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Now available in PDF format: Abstract Book [7.4 Mb] (posted 10 November 2005)

Abstracts for Posters

Food Production (P-FP)

Sub-Theme 3: Impact Modeling

P-FP3.1

Linking Seasonal Climate Forecasts with Crop Simulation to Optimize Maize Management

Ashok Mishra, International Research Institute for Climate Prediction, mishra@iri.columbia.edu

James Hansen, International Research Institute for Climate Prediction

K.P.C. Rao, ICRISAT, Nairobi, Kenya

Durton Nanja, Zambia Meteorological Department

Elijah Mukhala, SADC Regional Remote Sensing Unit, Gabarone, Botswana

Advance information about climatic conditions for the upcoming growing season provides an opportunity to improve agricultural risk management and to optimize management practices. Empirical or GCM-based seasonal forecasts linked with crop simulation models provide a means of translating climate forecasts into agricultural production terms, and of assessing the production and economic outcomes of management alternatives. We used a stochastic weather model to disaggregate monthly rainfall predictions into synthetic daily realizations as input into crop simulation models to predict field-scale maize yields at sites in southern Kenya and southern Zambia. The integrated climate-crop modeling predicted a significant portion of final yields well in advance of the normal planting dates. Potential value of a forecast system is established from expected income in response to management optimized for each year's forecast, minus expected response to management optimized for observed climatology, given current cost and price expectations. To estimate the potential value of forecasts for a set of maize management decisions, we identified management optimized for all years and for each year's yields predicted with hindcast rainfall, then applied the optimal management to yields simulated with observed rainfall. In Kenya, analyses of
management responses focused on N fertilizer rates and planting density. Yield response to N, averaged across years and in many individual years, fit a nonlinear Mitscherlich function quite well. In those cases, we identified optimal fertilizer rates based on analytical solution of the Mitscherlich function plugged into an enterprise budget to estimate gross margins. Where the Mitscherlich function did not approximate simulated response to fertilizer, we selected the discrete fertilizer level that gave the highest gross margins. In Zambia, we considered cultivar, N fertilizer, planting density and conventional vs. conservation tillage decisions and applied the same discrete gross margin maximization procedure, but without first fitting to the nonlinear production function. The results indicate that modest increases in average farmer income can be obtained by using available seasonal climate forecasts. We anticipate that improvements in methods for translating seasonal forecasts into local crop impacts, and consideration of a broader range of farm-level decisions will increase the potential value. However, there remain several challenges to achieving that potential.

[Poster PDF]

P-FP3.2

Effects of Climate Variability on Irrigation Water Use in the Southeastern United States

J.O. Paz, The University of Georgia, Griffin, GA, jpaz@griffin.uga.edu

G. Hoogenboom, The University of Georgia, Griffin, GA

A. Garcia y Garcia, The University of Georgia, Griffin, GA

L.C. Guerra1,

J.G. Bellow, Florida State University, Tallahassee, FL

C. W. Fraisse, University of Florida, Gainesville, FL

J.W. Jones, University of Florida, Gainesville, FL

Climate plays a key role in agricultural production in the southeastern United States. The goal of this study was to examine the effects of changes in El Niño-Southern Oscillation (ENSO) phases on irrigation water consumption for peanut production. Annual irrigation water use and the frequencies of irrigation applications were simulated using the CSM-CROPGRO-Peanut model for irrigated peanuts for several counties in Georgia, Florida and Alabama. These simulations were based on long-term historical weather data, different planting dates, and local soil types. Results showed that average annual irrigation amounts and number of irrigation events decreased with delayed planting. The reduction in irrigation water use was more pronounced for El Niño when compared to either the La Niña or neutral phase.

[Poster PDF]

P-FP3.3

Impact of Climate Information in Reducing Farm Risk by Selecting Crop Insurance Programs

Victor Cabrera, Southeast Climate Consortium, University of Miami, v.cabrera@miami.edu

Clyde Fraisse, Southeast Climate Consortium, University of Florida

David Letson, Southeast Climate Consortium, University of Miami

Guillermo Podesta (Southeast Climate Consortium, University of Miami

James Novak (Southeast Climate Consortium, Auburn University

Predictability of seasonal climate variability associated with El Niño Southern Oscillation (ENSO) suggests a potential to reduce farm risk by selecting crop insurance products with the purpose of increasing or maintaining farm income stability. A hypothetical 50% peanuts-50% cotton, non-irrigated, 40 ha (100-acre) north Florida farm was used to study the interactions of different crop insurance products with ENSO-based climate information and relative levels of risk of aversion under uncertain conditions of climate and prices. Crop yields simulated by the DSSAT suite of crop models using multiyear weather data combined with historical series of prices were used to generate long series of stochastic income distributions in a whole farm model portfolio. The farm model optimized planting dates and simulated uncertain net incomes for 50 alternative crop insurance combinations for different levels of relative risk aversion under different planning horizons. Results suggested that net incomes are greater and more stable for low risk averse farmers when catastrophic (CAT) insurance for cotton and 70 or 75 actual production history (APH) for peanuts are selected in all ENSO phases. For high risk averse farmers the best strategy depends on the ENSO phase: a) 70% crop revenue coverage (CRC) or CAT for cotton and 65% APH for peanuts during EL Niño years; b) CAT for cotton and 65, 70, or 75 APH for peanuts during Neutral years; and c) 65, 70 APH, or CAT for cotton and 70% APH during peanuts for La Niña years are selected. Optimal planting dates varied for all ENSO phases, risk aversion levels, and selected crop insurance products.

[Poster PDF]

P-FP3.4

A Climate-based Tool for Agricultural Nitrogen Management Decisions

Arthur T. DeGaetano, Northeast Regional Climate Center, atd2@cornell.edu

Laura Joseph, Northeast Regional Climate Center

Jeffrey J. Melkonian, Department of Crop and Soil Science, Cornell University, Ithaca, NY

Decisions related to the application of nitrogen on agricultural lands have both economic (crop yield and fertilizer expenses) and environmental (water quality issues related to nitrogen leaching) consequences. These decisions rely heavily on both real-time and recent historical climate information. The newly developed Precision Nitrogen Management (PNM) model links a soil nitrogen (N) dynamics model and a maize nitrogen uptake/growth and yield model with climate information from the NOAA Regional Climate Center Applied Climate Information System (ACIS).

A website provides a user-friendly input and output format for the model. It is intended specifically for crop consultants, growers and any other users who are interested in obtaining sidedress N recommendations for a maize crop. Users are prompted for information related to soil type, cultivar, and previous nitrogen inputs. These are necessary inputs to the nitrogen and crop model. A user-specified zip code allows the ACIS metadata to be queried for the nearest weather observation site. This allows access to the high quality ACIS climate data that are need by PNM model.

Based on recent climate conditions, the PNM model returns a recommended nitrogen application rate to the user via the web. This recommendation optimizes yield and generally specifies an application rate below that which is conventionally used since current sidedress N rate recommendations in New York State do not consider early season climate or early season crop N demand, and growers tend to fertilize for the wettest years. In most years, this results in a reduction in farm expenses and minimizes nitrogen runoff to water bodies.

[Poster PDF]