Estimating input allocation for farm supply models

When building an economic model for supply analysis the aim is to model a decision making process of one or more agents which fits the observed practice as good as possible. Hereby the modeller is often confronted with incomplete information about the production process; particular crop specific input data are rarely available. The problem of defining activity related technology inputs coefficients is not new. A good deal of literature comes from the mathematical programming perspective, where input coefficients were estimated using a standard linear regression function to fully represent the mathematical program. However this approach is a pure technical device and may result in an inconsistent model. The author of the paper wants to investigate whether it is possible, employing proper estimation techniques, to simultaneously estimate all unknown coefficients of a mathematical farm supply model. This includes the estimation of parameters of the non linear cost function, used to calibrate and catch the simulation behaviour and the crop specific input coefficients. It is shown that a simultaneous estimation of all parameters improves the goodness of fit of the estimated parameters and that such an approach is technically feasible.farm supply model, input allocation, entropy, HDP, Research Methods/ Statistical Methods,

EU-WIDE FARM TYPES SUPPLY IN CAPRI - HOW TO CONSISTENTLY DISAGGREGATE SECTOR MODELS INTO FARM TYPE MODEL

EU-wide farm supply analysis, highest posterior density estimator, CAPRI, Research Methods/ Statistical Methods,

MUNICIPALITY DISAGGREGATION OF GERMAN'S AGRICULTURAL SECTOR MODEL RAUMIS

Since several decades the RAUMIS modelling system is applied for policy impact assessments to measure the impact of agriculture on the environment. A disaggregation at the municipality level with more than 9.000 administrative units, instead of currently used 316 counties, would tremendously improve the environmental impact analysis. Two sets of data are used for this purpose. The first are geo-referenced data, that are, however, incomplete with respect its coverage of production activities in agriculture. The second set is the micro census statistic itself, that has a full coverage, but data protection rules (DPR) prohibit its straightforward use. The paper show how this bottleneck can be passed to obtain a reliable modelling data set at municipality level with a complete coverage of the agricultural sector in Germany. We successfully applied a Bayesian estimator, that uses prior information derived a cluster analysis based on the micro census and GIS information. Our test statistics of the estimation, calculated by the statistical office, comparing our estimates and the real protected data, reveals that the proposed approach adequately estimates most activities and can be used to fed the municipality layer in the RAUMIS modelling system for an extended policy analysis.Highest Posterior Density estimator (HPD), RAUMIS, Down scaling, Agricultural and Food Policy, C11, C61, C81, Q15,

RECOVERING LOCALIZED INFORMATION ON AGRICULTURAL STRUCTURE UNDERLYING DATA CONFIDENTIALITY REGULATIONS - POTENTIALS OF DIFFERENT DATA AGGREGATION AND SEGREGATION TECHNIQUES

The modelling and information system RAUMIS is used for policy impact assessment to measure the impact of agriculture on the environment. The county level resolution often limits the analysis and a further disaggregation at the municipality level would reduce aggregation bias and improve the assessment. Although the necessary data exists in Germany, data protection rules (DPR) prohibit their direct use. With methods such as the Locally Weighted Averages (LWA), and with aggregation singling production activities into larger groups of activities, the data at the municipality level can be made publicly available. However, this reduces the information content and introduces an additional error. This paper’s aim is to investigate how much information is necessary to satisfactorily estimate Germany-wide production activity levels at the municipality level and whether the data requirements are still in compliance with the DPR. We apply Highest Posterior Density (HPD) estimation, which is easily able to include sample information as prior. We tested different prior information content at the municipality level. However, the goodness of the developed estimation approach can only be evaluated having knowledge about the population. Because the real population is not known to us, we took advantage of the special situation in Bavaria and derived a pseudo population for that region. This is used to draw information conforming to DPR for our estimation and to evaluate the resulting estimates. We found that the proposed approach is capable of adequately estimating most activities without violating the DPR. These findings allow us to extend the approach towards the Germany-wide municipality coverage in RAUMIS.Highest Posterior Density estimator (HPD), RAUMIS, locally weighted average (LWA), Research Methods/ Statistical Methods,

Salvage the treasure of geographic information in Farm census data

In Germany, since several decades the RAUMIS modelling system is applied for policy impact assessments to measure the impact of agriculture on the environment. A disaggregation at the municipality level with more than 9.600 administrative units, instead of currently used 316 counties, would tremendously improve the environmental impact analysis. Two sets of data are used for this purpose. The first are geo-referenced data, that are, however, incomplete with respect its coverage of production activities in agriculture. The second set is the micro census statistic itself, that has a full coverage, but data protection rules (DPR) prohibit its straightforward use. The paper show how this bottleneck can be passed to obtain a reliable modelling data set at municipality level with a complete coverage of the agricultural sector in Germany. We successfully applied a Bayesian estimator, that uses prior information derived a cluster analysis based on the micro census and GIS information. Our test statistics of the estimation, calculated by the statistical office, comparing our estimates and the real protected data, reveals that the proposed approach adequately estimates most activities and can be used to fed the municipality layer in the RAUMIS modelling system for an extended policy analysis.Highest Posterior Density estimator (HPD), RAUMIS, Down scaling, Research Methods/ Statistical Methods, C11, C61, C81, Q15,

Certifying Correctness for Combinatorial Algorithms : by Using Pseudo-Boolean Reasoning

Over the last decades, dramatic improvements in combinatorialoptimisation algorithms have significantly impacted artificialintelligence, operations research, and other areas. These advances,however, are achieved through highly sophisticated algorithms that aredifficult to verify and prone to implementation errors that can causeincorrect results. A promising approach to detect wrong results is touse certifying algorithms that produce not only the desired output butalso a certificate or proof of correctness of the output. An externaltool can then verify the proof to determine that the given answer isvalid. In the Boolean satisfiability (SAT) community, this concept iswell established in the form of proof logging, which has become thestandard solution for generating trustworthy outputs. The problem isthat there are still some SAT solving techniques for which prooflogging is challenging and not yet used in practice. Additionally,there are many formalisms more expressive than SAT, such as constraintprogramming, various graph problems and maximum satisfiability(MaxSAT), for which efficient proof logging is out of reach forstate-of-the-art techniques.This work develops a new proof system building on the cutting planesproof system and operating on pseudo-Boolean constraints (0-1 linearinequalities). We explain how such machine-verifiable proofs can becreated for various problems, including parity reasoning, symmetry anddominance breaking, constraint programming, subgraph isomorphism andmaximum common subgraph problems, and pseudo-Boolean problems. Weimplement and evaluate the resulting algorithms and a verifier for theproof format, demonstrating that the approach is practical for a widerange of problems. We are optimistic that the proposed proof system issuitable for designing certifying variants of algorithms inpseudo-Boolean optimisation, MaxSAT and beyond

A New Approach for Automated Feature Selection

Feature selection or variable selection is an important step in different machine learning tasks. In a traditional approach, users specify the amount of features, which shall be selected. Afterwards, algorithm select features by using scores like the Joint Mutual Information (JMI). If users do not know the exact amount of features to select, they need to evaluate the full learning chain for different feature counts in order to determine, which amount leads to the lowest training error. To overcome this drawback, we extend the JMI score and mitigate the flaw by introducing a stopping criterion to the selection algorithm that can be specified depending on the learning task. With this, we enable developers to carry out the feature selection task before the actual learning is done. We call our new score Historical Joint Mutual Information (HJMI). Additionally, we compare our new algorithm, using the novel HJMI score, against traditional algorithms, which use the JMI score. With this, we demonstrate that the HJMI-based algorithm is able to automatically select a reasonable amount of features: Our approach delivers results as good as traditional approaches and sometimes even outperforms them, as it is not limited to a certain step size for feature evaluation

Methods in Economic Farm Modelling

The objective of this thesis is to develop methods for the evaluation of agricultural firms using efficiency analysis and to develop and assess farm responses in mathematical programming (MP) models to changing political and economic conditions. The dissertation is structured in four main parts.Chapter 2 extends Data Envelopment Analysis (DEA) by incorporating confidence intervals in the evaluation of the resulting point estimates. In the literature, agricultural farms are often evaluated and compared based on DEA, where causes of inefficiencies within a farm group are often analysed by regressing efficiency measures on other variables. However, when confidence intervals are taken into account, the results of this analysis show that neglecting the stochastic nature of efficiency measures cannot produce any valid conclusions about the real nature of inefficiencies. Hence, DEA efficiency measures need to be carefully interpreted, and further research is necessary before this methodology can be used as a standard approach for evaluating the efficiency of farms and other firms. Chapter 3 analyses the responses of MP farm group models induced by a change in political and economic conditions. MP models are widely used as decision models in agricultural economics. In contrast to an application on the farm level with considerable modelling detail, an analysis of macroeconomic effects is often only reasonable if it is based on representative farms. However, only sparse information is available for the specification of aggregated representative farm groups. Furthermore, decision variables should reflect observed behaviour through a process known as calibration of MP models. Positive Mathematical Programming (PMP) has been developed for this purpose, a method that calibrates the objective function with the help of a non-linear costs component and determines simulation behaviour. The influence of the different proposed PMP variants on simulation results is compared ex post with observed values using the representative farm model FARMIS. This is done through 45 farm groups; these data were obtained from the German Farm Accountancy Data Network (FADN). Based on these farm groups, PMP calibration methods are applied for the year 1996/97, and a shock is introduced for observed gross margins of 2002/03. Comparison of the calibration methods reveals that the simulation strongly depends on the PMP method applied.Chapter 4 develops an estimation method for the specification of crop-specific input coefficients in MP models. The lack of information about input allocations for different crop levels, e.g., fertiliser inputs for wheat or the level of pesticides used for sugar beets, provides a challenge for the specification of aggregated farm type models. In farm accounting records available for farm group models, often only total inputs per farm are reported. In aggregated MP farm type models, the explicit representation of input allocation plays an increasingly important role, for example in the representation of environmental effects such as nitrogen intake, and subsequently in the modelling of policy alternatives. In the past, crop-specific inputs were either implemented ad hoc in MP models based on management handbooks, or were based on total input levels that were estimated with input-output regressions. This chapter presents an approach that combines the regression approach with the estimation of a farm supply model using single farm data. The relationship between the MP and the linear regression model is defined, and an estimation approach based on the optimal condition of the farm is presented. The developed estimation approach is applied to Belgian FADN data, where input allocations for various crop levels are collected in the database. A comparison of observed and estimated data is possible to validate the suggested method. The results show that the developed estimation approach successfully models the observed values of input allocation, in contrast to the regression estimation. Furthermore, this approach leads to a crop-specific breakdown of variable inputs and a representation of the resulting farm type with a fully specified non-linear component.Chapter 5 presents the farm type module developed in the modelling system CAPRI (Common Agricultural Policy Regional Impact). The integration of farm types into the modelling system CAPRI provides the chance to directly quantify the effects of market policies and developments on the farm level and to reduce the aggregation bias, resulting in an improved localisation of farm type related environmental effects. The farm types in CAPRI are based on data from the European Farm Structure Survey (FSS). For several reasons, these data are not consistent with the CAPRI database. One possible way to overcome these inconsistencies would be a simple linear up- and down-scaling of FSS to the quantity structure of the CAPRI database. However, this method could lead to a loss of information about the type and size of the farm group from FSS. To avoid this effect, an estimation approach is developed covering EU-27 that does not violate the type of farming or the economic size of the farm types.Methoden zur ökonomischen Modellierung landwirtschaftlicher Betriebe Die Arbeit untersucht und entwickelt Methoden zur Bewertung von landwirtschaftlichen Betrieben im Rahmen der Effizienzanalyse und zur Abschätzung von Anpassungsreaktionen induziert durch die Veränderung von politischen und wirtschaftlichen Rahmenbedingungen. Die Dissertation ist in vier Hauptkapitel gegliedert. Im Kapitel 2 wird die Methodik der Effizienzanalyse, bekannt unter dem Namen Data Envelopment Analysis (DEA) um den Ansatz zur Ableitung von Konfidenzintervallen erweitert, um die Aussagekraft der Effizienzmaße zu überprüfen. Die Bewertung und der Vergleich von landwirtschaftlichen Betrieben mit DEA sind in der Literatur häufig zu finden. Dabei werden die Ursachen von Ineffizienz oft mittels einer anschließenden Regressionsanalyse ermittelt. Die abgeleiteten Konfidenzintervalle zeigen jedoch deutlich, dass ohne Berücksichtigung der stochastischen Natur der Effizienzmaße kaum aussagekräftige Schlussfolgerungen über die wahre Natur von Ineffizienzen gegeben werden können. Im Kapitel 3 wird das Simulationsverhalten von mathematischen Programmierungsmodellen (MP) induziert durch die Veränderung von politischen und wirtschaftlichen Rahmenbedingungen untersucht. Im Gegensatz zur Anwendung auf einzelbetrieblicher Ebene, wo eine Spezifizierung des Modells durch vergleichweise viele Informationen erfolgen kann, sind Analysen zur Politikfolgenabschätzung häufig nur sinnvoll, wenn diese auf repräsentativen Betriebsgruppen basieren und damit aggregierte Effekte quantifiziert werden können. Zur Spezifizierung der entsprechenden Modelle stehen jedoch oftmals nur wenige Informationen zur Verfügung. Weiterhin besteht das Problem, dass wichtige Entscheidungsvariablen den beobachteten Werten entsprechen sollten, was als Kalibrierung des MP-Modells bezeichnet wird. Um dennoch MP-Modelle für repräsentative Politikfolgenabschätzung auf Betriebsebene nutzen zu können, sind positiv-mathematische Programmierungsmodelle (PMP), die mittels einer nicht-linearen Komponente der Zielfunktion das Model kalibrieren und das Simulationsverhalten mitbestimmen, entwickelt worden. Der Einfluss verschiedener vorgeschlagener PMP Methoden auf das Simulationsergebnis werden mit dem Betriebsgruppenmodel FARMIS quantifiziert und ex post mit beobachteten Werten verglichen. Dafür werden 45 Betriebsgruppen benutzt. Auf diese Betriebsgruppenmodelle werden die PMPKalibrierungsmethoden für das Jahr 1996/97 angewendet und beobachtete Deckungsbeiträge aus dem Jahr 2002/03 als Schock implementiert. Aus dem Vergleich wird ersichtlich, dass das Simulationsverhalten stark durch die Wahl des PMP Verfahrens bestimmt wird. Im Kapitel 4 wird eine Schätzmethodik von fruchtartenspezifischen Input Koeffizienten in MP-Modellen entwickelt. Fehlende Daten über die Inputallokation auf Fruchtartenebene, wie zum Beispiel der Düngemitteleinsatz im Weizen oder die Höhe der Pflanzenschutzaufwendungen in der Zuckerrübenproduktion, sind ein Problem bei der Spezifizierung von aggregierten Betriebsgruppenmodellen. In Buchführungsergebnissen werden nur die Gesamtaufwendungen im Betrieb dokumentiert. In aggregierten MP-Modellen spielt die explizite Darstellung der Input Allokation jedoch eine immer wichtigere Rolle, um Umwelteffekte, wie zum Beispiel den Stickstoffeintrag aus der Landwirtschaft, abbilden und daraufhin Alternativen modellieren zu können. In der Vergangenheit wurden Input-Mengen entweder ad hoc von Informationen aus Bewirtschaftungshandbüchern auf alle Betriebsgruppen übertragen oder von den Gesamtinputmengen aus Betriebsabschlüssen eine Input-Output Regression geschätzt. Der in dieser Arbeit vorgestellte Ansatz kombiniert die Regression mit der Schätzung des MP-Models basierend auf einzelbetrieblichen Daten. Der entwickelte Schätzansatz wird auf belgische Buchführungsergebnisse angewandt, die Informationen über die Input Allokation auf Fruchtartenebene zur Evaluierung der Ergebnisse enthält. Im Vergleich zur Regression lassen die Ergebnisse erkennen, dass der Schätzansatz die Beobachtungswerte besser widerspiegelt.Kapitel 5 präsentiert ein Betriebsgruppenmodell für die EU-27 und ein dafür entwickelten Schätzansatz zur Konsistenzrechung der CAPRI Datenbank (Common Agricultural Policy Regional Impact) und der Daten der Europäischen Betriebsstrukturerhebung (FSS). Der Schätzansatz basiert auf Daten der FSS, die aus mehreren Gründen inkonsistent mit den Daten von CAPRI sind. Ein möglicher Weg die Konsistenz zu erreichen, könnte eine lineare Skalierung der Betriebsdaten sein. Als Folge könnte jedoch die Betriebsgruppenstruktur aus FSS (Betriebsgruppentyp und -größe) verloren gehen. Um dieses Problem zu umgehen wurde für das Betriebsgruppenmodell eine Methode zur betriebstypen- und betriebsgrößenkonsistenten Schätzung entwickelt. Ein Vergleich mit der linearen Skalierungsmethode zeigt, dass die entwickelte Methode einer einfachen Skalierung vorzuziehen ist, weil damit sichergestellt werden kann, dass die Betriebsstrukturinformationen von FSS in den geschätzten Betriebsmodellen erhalten bleiben

On the reliability of multistate systems with imprecise probabilities

Розглядається обчислення надійності в складних системах за наявності випадкового набору оцінок працездатності елементів. Виявлено, що підхід Демпстер-Шефера є відповідним математичним інструментом, який відповідає поставленим задачам. Для випадку, коли взаємозалежності елементів невідомі, наведено також оцінки ефективності системи переконань і правдоподібність функції.Рассматривается вычисления надежности в сложных системах при наличии случайного набора оце- нок работоспособности элементов. Выявлено, что подход Демпстер-Шефера является соответствующим математическим инструментом, который соответствует поставленным задачам. Для случая, когда взаимозависимости элементов неизвестны, приведены также оценки эффективности системы убеждений и правдоподобность функции.We consider the computation of multistate systems reliabilities in the presence of random set estimations for the elements' working abilities. It turns out that the Dempster-Shafer approach is a suitable mathematical tool. For the case that the interdependence of the elements is unknown, bounds for the system's performance belief and plausibility functions are given as well