تجزیه و تحلیل بهره وری اقتصادی و زیست محیطی جمعیت مزرعه با استفاده از مدل شبیه سازی میکرو
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|9645||2011||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Mathematics and Computers in Simulation, Volume 81, Issue 7, March 2011, Pages 1344–1352
New Zealand's success in raising agricultural productivity has been accompanied by higher input use, leading to adverse effects on the environment. Until recently, analysis of farm performance has tended to ignore such negative externalities. The current emphasis on environmental issues has led dairy farmers to target improvements in both environmental performance and productivity. Therefore, measuring the environmental performance of farms and integrating this information into farm productivity calculations should assist in making informed policy decisions which promote sustainable development. However, this is a challenging process since conventional environmental efficiency measures are usually based on simple input and output flows but nitrogen discharge is a complex process affected by climate, pasture composition, cow physiology and geophysical variability. Furthermore, the outdoor pastoral nature of New Zealand farming means that it is difficult to control input and output flows, particularly of nitrogen. We present a novel approach to measure the environmental and economic efficiency of farms, using the Overseer nutrient budget model and spatially micro-simulated virtual population data. The empirical analysis is based on dairy farms in the Karapiro catchment, where nitrogen discharge from dairy farming is a major source of nonpoint pollution.
New Zealand's success in raising agricultural productivity has been accompanied by higher input use, leading to adverse effects on the environment. Until recently, analysis of dairy farm performance in New Zealand has often ignored undesirable effects on the environment ,  and . The eco efficiency study by Basset Mens et al.  provides a notable exception by indentifying farms which were both economically and environmentally efficient. This was achieved by including nitrogen discharges into the analysis of farm production and financial performance. This paper extends this approach and provides separate measures of economic, environmental and joint economic and environmental performance. The current emphasis on environmental issues has led dairy farmers to target improvements in both environmental performance and productivity. Therefore, measuring the environmental performance of farms and integrating this information into farm productivity calculations should allow farmers to benchmark their performance and assist policy makers to make informed decisions which promote sustainable development. The remainder of this section outlines our use of Data Envelopment Analysis (DEA) and recent approaches to the measurement of environmental performance. We then outline some of the challenges in measuring dairy farm environmental efficiency. In Section 2 we detail the specification of the DEA model and our definitions of four different measures of efficiency (technical, economic, environmental and joint environmental and economic efficiency). This is followed by details of the empirical analysis (Section 3), results, implications and conclusions.
نتیجه گیری انگلیسی
DEA efficiency scores are summarized in Table 3 with the cumulative frequency distributions for each measure being illustrated in Fig. 1. It can be seen that there are substantial differences in both the level and the distribution of efficiencies among farms. The average level of technical efficiency (0.82) suggests that in principle farms could reduce their input use by 18% and still maintain the existing level of output, or could increase output without increasing input use. However, attitudes towards risk and farmer skill levels may affect their ability and desire to achieve this sort of efficiency. The level of technical efficiency found here is similar to that estimated by Jaforullah and Whiteman  in 1999 (0.83). From Fig. 1 it can be seen that farms perform best according to the technical efficiency measure with more than 60% of farms achieving at least 80% efficiency.