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--- input_allocation [2020/02/25 09:35] – [Input allocation for fertilisers and nutrient balances] matsz
+++ input_allocation [2020/03/31 08:12] – [Input allocation for feed] matsz
@@ Line 146: / Line 146: @@
 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}
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 {{: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__
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 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, 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.