Stress-Day Factor and Stress-Day Index as Indicators of Drainage Needs of Soils

Field data on water-table depths, plant parameters, and corn yields were collected for 3 years (1984 through 1986) on the Clarion-Nicollet-Webster Soil Association from 50 plots under naturally fluctuating water-table conditions. The stress-day factor (SEW30) and stress-day index (SDI) concepts were used to provide a quantitative means of determining drainage needs of soils. Corn yield and SDI were used to assess the crop production losses due to excessive wetness in the soils. The stress-day factor and index were found to be useful tools in evaluation of drainage needs of poorly drained soils. The highest values of SEW30 and SDI were obtained for naturally very poorly drained soils, while naturally well-drained soils gave about zero values of SDI. Significant differences were found in SEW30 and SDI between all natural drainage classes of soils. This study also showed a strong relationship between relative yield and SDI for corn. This relationship was compared with published data from three other sources -India, North Carolina, and Ohio and found to be in agreement. The information in this paper on transient waterlogging\u27s effect on crop growth under different natural soil drainage regimes should provide a basis for improving the design of field drainage systems

Dynamic Rank Maximal Matchings

We consider the problem of matching applicants to posts where applicants have preferences over posts. Thus the input to our problem is a bipartite graph G = (A U P,E), where A denotes a set of applicants, P is a set of posts, and there are ranks on edges which denote the preferences of applicants over posts. A matching M in G is called rank-maximal if it matches the maximum number of applicants to their rank 1 posts, subject to this the maximum number of applicants to their rank 2 posts, and so on. We consider this problem in a dynamic setting, where vertices and edges can be added and deleted at any point. Let n and m be the number of vertices and edges in an instance G, and r be the maximum rank used by any rank-maximal matching in G. We give a simple O(r(m+n))-time algorithm to update an existing rank-maximal matching under each of these changes. When r = o(n), this is faster than recomputing a rank-maximal matching completely using a known algorithm like that of Irving et al., which takes time O(min((r + n, r*sqrt(n))m)

Testing Uniformity of Stationary Distribution

A random walk on a directed graph gives a Markov chain on the vertices of the graph. An important question that arises often in the context of Markov chain is whether the uniform distribution on the vertices of the graph is a stationary distribution of the Markov chain. Stationary distribution of a Markov chain is a global property of the graph. In this paper, we prove that for a regular directed graph whether the uniform distribution on the vertices of the graph is a stationary distribution, depends on a local property of the graph, namely if (u,v) is an directed edge then outdegree(u) is equal to indegree(v). This result also has an application to the problem of testing whether a given distribution is uniform or "far" from being uniform. This is a well studied problem in property testing and statistics. If the distribution is the stationary distribution of the lazy random walk on a directed graph and the graph is given as an input, then how many bits of the input graph do one need to query in order to decide whether the distribution is uniform or "far" from it? This is a problem of graph property testing and we consider this problem in the orientation model (introduced by Halevy et al.). We reduce this problem to test (in the orientation model) whether a directed graph is Eulerian. And using result of Fischer et al. on query complexity of testing (in the orientation model) whether a graph is Eulerian, we obtain bounds on the query complexity for testing whether the stationary distribution is uniform

Efficient Compression Technique for Sparse Sets

Recent technological advancements have led to the generation of huge amounts of data over the web, such as text, image, audio and video. Most of this data is high dimensional and sparse, for e.g., the bag-of-words representation used for representing text. Often, an efficient search for similar data points needs to be performed in many applications like clustering, nearest neighbour search, ranking and indexing. Even though there have been significant increases in computational power, a simple brute-force similarity-search on such datasets is inefficient and at times impossible. Thus, it is desirable to get a compressed representation which preserves the similarity between data points. In this work, we consider the data points as sets and use Jaccard similarity as the similarity measure. Compression techniques are generally evaluated on the following parameters --1) Randomness required for compression, 2) Time required for compression, 3) Dimension of the data after compression, and 4) Space required to store the compressed data. Ideally, the compressed representation of the data should be such, that the similarity between each pair of data points is preserved, while keeping the time and the randomness required for compression as low as possible. We show that the compression technique suggested by Pratap and Kulkarni also works well for Jaccard similarity. We present a theoretical proof of the same and complement it with rigorous experimentations on synthetic as well as real-world datasets. We also compare our results with the state-of-the-art "min-wise independent permutation", and show that our compression algorithm achieves almost equal accuracy while significantly reducing the compression time and the randomness

Simulating NO3-N Transport to Subsurface Drain Flows as Affected by Tillage Under Continuous Corn Using Modified RZWQM

The Root Zone Water Quality Model (RZWQM) was previously modified to simulate subsurface drain flows and evaluate the impact of different tillage systems on subsurface drain flows (Singh and Kanwar, 1994). This article discusses further modifications made in the RZWQM to simulate nitrate-nitrogen (NO3-N) concentrations and NO3-N losses with subsurface drain flows. Daily NO3-N concentrations were simulated in subsurface drain flows under four different tillage systems: chisel plow (CP), moldboard plow (MB), no-tillage (NT), and ridge-tillage (RT) by using the modified RZWQM. Simulations were conducted for the growing seasons of three years (1990 to 1992). Simulated NO3-N concentrations and losses with subsurface drain flows were compared with the measured data obtained from a water quality research site at Nashua, Iowa. Predicted NO3-N concentrations generally followed the same pattern as the observed concentrations. Simulated annual average NO3-N concentrations in subsurface drain flows were within 11% (averaged over all three years) of observed annual average NO3-N concentrations in subsurface drain flows. The model correctly predicted maximum concentrations under MB treatment and minimum under NT for all three years. Simulated annual NO3-N losses were within 14% (averaged over all three years) of observed annual NO3-N losses. Various NO3-N transformation processes need to be calibrated as a function of tillage system to improve model performance

Predicting Soil Nitrate-Nitrogen Losses from Incorporated Poultry Manure Using the GLEAMS Model

Proper calibration and validation of computer models can inexpensively and quickly assess the impacts of different agricultural management practices on water quality. This study used Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) version 2.1 to determine the effects of two nitrogen (N) application rates (168 kg N/ha (168PM) and 336 kg –N/ha (336PM)) from poultry manure and one N application rate (168 kg N/ha (168UAN)) from urea ammonium nitrate (UAN) fertilizer on NO3–N loss with subsurface drainage. The simulated NO3–N losses by the GLEAMS model were compared with two–year (1999 and 2000) monthly measured NO3–N losses from six 2.1–m 2 field lysimeters under continuous corn production.Results indicated that the two–year average simulated subsurface drainage consistently followed the average measured subsurface drainage from 168UAN, 168PM, and 336PM treatments, with mean errors of 9%, 3%, and –2%, respectively. The model was capable of predicting overall NO3–N concentration in subsurface drainage from lysimeters under 168UAN, 168PM, and 336PM treatments reasonably well, giving mean errors of 19%, –29%, and 9%, respectively. The overall two–year results also show that there were no significant differences (p= 0.05) between average measured and simulated NO3–N losses with subsurface drainage from poultry manure treatments. The study concluded that the GLEAMS model can be used as a viable management and decision–making tool to assess the impacts of long–term poultry manure application on water quality and agricultural production for Iowa soils

Simulating Tillage Effects on Nonpoint Source Pollution from Agricultural Lands Using GLEAMS

A GLEAMS (ver. 2.10) model was calibrated and validated using three years (1990–1992) of field–measured data to simulate tillage effects on nitrate–nitrogen (NO3–N) and herbicide losses with subsurface drain “tile” water beneath a continuous corn production system. The model was calibrated for chisel plow systems using 1991 field data and was validated against field data of two years (1990 and 1992) for chisel plow and three years (1990–1992) for moldboard plow (MB), ridge till (RT), and no–till (NT) systems. The model simulations were made with a single run using data on measured tile flows; nitrate–nitrogen (NO3–N), atrazine, and alachlor losses with tile flows; N–uptake; and other biomass parameters for the period 1 January 1990 through 31 December 1992 for CP, MB, RT, and NT systems. All four tillage treatments, each replicated three times, were applied in a randomized complete block design at Iowa State University’s Northeastern Research Center, Nashua, Iowa, to collect field data. A specific calibration procedure for the nutrient component of the GLEAMS model was developed adjusting the N–uptake coefficient and using steady–state N–pool values obtained after multiple years of the model runs. The results of this study indicated that model simulations on subsurface drain water quality and plant parameters were adequate for chisel plow systems and the percent difference, on the average, was \u3c10% between predicted and measured data with R 2 = 0.99. The change in tillage management option for MB, RT, and NT systems slightly affected the model predictions on subsurface drain water quality, probably due to the lack of a macropore option in the model and perhaps also due to no fall plowing in 1991 because of wet weather. However, calibration of the nutrient submodel using steady–state N–pool values was successful and may be tested to simulate the long–term effects of different cropping systems on soil and water quality

Effect of Different Moisture Stress Levels on Corn Growth in Field Lysimeters

Experiments were conducted in field lysimeters to investigate the effects of high water table positions (created by subirrigation practice) on com growth. Various plant growth parameters (plant dry matter, canopy height, and yield) were measured before, during, and after the excessively wet periods. Two regression models were developed to characterize corn yield production as a function of durations of excessively wet periods and water-table positions. Results indicated that subirrigation practices can improve com growing conditions. Less com yield reduction occurred when water tables were maintained at 15 cm than when water tables were maintained at the soil surface during the six-leaf stage

Impact of Tillage and Tracer Application Method on Spatial Distribution of Leaching Losses

The impact of tillage and nitrogen (N) application method on the spatial variation on leaching losses was evaluated using eight, 1-m3 undisturbed soil monoliths. Monoliths were collected in 1992 from research plots with 15 years of continuous tillage and crop rotation history. Tillage practices imposed in the field were moldboard plow, chisel, or ridge tillage. Anion tracers were applied to simulate applying N as a surface broadcast, in a slot with surface compaction, and with water. A rainfall simulator was used to apply 100 mm of water followed 24 h later by an additional application of 430 mm to establish tracer concentrations over a range of drainage. Drainage was collected at the bottom of each monolith using fiberglass wick extractors placed in a 6 ×6 grid of 90 mm ×90 mm cells. Tracer leaching losses and flow-weighted concentrations were calculated from the initial flush of water through 24 h after water application. No significant differences were noted for cumulative drainage distribution curves or for the depth of drainage produced (p \u3c 0.05). However, in all cases, the cumulative distribution curves were above the 1:1 line, indicating that drainage from some cells was greater than others. Spatial analysis indicated that drainage was randomly distributed across the monolith. Tracer leaching losses were not significantly different among tillage treatments or tracer application methods for either water application event. However, results for the slot with surface compaction treatment suggest that 10 times more NO3-N from moldboard plow treatment in comparison with the ridge tillage treatment. Trends in leaching losses for the SLOT (Br) with surface compaction treatment suggested that a tracer leaching pattern existed directly below the application zone. Tracer concentrations peaked above 350 mg L–1 after 100 to 170 mm of drainage for the SLOT (Br) with surface compaction application method and final concentrations remained above 70 mg L–1. Peak concentrations for the surface broadcast (BROAD) and with water application (WATER) methods peaked at less than 110 mg L–1 and were consistent among monoliths. Spatial analysis indicated that leaching losses were randomly distributed. Data supported an assertion that the moldboard plow tillage treatment combined with the slot with surface compaction (SLOT) application of N should be avoided

Incorporating preferential flow and herbicide fate and transport into the drainage model

The DRAINAGE model was modified by incorporating a pesticide component and a preferential flow component in order to simulate pesticide concentrations in subsurface drain flows. Field data on subsurface drain flows and their atrazine concentrations were used to calibrate and validate the enhanced DRAINAGE model for growing seasons of 1990, 1991, and 1992. Simulated subsurface drain flows and their atrazine concentrations were compared with the measured values. Predicted daily flows by the modified DRAINAGE model were close to the observed values (difference over all years + 6.3%). Overall mean difference (Md) and correlation coefficient (R2) were + 0.1 mm and 0.70, respectively. The predicted atrazine concentrations in subsurface drainage water followed the observed trends well except in 1992. The overall timings of pesticide appearance in the drain water were predicted well by the model. The annual atrazine losses with subsurface drain flows predicted by the model were also in close agreement with the observed losses for 1990 and 1991 (with 1.1% difference). The results of this study indicated that the modified DRAINAGE model has good potential for simulating atrazine concentrations for normal rainfall years when a substantial amount of pesticides may be lost in the subsurface drainage water (overall Md = 2.03 ìg/kg and R2 = 0.58)