Cover Crops and Soil Quality Index

Over the years, several approaches were developed to quantify soil quality in response to management practices (Huddleston. 1984; Wymore, 1993; Islam, 1996; Andrews et al., 2004; Aziz et al„ 2013). Islam (1996) used inductive additive approach ‘ considering higher values of soil properties are better indicator of soil quality’. To assess the soil quality, he emphasised on ‘key indicator properties’ of soil or crops that are sensitive and early indicators of soil’s functionality, easily identifiable and precisely measurable, consistent changes hi response to management practices and complementary to soil and crop properties associated with crop production and annual nutrition. In contrast, Andrews et al. (2004) developed the soil management assessment framework (SMAF) approach to calculate soil quality indices (SQI) in response to macrosystem management practices. This framework outline comprised of three basic parts: (1) indicator selection; (2) indicator interpretation and index integration; and (3) scoring functions to get overall soil quality index (Andrews et al.,

  • 2002). Example:
    • a) Cornell Soil Health Test (CSHT'): By adopting the SMAF, Cornell University soil testing laboratory developed CSHT, which is an integrative soil quality assessment tool using physical, chemical and biological properties of the soil. There are 42 potential indicators and given weightage are required for this CSHT scoring (Moebius-Clune et al., 2016).
    • b) Alabama Soil Quality’ Index: This is similar as CSHT; however, a slight modification was done that is site-specific. A difference was that a weight was assigned to each factor based on the judgement of the scientists’ panel instead of the unweiglied average in CSHT (Bosarge, 2015).
    • c) Haney’s Soil Health Test: It is quite distinct compared to other soil-quality assessment methods. This test uses a unique set of parameters that are related to soil microbial activity and functions. This test uses water-extractable organic C and water-extractable organic N contents ratio, which was a sensitive indicator of soil microbial activity (Haney et al., 2012).
    • d) OSU Soil Quality Test: The Ohio State University soil quality test is a highly simplified method in which a dilute buffered reagent is used to react with active fractions of SOM, changing the deep purple colour of the solution to a light pink colour or colourless (Fig. 12).
The Ohio State University soil quality test (Islam and Simdenneier, 2008)

Fig. 12 The Ohio State University soil quality test (Islam and Simdenneier, 2008).

The lighter the colour of the suspension after reacting with soil, the greater the amount of active organic matter content and the better the quality of the soil (Islam and Sundenneier, 2008). This field test instantly estimated total SOM, active SOM, available N contents, microbial biomass and soil aggregate stability.

A comparative evaluation on soil health tests (Fig. 13) related to their cost, depth and simplicity of analysis, turnaround, cost and other characteristics were evaluated (Spiegel. 2017).

Several research studies have evaluated and/or measured the impacts of cover crops with other factors on soil quality, in terms of calculated soil-quality indices or ratings (Islam and Weil, 2000). Islam (2010) has calculated soil quality index (> 0 to < 1) based on additive integration of several indicator properties (inductive approach) in response to tillage x crop rotation with and without cover crops over time (2004-2009). The soil-quality properties were: (1) biological properties - total and active microbial biomass, basal and specific maintenance respir ation rates, potentially mineralisable carbon and nitrogen, tuease and dehydrogenase enzyme activities, and biodiversity; (2) chemical properties - total organic C and N, active C and N, particulate organic matter, particulate organic carbon and nitrogen, C and N lability; and (3) physical properties - bulk density and porosity, penetration resistance (compaction), aggregate stability and aggregation indices and plant available water capacity.

Results showed that crop rotation with conventional tillage, no-till had a significant impact on soil physical, chemical and biological quality at different soil depths (Fig. 14). Among crop rotations, no-till com-soybean-wheat (NT-CSW) performed the best in improving soil-quality properties and soil quality over time (2004-2009). The calculation of overall soil quality showed that no-till with cover crop treatments increased soil quality from 60 per cent to 80 per cent at 0-7.5 cm depth.

Further studies have shown that the impact of cover crops significantly influenced soil quality under tillage x cropping diversity systems (Fig. 15). From the results, it was found that soil quality significantly increased with cover crop treatments over time (2004-2014), which directly impacted crop yield (%). As a result, cover crop treatment in a no-till cropping system will undoubtedly increase the soil quality (%). The results imply that multiple cropping systems, along with cover- crops, could be more effective in enhancing soil quality than cropping systems alone.

Demir et al. (2019) studied the effects of different cover crop treatments on soil quality parameters in an apricot orchard. The treatment was carried oirt with hairy vetch, Hungarian vetch,

Soil health tests comparison (Source

Fig. 13 Soil health tests comparison (Source: Spiegel, 2017).

Management-induced changes in temporal soil quality changes (Source Islam, 2010)

Fig. 14 Management-induced changes in temporal soil quality changes (Source Islam, 2010).

Changes of soil quality (%) and crop yield (%) with cropping diversity

Fig. 15 Changes of soil quality (%) and crop yield (%) with cropping diversity.

a mixture of Hungarian vetch (70 per cent) and triticale (30 per cent) and lacy pliacelia winter cover crops. This study reported that cover crops enhanced soil quality parameters like SOM, TN, electrical conductivity, soil basal respiration, structural stability index, aggregate stability, saturated hydraulic conductivity, bulk density, permanent wilting pomt, available water capacity and field moisture capacity. It was found that hairy vetch increased the SOM by 63.5 per cent, hydraulic conductivity by 248.7 per cent, available water capacity by 19.4 per cent, and structural stability index by 9.4 per cent in the 0-20 cm soil depth. The SOM contents increased followed the sequence as control plot < mechanically cultivated plot < lacy phacelia < Triticale < Hungarian vetch < liaiiy vetch.

Mbuthia et al. (2015) studied the effect of long-term tillage, cover crops and fertilisation on microbial community structure and activities that were linked to improvements in soil quality. This smdy characterised the impact of long-term (31 years) tillage (till and no-till), cover crops (Hairy vetch - Vida villosa and winter wheat - Triticum aestivum) and a no-cover control on soil microbial community structure, activity and resultant soil quality calculated using the SMAF scoring index under continuous cotton (Gossypium hirsutum) production on a Lexington silt loam in western Tennessee. Soil quality indices were calculated based on the SMAF (Andrews et al., 2004). Seven of the 13 indices with scoring algorithms that are currently available under the SMAF quality scoring algorithms were used for this study. The selection of the SMAF indices is based on their- role in certain soil functions that can be used as measurements for attaining specific management goals. MBC, pH. P. К and p-glucosidase activity are indices selected for their- role in nutrient cycling. The SOC and bulk density were selected for then- role in soil-water relations, aggregate stability, as well as filtering and buffering. All the selected indices are measures used for the assessment of crop productivity and ecosystem functioning (Andrews et al., 2004). The scores obtained from each indicator are then integrated into a soil quality index by dividing their sum by the total number of indicators used, then multiplying that number by 100. In Table 8, the extractable nutrients P, K, pH, microbial biomass and bulk density resulted in the highest soil quality scores from 0.85 to 1.00; meanwhile, SOC, calculated from soil C and p-glucosidase, had scores below 0.50 resulting in an overall soil quality index (SQI) ranging between 61-71 per cent. The soil quality score of SOC was highest for vetch cover crop treatment as compared to no-cover crop treatments. Cover crop also had a significant effect on P and p-glucosidase scores, which were significantly greater in the vetch cover crop as compared to no cover crop and wheat. The hairy vetch cover crop resulted in a significantly greater SQI as compared to no-cover crop and wheat.

Jokela et al. (2009) studied the effect of cover crops and liquid manures on soil quality indicators in a com silage system. In this study, com was grown for four years on a Bertrand silt loam in rotation with a number of crops and SQI was determined. Table 9 shows the change of soil quality indicators. They reported that cover crop treatment increased the active or labile carbon significantly and showed a good relationship with aggregate stability and microbial biomass. Overall, the use of cover crops with companion crops appears beneficial for com silage systems. The SQI, a composite of scores, based on five soil parameters, ranged from 80-87 in the 0-5 cm depth and 73-83 in the 5-15 cm depth. The SQI differences were mainly due to differences in scores for total organic carbon, water stable aggregates and bulk density, even though differences in treatments were not significant for some of the individual sores (Table 9).

Table 8 Tillage and cover crop effect on soil quality indicators (average) based on the scores as determined by soil- management assessment framework (SMAF) (Source: Mbuthia et al., 2015).

Treatment

TOC

P

К

pH

BD

BG

MBC

SQI (%)

C Crop

Vetch

0.44

0,98

0,99

0.95

0.97

0.24

0.88

70.6

Wheat

0.38

0,95

1.01

0.97

0.94

0,19

0.83

68.2

No cover

0.35

0,95

1.01

0.96

0.95

0.24

0.78

67.5

Tillaae

No-till

0.44

0,93

1,00

0.96

0.93

0.27

0.79

68.5

Till

0.34

0,99

1.01

0.97

0.97

0.17

0.86

69.1

N-rate

0

0.31

0,91

1.03

099

0.93

0.21

0.93

66.2

34

0 39

0.97

1.02

0.98

0.95

0.27

0.84

69.7

67

0.36

1.00

0,99

0.96

097

0.21

0.90

69

101

0.49

1,00

1,00

0.92

0.97

0,19

0.96

70.2

TOC: total organic carbon; P-phosphorous; K-potassium; BD-soil bulk density; BG: P-glucosidase; MBC: microbial biomass C; Nitrogen fertilisation rate (N-rate) - 0,34,67 and 101N kg/ha; SQI: soil quality index (an integration of all the individual quality scores).

Table 9 Soil quality index (soil management-assessment framework) as affected by cover crops and liquid manure

application (Source: Jokela et al., 2009).

Treatment

Scores

TOC

AGG

pH

STP

BD

SQI

0-5 cm depth

KC-F

0.83

091

099

1.00

0,60

86.7

KC-C

0.84

0.94

0.95

1.00

0,61

86.9

RC-F

0.75

0 89

099

1.00

0.50

82.3

RC-C

0.83

091

0.95

1.00

0,60

85.7

IR-C

0.82

0 89

0.97

1.00

0,68

87.2

WR-C

0.72

095

0.92

1.00

0.65

84.6

NC-Man

0.66

0.88

0.98

1.00

0,48

80.0

NC-FN

0.79

0.96

0.94

1.00

0.58

85.3

5-15 cm depth

KC-F

060

0.84

0.96

1.00

0.50

78.0

KC-C

0.62

095

099

1.00

0.57

82.6

RC-F

0.59

0.79

0.94

1.00

0,46

75.5

RC-C

0.61

092

0.98

1.00

0.55

81.1

IR-C

0 60

090

0.91

1.00

0.53

78.7

WR-C

0.55

0.84

0.97

0.99

0.51

77.0

NC-Man

0.48

0.81

0.93

1.00

0.44

73.2

NC-FN

0.62

091

0.91

1.00

0.62

82.5

KC-C, Kura clover-corn; KC-F, Kura clover forage; RC-C, red clover-corn; RC-F, red clover forage; IR-C, Italian ryegrass-corn; WR-C, winter rye-corn; NC-FN, no-cover, N fertilised com; NC-Man, no-cover, manured com.

TOC, total organic carbon; AGG, aggregate stability (from Macro-All); pH, soil pH; STP, soil test P (Bray-1); BD, bulk density; SQI, soil quality index (Soil Management-assessment Framework).

 
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