No-tillage participatory quality index reflects the condition of soil management 1

- Conservation agriculture is fundamental for improving agricultural sustainability. However, the quality of soil management in conservation agriculture systems is highly variable. The goal of this study was to verify whether a no-tillage participatory quality index (PQI) is associated with the physical, chemical, and microbiological attributes of soil. Thus, we sought to validate its use as an indicator of soil management quality. A survey was conducted to assess the agricultural practices of farmers from the western mesoregion of the state of Paraná, Brazil to evaluate the PQI. The quality of soil management for annual crops was related to the PQI, as evidenced by its association with soil physical, chemical, and microbiological attributes. These results confirmed the usefulness of the PQI methodology as a tool for assessing the quality of soil management, demonstrating its sensitivity to short-term changes in management practices. Consequently, this may allow for the monitoring of management quality and inferences about the beneficial effects of the implemented practices.


INTRODUCTION
Conservation agriculture is fundamental for improving agricultural sustainability, especially in tropical and subtropical regions.It is based on three principles: i) minimum soil disturbance (such as no tillage), ii) permanent soil cover, and iii) diversifi ed crop rotation (KASSAM et al., 2009).These conservation practices are directly related to the maintenance and/or improvement of the chemical, physical, and biological attributes of the soil, which enable proper functioning of the soil, making it capable of sustaining agricultural productivity.
The no-tillage practice has been adopted in Brazil since 1972, initially in the state of Paraná, to reduce soil loss caused by water erosion.However, the quality of soil management involving no-tillage in Brazil is highly variable, particularly because most farmers do not adopt the other two principles (CASÃO JÚNIOR et al., 2006).
The quality of soil management is a conceptual variable, and consequently, cannot be measured directly.The use of operational variables is necessary to distinguish between good and poor soil management.Operational variables based on farmer perceptions have been used to quantify conceptual variables for many purposes in many regions of the world (NEZOMBA et al., 2017;NUNES et al., 2020a;TESFAHUNEGN;TAMENE;VLEK, 2011).To measure the quality of soil management, a participatory quality index (PQI) was created based on farmers' responses regarding their agricultural practices.
The PQI aims to assess the quality and effi ciency of the management of the production system, focusing on profi tability and environmental conservation.It is comprised of a set of eight indicators: RI, rotation intensity; RD, rotation diversity; RP, rotation persistence; TF, soil tillage frequency; CT, correct terracing; SC, soil conservation evaluation; BC, balanced fertilization; and AT, no-tillage adoption time.These indicators are evaluated, and a macro-indicator called the PQI is generated.
Since its creation, attempts have been made to validate the PQI as an indicator of the quality of soil management (NUNES et al., 2020a).However, recently, Telles et al. (2020) suggested that some indicators of the PQI should be reviewed because they had a weak correlation with the fi nal PQI index.These indicators were reviewed and altered by the Institute of Rural Development of Paraná (IDR-PR) and approved by the PQI Working Group, which is coordinated by the Brazilian Federation of No-tillage.The current methodology, which lacks validation, was applied for the fi rst time in this study.
Considering that many aspects of soil management are included in the questionnaire, we hypothesized that the quality of soil management, as measured by the no-tillage PQI, relates to soil attributes, and consequently, to its quality.The goal of this study was to verify whether the no-tillage PQI was associated with soil physical, chemical, and microbiological attributes.Therefore, we sought to validate its use as an indicator of good soil management.

Study area
The study area is located in the western mesoregion of the state of Paraná, Brazil (Figure 1).The geology consists of basaltic rocks from the Serra Geral Formation, and Latossolos and Nitossolos (SANTOS et al., 2018) are the predominant soil classes, which have a heavy clay texture and are mainly composed of kaolinite and iron oxides (MELO et al., 2019b).According to the Köppen-Geiger classifi cation system, the climate is Cfa, humid subtropical, oceanic, without a dry season, and with hot summers (ALVARES et al., 2013).
A total of 27 farms in which soil management practices were adopted in the last three years were selected and the farmers were interviewed (according to the questionnaire in the supplementary material).The study was carried out only with those farmers who adopted no-tillage, since farmers with recent plowing in the soil would present good ephemeral structure of the soil.Among the selected producers, the average size of the property was greater than 10 bushels, with the main crops being soybeans and off-season corn.According to the Brazilian Federation of Zero Planting in Crop and Irrigation Residues (FEBRAPDP), based on the evaluation carried out on the PQI (Table 6), the selected farmers were classifi ed as: very bad (0.00-1.99), bad (2.00-3.99),regular (4.00-5.99),good (6.00-7.99),and very good (8.00-10.00).The soil was sampled at three points on each property at a depth of 0-20 cm.

Physical attributes
Soil density, fi eld capacity, macroporosity, and microporosity were measured in stainless steel rings that were 5 cm each in diameter and height.The rings were collected from the center of the evaluated layer (0-20 cm) and protected against water loss until the analyses were performed.After saturation (24 h), the samples were placed on a tension table to determine their fi eld capacity and macroporosity.After this period, the samples were oven-dried (105 °C) until a constant mass was obtained for the determination of microporosity and soil density.
Soil aggregates (≤ 19 mm) were collected and tested for stability in water.After drying under laboratory No-tillage participatory quality index reflects the condition of soil management Figure 1 -Location of the study area conditions, the aggregates were wetted by capillarity for 10 min and sieved underwater (tap water) for 15 min at a rate of 40 vertical cycles per minute.Sieves with openings of 8, 4, 2, 1, 0.5, and 0.25 mm were used, and the mean weighted diameter (MWD), mean geometric diameter (MGD), and aggregate stability index (ASI) were calculated using the following formulas (1): (1) where, MWD is the mean weighted diameter, Diam i is the mean diameter of the size class i, Mass i is mass of aggregates within class i, Mass Sample is the total mass of the sample, MGD is the mean geometric diameter, ASI is the aggregate stability index, Mass >0.25 mm is the mass of water-stable aggregates retained in the sieves with openings of 0.25 mm or larger.

Chemical attributes
Before chemical analyses, air-dried soil aggregates were crushed and passed through a 2 mm sieve.The pH was measured in a 0.01 mol L −1 CaCl 2 solution (soil: solution ratio of 1:2.5, mass:volume).Exchangeable potassium (K + ) and available phosphorous (P) were extracted with Mehlich-1 solution and quantifi ed using fl ame photometry and spectrophotometry, respectively.Exchangeable aluminum (Al 3+ ), calcium (Ca 2+ ), and magnesium (Mg 2+ ) were extracted with a 1 mol L −1 KCl solution and quantifi ed by titration with NaOH (Al 3+ ) and by atomic absorption spectrometry (Ca 2+ and Mg 2+ ).The potential acidity (H+Al 3+ ) was estimated using potentiometry after equilibration with the SMP solution.The cation exchange capacity (CEC) was calculated as the sum of H+Al 3+ , Ca 2+ , Mg 2+ , and K + .These procedures have been described in Teixeira et al. (2017).

Biological attributes
Microbial biomass carbon (MBC) was determined using the fumigation-extraction method (VANCE; where, RP: rotation persistence.RP Grade : grade based on the number of grasses in the crop rotation within a 36-months period (grasses used for haying or silaging are not considered), Equation (4); this grading is presented in Table 1.PF: ponderation factor (1.5 for RP).
Grasses used for haying or silaging are not considered (5) where, TF: tillage frequency.TF Grade : grade based on the tillage frequency (within a 36-months period), equation ( 5); this grading is presented in Table 2 and depends on whether the farmers till the whole area, just the area's borders, or in the terrace channel.PF: ponderation factor (2.0 for TF). ( 6) CT: correct terracing.CT Grade : grade based on the number of times the terraces have overfl owed in the last fi ve years, equation ( 6); this grading is presented in Table 3. PF: ponderation factor (1.0 for CT). ( 7) SC: soil conservation evaluation.OL Grade : grade based on the operations performed at level.SC Grade : grade based on the observations of soil compaction.SE Grade : grade based on the observation of soil erosion, equation ( 7); these grades are presented in Table 4. PF: ponderation factor (1.0 for SC).

No-tillage PQI
The no-tillage PQI was obtained by applying a questionnaire that considered the crop and soil management practices adopted by the farmer in the last three years.Initially, the PQI indicators were calculated based on the farmers' responses.These indicators were: i) rotation intensity (RI); ii) rotation diversity (RD); iii) rotation persistence (RP); iv) soil tillage frequency (TF); v) correct terracing (CT); vi) soil conservation evaluation (SC); vii) balanced fertilization (BF); and viii) no-tillage option time (AT).The PQI indicators were calculated using the following formula: (2) where, RI: rotation intensity.N°C overed : number of months (within a 36-months period) that the soil remains covered.PF: ponderation factor (1.5 for RI), Equation ( 2).
(3) where, RD: rotation diversity.N°S pecies : number of diff erent species (within a 36-months period) used in the crop rotation.PF: ponderation factor (1.5 for RD), Equation (3).Each PQI indicator contributes diff erently to the PQI.The amplitudes and classifi cations of the values of each PQI indicator are presented in Table 6.

Statistical procedures
Principal component analysis (PCA) was performed based on the correlation matrix of the data using R software.The number of plotted components was defi ned by the Kaiser criterion, in which those with a variance higher than one were used.

RESULTS AND DISCUSSION
All the variables were within the expected ranges (Table 7).The coeffi cient of variation of most variables was relatively high, suggesting a considerable variation in soil management quality, particularly considering that these soils are pedogenetically similar and the same management was used (annual crops under no-tillage).
No-tillage is used over more than 33 million hectares in Brazil (IBGE, 2017).However, most farmers do not meet all the requirements for conservation agriculture (TELLES et al., 2019).This suggests that within no-tillage farmers, the quality of soil management and, consequently, soil quality is highly variable.In the present study, despite all soils being managed under no-tillage and presenting similar parent materials and climatic conditions, most soil attributes varied considerably (Table 7).
Most variables with marginal variability were physical, but this pattern was expected.Particle density was highly infl uenced by soil mineralogy and organic matter content.As all soils were pedogenetically similar and only the surface layer (0-20 cm) was analyzed, a marginal variability was expected.These soils commonly contain kaolinite as the main mineral in the clay fraction.However, these soils also contain a considerable proportion of Al and Fe oxides, which tends to increase particle density.Contrasting this, organic matter reduces particle density.Soil density, ASI, microporosity, and total porosity were naturally less variable in these soils (MELO et al., 2018).This was mainly because of the high clay content and the layer (0-10 cm) from which the samples were collected.The pH also showed marginal variability, probably because all the farmers used lime to reduce acidity.Most areas had adequate pH for crop development.However, a few areas presented a pH (in CaCl 2 solution) lower than 5.0, where Al 3+ is expected to be present at higher concentrations in the soil solution.
Not all classes of PQI indicators (very bad, bad, regular, good, and very good) were obtained in this study.This was probably a refl ection of factors such as: i) the study region, which has several technologies in areas of soybean/ maize production, and ii) the studied farmers, who included only those adopting no-tillage for at least three years.Despite this limitation, several associations with soil attributes were observed.All groups of soil attributes (physical, chemical, and microbiological) were related to PQI indicators.Consequently, the PQI methodology can be considered adequate for measuring soil management quality.Although the overall PQI was associated with many of the evaluated soil attributes, some associations were more evident with the PQI indicators that constituted the overall index.This suggests that all PQI indicators, as well as the overall index, should be considered when measuring the quality of soil management.
In these heavy clay soils, lower soil density and microporosity, and higher macroporosity and total porosity indicate soil structural improvement (TAVARES FILHO et al., 2014) because most of their porosity is composed of micropores (MELO et al., 2018).As expected, total porosity and macroporosity were inversely related to soil density and microporosity.However, these attributes were weakly related to the PQI indicators (Figure 2).This was unexpected because the soil structure is supposed to improve by improving the conservation practices as measured by the PQI indicators.For example, increasing the time the area remains with living plants improves the soil structure, mainly because of root growth (ADETUNJI et al., 2020).The aggregation indices (MWD, GWD, and ASI) were positively associated with most PQI indicators (except AC) and the PQI index.This suggests that the methodology accurately measured improvements in soil structural stability but not structural quality.The stability of aggregates in these soils responds intensely to organic matter increments because of their high clay content, which also has a signifi cant metallic sesquioxide content (MELO et al., 2018(MELO et al., , 2019a)).This explains why these attributes are related to many of the PQI indicators that refl ect organic matter dynamics (RP, TF, CT, SC, BF, and AT).
The soil attributes used in this study are associated with important soil processes, and can be considered adequate for validating the PQI methodology.MWD, GMD, and ASI are indicators of aggregate stability and consequently reflect their persistence against disrupting agents and hydration (BARBOSA et al., 2015;MELO et al., 2019aMELO et al., , 2019b)).Soil density, porosity (macroporosity, microporosity, and total porosity), and field capacity reflect the quality of soil structure, with implications for water and gas dynamics (CENTENO et al., 2020).Total organic carbon (TOC) is a central indicator of soil quality and is associated with several processes (LEHMAN et al., 2015).pH, CEC, base saturation, and phosphorous are indicators of nutrient availability, and Al 3+ is associated with plant toxicity.
The PQI and indicators were positively correlated with TOC, suggesting that these indicators were associated with higher organic matter input in the system.Rotation diversity, tillage frequency, correct terracing, and balanced fertilization are factors of soil management that are related to the TOC content in the soil.As an important indicator of soil quality, the strong association between PQI indicators and TOC is essential for its use as an indicator of soil management quality.Studies on these heavy clay soils have shown a high potential for organic matter increment by soil management practices such as manure application (MELO et al., 2019c) and adoption of no-tillage (TAVARES FILHO et al., 2014).
The remaining soil chemical attributes (cation exchange capacity, available phosphorous, base saturation, pH, and exchangeable aluminum) were not good indicators of soil quality in the present study because they were within adequate ranges for crops (Table 7) according to the high fertilization and liming rates suggested in the liming and fertilization recommendation manual for the state of Paraná (PAULETTI; MOTTA, 2019).This was expected because the farmers from the study region use several technologies, with a high input of chemical fertilizers and lime.Consequently, soil chemical attributes, such as nutrient concentration and pH, were not capable of refl ecting the quality of soil management, because most of them were associated with conservation practices (see the calculations of PQI indicators in the supplementary material).Additionally, these soils are weathered from basalt, which is refl ected in their natural slight acidity and high base availability.
The soil pH was measured in CaCl 2 (0.01 mol dm −3 ) solution.The observed values reveal that the acidity of these soils is adequately neutralized in most cases.The CEC of these soils is relatively high for intensely weathered soils but is a reflection of their high clay content and capacity to protect organic matter; consequently, it is within the expected range.Available phosphorus values indicate excess fertilization in several cases.According to the state's fertilization manual (PAULETTI; MOTTA, 2019), values higher than 60 mg dm −3 of available phosphorus can lead to problems in terms of environmental degradation and nutritional deficiency of other elements to plants.Some areas analyzed in the present study presented values higher than this threshold.This reinforces the need for a better evaluation of farmers' fertilization criteria, which is most likely not based on adequate parameters.The principal component analysis showed a correlation between the microbiological attributes that were indicative of soil quality and PQI indicators.The first two components explained 38.44% of the total variability in the data (Figure 2).MBC, acid phosphatase, and arylsulfatase were positively correlated with the PQI indicators, suggesting an increase in soil carbon and protein content, promoting a greater source of energy and nutrients for microbial communities (WEIL et al., 2003).These correlations separated the observations classifi ed as good and very good in the PCA (Figure 2).This suggests that the PQI methodology can accurately measure improvements in soil microbiological quality, whereas No-tillage participatory quality index reflects the condition of soil management biomass and microbial activity, as measured by MBC, β-glucosidase, arylsulfatase, and acid phosphatase, are useful for assessing short-term changes in soil quality (BONGIORNO et al., 2019;NUNES et al., 2020b;VAN ES;KARLEN, 2019;WANDER et al., 2019).
Microbial activity responds quickly to the increased addition of organic matter and proteins by living plants as it is a source of energy and nutrients for microbial communities.Another point is the positive effect of the time of adoption of the no-tillage system on microbiological indicators of soil quality, which are frequently reported and have been associated with a greater retention of crop residues on the soil surface (NUNES et al., 2018;VEUM et al., 2015;VEUM;LORENZ;KREMER, 2019).This explains why these attributes are related to most PQI indicators that refl ect the dynamics of organic matter (RI, RD, RP, TF, CT, BF, and AT).
The SC indicator was not associated with these microbiological variables, considering that this indicator reflects adequate soil management and conservation practices.Such a pattern was unexpected given the sensitivity of microbiological attributes to soil changes (BONGIORNO et al., 2019;VAN ES;KARLEN, 2019).According to the PCA results, β-glucosidase enzyme activity showed a low association with IQP indicators (Figure 2).However, the inverse association with BF indicator reflects the validation of this indicator through β-glucosidase activity.Several studies have shown that chemical fertilization reduces the enzymatic activity of β-glucosidase (ADETUNJI et al., 2017;ČUHEL;MALÝ;KRÁLOVEC, 2019;MULIDZI;WOOLDRIDGE, 2016).
Most soybean farmers in Brazil assess the soil nutrient status through soil analysis.However, they rarely measure soil physical or biological status, except for soil particle distribution (clay, silt, and sand content).Considering this scenario, the PQI, an easy-to-measure approach, can help fi ll this gap and allow farmers to self-assess the quality of soil management (NUNES et al., 2020a).Additionally, the PQI can be used as a tool for the evaluation of payment of ecosystem services as it considers conservation agriculture-related practices.
Finally, these results confirmed the usefulness of the PQI methodology as a tool for assessing the quality of soil management under annual crops, demonstrating their sensitivity to short-term changes in management practices.This will allow for the monitoring of management quality and inferences about the benefits of implemented practices.Despite this, not all major attributes of soil quality (such as compaction and nutrient availability) could be explained by PQI, suggesting that changes must be made in the index to include practices related to their improvement.

CONCLUSION
The quality of soil management under annual crops can be assessed using the no-tillage participatory quality index (PQI), as evidenced by its association with soil physical, chemical, and microbiological attributes.This method has been shown to be sensitive to short-term changes in soil management practices.Consequently, it may allow for the monitoring of management quality and inferences about the beneficial effects of the implemented practices.Practices that are more closely related to soil compaction and nutrient availability must be included to improve the capacity of the PQI to reflect the quality of soil management.

F i g u r e 2 -
Biplots of principal component analysis showing the relationship between soil physical (A), chemical (B) and microbiological (C) attributes (in black) with PQI indicators (in red)

Table 1 -
Grading of the RP indicator

Table 2 -
Grading of the TF indicator

Table 3 -
Grading of the CT indicator

Table 5 -
Grading of the BF indicator

Table 4 -
Grading of the SC indicator : rotation intensity.RD: rotation diversity.RP: rotation persistence.TF: soil tillage frequency.CT: correct terracing.SC: soil conservation evaluation.BF: balanced fertilization.AT: no-tillage adoption time.PQI: participatory quality index RI

Table 6 -
Amplitude and classifi cation of PQI indicatorsNo-tillage participatory quality index reflects the condition of soil management