Composting is the biological decomposition of organic materials, such as plant tissue, food scraps, paper, animal fodder, and wood chips. The end-product, compost, is a beneficial soil amendment because it can contain a diversity of beneficial microorganisms, has high nutrient and water-holding capacities, can increase total soil porosity, and contains essential plant nutrients that improve soil productivity. Coastal regions of the Gulf of Mexico, as well as the Atlantic and European shorelines, have witnessed a proliferation of brown seaweed (Sargassum sp.). When piled on beaches, tourism appeal is reduced, threatening the local economy. When amassed offshore, thick brown seaweed mats can hinder fishing. Excessive decomposition rates can lead to eutrophication, which threatens coastal areas economically and environmentally. Despite these problems, seaweed may be considered a valuable compost ingredient. Therefore, the purpose of this study was to conduct a market test to determine the potential value of a seaweed-incorporated compost to consumers in Texas and to identify attributes of likely consumers. A marketing survey was developed and distributed to gardeners in the central and south Texas regions. Contingent valuation questions measured participants’ willingness to pay for the seaweed compost products. Participants were able to see, smell, and touch a sample of the compost while completing the survey. Despite 92% of respondents ranking themselves as inexperienced in compost behavior, results indicated a potential for a specialty, competitively priced seaweed-incorporated compost to be introduced to the market. Respondents were most willing to pay $4.00/ft3 to $5.00/ft3 for seaweed-incorporated compost. Additionally, participants who responded positively to buying local, buying compost in the past, having positive environmental attitudes, and buying American were more likely to pay more for the seaweed-incorporated compost. There was not an obvious pattern between willingness to pay for seaweed-incorporated compost and demographic responses.

An extensive line of research has examined linkages among spatially‐distinct markets. We apply semi‐parametric, generalized additive vector autoregressive models to a consideration of basis linkages among North Carolina corn and soybean markets. An extensive suite of linearity tests suggests that basis and price relationships are nonlinear. Marginal effects, transmission elasticities, and generalized impulse responses are utilized to describe patterns of adjustment among markets. The semi‐parametric models are compared to standard threshold vector autoregressive models and are found to reveal more statistical significance and substantially more nonlinearity in basis adjustments. Marginal effects are nonlinear and impulse responses suggest greater adjustments to extreme shocks and asymmetric adjustment patterns. The results provide evidence in favor of efficiently linked markets.

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This paper assesses the exchange rate pass-through (ERPT) for forest product prices (i.e., sawnwood, logs) by applying a two-regime Self-Exciting Threshold Autoregressive (SETAR) model. We incorporate autoregressive second-order dynamics in the regime equations. This leads to better forecasts, as integrating more lags helps capture the cumulative effects of the price dynamics. We examine sawnwood and log products traded in the United States, Malaysia (Southeast Asia) and Cameroon (West Africa). Our results illustrate the importance of applying the two-regime SETAR-type models to analyze the non-linear exchange rate pass-through for forest product markets. The impulse response analysis of each price pair supports the changing behavior of price ratios in various regimes. This may be regarded as another justification to apply models accounting for structural changes to investigate the exchange rate pass-through in a non-linear fashion. The aftershock adjustment process is similar, but the amplitude of the impact differs among markets. The results reveal potential arbitrage opportunities in the forestry industry.