Crop Growth Model Research Articles

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

AbstractNortheast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally.

A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops

Changes in the frequency of extreme weather events related to climate change potentially pose significant challenges to UK agricultural production. There is a need for improved climate change risk assessments to support adaptation strategies and to ensure security of food production in future.We describe an innovative and practical framework for spatially explicit modelling of climate change impacts on crop yields, based on the UKCP18 climate projections. Our approach allows the integration of relatively simple crop growth models with high spatial and temporal resolution Earth Observation datasets, describing changes in crop growth parameters within year and over the longer term. We focus on modelling winter wheat, a commercially important crop. We evaluate the results of the model against precision yield data collected from 719 fields. We show that the assimilation of leaf area index data from Sentinel-2 satellite observations improves the agreement of the modelled yields with those observed. Our national-scale results indicate that wheat production initially becomes more favourable under climate change across much of the UK with the projected increase in temperature. From 2050 onwards, yields increase northwards, whilst they decline in South East England as the decrease in precipitation offsets the benefits of rising temperature.Our framework can readily accommodate growth models for other crops and LAI retrievals from other satellite sensors. The ability to explore impacts of crop yields at fine spatial resolutions is an important part of assessing the potential risks of climate change to UK agriculture and of designing more climate resilient agricultural systems.

Usefulness of techniques to measure and model crop growth and yield at different spatial scales

ContextWithin-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. ObjectiveThis study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. MethodsWe collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. ResultsWheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial variation in yield due largely to uncertainties in input data. However, APSIM simulations captured the average crop growth dynamics and yield well at 1 ha strip- and 10 ha whole field scales. ConclusionsThe results highlight the need for caution when using yield maps and remote sensing data to quantify spatial variability and inform spatially explicit management decisions at a fine resolution (e.g., 1 m2). In our case, remote sensing data and yield maps only became consistent and process-based modelling became skilful at scales larger than a 1 ha strip. ImplicationsDespite an increasing amount of high-resolution spatial data, the usefulness at fine resolution needs further investigation, particularly under heterogeneous field conditions. Such data need to be analysed in conjunction with the landscape, soil and climate data to understand the drivers of spatial variability and inform process understanding and modelling. This further implies potential problems in developing spatial management practices at finer scales using such data.

Study on the impact of low‐temperature stress on winter wheat based on multi‐model coupling

AbstractCrop growth models, such as the WOrld FOod STudies (WOFOST) model, mimic the mechanistic processes involved in crop development, growth, and yield production. The accuracy of simulation is decreased in unfavorable low‐temperature settings because these models do not accurately represent crop response processes in low‐temperature stress. Enhancing the WOFOST crop growth model's accuracy in simulating crops' responses to cold temperatures is the aim of this work. Given its vulnerability to low temperatures, the inquiry uses winter wheat in Henan Province as a focal point. It integrates the WHEATGROW wheat phenology model with the Frost model of Lethal Temperature 50 (FROSTOL) inside the framework of the crop growth model. This link aims to improve simulation accuracy and supplement the model's mechanisms, particularly when it comes to the impact of low temperatures on crop development. The study uses Long Short‐Term Memory networks to build a yield model that integrates remote sensing data with information from simulated crop models. Under low temperatures, the leaf area index, total above ground biomass, and total weight of storage organs of the model WWF—which combines FROSTOL and WHEATGROW with WOFOST—show a considerable decline. It was discovered that there is a greater improvement in simulation accuracy of the linked model WWF relative to the WOFOST model in frost years than in normal years, based on a comparison analysis between typical frost years and normal years. To be more precise, the improvement is 8.03% in frost years and 1.98% in regular years. When all is said and done, the coupled model advances our knowledge of how winter wheat is impacted by low temperatures.

Revisiting reference evapotranspiration calculation under regional advection and its effect on single and dual crop coefficients: An empirical approach for quinoa crop

AbstractAdvection is a prevailing meteorological phenomenon in the arid and semi‐arid environments that directly affects the crop and soil hydrology. It could have a great impact on the rate of standard crop evapotranspiration (ETc) in the irrigated areas. Therefore, it is required to take it into consideration while computing crop water requirement through the physically based meteorological procedures. The objectives of this study are (1) simple modification of the Penman–Monteith (PM) equation in calculation of the grass reference evapotranspiration (ETo) to implement the local advection, (2) comparing the single (Kc) and dual crop coefficient (Kcb) of two quinoa cultivars (Titicaca, and Q5) with and without advection correction and (3) presenting a simple model to calculate the advection factor using the maximum air temperature and mean relative humidity for the future crop growth modelling studies. Both Q5 and Titicaca showed unexpectedly very high ETc and potential transpiration (Tp) as 1568 mm and 1003 mm, and 1156 mm and 829 mm, respectively, due to occurrence of regional advection, whereas very high unrealistic Kc and Kcb values for Q5 revealed the impact of strong local advection and external energy. Therefore, we modified ETo to implement the advection effect through introducing the advection factor, ETc/Rn (Rn is the net radiation), as a function of maximum air temperature and mean relative air humidity during the growing season [ETc/Rn = exp (0.025Tmax – 0.015RHavg)] which resulted in higher ETo values, and consequently lower and more realistic Kc and Kcb. As a result, modified maximum Kc values of 1.14 and 1.55 and the modified maximum Kcb of 0.94 and 1.0 were obtained for Titicaca and Q5 cultivars, respectively. This procedure leads to a more accurate site‐specific Kc estimation and indirectly reliable ETc estimation as a function of advection factor and its multiplication by the non‐modified ETo. Furthermore, for direct estimation of ETc through the PM equation, the coefficients of aerodynamic and canopy resistance components of PM equation were modified for estimation of ETc by the non‐modified Rn, which resulted in accurate estimation of ETc.

Coupling field monitoring with crop growth modelling provides detailed insights on yield gaps at field level: A case study on ware potato production in the Netherlands

ContextYield gap analyses are useful to assess and benchmark the productivity of cropping systems. Often such analyses are performed at higher aggregation levels. As a result, these studies lack the detail to explain yield gaps at field level and hence make it difficult to translate findings into precise recommendations to farmers and extensionists. ObjectiveThis study provides a detailed approach for yield gap assessments at field level through coupling frequent field monitoring in farmers’ fields with crop growth modelling. We used ware potato production in the Netherlands as a case to study yield gaps at field level, as average productivity is high whilst yields are still highly variable among fields, and as ware potato is an important cash crop for farmers. MethodsOver two growing seasons, 96 ware potato fields were monitored throughout the growing season on a biweekly basis, taking measurements on soil, crop growth and yield. The crop growth model SWAP-WOFOST was used to simulate potential and water-limited potential yields. Various statistical methods were used to quantify yield gap explaining factors. ResultsThe average yield gap ranged from 20 to 31% depending on the year and soil type. Among fields, the yield gap ranged from 0 to 51%. On clayey soils, the yield gap was attributed mostly to oxygen stress. On sandy soils, the yield gap was determined mostly by drought stress in 2020, a relatively dry year, and by reducing factors (pests, diseases and poor agronomic practices) in 2021, an average year in terms of precipitation. The type of reducing factors differed per field. Furthermore, we found that earlier planting and later harvesting can increase yields, as Yp is radiation-limited. ConclusionsOverall, there is limited scope to narrow the yield gap as current ware potato production is already close to 80% of the potential yield, which is assumed to be approximately the maximum farmers can attain. However, yield and resource use efficiency gains are to be made for individual fields. Furthermore, we conclude that frequent field monitoring coupled with crop growth modelling is a powerful way to assess yield gap variability and to get detailed insight in the yield gap explaining factors at field level. SignificanceThis study showed that coupling frequent field monitoring with crop growth modelling allows to gain detailed insight in yield gap variability among fields. This method provides detailed information about yield gap explaining factors which can be used to improve yield and resource use efficiency at field level.

The potential of growing soybean in Saskatchewan and its irrigation water needs under climate change scenarios—a modelling study

The soybean industry in Canada is seeking opportunities to expand cultivation due to economic and environmental benefits of growing soybean. Climate projections indicate that soybean expansion into Saskatchewan would be possible with the increases in the available crop heat units under a future warmer climate; however, crop water availability could limit yields. Using a crop growth model, we simulated soybean yields within the Canadian Regional Agricultural Model regions in Saskatchewan for the near-term (2030s), mid-term (2050s), and distant future (2070s) periods under different climate scenarios. Soybean yields were simulated without water stress (potential yield), with water stress (rainfed yield), and under full and partial irrigation scenarios. Irrigation water needs were estimated under the irrigation scenarios and irrigation water availability was discussed. Our results suggest that reasonable and likely more profitable yields (∼2000–2500 kg ha−1) can be achieved under rainfed conditions in the Black soil zone neighbouring Manitoba but soybean production would be less favourable in the Dark Brown soil zone and least favourable in the Brown soil zone. Northeastern regions in the Black soil zone were found to be suitable for growing soybean cultivars in the maturity group (MG) 0 in the distant future and MG 00 in the mid-term under the medium–high greenhouse gas emission scenarios. Soybean would still not be suitable in the northwestern region. Our results indicate that regions in central Saskatchewan requiring 120–170 mm of irrigation are more likely to benefit from the proposed Lake Diefenbaker Irrigation Projects in the future.

Impacts of climate change on rice yields in the Nile River Delta of Egypt: A large-scale projection analysis based on CMIP6

Climate change directly affects crop yields, which would cause more future food security crises. Ensemble global climate models (GCMs) combined with crop growth models are an effective method to project such impacts. In the current study, five criteria were used to pick out ten GCMs. Three types of efficiency criteria, namely root-mean-squared error (RMSE), Pearson’s correlation coefficient (R), Nash-Sutcliffe efficiency coefficient (NSE), and bias (BISA) between predicted and observed temperature and precipitation, were used to evaluate and select the better-performing of the GCMs for the baseline period (1981–2010). Then, AquaCrop-GIS, driven by the downscaled projected climate data from the selected GCMs, was used to predict rice yields in the Nile River Delta (NRD) region under two Shared Socioeconomic Pathways (SSP) scenarios (SSP2–4.5 and SSP5–8.5) and four prediction periods (2021–2099). Four statistical indicators, namely, prediction error (Pe), normalized RMSE (RMSEn), index of agreement (Dindex), and coefficient of determination (R2), were used to evaluate the performance of AquaCrop-GIS. Our results showed that five GCMs, including the BCC-CSM2-MR, CMCC-ESM2, INM-CM5–0, MRI-ESM2–0, and UKESM1–0-LL, had better performances in simulating temperature and precipitation (0.81 ≤ RMSE ≤ 4.77, 0.30 ≤ NSE ≤ 0.97, and 0.57 ≤ R ≤ 0.99). In addition, AquaCrop-GIS showed excellent accuracy in simulating rice yields and predicted that, without CO2 effects, rice yields would increase by 2.19% and 4.23% under SSP2–4.5 and by 0.72% and 0.30% under SSP5–8.5 during the 2030s and 2050s, respectively. However, in the 2070s and 2090s, rice yields would decline by 7.20% and 9.0% under SSP2–4.5 and by 23.34% and 34.24% under SSP5–8.5 during the 2070s and 2090s, respectively. With CO2 effects, rice yields would rise by 14.49%, 24.97%, 15.96%, and 16.93% under SSP2–4.5 and by 14.33%, 26.22%, 8.06%, and 1.61% under SSP5–8.5 during the 2030s, 2050s, 2070s, and 2090s, respectively. Regardless of uncertainties and limitations, our findings are beneficial for farmers and policymakers to develop appropriate management strategies to improve rice yields in Egypt.

Subsurface fluxes dominate the dissolved nitrogen losses from rice paddies in central China

Nitrogen losses in rice paddy fields pose a high risk to surrounding aquatic environment. While the surface N losses have garnered considerable attention in recent years, subsurface flux, including vertical leaching and near-ditch lateral seepage, has barely been quantified. To understand how it impacts the N run off, we collected three-year high-frequency and systematic field observations and adopted a two-dimensional quantitative model coupling water, nitrogen dynamics and crop growth. We quantified the magnitude and dynamics of subsurface N loss during rice growing season. Our results showed that subsurface N loss, reaching 6.2 kg N ha−1 and 4% of total applied N in rice growing seasons, was 2-fold larger than surface N runoff, in which lateral seepage contributed 33%. The driving factors include hydraulic gradients brought by ponded water depth and ditch water level. Based on our study, optimized irrigation management and conservation tillage were priority in mitigating subsurface N loss, with 41% and 32% reduction potential, respectively, and improved ditch-pond system combined with irrigation-drainage management could also be an effective solution. Together, these findings highlight an important role of subsurface flux including lateral seepage, which could be instrumental for future water-quality improvements in rice paddy-ditch landscape.

Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle

Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances.

Development of a crop growth model for the energy analysis of controlled agriculture environment spaces

In controlled environment agriculture spaces, the conditions fluctuate between photoperiod and dark period, with crops growing continuously. As crops grow, their impact on the energy demand and energy use, often estimated using a building performance simulation tool, becomes more prominent. In this paper, a dynamic crop model integrated into a building performance simulation tool is proposed to estimate the yield and heat gain/loss from crops by combining a growth model and an energy balance model of the crops. The developed growth model is an adjusted version of a greenhouse lettuce growth model modified for high-density controlled environment agriculture applications by calibrating the sensitive parameters for several indoor environment conditions (temperature, lighting, etc.) using an experimental growth dataset. The yield, the energy demand and the energy use were assessed for a case study modelled in TRNSYS. The results obtained using the greenhouse and developed growth models were compared to those generated with the experimental growth dataset. Depending on the indoor environment conditions, the difference in specific energy use estimated using the experimental growth dataset and the developed model varied between 0.1% and 3.5%, indicating that the model led to an acceptable level of accuracy. The dynamic crop model estimates yield and heat gain/loss from crops for various indoor environment conditions, which are essential for carrying out energy, financial, and environmental analyses.

Rape Yield Estimation Considering Non-Foliar Green Organs Based on the General Crop Growth Model

To address the underestimation of rape yield by traditional gramineous crop yield simulation methods based on crop models, this study used the WOFOST crop model to estimate rape yield in the main producing areas of southern Hunan based on 2 years of field-measured data, with consideration given to the photosynthesis of siliques, which are non-foliar green organs. First, the total photosynthetic area index (TPAI), which considers the photosynthesis of siliques, was proposed as a substitute for the leaf area index (LAI) as the calibration variable in the model. Two parameter calibration methods were subsequently proposed, both of which consider photosynthesis by siliques: the TPAI-SPA method, which is based on the TPAI coupled with a specific pod area, and the TPAI-Curve method, which is based on the TPAI and curve fitting. Finally, the 2 proposed parameter calibration methods were validated via 2 years of observed rape data. The results indicate that compared with traditional LAI-based crop model calibration methods, the TPAI-SPA and TPAI-Curve methods can improve the accuracy of rape yield estimation. The estimation accuracy ( R 2 ) for the total weight of storage organs (TWSO) and above-ground biomass (TAGP) increased by 9.68% and 49.86%, respectively, for the TPAI-SPA method and by 14.04% and 42.94%, respectively, for the TPAI-Curve method. Thus, the 2 calibration methods proposed in this study are of important practical importance for improving the accuracy of rape yield simulations. This study provides a novel technical approach for utilizing crop growth models in the yield estimation of oilseed crops.

Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data.

Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird's-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R 2=0.95, RMSE = 7.6cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPAR f ), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.

Hierarchical Model-Based Irrigation Control for Vertical Farms

As high energy costs and water scarcity remain major problems in the context of vertical farming, we present a hierarchical control system developed for the optimization of irrigation processes in these farms. The main objective is to minimize the energy cost and water consumption associated with the operation of a vertical farm, with special emphasis on irrigation in a soil-based environment. At a higher level, an optimal control problem based on a dynamic crop growth model and the FAO Penman-Monteith equations is solved offline to determine the optimal daily inputs, considering temperature, radiation, and plant available water as decision variables. At a lower level, strategically placed Proportional-Integral-Derivative (PID) controllers are used to track these irrigation set-points online. These controllers are designed using a dynamic drip irrigation and soil model that simulates the movement of water through soil. In a simulation study, this integration of set-points and subordinate PID controllers shows a robust control system that effectively stabilizes the irrigation process. The results also show a uniform moisture distribution in the soil after a short time, which contributes to uniform plant growth in the farm. The proposed hierarchical control system combines computationally intensive offline open-loop optimization for daily input determination with fast and simple online PID control for real-time stabilization.

Development of fertilizer-distributed algorithms based on crop growth models

Development of fertilizer-distributed algorithms based on crop growth models

Development of fertilizer-distributed algorithms based on crop growth models

Development of fertilizer-distributed algorithms based on crop growth models

干旱区谷子生长发育模拟模型的构建与评价

The establishment of crop growth models enables the simulation of the impacts of environmental changes on crop growth, providing theoretical guidance for exploring the relationship between environmental factors and crop growth. The model used the growth cycle (GC) as the simulation time step and was built upon four sub-models: topology, photosynthesis, biomass allocation, and geometric morphology. It was quantitatively utilized the concept of effective accumulated temperature (EAT) and parameters such as sink and expansion rate were used to explain the allometric growth relationships among different organs. The R2 values for the geometric morphological parameters such as leaf length, leaf width, leaf area, and internode volume ranged from 0.78 to 0.94, while the F-values for the regression equations ranged from 1533.53 to 13949.51. The R2 values for simulating leaf, internode, and earhead biomass were 0.62-0.94, 0.74-0.97, and 0.98, respectively, with RMSE values ranging from 0.02 to 0.13 g for leaf biomass, 0.03 to 0.13 g for internode biomass, and 1.71 g for earhead biomass. The results indicated that the model exhibited good performance and reliability in simulating the growth and development of leaves, internodes, and earheads. This provides a solid foundation for the development of a millet model with functional-structural feedback.

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

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.

Improving crop model accuracy in the development of regional irrigation and nitrogen schedules by using data assimilation and spatial clustering algorithms

Crop growth models have been used to develop irrigation and nitrogen schedules (INSs). However, differences in crop cultivar coefficients are often ignored in the development of regional INSs. This study aimed to formulate suitable INSs under spatial heterogeneities in crop cultivar coefficients. Therefore, we propose two strategies for retrieving maize cultivar coefficients by using a data assimilation algorithm and spatial clustering algorithm. The first strategy involves determining the cultivar coefficients for each simulation unit in the examined region and then assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm, with the cultivar coefficients employed as clustering characteristics (CAs). The second strategy involves assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm on the basis of cultivar coefficients and geographical characteristics (CAGCs). By using observational data, the accuracy of cultivar coefficients CAs and CAGCs was compared with that of commonly used regional representative coefficients (RRs) and sub-region representative coefficients (SRRs). Furthermore, we formulated INSs with these four coefficients by using yield maximization as the objective and water use efficiency (WUE) and nitrogen use efficiency (NUE) as constraints. We examined the differences between the INSs, yields, WUE values, and NUE values obtained using each of the aforementioned four coefficients and those obtained using a point optimization approach. The results revealed that the highest accuracy in the simulation of the regional leaf area index, yield, and phenological stage was exhibited by the CAGCs-based strategy, followed by the CAs-based strategy. The RRs-based and SRRs-based strategies produced considerable errors. Crucially, the INSs obtained using the CAGCs-based strategy were more similar to those obtained through point optimization and more reasonable than were the INSs obtained using the other three strategies. In addition, more accurate yield, WUE, and NUE values were obtained with the CAGCs-based strategy than with the other three coefficients-based strategies. The results of this study indicate that the combination of a data assimilation algorithm and spatial clustering algorithm can improve the application potential of crop models in agricultural systems.

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery.

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R2 and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods.