轮作模式在农耕区土壤有机质推测制图中的应用
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摘要
人类活动近年成为数字土壤制图亟需考虑的要素。本文以农业活动中轮作模式为例,将轮作信息应用于数字土壤制图,探讨其对土壤空间变异刻画的有效性。以安徽宣城两个县市的耕地平区为研究区,通过野外调查获得近年三种主要轮作模式,基于监督分类对多期遥感影像解译得到轮作类型空间分布图,使用方差分析探讨轮作对土壤表层有机质空间变异是否有显著性影响,采用随机森林重要性指标对自然环境因子、轮作模式、土地利用方式和归一化植被指数进行重要性排序,并构建不同的环境因子组合,利用基于相似度的土壤推测模型和随机森林模型进行制图和交叉验证。结果表明,轮作模式对土壤表层有机质有显著性影响,其重要性排序为第二,引入轮作使得基于相似度的土壤推测模型和随机森林模型制图精度分别提高4.8%~65.9%和1.9%~2.7%。
Most of the current digital soil mapping uses natural environmental covariates. However, human activities have had a profound impact on soil properties and thus become an important factor influencing soil spatial variability.In this paper, we introduced crop rotation as one of the agricultural activities into digital soil mapping and explored its effectiveness in characterizing soil spatial variability. Flat cultivated areas of Xuanzhou City and Langxi County in Anhui Province was chosen as the study area. Three main crop rotations were obtained through field investigation in2010. The spatial distribution of crop rotation in the studied area was obtained by multi-phase remote sensing image interpretation.One-way analysis of variance(ANOVA) was used to analyzewhether topsoil organic matter content had significant different contents statistically among three crop rotation groups. Factor importance of seven natural environmental covariates, crop rotation, land use and NDVI were generated by variable importance measurements of Random Forest. Different combination of environmental covariates were constructed according to the rankings of their importance. The Soil Landscape Inference Model(So LIM) and Random Forest were used to predict soil organic matter mapping and the cross validation was used to evaluate the mapping accuracies. The results showed that topsoil organic matter content was significantly different statistically between three crop rotation groups, and the crop rotation was more important than parent material, land use or NDVI. In addition, the introduction of crop rotation improved the accuracy of soil mapping. This study demonstrated the usefulness of human activities in digital soil mapping and indicated the necessity for using human activity factors in digital soil mapping studies.
Most of the current digital soil mapping uses natural environmental covariates. However, human activities have had a profound impact on soil properties and thus become an important factor influencing soil spatial variability.In this paper, we introduced crop rotation as one of the agricultural activities into digital soil mapping and explored its effectiveness in characterizing soil spatial variability. Flat cultivated areas of Xuanzhou City and Langxi County in Anhui Province was chosen as the study area. Three main crop rotations were obtained through field investigation in2010. The spatial distribution of crop rotation in the studied area was obtained by multi-phase remote sensing image interpretation.One-way analysis of variance(ANOVA) was used to analyzewhether topsoil organic matter content had significant different contents statistically among three crop rotation groups. Factor importance of seven natural environmental covariates, crop rotation, land use and NDVI were generated by variable importance measurements of Random Forest. Different combination of environmental covariates were constructed according to the rankings of their importance. The Soil Landscape Inference Model(So LIM) and Random Forest were used to predict soil organic matter mapping and the cross validation was used to evaluate the mapping accuracies. The results showed that topsoil organic matter content was significantly different statistically between three crop rotation groups, and the crop rotation was more important than parent material, land use or NDVI. In addition, the introduction of crop rotation improved the accuracy of soil mapping. This study demonstrated the usefulness of human activities in digital soil mapping and indicated the necessity for using human activity factors in digital soil mapping studies.
引文
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[20]陈书涛,黄耀,郑循华,等.轮作制度对农田氧化亚氮排放的影响及驱动因子[J].中国农业科学,2005,38(10):2053-2060.
[21]王俊,李凤民,贾宇,等.半干旱黄土区苜蓿草地轮作农田土壤氮,磷和有机质变化[J].应用生态学报,2005,16(3):439-444.
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[24]QIN C,ZHUN A X,PEI T,et al.An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm[J].International Journal of Geographical Information Science,2007,21(4):443-458.
[25]QIN C-Z,ZHU A-X,PEI T,et al.An approach to computing topographic wetness index based on maximum downslope gradient[J].Precision Agriculture,2011,12(1):32-43.
[26]张淑杰,朱阿兴,刘京,等.整合已有土壤样点的数字土壤制图补样方案[J].地理科学进展,2012,31(10):1318-1325.
[27]张文彤,闫洁.SPSS统计分析基础教程[J].北京:高等教育版社,2004,335-340.
[28]郭乃嘉,史学正,赵永存,等.人为与环境因子对农田土壤有机质影响的比较研究——以典型黑土区和水稻土区为例[J].土壤学报,2016,5:1097-1106.
[29]ZHU A,BAND L,VERTESSY R,et al.Derivation of soil properties using a soil land inference model(So LIM)[J].Soil Science Society of America Journal,1997,61(2):523-533.
[30]BREIMAN L.Random forests[J].Machine learning,2001,45(1):5-32.
[31]WERE K,BUI D T,DICK?B,et al.A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape[J].Ecological Indicators,2015,52:394-403.
[32]WIESMEIER M,BARTHOLD F,SSPORLEIN P,et al.Estimation of total organic carbon storage and its driving factors in soils of Bavaria(southeast Germany)[J].Geoderma Regional,2014,1:67-78.
[33]REFAEILZADEH P,TANG L,LIU H.Cross-validation[M].Encyclopedia of database systems.Springer.2009:532-538.
[34]REDDY P P.Sustainable Intensification of Crop Production[M].Springer.2016.
[35]HARGROVE W,FRYE W.The need for legume cover crops in conservation tillage production[J].1987,11(1):1-18.
[2]BARRIOS E.Soil biota,ecosystem services and land productivity[J].Ecological economics,2007,64(2):269-285.
[3]BAURE A,BLACK A.Quantification of the effect of soil organic matter content on soil productivity[J].Soil Science Society of America Journal,1994,58(1):185-193.
[4]BEZDICEK D,PAPENDICK R I,Lal R,et al.Importance of soil quality to health and sustainable land management[J].Methods for assessing soil quality,1996,1-8.
[5]SAXTON K,RAWLS W.Soil water characteristic estimates by texture and organic matter for hydrologic solutions[J].Soil science society of America Journal,2006,70(5):1569-1578.
[6]MC BRATNEY A B,SSANTOS M M,MINASNY B.On digital soil mapping[J].Geoderma,2003,117(1):3-52.
[7]ZHU A X,LIU F,LI B,et al.Differentiation of Soil Conditions over Low Relief Areas Using Feedback Dynamic Patterns[J].Soil Science Society of America Journal,2010,74(3):861-869.
[8]孙福军,雷秋良,刘颖,等.数字土壤制图技术研究进展与展望[J].土壤通报,2011,42(6):1502-1507.
[9]孙孝林,赵玉国,刘峰,等.数字土壤制图及其研究进展[J].土壤通报,2013,44(3):752-759.
[10]LAGCHERIE P,MC BRATNEY A,VOLTZ M.Digital soil mapping:An introductory perspective[M].Elsevier,2006.
[11]MINASNY B,HARTEMINK A E.Predicting soil properties in the tropics[J].Earth-Science Reviews,2011,106(1):52-62.
[12]杨琳,朱阿兴,秦承志,等.基于典型点的目的性采样设计方法及其在土壤制图中的应用[J].地理科学进展,2010,29(3):279-286.
[13]BALSTROM T,BREUNING-MADSEN H,KRUGER J,et al.A statistically based mapping of the influence of geology and land use on soil p H:A case study from Denmark[J].Geoderma,2013,192:453-462.
[14]GERI F,AMICI V,ROCCHINI D.Human activity impact on the heterogeneity of a Mediterranean landscape[J].Applied geography,2010,30(3):370-379.
[15]周涛,史培军,王绍强.气候变化及人类活动对中国土壤有机碳储量的影响[J].地理学报,2004,58(5):727-734.
[16]KATO H,KIMURA R,ELBEIH S F,et al.Land use change and crop rotation analysis of a government well district in Rashda village Dakhla Oasis,Egypt based on satellite data[J].The Egyptian Journal of Remote Sensing and Space Science,2012,15(2):185-195.
[17]LU D,MAUSEL P,BRONDIZIO E,et al.Change detection techniques[J].International journal of remote sensing,2004,25(12):2365-2401.
[18]USMAN M,LIEDLR,SHAHID M A,et al.Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data[J].Journal of Geographical Sciences,2015,25(12):1479-1506.
[19]韩卫东.耕地土壤肥力数字化制图方法对比研究[D];浙江大学,2014.
[20]陈书涛,黄耀,郑循华,等.轮作制度对农田氧化亚氮排放的影响及驱动因子[J].中国农业科学,2005,38(10):2053-2060.
[21]王俊,李凤民,贾宇,等.半干旱黄土区苜蓿草地轮作农田土壤氮,磷和有机质变化[J].应用生态学报,2005,16(3):439-444.
[22]ZHANG S,ZHU A,LIU W,et al.Mapping detailed soil property using small scale soil type maps and sparse typical samples[J].Chinese Geographical Science,2013,23(6):680-691.
[23]杨琳,朱阿兴,张淑杰,等.土壤制图中多等级代表性采样与分层随机采样的对比研究[J].土壤学报,2015,1:28-37.
[24]QIN C,ZHUN A X,PEI T,et al.An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm[J].International Journal of Geographical Information Science,2007,21(4):443-458.
[25]QIN C-Z,ZHU A-X,PEI T,et al.An approach to computing topographic wetness index based on maximum downslope gradient[J].Precision Agriculture,2011,12(1):32-43.
[26]张淑杰,朱阿兴,刘京,等.整合已有土壤样点的数字土壤制图补样方案[J].地理科学进展,2012,31(10):1318-1325.
[27]张文彤,闫洁.SPSS统计分析基础教程[J].北京:高等教育版社,2004,335-340.
[28]郭乃嘉,史学正,赵永存,等.人为与环境因子对农田土壤有机质影响的比较研究——以典型黑土区和水稻土区为例[J].土壤学报,2016,5:1097-1106.
[29]ZHU A,BAND L,VERTESSY R,et al.Derivation of soil properties using a soil land inference model(So LIM)[J].Soil Science Society of America Journal,1997,61(2):523-533.
[30]BREIMAN L.Random forests[J].Machine learning,2001,45(1):5-32.
[31]WERE K,BUI D T,DICK?B,et al.A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape[J].Ecological Indicators,2015,52:394-403.
[32]WIESMEIER M,BARTHOLD F,SSPORLEIN P,et al.Estimation of total organic carbon storage and its driving factors in soils of Bavaria(southeast Germany)[J].Geoderma Regional,2014,1:67-78.
[33]REFAEILZADEH P,TANG L,LIU H.Cross-validation[M].Encyclopedia of database systems.Springer.2009:532-538.
[34]REDDY P P.Sustainable Intensification of Crop Production[M].Springer.2016.
[35]HARGROVE W,FRYE W.The need for legume cover crops in conservation tillage production[J].1987,11(1):1-18.