Browsing by Subject "Yield"

Now showing 1 - 5 of 5
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    A bio-economic assessment of the spatial dynamics of U.S. corn production and yields
    (2012-02) Beddow, Jason Michael
    This dissertation reports on an investigation into the effects of location on corn production and productivity. The landscape of crop production is dynamic--where crops are produced changes dramatically over time. The answers to important questions about the potential impacts of global climate change and whether agriculture will be able to meet the world's increasing need for food are affected by the moving footprint of production. However, most studies of agricultural productivity and the effects of global warming do not consider that agriculture moves, and that the concomitant changes in natural services have important effects. A full set of county-level census data on corn production and area in the United States have been digitized and assembled for the first time, and new methods have been applied to account for changing geopolitical boundaries. Concepts adapted from economic index number theory are used to show that some 15 to 20 percent of the change in U.S. corn output over the past 130 years has come about due to shifts in where corn is produced. A newly developed, long-run, corn-specific weather dataset is used with the county data to show that, because of changes in the location of production, U.S. corn is now grown in cooler climates than it was a century ago, possibly offsetting some of the potential impacts of climate change. Finally, methods from ecological modeling, spatial econometrics, and crop modeling are combined to create a corn yield model that is then used to develop a location- and season-specific crop suitability indicator that takes into account the intra-seasonal dynamics of weather and the complex relationships between weather and yields. It will be shown that the suitability metric developed in this study gives results that are both consistent and more interpretable than more traditional approaches.
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    Data and R code: Towards sustainable maize production in the U.S. upper Midwest with interseeded cover crops
    (2019-07-17) Rusch, Hannah L; Garcia y Garcia, Axel; Coulter, Jeffrey A; Johnson, Gregg A; Grossman, Julie M; Porter, Paul M; axel@umn.edu; Garcia y Garcia, Axel; Sustainable Cropping Systems Lab
    Six cover crop treatments were interseeded into maize at two distinct timings: at the four to six-leaf collar stage and at physiological maturity. The canopy cover and biomass of cover crops, soil moisture at planting maize, and maize biomass and yield were evaluated to determine the potential impacts of interseeded cover crops on maize productivity.
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    Estimation And Analysis Of Total Suspended Solid Yields From The Mica Creek Experimental Watershed, Idaho
    (2016-08) Elverson, Charlie
    Transport and concentration of Total Suspended Solids (TSS) in forested streams play an important role in ecosystem health, affecting the health of fish populations and playing a role in nutrient delivery to floodplains. Despite a long history of studying TSS yields, estimates of TSS yields remain uncertain. Multiple methods have been commonly used to estimate time-aggregated TSS yields from water samples. This study investigated the effects of contemporary timber harvest practices on TSS yields as well as the robustness of conclusions to different methods of TSS yield calculation. TSS yields were calculated using linear interpolation, flow-weighted averaging, and statistical regression. The study utilized a paired-watershed design, with 6 years of calibration data between the control watershed and 2 treatment watersheds. One watershed was clearcut across 50% of its area and allowed to recover for the remaining 12 years of the study period. The other treatment watershed had 50% of basal area removed across 50% of it's total area. After the thinning treatment, the watershed was allowed to recover for 9 years before being clearcut across 46% of the area which was originally thinned. The watershed was then allowed to recover for the remaining 3 years of the study. Harvest impacts on TSS yield varied between treatment watershed and season, with the most significant changes occurring during spring (March through June). Furthermore, the different methods of TSS yield estimation provided different conclusions about TSS yield trends as well as total yields, with statistical regression providing the most consistently defensible estimates.
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    Spatial Quantification of the Gap between Farm Field and University Trial Maize Yields in the United States
    (2015-08) Forland, Christine
    Greater crop production will be required to support both an increase in biofuel use and a forecasted doubling of global food demand by 2050. An improved understanding of yield potential and realistic estimates of the magnitude and spatial variability of the gap between actual yield and yield potentials are critical to achieving maximum crop production. This study examines near-term yield potentials and gaps of maize (Zea mays L.) yield data over the years of 2006 to 2011 from two sources: university crop variety trials and the United States Department of Agriculture yield surveys. Yield potentials are analyzed across 32 states through a compiled database of 129,499 trial maize hybrid entries. From the database, 1,102 direct, irrigation-specific, year-to-year, county-to-county yield comparisons are made across 27 states. These 32 and 27 states comprise nearly all United States maize production—99% and 97%, respectively. Trial yield is calculated as the 90th percentile of hybrid yields in a given county in a given year, and farm yield is the USDA-reported county-level yield in that same trial-performing county in that same year. Analysis of the median yield gap values in each state shows a yield gap of 13% to 53% in rainfed maize and a yield gap of 16% to 39% in irrigated maize. The magnitude of these differences between farming and trial yields indicates that maize yields in the United States, particularly rainfed, have considerable room for improvement. Additionally, the 40% range of median rainfed yield gap values and the 23% range of median irrigated yield gap values suggest that the yield gap varies greatly between states. The results of this study are expected to support the production of more accurate biofuel crop projections and identify where yields might be increased, thereby avoiding further land conversion to cropland while reaching the goal of increasing biofuel production and sustaining ample food production.
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    UAV-based hyperspectral dataset for high-throughput yield phenotyping in wheat
    (2020-01-14) Moghimi, Ali; Yang, Ce; Anderson, James A.; moghi005@umn.edu; Moghimi, Ali; University of Minnesota
    The dataset was collected by a hyperspectral camera (PIKA II, Resonon, Inc.) mounted on an unmanned aerial vehicle (UAV, DJI Matrice 600 Pro) from three experimental yield trial fields (C3, C4, and C9) during two consecutive growing seasons 2017 (C3 and C9) and 2018 (C4). The aerial hyperspectral images were captured within two weeks prior to harvest over 240 spectral channels in visible and near infrared region (400 nm to 900 nm) with about 2.1 nm spectral resolution and about 2 cm spatial resolution. Subsequent to radiometric calibration and noisy band removal, plots were cropped from the hyperspectral images and saved as 3D matrices with Matlab (MAT files) and Python (NPY files) format. The dataset entails hyperspectral cubes of 1021 wheat plots and the grain yield of plots harvested by a combine. The corresponding ground truth data (yield) for each hyperspectral cube representing a plot can be found based on the field (e.g., C3, C4, and C9) and plot ID.