D5-K1-Janos Tamas-Agroecologica Evaluation C5 4Ver.Pdf
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Agroecological evaluation of agroforestry Prof. Dr. Janos Tamás Water and Environmental Management Institute University of Debrecen European Agroforestry Week Agrof-MM C5 - International training OVERVIEW • AGROFORESTRY - ECOLOGICAL SERVICE • SOIL –SOIL EROSION • WHY GIS • HOW CAN BE USE GIS AS SDSS • SOFTWARE TOOLS Agroecology - Agroforestry Mind Map Silvopastoral system weed control – over grazing FIGECZKY GÁBOR (WWF MAGYARORSZÁG) ÉS SZÉKELYHIDI TAMÁS SZERKESZTETTE: FIGECZKY GÁBOR Agroecological Capacity - Agroforestry • Soil Fertility • Water storage, infiltration an available water content • Provide nutrients to plants. (macro/micro) • Physical-Chemical-Biological parameters • Elevation • Slope • Aspects • Runoff - Erosion • Micro climate • Radiation • Precipitation • Temperature • Evapotranspiration • Optimization of agro technology • Genetics- Species • Cultivation • Water-Nutrition management • Plant protection • Harvesting Soil • Keys to Soil Taxonomy WRB Basic principles The classification of soils is based on soil properties defined in terms of: diagnostic horizons, diagnostic properties, diagnostic materials. 1. Soils with thick organic layers: HISTOSOLS 2. Soils with strong human influence Soils with long and intensive agricultural use: ANTHROSOLS Soils containing many artefacts: TECHNOSOLS Soil organic matter types to determine Organic matter Living biomass under decomposition (biodiversity) Undecomposed organic matter under dry conditions under wet „easy fraction”, litter conditions(peats) Decomposed organic matter humic non humic substances substances Fulvic Humic Humin carbohidrates, acids acids proteines, etc Eight threats for soil degradation - erosion - decline of soil organic matter content - contamination -sealing - compaction - decline in soil biodiversity - salinization - landslides - desertification • Color • Texture • Structure • Bulk Density • Density • Porosity Soil Textural Triangle 10 % clay 60 % silt 30 % sand Silt Loam Soil Water Content Soil degradation Landuse change Global landuse Arable land 11% Grassland 26% Forest 32% Others 31% Agroforestry in erosion control (Young, 1989) Water Erosion Indices of soil erobility for water erosion Strategy for Erosion Control Source Hudson: Soil Erosion and Conservation Land use management (Poel and Kaya, 1991) Agroforestry Soil Conservation Strategy (Dangler and Amstrong, 1982) Agroforestry RUSLE Wischmeier and Smith's Empirical Soil Loss Model (USLE) Soil Erosion Model IDRISI Soil Erosion • Soil Erosion is a common term that is often confused with soil degradation as a whole, but in fact refers only to absolute soil losses in terms of topsoil and nutrients. • This is indeed the most visible effect of soil degradation, but does not cover all of its aspects. Soil erosion is a natural process in mountainous areas, but is often made much worse by poor management practices. Soil Erosion • Erosion models play critical roles in soil and water resource conservation and nonpoint source pollution assessments, including: sediment load assessment and inventory, conservation planning and design for sediment control, and for the advancement of scientific understanding. Data sources USLE FAO documentation Title: Land husbandry - Components and strategy Division: Land and Water Division ISSN: 0253-2050 http://www.fao.org/3/a-t1765e/t1765e0e.htm On lone calculation http://www.iwr.msu.edu/rusle/ RUSLE 2 Science Documentation Revised Universal Soil Loss Equation Version 2 (RUSLE2) https://www.ars.usda.gov/ARSUserFiles/60600505 /RUSLE/RUSLE2_Science_Doc.pdf https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation- laboratory/watershed-physical-processes-research/research/rusle2/revised-universal-soil-loss- equation-2-rusle2-documentation/ RUSLE2 version WEPP (USDA - Water Erosion Prediction Project) The WEPP erosion model computes soil loss along a slope and sediment yield at the end of a hillslope https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion- research/docs/wepp / Environmental Policy Integrated Climate (EPIC) Model • Environmental Policy Integrated Climate (EPIC) model is a cropping systems model that was developed to estimate soil productivity as affected by erosion. EPIC simulates approximately eighty crops with one crop growth model using unique parameter values for each crop. It predicts effects of management decisions on soil, water, nutrient and pesticide movements, and their combined impact on soil loss, water quality, and crop yields for areas with homogeneous soils and management. • DOWNLOAD: http://epicapex.tamu.edu/epic/ • APEX – Agricultural Policy/Environmental eXtender Model http://epicapex.tamu.edu/apex/ SWAT http://swat.tamu.edu/software/ Erosion 2015 Europe SOIL LOSS MODELING E = R * K * C * LS * P Where E: Annual average soil loss (t ha-1 yr-1), R: Rainfall Erosion factor (MJ mm ha-1 h-1 yr-1), K: Soil Erodibility factor (t ha h ha-1 MJ-1 mm-1), EUROPEAN SOIL DATA CENTRE (ESDAC) C: Cover-Management factor (dimensionless), LS: Slope Length and Slope Steepness factor (dimensionless), P: Support practices factor (dimensionless). Farm level model - RUSLE TERRSET IDRISI • RUSLEDEM.rst + FIELDS (transparency) The data used in this example is derived from a dairy farm in Rutland, Massachusetts (about 10 miles (16 km) north of Worcester in Central Massachusetts). • Kfactor; Rfactor; Cfactor; Pfactor C= 0.25 maize C=0.005 hay C= 0.01 tree line RUSLE parameters: DEM: RUSLEDEM field: fields R: rfactor K: kfactor C: cfactor P: pfactor slope threshold: 3.000 aspect threshold: 3.000 length limit: 200.000 unit: feet area threshold: 43560.000 average soil: yes rounding: to shorter patch table: run1PatchTable.txt patch ID: run1PatchID.txt patch total loss: run1PatchTotalSoilLoss.txt field table: run1FieldTable.txt field unit loss: run1FieldAverageSoilLoss.txt field total loss: run1FieldTotalSoilLoss.txt unit soil loss total soil loss ID # of patches area(ac) (t/ac/yr) (t/field/yr) 1 29 7.616 1.878 14.305 •Slope Threshold = 3, Maximum slope 2 9 2.363 0.112 0.265 3 18 4.932 3.179 15.678 length = 200 (feet), select round to 4 3 2.832 0.140 0.397 shorter, set the aspect threshold to 3, the 5 13 3.981 2.784 11.082 6 8 2.276 4.545 10.343 smallest patch size to 43,560 (ft2), the 7 5 5.581 2.371 13.233 default background to 0, and check the unit soil loss total soil loss box to average soil factor within patches. ID # of patches area(ac) (t/ac/yr) (t/field/yr) Total 85 29.579 65.304 Patch Total soil loss 1. What is the maximum and minimum soil loss (tons/acre/year) that occurs on the seven fields? 2 Look at the C, K, P, and R values for the seven fields. Which of these four factors contains the most explanatory value for the low average soil loss for these two fields? 3 Which field has the highest average soil loss per acre? Which factor (L, S, C, K, P, R) is the likely major contributing factor for this field’s average soil loss? 4 Which patch had the highest soil loss? In what field is this patch located? 5. How changed total soil loss if we apply Agroforestry trees? RUSLE parameters: DEM: RUSLEDEM 6, How changed total soil loss if we apply higher R value ID # of patches area(ac) (t/ac/yr) (t/field/yr) to simulate climate change? 1 29 7.616 2.087 15.895 2 9 2.363 6.956 16.435 3 18 4.932 1.295 6.388 4 3 2.832 0.140 0.397 5 13 3.981 1.340 5.336 6 8 2.276 0.168 0.383 7 5 5.581 0.439 2.451 unit soil loss total soil loss ID # of patches area(ac) (t/ac/yr) (t/field/yr) Total 85 29.579 47.284 Why GIS? • The Geographic Information System (GIS) technology is one of the most important examination methods for decision support to solve of the global or local environmental problems. • GIS data is a digital representation of objects or phenomena that take place on or below the surface. • It could provide different parameters of objects such as area, temperature, high, elevation; and categorize based on attributes. Why use GIS? ... because GIS can answer the following questions: • Where is? • What is there? • What has changes since? • What is the best route between? • What relations exist between? • What if? GIS as an Integrating Technology • GIS is able to integrate • different data sources ( e.g. ground survey, remote sensing, etc.) • different disciplines • Obstacles: • „specialized“ software • missing exchange standards, etc. Forestry • Had been among the first users of GIS • In the beginning just inventory of forest. • Now GIS is used for all areas of management. Source: BUCKLEY, David J. (1997) Field sensors in Agroforestry Airborne data aquisition Spectral fingerprint Common oak (Quercus robur) Red oak (Quercus rubra) Scots pine (Pinus sylvestris) Glade area Sparsely covered with vegetation parcel Trees shadow Lidar TOPOGRAPHY Of Agroforestry Tree volume DSM-DEM Tree density Evapotranspiration Runoff Infiltration LIDAR based products (a) True-color orthophoto (© http://maps.live.de), (b) slope-adaptive echo ratio (sER), (c) nDSM overlaid with segmentation result and mean values per segment of the (d) echo width (sp;mean), (e) backscatter cross-section (mean), and (f) final tree species classification result (From Hollaus et al. (2009a)) Modeling Process Step 1: Problem Statement Step 4: Step 2: Evaluation Basic Items Step 3: Relationships and Rules Environment Spatial thinking Real word Expert User knowledge knowledge Spatial- Time Spatial Mapping Map Cartographic Users’ concept language demand Potential Users Legend Paper map Map reading Scale Computer graphics Rigid, digital map Disciple Change treatment Physical model Interactív