input_allocation
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input_allocation [2020/02/25 09:43] – [Input allocation for labour] matsz | input_allocation [2022/11/07 10:23] (current) – external edit 127.0.0.1 | ||
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**Figure 5: The cattle chain** | **Figure 5: The cattle chain** | ||
- | {{: | + | {{: |
Accordingly, | Accordingly, | ||
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|GROFYCOW| Numer of heifers raised to young cows| 235, | |GROFYCOW| Numer of heifers raised to young cows| 235, | ||
|HEIRLEVL| Activity level of the heifers raising process |235, | |HEIRLEVL| Activity level of the heifers raising process |235, | ||
+ | \\ Source: CAPRI Modelling System | ||
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|Bull fattening (BULF) |BULL: | |Bull fattening (BULF) |BULL: | ||
|Heifers fattening (HEIF)| HEIL: | |Heifers fattening (HEIF)| HEIL: | ||
+ | \\ Source: CAPRI Modelling System | ||
====Input allocation for feed==== | ====Input allocation for feed==== | ||
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Wide supports for the Gross Value Added of the fodder activities mirror the problem of finding good internal prices but also the dubious data quality both of fodder output as reported in statistics and the value attached to it in the EAA. The wide supports allow for negative Gross Value Added, which may certainly occur in certain years depending on realised yields. In order to exclude such estimation outcomes as far as possible an additional constraint is introduced: | Wide supports for the Gross Value Added of the fodder activities mirror the problem of finding good internal prices but also the dubious data quality both of fodder output as reported in statistics and the value attached to it in the EAA. The wide supports allow for negative Gross Value Added, which may certainly occur in certain years depending on realised yields. In order to exclude such estimation outcomes as far as possible an additional constraint is introduced: | ||
- | |||
- | FIXME | ||
\begin{equation} | \begin{equation} | ||
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| |FEDAGGR_ |aggregate to roughage, concentarte feed, etc|Defines feed aggregates from single bulks FEED| | | |FEDAGGR_ |aggregate to roughage, concentarte feed, etc|Defines feed aggregates from single bulks FEED| | ||
| |FeedAggrShare_ |Calculate share of feed aggregates (roughage, concentrates, | | |FeedAggrShare_ |Calculate share of feed aggregates (roughage, concentrates, | ||
- | | |MeanFeedTotal_ |Calculates total feed intake in DM per animal|Part of revised objective function| | + | | |MeanFeedTotal_ |Calculates total feed intake in DM per animal|Part of revised objective function| |
The four additional equations developed in the new feed allocation procedure are described in more detail in the following. | The four additional equations developed in the new feed allocation procedure are described in more detail in the following. | ||
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^FeedCons| | | | | | | | X | X | X | X | | | ^FeedCons| | | | | | | | X | X | X | X | | | ||
^FeedOth| | | | | X | X | X | | | | | X | | ^FeedOth| | | | | X | X | X | | | | | X | | ||
- | ^FeedTotal| | + | ^FeedTotal| |
__ FeedAggrShare_ __ | __ FeedAggrShare_ __ | ||
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{{: | {{: | ||
- | This part of the objective functions tries to minimize the difference between the requirements calculated from the feed input coefficients (v_animReq) and the expected (mean) requirements (p_animReq) coming from literature. Due to the weighting with number of animals (v_actLevl) and expected requirements (p_animReq) the optimal solution tends to distribute over or under supply of nutrients relatively even over all activities and regions. It has been decided to attach an exponent smaller one to these weights which strongly pulls them towards unity (see: [...] FIXME (doppelstern) | + | This part of the objective functions tries to minimize the difference between the requirements calculated from the feed input coefficients (v_animReq) and the expected (mean) requirements (p_animReq) coming from literature. Due to the weighting with number of animals (v_actLevl) and expected requirements (p_animReq) the optimal solution tends to distribute over or under supply of nutrients relatively even over all activities and regions. It has been decided to attach an exponent smaller one to these weights which strongly pulls them towards unity (see: [...] FIXME (section? |
__Deviation of sub regional total feed intake from regional average__ | __Deviation of sub regional total feed intake from regional average__ | ||
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^ SHGF | 6.3 | 5.8 | 7 | 0.155 | 0.14 | 0.17 | | ^ SHGF | 6.3 | 5.8 | 7 | 0.155 | 0.14 | 0.17 | | ||
^ HENS | 8 | 7.8 | 8.2 | 0.18 | 0.14 | ^ HENS | 8 | 7.8 | 8.2 | 0.18 | 0.14 | ||
- | ^ POUF | 8 | 7.8 | 8.2 | 0.18 | 0.14 | 0.2 | | + | ^ POUF | 8 | 7.8 | 8.2 | 0.18 | 0.14 | 0.2 | \\ |
__Shares of feed aggregates in total feed intake in DRMA __ | __Shares of feed aggregates in total feed intake in DRMA __ | ||
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^ SHGF | | 0.3 | | 0.05 | | ^ SHGF | | 0.3 | | 0.05 | | ||
^ HENS | | | | 0.99 | | ^ HENS | | | | 0.99 | | ||
- | ^ POUF | | | | 0.99 | | + | ^ POUF | | | | 0.99 | \\ Source: own compilation |
For „other feed“ there are no lower bounds but rather low upper bounds: 10% for adult cattle, 5% for calves and sheep, 1% for pigs and 1E-6 (so near zero) for poultry. | For „other feed“ there are no lower bounds but rather low upper bounds: 10% for adult cattle, 5% for calves and sheep, 1% for pigs and 1E-6 (so near zero) for poultry. | ||
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| | | |Nitrogen in ammonia, NOx, N2O and runoff losses from mineral fertiliser| | | | | |Nitrogen in ammonia, NOx, N2O and runoff losses from mineral fertiliser| | ||
| **TOTAL INPUT** | | **TOTAL INPUT** | ||
- | | | | | **Nutrient losses at soil level (SURPLUS)** | + | | | | | **Nutrient losses at soil level (SURPLUS)** |
The difference between nutrient inputs and outputs corresponds to the soil surplus. For nitrates the leaching is calculated as a fraction of the soil surplus, which is based on estimates from the MITERRA project, and depends on the soil type, the land use (grassland or cropland), the precipitation surplus, the average temperature and the carbon content in soils. For details see Velthof et al. 2007 “Development and application of the integrated nitrogen model MITERRA-EUROPE”. Alternatively, | The difference between nutrient inputs and outputs corresponds to the soil surplus. For nitrates the leaching is calculated as a fraction of the soil surplus, which is based on estimates from the MITERRA project, and depends on the soil type, the land use (grassland or cropland), the precipitation surplus, the average temperature and the carbon content in soils. For details see Velthof et al. 2007 “Development and application of the integrated nitrogen model MITERRA-EUROPE”. Alternatively, | ||
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|**Cattle**| | |**Cattle**| | ||
|**Swine**| | |**Swine**| | ||
- | |**Poultry**| | + | |**Poultry**| |
- | Source:Lufa von Weser-Ems, Stand April 1990, Naehrstoffanfall. | + | |
These data are converted into typical pure nutrient emission at tail per day and kg live weight in order to apply them for the different type of animals. For cattle, it is assumed that one live stock unit (=500 kg) produces 18 m³ manure per year, so that the numbers in the table above are multiplied with 18 m³ and divided by (500 kg *365 days). | These data are converted into typical pure nutrient emission at tail per day and kg live weight in order to apply them for the different type of animals. For cattle, it is assumed that one live stock unit (=500 kg) produces 18 m³ manure per year, so that the numbers in the table above are multiplied with 18 m³ and divided by (500 kg *365 days). | ||
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|N|0.0084| | |N|0.0084| | ||
|P|0.004| | |P|0.004| | ||
- | |K|0.0047| | + | |K|0.0047| |
- | Source: RAUMIS Model [[http:// | + | FIXME |
The factors shown above for pigs are converted into a per day and live weight factor for sows by assuming a production of 5 m³ of manure per sow (200 kg sow) and 15 piglets at 10 kg over a period of 42 days. Consequently, | The factors shown above for pigs are converted into a per day and live weight factor for sows by assuming a production of 5 m³ of manure per sow (200 kg sow) and 15 piglets at 10 kg over a period of 42 days. Consequently, | ||
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**Figure 6. Ex-post calibration of NPK balances and the ammonia module** | **Figure 6. Ex-post calibration of NPK balances and the ammonia module** | ||
- | {{:: | + | {{:: |
The following equations comprise together the cross-entropy estimator for the NPK (Fnut=N, P or K) balancing problem. Firstly, the purchases (NETTRD) of anorganic fertiliser for the regions must add up to the given inorganic fertiliser purchases at Member State level: | The following equations comprise together the cross-entropy estimator for the NPK (Fnut=N, P or K) balancing problem. Firstly, the purchases (NETTRD) of anorganic fertiliser for the regions must add up to the given inorganic fertiliser purchases at Member State level: | ||
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**Figure 8: Carbon flows in the agricultural production process** | **Figure 8: Carbon flows in the agricultural production process** | ||
- | {{: | + | {{: |
- | Source: Weiss and Leip (2016) | + | |
In the following, we briefly describe the general methodology for the quantification of the carbon flows that are taken into account in the CAPRI approach. | In the following, we briefly describe the general methodology for the quantification of the carbon flows that are taken into account in the CAPRI approach. | ||
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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. | 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