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Input Requirement Estimations Estimating Input Requirements using FADN Data Stephan Pfaffenzeller, NUI, Galway.

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Presentation on theme: "Input Requirement Estimations Estimating Input Requirements using FADN Data Stephan Pfaffenzeller, NUI, Galway."— Presentation transcript:

1 Input Requirement Estimations Estimating Input Requirements using FADN Data Stephan Pfaffenzeller, NUI, Galway

2 Objective To Estimate Intput Requirement Coefficients for Each CAPRI Activity for insertion into the CAPRI database.

3 Data FADN DATA from 1990-1999 AT - Austria - 2451 farms BL - Belgium, 2601 farms DE - Germany, 15110 farms --> price data from ‘91-’99 DK - Denmark, 6625 farms EL - Greece, 11877 farms --> price data from ‘95-’99 FI - Finland, 1324 farms IR - Ireland, 3409 farms --> no price data prior to 1995

4 Data FADN DATA from 1990-1999 IT - Italy, 57264 farms PT - Portugal, 6379 farms SE - Sweden, 1191 farms UK - United Kingdom, 6668 farms ES - Spain, 22609 farms NL - Netherlands FR - France

5 Variables I: CAPRI INPUTS L: CAPRI LEVELS (hect./head) Y: CAPRI YIELDS (tonnes p/h etc.) O: CAPRI OUTPUTS (tonnes, litres etc.) 11 Input Variables 53 Level Variables and 53 Output/Yield Variables in Full Model

6 Model (for Any One Input) i is the farm i.d., t the year, L are Relevant Level Values Basic specification

7 Model (for Any One Input) i is the farm i.d., t the year, L are Relevant Level Values and O are Output Values Alternative specification I

8 Model (for Any One Input) i is the farm i.d., t the year, L are Relevant Level Values and Y are Yield Values Alternative specification II

9 Choice of Estimator SUR eliminated (not feasible – problems last summer with SUR estimation) Choice between OLS, FE and RE for each model Preliminary Tests revealed FE was far superior: decided to be consistent

10 Results Generally good (pending final evaluation of specifications) Problems with Negative coefficient estimates in some cases Fitted values need to be reconciled with EAA

11 Negative Coefficients These problems are mainly with the OTHER categories of activities/inputs (OFRU, OCRO, OANI etc.). The chosen solution is to use the average coefficient from the relevant activity category (e.g. the Fruit Average if OFRU is negative)

12 Selected Results

13 Level Coefficient Estimates  L estimates for selected Crop activity levels: NLSEELPT swhe584.13*-62.00336.17*315.43* barl501.7569.57372.24*161.80* oats433.4640.99247.51*91.43* rye1623.70*-37.47272.94*69.41*

14 Level Coefficient Estimates  L estimates for selected animal activity levels: NLSEELPT dcow1229.45*1520.40*493.30*1118.36* hens6.74*4.44*7.71*4.91* shgm93.006.2021.02*40.96*


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