongly communal in character [26]. The long history of collective land management, the regulation of the Norwegian land market and the selfimposed limits to farm scale are in contrast to the generally unregulated land market and existence of larger-scale farms in the UK.

#### *2.2. Research Design*

Q-methodology is a mixed-methods approach using interviews to explore participants' subjective understanding of a topic using Q sorts where respondents rate the extent to which they agree with statements, which are then analysed using by-person factor analysis, correlating people with others who hold similar opinions based on their Q-sorts. Q-methodology was chosen due to its capacity to shed light on complex, subjective phenomena where individuals hold differing views and values [30]. It allows for exploration of tensions in knowledge and perspectives between stakeholders that may affect the effectiveness and acceptability of a land use policy. The results can show areas of statistical agreement and disagreement, whilst also revealing distinct narratives emerging from groups of respondents [31,32]. When applied to situations with conflicted stakeholder dynamics, Q-methodology can be useful in identifying common ground among diverse stakeholders in situations where conservation or resource management is contested [16,33]. This makes the method particularly useful for this study due to the above benefits.

#### *2.3. Data Collection*

The research undertaken in the UK took place in late 2018 (when the UK was still part of the EU) and in Norway in mid-2019. Q-methodology studies commonly begin by using a qualitative approach, where interviews are undertaken with a range of stakeholders on a study's topic to gather the diversity of opinions on the phenomenon in question. This data collection can be enhanced or replaced with a literature review. This qualitative step is used to develop the "concourse", which is the range of views (listed as statements) held on a topic, followed by a structured, quantitative interview where participants rank the concourse statements, usually based on the extent to which they agree/disagree. During these interviews, qualitative information is gathered from participants on their decisionmaking processes and preferences. Because the concourse is designed to cover as closely as possible all perspectives on a topic, and participants are chosen to cover the range of views, then random sampling from the wider population is not necessary. Because of non-random sampling and smaller sample sizes, conclusions cannot be generalised but the aim is to understand the range rather than the frequency of the views, and to find points of convergence or divergence of opinion.

The concourse for this study was developed by interviewing 18 European agricultural stakeholders on causes of declining soil quality and corresponding solutions. Interviewees were purposefully chosen to represent researchers, land managers and other stakeholders from ten European countries participating in the wider project, SoilCare, on which this study is based. Ten researchers and eight other stakeholders (representing agricultural unions, farmers and other landowners) were interviewed. An interview guide was used, which was piloted on a subset of the sample population and amended due to feedback. Interview themes and prompts are shown in Table 1. Interviews were undertaken by telephone or Skype and lasted an average of an hour. Free, prior informed consent was obtained from all interviewees and ethical approval was gained from Newcastle University. Interviews were recorded with permission from the participants and later transcribed. Interviews were conducted in English, apart from one which took place in Italian, which was later translated to English for analysis.

**Table 1.** Interview guide used to develop the concourse.

A narrative review was undertaken, based on a broad-based search for relevant material, to provide further evidence to supplement the interviews and further expand the concourse. This review was to ensure that the topic was sufficiently covered by the statements developed from the interview data. Data were then analysed using a thematic analysis focusing on reasons for soil quality decline and solutions for how to fix this. A total of 142 statements was obtained from across all interviews and the literature review, which included statements both for the problem Q-set and the solution Q-set.

Similar statements for each set were merged, whilst trying to retain as far as possible the original wording of the interviews to capture the intent of the source. For both studies, some statements arising from the literature were amended subtly to match the country's context, e.g., changing the statement "EU agricultural policy" to "Norwegian agricultural policy", and adding local problems such as drainage. For the UK study, this resulted in 41 statements for the "problems" Q-set and 34 statements for the "solutions" Q-set, and in Norway, this resulted in 42 problem statements and 36 solution statements (see Tables A1–A4, Appendix A).

A "Q-sort" is the ranking of the Q-set by participants. Data collection for the Q-sort was undertaken via an online survey using Google Forms. The Q-sort survey was first piloted on a subset of the target population and subsequently adapted following feedback to improve question clarity and to include additional statements that were not captured via the interviews or literature review. Participations then ranked the statements on a scale of −2 (strongly disagree) to +2 (strongly agree). The UK survey was distributed via soilspecific newsgroups, British agricultural union members and by sharing on agricultural social media channels. A total of 61 UK respondents undertook the survey: 19 scientists, 19 farmers, 16 agricultural advisers, three water company employees who work in agriculture, two nature conservationists, one agricultural union representative and one civil servant. For the Norwegian study, a link to the survey was distributed in "Plantenytt", a newsletter from the government extension service Norsk Landbruksrådgivning Øst and to a local "soil education group". Forty-two Norwegian farmers took part in the survey, as well as six agricultural advisers and five scientists, totaling 53 respondents. The substantial weighting towards farmers in the Norwegian study was deemed acceptable due to the smaller average farm size in Norway and the historical legacy of communal land management which is embedded in national agricultural institutions [26]. However, the findings of our analysis may need to be interpreted in light of the greater diversity of stakeholders in the UK study.

At the end of the survey, participants were asked what they thought was the leading cause of declining soil quality and the most important solution to solve this problem. Participants could choose a statement from the Q-sort or add a new statement. These open-ended questions were used to find out what, subjectively, respondents thought were the most important drivers for causing declining soil quality and how to fix these. Data from these open-ended questions were analysed via thematic analysis. Quotes in the results section are used to highlight common sentiments as well as responses that stood apart from the rest. Quotes from the Norwegian study were translated into English.

#### *2.4. Analysis*

Data from the Q-sorts were analysed using KenQ (https://shawnbanasick.github. io/ken-q-analysis, accessed on 10 January 2022). First, a principal component analysis (PCA) was used to identify the groups of participants who ranked their Q-sorts similarly, also known as a "loaded factor". Flags were automatically added to respondents that significantly loaded onto these factors at *p* < 0.05.

For the UK study, the PCA for the problem Q-sort revealed eight factors with Eigenvalues >1 (which together explained 67% of the variance) but most loaded onto factors 1–4 (which together explained 53% of the variance). Large datasets, such as in this study, run the risk of inflating the Eigenvalues [34]. Because of this, we focused on the first four factors for the problem set as this explained over half the variance. A Varimax rotation was then applied to the four factors, which calculated the highest variability between factors. A *z*-score was calculated based on the average ranking participants gave to the statement within each factor group. Respondents that significantly loaded onto more than one factor were excluded from subsequent analysis because their inclusion gives little information about the clustering of opinions. Statistical disagreement (and agreement) between participants was set where *p* > 0.01, which meant that the groups of participants did (not) rank the statements differently at the 99% confidence level. The PCA for the solutions Q-sort revealed eight factors with Eigenvalues > 1 (which together explained 79% of the variance) but most loaded onto factors 1–3 (which together explained 65% of the variance). The rest of the solutions analysis followed the same process as with the problem Q-sort.

For the Norwegian study, the analysis followed the same procedure as the UK study. For the problem sort, eight principal components with Eigenvalue above 1 were extracted through the PCA, which explained 69% of the variance. Most of the participants loaded onto the first three problem factors, which together explained 51% of the variation, and these three factors were carried forward for further analysis. For the solution sort, eight factors with Eigenvalues above 1 were extracted, explaining 78% of the variance, though as respondents loaded onto factors 1–3, explaining 63% of the variance, these three factors were used in further analysis.

#### **3. Results**

This section describes results from the problems Q-sorts (Table A1, Appendix A: UK; Table A2, Appendix A: Norway) and solutions Q-sorts (Table A3, Appendix A: UK; Table A4, Appendix A: Norway). The number of respondents loading onto each factor (i.e., who ranked statements similarly) is shown in Figure 1 (UK problem Q-sort), Figure 2 (Norway problem Q-sort), Figure 3 (UK solution Q-sort) and Figure 4 (Norway solution Q-sort). Results are grouped under the key defining factors that emerged from each Q-sort, which are summarised in short, narrative phrases based on the main defining traits of each factor. Key areas of consensus and disagreement that emerged across these different groupings are then highlighted.

**Figure 1.** Professions of UK respondents loading onto the four problem factor groups.

**Figure 2.** Professions of Norwegian respondents loading onto the three problem factor groups.

**Figure 3.** Professions of UK respondents loading onto the three solution factor groups.

**Figure 4.** Professions of Norwegian respondents loading onto the three solution factor groups.

*3.1. Perceived Problems Causing Declining Soil Quality*

3.1.1. UK Study

Factor 1: "Intensive Agriculture to Blame"

This factor was defined by respondents who were significantly more likely to think the problems causing declining soil quality were due to "intensive use of soil without time to recover" and "overuse of inputs", more strongly agreeing with these statements than other factors. In contrast, they strongly disagreed that the problem was caused by the fact that "soil has become too saline", ranking this statement more negatively than other factors.

#### Factor 2: "Farmers Need to Change"

This factor was defined by respondents more strongly agreeing than other factors that "lack of knowledge of soils amongst farmers" and "some traditions of farmers are damaging" were causing problems in soil quality. Conversely, they more strongly disagreed with statements regarding "declining level of nutrient status", "loss of number of wild species" and "I do not believe there is a problem with soil quality" than other respondents.

#### Factor 3: "It's the EU, Not Farmers That Are to Blame"

Respondents here were defined by more strongly agreeing than other factors with the idea that "EU agricultural policy" was the cause of declining soil quality, along with "lack of knowledge of soils amongst farmers" and "natural local climate constraints". Conversely, they strongly disagreed that the problems were "use of contractors" and "loss of numbers of wild species" compared with other respondents.

#### Factor 4: "Weather and Farm Management to Blame"

Respondents here more strongly agreed than other factors that "pressure on farmers to produce at a low cost", "choice of cropping system" and "flooding or drought" were causing problems with soil. Conversely, they more strongly disagreed with statements regarding "lack of knowledge of soils amongst farmers", "overuse of inputs" and "distrust of scientists by farmers" were causing problems, when compared with other respondents.

#### Areas of Agreement and Disagreement

Respondents in all factors strongly agreed that soil quality was declining due to loss of soil structure, and agreed/strongly agreed that compaction, soil erosion, loss of organic matter and insufficient knowledge exchange were other causes (Table 1). In contrast, they did not think that the causes were due to farmers having little control over their land or due to a distrust of scientists by farmers. Conversely, the only area of statistical dissensus between each factor was "lack of knowledge of soils amongst farmers" (Table 1).

#### Leading Causes of Declining Soil Quality

There were two themes that were frequently mentioned by UK respondents as the leading causes of declining soil quality when answering this open-ended question. The first group blamed market pressures for pushing farmers into intensifying farming, with a sentiment that an ever-increasing drive to produce more food at cheaper costs was a fundamental driver of unsustainable land management, including soil quality decline. This could relate to the Q-set statement 21, "pressure on farmers to produce at low cost", which respondents in Factors 1, 3 and 4 strongly agreed was a cause for declining soil quality. This sentiment is captured by an agricultural adviser (UK2) who said:

"There is an increasing demand to produce cheaper food for a larger population using the same/declining land area. Pressure is put on producers by supermarkets and the general public to provide food to contracts, often unknowingly, which results in poor management choices".

Conversely, the second group blamed farmers and thought that intensive agricultural practices, such as ploughing and insufficient crop rotation, were the leading causes of soil quality decline. Many respondents felt this was due to a lack of understanding by the farmer of better soil management practices. One nature conservationist (UK4) summarised this theme by saying the problems causing declining soil quality were due to:

"Traditional' farming practices and cropping, which means too many farmers not being innovative/open to new methods. Time to start re-thinking about how we measure what makes a successful farm-it's not all about productivity".

This sentiment was not reflected in the answers to the problem Q-sort. One of the reasons for this could be that it encapsulates many of the problem statements related to farm management, as it is a multi-faceted and complex problem.

#### 3.1.2. Norway Study

Factor 5: "Disconnection between Farmer and Soil"

Respondents in this factor were more likely to rank "poor management of the soil" as one of the main reasons for the decline in soil quality. This group disagreed more strongly with the statement "I do not think there is a problem with soil quality". Instead, they ranked statements on agricultural practices and farmers' knowledge as leading causes for declining soil quality, such as "farmer has lost the finer touch with his land", "overuse of input like fertiliser and chemicals", and "lack of knowledge of soil amongst farmers".

#### Factor 6: "There Is No Problem with the Soil Quality"

Respondents in this factor disagreed that agricultural practices are reasons leading to a decline in soil quality such as "intensive agriculture to blame" and "overuse of input like fertilisers and chemicals". They also strongly disagreed with the statements "use of contractors", "too much leased land" or "farmers have lost the finer touch with their land". They agreed more strongly than others with the statements "too little advice on soil-improving practices" and "lack of knowledge-sharing between scientists, advisors, and farmers" as problems for soil quality.

#### Factor 7: "Industrialised/Intensive Agriculture to Blame"

Respondents in this factor thought the problems were often outside of the farmer's actions and responsibility compared to factor 5, being significantly more likely to agree on "pressure on the farmer to produce at low cost", and "intensive agriculture" than the other factors. This group also more strongly agreed about structural characteristics like "too large farms" and "high share of leased land" as problems of declining soil quality compared with other factor groups.

#### Areas of Agreement and Disagreement

Respondents in the three factors agreed/strongly agreed that soil quality was declining because of "soil erosion", "repetition of the same crop, year after year", and "loss of soil structure". There were also numerous areas of statistical disagreement between the factor groups (Table A2, Appendix A), such as knowledge/education, environmental conditions and management of the farm.

#### Leading Causes of Declining Soil Quality

There were two common perceived causes of declining soil quality. The first was an increase in use of large machinery causing soil compaction, captured by the statement (N5):

"Larger farms stimulate heavier machinery leading to more compaction" and " . . . modern machinery can drive in unfavourable conditions".

The second aspect was lack of crop rotation, which respondents felt contributed to declining levels of SOM, while some connected monocultures to the regional specialisation policy.

#### *3.2. Perceived Solutions to Address Declining Soil Quality*

#### 3.2.1. UK Study

#### Factor A: "Anti-Innovation"

This factor was defined by respondents more strongly agreeing that there is not much we can do to improve soil quality and that the problems were due to natural climatic constraints. They also disagreed with innovations and increasing early adoption of new techniques to solve the issue, ranking these statements more negatively than other factors.

#### Factor B: "Yes to Financial Incentives but No to Regulation"

Respondents here more strongly agreed that financial incentives could be a solution but more strongly disagreed that restrictive policies, such as more regulation (including for fertilisers and to reduce water usage) and creating a Soil Directive, would improve soil quality.

#### Factor C: "Early Adoption of New Techniques"

This factor was defined by respondents more significantly agreeing to increasing early adoption of new techniques as a solution to declining soil quality. They also more strongly disagreed that solutions were maintaining small farms, giving more freedom for farmers to manage their land as they would like, and that farmers have already tried lots of things.

#### Areas of Agreement and Disagreement

Respondents loading onto the three factors strongly agreed that more research should be done in collaboration with farmers, and all agreed in investing in education and training (Table A3, Appendix A). They also agreed that we should work towards improving trust between farmers and regulatory agencies and initiatives to reduce compaction. Respondents did not think changing the timing of tillage would improve soil quality. There was disagreement on numerous solutions, particularly around maintaining small farms, increasing the early adoption of new techniques and giving more freedom to farmers to manage their land.

#### The Most Important Perceived Solutions to Addressing Soil Quality Decline

There were two main themes that emerged in the responses to the open-ended question, with the first (and most common) requesting improved knowledge exchange between agricultural stakeholders. This links to the Q-set statements on "more research should be done in collaboration with farmers" and "investing in education and training", to which all factors agreed. This theme can be best encapsulated by a quote from a researcher (UK5) who said the solution lay with:

"Two-way communication between farmers, researchers and policy makers. Even the best solutions will not work if they can't be shown as favourable or acceptable to the farmer".

The second theme was around suggestions of using soil-improving cropping systems, or derivatives thereof, such as diverse crop rotations, direct drilling and reduced tillage. This related to many of the solution Q-set statements, such as on cover crops, rotation and less use of heavy machinery.

#### 3.2.2. Norway Study

Factor D: "Farmer-Led Demonstration and Innovation"

Respondents in this factor were more likely to rank "setting examples to follow; if one farmer succeeds others will follow", "more innovation" and "more targeted mapping of soil threats" as solutions to declining soil quality than others. This group disagreed more strongly than others on "more small farms" and "reduction of leased land" as solutions to increase soil quality and was the only group that was neutral on the statement "reduce use of heavy machinery".

#### Factor E: "No More Regulation or Financial Incentives"

Respondents in this group agreed more strongly on "farmers have already tried many measures to improve soil quality" compared with other factors. They disagreed on "more use of cover crops", "financial incentives", "creation of a soil directive", "more regulation of fertiliser use", and "more regulation" as solutions.

#### Factor F: "Society Needs to Change"

The respondents in this factor distinguished themselves from the others by strongly agreeing on "society needs to change focus on what farmers produce". This group also agreed that the solutions could be to "reduce use of heavy machinery" and "more use of cover crops", though not at the *p* < 0.01 level.

#### Areas of Agreement and Disagreement

Respondents agreed on "less soil compaction" and "more variation in crop rotation" as ways to improve soil quality (Table A4, Appendix A), as well as on statements related to education, such as "investment in education and training" and "more farmer demonstration days". Respondents did not think "there is not much we can do with the cropping system to improve soil quality" or that the "problems are due to natural, climatic variations". Further, respondents were strongly against "more use of financial penalties" and were neutral or disagreed with "financial incentives" as a solution.

#### The Most Important Perceived Solution to Declining Soil Quality

More than half of the respondents mentioned "soil organic matter", "cover crops" or "crop rotation" in the open-ended section as solutions to improve soil quality. In addition, more drainage was mentioned by eight respondents as the most critical measure to increase soil quality in the open-answer section, a factor not discussed at all in the UK survey.

#### **4. Discussion**

Understanding the range of stakeholder perceptions of the causes of, and solutions to, declining soil quality is useful as it can highlight potential tensions and agreement that might affect the acceptability of land management policies and measures. In our work in the UK and Norway, whilst there were disagreements between respondents on the perceived causes of soil degradation, there was consensus on numerous soil-specific factors, e.g., compaction, soil erosion and loss of organic matter. Both groups agreed that the underlying drivers of declining soil quality were related to wider issues around industrialised agriculture and demand for cheap food, which many farmers felt were out of their control. When it came to solutions, some stakeholders felt that society needs to change in order to address these underlying drivers. Knowledge exchange between agricultural stakeholders was also seen as key. However, many respondents were against further regulation or financial mechanisms including both incentives and penalties.

When focusing on the causes of declining soil quality, studies show that UK soils are threatened by soil erosion, compaction and organic matter decline [21], which reflected the main problems that UK respondents believed were causing declining soil quality. Respondents in Norway also thought soil degradation was due to soil erosion and loss of soil structure, reflecting findings in southeast Norway, where erosion and loss of soil structure have been linked to increased soil compaction [35]. However, Norwegian respondents considered lack of crop rotation as a problem causing soil decline, which was not noted in the UK study, perhaps reflecting the fact that crop rotations were at the time incentivised in the UK via the EU Common Agricultural Policy's three crop rule [36].

Reducing compaction was agreed to be key to improving soil quality for UK and Norwegian respondents. There has been significant interest in the effects of compaction over the last few years in both Norway and the UK, with numerous research projects, training events, innovations and industry-led technology to help address this problem (e.g., [35,37,38]). This suggests compaction is a salient issue for respondents. However, some of the ways for dealing with compaction, such as reducing usage of heavy machinery, were not highly rated by respondents in this survey. More research would be needed to understand why this is.

Industrialisation of the agri-food sector was thought to be a driver of soil degradation. This perception reflects the significant structural changes in southeastern Norway, described by Bjørlo and Rognstad [39] in their report "Barely recognisable" (translated from Norwegian). When analysing the answers to the open-ended question about the main problem causing declining soil quality, both British and Norwegian respondents often highlighted the complex nature of soil degradation, related to external pressures

along the food supply chain such as consumers demanding cheap food and agricultural policy and supermarkets dictating farm management. One UK farmer summarised this sentiment succinctly by stating: "give a farmer the right tools and he can put things right but remember he is only a puppet in a political system". Competing demands were thought to be placed on farmers, pulling them in different directions and this was thought to have a negative effect on the soil. To illustrate this tension, one UK adviser stated that "the machinery industry wants to sell big heavy machinery and the agronomy industry wants to sell more chemistry and soil health is the loser". The pressure for farmers to produce more food as cheaply as possible appeared to be part of the symptom of industrialised agriculture where farmers felt trapped and unable to improve their soil quality due to these powerful external market forces.

Whilst respondents in the UK study felt there had been a decline in soil quality, many respondents in factor 6 of the Norwegian study ("I do not think there is a problem with soil quality") did not agree. There can be several reasons to why this may be, such as respondents in factor 6 conceptualising "soil quality" in a different way to others, thereby not considering there to be a decline. For instance, a crop consultant wrote, "I do not think that soil quality has gone down, farmers are harvesting higher and higher yields". This might reflect a more historic definition of the term "soil quality" that focused on productivity rather than wider ecosystem services [40], where continued application of fertiliser and pesticides can mask underlying soil quality issues [41]. In addition, the larger proportion of non-farmers in the UK study group may have resulted in a greater emphasis on declining soil quality, with the scientists in the UK group most commonly identifying industrial agriculture as a causal factor in declining soil quality.

When it came to improving soil quality, many respondents in both studies were neutral towards or disagreed with financial measures, including penalties and incentives. This is a finding also established in other studies [16,42] whereby farmers felt financial incentives in particular were bribes to coerce farmers into doing what others wanted them to do. Whilst there may be some reluctance to agree to using financial incentives to change farmer behaviour, when implemented effectively they can change farmer practices and produce environmental benefits [43,44]. Leaving the EU presents the UK with an opportunity to revolutionise its agricultural policy and there is increasing interest in paying farmers for providing essential ecosystem services, such as soil conservation [45]. However, given that numerous respondents in this study did not think financial incentives could reduce soil degradation, it remains to be seen whether this approach will result in widespread uptake or improved soil quality, especially if fundamental drivers of soil degradation are not also addressed. For instance, if supermarkets and consumers continue to demand and purchase high quantities of cheap food, it is possible that market forces may undermine Government incentive schemes if the profits that farmers make from selling cheap, industrially produced food is more than what the Government can offer. Equally, supermarkets often tie farmers into contracts, with strict requirements on yield and quality of food but limited attention towards how the food is produced. To transform the agri-food system, supermarkets should also start requiring food to be produced in more sustainable ways [46].

Many UK and Norwegian farmers in this study did not believe EU or national intervention could improve soils, such as by creating a Soils Directive, and were also more negative with regards to any form of regulation. In the UK, this may partly be as a result of longstanding political opposition on the issue; in 2012, UK Ministers, together with Germany, France, The Netherlands and Austria, played a key role in blocking an EU Soils Directive [47]. This finding may also reflect the fact that the survey ran during the EU "Brexit" negotiations, where trust in the EU by many UK citizens was at a low point, suggesting a lack of faith in the UK's national application of the Common Agricultural Policy. It remains to be seen whether trust can be rebuilt between British farmers and policymakers as the UK leaves the EU and devises its own agricultural policies.

Investing in education and training were additional solutions that respondents agreed upon. Research has shown that education and training can be effective at spreading awareness and encouraging uptake of more sustainable agricultural practices [48,49]. This may work best with farmers who are more open to learning about new topics and trying novel approaches. However, conservative farmers that are more risk-averse and less willing to change might be less likely to attend training events or try new practices [50] and it could be these farmers that are undertaking the most soil-damaging practices. Targeting these hard-to-reach farmers has continued to prove challenging though one way of addressing this could be to frame knowledge exchange events in ways that attract these farmers by focusing on aspects they are passionate about, and where the event is run by someone whom they respect and relate to. In particular, local peer-to-peer knowledge exchange events have been identified as offering scope for strengthening land manager networks and facilitating behavioural change through exchange of information and experience [51,52]. Opportunities also exist to use online information and integration to influence farmers to change their practice, although more understanding of the effectiveness of this type of approach is needed [53]. Another solution agreed upon by many respondents was to undertake more research in collaboration with farmers. The EU Horizon 2020 funding stream promotes a "multi-actor approach" for agricultural research projects, encouraging a diverse group of stakeholders to work together rather than research solely (or primarily) being conducted by researchers [54]. This approach has many potential benefits as it can help promote greater understanding of different perspectives, building empathy, making research more robust, allowing quicker uptake of results, and grounding research in nonacademic stakeholder experiences and knowledge, as well as others [55]. This collaborative approach may help stakeholders understand their epistemological differences and build trust to work together more effectively and respect each other's perspectives. Given that one of the suggested solutions was to build trust between regulators and farmers, future work should encourage participation of regulators in multi-actor projects. In the Norwegian study, "farmer demonstration days" were considered as an agreed solution to improve soil quality, where researchers, extension services and farmers meet to discuss both theoretical and practical aspects of agriculture. These demonstration days could provide a valuable opportunity for knowledge exchange between researchers, farmers and other stakeholders. Similar events are held in the UK and have been met with great success from the farmers attending.

#### **5. Conclusions**

To be well-informed, equitable and transparent, public decision-making needs to take account of the views of the diversity of stakeholders they may affect. Taking these perspectives into account in the policy-making process has the potential to deliver more robust decisions that are more beneficial for the environment and more likely to be implemented. Participatory processes can elicit a more inclusive range of perspectives than conventional consultative processes, revealing areas of consensus and disagreement that can inform policy development. They are also able to capture the likely social impacts of proposed policies, which are often neglected in favour of more straightforward environmental and economic appraisals [16,56]. Using Q-methodology to analyse the diversity of stakeholder perspectives, this research has shown that there is a diversity of perspectives on the problems of and solutions to declining soil quality across different professions within the agricultural sector in Norway and the UK.

Respondents in both countries found it easy to agree on the physical processes causing declining soil quality (in both countries, respondents pointed to a loss of soil structure and soil erosion). It was harder to find agreement on social and political drivers from the Qsorts, other than a lack of knowledge exchange in the UK. However, analysis of qualitative data suggested that respondents primarily blamed industrial agricultural methods, which in turn, they blamed on market drivers, pushing down farm-gate prices (in the UK) and regional specialisation policies (in Norway). Although these drivers of declining soil quality are difficult to address in the short term, and market drivers are outside the control of policymakers, the proposed solutions were pragmatic, focusing primarily on capacity

building measures. Respondents in both countries agreed that more investment was needed in training for farmers to use soil-improving cropping systems as an important way of improving soil quality. Respondents in Norway were strongly against the use of financial penalties to encourage the use of these cropping systems, and UK respondents believed that trust needed to be built between farmers and regulators, and that more research needed to be done in collaboration with farmers. It may be possible to engage farmers in action research, including the development of evidence-based training tailored to their needs, drawing on both existing evidence and findings from new collaborative research.

Linked to this, future research might integrate Q methodology with Delphi or other structured elicitation techniques to further triangulate and increase the robustness of findings. Notwithstanding the sample sizes in this research, it is important to note that Q methodology alone should not be used to generalise findings to wider populations, and so these findings should be seen as indicative of the views of some stakeholders in each country, rather than as an authoritative representation of the perspectives of these groups in general.

Although the limited sample makes generalisations inadvisable at national scales, areas of consensus are important for policy makers to understand, as they could indicate areas where policy changes might be more acceptable to a range of stakeholders, addressing the challenge of creating scalable policy options noted by the UNCCD. It is also useful to highlight areas of disagreement among stakeholders, so that further consultation can be carried out to understand the basis of dissensus and its likely impact on policy implementation. For example, in this study we highlighted the diverging view of perspectives on the underlying causes of declining soil quality, which variously blamed farmers (who "have lost touch with their land and are afraid of doing something new"), policy-makers ("it's EU policy, not farmers that are to blame"), the industrial system ("intensive agriculture to blame") and external forces from society ("pressure to produce at low cost").

As policymakers in the UK, Norway and other countries grapple with the challenge of feeding growing populations whilst mitigating climate change, there is a greater need than ever before to develop policies that are acceptable, implementable and sustainable. In this context, policies are needed that address the widest possible range of real and perceived causes of declining soil quality, harnessing the adaptability and ingenuity of farmers and other stakeholders as part of wider attempts to address systemic market and policy failures across the agri-food system. Dealing with soil degradation requires tackling underlying drivers and this study has highlighted numerous solutions for addressing this challenge that are acceptable to a range of agricultural stakeholders.

**Author Contributions:** Conceptualization: M.S.R., E.A.O.; Data curation: N.R., O.E.L., N.R., S.I., S.V., E.A.O., T.A.B.; Formal analysis: N.R., O.E.L.; Funding acquisition: M.S.R.; Investigation: N.R., O.E.L., S.I., S.V., E.A.O., T.A.B., M.S.R.; Methodology: N.R., O.E.L., S.I., E.A.O., T.A.B., M.S.R.; Supervision: M.S.R., N.R., T.A.B.; Project administration: M.S.R., N.R.; Writing—original draft: N.R., O.E.L., N.R., S.I., S.V., E.A.O., T.A.B., M.S.R.; Writing—review & editing: J.H.G., C.M.M., R.M., M.S.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by EU Horizon 2020 project 'SoilCare' (Soil Care for profitable and sustainable crop production in Europe, H2020-SFS-2015-2). This funder had no role to play in the design, data collection, analysis or interpretation of results.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Newcastle University SAGE Faculty Ethics Committee.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request and approval from the study sites representative co-authors.

**Conflicts of Interest:** MR is CEO of Fast Track Impact Ltd. and Research Lead for IUCN UK Peatland Programme. The other authors declare no competing interests.

#### **Appendix A**

**Table A1.** Average Q-sort scores for each of the four factors identified as causing problems by UK respondents. Bold text indicates distinguishing statement at *p* < 0.01, underlined text indicates consensus statements; scale from −2 (strongly disagree) to 2 (strongly agree).

**\*** Factor 1—Intensive use of agriculture to blame; Factor 2—Farmers need to change; Factor 3—It's the EU, not farmers that are to blame; Factor 4—Weather and farm management are to blame.

**Table A2.** Average Q-sort scores for each of the four factors identified as causing problems by Norwegian respondents. Bold text indicates distinguishing statement at *p* < 0.01, underlined text indicates consensus statements; scale from −2 (strongly disagree) to 2 (strongly agree).

\* Factor 5—Disconnection between farmer and soil; Factor 6—There is no problem with the soil quality; Factor 7—Industrialised agriculture to blame.

**Table A3.** Average Q-sort scores for each of the four factors identified as solutions to improve soil by UK respondents. Bold text indicates distinguishing statement at *p* < 0.01, underlined text indicates consensus statements; scale from −2 (strongly disagree) to 2 (strongly agree).

\* Factor A—Anti-innovation; Factor B—Yes to financial incentives but no to regulation; Factor C—Early adoption of new techniques.

**Table A4.** Average Q-sort scores for each of the four factors identified as solutions to improve soil by Norwegian respondents. Bold text indicates distinguishing statement at *p* < 0.01, underlined text indicates consensus statements; scale from −2 (strongly disagree) to 2 (strongly agree).

\* Factor D-Farmer-led demonstration and innovation; Factor E—No more regulation or financial incentives; Factor F—Society needs to change.

#### **Note**

1 Definitions of "soil quality" vary and have progressed from focusing solely on agricultural production to a broader focus on the complex and diverse functions that soil confers to humans and our environment [7]. Here, we define soil quality as "the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation. In short, the capacity of the soil to function" [57].

#### **References**

**Felice Sartori, Ilaria Piccoli \*, Riccardo Polese and Antonio Berti**

Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy<EMAIL_ADDRESS>(F.S<EMAIL_ADDRESS>(R.P<EMAIL_ADDRESS>(A.B.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-049-827-2841

**Abstract:** The evaluation of the effects of conservation agriculture during the transition from conventional tillage to no-tillage requires numerous indicators to be considered. For this purpose, we monitored changes in a multi-parameter dataset during a three-year experiment that combined three tillage intensities (conventional tillage—CT; minimum tillage—MT; and no tillage—NT) with three soil covering managements (tillage radish cover crop, winter wheat cover crop and bare soil). Using a multivariate analysis, we developed a Relative Sustainability Index (*RSI*) based on 11 physical (e.g., bulk density and penetration resistance), chemical (e.g., soil organic carbon and pH) and biological soil properties (e.g., earthworm density) to evaluate cropping systems sustainability. The *RSI* was most affected by tillage intensity showing higher *RSI* values (i.e., better performances) in reduced tillage systems. Specifically, the *RSI* under NT was 42% greater than that of CT and 13% greater than that of MT. Soil covering had little impact on the *RSI*. Among the tested parameters, the *RSI* was increased most by saturated hydraulic conductivity (+193%) and earthworm density (+339%) across CT and NT treatments. Our results suggest that conservation agriculture and, particularly, reduced tillage systems, have the potential to increase farm environmental and agronomic sustainability.

**Keywords:** conservation agriculture; no tillage; minimum tillage; principal component analysis; soil quality index; scoring function

#### **1. Introduction**

Conservation agriculture (CA) is defined as the combination of three principles: minimum soil disturbance, permanent soil organic cover and species diversification [1]. In addition to reduced management costs, CA is considered to enhance several ecosystem services (soil physical and chemical properties, soil organic carbon (SOC) and biodiversity) [2,3] and prevent some soil threats, such as soil erosion [4–6]. Although some of these benefits remain less clear, the worldwide adoption of CA grew to 12.5% of 2016 global cropland. At odds with this growth trend, there is Europe, where only 5% of total cropland is managed with CA. One country that has shown particularly limited adoption is Italy—less than 300,000 ha (a mere 2% of agricultural land) [7].

Adoption of CA has suffered slow adoption in Europe primarily due to the long transition time that follows conversion from conventional agriculture to CA before the positive effects are realized. During this crucial period, farmers face reduced crop yield and new equipment expenditures. Conversion to CA also requires a permanent soil covering, yet another cost that would benefit from economic support [8]. A key reason behind the very limited use of CA in Italian agrosystems is the long conversion time (more than five years) required before SOC, fertility and nutrient use efficiency benefits are observed [9,10]. Most studies have considered transition time only as a function of a single parameter, such as soil physical properties [11,12], yield [13–16], net SOC stock [17,18], soil aggregate stability, biodiversity, SOC content [19,20], earthworm density, or CO2 emission reduction [21]. However, each of these exerts an effect on CA. As such, we suggest that a

**Citation:** Sartori, F.; Piccoli, I.; Polese, R.; Berti, A. A Multivariate Approach to Evaluate Reduced Tillage Systems and Cover Crop Sustainability. *Land* **2022**, *11*, 55. https://doi.org/ 10.3390/land11010055

Academic Editors: Amrakh I. Mamedov, Guido Wyseure, Julián Cuevas González and Jean Poesen

Received: 14 December 2021 Accepted: 30 December 2021 Published: 31 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

holistic approach capable of considering multiple parameters may provide a better means by which to evaluate the effects of CA.

The ecosystem services delivered by CA justify the need for environmentally conscious policymakers to consider economic support of the practice through a program such as the "green payments" program already established in the EU Common Agricultural Policy [22]. Alternatively, yield losses and/or other negative effects could be limited or compensated in some fashion. In either case, programs such as these are effective only when the protocol created is adaptable to local area specifics but is assessable by a single, consistent set of criteria. As is often described in the literature, an index represents one way to determine and compare the impact of different management strategies [23,24]. Similarly, the literature has already identified potential soil quality indicators to comprise an index: physical soil property measures (soil hydraulics, penetration resistance and bulk density) [25,26] plus soil aggregate stability [27], soil C and N content and earthworm density [28]. Masto et al. [29] previously adopted a statistical methodology to determine the impact of different management strategies on soil quality and sustainability using a dataset with several soil characteristics, as reported above. The method involved the application of a principal component analysis (PCA) to derive the weight of the different soil parameters in promoting the sustainability. The derived index showed to be a reliable tool to assess the performance and impacts of alternative land uses and soil management options [23].

In this work, three tillage systems (no tillage, minimum tillage and conventional tillage) were combined with three different soil coverings (tillage radish cover crop, winter wheat cover crop and bare soil) to compare the effects of the main factors influencing CA. A multivariate approach was applied to a dataset of soil quality measures taken during a north Italy field experiment. A sustainability index was calculated to compare different treatment combinations as a function of the selected indicator variability. This study aims to determine the short-term effects of reduced tillage and cover crops on the studied parameters. Our starting hypothesis was that a reduced tillage system combined with tillage radish could minimize conversion time side effects and improve soil properties.

#### **2. Materials and Methods**

#### *2.1. Experimental Design*

The experiment took place at the Lucio Toniolo Experimental Farm, located in Legnaro, PD (NE Italy, 45◦21 N; 11◦58 E; 6 m a.s.l.). The climate is sub-humid with average temperatures between −1.5 ◦C in January and 27.2 ◦C in July. Rainfalls reaches 850 mm annually. The reference evapotranspiration of 945 mm exceeds rainfalls from April to September. The highest rainfalls occur in June (100 m) and October (90 mm), while winter is the driest season, with average rainfalls of 55 mm. The shallow water table ranges in depth from 0.5 to 2 m, with the lowest values recorded in summer.

This three-year study began in spring 2018 and it was designed as a split plot, with two replicates located in a flat area of the Po valley with a maximum slope < 1%. An area of 2 ha was divided into 18 plots, each of 1111 m2. The soil at the site was Fluvi-Calcaric Cambisol [30] with a silt loam texture (25% clay, 50% silt and 25% sand), pH 7.8, 27.1% total carbonate content, <1% soil organic carbon and <0.1% total nitrogen. The main factor was tillage intensity; conventional tillage (CT) was ploughed to a depth of 30 cm and then harrowed to 15 cm; minimum tillage (MT) was tilled with a harrow to a depth of 15 cm; no tillage (NT) was sod seeded with a zero-tillage seeder that included double disks for furrow openers and press wheels for soil firming. Within each main plot, three winter soil coverings were randomized: tillage radish (TR—*Raphanus sativus* L.), winter wheat (WW—*Triticum aestivum* L.) and bare soil (BS), where only residues from the previous year crop were present. Cover crops were seeded on residues from the main crop (always maize, *Zea mays* L.) in autumn 2018 and 2019.

#### *2.2. Field Surveys*

According to what already reported by other authors [25–28], we chose 11 parameters to monitor changes in the condition of the soil: (1) aggregate stability (Agg), (2) bulk density (BD), (3) soil organic carbon (C org), (4) total nitrogen (N tot), (5) gravimetric water content (GWC), (6) penetration resistance (PR), (7) saturated hydraulic conductivity (Ks), (8) earthworm density (EW), (9) mineral nitrogen (N min), (10) pH and (11) cash crop yield (Y) (Table 1). Each parameter was measured at two times. The first measurement was taken immediately after treatment combination adoption (T0) and the second measurement was taken at the end of the three-year period (T1). The method for determination of the measure of each parameter is fully described below.

**Table 1.** Soil parameters used for building the sustainability index.

A continuous value of Agg was determined. The Slakes application [27,31] was employed to soil aggregates in the 0.2–2 cm fraction sampled from the 0–20 cm soil layer. Three randomly selected aggregates from each sample were analyzed to produce a dimensionless slaking index (*SI*) with a value ≥ 0. A low *SI* (<3) represents high aggregate stability, an *SI* between 3 and 7 indicates moderate stability and an *SI* above 7 indicates that the aggregates have low stability. The *SI* was calculated as the difference between the wet aggregate area (*At*) after 10 min of water saturation and the dry aggregate area (*At*0), divided by *At*0, as shown in Equation (1).

$$SI = \frac{A\_{\rm f} - A\_{\rm t0}}{A\_{\rm t0}} \tag{1}$$

The BD was measured in the 0–30 cm soil profile with the core method as described in Grossman and Reinsch [32]. In the studied soil, a BD value of 1.55 g cm−3 was considered a limiting condition to the growth of plant roots [33].

The C org and N tot contents were determined from shallow layer (0–30 cm) samples. The soil was air-dried and sieved at 0.5 mm and the inorganic carbon was removed with an acid pre-treatment. Subsequently, SOC and N tot were determined with flash combustion using a CNS Elemental analyzer (Vario Max; Analysensysteme GmbH, Langenselbold, Germany).

Four sampling areas were selected in each plot for GWC and PR measurements. For the PR, the measures were taken from the 0–20 cm layer and an average PR value was calculated. In each sampling area, a disturbed soil core was collected, weighted and ovendried at 105 ◦C to determine the GWC. For the PR, the measures were taken in each plot with the Penetrologger (Eijkelkamp, Giesbeek, The Netherland). A PR value above 2.5 MPa was considered a limiting factor to plant root growth [34].

The *Ks* [35] was determined using the double-ring infiltrometer method [36]. An inner ring of 60 cm in diameter was used to measure both the row and inter-row areas in the tillage radish plots. The water within the inner ring was maintained at two levels. As one operator measured the time for the water to reach the lower level from the upper level, another added more water to reach the upper level again. This operation was replicated

until the infiltration rate was constant. Meanwhile, the water in the external ring was suspended at an average value between the two levels of the inner ring. Then, the data were analyzed by fitting Philip's equations [37] with the Solver Add-in for Microsoft Excel.

$$i(t) = S \times t^{1/2} + At \tag{2}$$

$$v(t) = \frac{\mathcal{S} \times t}{2}^{-1/2} + A \tag{3}$$

where *i(t)* is the water infiltration (m) and *v(t)* is the infiltration rate (m s−1) expressed as a function of time. Parameters S and A were calculated with the Solver add-in by minimizing the square difference between predicted and observed *i(t)* and *v(t)*. The *Ks* was calculated as below and m is a constant equal to 2/3.

$$\text{Ks} = \frac{A}{m} \tag{4}$$

The EW was measured with a mustard extraction as described by Valckx et al. [38]. The measure was performed by taking an earthworm extraction from the soil surface using a water-suspended mustard in a 25 × 25 cm2 frame [38]. First, we used the number of extracted earthworms to score soil quality [39]. A density of <4 was the lowest score or of "poor" soil condition, a density of 4–8 was "moderate" soil condition and the highest density (>8) was "good" soil condition. Then, the earthworm count was compared amongst the different treatment combinations.

We estimated N min based on samples of the 0–20 cm soil layer. Concentrations of ammonium, nitrite and nitrate were measured using a KCl extraction followed by photometry, as described by García-Robledo et al. [40].

Soil pH was determined from air dried, mixed and sieved (0.5 mm) samples taken from the 0–20 cm soil layer. The pH was measured in a 1 M KCl solution (1:2.5 solid–liquid ratio) [41].

At the end of the cropping season, four biomass samples were collected from each subplot to determine maize grain Y at 27% grain moisture. After the harvest, a grain sample was oven dried at 105 ◦C until it maintained a constant weight to determine the dry mass weight. The Y was expressed in kilogram of dry grain per hectare.

#### *2.3. Data Analyses and Statistics*

First, a mixed-effects model was constructed using tillage, covering and their interactions in each monitored year. These effects were treated as fixed effects and the block effects as random. Post hoc pairwise comparisons of least-squares means were performed, using Tukey's method to adjust for multiple comparisons, with a *p* < 0.05.

To calculate a soil quality index, we relied on the method of Masto et al. [23,29]. The procedure requires that indicators be selected once they have been surveyed and normalized with linear or non-linear scoring functions, so that higher scores represent better-performing observations. The indicators and their weights were determined using the multivariate analysis method of Andrews et al. [42,43], which has been adapted and applied to many studies evaluating long-term practices [23,29], combinations of various crop rotations under different residue managements [44,45] and different tillage practices [46].

The sampled data were normalized with a linear scoring function [23] by applying Equations (5)–(7).

$$S = \frac{\mathbf{x}\_{ij} - \mathbf{x}\_{i\text{ min}}}{\mathbf{x}\_{i\text{ max}} - \mathbf{x}\_{i\text{ min}}} \tag{5}$$

$$S = -\frac{\mathbf{x}\_{ij} - \mathbf{x}\_{i\max}}{\mathbf{x}\_{i\max} - \mathbf{x}\_{i\min}}\tag{6}$$

$$S = \frac{|\mathbf{x}\_{ij} - \mathbf{7}|}{|\mathbf{x}\_i - \mathbf{7}|\_{\max} - |\mathbf{x}\_i - \mathbf{7}|\_{\min}}\tag{7}$$

where *xi max* is the maximum value measured during the *i* parameter survey and *xi min* is the smallest. The *S* value ranges between 0 and 1, which corresponds to the minimum and maximum values, respectively, observed in the *i* parameter. Equation (5) was used as a "more is better" scoring function for C org, GWC, Ks, EW, N min, N tot and Y. Alternatively, the parameters Agg, BD and PR were scored with Equation (6), according to a "less is better" approach. Finally, Equation (7) was used for pH scoring. In this way, treatment combinations that most favorably impacted the parameters scored highest.

The Relative Sustainability Index (*RSI*) was calculated as the sum of the observed parameter score, weighted with principal component analysis weighting factors (*PWs*). These factors were calculated according to Masto et al. [23], by selecting principal components (PCs) explaining at least 10% of the variability. Within each of these PCs, loaded factors (values > |0.2|) were selected and their correlations were measured [43]. In cases in which r > |0.8|, only the factor with the highest load was used for *RSI* calculation, together with all the other uncorrelated highly loaded factors. The percentage of variation explained by each PC provided the *PW*. The *RSI* was calculated with Equation (8).

$$RSI = \sum\_{i=1}^{n} PW\_i \times S\_i \tag{8}$$

To normalize the *RSI*, this was divided by the highest *RSI* value obtained. A total of 36 *RSIs* were calculated, one per treatment combination replication in survey T0 and another in T1.

*RSI* differences amongst tillage, soil covering and their interaction were tested with mixed models and the model with the smallest Akaike's Information Criterion (AIC) was selected [47]. Post hoc pairwise comparisons of least-squares means were performed using Tukey's method to adjust for multiple comparisons, with a *p* < 0.05. The statistical analyses were performed using Microsoft Excel 2016, ClustVis [48] and SAS (SAS Institute Inc., Cary, NC, USA), version 5.1.

#### **3. Results**

Below is a description of the mixed model results comparing changes in the 11 indicators of soil quality under the tested treatments over time (Table 2). Table 3 reports the average 2019 and 2020 values used to calculate RSIs.

**Table 2.** Comparison of *p*-values among the linear mixed-effect model analysis of observed parameters (Agg—aggregate stability; BD—bulk density; C org—soil organic carbon; N tot—soil total nitrogen; GWC—gravimetric water content; PR—penetration resistance; *Ks*—saturated hydraulic conductivity; EW—earthworm density; N min—mineral nitrogen; Y—yield, CC—cover crop).

\*, \*\* and \*\*\* mean *p* < 0.05, <0.01 and <0.001, respectively.

**Table 3.** Descriptive statistics of studied parameters for T0 and T1 surveys (Agg—aggregate stability; BD—bulk density; C org—soil organic carbon; N tot—soil total nitrogen; GWC—gravimetric water content; PR—penetration resistance; *Ks*—saturated hydraulic conductivity; EW—earthworm density; N min—mineral nitrogen; St. Dev.—standard deviation; Var. Coef.—coefficient of variation).

All values of Agg, GWC and pH were significantly higher at T0 than at T1. Overall, average Agg was higher at T0 (4.50) than at T1 (3.19) and was characterized as of high-tomoderate stability, according to the *SI* range (0.3–6.1). At T0, all aggregate samples, except one, were of moderate stability (>3). The exception sample value, collected from treatment combination MT–BS, was 2.9. During the T1 survey, 44% of the observations were <3 (high aggregate stability) and the lowest values found in the reduced tillage systems (NT and MT). The measures of the GWC were strictly related to the pedoclimatic conditions on the sampling dates, with the GWC ranging from 20% to 25% in 2019 and from 12% to 22% in 2020. Cover crop treatments showed significant effects on GWC, as demonstrated by values of 18.3% in TR and 20.3% in WW, while BS had an intermediate value. Despite the significantly lower pH values at T0 versus T1, the pH values maintained non-critical averages (7.36 in T0 and 7.05 in T1).

Between survey T0 and T1, the N tot, PR and Ks all increased significantly. The N tot rose from 0.88‰ at T0 to 1.01‰ at T1. During each survey, the N tot maintained a modest variability, as indicated by the coefficients of variation at T0 (0.26) and T1 (0.13). The PR test values differed from an average of 0.70 MPa in T0 to an average of 1.34 MPa in T1. In the second survey, the PR was not only significantly higher, but also more variable than it was in T0; all of the PR observations across both surveys registered below the 2.5 MPa threshold. The PR differences occurred among the differing tillage systems. Specifically, CT reported a PR of 0.88 MPa, which proved to be significantly higher than the 1.18 MPa observed under NT. The PR result under MT was intermediate. Last, the *Ks* increased by 158% between T0 (3.4 × 10−5 m s−1) and T1 (8.7 × 10−5 m s−1). This parameter showed it was also significantly impacted by different tillage intensities, as shown by the average *Ks* values of 1.05 × 10−4 m s−1 in NT, 3.58 × 10−5 m s−1 in CT and an intermediate value in MT.

The parameters BD and EW were affected by the time × tillage interaction. Despite a generally limited effect on the BD across the various treatments, the average BD under CT was lower during the first survey (1.39 g cm−3) versus the second survey (1.45 g cm−3). All measures of BD were less than its 1.55 g cm−3 threshold. In the case of EW, variability was higher; it ranged between 0 and 20 (Table 3). Among the treatments, during T1, the EW differences were, on average, significantly higher (13.17) under NT than under CT (3.00).

The C org, Y and N min parameters resulted as unaffected by all factors tested. On average, the C org was 0.83% and displayed only a modest variability within and between the surveys. Similarly, Y (10.00 Mg ha−1, on average) and N min (24.54 mg kg−1, on average) showed no significance among the treatment combinations in the different surveys.

The values presented in Table 3 were normalized. The average of each treatment combination is presented in Figure 1 (biochemical parameters) and Figure 2 (physical parameters). Normalization allows higher values to be associated with parameter improvement and wider areas to represent an overall sustainability increment.

**Figure 1.** Biochemical parameter scores with average values in treatment combinations at T0 and T1 surveys (C org—soil organic carbon; N tot—soil total nitrogen; EW—earthworm density; N min—mineral nitrogen; Y—yield; BS—bare soil; TR—tillage radish; WW—winter wheat; CT—conventional tillage; MT—minimum tillage; NT—no tillage).

**Figure 2.** Physical parameter scores with average values in treatment combinations in T0 and T1 surveys (Agg—aggregate stability; BD—bulk density; GWC—gravimetric water content; PR—penetration resistance; *Ks*—saturated hydraulic conductivity; BS—bare soil; TR—tillage radish; WW—winter wheat; CT—conventional tillage; MT—minimum tillage; NT—no tillage).

Figures 1 and 2 show sizeable differences between the treatment combinations and two years. The correlation matrix between each parameter pair is shown in Table 4. As expected, the highest correlation resulted between the C org and N tot (r = 0.924). To identify which of these highly-correlated parameters could best explain treatment variation—and warrant inclusion in the *RSI*—we performed a principal component analysis (PCA).

Table 5 presents the PCA results. Each parameter was weighted according to the treatment variation it explained based on the PC selected.

**Table 5.** Results of principal component analysis under different treatment combination in different years. Bolded factor loads were considered as high. Bolded and underlined factor loads determined for each variable were those the PC considered in the *RSI* calculation. The weighting factor (*PW*) for each variable was equal to the variation explained by the PC selected (Agg—aggregate stability; BD—bulk density; C org—soil organic carbon; N tot—soil total nitrogen; GWC—gravimetric water content; PR—penetration resistance; *Ks*—saturated hydraulic conductivity; EW—earthworm density; N min—mineral nitrogen).