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Analyzing Long-term Historical Peanut Yield in Georgia with a Crop Simulation Model: the Southeast Climate Consortium Experience
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Paper number 053005, 2005 ASAE Annual Meeting . (doi: 10.13031/2013.19888) @2005
Authors: Axel Garcia y Garcia, Larry C. Guerra, Gerrit Hoogenboom
Keywords: DSSAT, CSM-CROPGRO-Peanut, Climate Variability, ENSO Phases
It is common practice to use crop simulation models and long-term historical weather data to study the impact of climate variability on agricultural production. The variation in simulated yield using this approach reflects the inter-annual and intra-annual weather variability while crop improvement through breeding and management practices are not reflected. Therefore, historical yield data cannot be readily used for evaluation of crop simulation models. The objectives of this study were to analyze long-term historical peanut yields in Georgia from a dynamic crop simulation model and to assess the use of long-term county average yield from the USDA-National Agricultural Statistics Services (NASS) for evaluation of simulated yield. Yield data for Burke, Sumter, and Tift counties from 1934 to 2003 were obtained from the USDA-NASS. The CSM-CROPGRO-Peanut model was used to simulate yield by grouping the period 1934-2003 in three technological periods (TP). For each TP, three soil types, three planting dates, one to three peanut varieties, and irrigated and/or rainfed conditions were used for the simulations. A unique cropping season yield for each period was obtained with a weighted average based on the acreage of the soil type, the peanut variety type, and the proportion of rainfed and irrigated land in each county. Then, observed and simulated yield, total rainfall, and air temperature of the cropping season were grouped with respect to the climatological, El Nio Southern Oscillation (ENSO) phases, and TPs data sets. Each set of data was standardized using the Z-score, which converts all values into compatible units with a distribution that has an average of 0 and a standard deviation of 1. Summary statistics were obtained and the Pearsons coefficient of correlation was used as a measure of similarity between observed and simulated yields. Linear regressions were also calculated to assess the relationship between rainfall and yield patterns. The inter-annual variation of peanut yield, mainly due to climate variability was clearly observed in the simulated series. We also found that the use of observed and simulated yields provided a better understanding of the historical peanut production. The impact of the climate variability on observed yield was low from 1934 to 1964 but high from 1974 to 2003. The technological periods provided an improved characterization for peanut production in Georgia. The 1934-1954 period was characterized by low and stable yields. A significant increase in yield due to new technologies occurred during the 1955-1978 period but yields were generally stable during the 1979-2003 period. The results from this study showed that crop models can be useful tools for understanding the historical variation in yield due to climate variability if appropriate adjustments are made to account for changes in agrotechnology.(Download PDF) (Export to EndNotes)