Investigating data-driven approaches to optimize nitrogen recommendations for winter wheat

Guojie Ruan; Davide Cammarano; Syed Tahir Ata-UI-Karim; Xiaojun Liu; Yongchao Tian; Yan Zhu; Weixing Cao; Qiang Cao

doi:10.1016/j.compag.2024.108857

Investigating data-driven approaches to optimize nitrogen recommendations for winter wheat

Guojie Ruan, Davide Cammarano, Syed Tahir Ata-UI-Karim, Xiaojun Liu, Yongchao Tian, Yan Zhu, Weixing Cao, Qiang Cao^*

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

4 Citationer (Scopus)

Abstract

The optimal nitrogen (N) application rate is an important concept to guide the strategic/static N management of crops. However, there is still a knowledge gap on how to combine the optimal N application rate, crop growth status, meteorological, and soil multi-source information to make tactic/dynamic N recommendation. For investigating the data-driven approaches to optimize nitrogen recommendations for winter wheat, this study proposed the concept of optimal topdressing N rate that coupling ‘strategic’ with ‘tactic’ ideas. Then a direct N fertilizer recommendation algorithm based on the optimal topdressing N rate was constructed integrating multi-source data with Random Forest (RF). The agronomic, economic, and environmental benefits of the algorithm were further evaluated based on a large-scale field-level validation experiments, and compared with the indirect N recommendation algorithm of RF-based yield prediction, farmers’ conventional N application rate, and regional optimal N application rate. Finally, multi-objective evolutionary optimization was used to verify the reasonability of the N fertilizer recommendation algorithms. The results showed that the optimal topdressing N algorithm had good model performance (Random Forest prediction R² = 0.59 ∼ 0.72, RMSE = 39.63 ∼ 40.48 kg/ha), and vegetation indices, maximum temperature, and solar radiation were important features. The validation experiment results showed that compared to conventional management measures, the optimal topdressing N algorithm could reduce N fertilizer application by 18.9 % ∼ 48.4 % with intelligent adjustment, improve partial factor productivity by 12.99 % to 46.09 %, and reduce reactive N losses and greenhouse gas emissions by 20.56 % ∼ 47.24 % and 16.34 % ∼ 40.45 %, respectively. The rationality verification of solving nitrogen topdressing rate through multi-objective evolutionary algorithm proved that both indirect and direct N recommendation algorithms could make decisions within a reasonable threshold. The study provided insights for the data-driven based N management, strategy selection, and benefit evaluation process of N recommendation.

Originalsprog	Engelsk
Artikelnummer	108857
Tidsskrift	Computers and Electronics in Agriculture
Vol/bind	220
ISSN	0168-1699
DOI	https://doi.org/10.1016/j.compag.2024.108857
Status	Udgivet - maj 2024

Adgang til dokumentet

10.1016/j.compag.2024.108857

Andre filer og links

Link to publication in Scopus

Citationsformater

@article{ca23e318c9f440f7882e8560adacd809,

title = "Investigating data-driven approaches to optimize nitrogen recommendations for winter wheat",

abstract = "The optimal nitrogen (N) application rate is an important concept to guide the strategic/static N management of crops. However, there is still a knowledge gap on how to combine the optimal N application rate, crop growth status, meteorological, and soil multi-source information to make tactic/dynamic N recommendation. For investigating the data-driven approaches to optimize nitrogen recommendations for winter wheat, this study proposed the concept of optimal topdressing N rate that coupling {\textquoteleft}strategic{\textquoteright} with {\textquoteleft}tactic{\textquoteright} ideas. Then a direct N fertilizer recommendation algorithm based on the optimal topdressing N rate was constructed integrating multi-source data with Random Forest (RF). The agronomic, economic, and environmental benefits of the algorithm were further evaluated based on a large-scale field-level validation experiments, and compared with the indirect N recommendation algorithm of RF-based yield prediction, farmers{\textquoteright} conventional N application rate, and regional optimal N application rate. Finally, multi-objective evolutionary optimization was used to verify the reasonability of the N fertilizer recommendation algorithms. The results showed that the optimal topdressing N algorithm had good model performance (Random Forest prediction R2 = 0.59 ∼ 0.72, RMSE = 39.63 ∼ 40.48 kg/ha), and vegetation indices, maximum temperature, and solar radiation were important features. The validation experiment results showed that compared to conventional management measures, the optimal topdressing N algorithm could reduce N fertilizer application by 18.9 % ∼ 48.4 % with intelligent adjustment, improve partial factor productivity by 12.99 % to 46.09 %, and reduce reactive N losses and greenhouse gas emissions by 20.56 % ∼ 47.24 % and 16.34 % ∼ 40.45 %, respectively. The rationality verification of solving nitrogen topdressing rate through multi-objective evolutionary algorithm proved that both indirect and direct N recommendation algorithms could make decisions within a reasonable threshold. The study provided insights for the data-driven based N management, strategy selection, and benefit evaluation process of N recommendation.",

keywords = "Multi-objective optimization, N recommendation algorithms, Optimal topdressing N rate, Winter wheat",

author = "Guojie Ruan and Davide Cammarano and Ata-UI-Karim, {Syed Tahir} and Xiaojun Liu and Yongchao Tian and Yan Zhu and Weixing Cao and Qiang Cao",

note = "Publisher Copyright: {\textcopyright} 2024 Elsevier B.V.",

year = "2024",

month = may,

doi = "10.1016/j.compag.2024.108857",

language = "English",

volume = "220",

journal = "Computers and Electronics in Agriculture",

issn = "0168-1699",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Investigating data-driven approaches to optimize nitrogen recommendations for winter wheat

AU - Ruan, Guojie

AU - Cammarano, Davide

AU - Ata-UI-Karim, Syed Tahir

AU - Liu, Xiaojun

AU - Tian, Yongchao

AU - Zhu, Yan

AU - Cao, Weixing

AU - Cao, Qiang

PY - 2024/5

Y1 - 2024/5

N2 - The optimal nitrogen (N) application rate is an important concept to guide the strategic/static N management of crops. However, there is still a knowledge gap on how to combine the optimal N application rate, crop growth status, meteorological, and soil multi-source information to make tactic/dynamic N recommendation. For investigating the data-driven approaches to optimize nitrogen recommendations for winter wheat, this study proposed the concept of optimal topdressing N rate that coupling ‘strategic’ with ‘tactic’ ideas. Then a direct N fertilizer recommendation algorithm based on the optimal topdressing N rate was constructed integrating multi-source data with Random Forest (RF). The agronomic, economic, and environmental benefits of the algorithm were further evaluated based on a large-scale field-level validation experiments, and compared with the indirect N recommendation algorithm of RF-based yield prediction, farmers’ conventional N application rate, and regional optimal N application rate. Finally, multi-objective evolutionary optimization was used to verify the reasonability of the N fertilizer recommendation algorithms. The results showed that the optimal topdressing N algorithm had good model performance (Random Forest prediction R2 = 0.59 ∼ 0.72, RMSE = 39.63 ∼ 40.48 kg/ha), and vegetation indices, maximum temperature, and solar radiation were important features. The validation experiment results showed that compared to conventional management measures, the optimal topdressing N algorithm could reduce N fertilizer application by 18.9 % ∼ 48.4 % with intelligent adjustment, improve partial factor productivity by 12.99 % to 46.09 %, and reduce reactive N losses and greenhouse gas emissions by 20.56 % ∼ 47.24 % and 16.34 % ∼ 40.45 %, respectively. The rationality verification of solving nitrogen topdressing rate through multi-objective evolutionary algorithm proved that both indirect and direct N recommendation algorithms could make decisions within a reasonable threshold. The study provided insights for the data-driven based N management, strategy selection, and benefit evaluation process of N recommendation.

AB - The optimal nitrogen (N) application rate is an important concept to guide the strategic/static N management of crops. However, there is still a knowledge gap on how to combine the optimal N application rate, crop growth status, meteorological, and soil multi-source information to make tactic/dynamic N recommendation. For investigating the data-driven approaches to optimize nitrogen recommendations for winter wheat, this study proposed the concept of optimal topdressing N rate that coupling ‘strategic’ with ‘tactic’ ideas. Then a direct N fertilizer recommendation algorithm based on the optimal topdressing N rate was constructed integrating multi-source data with Random Forest (RF). The agronomic, economic, and environmental benefits of the algorithm were further evaluated based on a large-scale field-level validation experiments, and compared with the indirect N recommendation algorithm of RF-based yield prediction, farmers’ conventional N application rate, and regional optimal N application rate. Finally, multi-objective evolutionary optimization was used to verify the reasonability of the N fertilizer recommendation algorithms. The results showed that the optimal topdressing N algorithm had good model performance (Random Forest prediction R2 = 0.59 ∼ 0.72, RMSE = 39.63 ∼ 40.48 kg/ha), and vegetation indices, maximum temperature, and solar radiation were important features. The validation experiment results showed that compared to conventional management measures, the optimal topdressing N algorithm could reduce N fertilizer application by 18.9 % ∼ 48.4 % with intelligent adjustment, improve partial factor productivity by 12.99 % to 46.09 %, and reduce reactive N losses and greenhouse gas emissions by 20.56 % ∼ 47.24 % and 16.34 % ∼ 40.45 %, respectively. The rationality verification of solving nitrogen topdressing rate through multi-objective evolutionary algorithm proved that both indirect and direct N recommendation algorithms could make decisions within a reasonable threshold. The study provided insights for the data-driven based N management, strategy selection, and benefit evaluation process of N recommendation.

KW - Multi-objective optimization

KW - N recommendation algorithms

KW - Optimal topdressing N rate

KW - Winter wheat

UR - http://www.scopus.com/inward/record.url?scp=85189029093&partnerID=8YFLogxK

U2 - 10.1016/j.compag.2024.108857

DO - 10.1016/j.compag.2024.108857

M3 - Journal article

AN - SCOPUS:85189029093

SN - 0168-1699

VL - 220

JO - Computers and Electronics in Agriculture

JF - Computers and Electronics in Agriculture

M1 - 108857

ER -

Investigating data-driven approaches to optimize nitrogen recommendations for winter wheat

Abstract

Adgang til dokumentet

Andre filer og links

Fingeraftryk

Citationsformater