Combining mobile proximal soil sensors and a crop model to produce high spatial resolution yield prediction maps

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00638306" target="_blank" >RIV/86652079:_____/25:00638306 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11119-025-10274-w" target="_blank" >https://link.springer.com/article/10.1007/s11119-025-10274-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11119-025-10274-w" target="_blank" >10.1007/s11119-025-10274-w</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining mobile proximal soil sensors and a crop model to produce high spatial resolution yield prediction maps

Popis výsledku v původním jazyce

Purpose:Precision agriculture requires detailed knowledge of the within-field variation of nyield forming factors and the productivity potential of each area of the field. The goal of this nwork was to use a case study to test all the steps of the process of creating a crop modelnbased yield map from soil mobile soil sensors and determine the impact of uncertainties and ninaccuracies on the results.nMethods: Soil texture maps (0- 90 cm of depth) of a field were derived from mobile sennsors and used as input for the process based deterministic crop growth model HERMES nto produce a high-resolution yield map.Results Compared to actual yield maps, the simnulated yield map successfully identified the major differences in productivity within the nfield, although some spatial variation was lost during the simulation, mostly at the point of ntranslating soil texture maps into soil water retention parameters. The model also showed na tendency to overestimate yield across the entire field. A crop model simulation based on nmeasured soil parameters resulted in a yield prediction accuracy of about 10% higher than na simulation based on estimated (mapped) soil parameters.nConclusion: The loss of spatial variability, although measurable, occurred at a scale that nmight not have a significant impact on the site-specific management plan. Most yield map ninaccuracies can be attributed more to model calibration than to the mapping process itself.

Název v anglickém jazyce

Combining mobile proximal soil sensors and a crop model to produce high spatial resolution yield prediction maps

Popis výsledku anglicky

Purpose:Precision agriculture requires detailed knowledge of the within-field variation of nyield forming factors and the productivity potential of each area of the field. The goal of this nwork was to use a case study to test all the steps of the process of creating a crop modelnbased yield map from soil mobile soil sensors and determine the impact of uncertainties and ninaccuracies on the results.nMethods: Soil texture maps (0- 90 cm of depth) of a field were derived from mobile sennsors and used as input for the process based deterministic crop growth model HERMES nto produce a high-resolution yield map.Results Compared to actual yield maps, the simnulated yield map successfully identified the major differences in productivity within the nfield, although some spatial variation was lost during the simulation, mostly at the point of ntranslating soil texture maps into soil water retention parameters. The model also showed na tendency to overestimate yield across the entire field. A crop model simulation based on nmeasured soil parameters resulted in a yield prediction accuracy of about 10% higher than na simulation based on estimated (mapped) soil parameters.nConclusion: The loss of spatial variability, although measurable, occurred at a scale that nmight not have a significant impact on the site-specific management plan. Most yield map ninaccuracies can be attributed more to model calibration than to the mapping process itself.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40101 - Agriculture

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Precision Agriculture

ISSN

1385-2256

e-ISSN

1573-1618

Svazek periodika

26

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

25

Strana od-do

79

Kód UT WoS článku

001550042400001

EID výsledku v databázi Scopus

2-s2.0-105013461379