Impact of Climate and Soils on Crop Selection and Management

IMPACT OF CLIMATE AND SOILS ON CROP SELECTION AND MANAGEMENT

Sponsoring Institution

National Institute of Food and Agriculture

Project Status

COMPLETE

Funding Source

HATCH

Reporting Frequency

Annual

Accession No.

0184617

Grant No.

(N/A)

Cumulative Award Amt.

(N/A)

Proposal No.

(N/A)

Multistate No.

NC-94

Project Start Date

Oct 1, 1999

Project End Date

Sep 30, 2004

Grant Year

(N/A)

Program Code

[(N/A)]- (N/A)

Recipient Organization
UNIVERSITY OF GEORGIA
200 D.W. BROOKS DR
ATHENS,GA 30602-5016

Performing Department
BIOLOGICAL & AGRICULTURAL ENGINEERING

Non Technical Summary
Producers need guidance on the relative benefits and probable impacts of soils and climate resources on crop production. This need for planning and decision aids exists in spite of the increase in data availability on the web and ever accumulating research knowledge-and may be labeled the information age paradox. This project will bring together the data from natural resources and the simulation models to provide guidance on the relative benefits and probable outcomes associated with agronomic decisions.

Animal Health Component

50%

Research Effort Categories

Basic

25%

Applied

50%

Developmental

25%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
132	0210	2080	15%
132	0420	2080	15%
132	1899	2080	10%
132	1599	2080	10%
132	1799	2080	10%
205	0210	2070	10%
205	0420	2070	10%
205	1899	2070	10%
205	1599	2070	10%

Knowledge Area
205 - Plant Management Systems; 132 - Weather and Climate;

Subject Of Investigation
0420 - Weather; 1799 - Fiber crops, general/other; 0210 - Water resources; 1599 - Grain crops, general/other; 1899 - Oilseed and oil crops, general/other;

Field Of Science
2070 - Meteorology and climatology; 2080 - Mathematics and computer sciences;

Goals / Objectives
1) Update and maintain the regional databases on soils, crop production and weather. 2) Develop predictive relationships for crop yield as a function of ENSO, SST, and frequencies associated with natural variability. 3) Viewing the optimum crop(s) selection as a dynamic decision on the basis of variability in natural resource patterns in the region.

Project Methods
1) With the expansion of automated weather station networks there is an opportunity to expand the number of input variables for many models, as well as the time scale by moving from daily to hourly inputs. Data on the rate of crop development will be assembled. A tabulation of these phenology data for many years will be obtained from the NASS. 2) We propose to assemble weather and additional crop production data and to determine the strength of the ENSO relationships. We will partition the yield data by El Nino, LaNina, and neutral years and study the correlation between ENSO activity in the Pacific and production of corn, soybean, wheat, peanut, etc. The weather data will be analyzed to find any temporal variations of the maximum and minimum temperatures and precipitation over a long time period. 3) One of the first tasks is to determine the availability of different crop models for simulation of maize and soybean growth and development. The weather information available for the region will be converted into file formats compatible with the individual models. Similarly soil information will be obtained and converted into file formats that can be read by the individual models. The third set of data relates to crop management information, including cultivar, planting date, spacing, irrigation and fertilizer applications, and other management information needed by the models. The models will be run for selected regions where input conditions with respect to the soil and climate are unique. Appropriate years will be selected, which represent the various climatic conditions to be expected in the region, such as dry and hot years, versus wet and cool years. The models will be compared with available crop development, yield and yield component data. Statistical analyses will be conducted to determine significant differences between the performance of the crop simulation models, as compared to observed data. The best models for each crop will be selected and a more detailed performance analysis will be conducted for all the counties and years for which measured data are available. We will identify and document any lack of performance by the models and collaborate with model developers to improve performance if needed. We will run the simulation models for a group of Mesonet stations and calculate the RMSE between model output at any two sites. This will be repeated for the Coop stations and combinations of Mesonet and Coop stations. Significant findings in regard to the station density required to maintain reasonable spatial accuracy as determined from these simulations will be reported in light of the future need for wether data in agriculture. We will simulate the response of different crop varieties to identify those areas that are best suited for a specific crop variety. Other management decisions that will be evaluated will relate to crop rotations in an area, planting date management, and harvest decisions. Performance of the crops over time will be compared and probabilities derived for the expected crop yields. Economic models will be used to evaluate how the optimum growing regions change with different economic conditions.

Progress 10/01/99 to 09/30/04

Outputs
The coordination of the Development of the Decision Support System for Agrotechnology Transfer (DSSAT) has been one of the main activities of our research group. DSSAT is being developed in collaboration with scientists at the University of Florida, Iowa State University and the International Center for Soil Fertility and Agricultural Development. We released DSSAT Version 4.0 in 2004, which encompasses the new Cropping System Model (CSM) for grain legumes, cereals, and various other crops. The CSM-CROPGRO-Peanut model was evaluated in farmers fields in both 2003 and 2004. This evaluation showed that the model was fairly accurate in being able to simulate growth, development and yield and to emulate the local irrigation strategies that are being applied by farmers. The model is also being run for each county and three different soil types to determine optimum planting dates under both rainfed and irrigated conditions, as well as changes in management strategies that are needed due the three ENSO phases. The Georgia Automated Environmental Monitoring Network (AEMN) was increased to 60 automated weather stations. Delivery of weather data and applications via the web site www.georgiaweather.net continued to be our main method for information dissemination. The number of page views increased significantly, reaching values as high as 170,000 per month in September when the remnants of three hurricanes affected the state of Georgia It has been found that providing weather data in near real-time is one of the main methods to help provide outcomes of research results directly to our clientele and stakeholders. An artificial neural network model was developed to predict temperature up to 12 hours ahead during the winter months. The main goal of this model is to provide growers with additional information for frost protection. Research continues on the impact of climate on peaches and blueberries. The focus of this year's research was on the impact of ENSO on chill-hours and last freeze. The findings are that critical chill-hour are accumulated earlier under el nino and neutral conditions compared to la nina conditions. There is not a strong relationship between ENSO phase and date of last spring freeze. Initial findings indicate that there is a greater chance of spring cold damage under el nino and neutral conditions.

Impacts
Computer models combined with current weather conditions can play a critical role in providing farmers with state-of-the-art technologies to help determine optimum management practices that reduce the use of natural resources, protect the environment, as well as provide long-term economic sustainability.

Publications

Ashish, D., G. Hoogenboom, and R.W. McClendon. 2004. Land-use classification of gray-scale aerial images using probabilistic neural networks. Transactions of the ASAE. 47(5):1813-1819.
Bannayan, M., G. Hoogenboom, and N.M.J. Crout. 2004. Photothermal impact on maize performance: a simulation approach. Ecological Modeling 180(2-3):277-290.
Banterng, P., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2004. Determination of genetic coefficients of peanut lines for breeding applications. European Journal of Agronomy 21(3):297-310.
Boken, V.K, G. Hoogenboom, J.E. Hook, D.L. Thomas, L.C. Guerra, and K.A. Harrison. 2004. Agricultural water use estimation using geospatial modeling and a geographic information system. Agricultural Water Management 67(3):185-199.
Boken, V.K., G. Hoogenboom, F.N. Kogan, J.E. Hook, D.L. Thomas, and K.A. Harrison. 2004. Potential of using NOAA-AVHRR data for estimating irrigated area to help solve an inter-state water dispute. International Journal of Remote Sensing 24(2):2277-2286.
Bostick, W.M., J. Koo, V.K. Walen, J.W. Jones, and G. Hoogenboom. 2004. A web-based data exchange system for crop model applications. Agronomy Journal 96(3):853-856. Li, B., R.W. McClendon and G. Hoogenboom. 2004. Spatial interpolation of weather variables for single locations using artificial neural networks. Transactions of the ASAE 42(2):629-637.
Guerra, L.C., G. Hoogenboom, V.K. Boken, J.E. Hook, D.L. Thomas, and K.A. Harrison. 2004. Evaluation of the model EPIC for simulating crop yield and irrigation demand. Transactions of the ASAE 42(6):2091-2100.
Boote, K.J., J.W. Jones, G. Hoogenboom, A. DuToit, E.L. Piper, P.J. Sexton and F. Sau. 2004. The origin of the temperature functions for processes in CROPGRO-Soybean. In:19-20. Proceedings Biological Systems Simulation Conference. University of Florida, Gainesville, Florida.
Garcia y Garcia, A., L.C. Guerra, G. Hoogenboom, J.E. Hook and K.A. Harrison. 2004. Simulating peanut irrigation water use and yield in farmers fields in southwest Georgia. In:34-35. Proceedings Biological Systems Simulation Conference. University of Florida, Gainesville, Florida.
Guerra, L.C., A. Garcia y Garcia, and G. Hoogenboom. 2004. Evaluation of cultivar coefficients derived from variety trials with on-farm monitoring data. In:36-37. Proceedings Biological Systems Simulation Conference. University of Florida, Gainesville, Florida.
Hoogenboom, G., J.W. Jones, P.W. Wilkens, and G.Y. Tsuji. 2004. Experiences with the Decision Support System for Agrotechnology Transfer. In:56-57. Proceedings International Workshop on Applications, Enhancements, and Collaborations of ARS RZWQM and GPFarm Models. USDA-ARS-GPSR, Fort Collins, Colorado.
Soler, C.M.T., P.C. Sentelhas, and G. Hoogenboom. 2004. The impact of climate variability on yield of maize grown off-season in the state of Sao Paulo, Brazil. In:68-69. Proceedings Biological Systems Simulation Conference. University of Florida, Gainesville, Florida.
Garcia y Garcia, A., G. Hoogenboom, and L.C. Guerra. 2004. Container temperature and moisture for estimating evapotranspiration of nursery crops. ASAE Paper 04-2295. American Society of Agricultural Engineers, St. Joseph, Michigan.
Guerra, L.C., A. Garcia y Garcia, and G. Hoogenboom. 2003. Cotton growth and development monitoring during 2003. 2003 Georgia Cotton Research and Extension Reports 46-52.
Guerra, L.C., G. Hoogenboom, A. Garcia y Garcia, and J.E. Hook. 2004. Simulating peanut yield and irrigation applications with the CSM-CROPGRO-Peanut model. ASAE Paper 04-3046. American Society of Agricultural Engineers, St. Joseph, Michigan.
Hoogenboom, G. 2004. Application of weather data for management of cotton production in 2003. 2003 Georgia Cotton Research and Extension Reports 52-58.
Hook, J,E,., K.A. Harrison, G. Hoogenboom, D.L. Thomas and V.K. Boken. 2004. Irrigation practices during long-term drought in the Southeast. In: 2004 Conference Proceedings. The Irrigation Association, Tampa, Florida. (In Press).
Messina, C., P.B. Ramkrishnan, J.W. Jones, K.J. Boote, G. Hoogenboom, and J.T.Ritchie. 2004. A simulation model of cotton growth and development for CSM. In:54-55. Proceedings Biological Systems Simulation Conference. University of Florida, Gainesville, Florida.

Progress 01/01/03 to 12/31/03

Outputs
The coordination of the Development of the Decision Support System for Agrotechnology Transfer (DSSAT) has been one of the main activities of our research group. DSSAT is being developed in collaboration with scientists at the University of Florida, Iowa State University and the International Fertilizer Development Center. The generic grain legume model CROPGRO and the generic grain cereal model CERES were improved and replaced with the Cropping System Model (CSM), which is the next generation of crop simulation models. The new CSM model is more adapted to study the impact of climate and soil on crop selection and management, especially for crop rotations and spatial applications, including regional assessments. Many crop application studies require long-term daily weather data series as inputs to account for seasonal weather variability. However, in many cases these long-term series are lacking Weather generators have been developed for generating daily weather data, including solar radiation, maximum and minimum temperature and precipitation. Inputs for these weather generators are normally local climate variables. A study was conducted to determine the impact of using different weather data sets as inputs, including different durations, for deriving these climate variables. As expected, the longer the time series used to determine the local climate variables, the more accurately the weather generators can represent local weather conditions. However, it was found that in many cases the weather generators were unable to determine weather extremes very accurately. This could be a concern, as it is expected that for future climate change scenarios, climate variability will increase with especially more frequent occurrence of weather extremes related to temperature and precipitation. The Georgia Automated Environmental Monitoring Network (AEMN) was increased to 57 automated weather station. Delivery of weather data and applications via the web site www.georgiaweather.net continued to be our main method for information dissemination. The number of page views increased significantly, reaching values as high as 100,000 per month during the colder months.A research engineer was employed to help develop new decision support tools, including the Brunt equation to estimate tomorrow's minimum temperature at sunrise based on today's conditions at sunset. This information is critical for frost protection for peach and blueberry growers. Research continues on the climatology of chill-hours for peaches and blueberries and the last -2 C freeze. Initial research has found a relationship between ENSO and the dates that critical chill-hours are met in the major peach growing regions of the Southeast. Critical chill-hours are met earlier under La Nina and neutral ENSO climate patterns. During El Nino patterns, the date of meeting critical chill-hours is much later (and some years are not met). The date of the last freeze shows no relationship with ENSO in the major peach growing regions of the Southeast. Preliminary analysis indicates that the peach crop maybe at a higher risk of late winter/spring freeze damage during La Nina and neutral climate patterns.

Impacts
Computer models combined with current weather conditions can play a critical role in providing farmers with state-of-the-art technologies to help determine optimum management practices that reduce the use of natural resources, protect the environment, as well as provide long-term economic sustainability.

Publications

Guerra, L.C., G. Hoogenboom, J.E. Hook, D.L. Thomas, V.K. Boken, and K.A. Harrison. 2003. On-farm evaluation of the model EPIC for simulating irrigation demand. ASAE Paper 03-3039. American Society of Agricultural Engineers, St. Joseph, Michigan.
Guerra, L.C., G. Hoogenboom, J.E. Hook, D.L. Thomas, and K.A. Harrison. 2003. Predicting water demand for irrigation under varying soil and weather conditions. International Water and Irrigation 23(2):21-24.
Hoogenboom, G. 2003. Application of weather data for irrigation management of cotton. 2002 Georgia Cotton Research and Extension Reports:51-57.
Jain, A., R.W. McClendon, G. Hoogenboom, and R. Ramyaa. 2003.Prediction of frost for fruit protection using artificial neural networks. ASAE Paper 03-3075. American Society of Agricultural Engineers, St. Joseph, Michigan.
Jones, J.W., C.H. Porter, G. Hoogenboom, W.D. Batchelor, and P. Papajorgji. 2003. A modular framework for developing models of crops and other ecological systems. ASAE Paper 03-3037. American Society of Agricultural Engineers, St. Joseph, Michigan.
Kemerait, R.C., T. Brenneman and G. Hoogenboom. 2003. Assessment of the Doppler radar based AU-Peanut disease advisory in two trials in Georgia. 2002 Georgia Peanut Research-Extension Report:83-89.
Koo, J, W.M. Bostick, V.K. Walen, G. Hoogenboom, and J.W. Jones. 2003. A web-based agricultural data exchange and cataloging system. ASAE Paper No. 03-3070. American Society of Agricultural Engineers, St. Joseph, Michigan.
White, J.W., and G. Hoogenboom. 2003. Gene-based approaches to crop simulation: past experiences and future opportunities. Agronomy Journal 95(1):52-64.
Boken, V.K., G. Hoogenboom, L.C. Guerra, J.E. Hook, D.L. Thomas and K.A. Harrison. 2003. Water use estimation for major crops in Georgia using geospatial modeling. p. 562-565. In : [K. J. Hatcher, editor] Proceedings of the 2003 Georgia Water Resources Conference. Institute of Ecology, The University of Georgia, Athens, Georgia.
Guerra, L.C., G. Hoogenboom, V.K. Boken, D.L. Thomas, J.E. Hook, and K.A. Harrison. 2003. Estimating statewide irrigation requirements using a crop simulation model. p. 558-561. In : [K. J. Hatcher, editor] Proceedings of the 2003 Georgia Water Resources Conference. Institute of Ecology, The University of Georgia, Athens, Georgia.
Hoogenboom, G. 2003. Crop growth and development. p. 655-691. In: [D.K. Bendi and R. Nieder] Handbook of Processes and Modeling in the Soil-Plant System. The Haworth Press, Binghamton, New York.
Hoogenboom, G., D.D. Coker, J.M. Edenfield, D.M. Evans and C. Fang. 2003. The Georgia Automated Environmental Monitoring Network: 10 years of weather information for water resources management. p.896-900. In : [K. J. Hatcher, editor] Proceedings of the 2003 Georgia Water Resources Conference. Institute of Ecology, The University of Georgia, Athens, Georgia.
Newton, G.L., J.F. Baker, C.R. Dove, J.K. Bernard, M.D. McCranie, V.K. Boken, D.L. Thomas and G. Hoogenboom. 2003. Agricultural water use associated with animal production systems in Georgia. p. 554-557. In : [K. J. Hatcher, editor] Proceedings of the 2003 Georgia Water Resources Conference. Institute of Ecology, The University of Georgia, Athens, Georgia.
Thomas, D.L., K.A. Harrison, J.E. Hook, G. Hoogenboom, R.W. McClendon, and L.R. Wheeler. 2003. Agricultural water use in Georgia: Results from the Ag. Water Pumping Program. p. 566-570. In : [K. J. Hatcher, editor] Proceedings of the 2003 Georgia Water Resources Conference. Institute of Ecology, The University of Georgia, Athens, Georgia.
Badenko, V., D. Kurtener, G. Hoogenboom, and V. Yakushev. 2003. Semi-structured GIS algorithm for agrotechnology management. Proceedings of the 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, June 23-26, 2003, Loughborough University, Loughborough, UK. (In Press).
Bannayan, M., N.M.J. Crout, and G. Hoogenboom. 2003. Applying the CERES-Wheat model for real-time forecasting of winter wheat. Agronomy Journal 95(1):114-125.
Hartkamp, A.D., J.W. White, and G. Hoogenboom. 2003. Comparison of three weather generators for crop modeling: a case study for subtropical environments. Agricultural Systems 76(2):539-560.
Hoogenboom, G., and J.W. White. 2003. Improving physiological assumptions of simulation models by using gene-based approaches. Agronomy Journal 95(1):82-89.
Ingram, K.T., B.C. Ahohuendo, B. Diarra, and G. Hoogenboom. 2003. Aspergillus flavus infection and aflatoxin contamination of peanut. New tools for research on Aspergillus flavus infection and aflatoxin contamination of peanut. Annales des Sciences Agronomiques 4(2):105-119.
Irmak, S., A. Irmak, J.W. Jones, T.A. Howell, J.M. Jacobs, R.G. Allen, and G. Hoogenboom. 2003. Predicting daily net radiation using minimum climatological data. Journal of Irrigation and Drainage Engineering 129(4):256-269.
Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. DSSAT Cropping System Model. European Journal of Agronomy 18:235-265.
Nijbroek, R., G. Hoogenboom, and J.W. Jones. 2003. Optimal irrigation strategy for a spatially variable soybean field: a modeling approach. Agricultural Systems 76(1):359-377.
Soltani, A., and G. Hoogenboom. 2003. A statistical comparison of the stochastic weather generators WGEN and SIMMETEO for climate conditions in Iran. Climate Research 24(3):215-230.
Soltani, A., and G. Hoogenboom. 2003. Minimum data requirements for parameter estimation of the stochastic weather generators WGEN and SIMMETEO. Climate Research 25(2):109-119.
Garcia y Garcia, A., D. Dourado Neto and G. Hoogenboom. 2003. The effect of nitrogen fertilizer on dry bean production under rainfed and irrigated conditions. In: 395-396. Annals of the XIII Brazilian Congress of Agrometeorology, Volume 1. Santa Maria, RS. Brazil. Santa Maria: Soceidade Brasileira de Agrometeorologia/Univesidade de Federal de Santa Maria.
Hook, J.E., G. Abts, L. Wheeler, K.A. Harrison, G. Hoogenboom, and D.L. Thomas. 2003. Center pivot and irrigation system monitoring for farmers and regulators. In: 2003 Conference Proceedings. The Irrigation Association, San Diego, California. (In Press).
Tojo Soler, C.M., P.C. Sentelhas and G. Hoogenboom. 2003. Predicting yield variability of maize sown out of season with the CERES-Maize model. In: 933-934. Annals of the XIII Brazilian Congress of Agrometeorology, Volume 2. Santa Maria, RS. Brazil. Santa Maria: Soceidade Brasileira de Agrometeorologia/Univesidade de Federal de Santa Maria.
Garcia y Garcia, A., and G. Hoogenboom. 2003. Automated measurement of container temperature and moisture for improvement of irrigation scheduling in nurseries. Georgia Green Industry Association Journal (February 2003):41,52-53.
Garcia y Garcia, A. and G. Hoogenboom. 2003. Automated measurement of container temperature and moisture for improvement of irrigation scheduling in nurseries. In: 2003 Open House (Program and Projects):19-20. Center for Applied Nursery Research, Dearing, Georgia.

Progress 01/01/02 to 12/31/02

Outputs
Research continues on the climatology of chill-hours for peaches and blueberries and the last -2 C freeze. Initial research has not found any change in the dates that critical chill-hours are met in the major peach growing regions of the Southeast. The date of the last freeze has not changed in the major peach growing regions of the Southeast. Working hypothesis is that producers have pushed the climate envelope, by cultivar selections, to a point that there is an increased probability that peaches will bloom before the last freeze. The study is being expanded to blueberries. I second question is to determine if there is a relationship between the chill-hour requirements being met and the date of the last freeze. Initial work involving climatology and forest production was begun. The Georgia Automated Environmental Monitoring Network (AEMN) was increased to 50 automated weather station. A special person was employed to improve the quality control and preventive maintenance of the weather stations. Delivery of weather data and applications via the web site www.georgiaweather.net continued to be our main method for information dissemination. A study was conducted to study the impact of weather data accuracy on yield predictions of crop simulation models. It was found that there was a strong correlation between weather accuracy and the amount of rainfall received during the growing season. Different modeling approaches were also applied to determine water use in agriculture. These approached included geospatial interpolation, point-based simulation, and up-scaling using crop simulation models and Geographic Information Systems. In collaboration with scientists at the University of Florida, Iowa State University and the International Fertilizer Development Center, the simulation models of the Decision Support System for Agrotechnology Transfer (DSSAT), including the grain legume model CROPGRO and the grain cereal model were improved. New programs have also been developed for Windows compatibility and for improving interaction with the users. A final version of DSSAT was evaluated during a training workshop, organized in collaboration with scientists from Florida, Alabama and Iowa, and attended by more than 40 international scientists. The cropping system models of DSSAT can be effective tools for determining the impact of climate, soils and crop management on crop yield and resource use.

Impacts
Computer models combined with current weather conditions can play a critical role in providing farmers with state-art-technologies to help determine optimum management practices that reduce the use of natural resources, protect the environment, and at the same time provide long-term economic sustainability.

Publications

Carbone, G.J., L.O. Mearns, T. Mavromatis, E.J. Sadler, D. Stooksbury. 2003. Evaluating CROPGRO-Soybean Performance for use in Climate Impact Studies. Agronomy Journal 95(3).
Sentelhas, P.C., R. T. de Faria, M.O. Chaves, and G. Hoogenboom. 2001. Evaluation of the WGEN and SIMMETEO generators for the brazilian tropics and subtropics, using crop simulation models. Revista Brasileira de Agrometeorologia 9(2):357-376.
Chojnicki, B.H., and G. Hoogenboom. 2002. Automated meteorological network as a source of agrometeorological information. Acta Agrophysica 60:39-44.
Gijsman, A.J., G. Hoogenboom, W.J. Parton, and P.C. Kerridge. 2002. Modifying the DSSAT crop models for low-input agricultural systems, using a SOM/residue module from CENTURY. Agronomy Journal 94:462-474.
Hartkamp, A.D., G. Hoogenboom, and J.W. White. 2002. Adaptation of the CROPGRO growth model to velvet bean as a green manure cover crop: I. Model development. Field Crops Research 78(1):9-25.
Hartkamp, A.D., G. Hoogenboom, R. Gilbert, T. Benson, S.A. Tarawali, A. Gijsman, W. Bowen, and J.W. White. 2002. Adaptation of the CROPGRO growth model to velvet bean as a green manure cover crop: II. Model evaluation and testing. Field Crops Research 78(1):27-40.
Heinemann, A.B., G. Hoogenboom, and B. Chojnicki. 2002. The impact of potential errors in rainfall observation on the simulation of crop growth, development, and yield. Ecological Modeling 157(1):1-21.
Ma, L., D.C. Nielsen, L.R. Ahuja, J.R. Kiniry, J.D. Hanson, and G. Hoogenboom. 2002. An evaluation of RZWQM, CROPGRO and CERES-Maize for responses to water stress in the Central Great Plains of the U.S. p. 119-148. In: (L.R. Ahuja, L. Ma, and T.A. Howell, editors). Agricultural System Models in Field Research and Technology Transfer. Lewis Publishers, Boca Raton, FL.
Mavromatis, T., K.J. Boote, J.W. Jones, G.G. Wilkerson, and G. Hoogenboom. 2002. Repeatability of model genetic coefficients derived from soybean performance trails across different states. Crop Science 42(1)76-89.
Nielsen, D.C., L. Ma, L.R. Ahuja, and G. Hoogenboom. 2002. Simulating soybean water stress effects with RZWQM and CROPGRO models. Agronomy Journal.94(6):1234-1243.
Heinemann, A.B., G. Hoogenboom, and R.T. de Faria. 2002. Determination of spatial water requirements at county and regional levels using crop models and GIS: An example for the State of Parana. Agricultural Water Management 52(3):177-196.

Progress 01/01/01 to 12/31/01

Outputs
Water has become an important resource in the state of Georgia. Since 1998 Georgia has been in a continuous drought. Some of the major rivers in Alabama and Florida originate in Georgia. This has resulted in a Federal lawsuit in which the state of Georgia will be required to provide a minimum amount of water to the surrounding states, including drought years. This will significantly impact agriculture, as most of the major crops, including cotton and peanut, are grown under irrigated conditions. Several projects have been initiated to monitor water use on the farm level, using manual as well as automated monitoring techniques. The monitoring sites represent only a 2% sample of all permitted sites. Different statistical and dynamic modeling methodologies are therefore needed to analyze this information and to determine agricultural water use at the state level. Because of the diversity of crops in Georgia, simple ET methods will initially be used, as well as kriging analysis methods. As part of this initiative we evaluated the impact of using the Penman-Monteith method versus the Priestley-Taylor method to calculate potential ET when only a limited set of data is available, such as the daily data collected by the NWS Cooperative Observer network. Due to budget and personnel problems, the Georgia Automated Environmental Monitoring Network (AEMN) was maintained at a level of 45 automated weather stations in 2001. An effort was initiated to improve the quality control and preventive maintenance of the weather stations. Delivery of weather data and applications via the web site www.georgiaweather.net continuous to be our main method for information dissemination. Dynamic tables to calculate degree days for cotton and chilling hours for peaches were added to the web site. In collaboration with scientists at the University of Florida, Iowa State University and the International Fertilizer Development Center, the simulation models of the Decision Support System for Agrotechnology Transfer (DSSAT), including the grain legume model CROPGRO and the grain cereal model are being improved. New programs are also being developed for user and Windows compatibility. These models are effective tools for determining the impact of climate, soils and crop management on crop yield and resource use.

Impacts
Computer models combined with current weather conditions can play a critical role in providing farmers with state-art-technologies to help determine optimum management practices that reduce the use of natural resources, protect the environment, and at the same time provide long-term economic sustainability.

Publications

Hoogenboom, G. 2001. Weather monitoring for management of water resources. p. 778-781. In : [K. J. Hatcher, editor] Proceedings of the 2001 Georgia Water Resources Conference. Institute of Ecology, The University of Georgia, Athens, Georgia.
Houser, J.B., G. Hoogenboom, J.E. Hook, D.L. Thomas, K.A. Harrison. 2001. Using a small sub-sample to project state-wide agricultural water use in 2000. p. 110-113. In : [K. J. Hatcher, editor] Proceedings of the 2001 Georgia Water Resources Conference. Institute of Ecology, The University of Georgia, Athens, Georgia.
Chojnicki, B.H., and G. Hoogenboom. 2000/2001. The structure and operation of automated agrometeorological weather station network - Georgia Automated Environmental Monitoring Network. Annales Universitatis Mariae Curie-Sklodowska, Sectio B LV/LVI(13):113-118.
Alexandrov, V.A., and G. Hoogenboom. 2001. Climate variation and crop production in Georgia, USA, during the 20th century. Climate Research 17(1):33-43.
Hunt, L.A., J.W. White, and G. Hoogenboom. 2001. Agronomic data: Advances in documentation and protocols for exchange and use. Agricultural Systems 70:477-492.
Mavromatis, T., K.J. Boote, J.W. Jones, A. Irmak, D. Shinde, and G. Hoogenboom. 2001. Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Science 41(1):40-51.
Heinemann, A.B., G. Hoogenboom, and R.T. de Faria. 2002. Determination of spatial water requirements at county and regional levels using crop models and GIS: An example for the State of Parana. Agricultural Water Management 52(3):177-196.
Hartkamp, A.D., J.W. White, and G. Hoogenboom. 2002. Comparison of three weather generators for crop modeling: a case study for subtropical environments. Agricultural Systems 74(1) (In Press).

Progress 01/01/00 to 12/31/00

Outputs
A project to develop a southeastern chill-hour/thermal unit climatology for peaches was started in 2000. Initial results indicate that the day of year at which critical chill-hour values are reached has not changed. The date of the last 28F occurrence in the spring also has not changed. Initial results indicate that cultural practices maybe increasing peaches to greater spring freeze vulnerability. The Georgia Automated Environmental Monitoring Network (AEMN) was expanded to 45 automated weather stations in 2000. These stations are located across the state in the main agricultural areas. Each station monitors air temperature, relative humidity, wind speed, wind direction, solar radiation, soil temperature at three different depths, rainfall and various other variables. All sensors are scanned at a one-second frequency and the data are summarized every 15 minutes. At midnight daily totals and daily extremes are determined. A centrally located computer at the Georgia Station communicates with each station through dedicated telephone lines. More than 20 sites are called at least once an hour to download the most recent weather information. All remaining sites are called once a day at midnight. The data are processed and transferred to data and web servers. The weather data, weather data-based products, and simple models, including a degree day calculator and a chilling hour calculator, are made available via the world wide web (http://www.georgiaweather.net). The rainfall data collected in 2000 showed a significant drought for the third consecutive year; this has a major impact on water resources for agriculture in the state of Georgia. Temperatures recorded during the November and December months were some of the coldest since local recording of weather data has started. A detailed study was conducted to review the impact of agrometeorology on crop model development and applications. Initial results showed that for most applications, weather and climate variability are the main cause for predictions in yield variability, especially under rainfed conditions. Various methodologies are being developed to link dynamic crop simulation models with weather data, including current weather conditions, and Geographic Information Systems, for spatial prediction of yield and natural resource use, especially water, and to determine the overall impact on the agricultural system.

Impacts
Computer models combined with current weather conditions can play a critical role in providing farmers with state-art-technologies to help determine optimum management practices that reduce the use of natural resources, protect the environment, and at the same time provide long-term economic sustainability.

Publications

Stooksbury, D.E., P.L. Davis, R. Weikel, and S. Baker. 2000. Chill-hour climatology for the Southeastern United States. Proc. 12th Conf. Applied Climatology, Am. Met. Soc., Boston. May 2000 in Asheville, NC. pp. 298-299.
Summers, K.R. and D.E. Stooksbury. 2000. Application of Hourly-Temperature Heat Index as a Measure of Heat Stress. 12th Conf. Applied Climatology, Am. Met. Soc., Boston. May 200 in Asheville, NC pp. 212-215.
Alagarswamy, G., P. Singh, G. Hoogenboom, S.P. Wani, P. Pathak, and S.M. Virmani. 2000. Evaluation and application of the CROPGRO-Soybean simulation model in Vertic Inceptisols of Peninsular India. Agricultural Systems 63(1):19-32.
Alexandrov, V.A., and G. Hoogenboom. 2000. The impact of climate variability and change on major crops in Bulgaria. Agricultural and Forest Meteorology 104(4):315-327.
Alexandrov, V.A., and G. Hoogenboom. 2000. Vulnerability and adaptation assessments of agricultural crops under climate change in the Southeastern USA. Theoretical and Applied Climatology 67:45-63.
Henderson, C.E., W.D. Potter, R.W. McClendon, and G. Hoogenboom. 2000. Predicting aflatoxin contamination in peanuts: A genetic algorithm/neural network approach. International Journal of Applied Intelligence 12(3):183-192.
Hoogenboom, G.. 2000. Contribution of agrometeorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology 103(1-2):137-157.
Liu, L., G. Hoogenboom, and K.T. Ingram. 2000. Controlled-environment sunlit plant growth chambers. Critical Reviews in Plant Sciences 19(4):347-375.
Ravagnolo, O., I. Misztal, and G. Hoogenboom. 2000. Genetic component of heat stress in dairy cattle. 2. Development of Heat Index. Journal of Dairy Science 83(9):2120-2125.
Hoogenboom, G., G.A. Georgiev, D.D. Gresham. 2000. Dissemination of near real-time weather data for agricultural applications. p. 158-161. In: Proceedings of the International Forum on Climate Prediction, Agriculture and Development. IRI-CW/00/01. International Research Institute for Climate Prediction, Palisades, New York (ISBN 0-9705907-0-9).
Hoogenboom, G., C. L. Perez, D. Rossiter, and P. van Laake. 2000. Biophysical agricultural assessment and management models for developing countries. p. 403-422. In : [C.A.S. Hall, editor] Quantifying Sustainable Development. The Future of Tropical Economies. Academic Press, San Diego, California (ISBN 0-12-318860-1).
Kiker, G.A., N.G. Inman-Bamber, G. Hoogenboom, and M.G. McGlinchey. 2000. Further progress in the validation of the CANEGRO-DSSAT model. p. 13-14. In: [G. O'Leary and G.A. Kiker, editors] Proceedings of the International Workshop on the CANEGRO Sugarcane Simulation Model. South African Sugar Association Experiment Station, Mount Edgecombe, KwaZulu-Natal, South Africa.
Chojnicki, B., G. Hoogenboom, and G. A. Georgiev. 2000. Estimation of rainfall intensity and spatial distribution based on data collected by automated weather stations and radar systems. p. 300-303. In: Preprints 12th Conference on Applied Climatology. American Meteorological Society, Boston, Massachusetts.
Hoogenboom, G. 2000. The Georgia Automated Environmental Monitoring Network. 2000. p. 24-25. In: Preprints 24rd Conference on Agricultural and Forest Meteorology. American Meteorological Society, Boston, Massachusetts.
Hoogenboom, G. 2000. The state-of-the-art in crop modeling. p. 69-75. In: (M.V.K. Sivakumar, editor) Climate Prediction and Agriculture. Proceedings of the START/WMO International Workshop. International START Secretariat, Washington, DC.
Hoogenboom, G., G.A. Georgiev, and D.D. Gresham. 2000. Development of weather-based products for agricultural and environmental applications. p. 66-67. In: Preprints 24rd Conference on Agricultural and Forest Meteorology. American Meteorological Society, Boston, Massachusetts.
Hoogenboom, G., K.T. Ingram, and R.W. McClendon. 2000. Prediction of aflatoxin contamination in preharvest groundnut. p. 59. In: [F. Waliyar and B.R. Ntare, editors]. Summary Proceedings of the Sixth ICRISAT Regional Groundnut Meeting for Western and Central Africa. ICRISAT, Patancheru, Andhra Pradesh, India.
Hunt, L.A., G. Hoogenboom, J.W. Jones, and J.W. White. 2000. The ICASA file standards. p. 49-59. In: (J.W. White and P. Grace, editors) Modeling Extremes of Wheat and Maize Crop Performance in the Tropics. NRG-GIS Series 00-01. Centro Internacional de Mejoramiento Maiz y Trigo (CIMMYT), Mexico, DF, Mexico (ISBN 970-648-47-1).
Hoogenboom, G. 2000. Future of CANEGRO in the DSSAT suite of models. p. 9-12. In: [G. O'Leary and G.A. Kiker, editors] Proceedings of the International Workshop on the CANEGRO Sugarcane Simulation Model. South African Sugar Association Experiment Station, Mount Edgecombe, KwaZulu-Natal, South Africa.
Hoogenboom, G. 2000. International Consortium for Agricultural Systems Applications - ICASA. p. 311. In: Proceedings of the International Forum on Climate Prediction, Agriculture and Development. IRI-CW/00/01. International Research Institute for Climate Prediction, Palisades, New York (ISBN 0-9705907-0-9).