Evaluation of Sensor-Based Nitrogen Rates and Sources in Wheat

Walsh, Olga S.; Shafian, Sanaz; Christiaens, Robin J.

doi:10.1155/2018/5670479

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…Second, there were not any significant differences with the further increase of N rate. In accordance with the present results, previous studies have demonstrated that there is no any considerable increase in the yield with top dressing of N [29] .…”

Section: Discussionsupporting

confidence: 94%

“…The result of the statistical data analysis showed that NDVI values increased with the amount of N applied. Another important finding was that this study did not find any significant difference between the higher throughput of the N. This results match those observed in earlier studies [28,29] . The result of anova demonstrated two things; first, the N application amount resulted in higher NDVI values to a certain level.…”

Section: Discussionsupporting

confidence: 92%

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Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle

Pathak

Barzin

Bora

2018

International Journal of Precision Agricultural Aviation

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Unmanned Aerial Vehicle (UAVs) and crop sensors are the most widely used remote sensing tool in precision agriculture. Use of UAVs in precision agriculture is attracting increasing interest due to its unique non-destructive approach. In recent years, dramatic evolution of precision agriculture technology has been driven by technologies such as sensors and controllers, telematics, and UAVs. An intriguing area in the field of precision agriculture and UAVs is big volume of data and its analysis that have not been dealt with in depth. The objective of this study was to validate the crop data acquisition procedure and the crop relationship with different type of data acquisition technique. In particular, this paper will compare ground based active optical sensor data collection with UAVs imagery for site-specific nitrogen management. To accomplish these objectives randomized complete block plantation experimental design was used with four treatments and four replicates. The plots were 12 rows wide at 38" spacing and were 125 ft. in length. GreenSeeker field sensor and Micasense sensor for UAVs was used to evaluate the Normalized Difference Vegetation Index. The software used to analyze the data were Microsoft ® Excel ® 2013, Statistical Analysis Software (ver.9.4) and ESRI ArcGIS (ver. 10.3). The results showed that UAVs assessed NDVI are good indicator of crop nutrition along with the ground based crop sensors. The result of the statistical data analysis showed that NDVI values are dependent on nitrogen application rate. The average NDVI value for no nitrogen application was recorded 0.54 whereas for 240 lb./acre nitrogen application it was noted to be 0.76. Crucially, this correlation holds true for definite extent of nitrogen application rate. Because there was not any significant change in NDVI for 160 lb./acre and 240 lb./acre. The NDVI values being 0.74 and 0.76 respectively. The results are significant because it shows the potential of further validating the use of aerial imagery derived NDVI for real time application of crop nutrient. This research has also proven that UAVs are reliable platform for nutrient assessment and making crop management decisions.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionsupporting

confidence: 92%

Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle

Pathak

Barzin

Bora

2018

International Journal of Precision Agricultural Aviation

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“…Nitrogen (N) is one of the essential factors for crop production in terms of plant growth and development and crop quality [1] [2]. Adequate supply of N is fundamental for optimizing wheat (Triticum aestivum L.) yield and grain quality [3] [4]. Nitrogen regulates plant growth processes and plays a vital role in chlorophyll (CL) production-the basis for the photosynthesis process [5].…”

Section: Introductionmentioning

confidence: 99%

“…Nitrogen regulates plant growth processes and plays a vital role in chlorophyll (CL) production-the basis for the photosynthesis process [5]. Insufficient N supply can negatively affect photosynthesis process and result in crop yield and quality penalties [3]. On the other hand, excessive N application to agricultural crops has been associated with nitrate leaching, soil denitrification, ammonia volatilization, and nitrous oxide contamination of aquifers and aggravating the climate change [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat

Walsh¹,

Shafian²,

Marshall³

et al. 2018

ARS

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Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVI red edge), Enhanced Vegetation Index 2 (EVI 2), Red Edge Simple Ratio (SR red edge), Green Chlorophyll Index (CI green), Red Edge Chlorophyll Index (CI red edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVI core) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration.

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“…In the United States, there are 2.1 million farms spread over approximately 33 million hectares of land (Kanter and Searchinger, 2018). The use of crop sensors, combined with reference strips in experimented sites, has recorded savings of USD 24-48 per hectare in N fertilizer costs as well as increased N use efficiency (Olga, 2015, Olga et al, 2018. Another study conducted at 55 sites in Missouri, USA has compared sensor determined N rates to farmer determined N rates.…”

Section: Discussionmentioning

confidence: 99%

Ecological Boundaries and Interference with the Global Nitrogen Cycle: A Review on Soil Nitrogen Management Strategies

Perera

Maharjan

2021

J Agric Sciences

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Purpose : Nitrogen (N) fertilizer is a major input in agro-ecosystems and has health, economic, and environmental implications. Changes in the global N cycle has transgressed the ecological safe boundary at present. Therefore, sustainable soil N management tools should be identified and implemented to reduce environmental implications of agriculture. This review paper intends to describe the magnitude of the global N based pollution, its health, economic, and environmental implications and suggest for approaches to achieve sustainable soil N management. Research Method : This paper shares a literature review on current efforts in optimizing soil N management. The central topic is an on-farm experiment conducted in the University of Nebraska-Lincoln, USA that monitors and manages in-season N in maize using a crop sensor technology. Other reliable findings from previously conducted studies worldwide are presented as well. Findings : Results of field studies revealed that there were no significant variations in maize grain yields between sensor-based treatment and farmer’s business-as-usual treatment However, the crop sensor-based treatment recorded savings of N at an average rate of 32 kg/ha. Based on the reported findings in literature, advanced fertilizer technology, manipulation of fertilizer application methods and development of conceptual models to predict the crop N need are other potential tools to optimize N in agriculture. Research Limitation : The behavior of reactive N in soil is unpredictable due to a complex interacting effect of crop, soil, climatic and management factors.Originality/ Value : In Sri Lanka as a country having a significant stake in agriculture, understanding and adoption of some of these available new technologies for efficient N management will serve well in our efforts for ecological sustainability.

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Evaluation of Sensor-Based Nitrogen Rates and Sources in Wheat

Cited by 10 publications

References 15 publications

Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle

Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle

Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat

Ecological Boundaries and Interference with the Global Nitrogen Cycle: A Review on Soil Nitrogen Management Strategies

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