bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parameterized Model of Gramineous Crop Growth: I. Leaf Area and Dry Mass Simulation
Authors: Maas Stephan J. 
Published: July 2010

AbstractParameterization is a technique used by modelers to approximate the response of a physical system using an empirical function. This technique has the advantages that it reduces the complexity of models and often simplifies their input requirements, making the models more easily usable for operational purposes. In this study, a relatively simple model of leaf area and dry mass growth for the gramineous crops was developed to demonstrate the use of parameterization. Results of model simulations for grain sorghum [Sorghum bicolor (L.) Moench], corn (Zea mays L.), and spring wheat (Triticum aestivum L.) were in reasonable agreement with field observations. The effects of plant population, fertilization level, and water stress on crop growth were implicitly accounted for in the simulations through the appropriate assignment of parameter values in empirical functions controlling leaf area appearance and senescence.
A Crop‐Specific Drought Index for Corn: I. Model Development and Validation
Authors: Meyer Steven J. , Hubbard Kenneth G. , Wilhite Donald A. 
Published: July 2010

AbstractMost popular indices in use today that address agricultural drought [e.g., the Palmer Drought Severity Index (PDSI) and the Crop Moisture Index (CMI)] do not consider specific crop responses to the drought event. Thus, values produced by indices such as the PDSI and CMI cannot be directly linked to drought impacts on crop production and yield since each crop responds differently to moisture and heat stress. This paper presents a new drought index that, unlike other drought indices, is specific to a single crop (corn—Zea mays L.) and takes into consideration water use during specific periods of growth. The Crop‐Specific Drought Index (CSDI) model was developed with 8 yr of data from Nebraska's East Central Crop Reporting District (CRD), and validated with 10 yr of data from Nebraska's East Central CRD, 9 yr of data from Missouri's Northeast CRD, 8 yr of data from Wisconsin's South Central CRD, and 9 yr of data from Indiana's Central CRD. Plots of model predicted vs. actual CSDI values yielded coefficients of determination (r2) ranging from 0.32 (Central Indiana) to 0.76 (Northeast Missouri); D‐index of agreement values ranging from 0.72 (Central Indiana) to 0.90 (South Central Wisconsin); and root mean square errors ranging from 7.7% (South Central Wisconsin) to 13.4% (Northeast Missouri). The CSDI model may provide an effective drought monitoring and assessment tool for the Corn Belt of the United States.
Yield Responses of Alfalfa to Simulated Alfalfa Weevil Injury and Development of Economic Injury Levels
Authors: Peterson Robert K. D. , Danielson Stephen D. , Higley Leon G. 
Published: July 2010

AbstractAlthough the alfalfa weevil Hypera postica (Gyllenhal)] is an important alfalfa (Medicago sativa L.) pest, relatively little is known about how defoliation at specific plant developmental stages affects yield. This study was conducted to characterize yield responses of alfalfa to simulated alfalfa weevil injury when injury was initiated at the early bud stage of the first growth cycle and to develop economic injury levels for the alfalfa weevil. The study was conducted on a silty clay loam (fine, montmorillonitic, mesic Typic Argiudoll) in a 3.6‐ha alfalfa (cv. Haymaker) field near Walton, NE. Sixteen 1 by 1‐m plots were arranged in a randomized complete block design with four treatments in each of four replicates. Treatments consisted of three levels of simulated alfalfa weevil defoliation and an undefoliated check. Leaflets were removed over 10 d. Dry matter yield and stem density responses were measured for both the first and second growth cycles. Simulated alfalfa weevil injury did not affect stem density before the first cutting or during the entire second growth cycle. When injury occurred during the early bud stage, the most significant effect was the loss of leaf tissue. Yield differences among treatments for the first growth were highly significant, and the relationship between percentage defoliation and percentage yield reduction was linear both years. Defoliation injury imposed during the early bud stage of the first growth cycle did not affect yields or stem densities of the second growth cycle. Dry matter yield responses of the first growth cycle were used to calculate economic injury levels for third and fourth instars feeding at the early bud stage of alfalfa development.
Simultaneous Selection for Yield and Stability in Crop Performance Trials: Consequences for Growers
Authors: Kang Manjit S. 
Published: July 2010

AbstractUtilization of genotype × environment (GE) interaction encountered in crop performance trials is an important issue among plant breeders and agronomists. Practical integration of yield and stability of performance has not been achieved. The purposes of this paper are (i) to examine consequences to growers when researchers commit Type I (rejecting the null hypothesis or Ho when it is true) and Type II (accepting the Ho when it is false) errors under a yield‐based, conventional selection method (CM) and a proposed method (yield‐stability statistic or YSi) that uses GE interaction, and (ii) to show why a greater emphasis on the stability component would be advantageous to growers. A corn (Zea mays L.) dataset was used to compute the YSi and to estimate Type II error rates for overall mean yield comparisons and for the stability‐variance statistic, σ2i(σ2i measures contribution of ith genotype to the total GE interaction) at different α (Type I error rate) and δ (minimal detectable difference) levels. When pairwise yield comparisons are made, a higher level of a will not be as harmful to growers as a higher level of β (Type II error rate). Since researchers would prefer to have power (1 ‐ β) of a test between 0.70 and 0.80, choosing an α(1) level (one‐tailed) between 0.10 and 0.20 should be appropriate. If an α(1) value of 0.25 is selected δ between 1.0 and 1.4, the Type II error rate would be almost zero. For stability, the Ho tested was that yield means of a genotype in different environments were equal, i.e., σ2i = 0. A consequence of committing a Type I error would be that growers could miss using a stable cultivar, but a consequence of committing a Type II error can be disastrous for growers, as they could choose an unstable genotype and suffer economically. A greater emphasis on performance stability during selection would benefit growers.
Microcomputer‐Based Software System to Facilitate Mechanical Planting for Agricultural Experiments
Authors: Srinivasan G. , Herrera R. 
Published: July 2010

AbstractMechanized planting for experimental purposes such as yield trials and breeding nurseries is plagued by time consuming and often error‐prone manual arrangement of seed packets, especially with multiple row planters. At CIMMYT, a four‐row planter is used when feasible, for machine planting maize. Careful planning for error‐free planting is necessary to fully realize the benefits of mechanized planting. The software system described in this article attempts to address this concern. The MACHPLAN program is written in the Pascal language using commercially available Turbo Pascal. MACHPLAN generates planting plans which can be used for a four‐row planter. Tables, created in an ASCII file, contain the field row number (going left to right, right to left in a serpentine fashion in the field) and the four‐digit mechanical planter number (from bottom to top and top to bottom in a serpentine fashion in the field). This plan is merged with a planting plan created by the user in a spreadsheet program (e.g., Lotus 1‐2‐3). Labels are then printed either using any commercially available package or by converting the spreadsheet file to a database file (e.g., dBASE). Using the mechanical planter number as key, seed packets can be arranged for machine planting.
Barley Performance under Heavy Applications of Cattle Feedlot Manure
Authors: Chang C. , Sommerfeldt T. G. , Entz T. 
Published: July 2010

AbstractApproximately 4800 cattle feedlots currently operate in Alberta, Canada. The large amount of manure produced annually creates disposal problems for feedlot operators. The objective of the present study was to evaluate the effects of long‐term annual manure application on barley (Hordeum vulgare L.) performance grown on clay loam soil (Dark Brown Chernozemic, Typic Haploboroll) near Lethbridge, AB. Feedlot cattle manure was applied annually from 1973 to 1989 to irrigated plots at 0, 60, 120, and 180 Mg ha−1 (wet wt. basis) and to nonirrigated plots at 0, 30, 60, and 90 Mg ha−1. Manure was incorporated with a cultivator plus disk, rototiller, or plow. Method of incorporation did not affect barley yields, but timeliness and amount of precipitation in the current and previous year were important in determining barley response to manure treatments. When moisture conditions were below normal, manure at any rate of application had a negative effect on yield of nonirrigated barley. Under nonirrigated conditions, average barley yield was depressed by 10 and 16% for the 60 and 90 Mg ha−1 manure rates, respectively. Barley yield increased when manure was applied under irrigation, with the 60 Mg ha−1 rate producing a 20% higher average yield than the control. Although manure was disposed of annually on the soil at up to three times the recommended rate without reducing barley yields below those of the unmanured plots under irrigated conditions, such practice is not recommended because of potential soil and water pollution.
Comparison of Near‐Infrared Spectroscopy and Other Soil Nitrogen Availability Quick Tests for Corn
Authors: Fox Richard H. , Shenk John S. , Piekielek William P. , Westerhaus Mark O. , Toth John D. , Macneal Kirsten E. 
Published: July 2010

AbstractOur ability to predict N fertilizer needs for corn (Zea mays L.) is improving, but more accurate and convenient tests are still needed. This work compared a new quick test for soil N availability using a near‐infrared spectrophotometer (NIRS) with three published quick tests for predicting soil N‐supplying capability (NSC) and relative corn grain yield. The other tests were the pre‐sidedress nitrate test (PSNT), nitrate concentration (at‐plant NO3), and absorbance at 200 nm of a 0.01 M NaHCO3 extract (UV‐200 test) of 0‐ to 20‐cm soil samples taken at planting. Soil samples taken at planting from 95 field experiments in Pennsylvania were analyzed at reflectance wavelengths from 400 nm to 2500 nm with NIRS. The coefficients of determination were the same (R2 = 0.49) for both linear and quadratic regressions of NSC and NIRS test values. The abilities of the four tests to predict NSC and relative corn grain yield were compared using data from 90 of the 95 experiments. The R2 values for linear and quadratic regressions between soil test values and NSC ranged from 0.49 to 0.58 for the NIRS, PSNT, and UV‐200 tests; for the at‐plant NO3 test, R2 was lower (=0.40). Eliminating sites where corn directly followed a legume, R2 values for quadratic regressions between test values and NSC increased to =0.60 for the NIRS, PSNT, and UV‐200 tests. The PSNT test was slightly better than the other tests in predicting a grain yield response to N fertilizer, but this advantage lessened when first‐year‐after‐legume sites were eliminated. No test could accurately predict relative grain yield or NSC in fields responding to N fertilizer (R2 = 0.08−0.36). The NIRS test offers a convenient, rapid, and inexpensive alternative to the PSNT for predicting whether humid‐region corn fields will respond to N fertilizer.
GENCALC: Software to Facilitate the Use of Crop Models for Analyzing Field Experiments
Authors: Hunt L. A. , Pararajasingham S. , Jones J. W. , Hoogenboom G. , Imamura D. T. , Ogoshi R. M. 
Published: July 2010

AbstractA number of crop simulation models use as model inputs certain coefficients that account for differences among cultivars. These coefficients, often referred to as genotype coefficients, allow the models to simulate performance of diverse genotypes under different soil, weather, and management conditions. The models therefore can potentially be used to resolve the genotype × environment interaction into underlying coefficients. GENCALC (Genotype Coefficient Calculator) is a software package that facilitates the calculation of these coefficients for use in existing crop models. In GENCALC, the coefficients for a genotype are estimated iteratively by running the appropriate crop model with model input data and approximate coefficients, comparing the model output with actual data, and then altering the coefficients until the simulated and measured values match. The coefficients are determined in a specified sequence, starting with those that relate to developmental aspects. GENCALC also allows for calculation of averages (±SD) for the coefficients determined from specific experiments. This facilitates the selection of coefficients with the lowest variability, which can then be stored in crop‐specific database files. GENCALC comprises several programs and requires additional models and model input files, so a hard disk with =2 Mb free space is required. GENCALC will run on any IBM or IBM‐compatible computer with DOS version 3.0 or later.
Spring Nitrogen on Winter Wheat: I. Farmer‐Field Validation of Tissue Test—Based Rate Recommendations
Authors: Scharf Peter C. , Alley Marcus M. , Lei Yong Zhen 
Published: July 2010

AbstractCorrect N application rates on winter wheat (Triticum aestivum L. emend. Theil) are needed to minimize NO3 leaching to ground and surface waters and to maximize profit. A system for predicting optimum spring N fertilizer rates for winter wheat in the U.S. mid‐Atlantic was previously developed from a small set of experiments using a single wheat variety and in which all cultural operations were conducted by researchers. The objective of the research reported here was to evaluate whether this system could be successfully applied to the more varied environment of farmer fields. Thirty‐nine N response experiments were carried out in farmer fields over five growing seasons encompassing a wide and representative range of soil types and wheat varieties. The relationship between wheat tissue N content at Zadoks growth stage (GS) 30 and optimum N rate at GS 30 was weaker in farmer fields (r2 = 0.51) than in the researcher‐planted experiments (r2 = 0.59) but was still strong enough to be useful. Economic analyses indicate that tissue test‐based N rate recommendations increased profit by an average of $36 ha−1 relative to traditional N application practices. Apparent fertilizer efficiency was measured in 10 of the experiments and was significantly higher for N applications based on the tissue test system than for traditional N applications.
Crop Water Stress Index Models for Bermudagrass Turf: A Comparison
Authors: Jalali‐Farahani H. R. , Slack Donald C. , Kopec David M. , Matthias Allan D. 
Published: July 2010

AbstractA field study assessed the accuracy and consistency of crop water stress index (CWSI) estimates determined by two empirical models and one theoretical model for evaluation of bermudagrass [Cynodon dactylon (L.) Pers. cv. Midiron] water status. The simplest empirical model, describing canopy temperature minus air temperature (Tc — Ta) of well‐watered turf as a linear function of air vapor pressure deficit, was inadequate for estimating CWSI, since seasonal averages of CWSI for well‐watered, moderately stressed, and stressed turf had corresponding coefficients of variation of 1170, 160, and 86, respectively. The CWSI values were also highly influenced by net radiation, as indicated by coefficients of determination for regressions of calculated CWSI on net radiation of 0.68, 0.76, and 0.33 for the three levels of irrigation treatments, respectively. The second empirical model included net radiation in the regression analysis of of Tc — Ta. The inclusion of net radiation in the equation for the unstressed baseline increased the coefficient of determination from 0.76 to 0.90 as compared with Model 1. The coefficient of determination for the upper baseline (which included net radiation) was 0.872, indicating a strong dependency of CWSI on net radiation. The theoretical CWSI model yielded the most accurate estimates of turfgrass water stress, as indicated by coefficients of variation for seasonal averages of CWSI for well‐watered, moderately stressed, and stressed turf of 76, 3, and 86, respectively. Midday estimates of CWSI were related to percent available extractable water, with coefficients of determination for a regression of midday CWSI on percent available extractable water of 0.988, 0.989, and 0.991 for the empirical, modified empirical, and theoretical models, respectively. The theoretical CWSI appears to be the most promising approach for a turfgrass crop water stress index for irrigation scheduling.
Simulation by NCSWAP of Seasonal Nitrogen Dynamics in Corn: I. Soil Nitrate
Authors: Lengnick Laura L. , Fox Richard H. 
Published: July 2010

AbstractComputer simulation models of crop—soil systems offer the potential to increase understanding of soil N cycle processes, thereby improving management of N resources in agricultural systems. NCSWAP (Nitrogen, Carbon, Soil, Water, And Plant) is a comprehensive, deterministic computer model of the plant‐soil system that simulates seasonal soil C and N cycles under the control of temperature, moisture, microbial activity, and crop growth. The objective of this study was to validate NCSWAP using data collected during a 3‐yr N‐rate study in central Pennsylvania that investigated seasonal N dynamics in corn (Zea mays L.) provided with N as liquid dairy manure or as NH4NO3. Seasonal soil NO3 concentration in the upper soil layer, seasonal aboveground N accumulation by corn, and water leached past 1.2 m during the second year of the study were used to calibrate input values controlling soil water flow and NO3 production from mineralization of soil organic N sources. The validation of NCSWAP identified several limitations in the water flow and C and N cycling submodels as well as in the potential of the model to simulate seasonal N dynamics in corn. Validation simulations were about as accurate as calibration simulations, reflecting the ability of the model to simulate C and N dynamics without recalibration from year to year. Much of the simulation error was related to an overestimation of NO3 leaching caused by the inability of the model's microporous flow submodel to simulate the macropore‐influenced water flow in the well‐structured soil used in the validation.
BEANGRO: A Process‐Oriented Dry Bean Model with a Versatile User Interface
Authors: Hoogenboom Gerrit , White Jeffrey W. , Jones James W , Boote Kenneth J. 
Published: July 2010

AbstractMicrocomputer‐based crop simulation models are increasingly used in agricultural research. Although most models have been developed for specific applications, there is a trend toward developing more versatile models, with user interfaces that permit a wide range of applications without the user having to modify the model structure. BEANGRO is such a versatile, user‐friendly simulation model for dry bean (Phaseolus vulgaris L.). It is written in the FORTRAN computer language and operates on personal computers. The flexible user interface allows researchers to specify diverse model inputs and outputs, easily compare simulation results with observed data, and conduct simulation experiments interactively through menu‐driven options for sensitivity analysis. BEANGRO follows the standard file formats of the Decision Support System for Agrotechnology Transfer (DSSAT) software system, and may be used within DSSAT to assess effects of different agricultural management practices for runs over multiple years using either historical or generated weather data. The model responds to environmental variables (including air temperature, solar radiation, precipitation, and soil moisture retention characteristics), as well as to crop management conditions. Cultivar traits are specified through an input file, and traits may be modified interactively. BEANGRO operates at daily time steps and simulates bean growth and development from planting until harvest maturity. This traditional process‐oriented dry bean model and its user interface has created an interest among nontraditional modeler users and has been distributed to researchers for application in various projects, ranging from landuse planning to plant breeding and plant physiology.
A Compression‐Suction‐Temperature (CST) Cell for Simulating the Physical Environment of Preemergent Seedlings
Authors: Weaich K. , Bristow K. L. , Cass A. 
Published: July 2010

AbstractField investigations of the influence of soil physical conditions on seedling emergence are hampered by a lack of control and reproducibility of the physical variables. Controlled environment studies frequently fail to adequately simulate field conditions and seldom permit investigation of interactive physical conditions. In this paper, we describe a compression‐suction‐temperature (CST) cell designed to enable investigation of the response of preemergent seedlings to various strength and temperature regimes under unsaturated conditions. The cells allow independent control of soil strength, temperature, and matric potential. Suction within the CST cells is controlled by a hanging water column, soil strength by application of a uniaxial load, and soil temperature by a combination of heating and cooling of the upper and lower cell boundaries. Shoot growth in six cells was uniform, reflecting the minimal temperature discrepancies between cells. Temperature profiles typical of those experienced in the field were reproduced in the cells. Maize (Zea mays L.) shoot growth was responsive to temperature, with maximum elongation rates occurring at a constant temperature of 31.5 ¼. Application of 80 kPa of pressure (estimated shear strength of 42.0 kPa) to the seedbed stopped shoot growth, demonstrating the cell's capacity to alter the strength environment of the preemergent seedling.
Nitrogen Fertilizer Requirements in an Annual Dryland Cropping System
Authors: Halvorson Ardell D. , Reule Curtis A. 
Published: July 2010

AbstractReduced tillage systems in the Central Great Plains have improved precipitation storage efficiency and increased the potential to crop more intensively than with the traditional crop‐fallow system. More intensive cropping will require additional N input to maintain economical yields. Nitrogen fertility requirements for optimum crop yields in a dryland, annual cropping system were studied. Six N fertilizer rates (0, 22, 45, 67, 90, and 134 kg N ha−1) were applied to the same plots for 8 crops on a Weld silt loam (montmorillonitic, mesic Aridic Paleustoll). Spring barley (Hordeum vulgare L.), corn (Zea mays L.), and winter wheat (Triticum aestivum L.) were grown in rotation from 1984 through 1991. Although grain yields varied with crop and year, average annual grain production was 2108, 2683, 3162, 3459, 3382, and 3411 kg ha−1 for the above N rates, respectively. This included 1 yr of zero yield, when a corn crop was lost due to hail, and 1 yr of low barley yields due to heat and water stress at anthesis. This compares with annualized winter wheat yields of 1381 kg ha−1 yr−1 produced in an adjacent crop‐fallow system with adequate applied N. Application of 67 kg N ha−1 each crop year or, based on regression analysis, an average available N (soil plus fertilizer) supply of 170 kg N ha−1 was sufficient to optimize (95% of maximum) grain yields over the 8‐yr period. These results indicate a high potential for adopting more intensive dryland cropping systems in the Central Great Plains to increase water use efficiency and better maintain soil quality.