scholarly journals A Real-Time Gridded Crop Model for Assessing Spatial Drought Stress on Crops in the Southeastern United States

2011 ◽  
Vol 50 (7) ◽  
pp. 1459-1475 ◽  
Author(s):  
Richard T. McNider ◽  
John R. Christy ◽  
Don Moss ◽  
Kevin Doty ◽  
Cameron Handyside ◽  
...  
Keyword(s):  
United States ◽  
High Resolution ◽  
Real Time ◽  
Physiological State ◽  
Southeastern United States ◽  
Rapid Change ◽  
Crop Model ◽  
Crop Evolution ◽  
Large Variability ◽  
Growing Seasons

AbstractThe severity of drought has many implications for society. Its impacts on rain-fed agriculture are especially direct, however. The southeastern United States, with substantial rain-fed agriculture and large variability in growing-season precipitation, is especially vulnerable to drought. As commodity markets, drought assistance programs, and crop insurance have matured, more advanced information is needed on the evolution and impacts of drought. So far many new drought products and indices have been developed. These products generally do not include spatial details needed in the Southeast or do not include the physiological state of the crop, however. Here, a new type of drought measure is described that incorporates high-resolution physical inputs into a crop model (corn) that evolves based on the physical–biophysical conditions. The inputs include relatively high resolution (as compared with standard surface or NOAA Cooperative Observer Program data) (5 km) radar-derived precipitation, satellite-derived insolation, and temperature analyses. The system (referred to as CropRT for gridded crop real time) is run in real time under script control to provide daily maps of crop evolution and stress. Examples of the results from the system are provided for the 2008–10 growing seasons. Plots of daily crop water stress show small subcounty-scale variations in stress and the rapid change in stress over time. Depictions of final crop yield in comparison with seasonal average stress are provided.

scholarly journals Improving Numerical Weather Predictions of Summertime Precipitation over the Southeastern United States through a High-Resolution Initialization of the Surface State

2011 ◽  
Vol 26 (6) ◽  
pp. 785-807 ◽  
Author(s):  
Jonathan L. Case ◽  
Sujay V. Kumar ◽  
Jayanthi Srikishen ◽  
Gary J. Jedlovec
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Southeastern United States ◽  
Model Verification ◽  
Sea Surface ◽  
Convective Precipitation ◽  
Numerical Weather

Abstract It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h−1 matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.

scholarly journals Nitrogen Fertilizer Management and Cultivar Selection for Cabbage Production in the Southeastern United States

2020 ◽  
Vol 30 (6) ◽  
pp. 685-691
Author(s):  
Andre Luiz Biscaia Ribeiro da Silva ◽  
Joara Secchi Candian ◽  
Lincoln Zotarelli ◽  
Timothy Coolong ◽  
Christian Christensen
Keyword(s):  
United States ◽  
Field Experiments ◽  
Southeastern United States ◽  
Total Yield ◽  
Crop Evapotranspiration ◽  
Fertilizer N ◽  
Marketable Yield ◽  
Rainfall Events ◽  
Growing Seasons ◽  
N Rates

Soil nitrogen (N) is easily leached in cabbage (Brassica oleracea var. capitata) production areas of southeastern United States characterized by sandy soils with low water-holding capacity. Soil N leaching in these areas is increased after rainfall events; consequently, growers increase the fertilizer N application to protect against N deficiencies and yield loss. The objective of this study was to evaluate the effects of three fertilizer N rates on yield and head quality for common cabbage cultivars used by Florida and Georgia growers during four cabbage growing seasons. Field experiments were conducted in Hastings, FL, in 2016 and 2017, and in Tifton, GA, in 2018 and 2019. A randomized complete block design was used with a split-plot design of fertilizer N rate and cabbage cultivar. Fertilizer N rate treatments consisted of the application of 170, 225, and 280 lb/acre N and were assigned as the main plot. Cabbage cultivars Bravo, Bronco, Bruno, Capture, Cheers, and Ramada were assigned as the sub-plots. Weather conditions were monitored during all growing seasons, and total, marketable, and unmarketable yields, as well as cabbage head polar and equatorial diameters, and core height and width were measured. In Florida, there was a significant interaction for growing season and fertilizer N rate. The Florida 2016 cabbage season experienced 10.5 inches of rainfall, and fertilizer N rates had no effect on cabbage yields. Total and marketable yield averaged 45,391 and 38,618 lb/acre among fertilizer N rates in 2016, respectively. Rainfall accumulated 2.1 inches during the 2017 study in Florida, which was less than the crop evapotranspiration. In response, total and marketable yield were higher for the applications of 225 lb/acre N (51,865 and 49,335 lb/acre, respectively) and 280 lb/acre N (54,564 and 52,219 lb/acre, respectively) compared with the application of 170 lb/acre N (47,929 and 43,710 lb/acre, respectively). In Georgia, there were no significant interactions between production season and fertilizer N rates. In addition, there were no significant main effects of season or fertilizer N rate. Rainfall events accumulated 20.9 and 7.8 inches during the 2018 and 2019 growing seasons, respectively. Total and marketable yields averaged 37,290 and 33,355 lb/acre, respectively for the two growing seasons in Georgia. Cabbage cultivar had no interaction with fertilizer N rate in any location. ‘Cheers’ (52,706 lb/acre) had the highest total yield in Florida, and ‘Ramada’ (38,462 lb/acre) and ‘Bronco’ (39,379 lb/acre) had the highest total yields in Georgia. In conclusion, the application of 225 lb/acre N was sufficient to sustain cabbage yields, but yields of the 170- and 225-lb/acre N treatments were not different when rainfall events exceeded crop evapotranspiration.

scholarly journals An Economic Evaluation of Soybean Stink Bug Control Alternatives for the Southeastern United States

1992 ◽  
Vol 24 (2) ◽  
pp. 83-94 ◽  
Author(s):  
David Chyen ◽  
Michael E. Wetzstein ◽  
Robert M. McPherson ◽  
William D. Givan
Keyword(s):  
United States ◽  
Experimental Data ◽  
Economic Evaluation ◽  
Methyl Parathion ◽  
Southeastern United States ◽  
The United States ◽  
Stink Bug ◽  
Growing Seasons ◽  
Lambda Cyhalothrin ◽  
Environmentally Sound

AbstractMethyl parathion or Penncap M (an encapsulated methyl parathion) are used extensively throughout the United States for controlling stink bug pests in soybeans, Glycine Max (L.) Merrill. However, this insecticide is highly toxic to mammals, birds, and non-target arthropods, and thus is less environmentally sound than other insecticides. For environmental and human health considerations, investigating alternative insecticides for control is desired. For this investigation, research based on field experimental data from Florida, Georgia, and Louisiana during the 1988 and 1989 growing seasons were employed. Results indicate that alternative, currently available, and less toxic insecticides may reduce producer costs, increase yield, and improve soybean quality. These alternative insecticides include Scout (tralomethrin), Karate (lambda-cyhalothrin), Orthene (acephate), and Baythroid (cyfluthrin). In terms of improved profits these alternative insecticides may dominate methyl parathion or encapsulated methyl parathion.

scholarly journals Impact of Fungal Gummosis on Peach Trees

HortScience ◽  
2003 ◽  
Vol 38 (6) ◽  
pp. 1141-1143 ◽  
Author(s):  
T.G. Beckman ◽  
P.L. Pusey ◽  
P.F. Bertrand
Keyword(s):  
United States ◽  
Disease Severity ◽  
Prunus Persica ◽  
Southeastern United States ◽  
Fruit Yield ◽  
Peach Tree ◽  
Botryosphaeria Dothidea ◽  
Trunk Diameter ◽  
Growing Seasons ◽  
Peach Trees

Peach tree fungal gummosis caused by Botryosphaeria dothidea [(Moug.:Fr.) Cos & de Not.] is widespread throughout the southeastern United States. Until recently, its economic impact on peach [Prunus persica (L.) Batsch] has been impossible to estimate, since no effective controls were known. Significant, though not total, suppression of gummosis on `Summergold' peach trees was achieved with an intensive 5-year spray program with captafol. Captan was far less effective than captafol. Both trunk diameter and fruit yield were negatively correlated with disease severity. After eight growing seasons, trees treated with captafol were 18% larger than the untreated trees. Yield of mature captafol-treated trees was 40% to 60% high er than that of untreated ones. Following termination of the spray program after 5 years, disease severity gradually increased on both captafol- and captan-treated trees. However, through eight growing seasons, disease severity was significantly lower on captafol-treated trees. This study demonstrates that peach tree fungal gummosis significantly depresses tree growth and fruit yield on susceptible peach cultivars.

scholarly journals Downscaling of Seasonal Precipitation for Crop Simulation

2007 ◽  
Vol 46 (6) ◽  
pp. 677-693 ◽  
Author(s):  
Andrew W. Robertson ◽  
Amor V. M. Ines ◽  
James W. Hansen
Keyword(s):  
United States ◽  
Rainfall Variability ◽  
Southeastern United States ◽  
Daily Rainfall ◽  
Summer Rainfall ◽  
Crop Model ◽  
Stochastic Simulations ◽  
Temporal Smoothing ◽  
Climate Forecasts ◽  
Low Pass

Abstract A nonhomogeneous hidden Markov model (NHMM) is used to make stochastic simulations of March–August daily rainfall at 10 stations over the southeastern United States, 1923–98. Station-averaged observed daily rainfall amount is prescribed as an input to the NHMM, which is then used to disaggregate the rainfall in space. These rainfall simulations are then used as inputs to a Crop Estimation through Resource and Environment Synthesis (CERES) crop model for maize. Regionally averaged yields derived from the NHMM rainfall simulations are found to correlate very highly (r = 0.93) with those generated by the crop model using observed rainfall; stationwise correlations range between 0.44 and 0.74. Rainfall and crop simulations are then constructed under increasing degrees of temporal smoothing applied to the regional rainfall input to the NHMM, designed to exclude the submonthly weather details that would be unpredictable in seasonal climate forecasts. Regional yields are found to be remarkably insensitive to this temporal smoothing; even with 90-day low-pass-filtered inputs to the NHMM, resulting yields are still correlated at 0.85 with the baseline simulation, whereas stationwise correlations range between 0.18 and 0.68. From these findings, it is expected that regional maize yields over the southeastern United States will be largely insensitive to year-to-year details of subseasonal rainfall variability; they should be downscalable, in principle, using an NHMM from climate forecasts archived at daily resolution, with the important caveat that the latter need to be skillful enough at the 90-day time scale. As a by-product of the analysis, subseasonal-to-interdecadal summer rainfall variability over the southeastern United States is interpretable in terms of six discrete weather states indicative of a monsoonlike climate regime. Low-simulated-yield years are found to be associated with delayed summer rainfall onset.

scholarly journals Characterizing severe weather potential in synoptically weakly forced thunderstorm environments

2018 ◽  
Vol 18 (4) ◽  
pp. 1261-1277 ◽  
Author(s):  
Paul W. Miller ◽  
Thomas L. Mote
Keyword(s):  
United States ◽  
High Resolution ◽  
Southeastern United States ◽  
Model Error ◽  
Severe Weather ◽  
Forecasting Model ◽  
Weather Event ◽  
Accurate Representation ◽  
Relative Odds ◽  
Severe Weather Event

Abstract. Weakly forced thunderstorms (WFTs), short-lived convection forming in synoptically quiescent regimes, are a contemporary forecasting challenge. The convective environments that support severe WFTs are often similar to those that yield only non-severe WFTs and, additionally, only a small proportion of individual WFTs will ultimately produce severe weather. The purpose of this study is to better characterize the relative severe weather potential in these settings as a function of the convective environment. Thirty-one near-storm convective parameters for > 200 000 WFTs in the Southeastern United States are calculated from a high-resolution numerical forecasting model, the Rapid Refresh (RAP). For each parameter, the relative odds of WFT days with at least one severe weather event is assessed along a moving threshold. Parameters (and the values of them) that reliably separate severe-weather-supporting from non-severe WFT days are highlighted. Only two convective parameters, vertical totals (VTs) and total totals (TTs), appreciably differentiate severe-wind-supporting and severe-hail-supporting days from non-severe WFT days. When VTs exceeded values between 24.6 and 25.1 ∘C or TTs between 46.5 and 47.3 ∘C, odds of severe-wind days were roughly 5× greater. Meanwhile, odds of severe-hail days became roughly 10× greater when VTs exceeded 24.4–26.0 ∘C or TTs exceeded 46.3–49.2 ∘C. The stronger performance of VT and TT is partly attributed to the more accurate representation of these parameters in the numerical model. Under-reporting of severe weather and model error are posited to exacerbate the forecasting challenge by obscuring the subtle convective environmental differences enhancing storm severity.

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