Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.3
Journals
Agronomy
Special Issues
Recent Advances in Crop Modelling
share announcement

Recent Advances in Crop Modelling

A special issue of Agronomy (ISSN 2073-4395).

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 25245

Special Issue Editor

Dr. Xing Liu

E-Mail Website
Guest Editor
Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
Interests: spatial data analysis; numerical weather modeling; regional crop modeling; model development; data management

Special Issue Information

Dear Colleagues,

Under the circumstances of climate change, we are facing a world with an increasing amount and severity of extreme weather events. Crop production is becoming more and more vulnerable to such extreme events. To better understand and quantify the impacts of these weather events on crop yield and production, we need innovative improvements in crop models. Those improvements are crucial in assessing the effects of extreme weather. The advanced models can help researchers and policymakers to address global food security issues. The models can also help crop producers to make more informed decisions in crop management.

In the Special Issue, we are looking forward to receiving new ideas and new suggestions on any aspect related to crop modeling across different climate conditions and crop types.

Dr. Xing Liu
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 model
  • crop yield
  • crop phenology
  • climate change
  • extreme weather
  • crop production

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 5330 KiB  
Article
Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA
by Uvirkaa Akumaga, Feng Gao, Martha Anderson, Wayne P. Dulaney, Rasmus Houborg, Andrew Russ and W. Dean Hively
Agronomy 2023, 13(6), 1540; https://doi.org/10.3390/agronomy13061540 - 1 Jun 2023
Cited by 9 | Viewed by 3130
Abstract
Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the [...] Read more.
Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data. Full article
(This article belongs to the Special Issue Recent Advances in Crop Modelling)
►▼ Show Figures

Figure 1

17 pages, 4474 KiB  
Article
Impact of Spatial Soil Variability on Rainfed Maize Yield in Kansas under a Changing Climate
by Rintu Sen, Zachary T. Zambreski and Vaishali Sharda
Agronomy 2023, 13(3), 906; https://doi.org/10.3390/agronomy13030906 - 18 Mar 2023
Cited by 6 | Viewed by 2495
Abstract
As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten [...] Read more.
As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten maize-producing counties. A cropping system model, CERES-Maize of Decision Support System for Agrotechnology Transfer (DSSAT) was calibrated using observed maize yield. To account for the spatial variability of soils, the gSSURGO soil database was used. The model was run for a baseline and future climate change scenarios under two Representative Concentration Pathways (RCP4.5 and RCP8.5) to assess the impact of future climate change on rainfed maize yield. The simulation results showed that maize yield was impacted by spatial soil variability, and that using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county. The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss. Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively. The results indicate the uncertainties of growing maize in our study region under the changing climate, emphasizing the need for developing strategies to sustain maize production in the region. Full article
(This article belongs to the Special Issue Recent Advances in Crop Modelling)
►▼ Show Figures

Figure 1

22 pages, 2831 KiB  
Article
Calibration for an Ensemble of Grapevine Phenology Models under Different Optimization Algorithms
by Chenyao Yang, Christoph Menz, Samuel Reis, Nelson Machado, João A. Santos and Jairo Arturo Torres-Matallana
Agronomy 2023, 13(3), 679; https://doi.org/10.3390/agronomy13030679 - 26 Feb 2023
Cited by 1 | Viewed by 1677
Abstract
Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. [...] Read more.
Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. We mainly aim to investigate the main source of the variability in the modelling errors for the flowering timings of two important varieties of vine in the Douro Demarcated Region (DDR) of Portugal; this is based on five phenology model simulations that use optimal parameters and that are estimated by three optimization algorithms (MLE, SA and SCE-UA). Our results indicate that the main source of the variability in calibration can be affected by the initially assumed parameter boundary. Restricting the initial parameter distribution to a narrow range impedes the algorithm from exploring the full parameter space and searching for optimal parameters. This can lead to the largest variation in different models. At an identified appropriate boundary, the difference between the two varieties represents the largest source of uncertainty, while the choice of algorithm for calibration contributes least to the overall uncertainty. The smaller variability among different models or algorithms (tools for analysis) compared to between different varieties could indicate the overall reliability of the calibration. All optimization algorithms show similar results in terms of the obtained goodness-of-fit: the RMSE (MAE) is 5–6 (4–5) days with a negligible mean bias and moderately good R2 (0.5–0.6) for the ensemble median predictor. Nevertheless, a similar predictive performance can result from differently estimated parameter values, due to the equifinality or multi-modal issue in which different parameter combinations give similar results. This mainly occurs for models with a non-linear structure compared to those with a near-linear one. Yet, the former models are found to outperform the latter ones in predicting the flowering timing of the two varieties in the DDR. Overall, our findings highlight the importance of carefully defining the initial parameter boundary and decomposing the total variance of prediction errors. This study is expected to bring new insights that will help to better inform users about the importance of choice when these factors are involved in calibration. Nonetheless, the importance of each factor can change depending on the specific situation. Details of how the optimization methods are applied and of the continuous model improvement are important. Full article
(This article belongs to the Special Issue Recent Advances in Crop Modelling)
►▼ Show Figures

Figure 1

17 pages, 3444 KiB  
Article
Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index
by Hongzhao Shi, Jinjin Guo, Jiaqi An, Zijun Tang, Xin Wang, Wangyang Li, Xiao Zhao, Lin Jin, Youzhen Xiang, Zhijun Li and Fucang Zhang
Agronomy 2023, 13(3), 663; https://doi.org/10.3390/agronomy13030663 - 24 Feb 2023
Cited by 49 | Viewed by 7062
Abstract
Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop [...] Read more.
Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R2) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages. Full article
(This article belongs to the Special Issue Recent Advances in Crop Modelling)
►▼ Show Figures

Figure 1

17 pages, 621 KiB  
Article
Tropical Tree Crop Simulation with a Process-Based, Daily Timestep Simulation Model (ALMANAC): Description of Model Adaptation and Examples with Coffee and Cocoa Simulations
by James R. Kiniry, J. G. Fernandez, Fati Aziz, Jacqueline Jacot, Amber S. Williams, Manyowa N. Meki, Javier Osorio Leyton, Alma Delia Baez-Gonzalez and Mari-Vaughn V. Johnson
Agronomy 2023, 13(2), 580; https://doi.org/10.3390/agronomy13020580 - 17 Feb 2023
Cited by 1 | Viewed by 3699
Abstract
Coffee (Coffea species) and Cocoa (Theobroma cacao) are important cash crops grown in the tropics but traded globally. This study was conducted to apply the ALMANAC model to these crops for the first time, and to test its ability to [...] Read more.
Coffee (Coffea species) and Cocoa (Theobroma cacao) are important cash crops grown in the tropics but traded globally. This study was conducted to apply the ALMANAC model to these crops for the first time, and to test its ability to simulate them under agroforestry management schemes and varying precipitation amounts. To create this simulation, coffee was grown on a site in Kaua’i, Hawai’i, USA, and cocoa was grown on a site in Sefwi Bekwai, Ghana. A stand-in for a tropical overstory tree was created for agroforestry simulations using altered parameters for carob, a common taller tropical tree for these regions. For both crops, ALMANAC was able to realistically simulate yields when compared to the collected total yield data. On Kaua’i, the mean simulated yield was 2% different from the mean measured yield, and in all three years, the simulated values were within 10% of the measured values. For cocoa, the mean simulated yield was 3% different from the mean measured yield and the simulated yield was within 10% of measured yields for all four available years. When precipitation patterns were altered, in Ghana, the wetter site showed lower percent changes in yield than the drier site in Hawai’i. When agroforestry-style management was simulated, a low Leaf Area Index (LAI) of the overstory showed positive or no effect on yields, but when LAI climbed too high, the simulation was able to show the detrimental effect this competition had on crop yields. These simulation results are supported by other literature documenting the effects of agroforestry on tropical crops. This research has applied ALMANAC to new crops and demonstrated its simulation of different management and environmental conditions. The results show promise for ALMANAC’s applicability to these scenarios as well as its potential to be further tested and utilized in new circumstances. Full article
(This article belongs to the Special Issue Recent Advances in Crop Modelling)
►▼ Show Figures

Figure 1

18 pages, 7652 KiB  
Article
Growth Indexes and Yield Prediction of Summer Maize in China Based on Supervised Machine Learning Method
by Lijun Su, Tianyang Wen, Wanghai Tao, Mingjiang Deng, Shuai Yuan, Senlin Zeng and Quanjiu Wang
Agronomy 2023, 13(1), 132; https://doi.org/10.3390/agronomy13010132 - 30 Dec 2022
Cited by 5 | Viewed by 2600
Abstract
Leaf area index and dry matter mass are important indicators for crop growth and yields. In order to solve the problem of predicting the summer maize growth index and yield under different soil quality and field management conditions, this study proposes a prediction [...] Read more.
Leaf area index and dry matter mass are important indicators for crop growth and yields. In order to solve the problem of predicting the summer maize growth index and yield under different soil quality and field management conditions, this study proposes a prediction model based on the supervised machine learning regression algorithm. Firstly, the data pool was constructed by collecting the measured data for maize in the main planting area. The total water input (rainfall plus irrigation water), fertilization, soil quality, and planting density were selected as the training set. Then, the maximum leaf area index (LAImax), maximum dry material mass (Dmax), and summer maize yields (Y) in the data pool were trained by using Gaussian regression (rational quadratic kernel function and Matern kernel function), support vector machine (SVM) and linear regression models. The training models were verified with the data-set not included in the data pool, and the water and fertilizer coupling functions were developed. The prediction results showed that compared to the support vector machine models and the linear regression models, the Gaussian regression prediction models comprising the rational quadratic and Matern kernel functions had good prediction accuracy. The coefficients of determination (R2) of the prediction results were 0.91, 0.89 and 0.88; the root-mean-square errors (RMSEs) were 0.3, 1138.6 and 666.16 kg/hm2; and the relative root-mean-square errors (rRMSEs) were 6.3%, 5.94% and 6.53% for LAImax, Dmax and Y, respectively. The optimal total water inputs and nitrogen applications indicated by the prediction results and the water and fertilizer coupling functions were consistent with the measured range from the field tests. The supervised machine learning regression algorithm provides a simple method to predict the yield of maize and optimize the total water inputs and nitrogen applications using only the soil quality and planting density. Full article
(This article belongs to the Special Issue Recent Advances in Crop Modelling)
►▼ Show Figures

Figure 1

15 pages, 5515 KiB  
Article
Determination of Cassava Leaf Area for Breeding Programs
by Phanupong Phoncharoen, Poramate Banterng, Nimitr Vorasoot, Sanun Jogloy and Piyada Theerakulpisut
Agronomy 2022, 12(12), 3013; https://doi.org/10.3390/agronomy12123013 - 29 Nov 2022
Cited by 6 | Viewed by 2637
Abstract
The evaluation of leaf area provides valuable information for decision-making for the cassava yield trail. The objectives of this study were (1) to determine the relationship between the leaf area and yield of the segregating populations and (2) to investigate the suitable mathematical [...] Read more.
The evaluation of leaf area provides valuable information for decision-making for the cassava yield trail. The objectives of this study were (1) to determine the relationship between the leaf area and yield of the segregating populations and (2) to investigate the suitable mathematical model for calculating cassava leaf area. The single-row trial for 60 segregating progenies of Kasetsart 50 × CMR38–125–77 was conducted from 2021 to 2022. The trial for eighteen progenies and the Kasetsart 50 and CMR38–125–77 was carried out in 2022. The sampled leaves for each genotype were collected to measure the leaf area. The length (L) and width of the central lobe (W), number of lobes (N), the product of the length and width (L × W; K), and the product of the length and number of lobes (L × N; J) were recorded for developing the mathematical models. The result showed that there were statistically significant correlations between the maximum individual leaf area and the total crop fresh weight and storage root fresh weight. The mathematical model LA = −3.39L + 2.04K + 1.01J − 15.10 is appropriate to estimate the maximum individual leaf area and leaf area index (LAI). This mathematical model also provided the estimated individual maximum leaf area that had the highest correlation with actual biomass at the final harvest as compared to the other three functions. The results showed statistical significance for the estimated LAI and biomass correlation. Full article
(This article belongs to the Special Issue Recent Advances in Crop Modelling)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop