新石器时代晚期华北地区耕地重建

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陈强强¹, 刘峰贵^,², 方修琦³, 周强¹, 陈琼¹, 陈锐杰¹1. 青海师范大学地理科学学院, 西宁 810008
2. 高原科学与可持续发展研究院, 西宁 810008
3. 北京师范大学地理科学学部, 北京 100875

Reconstruction of cropland distribution in the Late Neolithic period in Northern China

CHEN Qiangqiang¹, LIU Fenggui^,², FANG Xiuqi³, ZHOU Qiang¹, CHEN qiong¹, CHEN Ruijie¹1. College of Geographic Sciences, Qinghai Normal University, Xining 810008, China
2. Academy of Plateau Science and Sustainability, Xining 810008, China
3. Department of Geography, Beijing Normal University, Beijing 100875, China

通讯作者: 刘峰贵（1966- ）,男,青海门源人,教授,博士生导师,主要从事环境演变与自然灾害研究。E-mail: lfg_918@163.com

收稿日期:2018-11-21修回日期:2019-04-9网络出版日期:2019-12-20

基金资助:

国家重点研发计划项目.2017YFA0603304
国家自然科学基金项目.41271123

Received:2018-11-21Revised:2019-04-9Online:2019-12-20
作者简介 About authors
陈强强（1992-）,男,甘肃榆中人,硕士,主要从事自然地理综合研究E-mail:qqc921025@163.com。

摘要
全新世以来农业的起源使全球人类活动日益加剧,而新石器时代晚期（在中国大约距今7 ka—5 ka）农业发展正处于从刀耕火种向耒耜翻耕过渡阶段,由此被认为是人类活动改变自然植被的关键时期。中国华北地区丰富的考古遗迹记录了该时期人类活动的状况。基于已发现和挖掘的考古资料,运用考古学关联建模方法推算出新石器时代晚期华北地区各聚落遗址的人口规模与耕地面积,并在此基础上重建了耕地空间分布格局。结果显示：① 新石器时代晚期华北地区的人口规模至少为50.61×10 ⁴人。其中,河南地区人口最多,约为30.46×10 ⁴人,山东地区约13.72×10 ⁴人,河北地区约6.43×10 ⁴人。② 耕地面积至少约102.22×10 ⁴ hm2,约是现代耕地的4.6%,整个华北地区垦殖率达到1.99%。河南地区耕地面积最大,约为61.52×10 ⁴ hm2,垦殖率达3.68%;山东地区次之,约为27.72×10 ⁴ hm2,垦殖率为1.75%;河北地区最少,约为12.98×10 ⁴ hm2,垦殖率为0.69%。③ 遗址周围3 km范围内是个体聚落耕地的主要分布区,约占总耕地面积的83.43%。④ 在地形分布上,63.36%的耕地分布在低海拔的平原和台地,仅2.43%分布在海拔1000 m以上的地区。就耕地分布的坡度而言,81.55%的耕地分布在坡度小于2°的地区;16.61%的耕地介于2°~6°的缓坡地区;6°~15°的坡地地区仅占耕地的1.84%。
关键词： 新石器时代晚期;华北地区;耕地;空间分布

Abstract

Since the Holocene, the origin of agriculture has contributed to the intensification of human activities across the globe, and its development in the Late Neolithic period (about 7-5 ka B.P. in China) was in the transition from slash-and-burn agriculture to lei-and-si ploughing, which was considered to be the key period for human activities to change the natural environment. Fortunately, the rich archaeological sites in Northern China documented various human activities during this period. In this study, we choose the archaeological data and using the archaeology associated modeling method to calculate the size of the population and the cultivated land area of the settlement sites in Northern China in the Late Neolithic period, and then to reconstruct the spatial pattern of the cultivated land. The results showed that: (1) There were at least 506 100 people in Northern China in the Late Neolithic period, and Henan province had the largest population (about 304 600), followed by Shandong and Hebei provinces (about 137 200 and 64 300 people, respectively). (2) In the study period, the area of cultivated land was 102.22×10 ⁴ hm ², which accounted for 4.6% of the modern cultivated land. For the whole study area, the average reclamation index reached 1.99% in this period. Like the pattern of population size, the cultivated land area and the reclamation rate for Henan province were the largest, reaching 61.52×10 ⁴ hm ² and 3.68%, respectively, followed by Shandong and Hebei provinces, both the cultivated land area and the reclamation rate were approximately 27.72×10 ⁴ hm ² and 1.75%, and 12.98 ×10 ⁴ hm and 0.69%, respectively. (3) In the 3-km range of the historical sites, the main distribution area of individual settlement cultivated land accounted for about 83.43% of the total cultivated area; (4) On spatial scale, 63.36% of cultivated land was located in the low-altitude plain and platform areas, and only 2.43% was located in the higher-altitude areas (above 1000 m). In terms of slope, 81.55% of cultivated land was located in areas less than 2°, 16.61% was found between 2°~6° of the gentle slope, and in the 6°~15° slope areas, the cultivated land area only accounted for 1.84%.

Keywords：Late Neolithic;Northern China;cropland;spatial distribution

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本文引用格式
陈强强, 刘峰贵, 方修琦, 周强, 陈琼, 陈锐杰. 新石器时代晚期华北地区耕地重建. 地理研究[J], 2019, 38(12): 2927-2940 doi:10.11821/dlyj020181293
CHEN Qiangqiang. Reconstruction of cropland distribution in the Late Neolithic period in Northern China. Geographical Research[J], 2019, 38(12): 2927-2940 doi:10.11821/dlyj020181293

1 引言

新石器时代是人类在过去几百万年进化中,在使用劳动工具方面发生的一次革命,这次工具革命和农业兴起、陶器制作几乎前后发生在（不同的地区发生时序有差异）全新世以来,这次工具革命不仅改变了人类生存的方式,也推动原始社会组织由母系氏族公社向父系氏族公社转制,使对土地的争夺代替了自然界威胁成为了人类进化的主要动力,自此以后,人类活动对地球系统开始产生普遍影响,以至后来工业时代的人类活动已经成为战胜大自然的巨大力量,成为一种新的全球变化的强制力量^[1,2,3,4]。尽管在全新世进入新石器时代的10 ka B.P.—7 ka B.P.的时间里人类活动对环境产生了影响,其由于生产技术仍很原始,自然趋势似乎并未受到重大影响^[5,6]。但新石器时代晚期的7 ka B.P.—5 ka B.P.前后,中国东南部山地森林的孢粉明显下降,碳屑数量明显增加^[6],同时在中国南方广西山区的晓锦、顶蛳山等文化遗存中发现了大量6 ka B.P.—5 ka B.P.之间的水稻遗存^[7],在中国北方华北平原早于仰韶时期文化的河北武安磁山遗址已经大规模种植粟,且有大量余粮被储存和堆积^[8],说明全新世进入新石器时代的晚期生产工具不断进步、生产能力大大提高。大约7 ka B.P.—5 ka B.P.前后,人类活动通过大规模新石器技术的运用,引起了土地利用方式的改变,从而改变了自然植被的自然演变趋势,土地覆盖发生明显变化^[6]。中国华北地区的华北平原是全球农业的发源地之一^[9],是全新世以来人类活动影响最为深刻的地区之一。从已发现和挖掘的大量新石器时代晚期遗存来看,新石器时代晚期该地区人类农业活动范围广泛、农业技术和文化相对稳定,考古遗址发现和挖掘最为全面,因此,本文以国际“Land Cover 6k”计划中的6 ka B.P.作为关键时间点,综合7 ka B.P.—5 ka B.P.期间的遗址点,重建该时期耕地格局,为过去10 ka 以来耕地和土地利用数据重建提供方法和数据依据。

土地利用/土地覆盖变化（LUCC）重建是揭示过去全球变化和气候变化的基础和核心之一^{[10,11,12,13,14]},目前大多数重建基于历史文献和遥感推演^{[15,16,17,18,19]},在时间尺度上聚焦于近300年^[20],历史时期以及更长时间尺度的LUCC重建,是研究过去气候变化和人类土地利用耦合、地表环境过程的重要基础^{[21,22,23,24]},对预测未来气候变化、制定政策、规避风险以及人类适应等方面具有重大的科学和实际应用价值。国际“Land Cover 6 k”计划致力于重建过去10 ka以来的人类土地利用状况,其总体目标是为气候建模生成人类活动土地覆盖变化（ALCC）的新数据集,同时加深人们对土地覆盖-气候相互作用的理解,并在不同的人为土地覆盖变化情景下对未来气候变化进行预测。目前,对史前土地利用重建途径主要有两类：一是利用孢粉等重建。孢粉学研究是重建古植被与古环境的手段之一^[25],可以半定量-定量化揭示过去人类活动对生态景观的影响^[26],以孢粉为手段建立的REVEALS和LOVE模型在欧洲、北美重建土地覆被已取得较好应用^[27,28,29],中国****也基于相对花粉产量,并利用相关模型重建了北方阿拉善、兴隆山、青藏高原等地区的植被景观^{[30,31,32,33,34,35]};二是利用考古遗址重建。考古数据包含了丰富的人类活动信息,进一步挖掘考古遗址信息并建立关联模型,可更好地重建史前土地利用状况^[29,36-39]。例如Yu等运用PLUM模型先后重建了伊洛河与渭河谷地地区的史前土地利用状况^[38,39]。

依据中国华北地区保存和挖掘较为完整的考古遗址而又丰富的考古信息为基础,运用考古学关联建模方法,重建区域典型聚落遗址的人口规模及人均占地面积,再根据区域典型遗址的人均占地面积及其各聚落遗址的面积推算出各聚落遗址点的人口规模,再依次根据推算和重建的每个新石器时代晚期遗址的人口规模,估算和重建出聚落遗址点的耕地面积及空间分布格局,还原华北地区新石器时代晚期已经被人类改变了的土地覆盖（耕地）,为区域环境变化和环境模拟提供数据支持。

2 研究区概况

以华北地区新石器时代晚期^[40]考古遗址发现和挖掘较为全面的河南、山东和河北三地区为研究对象（图1）,该地区史前文化及农业发展历史极其悠久,是中国粟作农业起源的中心区域之一^{[41,42,43,44]},考古遗迹众多,类型丰富,考古发掘和历史文献记录完整,新石器时代晚期处于刀耕火种向耒耜翻耕农业发展的阶段^[41,45],农业生产技术较前期有很大提高。该区域属东部季风区,气候适宜,地形以平原、丘陵为主,地势起伏小,新石器时代晚期的全新世中期,气温比现在高2~3℃,降水较现在高约100 mm^[46],气候温暖湿润,是当时中国区域农业社会的中心区域,也是研究LUCC重建的理想区域。

图1

新窗口打开|下载原图ZIP|生成PPT
图1研究区范围

Fig. 1Scope of the study area

目前,河北地区已发现的新石器时代遗址有460余处,其中新石器时代晚期主要文化遗址有红山文化（6.6 ka B.P.—4.8 ka B.P.）、赵宝沟文化（6.9 ka B.P.—5.9 ka B.P.）、后岗一期文化（6.8 ka B.P.—6 ka B.P.）、仰韶文化（6 ka B.P.—5.5 ka B.P.）^{[47,48,49,50]};河南地区已发现的新石器时代遗址超过1842处,其中属于新石器时代晚期的文化遗址主要是仰韶文化（7 ka B.P.—5 ka B.P.）^[51];山东地区已发现新石器时代遗址共计2155处,属于新石器时代晚期的有北辛文化（7.3 ka B.P.—6.1 ka B.P.）、大汶口文化（6.1 ka B.P.—4.6 ka B.P.）^[52,53]。

3 数据来源与研究方法

3.1 数据来源与处理

3.1.1 数据来源考古数据来源：考古遗址信息来自《中国文物地图集》各分册^[54,55,56],以及查阅考古资料^[57,58],插补了1992—2010年前后河南地区发现和挖掘的新石器时代晚期遗址信息,通过筛选、甄别共提取可重建耕地的聚落遗址信息点1 854个。

地图数据：研究区范围边界依据中国地图出版社、国家基础地理信息中心标准地图服务（http://bzdt.nasg.gov.cn）;海拔、坡度和坡向信息由地理空间数据云提供的90 m分辨率的DEM中获取（www.gscloud.cn）;1980年1 km耕地数据来自中国科学院资源环境科学数据中心土地利用图（http://www.resdc.cn/data.aspx?DATAID=212）。

3.1.2 聚落遗址信息数据库的构建根据3.1.1提供的考古文献资料,过滤和筛选新石器时代晚期考古遗址,剔除非聚落遗址点,并建立1 854个聚落遗址信息数据库,要素包括：聚落遗址规模、文化类型及空间位置信息等（表1）。

Tab. 1
表1
表1新石器时代晚期华北地区聚落遗址信息数据库结构
Tab. 1Information database structure of the settlement sites in the Late Neolithic period in North China

地点	遗址名称	规模（万m²）	经度（°）	纬度（°）	文化类型
洛阳市	高平寨遗址	45	112.082539	34.741394	仰韶文化
郑州市	尚岗阳遗址	3	113.720175	34.717497	仰韶文化
济宁市	王因遗址	4	116.770287	35.453453	北辛、大汶口文化
承德市	沙梁沟遗址	3	116.547274	41.878647	红山文化
……	……	……	……	……	……

新窗口打开|下载CSV

3.1.3 影响因子的遴选与量化聚落遗址作为人类活动的遗迹,反映当时遗址内人口数量、聚落规模、生产方式、社会结构等,遗址规模和人口数量是直接反映当时耕地开垦规模的决定因素,遗址规模越大,说明聚集的人口越多,土地的需求量越大,对自然植被的开垦程度就越强,开垦范围也越大,反之则越小。而影响耕地空间分布格局的主要限制因素则是坡度、海拔、距离聚落地的远近程度等,其中华北地区当时主要种植粟,粟是一种耐旱作物,无需灌溉,故距离河流的距离未加以考虑。同时河道虽然对耕地有较大的限制作用,但其面积有限,而且限制因子提取难度较大,故本文也未将其作为限制性因子。华北地区由于地形起伏不大,均位于中温带气候区,气候因子对作物的影响较小,故也未进行考虑。因此,本文根据实际,将华北地区耕地重建中最主要的自然限制性因子确定为海拔和坡度、坡向^[59]3个地形因子。假设新石器时代晚期华北地区的海拔、坡度、坡向与现在一致,选取与聚落遗址的距离、海拔、坡度、坡向作为影响耕地分布的自变量因子,对华北地区新石器时代晚期的耕地进行重建。

由于各因子的单位不同,数值大小差异大,所以对每个因子进行了标准化处理,从而使每种因子的贡献率判断较为精准。

（1）

D' (i) = \frac{D (i)}{Max [D (i)]}

（2）

S' (i) = \frac{S (i)}{Max [S (i)]}

（3）

A' (i) = \frac{A (i)}{Max [A (i)]}

（4）

Dr' (i) = \frac{Dr (i)}{Max [Dr (i)]}

式中：i为网格编号;D'(i)、S'(i)、A'(i)、Dr'(i)表示标准化后i网格的各因子（分别为海拔、坡度、坡向、距聚落遗址距离）;D(i)、S(i)、A(i)、Dr(i)表示i网格各因子的平均值;Max[D(i)]、Max[S(i)]、Max[A(i)]、Max[Dr(i)]表示i网格各因子平均值中的最大值。

3.1.4 数据网格化与重采样首先,利用ArcGIS 10.1中的create fishnet功能,将华北地区网格化,由于研究区内的部分聚落遗址点耕地重建中可能出现相互重叠的情况,为了提高重建精度,特将网格大小设为500 m×500 m,并在每个网格上建立Label点,使每个网格与Label点相互对应;其次,计算出Label点距聚落遗址的距离,并将计算结果转化成栅格距离。

考虑到研究区空间尺度相对较大,像元尺度上模型构建要相对合理。故将海拔、坡度、坡向、栅格距离进行重采样处理,栅格大小也均为500 m×500 m。

3.2 研究方法

3.2.1 聚落遗址的人口估算方法目前关于史前遗址人口规模的估算主要用聚落考古学、墓葬分析、生物考古学遗物、民族历史学资料等分析方法进行估算。本文主要基于考古资料的墓葬分析和聚落考古学^[60,61,62]相结合的分析方法,对华北地区新石器时代晚期聚落遗址的人口规模进行估算。

墓葬分析法是通过考古发掘的墓葬状况提取信息,假定年出生人数等于年死亡人数,单个遗址的总人数等于年出生人数乘以墓葬死亡人口的平均寿命,即年死亡人数乘以平均寿命,得到聚落遗址的人口数量,这是采用静止的人口模式估算人口的一种方法^[63]。估算公式为：

（5）

P = \frac{A \times D}{T}

式中：P为遗址的年均人口规模;A为平均亡龄,可由墓葬的人骨年龄鉴定资料确定;D为墓地总死亡人数,根据发掘的人骨数确定,通过遗址面积与发掘面积的比例关系可以保证D的完整性与相对准确性^[63];T代表墓地延续的时间^[61]。

聚落考古学分析法是以聚落为单位来研究社会关系及人与生态环境关系的一种考古学方法,其在史前人口研究中具有较好的适用性^[62]。计算原理为：遗址的年均人口规模等于遗址的房屋数量与单个房子的人口规模之积除以房子的更新次数。由遗址面积与发掘面积的比例关系可以估算出整个遗址的房址数量^[62]。估算公式为：

（6）

P = \frac{H \times P_{h} \times (S_{t} / S_{e})}{T / U_{c}}

式中：P为人口规模;H为房子数量;P_h表示单个房屋的人口规模;S_t、S_e、T、U_c分别表示该典型聚落遗址的总面积、发掘面积、遗址延续时间和房屋的平均使用周期。

综合以上两种方法,计算得到典型区域聚落遗址的人口规模,然后通过典型遗址规模与人口规模的比例关系,求得当时人均占地面积（式7）。为保证结果的可靠性,将墓葬分析法和聚落考古学分析法所得人均占地面积平均值作为该区域各聚落遗址的人均占地面积。最后以聚落遗址和人均占地面积的比例关系估算出各遗址的人口规模（式8）：

（7）

S_{p} = S_{m} / P

式中：S_p为典型遗址的人均占地面积;S_m为典型遗址的规模;P为典型遗址的人口规模。

（8）

P_{m} = \frac{S_{m}}{S_{p}}

式中：P_m为m遗址的人口规模;S_m、S_p分别为m遗址的规模与人均占地面积。

3.2.2 聚落遗址的耕地面积估算方法为了更好地模拟重建新石器时代晚期的耕地,在认真分析考古资料与历史文献的基础上,提出4点假设：① 新石器时代晚期仍属于全新世气候适宜期,根据聚落遗址和墓葬挖掘信息反映,农业生产的种植结构相对单一,粟作农业是其居民生业的主要来源;② 新石器时代晚期华北地区粟作农业的粮食单产借鉴与其时间较为接近的夏代的粟的单产产量,即300 kg/hm^2[64],除去15%种子量^[65],取255 kg/hm²为粟的单位面积产量,其理由是：当时生产工具在数量与类型上都有重大突破^[66],生产力有了很大提高,但与后期的青铜时代和铁器时代相比仍处于较低水平,但是该时期气候较温暖于夏时期,自然生产潜力应该略大于后来的夏时期。此外,将258 kg/人设定为史前居民粟的人均年消费量^[65];③ 将维持聚落遗址内人口生存所必须的耕地面积设定为人均需要可耕地面积与聚落遗址的人口规模的乘积,其中,人均需要可耕地的面积为粟的人均年消费量与粟的单位面积产量之比^[65];④ 假定新石器时代晚期的耕作制度已完成了由生荒耕作制向熟荒制的过渡,主要农业区已采用较短期和定期的轮荒耕作制（3~5年的熟荒耕作）^[66],设定3年轮耕政策是这一时代的基本耕作制度。

基于上述假设,人均需要耕地面积可通过式（9）计算得到：

（9）

S_{a} = \frac{\frac{258 kg}{人}}{\frac{255 kg}{h m^{2}}} = 1.01 h m^{2} / 人

聚落遗址m的耕地面积可用公式（10）表示：

（10）

S_{cm} = S_{a} \times R \times P_{m} = 1.01 \times \frac{(T_{f} + T_{c})}{T_{c}} \times P_{m} = 1.01 \times 2 \times P_{m} = 2.02 \times P_{m}

式中：S_cm为m聚落遗址的耕地面积;S_a为人均需要可耕地的面积;R为一个栽培周期;P_m为m遗址的人口规模;T_f为休耕期;T_c是耕作期。

3.2.3 聚落遗址的耕地重建模型及验证耕地的分布受自然条件（如地形、温度、降水等）和社会经济条件（如人口、政策、战争等）的影响。多元Logistic回归模型是以二值变量（0或1,是或否）为因变量,连续变量或二元变量为自变量的方程模型^[67,68],优点是对每个解释变量生成单独系数,并通过一定的权重运算法计算出被解释变量发生的概率^[18]。本文以现代耕地分布为因变量,选取影响耕地分布的主要自然因子及社会因子作为自变量,创建多元Logistic回归模型。通过确定影响耕地分布各因子的权重,对华北地区新石器时代晚期耕地分布概率进行判定,最后实现耕地面积数据的空间重建。多元Logistic回归模型的基本形式为：

（11）

\log (\frac{P_{i}}{1 - P_{i}}) = α + \sum_{k}^{-} β_{k} X_{ki}

式中：i是网格编号;P_i为i网格成为耕地的概率;

α

为截距;X_i为变量值;β为回归系数;K为解释变量的种类。

将式（11）转化为式（12）：

（12）

P_{i} = \frac{\exp (α + \sum_{k}^{-} β_{k} X_{ki})}{1 + \exp (α + \sum_{k}^{-} β_{k} X_{ki})}

式中：P_i为i网格成为耕地的概率,P_i值范围在0~1间,值越大则耕地分布的可能性越大。

对模型的系数、关联强度进行检验,若P<0.05,达到显著性水平,则所选取的变量可解释耕地的分布概率,且模型的整体适配度符合要求;对预测准确率进行检验,将模型预测概率与实际观测值进行比较,模型假定概率值0.5及以上为耕地,小于0.5为非耕地,整体分类正确率越高则模型越理想。

将研究区新石器时代晚期与聚落遗址的距离以及海拔、坡度、坡向作为自变量,带入回归模型计算,可得到华北地区这一时期1 930 499个网格的概率值,即得出这一时期的耕地分布概率图（图2）。其概率值越大,耕地分布的可能性越大,华北地区新石器时代晚期的耕地面积就相应按概率分配在该网格中。本文所建网格大小为500 m×500 m,因此每个网格能分配耕地的最大面积为25 hm²×91%^[69]。

图2

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图2新石器时代晚期华北地区耕地概率分布

Fig. 2Probability distribution of cropland in the Late Neolithic period in North China

根据距离聚落遗址耕作路途需要花费1 h、2 h、3 h、4 h的步行路程^[70],对遗址分别做3 km、6 km、9 km及12 km的缓冲区。遵循距离聚落遗址越近且概率越大,则耕地优先分配的原则,将每个遗址的耕地面积根据概率值由高到低,优先分配3 km范围内的耕地,3 km内所有网格分配的耕地面积达到91%时,如果遗址耕地面积还没有分配完,则由近到远再在3 km~6 km的范围内分配,若还未分完则依次类推在6 km~9 km和9 km~12 km的范围内分配,直到估算的聚落遗址耕地面积分配完为止。

4 结果分析

4.1 模型构建指标分析

据表2,所选自变量的显著性P<0.05,均达到显著,表明海拔、坡度、坡向、与聚落遗址（居民点）的距离四个变量是影响耕地分布概率的重要变量,可以解释耕地的分布水平。自变量因子中坡度是耕地分布最大的影响因子,其他依次为与聚落遗址距离、海拔、坡向,其各因子系数分别为-11.438、-38.255、-2.865、0.203。

Tab. 2
表2
表2自变量显著性参数估计
Tab. 2The estimation of the significant parameter of independent variables

自变量	B	标准误差	Wald	自由度
坡度	-11.438	0.113	10 226.640	1
距遗址距离	-38.255	0.506	5 724.674	1
海拔	-2.865	0.058	2 461.304	1
坡向	0.203	0.028	51.249	1
常量	2.602	0.024	12 105.393	1

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4.2 耕地空间重建模型

为了使耕地模型构建更为合理、客观,从创建的1 930 499个（其中耕地1 707 192个;非耕地223 307个）Label点中随机各抽取5万个耕地与非耕地样本点进行模型构建。其中耕地网格赋值为1,非耕地网格赋值为0,将其作为因变量。将与之对应网格的海拔、坡度、坡向、距聚落遗址的距离（此处为距离现代居民点的距离）作为自变量进行多元Logistic回归建模（式13）。

（13）

\log (\frac{P_{i}}{1 - P_{i}}) = - 11.438 \times S' (i) - 38.255 \times Dr' (i) - 2.865 \times D' (i) + 0.203 \times A' (i) + 2.602

模型最终确定为：

（14）

P_{i} = \frac{\exp [- 11.438 \times S' (i) - 38.255 \times Dr' (i) - 2.865 \times D' (i) + 0.203 \times A' (i) + 2.602]}{1 + \exp [- 11.438 \times S' (i) - 38.255 \times Dr' (i) - 2.865 \times D' (i) + 0.203 \times A' (i) + 2.602]}

式中：S '（i）、Dr'（i）、D'（i）、A'（i）分别表示标准化后i网格的坡度、距聚落遗址的距离、海拔和坡向;P_i表示耕地分布的可能性,P_i值越大,表示耕地分布的可能性就越大。其影响因子中,海拔、坡度、距聚落遗址距离与耕地分布成负相关,海拔越高,坡度越大,耕地分布的可能性越小。当坡度超过25°时,易引起水土流失,基本不能耕作。耕地分布受聚落遗址距离远近的制约,与聚落遗址距离越近耕地分布的可能性越大,耕地分布在聚落遗址周边一定的空间范围之内。

4.3 模型评价

表3是整体模型显著性检验的结果,4个自变量建立的模型整体适配度检验的卡方值为49 104.286,P=0.000<0.05,达到显著性水平,因此,模型的整体适配度符合计算要求。此外,Nagelkerke R方值为0.517,则说明4个自变量与因变量之间有中等强度的关联。

Tab. 3
表3
表3模型综合检验结果
Tab. 3Comprehensive test of model coefficients

模型	卡方	df	显著水平
最终	49 104.286	4	0.000

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表4中,根据回归模型的分类预测,在实际观测的5万个非耕地网格中,有38 615个归类于非耕地,有11 385个被归类于耕地。实际观测的5万个耕地网格中,有41 748个耕地网格,非耕地网格是8 252个。预测分类正确率百分比整体为80.4%。

Tab. 4
表4
表4预测分类正确率交叉表
Tab. 4Forecast accuracy of model

		预测值		百分比校正(%)
观察值		非耕地（网格数）	耕地（网格数）
	非耕地（网格数）	38 615	11 385	77.2
	耕地（网格数）	8 252	41 748	83.5
	总百分比			80.4

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通过以上对模型关联强度以及预测准确率的检验结果,可以看出总体模型较为理想,可以重建。

4.4 重建结果分析

河南和山东地区聚落遗址信息相对完整,考古学上已经测算得出新石器时代晚期早、中、晚相对应的人均占地面积^[62],本文重建分别采用求得其平均值,河南地区人均占地面积为：[250（早）+186（中）+168（晚）]/3≈ 201 m²/人,山东地区人均占地面积为：[209（早）+323（中）+209（晚）]/3=247 m²/人。

河北地区的人均占地面积估算相对复杂,本文运用3.2.1中墓葬分析和聚落考古学分析相结合的方法,以河北西北部发掘规模最大、信息最完整的姜家梁新石器时代遗址为案例进行估算。

姜家梁遗址属小河沿文化的前期阶段（5.316 ka B.P.—5.016 ka B.P.）^[71],遗址规模4.5万 m²,发掘总面积0.16万 m²,其中房址9座,墓葬78座,同时期的77座,除了2座墓穴是3人葬外,其余均为单人葬。其中对15具人骨进行年龄测定并统计得出该遗址人口平均亡龄为34.5岁^[72],因此,假定河北地区当时每个聚落遗址的人口平均亡龄基本相同,故以34.5岁作为当时河北地区人口的平均亡龄^[72,73]。

根据墓葬分析法,姜家梁聚落遗址墓地总死亡人数为83人,延续时间大约为300年^[72,74]。用公式（5）和公式（7）可估算出姜家梁遗址的人口规模为268人,人均占地面积为168 m²/人。

根据聚落考古学方法,姜家梁遗址发掘的房屋数量有9间,面积最大者为30.25 m²,最小者为10.5 m²,仰韶时期房屋平均使用周期为30年,每个房址中居住的人口规模约在3~11人之间,平均数为每间房屋住7人^[62,75]。则使用公式（6）和公式（7）求得姜家梁遗址的人口规模为177人,人均占地面积为254 m²/人。

综合上述两种方法求得姜家梁遗址的人均占地面积的平均值为211 m²/人。因此,根据考古学界对河南、山东等地区推算人均占地面积的方法^[62],本文将211 m²/人作为河北地区新石器时代晚期遗址的人均占地面积,由此可根据公式（8）推算出河北地区每个聚落遗址的人口规模。

根据计算出的各地区单个聚落遗址人口规模,依据公式（10）分别推算出河南、山东、河北地区各聚落遗址点新石器时代晚期的耕地面积（表5）。

Tab. 5
表5
表5单个遗址的人口规模及耕地面积
Tab. 5Population and cropland area of a single site

地点	遗址	纬度（°）	经度（°）	规模（万m²）	人口规模（人）	耕地（hm²）
洛阳市	高平寨遗址	34.741394	112.082539	45	2 239	4 522.78
郑州市	尚岗阳遗址	34.717497	113.720175	3	149	300.98
济宁市	王因遗址	35.453453	116.770287	4	162	327.24
承德市	沙梁沟遗址	41.878647	116.547274	0.6	28	56.56
……	……	……	……	……	……	……

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将以上华北地区各聚落遗址点新石器时代晚期的耕地面积按照3.2.3所述的分配原则,应用GIS将其分配在相应聚落信息点附近的地理空间上,从而得到新石器时代晚期华北地区耕地重建结果（图3）。

图3

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图3新石器时代晚期华北地区耕地重建结果

Fig. 3Results of cropland reconstruction in the Late Neolithic period in North China

综上所述,新石器时代晚期整个华北地区的人口规模约为50.61万人,其中,河北地区约6.43万人,河南地区约30.46万人,山东地区约13.72万人。华北地区总耕地面积约为102.22万hm2,约是现代耕地^[76]的4.6%。其中河北地区约12.98万hm2,河南地区约61.52万hm2,山东地区约27.72×万hm2。整个华北地区垦殖率达到1.99%,河南地区垦殖强度最大约为3.68%,河北地区约0.69%,山东地区达1.75%。

对耕地的分布范围特征、地形分布特征及坡度分布特征进行统计分析（表7~表9）,结果表明：耕地主要分布在距离聚落遗址3 km的范围内,占整个区域耕地面积的83.43%,3~6 km范围内占到了15.47%,仅有0.93%的耕地分布在6~9 km范围内;按照中国基本地貌类型的划分标准^[77]进行统计得出,耕地主要分布在海拔200 m以下且地形起伏度200 m以下的低海拔平原和台地地区,占耕地面积总数的63.36%;从坡度来看,耕地分布在平耕地,占耕地面积的81.55%,2°以上的缓坡耕地只占16.61%,坡度15.94°是耕地分布的上限,虽然6°~15°的坡耕地只占1.84%,但这表明在新石器时代晚期,人类通过农业生产等活动对地表环境过程已经开始了前所未有的改造。

Tab. 7
表7
表7耕地的分布范围特征
Tab. 7Distribution characteristics of the range of cropland

范围	≤ 3 km	≤ 2 km	≤ 1 km	>3 km
网格数（个）	37 699	28 953	12 848	7 488
占比（%）	83.43	64.07	28.43	16.57

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Tab. 8
表8
表8耕地的坡度分布特征
Tab. 8Slope distribution characteristics of cropland

坡度（°）	网格数（个）	占比（%）
平耕地（≤ 2°）	36 850	81.55
缓坡耕地（2°~6°）	7 507	16.61
坡耕地（6°~15°）	830	1.84

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Tab. 9
表9
表9耕地的海拔分布特征
Tab. 9Elevation distribution characteristics of cropland

		地形起伏度
		200 m以下的网格数（个）	占比（%）	200~500 m的网格数（个）	占比（%）
海拔（m）	1000以上	1 098	2.43	7	0.02
	500~1000	4 346	9.62	22	0.05
	200~500	11 057	24.47	19	0.04
	200以下	28 632	63.36	6	0.01

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就整体空间分布格局来看,耕地分布与遗址分布关系密切,主要分布在河南中西部的洛阳盆地、涧河、洛河和伊河河谷地区,山东中南山地周围的丘陵、山前冲洪积平原地区,河北太行山东麓山前地区、燕山南北麓地区。

5 结论与讨论

基于现有考古资料,运用考古学关联建模方法估算了新石器时代晚期华北地区单个遗址的人口规模、耕地面积,重建了其耕地的空间分布格局。通过分析得出以下结论：

（1）运用墓葬分析与聚落考古学分析相结合的方法得到新石器时代晚期华北地区的人口规模至少为50.61万人,其中,河北为6.43万人,河南为30.46万人,山东为13.72万人;并估算出新石器时代晚期华北地区的耕地至少约102.22万hm2,约占现代耕地的4.6%,其中河北约12.98万hm2,河南约61.52万hm2,山东约27.72万hm2;华北地区新石器时代晚期垦殖率约为1.99%,其中河南地区约3.68%,河北地区为0.69%,山东地区达1.75%。

（2）从重建的耕地分布范围来看,约有83.43%的耕地面积在距离遗址3 km的范围内分布;63.36%的耕地分布在低海拔的平原和台地上,分布在海拔1 000 m以上的耕地极少,仅占全区耕地的2.43%;坡度15.94°是耕地分布的上限,81.55%的耕地分布在小于2°的平耕地,16.61%的耕地分布在2°~6°的缓坡耕地,6°~15°的坡耕地占1.84%。

（3）本文对于新石器时代晚期华北地区耕地的网格化重建,考虑到每个聚落遗址的规模大小不同,其居住的人口规模及粮食需求差异较大,因此按照每个聚落一定范围内耕地分布的概率值进行分配,规模大且集聚性强的聚落遗址,其分配的耕地面积相对较多,分散且规模小的聚落遗址,分配的耕地面积较少。本文所建的分配模型对于本研究区中的不同地形区具有适用性,同时,对于耕地的网格化重建为后续工作的开展奠定了基础。本文也存在不足之处：① 重建以2010年以前已发现的聚落遗址为基础,未包含尚未发现和挖掘的聚落遗址,但其数量规模不大,不影响整体结果。② 本文创建的网格大小为500 m×500 m,每个网格的面积为22.5×91% hm²,因此分配到网格的耕地与估算的耕地面积有一定的误差,但是本文运用人均占地面积所建的人口规模及耕地数量代表当时人口规模及土地开垦的最低基数,且从整个空间分布格局来看,可以说明当时生产力水平下该区域土地开发程度的空间差异性。

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不同农业单元122个表土样品花粉分析结果表明，安阳地区表土花粉组合中草本植物花粉含量最高，以禾本科、蒿属、藜科和十字花科为主，乔木植物花粉百分含量较低，以松属、桦属、胡桃属、黄栌属、栎属为主，应为西部太行山区传播而来。汪家店和茶店陂两个全新世地层自然剖面孢粉分析表明，约 10000cal.aB.P.以前，安阳地区分布着以松属、栎属、桦属等为主的落叶阔叶林；  约 10000～8200cal.aB.P.，气候变暖，降水量增加，森林植被中夹杂着常绿栎等亚热带树种；  约 8200～3400cal.aB.P.，安阳地区进入全新世大暖期，森林植被演变为亚热带落叶阔叶和常绿阔叶混交林；  商王朝在安阳地区建都后（3400cal.aB.P.后），乔木植物花粉急剧减少，草本植物花粉和中华卷柏百分含量升高，表明森林被大量砍伐，砍伐后的林地开垦为农田，主要种植禾本科等农作物；  两条剖面上部的冲积层中出现大量中华卷柏孢子，表明森林植被破坏后水土流失加剧。古地貌面孢粉分析也证明殷商时期安阳地区生长有亚热带落叶阔叶和常绿阔叶混交林，气候和植被条件适于野象生存；  殷墟附近为禾本科农作物花粉高含量区，表明殷商时期农业已具一定规模。

[ Xu

Qinghai

, Cao

Xianyong

, Wang

Xueli

, et al. Generation of Yinxu culture: Environmental background and impacts of human activities
Quaternary Sciences, 2010,30(2):273-286.]

URL [本文引用: 1]

Wang

, Herzschuh

. Reassessment of Holocene vegetation change on the upper Tibetan Plateau using the pollen-based REVEALS model
Review of Palaeobotany and Palynology, 2011,168(1):31-40.

DOI:10.1016/j.revpalbo.2011.09.004 URL [本文引用: 1]

Previous studies based on fossil pollen data have reported significant changes in vegetation on the alpine Tibetan Plateau during the Holocene. However, since the relative proportions of fossil pollen taxa are largely influenced by individual pollen productivities and the dispersal characteristics, such inferences on vegetation have the potential to be considerably biased. We therefore examined the modern pollen-vegetation relationships for four common pollen species on the Tibetan Plateau, using Extended R-value (ERV) models. Assuming an average radius of 100 m for the sampled lakes, we estimated the relevant source area of pollen (RSAP) to be 2200 m (which represents the distance from the lake). Using Poaceae as the reference taxa (Pollen Productivity Estimate, PPE = 1), ERV Submodel 2 derived relative high PPEs for the steppe and desert taxa: 2.079 +/- 0.432 for Artemisia and 5.379 +/- 1.077 for Chenopodiaceae. Low PPEs were estimated for the Cyperaceae (1.036 +/- 0.012). whose plants are characteristic of the alpine Kobresia meadows. Applying these PPEs to four fossil pollen sequences since the Late Glacial, the plant abundances on the central and north-eastern Tibetan Plateau were quantified using the "Regional Estimates of Vegetation Abundance from Large Sites" (REVEALS) model. The proportions of Artemisia and Chenopodiaceae were greatly reduced compared to their original pollen percentages in the reconstructed vegetation, owing to their high productivities and their dispersal characteristics, while Cyperaceae showed a relative increase in the vegetation reconstruction. The reconstructed vegetation assemblages of the four pollen sequence sites always yielded smaller compositional species turnovers than suggested by the pollen spectra, as revealed by Detrended Canonical Correspondence Analyses (DCCA) of the Holocene sections. The strength of the previously reported vegetation changes may therefore have been overestimated, which indicates the importance of taking into account pollen-vegetation relationships when discussing the potential drivers (such as climate, land use, atmospheric CO(2) concentrations) and implications (such as for land surface-climate feedbacks, carbon storage, and biodiversity) of vegetation change. (C) 2011 Elsevier B.V.

[33]

Jie

, Xu

Qinghai

, Zhang

Shengrui

, et al. Relative pollen productivity and its use in quantitative reconstruction of paleovegetation
Quaternary Sciences, 2013,33(6):1101-1110.

DOI:10.3969/j.issn.1001-7410.2013.06.06 URL [本文引用: 1]

Pollen productivity estimates are the necessary condition for quantitative reconstruction the paleovegetation and paleoclimate.Pollen productivity estimates include relative pollen productivity and absolute pollen productivity.Due to it is hard to get long-term and successive pollen records for absolute pollen productivity estimated,most palynologists tend to use relative pollen productivity to reconstruct paleovegetation.Relative pollen productivity estimates is the relative value for species to a reference taxon(the reference taxon is frequently found in both modern and fossil pollen records),it can be accomplished by ERV model.However,the accurate of the estimated results have strongly influenced by the vegetation survey methods and the assumptions of the model.In this paper,we try to introduce the vegetation survey methods and the models which were used to reconstruction the paleovegetation.Nowdays,the most common used method for reconstructing paleovegetation using pollen production estimates is Landscape Reconstruction Algorithm,which includes REVEALS model(Regional Estimates of Vegetation Abundance from Large Site,which fit the size of lake or mire ≥1~5km²) and LOVE model(Local Vegetation Estimates,which fit the size of lake or mire 0.1~1.0km²).Whoever European researchers use their relative pollen productivity and the REVEALS model to reconstruct vegetation succession in Denmark,Sweden,Swiss and our work on typical steppe and meadow steppe in Northern China,showing the results can reflect the paleovegetation reasonably.But,the studies on relative pollen productivity are still on first step,especially in China.The relative pollen productivities for the same pollen taxa vary clearly,and one place can not apply to others. So,we should centralize and develop research method to fit actual situation,and do more work to estimate relative pollen productivities of major pollen types in China.

[34]

伍婧, 马玉贞, 桑艳礼 , 等. 古植被定量重建与R值模型的发展: R值和ERV模型在兴隆山地区的应用
第四纪研究, 2013,33(3):554-564.

DOI:10.3969/j.issn.1001-7410.2013.03.16 URL [本文引用: 1]

基于兴隆山自然保护区及其周边地区30个样方的植被调查和表土花粉分析,运用R值计算方法和ERV模型估算了我国北方常见的几种乔木和草本植物花粉的代表性和相对花粉产量。R值显示蒿属花粉为超代表性,云杉属和沙棘属代表性略高,栎属和桦属代表性适中,禾本科和莎草科为低代表性。ERV模型估算结果显示云杉属、沙棘属和栎属的花粉产量较高,桦木属的花粉产量较低,而禾本科和莎草科的花粉产量很低。通过对结果的分析表明:云杉属、沙棘属、栎属、莎草科和禾本科根据R值计算得到的代表性结果和ERV模型估算得到的花粉产量结果有较好的一致性,但有很高背景花粉成分的桦属和蒿属的花粉产量较低,主要是由于R值没有考虑背景花粉成分对花粉百分比的影响。因此,在进行景观尺度的植被重建时一定要考虑背景花粉成分的影响,以确定当地是否有对应的母体植物大量分布。

[ Wu

Jing

, Ma

Yuzhen

, Sang

Yanli

, et al. Quantitative reconstruction of paleovegetation and development R-value model: An application of R-value and ERV model in Xinglong Mountain Natural Protection Region
Quaternary Sciences, 2013,33(3):554-564.]

DOI:10.3969/j.issn.1001-7410.2013.03.16 URL [本文引用: 1]

许清海, 曹现勇, Tian

, 等. 中国北方典型草原区花粉产量及其定量重建古植被的作用
中国科学: 地球科学, 2013,43(12):2016-2028.

[本文引用: 1]

[ Xu

Qinghai

, Cao

Xianyong

, Tian

, et al. Relative pollen productivity of typical steppe plants and its potential for reconstructing palaeovegetation in Northern China
Scientia Sinica: Terrae, 2013,43(12):2016-2028.]

[本文引用: 1]

[36]

Zhang

, Bevan

, Fuller

, et al. Archaeobotanical and GIS-based approaches to prehistoric agriculture in the upper Ying valley, Henan, China
Journal of Archaeological Science, 2010,37(2):1480-1489.

DOI:10.1016/j.jas.2010.01.008 URL [本文引用: 1]

[37]

Hou

Guangliang

, Lai

Zhongping

, Xiao

Jingyi

, et al. Reconstruction of cultivated land during mid-Holocene in the middle and lower reaches of Yellow River and human impact on vegetations
Journal of Geographical Sciences, 2012,22(5):933-945.

DOI:10.1007/s11442-012-0974-6 URL

Yangshao culture is the most important mid-Holocene Neolithic culture in the Yellow River catchment, and thus, a study on the impact of human activities on the environment is important. In the current study, the distribution pattern of the cultivated land in late Yangshao culture is reconstructed using GIS tool and site domain analysis (SDA). The results show that the cultivated land during 5.5-5.0 ka BP was mainly distributed in the Weihe River valley, Luohe River valley, northwestern Henan Plain, Fenhe River valley and eastern Gansu region, especially concentrated in the Xi’an-Baoji line of the Weihe River valley. At that time, at least 37,000 km² of lands were reclaimed in the middle and lower reaches of the Yellow River, and 132,000 km² of lands were affected by agricultural activities. Human activities had become the driving force of land use/land coverage. Charcoal records indicate that the ancestors of Yangshao culture burnt forests for reclamation, leading to the decrease of arbor pollen at 5 ka BP in core areas of the Yangshao culture. The areas that were significantly affected by human activities accounted for 3.2% of the Yangshao culture influenced area, while the moderately affected areas accounted for 20.1% of Yangshao culture influenced area. Meanwhile, 92% of the land areas on the edge and outside of the Yangshao culture influenced area were not affected by human activities. The arbor pollen in these areas did not decrease until 4.0 ka BP.

[38]

, Guo

, Wu

, et al. Reconstructing prehistoric land use change from archeological data: Validation and application of a new model in Yiluo valley, northern China
Agriculture Ecosystems & Environment, 2012,156(6):99-107.

DOI:10.1016/j.jenvman.2019.110010 URLPMID:31868636 [本文引用: 1]

Traditional orchard meadows are among the most valuable cultural and agricultural systems for nature conservation in Europe. They comprise scattered fruit trees over a highly diverse herbaceous layer and provide a wide range of ecosystem services. However, they are strongly endangered due to farmland intensification and abandonment. Livestock grazing is known to promote grassland diversity but it may also cause tree damage through debarking. In this study, we evaluated the effect of different grazers (cattle, horse and sheep) on fruit trees in 42 traditional orchards of the Rhenish uplands (Germany). Overall, we found that 70% of the study trees showed debarking damage, although most of them (40%) were slightly damaged (1-10% of the trunk debarked). Most debarked trees showed accumulated damage over time, and only 8% of the study trees were damaged during the last year. The probability of strong debarking (&gt;50% of the trunk damaged) was higher in orchards grazed by cattle and horses than on those grazed by sheep (5.3 and 3.7-fold difference, respectively). Importantly, unsustainable levels of cumulative debarking caused a decay of crown development, which may strongly affect fruit production. Additionally, lower tree densities favored higher levels of debarking intensity but did not affect the probability of occurrence. Individual tree-protection was an effective practice in decreasing trunk debarking (95% reduction in tree damage occurrence). The impact of grazing animals on trees might represent a useful indicator to assess the sustainability of each grazing system and should be taken into account in future agriculture and conservation policies.

[39]

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, Finke P

, et al. Spatial and temporal changes of prehistoric human land use in the Wei River valley, northern China
Holocene, 2016,26(11):1-14.

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[ Institute of Archaeology Chinese Academy of Social Sciences (IA CASS). The Neolithic Volume of Chinese Archaeology. Beijing: China Social Scienses Press, 2010.]

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[ Shi

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朱乃诚 . 中国农作物栽培的起源和原始农业的兴起
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URL [本文引用: 1]

农作物栽培的起源及原始农业的兴起与发展，对人类社会的发展产生了深远的影响，它为古代文明社会的形成奠定了基础，并且泽被至今，文明社会里的一切发明与创造都受其恩惠。所以，探索栽培作物的起源和原始农业的兴起，对于研究古代文明的形成和发展具有十分重要的意义。……

[ Zhu

Naicheng

. The origin of crops and the rise of ancient agriculture in China
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URL [本文引用: 1]

农作物栽培的起源及原始农业的兴起与发展，对人类社会的发展产生了深远的影响，它为古代文明社会的形成奠定了基础，并且泽被至今，文明社会里的一切发明与创造都受其恩惠。所以，探索栽培作物的起源和原始农业的兴起，对于研究古代文明的形成和发展具有十分重要的意义。……

[43]

王星光, 李秋芳 . 太行山地区与粟作农业的起源
中国农史, 2002, ( 1):27-36.

URL [本文引用: 1]

太行山地区是中国粟作农业起源地之一.第四纪全新世前后,太行山地区优越的自然环境为粟作农业的产生和发展提供了有利的条件;早期人类经常的采集活动为粟作农业的诞生准备了技术条件;多种原因造成的食物匮乏为粟作农业的产生提供了契机;野生狗尾草的存在为粟作农业的起源提供了生物依据.

[ Wang

Xingguang

, Li

Qiufang

. District of Mount Taihang and origin of millet agriculture
Agricultural History of China, 2002, ( 1):27-36.]

URL [本文引用: 1]

太行山地区是中国粟作农业起源地之一.第四纪全新世前后,太行山地区优越的自然环境为粟作农业的产生和发展提供了有利的条件;早期人类经常的采集活动为粟作农业的诞生准备了技术条件;多种原因造成的食物匮乏为粟作农业的产生提供了契机;野生狗尾草的存在为粟作农业的起源提供了生物依据.

[44]

侯毅 . 试论我国北方粟作农业的起源问题
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URL [本文引用: 1]

农业的产生是人类发展历史上的一件大事,著名考古学家柴尔德称其为"农业革命"是恰如其分的.没有农业的产生,就没有定居生活和村落,也无法产生聚落和中心聚落,人类也只能永远停留狩猎、捕捞和采集的原始生存状态;没有农业的产生,人类也不会饲养家畜,后来的手工业和各种社会分工也无从谈起;没有农业作为基础,人类的人口增殖也只能维持在很低的水平,这也是显而易见的.

[ Hou

. The discussion of origin of millet agriculture in northern China
Agricultural Archaeology, 2007, ( 1):152-155.]

URL [本文引用: 1]

王玉堂, 吴仁德, 张之恒 . 农业的起源和发展. 南京: 南京大学出版社, 1996.

[本文引用: 1]

[ Wang

Yutang

, Wu

Rende

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Zhiheng

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连鹏灵, 方修琦 . 岱海地区原始农业文化的兴衰与环境演变的关系
地理研究, 2001,20(5):623-628.

URL [本文引用: 1]

内蒙古中南部岱海地区考古学文化的发展受到环境条件的制约。原始农业文化的兴起与发展对应于全新世暖期的暖湿条件;农业文化的间断、南移与由农向牧的转变与4～35kaBP发生的变冷、变干的突变事件相对应;而原始农业文化阶段中间每一次文化的间断、侵入、融合总是可以在气候变化中找到相应的变化阶段,与短期寒冷事件的开始或结束对应。

[ Lian

Pengling

, Fang

Xiuqi

. On the relationship between the development of pre-historical cultures and environmental change in the Daihai region, Inner Mongolia
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李月丛, 胡金华, 许清海 . 河北省南部新石器时代人地关系研究
地理与地理信息科学, 2004,20(2):91-96.

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[ Li

Yuecong

, Hu

Jinhua

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Qinghai

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Jianxin

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Guoqing

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索秀芬, 李少兵 . 红山文化研究
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Xiufen

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Shaobing

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李中轩, 朱诚, 吴国玺 , 等. 河南省史前人类遗址的时空分布及其驱动因子
地理学报, 2013,68(11):1527-1537.

DOI:10.11821/dlxb201311008 URL [本文引用: 1]

基于ArcGis10.0 平台分析了河南省史前时期的仰韶文化遗址和龙山文化遗址的空间分布、濒河性、空间集聚度等指标。结果显示,18.8%的仰韶期遗址分布在河流的1 km缓冲区内,而龙山期遗址只有16.5%,其濒河性呈减弱趋势。豫西山区、南阳盆地、颍河—淮河地区龙山期遗址的空间集聚度比仰韶期分别增加了0.006,0.016 和0.021,暗示龙山时期的自然条件逊色于仰韶期导致人类活动范围受到局限。遗址密度3-D分析表明,河南地区史前人类活动格局从仰韶期的“单核型”演化为龙山时期的“多核型”。这种空间格局的变化可能与5.4 kaBP降温事件相关,而且龙山期的气候特征与仰韶时期相比显得温凉、干燥,加之农业生产技术的进步和人口增加促使史前人类活动核心区从豫西山区向豫东平原和豫北平原地区扩散。同时4.0 ka BP前后河南地区的干旱、洪水、低温等自然灾害频发亦加速了人类活动范围的快速扩展,并且石家河、大汶口、关中等史前文化类型向河南地区的渗透,造成河南龙山文化类型的多元化和空间分布的复杂化。而全新世早期的裴李岗遗址和晚期的二里头遗址数目较少且均匀分布于嵩山两翼,初步推测环嵩山地区是河南史前文化的肇源地区。

[ Li Zhong

Xuan

, Zhu

Cheng

, Wu

Guoxi

, et al. Spatial and temporal distribution of prehistoric human sites and its driving factors in Henan province
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建立具有空间属性的历史时期土地覆被数据集有助于更好地模拟土地覆被变化的气候与生态效应。根据我国历史时期土地开发利用的特点, 深入分析了影响我国历史时期土地开发利用的主导因子, 量化了海拔高度、坡度、人口分布等自然及人文要素与耕地分布的关系, 并以此为依据设计了一套将我国传统农区历史耕地数据网格化的方法与算法模型, 计算各网格(分辨率为60 km×60 km) 耕地面积占整个农区耕地总面积的比重、以及各网格的耕地面积与垦殖率; 同时利用基于《嘉庆重修一统志》重建的中国传统农区历史耕地资料, 采用上述方法与算法建立了研究区内1820 年(清嘉庆二十五年) 60 km×60 km 空间尺度的耕地数据集, 并绘制了分布图。重建结果与册载府级田亩数据的比较分析表明: 该方法与算法可以有效地将以行政区域为统计单元的中国历史耕地数据量化为具有统一且更高空间分辨率的网格化数据集。

[ Lin

Shanshan

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Jingyun

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Fanneng

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[ Wang

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针对中国国家尺度层面耕地历史数据集的缺乏,提出了分区建模的方案.首先,将中国定性划分为四大区域,即传统农区、东北地区、西北地区、青藏地区;在此基础上,分别量化地形、人口要素与耕地分布之间的关系,构建空间格网化模型.利用该方案,重建了中国1913、1933、1950、1970、1990 和2000 年6 个时间断面空间分辨率为10 km的格网化耕地数据.对比1990 年的重建结果与遥感解译结果,发现无论是县域尺度,还是栅格尺度,数据集的准确性都较高.对重建的耕地数据集进行分析发现,近百年来中国的耕地面积出现先增后减的趋势,拐点大体在20 世纪后期,不仅是耕地总量的先增后减,而且垦殖强度也是先增后减,但区域之间并不一致,其中变化较大的是东北和西北地区.

[ Feng

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DOI:10.11821/dlxb201407002 URL [本文引用: 1]

针对中国历史时期不同政体耕地记录的特点,分别采用因素修正、引用替换、线性内插、衔接对比、人地关系检验、垦殖趋势检验、行政面积比例调整等方法对历史耕地数据进行修正,重建了基于现代省界的近300年中国分省耕地数据集,从数量角度对中国耕地总量和分省耕地变动特点进行分析,得到以下主要结论：① 在耕地总量上,SAGE和周荣的数据明显高估,本文结果与HYDE、CHCD和章有义数据的平均差异率在15%以下;但在省域尺度上,与SAGE、HYDE数据库存在明显差异,相对差异率>30%的省份比重分别为94%和61%,与CHCD数据较为接近,相对差异率>30%的省份比重为22%,但部分省份差异明显,仍需进一步分析研究;② 伴随清中期后的人口爆炸,近300年中国耕地增长近3.2倍,由清朝初年的42.4×10⁶ hm²增加至1985年的136.9×10⁶ hm²,根据增长速率变化可分为五个阶段,即清前中期快速增长阶段、清后期低速增长阶段、民国时期波动阶段、建国初期剧烈增长阶段和建国后耕地流失阶段,影响耕地变化主要是国家政策、战乱、经济发展等驱动因素。③ 从省域尺度看,近300年中国各区域耕地变化差异显著。清初,中国耕地主要集中于长江中下游平原、黄淮海平原、关中盆地及银川平原等地,此后,内地的垦殖活动不断增强,外围农区呈由南向北的趋势不断开荒。建国后,耕地开垦逐步向西北和东北方向发展。

[ Cao

Xue

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Xiaobin

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Jinshuo

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DOI:10.11821/dlxb201407002 URL [本文引用: 1]

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平原、台地、丘陵、山地等是地表最基本的地貌形态,这些名称不仅为专业领域广泛引用,也为普通人所知晓。近100年来多种地貌分类方案中都涉及这些地貌类型名称,有的称其为地貌"基本形态"。由于每种地貌形态都不仅包含形态特征,而且还有一定的成因意义,因此应称其为基本地貌类型。通过对已有的基本地貌分类及其划分指标进行系统分析和评估,认为中国陆地基本地貌类型按照起伏高度和海拔高度两个分级指标组合来划分的原则符合起伏复杂、多台阶中国地貌的基本特点。在传统的平原、台地、丘陵和山地分类的基础上,按起伏高度对山地进一步细分,即划分平原、台地、丘陵（<200m）、小起伏山地（200～500m）、中起伏山地（500～1000m）、大起伏山地（1000～2500m）和极大起伏山地（>2500m）等7个基本地貌"形态"。本文对前人以现代雪线、多年冻土下线和森林上线高度为依据确定地貌面海拔高度的分级指标进行了全面分析,由于它们的海拔高度在全国各地存在巨大差异,我们认为海拔高度等级指标并不符合中国实际。通过全国重点地区1∶500000地形图山地顶面海拔高度分布和1∶1000000国家数字高程模型（DTM）数据库编制的中国地面高程分布图进行较系统的分析,我们提出了应以1000m,2000m,4000m和6000m作为划分低海拔（<1000m）、中海拔（1000～2000m）、亚高海拔（2000～4000m）、高海拔（4000～6000m）和极高海拔（>6000m）地貌海拔高度分级指标。根据7个地貌起伏高度形态和5个海拔高度等级,将全国组合成从低海拔平原至极大起伏极高山28个基本地貌类型。

[ Li

Binyuan

, Pan

Baotian

, Han

Jiafu

. Basic terrestrial geomorphological types in China and their circumscriptions
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URL [本文引用: 1]