Steve Starrett

Movement of herbicides under two irrigation regimes applied to turfgrass

Abstract Our objective was to investigate the movement of the dimethylamine salt of (2,4-dichlorophenoxy) acetic acid (2,4-D), dicamba, and 2-(2-methyl-4-chlorophenoxy) propionic acid (MCPP), when applied to Kentucky bluegrass turf under both infrequent and frequent irrigation regimes. The turfgrass was established on 50-cm length and 20-cm diameter undisturbed soil columns of Nicollet (fine-loamy, mixed, mesic-Aquic Hapludolls) with intact macropores. The infrequent irrigation regime consisted of four 2.54-cm applications, and the frequent regime consisted of 16 0.64-cm applications. On average, the amounts of 2,4-D, dicamba, and MCPP recovered from the soil columns, and in the leachate, under the infrequent irrigation regime were 3.0, 22.4, and 1.1%, respectively. The average amounts of 2,4-D, dicamba, and MCPP recovered from soil columns, and in the leachate, under the frequent irrigation regime were 3.2, 6.3, and 2.3%, respectively. Average values of 1.9, 21.8, and 0.7% of the applied 2,4-D, dicamba, and MCPP, respectively, were measured in the leachate from the soil columns under infrequent irrigation, in contrast to 0.1, 3.4, and 0.2%, respectively, from the soil columns under frequent irrigation. We concluded that the frequency of irrigation can have an impact on the movement of these herbicides through soil profiles.

Using artificial neural networks and regression to predict percentage of applied nitrogen leached under turfgrass

Abstract The objective of this study was to develop an Artificial Neural Network (ANN) model that accurately predicts the percentage of applied nitrogen (N) that leaches through the upper 50 cm of soil under a variety of conditions. The statistical regression models were used for comparison with the ANN model. The Sum of the Squared Error (SSE) between the anticipated values (from research data) and the predicted values (produced by the model) was calculated to be 0.3 for the ANN model and 0.1 for the third order regression. In this particular project, the first and second order regression equations are not useful; however, the third order equation could be used by turf managers along side the ANN model to accurately predict leachate under given field conditions. These models enable the turfgrass manager to determine the effects of management practices on N leaching.

COMPARING DISPERSIVITIES AND SOIL CHLORIDE CONCENTRATIONS OF TURFGRASS-COVERED UNDISTURBED AND DISTURBED SOIL COLUMNS

Research relating to soil leaching properties under turfgrass conditions has often been conducted on disturbed soils where macropore structure has been destroyed. The objective of this study was to compare the solute movement characteristics of undisturbed and disturbed soil columns covered with turfgrass. Dispersivities and chloride (Cl) breakthrough curves of undisturbed and disturbed soils were investigated. Soil columns were excavated into three sections after testing, for which the mean bulk density was 1.33 Mg M−3 for the undisturbed columns and 1.16 Mg m−3 for the disturbed columns. The dispersivity for the undisturbed columns was over three times greater than for the disturbed columns. Chloride concentration found in Layer 1 (0–6.7 cm), Layer 2 (6.7–13.4 cm), and Layer 3 (13.4–20.0 cm) were 2.8, 5.3, and 4.8 times higher, respectively, for the disturbed soils than for the undisturbed. Applying conclusions from solute movement studies using repacked columns covered with turfgrass to actual undisturbed field conditions could lead to errors in interpretation because of the effect of macropores.

Modeling pesticide leaching from golf courses using artificial neural networks

Abstract The objective of this work was to develop a computer model that accurately predicted pesticide leaching of pesticides applied to turfgrass areas. After much investigation, the number of inputs used to train the Artificial Neural Networks (ANN) was reduced to pesticide solubility, pesticide soikwater partitioning coefficient (Koc), time after application, and the irrigation application practice. For comparison reasons, 1st and 2nd order polynomial regression models were developed. An artificial neural network is a form of artificial intelligence enabling the program to learn relationships instead of the relationships being defined by the programmer. The ANN proved to be a feasible modeling technique for pesticide leaching. The ANN predictions for the test cases had much less error than the 1st or 2nd order regression equations (sum of the squared error between measured and predicted values were 17.4, 528.4, and 522.3, respectively). An interactive World Wide Web (www) site has been developed where...

Mentoring New Water Resources Professionals on Engineering Ethics

Engineers maintaining high level of ethical standards is important to society and to the respective profession. We serve as the technical experts on behalf of society and with that role comes significant responsibilities. There are many causes for engineers to make unethical decisions, i.e., placing profit above protection of the public. Such reasons, for example, overbooked schedules, the unethical conduct of business associates, doing the “right thing” would cause a lot of trouble, and the pursuit of fame and fortune above all, etc. There are some common ethical dilemmas that new professionals may face: the acceptance of gifts from vendors or contractors, the billing of labor hours to unrelated project, and the inadequate reviewing of design work. New professions may lack the experience and abilities to successfully handle ethical dilemmas early in their careers. Mentoring new professionals, related to ethical standards may have a long-lasting effect on individuals who are new in the engineering profession. Experienced engineers bring vast knowledge in discussions on how to resolve ethical dilemmas. A key aspect to being a successful mentor is to thoroughly understand the Code of Ethics. If an engineer’s decision are highly supported by the Code of Ethics then it is much more likely that his(her) conduct will be more appropriate and acceptable.

Case study: engineer discovers problem but supervisor wants to ignore

When an engineer suspects that there is a public safety hazard in a project he or she is assigned to, what is the engineers ethical obligation? If the engineer believes that the possible hazard should be investigated, but his/her supervisor does not, how should the engineer proceed? We examine a realistic case of a disagreement between an engineer and his supervisor. We argue that the engineers obligations in this case are not obvious, and that we need to draw on both the IEEE code of ethics and the underlying philosophical principles of that code to formulate and justify an ethical response to the situation.

Nutrient Losses in Runoff from a New Golf Course

A new golf course community occupies a land area of approximately 1000 ac with 60% of its area in the Little Kitten Creek watershed. The watershed, previously grassland pasture, undertook a dramatic change in land-use and watershed management since the golf course construction began in July 1998. A USGA-funded research project started in 1996 enabled us to evaluate changes in surface-water quality. Water quality data were divided into three sets: pre-construction, during-construction, and post-construction. The mean concentrations of TN, TP, and sediment (TSS) in pre-construction period were 1.18, 0.39, and 477 mg/L; during construction were 3.88, 0.93, and 2,754 mg/L; and during post-construction were 2.10, 0.53, and 594 mg/L; respectively. Construction activities had the greatest adverse impacts on water quality. During the first 6 yrs of golf course operation, the TSS and nutrient concentrations returned nearly to the previous pasture values.

SWMM AND SMADA MODEL PREDICTIONS VS. FIELD DATA

The objectives of this study were: (1) conduct sediment and nutrient runoff research during golf course operation on a watershed of 416 ha, (2) determine how the values of SWMM and SMADA-PLOAD compared to 8 years of measured data for modeled total suspended solids, SS, total nitrogen (organic and inorganic nitrogen), total N, and total phosphorus (othrophosphates, condensed phosphates, and organically bound phosphates), total P, for the native prairie and during golf course construction and operation, and (3) make recommendations as to how to use SWMM and SMADA-PLOAD to model golf course dominated watersheds. At the watershed outlet during early golf course operation, field measurements showed average annual loadings of 475, 0.46, and 1.14 kg/ha/yr for SS, total P, and total N respectively. SMADA-PLOAD predicted 309 (-35%), 0.45 (-2%), and 6.49 (+468%) kg/ha/yr for SS, total P, and total N respectively. SWMM predicted 582 (+23%), 0.63 (+36%), and 1.53 (+34%) kg/ha/yr for SS, total P, and total N respectively. SMADA-PLOAD was recommended if a very general estimation was necessary. If field measurements were available, SWMM would be model of choice because of calibration capabilities.

USING SWMM TO MODEL SEDIMENT RUNOFF FROM A GOLF COURSE

Kansas State University has built an 18-hole championship golf course near Manhattan, Kansas, on the Little Kitten Creek watershed. The watershed, previously native grassland, underwent dramatic changes in land-uses since the beginning of the construction. A seven-year study was done to evaluate changes in surface water in terms of sediment concentrations. This paper was designed to compare PCSWMM (2002 version) water quality predictions with those measured values and predicted values from an AGNPS model. All attributes of the watershed were inputted into the program including area, slope, land-use, soil characteristics, channel characteristics, infiltration variables, precipitation values, and evaporation values. A drastic change was noticed during the construction phase compared to the operational phase. The sediment concentrations during some storms during construction were almost 10 times the concentrations observed prior to construction of the golf course. The sediment yield during the pre-construction phase was measured to be 845,000 kg/yr compared to 905,400 kg/yr predicted in AGNPS and 943,500 predicted in SWMM. During construction, the measured value was 1,574,000 kg/yr compared to 2,708,000 kg/yr predicted in AGNPS and 1,832,500 kg/yr predicted in SWMM. During operation, the concentrations returned to the level observed prior to construction. PCSWMM and AGNPS accurately predicted sediment yield increases during the construction of this golf course.