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Verdi, A.K

Maestrini, B

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Authors

Verdi, A.K

Garg, A

Sapkota, A

van Evert, F

Van Oort, P

Maestrini, B

Pronk, A

Boersma, S

Kopanja, M

Mimić, G

Maestrini, B

Pott, L.P

Bamberg, D

Liska, T

Rosado, T

Sander, L

Ruiz Moreno, T

Garcia Dutrez, N

Doeler, F

Van Der Wal, T

Kaster Marini, V

Amado, T

Nieuwenhuizen

Topics

Decision Support Systems

In-Season Nitrogen Management

Site-Specific Nutrient, Lime and Seed Management

Type

Oral

Poster

Year

2024

2026

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Authors

Filter results3 paper(s) found.
1. Field-level Zoning at Regional Scale Using Remote Sensing and GIS: Lessons Learned from the Desert Agriculture Region of Southern California A decision support tool, SAMZ-Desert, utilizing GIS and remote sensing techniques, was created to delineate management zones (MZs) for a total of 6852 fields in California's Imperial County. Landsat-8 NDVI data from April 27, 2018, was employed for this purpose. Furthermore, 11 cloud-free images captured between 2018 and 2020 were statistically analyzed to assess within-field NDVI variability and the temporal stability of MZs at the regional level. Approximately 37% of the fields in the region... A.K. Verdi, A. Garg, A. Sapkota
2. A Digital Twin for Arable Crops and for Grass There is an opportunity to use process-based cropping systems models (CSMs) to support tactical farm management decisions, by monitoring the status of the farm, by predicting what will happen in the next few weeks, and by exploring scenarios. In practice, the responses of a CSM will deviate more and more from reality as time progresses because the model is an abstraction of the real system and only approximates the responses of the real system. This limitation may be overcome by using the CSM... F. Van Evert, P. Van Oort, B. Maestrini, A. Pronk, S. Boersma, M. Kopanja, G. Mimić Download paper
3. Variable Seeding Rate to Manage Within-field Variability Within-field variability can strongly influence final crop yield and the efficiency of agricultural inputs such as seeds, fertilizer, water, and agrochemicals, thus managing spatial variability through precision agriculture to optimize input use and improve sustainability can yield significant gains, provided that the mechanisms driving field variability are understood. Despite extensive research on the relationship between seeding density and yield, relatively little attention has been given... B. Maestrini, L.P. Pott, D. Bamberg, T. Liska, T. Rosado, L. Sander, T. Ruiz Moreno, N. Garcia Dutrez, F. Doeler, T. Van Der Wal, V. Kaster Marini, T. Amado, Nieuwenhuizen Download paper

Filter results3 paper(s) found.

1. Field-level Zoning at Regional Scale Using Remote Sensing and GIS: Lessons Learned from the Desert Agriculture Region of Southern California

A decision support tool, SAMZ-Desert, utilizing GIS and remote sensing techniques, was created to delineate management zones (MZs) for a total of 6852 fields in California's Imperial County. Landsat-8 NDVI data from April 27, 2018, was employed for this purpose. Furthermore, 11 cloud-free images captured between 2018 and 2020 were statistically analyzed to assess within-field NDVI variability and the temporal stability of MZs at the regional level. Approximately 37% of the fields in the region... A.K. Verdi, A. Garg, A. Sapkota

2. A Digital Twin for Arable Crops and for Grass

There is an opportunity to use process-based cropping systems models (CSMs) to support tactical farm management decisions, by monitoring the status of the farm, by predicting what will happen in the next few weeks, and by exploring scenarios. In practice, the responses of a CSM will deviate more and more from reality as time progresses because the model is an abstraction of the real system and only approximates the responses of the real system. This limitation may be overcome by using the CSM... F. Van Evert, P. Van Oort, B. Maestrini, A. Pronk, S. Boersma, M. Kopanja, G. Mimić

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3. Variable Seeding Rate to Manage Within-field Variability

Within-field variability can strongly influence final crop yield and the efficiency of agricultural inputs such as seeds, fertilizer, water, and agrochemicals, thus managing spatial variability through precision agriculture to optimize input use and improve sustainability can yield significant gains, provided that the mechanisms driving field variability are understood. Despite extensive research on the relationship between seeding density and yield, relatively little attention has been given... B. Maestrini, L.P. Pott, D. Bamberg, T. Liska, T. Rosado, L. Sander, T. Ruiz Moreno, N. Garcia Dutrez, F. Doeler, T. Van Der Wal, V. Kaster Marini, T. Amado, Nieuwenhuizen

Download paper