input_allocation
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input_allocation [2020/02/25 09:13] – [Input allocation for fertilisers and nutrient balances] matsz | input_allocation [2020/02/25 09:57] – [Input allocation for labour] matsz | ||
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//Carbon dioxide emissions from manure management in livestock production// | //Carbon dioxide emissions from manure management in livestock production// | ||
- | During storage or grazing, carbon is not only emitted in form of methane, but part of the organic material is mineralized and carbon released as carbon dioxide. Following the FarmAC model((The FarmAC model simulates the flows of carbon and nitrogen on arable and livestock farms, enabling the quantification of GHG emissions, soil C sequestration and N losses to the environment (for more information see: [[www.farmac.dk]]). )), we assume a constant relation between carbon emitted as methane and total carbon emissions (methane plus carbon dioxide) of 63%. Therefore, the carbon loss through carbon dioxide emissions can be quantified as: | + | During storage or grazing, carbon is not only emitted in form of methane, but part of the organic material is mineralized and carbon released as carbon dioxide. Following the FarmAC model((The FarmAC model simulates the flows of carbon and nitrogen on arable and livestock farms, enabling the quantification of GHG emissions, soil C sequestration and N losses to the environment (for more information see: [[http://farmac.dk]]). )), we assume a constant relation between carbon emitted as methane and total carbon emissions (methane plus carbon dioxide) of 63%. Therefore, the carbon loss through carbon dioxide emissions can be quantified as: |
\begin{equation} | \begin{equation} | ||
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//Carbon sequestration in soils// \\ | //Carbon sequestration in soils// \\ | ||
- | Finally, we quantify the sequestered material after 20 years. The carbon change is based on simulations with the CENTURY agroecosystem model (Lugato et al. 2014) (aggregated from 1 km2 to NUTS2 level), and calculated from the difference in the manure and crop residue input to soils between the simulation year and the base year. This is done because carbon sequestration is only achieved from an increased carbon input, assuming that the carbon balance in the base year is already in equilibrium. The total cumulative carbon increase is divided by 20, in order to spread the effect over a standardised number of years (consistent with the 2006 IPCC guidelines). | + | Finally, we quantify the sequestered material after 20 years. The carbon change is based on simulations with the CENTURY agroecosystem model (Lugato et al. 2014) (aggregated from 1 km2 to NUTS2 level), and calculated from the difference in the manure and crop residue input to soils between the simulation year and the base year. This is done because carbon sequestration is only achieved from an increased carbon input, assuming that the carbon balance in the base year is already in equilibrium. The total cumulative carbon increase is divided by 20, in order to spread the effect over a standardised number of years (consistent with the 2006 IPCC guidelines).((The simulations with the CENTURY model were carried out by Emanuele Lugato from JRC.D3 in Ispra (for more details see Lugato et al. 2014).)) |
//Carbon losses from soil erosion// \\ | //Carbon losses from soil erosion// \\ | ||
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//Carbon dioxide emissions from respiration of carbon inputs to soils// \\ | //Carbon dioxide emissions from respiration of carbon inputs to soils// \\ | ||
Carbon losses from soil are quantified as the residual between all carbon inputs to soils, the emissions and the carbon sequestered in the soils: | Carbon losses from soil are quantified as the residual between all carbon inputs to soils, the emissions and the carbon sequestered in the soils: | ||
+ | |||
+ | \begin{align} | ||
+ | \begin{split} | ||
+ | &Carbon \; losses\; via\; soil\; and\; root\; respiration = \\ | ||
+ | & | ||
+ | &+ input\; from\; crop\; residues \\ | ||
+ | &- carbon \;losses \;(CH4)\; from \;rice\; production \\ | ||
+ | &- carbon \;losses \;(CO2) \;from \;the \; | ||
+ | &- carbon \;losses \;from \;runoff \;from \;soils \\ | ||
+ | &- carbon \;losses\; from \;soil \;erosion \\ | ||
+ | &- carbon \; | ||
+ | \end{split} | ||
+ | \end{align} | ||
+ | |||
+ | Carbon losses from leaching should also be subtracted, but they are not specifically quantified in the CAPRI carbon cycle model so far. Therefore, the share of soil respiration is currently overestimated by the model. | ||
+ | |||
+ | ===Greenhouse Gases=== | ||
+ | |||
+ | For the purpose of modelling GHG emissions from agriculture, | ||
+ | |||
+ | In CAPRI consistent GHG emission inventories for the European agricultural sector are constructed. As already mentioned, //land use// and //nitrogen flows// are estimated at a regional level. This is the main information needed to calculate the parameters included in the IPCC Good Practice Guidance (IPCC, 2006). The following table lists the emission sources modelled: | ||
+ | |||
+ | **Table 19: Agricultural greenhouse gas emission sources included in the model** | ||
+ | | **Greenhouse Gas** | **Emission source** | ||
+ | |**Methane**|Enteric fermentation|CH4Ent| | ||
+ | |::: | ||
+ | |::: |Rice production|CH4Ric| | ||
+ | |::: |Land use change emissions from\\ biomass burning|CH4bur| | ||
+ | |**Nitrous Oxide**|Manure management|N2OMan| | ||
+ | |::: | ||
+ | |::: | ||
+ | |::: | ||
+ | |::: |Crop residues|N2OCro| | ||
+ | |::: | ||
+ | |::: | ||
+ | |::: | ||
+ | |::: |Land use change emissions from the \\ burning of biomass|N2Obur| | ||
+ | |**Carbon dioxide**|Cultivation of histosols|CO2his| | ||
+ | |::: | ||
+ | |::: | ||
+ | |::: |Land use change emissions from above \\ and below ground biomass|CO2bio| | ||
+ | |::: |Land use change emissions from soil \\ carbon changes|CO2soi| \\ Source: CAPRI Modelling System | ||
+ | |||
+ | For a detailed analysis of these single emission sources refer to Pérez 2006: Greenhouse Gases: Inventories, | ||
+ | |||
+ | The model code also comprises a life-cycle assessment for GHGs (first approach explained in Leip et al, 2010, but newer approach not yet documented in an official publication), | ||
+ | |||
+ | * Anaerobic digestion | ||
+ | * Feed additives to reduce methane emissions from ruminants (lineseed, nitrate) | ||
+ | * Precision farming | ||
+ | * Variable Rate Technology | ||
+ | * Nitrification Inhibitors | ||
+ | * Better timing of fertilizer application | ||
+ | * Winter cover crops | ||
+ | * No Tillage | ||
+ | * Conservation Tillage | ||
+ | * Buffer strips | ||
+ | * Fallowing of histosols | ||
+ | * Measures to reduce methane emissions in rice production | ||
+ | * Increased legume share on temporary grassland | ||
+ | * Genetic measures to increase milk yields and feed efficiency | ||
+ | * Urea Substitution | ||
+ | * Manure application measures to reduce ammonia emissions (high and low efficiency) | ||
+ | * Manure storage measures to reduce ammonia emissions (high and low efficiency) | ||
+ | * Stable design measures to reduce ammonia emissions | ||
+ | * Low Nitrogen Feed | ||
+ | * Manure storage basins in concrete to reduce nitrate leaching | ||
+ | * Flexible limits for nitrogen application to soils | ||
+ | * Flexible limits for livestock density | ||
+ | * Vaccination against methanogenic bacteria | ||
+ | |||
+ | For details see Van Doorslaer et al. 2015, and Perez et.al 2016 (Most recent developments not yet published). | ||
+ | |||
+ | ===Soil erosion=== | ||
+ | |||
+ | Soil erosion is calculated on the basis of the RUSLE equation. The equation has the following form: | ||
+ | |||
+ | \begin{equation} | ||
+ | A = R \cdot K \cdot L \cdot S \cdot C \cdot P | ||
+ | \end{equation} | ||
+ | |||
+ | where \\ | ||
+ | A = soil loss in ton per ha/acre per year \\ | ||
+ | R = rainfall-runoff erosivity factor \\ | ||
+ | K = soil erodibility factor \\ | ||
+ | L = slope length factor \\ | ||
+ | S = slope steepness factor \\ | ||
+ | C = cover management factor \\ | ||
+ | P = support practice factor \\ | ||
+ | |||
+ | For more details on the factors used see Panagos et al. (2015). | ||
+ | |||
+ | ==== Input allocation for labour ==== | ||
+ | |||
+ | Labour (and other inputs) in CAPRI are estimated from a Farm Accounting Data Network (FADN) sample ((More details on the FADN estimation were reported older versions of this section (originally drafted by Markus Kempen and Eoghan Garvey) the CAPRI documentation, | ||
+ | |||
+ | ===Labour Input Allocation=== | ||
+ | |||
+ | Input coefficients (family labour and paid labour, both in hours, as well as wage regressions for paid labour) were estimated using standard econometrics from single farm records as found in FADN. While many of results from this process are plausible a number of CAPRI estimates of labour input are inaccurate and untrustworthy, | ||
+ | |||
+ | The reconciliation process has two components. The first component is to fix on a set of plausible estimates for the labour input coefficients (based on the econometric results) while the second involves a final reconciliation, | ||
+ | |||
+ | Step one involves preparing the econometric estimates in order to remove unreliable entries. This process removes specific unsuitable estimates for particular regions and crop types. In addition, this process also involves adjusting certain agricultural activities labour input coefficients (such as the estimates for triticale) so as to bring them into line with similar activities (such as for soft wheat). Furthermore, | ||
+ | |||
+ | While the procedure described above help to ensure plausible estimates, the labour input values generated will still not be such as to reconcile total fitted labour with total actual labour at a regional or national level (as estimated by FADN). Step 2 in this process is to implement a final reconciliation, | ||
+ | |||
+ | As well as the reconciliation process, two other procedures have to be carried out. The first results from the fact that a number of activities don’t have labour input coefficient estimates. In order to estimate them, the revenue shares for the relevant activities are used as a proxy for the amount of labour they require. | ||
+ | |||
+ | It should be noted that the reconciliation process has to be divided into these two steps because it is highly computationally burdensome. For the model to run properly (or even at all), it is necessary to divide it into two parts, with the one part obtaining plausible elements and the other implementing the final reconciliation. | ||
+ | |||
+ | **Table 20: Total labour input coefficients from different econometric estimations and steps in reconciliation procedure (selected regions and crops)** | ||
+ | |||
+ | | Region | ||
+ | |:::| | regional | ||
+ | |Belgium (BL24)|Soft wheat| 31.49| 31.26| 31.49| 24.99| 32.73| 53.88| | ||
+ | |:::|Sugar beet | | ||
+ | |::: | ||
+ | |:::|Root crops | | ||
+ | |Germany (DEA1)|Soft wheat| 36.78| 35.32| 36.78| 36.98| 38.62| 34.46| | ||
+ | |:::|Sugar beet | | ||
+ | |::: | ||
+ | |:::|Root crops | | ||
+ | |France (FR24) |Soft wheat| 14.65| 23.3| 23.68| 14.71| 16.5| 13.22| | ||
+ | |:::|Sugar beet | | ||
+ | |::: | ||
+ | |:::|Root crops | | ||
+ | |||
+ | The Table visualizes the adjustments regarding an implausible labour input coefficient for sugar beet in a French region. The econometric estimation come up with very low or negative values. The HPD solution combining crop specific estimates with corresponding averages of crop aggregates corrects this untrustworthy value to 11.08 h/ha. This value is in an acceptable range but it strikes that in opposite to many other regions the labour input for sugar beet is still less than for soft wheat. After adding equations in the reconciliation procedure that ensure that the relation of labour input coefficients among crops follows an similar “European” pattern the labour input is supposed to be 19.72 h/ha. There is up to now no theoretical or empirical evidence for this similar pattern regarding relation of input coefficients but the results seem to be more plausible when checked with expert knowledge. In the last column bounds on regional labour supply derived from FADN are added which “scales” the regional value. This final result is and is now part of the CAPRI model. | ||
+ | |||
+ | ===Projecting Labour Use=== | ||
+ | |||
+ | For typical applications of CAPRI, regional projections of labour use are needed. Such projections have been prepared as well in the CAPSTRAT project, using a cohort analysis to separate 2 components of changes over time: (1) an autonomous component, which comprises structural changes due to demographic factors such as ageing, death, disability and early retirement, and (2) a non-autonomous component, which incorporates all other factors that influence changes in farm structure and has been analysed econometrically. | ||
+ | |||
+ | The results of this analysis are loaded in the context of CAPRI task “Generate trend projection” in file baseline\labour_ageline.gms, | ||
+ | |||
+ | |||
input_allocation.txt · Last modified: 2022/11/07 10:23 by 127.0.0.1