James E. Hook

Using crop models to plan water withdrawals for irrigation in drought years

Abstract Knowledge of water demands during periods of severe drought is needed to develop strategies for water management. The potential (no-water stress) and the lowest (no irrigation) yields for corn, soybean and peanut were calculated using three crop growth and water use models—CERES-Maize, SOYGRO, and PNUTGRO. Rainfall, temperature, and solar radiation records were used with these models to identify the 15 most severe drought years in the 53 year record in a 36-county region of Georgia that contains almost 75% of Georgias irrigated land. In the 15 driest years, simulated yield losses averaged 75% for corn, 73% for soybean, and 64% for peanut. Irrigation amount and timing needed to provide 90% of the no-stress yields were calculated. Most of the irrigation needs of corn in these drought years occurred before that of peanut or soybean. For the reported irrigated crop acreage of the study area, simulated water withdrawals exceeded 3 million m 3 per day, on the average, for most of the 130 days between late May and late September. Further application of the techniques used here could lead to regional or watershed specific estimates of maximum water needs.

Dynamic programming for improving irrigation scheduling strategies of maize

Abstract Efficient and profitable irrigation scheduling strategies are needed, particularly where water available for irrigation is limited. The purpose of the study was to identify optimal irrigation thresholds for several maize irrigation strategies in the southeastern USA. Using dynamic programming (DP), decisions among six possible thresholds were made for each of five maize growth stages. Net returns were maximized subject to a constraint on the total amount of irrigation. Net returns and irrigation amounts were determined for each growth stage using the growth simulator CERES-Maize. Net returns were computed by stage using the futures price for grain and constant ratio of grain yield to biomass. Net returns and water consumption were also computed for fixed threshold irrigation strategies. Dynamic irrigation strategies with differing thresholds for each growth stage produced higher average net returns and required less irrigation water than the best fixed threshold irrigation strategies. Further, irrigation amounts with the dynamic strategies were held below the maximum irrigation level. Irrigation amounts for the fixed strategies often exceeded the allowed maximum.

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).

Potential of using NOAA-AVHRR data for estimating irrigated area to help solve an inter-state water dispute

The states of Alabama, Florida and Georgia dispute the apportioning of water from rivers that originate in Georgia and flow through the other two states. Florida and Alabama often claim that Georgia uses more than its fair share of water. In order to address such a dispute, an estimation of the total amount of water used for irrigation by different crops is required. Current estimates of irrigated areas are subject to errors because they are based entirely on survey questionnaires. In this paper, the potential of Advanced Very High Resolution Radiometer (AVHRR) on-board the National Oceanic Space Administration (NOAA) satellites is examined for estimating irrigated area. Two indices, a widely used Normalized Difference Vegetation Index (NDVI) and a newer Vegetation Health Index (VHI), were regressed against irrigated area for 1986, 1989, 1992, 1995 and 2000 for selected regions in Georgia (Baker and Mitchell counties, and Seminole and Decatur counties). The average VHI during a period from the third week of February to the end of September was better related to irrigated area than the corresponding NDVI; R 2 was above 0.80 as opposed to 0.49. It is concluded that the VHI, derived from three-channel AVHRR data, can be used to estimate irrigated area. By multiplying irrigated area with the application rate, the volume of irrigation used in a state can be determined, which can contribute to the solution of the water dispute.

Influence of tractor wheel tracks and crusts/seals on runoff: Observations and simulations with the RZWQM

In simulations on the fate of agricultural chemicals applied to crops, accurate partitioning of rainfall between infiltration and runoff is fundamental to chemical runoff predictions. We evaluated the Root Zone Water Quality Model (RZWQM version 3.1) against measured runoff from two field plots (15×45 m with 3% slope) on a Tifton loamy sand (fine-loamy, siliceous, thermic Plinthic Kandiudult). Six simulated rainfall events, each 25 mm h−1 for 2 h, were applied to maize (Zea mays, L.) each year. In the uncalibrated mode, RZWQM under-predicted runoff by 40% on average, with the closest fit for events that occurred after full canopy. Saturated hydraulic conductivity (Ks) accounted for the majority of the uncertainty in predicted runoff. When Ks of the surface crust was back calibrated from the measured runoff, RZWQM predicted runoff closely for the remaining plots and events. Alternatively, using different Ks values for wheel track and crop beds, running the model for each and, then, proportionally assigning runoff also led to predictions that agreed with measured runoff. When spatial and temporal changes in Ks were calibrated to specific conditions at the site, RZWQM effectively predicted runoff.

Evapotranspiration predictions: a comparison among GLEAMS, Opus, PRZM-2, and RZWQM models in a humid and thermic climate

Abstract Environmental fate models are increasingly used to evaluate potential impacts of agrochemicals on water quality to aid in decision making. However, errors in predicting processes like evapotranspiration (ET), which is rarely measured during model validation studies, can significantly affect predictions of chemical fate and transport. This study compared approaches and predictions for ET by GLEAMS, Opus, PRZM-2, and RZWQM and determined effects of the predicted ET on simulations of other hydrology components. The ET was investigated for 2 years of various fallow–corn growing seasons under sprinkler irrigation. The comparison included annual cumulative daily potential ET (ET p ), actual ET, and partitioning of total ET between soil evaporation ( E s ) and crop transpiration ( E t ). When measured pan evaporation was used for calculating ET p (the pan evaporation method), Opus, PRZM-2, and RZWQM predicted 74, 65, and 59%, respectively, of the 10-year average ET reported for a nearby site. When the energy-balance equations were used for calculating ET p (the combination methods), GLEAMS, Opus, PRZM-2, and RZWQM predicted 84, 105, 60, and 72% of the reported ET, respectively. The pan evaporation method predicted a similar amount of ET to the combination methods for bare soil, but predicted less ET when both E s and E t occurred. RZWQM reasonably predicted partitioning of ET to E s , while GLEAMS and Opus over-predicted this partitioning. A close correlation between soil water storage in the root zone and ET suggests that accurate soil water content predictions were fundamental to ET predictions. ©

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.