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In-Season Nitrogen Management
Precision Agriculture and Climate Change
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Authors
Albrecht, H
Amaral, L.R
Balboa, G
Cambouris, A
Cammarano, D
Cendrero Mateo, M.P
Cesario Pereira Pinto, J
Charvat Jr., K
Charvat, K
Chen, Z
Coulter, J.A
Custer, S
Dong, R
Dos Reis, A.A
Duchemin, M
Figueiredo, G.K
Flint, E.A
Freitas, R.G
Fulton, J.P
Ghimire, D
Gunzenhauser, R
Gupta, S
Hartschuh, J
Hatfield, J.L
Hawkins, E
Hopkins, B.G
Horakova, S
Jia, M
Kalmar, J
Keller, B
Kepka, M
Khosla, R
Kitchen, N.R
Klopfenstein, A
Kovacs, A.J
Kuehner, K
Lacerda, L.N
Lamparelli, R.A
Li, D
Li, Y
Lima, J.P
Longchamps, L
Lu, J
Lukas, V
Magalhães, P.S
Maharjan, B
Massey, R
McArtor, B
Miao, Y
Miao, Y
Miao, Y
Miao, Y
Mieno, T
Milics, G
Mizuta, K
Mizuta, K
Morales, A.C
Mueller, N
Mueller-Linow, M
Mulla, D.J
Muller, O
Neményi, M
Nielsen, R.L
Nyeki, A
Otto, R
Pereira, F.R
Pereira, F.R
Pereira, J.C
Pieruschka, R
Pinto, F
Puntel, L
Quinn, D.J
Ransom, C.J
Rascher, U
Reich, R
Reznik, T
Rosen, C
Sanches, G.M
Scharf, P
Schepters, J.S
Schickling, A
Schurr, U
Shearer, S
Splichal, M
Thompson, L
Wakahara, S
Wakahara, S
Wang, X
Yost, M
Zhang, J
Zhang, Y
Zhao, X
Ziadi, N
Topics
Precision Agriculture and Climate Change
In-Season Nitrogen Management
Type
Oral
Poster
Year
2016
2022
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1. Quo Vadis Precision Farming

The agriculture sector is a unique sector due to its strategic importance for both citizens and economy which, ideally, should make the whole sector a network of interacting organizations. There is an increasing tension, the like of which is not experienced in any other sector, between the requirements to assure full safety and keep costs under control, but also assure the long-term strategic interests of Europe and worldwide. In that sense, agricultural production influences, and is influenc... K. Charvat, T. Reznik, V. Lukas, K. Charvat jr., S. Horakova, M. Splichal, M. Kepka

2. Climate Smart Precision Nitrogen Management

Climate Smart Agriculture (CSA) aims at improving farm productivity and profitability in a sustainable way while building resilience to climate change and mitigating the impacts of agriculture on greenhouse gas emissions. The idea behind this concept is that informed management decision can help achieve these goals. In that matter, Precision Agriculture goes hand-in-hand with CSA. The Colorado State University Laboratory of Precision Agriculture (CSU-PA) is conducting research on CSA practice... L. Longchamps, R. Khosla, R. Reich

3. Field Phenotyping Infrastructure in a Future World - Quantifying Information on Plant Structure and Function for Precision Agriculture and Climate Change

Phenotyping in the field is an essential step in the phenotyping chain. Phenotyping begins in the well-defined, controlled conditions in laboratories and greenhouses and extends to heterogeneous, fluctuating environments in the field. Field measurements represent a significant reference point for the relevance of the laboratory and greenhouse approaches and an important source of information on potential mechanisms and constraints for plant performance tested at controlled conditions. In this... O. Muller, M.P. Cendrero mateo, H. Albrecht, F. Pinto, M. Mueller-linow, R. Pieruschka, U. Schurr, U. Rascher, A. Schickling, B. Keller

4. Sensor-based Variable-rate N on Corn Reduced Nitrous Oxide Emissions

More nitrogen fertilizer is applied to corn than to all other U.S. crops combined, contributing to atmospheric heat trapping when nitrous oxide is produced.  Higher nitrogen rate is well known to increase nitrous oxide emissions, and earlier N application time may increase the window during which nitrous oxide can form.  An experiment was initiated in 2012 comparing nitrogen management and drainage effects on corn yield and nitrous oxide emissions.  Two nitrogen treatments... P. Scharf

5. Climate Sensitivity Analysis on Maize Yield on the Basis of Precision Crop Production

In this paper by prediction we have defined maize yield in precision plant production technologies according to five different climate change scenarios (Ensembles Project) until 2100 and in one scenario until 2075 using DSSAT v. 4.5.0. CERES-Maize decision support model. Sensitivity analyses were carried out. The novelty of the method presented here is that precision, variable rate technologies from relatively small areas (in our case 2500 m2) enable a large amount of data to be co... A. Nyeki, G. Milics, A.J. Kovacs, M. Neményi, J. Kalmar

6. Soil and Crop Factors to Site-specific Nitrogen Management on Sugarcane Fields

Nitrogen (N) is one of the most widely used fertilizers in crops and the most harmful to the environment. The increase fertilizers consumption, mainly N sources (one of the most widely fertilizer used in sugarcane fields), is one of the main factors underlying the sustainability of the entire production process. Currently, N recommendations in sugarcane are based only on the expected yield. However, there is little agronomic support for nitrogen (N) recommendations based on expected yield, de... G.M. Sanches, R. Otto, F.R. Pereira

7. Spatial and Temporal Factors Impacting Incremental Corn Nitrogen Fertilier Use Efficiency

Current tools for making crop N fertilizer recommendations are primarily based on plot and field studies that relate the recommendation to the economic optional N rate (EONR).  Some tools rely entirely on localized EONR (e.g., MRTN). In recent years, tools have been developed or adapted to  account for within-field variation in crop N need or variable within season factors. Separately, attention continues to elevate for how N fertilizer recommendations might account for environmenta... N.R. Kitchen, C.J. Ransom, J.S. Schepters, J.L. Hatfield, R. Massey

8. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and Indiana

Precision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minne... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor

9. Nitrogen Fertilization of Potato Using Management Zone in Prince Edward Island, Canada

Potato is sensible to nitrogen (N) and optimal N fertilization improve the tuber yield and its quality. Potato crop N response varies widely within fields. It is also well recognized that significant spatial and temporal variation in soil N availability occurs within crop fields. However, uniform application of N fertilizer is still the most common practice under potato production. Management zone (MZ) approach can help growers to achieve a part of this. The goal of the project is to compare ... A. Cambouris, M. Duchemin, N. Ziadi

10. Evaluating the Potential of Improving In-season Nitrogen Status Diagnosis of Potato Using Leaf Fluorescence Sensors and Machine Learning

Precision nitrogen (N) management is particularly important for potato crops due to their high N fertilizer demand and high N leaching potential caused by their shallow root systems and preference for coarse-textured soils. Potato farmers have been using a standard lab analysis called petiole nitrate-N (PNN) test as a tool to diagnose potato N status and guide in-season N management. However, the PNN test suffers from many disadvantages including time constraints, labor, and cost of analysis.... S. Wakahara, Y. Miao, S. Gupta, C. Rosen, K. Mizuta, J. Zhang, D. Li

11. Nitrogen Status Prediction on Pasture Fields Can Be Reached Using Visible Light UAV Data Combined with Sentinel-2 Imagery

Pasture fields under integrated crop-livestock system usually receive low or no nitrogen fertilization rates, since the expectation is that nitrogen demand will be provided by the soybean remaining straw cropped previously. However, keeping nitrogen at suitable levels in the entire field is the key to achieving sustainability in agricultural production systems. In this sense, remote sensing technologies play an essential role in nitrogen monitoring in pastures and crops. With the launch of th... F.R. Pereira, J.P. Lima, R.G. Freitas, A.A. Dos reis, L.R. Amaral, G.K. Figueiredo, R.A. Lamparelli, J.C. Pereira, P.S. Magalhães

12. Variable Rate Nitrogen Approach in a Potato-wheat-wheat Cropping System

Nitrogen application in agriculture is a vital process for optimal plant growth and yield outcomes. Different factors such as topography, soil properties, historical yield, and crop stress affect nitrogen (N) needs within a field. Applying variable N within a field could improve precision agriculture. Optimal N management is a system that involves applying a conservative variable base rate at or shortly after planting followed by in-season assessment and, if needed, variable rate application&... E.A. Flint, M. Yost, B.G. Hopkins

13. Evaluation of Nitrogen Recommendation Tools for Winter Wheat in Nebraska

Attaining both high yield and high nitrogen (N) use efficiency (NUE) simultaneously remains a current research challenge in crop production. Digital ag technologies for site-specific N management have been demonstrated to improve NUE. This is due to the ability of digital technologies to account for the spatial and temporal distribution of crop N demand and available soil N in the field which varies greatly according t... J. Cesario pereira pinto, L. Thompson, N. Mueller, T. Mieno, G. Balboa, L. Puntel

14. Nitrogen Placement Considerations for Maize Production in the Eastern US Cornbelt

Proper fertilizer placement is essential to optimize crop performance and amount of applied nitrogen (N) along with crop yield potential. There exists several practices currently used in both research within farming operations on how and when to apply N to maize (Zea mays L). Split applications of N in Ohio is popular with farmers and provides an economic benefit but more recently some farmers have been using mid- and late-season N fertilizer applications for their maize production.&... J.P. Fulton, E. Hawkins, S. Shearer, A. Klopfenstein, J. Hartschuh, S. Custer

15. In-season Nitrogen Management of Maize Based on Nitrogen Status and Lodging Risk Prediction

Development of effective precision nitrogen (N) management strategies is crucially important for food security and sustainable development. Lodging is one of the major constraints to increasing maize yield that can be induced by strong winds, and is also influenced by management practices, like N rate. When making in-season N application decisions, lodging risk should be considered to avoid yield loss. Little has been reported on in-season N management strategies that also incorporate lodging... R. Dong, Y. Miao, X. Wang

16. Assessment of Active Crop Canopy Sensor As a Tool for Optimal Nitrogen Management in Dryland Winter Wheat

Optimum nitrogen (N) fertilizer application is important for agronomic, economic, and environmental reasons. Among different N management tools, active crop canopy sensors are a recent and promising tool widely evaluated for use in corn but still under-evaluated for use in winter wheat. The objective of this study was to determine whether vegetation indices derived from in-season active crop canopy sensor data can be used to predict winter wheat grain yield and protein content and subsequentl... D. Ghimire

17. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data

Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by c... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia