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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: closed (31 May 2025) | Viewed by 4802

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

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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 (4 papers)

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Research

19 pages, 3249 KiB  
Article
Method and Optimization of Key Parameters of Soil Organic Matter Detection Based on Pyrolysis Coupled with Artificial Olfaction
by Mingwei Li, Xiao Li, Xuexun Li, Wenjun Wang, Yulong Chen, Long Zhou and Xiaomeng Xia
Agronomy 2025, 15(7), 1740; https://doi.org/10.3390/agronomy15071740 - 19 Jul 2025
Viewed by 298
Abstract
Accurate quantification of soil organic matter (SOM) is crucial for improving soil fertility and maintaining ecosystem health. The content of SOM affects soil nutrient availability and is closely linked to the global carbon cycle. The use of an electronic nose to detect SOM [...] Read more.
Accurate quantification of soil organic matter (SOM) is crucial for improving soil fertility and maintaining ecosystem health. The content of SOM affects soil nutrient availability and is closely linked to the global carbon cycle. The use of an electronic nose to detect SOM contents has the advantages of rapidity, accuracy, and low pollution to the environment. This study proposes a method for obtaining SOM contents via pyrolysis coupled with an artificial olfaction system. To improve the accuracy of SOM content determination, the effects of three parameters (pyrolysis temperature, pyrolysis time, and soil sample mass) related to the pyrolysis process on the distinguishability of pyrolysis gases were investigated. Firstly, single-factor experiments were conducted to determine the optimal values of three parameters that can improve the differentiation of pyrolysis gases. Secondly, a regression model based on the Box–Behnken experiment was established to analyze the interrelationships between the three parameters and the discrete ratio. The experimental results showed that the three parameters exerted significant influences on the discrete ratio, with pyrolysis time having the greatest impact, followed by soil sample mass and pyrolysis temperature. The optimal discrimination and minimal dispersion ratio of the pyrolysis gases were achieved at a pyrolysis temperature of 384 °C, with a pyrolysis time of 2 min 41 s and a soil sample mass of 1.68 g. Finally, the Back-Propagation Neural Network (BPNN) and Partial Least-Squares Regression (PLSR) algorithms were used to establish an SOM prediction model after obtaining soil pyrolysis gases under the optimal combination of pyrolysis parameters. The experimental results demonstrated that the SOM prediction model based on PLSR achieved the best accuracy and the highest generalization capability, with R2 > 0.85 and RMSE < 7.21. This study could provide a theoretical basis for the prediction of SOM contents via pyrolysis coupled with an artificial olfaction system. 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, 9210 KiB  
Article
Topographic Position Index Predicts Within-Field Yield Variation in a Dryland Cereal Production System
by Jacob A. Macdonald, David M. Barnard, Kyle R. Mankin, Grace L. Miner, Robert H. Erskine, David J. Poss, Sushant Mehan, Adam L. Mahood and Maysoon M. Mikha
Agronomy 2025, 15(6), 1304; https://doi.org/10.3390/agronomy15061304 - 27 May 2025
Cited by 1 | Viewed by 569
Abstract
Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, [...] Read more.
Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings a multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6–4.3 ha management units, over 4 years, and included high-resolution topographic data, densely sampled soil properties, and on-site weather data. We modeled yield for each crop separately using random forest regression and evaluated model performance using spatially blocked cross-validation. The topographic position index (TPI) and increasing percent sand had a strong negative effect on yield, while the nitrogen application rate (N) and total soil carbon had strong positive effects on yield in both the wheat and millet models. Remarkably, TPI had almost as large of an effect size as N, and outperformed other more commonly used topographic predictors of yield such as the topographic wetness index (TWI), elevation, and slope. Despite the size and quality of our dataset, cross-validation results revealed that our models account for approximately one-quarter of the total yield variance, highlighting the need for continued research into drivers of spatial variability within fields. 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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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 4 | Viewed by 1630
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 2 | Viewed by 1402
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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