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PK05:Soil quality and health:A review
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PK05:Soil quality and health:A review

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a presentation by Prof. Krishna Saxena on the Soil quality and health

a presentation by Prof. Krishna Saxena on the Soil quality and health

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  • 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 1
  • 2. 5/27/2010 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 2
  • 3. 5/27/2010 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 3
  • 4. 5/27/2010 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) 4
  • 5. 5/27/2010 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). 5
  • 6. 5/27/2010 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) 6