Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (4): 679-697.doi: 10.3864/j.issn.0578-1752.2024.04.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Stacking Ensemble Learning Modeling and Forecasting of Maize Yield Based on Meteorological Factors

LI QianChuan(), XU ShiWei(), ZHANG YongEn, ZHUANG JiaYu, LI DengHua, LIU BaoHua, ZHU ZhiXun, LIU Hao   

  1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081
  • Received:2023-06-12 Accepted:2023-08-02 Online:2024-02-16 Published:2024-02-20
  • Contact: XU ShiWei

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Abstract:

【Objective】In the context of intensified global climate change and frequent meteorological disasters, exploring the significance of meteorological factors on maize yield and accurately predicting maize yield is crucial for enhancing agricultural production and field management. This paper aims to quantitatively analyze the importance of meteorological factors during various growth stages of maize on yield and to establish a highly accurate and reliable maize meteorological yield stacking ensemble learning estimation model for yield prediction.【Method】Using the HP filter method and moving average method, trend yield models for various counties were determined, and county-level meteorological yields were isolated. Three ensemble learning methods (light gradient boosting machine (LightGBM), Bagging, and Stacking) were employed. By analyzing daily meteorological data and maize yield data over 34 years from 596 county-level administrative regions and meteorological observation stations across 12 provinces in China, three maize meteorological yield prediction models based on different ensemble learning frameworks (LightGBM, Bagging, and Stacking) were established.【Result】The HP filter method as the trend yield model was mainly applicable in the regions of Shaanxi, Henan, Jiangsu, and Anhui. Compared to the HP filter method, more counties were suitable for the moving average method, with most counties having the R2 distribution above 0.8. Based on a 5-year sliding forecast and model accuracy evaluation indicators, the mean absolute percentage error (MAPE) for the three models on maize yield was below 6%. The Stacking model achieved a MAPE of 4.60%, indicating high prediction accuracy and strong generalizability. The results demonstrate that the maize meteorological yield stack-integrated learning prediction model has higher accuracy and stronger robustness. It effectively utilizes the characteristics and advantages of each base learner to improve prediction accuracy, making it the optimal model for predicting maize yield based on meteorological factors. Furthermore, a quantitative analysis of the impact of 27 meteorological factors during the maize growth stages in 12 provinces, using the random forest feature importance score, is of reference value for crop monitoring and field management.【Conclusion】The three ensemble learning methods, especially the stack-integrated learning model (Stacking), can accurately reflect the spatiotemporal distribution changes in maize yield. The stack-integrated learning model for maize yield based on meteorological factors provides a new method for field management and accurate prediction of maize yield.

Key words: maize meteorological yield, ensemble learning, yield estimation, county-level data, feature importance

Fig. 1

Distribution of climatic zones in China, geographic information and meteorological observation points of maize planting counties in 12 provinces of Southern and Central-Western China"

Fig. 2

Schematic diagram of specific date division for each growth stage of maize in 12 provinces"

Fig. 3

Framework of the maize meteorological yield Bagging ensemble learning prediction model"

Fig. 4

Framework of the maize meteorological yield LightGBM ensemble learning prediction model"

Fig. 5

Framework of the maize meteorological yield Stacking ensemble learning prediction model"

Fig. 6

Schematic diagram of county-level trend yield models in 12 provinces"

Fig. 7

Bar chart of meteorological factor feature importance scores for maize in 12 provinces"

Fig. 8

Heat map of meteorological factor feature importance in 12 provinces"

Table 1

Evaluation of 5-year sliding average prediction indicators for 5 types of maize meteorological yield prediction models in 596 counties of 12 provinces"

省份
Province		 模型
Model		 RMSE
(kg·hm-2)		 R2 MAE
(kg·hm-2)		 MAPE
(%)
安徽Anhui Lasso 429.985 0.883 350.259 7.466
BP neural network 434.525 0.881 353.761 7.554
Bagging 439.796 0.878 360.207 7.685
LightGBM 394.414 0.902 302.532 6.273
Stacking 407.022 0.896 323.037 6.755
福建Fujian Lasso 125.812 0.977 84.344 2.780
BP neural network 130.215 0.976 90.654 2.950
Bagging 126.978 0.977 86.058 2.833
LightGBM 190.409 0.948 129.502 4.132
Stacking 128.378 0.976 87.562 2.893
贵州Guizhou Lasso 640.684 0.818 477.757 10.937
BP neural network 636.296 0.820 474.561 10.621
Bagging 635.883 0.820 473.537 10.651
LightGBM 661.118 0.806 489.157 11.259
Stacking 642.441 0.817 470.063 10.775
河南Henan Lasso 391.623 0.842 327.031 6.120
BP neural network 397.196 0.837 331.677 6.204
Bagging 385.328 0.847 321.127 6.007
LightGBM 229.429 0.946 169.439 3.306
Stacking 255.944 0.932 200.405 3.867
湖北Hubei Lasso 324.475 0.934 229.117 5.140
BP neural network 333.874 0.930 233.386 5.251
Bagging 324.153 0.934 226.468 5.086
LightGBM 429.661 0.884 278.379 6.228
Stacking 327.289 0.933 227.862 5.106
湖南Hunan Lasso 188.099 0.965 116.883 3.099
BP neural network 189.222 0.965 121.794 3.203
Bagging 184.516 0.966 113.240 2.997
LightGBM 220.178 0.952 133.447 3.432
Stacking 184.619 0.966 109.678 2.919
江苏Jiangsu Lasso 355.866 0.871 256.219 4.097
BP neural network 362.406 0.866 263.944 4.240
Bagging 361.581 0.866 260.011 4.160
LightGBM 420.158 0.820 324.532 5.223
Stacking 396.549 0.839 290.822 4.658
江西Jiangxi Lasso 268.408 0.954 181.344 5.682
BP neural network 268.240 0.954 181.776 5.667
Bagging 266.050 0.955 178.464 5.573
LightGBM 332.810 0.929 229.034 6.522
Stacking 268.489 0.954 177.111 5.439
陕西Shaanxi Lasso 326.690 0.961 232.629 4.855
BP neural network 328.045 0.960 234.162 4.887
Bagging 327.698 0.961 232.380 4.851
LightGBM 363.645 0.951 254.505 5.325
Stacking 333.508 0.959 233.337 4.820
四川Sichuan Lasso 163.837 0.983 107.752 2.162
BP neural network 167.203 0.982 109.679 2.197
Bagging 164.037 0.983 106.993 2.144
LightGBM 184.138 0.978 123.460 2.497
Stacking 163.299 0.983 107.347 2.149
浙江Zhejiang Lasso 153.359 0.961 104.611 2.509
BP neural network 154.878 0.961 106.501 2.549
Bagging 153.905 0.961 105.390 2.526
LightGBM 184.471 0.944 116.249 2.774
Stacking 152.227 0.962 103.882 2.490
重庆Chongqing Lasso 88.538 0.993 67.871 1.373
BP neural network 80.281 0.994 61.049 1.248
Bagging 77.610 0.995 61.309 1.237
LightGBM 127.141 0.985 98.977 2.022
Stacking 83.661 0.994 64.649 1.319

Fig. 9

MAPE accuracy evaluation of 5-year sliding estimation of maize yield in 596 county-level administrative regions"

Table 2

Comparison of single model and 3 ensemble learning models in 5-year moving prediction"

模型Model RMSE (kg·hm-2) MAPE (%) MAE (kg·hm-2) R2
Lasso 343.24 5.00 228.17 0.9406
BP neural network 345.12 5.02 230.62 0.9399
Bagging 342.42 4.95 227.09 0.9408
LightGBM 348.09 4.97 223.90 0.9389
Stacking 326.14 4.60 208.51 0.9463

Fig. 10

Schematic diagram of the 5-year spatiotemporal variation pattern in predicted yield by the ensemble prediction model in 596 counties"

[1]
EGERER S, PUENTE A F, PEICHL M, RAKOVEC O, SAMANIEGO L, SCHNEIDER U A. Limited potential of irrigation to prevent potato yield losses in Germany under climate change. Agricultural Systems, 2023, 207: 103633.

doi: 10.1016/j.agsy.2023.103633
[2]
ZHANG Z Y, LI Y, CHEN X G, WANG, Y Z, NIU B, LIU D L, HE J Q, PULATOV B, HASSAN I, MENG Q T. Impact of climate change and planting date shifts on growth and yields of double cropping rice in southeastern China in future. Agricultural Systems, 2023, 205: 103581.

doi: 10.1016/j.agsy.2022.103581
[3]
KIKSTRA J S, NICHOLLS Z R J, SMITH C J, LEWIS J, LAMBOLL R D, BYERS E, SANDSTAD M, MEINSHAUSEN M, GIDDEN M J, ROGELJ J, et al. The IPCC sixth assessment report WGIII climate assessment of mitigation pathways: From emissions to global temperatures. Geoscientific Model Development, 2022, 15(24): 9075-9109.
[4]
SCHENUIT F. Staging science:Dramaturgical politics of the IPCC’s special report on 1.5 ℃. Environmental Science and Policy, 2023, 139: 166-176.
[5]
WEI W Y, KASHAGAN K, LI L H. Sensitivities of wheat and maize productivity in Kazakhstan to future climate change scenarios. International Journal of Plant Production, 2022, 16(3): 365-383.

doi: 10.1007/s42106-022-00193-5
[6]
PALACIOS-ROJAS N, MCCULLEY L, KAEPPLER M, TITCOMB T J, GUNARATNA N S, LOPEZ-RIDAURA S, TANUMIHARDJO S A. Mining maize diversity and improving its nutritional aspects within agro-food systems. Comprehensive Reviews in Food Science and Food Safety, 2020, 19(4): 1809-1834.

doi: 10.1111/crf3.v19.4
[7]
BAI Y Y, ZHANG T Z, ZHAI Y J, SHEN X X, MA X T, ZHANG R R, JI C X, HONG J L. Water footprint coupled economic impact assessment for maize production in China. Science of the Total Environment, 2021, 752: 141963.

doi: 10.1016/j.scitotenv.2020.141963
[8]
WANG Z Q, LUO H L, YANG S.Different mechanisms for the extremely hot central-eastern China in July-August 2022 from a Eurasian large-scale circulation perspective. Environmental Research Letters, 2023, 18(2): 024023.

doi: 10.1088/1748-9326/acb3e5
[9]
QIN Y, QIN Y J, SHEN Y C, LI Y H, XIANG B.Numerical study on the effects of intraseasonal oscillations for a persistent drought and hot event in South China summer 2022. Remote Sensing, 2023, 15(4): 892.

doi: 10.3390/rs15040892
[10]
ZHAO C, LIU B, PIAO S, WANG X H, LOBELL D B, HUANG Y, HUANG M, YAO Y T, BASSU S, CIAIS P, et al.Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(35): 9326-9331.
[11]
BAIO F H R, SANTANA D C, TEODORO L P R, DE OLIVEIRA I C, GAVA R, DE OLIVEIRA J L G, DA SILVA C A, TEODORO P E, SHIRATSUCHI L S. Maize yield prediction with machine learning, spectral variables and irrigation management. Remote Sensing, 2022, 15(1): 79.

doi: 10.3390/rs15010079
[12]
MAITAH M, MALEC K, GE Y, GEBELTOVA Z, SMUTKA L, BLAZEK V, PANKOVA L, MAITAH K, MACH J. Assessment and prediction of maize production considering climate change by extreme learning machine in Czechia. Agronomy, 2021, 11(11): 2344.

doi: 10.3390/agronomy11112344
[13]
CHEN X X, FENG L, YAO R, WU X J, SUN J, GONG W. Prediction of maize yield at the city level in China using multi-source data. Remote Sensing, 2021, 13(1): 146.

doi: 10.3390/rs13010146
[14]
陈志君, 朱振闯, 孙仕军, 王秋瑶, 苏通宇, 付玉娟. Stacking集成模型模拟膜下滴灌玉米逐日蒸散量和作物系数. 农业工程学报, 2021, 37(5): 95-104.
CHEN Z J, ZHU Z C, SUN S J, WANG Q Y, SU T Y, FU Y J. Estimation of daily evapotranspiration and crop coefficient of maize under mulched drip irrigation by Stacking ensemble learning model. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(5): 95-104. (in Chinese)
[15]
ELBELTAGI A, SRIVASTAVA A, KUSHWAHA N L, JUHASZ C, TAMAS J, NAGY A. Meteorological data fusion approach for modeling crop water productivity based on ensemble machine learning. Water, 2022, 15(1): 30.

doi: 10.3390/w15010030
[16]
CHENG Q, XU H G, FEI S P, LI Z P, CHEN Z. Estimation of maize LAI using ensemble learning and UAV multispectral imagery under different water and fertilizer treatments. Agriculture, 2022, 12(8): 1267.

doi: 10.3390/agriculture12081267
[17]
张杰, 徐波, 冯海宽, 竞霞, 王娇娇, 明世康, 傅友强, 宋晓宇. 基于集成学习的水稻氮素营养及籽粒蛋白含量监测. 光谱学与光谱分析, 2022, 42(6): 1956-1964.
ZHANG J, XU B, FENG H K, JING X, WANG J J, MING S K, FU Y Q, SONG X Y. Monitoring nitrogen nutrition and grain protein content of rice based on ensemble learning. Spectroscopy and Spectral Analysis, 2022, 42(6): 1956-1964. (in Chinese)
[18]
ZHAO W, ZHAO X N, LUO B, BAI W W, KANG K, HOU P C, ZHANG H. Identification of wheat seed endosperm texture using hyperspectral imaging combined with an ensemble learning model. Journal of Food Composition and Analysis, 2023, 121: 105398.

doi: 10.1016/j.jfca.2023.105398
[19]
AL-GAASHANI M S A M, SHANG F J, ABD EL-LATIF A A. Ensemble learning of lightweight deep convolutional neural networks for crop disease image detection. Journal of Circuits, Systems and Computers, 2023, 32(5): 2350086.

doi: 10.1142/S021812662350086X
[20]
侯志松, 冀金泉, 李国厚, 焦红伟, 王良. 集成学习与迁移学习的作物病害图像识别算法. 中国科技论文, 2021, 16(7): 708-714.
HOU Z S, JI J Q, LI G H, JIAO H W, WANG L. Crop disease image recognition algorithm based on ensemble learning and transfer learning. China Sciencepaper, 2021, 16(7): 708-714. (in Chinese)
[21]
史飞飞, 高小红, 肖建设, 李宏达, 李润祥, 张昊. 基于集成学习和多时相遥感影像的枸杞种植区分类. 自然资源遥感, 2022, 34(1): 115-126.
SHI F F, GAO X H, XIAO J S, LI H D, LI R X, ZHANG H. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images. Remote Sensing for Natural Resources, 2022, 34(1): 115-126. (in Chinese)
[22]
DAS A, KUMAR M, KUSHWAHA A, DAVE R, DAKHORE K K, CHAUDHARI K, BHATTACHARYA B K. Machine learning model ensemble for predicting sugarcane yield through synergy of optical and SAR remote sensing. Remote Sensing Applications: Society and Environment, 2023, 30: 100962.

doi: 10.1016/j.rsase.2023.100962
[23]
OLOFINTUYI S S, OLAJUBU E A, OLANIKE D. An ensemble deep learning approach for predicting cocoa yield. Heliyon, 2023, 9(4): e15245.

doi: 10.1016/j.heliyon.2023.e15245
[24]
FEI S P, HASSAN M A, HE Z H, CHEN Z, SHU M Y, WANG J K, LI C C, XIAO Y G. Assessment of ensemble learning to predict wheat grain yield based on UAV-multispectral reflectance. Remote Sensing, 2021, 13(12): 2338.

doi: 10.3390/rs13122338
[25]
LI Z P, CHEN Z, CHENG Q, DUAN F Y, SUI R X, HUANG X Q, XU H G. UAV-based hyperspectral and ensemble machine learning for predicting yield in winter wheat. Agronomy, 2022, 12(1): 202.

doi: 10.3390/agronomy12010202
[26]
DHILLON J S, RAUN W R. Effect of topdress nitrogen rates applied based on growing degree days on winter wheat grain yield. Agronomy Journal, 2020, 112(4): 3114-3128.

doi: 10.1002/agj2.v112.4
[27]
SONG Y L, WANG C Y, LINDERHOLM H W, FU Y, CAI W Y, XU J X, ZHUANG L W, WU M X, SHI Y X, WANG G F, CHEN D L. The negative impact of increasing temperatures on rice yields in southern China. Science of the Total Environment, 2022, 820: 153262.

doi: 10.1016/j.scitotenv.2022.153262
[28]
LI S, WEI F L, WANG Z, SHEN J S, LIANG Z, WANG H, LI S C. Spatial heterogeneity and complexity of the impact of extreme climate on vegetation in China. Sustainability, 2021, 13(10): 5748.

doi: 10.3390/su13105748
[29]
YU X Y, MA Y Y. Spatial and temporal analysis of extreme climate events over Northeast China. Atmosphere, 2022, 13(8): 1197.

doi: 10.3390/atmos13081197
[30]
XIAO D P, BAI H Z, LIU D L, TANG J Z, WANG B, SHEN Y J, CAO J S, FENG P Y. Projecting future changes in extreme climate for maize production in the North China Plain and the role of adjusting the sowing date. Mitigation and Adaptation Strategies for Global Change, 2022, 27(3): 21.

doi: 10.1007/s11027-022-09995-4
[31]
WANG S, LIM T H, OH K, SEO C, CHOO H. Prediction of wide range two-dimensional refractivity using an IDW interpolation method from high-altitude refractivity data of multiple meteorological observatories. Applied Sciences, 2021, 11(4): 1431.

doi: 10.3390/app11041431
[32]
SIATWIINDA S M, SUPIT I, VAN HOVE B, YEROKUN O, ROS G H, DE VRIES W. Climate change impacts on rainfed maize yields in Zambia under conventional and optimized crop management. Climatic Change, 2021, 167: 1-23.

doi: 10.1007/s10584-021-03143-8
[33]
WANG X Y, ZHANG X H, YANG M X, GOU X N, LIU B B, HAO Y C, XU S T, XUE J Q, QIN X L, SIDDIQUE K H M. Multi-site evaluation of accumulated temperature and rainfall for maize yield and disease in Loess Plateau. Agriculture, 2021, 11(4): 373.

doi: 10.3390/agriculture11040373
[34]
HATFIELD J L, PRUEGER J H. Temperature extremes: Effect on plant growth and development. Weather and Climate Extremes, 2015, 10: 4-10.

doi: 10.1016/j.wace.2015.08.001
[35]
WAQAS M A, WANG X K, ZAFAR S A, NOOR M A, HUSSAIN H A, NAWAZ M A, FAROOQ M. Thermal stresses in maize: Effects and management strategies. Plants, 2021, 10(2): 293.

doi: 10.3390/plants10020293
[36]
SANCHEZ B, RASMUSSEN A, PORTER J R. Temperatures and the growth and development of maize and rice: A review. Global Change Biology, 2014, 20(2): 408-417.

doi: 10.1111/gcb.12389 pmid: 24038930
[37]
HANWAY J J.How a corn plant develops. Special Report. Iowa State University, 1966: 38.
[38]
LING M H, HAN H B, HU X Y, XIA Q Y, GUO X M. Drought characteristics and causes during summer maize growth period on Huang-Huai-Hai Plain based on daily scale SPEI. Agricultural Water Management, 2023, 280: 108198.

doi: 10.1016/j.agwat.2023.108198
[39]
WANG X W, LI X Y, GU J T, SHI W Q, ZHAO H G, SUN C, YOU S C. Drought and waterlogging status and dominant meteorological factors affecting maize (Zea mays L.) in different growth and development stages in Northeast China. Agronomy, 2023, 13(2): 374.

doi: 10.3390/agronomy13020374
[40]
GAO C, LI X W, SUN Y W, ZHOU T, LUO G, CHEN C. Water requirement of summer maize at different growth stages and the spatiotemporal characteristics of agricultural drought in the Huaihe River Basin, China. Theoretical and Applied Climatology, 2019, 136: 1289-1302.

doi: 10.1007/s00704-018-2558-6
[41]
WANG C L, WU J D, WANG X, HE X, LI N. Non-linear trends and fluctuations in temperature during different growth stages of summer maize in the North China Plain from 1960 to 2014. Theoretical and Applied Climatology, 2019, 135: 61-70.

doi: 10.1007/s00704-017-2352-x
[42]
LOBELL D B, SCHLENKER W, COSTA-ROBERTS J.Climate trends and global crop production since 1980. Science, 2011, 333(6042): 616-620.

doi: 10.1126/science.1204531
[43]
SLOAT L L, DAVIS S J, GERBER J S, MOORE F C, RAY D K, WEST P C, MUELLER N D. Climate adaptation by crop migration. Nature Communications, 2020, 11(1): 1243.

doi: 10.1038/s41467-020-15076-4 pmid: 32144261
[44]
KUKAL M S, IRMAK S. Climate-driven crop yield and yield variability and climate change impacts on the U.S. Great Plains agricultural production. Scientific Reports, 2018, 8(1): 3450.

doi: 10.1038/s41598-018-21848-2 pmid: 29472598
[45]
PENG B, GUAN K Y, TANG J Y, AINSWORTH E A, ASSENG S, BERNACCHI C J, COOPER M, DELUCIA E H, ELLIOTT J W, EWERT F, et al. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants, 2020, 6(4): 338-348.

doi: 10.1038/s41477-020-0625-3 pmid: 32296143
[46]
VAN DRIEL J, OLIVERS C N L, FAHRENFORT J J. High-pass filtering artifacts in multivariate classification of neural time series data. Journal of Neuroscience Methods, 2021, 352: 109080.

doi: 10.1016/j.jneumeth.2021.109080
[47]
ZAHRA H S, OWEIS H T. Application of high-pass filtering techniques on gravity and magnetic data of the eastern Qattara Depression area, Western Desert, Egypt. NRIAG Journal of Astronomy and Geophysics, 2016, 5(1): 106-123.

doi: 10.1016/j.nrjag.2016.01.005
[48]
AL-ALWAN A, FEROZE N, NAZAKAT A, ALMUHAYFITH F E, ALSHENAWY R. Analysis of trends in awareness regarding hepatitis using Bayesian multiple logistic regression model. Mathematical Problems in Engineering, 2022, 2022: 4120711.
[49]
SHANG J, CHEN M Y, JI H Q, ZHOU D H, ZHANG H F, LI M L. Dominant trend based logistic regression for fault diagnosis in nonstationary processes. Control Engineering Practice, 2017, 66: 156-168.

doi: 10.1016/j.conengprac.2017.06.011
[50]
CHERNYKH M, VODIANYK B, SELEZNOV I, HARMATIUK D, ZYMA I, POPOV A, KIYONO K. Detrending moving average, power spectral density, and coherence: Three EEG-based methods to assess emotion irradiation during facial perception. Applied Sciences, 2022, 12(15): 7849.

doi: 10.3390/app12157849
[51]
孟品超, 李学源, 贾洪飞, 李延忠. 基于滑动平均法的轨道交通短时客流实时预测. 吉林大学学报(工学版), 2018, 48(2): 448-453.
MENG P C, LI X Y, JIA H F, LI Y Z. Short-time rail transit passenger flow real-time prediction based on moving average. Journal of Jilin University (Engineering and Technology Edition), 2018, 48(2): 448-453. (in Chinese)
[52]
CETIN B, YAVUZ I. Comparison of forecast accuracy of Ata and exponential smoothing. Journal of Applied Statistics, 2021, 48(13/15): 2580-2590.

doi: 10.1080/02664763.2020.1803813
[53]
BUTT U M, LETCHMUNAN S, HASSAN F H, KOH T W. Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy. PLoS ONE, 2022, 17(9): e0274172.

doi: 10.1371/journal.pone.0274172
[54]
RAY D K, GERBER J S, MACDONALD G K, WEST P C. Climate variation explains a third of global crop yield variability. Nature Communications, 2015, 6(1): 5989.

doi: 10.1038/ncomms6989
[55]
NETZEL P, STEPINSKI T. Climate similarity search: GeoWeb tool for exploring climate variability. Bulletin of the American Meteorological Society, 2018, 99(3): 475-477.

doi: 10.1175/BAMS-D-16-0334.1
[56]
YU Z X, GUINDANI M, GRIECO S F, CHEN L J, HOLMES T C, XU X M. Beyond t test and ANOVA: Applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron, 2022, 110(1): 21-35.

doi: 10.1016/j.neuron.2021.10.030
[57]
BAR-GERA H. The target parameter of adjusted R-squared in fixed-design experiments. The American Statistician, 2017, 71(2): 112-119.

doi: 10.1080/00031305.2016.1200489
[58]
ZHUANG J Y, XU S W, LI G Q, ZHANG Y E, WU J Z, LIU J J. The influence of meteorological factors on wheat and rice yields in China. Crop Science, 2018, 58: 837-852.

doi: 10.2135/cropsci2017.01.0048
[59]
NGO G, BEARD R, CHANDRA R. Evolutionary bagging for ensemble learning. Neurocomputing, 2022, 510: 1-14.

doi: 10.1016/j.neucom.2022.08.055
[60]
KIM K, KIM J, CHOI H, KWON O, JANG Y, RYU S, LEE H, SHIM K, PARK T, CHA S W. Pre-diagnosis of flooding and drying in proton exchange membrane fuel cells by bagging ensemble deep learning models using long short-term memory and convolutional neural networks. Energy, 2023, 266: 126441.

doi: 10.1016/j.energy.2022.126441
[61]
WONG A, KRAMER S C, PICCININNI M, ROHMANN J L, KURTH T, ESCOLANO S, GRITTNER U, DE CELLES M D. Using LASSO regression to estimate the population-level impact of pneumococcal conjugate vaccines. American Journal of Epidemiology, 2023, 192(7): 1166-1180.

doi: 10.1093/aje/kwad061 pmid: 36935107
[62]
奚丽婧, 郭昭艳, 杨雪珂, 平智广. LASSO及其拓展方法在回归分析变量筛选中的应用. 中华预防医学杂志, 2023, 57(1): 107-111.
XI L J, GUO Z Y, YANG X K, PING Z G. Application of LASSO and its extended method in variable selection of regression analysis. Chinese Journal of Preventive Medicine, 2023, 57(1): 107-111. (in Chinese)
[63]
JI Q Q, ZHANG S Y, DUAN Q, GONG Y H, LI Y W, XIE X T, BAI J K, HUANG C L, ZHAO X. Short- and medium-term power demand forecasting with multiple factors based on multi-model fusion. Mathematics, 2022, 10(12): 2148.

doi: 10.3390/math10122148
[64]
LYU J Y, ZHENG P J, QI Y, HUANG G H. LightGBM-LncLoc: A LightGBM-based computational predictor for recognizing long non-coding RNA subcellular localization. Mathematics, 2023, 11(3): 602.

doi: 10.3390/math11030602
[65]
ZHAO L N, LU S, QI D. Improvement of maximum air temperature forecasts using a stacking ensemble technique. Atmosphere, 2023, 14(3): 600.

doi: 10.3390/atmos14030600
[66]
WU X L, WANG J Y. Application of bagging, boosting and stacking ensemble and EasyEnsemble methods for landslide susceptibility mapping in the Three Gorges Reservoir area of China. International Journal of Environmental Research and Public Health, 2023, 20(6): 4977.

doi: 10.3390/ijerph20064977
[67]
IBRAHIM S. Improving land use/cover classification accuracy from random forest feature importance selection based on synergistic use of sentinel data and digital elevation model in agriculturally dominated landscape. Agriculture, 2022, 13(1): 98.

doi: 10.3390/agriculture13010098
[68]
HWANG S W, CHUNG H W, LEE T Y, KIM J, KIM Y, KIM J C, KWAK H W, CHOI I G, YEO H M. Feature importance measures from random forest regressor using near-infrared spectra for predicting carbonization characteristics of kraft lignin-derived hydrochar. Journal of Wood Science, 2023, 69(1): 1-12.

doi: 10.1186/s10086-022-02073-y
[69]
王晓伟, 李晓玉, 史雯琪, 赵海根, 孙琛, 游松财. 黄淮海地区玉米生育期制图研究. 江苏农业科学, 2023, 51(4): 105-113.
WANG X W, LI X Y, SHI W Q, ZHAO H G, SUN C, YOU S C. Study on mapping of maize growth period in Huang-Huai-Hai region. Jiangsu Agricultural Sciences, 2023, 51(4): 105-113. (in Chinese)
[70]
尹小刚, 王猛, 孔箐锌, 王占彪, 张海林, 褚庆全, 文新亚, 陈阜. 东北地区高温对玉米生产的影响及对策. 应用生态学报, 2015, 26(1): 186-198.
YIN X G, WANG M, KONG Q X, WANG Z B, ZHANG H L, CHU Q Q, WEN X Y, CHEN F. Impacts of high temperature on maize production and adaptation measures in Northeast China. Chinese Journal of Applied Ecology, 2015, 26(1): 186-198. (in Chinese)
[71]
冯小杰, 郑子成, 李廷轩. 紫色土区坡耕地玉米季地表径流及其氮素流失特征. 水土保持学报, 2017, 31(1): 43-48, 54.
FENG X J, ZHENG Z C, LI T X. Characteristics of runoff and nitrogen loss in sloping cropland of purple soil during corn growing season. Journal of Soil and Water Conservation, 2017, 31(1): 43-48, 54. (in Chinese)
[72]
周新国, 韩会玲, 李彩霞, 郭树龙, 郭冬冬, 陈金平. 拔节期淹水玉米的生理性状和产量形成. 农业工程学报, 2014, 30(9): 119-125.
ZHOU X G, HAN H L, LI C X, GUO S L, GUO D D, CHEN J P. Physiological characters and yield formation of corn (Zea mays L.) under waterlogging stress in jointing stage. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(9): 119-125. (in Chinese)
[73]
任小龙, 贾志宽, 陈小莉, 韩娟, 韩清芳, 丁瑞霞. 半干旱区沟垄集雨对玉米光合特性及产量的影响. 作物学报, 2008, 34(5): 838-845.
REN X L, JIA Z K, CHEN X L, HAN J, HAN Q F, DING R X. Effects of ridge and furrow planting for rainfall harvesting on photo-synthetic characteristics and yield in corn in semi-arid regions. Acta Agronomica Sinica, 2008, 34(5): 838-845. (in Chinese)

doi: 10.3724/SP.J.1006.2008.00838
[74]
LI Q C, XU S W, ZHUANG J Y, LIU J J, ZHOU Y, ZHANG Z X. Ensemble learning prediction of soybean yields in China based on meteorological data. Journal of Integrative Agriculture, 2023, 22(6): 1909-1927.

doi: 10.1016/j.jia.2023.02.011
[75]
DUARTE Y C N, SENTELHAS P C. Intercomparison and performance of maize crop models and their ensemble for yield simulations in Brazil. International Journal of Plant Production, 2020, 14: 127-139.

doi: 10.1007/s42106-019-00073-5
[76]
XU S W, LI G Q, LI Z M.China agricultural outlook for 2015-2024 based on China Agricultural Monitoring and Early-warning System (CAMES). Journal of Integrative Agriculture, 2015, 14(9): 1889-1902.
[77]
许世卫, 邸佳颖, 李干琼, 庄家煜. 农产品监测预警模型集群构建理论方法与应用. 中国农业科学, 2020, 53(14): 2859-2871. doi: 10.3864/j.issn.0578-1752.2020.14.010.
XU S W, DI J Y, LI G Q, ZHUANG J Y. The methodology and application of agricultural monitoring and early warning model cluster. Scientia Agricultura Sinica, 2020, 53(14): 2859-2871. doi: 10.3864/j. issn.0578-1752.2020.14.010. (in Chinese)
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