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基于模糊c均值聚類(lèi)法的玉米農(nóng)田管理分區(qū)研究
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農(nóng)業(yè)部公益性行業(yè)科研專(zhuān)項(xiàng)(201503125)和國(guó)家自然科學(xué)基金項(xiàng)目(51725904、51439006)


Delineating Management Zones in Maize Field Based on Fuzzy C-means Algorithm
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    摘要:

    為提高大面積農(nóng)田作物管理的精確性,以甘肅黃羊河農(nóng)場(chǎng)玉米膜下滴灌示范區(qū)為研究對(duì)象,對(duì)大面積農(nóng)田進(jìn)行管理分區(qū)研究。綜合考慮地形屬性(高程、坡度、坡向)、土壤質(zhì)地(砂粒、粘粒、粉粒含量)、土壤含水率(SWC)、速效氮含量(AN)、電導(dǎo)率(EC1:5)以及玉米產(chǎn)量,根據(jù)相關(guān)性分析結(jié)果篩選產(chǎn)量主控因子,使用主成分分析得到3個(gè)主成分作為分區(qū)依據(jù),進(jìn)而使用模糊c均值聚類(lèi)法(Fuzzy c-means algorithm, FCM)進(jìn)行管理區(qū)劃分,以模糊性能指數(shù)和歸一化分類(lèi)熵作為最佳分區(qū)數(shù)的評(píng)判依據(jù),分析管理分區(qū)后各分區(qū)間的差異。結(jié)果表明:玉米產(chǎn)量的主控因子分別為土壤粉粒含量、土壤砂粒含量、SWC、AN、EC1∶5和高程,使用模糊c均值聚類(lèi)法進(jìn)行聚類(lèi)分區(qū)得到最優(yōu)分區(qū)數(shù)為3個(gè)。管理區(qū)之間各主控因子呈現(xiàn)極顯著差異性(P<0.01),且生育期內(nèi)作物株高、葉面積指數(shù)(LAI)和SWC在不同分區(qū)中也有明顯差異;同時(shí),分區(qū)內(nèi)的各因子變異性均有不同程度的下降。研究結(jié)果說(shuō)明,農(nóng)田分區(qū)管理可以依據(jù)不同分區(qū)特點(diǎn)制定管理策略,為“精準(zhǔn)農(nóng)業(yè)”的實(shí)施提供理論基礎(chǔ)。

    Abstract:

    Taking the demonstration area of drip irrigation under film as the research object, and aiming at delineating management zones in large areas of farmland, in Huangyanghe Farm, Gansu Province. The topographical attributes (elevation slope and aspect), soil texture (sand, clay and silt), soil moisture content, available nitrogen, electrical conductivity and yield of maize were considered, the degree of variation and correlation of each factor were analyzed, and then the master factors of maize yield were extracted by the results of correlation analysis. Three principal components were obtained by principal component analysis (PCA) based on the master factors. Fuzzy c-means clustering algorithm (FCM) was used to delineate management zones based on the spatial variation of the principal components, the optimal partition number was determined by the fuzzy performance index (FPI), and normalized classification entropy (NCE) were minimum at the same time, and then the differences of the master factors among the management zones were analyzed. Results showed that the master factors were silt, sand, soil water content, available nitrogen, electrical conductivity and elevation, and three management zones were determined by FCM. Statistically significant differences in the master factors were found among the three management zones. Soil water content, crop height and LAI were also significantly different in different management zones during the crop growth period. The spatial variation of the factors within the same management zones was smaller than that of the factors in the whole field, and the variation between zones was large. Delineation of management zones should be adopted based on the characteristics of each zone, and the research result provided a theoretical basis for the implementation of precision agriculture.

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陳世超,杜太生,王素芬.基于模糊c均值聚類(lèi)法的玉米農(nóng)田管理分區(qū)研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2019,50(11):293-300. CHEN Shichao, DU Taisheng, WANG Sufen. Delineating Management Zones in Maize Field Based on Fuzzy C-means Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2019,50(11):293-300.

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  • 收稿日期:2019-04-08
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  • 在線發(fā)布日期: 2019-11-10
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