Exporting northwest Arkansas excess turkey and broiler litter to partially fertilize nutrient-deficient cropland in eastern Arkansas can be more cost effective than to supply all crop nutrients with chemical fertilizer only, given current high fertilizer prices. Cost savings are greater if litter is baled in ultraviolet resistant plastic and transported via truck, since backhaul opportunities reduce truck rates, or alternatively, if raw litter is shipped via a truck-barge combination. Rice is the crop that allows for greater savings according to a mathematical programming model implemented in General Algebraic Modeling System (GAMS).
A risk-averse irrigated corn producer would be better off choosing the more expensive subsurface drip irrigation (SDI) over center-pivot sprinkler (CPS), given limited aquifer life and swine effluent and urea fertilization. A stochastic optimization using EPIC data maximized expected utility of 100 years' worth of net revenues for a quarter section. Phosphorus accumulation was more likely with the CPS than with the SDI but soil nitrogen was constant under both systems. SDI conserves more water than CPS per acre but depletes the aquifer faster because a greater area is irrigated. These results were invariant in the sensitivity analysis.
Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to traditional Granger Causality tests.
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