|12-348 Project Manager: E. M. Barnes|
APEX TO EVALUATE COTTON PRODUCTION SYSTEMS
Susan Wang, Texas AgriLife Research
The purpose of this project is to have a science-based approach to evaluate the potential environmental impact of cotton production systems on water and soil quality. Some environmental models have been developed to portray manufacturing industries and now some are applying those same models to agricultural systems. Unfortunately, the models are not sophisticated enough to account for the complexities of a biological system, especially chemical fate and transport outside of the farm boundaries. One solution would be to use models already developed and validated for agricultural systems to provide the data needed as an alternative or input to the industrial models. The first step in this process was to set up some simulation scenarios and get model outputs. In 2012, the focus was to first conduct simulations with EPIC that will be later compared to other models.
Four locations with variations in climate and crop management, which are representative of the four cotton growing regions within the U.S., were selected for EPIC simulation. Each simulation site was tested with three selected soil types, average or common cropping practices, application rates of fertilizer, pesticide, irrigation, and application dates of that region were used for each respective location. We were essentially looking at long-term of 40-year different weather scenarios to determine soil erosion, soil carbon change, nitrogen and phosphorus loss, pesticide fate and transport.
As simulated by the EPIC model, soil losses, organic carbon change, nutrient and pesticide losses vary considerably. Water is a potent force that interacts with or drives almost all environmental processes acting within an agricultural production system. The hydrologic conditions prevalent in the regions are critical to understanding the estimates of sediment, nutrient, and pesticide loss. As the precipitation ranged from 310 mm (California site) to 1317 mm (Georgia site), the average annual soil losses ranged from 0 Mg ha-1 for Atwater sand soil at the California site to 3 Mg ha-1 for Alviso clay at the Georgia site based on RUSLE2 approach. Soil characteristics also play an important role in the system. Surface runoff and soil loss from clay soils were the largest, and erosion smallest from sand soils. The soils at the Georgia simulation site had low initial organic carbon (26 to 47 Mg ha-1), EPIC simulated that the soils gain carbon for the first 10 to 15 years of cultivation and gradually reach steady state, with total gains by 25-60% during the 40 years of simulation period. Low initial carbon stocks, cotton rotated with soybean and cover crop, and no-till all contributed to the improvement of the soil carbon storage for the Georgia three soils. The soils at the California site had relatively high initial organic carbon; they began to lose carbon under continuous cotton for the first 10 to 15 years of cultivation and gradually reached steady state.
For sandy soils, nitrogen and phosphorus loss were primarily through leaching. The average annual total simulated nitrogen loss through water (surface and subsurface) and sediment ranged from the lowest value at the non-irrigate Texas site, to a high value for a sandy, irrigated California soil. The average annual total phosphorus loss had an almost opposite trend, with the lowest loss predicted at the California site and highest predicted to occur at the Arkansas site.
The dominant pesticide loss pathway was predicted to be pesticides dissolved in surface water runoff for most of the pesticides applied for cotton. Other pesticides, such as Sodium chlorate, have the main loss pathway through leaching loss; yet pesticides, like Tribufos is prone to adsorbed to sediment loss. Due to dry climate for the California site and Texas site the total annual pesticide mass losses predicted were lower than the sites in Georgia and Arkansas.
Cotton production practices vary broadly. Regions within a country can have high variability in cotton practices. In this study, reasonable management operations were applied and the soil choices were selected to provide a maximum range of conditions for simulation. These simulations provided a mechanism to evaluate potential responses of cotton production systems across a wide range of weather and geographic conditions. Opportunity exists for further data collection, including detailed data of field management and soils for specific field, and conservation practice scenario simulations, which will allow the model to provide greater insight in identifying sensitive areas for specific recommendation.
|Project Year: 2012|
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