Advances in Crop Simulation Modelling

Topic Information

Dear Colleagues,

In contrast to statistical models, process-based crop simulation models consider dynamic interactions between environment, genotype, and management (including but not limited to agricultural water) factors, something that justifies their application in decision making, in the assessment of the impacts of climate change/variability, and management practices on productivity and environmental performance of alternative cropping systems, to promote better and sustainable agriculture. However, application of these models is often hindered by limited input data availability (such as climate, cultivar and soil characteristics, and management practices) for model calibration and testing and extensive computing time.

Although it is well recognized that the choice of model calibration strategy and incomplete/ poor in quality/not easily accessible input data have implications on the overall reliability of the crop model simulations, only few attempts have been made to quantify errors in crop simulation results related to the above-mentioned issues on a variety of spatial scales (from field to large area applications).

In this context, this Topic aims to highlight the challenges of producing locally relevant and climate informed results from crop simulation models, promoting this way their effective use, across various time frames (from seasonal to future climate change) for agriculture, under historical and future climate conditions.

Dr. Mavromatis Theodoros
Dr. Thomas Alexandridis
Dr. Vassilis Aschonitis
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agronomy agronomy	3.7	5.2	2011	15.8 Days	CHF 2600	Submit
Climate climate	3.7	5.2	2013	19.7 Days	CHF 1800	Submit
Earth earth	-	1.6	2020	17.6 Days	CHF 1200	Submit
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700	Submit
Water water	3.4	5.5	2009	16.5 Days	CHF 2600	Submit

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Published Papers (9 papers)

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15 pages, 2645 KiB  

Open AccessArticle

Synchronous Retrieval of Wheat Cab and LAI from UAV Remote Sensing: Application of the Optimized Estimation Inversion Framework

by Jiangtao Ji, Xiaofei Wang, Hao Ma, Fengxun Zheng, Yi Shi, Hongwei Cui and Shaoshuai Zhao

Agronomy 2024, 14(2), 359; https://doi.org/10.3390/agronomy14020359 - 10 Feb 2024

Viewed by 560

Abstract

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of [...] Read more.

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R² = 0.835, RMSE = 14.357; for LAI, R² = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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18 pages, 4156 KiB  

Open AccessArticle

Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study

by Efrain Noa-Yarasca, Javier M. Osorio Leyton and Jay P. Angerer

Agronomy 2024, 14(2), 349; https://doi.org/10.3390/agronomy14020349 - 08 Feb 2024

Viewed by 801

Abstract

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates [...] Read more.

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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18 pages, 8310 KiB  

Open AccessArticle

Estimating the Light Interception and Photosynthesis of Greenhouse-Cultivated Tomato Crops under Different Canopy Configurations

by Yue Zhang, Michael Henke, Yiming Li, Zhouping Sun, Weijia Li, Xingan Liu and Tianlai Li

Agronomy 2024, 14(2), 249; https://doi.org/10.3390/agronomy14020249 - 24 Jan 2024

Viewed by 743

Abstract

Understanding the spatial heterogeneity of light and photosynthesis distribution within a canopy is crucial for optimizing plant growth and yield, especially in the context of greenhouse structures. In previous studies, we developed a 3D functional-structural plant model (FSPM) of the Chinese solar greenhouse [...] Read more.

Understanding the spatial heterogeneity of light and photosynthesis distribution within a canopy is crucial for optimizing plant growth and yield, especially in the context of greenhouse structures. In previous studies, we developed a 3D functional-structural plant model (FSPM) of the Chinese solar greenhouse (CSG) and tomato plants, in which the greenhouse was reconstructed as a 3D mockup and implemented in the virtual scene. This model, which accounts for various environmental factors, allows for precise calculations of radiation, temperature, and photosynthesis at the organ level. This study focuses on elucidating optimal canopy configurations for mechanized planting in greenhouses, building upon the commonly used north–south (N–S) orientation by exploring the east–west (E–W) orientation. Investigating sixteen scenarios with varying furrow distance (1 m, 1.2 m, 1.4 m, 1.6 m) and row spacing (0.3 m, 0.4 m, 0.5 m, 0.6 m), corresponding to 16 treatments of plant spacing, four planting patterns (homogeneous row, double row, staggered row, incremental row) and two orientations were investigated. The results show that in Shenyang city, an E–W orientation with the path width = 0.5 (furrow distance + row distance) = 0.8 m (homogeneous row), and a plant distance of 0.32 m, is the optimal solution for mechanized planting at a density of 39,000 plants/ha. Our findings reveal a nuanced understanding of how altering planting configurations impacts the light environment and photosynthesis rate within solar greenhouses. Looking forward, these insights not only contribute to the field of CSG mechanized planting, but also provide a basis for enhanced CSG planting management. Future research could further explore the broader implications of these optimized configurations in diverse geographic and climatic conditions. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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22 pages, 1766 KiB  

Open AccessArticle

ARMOSA Model Parametrization for Winter Durum Wheat Cultivation under Diverse Cropping Management Practices in a Mediterranean Environment

by Pasquale Garofalo, Marco Parlavecchia, Luisa Giglio, Ivana Campobasso, Alessandro Vittorio Vonella, Marco Botta, Tommaso Tadiello, Vincenzo Tucci, Francesco Fornaro, Rita Leogrande, Carolina Vitti, Alessia Perego, Marco Acutis and Domenico Ventrella

Agronomy 2024, 14(1), 164; https://doi.org/10.3390/agronomy14010164 - 11 Jan 2024

Viewed by 673

Abstract

In anticipation of climate changes, strategic soil management, encompassing reduced tillage and optimized crop residue utilization, emerges as a pivotal strategy for climate impact mitigation. Evaluating the transition from conventional to conservative cropping systems, especially the equilibrium shift in the medium to long [...] Read more.

In anticipation of climate changes, strategic soil management, encompassing reduced tillage and optimized crop residue utilization, emerges as a pivotal strategy for climate impact mitigation. Evaluating the transition from conventional to conservative cropping systems, especially the equilibrium shift in the medium to long term, is essential. ARMOSA, a robust crop simulation model, adeptly responds to varied soil management practices such as no tillage, minimum tillage, and specific straw management options such as chopping and incorporating crop residue into the soil (with or without prior nitrogen and water addition before ploughing). It effectively captures dynamic fluctuations in total organic carbon over an extended period. While challenges persist in precisely predicting grain yield due to climatic intricacies, ARMOSA stands out as a valuable and versatile tool. The model excels in comprehending and simulating wheat cultivar responses in dynamic agricultural ecosystems, shedding light on phenological patterns, biomass accumulation, and soil organic carbon dynamics. This research significantly advances our understanding of the intricate complexities associated with past wheat cultivation in diverse environmental conditions. ARMOSA’s ability to inform decisions on conservation practices positions it as a valuable asset for researchers, agronomists, and policymakers navigating the challenges of sustainable agriculture amidst climate change. Its real-world significance lies in its potential to guide informed decisions, contributing to global efforts in sustainable agriculture and climate resilience. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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15 pages, 4203 KiB  

Open AccessArticle

Estimation of Spring Maize Planting Dates in China Using the Environmental Similarity Method

by Meiling Sheng, A-Xing Zhu, Tianwu Ma, Xufeng Fei, Zhouqiao Ren and Xunfei Deng

Agronomy 2024, 14(1), 97; https://doi.org/10.3390/agronomy14010097 - 30 Dec 2023

Viewed by 618

Abstract

Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date is one of the important model parameters. Larger-scale spatial distribution with high [...] Read more.

Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date is one of the important model parameters. Larger-scale spatial distribution with high accuracy for planting dates is essential for the widespread application of crop growth models. In this study, a planting date prediction method based on environmental similarity was developed in accordance with the third law of geography. Spring maize planting date observations from 124 agricultural meteorological experiment stations in China over the years 1992–2010 were used as the data source. Samples spanning from 1992 to 2009 were allocated as training data, while samples from 2010 constituted the independent validation set. The results indicated that the root mean square error (RMSE) for spring maize planting date based on environmental similarity was 10 days, which is better than that of multiple regression analysis (RMSE = 13 days) in 2010. Additionally, when applied at varying scales, the accuracy of national-scale prediction was better than that of regional-scale prediction in areas with large differences in planting dates. Consequently, the method based on environmental similarity can effectively and accurately estimate planting date parameters at multiple scales and provide reasonable parameter support for large-scale crop growth modelling. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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21 pages, 1949 KiB  

Open AccessArticle

Optimal Planting Density and Nutrient Application of Soybeans: A Case Study in Northeastern China

by Huicheng Hao, Shixin Lv and Fulin Wang

Agronomy 2023, 13(12), 2902; https://doi.org/10.3390/agronomy13122902 - 26 Nov 2023

Viewed by 736

Abstract

In the context of the Chinese government’s policy guidance, there is black soil protection and ecological environment protection. The purpose of this paper is to solve the problem that the soil ecology of the black soil in Northeast China is changing year by [...] Read more.

In the context of the Chinese government’s policy guidance, there is black soil protection and ecological environment protection. The purpose of this paper is to solve the problem that the soil ecology of the black soil in Northeast China is changing year by year, and it is necessary to explore the sowing and fertilization strategy under the new situation; most Chinese growers rely excessively on their personal experience in the process of soybean sowing and fertilization. In this study, we used “Heihe 43” soybeans and used regression experimental design methods to analyze the effects of planting density, nitrogen, phosphorus, and potassium fertilizer application on soybean yield and to determine the optimal planting density and fertilizer ratios. The study reveals that the optimal soybean planting density in Northeast China is 45.37 × 10⁴ plants/ha, with nitrogen at 98.4 kg/ha, phosphorus at 218.96 kg/ha, and potash at 47.62 kg/ha. Under these conditions, soybean yields can reach 3816.67 kg/ha. This study can provide a theoretical method for decision-making to obtain the optimal planting density and fertilizer ratio for different regions of the farming system. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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20 pages, 7141 KiB  

Open AccessArticle

Comprehensive Growth Index (CGI): A Comprehensive Indicator from UAV-Observed Data for Winter Wheat Growth Status Monitoring

by Yuanyuan Tang, Yuzhuang Zhou, Minghan Cheng and Chengming Sun

Agronomy 2023, 13(12), 2883; https://doi.org/10.3390/agronomy13122883 - 24 Nov 2023

Viewed by 833

Abstract

Crop growth monitoring plays an important role in estimating the scale of food production and providing a decision-making basis for agricultural policies. Moreover, it can allow understanding of the growth status of crops, seedling conditions, and changes in a timely manner, overcoming the [...] Read more.

Crop growth monitoring plays an important role in estimating the scale of food production and providing a decision-making basis for agricultural policies. Moreover, it can allow understanding of the growth status of crops, seedling conditions, and changes in a timely manner, overcoming the disadvantages of traditional monitoring methods such as low efficiency and inaccuracy. In order to realize rapid and non-destructive monitoring of winter wheat growth status, this study introduced an equal weight method and coefficient of variation method to construct new comprehensive growth indicators based on drone images and measured data obtained from field experiments. The accuracy of the indicators in evaluating the growth of winter wheat can be judged by the construction, and the effects of different machine learning methods on the construction of indicators can be compared. Correlation analysis and variable screening were carried out on the constructed comprehensive growth indicators and the characteristic parameters extracted by the drone, and the comprehensive growth index estimation model was constructed using the selected parameter combination. Among them, when estimating the comprehensive growth index (CGIavg), the optimal model at the jointing stage is the support vector regression (SVR) model: R2 is 0.77, RMSE is 0.095; at the booting stage, the optimal model is the Gaussian process regression (GPR) model: R2 is 0.71, RMSE is 0.098; at the flowering stage, the optimal model is the SVR model: R2 is 0.78, RMSE is 0.087. When estimating the comprehensive growth index based on the coefficient of variation method (CGIcv), the optimal model at the jointing stage is the multi-scale retinex (MSR) model: R2 is 0.73, RMSE is 0.084; at the booting stage, the optimal model is the GPR model: R2 is 0.74, RMSE is 0.092; at the flowering stage, the optimal model is the SVR model, R2 is 0.78: RMSE is 0.085. The conclusion shows that the method of constructing the comprehensive growth index is superior to the function of a single parameter to some extent, providing a new way for wheat growth monitoring and process management. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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26 pages, 5989 KiB  

Open AccessArticle

STICS Soil–Crop Model Performance for Predicting Biomass and Nitrogen Status of Spring Barley Cropped for 31 Years in a Gleysolic Soil from Northeastern Quebec (Canada)

by Nomena Ravelojaona, Guillaume Jégo, Noura Ziadi, Alain Mollier, Jean Lafond, Antoine Karam and Christian Morel

Agronomy 2023, 13(10), 2540; https://doi.org/10.3390/agronomy13102540 - 30 Sep 2023

Cited by 3 | Viewed by 1564

Abstract

Spring barley (Hordeum vulgare L.) is an increasingly important cash crop in the province of Quebec (Canada). Soil–crop models are powerful tools for analyzing and supporting sustainable crop production. STICS model has not yet been tested for spring barley grown over several [...] Read more.

Spring barley (Hordeum vulgare L.) is an increasingly important cash crop in the province of Quebec (Canada). Soil–crop models are powerful tools for analyzing and supporting sustainable crop production. STICS model has not yet been tested for spring barley grown over several decades. This study was conducted to calibrate and evaluate the STICS model, without annual reinitialization, for predicting aboveground biomass and N nutrition attributes at harvest during 31 years of successive cropping of spring barley grown in soil (silty clay, Humic Gleysol) from the Saguenay–Lac-Saint-Jean region (northeastern Quebec, Canada). There is a good agreement between observed and predicted variables during the 31 successive barley cropping years. STICS predicted well biomass accumulation and plant N content with a low relative bias (|normalized mean error| = 0–13%) and small prediction error (normalized root mean square error = 6–25%). Overall, the STICS outputs reproduced the same trends as the field-observed data with various tillage systems and N sources. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. These ‘newly calibrated’ parameters in STICS for spring barley cropped under continental cold and humid climates require validation using independent observation datasets from other sites. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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10 pages, 1049 KiB  

Open AccessArticle

GGE Biplot-Based Transcriptional Analysis of 7 Genes Involved in Steroidal Glycoalkaloid Biosynthesis in Potato (Solanum tuberosum L.)

by Feng Zhao, Yajie Li, Tongxia Cui and Jiangping Bai

Agronomy 2023, 13(8), 2127; https://doi.org/10.3390/agronomy13082127 - 14 Aug 2023

Viewed by 1036

Abstract

Steroidal glycoalkaloids (SGAs) are secondary metabolites that are closely associated with the sensory and processing qualities of potato tubers. GGE biplots are a widely used tool for analyzing crop breeding analysis. This study aimed to investigate the effect of light on SGA biosynthesis [...] Read more.

Steroidal glycoalkaloids (SGAs) are secondary metabolites that are closely associated with the sensory and processing qualities of potato tubers. GGE biplots are a widely used tool for analyzing crop breeding analysis. This study aimed to investigate the effect of light on SGA biosynthesis by employing GGE biplots to analyze the transcriptional gene expression of seven genes involved in the SGA biosynthesis pathway. Tubers of five different potato genotypes were incubated for 6, 12, and 24 h under red light. The expression levels of the seven genes were measured using qRT-PCR for analysis. Further analysis of the data was performed using GGE biplots. Our results indicated significantly higher expression levels for Pvs1, Sgt1, and Sgt3 genes than those of the remaining tested genes. Across the three red light illumination durations, Sgt3 showed high and stable expression, although it showed less stability across the different genotypes. Interestingly, the expression patterns of the seven genes were extremely similar for the 12 h and 24 h treatments. It was found that at least 6 h of red light illumination was required for optimal gene expression in all five genotypes, particularly in the genotype Zhuangshu-3 (DXY) after 24 h of treatment. Additionally, significant expression of the seven genes was observed in the L-6 genotype after 12 and 6 h of red light illumination. These results highlight that GGE biplots are an appropriate tool for analyzing and illustrating the differential expression profiles of the seven key genes involved in SGA biosynthesis in potato tubers. This study provides valuable insights into the biosynthesis and metabolism of SGAs in potatoes. Moreover, it demonstrates the potential application of GGE biplots in crop breeding and other research fields. Full article

(This article belongs to the Topic Advances in Crop Simulation Modelling)

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