Additional index words. fruit size, Lycopersicon esculentum, shoot removal Abstract. Unstable prices and increased competitive market pressures have caused many staked-tomato (Lycopersicon esculentum Mill.) producers to reexamine the costs and benefits of various production practices. In 1988 and 1989, field studies were conducted to determine if changes in plant in-row spacing and pruning could reduce production costs, increase yields, and improve grower net returns of staked 'Mountain Pride' tomatoes. In both years, early-season yields were highest using early pruning (when lateral shoots were 5 to 10 cm long) or delayed pruning (when lateral shoots were 30 to 36 cm long) and in-row spacings ≤46 ≤46 cm. In 1988, total-season yields per hectare of pruned plants increased as in-row spacing decreased. For nonpruned plants, however, total-season yields were high at all spacings. In 1989, total-season yields were lower from delayed-pruned plants than from nonpruned plants and there was little yield difference due to in-row spacing. In both years, nonpruned plants produced low yields of fruit >72 mm in diameter but their total yields were greater than those of pruned plants. Net returns per hectare, calculated from combined data of both years, were highest when 1) plants spaced closely in-row were pruned early and 2) plants were spaced 46 to 76 cm apart and either pruned early or not pruned.

The effects of plant spacing (15, 23, 31, and 38 cm) and date of harvest on yield and economic return of `Beauregard' sweetpotato [Ipomoea batatas (L.) Lam.] were studied. For comparison, `Jewel' was included at one spacing. As in-row plant spacing decreased, yield of U.S. No. 1, canners, and total marketable root production increased when plants were harvested 103 days or later after transplanting. The yield of jumbo roots generally increased with in-row spacing because of less plant-to-plant competition. Total marketable and No. 1 grade yields of `Beauregard' at the closest spacing (15 cm) were greater than those of `Jewel'. `Beauregard' roots sized more quickly than `Jewel' roots regardless of spacing. The optimal time for harvesting `Beauregard' was 100 to 110 days after transplanting, while acceptable yields could be obtained as early as 90 days after transplanting depending on market prices. Economic analysis of `Beauregard' spacing data indicated that 23 cm would be the preferred spacing if a late harvest was anticipated, while the 15-cm spacing would be best if harvested at ≈110 days after transplanting. Thus, sweetpotato growers should place `Beauregard' at an in-row spacing of 15 or 23 cm, depending on projected date of harvest, on or before 10 June, to obtain the best yields with the highest return on investment.

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[Y t -rix.; ... , X lk ; {3)]2. 1=1In practice the maximum likelihood estimator /:J can be found either by solving a set of nonlinear normal equations or by using a search algorithm. In certain important applications the function F will have a form so that conditional on values of p < m of the m parameters in the {3 vector, and on the X's, the function is linear in the remaining parameters.Notable examples of models that have this property include the model that results from a Box-Cox power transformation of explanatory variables asNote that (3) is linear in its parameters, given A = {3k+I' The constant elasticity of likelihood (CES) production function, equation (4), is a second example. After logarithmic transformation it is linear in {33and In f30 if {31 and f32 are given. Just's (l974a,b, 1977) generalized adaptive expectations model is a third example. In its basic form, it can be written asThe variable Y t above is acreage harvested in year t , PI is price received for the crop in year t, and P/(f33) and V/({33) are the mean and variance, respectively, of the subjective distribution of year t + 1 prices as perceived in year t. A consistent finite approximation to (5) is defined asAmong the class of models that are nonlinear in the parameters, some lend themselves to a simple estimation method requiring repeated application of linear least squares. However, there is confusion over methods for computing standard errors of such estimates.In this paper we define a simple method for estimating parameters of certain nonlinear models and clarify the interpretation of results. This method is based on conditional linear least squares. It has been used by Just (l974a,b, 1977) to estimate a generalized adaptive-expectations supply model. We show that a method commonly used to estimate asymptotic standard errors of such parameter estimates systematically overstates precision. Factors that affect the degree of overstatement are delineated. A method of calculating correct precision measures is contrasted empirically with the incorrect procedure. Conditional Linear Least SquaresA wide range of stochastic models used in applied research can be written in the form (1) Y t = F(X/l, X/2, ... , X tk ; {3) + VI' It is assumed that the disturbances in (1) are independently and identically distributed normal random variables, and, under this assumption, nonlinear squares and maximum likelihood procedures applied to estimate {3 in (1) are equivalent. Note that the natural logarithm of the likelihood function associated with (1) takes the form n n (2) In L({3, cr-) = -T In 27T -T In cr-1 n --::2 L [Y t -F(X/l, ... , X l k ; {3)]2, 2u-t=1 so that for any cr-> 0, In L is maximized when {3 is chosen to minimize the residual sum of squares, Edmund A. where where co P t-{({33) = {33 L (l -f33)iP t -i -l , and i=O t-tb-J P*t-I ({33) = {33 I (l -{33) iPt-i-J' i=O