This document discusses various methods for assessing soil quality and health, including the use of indicators and indices. It proposes that a combination of indicators capturing different soil properties and functions are needed to adequately characterize soil quality, as no single attribute can reflect all soil functions. Several specific soil quality indices are described, including the soil microbiological degradation index, general soil quality index, carbon management index, and QBX index, each of which synthesizes multiple indicator measurements into an overall score. Challenges in selecting appropriate indicators and designing sampling methods are also noted.
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PK05:Soil quality and health:A review
1. 5/27/2010
Soil quality and health Defining soil
• Quality: the fitness of soil, to function within its
• Fundamental resources: Air, water and capacity and within natural/managed ecosystem
soil boundaries, to sustain plant/animal (which!)
productivity, maintain/ enhance water/air quality, and
• Interconnections support human health and habitation
• Policy concerns – Federal Soil Protection • Health: promote plant/animal health; ability to
Act, 1999 (Germany) suppress diseases; Bioprotectants-soil food web-
soil biodiversity
• Primary standards (human health) and
• High quality (high SOC) but poor health (abundance
secondary standards (human well being of pathogens)
and sustainability) • Ecosystem/landscape health inclusive of soil health:
resistance/resilience to stresses and disturbances
Ecological functions of soil: dealing with multiplicity
(FAO, 1995; Masto et al., 2007 ) Why indicators
Production function High yields/incomes • Indicators: measurable changes in
ecosystem structure, composition and
Biotic High bioidversity; abundance of
environmental/living beneficial organisms/functions
function
space function • Reduce the information overload
Climate- High levels of carbon stocks/low (oversimplify!)
regulative/storage levels greenhouse gas emissions
function • Document large scale patterns
Hydrologic function Water availability/reduced flood risks • Help determine appropriate actions: early
warning and corrective measures
Waste/pollution High yields/incomes; good human
control function health
Realizing the neglect of soil
The best indicators (Parisi et al., 2005).
biodiversity
• sensitivity (response vs background natural
variability),
• Neglect in conservation inventories
• good correlation with the beneficial soil
functions, • Lack of methodologies that can extract,
• h l f l
helpfulness i revealing ecosystem processes
in li t identify and quantify diversity
• comprehensibility and utility for land managers: • Soil organisms taxonomy – time-
policy relevance and public acceptance consuming, monotonous, painful, IPR
• Simple, cheap and easy to measure Contrasting hypotheses: most species must
• Knowledge of critical limits, thresholds, be redundant versus a significant role for
standards diversity
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Methodological challenge Forest-farmland linkages
• As a single measurable soil attribute is
unlikely to be correlated with soil
function(s) and measurement of ‘all’ soil
attributes is not practical: minimum
number of indicators (minimum data set)
• a single, affordable, workable soil quality
index is unattainable! (Sojka and
Upchurch 1999)
Earthworm diversity in Himalaya Indicator taxa: an illustration from
Species Colour Size Habitat Functional group
cm3) Himalayan region
Perionyx Dark 5.2 FYM, Vermicompost, Epigeic Indicator feature Indicator of
excavata purple oak forests
Drawida Light 1.9 Agriculture, pasture Endogeic-top soil Perionyx Absence in oak Fire, high intensity of litter
nepalensis grey excavatus forests removal, convex slopes, poor soil
Amynthas Dark 9.5 Agriculture, forests, Endogeic-anecic aggregation
alexandri pink pasture Drawida Abundant Tillage, FYM input, shaded-moist
shaded moist
Metaphire Dark 6.2 Agriculture, forests Endogeic-top soil nepalensis microsites in rainfed agroforestry
anomala red and pasture systems
Metaphire Dark 5.7 Agriculture, forests Endogeic-top soil Metaphire Abundant Irrigated paddy systems on clayey
birmanica brown and pasture
anomala soils
Octochaetona Light 3.1 Agriculture Endogeic-top soil
beatrix pink Collembola with Abundant Low intensity of leaf litter removal
Lennogaster Light 0.4 FYM Epigeic long antennae in forests
sp. brown covered with
Eisenia fetida Dark 1.6 Vermicompost Epigeic dense bristles
red
Cultivated legume diversity in central Himalaya
Scientific name Local name Altitudinal range Nodule Rhizobium- cross inoculation
(mg)
Cajanus cajan Tor 500-1650 m 37 Isolate V. mungo V. V. V.
Glycine max Safed Bhatt 700-1700 m 19 from/Nodul radiata unguiculata angularis
Glycine max Black Bhatt 1000-1500 m 16 ation in
Macrotyloma uniflorum Gehet 500-2000 m 12 V. mungo Yes No Yes No
Phaseolus vulgaris Rajma 1500 2500
1500-2500 m 12
Vigna angularis Rains 1000-2250 m 11 V. radiata No Yes Yes No
Vigna mungo Urd 500-1750 m 31
V. Yes Yes Yes No
Vigna unguiculata Sontha 500-1750 m 10
unguiculata
Lens culinaris Masoor 500-1500 m 9
Pisum arvense Kong 2200-2650 m 5 V. angularis No No No Yes
Pisum sativum Matar 500-2650 m 8
Vicia faba Shiv Chana 500-1500 m 9
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2500
Roots Aboveground residues Seeds Nodulation behaviour
Pisum sativum
Phaseolus vulgaris
2000 200
200
Number of nodules
Number of nodules
30 d
150 30 60 90
60 d 150
B io m as s (g /m 2 )
90 d
100 100
50 50
1500
0 0
RA IA PF OF DF RA BF DF RA CF RA IA PF OF DF RA BF DF RA CF
1000 m 2250 m 2800 m 1000 m 2250 m 2800 m
1000
Glycine m ax Glycine soja
Number of nodules
Number of nodules
200 200
30 60 90
150 150 30 60 90
500 100 100
50 50
0 0
RA IA PF OF DF RA BF DF RA CF RA IA PF OF DF RA BF DF RA CF
0 1000 m 2250 m 2800 m 1000 m 2250 m 2800 m
C.caj V.mun V. ang G.max G.sp V.ung M.uni E.cor
Soil quality index Soil organic matter
• Selection: soil properties/indicators constituting • a primary indicator of soil quality and
the minimum data set
• Transformation: bringing all indicators to a health for both scientists and farmers
common measurement scale • the best surrogate for soil health
• Synthesis: combining the indicator scores into
the index • L bil / ti l t / i bi l/t t l
Labile/particulate/microbial/total
• Statistical tools to avoid disciplinary biases in • soil microbial carbon : total organic carbon
expert opinion based approaches (Bachmann
and Kinzel, 1992; Doran and Parkin, 1996). ratio
• QBX index, soil microbiological degradation • Carbon management index
index (MDI),general index of soil quality (GISQ)
• Soil depth
0-10 cm 10-20 cm 20-50 cm 50-100 cm Mean
Soil organic carbon stock
S o il o rg a n ic c a rb o n (% )
• Cm = Cn * B * T * I 2.8
Cm, the amount of soil carbon some time after land use change
Cn, the amount of soil carbon under the original native vegetation
2.4
B, base factor, with values varying from 0.5 to 1.1 depending on 2
environmental factors and the type of agricultural activities - the
lowest values referring to long term cultivated aquic soils or 1.6
degraded land in the tropics and the highest values to improved
c
pasture and rice paddies 1.2
T, tillage factor - higher values (1.1) for no tillage and lower values for
full tillage (0.9-1.0) 0.8
I, input factor accounting for different levels of input from different
residue management systems: 0.8 for shortened fallow under 0.4
shifting cultivation to 1.2 for high input systems, such as those
receiving regular fertilizer additions. 0
RA HG PF OF
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Land use differentiation in village landscape
Agricultural land use intensification Oak forests Pine forests Rainfed
agriculture
Homegarden
Dominant tree Quercus Pinus Grewia Grewia
Relative area (%) 14 74 11 1
• Population growth, market demand, Tree density (ind/ha) 578 503 107 501
market risks, loss/gain in biodiversity and Irrigation No No No Yes
Tillage No No Less More frequent
ecosystem functions frequent
Manure (t/ha/year) Nil Nil 18 38
• Increase in productivity-lack of agricultural
p y g Leaf litter removal 50-70% 80-90% Nil Nil
land use expansion-increase in Woody litter removal
Lopping
80-90%
20-60%
80-90%
80-90%
Nil
80-90%
Nil
Low intensity
external/modern inputs-decrease in inputs canopy
removal
canopy
removal
canopy
removal
removal all
through the year
during during during
but increase or no change in outputs- winter winter winter
Grazing 1001 LU 513 LU 637 LU Nil
susbstitution of labour, capital or days/ha/yr days/ha/yr days/ha/yr
technology for land Fire Nil Yes Nil Nil
Net primary productivity 12.8 10.9 8.1 10.2
(t/ha/yr)
Annual biomass removal/NPP 53.1 64.2 85.7 84.1
3.5 Methodological puzzles and
3 challenges
2.5
• sample soil from similar depths in different land uses and
2 express SOC as t carbon/ha using bulk density values.
1.5 HG • measure bulk density first and then calculate the
DC A axis 2
1 RA sampling depths in different land uses to obtain the
0.5 PF same mass
• Selection of soil attributes
0 OF
-2 -1 • Sampling design and intensity
-0.5 0 1 2 3 4
• Cordyceps sinensis (ascomycetes growing on
-1 caterpillars)
-1.5 • Application of biodiversity science to the benefit of the
-2 society
DCA axis 1
Carbon management index, CMI,
Change/impact studies
(Blair et al. 1995)
• an indicator of the rate of change of SOM in response to
land management changes, relative to a more stable • repeated measurements on a single site
reference soil:
• Carbon pool index (CPI) = Total C of a given land
• paired sites
use/Total C of the reference land use • chronosequences where neighbouring
• Lability index (LI) = [Labile carbon content of a given
land use/Non-labile carbon content of a given land use] * sites experienced l d use change at
it i d land h t
[Labile carbon content of the reference land use/Non- different times in the past
labile carbon content of the reference land use]
• Carbon management index (CMI) = CPI * LI * 100 (Murty et al., 2002).
• Landscape CMI: sum of the products of multiplication of
the CMI values of different land uses in a landscape and
their relative areas (%) Collard and Zammit (2006)
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Choosing from the basket of Soil microbiological degradation
enzymes index (MDI)
• Three enzymes viz., phosphomonoesterase, chitinase and phenol
oxidase, as a group reflect relative importance of bacteria and fungi, • the sum of the normalized and weighted
as well as the nature of organic matter complex (Giai and Boerner,
2007). values of the most important parameter
• Phosphomonoesterase (acid phosphatase) activity is often
correlated with microbial biomass (Clarholm, 1993; Kandeler and
Eder, 1993), fungal hyphal length (Haussling and Marschner, 1989)
and nitrogen mineralization (D k et al., 1999)
d it i li ti (Decker t l 1999).
• Chitinase is a bacterial enzyme which converts chitininto
carbohydrates and inorganic nitrogen (Hanzlikova and Jandera,
1993).
• Phenol oxidase is produced primarily by white rot fungi, and is
specific for highly recalcitrant organic matter, such as lignin (Carlisle
and Watkinson, 1994).
General indicator of soil quality
QBX index(Parisi et al. (2000)
(GISQ) (Velasquez et al., 2007).
• PCA analysis of the variables (50) allowing • values based on evaluation of microarthropods’ level of adaptation
to the soil environment life rather than the species richness/diversity:
testing of the significance of their variation Reduction or loss of pigmentation and visual apparatus, streamlined
among land use types; body form, with reduced and more compact appendages, reduction
or loss of flying, jumping or running adaptations and reduced water
• identification of the variables that best retention capacity (e.g., by having thinner cuticle and lack of
differentiate the sites according to the soil
g hydrophobic compounds) are some of the adaptations of
microarthropods to soil environment (Parisi, 1974)
(Parisi 1974).
quality; • the morphotypes varying in terms of their degree of adaptation to
• creation of sub-indicators of soil physical quality, soil quantified as eco-morphological score: eu-edaphic (i.e., deep
soil-living) forms get a score of 20, epi-edaphic forms (surface living
chemical fertility, organic matter, morphology forms) of 1 and Groups like Protura and Diplura have a single value
and soil macrofauna, with values ranging from of 20, because all species belonging to these groups show a similar
0.1 to 1.0; level of adaptation to soil (Parisi et al., 2005).
• combination of all five subindicators into a
general one.
Vegetation attributes as a
Indicator species/taxa
surrogate to the soil quality
• environmental parameters which are expected to regulate soil fauna • Reflect abiotic state of the environment
composition, e.g., climate, soil and vegetation characteristics
• measures inherent to soil fauna community itself, such as higher taxon
richness, indicator taxa and maximum dominance.
• Reveal evidence of impacts or
• only 34-60% of the variance in soil animal richness explained by
environmental variables; Coefficient of variation of soil animal richness
environmental changes
between replicate samples as as high as 60% indicating a high degree of
independence of richness from environmental conditions Ekschmitt et al. • I di t di
Indicate diversity of other species, t
it f th i taxa or
(2003)
• outcome of significant influence of autogeneous dynamics of the population communities (Lawton and Gaston 2001)
under consideration, interaction of this population with predators, parasites
and competitors and by presently indiscernible past conditions (Salt and
Hollick, 1946).
• Focal species, umbrella species, flagship
• positive correlations between species richness of all termites and mean
canopy height, woody plant basal area, ratio of plant richness to plant
species, guilds
functional types, while there was no significant correlation between
individual plant and termite species (Gillison et al. 2003).
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Soil fertility, land quality and farm level
environmental indicators
• Land quality indicators represent generic directives for the functional role of
land, indicating condition and capacity of land, including its soil, weather
and biological properties, for purposes of production, conservation and
environmental management (Pieri et al., 2000).
• (i) measurable in space, i.e., over the landscape and in all countries (ii)
reflect change over recognizable time periods (5-10 years) (iii) showing
relationships with independent variables (iv) quantifiable and usually
dimensionless (v) cost effectiveness and precison of its measurement and
availability of an interpretative framework to translate it in terms of
identifying sustainable management practices (Sparling et al., 2004).
• (i) the yield gap indicator - a measure of the difference between yields
under optimum management conditions and actual yields of the ‘most
suitable crop’ (Monteith, 1990) (ii) soil nutrient balance indicator - measure
of the rate with which soil fertility changes - net differences between
nutrient inputs and outputs (Stoorvogel and Smaling, 1990).
• Control indicators (those based on farmers’ management practices) and
state indicators (those based on recordings of consequences for the farming
system) (Halberg 1998)
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