Zhang Lei, Hou Yingyu, Zheng Changling, et al. The construction and application of assessing index to crop growing condition. J Appl Meteor Sci, 2019, 30(5): 543-554. DOI:  10.11898/1001-7313.20190503.
Citation: Zhang Lei, Hou Yingyu, Zheng Changling, et al. The construction and application of assessing index to crop growing condition. J Appl Meteor Sci, 2019, 30(5): 543-554. DOI:  10.11898/1001-7313.20190503.

The Construction and Application of Assessing Index to Crop Growing Condition

DOI: 10.11898/1001-7313.20190503
  • Received Date: 2019-05-10
  • Rev Recd Date: 2019-07-24
  • Publish Date: 2019-09-30
  • It is generally accepted that crop growing assessment can reveal its temporary condition and response to weather and climate when crop growing condition is assessed in a reasonable and effective way. To address this, normal field observation and remote sensing monitoring are the major techniques to quantify the crop growing condition. However, limited by their time-efficiency and uncertainties in algorithm, there are some deficiencies within them. Crop model, as an alternative way, is proposed to detect crop growing condition, with the advantage of its better mechanism and timeliness. Currently, WOFOST and ORYZA2000 are widely used to simulate the growth of winter wheat, spring maize, summer maize, single-season rice and double-season rice. Derived from WOFOST and ORYZA2000, the daily outputs, i.e., the development stage, leaf area index and total aboveground production, are simulated from 2001. Three outputs are selected as impacted variables for crop growing, and quantified through membership function. In the initial stage of crop growth, development stage, leaf area index and total aboveground production generate comprehensive effect, and they are weighted aggregated to a integrated index by the respective weight of 0.3, 0.3 and 0.4 according to expert scoring method. In the later stage, development stage and total aboveground production are the major factors influencing crop condition, and they are aggregated to a integrated index by the weight derived from their relative relationship with dry weight of storage organs. The assessing index is keeping well with experimental observation and remote sensing monitoring, implying its effectivity in evaluation service. The crop growing condition is assessed on any day during the growing season, and the corresponding daily integrated index is built in datasets under Crop Growth Simulating and Monitoring System in China (CGMS-China). According to daily integrated index, crop growing condition is divided into 5 levels, i.e., better, good, normal, bad and worse. Based on CGMS-China, daily crop growing condition is illustrated in the spatial distribution, which can distinguish the regional or local distinction of condition. Moreover, assessing index is spatially aggregated to the index at the province scale, which is a base quantity for comparing the provincial crop growing condition, corresponding to the assessing scale of yield prediction in agrometeorological services. Under the typical weather conditions, assessing index is efficient in specific regions and even local stations. For example, impacts of high temperature and drought from 21 July to 10 August in 2018 are well performed in the assessing index change at spatial and temporal scales. The assessing index for crop growing assessment based on crop model can provide more accurate and quantifier outputs, fitting with the demand of modern agriculture and agrometeorological service.
  • Fig. 1  Growth assessment for spring maize on 30 May 2018(a) and anomaly of observed phenology at agro-meteorological experiment stations(b)

    Fig. 2  Growth assessment(a) and remote monitoring(b) for winter wheat on 20 Mar 2018

    Fig. 3  Drought days with intensity from 21 Jul to 10 Aug during 1981-2018(a) and growth assessment for spring maize from 21 Jul to 10 Aug during 2014-2018(b) at Kangping in Liaoning

    Fig. 4  The percentage of station in soil water deficit at 10 cm depth and surface precipitation(a) and growth assessment for spring maize(b), in Jilin and Liaoning during 11-30 May 2018

    Fig. 5  Growth assessment for single-season rice(a) and double-season early rice(b) on 30 Jun 2018

    Fig. 6  Weight of total aboveground production(a) and growth assessment(b) for winter wheat on 15 Mar 2019

    Fig. 7  Growth assessment for single-season rice on 5 Aug 2018(a) and 5 Aug 2013(b)

    Fig. 8  Growth assessment for spring maize in provinces on 31 May 2018

  • [1]
    杨邦杰, 裴志远.农作物长势的定义与遥感监测.农业工程学报, 1999, 15(3):214-218. doi:  10.3321/j.issn:1002-6819.1999.03.044
    李树强, 孙红, 张彦娥, 等.作物长势信息空间分析系统设计.农业机械学报, 2013, 44(11):234-240. doi:  10.6041/j.issn.1000-1298.2013.11.040
    中国气象局.生态气象观测规范(试行).北京:气象出版社, 2005:53-59.
    李颖, 陈怀亮, 李耀辉, 等.一种利用MODIS数据的夏玉米物候期监测方法.应用气象学报, 2018, 29(1):111-119. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180110&flag=1
    吴炳芳, 张峰, 刘成林, 等.农作物长势综合遥感监测方法.遥感学报, 2004, 8(6):498-514. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ygxb200406002
    江东, 王乃斌, 杨小唤, 等.NDVI曲线与农作物长势的时序互动规律.生态学报, 2002, 22(2):247-253. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=stxb200202015
    钱永兰, 侯英雨, 延昊, 等.基于遥感的国外作物长势监测与产量趋势估计.农业工程学报, 2012, 28(13):166-171. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201213027
    侯英雨, 张蕾, 吴门新, 等.国家级现代农业气象业务技术进展.应用气象学报, 2018, 29(6):641-656. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180601&flag=1
    王建林.现代化农业气象业务.北京:气象出版社, 2010.
    马玉平, 王石立, 王馥棠.作物模拟模型在农业气象业务应用中的研究初探.应用气象学报, 2005, 16(3):293-303. doi:  10.3969/j.issn.1001-7313.2005.03.003
    帅细强, 陆魁东, 黄晚华.不同方法在湖南省早稻产量动态预报中的比较.应用气象学报, 2015, 26(1):103-111. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20150111&flag=1
    Supit I, Hoojer A A, Diepen C A V.System Description of the WOFOST 6.0 Crop Simulation Model Implemented in CGMS.Theory and Algorithms.Luxembourg: Commission of the European Communities, 1994.
    Ma Y P, Wang S L, Li W J.Monitoring and predicting of maize chilling damage based on crop growth model in Northeast China.Acta Agronomica Sinica, 2011, 37(10):1868-1878. doi:  10.3724/SP.J.1006.2011.01868
    Li Z H, Jin XL, Zhao C J, et al.Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing.European Journal of Agronomy, 2015, 71:53-62. doi:  10.1016/j.eja.2015.08.006
    Li H, Jiang Z W, Chen Z X, et al.Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation.Journal of Integrative Agriculture, 2016, 15(10):60345-7. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgnykx-e201710019
    Yao F M, Tang Y J, Wang P J, et al.Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain.Physics and Chemistry of the Earth, 2015, 87:142-152. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4f1b889430cda01c0003836055ddd092
    张建平, 王靖, 何永坤, 等.基于WOFOST作物模型的玉米区域干旱影响评估技术.中国生态农业学报, 2017, 25(3):451-459. http://d.old.wanfangdata.com.cn/Periodical/stnyyj201703016
    秦鹏程, 刘敏, 万素琴.不完整气象资料下基于作物模型的产量预报方法.应用气象学报, 2016, 27(4):407-416. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160403&flag=1
    侯英雨, 何亮, 靳宁, 等.中国作物生长模拟监测系统构建及应用.农业工程学报, 2018, 34(21):165-175. doi:  10.11975/j.issn.1002-6819.2018.21.020
    Diepen C A, Wolf J, Keulen H, et al.WOFOST:A simulation model of crop production.Soil Use and Management, 1989, 5(1):16-24. doi:  10.1111/j.1475-2743.1989.tb00755.x
    Bouman B A M, Kropff M J, Tuong T P, et al.ORYZA2000: Modeling Lowland Rice.Los Baos: International Rice Research Institute, 2003.
    邱美娟, 宋迎波, 王建林, 等.山东省冬小麦产量动态集成预报方法.应用气象学报, 2016, 27(2):191-200. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160207&flag=1
    帅细强, 王石立, 马玉平, 等.基于水稻生长模型的气象影响评价和产量动态预测.应用气象学报, 2008, 19(1):71-81. doi:  10.3969/j.issn.1001-7313.2008.01.010
    孙琳丽, 马玉平, 俄有浩, 等.基于约束性分析的数据与作物模型同化方法.应用气象学报, 2013, 24(3):287-296. doi:  10.3969/j.issn.1001-7313.2013.03.004
    刘维, 王冬妮, 侯英雨, 等.基于吉林省观测土壤水分的WOFOST模型模拟研究.气象, 2018, 44(10):1352-1359. http://d.old.wanfangdata.com.cn/Periodical/qx201810012
    宣守丽, 石春林, 刘杨, 等.自然环境高温对长江中下游地区中稻结实率的影响及模拟.气象与环境科学, 2017, 40(1):73-77. http://d.old.wanfangdata.com.cn/Periodical/hnqx201701011
    董姝娜, 庞泽源, 张继权, 等.基于CERES-Maize模型的吉林西部玉米干旱脆弱性曲线研究.灾害学, 2014, 29(3):115-119. http://d.old.wanfangdata.com.cn/Periodical/zhx201403021
    胡标林, 余守武, 万勇, 等.东乡普通野生稻全生育期抗旱性坚定.作物学报, 2007, 33(3):425-432. http://www.cnki.com.cn/Article/CJFD2007-XBZW200703011.htm
    钱永兰, 侯英雨, 延昊, 等.基于遥感的国外作物长势监测与产量趋势估计.农业工程学报, 2012, 28(13):166-171. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201213027
    王培娟, 霍治国, 杨建莹, 等.基于热量指数的东北春玉米冷害指标.应用气象学报, 2019, 30(1):13-24. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20190102&flag=1
    宋艳玲, 王建林, 田靳峰, 等.气象干旱指数在东北春玉米干旱监测中的改进.应用气象学报, 2019, 30(1):25-34. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20190103&flag=1
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