Improved Extraction Method of Soil Nitrite DOI Open Access

Yaqi Song,

Dianming Wu, Peter Dörsch

et al.

Published: Dec. 27, 2023

Soil nitrite (NO2‒) is an important reactive intermediate in many nitrogen transformation processes, but it unstable under acidic conditions. Canonical extraction method of soil NO2‒ with potassium chloride (KCl) solution greatly underestimates its concentration. In order to reflect the concentration more accurately, we optimized this study. Moreover, ammonium (NH4+) and nitrate (NO3‒) were also systematically investigated achieve efficient inorganic nitrogen. The results showed that un-buffered KCl significantly underestimated compared DIW. highest recovery was obtained by extracting DIW at 10 min oscillation for three soils. Compared DIW, NH4+ NO3‒ extracted from increased significantly. Furthermore, content extracts stored 4°C one day closer directly measurements fresh samples than other storage methods. Overall, recommend analysis oscillated min, filtered a 0.45 µm filter, 30 min. extract should be analyzed within 24 hours.

Language: Английский

Will artificial intelligence and machine learning change agriculture: A special issue DOI Creative Commons
David E. Clay, Skye Brugler, Bhavna Joshi

et al.

Agronomy Journal, Journal Year: 2024, Volume and Issue: 116(3), P. 791 - 794

Published: March 5, 2024

Abstract In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced ML algorithms replace or partially farmers in the decision process. As technologies become more accurate, they have potential to improve profitability while reducing impact of agriculture on environment. However, despite these benefits, there are many adoption barriers including cost, that may be reluctant adopt a tool do not understand. The goal this special issue discuss cutting‐edge research use technologies, can affect our current workforce. papers separated into three sections: Machine Learning within Crops, Pasture, Irrigation; Predicting Crop Disease; Society Policy Learning.

Language: Английский

Citations

4
Evaluating evapotranspiration models for precise aridity mapping based on UNEP- aridity classification DOI

Dauda Pius Awhari,

Mohamad Hidayat Jamal, Mohd Khairul Idlan Muhammad

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 20, 2025

Language: Английский

Citations

0
Enhancing the prediction of irrigation demand for open field vegetable crops in Germany through neural networks, transfer learning, and ensemble models DOI Creative Commons
Samantha Rubo, Jana Zinkernagel

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 312, P. 109402 - 109402

Published: March 18, 2025

Language: Английский

Citations

0
Optimization of Support Vector Machine with Biological Heuristic Algorithms for Estimation of Daily Reference Evapotranspiration Using Limited Meteorological Data in China DOI Creative Commons
H. Alex Guo,

Liance Wu,

Xianlong Wang

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(8), P. 1780 - 1780

Published: Aug. 13, 2024

Precise estimation of daily reference crop evapotranspiration (ET0) is critical for water resource management and agricultural irrigation optimization worldwide. In China, diverse climatic zones pose challenges accurate ET0 prediction. Here, we evaluate the performance a support vector machine (SVM) its hybrid models, PSO-SVM WOA-SVM, utilizing meteorological data spanning 1960–2020. Our study aims to identify high-precision, low-input tool. The findings indicate that particularly demonstrated superior accuracy with R2 values ranging from 0.973 0.999 RMSE between 0.123 0.863 mm/d, outperforming standalone SVM model 0.955 0.989 0.168 0.982 mm/d. showed relatively lower 0.822 0.887 0.381 1.951 Notably, WOA-SVM model, 0.990 0.992 0.092 0.160 emerged as top performer, showcasing benefits whale algorithm in enhancing SVM’s predictive capabilities. also presented improved performance, especially temperate continental zone (TCZ), subtropical monsoon region (SMZ), (TMZ), when using limited input. concludes promising tool high-precision fewer parameters across different China.

Language: Английский

Citations

1
Calibration and evaluation of various reference evapotranspiration estimation methods in a humid subtropical climate: A case study in Samsun Province, Türkiye DOI
Amin Gharehbaghi, Ehsan Afaridegan, Birol KAYA

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 136, P. 103734 - 103734

Published: Sept. 12, 2024

Language: Английский

Citations

1
Evaluation of crop water stress index of wheat by using machine learning models DOI

Aditi Yadav,

Likith Muni Narakala,

Hitesh Upreti

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(10)

Published: Sept. 23, 2024

Language: Английский

Citations

1
Improved Extraction Method of Soil Nitrite DOI Open Access

Yaqi Song,

Dianming Wu, Peter Dörsch

et al.

Published: Dec. 27, 2023

Soil nitrite (NO2‒) is an important reactive intermediate in many nitrogen transformation processes, but it unstable under acidic conditions. Canonical extraction method of soil NO2‒ with potassium chloride (KCl) solution greatly underestimates its concentration. In order to reflect the concentration more accurately, we optimized this study. Moreover, ammonium (NH4+) and nitrate (NO3‒) were also systematically investigated achieve efficient inorganic nitrogen. The results showed that un-buffered KCl significantly underestimated compared DIW. highest recovery was obtained by extracting DIW at 10 min oscillation for three soils. Compared DIW, NH4+ NO3‒ extracted from increased significantly. Furthermore, content extracts stored 4°C one day closer directly measurements fresh samples than other storage methods. Overall, recommend analysis oscillated min, filtered a 0.45 µm filter, 30 min. extract should be analyzed within 24 hours.

Language: Английский

Citations

1