Larry C. Guerra

Analysis of the Inter-Annual Variation of Peanut Yield in Georgia Using a Dynamic Crop Simulation Model

It is common practice to use crop simulation models and long-term weather data to study the impact of climate variability on yield. Simulated yields mainly reflect the weather variability but not the adoption of new technologies; both sources of variation are reflected in long-term observed yields. Therefore, long-term observed yields, if available, cannot be readily used for evaluation of crop models. The objectives of this study were to analyze the impact of climate variability on long-term historical peanut yield in Georgia obtained with a dynamic crop simulation model and to assess the applicability of using long-term average county yield determined from statistical estimates for evaluation of the simulated yield. Observed yields obtained from state variety trials as well as yield estimates from the USDA-NASS for three counties in the Georgia peanut belt from 1934 to 2003 were used for evaluating simulated yield series. Simulated yields based on the CSM-CROPGRO-Peanut model were categorized into three technological periods (TP). A weighted average based on the acreage of the soil type, the peanut type, and the irrigated land in each county was calculated to obtain a unique simulated yield. Then yields and weather data of the 70-year period were grouped with respect to El Nino Southern Oscillation phases and TPs. Pearsons coefficient of correlation, the least significant difference (LSD), and the t-test were used to evaluate the results. When compared with observed yields, NASS estimates failed to estimate the weather variability at the beginning of the period, but simulated yields clearly reflected that variability during the 70-year period. NASS yield estimates seemed to be useful for evaluating simulated yields from the mid-1970s. The results showed that crop models can be useful in understanding the inter-annual variation of yield due to climate variability if appropriate adjustments are made to account for changes and improvements in agrotechnology.

Evaluation of on-farm irrigation applications using the simulation model EPIC

An understanding of water needs in agriculture is a critical input in resolving the water resource issues that confront many southeastern and other US states. The objective of this study was to evaluate on-farm irrigation applications for three major crops grown in Georgia, USA using the Environmental Policy Integrated Climate (EPIC) model. For cotton, 16, 58, and 75 farmers’ fields in 2000, 2001, and 2002, respectively, were selected from among the Agricultural Water Pumping (AWP) program sites across the state of Georgia. For maize, 9, 20, and 28 fields were selected in 2000, 2001, and 2002, respectively, and for peanut, 18, 51, and 54 fields were selected in 2000, 2001, and 2002, respectively. The majority of these fields were located in the southwest region of Georgia, where traditional row-crop agriculture is most dominant. We compared the simulated irrigation requirements with the amount of water that the farmers actually applied during the 2000, 2001, and 2002 growing seasons. For cotton and peanut, the means of farmer-applied irrigation amounts and simulated irrigation requirements agreed very well, with similar values for root mean squared deviation (RMSD) of the two crops. For maize, good agreement between simulated and farmer-applied irrigation amounts were found only in 2001. Farmers applied more water to their maize crop when compared to simulated irrigation requirements, especially when rainfall was very low and potential evapotranspiration was high during the 2000 and 2002 growing seasons. The component of the mean squared deviation (MSD = RMSD2) related to the pattern of variability in seasonal irrigation applications contributed most to MSD. Accurate estimates of the mean and the magnitude of variability in seasonal irrigation applications could be very useful for the estimation of overall water use by agriculture in Georgia and other southeastern states. This study showed that the EPIC model would be an adequate tool for this purpose; potential users could include policy makers, planners and regulators, including the Georgia Department of Natural Resources (DNR).

Evaluation of the EPIC model for simulating crop yield and irrigation demand

An understanding of water needs in agriculture is a critical input in resolving the water resource issues that confront many southeastern states. Unfortunately, how much water is required and how much water is actually being used for irrigation in Georgia is primarily estimated and largely unknown. The objective of this study was to evaluate the performance of the Environmental Policy Integrated Climate (EPIC) model in simulating crop yield and irrigation demand for three major crops in Georgia. Model evaluation is necessary to provide credibility in applying the model for simulating water use by agriculture. Seasonal yield and irrigation data for the 1990 through 2001 crop variety trials conducted at five agricultural experiment stations were used to evaluate simulation of yield and irrigation amount. The root mean squared deviation (RMSD) for yield was 0.29 t/ha for cotton, 0.39 t/ha for soybean, and 1.02 t/ha for peanut. The RMSD for peanut was large because the model tended to underestimate high yields and was not as sensitive to the factors responsible for the year-to-year variability of peanut yield. The RMSD for total amount of irrigation was 75 mm for cotton, 83 mm for soybean, and 87 mm for peanut. The model simulated the mean irrigation amount and the magnitude of annual variability very well. The component of mean squared deviation (MSD = RMSD2) related to the pattern of annual variability in irrigation amount contributed most to MSD. Overall, the results showed that the EPIC model can be a useful tool for simulating crop yield and irrigation demand at a field level. Future efforts will focus on using the model for regional estimation of water use for irrigation in Georgia and other southeastern states.

Container Temperature and Moisture for Estimating Evapotranspiration of Nursery Crops

The availability of water for agriculture has become an important issue, especially due to the continuing drought in the southeastern USA and because water is one of the most critical inputs for nursery plants. The goal of this study was to estimate the amount of water required for nursery crops based on the soil temperature and moisture measurements of nursery containers. The experiment was conducted at the Center for Applied Nursery Research (CANR), located in Dearing, McDuffie County, Georgia. Fifteen soil temperature probes and fifteen soil moisture probes were installed in three different sizes of containers, including 11.4-, 19.0- and 26.5-L pots filled with a soil mixture consisting of bark, lime, fertilizer, and sand. The probes were connected to an automated data logger, which recorded the container conditions every 15 minutes. At midnight the data logger also calculated the daily extremes and averages. This information was retrieved hourly via a dedicated telephone line and modem by a computer located at the College of Agricultural and Environmental Science-Griffin Campus. At the same time an automatic weather station recorded several weather variables for the same period The experiment was carried out from November 19th 2002 through May 20th 2003. The 11.4-L containers were planted with Ilex chinensis “Bufordi”, Dwarf Burford, the 19.0-L containers were planted with Cuppressocyparis “Leylandi” and the 26.5-L containers were planted with Ilex x “Ruby Sceptor”. The soil moisture and soil temperature probes homogeneity was evaluated through the analysis of the cumulative distribution functions (CDFs) of the 15 minute observations. The Kolmogorov-Smirnov two-sample, two-side test was used to compare all CDFs combinations by treatment. Kolmogorov-Smirnov’s test can detect all type of differences that might exist between two distribution functions and a statistic test (D) is calculated. Thereafter, simple equation regressions of one and two order were fitted. Container moisture was affected by the air temperature seasonal variation. High variability and increase of container soil water content for days without rainfall and irrigation was observed during the winter. This phenomenon was probably due to temperature gradient that could transport water from warm to cool fronts. Meanwhile, the soil moisture probes showed an adequate variation of the soil moisture content during the spring. The experiment received 434mm of water from irrigation and 690mm from rainfall. The irrigation efficiencies were 44, 40, and 53% and the total water consumption was 193, 173, and 230mm in the 11.4, 19.0, and 26.5-L containers, respectively. Functional relationships between container temperature and moisture with air temperature provided satisfactory evidences for estimation of water use by nursery crops and could be used for irrigation scheduling to conserve water use.

Estimating Water Demand For Irrigation Using A Crop Simulation Model

Crop yield and water demand for irrigation under rainfed and irrigated conditions for four major crops in Georgia were estimated using the Environmental Policy Integrated Climate (EPIC) model. Seasonal yield and irrigation data during 1990-2001 for Tifton, Plains and Midville in the Coastal Plain region, Griffin and Athens in the Piedmont region, and Calhoun in North Georgia were used for evaluating simulated yield and irrigation. Under rainfed conditions, the model performs fairly well for different crops, weather and soil conditions across Georgia. In general, the model tends to overpredict for low yielding conditions and underpredict for high yielding conditions. Under irrigated conditions, the model overpredicted to a greater extent for low yielding conditions and underpredicted to a greater extent for high yielding conditions. Only for cotton, the model simulated the year-to -year variability in measured irrigation fairly well.

Analyzing long-term historical peanut yield in georgia with a crop simulation model: the southeast climate consortium experience

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 Nino 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 Pearson’s 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.