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====== 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). **Figure 1 **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// T//mean≥ 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|
//(Figure 1: 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 ****Season****Start**** 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)].
__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__
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