Life Sciences Commons^™

Integration Of Cover Crops Into Midwest Corn-Soybean Cropping Systems And Potential For Weed Suppression, Joshua S. Wehrbein

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Cover crops have potential to provide benefits to agricultural systems, such as improved soil productivity, nutrient scavenging, weed suppression, and livestock forage. There are several challenges associated with cover crop integration into traditional Midwest corn-soybean cropping systems. One of these challenges is timely establishment in the fall, which is limited by the relatively late harvest of corn and soybean. Cover crop effectiveness is related to the amount of biomass produced, thus maximizing the growth period in the fall is desired. To address this challenge, we evaluated the potential to utilize early-season soybean maturity groups (MGs) to allow for earlier soybean …

Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan

John E. Sawyer

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset …

Divergence Of Usda Trade Payments For Corn, Soybean, And Wheat Producers And ‘Nowcasts’ Of Tariff Impacts, Matthew Elliott, Lisa Elliott

Matthew Elliott

No abstract provided.

Utilizing Growing Degree Days For Corn Production, Dexton Lake, Matt Yost, Clark Israelsen

All Current Publications

This fact sheet explains the concepts and formulas in determining growing degree days.

Development Of A Nitrogen Recommendation Tool For Corn Considering Static And Dynamic Variables, Laila A. Puntel, Agustin Pagani, Sotirios V. Archontoulis

Department of Agronomy and Horticulture: Faculty Publications

Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha⁻¹ and the average optimum yield was 12.3 ± 2.2 Mg ha⁻¹, which is roughly 50% higher than the current N rates used …

Evaluation Of Stabilized Fertilizer And Crop Canopy Sensors As Next-Generation Nitrogen Management Technologies In Irrigated Corn, Leonardo Mendes Bastos

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Nitrogen (N) is often the most limiting nutrient to corn. Once applied to the field, N can be lost through different pathways, which contributes to low N use efficiency (NUE) by plants. Increases in NUE and decreases in N losses can be potentially achieved by using management options that allow a better synchrony between N supply and demand, such as stabilized fertilizers, and spatially-variable sensor-derived in-season N application. Three studies were conducted in order to assess the effects of different stabilized fertilizers and crop canopy sensors on irrigated corn yield. The first study evaluated the effect of urease inhibitor on …

Vermont Organic Silage Corn Performance Trial, Heather Darby, Sara Ziegler, John Bruce, Ivy Krezinski, Rory Malone

Northwest Crops & Soils Program

The University of Vermont Extension Northwest Crops and Soils Program conducted an organic silage corn variety trial in 2019 to provide unbiased performance comparisons of commercially available varieties. With the expansion of the organic dairy industry in our region there is increased interest in organic corn silage production. To determine varieties that are best suited to this production system and our region’s climate, we evaluated 11 commercially available organic corn silage varieties. It is important to remember that the data presented are from a replicated research trial from only one location in Vermont and represent only one season. Crop performance …

Long Season Corn Silage Performance Trials Summary, Heather Darby

Northwest Crops & Soils Program

Long season corn (96-110 day relative maturity) silage hybrids in Alburgh, VT

Planting Date: 05-13-19

Harvest Date: 10-02-19

Short Season Corn Silage Performance Trials Summary, Heather Darby

Northwest Crops & Soils Program

Short season corn (85-95 day relative maturity) silage hybrids in Alburgh, VT

Planting Date: 05-22-19

Harvest Date: 09-25-19

Vermont Flint And Dent Corn Performance Trial, Heather Darby, Sara Ziegler, John Bruce, Ivy Krezinski, Rory Malone

Northwest Crops & Soils Program

In the northeast there is a strong demand from consumers to have access to a wide range of locally produced food products. This demand creates opportunities for specialty value-added markets and crops to emerge. One market that has been gaining popularity and expanding recently in the northeast is the specialty corn market. Flint corn has very hard starch and can be ground and used in tortillas, tamales, corn meal, grits, and other products. Flint has a high proportion of hard starch in the kernel that produces a coarse meal. This is different than a soft-starch flour corn that, when ground, …

Vermont Non-Gmo Corn Silage Performance Trial, Heather Darby, Sara Ziegler, John Bruce, Ivy Luke, Rory Malone

Northwest Crops & Soils Program

In 2019, the University of Vermont Extension Northwest Crops and Soils Program evaluated yield and quality of 10 non-GMO corn silage varieties at Borderview Research Farm, Alburgh, VT. A non-GMO milk market has prompted some dairy farmers to start growing corn silage that has not been genetically modified. Conventional farmers have countless corn silage varieties available supported by performance data and trait information. To successfully transition to growing non-GMO corn, farmers are looking for more information on non-GMO varieties that are available and perform well in our region. While the information presented can begin to describe the yield and quality …

New York And Vermont Corn Silage Hybrid Evaluation Program, Joseph Lawrence, Allison Kerwin, Thomas Overton, Heather Darby

Northwest Crops & Soils Program

In 2019, the corn silage hybrid evaluation program received 75 entries from 14 seed brands. Hybrid evaluation at multiple environments helps in decision making and expands the reach of this type of data to more farmers. With this in mind Cornell, UVM, and seed companies collaborate to provide this robust evaluation. Hybrids were either entered into the 80-95 day relative maturity (RM) group (Early-Mid) and were tested at two locations in NY (n = 26; Hu-Lane Farm in Albion and the Willsboro Research Farm in Willsboro) and one location in VT (n = 26; Borderview Farm in Alburgh) …

Syngenta Silage Corn Variety Trial, Heather Darby, Rory Malone, Lindsey Ruhl, Sara Ziegler

Northwest Crops & Soils Program

The University of Vermont Extension Northwest Crops and Soils Program conducted a variety trial of silage corn from Syngenta AG (Greensboro, NC) to provide unbiased performance comparison of eight commercially available varieties, and to determine varieties best suited to this production system and local climate. It is important to remember that the data presented are from a replicated research trial from only one location in Vermont and represent only one season. Crop performance data from additional tests in different locations and over several years should be compared before making varietal selections.

Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan

Department of Agronomy and Horticulture: Faculty Publications

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset …