In this study, we investigate the economic viability and environmental impact of three different soil management systems used for strawberry (Fragaria ×ananassa) production in the southeastern United States: 1) a conventional production system that is based on the current production practices implemented by growers, 2) a nonfumigated compost system with summer cover crop rotations and beneficial soil inoculants, and 3) an organic production system that includes practices approved for use under the National Organic Program (NOP). Under our assumptions, all three systems resulted in positive net returns estimated at $14,979, $11,100, and $19,394 per acre, respectively. The nonfumigated compost system and organic system also both resulted in considerable reductions in negative environmental and human health impacts measured by a set of selected indicators. For example, the total number of lethal doses (LD50) applied per acre from all chemicals used in each system and measuring acute human risk associated with each system declined from 118,000 doses/acre in the conventional system to 6649 doses/acre in the compost system and to 0 doses/acre in the organic system. Chronic human health risk, groundwater pollution risk, and fertilizer use declined as well in the compost and organic systems as compared with the conventional system.
Commercial compost is the inherently variable organic product of a controlled decomposition process. In the USA, assessment of compost's physicochemical parameters presently relies on standard laboratory analyses set forth in Test Methods for the Examination of Composting and Compost (TMECC). A rapid, field-portable means of assessing the organic matter (OM) content of compost products would be useful to help producers ensure optimal uniformity in their compost products. Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) is a rapid, proximal-sensing technology proven effective at quantifying organic matter levels in soils. As such, VisNIR DRS was evaluated to assess its applicability to compost. Thirty-six compost samples representing a wide variety of source materials and moisture content were collected and scanned with VisNIR DRS under moist and oven-dry conditions. Partial least squares (PLS) regression and principal component regression (PCR) were used to relate the VisNIR DRS spectra with laboratory-measured OM to build compost OM prediction models. Raw reflectance, and first- and second-derivatives of the reflectance spectra were considered. In general, PLS regression outperformed PCR and the oven-dried first-derivative PLS model produced an r(2) value of 0.82 along with a residual prediction deviation value of 1.72. As such, VisNIR DRS shows promise as a suitable technique for the analysis of compost OM content for dried samples.
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