Search Results

You are looking at 1 - 10 of 144 items for :

  • risk prediction x
  • Refine by Access: All x
Clear All
Full access

Rachel Leisso, Ines Hanrahan, and Jim Mattheis

temperature data and at-harvest quality measures that were evaluated for their ability to predict incidence of soft scald at 12 weeks of storage. The dashed line indicates the threshold value for either low- or high-risk prediction. GDD = growing degree day

Full access

Steve M. Spangler, Dennis D. Calvin, Joe Russo, and Jay Schlegel

predict the risk of harvest infestation by european corn borer at the time of planting. Development of an at-planting prediction system would increase the ability of growers to anticipate harvest infestation, and, therefore, needed management resources (i

Full access

Mark P. Widrlechner, Christopher Daly, Markus Keller, and Kim Kaplan

Horticulturists have long recognized that the accurate prediction of winter injury is a key component of the effective cultivation of long-lived woody and herbaceous perennial plants in many climates. Winter injury can limit long-term plant survival

Free access

D. Clay Collins and Steven E. Newman

The Leaf Wetness Data Logger (LWL) and accompanying Logbook software were designed by Spectrum Technologies Inc. as a low-maintenance tool to aid in disease prediction and spray scheduling for outdoor field-grown crops. The LWL mimics leaf surface moisture represented as a value between 0 (dry) and 15 (wet). We explored an expanded use of the LWL to large-scale commercial greenhouses for the purpose of humidity control and disease prevention. Data were collected over 15 days in a commercial hydroponic tomato production greenhouse and repeated. Results indicated that leaf wetness, as determined by the LWL, increased during irrigation periods, with cumulative effects dependent on daily irrigation requirements and climate. Irrigation was controlled by the climate control computer in response to cumulative radiation intensity. By analyzing leaf wetness in correlation with climatic conditions, more adequate irrigation scheduling may be implemented, reducing the risk of disease spread and infection.

Full access

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.

Free access

Arthur T. DeGaetano

., 2005 ) and native species ( Schwartz et al., 2006 ). This has raised concerns that warming winter temperatures may paradoxically increase the risk of spring freeze injury as accelerated development may not outpace the decline in the probability of

Free access

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

). This model mainly uses functions that include maximum and minimum temperatures and solar radiation during the months of July and August. A daily risk index is calculated starting from the phenological stage of fruit set, providing prediction in real

Free access

Charles E. Barrett, Lincoln Zotarelli, Lucas G. Paranhos, Peter Dittmar, Clyde W. Fraisse, and John VanSickle

Incorporating weather-related risk into yield prediction models can help the vegetable growers to decide between alternative production systems, given uncertain weather during the growing season. Regression models were simulated stochastically to predict cabbage

Full access

W.G. Harris, M. Chrysostome, T.A. Obreza, and V.D. Nair

schedule. Another approach to assessing risk of P leaching is to model vertical P transport based on P sorption characteristics and loading factors (e.g., Rhue et al., 2006 ). Such models can provide a prediction of the time it would take for elevated P

Free access

Robert C. Ebel, Monte Nesbitt, William A. Dozier Jr., and Fenny Dane

freeze injury. Furthermore, more cold-hardy cultivars ( Zhang et al., 2002 ) and the potential of genetic modification to enhance cold tolerance ( Iba, 2002 ; Nolte et al., 1997 ) may reduce the risk of freeze injury. Satsuma mandarins represent a