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Agronomy
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A Model-Based Approach to Crop Yield Forecasting and Predictive Mapping...
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A Model-Based Approach to Crop Yield Forecasting and Predictive Mapping of Soil Properties in Precision Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3222

Special Issue Editor

Dr. Kingsley John

E-Mail Website
Guest Editor
Faculty of Agriculture, Dalhousie University, Truro, NS, Canada
Interests: spatial statistics; digital soil mapping; process-based modeling; carbon farming; soil management; soil quality assessment; improved crop yield; data science

Special Issue Information

Dear Colleagues,

Precision agriculture stands at the forefront of transformative innovations, revolutionizing how we approach crop management and soil optimization. This Special Issue is dedicated to unraveling the intricacies of a model-based approach, a paradigm that holds immense potential in advancing our understanding of two critical facets: crop yield forecasting and predictive mapping of soil properties. This Special Issue is a call to action for researchers and professionals passionate about shaping the future of precision agriculture. We invite submissions that showcase innovative models, methodologies, and case studies related to crop yield forecasting and predictive mapping of soil properties. Share your expertise and contribute to the collective knowledge that propels the field forward.

Dr. Kingsley John
Guest Editor

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

  • crop models
  • yield influencing variables
  • crop yield
  • soil mapping
  • precision agriculture
  • smart farming

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Published Papers (2 papers)

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Research

18 pages, 5873 KiB  
Article
Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data
by Zhicheng Ye, Xu Zhai, Tianlong She, Xiaoyan Liu, Yuanyuan Hong, Lihui Wang, Lili Zhang and Qiang Wang
Agronomy 2024, 14(10), 2262; https://doi.org/10.3390/agronomy14102262 - 1 Oct 2024
Cited by 1 | Viewed by 1266
Abstract
Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning [...] Read more.
Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture. Full article
(This article belongs to the Special Issue A Model-Based Approach to Crop Yield Forecasting and Predictive Mapping of Soil Properties in Precision Agriculture)
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23 pages, 13069 KiB  
Article
Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China
by Yuanyuan Sui, Ranzhe Jiang, Nan Lin, Haiye Yu and Xin Zhang
Agronomy 2024, 14(5), 1067; https://doi.org/10.3390/agronomy14051067 - 17 May 2024
Cited by 1 | Viewed by 1204
Abstract
Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and [...] Read more.
Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions. Full article
(This article belongs to the Special Issue A Model-Based Approach to Crop Yield Forecasting and Predictive Mapping of Soil Properties in Precision Agriculture)
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