Efficient sampling for predictive nutrient mapping in farm-scale crop management

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027049%3A_____%2F24%3AN0000099" target="_blank" >RIV/00027049:_____/24:N0000099 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Efficient sampling for predictive nutrient mapping in farm-scale crop management

Original language description

Plant nutrition and balanced fertilization require very accurate information from the field at the lowest possible cost and therefore limited number of samples. It is therefore essential to optimise sampling schemes. The aim of this study is to compare widely used sampling schemes combined with variable sample size for the prediction of common soil macronutrients. Conditional Latin hypercube sampling (cLHS), feature space coverage sampling with k-means (FSCS) and simple random sampling (SRS) were compared. The effect of sampling scheme and sample size on the accuracy of predicted nutrient maps was investigated in a real case study field (35 ha) with heterogeneous soil properties. A total of 200 training points were placed in 6 grids: cLHS and FSCS with 10, 30 and 60 samples, corresponding to 1 sample per 3, 1 and 0.5 ha, respectively. For the numerical experiment with different sampling frequencies, all 200 training samples were interpolated into a set of nutrient maps, which were considered as an error-free dataset for both calibration and validation samples included in the predictive modelling. Sampling grids with variable sample sizes from 2 to 60 were created using SRS, cLHS and FSCS in combination with a pragmatic set of environmental covariates. Each network of each method was automatically generated 100 times using the same algorithm settings. These were used to make predictions using the covariates. The performance of the models was monitored. The results show the advantage of using FSCS, which shows both less variation in prediction accuracy compared to SRS and cLHS and better results with sparse sampling.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

40104 - Soil science

Project

<a href="/en/project/QK21010247" target="_blank" >QK21010247: Management optimization of unbalanced fields by means of digital soil mapping and soil moisture changes monitoring in order to stabilize the achievable yield</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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