Chapter Assessment of agricultural productivity change at country level: A stochastic frontier approach

In this paper, we estimate agricultural productivity change at country level based on the same data employed by the United States Department of Agriculture (USDA), the current reference data source, using a stochastic frontier model instead of the growth accounting method. The use of a stochastic frontier model is motivated by the opportunity to overcome the limitation of USDA estimates which rely on approximated and imputed input cost shares, and of the growth accounting method in general, which ignores technical inefficiency. We found that, in general, USDA estimates are higher in absolute value than ours but in substantial agreement, confirming the different theoretical foundations of the two methods and suggesting the empirical validity of both of them. Furthermore, our results show that the assumption of constant returns to scale made by many authors appears just a simplification and not a real property of the production processes of the various countries. This work has the value to provide, for the first time in the literature, a comparison between agricultural productivity changes estimated with different methodologies, and an additional data source that can be employed in a large variety of longitudinal economic analyses at country level

Chapter The impact of public research expenditure on agricultural productivity: evidence from developed European countries

The objective of this paper is to assess the impact of public research expenditure on agricultural productivity in developed European countries. Our research provides original evidence, making possible a comparison with existing studies focused on United States of America (USA). We apply a fixed effects Gamma distributed-lag model to yearly data in 1970-2016 sourced from the United States Department of Agriculture (USDA), the Organisation for Economic Cooperation and Development (OECD), and the Food and Agriculture Organization (FAO). In our results, public research expenditure has a significant impact on agricultural productivity up to 35 years, with peak at 17 years and long-term elasticity equal to 0.172. Based on our model, the countries with the highest internal rate of return of agricultural research expenditure resulted Germany, Spain, France and Italy (24.5-25.2%), followed by Netherlands, United Kingdom, Denmark, Greece, Belgium and Luxembourg (20.5-21.8%). However, only Germany, Denmark and Greece increased agricultural research expenditure in recent years. The estimated internal rates of return are in line with the ones reported by existing studies on USA, and they suggest that developed European countries, just like USA, could benefit from research investments in Agriculture to a much greater extent than they currently do

A model-based residual approach for human-robot collaboration during manual polishing operations

A fully robotized polishing of metallic surfaces may be insufficient in case of parts with complex geometric shapes, where a manual intervention is still preferable. Within the EU SYMPLEXITY project, we are considering tasks where manual polishing operations are performed in strict physical Human-Robot Collaboration (HRC) between a robot holding the part and a human operator equipped with an abrasive tool. During the polishing task, the robot should firmly keep the workpiece in a prescribed sequence of poses, by monitoring and resisting to the external forces applied by the operator. However, the user may also wish to change the orientation of the part mounted on the robot, simply by pushing or pulling the robot body and changing thus its configuration. We propose a control algorithm that is able to distinguish the external torques acting at the robot joints in two components, one due to the polishing forces being applied at the end-effector level, the other due to the intentional physical interaction engaged by the human. The latter component is used to reconfigure the manipulator arm and, accordingly, its end-effector orientation. The workpiece position is kept instead fixed, by exploiting the intrinsic redundancy of this subtask. The controller uses a F/T sensor mounted at the robot wrist, together with our recently developed model-based technique (the residual method) that is able to estimate online the joint torques due to contact forces/torques applied at any place along the robot structure. In order to obtain a reliable residual, which is necessary to implement the control algorithm, an accurate robot dynamic model (including also friction effects at the joints and drive gains) needs to be identified first. The complete dynamic identification and the proposed control method for the human-robot collaborative polishing task are illustrated on a 6R UR10 lightweight manipulator mounting an ATI 6D sensor

A tale of two cities: Communication, innovation, and divergence

We present a two‐area endogenous growth model where abstract knowledge flows at no cost across space but tacitk nowledge arises from the interaction among researchers and is hampered by distance. Digital communication reduces this “cost of distance” and reinforces productive specialization, leading to an increase in the system‐wide growth rate but at the cost of more inequality within and across areas. These results are consistent with evidences on the rise in the concentration of innovative activities, income inequality, and skills and income divergence across US urban areas

A Tale of Two Cities: Communication, Innovation, and Divergence

We present a two-area endogenous growth model where abstract knowledge flows at no cost across space but tacit knowledge arises from the interaction among researchers and is hampered by distance. Digital communication reduces this “cost of distance” and reinforces productive specialization, leading to an increase in the system-wide growth rate but at the cost of more inequality within and across areas. These results are consistent with evidences on the rise in the concentration of innovative activities, income inequality, and skills and income divergence across US urban areas

Distributed-lag Linear Structural Equation Models in R: the dlsem Package

In this paper, an extension of linear Markovian structural causal models is introduced,called distributed-lag linear structural equation models (DLSEMs),where each factor of the joint probability distribution is adistributed-lag linear regression with constrained lag shapes.DLSEMs account for temporal delays in the dependence relationshipsamong the variables and allow to assess dynamic causal effects.As such, they represent a suitable methodology to investigate the effectof an external impulse on a multidimensional system through time.In this paper, we present the dlsem package for Rimplementing inference functionalities for DLSEMs.The use of the package is illustrated through an example on simulated dataand a real-world application aiming at assessing the impact of agriculturalresearch expenditure on multiple dimensions in Europe

linear markovian models for lag exposure assessment

SummaryLinear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited to a single biomarker of interest and cannot provide insights on the relationships holding among the pathogen exposures, thus precluding the assessment of causal effects in a general context. In this paper, to overcome these limitations, distributed-lag linear regression is applied to Markovian structural causal models. Dynamic causal effects are defined as a function of regression coefficients at different time lags. The proposed methodology is illustrated using a simple lag exposure assessment problem

A comparative study of the seed germination capabilities of Anacamptis palustris (Orchidaceae), a threatened terrestrial orchid, and other more common Anacamptis species, by asymbiotic culture in vitro

The increasing emphasis on terrestrial orchid conservation has led to conservation actions for a wide range of threatened Mediterranean species. Many terrestrial orchids are currently at great risk for extinction as a result of a multiplicity of threatening processes. We focus on orchid seed germination capabilities in vitro, specifically germination capability of a threatened species, Anacamptis palustris, compared to three other more common species (A. laxiflora, A. morio, and A. papilionacea), and also discuss its potential impact on orchid distribution and conservation. Asymbiotic germination tests were performed with mature seeds using BM-1 medium. In vitro seed germination and protocorm developmental stages were evaluated up to 20 weeks after sowing. Significant differences in seedling development were detected among the species, and a correlation was evident between the rarity of the species and their germinability. Thus, the presence of intrinsic, biological factors that affect and limit the distribution of A. palustris may exist