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This project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme

This project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme

This project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme

wiki:gross_fodder_production

Biomass production from grassland - Supply

General description

The approach presented here is comprised of two main parts. The first part (steps 1 and 2) assesses the optimal yield (ES potential) according to the length of the growing season, the respective growth functions and the specific land use types. The second part refines the biomass productivity according to region-specific precipitation patterns (steps 3 to 7) and local small-scale topographic conditions (steps 8 to 10), in order to provide more reliable local yield estimates (ES status). Flowchart below describes in detail the calculation procedure to derive the Supply (DM kg/ha) for each local administrative units (LAU2) of the Alpine Space.

Input data

  • DEM (slope, aspect)
  • Precipitation (in mm)
  • Climate Data (number of Vegetation days, start of growing season)
  • Land use types (intensively used, moderately used and extensively used grassland, Natural Grassland (CLC)…)

Calculation processes

(1) Calculate Vegetation Days (days with Tmean≥ 5 C)

The approach is based on the assumption that biomass production does not start if the daily average temperature is below 5°C, hence the year is divided into a growing season and a dormant season.

(2) Calculate Optimal Yield

This is done according to the productivity type of the grassland types of your study area. In the table below you find the factors we used for the Alpine-wide approach according to the dataset we had at our disposal.


Land use type

Productivity type

Permanent Grassland

4

Natural Grassland (CLC)

3

Natural Grassland (HRL)

3

Bogs

2

Dwarf bushes

2

Larch meadows

1

Alpine grasses

1

The optimal yield is then derived using the following functions, where x is the number of vegetation days.


Forage type

Yield function (dt/ha)

4

y=(0.0021*(x²))-(0.419*x)+93.774

3

y=(0.0007*(x²))-(0.1513*x)+26.585

2

y=(0.0006*(x²))-(0.1613*x)+25.321

1

y=(-0.00007*(x²))+(0.1084*x)-4.7726

(Yield calculations Source: Egger, G., et al. (2004). GIS-gestützte Ertragsmodellierung zur Optimierung des Weidemanagements auf Almweiden. Irdning, Irdning: BAL. Modified by Jaeger and Tasser)

(5) Indicate Growing SeasonStart and End in days of the year (DOY)If no specific data is available for your test region, you can use a DEM-based method (i.e. Krautzer et. al. 2012) to approximate the start of the growing-season.

  • (3) Calculate start of growing season based on DEM (DOY). Here the example function we used for the entire Alpine space:[(0.0689*DEM)+0.4444]
  • (4) Calculate end of growing season. Start of Growing Season + Vegetation Days (in DOY).

(6) Calculate average precipitation needed during growing season

  • Sum the average precipitation data for the growing season (in mm).

(7) Optimizing (reducing) regional yield by applying a correction factor if precipitation during the growing season is below a certain threshold

  • precipitation sums (in mm) in vegetation season are lower than (Vegetation days * 3.33) then the regional yield (unit: dt) ⇒ regional yield =[(Precipitation in growing season (in mm) / Vegetation days * 3.33) * optimal yield]
  • else use optimal yield.

(8) Calculate slope yield

  • Calculate the yield reduction caused by slope because of radiation reduction* the slope is > 10, then use the following formula [(1- (Slope/ 100)) * regional yield], else keep the regional yield value.

(9) Reclassify the “Aspect raster” to “Aspect modified” in preparation of step (10)

  • For the purposes of the model, simplified aspect values are required that account for losses caused by radiation decrease due to unfavorable exposition. Only values between 0° (north) – 180° (south) are valid inputs, where 90° refers to both east and west exposition. The reduction factors range between 0% and 20%, respectively for southerly and northerly facing slopes. We applied a linear distribution of the reduction factor from south to north.* Hence, first aspect values have to be reclassified and inverted using the following formula: aspect_recl =180 - (180 - (Aspect - 180)
  • And second, this raster has to be multiplied with its specific reduction factor. Aspect modified = aspect_recl * reduction factor

The result is the layer „Aspect modified“ with cell values ranging from 0 (for southern faced slopes) up to 20 (for northern faced slopes).

(10) Calculate local yield

  • In the final step local yield is calculated without the losses caused by exposition:* the Annual Precipitation ⇐ 1500 mm then use the following formula: [(100 - (Aspect modified / 2)) / 100) * Slope Yield] else this other formula [((100 – Aspect modified) / 100) * Slope Yield)].

biomass_grassland_supply.jpg

test_legens.jpg

References:

Krautzer, Bernhard, Christian Uhlig, and Helmut Wittmann. “Restoration of Arctic–Alpine Ecosystems.”Restoration Ecology: The New Frontier 189 (2012)

Egger, G., et al. (2004). GIS-gestützte Ertragsmodellierung zur Optimierung des Weidemanagements auf Almweiden.Irdning, Irdning: BAL. modified by Jaeger and Tasser et al.

Urthaler, K. (2016). Modellierung und Validierung des landwirtschaftlichen Ertrages der Grünlandflächen Südtirols. Institut für Ökologie. Innsbruck, Leopold Franzens Universität. Master of Science


wiki/gross_fodder_production.txt · Last modified: 2018/07/18 08:39 by sebastian