Crop Growth Model Research Articles

Estimation of Surface and Subsurface Soil Moisture Using Microwave Remote Sensing: A Typical Analysis

Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface & the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

Predicting maize yield in Northeast China by a hybrid approach combining biophysical modelling and machine learning

ContextChina produces more than 20 % of maize grain in the world, and Northeast China (NEC) accounts for ∼30 % of the nation's total maize production. Previous studies have used either climate data, satellite data, or crop growth model (CGM) to predict or forecast maize yield. However, maize is highly susceptible to the effect of extreme climate events (such as drought, heat) in NEC, and there is a lack of studies to predict/forecast maize yield by integrating climate data, satellite data, extreme climate events, and CGM-simulated data. ObjectiveWe aim to develop a hybrid approach with machine learning to blend different sources of data (climate data, satellite data, extreme climate events) and process-based modelling results to improve predictive accuracy of maize yield in NEC. MethodsUsing maize data from 44 sites during the period of 2000–2013 in NEC, we firstly optimized Agricultural Production System sIMulator (APSIM) using Differential Evolution Adaptive Metropolis combined with Gaussian likelihood function and Bayesian multiplication method. Next, we divided the growing season into five phases, and selected variables of different phases using exploratory data analysis and Random Forest. Then, we developed a hybrid model using Random Forest by blending of multiple sources of data and APSIM simulations to predict maize yield from the start to the end of the growing season, and quantified the relative contribution of predictors. Results and conclusionsA hybrid model developed with random forest by combining climate data, NDVI, extreme climate events and APSIM simulations can achieve high performance for predicting yield toward the end of the growing season. The accuracy of in-season yield prediction showed a linear increase with MAPE/KGE changing from 19 %/0.05 to 13 %/0.53 from start to end of the growing season. Yield forecasts are acceptable with RMSE/MAE of 1.20/1.01 Mg ha−1 (16 %/13 % of the observed mean yield) approximately one-month prior to harvest. The most important predictor that affect yield forecast was APSIM-simulated biomass or yield, and the most important extreme climate event was drought during early grain-filling stage. SignificanceWith the increasing availability of crop-related data, we expect that the in-season forecasting capacity of the proposed methodology could be further improved, and the methodology can be extended to other crops and other regions for yield forecast.

An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

Adaptive predictive control of a fan-ventilated tunnel greenhouse with evaporative cooling

In the present work, micro-climate control of a fan-ventilated tunnel greenhouse with evaporative cooling is investigated via simulation study. For this purpose, the adaptive generalized predictive control (AGPC) strategy is adopted to control temperature, relative humidity, and CO2 concentration in the greenhouse on extremely hot summer days. A dynamic model which integrates a distributed model of greenhouse micro-climate with a crop growth model is used for numerical simulation of the tunnel greenhouse. The controller performance is analyzed via numerical simulation. The relative gain array analysis confirms that greenhouse micro-climatic parameters are highly interactive. However, simulation results showed that AGPC can efficiently handle variables interaction and follow set-points despite actuator limitations and high outside temperature. Besides, by considering output constraints in applying AGPC, the control system can keep the humidity of the greenhouse in a specific range instead of in a particular set point. The calculation burden for the AGPC strategy is kept low without increasing prediction error by employing a linear model and on-line adaptive identification. The applied strategy can handle the uncertainty of the design model due to the model mismatch by on-line identification of model parameters using the recursive least square method.

Monitoring Waterlogging Damage of Winter Wheat Based on HYDRUS-1D and WOFOST Coupled Model and Assimilated Soil Moisture Data of Remote Sensing

Waterlogging harms winter wheat growth. To enable accurate monitoring of agricultural waterlogging, this paper conducts a winter wheat waterlogging monitoring study using multi-source data in Guzhen County, Anhui Province, China. The hydrological model HYDRUS-1D is coupled with the crop growth model WOFOST, and the Ensemble Kalman Filter is used to assimilate Sentinel-1 inversion soil moisture data. According to the precision and continuity of soil moisture, the damage of winter wheat waterlogging were obtained. The experimental results show that the accuracy of the soil moisture is improved after data assimilation compared with that before data assimilation, and the Nash–Sutcliffe efficiency (NSE) of the simulated soil moisture values at three monitoring sites increased from 0.528, 0.541 and 0.575 to 0.752, 0.692 and 0.731, respectively. A new waterlogging identification criterion has been proposed based on the growth periods and probability distribution of soil moisture. The proportion, calculated from this identification criterion, of the waterlogging wheat farmland in total farmland shows a high correlation with the yield reduction rate. The correlation coefficient of the waterlogging farmland proportion and the yield reduction rate in 11 towns of Guzhen County reaches 0.78. Through the synchronization of geography, agriculture and meteorology, the framework shows great potential in waterlogging monitoring.

Performance evaluation of vertical bifacial and single-axis tracked agrivoltaic systems on arable land

This study evaluates and compares two types of interspersed bifacial agrivoltaic systems in Belgium: a fixed vertical system and a dynamic single-axis tracker, focusing on sugarbeet cultivation. Additionally, the impact of different tracking algorithms on crop yield and quality is investigated. The main approach involves developing an empirical crop model based on radiation use efficiency (RUE) and utilizing a radiation tool to simulate crop yield and quality. Field measurements of electricity and crop output are conducted over a span of two years, 2021 and 2022. The findings reveal that the dynamic solar tracker outperforms the fixed vertical bifacial setup in both years. The smart-tracking algorithm, applied under optimal watering conditions in the 2021 season, leads to a significant increase in energy yield (＋30%) and land use efficiency (＋20%) at a lower cost, while maintaining comparable crop yields to the vertical setup. However, limitations are observed in the empirical crop growth model’s ability to account for climatic variability in the dry 2022 year, reducing its usefulness during the design phase. Notably, the practical implementation of the systems highlights challenges during construction and exploitation, emphasizing the significance of considering these practical factors together with climate, structure and crop choice assessing the effectiveness and to de-risk investments.

A review on food subsystem simulation models for the water-food-energy nexus: development perspective.

Agricultural crops are the primary food source because livestock and poultry products also indirectly depend on crops. A significant obstacle to adopting the water, food, and energy (WFE) nexus is the lack of a comprehensive and easy-to-use simulation model for the food subsystem focusing on crops. By reviewing the articles in Scopus and Google Scholar databases, WFE nexus studies can be divided into two categories: simulation-based and conceptual-based studies of WFE nexus. Based on the developmental perspective on food subsystem modeling in the WFE nexus, the conceptual studies were excluded, and the modeling studies were reviewed. Two points of view can be used for WFE nexus modeling: 1. hard-link modeling and 2. soft-link modeling. Comparing these two types of modeling showed that hard-link modeling cannot model the interrelations of the food subsystem, and this shortcoming is of great importance. This study reviewed the crop growth models (CGMs) used in the WFE nexus system from the development perspective. The technical characteristics of the CGMs have been evaluated according to the requirements of the CGMs. Finally, a checklist based on the criteria defined for the nexus system has been provided, which can guide researchers in choosing the appropriate CGMs for the food subsystem with the nexus approach. The analysis revealed that none of the CGMs studied alone were sufficient to develop a simulation model for the food subsystem with the WFE nexus. However, the AquaCrop model met more criteria.

Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model

This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts.

Approximation of multi-year time series of XCO2 concentrations using satellite observations and statistical interpolation methods

Long-term time series of CO2 concentrations are required for studying e.g., the effects of rising CO2 concentrations on plants and ecosystems. Different methods for approximating column-averaged dry-air mole fractions of CO2 (XCO2) have been developed to substitute missing local CO2 measurements.This study presents a novel method (XCO2SAT+) for determining monthly XCO2 concentrations for selected sites, based entirely on observations from multiple satellites as well as statistical methods in the case of missing satellite data. The product currently covers a period of 16 years from 2003 to 2018. XCO2SAT+ results were validated against data of five European sites of the Total Carbon Column Observing Network (TCCON) and compared to two established XCO2 approximation methods. All three approximation methods were compared to CO2 measurements of German towers from the Integrated Carbon Observation System (ICOS).The mean absolute error (MAE) between the XCO2SAT+ method and TCCON site Karlsruhe, Germany ranges between 0.5 and 1.1 ppm (0.1–0.3%) for years 2014–2018. Mean differences between XCO2SAT+ and XCO2 approximation method CAMS-GCM shows a slightly better MAE of 0.1–0.3 ppm for CAMS-GCM at two German TCCON sites. Multi-year comparisons with ground-level ICOS measurements revealed a comparable seasonality of CO2 concentrations during the vegetation period, but underestimated high winter concentrations, a pattern also observed when comparing with CAMS-GCM. XCO2SAT+ is a suitable alternative and simple method for providing long-term time series of monthly averaged XCO2 concentrations for selected sites, expanding the methodological toolbox for in-situ CO2 research. The new method, which so far provides XCO2 concentrations, will thus serve as a first step towards providing further CO2 data sources, which could be used to further improve numerical models such as atmospheric models or crop growth models and to study CO2 impacts in agriculture and natural ecosystems.

Coupled maize model: A 4D maize growth model based on growing degree days

Crop canopy parameters are critical for environmental remote sensing, describing crop phenotypes, and ensuring food security. Evaluating the effect of temperature on crop growth is crucial for estimating crop canopy parameters. However, existing crop growth and plant functional-structural models cannot simultaneously model temperature responses, perform accurate dynamic simulations, and provide multi-scale computer visualizations. This limitation has hindered the application of structural models of maize plants for use in 3D radiative transfer models, crop structure evaluations, and crop phenotype descriptions. We improve the leaf/organ-level thermal-driven crop growth model (MAIZSIM) and the plant functional-structural algorithm. To address these limitations, we propose the coupled maize model, a four-dimensional (4D) growth model based on growing degree days. This model can simulate and visualize the structural parameters of the maize canopy at the organ, plant, seasonal, and population levels. The model outputs three-dimensional (3D) predictions of the maize structure (file format.obj), enabling editing and 3D visualizations. We use maize datasets from multiple phenological periods to test the proposed model’s accuracy and stability in simulating the canopy parameters at multiple levels. The results show that the normalized root mean square errors (NRMSEs) between the simulated and measured maize leaf size, area, leaf node height, and vein curve derived from the coupled maize model are below 0.1, demonstrating the model's high accuracy.

Modelling potato yield losses caused by Phytophthora infestans: Aspects of disease growth rate, infection time and temperature under climate change

Modelling crop yield loss due to pathogens remains a challenge due to the complex and dynamic interactions involved. Here, we develop a parsimonious dynamic yield loss simulation model that couples a simple epidemiological model of late blight disease with a basic model of potato crop growth. The model is parameterized using 17 years of field data on disease progress of potato late blight and potato yield in southern Sweden. Simulated actual yields (yields obtained under the presence of Phytopthora infestans) correlated well (R2 = 0.66) with the actual yields observed in field trials, using a few widely accessible meteorological variables. The results also underline the impact of disease growth rate and the time of infection on yield losses, with relatively low rates and early onset of the epidemic producing large yield reductions. In addition, the importance of increasing temperature due to climate change on both potato crop and Phytophthora infestans development was simulated, suggesting that higher temperatures may be more detrimental for the pathogen than for the crop. Additional ways to expand the model are discussed, and the inclusion of other diseases and pests are encouraged, indicating the potential to improve disease management in potato production.

Assessing marginality of Camelina (C. sativa L. Crantz) in rotation with barley production in Southern Europe: A modelling approach

Camelina (C. sativa L. Crantz) is a promising biofuel crop with high potential for cultivation on marginal soils. In this work, seed yields have been modelled to assess suitable areas based on experimental field trials, meteorological data from the Monitoring Agricultural ResourceS (MARS) gridded agro-meteorological in Europe, soil properties from LUCAS, topography and land cover. Potential yields for Camelina-Barley rotation (CAMBAR) were modelled for the past 20 years using the mechanistic crop growth model ARMOSA that can estimate quantitatively several soil and water parameters and future forcing scenarios (RCP4.5 and RCP8.5) using the generation model HadGEM2-ES. The term marginal land is found in the literature to indicate unused agriculture land such as abandoned, underused, degraded and fallow. In this work, marginality is considered the economic feasibility of cultivation, which aims to identifying land where cost effective agricultural production is not possible under a given set of conditions. Marginal lands were identified when the average Camelina seed yield from the crop growth modelling was lower than average in European countries based on a comprehensive literature assessment. The analysis was targeted at regions with a predominantly Mediterranean climate. Simulation by the mechanistic crop model was carried out on a 25 km grid for soil texture, soil carbon average stock, slope and aspect found in the MARS agricultural area mask. Spatial data and subsequent editing and processing of each simulation were allocated to a 500 m spatial resolution via a Geographic Information Systems (GIS). The study area covers around 500,000 km2. The CAMBAR scenario obtained an average yield of 2468 kg ha-1 yr-1, with standard deviation (+- 641) due to fluctuations for extreme weather patterns. Regarding soil organic carbon (SOC), CAMBAR showed an increase of + 43 kg ha-1 yr-1, which aligns with other studies carried out in Mediterranean or continental climates under crop rotation with minimum tillage and straw retention. The results of the present scenarios showed a slight increase in SOC stock (0.1–0.15 % yr-1). In regions with sufficient precipitation throughout the crop cycle, the increase of SOC is lower than the entire study area average, and in few cases, the SOC stock was drastically decreased. However, the model shows that SOC stock can increase when Camelina is introduced in rotation with cereals in areas with high desertification risk. In Spain, in the regions of Castilla La Mancha, Castilla y León and Comunidad de Madrid accounting for 40% of the total area of investigation in Spain, equivalent to an area of 88,233 km2, the SOC increase was + 188, + 255 and + 236 kg ha-1 yr-1, respectively. The results of this work constitute a key contribution to policy development at the sub-national, national and EU level, through the investigation of low LUC/ILUC biofuel from marginal areas before these are lost due to land degradation processes and other anthropogenic impacts. Mapping the marginal areas is fundamental to showing potential for producing advanced biofuel crops.

Methodological evolution of potato yield prediction: a comprehensive review.

Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato (Solanum tuberosum L.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato.

Modelling water/heat transfer and crop growth under film mulching condition in a seed–maize field

Film mulching has been widely applied in arid and semi–arid areas around the world. Current models simulating the process of water, heat, and crop growth generally neglect or simplify the role of film mulching in energy balance and water/heat exchange. In this study, CropSMPAC model was established based on previously developed model (CropSPAC, the soil water/heat transfer and crop growth model), by incorporating the influence of film mulching on energy budget and partition, sensible and latent heat fluxes of the soil surface, precipitation interception, and crop growth. The CropSMPAC model was calibrated and validated using experimental data from a seed–maize field located in arid Northwest China for the years 2017, 2018, and 2019. The experimental setup included two mulching treatments, i.e., full film mulching (M1) and no mulching (M0) along with three irrigation treatments, i.e., full irrigation (WF), medium irrigation (WM, 70%WF), low irrigation (WL, 40%WF). The results demonstrated that the model performed well in simulating the dynamics of field water, heat, and crop growth under various film mulching and irrigation conditions. The Nash-Sutcliffe efficiency coefficients for net radiation, soil heat flux, soil water storage (SWS), soil temperature (ST), leaf area index (LAI), aboveground dry biomass (ADB) were 0.57, 0.37, 0.79, 0.76, 0.95, 0.95, respectively. The relative errors for seed-maize yield, crop evapotranspiration (ET) were 9.38%, 9.10%, respectively. Furthermore, the model accurately reflected the differences in SWS and ST due to different mulching conditions (M1 and M0). The observed differences in SWS between M1 and M0 ranged from –30.6 to 32.9 mm, while the simulated differences ranged from –30.5 to 30.2 mm under various irrigation treatments. Similarly, the observed differences in ST between M1 and M0 were 1.8, 1.7, 1.0 and 1.4 °C at 10, 20, 40 and 80 cm soil depths, respectively, while the simulated differences were 2.3, 2.1, 1.7 and 0.8 °C under WF treatment. Additionally, the model effectively simulated the effects of film mulching on advancing the crop growth period and promoting LAI, ADB and yield. Overall, the model accurately captured the changes in energy fluxes, soil water, heat and crop growth caused by plastic film mulching, demonstrating that it is a useful tool for irrigation water management and crop production in crop land with film mulching.

Development of a cotton fiber quality simulation module and its incorporation into cotton crop growth and development model: GOSSYM

GOSSYM is a mechanistic, process-level cotton crop simulation model. It can simulate the growth and development of cotton under different environmental and management conditions and provide information on crop growth and development, various stresses, and yield. The current model version does not have the capability of simulating cotton fiber quality. Cotton fiber quality is as significant as fiber quantity. In this study, a cotton fiber quality simulation module is developed and integrated into GOSSYM. The functional relationships between the fiber quality and the major factors influencing fiber quality (temperature, water, and nutrient status) are established based on the temperature, water, and nutrient-controlled sunlit soil plant atmospheric research (SPAR) chamber experiments. In the developed model, the potential fiber quality in terms of four major fiber quality indices (fiber length, fiber strength, micronaire, and uniformity) is estimated based on the temperature function, and actual fiber quality indices are determined by reducing potential quality as a function of water and nutrient status in the crop. The newly developed model's fiber quality modeling capabilities are demonstrated using illustrative examples from a case study. Illustrated examples analyze the effect of different temperatures, nitrogen fertilizer application rates, irrigation, planting date, and atmospheric carbon dioxide concentrations on fiber quality indices. Simulation results are discussed by correlating them with underlying processes (growth and development, water, and nitrogen status), literature data, and functional relationships incorporated into the fiber quality module in GOSSYM. The model has demonstrated a reasonable simulation of fiber quality. The developed model can be a valuable tool for accurate cotton fiber quality simulations, lint yield production, fiber quality optimization, and making policy decisions under varying environmental and management conditions.

Upcycling rice yield trial data using a weather-driven crop growth model

Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the “YpCGM method”. We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world’s changing climate.

Incorporation of carbon dioxide production and transport module into a Soil-Plant-Atmosphere continuum model

Carbon dioxide release from agricultural soils is influenced by multiple factors, including soil (soil properties, soil-microbial respiration, water content, temperature, soil diffusivity), plant (carbon assimilation, rhizosphere respiration), atmosphere (climate, atmospheric carbon dioxide), etc. Accurate estimation of the carbon dioxide (CO2) fluxes in the soil and soil respiration (CO2 flux between soil and atmosphere) requires a process-based modeling approach that accounts for the influence of all these factors. In this study, a module for CO2 production via root and microbial respiration and diffusion-based carbon dioxide transport is developed and integrated with MAIZSIM (a process-based maize crop growth model that accounts for detailed soil and atmospheric processes) based on a modularized architecture. The developed model simulates root respiration based on root mass, root age, soil water content, and temperature. Microbial respiration is based on the soil microbial processes by accounting for the carbon dynamics in the litter, humus, and organic fertilizer pools as moderated by the soil water content, temperature, microbial synthesis, humification, and decomposition of the carbon pools. Case studies presented include scenarios with different soil, climate, and carbon pools that simulated the soil respiration with an average index of agreement of 0.73 and root mean squared error of 11.4 kg carbon ha−1 between the measured and simulated soil respiration. The modular architecture used in the model development facilitates easy integration with other existing crop models and future modifications.

CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation

Crop planting timing is critical in regulating environmental conditions of crop growth throughout the season, and is an essential parameter in crop simulation models for estimating dry matter accumulation and yields. Accurate planting date information is key to characterizing crop growing dynamics under varying farming practices and facilitating agricultural adaptation to climate change. To date, the main methods to acquire planting dates include field survey methods, weather-dependent methods, and remote sensing phenological detecting methods. However, it is still challenging to effectively estimate the crop planting dates at field levels due to the lack of appropriate field-level modeling design as well as the dearth of ground planting reference data. In our study, we develop a novel CropSow modeling framework to estimate field-level planting dates by integrating the remote sensing phenological detecting method with the crop growth model. The remote sensing phenological detecting method is devised to retrieve the critical crop phenological metrics of farm fields from remote sensing time series, which are then integrated into the crop growth model for field planting date estimation in consideration of soil-crop-atmosphere continuum. CropSow leverages the rich physiological knowledge embedded in the crop growth model to scalably interpret satellite observations under a variety of environmental and management conditions for field-level planting date retrievals. With corn in Illinois, US as a case study, the developed CropSow outperforms three advanced benchmark models (i.e., the remote sensing accumulative growing degree day method, the weather-dependent method, and the shape model) in crop planting date estimation at the field level, with R square higher than 0.68, root mean square error (RMSE) lower than 10 days, and mean bias error (MBE) around 5 days from 2016 to 2020. It achieves better generalization performance than the benchmark models, as well as stronger adaptability to abnormal weather conditions with more robust performance in estimating the planting dates of farm fields. CropSow holds considerable promise to extrapolate over space and time for estimating the timing of crop planting of individual farm fields at large scales.

Effects of Oxygenated Brackish Water on Pakchoi (Brassica chinensis L.) Growth Characteristics Based on a Logistic Crop Growth Model

Oxygenated irrigation can improve soil physical and chemical properties and increase vegetable yields. It provides an effective method for safe and efficient utilization of brackish water, but its growth-promoting pathway is unclear. We investigated the effects of brackish water culture at five dissolved oxygen concentrations (9.5 (CK), 12.5, 15.5, 18.5, and 22.5 mg/L) on pakchoi (Brassica chinensis L.) growth characteristics by hydroponics experiment, and the logistic model to fit and analyze pakchoi growth characteristics. At a brackish water dissolved oxygen concentration of 18.5 mg/L, nitrogen mass fraction was significantly higher than in a control treatment by 43.4%, and pakchoi effective accumulated temperature increment during vigorous plant height and root length growth was significantly lower than other treatments. The logistic model effectively simulated pakchoi plant height and root length growth, and both theoretical maximum plant height and root length reached their maximum values at 18.5 mg/L dissolved oxygen concentration. Path analysis showed that the maximum net photosynthetic rate and nitrogen mass fraction were the main factors affecting aboveground pakchoi fresh weight. In conclusion, a dissolved oxygen concentration of 18.5 mg/L in brackish water is more appropriate for pakchoi to achieve high yield under brackish water hydroponics conditions. Our results provide guidance for the safe and efficient utilization of brackish water in green and efficient vegetable production.