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基于AquaCrop模型的茶叶产量和开采期预报

马于茗 陈捷 金志凤 郝璐

马于茗, 陈捷, 金志凤, 郝璐. 基于AquaCrop模型的茶叶产量和开采期预报[J]. 中国生态农业学报(中英文), 2021, 29(8): 1339-1349. doi: 10.13930/j.cnki.cjea.210018
引用本文: 马于茗, 陈捷, 金志凤, 郝璐. 基于AquaCrop模型的茶叶产量和开采期预报[J]. 中国生态农业学报(中英文), 2021, 29(8): 1339-1349. doi: 10.13930/j.cnki.cjea.210018
MA Yuming, CHEN Jie, JIN Zhifeng, HAO Lu. Prediction of tea yield and picking date based on the AquaCrop model[J]. Chinese Journal of Eco-Agriculture, 2021, 29(8): 1339-1349. doi: 10.13930/j.cnki.cjea.210018
Citation: MA Yuming, CHEN Jie, JIN Zhifeng, HAO Lu. Prediction of tea yield and picking date based on the AquaCrop model[J]. Chinese Journal of Eco-Agriculture, 2021, 29(8): 1339-1349. doi: 10.13930/j.cnki.cjea.210018

基于AquaCrop模型的茶叶产量和开采期预报

doi: 10.13930/j.cnki.cjea.210018
基金项目: 

国家重点研发计划项目 2019YFC1510202

国家自然科学基金项目 41877151

国家自然科学基金项目 41977409

浙江省科技计划项目 2021C02036

详细信息
    作者简介:

    马于茗, 主要研究方向为应用气象。E-mail: 849571014@qq.com

    通讯作者:

    郝璐, 主要研究方向为应用气象。E-mail: hl_haolu@163.com

  • 中图分类号: S571.1

Prediction of tea yield and picking date based on the AquaCrop model

Funds: 

the National Key Research and Development Project of China 2019YFC1510202

the National Natural Science Foundation of China 41877151

the National Natural Science Foundation of China 41977409

the Science and Technology Project of Zhejiang Province 2021C02036

More Information
  • 摘要: 为验证作物模型在浙江地区不同茶树品种产量和开采期的适用性,基于FAO推荐的AquaCrop作物模型,在浙江省典型茶树种植区选择‘白叶一号’‘龙井43’以及‘龙井群体种’等3个茶树主栽品种,通过田间试验、数据收集和参数敏感性分析等方式获取AquaCrop模型所需的茶树生长参数,并使用历史数据对模型进行本地化校正,建立了基于AquaCrop模型的安吉县和松阳县茶叶产量预报模型以及3个茶树品种的春茶开采期预报模型。AquaCrop模型模拟2013—2017年松阳县茶叶平均年总产量为1.497 t·hm-2,相对误差为1.98%;2014—2018年安吉县春茶平均年产量为0.164 t·hm-2,相对误差为0.99%;模拟松阳县和安吉县茶叶产量归一化均方根误差(NRMSE)分别为2.20%和1.10%,均方根误差(RMSE)分别为0.0325 t·hm-2和0.0018 t·hm-2;协同指数(d)值分别为0.84和0.88。确定了‘白叶一号’‘龙井43’和‘龙井群体种’茶叶的生长度日预报标准,并通过逐步回归方法获得了3品种茶叶生长度日预测公式;利用AquaCrop分别基于生长度日预报法和逐步回归预报法对3品种开采期进行预测。基于生长度日的3品种开采期预报模型的回代后平均绝对误差(MAE)分别为1.1 d、2.1 d和1.1 d;基于逐步回归的预报模型均通过P < 0.01显著性检验,3品种的MAE分别为0.7 d、0.7 d和0.9 d。结果表明,AquaCrop模型经过校正后对浙江地区不同品种茶叶具有较好的适应性,本地化后的AquaCrop可以用于茶园水分管理,产量潜力等研究。基于生长度日和逐步回归的两种AquaCrop茶树开采期预报模型均具有应用价值,逐步回归预报模型的预报效果更理想,具有实际生产指导作用。
  • 图  1  松阳县(a)和安吉县(b)的茶叶产量模拟值和实测值分析

    Figure  1.  Analysis of simulated and measured tea yield in Songyang County (a) and Anji County (b)

    图  2  2013—2019年AquaCrop模型基于生长度日(a)和回归方程(b)的‘白叶一号’茶叶开采期模拟结果

    Figure  2.  Simulation results of AquaCrop model based on the growth degree days (GDDs, a) and regression equation (b) for 'White Leaf 1' tea picking date from 2013 to 2019

    图  3  2005—2019年AquaCrop模型基于生长度日的‘龙井43’(a)和‘龙井群体种’(b)的茶叶开采期模拟结果

    Figure  3.  Simulation results of AquaCrop model based on growth degree days (GDDs) for 'Longjing 43' (a) and 'Longjingqunti' (b) tea picking date from 2005 to 2019

    图  4  2005—2019年AquaCrop模型基于回归方程的‘龙井43’(a)和‘龙井群体种’(b)的茶叶开采期模拟结果

    Figure  4.  Simulation results of AquaCrop model based on regression equation for 'Longjing 43' (a) and 'Longjingqunti' (b) tea picking date from 2005 to 2019

    表  1  不同研究区AquaCrop模型中茶叶作物参数相对生物量和产量的敏感度

    Table  1.   Sensitivities to biomass and yield of tea parameters of AquaCrop model in different study areas

    参数Parameter 生物量Biomass 产量Yield
    安吉
    Anji
    松阳
    Songyang
    安吉
    Anji
    松阳
    Songyang
    种植密度Planting density 0.1729 0.2318 0.1840 0.2318
    初始冠层覆盖度Initial canopy cover after pruning 0.2324 0.2339 0.2454 0.2357
    最大冠层覆盖度Maximum canopy cover 0.4071 0.4062 0.4417 0.4050
    达到最大冠层覆盖度的时间Time to maximum canopy coverage 0.0618 0.0808 0.0644 0.0826
    达到成熟的天数Time from transplanting to maturity 2.0357 1.2429 2.0429 1.2450
    最大根深Maximum effective rooting depth 0 0.0433 0 0.0451
    作物系数Crop coefficient 0.9996 0.9390 0.9939 0.9375
    标准化水分生产力Water productivity normalized for ET0 and CO2 0.9989 1.0000 0.9448 1.0045
    参考作物收获指数Reference harvest index 0 0 0.9939 1.0006
    基底温度Base temperature 0.9834 0.4105 0.9877 0.4121
    全生产所需的最小生长温度Minimum growing degrees required for full biomass production 0.4840 0.2835 0.4724 0.2859
    下载: 导出CSV

    表  2  AquaCrop模型中茶叶主要参数及研究区的取值

    Table  2.   Main parameters and their value of tea in AquaCrop model in different study areas

    茶叶参数Tea parameter 安吉Anji 松阳Songyang
    种植密度Planting density (plants∙hm–2) 60 000 75 000
    CCini 初始冠层覆盖度Initial canopy cover after pruning (%) 80 60
    CCx 最大冠层覆盖度Maximum canopy cover (%) 90 85
    达到最大冠层覆盖度的天数Time to maximum canopy coverage (d) 63 365
    达到成熟的天数Time to maturity (d) 45 365
    Zx 最大根深Maximum effective rooting depth (m) 2 2
    Zn 最小根深Minimum effective rooting depth (m) 2 2
    KcTr, x 作物系数Crop coefficient 0.9 0.8
    WP* 标准化水分生产力Water productivity normalized for ETo and CO2 (g∙m–2) 14 13
    Hio 参考作物收获指数Reference harvest index (%) 6 6
    Tbase 基底温度Base temperature (℃) 7 8
    全生产所需的最小生长温度Minimum growing degrees required for full biomass production (℃∙d) 7 11.1
    Tupper 上限温度Upper temperature (℃) 30 30
    Pexp, lower 冠层扩张的土壤水分消耗下限阈值Lower limit of soil water depletion threshold for canopy expansion 0.1 0.1
    Pexp, upper 冠层扩张的土壤水分消耗上限阈值Upper limit of soil water depletion threshold for canopy expansion 0.4 0.4
    冠层扩张的水分胁迫形状因子Shape factor of water stress coefficient for canopy expansion 3 3
    Psto 气孔控制的土壤水分消耗上限阈值Upper limit of soil water depletion threshold for stomatal control 0.25 0.25
    气孔控制的水分胁迫形状因子Shape factor for water stress coefficient for stomatal control 3 3
    Psen 引起冠层早衰的土壤水分消耗上限Upper limit of soil water depletion threshold for canopy senescence 0.6 0.6
    引起冠层早衰的水分胁迫形状因子Shape factor for water stress coefficient for canopy senescence 3 3
    下载: 导出CSV

    表  3  松阳县和安吉县历年茶叶产量模拟值和实测值

    Table  3.   Simulated and measured values of tea yield over the years in Songyang County and Anji County

    2013 2014 2015 2016 2017 2018
    松阳县
    Songyang County
    实际值Actual value (t∙hm–2) 1.407 1.475 1.455 1.498 1.553
    模拟值Simulation value (t∙hm–2) 1.443 1.485 1.508 1.519 1.528
    相对误差Relative error (%) 2.56 0.68 3.64 1.40 1.61
    安吉县
    Anji County
    实际值Actual value (t∙hm–2) 0.159 0.165 0.160 0.164 0.167
    模拟值Simulation value (t∙hm–2) 0.161 0.164 0.163 0.163 0.168
    相对误差Relative error (%) 1.26 0.61 1.88 0.61 0.60
    下载: 导出CSV
  • [1] 骆耀平. 茶树栽培学[M]. 北京: 中国农业出版社, 2015

    LUO Y P. Tea Cultivation[M]. Beijing: China Agriculture Press, 2015
    [2] 金志凤, 黄敬峰, 李波, 等. 基于GIS及气候-土壤-地形因子的浙江省茶树栽培适宜性评价[J]. 农业工程学报, 2011, 27(3): 231-236 doi: 10.3969/j.issn.1002-6819.2011.03.044

    JIN Z F, HUANG J F, LI B, et al. Suitability evaluation of tea trees cultivation based on GIS in Zhejiang Province[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(3): 231-236 doi: 10.3969/j.issn.1002-6819.2011.03.044
    [3] 李时睿, 王治海, 杨再强, 等. 江南茶区茶叶生产现状和气候资源特征分析[J]. 干旱气象, 2014, 32(6): 1007-1014

    LI S R, WANG Z H, YANG Z Q, et al. Analysis on production status of tea plant and climate characteristics in tea regions in southern Yangtze River[J]. Journal of Arid Meteorology, 2014, 32(6): 1007-1014
    [4] 金晶, 陆德彪. 浙江茶叶产业发展七十年回顾[J]. 茶叶, 2019, 45(3): 121-125 doi: 10.3969/j.issn.1005-2291.2019.03.073

    JIN J, LU D B. Review of the 70-year development of Zhejiang tea industry[J]. Journal of Tea, 2019, 45(3): 121-125 doi: 10.3969/j.issn.1005-2291.2019.03.073
    [5] 郭书坡. 基于灰色马尔科夫链的优化模型及其在茶叶产量预测中的应用[D]. 兰州: 兰州大学, 2011: 1-5

    GUO S P. The optimized model based on grey Markov chain and its application in prediction of tea output[D]. Lanzhou: Lanzhou University, 2011: 1-5
    [6] PHAN P, CHEN N C, XU L, et al. Using multi-temporal MODIS NDVI data to monitor tea status and forecast yield: a case study at Tanuyen, Laichau, Vietnam[J]. Remote Sensing, 2020, 12(11): 1814 doi: 10.3390/rs12111814
    [7] 朱兰娟, 金志凤, 张玉静, 等. 西湖龙井茶开采期影响因子及预报模型[J]. 中国农业气象, 2019, 40(3): 159-169 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY201903003.htm

    ZHU L J, JIN Z F, ZHANG Y J, et al. Research on the factors of Xihulongjing tea picking date and its prediction model[J]. Chinese Journal of Agrometeorology, 2019, 40(3): 159-169 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY201903003.htm
    [8] NUGROHO A P, OKAYASU T, INOUE E, et al. Development of actuation framework for agricultural informatization supporting system[J]. IFAC Proceedings Volumes, 2013, 46(4): 181-186 doi: 10.3182/20130327-3-JP-3017.00041
    [9] 党玉梅. 数字化农业信息咨询决策集成平台的构建研究[D]. 石河子: 石河子大学, 2013: 1-7

    DANG Y M. Construction research on the integrates platform of digitized agriculture information consultant decision-making[D]. Shihezi: Shihezi University, 2013: 1-7
    [10] 曹宏鑫, 金之庆, 石春林, 等. 中国作物模型系列的研究与应用[J]. 农业网络信息, 2006, (5): 45-48 doi: 10.3969/j.issn.1672-6251.2006.05.013

    CAO H X, JIN Z Q, SHI C L, et al. Researches and application of crop model series in China[J]. Agriculture Network Information, 2006, (5): 45-48 doi: 10.3969/j.issn.1672-6251.2006.05.013
    [11] 孙仕军, 张琳琳, 陈志君, 等. AquaCrop作物模型应用研究进展[J]. 中国农业科学, 2017, 50(17): 3286-3299 doi: 10.3864/j.issn.0578-1752.2017.17.004

    SUN S J, ZHANG L L, CHEN Z J, et al. Advances in AquaCrop model research and application[J]. Scientia Agricultura Sinica, 2017, 50(17): 3286-3299 doi: 10.3864/j.issn.0578-1752.2017.17.004
    [12] BATTILANI A, LETTERIO T, CHIARI G. Aquacrop model calibration and validation for processing tomato crop in a sub-humid climate[J]. Acta Horticulturae, 2015, (1081): 167-174 http://agris.fao.org/agris-search/search.do?recordID=US201600104197
    [13] GREAVES G, WANG Y M. Assessment of FAO AquaCrop model for simulating maize growth and productivity under deficit irrigation in a tropical environment[J]. Water, 2016, 8(12): 557 doi: 10.3390/w8120557
    [14] 王连喜, 吴建生, 李琪, 等. AquaCrop作物模型应用研究进展[J]. 地球科学进展, 2015, 30(10): 1100-1106 https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201510007.htm

    WANG L X, WU J S, LI Q, et al. A review on the research and application of aqua crop model[J]. Advances in Earth Science, 2015, 30(10): 1100-1106 https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201510007.htm
    [15] 周彤, 刘涛, 武威, 等. 几种常见作物模型的研究进展及其参数优化[J]. 上海农业学报, 2017, 33(4): 152-159 https://www.cnki.com.cn/Article/CJFDTOTAL-SHLB201704034.htm

    ZHOU T, LIU T, WU W, et al. Research progress and parameter optimization of several maincrop models[J]. Acta Agriculturae Shanghai, 2017, 33(4): 152-159 https://www.cnki.com.cn/Article/CJFDTOTAL-SHLB201704034.htm
    [16] 孙扬越, 申双和. 作物生长模型的应用研究进展[J]. 中国农业气象, 2019, 40(7): 444-459 doi: 10.3969/j.issn.1000-6362.2019.07.004

    SUN Y Y, SHEN S H. Research progress in application of crop growth models[J]. Chinese Journal of Agrometeorology, 2019, 40(7): 444-459 doi: 10.3969/j.issn.1000-6362.2019.07.004
    [17] 范兰, 吕昌河, 陈朝. EPIC模型及其应用[J]. 地理科学进展, 2012, 31(5): 584-592

    FAN L, LU C H, CHEN Z. A review of EPIC model and its applications[J]. Progress in Geography, 2012, 31(5): 584-592
    [18] JONES J W, HOOGENBOOM G, PORTER C H, et al. The DSSAT cropping system model[J]. European Journal of Agronomy, 2003, 18(3/4): 235-265 http://europepmc.org/abstract/AGR/IND44696208
    [19] DOORENBOS J, KASSAM A H. FAO. Yield Response to Water. FAO Irrig. Drain. Pap. 33[M]. Food and Agriculture Organization of the United Nations. Rome: 1979
    [20] 张涛. 西北半干旱区春玉米生产力对气象因子的响应及模拟研究[D]. 兰州: 甘肃农业大学, 2018: 2-5

    ZHANG T. Response and simulation study on spring maize productivity of meteorological factor in the northwest semiarid region[D]. Lanzhou: Gansu Agricultural University, 2018: 2-5
    [21] 冉辉. 西北旱区制种玉米生长与产量对土壤水氮的响应及模拟研究[D]. 北京: 中国农业大学, 2017

    RAN H. Response mechanism of hybrid maize seed production to water and nitrogen and crop modeling in arid northwest China[D]. Beijing: China Agricultural University, 2017
    [22] ELBEHRI A, FAO R E, AZAPAGIC A, et al. Kenya's Tea Sector Under Climate Change: An Impact Assessment and Formulation of a Climate-smart Strategy[R]. Roma: Food and Agriculture Organization of the United Nations, 2015: 83-98
    [23] 陈超飞, 柳双环, 郭大辛, 等. 基于AquaCrop模型的夏玉米生长模拟及灌溉制度优化[J]. 干旱地区农业研究, 2019, 37(3): 72-82 https://www.cnki.com.cn/Article/CJFDTOTAL-GHDQ201903010.htm

    CHEN C F, LIU S H, GUO D X, et al. Growth simulation and optimization of irrigation scheme forsummer maize using AquaCrop model[J]. Agricultural Research in the Arid Areas, 2019, 37(3): 72-82 https://www.cnki.com.cn/Article/CJFDTOTAL-GHDQ201903010.htm
    [24] 宋明义, 任荣富, 周涛发, 等. 浙江"安吉白茶"产地地质地球化学特征[J]. 现代地质, 2008, 22(6): 954-959 doi: 10.3969/j.issn.1000-8527.2008.06.009

    SONG M Y, REN R F, ZHOU T F, et al. Characteristics of geology and geochemistry in producing area of the Anji white tea, Zhejiang Province[J]. Geoscience, 2008, 22(6): 954-959 doi: 10.3969/j.issn.1000-8527.2008.06.009
    [25] 倪玲. 基于AquaCrop模型的冬小麦灌溉制度研究[D]. 杨凌: 西北农林科技大学, 2015: 7-24

    NI L. Irrigation management for winter wheat based on aquacrop model[D]. Yangling: Northwest A&F University, 2015: 7-24
    [26] 姜润, 钱半吨, 蒋文妍, 等. '白叶1号'茶树品种在溧阳开采期预测研究[J]. 茶叶, 2014, 40(3): 134-137 doi: 10.3969/j.issn.0577-8921.2014.03.002

    JIANG R, QIAN B D, JIANG W Y, et al. Forecast of first plucking date of white tea cultivar 'White Leaf 1' in Liyang[J]. Journal of Tea, 2014, 40(3): 134-137 doi: 10.3969/j.issn.0577-8921.2014.03.002
    [27] 黄寿波. 茶树生长的农业气象指标[J]. 农业气象, 1981, 2(3): 54-58 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY198103011.htm

    HUANG S B. Agrometeorological index for the growth of tea[J]. Chinese Journal of Agrometeorology, 1981, 2(3): 54-58 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY198103011.htm
    [28] 黄寿波. 气象因子与茶树生育、产量、品质的关系[J]. 中国农业科学, 1986, 19(4): 96 https://www.cnki.com.cn/Article/CJFDTOTAL-ZNYK198604020.htm

    HUANG S B. Relationship between the meteorological factors and the growth development, yield and quality of tea trees[J]. Scientia Agricultura Sinica, 1986, 19(4): 96 https://www.cnki.com.cn/Article/CJFDTOTAL-ZNYK198604020.htm
    [29] 唐湖, 郝心愿, 王璐, 等. 茶树越冬芽在休眠与萌发时期的物质交流变化及其分子调控[J]. 作物学报, 2017, 43(5): 669-677 https://www.cnki.com.cn/Article/CJFDTOTAL-XBZW201705006.htm

    TANG H, HAO X Y, WANG L, et al. Molecular regulation and substance exchange dynamics at dormancy and budbreak stages in overwintering buds of tea plant[J]. Acta Agronomica Sinica, 2017, 43(5): 669-677 https://www.cnki.com.cn/Article/CJFDTOTAL-XBZW201705006.htm
    [30] 肖静, 李楠, 姜会飞. 作物发育期积温计算方法及其稳定性[J]. 气象研究与应用, 2010, 31(2): 64-67 doi: 10.3969/j.issn.1673-8411.2010.02.020

    XIAO J, LI N, JIANG H F. Calculation and stability of accumulated temperatures in the growing season of winter wheat[J]. Journal of Meteorological Research and Application, 2010, 31(2): 64-67 doi: 10.3969/j.issn.1673-8411.2010.02.020
    [31] 沈天琦. 茶叶春霜冻灾害精细化概率评估——以浙江松阳县为例[D]. 南京: 南京信息工程大学, 2018: 1-3

    SHEN T Q. Detailed probability assessment of tea spring frost disaster-A case of Songyang County, Zhejiang Province[D]. Nanjing: Nanjing University of Information Science & Technology, 2018: 1-3
    [32] FOSTER T, BROZOVIĆ N, BUTLER A P, et al. AquaCrop-OS: an open source version of FAO's crop water productivity model[J]. Agricultural Water Management, 2017, 181: 18-22 doi: 10.1016/j.agwat.2016.11.015
    [33] RODRIGUEZ, OBER. AquaCropR: crop growth model for R[J]. Agronomy, 2019, 9(7): 378 doi: 10.3390/agronomy9070378
    [34] 宫诏健, 刘利民, 陈杰, 等. 基于MODIS NDVI数据的辽宁省春玉米物候期提取研究[J]. 沈阳农业大学学报, 2018, 49(3): 257-265 https://www.cnki.com.cn/Article/CJFDTOTAL-SYNY201803002.htm

    GONG Z J, LIU L M, CHEN J, et al. Phenophase extraction of spring maize in Liaoning Province based on MODIS NDVI data[J]. Journal of Shenyang Agricultural University, 2018, 49(3): 257-265 https://www.cnki.com.cn/Article/CJFDTOTAL-SYNY201803002.htm
    [35] 陈小敏, 陈汇林, 李伟光, 等. 海南岛天然橡胶林春季物候期的遥感监测[J]. 中国农业气象, 2016, 37(1): 111-116 doi: 10.3969/j.issn.1000-6362.2016.01.014

    CHEN X M, CHEN H L, LI W G, et al. Remote sensing monitoring of spring phenophase of natural rubber forest in Hainan Province[J]. Chinese Journal of Agrometeorology, 2016, 37(1): 111-116 doi: 10.3969/j.issn.1000-6362.2016.01.014
    [36] LORITE I J, GARCÍA-VILA M, SANTOS C, et al. AquaData and AquaGIS: Two computer utilities for temporal and spatial simulations of water-limited yield with AquaCrop[J]. Computers and Electronics in Agriculture, 2013, 96: 227-237 doi: 10.1016/j.compag.2013.05.010
    [37] 冯宗炜, 王效科. 中国森林生态系统的生物量和生产力[M]. 北京: 科学出版社, 1999

    FENG Z W, WANG X K. Biomass and Productivity of Chinese Forest Ecosystem[M]. Beijing: China Science Press, 1999
    [38] 赵蓓, 郭泉水, 牛树奎, 等. 大岗山林区几种常见灌木生物量估算与分析[J]. 东北林业大学学报, 2012, 40(9): 28-33 doi: 10.3969/j.issn.1000-5382.2012.09.008

    ZHAO B, GUO Q S, NIU S K, et al. Estimation and analysis on biomass of several common shrubs in Dagang Mountain[J]. Journal of Northeast Forestry University, 2012, 40(9): 28-33 doi: 10.3969/j.issn.1000-5382.2012.09.008
    [39] 万五星, 王效科, 李东义, 等. 暖温带森林生态系统林下灌木生物量相对生长模型[J]. 生态学报, 2014, 34(23): 6985-6992 https://www.cnki.com.cn/Article/CJFDTOTAL-STXB201423023.htm

    WAN W X, WANG X K, LI D Y, et al. Biomass allometric models for understory shrubs of warm temperate forest ecosystem[J]. Acta Ecologica Sinica, 2014, 34(23): 6985-6992 https://www.cnki.com.cn/Article/CJFDTOTAL-STXB201423023.htm
    [40] 潘灼坤. 耦合遥感信息与作物生长模型的区域低温影响监测、预警与估产[D]. 杭州: 浙江大学, 2016: 9-20

    PAN Z K. Integration of remote sensing and crop growth model for regional low temperature impact monitoring, early warning, and yield estimation[D]. Hangzhou: Zhejiang University, 2016: 9-20
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  • 收稿日期:  2021-01-09
  • 录用日期:  2021-03-23
  • 刊出日期:  2021-08-01

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