Irrigation for Row Crops

The monthly irrigation amounts were calculated with the dynamic crop simulation models of the Decision Support System for Agrotechnology Transfer (DSSAT; Jones et al., 2003; Hoogenboom et al., 2004) Version 4.5. The Cropping System Model (CSM)-CERES (Jones and Kiniry, 1986) and –CROPGRO models (Boote et al., 1998) were used to simulate water use for cotton, peanut, corn, and soybean. To simulate water use for vegetables for both spring and fall seasons, the CSM-CROPGRO-Tomato model was used.

The DSSAT models operate on a daily time step and simulate growth and development from planting until harvest maturity is predicted. The soil water balance model of the DSSAT crop models is a one-dimensional model that computes the daily changes in water content at different soil layers as a function of infiltration of rainfall and irrigation, vertical drainage, unsaturated flow, soil evaporation, and root water uptake (Ritchie, 1998). The Priestley-Taylor method was used to calculate the potential evapotranspiration, as only daily weather data for temperature and solar radiation were available. The initial water content was set at field capacity, with simulations starting on January 1.

In order to simulate irrigation applications, the automatic irrigation routine of the crop models was selected. Based on this methodology, the model checks the available soil water for a crop-specific irrigation depth. Whenever the available soil water drops below a set threshold value, the model applies a fixed amount of irrigation based on the recommendations of the Cooperative Extension Service. The irrigation efficiency was assumed to be 75%. The crop-specific irrigation depth (ID) and irrigation threshold (IT) values that were used are presented in Table 1. Both the irrigation depth and irrigation threshold value were based on a sensitivity analysis that was conducted for each crop. The monthly irrigation totals simulated by the model were compared with observed monthly irrigation amounts from a select set of counties obtained through the Ag Water Pumping project (Hook et al., 2005). The values for ID an IT that provided the best match were used for all counties. Based on the observed data it was also assumed that a small amount of irrigation was applied at planting in order to provide a uniform plant stand for germination.

The recommendations from the Cooperative Extension Service (http://www.caes.uga.edu/extension) and commodity specialists (http://www.caes.uga.edu/commodities/) were used to define a representative management practice for each crop, as listed in Table 1.

Simulations of daily water use, starting on January 1 of each year, were conducted with one representative weather station for each county and three representative soil profiles as described below. Simulations were conducted for a period of 58 years, starting with the year 1950 and ending in 2007. Because of the significant droughts experienced in the 1950's, this period was explicitly included in the simulations. To determine monthly irrigation totals, the daily irrigation totals, if applied as per model simulation, were integrated on a monthly basis.

Irrigation for Pecans

DSSAT does not include a model for pecans. Thus, the FAO-56 Penman-Monteith method, which has been implemented for many locations across the world (Allen, et al., 1998), was used as an approximation to calculate the water requirements for pecans. The procedure was based on the approach of combining reference evapotranspiration ETo and crop coefficients (Kc).

In addition to the weather variables provided by National Weather Service-Cooperative Observer Network, the FAO56 Penman-Monteith equation also requires wind speed and relative humidity (RH). For the former, data were extracted from weather stations that are part of the Georgia Automated Environmental Monitoring Network. For each location of interest, all years with recorded wind speed were extracted and then the daily average wind speed was calculated. The one-year average daily wind speed was replicated and used for all 58 years weather data. The RH was estimated following the approach proposed by FAO56 (Allen et al., 1998), which is based on vapor pressure, which in turn was also estimated using the maximum and minimum air temperature.

The FAO56 (Allen et al., 1998) does not provide specific crop coefficients (Kc) for pecans. Thus, Kc was estimated on a daily basis using an equation derived from results obtained by Sammis et al. (2004). The crop daily evapotranspiration (ETc) was estimated as ETo * Kc

The available soil water content at 3 feet depth was calculated for the three soils representing the conditions of the county of interest. The soil water depletion fraction used was 0.5 for ETc = 5 mm d-1 and adjusted at daily basis. Then, the amount of irrigation water was derived from a daily water balance considering rainfall, Etc, and soil moisture variation. The monthly irrigation amounts were finally obtained and evaluated against observed data from the Agricultural Water Pumping (AWP) project (Hook et al., 2005). Pecans are normally irrigated from bud break through shuck split, a period from the end of March through at least mid October for Georgia conditions (Wells, 2007). Pecans receive most of the irrigation from mid August to mid September when the kernels are filling (Dr. L. Wells, Pecan Horticulturist, UGA-Tifton Campus, personal communication, March 10/2009).

Model Evaluation

Simulated irrigation amounts for all crops were compared to the observed irrigation amounts at a county level as obtained from the AWP database (Hook et al. 2005). This model evaluation ensured that the simulated values were within an acceptable range of the observed data. However, the observed data represented a limited set of years, i.e., 2000-2004, and a limited set of environmental conditions and management scenarios.

Weather data

Daily maximum and minimum air temperature and precipitation for 112 counties were obtained from the National Weather Service (NWS) Cooperative Observer Program (COOP) through the National Climate Data Center (NCDC), located in Ashville, North Carolina. Daily solar radiation was estimated based on daily observed maximum and minimum air temperature and rainfall using the Weather Generator for Solar Radiation (WGENR; Hodges et al., 1985), as modified by Garcia y Garcia and Hoogenboom (2005).

The 112 counties selected to represent the State were, in turn, represented by 51 weather stations. Most weather stations covered the period of interest from 1950 to 2007. Forty three (43) weather stations represented 98 of the 112 counties or interest (88%) while the remaining 14 counties were represented by eight weather stations located in counties other than the 112. Twelve counties (11%) had weather data starting after 1950 and three counties (less than 3%) had weather data ending before 2007. Data from the nearest neighboring station and from the Georgia Automated Environmental Monitoring Network (AEMN; www.GeorgiaWeather.net) were used to fill the gaps.

Climate Change and Climate Extremes

No climate change scenarios were applied in this study due to the relatively short period to provide deliverables as well as the wide range of options that are available to represent future climate conditions. Instead, we assumed that the historical climate data were representative for future conditions. A follow up study could include implementing different types of climate change scenarios.

To deal with climate extremes, such as extreme dry or extreme wet periods, the analyzed results will separated into probability distributions and percentiles, including the 10%, 25%, median or 50%, 75% and 90% percentiles.

Soil data

Soil associations, identified using GIS techniques and USDA - Natural Resources Conservation Service (NRCS) soil maps were provided for each of the 112 counties of interest. Each association was composed of three soil types and each county had one to four different soil associations. For counties with only one soil association, we obtained profile information for all three soils. For counties with two associations, profile information was for one soil from each association, while the third profile was from either of the two associations or from the nearest neighboring soil profile. For counties with three to four associations, the three soil profiles represented the three associations.

The County-extent of each soil type was extracted from the STATSGO spatial and tabular data, available online at http://soils.usda.gov/survey/geography/statsgo/ and for download from the Soil Data Mart. STATSGO data has some limitations for crop modeling applications due to its insufficient soil profile information. Perkins, et al. (1987) and the online soil characterization database of the USDA-NRCS (http://ssldata.nrcs.usda.gov), which provides an excellent source of soil profile data, were used to extract profile information for each soil. The main characteristics that were extracted for each soil layer included texture (percentage of clay, silt, and sand), soil water content of the drained upper limit (or field capacity; cm3 cm-3) and lower limit of plant available soil water (or permanent wilting point; cm3 cm-3), saturated soil water content (cm3 cm-3), saturated hydraulic conductivity (cm h-1), bulk density (g cm-3), organic carbon (%), total nitrogen (%), pH, cation exchange capacity (cmol kg-1). The program Sbuild, a tool distributed with DSSAT version 4 (Hoogenboom et al., 2004), was used to estimate missing data for bulk density, saturated hydraulic conductivity, saturated soil water content, permanent wilting point, and field capacity.

References

Allen, R.G., L.A. Pereira., D. Raes, M. Smith. 1998. Crop evapotranspiration. FAO Irrigation and Drainage Paper 56. FAO, Rome, Italy, 293 pp.

Boote, K.J., J.W. Jones, G. Hoogenboom and N.B. Pickering, The CROPGRO model for grain legumes. In: G.Y. Tsuji, G. Hoogenboom and P.K. Thornton, Editors, Understanding Options for Agricultural Production, Kluwer Academic Publishers, Dordrecht, The Netherlands (1998), pp. 99–128.

Garcia y Garcia, A., and G. Hoogenboom. 2005. Evaluation of an improved daily solar radiation generator for the southeastern USA. Climate Research, 29:91-102.

Georgia Agricultural Facts, 2008 Edition. Georgia Department of Agriculture. http://www.nass.usda.gov/Statistics_by_State/Georgia/Publications/Annual_Statistical_Bulletin/2008/2008aIntro.pdf

Hodges T, V. French S.K. LeDuc. 1985. Estimating solar radiation for plant simulation models. AgRISTARS Tech. Rep. JSC-20239; YM-15-00403, 21 pp.

Hoogenboom, G., J.W. Jones, P.W. Wilkens, C.H. Porter, W.D. Batchelor, L.A. Hunt, K.J. Boote, U. Singh, O. Uryasev, W.T. Bowen, A. Gijsman, A. du Toit, J.W. White, and G.Y. Tsuji. 2004. Decision support system for agrotechnology transfer [CD-ROM]. Version 4.0. Univ. of Hawaii, Honolulu.

Hook, J.E., Harrison, K.A., Hoogenboom, G., Thomas, D.L., 2005. Ag water pumping. Project Report 52-Final Report. Statewide Irrigation Monitoring (unpubl.) 125 pp.

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.

Jones, C.A., and J.R. Kiniry. 1986. CERES-Maize: A Simulation Model of Maize Growth and Development. Texas A & M University Press, College Station, Texas.

Perkins, H.F. 1987. Characterization Data for Selected Georgia Soils. Special Publication 43, The Georgia Agricultural Experiment Stations, Athens, GA.

Ritchie, J.T. 1998. Soil water balance and plant water stress. In: Tsugi, G.Y., Hoogenboom, G., Thornton, P.K. (Eds.), Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, pp. 41–54.

Sammis, T.W., J.G. Mexal, and D. Miller. 2004. Evapotranspiration of flood-irrigated pecans. Agricultural Water Management, 69(3,1):179-190.

SAS Institute Inc. 2004. SAS OnlineDoc® 9.1.3. Cary, NC: SAS Institute Inc.

Tomato Production Guide for Florida. Cooperative Extension Service, University of Florida http://hammock.ifas.ufl.edu./txt/fairs/56332

Wells, L. (ed.). 2007 Southeastern pecan growers' handbook. Cooperative Extension Service, The University of Georgia, College of Agricultural and Enviromental Sciences, 236p. (Bulletin 1327).