To improve management of winter wheat (Triticum aestivum L.), more information is needed on how grain yield is influenced by planting date, seeding rate, and applied P. A 3‐yr study was conducted to measure the effects of these variables on grain yield and yield components of wheat grown in low‐P soils. All soils were Crete silty clay loams (fine Montmorillonitic mesic Pachic Argiustoll) and had Bray & Kurtz no. 1 soil tests of less than 10 mg kg−1. A randomized complete block designed experiment with a split plot treatment arrangement using three planting dates (each in 1985, 1986, and 1988) as whole plots, and factorial combinations of three seeding rates and three P rates as split plots. Grain yield, spikes meter−2, kernels spike−1, and kernel weight data were collected. Relative grain yield was greatest when 400 growing degree days (GDD, 4.4 °C base temperature) accumulated between the planting date and 31 December. Increasing the seeding rate from 34 to 101 kg ha−1 resulted in yield increases of 0.39, 0.48, and 0.21 Mg ha−1 in 1986, 1987, and 1988, respectively. Increasing the P rate from 0 to 34 kg P ha−1 resulted in 0.67, 0.53, and 0.79 Mg ha−1 yield increase in 1986, 1987, and 1988, respectively. Planting date by P rate and seeding rate by P rate interactions in 1988 indicated that P reduced the negative influence of late planting and low seeding rate on grain yield. Path coefficient analysis indicated that under conditions resulting in low tiller numbers, kernel weight contributed most in yield determination, while under high tillering conditions the number of spikes meter−2 was the most important yield component. This study showed that wheat grain yields were optimized with planting dates that allowed 400 GDD accumulation before 31 December with a 101 kg ha−1 seeding rate when available soil P is sufficient.

Grain producers price grain prior to harvest to reduce financial risk and to enhance net returns. Because accomplishing the second objective is debatable, altemative com and soybean preharvest options and hedge marketing strategies were designed to test the hypothesis that preharvest pricing could generate statistically higher average net returns than harvest sales without increasing variability. Weekly seasonal futures price patterns from 1975 to 1994 were used to time marketings. The strategies were applied to Iowa and Ohio model farms. The hypothesis was accepted for some strategies that included options but not for futures-only strategies.I 'n the 1960s, Cootner and Samuelson popularized the random walk theory and .the efficient-market hypothesis. These irnply that prices fluctuate randomly about their intrinsic value and, at any point in time, reflect all available market information. The concept was initially applied to stock markets, which unlike grain are not influenced by seasonal factors related to weather. Using this concept, authors have argued that the optimum investment strategy in the stock market is to routinely buy and hold an index of stocks and bonds rather than attempting to time investments to beat the market (Malkiel, Murphy).During the last thirty years, authors have applied this concept to agricultural futures markets and have conducted marketing efficiency tests (Kamara). Results from these investigations have advanced the debate as to whether preharvest marketing strategies employing hedges and/or options can be used by grain producers to increase profits above those earned through a naive, harvest-time cash marketing strategy. This paper adds to the discussion by examining altemative com and soybean preharvest rnarketing strategies and tests the hypothesis that a set of pre-

A quality‐of‐life index (QLI), a proxy measure of utility, is constructed by factor‐weighted and simple‐summation weighted aggregation of socio‐psychological measures of well‐being. The socio‐psychological measures were constructed from quality of life domains taken from selected years of the General Social Surveys [General Social Surveys, 1972–1993: Cumulative Code Book. Principal Investigator, James A. Davis; Director and Co‐Principal Investigator, Tom W. Smith — Chicago: National Opinion Research Center, 1993. (National Data Program for the Social Sciences Series, no. 13).]. The Quality of Life Indices (QLI) indices are regressed on selected socio‐demographic variables using quadratic. Cobb‐Douglas, square root, and semilog functional forms. QLI is much influenced by income, education, and health. As measured here, QLI is not much influenced by year of measurement, sector, or by region of residence. Much variability in the QLI is unique to individuals, and our results are suited to predict group, rather than individual well being. Practitioners computing the benefit–cost ratio for a public program, project, or policy can weight dollars by income groups with marginal utilities derived from this study. That methodology will matter: even the ‘conservative’ quadratic equation indicates that the marginal utility of income (MUI) for families with very low incomes is half as large as for families with median incomes.

Following enactment of the 1996 Farm Bill, corn and soybean implied volatilities covering the preharvest and storage seasons increased 16–23% between 1987–1995 and 1997–2001. The increase was statistically significant at the 90% confidence level. Standard deviation of corn and soybean prices derived from the implied volatilities increased 7–25%, but only the increase for preharvest corn was statistically significant. Further muddling the picture is the decline in variability of annual U.S. average corn and soybean cash price. These mixed findings point to continuing disagreement about government's role in managing farm risk in the post‐1996 Farm Bill world.

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