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--- input_allocation [2020/02/25 09:42] – [Input allocation for fertilisers and nutrient balances] matsz
+++ input_allocation [2022/11/07 10:23] (current) – external edit 127.0.0.1
@@ Line 43: / Line 43: @@
 **Figure 5: The cattle chain**
-{{:figure_5.png?600|}}
+{{:figure_5.png?600|}} \\ Source: CAPRI Modelling System
 Accordingly, each raising and fattening process takes exactly one young animal on the input side. The raising processes produce exactly one animal on the output side which is one year older. The output of calves per cow, piglets per sow, lambs per mother sheep or mother goat is derived ex post, e.g. simultaneously from the number of cows in t-1, the number of slaughtered bulls and heifers and replaced in t+1 which determine the level of the raising processes in t and number of slaughtered calves in t. The herd flow models for pig, sheep and goat and poultry are similar, but less complex, as all interactions happen in the same year, and no specific raising processes are introduced.
@@ Line 105: / Line 105: @@
 |GROFYCOW|	Numer of heifers raised to young cows|	235,45	|227,16	|229,4|
 |HEIRLEVL|	Activity level of the heifers raising process	|235,45	|227,16	|229,4|
+ \\ Source: CAPRI Modelling System
@@ Line 115: / Line 116: @@
 |Bull fattening (BULF)	|BULL: 20% lower meat output, variable inputs besides feed an young animals at 80% of average	|BULH: 20% higher meat output, variable inputs besides feed an young animals at 120% of average|
 |Heifers fattening (HEIF)|	HEIL: 20% lower meat output, variable inputs besides feed an young animals at 80% of average	|HEIH: 20% higher meat output, variable inputs besides feed an young animals at 120% of average|
+ \\ Source: CAPRI Modelling System
 ====Input allocation for feed====
@@ Line 146: / Line 148: @@
 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}
@@ Line 190: / Line 190: @@
 | |FEDAGGR_ |aggregate to roughage, concentarte feed, etc|Defines feed aggregates from single bulks FEED|
 | |FeedAggrShare_ |Calculate share of feed aggregates (roughage, concentrates, other)|shares of roughage and concentrate feed enter objective|
-| |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| \\ Source: own compilation
 The four additional equations developed in the new feed allocation procedure are described in more detail in the following.
@@ Line 211: / Line 211: @@
 ^FeedCons| | | | | | | |  X  |  X  |  X  |  X  | |
 ^FeedOth| | | | |  X  |  X  |  X  | | | | |  X  |
-^FeedTotal|  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |
+^FeedTotal|  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  |  X  | \\ Source: own compilation
 __ FeedAggrShare_ __
@@ Line 235: / Line 235: @@
 {{:code_p_73.png?600|}}
-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) .1). This tends to give more weight to “less important” animal types compared with untransformed weights.
+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? .1). This tends to give more weight to “less important” animal types compared with untransformed weights.
 __Deviation of sub regional total feed intake from regional average__
@@ Line 273: / Line 273: @@
 ^  SHGF  |  6.3	  |  5.8	  |  7	  |  0.155  |  	0.14  |  	0.17  |
 ^  HENS  |  8  |  	7.8	  |  8.2	  |  0.18  |  	0.14	  |  0.2  |
-^  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 __
@@ Line 301: / Line 301: @@
 ^  SHGF	 | |  0.3  | | 	0.05  |
 ^  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.
@@ Line 376: / Line 376: @@
 | | | |Nitrogen in ammonia, NOx, N2O and runoff losses from mineral fertiliser|  l  |  2.89  |
 |  **TOTAL INPUT**  |  **e=a+b+c+d**  |  **162.768**  |  **TOTAL OUTPUT**  |  **n=f+k+l+m**  |  **103.92**  |
-| | | |  **Nutrient losses at soil level (SURPLUS)**  |  **m=e-f-k-l**  |  **58.85**  |
+| | | |  **Nutrient losses at soil level (SURPLUS)**  |  **m=e-f-k-l**  |  **58.85**  | \\ Source: CAPRI modelling system
 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, a version was developed which uses the leaching fractions from the official Greenhouse gas inventories of the member states. For phosphate, currently emissions (mainly superficial runoff) are not quantified.
@@ Line 389: / Line 389: @@
 |**Cattle**|  2.0  |  5.5  |
 |**Swine**|  3.3  |  3.3  |
-|**Poultry**|  6.3  |  5.1  |
+|**Poultry**|  6.3  |  5.1  | \\ Source: Lufa von Weser-Ems, Stand April 1990, Naehrstoffanfall.
-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).
@@ Line 409: / Line 408: @@
 |N|0.0084|
 |P|0.004|
-|K|0.0047|
+|K|0.0047| \\ Source: RAUMIS Model [[http://www.agp.uni-bonn.de/agpo/rsrch/raumis_e.htm]].
-Source: RAUMIS Model [[http://www.agp.uni-bonn.de/agpo/rsrch/raumis_e.htm]]. FIXME
+ 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 manure output of sows varies in the model with the number of piglets produced.
@@ Line 493: / Line 492: @@
 **Figure 6. Ex-post calibration of NPK balances and the ammonia module**
-{{::figure_6.png?600|}}
+{{::figure_6.png?600|}} \\ Source: CAPRI modelling system
 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:
@@ Line 607: / Line 606: @@
 **Figure 8: Carbon flows in the agricultural production process**
-{{:figure_8.png?600|}} \\
+{{:figure_8.png?600|}} \\ Source: Weiss and Leip (2016)
-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.
@@ Line 783: / Line 781: @@
 ==== Input allocation for labour ====
-Labour (and other inputs) in CAPRI are estimated from a Farm Accounting Data Network (FADN) sample  and then these estimation results are combined with total labour requirements within a region (or aggregate national input demand reported in the EAA), using a Highest Posterior Density (HPD) estimation framework.
+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, accessible in the \doc folder of any stable release of the CAPRI system up to star 2.4 from [[https://www.capri-model.org/dokuwiki/doku.php?id=capri:get-capri.]])) and then these estimation results are combined with total labour requirements within a region (or aggregate national input demand reported in the EAA), using a Highest Posterior Density (HPD) estimation framework.
 ===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, not least when fitted values for labour using the econometric coefficients are compared with total regional labour inputs recoverable from FADN data survey weights. To remedy this, a reconciliation process is undertaken to correct figures for labour input by adjusting the labour input coefficients for both total labour and family labour, handled in file gams\inputs\labour_calc.gms.
+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, where further adjustments are made to bring the estimates into line with the FADN values for labour inputs. Implementing these two steps involves the following procedures.
+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, a Bayesian probability density function is used where EU averages are used as priors, and a number of bounds are added, in order to generate realistic labour input coefficients.
+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, where the labour input coefficients are adjusted in order to bring estimates of labour input closer to the total labour used in the region/country. However, this adjustment process has to be balanced with a recognition that many of the labour input coefficient estimates are relatively reliable and that we don’t need or want to radically adjust all of them. Therefore the final reconciliation has to specify which input coefficients have to be adjusted most.  The main way in which this is achieved is through the consideration of the coefficients’ standard errors in a second Bayesian posterior density function.
+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.  Labour input for the different activities is then calculated based on these shares. The second procedure is due to the presence of infeasibilities in this model. In order to try and eliminate them, a number of courses of action can be followed from excluding outlying estimates to dropping regional estimates.
+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  |  crop or aggregate  |  Econometric estimation  |||  HPD solution including  |||
+|:::| |  regional  |  national- \\ including yield  |  national - \\ without yield  |  regional, \\ national, crop \\ aggregates  |  + expert assumption  |  + regional \\ labour supply  |
+|Belgium (BL24)|Soft wheat|	31.49|	31.26|	31.49|	24.99|	32.73|	53.88|
+|:::|Sugar beet	|  76.25|	77.39|	76.25|	62.19|	48.27|	68.36|
+|:::|Cereals	|  28.23|	32.89|	28.23|	32.78|	28.16|	32.66|
+|:::|Root crops	|  58.75|	65.43|	58.75|	58.8|	64.52|	105.89|
+|Germany (DEA1)|Soft wheat|	36.78|	35.32|	36.78|	36.98|	38.62|	34.46|
+|:::|Sugar beet	|  82.01|	58.99|	82.01|	55.06|	39.61|	43.58|
+|:::|Cereals	|  40.13|	32.63|	40.13|	39.94|	41.65|	35.12|
+|:::|Root crops	|  28.83|	14.23|	28.83|	38.32|	41.26|	0.01|
+|France (FR24)	|Soft wheat|	14.65|	23.3|	23.68|	14.71|	16.5|	13.22|
+|:::|Sugar beet	|  -7.42|	2.24|	-1.68|	11.08|	19.72|	18.5|
+|:::|Cereals	|  10.48|	35.9|	22.7|	15.61|	15.43|	12.7|
+|:::|Root crops	|  11.68|	29.78|	19.42|	17.05|	24.64|	18.43| \\ Source: CAPRI Modelling System
+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, but only to serve as one type of bounds for labour use in the contrained trends for European regions. Other bounds are derived from engineering knowledge (or assumptions) on plausible labur use per activity which is based on the initial estimation of labour allocation by activity.