Value Of Forecast Information Research Articles

Decadal climate variability, water availability and cropping: the value of forecast information in South-Central Texas

Decadal climate variability (DCV) phenomena are ocean-based conditions that have climate influences persisting for a decade or more. Few studies have addressed the coupled agricultural and economic implications of these phenomena. Here, we investigate the economic value of DCV information in a South Central Texas case study along with the nature of possible adaptations to the information. To do this we first perform an econometric estimation of DCV impacts on regional crop yields and groundwater recharge. We then expand a regional economic and hydrological model to include DCV information. We find significant DCV impacts on regional aquifer recharge and crop yields for oats, dryland sorghum, and irrigated winter wheat. DCV information has substantial value with perfect information ranging from US$3.79-3.93 million yr-1 plus benefits for springflow protection, while a less perfect forecast is worth $1.47-1.89 million yr-1. We also find DCV adaptations involve changes in crop mix and water use.

A Two-Tier Statistical Forecast Method for Agricultural and Resource Management Simulations

Abstract Simple phase schemes to predict seasonal climate based on leading ENSO indicators can be used to estimate the value of forecast information in agriculture and watershed management, but may be limited in predictive skill. Here, a simple two-tier statistical method is used to hindcast seasonal precipitation over the continental United States, and the resulting skill is compared with that of ENSO phase systems based on Niño-3 sea surface temperature anomaly (SSTA) and Southern Oscillation index (SOI) persistence. The two-tier approach first predicts Niño-3 winter season SSTA, and then converts those predictions to categorical precipitation hindcasts via a simple phase translation process. The hindcasting problem used to make these comparisons is relevant to winter wheat production over the central United States. Thus, given the state of seasonal SOI and Niño-3 indicators defined before August, the goal is to predict the tercile category of the following November–March precipitation. Generally, it was found that the methods based on either predicted or persisted winter Niño-3 conditions were skillful over areas where ENSO affects U.S. winter precipitation—that is, the Southeast and the Gulf Coast, Texas, the southern and central plains, the Southwest, Northwest, and the Ohio River valley—and that the two-tier approach based on predicted Niño-3 conditions was more likely to provide the best skill. Skill based on SOI persistence was generally lower over many of those regions and was insignificant over broad parts of the central and southwest United States, but did lead the other methods over the Ohio River valley and the northwest. A more restrictive test of leading hindcast skill showed that the skill advantages of the two-tier approach over the central and western United States were not substantial, and mainly highlighted SOI persistence’s lack of skill over the central United States and leading skill over the Ohio River valley. However, two-tier hindcasts based on neural-network-predicted Niño-3 SSTA were clearly more skillful than both ENSO phase methods over areas of the Southeast. It is suggested that the relative skill advantage of the two-tier approach may be due in part to the use of arbitrary thresholds in ENSO phase systems.

Economic Evaluation Of The Information Content Of Alternative Sales Forecasting Technioues

AbstractViewing the selection of forecasting procedures as a problem of decision-making under uncertainty, operational measures of the value of forecast information and the effect of the forecast on the decision-maker’s behaviour are developed from the economics of information, as is a concept of forecast accuracy. These operational measures of economic value, illustrated and interpreted in the context of variations on a hypothetical example, are useful for cost-benefit evaluation.