ABSTRACT. The first comprehensive life-cycle assessment (LCA)

ABSTRACT. A machine vision-basedorn plant populations that are higher or lower than optimal can reduce crop yield. Duncan (1958) found that corn yield was maximized at particular plant populations depending on nutrient availability. Wiley and Heath (1969) investigated the relationships established by different researchers between corn population density and yield and found that the predictions had similar trends of yield maximization at particular plant population densities. Duncan (1984) presented the theory of crowding as a reason for yield reduction. However, optimum plant densities have not been constant over time but have increased substantially over the last several decades (Troyer and Rosenbrook, 1983;Nafziger, 1994).Even if a corn variety is planted at its optimal population, row spacing and interplant distance within a row can also affect the final yield. Plant population density, as well as interplant distribution, is important in effective utilization of available resources like nutrients and sunlight. Barbieri et al. (2000) studied the row spacing effect at different levels of nitrogen availability in corn. They found that the corn yield was higher when the row spacing was decreased for the same population density. The relative yield increase was higher for nitrogen-deficient fields. Doerge et al. (2002) measured spacing of 6,000 plants in research conducted in Missouri, Iowa, and Minnesota. The whole-field plant spacing standard deviation ranged from 3.2 to 6.9 inches. They estimated that every inch reduction in plant spacing standard deviation in a commercial field would increase the yield by about 3.4 bu/acre. Nafziger (1996) found that when there is a missing plant, the plants on either side compensated for only 47% of the reduced yield in lower population density fields (18,000 plants /acre) and 19% in higher plant density (30,000 plants/acre) fields, hence decreasing the final yield.There are three main causes of variability in plant spacing: seed germination, planter seed placement, and plant death. Seed germination rates typically range from 90% to 95% (Nielsen, 2001). Planter performance depends both on planter maintenance and speed. Nielsen (1995) reported that when the planter speed varied from 6.4 to 11.2 km/h (4 to 7 mph), the planted seed rate at higher speeds was significantly different than the planted seed rate at lower speeds. He concluded that a yield loss of at least 1.9 bu/acre occurs at every 1 mph speed increase in the range of 4 to 7 mph. Weather-and pest-related damage may result in unevenly spaced plant survivors within a row (Nielsen, 2001). Because of these factors, established plant population and spacing may be different than target plant population. Bullock et al. (1998) found that for variable-rate seeding to be profitable, a farmer needs extensive knowledge of site-specific plant population versus yield data from many years. Manual stand counts would not be feasible for a large field and are also susceptible to human error. An automated plant counting system provide...

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Biodiesel is often blended with regular U.S. No. 2 diesel. The blending level influences engine performance, emissions, and fuel cold-flow properties. In this article, ultraviolet (UV) absorption spectroscopy is presented as a reliable and affordable technology for blend level detection based on the absorbance patterns of the aromatic compounds in the proposed spectrum. Blends of biodiesel from six different feedstocks and U.S. No. 2 diesels from five different sources were used to test the robustness of the method. Since the absorbance of undiluted samples was too high to measure reliably, the samples were diluted with n-heptane. It was found that the feedstock and alcohol used (methyl or ethyl) did not make a significant difference in the absorbance of diluted biodiesel in the 245 to 305 nm range, while absorbance from 254 to 281Ănm was correlated with blend level with R 2 > 0.99. It was also observed that if the absorbance of the diesel source was known, then a single wavelength could be used to detect the biodiesel blend level. However, a single wavelength was inadequate when the diesel source was unknown because of variation in the level of aromatics in diesel fuel. Absorbances at 265, 273, and 280Ănm were used to calculate the absorbance index, which was found to be independent of the diesel fuel used. Using three wavelengths captured the shape information of the absorbance curve and eliminated the variation from the aromatics content. The root mean square error in determining blend level with this method was estimated to be 2.88%, and the R 2 for the linear model was 0.99. The method worked well with biodiesel from the different feedstocks tested in this research and was independent of the diesel fuel used.