Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps
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 southea...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3148 |
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| author | Salman Mirzaee Ali Mirzakhani Nafchi |
| author_facet | Salman Mirzaee Ali Mirzakhani Nafchi |
| author_sort | Salman Mirzaee |
| collection | DOAJ |
| description | 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 (<i>p</i> < 0.05). The remote sensing-based method improved NUE compared to the flat-rate method, with increases of 2.21, 29.24, 29.6, and 82.09% in zones 1, 2, 3, and 4, respectively. However, adjusting the spatial and temporal nitrogen requirement rates using a soil-based sensor was difficult. The findings suggest remote sensing-based method can offer nitrogen optimization by incorporating in-season environmental variability, enhancing agronomic efficiency and sustainability. |
| format | Article |
| id | doaj-art-76cb08e51b794002b09c288d53e83802 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-76cb08e51b794002b09c288d53e838022025-08-20T03:48:01ZengMDPI AGSensors1424-82202025-05-012510314810.3390/s25103148Enhancing NUE in Corn Through Optimized Sensor-Based Prescription MapsSalman Mirzaee0Ali Mirzakhani Nafchi1Department of Agronomy, Horticulture and Plant Sciences, College of Agriculture, Food and Environmental Sciences, South Dakota State University, Brookings, SD 57007, USADepartment of Agronomy, Horticulture and Plant Sciences, College of Agriculture, Food and Environmental Sciences, South Dakota State University, Brookings, SD 57007, USAEnhancing 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 (<i>p</i> < 0.05). The remote sensing-based method improved NUE compared to the flat-rate method, with increases of 2.21, 29.24, 29.6, and 82.09% in zones 1, 2, 3, and 4, respectively. However, adjusting the spatial and temporal nitrogen requirement rates using a soil-based sensor was difficult. The findings suggest remote sensing-based method can offer nitrogen optimization by incorporating in-season environmental variability, enhancing agronomic efficiency and sustainability.https://www.mdpi.com/1424-8220/25/10/3148flat-rate methodnitrogen environmental risksremote sensing datavariable-rate method |
| spellingShingle | Salman Mirzaee Ali Mirzakhani Nafchi Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps Sensors flat-rate method nitrogen environmental risks remote sensing data variable-rate method |
| title | Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps |
| title_full | Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps |
| title_fullStr | Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps |
| title_full_unstemmed | Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps |
| title_short | Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps |
| title_sort | enhancing nue in corn through optimized sensor based prescription maps |
| topic | flat-rate method nitrogen environmental risks remote sensing data variable-rate method |
| url | https://www.mdpi.com/1424-8220/25/10/3148 |
| work_keys_str_mv | AT salmanmirzaee enhancingnueincornthroughoptimizedsensorbasedprescriptionmaps AT alimirzakhaninafchi enhancingnueincornthroughoptimizedsensorbasedprescriptionmaps |