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Technical Summary:
Our goal is to find ways that agricultural spatial technologies can be
employed to improve water quality, while maintaining or enhancing
agricultural producers’ profitability. To meet this goal, we (1) use
biophysical simulation models to establish relationships between field-level
runoff and offsite ambient water quality; (2) use economic models to find
optimal net return of spatial technology application, in comparison with
conventional agricultural practices while meeting environmental goals of
reduced runoff; and (3) educate agricultural producers through public forums
about how spatial technologies can be used to reduce runoff and enhance
profitability. To fulfill the first objective, a biophysical model of the
Big Sunflower watershed, located in the Mississippi Delta will be
developed. SWAT will be used to simulate a combination of spatial
technology application such as variable seeding rate, fertilizer and
pesticide applications, yield monitoring to obtain estimates of offsite
ambient water quality, field edge loadings, channel, stream, and river
loadings, and crop yields. A mathematical economic optimization modeling
will fulfill the second objective. Objective 3) will be fulfilled through
cooperation with extension and conservation groups.
Objectives:
Our primary goal is to investigate ways in which water quality can be
improved to meet set environmental standards, while maintaining or enhancing
producer profitability. To meet this goal, the following objectives are to
be met: (1) use watershed-level biophysical simulation models to establish
relationships between runoff loading at field level and offsite ambient
water quality; (2) use economic models to find optimal practices of spatial
technology that will maximize profit while meeting standards established in
goal (1); and (3) educate agricultural producers through extension and other
public forums about optimal agricultural practice, using spatial
agricultural technology to reduce runoff and enhance profitability. To
fulfill these goals our objectives are to:
1.
Quantify the contribution of farm-level sediment, nutrient and chemical
runoff to the ambient quality of offsite water bodies within the row crop
growing region of Mississippi;
2.
Use the results from 1) to
find optimal practice, using spatial agricultural technology, in comparison
with conventional practices that will maximize profit while meeting
environmental goals of runoff reduction; and
3.
Use the results to educate
extension personnel and agricultural producers to better understand ways to
limit runoff using spatial technology, while maintaining farm profits.
Procedures:
Accurate determination of off-site impacts of nonpoint pollution is costly
and logistically difficult. The proposed research will facilitate
estimation of those impacts through a watershed-level modeling approach.
Using a spatially explicit modeling tool, Soil and Water Assessment Tool
(SWAT), we will evaluate a number of scenarios for the Big Sunflower
watershed. SWAT is a physically based, continuous time model which requires
specific information about weather, soil properties, topography, vegetation
and land management practices. Physical processes, which include water
movement, sediment movement, crop growth, nutrient cycling, etc., are inputs
into SWAT. Crop yield, nutrient runoff and sediment are output parameters
of interest. Experts on biophysical modeling at Blackland Research Center,
Texas Agricultural Experiment Station, Texas A & M will be consulted on an
ongoing basis to address problems of hydrology and water quality modeling.
First, we will
identify offsite water bodies in Big Sunflower watershed for which we want
to establish a maximum level of ambient nutrients, sediment and chemicals
that will provide a healthy ecosystem. We will consult with an ecologist
who will determine the targeted ambient water quality levels used in our
models. For example, the ecologist working on the Mississippi Delta
Management Systems Evaluation Areas (MDMSEA) project has established that a
maximum ambient level of sediment of 150 mg per liter in the Delta’s oxbow
lakes would result in a healthy ecosystem. Once the targeted waterbodies
and ambient qualities are established, we will use SWAT to simulate a large
number of combinations of different cropping and structural practices to
obtain estimates of offsite ambient water quality, field edge loadings,
channel, stream, and river loadings, and crop yields. Because SWAT is
capable of determining nutrient discharges for long time periods to measure
net environmental effects of management strategies within a watershed, we
will be able to not only observe mean loads and ambient conditions, but will
be able to characterize the long run variability of these parameters under
many practices. From this step, we will be able to establish the long run
correlation between total watershed loading levels with offsite water
quality. SWAT will be used to simulate offsite ambient water quality, field
edge loadings, channel, stream, and river loadings, and crop yields that
result from combinations of spatial technology applications, in comparison
with conventional cropping practices. Spatial technologies that will be
considered in our study are variable seeding rate, fertilizer and pesticide
applications, and yield monitoring. Information and data on spatial
technology of interest will be obtained from various sources such as
existing studies, interviews with farmer and agricultural consulting
companies. For example, in our previous study on precision fertilizer
application, information on recommended fertilizer rates and levels of
phosphorus in soil were obtained from an agricultural consulting company and
the MSU Extension Soil Testing Laboratory (Intarapapong, W., et. al.,
2002). Regarding optimal net return estimation, information on Crop
Budgets, published by Agricultural Economic Department will be used where
available, along with data from any existing studies and from interviews
with producers.
To calibrate our
simulation model, Beasley Lake watershed with 2,100 acres of total drainage
area will be chosen as a preliminary study area within the Big Sunflower.
Loam is the predominant soil type, and the primary crops include cotton and
soybeans with some corn and rice. Beasley Lake, Sun Flower County,
Mississippi is part of the MDMSEA project to study agricultural nonpoint
pollution. On site data collected by MDMSEA will be used to calibrate the
simulation model. Once the simulation model is calibrated, it will be
applied for the target watershed, the Big Sunflower.
Our second objective
will be to establish the link between farm runoff and watershed level
ambient quality targets. To find target levels, it will be necessary to
take economic profits into account. It is possible, for instance, that
producers might enjoy greater profits with spatial technology than under
conventional practices, while meeting an even more rigid ambient quality
level than a targeted minimum. In order to meet this objective, we will
estimate a constrained economic optimization model with total loading
limited to be less than or equal to the amount that would provide our
targeted ambient quality level. The objective of the model is to maximize
net farm returns while meeting an environmental standard by using
agricultural spatial technology, as compared to conventional practices.
Furthermore, the solution will provide estimates of total watershed
pollutant loadings under use of different spatial technology practices and
conventional agricultural practices.
The third objective
will be fulfilled with the cooperation of extension and conservation
groups. We will develop a series of bulletins, demonstrations and
educational materials that can be used in workshops and other meetings to
demonstrate the results of our research. Through these meetings, producers
will learn how profit risk can be reduced through total watershed
management. One of our goals is to engender cooperation among producers
within watersheds to meet environmental goals. To this end, we will use
the bioeconomic model to demonstrate differences in profitability between
current conventional practices and spatial agricultural technology without
constraints and optimal practices with environmental constraints. Such
analyses may encourage more producers to adopt spatial technology to reduce
nonpoint pollution. Furthermore, we will publish a series of research
papers and general interest papers in appropriate outlets for both
professional and lay audiences. The results of the research will be
demonstrated through personnel at Delta Research Extension Center (Dr. Steve
Martin) and at Mississippi State University (Dr. Larry Oldham).
Justification:
Agricultural nonpoint pollution has been considered to be the largest
contributor to environmental degradation of surface water in the U.S.,
particularly in terms of nutrient runoff and sediment. Nitrate contaminated
water may pose health risks to humans and animals that drink it (Crosson and
Brubaker, 1982). Phosphorus runoff into waterways can cause eutrophication
that results in reduction of oxygen in water bodies, negatively impacting
aquatic organisms. Sediment loadings in waterways that result from soil
erosion have negative environmental impacts on biodiversity and recreational
and commercial uses of waterways. Chemical such as herbicides and
pesticides may have negative ecosystem impacts.
A
number of programs have been introduced to directly limit
environmental degradation from agricultural practices, including the Food
Quality Protection Act (FQPA) of 1996, the Endangered Species Act (ESA), and
the recent Total Maximum Daily Load rules (TMDLs). TMDLs, which require
state and local government to conduct studies to establish water quality
standards, are expected to become effective in the near future. Each state
must then develop and implement policies to improve the quality of impaired
waterways. It is desirable to find means to meet TMDLs by reducing
agriculturally related effluents while maintaining profitability in the
agricultural sector.
Spatial agricultural technology is applied in a variety of agricultural
management systems and agricultural products such as crops, livestock and
forestry. For this study, the variable rate fertilization and pesticide
components of precision agriculture are examined. To apply this technology,
site-specific data collected in advance using GPS or collected in real time
using local sensing will be utilized.
Literature Review:
Nonpoint pollution (NPP) problems associated with agricultural practices
have come under increasing scrutiny in recent years. Agricultural practices
are considered to be the largest contributor of surface water quality
degradation in terms of sediment, runoff of nutrients and leaching of
chemicals (Crutchfield et al., 1993), affecting one third of the surveyed
lake acres, streams and rivers in the U.S. (USEPA, 1998). Nitrate
contaminated water can pose health risks to humans and animals that drink it
(Crosson and Brubaker, 1982) and is a source of public concern (Hite et al.,
1999). Phosphorus loss in sediment is responsible for eutrophication,
causing a reduction of oxygen levels in lakes and rivers. Reduced oxygen
levels in turn have a negative impact on aquatic organisms, upsetting
ecosystem balance.
A number of studies
related to water quality and nutrient loading have been conducted. Smith
(1992) found that damages due to off-site soil erosion, water quality
reduction and wetlands conversion were about 5%, 6% and 2% of the value of
agricultural output, respectively. Clark, et al. (1985), Dendy, et al.
(1978), Gianessi, et al. (1980), McCabe, et al. (1982), and Ribaudo (1986)
have offered interpretations and estimates of off-farm impacts of erosion
levels. Walker (1980) found that on-site damages in the form of soil
erosion were decreased when conservation practices were employed. Regarding
pesticides, Davies, J.E. (1973) reported that higher concentration of DDT
and its metabolites may cause cancer, hypertension and disease of the
liver. Some pesticides such as Temik (Aldicarb), Vydate (Oxamyl), Bidrin (Dicrotophos),
and Methyl Parathion are very high toxic to birds
(http://ace.orst.edu/info/nptn/ppdmove.htm).
A number of studies
have been conducted to investigate runoff reductions and profitability
associated with alternative practices, using either experimental plot data
or simulation models. Precision
agriculture involves a range of management practices that attempt to utilize
site-specific information at the field level, such as soil characteristics
and weather conditions, in order to adjust the inputs used and ultimately
achieve optimal output. Precision agriculture, using spatial technology is
hypothesized to limit the amount of nutrient and chemical runoff to the
environment because it precisely matches fertilizer and pesticide
applications to the needs of the crop (in both quantity and timing).
Kitchen et al. (1995) found that precision agriculture technology could help
reduce the level of residual nitrogen found in soils, thereby reducing
nitrogen contamination through erosion. Despite the wealth of information
that studies on site-specific experimental plots can produce, conducting
such studies is a costly and time-consuming process. Application of the
results are also potentially limited because of an inability to extrapolate
results to different conditions. In addition, experiments are generally
conducted over the short run, so that the long run environmental and
economic impacts are not well understood.
Despite a number of
studies involving management practices and nonpoint pollution, little is
known about the relationship between onsite nutrient and sediment loading at
farm level and ambient water quality at offsite locations. This linkage is
very important for policy implementation and environmental management. In
addition, adoption of spatial technology by producers is not widespread
because of profit uncertainty. Our research will attempt to demonstrate the
potential long run economic and environmental benefits of optimal management
practices, using spatial technology.
References:
Clark, E.H., II, J.A. Haverkamp, and W. Chapman. 1985. “Eroding
Soils: The Off-Farm
Impacts.” The
Conservation Foundation, Washington, DC.
Crosson, P.R., and S. Brubaker. 1982. “Resource and Environment Effect of
U.S. Agriculture.” Resources for the Future, Washington DC.
Crutchfield, S.R., L.T. Hansen, and M.O.
Ribaudo. 1993. "Agricultural and Water
Quality Conflicts: Economic Dimensions of
the Problem", AIB-676, USDA,
Economic Research Service, Washington DC.
Dendy F.E. and W.A. Champion. 1978. “Sediment Deposition in U.S.
Reservoirs:
Summary of Data Reported through 1975.” Agricultural Research Service, USDA,
1978.
Gianessi, L.P., H.M. Peskin, and T.S. Poles. 1980. “Cropland Soil Erosion
and
Sediment: Discharge to Waterways in the United States.” Resources for the
Future, Washington D.C. 1980.
Hite, D., D. Hudson and D. Parisi. 1999. “Public Concern for Agricultural
Pollution:
Opportunities and Challenges.” Farm Policy Notes, Mississippi State
University
Extension Service Publication, August 1999.
Hrubovcak, James, Utpal Vasavada, and Joseph E. Aldy. 1999. "Green
Technologies for
a More Sustainable Agriculture", Economic Research Service, U.S. Department
of Agriculture, Agriculture Information Bulletin, No. 752. Washington DC.
Intarapapong,
Walaiporn, Diane Hite, and Darren Hudson, 2002. “The Economic and
Environmental Impacts of Variable Rate Fertilizer Application: The Case of
Mississippi”, Mississippi State University, Mississippi Agricultural and
Forestry Experimental Station,
Kitchen, N., D. Hughes, K. Sudduth, and S. Birrell. 1995. "Comparison of
Variable Rate
to Single Rate Nitrogen Fertilizer Application: Corn Production and Residual
NO3-N", in P. Robert, R. Rust, and W. Larson, eds., Site-Specific
Management for Agricultural Systems. Madison, WI: American Society of
Agronomy, Crop Science Society of America, and Soil Science Society of
America.
McCabe, J.M., R.P.
Glandon and C.D. McNabb, Jr. “Identification and Classification
of Agricultural Pollutants and Impacts on Surface Waters,” Publication No.
101,
Limnological Research Laboratory, Michigan State University, East Lansing.
1982.
McCarty, William and Keith Crouse. 1999. "Summary of Soil Testing Data by
Selected
Crops, " Extension Soil Testing Laboratory,
Mississippi State University.
Davies, J.E. 1973. “Pesticide Residuals in
Man,” in Environmental Pollution by
Pesticides, edit by C.A. Edwards, Plenum
Press, New York, 1973.
Ribaudo, M.O. 1986. “Reducing Soil
Erosion: Offsite Benefits,” Natural Resource
Economics Division, Economic Research Service, US Department of Agriculture,
Agricultural Economic Report No. 561.
Smith, V.K. 1992. “Environmental Costing
for Agriculture: Will It Be Standard Fare in
Farm Bill for 2000?” American Journal of Agricultural Economics,
74:1076-1088.
U.S. Environmental Protection Agency (USEPA),
Office of Water. 1998. “National
Water Quality Inventory: 1996 Report to Congress.”
Walker, D.J. 1980. “A Damage Function to Evaluate Erosion Control
Economics,”
American Journal of Agricultural Economics, Vol. 64(4) 1980.
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