Search Results

You are looking at 1 - 3 of 3 items for :

  • Author or Editor: Diana R. Cochran x
  • HortScience x
Clear All Modify Search

An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R 2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R 2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R 2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible.

Open Access

This study evaluated the effects of nine alternative substrates on herbicide efficacy in container-grown nursery crops: 1) VT (pine wood chips hammer-milled to pass a 0.4-cm screen); 2) USDA (pine wood chips hammer-milled to pass a 0.64-cm screen; 3) AUC (Pinus taeda chipped including needles); 4) AUHM (AUC hammer-milled to pass a 0.48-cm screen; 5) 1 VT: 1 commercial grade pinebark (v/v); 6) 1 USDA: 1 pinebark (v/v); 7) 1 AUC: 1 pinebark (v/v); 8) 1 AUHM: 1 pinebark (v/v); and 9) 6 pinebark: 1 sand (v/v). Each substrate was amended with 6.35 kg of 17–6–12 (17N–2.6P–10K) control-release fertilizer, 2.27 kg of lime, and 0.89 kg micromax per cubic meter. Containers (8.3 cm) were filled on 15 June and three herbicides applied the next day: Rout (oxyfluorfen + oryzalin at 2.24 + 1.12 kg·ha-1), Ronstar (oxadiazon at 4.48 kg·ha-1) and a nontreated control. The next day, containers were overseeded with 25 prostrate spurge seed. Data collected included weed counts 30 and 60 days after treatment (DAT) and weed fresh weights at 60 DAT. Spurge occurred less in the two treatments of 100% pine wood chips followed by the AUC treatment. With spurge, the least weed fresh weight occurred with the USDA and AUC treatments. For example, at 30 DAT, spurge count was reduced by 33%, 40%, and 70%, respectively, when comparing VT, USDA, and AUC to pinebark: sand. Spurge fresh weight at 60 DAT followed a similar trend. With all of the substrates except AUHM, the addition of commercially used pine bark resulted in less weed control. Rout provided superior control followed by Ronstar and the nontreated control. These data show that control of prostrate spurge with commonly used preemergent applied herbicides may actually be improved with some of the alternative substrates currently being tested.

Free access

Regalia®, a commercial extract of giant knotweed [Fallopia sachalinensis F. Schmidt (synonyms: Reynoutria sachalinensis (F. Schmidt) Nakai, Polygonum sachalinense F. Schmidt, Tiniaria sachalinesis (F. Schmidt) Janch.)], was evaluated for its potential to enhance drought tolerance of container-grown impatiens (Impatiens walleriana Hook. f. ‘Super Elfin XP White’). In two separate experiments, Regalia® was foliar-applied once a week for 4 weeks at four different rates (0, 5, 10, or 15 mL·L−1). In Expt. 1, Regalia® was applied to impatiens grown under three target substrate volumetric water contents (TVWCs): 85%, 55%, or 25%. In Expt. 2, Regalia® was applied to impatiens watered with 1, 3, or 6 days between waterings (DBW). In Expt. 1, root dry weight (RDW) of impatiens receiving applications of Regalia® at the 0.5× rate was greater compared with the 0.0× rate across all TVWCs. Additionally, soluble protein content was greater after Regalia® application at the 0.5×, 1.0×, or 1.5× rates compared with the 0.0× rate for plants grown at 55% TVWC. In Expt. 2, leaf greenness (SPAD) and leaf net photosynthetic rate (Pn) were greater with Regalia® applied at the 0.5× and 1.0× rates compared with the 0.0× rate, respectively. Soluble protein content was greater in impatiens treated with Regalia® at the 1.5× rate and 1 DBW and the 0.5× rate with 3 DBW compared with the 0.0× rate with 1 or 3 DBW. However, there was no indication that impatiens grown under different moisture levels had increased drought tolerance after application of Regalia®.

Free access