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Agronomy
Special Issues
Crop Models for Agricultural Yield Prediction under Climate Change
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Crop Models for Agricultural Yield Prediction under Climate Change

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agroecology Innovation: Achieving System Resilience".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3230

Special Issue Editors

Prof. Dr. Wen-Shin Lin

E-Mail Website
Guest Editor
Department of Plant Industry, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
Interests: crop prediction models; high throughput phenotyping; crop warning model
Dr. Yun Yang Chao

E-Mail Website
Guest Editor
Department of Plant Industry, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
Interests: crop science; seed physiology; plant stress physiology; plant physiology

Special Issue Information

Dear Colleagues,

The cultivation management of agricultural crops is greatly influenced by the environment, especially the climate and soil conditions. Among them, the changing climate has a significant impact on crop yields and cultivation methods. Moreover, the stability of the crop yield and cultivation methods in the agricultural cultivation management process are seriously affected by climate changes. Generally, the impact of climate change on agricultural production can be broadly categorized into climate warming, increasing CO2 concentration, frequency of extreme weather, water shortage, etc., which gradually affect the agricultural cultivation process, especially in crop growth patterns, cultivation management systems, agricultural environmental biodiversity, etc.

Therefore, in order to mitigate the impact of climate change on agricultural cultivation, researchers have conducted research on many aspects, including crop breeding, irrigation management methods, soil improvement, growth period regulation, ecosystem protection, early warning mechanisms for crop pests and diseases, and the utilization of agricultural climate information, etc. Furthermore, predictive and warning mechanisms and models are important research areas for future efforts to combat climate change.

In this Special Issue, we will focus on the predictability of agricultural crop production. By collecting and utilizing various climate and environmental data, phenotypic data, spectral data, crop physiological data, etc., we hope to support the creation of forecast models for crop yield and growth status. The aim of this Special Issue is to assemble a collection of manuscripts that showcase the latest research in the crop models field.

Prof. Dr. Wen-Shin Lin
Dr. Yun Yang Chao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agronomy
  • crop growth models
  • phenology
  • crop yield
  • climate change
  • prediction model
  • high throughput phenotyping
  • crop warning model

Published Papers (3 papers)

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Research

22 pages, 24829 KiB  
Article
A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada
by Parisa Sarzaeim and Francisco Muñoz-Arriola
Agronomy 2024, 14(4), 733; https://doi.org/10.3390/agronomy14040733 - 2 Apr 2024
Viewed by 493
Abstract
Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices intertwined in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to climate still hampers the identification of the underlying abilities of plants [...] Read more.
Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices intertwined in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to climate still hampers the identification of the underlying abilities of plants to adapt to climate change. We hypothesize that the PiAnosi and WagNer (PAWN) global sensitivity analysis (GSA) coupled with a genetic by environment (GxE) model built of environmental covariance and genetic markers structures, can evidence the contributions of climate on the predictability of maize yields in the U.S. and Ontario, Canada. The GSA-GxE framework estimates the relative contribution of climate variables to improving maize yield predictions. Using an enhanced version of the Genomes to Fields initiative database, the GSA-GxE framework shows that the spatially aggregated sensitivity of maize yield predictability is attributed to solar radiation, followed by temperature, rainfall, and relative humidity. In one-third of the individually assessed locations, rainfall was the primary responsible for maize yield predictability. Also, a consistent pattern of top sensitivities (Relative Humidity, Solar Radiation, and Temperature) as the main or the second most relevant drivers of maize yield predictability shed some light on the drivers of genetic improvement in response to climate change. Full article
(This article belongs to the Special Issue Crop Models for Agricultural Yield Prediction under Climate Change)
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24 pages, 27815 KiB  
Article
Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland
by Hexiang Zheng, Hongfei Hou, Ruiping Li and Changfu Tong
Agronomy 2024, 14(4), 708; https://doi.org/10.3390/agronomy14040708 - 28 Mar 2024
Viewed by 490
Abstract
To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study [...] Read more.
To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study’s findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions. Full article
(This article belongs to the Special Issue Crop Models for Agricultural Yield Prediction under Climate Change)
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17 pages, 16178 KiB  
Article
Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods
by Islombek Mirpulatov, Mikhail Gasanov and Sergey Matveev
Agronomy 2023, 13(8), 2185; https://doi.org/10.3390/agronomy13082185 - 21 Aug 2023
Viewed by 1621
Abstract
Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to [...] Read more.
Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, R2, while for sugar beet, the Neural Prophet model achieved superior R2 levels of 0.99 and 0.98, respectively. Full article
(This article belongs to the Special Issue Crop Models for Agricultural Yield Prediction under Climate Change)
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