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Reza Amiri, Kourosh Vahdati, Somayeh Mohsenipoor, Mohammad Reza Mozaffari, and Charles Leslie

Torrie, 1980 ). Two main traits, kernel percentage and blight susceptibility, were analyzed as dependent variables and traits influencing these were detected by stepwise regression ( Draper and Smith, 1998 ). Direct (standardized partial regression

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Maude Lachapelle, Gaétan Bourgeois, Jennifer R. DeEll, Katrine A. Stewart, and Philippe Séguin

the stepwise regression analyses. The prediction model for SBI dyn was based on the following basic equations: Temperature ( Temp) and precipitation ( Prec) were initially set to have equivalent relative levels of importance (i.e., 0 to 1) in

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Yun Kong, Xiangyue Kong, and Youbin Zheng

shoots. After that, five linear models with 6, 4, 3, 2, and 1 predictor variables were developed using stepwise regression. In addition, using SML or SMD as a single predictor variable, two nonlinear models (i.e., power function) and two linear models

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Han Xu, Cuihua Bai, Wei Wang, Changmin Zhou, Luwei Zhu, and Lixian Yao

proline (Pro)] and bitter-taste AA [the sum of Val, Met, Ile, Leu, Phe, Lys, arginine (Arg), and Pro] were classified as the method reported by Kato et al. (1989) . Multiple stepwise regression analysis was performed between foliar nutrient and pulp FAA

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Yuhung Lin and Yaling Qian

method ( Table 3 ). The Pearson correlation test was performed by Proc CORR to obtain Pearson statistic coefficients of individual minerals and its relationship with turf quality. Stepwise regression was conducted to determine whether any of the minerals

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Arthur Villordon, Christopher Clark, Tara Smith, Don Ferrin, and Don LaBonte

Forward and stepwise regression methods identified variables related to the influence of transplanting date on yield of U.S. #1 sweetpotatoes. The variables were mean minimum soil temperature 5 days after transplanting (DAT), wind direction at transplanting, and accumulated heat units (growing degree-days) 5 DAT. Machine learning techniques identified the same variables using leave-one-out and k-fold cross-validation methods. Growers and crop consultants, in collaboration with knowledge workers, can use this information in conjunction with public and subscription-based weather forecasts to further optimize transplanting date determination and for making risk-averse decisions. These results help to underscore the importance of consistent transplant establishment as one of the determinants of storage root yield in sweetpotatoes.

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Hudson Minshew, John Selker, Delbert Hemphill, and Richard P. Dick

Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R 2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.

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S.B. Sterrett, M.R. Henningre, and G.S. Lee

The progression of internal heat necrosis (IHN) of `Atlantic' potato was studied in seven plantings in two locations (Virginia and New Jersey) over 3 years. The incidence (percentage of tubers with necrosis), severity (rating), and distribution (percentage of 1/8 pieces with necrosis per tuber) of IHN increased with successive harvests, but varied with year and location. Significant but weak linear correlation coefficients were found for the IHN variables of incidence, rating, and distribution with either time in days after planting (DAP), yield, or percentage of tubers >64 mm in diameter. Models were developed using stepwise regression to relate IHN variables with DAP, yield, percentage of large tubers, and various temperature and rainfall measurements. Time (DAP), penalty (DAP to first occurrence of three consecutive days of negative accumulated heat units), and rainfall (1 to 60 DAP) were significant variables in regression models for incidence and rating. While DAP and penalty were significant variables in the regression model for distribution, the variable rainfall was not included in the model. These findings indicate that the potential of IHN in `Atlantic' varies with the growing season, and is influenced by more than one environmental

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S.B. Sterrett, G.S. Lee, M.R. Henninger, and M. Lentner

In `Atlantic' potato (Solanum tuberosum L.) the onset and development of internal heat necrosis (IHN) varied with planting date and location in 1989. Symptoms of IHN (first trace) took fewer days to appear in the later plantings in Virginia and New Jersey. However, the interval from first trace to offgrade was extended in the later plantings. Data from successive harvests in these two locations over the past 4 years were used to develop a two-stage model to predict first trace and offgrade by stepwise regression techniques. The predictive model for first trace included rainfall and variables calculated from a heat-sum model that reflected maximum and minimum air temperatures during tuber initiation and early enlargement. The addition of variables reflecting size distribution and rainfall events at first trace resulted in a strong predictive model for offgrade (R 2 = 0.98, Mallow's criterion = 2.97). These models indicate that onset and development of IHN are influenced by environmental stress during more than one stage of growth. A delay in the development of offgrade tubers would be expected in years with a cool, wet spring, fewer tubers >64 mm in diameter at first trace, and more rain events during the 10 days immediately after first trace.

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Rawia El-Motaium, Hening Hu, and Patrick H. Brown

The influence of B and salinity [3 Na2SO4 : 1 CaCl2, (molar ratio)] on B toxicity and the accumulation of B, sodium, and SO4 in six Prunus rootstocks was evaluated. High salinity reduced B uptake, stem B concentrations, and the severity of toxicity symptoms in five of the six rootstocks. Forward and backward stepwise regression analyses suggested that stem death (the major symptom observed) was related solely to the accumulation of B in the stem tissue in all rootstocks. The accumulation of B and the expression of toxicity symptoms increased with time and affected rootstock survival. No symptoms of B toxicity were observed in leaf tissue. The Prunus rootstocks studied differed greatly in stem B accumulation and sensitivity to B. The plum rootstock `Myrobalan' and the peach-almond hybrid `Bright's Hybrid' were the most tolerant of high B and salinity, whereas the peach rootstock `Nemared' was very sensitive to high B and salinity. In all rootstocks, adding B to the growth medium greatly depressed stem SO4 concentrations. In every rootstock except `Nemared' peach, adding salt significantly depressed tissue B concentrations. A strong negative correlation between tissue SO4 and B was observed. Grafting experiments, in which almond was grafted onto `Nemared' peach or `Bright's Hybrid', demonstrated the ability of rootstocks to influence B accumulation and scion survival.