Development status and trend of mechanization of rapeseed and wheat sowing in hilly and mountainous region of southern China
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摘要:
中国南方丘陵山地油菜和小麦的机械化生产是解决南方地区冬闲田难题的有效途径，也是保障国内粮油安全的重要措施，但受地形条件和种植模式影响，目前南方丘陵山地油菜、小麦机械化播种水平较低且发展缓慢，仍处于“无机可用”或“无好机可用”状态。该文从生产区划、应用场景、技术装备3个方面分析了南方丘陵山地油菜小麦机械化生产现状，通过梳理油菜和小麦典型生产场景与实际应用机具之间的矛盾，明确了湿黏稻茬土壤、复杂丘坡地形、油菜和小麦种植农艺要求、区域经济落后、经营主体倾向和技术市场空缺等是影响南方丘陵山地各油麦产区机械化播种发展水平不充分的主要因素；通过分析现阶段油菜小麦机械化播种装备应用、技术发展及南方丘陵山地油麦机械化播种的技术卡点和装备发展需求，提出了研发适应南方丘陵山地黏重湿烂土壤条件、丘坡地形和小地块播种的智能化油麦兼用联合直播机是提高油麦机播水平的装备基础，并明确黏重与湿烂土壤种床整备、丘陵山地起伏地表播深控制、油麦兼用精量排种、智能化播种控制等关键技术是研究重点。研究可为南方丘陵山地油菜、小麦机械化播种水平提升提供参考。
Abstract:The accurate location of targets can greatly contribute to the precision operations within agricultural scenes. Binocular stereo vision can be used to obtain the three-dimensional (3D) perception of the real world. Considerable application potential can depend mainly on the 3D localization and point cloud reconstruction of targets in agricultural environments. This study aims to review the latest research on binocular stereo vision and its applications in the agricultural field. Firstly, the pipeline of binocular stereo vision was summarized, including binocular camera calibration, epipolar rectification, and stereo matching. The binocular camera was utilized to calculate the depth of targets using disparity data. The objective of stereo vision calibration was then to determine the intrinsic and extrinsic parameters of the camera, including reference calibration, active vision calibration, self-calibration, and neural network calibration. A mapping was also established among points in pixel and world coordinates. In epipolar rectification, the constraints were employed to reduce the search space, in order to match the points from two dimensions to one. Stereo matching was used to calculate the disparity, in order to match the left and right images in both feature-based and deep learning. Furthermore, the local, global, and semi-global methods were categorized in the search range of matching pixels. The local method was used to search for the matching points within surrounding areas, the global method was to minimize the global energy function, and the semi-global method was to aggregate the costs from various directions. In contrast, more complex features were learned to enhance the stereo matching using deep learning. The network frameworks were introduced, such as convolutional neural networks (CNN), generative adversarial networks (GAN), transformers, neural architecture search (NAS), iterative optimization (IO) and graph neural network (GNN). CNNs performed extensive convolution operations to compute the matching costs for high accuracy, including convolutional encoders and decoders, hierarchical pyramids, as well as complex cost volumes. GANs synthesized the data through adversarial generation, in order to acquire the realistic disparity in binocular datasets. In Transformer, the self-attention mechanisms were utilized to capture the contextual information, indicating the limitations of CNN receptive fields. In NAS, the stereo-matching network architectures were automatically constructed to incorporate the human prior knowledge, in order to removal the peripolar need for manual design. The IO with no requirements on the construction of cost volumes and aggregation, leading to significant resource savings for the large ranges of disparity. GNN was used to simulate the complex relationships among features, and then extract the global information. Furthermore, the number and impact of publications were analyzed to examine the widespread applications of binocular stereo vision in agricultural research. The latest applications were explored from the recent literature. As such, 3D localization of fruit targets facilitated map navigation in practical operations using point cloud processing. The technology also supported the 3D reconstruction of crops or the segmentation of individual organs for growth parameter measurement. Additionally, the crop diseases or pests were identified to combine with precision spraying by agricultural machinery. Ultimately, the challenges were summarized to apply the binocular stereo vision to agriculture. The high precision was demonstrated in the localization, measurement, and identification. Some issues still remained, such as model complexity, scene limitations, scarcity of datasets, and fewer evaluation standards for stereo matching. Looking ahead, future research should focus on the algorithm design and optimization, the intelligent assistance platforms, the comprehensive datasets, and the evaluation system, in order to enhance the practicality and efficiency of binocular stereo vision systems in precision agriculture.
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Keywords:
- hilly and mountainous region of southern China /
- rapeseed /
- wheat /
- mechanized sowing /
- development status /
- trend
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0. 引 言
南疆地处中国西北内陆干旱区，光热资源充足，是中国棉花的重要生产区[1]。新疆盐碱土的面积为2 184.4万hm2，约占全国盐碱土总面积的1/3[2]，其中南疆土壤盐渍化问题更为突出。南疆地区水资源总量424.4亿m3，地均水资源量为全国平均水平的1/8，而农业用水占比超过总量的95%[3]，水资源短缺和土壤盐渍化已经成为制约该地区农业发展的两大关键因素[4]。利用微咸水灌溉可以缓解淡水资源短缺，据统计，新疆矿化度大于3 g/L的咸水资源约100亿m3[5]，南疆灌溉水矿化度普遍在2～6 g/L，适当的微咸水灌溉可以促进作物生长发育[6]，但也可能造成土壤盐分在根区累积。因此，探究微咸水灌溉对土壤水热盐运移的影响，科学合理地制定微咸水灌溉制度对该地区农业可持续发展具有重要意义。
近年来，地膜与滴灌结合的膜下滴灌技术得到广泛应用，具有减少蒸发和深层渗漏，提高土壤温度[7]的优势，有明显的节水增产效益。由于地膜阻碍土壤与近地面的气体交换，使膜内和膜间土壤中的水分、热量、盐分在二维剖面上产生差异，所以需要考虑水、热、盐在二维剖面的耦合运移。TIAN等[8]通过室内土箱试验对膜下滴灌土壤水、热、盐运移进行了研究，结果表明膜间的土壤比膜内更干，土壤含盐量更高，辐射升温幅度也更大。低滴灌强度的根区脱盐效率较高，横向盐分布不均匀。吉光鹏等[9]研究南疆多次微量滴水技术分析土壤水热盐二维分布的影响，得到了最佳土壤水热盐环境下的灌水量。孙贯芳等[10]的研究表明，不同灌溉制度下土壤盐分均随水分由膜内向膜外地表裸露区定向迁移，趋于膜外地表积累。微咸水与膜下滴灌结合的灌溉方式节水效果更好，但在淋洗土壤盐分的同时会带入土壤新的盐分，灌水矿化度较高时将导致土壤次生盐渍化。因此，有必要进一步探究微咸水膜下滴灌条件下土壤水热盐二维运移机制。
田间试验受到环境的影响较大，且费时费力，而田间试验与数值模拟相结合能够更方便有效地获取和预测农田土壤各项指标的动态。HYDRUS-2D模型是由美国盐土实验室开发，能够较好地模拟土壤水分、热量、溶质在二维可变饱和多孔介质中的运动过程[11]。DOU等[12]利用HYDRUS-2D模拟了3种排水条件下的土壤水盐运移，结果表明土壤水分主要分布在0～40 cm土层，且在生育后期出现不同程度的次生盐渍化；ZHAO等[13]的研究表明，覆膜提高了土壤水分和温度，HYDRUS-2D可以有效模拟覆膜情况下二维土壤水热耦合运移。王在敏等[14]基于田间数据构建的HYDRUS-2D模型，优化了生育期内微咸水灌溉制度，得到最佳滴灌制度为高频少量灌溉；LI等[15]模拟南疆膜下滴灌棉田不同灌水定额的土壤水热盐二维分布，并提出了适宜的淡水灌溉制度。尽管前人验证了HYDRUS-2D模型模拟膜下滴灌农田土壤水热盐运动过程的适用性，并根据模拟结果提出了适宜的灌溉制度，但针对不同灌水量和灌水矿化度组合下的土壤水盐运移研究较少。为此，本研究在南疆膜下滴灌棉田开展了不同水盐处理下的田间试验，分析不同灌水量及灌水矿化度对膜下滴灌棉田土壤水热盐二维迁移与分布特征的影响，并利用HYDRUS-2D进行模拟验证和情景预测，从而确定南疆微咸水膜下滴灌适宜的灌溉制度，以期为南疆水资源高效利用和防治土壤次生盐渍化提供参考。
1. 材料与方法
1.1 试验区概况
田间试验于2023年4—10月在新疆阿拉尔现代农业院士专家工作站（40°06′N，81°02′E）进行。该站位于新疆第一师阿拉尔市十团，属于典型的大陆性暖温带干旱荒漠气候，全年降雨稀少，蒸发强烈，生育期内（2023年5月7日—2023年10月15日）平均气温23.47 ℃，降雨总量为15 mm，参考作物潜在蒸散发量为962.59 mm。该地区光热资源丰富，属于纯灌溉农业[16]。逐日气象数据来源于试验站内安装的HOBO自动气象站（美国Onset公司），生育期内降雨蒸发数据见图1。试验区地下水埋深2～3 m，从地表至地下80 cm深度的土壤大致可分5层，土壤质地均为砂壤土，其基本物理性质见表1[17]。
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图 1 棉花生育期内降雨及参考作物腾发量Figure 1. Rainfall and reference crop evapotranspiration (ET0) in growing season of cotton1.2 试验设计
田间试验共设置3个灌水量和3个灌水矿化度水平，其中3个灌水量水平分别为作物灌溉需水量的75%（W1）、100%（W2）和125%（W3），3个灌水矿化度水平分别为1.5 g/L（S1，当地灌溉用水）、3.5 g/L（S2）和5.5 g/L（S3），共9个处理，每个处理设置3个重复，共27个小区，小区布置完全随机。试验小区长10 m，宽6.84 m，面积为68.4 m2。试验作物为棉花，品种为“塔河2号”，棉花种植模式为“一膜两带六行”（图2），采用膜下滴灌的灌溉方式。滴灌带为贴片式滴灌带，滴头间距24 cm，滴头流量2 L/h。棉花播种日期为4月24日，灌溉出苗水为淡水，5月7日进入苗期后，进行不同水盐灌溉，其中1.5 g/L的淡水和3.5 g/L的微咸水[18]分别为当地深层地下水和浅层地下微咸水，矿化度5.5 g/L的咸水是在3.5 g/L的基础上根据研究区地下水离子组成人工配制而成（NaCl：CaCl2=2：1），每隔7～10 d灌水一次，具体的灌溉制度见表2。棉花播种采用机播，株距10 cm，所有处理的施肥管理参照当地经验实行。
表 1 土壤物理性质Table 1. Soil physical properties土壤深度
Soil depth/cm容重
Bulk density/
（g·cm−3）田间持水量
Field capacity/
（cm3·cm−3）粒径组成Particle composition/% ＜0.002 mm 0.002～
0.05 mm＞0.05～2 mm 0～20 1.60 0.21 2.43 41.49 56.08 ＞20～40 1.55 0.24 2.55 41.40 56.05 ＞40～60 1.58 0.25 2.89 42.82 54.29 ＞60～80 1.59 0.25 2.60 41.40 56.00 表 2 灌溉制度Table 2. Irrigation schedule生育阶段
Growth stage灌水日期
Irrigation date灌水量Irrigation amount/mm W1 W2 W3 苗期
Squaring06-14 22.5 30.0 37.5 蕾期
Bolling06-23 30.0 40.0 50.0 07-03
07-0934.5 46.0 57.5 17.4 23.2 29.0 07-18 32.5 43.3 54.2 花铃期
Flowering07-26 36.8 49.0 61.3 08-03 28.1 37.5 46.9 08-10 33.8 45.0 56.3 08-16 22.5 30.0 37.5 08-26 33.8 45.0 56.3 总计
Total/mm291.8 389.0 486.3 1.3 土壤水热盐的监测与方法
在生育期始末和灌水前后，用土钻在每个小区对土壤进行取样，分别在棉花的中间行（地膜中线处）、滴灌带下、棉花边行和裸土中间（以下简称为“中行” “滴灌带下” “边行” “裸土”）4个位置处，取样位置如图2，取样深度为10、20、40、60和80 cm，共5层。取得的土样通过烘干法（105 ℃，8～12 h）得到各层土壤的质量含水率，经土壤容重转换为体积含水率。将土样风干、碾碎后过1 mm筛，采用1:5的土水比例制成土壤浸提液，测量土水比1:5的土壤浸提液的电导率（SEC1：5电导率仪DDSJ-308A，上海精科仪器公司）。
在W2灌水量下的3个不同灌水矿化度处理中，各随机选取1个试验小区，在中行、滴灌带下、边行和裸土这4个位置（与取样位置相同）的10、20、40、60和80 cm处，分别插入土壤水热盐监测探头（TDR315N，美国），探头与数据采集器相连，每隔1 h获取一次土壤体积含水率、电导率、温度等数据，代表不同位置处0～10、＞10～20、＞20～40、＞40～60、＞60～80 cm深度的数据，并用取土得到的实测数据对探头数据进行校准。
各采样点土壤含盐量（S1，g/kg）是将SEC1:5按照当地经验公式转换，公式如下[19]：
S1=0.004SEC1:5+1.029 (1) 1.4 土壤水热盐数值模拟
1.4.1 模型基本方程
1）忽略与滴灌带平行方向上水分分布的差异，采用与滴灌带垂直方向上的二维Richards方程描述土壤水分运移[20]：
∂θ∂t=∂∂x[K(θ)∂h∂x]+∂∂z[K(θ)∂h∂z]+∂K(θ)∂z−S0 (2) 式中θ为土壤体积含水率，cm3/cm3；K(θ)为非饱和土壤导水率，cm/d；h为压力水头（基质势），cm；t为时间，d；x为横坐标；z为纵坐标；S0为源汇项。式（2）中土壤含水率-基质势-导水率的关系采用van Genuchten-Mualem模型描述[21]：
K(θ)=KsSle[1−(1−S1me)]2,(m=1−1n,n>1) (3) Se=θ−θrθs−θr (4) 式中Ks为饱和导水率，cm/d；θr为土壤残余含水率，cm3/cm3；θs为土壤饱和含水率，cm3/cm3；Se为相对饱和系数；l为孔隙连通性参数；m、n为土壤水分特征曲线形状系数。
2）不考虑溶质的降解、吸附、沉淀等过程，土壤盐分运移采用二维对流-弥散方程描述[22]：
∂θc∂t=∂∂x[θD0∂c∂x]+∂∂z[θD0∂c∂z]−∂(qxc)∂x−∂(qzc)∂z (5) θD0=DT|q|δ+(DL−DT)qxqz|q|+θDWτWδ (6) 式中c为溶质质量浓度，mg/cm3；D0为弥散系数，cm2/d；qx、qz为体积通量，cm/d；DW为溶质分子在水中的扩散系数，cm2/d；DL为纵向弥散度，cm；DT为横向弥散度，cm；δ为Kronecker函数；τW为液相弯曲系数。
C(θ)∂T∂t=∂∂x[λ(θ)∂T∂x]+∂∂z[λ(θ)∂T∂z]−Cwqx∂T∂x−Cwqz∂T∂z (7) C(θ)=Cnθn+Coθ0+Cwθ+Cgαv (8) λ(θ)=λTCw|q|δ+(λL−λT)Cwqxqz|q|+λ0(θ)δ (9) λ0(θ)=b1+b2θ+b3θ0.5 (10) 式中C(θ)是总体积热容量，J/(cm3· ℃)；T为土壤热量， ℃；λ(θ)是土壤表观热导率，W/(cm· ℃)；Cn、Cw、Co分别为固相、液相、有机质的体积比热容，J/(cm3· ℃)；θn、θo分别为固相、有机质的含量，在本研究中忽略有机质的影响，θo取为0；λL为纵向导热弥散度，cm；λT为横向导热弥散度，cm；λ0(θ)是热导系数，W/(cm· ℃)；b1、b2、b3分别为热传导函数的参数，W/(cm· ℃)。
4）根系吸水采用修正的Feddes模型[26]描述：
S(h,hϕ,x,z)=α(h,hϕ,x,z)b(x,z)StTp (11) b(x,z)=b′(x,z)∫ΩRb′(x,z)dΩR (12) 式中S(h， hϕ， x， z)为根系吸水函数；α(h，hψ，x，z)为水盐胁迫函数；hψ为渗透压力，cm；b(x，z)为根系分布函数，1/cm2；St为与蒸腾有关的地表长度，cm；Tp为潜在蒸腾速率，cm/d；ΩR为根系分布区域面积，cm2。
5）由试验区实测的气象资料，根据Penman-Monteith公式[27]计算参考作物腾发量ET0，通过作物系数Kc[28]计算作物潜在腾发量ETc，并根据Beer定律划分为潜在蒸腾与棵间蒸发[29]：
ET0=0.408Δ(Rn−G)+γ900T+273μ2(es−ea)Δ+γ(1+0.34μ2) (13) ETc=Kc⋅ET0 (14) Tp=ETc⋅(1−e−β⋅LAI) (15) Ep=ETc−Tp (16) 式中ET0为参考作物腾发量，mm；Rn为净辐射量， MJ/(m2·d)；G为土壤热通量，MJ/(m2·d)；μ2为2 m高处的风速，m/s；es为饱和水汽压，kPa；ea为实际水汽压，kPa；△为饱和水汽压与温度曲线的斜率，kPa/ ℃；γ为湿度计常数， kPa/ ℃；Kc为作物系数，参考FAO-56的取值并考虑试验地气象、覆膜等情况进行调整[28]；Ep为潜在蒸发速率，cm/d；Tp为潜在蒸腾速率，cm/d；β为太阳辐射消光系数，参考文献[30]取值；LAI为叶面积指数，根据试验实测取值。
1.4.2 模型构建
利用HYDRUS-2D模型模拟棉花整个生育期从2023年5月7日—2023年10月15日共162 d的根区0～60 cm土壤水分、盐分和热运移的情况。试验小区在垂直于滴灌带的剖面上以地膜中线为轴，呈左右对称（图2），以膜中间划分，对右侧进行模拟，选取的模拟区域（图3）长度为120 cm，高度为80 cm（长度为膜宽的1/2，高度为棉花根系能达到的最大深度），模拟区域内有610个节点，
1138 个有限元网格，网格划分情况如图3。![]()
图 3 模拟区域及边界条件示意图Figure 3. Schematic diagram of simulated region and boundary conditions灌水量、灌水矿化度分别按照实际的水流通量[31]和盐分浓度输入[12]，土壤上边界温度按照膜下和膜间两部分分别输入[15]。在模拟中缺乏实测地表温度，根据经验公式[32]，由实测日平均气温得到5 cm深度的土壤平均温度，取日平均气温与5 cm土壤平均温度的平均值作为日平均地表温度。模拟的初始条件为土壤剖面上含水率、含盐量、温度的实测值。土壤水分运动上边界条件在滴头的湿润区[33]设为变通量边界（图3，30～50 cm处），灌水期间滴头的湿润区，根据灌水强度设置为通量边界，非灌水期为零通量边界。膜间裸地设为包含降水和蒸散发的大气边界（100～120 cm），膜下其他位置（0～30、50～100 cm）蒸发较小忽略不计，设为零通量边界，下边界为定水头边界（设置为土壤80 cm处的体积含水率）；土壤溶质运移的上下边界均为第三类边界；土壤热运移的上下边界为第一类边界。左、右边界均为零通量边界。
1.4.3 模型参数
采用W2S2、W1S3和W3S1共3个不同的水盐梯度组合对HYDRUS-2D模型的适用性进行分析，选取W2S2处理对模型进行参数率定，以W1S3和W3S1处理验证模型。
土壤水分特征参数根据表1实测的土壤粒径组成和干容重，利用Rosetta软件由ANN方法预测的van Genuchten-Mualem模型参数初始值[34]，然后根据试验实测值进行调试，得到最终的van Genuchten-Mualem模型参数见表3。土壤溶质运移和热运移参数初始取值参考文献[15,35]，经调试后的参数DW=5.5 cm2/d，λL=5 cm，λT=1 cm，θn=0.68，其他参数取值见表4。
表 3 土壤水分特征参数Table 3. Soil moisture characteristic parameters土壤深度
Soil depth/cmθr/(cm3·cm−3) θs/(cm3·cm−3) α/cm−1 n Ks/(cm·d−1) l 0～10 0.0269 0.3676 0.0296 2.1676 50.55 0.5 ＞10～20 0.0279 0.3456 0.0281 2.2141 49.34 0.5 ＞20～40 0.0277 0.3523 0.0372 2.1983 48.41 0.5 ＞40～60 0.0286 0.3657 0.0421 1.9152 40.48 0.5 ＞60～80 0.0272 0.3589 0.0474 2.1354 46.12 0.5 注：θr为土壤残余含水率；θs为土壤饱和含水率；α和n为土壤水分特征曲线形状系数；Ks为饱和导水率； l为孔隙连通性参数。 Note: θr is the residual moisture content of soil; θs is the saturated moisture content of soil; α and n are the shape coefficient of soil water characteristic curve; Ks is the saturated water conductivity; l is the pore connectivity parameter. 表 4 土壤溶质运移和热特性参数Table 4. Soil solute transport parameters and thermal parameters土壤深度
Soil depth/cmDL/cm DT/cm b1/(W·cm−1· ℃−1) b2/(W·cm−1· ℃−1) b3/(W·cm−1· ℃−1) Cn/(J·cm−3· ℃−1) Co/(J·cm−3· ℃−1) Cw/(J·cm−3· ℃−1) ＞0～10 65 45 8.67×1016 8.53×107 5.89×108 1.43×1014 1.87×1014 3.12×1014 ＞10～20 60 45 2.87×1017 7.95×107 6.24×108 1.43×1014 1.87×1014 3.12×1014 ＞20～40 45 40 6.57×1017 7.83×107 7.49×108 1.43×1014 1.87×1014 3.12×1014 ＞40～60 20 15 7.77×1017 7.65×107 8.29×108 1.43×1014 1.87×1014 3.12×1014 ＞60～80 20 15 4.57×1016 2.53×107 9.89×108 1.43×1014 1.87×1014 3.12×1014 注：DL为纵向弥散度；DT为横向弥散度；b1、b2、b3分别为热传导函数的参数；Cn、Cw、Co分别为固相、液相、有机质的体积比热容。
Note: DL is longitudinal dispersion; DT is lateral dispersion; b1、b2 and b3 are the parameters of the heat conduction function; Cn、Cw and Co are the volume specific heat capacities of solid phase, liquid phase and organic matter, respectively.1.4.4 模型评价指标
为了定量描述模型模拟的精度，采用模拟值与实测值之间的均方根误差（root mean squared error，RMSE）、平均绝对误差（mean absolute error，MAE）和决定系数（R2）对模型进行评价[36]。其中，RMSE、MAE越接近0，R2越接近1，表明模拟值的变化趋势与实测值吻合程度越高。
2. 结果与分析
2.1 土壤水分二维运移及模拟
2.1.1 生育期内土壤水分二维运移及分布特征
在棉花生育期内土壤水分会伴随着灌水、土壤蒸发、植株蒸腾等过程运动，影响土壤水分在剖面上的分布。选取棉花苗期（6月12日）、蕾期（7月15日）和花铃期（8月25日）3个时期代表生育进程中的前、中、后3个阶段（选取日期均为相应生育阶段末期最后一次灌水前取土日期），分析生育期内不同灌水量下土壤水分的二维运移及空间分布（图4），图中距离（水平坐标）表示与覆膜中间的水平距离，其中0、40、80、120 cm的距离分别代表中行、滴灌带下、边行、裸土。
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图 4 S1处理不同灌水量下土壤水分二维分布注：S1为灌水矿化度1.5 g·L−1。Figure 4. Two-dimensional distribution of soil moisture under various irrigation amounts of S1 treatmentNote: Mineralization is 1.5 g·L−1 in irrigation water由图4可知，苗期和蕾期土壤水分二维分布的差异性较大，由于棵间蒸发耗水严重，土壤表层0～20 cm含水率最低，在0.05～0.11 cm3/cm3之间，40～60 cm含水率在整个剖面中最高，可达到0.14～0.18 cm3/cm3，土壤含水率随深度的增加先增大后减小，水平方向上表现为由膜下向膜间裸土逐渐递减的变化趋势。随着灌水量的累加和植株发育，冠层遮蔽降低了膜间裸土蒸发，生育后期土壤剖面上含水率的分布逐渐均匀（图4c、图4f、图4i）。生育期内灌水对0～40 cm深度土壤水分的影响显著[37]，从苗期到花铃期，3个灌水量下土壤含水率均增加，W1、W2、W3的0～40 cm平均含水率分别增加了21.84%、29.13%、32.37%，40～80 cm均略有降低。总的来说，土壤含水率在二维剖面内呈现出随深度增加先增大后减少，由膜内向膜间递减的分布规律。
由图4a和图4b可以发现，在低灌水量下，土壤水分主要分布在水平0～20 cm和垂直40～60 cm，高灌水量下（图4g和图4h），土壤水分主要分布在水平0～60 cm和垂直30～80 cm，随着灌水量的增加，土壤湿润区域增大。对比W2S1和W3S1处理，在苗期（图4d和图4g）、蕾期（图4e和图4h）和花铃期（图4f和图4i），W2S1土壤40～80 cm含水率均保持在大于W3S1的水平，过量的灌溉定额会增加植株蒸腾，这表明过量灌水不一定提高根区土壤含水率。
2.1.2 生育期始末二维土壤剖面水分变化
以W2S1处理（当地常规灌溉）为例分析不同位置土壤含水率在生育期始末的变化（生育期末-生育期初），如表5。
表 5 生育期始末土壤含水率变化Table 5. Changes of soil moisture from the initial to the end of growth stage土壤深度
Soil depth/cm中行
Middle row/
(cm3·cm−3)滴灌带
Drip irrigation tape/
(cm3·cm−3)边行
Border row/
(cm3·cm−3)裸土
Bare soil/
(cm3·cm−3)0～10 −0.038 −0.082 −0.040 −0.013 ＞10～20 −0.044 −0.053 −0.048 −0.015 ＞20～40 −0.041 −0.033 −0.049 −0.037 ＞40～60 −0.037 0.024 −0.045 −0.046 ＞60～80 −0.045 −0.011 −0.047 −0.034 平均值Average −0.041 −0.031 −0.046 −0.029 注：负号表示生育期末土壤含水率下降。 Note: The negative sign indicates that the soil moisture content decreases at the end of the growth period. 生育期内土壤含水率在灌溉、植株蒸腾和土壤蒸发影响下不断波动，但生育期结束后的总体土壤含水率低于初始值。覆膜春灌的非生育期盐分淋洗模式有效提升了生育期初的土壤含水率，但膜间裸土含水率提高较少，同时生育期内的灌水也较少运移到膜间，因此裸土水分降低最少，0～80 cm土层平均降低0.029 cm3/cm3；而边行和中行初始含水率较高，同时在棉花旺盛的蒸腾作用下导致土壤水分降低最多，分别为0.046、0.041 cm3/cm3；生育期内频繁的灌水使得滴灌带下60 cm处始终处在湿润锋边缘，同时棉花根系吸水和土壤蒸发影响较小，使其土壤含水率在生育期末上升0.024 cm3/cm3。
2.1.3 不同水盐处理下0～60 cm土壤水分动态
图5为不同水盐处理下根区0～60 cm土壤含水率模拟值与实测值的比较结果，在每次灌水之后土壤水分得到补充，随着作物耗水和土壤蒸发缓慢下降，各层土壤水分呈现锯齿状波动，其中土壤0～20 cm波动较深层大。土壤含水率的模拟效果较好，R2、RMSE、MAE分别在0.64～0.85、0.01～0.03 cm3/cm3、0.01～0.03之间，深层土壤含水率模拟效果优于表层。
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图 5 不同水盐处理下0～60 cm土壤含水率模拟值与实测值的比较注：RMSE为均方根误差。MAE为平均决定误差。S2和S3中，灌水矿化度分别为3.5和5.5 g·L−1。Figure 5. Comparison of simulated and measured water content in 0-60 cm soil depth under different water and salinity treatmentsNote: RMSE is root mean square error. MAE is mean relative error. Irrigation mineralization is 3.5 and 5.5 g·L−1 for S2 and S3respectively.2.2 土壤盐分二维运移及模拟
2.2.1 单次灌水前后土壤剖面盐分动态
本文以棉花生育中期的第5次灌水事件（灌水日期为7月18日，灌水量见表2）为例，分析不同灌水量下单次灌水前后土壤剖面上的盐分运移（图6）。灌水前，垂直方向上土壤盐分主要分布在深层，W1S1处理（图6a）盐分在土壤40～80 cm深度累积，平均盐含量是0～40 cm的2.4倍，而W2S1（图6b）和W3S1（图6c）处理主要分布在土壤60～80 cm深度，平均盐含量是0～60 cm的1.91、2.26倍；水平方向上，W1、W2处理土壤盐分在边行和裸土的20～40 cm累积，W3处理在30～60 cm累积，灌水量增加提高了低矿化度处理下的洗盐效率，导致土壤积盐深度增大。灌水后，由于滴灌的点源入渗特性，在滴头下方形成一个半圆形脱盐区，土层加深淋洗作用减弱，40～80 cm土层土壤盐分在W1时基本不变，W2、W3盐分向下迁移而呈现降低趋势。S1矿化度低，0～40 cm土层中行和滴灌带下的盐含量均小于灌水前，以盐分淋洗为主，而对于不同的灌水量，边行和裸土的盐分变化不同，低灌水量下边行盐分增加，积盐率为11.55%，中、高灌水量下盐分在裸土增加，积盐率分别为5.53%、15.21%（图6d～图6f），表明随着灌水量增大，盐分淋洗到较深的土层中，脱盐区域向膜外移动扩展。
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图 6 S1处理不同灌水量下灌水前后土壤盐分的剖面分布Figure 6. Distribution of soil salinity before and after irrigation under various irrigation amounts of S1 treatment2.2.2 全生育期土壤剖面盐分的二维运移特征
微咸水灌溉能够淋洗土壤中的盐分，同时也会将灌水将盐分带入土壤中，不同灌溉量下盐分淋洗速率与累积速率的大小将影响土壤盐分的运移及分布。为了明晰生育期内0～80 cm土壤盐分的动态变化及运移趋势，与土壤水分相同，以棉花苗期（6月12日）、蕾期（7月15日）和花铃期（8月25日）3个阶段为生育前期、中期和后期进行分析。生育期内不同灌水矿化度下土壤剖面盐分的分布如图7所示。
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图 7 生育期内不同灌水矿化度下土壤盐分二维分布Figure 7. Two-dimensional distribution of soil salinity in different growth stages under various irrigation salinity苗期土壤盐分主要分布在40～80 cm土层，盐分在上层土壤的水平方向上没有显著差异（图7a、图7d、图7g），这是由于播前采用覆膜春灌的方式将盐分淋洗至深层土壤，膜间裸土的蒸发对于膜内外盐分运移的影响较小。此时正处生育期的第1次灌水前，不同矿化度之间的盐分分布无显著差异。
与生育初期相比，蕾期（图7b、图7e、图7h）的土壤盐分分布出现差异，在此期间较高的灌水频率有利于盐分向下迁移并在湿润锋边缘积聚，导致深层土壤的盐分升高。3种灌水矿化度处理下40～80 cm土壤深度中行和滴灌带位置的盐分均相对较高，其中，W2S2和W2S3处理中行和滴灌带下的40～80 cm平均含盐量分别比苗期增加了36.61%和31.07%，这是在“一膜两带六行”的种植模式下，中行位于两侧滴灌带形成的湿润峰交汇处，高盐浓度时导致盐分在40～80 cm的中行和滴灌带下积聚。W2S1处理在裸土的0～40 cm盐分累积，0～40 cm平均含盐量分别比苗期增加了77.47%，由于蒸发驱动的盐分向上迁移，导致裸土的40～80 cm盐分有所下降；而W2S2和W2S3处理0～40 cm边行土壤含盐量要高于膜间裸土。
在花铃期（图7c、图7f、图7i），不同灌水矿化度对盐分的空间分布产生显著的影响。与生育初期相比， S1矿化度灌溉下的中行和滴灌带下40～80 cm深度土壤盐分分别降低了45.71%、29.03%，盐分淋洗效果明显，而S3盐浓度较高，中行和滴灌带下土壤40～80 cm盐分分别增加了56.28%、80.14%。S1处理0～80 cm土壤含盐量相对较低，水平方向上主要在边行和裸土积聚；S2处理60～80 cm深度为主要积盐区，水平方向上除滴灌带下均发生积盐；S3处理灌水矿化度较高，在40～80 cm深度发生盐分累积，水平方向上在边行和裸土处积盐。
2.2.3 生育期始末二维土壤剖面盐分变化
表6为W2S1处理土壤剖面上不同位置盐分在生育期始末的变化。灌溉是土壤盐分运移的主要驱动因素，由表可知，低矿化度的灌水淋洗滴灌带下0～40 cm土壤盐分，其中表层0～10 cm土壤生育期末盐分降低0.46 g/kg，随着土壤深度的增加淋洗效果减弱，盐分在40～80 cm积聚，生育期末上升1.51 g/kg；边行10～40 cm处于湿润范围内盐分得到淋洗，而0～10 cm处于湿润锋边缘，生育期末盐分上升0.30 g/kg；膜间裸土是主要的积盐区，田间土壤较砂且10～40 cm处于湿润锋边缘，因此在生育期末膜间土壤10～40 cm盐分累积高于膜间0～10 cm土壤；中行处于两根滴灌带造成的湿润锋重合处，生育期末在中行20～40 cm处累积的盐分最多，为1.05 g/kg。
表 6 生育期始末土壤含盐量变化Table 6. Changes of soil salinity from the initial to the end of growth stage土壤深度
Soil depth/cm中行
Middle row/
(g·kg−1)滴灌带
Drip irrigation
tape/(g·kg−1)边行
Border row/
(g·kg−1)裸土
Bare soil/
(g·kg−1)0～10 0.60 −0.46 0.30 0.28 ＞10～20 0.42 −0.23 −0.02 0.91 ＞20～40 1.05 −0.07 −0.13 0.79 ＞40～60 0.07 1.04 0.39 0.46 ＞60～80 −0.20 0.47 0.17 −0.05 平均值Average 0.39 0.15 0.14 0.48 注：负号表示生育期末土壤含盐量下降。 Note: The negative sign indicates that the soil salinity decreases at the end of the growth period. 2.2.4 不同水盐处理下0～60 cm土壤含盐量动态
生育期内0～60 cm土壤含盐量的动态变化模拟与实测值的比较如图8所示，模拟的盐分变化趋势与实际基本一致，图8展示的3个处理中，灌溉水矿化度大于S2时，0～40 cm土壤盐分波动式上升，而W3S1处理的含盐量基本不变，40～60 cm含盐量均呈降低趋势。模拟结果较好，R2、RMSE、MAE分别在0.56～0.79、0.27～1.28 g/kg、0.25～1.24之间。
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图 8 不同水盐处理下0～60 cm土壤含盐量模拟与实测的比较Figure 8. Comparison of simulated and measured soil salinity in 0-60 cm soil depth under different water and salinity treatments2.3 土壤二维剖面的热运移及模拟
2.3.1 土壤剖面温度的动态及分布特证
由于试验设置只在W2S1、W2S2和W2S3这3个处理下埋设土壤温度传感器，而经过比较发现在不同处理之间生育期内土壤温度差异较小，所以本文选择以W2S1（当地常规灌溉）为例进行分析。通过分析各层土壤温度随棉花生育阶段的变化规律（图9），可以发现各层土壤温度变化趋势一致，生育初期土壤温度迅速升高，进入蕾期后在冠层遮蔽和灌水的双重作用下，土壤温度呈波动式下降。进入吐絮期后土壤温度略有回升，此后随空气温度迅速降低。
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图 9 生育期内0～80 cm土层温度动态变化Figure 9. Dynamic changes of soil temperature of 0-80 cm soil depth in different growth stages土壤0～40 cm温度受外界环境影响较大引起波动幅度大，各生育阶段始末的平均温差在3.56～8.41 ℃之间，而土壤40～80 cm温度波动较小，各生育阶段始末的平均温差在1.61～4.55 ℃之间。从空间尺度上看，随深度增加土壤平均温度逐渐降低，0～80 cm的土壤各层平均温度从24.06 ℃降至22.41 ℃。覆膜可以显著提高生育初期的土壤温度，这种增温效果对0～40 cm土壤影响较大。棉花苗期由于覆膜导致0～40 cm的膜内外土壤平均温差为1.35 ℃，此后随着冠层发育遮蔽土壤，覆膜的增温效果减弱。
2.3.2 全生育期0～60 cm土壤温度模拟和实测比较
在田间试验中只收集了W2灌水量下3个处理的温度实测值，所以在土壤水热盐耦合模拟中，仅对比了W2S2处理土壤温度的实测与模拟结果，图10为0～60 cm土壤日平均温度的模拟值与实测值比较，表现出良好的一致性，R2在0.77～0.87之间。
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图 10 0～60 cm土壤温度模拟值与实测值比较Figure 10. Comparison of simulated and measured soil temperature in 0-60 cm soil depth2.4 情景模拟
由以上模拟与实测值的对比可知，本文建立的模型能够可靠地反映膜下滴灌农田不同水盐处理下的土壤水盐运移规律。进一步采用本模型进行情景模拟，设置W0.7、W0.8、W0.9、W1.0共4个灌水量水平，分别为灌溉需水量的70%、80%、90%、100%和S1.5、S2.5、S3.5、S4.5、S5.5共5个灌溉水矿化度水平，分别为1.5、2.5、3.5、4.5、5.5 g/L，以便模拟多水盐处理下膜下滴灌农田棉花根区的土壤水盐动态，确定南疆适宜的膜下滴灌灌溉制度。情景模拟除灌水量和灌水矿化度的输入外，模型设置均与以上模拟相同。
根据前人的研究，膜下滴灌棉花根系分布主要集中在土壤25～35 cm之间[38]，0～40 cm是棉花的主根区，因此情景模拟结果主要分析不同灌溉水盐条件下土壤0～40 cm膜下与膜间的土壤盐分迁移规律。生育期始末膜下和膜间的土壤积、脱盐率如图11，可以发现，土壤盐分淋洗主要发生在膜下（图11a），当矿化度小于2.5 g/L时，4个灌水量的膜下0～40 cm深度均没有积盐。同一灌水矿化度下，随着灌水量的增加，土壤湿润范围扩大，膜下的盐分淋洗作用加强（低矿化度时的脱盐率增大，高矿化度时的积盐率减小），向膜间迁移的盐分增加，而在灌水量超过W0.9时，膜下土壤脱盐率有所降低。
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图 11 0～40 cm土壤膜内外积/脱盐率注：W0.7～W1.0分别为灌水需水量70%、80%、90%、和100%。Figure 11. Salt accumulation rate or desalting rate of 0-40 cm soil depth in and out of mulchNote: W0.7-W1.0 are 70%, 80%, 90% and 100% of water demand, respectively.在相同的灌水量下，随着灌水矿化度的增加，土壤脱盐率减少，积盐率逐渐增大，当矿化度大于3.5 g/L时，所有灌水量的膜下0～40 cm土壤均出现积盐。随着灌水量的增加，更多的盐分淋洗至膜间（图11b），灌水矿化度为1.5 g/L条件下，灌溉水量高于W0.95会导致膜间0～40 cm土壤出现盐分累积；当灌水矿化度高于2.5 g/L时，所有灌水量均会导致膜间土壤积盐。生育期内根区土壤盐分淋洗效率不会随着灌水量的增大而增加，同一灌水矿化度下，当灌水量为W0.9时，可以达到膜下土壤最高的脱盐率或最小的土壤积盐率。充分灌溉条件下，保证膜下0～40 cm土壤不积盐的最高灌水矿化度为3.2 g/L；在90%的灌溉需水量下，保证膜下土壤根区不积盐的最高灌水矿化度为3.5 g/L；灌水矿化度低于2.5 g/L时，所有灌溉水平均不会导致根区土壤出现盐分累积。
3. 讨 论
3.1 土壤水热盐二维运移及分布特征
本试验观测到土壤水分在垂直方向上呈现出先增大后减小的分布规律，在40～60 cm土层含水率较高。这是由于南疆地区土壤质地偏砂性，且地下水埋深多在2.5 m[16]以下，土壤水分主要靠灌溉进行补给，由于覆膜滴灌单次灌水量少且灌水强度小，生育期内灌水湿润深度多在60 cm左右，而0～40 cm为棉花的主根区，在植株蒸腾耗水影响下导致土壤水分在垂直方向上呈现先增大后减小的特征。这与CHEN等[37]得到的研究结果相同。“一膜两带六行”的种植模式下，由于生育期内持续灌水使相互对称的两侧滴灌带湿润区在膜中间的中行处产生重叠，在覆膜作用下蒸发较小，而膜间裸土距离滴灌带较远且裸露土壤蒸发强烈，水分散失快，生育期内的少量降雨（图1，单次降雨量在0.2～4.4 mm之间）虽然会在短时间内提高膜间裸土含水率，但对膜间10 cm以下土壤无显著影响，同时在强烈的蒸发作用下膜间裸土含水率迅速降低，单次降雨对土壤水分二维分布影响较小，因此水平方向上土壤含水率呈现出从膜内中行到膜间裸土逐渐降低，这也与以往研究一致[39-40]。生育前期土壤水分有较大的分布差异，随着灌水量的增加，湿润体逐渐向膜外移动，湿润区范围扩大（图4），在生育后期土壤剖面上水分分布的均匀性增加，这与CHEN等[41]的研究结果相同。125%的灌溉需水量并不能提高根区土壤含水率（图4h、图4i），这是因为过量的灌溉定额促进作物生长发育，旺盛的蒸腾作用在一定程度上降低了土壤含水率。
盐随水走，随着裸土蒸发和定期灌溉，土壤盐分在不同灌水量和灌水矿化度的影响下再分布。S1矿化度下，灌水量增加提高了此矿化度处理的洗盐效率。由灌前土壤盐分分布（图6）可知，低灌水量下，土壤水盐向下迁移，在40～80 cm深度积聚，较低的灌水量水盐横向运移较少（图6a）；随着灌水量的增加，湿润区不断扩大，在膜间裸土蒸发作用下盐分被淋洗到膜间，而W3处理充足的灌溉水量也淋洗了裸土0～20 cm土壤盐分（图6b、图6c）。灌后土壤在滴灌带处出现明显的脱盐区（图6d、图6e、图6f），其中低（W1）灌水量处理盐分分布与灌前相同，高灌水处理盐分淋洗效果显著，盐分被淋洗至深层土壤，这与HOU等[42]的研究结论相同。
随着生育期的进行，中、高矿化度灌水处理土壤盐分不断累积，累积位置主要在中行和滴灌带处的40～80 cm（图7e、图7h），这是因为“一膜两带六行”的模式下中行和滴灌带处为土壤主要湿润区，中、高矿化度的灌溉水在此累积提高了土壤盐分。进入花铃期，停止灌水后盐分的主要驱动力来自膜间裸土的蒸发，水平方向上边行和膜间裸土处出现盐分累积（图7c、图7f、图7 i）。与前人研究结果[42-43]不同的是，膜间0～20 cm含盐量低于20～40 cm，盐分表聚现象不明显，这是由于试验地土壤质地偏砂，导水率较大，垂直方向的盐分运移远高于水平方向，因此盐分在随湿润峰运移时导致膜间20～40 cm土壤盐分高于0～20 cm。同一灌水量下，随着灌水矿化度的增加，生育期内灌水对盐分的淋洗效果减弱，高矿化度的处理60～80 cm土壤积盐更加显著，灌水带入的盐分在此处产生积聚（图7）。灌水矿化度增大，盐分累积占主导，盐分累积深度降低。
苗期土壤温度随空气温度的升高迅速增高，进入蕾期后作物生长旺盛，同时频繁的灌水降低了土壤温度，使得进入蕾期后土壤温度开始降低，覆膜的增温效果减弱。进入吐絮期后停止灌水，棉花叶片脱落，土壤温度略有回升，此后又在空气温度影响下迅速降低。生育期内土壤温度的分布差异表现为膜内温度高于膜间温度，上层土壤温度高于下层。同时，覆膜的增温效应在棉花生育初期土壤表层（0～40 cm）表现更加明显，这是因为生育初期植株的遮蔽作用较弱，受太阳直射导致近地表的气体交换频繁，温度波动大，后期植株冠层发育遮蔽膜间裸土，导致覆膜的增温效减弱，这与孙贯芳等[10，44-45]的研究结果一致。
3.2 灌溉制度对土壤积/脱盐率的影响
随着灌水矿化度的增加，盐分累积趋势增大[46]，当矿化度小于3.5 g/L时，棉花主根区0～40 cm土壤盐分淋洗速率大，这与杨广等[22]的研究得出的矿化度在4 g/L以下，盐分淋洗速率大于累积速率，结果基本一致。低矿化度下，膜内0～40 cm土壤脱盐率随灌水量的增加先增大后减小，高矿化度下，膜内土壤积盐率随灌水量的增加先减小后增大，这是由于灌水量增加提高了土壤淋洗深度，W0.9对于根区0～40 cm土壤淋洗效果最好，而过量灌水湿润峰位置处于深层土壤，对根区土壤的淋洗效果与W0.9相同，此外灌水的同时伴随着带入土壤中更多的盐分，除盐分横向运移外，也导致膜下土壤盐分增加。膜间土壤盐分累积与灌水定额密切相关[47]，增大灌水量能够将更多盐分淋洗至膜间0～40 cm，在不同灌溉制度下膜间0～40 cm土壤主要以积盐为主。由于初始盐含量对盐分的变化有很大影响，情景模拟仅在轻度盐渍土上进行，在不同盐渍程度的区域，其适用性还需要进一步验证。
本文利用HYDRUS-2D模型能够较好地模拟膜下滴灌农田的土壤二维水热盐运移，但仍存在不足之处，例如模型将土壤含水率的覆膜边界在非灌水期简单设置为零通量边界，没有考虑到膜的破损导致的膜下土壤水分蒸发，导致在灌水后土壤湿润条件下，0～20 cm的土壤含水率被高估，峰值较高，而在土壤较干燥的时期，模拟值更加接近实测值。此外还缺少对地上作物生长的模拟，在模拟中采用了已知的土壤水热盐上边界条件。而在实际应用中，土壤的上边界温度往往是未知的，需要考虑根据气象观测结合下垫面能量平衡和水热传输过程来进行量化。
4. 结 论
本研究通过田间试验和数值模拟，分析了南疆膜下滴灌农田土壤水热盐在二维剖面上的分布特征及运移规律，并通过情景模拟探究了不同灌水量和灌水矿化度对土壤积/脱盐率的影响，主要得到以下结论：
1）生育前期土壤二维剖面内水分分布差异明显，后期逐渐均匀；125%的灌溉需水量不能提高根区土壤含水率；低灌水矿化度下土壤盐分累积位置随灌水量增大向深层、膜间土壤运移，中、高矿化度灌水提高了滴灌带下、中行位置处的土壤盐分；覆膜对生育初期0～40 cm土壤增温效果明显。
2）HYDRUS-2D模型能可靠模拟膜下滴灌棉田土壤水热盐二维运移，0～60 cm土层土壤含水率、含盐量、温度模拟值与实测值的动态变化表现出良好的一致性，R2均大于0.56。
3）膜间土壤主要以积盐为主，微咸水灌溉下较高的灌水定额会提高膜下根区土壤含盐量。灌水矿化度小于2.5 g/L时，不同灌溉水量均不会造成膜下根区土壤在生育期末的盐分累积。充分灌水条件下，保证膜下0～40 cm土壤不积盐的最高灌水矿化度为3.2 g/L；在90%的灌溉需水量下，灌水矿化度为3.5 g/L时，是微咸水灌溉下保证膜下土壤根区不积盐的最大阈值。
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图 2 兴赣黑芝麻专业合作社地形地貌
Figure 2. Topography and landforms of Xinggan Black Sesame Professional Cooperative
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图 3 特色粮油作物江津综合试验站地形地貌
Figure 3. Topography and landforms of Jiangjin Comprehensive Experimental Station of Characteristic Grain and Oil Crops
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图 4 机械与人工结合的分段播种
Figure 4. Segmented sowing with combination of mechanical and human labor
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图 5 机械耕整地开沟/免耕+飞播
Figure 5. Mechanical plowing and trenching/no tillage with aerial sowing
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图 7 2BYM-6/8型油麦兼用联合直播机
Figure 7. 2BYM-6/8 direct sowing machine for rapeseed and wheat
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图 11 机械式油麦兼用集排器[39]
1.充种仓 2.种层调节装置 3.传动主轴 4.型孔轮组件 5.外壳 6.电机 7.气力辅助导种装置 8.风机组 9.种箱
Figure 11. Mechanical rapeseed and wheat dual-purpose metering device[39]
1.Seed filling warehouse 2.Seed layer adjustment device 3.Drive spindle 4.Type-hole wheel assembly 5.Shell 6.Motor 7.Pneumatic assisted seed guide device 8.Fan unit 9. Seed box
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表 1 南方丘陵山地油麦种植场景耕地分类
Table 1 Farmland classification of rapeseed and wheat planting scene in hilly and mountainous region of southern China
耕地类型
Farmland type地形特点
Topographic features适机耕播特征
Characteristics of suitable tillage and sowing典型地形地貌
Typical landform梯田
Terrace丘陵山地上沿等高线方向
修筑的条状阶台式或波浪式
断面的耕地水旱轮作梯田：精细等高水平改造形成，田块平整，适宜水旱轮作，
但田面弯曲狭长，单块面积不足0.03hm2，中大型机组作业和转运困难，
导致机械化生产率普遍偏低
旱作梯田：粗放改造形成，以坡式梯田和隔坡梯田为主，田块间落差较大，单田块面积0.20～0.33hm2，部分田块有较小坡度（6°以下），机具作业时可顺坡运移、入田作业，机械化生产难度较水旱轮作梯田低 
平坝
Flat plains丘陵山地山间盆地、峰丛洼地及河谷地带的坡度起伏小、地势相对平缓（6°以下）的耕地 大平坝：耕地面积从6.67~666.67hm2不等，地形地貌与平原地区相似，适宜发展农业机械化生产，机械化作业主要难点在于机耕道不完善、农业技术服务机构不足等基础建设问题，技术难度相对较低 
小平坝：面积在6.67hm2以下，普遍存在于丘陵山地谷底区域，一般为两山所夹的底部平缓耕地，地形平坦、地势较低，生产上多采用稻-油/麦水旱轮作
模式，油麦播种时土壤含水量较大且长期浸水，加之受雨热同期气候特征
影响，导致耕地黏重湿烂特征显著，现有播种机具作业时存在机组易下陷、开沟起垄稳定性差、易缠绕拥堵等问题
坡田
Sloping field丘陵山地中未经改造或简单改造后仍具有一定坡度的耕地 坡度在6°～15°之间，地势起伏无规律，降水易通过径流流失，
土壤蓄水保土保肥能力弱，易旱特征显著[20-21]，机械化播种对
机具仿形和播深控制能力要求较高
下载: 导出CSV
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