BackgroundEconomic impact assessment of invasive species requires integration of information on pest entry, establishment and spread, valuation of assets at risk and market consequences at large spatial scales. Here we develop such a framework and demonstrate its application to the pinewood nematode, Bursaphelenchus xylophilus, which threatens the European forestry industry. The effect of spatial resolution on the assessment result is analysed.Methodology/Principal FindingsDirect economic impacts resulting from wood loss are computed using partial budgeting at regional scale, while impacts on social welfare are computed by a partial equilibrium analysis of the round wood market at EU scale. Substantial impacts in terms of infested stock are expected in Portugal, Spain, Southern France, and North West Italy but not elsewhere in EU in the near future. The cumulative value of lost forestry stock over a period of 22 years (2008–2030), assuming no regulatory control measures, is estimated at €22 billion. The greatest yearly loss of stock is expected to occur in the period 2014–2019, with a peak of three billion euros in 2016, but stabilizing afterwards at 300–800 million euros/year. The reduction in social welfare follows the loss of stock with considerable delay because the yearly harvest from the forest is only 1.8%. The reduction in social welfare for the downstream round wood market is estimated at €218 million in 2030, whereby consumers incur a welfare loss of €357 million, while producers experience a €139 million increase, due to higher wood prices. The societal impact is expected to extend to well beyond the time horizon of the analysis, and long after the invasion has stopped.Conclusions/SignificancePinewood nematode has large economic consequences for the conifer forestry industry in the EU. A change in spatial resolution affected the calculated directed losses by 24%, but did not critically affect conclusions.
Harmful non-indigenous species (NIS) impose great economic and environmental impacts globally, but little is known about their impacts in Southeast Asia. Lack of knowledge of the magnitude of the problem hinders the allocation of appropriate resources for NIS prevention and management. We used benefit-cost analysis embedded in a Monte-Carlo simulation model and analysed economic and environmental impacts of NIS in the region to estimate the total burden of NIS in Southeast Asia. The total annual loss caused by NIS to agriculture, human health and the environment in Southeast Asia is estimated to be US$33.5 billion (5th and 95th percentile US$25.8–39.8 billion). Losses and costs to the agricultural sector are estimated to be nearly 90% of the total (US$23.4–33.9 billion), while the annual costs associated with human health and the environment are US$1.85 billion (US$1.4–2.5 billion) and US$2.1 billion (US$0.9–3.3 billion), respectively, although these estimates are based on conservative assumptions. We demonstrate that the economic and environmental impacts of NIS in low and middle-income regions can be considerable and that further measures, such as the adoption of regional risk assessment protocols to inform decisions on prevention and control of NIS in Southeast Asia, could be beneficial.
This paper describes a decision‐support scheme (DSS) for mapping the area where economically important loss is likely to occur (the endangered area). It has been designed by the PRATIQUE project to help pest risk analysts address the numerous risk mapping challenges and decide on the most suitable methods to follow. The introduction to the DSS indicates the time and expertise that is needed, the data requirements and the situations when mapping the endangered areas is most useful. The DSS itself has four stages. In stage 1, the key factors that influence the endangered area are identified, the data are assembled and, where appropriate, maps of the key factors are produced listing any significant assumptions. In stage 2, methods for combining these maps to identify the area of potential establishment and the area at highest risk from pest impacts are described, documenting any assumptions and combination rules utilised. When possible and appropriate, Stage 3 can then be followed to show whether economic loss will occur in the area at highest risk and to identify the endangered area. As required, Stage 4, described elsewhere, provides techniques for producing a dynamic picture of the invasion process using a suite of spread models. To illustrate how the DSS functions, a maize pest, Diabrotica virgifera virgifera, and a freshwater invasive alien plant, Eichhornia crassipes, have been used as examples.
Oil palm production has led to large losses of valuable habitats for tropical biodiversity. Sparing of land for nature could in theory be attained if oil palm yields increased. The efficiency of oil palm smallholders is below its potential capacity, but the factors determining efficiency are poorly understood. We employed a two-stage data envelopment analysis approach to assess the influence of agronomic, supply chain and management factors on oil palm production efficiency in 190 smallholders in six villages in Indonesia. The results show that, on average, yield increases of 65% were possible and that fertilizer and herbicide use was excessive and inefficient. Adopting industry-supported scheme management practices, use of high-quality seeds and higher pruning and weeding rates were found to improve efficiency. Smallholder oil palm production intensification in Indonesia has the capacity to increase production by 26%, an equivalent of 1.75 million hectares of land.
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