Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Gerrit Hoogenboom x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Jason Allard
,
Paul C. Vincent
,
Jeromy R. McElwaney
, and
Gerrit Hoogenboom

Abstract

The objectives of this study were to compare average monthly and seasonal maximum and minimum temperatures of the Georgia Automated Environmental Monitoring Network (AEMN) to those of geographically close (i.e., paired) manual observations from U.S. Historical Climatology Network (USHCN) stations and Cooperative Observer Program (COOP) stations for the period 2002–13, and to evaluate the extent to which differences in siting characteristics of paired AEMN–USHCN stations contribute to the temperature differences. Correlations for monthly and seasonal maximum and minimum temperatures of paired AEMN–USHCN and AEMN–COOP stations were high and almost always significant, although the correlations for seasonal minimum temperatures were slightly lower than those of maximum temperatures, especially for summer. Monthly maximum and minimum temperatures and seasonal maximum temperatures of paired AEMN–USHCN and AEMN–COOP stations were significantly different in only a few instances, while seasonal minimum temperatures were more often significantly different, particularly for summer. The stronger relationship between maximum temperatures than minimum temperatures for paired stations is logical given that minimum temperatures typically occur when a shallow, decoupled nocturnal boundary layer is more sensitive to local conditions [e.g., land use/land cover (LULC)]. Stepwise regressions confirmed that a portion of the variance of seasonal minimum temperatures of paired AEMN–USHCN stations was explained by differences in LULC, while the variance in seasonal maximum temperatures was explained better by differences in elevation. Despite the generally close relationships between temperatures of paired stations and a portion of the differences being explained, an abrupt change from manual networks to the AEMN without data adjustments would change the Georgia climate record on monthly and seasonal time scales.

Full access
Richard T. McNider
,
John R. Christy
,
Don Moss
,
Kevin Doty
,
Cameron Handyside
,
Ashutosh Limaye
,
Axel Garcia y Garcia
, and
Gerrit Hoogenboom

Abstract

The 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.

Full access
Todd A. Crane
,
Carla Roncoli
,
Joel Paz
,
Norman Breuer
,
Kenneth Broad
,
Keith T. Ingram
, and
Gerrit Hoogenboom
Full access
Todd A. Crane
,
Carla Roncoli
,
Joel Paz
,
Norman Breuer
,
Kenneth Broad
,
Keith T. Ingram
, and
Gerrit Hoogenboom

Abstract

During the last 10 yr, research on seasonal climate forecasts as an agricultural risk management tool has pursued three directions: modeling potential impacts and responses, identifying opportunities and constraints, and analyzing risk communication aspects. Most of these approaches tend to frame seasonal climate forecasts as a discrete product with direct and linear effects. In contrast, the authors propose that agricultural management is a performative process, constituted by a combination of planning, experimentation, and improvisation and drawing on a mix of technical expertise, situated knowledge, cumulative experience, and intuitive skill as farmers navigate a myriad of risks in the pursuit of livelihood goals and economic opportunities. This study draws on ethnographic interviews conducted with 38 family farmers in southern Georgia, examining their livelihood goals and social values, strategies for managing risk, and interactions with weather and climate information, specifically their responses to seasonal climate forecasts. Findings highlight the social nature of information processing and risk management, indicating that both material conditions and value-based attitudes bear upon the ways farmers may integrate climate predictions into their agricultural management practices. These insights translate into specific recommendations that will enhance the salience, credibility, and legitimacy of seasonal climate forecasts among farmers and will promote the incorporation of such information into a skillful performance in the face of climate uncertainty.

Full access