Analysis of Corn Leaf Area Index and Dry Matter Simulation Under Different Fertilization Treatments Based on APSIM Model

Analysis of Corn Leaf Area Index and Dry Matter Simulation Under Different Fertilization Treatments Based on APSIM Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Corn Leaf Area Index and Dry Matter Simulation Under Different Fertilization Treatments Based on APSIM Model Gulaimubaier Aierken, Renjuan Wei, Yi Liang, Xin Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4677500/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study simulated the effects of three fertilization schemes on maize leaf area index and dry matter accumulation using the APSIM model, and also investigated the adaptability of the APSIM model in simulating leaf area index and dry matter accumulation of summer maize in the region. Results indicated that the maize leaf area index was highest during the silking stage and lowest at the 9-leaf stage throughout the growth period. The maize leaf area index of the fertilization treatments was significantly higher than that of the non-fertilization treatment. As fertilization rates increased, the maize leaf area index gradually increased. Maize dry matter accumulation also increased with higher fertilization rates, with the fertilization treatments showing significantly higher accumulation compared to the control (CK). The APSIM model demonstrated high precision in simulating maize leaf area index and dry matter accumulation under different fertilization schemes, with root mean square error (RMSE) for leaf area index ranging from 0.093 to 0.250, coefficient of determination (R 2 ) ranging from 0.956 to 0.992, and efficiency index (Me) ranging from 0.936 to 0.990. The RMSE for maize dry matter accumulation ranged from 0.03 to 0.332, R 2 ranged from 0.961 to 0.998, and Me ranged from 0.880 to 0.980. Among the fertilization schemes, N3 exhibited the highest RMSE, R 2 , and Me values for both maize leaf area index and dry matter accumulation. Maize APSIM model Compound fertilizer Leaf area index Dry matter accumulation Figures Figure 1 Figure 2 0 Introduction Due to the increase in food production, maize is considered the primary food security crop globally. With the demand driven by population growth, maize demand is projected to increase by 70% by 2050 to feed the continuously growing rural and urban populations(Lobell, D.B,2008). Agriculture must provide food for an additional 3.5 billion people in the next 50 years. Maize, being one of the world's largest food crops, plays a crucial role in ensuring food security(Borlaug, N.,2007). Due to its wide range of uses and greater climate adaptability, maize is grown in most regions of the world, with many farmers believing in its potential for higher yields(Chen Y,2007;Zhao JR,2016).As arable land resources become scarce, the future expansion of grain cultivation in China is limited, and the key to increasing production lies in improving yield levels, with maize showing the greatest potential for yield improvement. The Central Document No.1 of 2023 emphasizes the importance of focusing on food production, implementing maize yield increase projects, and carrying out ton-grain field construction. Achieving high maize yields is essential for ensuring China's food security and remains a central focus in agricultural production(Evans LT,1999). Leaves serve as the primary sites for photosynthesis in plants. Leaf Area Index (LAI) is a crucial indicator that reflects changes in leaf area of crop populations and is closely linked to final yield levels(WANG Z,1998;ZHANG X D,2006;CAO W X,2005) Chongzhou City, known as the "Granary of Western Sichuan," is a high-quality grain and oil production area in Sichuan Province. However, there is limited research on maize growth and yield in this region. Therefore, this study focuses on Chongzhou City, investigating the effects of different fertilization levels on maize LAI and dry matter accumulation under varying fertilization conditions to provide a scientific foundation for increasing local maize yields. Many scholars are currently conducting field experiments to study the effects of different fertilization levels on maize leaf area index (LAI) and dry matter accumulation. Both domestic and international scholars have conducted extensive research on the growth laws of maize leaf area and dynamic models of LAI. The current LAI estimation models generally include rational equation models, quadratic polynomial models, logistic models, double logistic models, modified logistic models, and remote sensing inversion models(Zhang B,2007;Tong P Y,1994;Han X L,1984;Tong P Y,1996;Wang X L,1986;Zhu J M,2019;Yang W,2018;Liu Y,2018;Chen Y L,2018). Wang Ling(2004)et al. established a logistic model to study the dynamic changes of LAI in summer maize of different varieties, planting dates, and densities. Li Xiangling(2011)et al. constructed multiple LAI estimation models for different maize varieties and planting dates, with the analysis showing that the rational equation model has the highest accuracy. Zhang Bin(2007) et al. developed dynamic estimation models for LAI of different varieties and planting dates of maize, wheat, and rice. However, there is limited research on simulating the processes of maize LAI and dry matter accumulation based on the APSIM model. The APSIM model is a mechanistic model with strong capabilities in crop planting systems, crop ecophysiological mechanisms, and crop rotation. Compared to other crop models, the APSIM model is more widely used and more accurate. This study utilizes the APSIM model to analyze the effects of different fertilization levels on LAI and dry matter accumulation in summer maize, aiming to provide scientific support for high-yield and efficient maize production. 1 Materials and Methods 1.1 Overview of the Study Area The experiment was conducted at the experimental base of Sichuan Water Conservancy Technical College, Chongzhou, Chengdu, Sichuan Province, China, according to the "National Management Standards for Crop Germplasm Resources Bank" issued by the Ministry of Agriculture and Rural Affairs of the People's Republic of China.The region experiences distinct seasons, with short springs and autumns, long winters and summers, abundant rainfall, low sunshine, and a relatively long frost-free period. The average annual temperature is 15.9°C, with July being the hottest month at an average of 25°C, and January being the coldest month at an average of 5.4°C. The annual average sunshine hours are 1161.5 hours, and the annual average rainfall is 1012.4mm. The predominant wind direction is calm, with an average annual wind speed of 1.3m/s. 1.2 Field Experiment Design The experiment will be conducted at the Sichuan Water Conservancy Technology Research and Training Base from 2022 to 2023, using spring corn (Nongke Nuò 336) as the test crop. Sowing will occur in mid-March, with harvest in July. The application rates of compound fertilizer (15-15-15) will be N1 (575kg/ha), N2 (675kg/ha), N3 (890kg/ha), and CK (no fertilizer applied). Each treatment in the experimental design will have 3 replications, with a plot area of 30m 2 . The cropping methods for all crops will be traditional, with a planting density of 50,000 plants/ha and a sowing depth of 45mm. 1.3 Test Items and Methods Select 10 plants with consistent growth at the 9-leaf stage, 12-leaf stage, silking stage, mid-grain filling stage (25 days after silking), and mature stage. Measure leaf area and calculate the Leaf Area Index (LAI). Then, choose 5 plants, wilt them at 105°C in an oven for 30 minutes, dry them at 80°C until a constant weight is reached, and weigh the dry matter. Harvest at maturity for yield measurement, selecting 20 panicles per subplot for seed inspection. (Measurements taken every 7 days) Single leaf area = length ×width × coefficient (0.75); LAI = single plant leaf area × number of plants per unit land area ÷unit area 1.4 APSIM Model 1.4.1 Model Overview APSIM, a mechanistic model developed by the Agricultural Production Systems Research Unit since 1991, can simulate various aspects of crop, soil, animal, and climate research. In this study, a soil model is utilized to simulate the fertilizer requirements and yield formation processes of spring maize(Asseng S,1998;Asseng S,2000). The crop-related parameters are outlined in Table. Table 1 Crop parameters Parameters Numerals Weight of a Thousand Grains (g) 400 Line spacing (mm) 500 Sow Deep (mm) 45 Plant density (plant/hm-2) 50000 Interplant distance (mm) 400 1.4.2 Model Calibration and Validation Methods The tuning and validation of the APSIM model typically involve statistical parameters for verification, such as the determination coefficient R 2 , the root mean square error RMSE, and the model's effectiveness index (Me)(REN Zhu,2014). The degree of fit between the model's simulated values and actual observations is influenced by the model's accuracy and observation errors. R 2 reflects the deviation of the model's simulated values from the measured values(ZhouJuncheng,2022); a higher R 2 value indicates a higher percentage of total variation explained by the independent variable. A smaller RMSE value suggests better consistency between simulated and measured values(ZHOU Juncheng,2022). Wang(2020) suggests that the model's simulation results are better when Me is above 0.5. The calculation formulas for RMSE, R 2 , and Me indices are as follows: In the formula, OBS represents the measured value, SIM represents the simulated value, n is the sample size, i is the sample number, and MEAN is the average value of the measured data. 1.5 Data Source and Processing The meteorological data utilized in this study were automatically collected by the Chongzhou Meteorological Station. The data pertaining to the growth and development of summer maize, soil parameters, and field management information were obtained from actual field measurements conducted in the research area (Table 2). Summer maize yield data under various fertilization treatments for model calibration were derived from field measurements at the experimental site between 2022 and 2023, while other data were simulated using the APSIM model. Data organization was initially carried out using Microsoft Excel 2010, linear fitting was conducted using Origin 2019, and statistical analysis was performed using SPSS. Table 2 Main parameters of soil module Depth of soil/ cm Weight Capacity/ （g·cm -3 ） Water content in the air-dried state/ （mm·mm -1 ） Wilt coefficient/ （mm·mm -1 ） The maximum water holding capacity of the field/ （mm·mm -1 ） Saturated moisture content/ （mm·mm -1 ） 0~10 0.95 0.013 0.112 0.252 0.48 10~20 1.21 0.027 0.143 0.252 0.45 20~30 1.41 0.041 0.159 0.252 0.49 2 Results and Analysis 2.1 Influence of Different Fertilization Treatments on Maize Leaf Area Index The leaf area index of maize is highest during the silking stage and lowest at the 9-leaf stage, as shown in Figure 1. Maize under fertilization treatment exhibits a significantly greater leaf area index compared to no fertilization treatment. The leaf area index of maize increases gradually with higher fertilization amounts. Specifically, at the 9-leaf stage, the leaf area index of maize under treatments NI, N2, and N3 increased by 21.83%, 26.95%, and 43.40% respectively compared to CK. At the 12-leaf stage, the leaf area index increased by 29.74%, 35.84%, and 42.67% under treatments NI, N2, and N3 compared to CK. During the silking stage, the leaf area index increased by 23.25%, 27.71%, and 32.63% under treatments NI, N2, and N3 compared to CK. 25 days after silking, the leaf area index increased by 10.77%, 14.17%, and 28.75% under treatments NI, N2, and N3 compared to CK. At maturity, the leaf area index increased by 24.41%, 29.17%, and 54.35% under treatments NI, N2, and N3 compared to CK. Table 3 Statistical analysis table of actual and simulated leaf area index values under different fertilization treatments Schemes hindex 9-leaf spread 12-leaf spread Spinning stage 25 days after spinning Mature stage CK RMSE 0.090 0.099 0.090 0.098 0.200 R 2 99.26% 99.02% 99.19% 99.04% 95.84% Me 0.993 0.990 0.992 0.990 0.958 N1 RMSE 0.200 0.132 0.198 0.200 0.200 R 2 95.96% 98.26% 96.08% 96.04% 95.84% Me 0.960 0.983 0.961 0.960 0.958 N2 RMSE 0.099 0.093 0.100 0.198 0.200 R 2 99.02% 99.14% 99.10% 96.08% 96.12% Me 0.990 0.991 0.991 0.961 0.961 N3 RMSE 0.090 0.200 0.140 0.190 0.250 R 2 99.26% 95.84% 98.07% 96.24% 93.70% Me 0.993 0.958 0.981 0.962 0.937 Utilizing the APSIM model to analyze the results of fertilization experiments conducted from 2022 to 2023, Table 3 presents the RMSE, R 2 , and Me values of the model under various fertilization schemes. The model is comprehensively evaluated based on the three statistical measures: RMSE, R 2 , and Me. 1) N1 Scheme: Throughout the maize growth period, the RMSE values of the model remained relatively stable, ranging from 0.132 to 0.200, indicating a consistent match between the simulated values of the APSIM model and the measured values, resulting in a good simulation effect. The coefficient of determination (R 2 ) is close to 1, ranging from 0.956 to 0.982, indicating a high level of agreement between the model's calculated data and actual data. The Me values of the APSIM model range from 0.958 to 0.983, showing stability and an increasing trend during the critical period of maize development, reflecting effective fitting of the APSIM model to LAI. 2) N2 Scheme: The RMSE values of the model during the entire maize growth period ranged from 0.093 to 0.200, indicating good consistency between the simulated values of the APSIM model and the measured values, resulting in a good simulation effect. The R 2 values ranged from 0.960 to 0.991, showing a high level of agreement between the model's calculated data and actual data. The Me values ranged from 0.961 to 0.990, with values exceeding 0.75 in each stage, demonstrating a very good simulation effect. 3) N3 Scheme: The RMSE values of the model ranged from 0.140 to 0.250 during the entire maize growth period, indicating good consistency between the simulated values of the APSIM model and the measured values, resulting in a good simulation effect. The R 2 values ranged from 0.937 to 0.992, showing a high level of agreement between the model's calculated data and actual data. The Me values ranged from 0.936 to 0.990, demonstrating a very good simulation effect. 2.2 Impact of Different Fertilization Treatments on Maize Dry Matter Accumulation The accumulation of maize dry matter increases with the fertilization amount throughout the growth period, as shown in Figure 2. Fertilization treatments exhibit significantly greater maize dry matter accumulation compared to the CK treatment. The leaf area index of maize in the NI, N2, and N3 treatments increased significantly during the jointing stage by 42.92%, 54.03%, and 85.84% respectively, compared to CK. Similarly, during the tasseling stage, the leaf area index increased significantly by 6.85%, 9.31%, and 16.32% in the NI, N2, and N3 treatments compared to CK. Furthermore, during the flowering stage, the leaf area index in the NI, N2, and N3 treatments increased significantly by 2.61%, 2.83%, and 8.81% compared to CK. Finally, during the maturity stage, the leaf area index in the NI, N2, and N3 treatments increased significantly by 7.98%, 11.67%, and 23.59% compared to CK. Using the APSIM model to assess the results of the fertilization experiment conducted from 2022 to 2023, Table 4 presents the RMSE, R 2 , and Me values of the model under different fertilization schemes. A comprehensive evaluation of the model is carried out based on the three statistical metrics of RMSE, R 2 , and Me: 1) N1 scheme: Throughout the maize growth period, the RMSE values of the model remained relatively stable, ranging from 0.110 to 0.332, indicating good agreement between the simulated values of the APSIM model and the observed values. The R 2 values are close to 1, ranging from 0.961 to 0.981, showing a high level of correlation between the model's calculated data and the actual data. The Me values range from 0.88 to 0.95, with a gradual increase during key stages of maize development, suggesting a good fit of the APSIM model to maize dry matter accumulation. 2) N2 scheme: The RMSE values of the model remained stable throughout the maize growth period, ranging from0.033 to 0.109, indicating good agreement between the simulated values of the APSIM model and the observed values. The R 2 values are close to 1, ranging from 0.968 to 0.987, demonstrating a high level of correlation between the model's calculated data and the actual data. The Me values range from 0.89 to 0.95, with values exceeding 0.75 at each stage, indicating very good simulation effects. 3) N3 scheme: The RMSE values of the model remained stable throughout the maize growth period, ranging from 0.030to 0.060, indicating good agreement between the simulated values of the APSIM model and the observed values. The R 2 values are close to 1, ranging from 0.967 to 0.998, showing a high level of correlation between the model's calculated data and the actual data. The Me values range from 0.92 to 0.98, demonstrating very good simulation effects. Table 4 Statistical Analysis Table of Measured and Simulated Dry Matter Accumulation under Different Fertilization Treatments Schemes hindex Jointing stage Big flare period Efflorescence Mature stage CK RMSE 0.118 0.385 0.485 0.302 R 2 96.42% 95.83% 95.08% 96.84% Me 0.86 0.91 0.92 0.96 N1 RMSE 0.118 0.200 0.332 0.110 R 2 98.02% 96.14% 98.10% 96.42% Me 0.88 0.93 0.95 0.94 N2 RMSE 0.033 0.063 0.081 0.109 R 2 97.26% 96.84% 98.07% 98.70% Me 0.90 0.91 0.89 0.95 N3 RMSE 0.058 0.060 0.040 0.030 R 2 96.77% 99.29% 99.68% 99.84% Me 0.92 0.96 0.98 0.94 3 Discussion Grain accumulation serves as a crucial indicator of crop growth and development(DORDAS C A,2009). The level of grain yield in crops is primarily determined by the amount of dry matter accumulation and is also influenced by the harvest index(TOLLENAAR M,1982). Studies have demonstrated a positive correlation between dry matter accumulation and yield within a certain range(HuangZhenxi,2007). High biomass is the foundation for achieving high yield(HuangZhihong,2007), therefore, enhancing dry matter accumulation during the maize growing period is an effective approach to increasing grain yield(CongYanxia,2008). Wang Qingcheng(2004) et al argue that dry matter serves as the material basis for yield formation, and only high accumulation of dry matter can result in high grain yield. This experiment reveals a significant increase in maize dry matter accumulation with the gradual rise in fertilizer application. Among treatments with the same amount of fertilization, the one with the highest dry matter accumulation during the maturity stage also exhibits the highest yield, in line with previous research findings. Leaf area, a significant dynamic indicator in crop growth and development, reflects the longitudinal and horizontal expansion capabilities of crops and is closely associated with the dry matter and yield of crops(WEI S,2010). Research conducted by Qin Wenli(2006) et al indicates that with a certain amount of nitrogen and phosphorus fertilizers, potassium application enhances the height of summer maize, although the degree of change is relatively minor, and excessive potassium fertilizer can actually decrease maize plant height. Yu Ning(2020) et al.'s study reveals that inadequate fertilization notably reduces the leaf area index (LAI) of summer maize. The results of this study suggest that maize leaf area index increases with an increase in fertilizer application, aligning with previous research outcomes. 4 Conclusion This study is based on observational data collected from the experimental fields at the Sichuan Water Conservancy Technology Research and Training Base in 2022-2023. Three different fertilization schemes were designed and implemented, and the APSIM model was utilized to simulate the leaf area index (LAI) and dry matter accumulation of maize under these schemes. The consistency, explanatory power, and effectiveness of the APSIM model in simulating summer maize LAI were analyzed using statistical indicators such as RMSE, R 2 , and Me. The main conclusions are as follows: (1) Throughout the growth period, maize LAI reached its peak at the silking stage and was lowest at the 9-leaf stage. Maize LAI was significantly higher under fertilization treatments compared to no fertilization. Increasing fertilizer application led to a gradual increase in maize LAI. When simulating summer maize LAI with the APSIM model, RMSE values for the entire growth period ranged from [0.132, 0.200] under the N1 scheme, R 2 ranged from [0.956, 0.982], and Me ranged from [0.958, 0.983]; under the N2 scheme, RMSE ranged from [0.093, 0.200], R 2 ranged from [0.960, 0.991], and Me ranged from [0.961, 0.990]; under the N3 scheme, RMSE ranged from [0.140, 0.250], R 2 ranged from [0.937, 0.992], and Me ranged from [0.936, 0.990]. (2) Throughout the growth period, maize dry matter accumulation increased with higher fertilizer application, with significantly higher accumulation under fertilization treatments compared to the control (CK). Under the N1 scheme, RMSE ranged from [0.11, 0.332], R 2 ranged from [0.961, 0.981], and Me ranged from [0.88, 0.95]; under the N2 scheme, RMSE ranged from [0.033, 0.109], R 2 ranged from [0.968, 0.987], and Me ranged from [0.89, 0.95]; under the N3 scheme, RMSE ranged from [0.03, 0.06], R 2 ranged from [0.967, 0.998], and Me ranged from [0.92, 0.98], indicating excellent simulation effects. Declarations Author Contribution The author, gulaimubaier, was responsible for conducting experiments, collecting data and writing the analytical part of the paper. The author Liang Yi was mainly responsible for sorting and analyzing the data; The author Wei Renjuan was mainly responsible for drawing, making tables and collecting documents; The author, Zhao Xin, was responsible for the final review. Data Availability The data has already been submitted in the document, and the data in Table 1 and Table 2 are the data used in the model Plant Ethics: The experiment was conducted at the experimental base of Sichuan Water Conservancy Technical College, Chongzhou, Chengdu, Sichuan Province, China, according to the "National Management Standards for Crop Germplasm Resources Bank" issued by the Ministry of Agriculture and Rural Affairs of the People's Republic of China. References Lobell, D.B., M.B. Burke, C. Tebaldi, et al., Prioritizing climate change adaptation needs for food security in 2030 [J]. Science, 2008. 319(5863): p. 607-10. Borlaug, N., Feeding a hungry world [J]. Science, 2007. 318(5849): p. 359. Chen YJ, Wang QQ, Xiang Y. Analysis on the status, superiority and self-sufficiency ratio of maize in China[J].Chin J Agric Resour Reg Plan, 2019, 40(1):7-16. Zhao JR, Wang RH, Liu XX. Present situation of maize industry in China and development trend of biological breeding[J].Biotechnol Bus, 2016(3):45-52. Evans LT, Fischer RA. Yield potential: its definition, measurement, and significance[J].Crop Sci, 1999, 39(6):1544-1551. WANG Z, JU Z G, LU J D. Grain leaf ratio in relation to photosynthesis and grain yield in maize. Journal of Nanjing Agricultural University, 1998(1): 3-5. (in Chinese) ZHANG X D , CAI H J , FU Y J, WANG J. Study on leaf area index of summer maize in loess areas. Agricultural Research in Arid Areas, 2006, 24(2): 25-29. (in Chinese) CAO W X, ZHU Y. Crop Management Knowledge Model. Beijing: China Agriculture Press, 2005: 104-107. (in Chinese) Zhang B,Zhao M,Dong Z Q,et al.Establishment and test of LAI dynamic simulation model for high yield population[J]. Acta Agronomica Sinica,2007,33(4):612-619.(in Chinese) Tong P Y,Cheng Y N.Dynamics of leaf area of different plant-types in maize[J].Beijing Agricultural Sciences,1994, 12(6):1-4.(in Chinese) Han X L,Kong Y Z,Chen L.Preliminary analysis of climate and corn productivity[J].Chinese Journal of Agrometeorology, 1984(2):15-20.(in Chinese) Tong P Y,Ling B Y,Guan Y X.Dynamic simulating of dry matter accumulation in summer corn[J].Beijing Agricultural Sciences,1996,14(5):22-25.(in Chinese) Wang X L.How to use the Logistic equation reasonably in the simulation of dry matter accumulation of crops[J]. Chinese Journal of Agrometeorology,1986,8(1):14-19.(in Chinese) Zhu J M,Guo Y K,Liu H Y,et al.A method of combining the inversion of leaf area index[J].Science of Surveying and Mapping,2019,44(1):60-65.(in Chinese) Yang W,Zhang X X,Zhao J Y.Remote sensing inversion of leaf area index based on Geosail model and SVR algorithm [J].Science of Soil and Water Conservation,2018,16(6): 48-55.(in Chinese) Liu Y,Yue H,Zhang F,et al.Drought monitoring of winter wheat in Henan province based on LAI-Ts space[J].Chinese Journal of Agrometeorology,2018,39(2):129-139.(in Chinese) Chen Y L,Gu X H,Gong A D,et al.Estimation of winter wheat assimilation based on remote sensing information and WOFOST crop model[J].Journal of Triticeae Crops,2018,38 (9):1127-1136.(in Chinese) Wang L,Xie D T,Liu H L,et al.A universal growth model for maize leaf area index[J].Journal of Southwest Agricultural University(Natural Science),2004,26(3):303-306.(in Chinese) Li X L,Zhao M,Li C F,et al.Dynamic characteristics of leaf area index in maize and its model establishment based on accumulated temperature[J].Acta Agronomica Sinica,2011, 37(2):321-330.(in Chinese) Asseng S. Keating B A, Fillery I R P, et al. Performance of the APSIMwheat model in Western Australia [J]. Field Crops Res, 1998, 57(2):163- 179. Asseng S, Keulen H V, Stol W, et al. Performance and application of the APSIM N- wheat model in the Netherlands [J]. Europ J Agronomy, 2000, 12(1):37- 54. REN Zhu,ZHAO Huina,ZENG Yuntao,et al.Changes of Key Enzymes in Sucrose Metabolism of Flue-cured Tobacco in Different Ecological Regions[J].Guizhou Agricultural Sciences,2014,42(9):52～54. ZhouJuncheng,WuJingjing,ChenXiaoyang,et al.The influence of transplanting period on the carbon and nitrogen metabolism products and sensory quality of 'Yunyan 87' cured tobacco[J].Specialty Research,2022,44(5):53～57. ZHOU Juncheng,WU Jingjing,CHEN Xiaoxiang,et al.Effects of Transplanting Period on Carbon And Nitrogen Metabolites And Sensory Quality of Cured Yunyan 87 Tobacco Leaves[J],Special Wild Economic Animal and Plant Research,2022,44(5):53～57. Wang L,Li Q,Coulter J A,et al.Winter wheat yield and water use efficiency response to organic fertilization in northern China:a meta-analysis[J].Agricultural Water Management,2020(229):105934. DORDAS C A,SIOULAS C.Laboratory dry matter and nitrogen accumulation,partitioning，and retranslocation in safflower（Carthamustinctorius L.）as affected by nitrogen fertilization[J].Field Crops Res,2009,110（1）:35-43. TOLLENAAR M,DAYNARD T B.Effect of source-sink ratio on dry matter accumulation and leaf senescence of maize[J].Can J Plant Sci,1982,62（4）:855-860． HuangZhenxi,WangYongjun,WangKongjun,et al.Photosynthetic characteristics during grain filling period of summer maize with a yield of over 15,000 kg/ha[J].Chinese Agricultural Science,2007,40（9）:1898-1906． HuangZhihong,WangSiyuan,BaoYan,et al.Research on the characteristics of dry matter accumulation and distribution in high-yielding maize varieties[J].Corn Science,2007,15（3）:95-98． CongYanxia,zhaoming,Dongzhiqiang,et al.Regulation of Spring Maize Dry Matter Accumulation and Stem Morphology by Yichong Mixture[J].Crop Magazine,2008（4）:68-71． Wangqingcheng,Liukaichang.Cultivation Theory and Practice of High-yield Maize in Shandong Summer[J].Corn Science,2004,12:60-62,65． WEI S. Ecological Basis of Maize. Beijing: China Agriculture Press, 2010: 40-49. (in Chinese) QIN W L, LI C J, LIU M C, HAN B W. Effect of different NPK ratios on main characteristics and yield of summer corn. Journal of Hebei Agricultural Sciences, 2006, 10(3): 27-29. (in Chinese) YU N N, ZHANG J W, REN B Z, ZHAO B, LIU P. Effect of integrated agronomic managements on leaf growth and endogenous hormone content of summer maize. Acta Agronomica Sinica, 2020, 46(6): 960-967. (in Chinese) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4677500","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336011352,"identity":"a486e7c4-f70b-4c3f-a484-27af56d263cd","order_by":0,"name":"Gulaimubaier Aierken","email":"","orcid":"","institution":"Sichuan Water Conservancy Technical College","correspondingAuthor":false,"prefix":"","firstName":"Gulaimubaier","middleName":"","lastName":"Aierken","suffix":""},{"id":336011353,"identity":"8625aa96-80bc-4a14-9639-4cd44964a9cf","order_by":1,"name":"Renjuan Wei","email":"","orcid":"","institution":"Sichuan Water Conservancy Technical College","correspondingAuthor":false,"prefix":"","firstName":"Renjuan","middleName":"","lastName":"Wei","suffix":""},{"id":336011354,"identity":"cccc05a3-e921-404f-b929-ee454620da22","order_by":2,"name":"Yi Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACNvbmAwc/GNjU8xOthY/nWOJjiYq0BMkGYrXISeQoG/CcOZxgcIBohzHksElItqXlGR9P3sDwo2IbMVrOHpMobLMpNjvzrICx58xtIrQw9qWBbGHcdiPHgJmxjRgtzDxmErxthxk3zyBaCxuPMcj7iRskiNbCwwYOZGMJoF8OEuUX+fmPwVEpx9+evPHBjwoitCABEqIGoYVUHaNgFIyCUTBCAACffDz6s/J43gAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan Water Conservancy Technical College","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liang","suffix":""},{"id":336011355,"identity":"282a64eb-8626-4746-8ffb-7bf781981004","order_by":3,"name":"Xin Zhao","email":"","orcid":"","institution":"Sichuan Water Conservancy Technical College","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-07-03 04:22:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4677500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4677500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62280567,"identity":"b82ba887-cf5e-49ef-8ded-7d2a84c85443","added_by":"auto","created_at":"2024-08-12 12:19:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34488,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic changes of LAI under different fertilization schemes based on APSIM model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4677500/v1/cfe9c8d350026c25ff581820.png"},{"id":62281254,"identity":"d4c0eea9-cd5c-4142-9f01-20ac6e7b69b4","added_by":"auto","created_at":"2024-08-12 12:27:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37886,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic changes in dry matter accumulation under different fertilization schemes based on the APSIM model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4677500/v1/da1a39a761df881dc1a04fc8.png"},{"id":81426397,"identity":"6924bac1-ab4c-4e60-a545-728b30bf998e","added_by":"auto","created_at":"2025-04-26 07:31:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":631221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4677500/v1/e3bcc895-fcef-4eb7-89ec-1d858de9548b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAnalysis of Corn Leaf Area Index and Dry Matter Simulation Under Different Fertilization Treatments Based on APSIM Model\u003c/p\u003e","fulltext":[{"header":"0 Introduction","content":"\u003cp\u003eDue to the increase in food production, maize is considered the primary food security crop globally. With the demand driven by population growth, maize demand is projected to increase by 70% by 2050 to feed the continuously growing rural and urban populations(Lobell, D.B,2008). Agriculture must provide food for an additional 3.5\u0026nbsp;billion people in the next 50 years. Maize, being one of the world's largest food crops, plays a crucial role in ensuring food security(Borlaug, N.,2007). Due to its wide range of uses and greater climate adaptability, maize is grown in most regions of the world, with many farmers believing in its potential for higher yields(Chen Y,2007;Zhao JR,2016).As arable land resources become scarce, the future expansion of grain cultivation in China is limited, and the key to increasing production lies in improving yield levels, with maize showing the greatest potential for yield improvement. The Central Document No.1 of 2023 emphasizes the importance of focusing on food production, implementing maize yield increase projects, and carrying out ton-grain field construction. Achieving high maize yields is essential for ensuring China's food security and remains a central focus in agricultural production(Evans LT,1999). Leaves serve as the primary sites for photosynthesis in plants. Leaf Area Index (LAI) is a crucial indicator that reflects changes in leaf area of crop populations and is closely linked to final yield levels(WANG Z,1998;ZHANG X D,2006;CAO W X,2005) Chongzhou City, known as the \"Granary of Western Sichuan,\" is a high-quality grain and oil production area in Sichuan Province. However, there is limited research on maize growth and yield in this region. Therefore, this study focuses on Chongzhou City, investigating the effects of different fertilization levels on maize LAI and dry matter accumulation under varying fertilization conditions to provide a scientific foundation for increasing local maize yields.\u003c/p\u003e \u003cp\u003eMany scholars are currently conducting field experiments to study the effects of different fertilization levels on maize leaf area index (LAI) and dry matter accumulation. Both domestic and international scholars have conducted extensive research on the growth laws of maize leaf area and dynamic models of LAI. The current LAI estimation models generally include rational equation models, quadratic polynomial models, logistic models, double logistic models, modified logistic models, and remote sensing inversion models(Zhang B,2007;Tong P Y,1994;Han X L,1984;Tong P Y,1996;Wang X L,1986;Zhu J M,2019;Yang W,2018;Liu Y,2018;Chen Y L,2018). Wang Ling(2004)et al. established a logistic model to study the dynamic changes of LAI in summer maize of different varieties, planting dates, and densities. Li Xiangling(2011)et al. constructed multiple LAI estimation models for different maize varieties and planting dates, with the analysis showing that the rational equation model has the highest accuracy. Zhang Bin(2007) et al. developed dynamic estimation models for LAI of different varieties and planting dates of maize, wheat, and rice. However, there is limited research on simulating the processes of maize LAI and dry matter accumulation based on the APSIM model. The APSIM model is a mechanistic model with strong capabilities in crop planting systems, crop ecophysiological mechanisms, and crop rotation. Compared to other crop models, the APSIM model is more widely used and more accurate. This study utilizes the APSIM model to analyze the effects of different fertilization levels on LAI and dry matter accumulation in summer maize, aiming to provide scientific support for high-yield and efficient maize production.\u003c/p\u003e"},{"header":"1 Materials and Methods","content":"\u003ch2\u003e\u003cem\u003e1.1\u0026nbsp;\u003c/em\u003e\u003cem\u003eOverview of the Study Area\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe experiment was conducted at the experimental base of Sichuan Water Conservancy Technical College, Chongzhou, Chengdu, Sichuan Province, China, \u0026nbsp;according to the \u0026quot;National Management Standards for Crop Germplasm Resources Bank\u0026quot; issued by the Ministry of Agriculture and Rural Affairs of the People\u0026apos;s Republic of China.The region experiences distinct seasons, with short springs and autumns, long winters and summers, abundant rainfall, low sunshine, and a relatively long frost-free period. The average annual temperature is 15.9\u0026deg;C, with July being the hottest month at an average of 25\u0026deg;C, and January being the coldest month at an average of 5.4\u0026deg;C. The annual average sunshine hours are 1161.5 hours, and the annual average rainfall is 1012.4mm. The predominant wind direction is calm, with an average annual wind speed of 1.3m/s.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e1.2 Field Experiment Design\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe experiment will be conducted at the Sichuan Water Conservancy Technology Research and Training Base from 2022 to 2023, using spring corn (Nongke Nu\u0026ograve; 336) as the test crop. Sowing will occur in mid-March, with harvest in July. The application rates of compound fertilizer (15-15-15) will be N1 (575kg/ha), N2 (675kg/ha), N3 (890kg/ha), and CK (no fertilizer applied). Each treatment in the experimental design will have 3 replications, with a plot area of 30m\u003csup\u003e2\u003c/sup\u003e. The cropping methods for all crops will be traditional, with a planting density of 50,000 plants/ha and a sowing depth of 45mm.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e\u0026nbsp;1.3 Test Items and Methods\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eSelect 10 plants with consistent growth at the 9-leaf stage, 12-leaf stage, silking stage, mid-grain filling stage (25 days after silking), and mature stage. Measure leaf area and calculate the Leaf Area Index (LAI). Then, choose 5 plants, wilt them at 105\u0026deg;C in an oven for 30 minutes, dry them at 80\u0026deg;C until a constant weight is reached, and weigh the dry matter. Harvest at maturity for yield measurement, selecting 20 panicles per subplot for seed inspection. (Measurements taken every 7 days)\u003c/p\u003e\n\u003cp\u003eSingle leaf area = length\u0026nbsp;\u0026times;width\u0026nbsp;\u0026times;\u0026nbsp;coefficient (0.75); LAI = single plant leaf area\u0026nbsp;\u0026times;\u0026nbsp;number of plants per unit land area\u0026nbsp;\u0026divide;unit area\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e1.4 \u0026nbsp; APSIM Model\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e1.4.1 \u0026nbsp;Model Overview\u003c/p\u003e\n\u003cp\u003eAPSIM, a mechanistic model developed by the Agricultural Production Systems Research Unit since 1991, can simulate various aspects of crop, soil, animal, and climate research. In this study, a soil model is utilized to simulate the fertilizer requirements and yield formation processes of spring maize(Asseng S,1998;Asseng S,2000). The crop-related parameters are outlined in Table.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 Crop parameters\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"66.66666666666667%\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eNumerals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"66.66666666666667%\"\u003e\n \u003cp\u003eWeight of a Thousand Grains (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"66.66666666666667%\"\u003e\n \u003cp\u003eLine spacing (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"66.66666666666667%\"\u003e\n \u003cp\u003eSow Deep (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"66.66666666666667%\"\u003e\n \u003cp\u003ePlant density (plant/hm-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e50000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"66.66666666666667%\"\u003e\n \u003cp\u003eInterplant distance (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1.4.2 \u0026nbsp;Model Calibration and Validation Methods\u003c/p\u003e\n\u003cp\u003eThe tuning and validation of the APSIM model typically involve statistical parameters for verification, such as the determination coefficient R\u003csup\u003e2\u003c/sup\u003e, the root mean square error RMSE, and the model\u0026apos;s effectiveness index (Me)(REN Zhu,2014). The degree of fit between the model\u0026apos;s simulated values and actual observations is influenced by the model\u0026apos;s accuracy and observation errors. R\u003csup\u003e2\u003c/sup\u003e reflects the deviation of the model\u0026apos;s simulated values from the measured values(ZhouJuncheng,2022); a higher R\u003csup\u003e2\u003c/sup\u003e value indicates a higher percentage of total variation explained by the independent variable. A smaller RMSE value suggests better consistency between simulated and measured values(ZHOU Juncheng,2022). Wang(2020) suggests that the model\u0026apos;s simulation results are better when Me is above 0.5. The calculation formulas for RMSE, R\u003csup\u003e2\u003c/sup\u003e, and Me indices are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" height=\"265\" width=\"560\"\u003e\u003c/p\u003e\n\u003cp\u003eIn the formula, OBS represents the measured value, SIM represents the simulated value, n is the sample size, i is the sample number, and MEAN is the average value of the measured data.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e1.5 \u0026nbsp;Data Source and Processing\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe meteorological data utilized in this study were automatically collected by the Chongzhou Meteorological Station. The data pertaining to the growth and development of summer maize, soil parameters, and field management information were obtained from actual field measurements conducted in the research area (Table 2). Summer maize yield data under various fertilization treatments for model calibration were derived from field measurements at the experimental site between 2022 and 2023, while other data were simulated using the APSIM model.\u003c/p\u003e\n\u003cp\u003eData organization was initially carried out using Microsoft Excel 2010, linear fitting was conducted using Origin 2019, and statistical analysis was performed using SPSS.\u003c/p\u003e\n\u003cp\u003eTable 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMain parameters of soil module\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.58206429780034%\" style=\"width: 14.9228%;\"\u003e\n \u003cp\u003eDepth of soil/\u003c/p\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.382402707275803%\" style=\"width: 14.2367%;\"\u003e\n \u003cp\u003eWeight Capacity/\u003c/p\u003e\n \u003cp\u003e（g\u0026middot;cm\u003csup\u003e-3\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.736040609137056%\" style=\"width: 16.9811%;\"\u003e\n \u003cp\u003eWater content in the air-dried state/\u003c/p\u003e\n \u003cp\u003e（mm\u0026middot;mm\u003csup\u003e-1\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" style=\"width: 16.9811%;\"\u003e\n \u003cp\u003eWilt coefficient/\u003c/p\u003e\n \u003cp\u003e（mm\u0026middot;mm\u003csup\u003e-1\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.473773265651438%\" style=\"width: 19.8971%;\"\u003e\n \u003cp\u003eThe maximum water holding capacity of the field/\u003c/p\u003e\n \u003cp\u003e（mm\u0026middot;mm\u003csup\u003e-1\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.751269035532996%\" style=\"width: 16.9811%;\"\u003e\n \u003cp\u003eSaturated moisture content/\u003c/p\u003e\n \u003cp\u003e（mm\u0026middot;mm\u003csup\u003e-1\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.9228%;\"\u003e\n \u003cp\u003e0~10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2367%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8971%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.9228%;\"\u003e\n \u003cp\u003e10~20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2367%;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8971%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.9228%;\"\u003e\n \u003cp\u003e20~30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2367%;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8971%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9811%;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"2 Results and Analysis","content":"\u003ch2\u003e\u003cem\u003e2.1 \u0026nbsp;Influence of Different Fertilization Treatments on Maize Leaf Area Index\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe leaf area index of maize is highest during the silking stage and lowest at the 9-leaf stage, as shown in Figure 1. Maize under fertilization treatment exhibits a significantly greater leaf area index compared to no fertilization treatment. The leaf area index of maize increases gradually with higher fertilization amounts. Specifically, at the 9-leaf stage, the leaf area index of maize under treatments NI, N2, and N3 increased by 21.83%, 26.95%, and 43.40% respectively compared to CK. At the 12-leaf stage, the leaf area index increased by 29.74%, 35.84%, and 42.67% under treatments NI, N2, and N3 compared to CK. During the silking stage, the leaf area index increased by 23.25%, 27.71%, and 32.63% under treatments NI, N2, and N3 compared to CK. 25 days after silking, the leaf area index increased by 10.77%, 14.17%, and 28.75% under treatments NI, N2, and N3 compared to CK. At maturity, the leaf area index increased by 24.41%, 29.17%, and 54.35% under treatments NI, N2, and N3 compared to CK.\u003c/p\u003e\n\u003cp\u003eTable 3 Statistical analysis table of actual and simulated leaf area index values under different fertilization treatments\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eSchemes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003ehindex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e9-leaf spread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e12-leaf spread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eSpinning stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e25 days after spinning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eMature stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" rowspan=\"3\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e99.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e99.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e99.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e99.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e95.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" rowspan=\"3\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e95.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e98.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e96.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e96.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e95.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" rowspan=\"3\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e99.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e99.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e99.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e96.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e96.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" rowspan=\"3\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e99.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e95.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e98.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e96.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e93.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUtilizing the APSIM model to analyze the results of fertilization experiments conducted from 2022 to 2023, Table 3 presents the RMSE, R\u003csup\u003e2\u003c/sup\u003e, and Me values of the model under various fertilization schemes. The model is comprehensively evaluated based on the three statistical measures: RMSE, R\u003csup\u003e2\u003c/sup\u003e, and Me.\u003c/p\u003e\n\u003cp\u003e1) N1 Scheme: Throughout the maize growth period, the RMSE values of the model remained relatively stable, ranging from 0.132 to 0.200, indicating a consistent match between the simulated values of the APSIM model and the measured values, resulting in a good simulation effect. The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) is close to 1, ranging from 0.956 to 0.982, indicating a high level of agreement between the model\u0026apos;s calculated data and actual data. The Me values of the APSIM model range from 0.958 to 0.983, showing stability and an increasing trend during the critical period of maize development, reflecting effective fitting of the APSIM model to LAI.\u003c/p\u003e\n\u003cp\u003e2) N2 Scheme: The RMSE values of the model during the entire maize growth period ranged from 0.093 to 0.200, indicating good consistency between the simulated values of the APSIM model and the measured values, resulting in a good simulation effect. The R\u003csup\u003e2\u003c/sup\u003e values ranged from 0.960 to 0.991, showing a high level of agreement between the model\u0026apos;s calculated data and actual data. The Me values ranged from 0.961 to 0.990, with values exceeding 0.75 in each stage, demonstrating a very good simulation effect.\u003c/p\u003e\n\u003cp\u003e3) N3 Scheme: The RMSE values of the model ranged from 0.140 to 0.250 during the entire maize growth period, indicating good consistency between the simulated values of the APSIM model and the measured values, resulting in a good simulation effect. The R\u003csup\u003e2\u003c/sup\u003e values ranged from 0.937 to 0.992, showing a high level of agreement between the model\u0026apos;s calculated data and actual data. The Me values ranged from 0.936 to 0.990, demonstrating a very good simulation effect.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.2 \u0026nbsp;Impact of Different Fertilization Treatments on Maize Dry Matter Accumulation\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe accumulation of maize dry matter increases with the fertilization amount throughout the growth period, as shown in Figure 2. Fertilization treatments exhibit significantly greater maize dry matter accumulation compared to the CK treatment. The leaf area index of maize in the NI, N2, and N3 treatments increased significantly during the jointing stage by 42.92%, 54.03%, and 85.84% respectively, compared to CK. Similarly, during the tasseling stage, the leaf area index increased significantly by 6.85%, 9.31%, and 16.32% in the NI, N2, and N3 treatments compared to CK. Furthermore, during the flowering stage, the leaf area index in the NI, N2, and N3 treatments increased significantly by 2.61%, 2.83%, and 8.81% compared to CK. Finally, during the maturity stage, the leaf area index in the NI, N2, and N3 treatments increased significantly by 7.98%, 11.67%, and 23.59% compared to CK.\u003c/p\u003e\n\u003cp\u003eUsing the APSIM model to assess the results of the fertilization experiment conducted from 2022 to 2023, Table 4 presents the RMSE, R\u003csup\u003e2\u003c/sup\u003e, and Me values of the model under different fertilization schemes. A comprehensive evaluation of the model is carried out based on the three statistical metrics of RMSE, R\u003csup\u003e2\u003c/sup\u003e, and Me:\u003c/p\u003e\n\u003cp\u003e1) N1 scheme: Throughout the maize growth period, the RMSE values of the model remained relatively stable, ranging from 0.110 to 0.332, indicating good agreement between the simulated values of the APSIM model and the observed values. The R\u003csup\u003e2\u003c/sup\u003e values are close to 1, ranging from 0.961 to 0.981, showing a high level of correlation between the model\u0026apos;s calculated data and the actual data. The Me values range from 0.88 to 0.95, with a gradual increase during key stages of maize development, suggesting a good fit of the APSIM model to maize dry matter accumulation.\u003c/p\u003e\n\u003cp\u003e2) N2 scheme: The RMSE values of the model remained stable throughout the maize growth period, ranging from0.033 to 0.109, indicating good agreement between the simulated values of the APSIM model and the observed values. The R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003evalues are close to 1, ranging from 0.968 to 0.987, demonstrating a high level of correlation between the model\u0026apos;s calculated data and the actual data. The Me values range from 0.89 to 0.95, with values exceeding 0.75 at each stage, indicating very good simulation effects.\u003c/p\u003e\n\u003cp\u003e3) N3 scheme: The RMSE values of the model remained stable throughout the maize growth period, ranging from 0.030to 0.060, indicating good agreement between the simulated values of the APSIM model and the observed values. The R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003evalues are close to 1, ranging from 0.967 to 0.998, showing a high level of correlation between the model\u0026apos;s calculated data and the actual data. The Me values range from 0.92 to 0.98, demonstrating very good simulation effects.\u003c/p\u003e\n\u003cp\u003eTable 4 \u0026nbsp;Statistical Analysis Table of Measured and Simulated Dry Matter Accumulation under Different Fertilization Treatments\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003eSchemes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003ehindex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eJointing stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eBig flare period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eEfflorescence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003eMature stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.121212121212121%\" rowspan=\"3\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.118\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.385\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.485\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e0.302\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e96.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e95.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e95.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e96.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.6490066225165565%\" rowspan=\"3\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.6490066225165565%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.282560706401767%\"\u003e\n \u003cp\u003e0.118\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.841059602649008%\"\u003e\n \u003cp\u003e0.200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.841059602649008%\"\u003e\n \u003cp\u003e0.332\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73730684326711%\"\u003e\n \u003cp\u003e0.110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e98.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e96.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e98.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e96.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e0.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.6490066225165565%\" rowspan=\"3\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.6490066225165565%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.282560706401767%\"\u003e\n \u003cp\u003e0.033\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.841059602649008%\"\u003e\n \u003cp\u003e0.063\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.841059602649008%\"\u003e\n \u003cp\u003e0.081\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73730684326711%\"\u003e\n \u003cp\u003e0.109\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e97.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e96.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e98.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e98.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.6490066225165565%\" rowspan=\"3\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.6490066225165565%\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.282560706401767%\"\u003e\n \u003cp\u003e0.058\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.841059602649008%\"\u003e\n \u003cp\u003e0.060\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.841059602649008%\"\u003e\n \u003cp\u003e0.040\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73730684326711%\"\u003e\n \u003cp\u003e0.030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e96.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e99.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e99.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e99.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\"\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e0.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eGrain accumulation serves as a crucial indicator of crop growth and development(DORDAS C A,2009). The level of grain yield in crops is primarily determined by the amount of dry matter accumulation and is also influenced by the harvest index(TOLLENAAR M,1982). Studies have demonstrated a positive correlation between dry matter accumulation and yield within a certain range(HuangZhenxi,2007). High biomass is the foundation for achieving high yield(HuangZhihong,2007), therefore, enhancing dry matter accumulation during the maize growing period is an effective approach to increasing grain yield(CongYanxia,2008). Wang Qingcheng(2004) et al argue that dry matter serves as the material basis for yield formation, and only high accumulation of dry matter can result in high grain yield. This experiment reveals a significant increase in maize dry matter accumulation with the gradual rise in fertilizer application. Among treatments with the same amount of fertilization, the one with the highest dry matter accumulation during the maturity stage also exhibits the highest yield, in line with previous research findings. Leaf area, a significant dynamic indicator in crop growth and development, reflects the longitudinal and horizontal expansion capabilities of crops and is closely associated with the dry matter and yield of crops(WEI S,2010). Research conducted by Qin Wenli(2006) et al indicates that with a certain amount of nitrogen and phosphorus fertilizers, potassium application enhances the height of summer maize, although the degree of change is relatively minor, and excessive potassium fertilizer can actually decrease maize plant height. Yu Ning(2020) et al.\u0026apos;s study \u0026nbsp;reveals that inadequate fertilization notably reduces the leaf area index (LAI) of summer maize. The results of this study suggest that maize leaf area index increases with an increase in fertilizer application, aligning with previous research outcomes.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study is based on observational data collected from the experimental fields at the Sichuan Water Conservancy Technology Research and Training Base in 2022-2023. Three different fertilization schemes were designed and implemented, and the APSIM model was utilized to simulate the leaf area index (LAI) and dry matter accumulation of maize under these schemes. The consistency, explanatory power, and effectiveness of the APSIM model in simulating summer maize LAI were analyzed using statistical indicators such as RMSE, R\u003csup\u003e2\u003c/sup\u003e, and Me. The main conclusions are as follows:\u003c/p\u003e\n\u003cp\u003e(1) Throughout the growth period, maize LAI reached its peak at the silking stage and was lowest at the 9-leaf stage. Maize LAI was significantly higher under fertilization treatments compared to no fertilization. Increasing fertilizer application led to a gradual increase in maize LAI. When simulating summer maize LAI with the APSIM model, RMSE values for the entire growth period ranged from [0.132, 0.200] under the N1 scheme, R\u003csup\u003e2\u003c/sup\u003e ranged from [0.956, 0.982], and Me ranged from [0.958, 0.983]; under the N2 scheme, RMSE ranged from [0.093, 0.200], R\u003csup\u003e2\u003c/sup\u003e ranged from [0.960, 0.991], and Me ranged from [0.961, 0.990]; under the N3 scheme, RMSE ranged from [0.140, 0.250], R\u003csup\u003e2\u003c/sup\u003e ranged from [0.937, 0.992], and Me ranged from [0.936, 0.990].\u003c/p\u003e\n\u003cp\u003e(2) Throughout the growth period, maize dry matter accumulation increased with higher fertilizer application, with significantly higher accumulation under fertilization treatments compared to the control (CK). Under the N1 scheme, RMSE ranged from [0.11, 0.332], R\u003csup\u003e2\u003c/sup\u003e ranged from [0.961, 0.981], and Me ranged from [0.88, 0.95]; under the N2 scheme, RMSE ranged from [0.033, 0.109], R\u003csup\u003e2\u003c/sup\u003e ranged from [0.968, 0.987], and Me ranged from [0.89, 0.95]; under the N3 scheme, RMSE ranged from [0.03, 0.06], R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eranged from [0.967, 0.998], and Me ranged from [0.92, 0.98], indicating excellent simulation effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author, gulaimubaier, was responsible for conducting experiments, collecting data and writing the analytical part of the paper. The author Liang Yi was mainly responsible for sorting and analyzing the data; The author Wei Renjuan was mainly responsible for drawing, making tables and collecting documents; The author, Zhao Xin, was responsible for the final review.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data has already been submitted in the document, and the data in Table 1 and Table 2 are the data used in the model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant Ethics:\u0026nbsp;\u003c/strong\u003eThe experiment was conducted at the experimental base of Sichuan Water Conservancy Technical College, Chongzhou, Chengdu, Sichuan Province, China, \u0026nbsp;according to the \u0026quot;National Management Standards for Crop Germplasm Resources Bank\u0026quot; issued by the Ministry of Agriculture and Rural Affairs of the People\u0026apos;s Republic of China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLobell, D.B., M.B. Burke, C. Tebaldi, et al., Prioritizing climate change adaptation needs for food security in 2030 [J]. Science, 2008. 319(5863): p. 607-10. \u003c/li\u003e\n\u003cli\u003eBorlaug, N., Feeding a hungry world [J]. Science, 2007. 318(5849): p. 359. \u003c/li\u003e\n\u003cli\u003eChen YJ, Wang QQ, Xiang Y. Analysis on the status, superiority and self-sufficiency ratio of maize in China[J].Chin J Agric Resour Reg Plan, 2019, 40(1):7-16. \u003c/li\u003e\n\u003cli\u003eZhao JR, Wang RH, Liu XX. Present situation of maize industry in China and development trend of biological breeding[J].Biotechnol Bus, 2016(3):45-52. \u003c/li\u003e\n\u003cli\u003eEvans LT, Fischer RA. Yield potential: its definition, measurement, and significance[J].Crop Sci, 1999, 39(6):1544-1551.\u003c/li\u003e\n\u003cli\u003eWANG Z, JU Z G, LU J D. Grain leaf ratio in relation to photosynthesis and grain yield in maize. Journal of Nanjing Agricultural University, 1998(1): 3-5. (in Chinese) \u003c/li\u003e\n\u003cli\u003eZHANG X D , CAI H J , FU Y J, WANG J. Study on leaf area index of summer maize in loess areas. Agricultural Research in Arid Areas, 2006, 24(2): 25-29. (in Chinese) \u003c/li\u003e\n\u003cli\u003eCAO W X, ZHU Y. Crop Management Knowledge Model. Beijing: China Agriculture Press, 2005: 104-107. (in Chinese)\u003c/li\u003e\n\u003cli\u003eZhang B,Zhao M,Dong Z Q,et al.Establishment and test of LAI dynamic simulation model for high yield population[J]. Acta Agronomica Sinica,2007,33(4):612-619.(in Chinese) \u003c/li\u003e\n\u003cli\u003eTong P Y,Cheng Y N.Dynamics of leaf area of different plant-types in maize[J].Beijing Agricultural Sciences,1994, 12(6):1-4.(in Chinese)\u003c/li\u003e\n\u003cli\u003eHan X L,Kong Y Z,Chen L.Preliminary analysis of climate and corn productivity[J].Chinese Journal of Agrometeorology, 1984(2):15-20.(in Chinese)\u003c/li\u003e\n\u003cli\u003eTong P Y,Ling B Y,Guan Y X.Dynamic simulating of dry matter accumulation in summer corn[J].Beijing Agricultural Sciences,1996,14(5):22-25.(in Chinese)\u003c/li\u003e\n\u003cli\u003eWang X L.How to use the Logistic equation reasonably in the simulation of dry matter accumulation of crops[J]. Chinese Journal of Agrometeorology,1986,8(1):14-19.(in Chinese) \u003c/li\u003e\n\u003cli\u003eZhu J M,Guo Y K,Liu H Y,et al.A method of combining the inversion of leaf area index[J].Science of Surveying and Mapping,2019,44(1):60-65.(in Chinese) \u003c/li\u003e\n\u003cli\u003eYang W,Zhang X X,Zhao J Y.Remote sensing inversion of leaf area index based on Geosail model and SVR algorithm [J].Science of Soil and Water Conservation,2018,16(6): 48-55.(in Chinese)\u003c/li\u003e\n\u003cli\u003eLiu Y,Yue H,Zhang F,et al.Drought monitoring of winter wheat in Henan province based on LAI-Ts space[J].Chinese Journal of Agrometeorology,2018,39(2):129-139.(in Chinese)\u003c/li\u003e\n\u003cli\u003eChen Y L,Gu X H,Gong A D,et al.Estimation of winter wheat assimilation based on remote sensing information and WOFOST crop model[J].Journal of Triticeae Crops,2018,38 (9):1127-1136.(in Chinese)\u003c/li\u003e\n\u003cli\u003eWang L,Xie D T,Liu H L,et al.A universal growth model for maize leaf area index[J].Journal of Southwest Agricultural University(Natural Science),2004,26(3):303-306.(in Chinese) \u003c/li\u003e\n\u003cli\u003eLi X L,Zhao M,Li C F,et al.Dynamic characteristics of leaf area index in maize and its model establishment based on accumulated temperature[J].Acta Agronomica Sinica,2011, 37(2):321-330.(in Chinese)\u003c/li\u003e\n\u003cli\u003eAsseng S. Keating B A, Fillery I R P, et al. Performance of the APSIMwheat model in Western Australia [J]. Field Crops Res, 1998, 57(2):163- 179. \u003c/li\u003e\n\u003cli\u003eAsseng S, Keulen H V, Stol W, et al. Performance and application of the APSIM N- wheat model in the Netherlands [J]. Europ J Agronomy, 2000, 12(1):37- 54.\u003c/li\u003e\n\u003cli\u003eREN Zhu,ZHAO Huina,ZENG Yuntao,et al.Changes of Key Enzymes in Sucrose Metabolism of Flue-cured Tobacco in Different Ecological Regions[J].Guizhou Agricultural Sciences,2014,42(9):52～54.\u003c/li\u003e\n\u003cli\u003eZhouJuncheng,WuJingjing,ChenXiaoyang,et al.The influence of transplanting period on the carbon and nitrogen metabolism products and sensory quality of \u0026apos;Yunyan 87\u0026apos; cured tobacco[J].Specialty Research,2022,44(5):53～57.\u003c/li\u003e\n\u003cli\u003eZHOU Juncheng,WU Jingjing,CHEN Xiaoxiang,et al.Effects of Transplanting Period on Carbon And Nitrogen Metabolites And Sensory Quality of Cured Yunyan 87 Tobacco Leaves[J],Special Wild Economic Animal and Plant Research,2022,44(5):53～57.\u003c/li\u003e\n\u003cli\u003eWang L,Li Q,Coulter J A,et al.Winter wheat yield and water use efficiency response to organic fertilization in northern China:a meta-analysis[J].Agricultural Water Management,2020(229):105934.\u003c/li\u003e\n\u003cli\u003eDORDAS C A,SIOULAS C.Laboratory dry matter and nitrogen accumulation,partitioning，and retranslocation in safflower（Carthamustinctorius L.）as affected by nitrogen fertilization[J].Field Crops Res,2009,110（1）:35-43.\u003c/li\u003e\n\u003cli\u003eTOLLENAAR M,DAYNARD T B.Effect of source-sink ratio on dry matter accumulation and leaf senescence of maize[J].Can J Plant Sci,1982,62（4）:855-860．\u003c/li\u003e\n\u003cli\u003eHuangZhenxi,WangYongjun,WangKongjun,et al.Photosynthetic characteristics during grain filling period of summer maize with a yield of over 15,000 kg/ha[J].Chinese Agricultural Science,2007,40（9）:1898-1906．\u003c/li\u003e\n\u003cli\u003eHuangZhihong,WangSiyuan,BaoYan,et al.Research on the characteristics of dry matter accumulation and distribution in high-yielding maize varieties[J].Corn Science,2007,15（3）:95-98． \u003c/li\u003e\n\u003cli\u003eCongYanxia,zhaoming,Dongzhiqiang,et al.Regulation of Spring Maize Dry Matter Accumulation and Stem Morphology by Yichong Mixture[J].Crop Magazine,2008（4）:68-71．\u003c/li\u003e\n\u003cli\u003eWangqingcheng,Liukaichang.Cultivation Theory and Practice of High-yield Maize in Shandong Summer[J].Corn Science,2004,12:60-62,65．\u003c/li\u003e\n\u003cli\u003eWEI S. Ecological Basis of Maize. Beijing: China Agriculture Press, 2010: 40-49. (in Chinese) \u003c/li\u003e\n\u003cli\u003eQIN W L, LI C J, LIU M C, HAN B W. Effect of different NPK ratios on main characteristics and yield of summer corn. Journal of Hebei Agricultural Sciences, 2006, 10(3): 27-29. (in Chinese)\u003c/li\u003e\n\u003cli\u003eYU N N, ZHANG J W, REN B Z, ZHAO B, LIU P. Effect of integrated agronomic managements on leaf growth and endogenous hormone content of summer maize. Acta Agronomica Sinica, 2020, 46(6): 960-967. (in Chinese)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Maize, APSIM model, Compound fertilizer, Leaf area index, Dry matter accumulation","lastPublishedDoi":"10.21203/rs.3.rs-4677500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4677500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study simulated the effects of three fertilization schemes on maize leaf area index and dry matter accumulation using the APSIM model, and also investigated the adaptability of the APSIM model in simulating leaf area index and dry matter accumulation of summer maize in the region. Results indicated that the maize leaf area index was highest during the silking stage and lowest at the 9-leaf stage throughout the growth period. The maize leaf area index of the fertilization treatments was significantly higher than that of the non-fertilization treatment. As fertilization rates increased, the maize leaf area index gradually increased. Maize dry matter accumulation also increased with higher fertilization rates, with the fertilization treatments showing significantly higher accumulation compared to the control (CK). The APSIM model demonstrated high precision in simulating maize leaf area index and dry matter accumulation under different fertilization schemes, with root mean square error (RMSE) for leaf area index ranging from 0.093 to 0.250, coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) ranging from 0.956 to 0.992, and efficiency index (Me) ranging from 0.936 to 0.990. The RMSE for maize dry matter accumulation ranged from 0.03 to 0.332, R\u003csup\u003e2\u003c/sup\u003e ranged from 0.961 to 0.998, and Me ranged from 0.880 to 0.980. Among the fertilization schemes, N3 exhibited the highest RMSE, R\u003csup\u003e2\u003c/sup\u003e, and Me values for both maize leaf area index and dry matter accumulation.\u003c/p\u003e","manuscriptTitle":"Analysis of Corn Leaf Area Index and Dry Matter Simulation Under Different Fertilization Treatments Based on APSIM Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-12 12:19:49","doi":"10.21203/rs.3.rs-4677500/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"653ad387-338b-40be-b1c4-e1058756054f","owner":[],"postedDate":"August 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-26T07:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-12 12:19:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4677500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4677500","identity":"rs-4677500","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}