Crop Growth Model Research Articles

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.

Improving sustainability of rice-canola rotation through water and nitrogen management in a humid region

Intensive cropping systems face significant environmental and economic challenges due to current water and nutrient management practices. This study addresses the limited research on optimizing these practices in rice-canola rotations, focusing on enhancing sustainability through improved water and nitrogen (N) management. Utilizing the DSSAT crop growth model, we evaluated three irrigation scenarios for rice- I1 (100 % irrigation requirement: IR), I2 (80 % IR), and I3 (60 % IR)-alongside three N application levels: N1 (30 % below RAN: regional average N), N2 (RAN), and N3 (30 % above RAN). Key sustainability indicators, including water productivity (WP), nitrogen use efficiency (NUE), and economic productivity, were analyzed. The DSSAT model demonstrated strong predictive accuracy, with yield differences of only 236 kg ha⁻¹ for rice and 121 kg ha⁻¹ for canola between observed and simulated values. A 40 % reduction in water use increased rice yield to a maximum of 5654 kg ha⁻¹ under I3N2 conditions. Additionally, this reduction in water consumption resulted in a 27 % decrease in production costs, significantly enhancing WP, net benefits, and gross benefits of rice by 46.7 %, 43.1 %, and 47.9 %, respectively. Conversely, a 30 % increase in N application for canola correlated with a 9 % yield increase, underscoring the importance of tailored N strategies. Furthermore, N3 raised WP, NUE, net benefits, and gross benefits of canola by 6.7 %, 1.4 %, 9.1 %, and 9.2 %, respectively, compared with current N application level. Overall, this study illustrates the potential for substantial water savings in rice cultivation while recommending stable or reduced N application levels. These management strategies enhance farm performance, lower production costs, and contribute to the sustainability of rice-canola cropping systems, promoting both agricultural productivity and environmental stewardship.

Phenological Adaptation Is Insufficient to Offset Climate Change-Induced Yield Losses in US Hybrid Maize.

Climate change is projected to decrease maize yields due to warmer temperatures and their consequences. Studies using crop growth models (CGMs), however, have predicted that, through a combination of alterations to planting date, flowering time, and maturity, these yield losses can be mitigated or even reversed. Here, we examine three assumptions of such studies: (1) that climate has driven historical phenological trends, (2) that CGM ensembles provide unbiased estimates of yields under high temperatures, and (3) that the effects of temperature on yields are an emergent property of interactions between phenology and environment. We used data on maize phenology from the United States Department of Agriculture, a statistical model of maize hybrid heat tolerance derived from 80 years of public yield trial records across four US states, and outputs of an ensemble of CMIP6 climate models. While planting dates have advanced historically, we found a trend toward later planting dates after 2005 and no trend for silking or maturity, shifting more time into the reproductive period. We then projected maize yields using the historical model and crop calendars devised using three previously proposed adaptation strategies. In contrast to studies using CGMs, our statistical yield model projected severe yield losses under all three strategies. Finally, we projected maize yields accounting for historical genetic variability for heat tolerance, discovering that it was insufficient to overcome the negative effects of projected warming. These projections are driven by greater heat stress exposure under all crop calendars and climate scenarios. Combined with analysis of the internal sensitivities of CGMs to temperature, our results suggest that current projections do not adequately account for the effects of increasing temperatures on maize yields. Climate adaptation in the US Midwest must utilize a richer set of strategies than phenological adaptation, including improvements to heat tolerance and crop diversification.

Spatial Simulation and Optimization of Cropping Structure Under Climate and Land Use Change Conditions Considering Synergistic Economic Benefits and Carbon Reduction in Crop Growth Processes

AbstractThe climate and land use changes caused by the natural environment and socioeconomic development have potential impacts on the green and sustainable development of agriculture. To accommodate agricultural production under multiple scenarios of future climate and land‐use change, this study proposes a “simulation–optimization” modeling approach based on a crop growth model with a synergistic “carbon emission–economic benefit” approach. This approach is based on climate change conditions and it accurately simulates future land use changes and crop growth processes, establishes a carbon emission intensity optimization model, and generates a spatial planting structure optimization and regulation scheme based on intelligent optimization algorithms under changing scenarios. The results of the model application show that the planting structure option in the future scenario can increase economic benefit by up to 14.8% compared to the current scenario while simultaneously reducing total greenhouse gas emissions by 6.77%. Correlation analysis of planting area, irrigation water volume, carbon intensity value and unilateral water use efficiency can be used to obtain the coordination level of each county under different regulation scenarios. This “simulation–optimization” modeling approach provides an effective approach to achieve synergistic and coordinated development of regional agricultural benefits and carbon reduction by fine‐tuning the planting structure, which promotes low‐carbon and high‐quality development of regional agriculture.

Vertical farming goes dynamic: optimizing resource use efficiency, product quality, and energy costs

Vertical farming is considered to be a key enabler for transforming agrifood systems, especially in or nearby urbanized areas. Vertical farming systems (VFS) are advanced indoor cropping systems that allow for highly intensified and standardized plant production. The close control of environmental parameters makes crop production stable and repeatable, ensuring year-round uniform product quality and quantity irrespective of location. However, due to continuous changes in plant physiology and development, as well as frequent changes in electricity prices, the optimum conditions for crop production and its associated costs can change within days or even minutes. This makes it beneficial to dynamically adjust setpoints for light (intensity, spectrum, pattern, and daylength), CO2, temperature, humidity, air flow, and water and nutrient availability. In this review, we highlight the beneficial effects that dynamic growth conditions can have on key plant processes, including improvements in photosynthetic gas exchange, transpiration, organ growth, development, light interception, flowering, and product quality. Our novel findings based on modeling and experimentation demonstrate that a dynamic daily light intensity pattern that responds to frequent changes in electricity prices can save costs without reducing biomass. Further, we argue that a smart, dynamic VFS climate management requires feedback mechanisms: several mobile and immobile sensors could work in combination to continuously monitor the crop, generating data that feeds into crop growth models, which, in turn, generate climate setpoints. In addition, we posit that breeding for the VFS environment is at a very early stage and highlight traits for breeding for this specialized environment. We envision a continuous feedback loop between dynamic crop management, crop monitoring, and trait selection for genotypes that are specialized for these conditions.

Pearl millet phenology assessment: An integration of field, a review, and in silico approach

AbstractPearl millet [Pennisetum glaucum (L.) R.Br.] is an essential subsistence cereal for food security in dryland farming systems of the semiarid tropics (e.g., in sub‐Saharan Africa) and has improved tolerance to drought, heat, and salinity stress compared to other domesticated cereals. Assessing the variation on phenology is critical toward devising effective adaptative management strategies for crop adaptation to current and future climate change. In this context, pearl millet presents a vast genetic diversity, exhibiting sensitivity to temperature and photoperiod. Hence, this study aims to describe the genotypic variability in the phenological responses of pearl millet to temperature and photoperiod, particularly affecting leaf number with implications on the overall total time to flowering. The dataset encompassed 21 publications from seven countries, with experiments conducted from 1965 to 2023, including three field studies from the United States. Broad variability has been reported for phyllochron values ranging from 45 to 111°Cd leaf−1, with a mean value of 67°Cd leaf−1. Thermal time to panicle initiation ranged from 340 to 594°C, but no response to photoperiod duration was found due to the nature of dataset. Maximum number of leaves per shoot ranged from 11 to 25, showing response (1.55–2.15 leaf h−1) to photoperiod due to variations in thermal time to flowering (from 875 to 1346°Cd). Thermal time to flowering increased ca. 323°Cd h−1 under day durations longer than 13.3 h, below which basic vegetative phase duration was close to 1033°Cd. Based on the Agricultural Production Systems sIMulator simulations, different combinations of the above responses (in silico cultivars) generated a great range of times to flowering (44–120 days) for locations in Senegal, Brazil, India, and United States. The findings of this study can help breeders to explore the phenological genetic variability of pearl millet and provide inputs for crop growth models to evaluate future in silico scenarios.

Agronomic dataset on potato growth and yield in the Netherlands

Data was collected at two field locations in the Netherlands: on the clayey soils in Lelystad and the sandy soils of Vredepeel during 2019 and 2020. The original aim of this dataset was to calibrate and evaluate crop growth models for estimating potential, water-limited and nitrogen-limited yield levels for modern potato cultivars. Therefore, treatments include different cultivars, irrigation regimes, nitrogen fertilization, and light interception. During the two seasons extensive data was collected either passively, via non-destructive or via destructive measurements. Passive measurements consisted of weather and soil moisture data and were taken continuously throughout the season. Non-destructive data was collected on photosynthesis, reflection/light intensity, SPAD chlorophyll values, plant height, crop phenology, and groundwater level roughly every other week throughout the seasons. Destructive measurements were taken on biomass (leaves, stems and tubers), leaf area, NPK content (leaves), and the number of tubers between five and seven times per season. Additionally, during the final harvest data was collected on the tuber size distribution, marketability, and NPK content of the tubers.

Projecting Irrigation Water and Crop Water Requirements for Paddies Using WEAP-MABIA under Climate Change

Monitoring future irrigation water demand as a part of agricultural interventions is crucial to ensure food security. In this study, the impact of climate change on paddy cultivation in Brunei is investigated, focusing on the Wasan rice scheme. This research aims to project irrigation water requirement (IWR) and crop water requirement (CWR) or the main and off season using the WEAP-MABIA model. Historical data analysis over the past 30 years and future projections up to 2100 are employed to assess potential impacts. An ensemble of statistically downscaled climate models, based on seven CMIP6 GCMs under shared socioeconomic pathways (SSPs) (SSP245, SSP370, and SSP585), was utilised to project the IWR and CWR. Using downscaled CMIP6 data, three future periods were bias-corrected using quantile delta mapping (QDM) for 2020–2046 (near future), 2047–2073 (mid future), and 2074–2100 (far future). The WEAP-MABIA model utilises a dual crop coefficient approach to evaluate crop evapotranspiration (ETc), a critical factor in computing IWR. Results indicate that changes in future temperatures will lead to higher average ETc. Consequently, this results in elevated demands in irrigation water during the off season, and it is especially prominent in high-emission scenarios (SSP370 and SSP585). While the main season experiences a relatively stable or slightly increasing IWR trend, the off season consistently shows a decreasing trend in IWR. Moreover, the off season benefits from substantial rainfall increases, effectively reducing IWR despite the rise in both maximum and minimum temperatures. This study also highlights some recommendations for implementing possible improvements in irrigation management to address the effects of climate change on rice cultivation in the region. Future investigation should focus on enhancing crop yield predictions under climate change by integrating a dynamic crop growth model that adjusts for changing crop coefficient (Kc) values.

An EnKF-LSTM Assimilation Algorithm for Crop Growth Model

An EnKF-LSTM Assimilation Algorithm for Crop Growth Model

A study on parameter calibration of a general crop growth model considering non-foliar green organs

Accurate crop yield estimation is essential for ensuring national food security and sustainable development. The crop growth model is one of the primary methods for estimating production and has been effectively applied in simulating crop growth and development, environmental factor effects and yield formation processes. However, many related studies have focused on Gramineae crops, such as wheat, rice and maize, while there has been little research on rape and soybean yield estimation. Non-foliar green organs such as rape siliques and soybean pods make vital contributions to plant photosynthesis and influence crop output. The parameter calibration method based on the leaf area index (LAI) cannot satisfy the existing demand for high-precision yield estimation for crops with active non-foliar green organs. Therefore, a new method for crop growth model calibration was proposed, which considers non-foliar green organs and plant photosynthetic succession processes and combines them with those of leaves to construct a photosynthetic area index (PAI). With wheat, rape and soybean as the research objects, their yields were simulated via the proposed crop growth model calibration method. The results showed that using the proposed calibration method to calibrate World Food Study (WOFOST) model parameters on the basis of the PAI improved the yield estimation accuracy over that of the crop model calibration method based on the LAI. The determination coefficient (R2) of the total dry weight of storage organs (TWSO) simulation value increased from 0.73 (using the LAI) to more than 0.90 (using the PAI). The R2 values of TWSO at the rape calibration and verification points were 0.910 and 0.922, respectively, and the R2 values of TWSO at the soybean calibration and verification points were 0.741 and 0.926, respectively. The above results verified the feasibility and effectiveness of the proposed calibration method. Consequently, the application of the crop model calibration method proposed in this paper is important for accurately estimating the crop yield of plants with active non-foliar green organs, promoting the expansion of general crop models for different types of crops and achieving high-precision crop yield estimations.

Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.

Improving the estimation accuracy of wheat maturity date by coupling WheatGrow with satellite images

Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale.

Simulating water-limited potato yields across the Netherlands with (SWAP-)WOFOST: Experimentation, model improvement and evaluation

Water availability explains a large part of the spatial and temporal yield variability of ware potato in the Netherlands. Climate change is projected to lead to greater variability in water availability. Therefore, an accurate simulation of crop yield as a function of water availability is of high importance. Many crop models can simulate water-limited potato yields, but as detailed experimental data on drought and oxygen stress are scarce, models are often not well calibrated and evaluated. We set up a large experiment and an on-farm observational trial to calibrate and evaluate the simulation of water-limited yield of different potato cultivars across the Netherlands. In addition, we investigated the required model complexity to accurately simulate water-limited potato yields. The crop growth model WOFOST was used for simulations either as a single model or coupled with SWAP. Yields were simulated applying either only drought stress or by including both drought and oxygen stress. We employed two methods for simulating oxygen stress in SWAP-WOFOST. The calibration improved accuracy of simulated water-limited yields as demonstrated in our model evaluation. Both on sandy and clayey soils, water-limited yields were best simulated using SWAP-WOFOST while simulating oxygen stress using a process-based method in combination with a rooting density that decreases linearly with rooting depth. On sandy soils, the bottom boundary condition of free drainage was applicable. On clayey soils measured groundwater levels (or soil water pressure heads when measurements are not available) should be used as bottom boundary condition. On sandy soils both WOFOST and SWAP-WOFOST may be used, as results were similar. On clayey soils, WOFOST is less suitable, as it cannot simulate capillary rise. SWAP-WOFOST however has many options and parameters, which can result in large differences in results. Hence, a careful model set up and evaluation is required for each application.

Responses of Leaf Expansion, Plant Transpiration and Leaf Senescence of Different Soybean (Glycine max. (L.) Merr.) Genotypes to Soil Water Deficit

ABSTRACTThe responses of eco‐physiological processes such as leaf expansion, plant transpiration and senescence to soil water deficit have been reported to be genotype‐dependent in different crops. To study such responses in soybean (Glycine max. (L.) Merr.), a 2‐year (2017 and 2021) outdoor pot experiment was carried out on the Heliaphen automated phenotyping platform at INRAE in Toulouse (France). Six soybean cultivars (Sultana‐MG 000, ES Pallador‐MG I, Isidor‐MG I, Santana‐MG I/II, Blancas‐MG II and Ecudor‐MG II) belonging to four maturity groups (MG) commonly grown in Europe were subjected to progressive soil water deficit from the reproductive stage R1 for 17 and 23 days in 2017 and 2021, respectively. The fraction of transpirable soil water (FTSW) was used as an indicator of soil water deficit. Non‐linear regression was used to calculate FTSWt, that is, the FTSW threshold for which the rate of the eco‐physiological process in stressed plants starts to diverge from a reference value. According to FTSWt, the three eco‐physiological processes showed significant differences in sensitivity to water deficit: leaf expansion exhibits the highest sensitivity and the widest range (FTSWt: 0.44–0.93), followed by plant transpiration (FTSWt: 0.17–0.56), with leaf senescence showing the narrowest range (FTSWt: 0.05–0.16). Among six cultivars, regarding leaf expansion, Cvs Santana (FTSWt = 0.48 in 2017; FTSWt = 0.44 in 2021), Blancas (FTSWt = 0.51 in 2017; FTSWt = 0.48 in 2021) and Ecudor (FTSWt = 0.46 in 2017; FTSWt = 0.52 in 2021) in late MGs (I/II to II) exhibited higher tolerance to soil drying. Conversely, the cv. Sultana in the earliest MG (000) showed the highest sensitivity (FTSWt = 0.91 in 2017; FTSWt = 0.93 in 2021) to water deficit. However, concerning the FTSWt values for plant transpiration (0.17–0.56 in 2017; 0.19–0.31 in 2021) and senescence (0.05–0.16 in 2017; 0.06–0.16 in 2021), their range did not demonstrate a correlated trend with the MG. In addition, a negative linear correlation was observed between values of FTSWt of normalised leaf expansion at the whole‐plant level (NLE) and specific leaf area (SLA) measured on irrigated plants for both years. This suggests that genotypes with high values of SLA could be associated with higher tolerance of leaf expansion to soil water deficit. Such a non‐destructive phenotyping method under outdoor conditions could bring new information to variety testing process and provide paths for integrating genotypic variability into crop growth models used for simulating soybean eco‐physiological responses to water deficit across the plant, field and even regional scales.

Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model

ContextProcess-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).ObjectiveWe calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.MethodsWe conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.Results and conclusionsThe APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.

Algorithm for estimating cultivar-specific parameters in crop models for newer crop cultivars

Model parameters in mechanistic process-based crop models are broadly classified into general parameters (environmental, management, and parameters related to soil processes), species-specific parameters, and cultivar-dependent parameters. While general and species-specific parameters typically remain unchanged, identifying cultivar-dependent parameters is crucial during calibration. With the continuous development of new cultivars, updating cultivar-specific parameters in crop models is necessary. However, gathering data on the behavior of these newer cultivars and calibrating the model to accommodate them is challenging. Currently, there is no standard approach for determining cultivar-specific parameters in crop models due to the difficulty of identifying variations in the growth and development process in newer cultivars, as well as the variations in the model structure, correlations between sub-model components and between different parameters in the crop models. The present study develops a standard methodology for determining cultivar-dependent parameters based on experiments and incorporating them into process-based crop growth models. The developed methodology is demonstrated using the cotton crop and the GOSSYM, a mechanistic process-based cotton crop simulation model. Experiments are conducted on 40 major cotton cultivars grown in the USA to quantify their growth and development under the same environmental and management conditions. Cultivar-dependent parameters are derived and integrated into the GOSSYM model based on the experimental data. By establishing a standardized methodology and demonstrating its application in the context of cotton and the GOSSYM model, this study provides practical guidance for incorporating cultivar-dependent parameters into crop simulation models. Researchers and agronomists can effectively utilize this methodology to integrate new cultivars into their crop models.

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.

Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

A Dynamic Process-Based Method for Screening Salt-Tolerant and High-Yielding Crop Varieties

Maize, the most important cereal crop worldwide, is moderately sensitive to salt stress. Given the challenges of soil salinization, developing methods to screen and cultivate salt-tolerant maize varieties is vital for enhancing food security. Among the options, process model-based assisted screening is an effective method for faster and more thorough screening of salt-tolerant varieties. In this study, a method for quickly screening salt-tolerant and high-yielding crop varieties is proposed by combining a coupled model of crop growth and water–salt response with fitness function. Then, this method was applied to the saline areas of the Hetao Plain in China to demonstrate its applicability. This study includes three parts. Firstly, field trial data from 10 commonly grown maize varieties in Hetao Plain (i.e., Xianyu 335, Yinyu 238, Jindan 73, Deke 622, DK-815, Heyu 157, Xianyu 1321, Jinrun 919, Tianci 19, and Xianyu 1225) were used to calibrate the model for different maize varieties. Moreover, model accuracy was evaluated using four indexes including the regression coefficient (b), coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). Secondly, scenario simulations were conducted using the calibrated model by setting nine initial salinity scenarios to simulate daily dynamic crop growth and soil water–salt changes for the 10 maize varieties. Finally, salt-tolerant and high-yielding maize varieties were screened based on the fitness function method during crop growth periods. The results showed that the simulation model was applicable and functioned effectively for all 10 maize varieties in the region, with the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) of simulated plant height and leaf area index being above 0.90. Furthermore, the R2 of soil water content, soil electrical conductivity, and groundwater depth are 0.51, 0.52, and 0.60, respectively. Afterward, the fitness function values were calculated to bridge the linkage between simulated indicators and scenarios to screen varieties step by step according to the predetermined percentage of screening. Jindan 73, Xianyu 1225, and DK-815 were eventually determined as the most suitable salt-tolerant and high-yielding maize varieties. Therefore, the above results show that the proposed method is suitable for saline crop variety screening with flexibility and applicability.

Development of a novel critical nitrogen concentration–cumulative transpiration curve for optimizing nitrogen management under varying irrigation conditions in winter wheat

Accurate nitrogen (N) nutrition diagnosis is essential for improving N use efficiency in crop production. The widely used critical N (Nc) dilution curve traditionally depends solely on agronomic variables, neglecting crop water status. With three-year field experiments with winter wheat, encompassing two irrigation levels (rainfed and irrigation at jointing and anthesis) and three N levels (0, 180, and 270 kg ha−1), this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration (T), providing a comprehensive analysis of the interaction between N and water availability. The Nc curves derived from both crop dry matter (DM) and T demonstrated N concentration dilution under different conditions with different parameters. The equation Nc = 6.43T−0.24 established a consistent relationship across varying irrigation regimes. Independent test results indicated that the nitrogen nutrition index (NNI), calculated from this curve, effectively identifies and quantifies the two sources of N deficiency: insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption. Additionally, the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit (Nand) and a positive correlation with relative grain yield (RGY). The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY, as indicated by larger R2 values and smaller AIC. The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status, predicting grain yield, and optimizing N fertilizer management across varying irrigation conditions. These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling

Efficient and accurate in-season diagnosis of crop nitrogen (N) status is crucially important for precision N management. The main objective of this study was to develop a strategy for in-season dynamic diagnosis of maize (Zea mays L.) N status across the growing season by integrating proximal sensing and crop growth modeling. In this study, we integrated plant N concentration (PNC) derived from leaf fluorescence sensor data and aboveground biomass (AGB) based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated PNC and AGB using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The results confirmed the applicability of leaf fluorescence sensing for PNC estimation and active canopy reflectance sensing for AGB estimation, respectively. The calibrated DSSAT CERES-Maize model performed well for simulating AGB (R2 = 0.96), which could be used for calculating the N status indicator, N nutrition index (NNI). However, the model did not perform satisfactorily for PNC simulation, with significant discrepancies between the simulated and measured PNC values. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of NNI (R2 = 0.95) and N status diagnostic outcomes (Kappa statistics = 0.64) for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support. More studies are needed to further improve this approach by multi-sensor and multi-source data fusion using machine learning models.