[HTML][HTML] Climate-based variability in the essential fatty acid composition of soybean oil

MR Bukowski, S Goslee - The American Journal of Clinical Nutrition, 2024 - Elsevier
Background Soybean oil is a major dietary source of the essential fatty acids linoleic acid
(LA) and α-linolenic acid (ALA); however, high-daytime temperatures during seed …

Applying the SIMPLE Crop Model to Assess Soybean (Glicine max. (L.) Merr.) Biomass and Yield in Tropical Climate Variation

QV Pham, TTN Nguyen, TTX Vo, PH Le, XTT Nguyen… - Agronomy, 2023 - mdpi.com
Soybean Glicine max.(L.) Merr. is one of the most major food crops. In some areas, its
responses to different climates have not been well studied, particularly in tropical countries …

Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain

H Zhuang, Z Zhang, F Cheng, J Han, Y Luo… - Agricultural and Forest …, 2024 - Elsevier
Timely and reliable regional crop yield forecasting before harvest is critical for managing
climate risk, adjusting agronomic management, and making food trade policy. Although …

Training machine learning algorithms using remote sensing and topographic indices for corn yield prediction

MF Oliveira, BV Ortiz, GT Morata, AF Jiménez… - Remote Sensing, 2022 - mdpi.com
Methods using remote sensing associated with artificial intelligence to forecast corn yield at
the management zone level can help farmers understand the spatial variability of yield …

[HTML][HTML] Effect of sulfur-and zinc-containing fertilizers on soybean yield and analysis of spatial and seasonal yield variability in Ghana, West Africa

AKK Kouame, PS Bindraban, L Jallal, B Kwesie… - European Journal of …, 2025 - Elsevier
Abstract Context Soybean [Glycine max (L.) Merr.] is an important crop in Ghana. However,
the variability of yields throughout the season and in space limits its potential to improve the …

Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning

MJ Hoque, MS Islam, J Uddin, MA Samad… - IEEE …, 2024 - ieeexplore.ieee.org
The agricultural sector is more vulnerable to the adverse effects of climate change and
excessive pesticide application, posing a significant risk to global food security. Accurately …

Measuring Sustainable Development of Cities Using Remote Sensing and Geospatial Technologies: A Review

S Shukla, Deeksha, S Chand, PK Rai… - … Livelihoods in the …, 2024 - Springer
Abstract The United Nations (UN) established 17 Sustainable Development Goals (SDGs) to
address global concerns such as poverty, inequality, and the consequences of climate …

A Systematic Review on Crop Yield Prediction Using Machine Learning

M Halder, A Datta, MKH Siam, S Mahmud… - … on Intelligent Systems & …, 2023 - Springer
Abstract Machine learning is an essential tool for crop yield prediction. Crop yield prediction
is a challenging task in the agriculture and agronomic field. In crop yield, many factors can …

An Extensive Study on Precision Farming Based on Crop Yield Using Integrated Approaches to Learning

K Geetha, BV Vidhya, A Kiran - 2023 International Conference …, 2023 - ieeexplore.ieee.org
Precision agriculture aims to optimize the use of resources by tailoring them to specific field
conditions. Previous research has investigated the use of IoT devices, remote sensing …

[PDF][PDF] Applying the SIMPLE Crop Model to Assess Soybean (Glicine max.(L.) Merr.) Biomass and Yield in Tropical Climate Variation. Agronomy 2023, 13, 1180

QV Pham, TTN Nguyen, TTX Vo, PH Le, XTT Nguyen… - 2023 - academia.edu
Soybean Glicine max.(L.) Merr. is one of the most major food crops. In some areas, its
responses to different climates have not been well studied, particularly in tropical countries …