Beleidsinstrumenten voor energie-neutrale en klimaatvriendelijke agrosectoren : zoektocht naar de optimale instrumentenmix

Greenhouse horticulture, dairy farming and intensive livestock farming have access to policy and otherinstruments that help these sectors move closer to their energy and climate objectives. However, whilesome objectives for 2020 are close to being achieved, others are more remote. This applies in particularto the production of renewable energy and the reduction of greenhouse-gas emissions in the dairysector, as well as the production of renewable energy using co-fermentation and the reduction of CO2emissions in intensive livestock farming. Education, economic incentives and regulations and legislationrepresent the major instruments used. Horticulture uses more specific instruments tailored to the natureof the sector, while livestock farming largely favours generic instruments. Regular reviews are carriedout to assess whether the instruments still incentivise the appropriate behaviour and do not fundinnovations that are already out of date or do not contribute to long-term objectives. A point forattention, however, is the limited number of tools – or limited support – for business owners to movecloser to their climate and energy objectives. This is in spite of the need for business owners to have aperspective for action. Alongside this, current communication and knowledge transfer appear toconcentrate on business owners who are actively working toward these objectives, even though more‘passive’ business owners should also be involved in developments---In de glastuinbouw, melkveehouderij en intensieve veehouderij zijn beleids- en andere instrumenteningezet om energie- en klimaatdoelstellingen te halen. De doelen voor 2020 zijn nog niet allemaalgehaald en zijn soms ook niet binnen handbereik. Dit geldt met name voor productie van duurzameenergie en reductie van broeikasgassen in de melkveehouderij en voor productie van duurzameenergie door co-vergisting en reductie van CO2-emissie in de intensieve veehouderij. Educatie,economische incentives en wet- en regelgeving zijn de belangrijkste instrumenten. De glastuinbouwgebruikt meer specifieke tuinbouwgerichte instrumenten, terwijl de veehouderij vooral generiekeinstrumenten inzet. Regelmatig wordt herzien of de instrumenten nog wel het juiste gedragondersteunen en niet inzetten op innovaties die al achterhaald zijn of niet bijdragen aan lange termijndoelen. Een aandachtspunt is dat het aantal tools of handvatten om ondernemers te helpen klimaatenenergiedoelstellingen na te streven nog beperkt is terwijl juist handelingsperspectief belangrijk isvoor ondernemers. Daarnaast lijkt communicatie en kennisoverdracht nu nog vooral gericht te zijn opondernemers die actief een bijdrage willen leveren, terwijl ook andere, meer passieve ondernemersmeegenomen moeten worden in de ontwikkelingen

Which factors affect the success or failure of eradication campaigns against alien species?

Although issues related to the management of invasive alien species are receiving increasing attention, little is known about which factors affect the likelihood of success of management measures. We applied two data mining techniques, classification trees and boosted trees, to identify factors that relate to the success of management campaigns aimed at eradicating invasive alien invertebrates, plants and plant pathogens. We assembled a dataset of 173 different eradication campaigns against 94 species worldwide, about a half of which (50.9%) were successful. Eradications in man-made habitats, greenhouses in particular, were more likely to succeed than those in (semi-)natural habitats. In man-made habitats the probability of success was generally high in Australasia, while in Europe and the Americas it was higher for local infestations that are easier to deal with, and for international campaigns that are likely to profit from cross-border cooperation. In (semi-) natural habitats, eradication campaigns were more likely to succeed for plants introduced as an ornamental and escaped from cultivation prior to invasion. Averaging out all other factors in boosted trees, pathogens, bacteria and viruses were most, and fungi the least likely to be eradicated; for plants and invertebrates the probability was intermediate. Our analysis indicates that initiating the campaign before the extent of infestation reaches the critical threshold, starting to eradicate within the first four years since the problem has been noticed, paying special attention to species introduced by the cultivation pathway, and applying sanitary measures can substantially increase the probability of eradication success. Our investigations also revealed that information on socioeconomic factors, which are often considered to be crucial for eradication success, is rarely available, and thus their relative importance cannot be evaluated. Future campaigns should carefully document socioeconomic factors to enable tests of their importance

Dr.ir. W. van der Werf Universitair hoofddocent bij de leerstoelgroep Gewas- en Onkruidecologie

brown rot in the Dutch potat

Costs and benefits of controlling quarantine diseases: a bio-economic modeling approach

This article describes a bio-economic model to quantify the costs and benefits of controlling plant quarantine diseases. The model integrates the epidemiology and economic consequences of a quarantine disease. It allows for "ex ante" evaluation of control scenarios for their cost-effectiveness, taking into account potential export losses resulting from presence of the disease. The model is applied to brown rot of potato in the Dutch potato production chain. Simulation results show that under the current (2006) control policy, the average yearly costs of brown rot are 7.7 million euros. Reducing monitoring frequency increases the costs to 12.5 million euros, 60% of which are export losses. It is also shown that, due to potential long-term effects of a strategy, conclusions on cost-effectiveness of a strategy depend on the length of the period over which that strategy is observed. These applications illustrate the potential of the bio-economic model to facilitate the development of cost-effective and soundly based control policies. Copyright 2008 International Association of Agricultural Economists.

Partial dependence plots based on the optimal boosted tree for (a) taxonomic Kingdoms, (b) biogeographic regions, (c) the reaction time between the arrival/detection of the organism and the start of the eradication campaign, (d) the spatial extent of the pest outbreak, (e) the level of biological knowledge and preparedness, and (f) insularity.

<p>The plots show probabilities of success of an eradication campaign for these predictors as net effects, i.e. averaging out the effects of all the other predictors included in the optimal boosted tree. The optimal boosted tree has overall misclassification rate 5.2% with 3.0% misclassified success and 8.0% failure cases. Sensitivity and specificity are respectively 97.0 and 92.0% for learning, and 82.2 and 68.1% for cross-validated samples. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048157#pone-0048157-t001" target="_blank">Table 1</a> for detail description of the predictors and Fig. 1 for detail explanation of misclassification rates, sensitivity and specificity.</p

Description of 29 potential success factors of 173 eradications against invertebrate plant pests, plant pathogens (viruses/viroids, bacteria and fungi) and weeds, used in data mining analyses.

<p>NA  =  information not available.</p>*<p>European Nature Information System habitat classification <a href="http://eunis.eea.europa.eu/habitats.jsp" target="_blank">http://eunis.eea.europa.eu/habitats.jsp</a>.</p>**<p>pathways were defined according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048157#pone.0048157-Hulme2" target="_blank">[44]</a>.</p

Optimal classification tree for factors relating to success and failure of 173 eradication campaigns against invertebrate plant pests, plant pathogens (viruses/viroids, bacteria and fungi) and weeds in a model without any predetermined structure.

<p>Splitting nodes (polygonal tables with splitting variable name) and terminal nodes (with a split criterion above each) show a table with columns for the outcome (success/failure) and % of weighted cases for each outcome, total number of unweighted cases (N), and graphical representation of the percentage of success (grey) and failure (black) weighted cases (horizontal bar). Vertical depth of each node is proportional to its improvement value that corresponds to explained variance at the node. Overall misclassification rate of the optimal tree is 15.8% compared to 50% for the null model, with 16.7% misclassified success and 14.8% failure cases. Sensitivity (true positive rate, defined as the ability of the model to predict that a case is eradicated when it actually is) is 83.3 and specificity (true negative rate, defined as the ability of the model to predict that a case is not eradicated when it is not) 85.2% for learning samples, i.e. the samples not used to build the models for assessment of cross-validation errors, and 77.1 and 69.0%, respectively, for cross-validated samples, i.e. the best estimates that would occur if the models were to be applied to new data, assuming that the new data were drawn from the same distribution as the learning data.</p

Optimal classification tree with event-specific factors placed at the top of the tree.

<p>Otherwise as in Fig. 1. Overall misclassification rate of the optimal tree is 18.0% with 15.3% misclassified success and 23.4% failure cases. Sensitivity and specificity are respectively 84.7 and 76.6% for learning, and 66.7 and 64.9% for cross-validated samples. Detail explanation of misclassification rates, sensitivity and specificity is in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048157#pone-0048157-g001" target="_blank">Fig. 1</a>.</p