The Role of Soil Test Information in Reducing Groundwater Pollution

Will nitrogen soil testing improve groundwater quality enough to decrease the demand for direct regulation? This question is addressed using a dynamic simulation model of irrigated agriculture in eastern Oregon. Results indicate that soil testing reduces applied nitrogen, increases farm profits and improves groundwater quality, but not enough to avoid regulation. Ronald A. Fleming is an Assistant Professor with the Department of Agricultural Economics, University of Kentucky, Lexington, KY, 40546-0276 The author thanks Drs. David Ervin and Richard Adams for their helpful comments. 1 The Role of Soil Test Information in Reducing Groundwater Pollution Soil tests provide information concerning the amount of nitrogen and other macro and micro nutrients in soil available for crop consumption. This information is valuable to producers who want to eliminating excess fertilizer and reduce crop production costs. An external consequence of eliminating excess fertilizer is reduced leaching of nutrients and improved groundwater quality. In agricultural regions where groundwater quality problems have been identified, producers may be able to avoid environmental regulation by taking advantage of information that reduces leaching of nutrients and improves groundwater quality. Past studies have shown that soil test information can be valuable to producers. However, these studies tend to focus on cost savings and only mention the potential for improved groundwater quality (Adams et. al., 1983; Babcock and Blackmer, 1992; Babcock et. al., 1996; Fuglie and Bosch, 1995; Musser et. al., 1995; Wu et. al., 1996). These studies indicate that soil testing can reduce nitrogen fertilizer applications 15 to 41 percent while increasing per acre return 20 to 50 dollars, depending upon location and crop. Musser et. al. acknowledge that the effect of a soil test on excess nitrogen is only an indicator of environmental performance. To measure actual environmental performance requires linking farm level input decisions with ambient levels of an environmental contaminant. Because of the difficulty involved in linking nitrogen input 2 decisions to groundwater nitrate concentration, no previous study has quantified changes in groundwater quality as a result of utilizing soil tests. While Babcock and Blackmer make no effort to measure the value of changes in nitrate contamination of groundwater, they do pose the following question which is the crux of this present investigation. Specifically, is it possible that voluntary adoption of soil testing will lower nitrogen applications sufficiently to decrease the demand for direct regulation? The purpose of this paper is to assess the impact of soil testing on ambient groundwater quality as well as producer profit. The empirical focus is an irrigated agricultural region in eastern Oregon. Soil testing is assessed using a spatially distributed, dynamic simulation model which links economic behavior with the physical processes that determine groundwater quality. The Study Region, Groundwater Concerns and the Use of Soil Tests The empirical focus of the study is an irrigated agricultural region in east, central Oregon. The study region is high desert; annual precipitation ranges from 5 to 16 inches, with an average of 10 inches. Irrigation is required for crop production and surface (flood) irrigation is the principal method of application. The study region encompasses 32 square miles (in Malheur county), of which 17,860 acres (28) square miles is farmed. Many crops (including fruit, vegetable and seed crops) are grown here. However, in this investigation, we focus on the five major crops in terms of acreage; soft white spring wheat, onions, potatoes, sugar beets and hay (a composite of meadow hay and alfalfa). These five crops represent approximately 72 percent of the crop acreage in the 3 county and 54 percent of total crop sales in 1992 (MCES, 1992). Onions, potatoes and sugar beets have a large impact on the county economy in terms of jobs created by processing, handling and field labor. Onions are the most valuable cash crop in the study area (6 percent of the acreage and 25 percent of crop sales in 1992). Between August of 1988 and April of 1990, 199 wells in the shallow aquifers were sampled. In this sampling, 32 percent of the wells were found to have nitrate levels which exceed the federal standard of 10 parts per million (ppm or mg/l) for municipal water supplies. Because of the groundwater quality problems identified here, the area was designated by the Oregon State Department of Environmental Quality (ODEQ) as a Groundwater Management Area (GMA). As a result, a groundwater management committee was formed and several years of extensive data collection and analyses of the geo-hydrology of the region followed. This data was used to develop an action plan to improve groundwater quality. The action plan relies on voluntary approaches, but if these are not successful then (unstated) mandatory actions will be considered. Specifically, a regulatory approach will be considered if there is not evidence that nitrate levels will reach 7 ppm by July 1, 2000. Producers in the study region who test their soils generally contract this work out to private companies who specialize in soil sampling and testing. Soil tests cost $15 per sample for nitrogen and $35 per sample for a complete nutrient profile which includes nitrogen, phosphorous, potassium, soil organic material and miner nutrients. One sample is taken per field where a sample consists of numerous probes taken at random throughout the field. The soil probes generally extract a core 1 foot in depth. An 4 exception is sugar beets where the first and second foot of soil is tested (the producer is charged for two soil tests). Soil testing is identified in the Groundwater Management Action Plan as a method for reducing groundwater nitrate concentration. Hence, there has been great effort on the part of the local Extension Service to educate producers and to encourage them to test their soils for nitrogen before applying fertilizer. Currently, 100% of the potato and sugar beet fields in the study region are soil tested. Wheat and onion fields are also soil tested, but at a much lower rate. Specifically, up to 10% of the wheat fields and 80% of the onion fields are soil tested. Further improvement in groundwater quality is possible if all fields were to be soil tested. In the analysis that follows, we assess whether voluntary adoption of soil testing on all fields is profitable to producers and if groundwater quality is improved. A Simulation Model of Soil Testing The affect that soil test information has on producer profit and groundwater quality is measured utilizing a spatially distributed, dynamic simulation model linking the economic and physical processes which determine groundwater quality. The integrated model is a composite of three sub-models (economic, soil water solute transport and groundwater solute transport) where each sub-model represents one level in the nitrate contamination process. In the economic sub-model, producers choose water and nitrogen fertilizer application rates to maximize profits. Results from the economic sub-model become input in the soil water solute transport sub-model which describes movement of 5 water and nitrogen through the unsaturated or vadose zone of soil. Results from the soil water solute transport sub-model are input in the groundwater solute transport sub-model, which tracks loading and movement of nitrates throughout the study aquifer. This model is an outgrowth of the extensive groundwater studies preformed in the area because of its GMA status. The integrated model is only summarized here with the greatest attention being given to the economic sub-model (for greater detail see ****, 1996). With the help of digitized USGS Soil Survey maps and other crop production maps, the study region is broken into 40 acre units. The 40 acre unit was chosen to conform with the needs of the groundwater solute transport sub-model. However, this is not believed to overly compromise the economic sub-model because average field size in the study region is 20 acres (Perry et. al., 1992). Using the soils maps, the soil type (subscript s in Equation 1) and corresponding crop mix (rotation) of each 40 acre unit is known. While up to five crops (wheat, onions, potatoes, sugar beets and hay; subscript c in Equation 1) can be grown in each unit (hence field sizes less than 20 acres), constraints are imposed that maintain the proper proportion of crops in each soil zone. This requires that constant returns to scale be assumed. Finally, all production units within a soil zone are treated as identical, based on information from OSU agricultural extension personnel (L. Jensen, personal communication, 1994). In the economic sub-model, nitrogen input (n ) and the crops to which this c,s,t nitrogen is applied (a ) are chosen to maximize profit (or net farm income) on 40 acres c,s,t through time (Equation 1) subject to a series of production constraints (Equations 2 Max nc,s,t ac,s,t sw c,s,t gw i,j,t c sw c,s,t c gw i,j,t ' T j t'1 ( S j s'1 C j c'1 [PcQ(nc,s,t)c,s,tqadjc&(np@nc,s,t)&cfcc]ac,s,t) 1 (1%r) dt nc,s,t # canc é c,s, t ac,s,t # cgrowc,s(nac é c, s, t C j c'1 ac,s,t # nac é s, t 6 through 4) and rotation or crop mix constraints that are not shown. In Equation 1, λ and sw λ are the co-state or shadow values for soil water and groundwater nitrates, c and c gw sw gw are the state or stock values for soil water and groundwater nitrates, P is crop price, Q is crop yield, qadj is an adjustment to crop yield, np is nitrogen fertilizer price, cfc represents crop production fixed costs and r is the discount rate. In the production constraints, the parameter "can" is the county average nitrogen fertilizer application rate, cgrow is a matrix of 0 and 1 values that defines relevant crop-soil combinations and the parameter nac is the number of acres in a production unit. Note that c and c are sw gw functions of n and a , but are calculated in, respectively, the soil water and c,s,t c,s,t groundwater solute transport sub-models.