Katharine B. Perry

Estimating temperature of mulched and bare soil from meteorological data

Abstract In order to investigate the application potential for soil solarization in the southern US without conducting labor intensive field tests and expensive experiments, a numerical model has been developed to estimate the temperature profile of both mulched and bare soils. Atmospheric and soil conditions, as well as the transmissivity, reflectivity and emissivity of mulch are considered in the model. The required dynamic inputs are hourly measurements of global radiation, air temperature, dewpoint, wind speed and rainfall. The model was validated using hourly observations from 12 contiguous days of July 6–18, 1990 at the North Carolina State University Horticultural Crops Research Station near Clinton. Different weather occurred during the period. The model worked very well on both clear and rainy days except July 17 when large, rapid changes of the air temperature and solar radiation occurred. However, the percentages of the absolute differences less than 2.0°C between the hourly estimated and measured soil temperatures at 10, 20, and 30 cm were 89, 95 and 95 for mulched soil, and 94, 98 and 100 for bare soil, respectively. The correlation between estimated and measured temperatures yielded R-square values between 0.82 and 0.93. The model was very successful to satisfy the main objectives in this study. Model sensitivities to 23 parameters were analyzed. Relative sensitivity coefficients were higher for soil bulk density, quartz fraction, and mulch transmissivity to solar radiation, than for surface roughness length, soil clay fraction and mulch transmissivity to long wave radiation.

Heat units to predict tomato harvest in the southeast USA

Abstract Planting and first harvest dates of tomato ( Lycopersicon esculentum Mill.) from 2 seasons in 3 years at eight locations in Georgia, North Carolina and South Carolina formed 38 environments which were used to determine the most reliable method to predict fast harvest date of tomato based on daily maximum and minimum air temperature. Eleven methods of calculating heat units were chosen for comparison based on their performance as described in the literature. The most reliable method was defined as the one with the smallest coefficient of variation (CV). CVs were calculated for each method over both seasons and locations, for each season over all locations, each location over all seasons, and for each season at each location. All heat unit summation methods had smaller coefficients of variation (CV) than the standard method of counting days from planting to first harvest. Heat unit summation methods improved harvest date prediction accuracy compared with the counting day method for tomatoes in the South Atlantic Coast (SAC) region. Prediction using location/season specific models were less variable than the models over all seasons and locations. Incorporating daylength improved model prediction accuracy when applied over all locations and seasons, all locations by season, and all seasons by location. Based on the results of this study, the heat unit summation technique recommended for this region (where the location and season specific models are not available) is the reduced ceiling method multiplied by daylength.

Heat units, solar radiation and daylength as pepper harvest predictors

Abstract Daily maximum and minimum air temperature, total solar radiation and daylength data from seven locations during three seasons of 3 years were used to compare 52 heat unit accumulation models with counting days as a harvest prediction method for pepper. The best model was defined as the one with the least variation, i.e. smallest coefficient of variation (CV). CVs were calculated for each method over all seasons and locations, for each method over all locations for each season, and for each method in each season at each location. In all cases heat unit accumulation methods were better than counting days. The location and season specific model was the most accurate, but the analysis over all seasons and locations did result in smaller CVs than counting days, so improved harvest prediction can be achieved by using regionally developed models.

Predicting harvest date of ‘Delicious’ and ‘Golden Delicious’ applesPredicting harvest date of ‘Delicious’ and ‘Golden Delicious’ apples using heat unit accumulations

Abstract Ten methods of calculating heat units for 30-, 40-, 50- and 60-day postbloom periods were applied to air temperatures observed for four years (1980–1984) in ‘Golden Delicious’ and ‘Delicious’ apple orchards in four production areas of the southeast U.S.A. The heat unit sums were correlated with the number of days from full bloom to objectively determined harvest dates to determine the method which provided the greatest significance probability for the correlation coefficient. No one method emerged superior. The resulting regression equations were then applied to data from the 1985 season not used in the development of the prediction equations. Heat unit accumulation equations predicted harvest within 1.0 day for ‘Delicious’ and 8.25 days for ‘Golden Delicious’.

Current and future agricultural meteorology and climatology education needs of the US extension service

Abstract A national system of extension programming that meets the basic meteorology needs of all agriculture with additional regional programming for particular crops is needed. Such a national system could be developed to optimize the capabilities of Federal and State agencies, and private sector entities. The United States Department of Agriculture (USDA) and State Cooperative Extension Services should be lead agencies in this system by designating agricultural meteorology as a programming area and providing leadership for state programs. The agricultural industry needs educational programs to improve the use of existing meteorological information and to prepare to use new information and technologies as they become available. Many agricultural producers and related agribusiness managers lack a complete understanding of the data analyses and forecast products that are now available. They do not know how to access these products or how to apply them in their management systems. Agricultural producers need to learn how to use weather-driven models as a component in their daily decision making. Extension personnel should work cooperatively with research counterparts and private sector agricultural meteorologists to improve linkages that bring agricultural weather information to the agribusiness community.