Evidence map›Paper›PMID 40431940›Full record

ArticleSensors (Basel, Switzerland)2025

Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps.

Salman Mirzaee, Ali Mirzakhani Nafchi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Salman MirzaeeDepartment of Agronomy, Horticulture and Plant Sciences, College of Agriculture, Food and Environmental Sciences, South Dakota State University, Brookings, SD 57007, USA.
Ali Mirzakhani NafchiDepartment of Agronomy, Horticulture and Plant Sciences, College of Agriculture, Food and Environmental Sciences, South Dakota State University, Brookings, SD 57007, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enhancing nitrogen use efficiency (NUE) through optimized application methods can benefit agronomic productivity and environmental sustainability. This study examined three nitrogen application strategies, flat rate, soil-based sensing, and remote sensing-based prescription maps, for corn in southeast South Dakota, USA. Soil-based sensing utilized an electrical conductivity (EC) sensor while the normalized difference vegetation index (NDVI) was extracted from remote sensing data using Sentinel-2 images to create different zones. In the flat-rate method, nitrogen is applied uniformly at all plots, regardless of field variations. On the other hand, the sensor-based methods recommended variable rates of nitrogen applications to address field variations. The results of the present study showed that remote sensing-based methods significantly identify field variations as different zones (

Indexed as

NitrogenRemote Sensing TechnologyZea maysAgricultureElectric ConductivityFertilizersSoilFertilizersNitrogenSoilflat-rate methodnitrogen environmental risksremote sensing datavariable-rate method

Identifiers

PMID40431940
PMCPMC12116061

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.