西北干旱区作物灌溉技术效率及影响因素

doi:10.31497/zrzyxb.20190414

摘要/Abstract

摘要：

水资源短缺是制约西北干旱区可持续发展的硬约束,提高作物灌溉技术效率、压缩农业灌溉用水是缓解水资源供需矛盾的可能途径之一。基于2014年张掖市农户调研数据,采用DEA-Tobit模型,分析了黑河流域中段不同类型灌区作物灌溉技术效率及其影响因素。结果表明：（1）典型灌区主要作物灌溉技术效率均存在改进空间,节水潜力较大。在其他投入保持不变的情况下,如果典型灌区主要作物灌溉技术效率达到目前的最高水平,平原灌区生产同样产量的制种玉米和大田玉米,灌溉用水可分别减少34.47%和38.15%;北部荒漠灌区生产同样产量的棉花、制种西瓜和玉米套小麦,灌溉用水可分别减少48.42%、34.82%和22.99%;沿山灌区生产同样产量的小麦、马铃薯、大麦和大田玉米,灌溉用水可分别减少14.48%、30.75%、25.50%和35.96%。（2）不同灌区之间作物灌溉技术效率的变异系数与作物种植面积占比呈负向关系,同一灌区内部种植相同作物的农户生产管理水平存在明显差异。（3）农地细碎化程度和农户耕地面积扩大会降低作物灌溉技术效率,改善耕地质量能提高北部荒漠灌区作物灌溉技术效率,增加井水灌溉会提高平原灌区大田玉米和沿山灌区作物灌溉技术效率,灌溉次数与多数作物灌溉技术效率呈“倒U型”关系,而农户耕作需求及其对风险态度的影响需结合具体情况进行判断。合理确定种植规模、加快农地空间优化,因地制宜地改善耕地质量,完善水利设施、合理使用井灌、增强河水灌溉放水的灵活性,是提升黑河流域作物灌溉技术效率的主要途径。

关键词: 张掖市, 黑河流域, 典型灌区, 作物灌溉技术效率, DEA-Tobit模型

Abstract:

The shortage of water resources has become a hard constraint to the sustainable development of the arid areas of Northwest China. Increasing the technical efficiency of crop irrigation (TECI) and reducing agricultural irrigation water use may be one of the possible ways to ease the contradiction between water supply and demand. Based on the survey data of farmers in Zhangye city in 2014, this paper uses the DEA-Tobit model to analyze the TECI and its determinants in different types of irrigation zones in the middle reaches of the Heihe River Basin. The results show that there is a certain improvement space of TECI of main crops in the typical irrigation zones. With the other inputs unchanged, if the TECI reaches the current highest level in different types of irrigation zones, in order to produce the same amount of the seed maize and maize, the irrigation water can be reduced by 34.47% and 38.15% in the plain irrigation zone; in order to produce the same amount of the cotton, seed watermelon and maize-wheat (inter-corn), the irrigation water can be reduced by 48.42%, 34.82% and 22.99% in the northern desert irrigation zone; in order to produce the same amount of the wheat, potato, barley and maize, the irrigation water can be reduced by 14.48%, 30.75%, 25.50% and 35.96% in the mountain irrigation zone. In addition, the coefficient of variation of the TECI in different irrigation zones is negatively related to the proportion of cropland area, and there is a significant difference in the production management level of farmers who grow the same crop in the same irrigation zone. What's more, the fragmentation degree of cultivated land and the expansion of cultivated area have negative effects on the TECI in different irrigation zones, improving the quality of cultivated land has a significant positive effect on main crops in the northern desert irrigation zone. Increasing well water irrigation has a positive effect on crops such as maize in plain irrigation zone and wheat, potato, maize in mountain irrigation zone, and there is an inverted U-shaped relationship between irrigation frequency and the TECI. However, the influence of farmers' demand for cultivation and the attitude towards risk need to be judged based on specific conditions. Therefore, determining the production scale, accelerating the optimization and reorganization of cultivated land and improving the quality of cultivated land, rationally guiding the use of well irrigation, and enhancing the water conservancy facility and the flexibility of river water irrigation are the main ways to improve the TECI in the Heihe River Basin.

Key words: Zhangye city, Heihe River Basin, irrigation zone, technical efficiency of crop irrigation, DEA-Tobit model

李贵芳, 周丁扬, 石敏俊. 西北干旱区作物灌溉技术效率及影响因素[J]. 自然资源学报, 2019, 34(4): 853-866.

LI Gui-fang, ZHOU Ding-yang, SHI Min-jun. Technical efficiency of crop irrigation and its determinants in the arid areas of Northwest China[J]. JOURNAL OF NATURAL RESOURCES, 2019, 34(4): 853-866.

图/表 8

图1

表1

表2

典型灌区主要作物投入产出指标描述性统计"

灌区	作物类型和指标		劳动/ （天/亩）	灌溉用水/ （m³/亩）	种子/ （元/亩）	农药/ （元/亩）	地膜/ （元/亩）	化肥/ （元/亩）	农机/ （元/亩）	作物产出/ （kg/亩）
平原灌区	制种玉米	平均值	7.55	807.20	71.97	49.00	38.59	256.45	100.10	962.10
		最大值	25.00	1146.67	120.00	200.00	75.60	433.28	400.00	1400.00
		最小值	2.50	537.50	40.00	0	0	64.02	0	450.00
		标准差	7.22	788.33	72.00	50.00	37.20	258.36	105.00	1000.00
	大田玉米	平均值	7.13	721.06	66.60	46.69	51.84	254.98	194.73	747.06
		最大值	30.67	1600.00	135.00	260.00	120.00	634.87	555.00	1300.00
		最小值	1.22	333.33	16.00	0	0	0	0	500.00
		标准差	6.00	666.67	64.00	30.00	60.00	267.68	220.00	750.00
北部荒漠灌区	棉花	平均值	13.73	432.33	87.22	37.38	49.63	215.76	53.13	156.32
		最大值	53.00	610.00	200.00	300.00	78.00	450.00	400.00	350.00
		最小值	5.00	401.67	24.00	0	30.00	0	0	55.50
		标准差	11.00	427.00	84.00	30.00	48.00	189.36	0	150.00
	制种西瓜	平均值	6.32	437.99	40.97	63.93	18.31	248.04	34.89	31.07
		最大值	29.00	610.00	150.00	300.00	70.00	774.58	180.00	58.00
		最小值	1.75	406.67	10.00	0	0	0	0	10.00
		标准差	4.50	423.95	39.75	50.00	0	227.53	0	30.25
沿山灌区	小麦	平均值	3.38	431.42	113.59	12.10	0	186.00	73.13	484.28
		最大值	32.25	1333.33	176.00	50.00	0	418.62	200.00	600.00
		最小值	0.88	254.17	76.00	0	0	0	0	300.00
		标准差	2.33	381.25	112.00	10.00	0	175.44	80.00	500.00
	马铃薯	平均值	11.70	417.37	441.81	37.16	53.82	255.44	80.44	2588.13
		最大值	56.50	666.67	640.00	150.00	195.00	596.27	250.00	4000.00
		最小值	0.79	254.17	245.00	0	0	90.81	0	1000.00
		标准差	9.80	381.25	455.00	20.00	46.80	243.10	80.00	2500.00
	大麦	平均值	3.61	416.98	78.00	12.19	0	186.23	65.74	490.00
		最大值	12.67	1047.92	98.67	50.00	0	472.76	190.00	650.00
		最小值	1.00	254.17	30.00	0	0	0	0	400.00
		标准差	3.00	305.00	80.17	10.00	0	181.15	50.00	500.00
	大田玉米	平均值	7.49	517.45	60.31	11.67	34.04	211.75	39.07	575.27
		最大值	27.00	1575.00	220.00	50.00	169.00	468.84	230.00	800.00
		最小值	1.17	203.33	10.00	0	0	0	0	400.00
		标准差	7.00	381.25	50.00	10.00	39.00	199.62	40.00	600.00

表2

表3

表4

表5

表6

表7

典型灌区主要作物灌溉技术效率影响因素Tobit回归结果"

类型	影响变量	平原灌区		北部荒漠灌区			沿山灌区
类型	影响变量	制种玉米	大田玉米	玉米—小麦	棉花	制种西瓜	小麦	马铃薯	大麦	大田玉米
	$β 0$	-0.535 (0.97)	0.744 (3.20^***)	1.066 (3.23^***)	0.681 (1.94^*)	0.014 (0.03)	0.866 (4.63^***)	0.687 (5.04^***)	-3.975 (-3.03^***)	3.146 (1.71^*)
人口社会学特征	lnAGE	0.041 (1.51)	0.021 (0.39)	-0.077 (-1.71^*)	-0.008 (-0.28)	0.030 (0.98)	-0.027 (-0.61)	0.070 (1.82^*)	0.595 (2.68^***)	-0.585 (-1.31)
	D1	0.015 (1.35)	0.032 (1.07)	0.008 (0.44)	-0.010 (-0.38)	0.008 (0.34)	0.016 (0.85)	0.081 (2.10^**)	0.182 (1.90^**)	0.449 (2.51^**)
	lnAL	0.008 (0.59)	0.029 (0.86)	0.043 (1.98^**)	0.058 (1.77^*)	0.041 (1.09)	0.016 (0.79)	-0.013 (-0.24)	-0.084 (-0.80)	0.017 (0.08)
生产管理特征	lnFLA	-0.034 (-2.78^***)	0.007 (0.23)	-0.021 (-0.95)	-0.006 (-0.18)	-0.021 (-0.50)	-0.001 (-0.03)	0.023 (0.52)	0.100 (0.90)	-0.021 (-0.11)
	lnSI	-0.109 (-2.35^**)	-0.335 (-2.09^***)	-0.369 (-3.13^***)	-0.042 (-2.37^**)	-0.186 (-3.69^***)	-0.169 (-1.74^*)	-0.820 (-2.49^**)	-2.373 (-3.16^***)	-3.865 (-4.15^***)
	D2	—	—	—	0.073 (2.45^**)	0.098 (3.06^***)	—	—	—	—
	D3	—	0.076 (2.42^**)	—	—	—	0.008 (0.36)	0.028 (0.49)	—	0.172 (0.63)
	lnIN	0.161 (0.23)	0.125 (0.42)	-0.149 (-0.61)	-0.226 (-0.78)	0.648 (1.38)	0.020 (0.17)	0.124 (0.84)	1.102 (3.40^***)	-1.190 (-1.50)
	(lnIN)²	-0.085 (-0.36)	-0.068 (-0.66)	0.039 (0.55)	0.120 (0.98)	-0.194 (-1.42)	-0.115 (-1.65)	-0.141 (-1.52)	-1.156 (-5.49^***)	0.510 (1.08)
	D4	—	—	0.042 (2.27)	—	—	0.010 (0.54)	—	—	—
	D5	-0.026 (-2.35^**)	-0.032 (-4.75^***)	-0.044 (-2.50^**)	-0.060 (-2.28^**)	-0.064 (-2.29^**)	0.006 (0.38)	-0.091 (-2.69^***)	0.150 (1.68^*)	-0.011 (-0.07)
耕作需求及对风险态度	D6	0.033 (1.65^*)	0.021 (0.51)	-0.006 (-0.32)	0.073 (2.45^**)	0.002 (0.05)	-0.043 (-0.88)	0.026 (0.52)	-0.168 (-0.83)	0.192 (0.46)
	lnAIR	0.013 (1.46)	0.024 (1.74^*)	-0.095 (-2.39^**)	-0.022 (-0.82)	0.025 (0.42)	-0.060 (-1.17)	0.095 (0.69)	0.165 (0.52)	-0.017 (-0.08)
	lnCT	—	0.017 (2.42)	0.098 (2.63^***)	-0.047 (-0.72)	0.018 (0.34)	0.034 (1.32)	0.068 (0.95)	0.448 (2.38^**)	0.721 (2.32^**)
	lnCP	-0.131 (-1.93^**)	-0.025 (-0.16)	-0.003 (-0.02)	-0.117 (-0.77)	-0.055 (-1.22)	-0.173 (-1.91^*)	0.091 (1.44)	2.275 (2.44^**)	5.534 (3.86^***)
	lnCP2 套小麦	—	—	0.109 (1.35)	—	—	—	—	—	—
LogLikehood		301.639	58.777	160.613	73.297	66.720	157.841	39.392	-73.658	-79.106
Obs		327	128	201	115	88	253	80	115	91
Prob>F=		<0.01	<0.01	<0.01	<0.01	<0.01	<0.01	<0.01	<0.01	<0.01

表7

参考文献 35

[1]	LI N, WANG X J, SHI M J, et al.Economic impacts of total water use control in the Heihe River Basin in Northwestern China: An integrated CGE-BEM modeling approach. Sustainability, 2015, 7(3): 3460-3478.
[2]	王晓君, 石敏俊, 王磊. 干旱缺水地区缓解水危机的途径: 水资源需求管理的政策效应. 自然资源学报, 2013, 28(7): 1117-1129.
	[WANG X J, SHI M J, WANG L.Solutions to water scarcity in arid regions: Effectiveness of water demand management policy. Journal of Natural Resources, 2013, 28(7): 1117-1129.]
[3]	王晓娟, 李周. 灌溉用水效率及影响因素分析. 中国农村经济, 2005, (7): 11-18.
	[WANG X J, LI Z.Analysis of efficiency of irrigation water use and influencing factors. Chinese Rural Economy, 2005, (7): 11-18.]
[4]	OMEZZINE A, ZAIBET L.Management of modern irrigation systems in Oman: Allocative vs. irrigation efficiency. Agricultural Water Management, 1998, 37(2): 99-107.
[5]	HAJI J. Production efficiency of smallholders' vegetable-dominated mixed farming system in Eastern Ethiopia: A non-parametric approach. Social Science Electronic Publishing, 2008, 16(1): 1-27.
[6]	SPEELMAN S, HAESE M, BUYSSE J, et al.A measure for the efficiency of water use and its determinants: A case study of small-scale irrigation schemes in North-West province, South Africa. Agricultural Systems, 2008, 98(1): 31-39.
[7]	韩洪云, 赵连阁, 王学渊. 农业水权转移的条件: 基于甘肃、内蒙典型灌区的实证研究. 中国人口·资源与环境, 2010, 20(3): 100-106.
	[HAN H Y, ZHAO L G, WANG X Y.The precondition for water right out of agriculture: A positive analysis based on two typical irrigation districts. China Population, Resources and Environment, 2010, 20(3): 100-106.]
[8]	NJIRAINI G W, GUTHIGA P M.Are small-scale irrigators water use efficient? Evidence from Lake Naivasha Basin, Kenya. Environmental Management, 2013, 52(5): 1192-1201.
[9]	WANG X Y.Irrigation water use efficiency of farmers and its determinants: Evidence from a survey in Northwestern China. Journal of Integrative Agriculture, 2010, 9(9): 1326-1337.
[10]	DHUNGANA B R, NUTHALL P L, NARTEA G V.Measuring the economic inefficiency of Nepalese rice farms using data envelopment analysis. Australian Journal of Agricultural & Resource Economics, 2004, 48(2): 347-369.
[11]	CHAVAS J P, PETRIE R, ROTH M.Farm household production efficiency: Evidence from the Gama. American Journal of Agricultural Economics, 2005, 87(1): 160-179.
[12]	FARRELL M J.The measurement of productive efficiency. Journal of the Royal Statistical Society, 1957, 120(3): 253-290.
[13]	KARAGIANNIS G, TZOUVELEKAS V, XEPAPADEAS A.Measuring irrigation water efficiency with a stochastic production frontier. Environmental & Resource Economics, 2003, 26(1): 57-72.
[14]	耿献辉, 张晓恒, 宋玉兰. 农业灌溉用水效率及其影响因素实证分析: 基于随机前沿生产函数和新疆棉农调研数据. 自然资源学报, 2014, 29(6): 934-943.
	[GENG X H, ZHANG X H, SONG Y L.Measurement of irrigation water efficiency and analysis of influential factors: An empirical study based on stochastic production frontier and cotton farmers' data in Xinjiang. Journal of Natural Resources, 2014, 29(6): 934-943.]
[15]	赵连阁, 王学渊. 农户灌溉用水的效率差异: 基于甘肃、内蒙古两个典型灌区实地调查的比较分析. 农业经济问题, 2010, (3): 71-78.
	[ZHAO L G, WANG X Y.Farmers' irrigation water use efficiency variance: A comparative analysis based on field survey of two typical irrigated areas in Gansu and Inner Mongolia. Issues in Agricultural Economy, 2010, (3): 71-78.]
[16]	RAJU K S, KUMAR D N.Ranking irrigation planning alternatives using data envelopment analysis. Water Resources Management, 2006, 20(4): 553-566.
[17]	REIG-MARTINEZ E, PICAZO-TADEO A J. Analysing farming systems with data envelopment analysis: Citrus farming in Spain. Agricultural Systems, 2004, 82(1): 17-30.
[18]	THIAM A, BRAVOURETA B E, RIVAS T E.Technical efficiency in developing country agriculture: A meta-analysis. Agricultural Economics, 2001, 25(2-3): 235-243.
[19]	CHAMBERS R G, FÄRE R. Using dominance in forming bounds on DEA models: The case of experimental agricultural data. Journal of Econometrics, 2004, 85(1): 189-203.
[20]	DHEHIBI B, LACHAAL L, ELLOUMI M, et al.Measuring irrigation water use efficiency using stochastic production frontier: An application on citrus producing farms in Tunisia. African Journal of Agricultural & Resource Economics, 2007, 1(2): 1-15.
[21]	FRIJA A, CHEBIL A, SPEELMAN S, et al.Water use and technical efficiency in horticultural greenhouses in Tunisia. Agricultural Water Management, 2009, 96(11): 1509-1516.
[22]	许朗, 黄莺. 农业灌溉用水效率及其影响因素分析: 基于安徽省蒙城县的实地调查. 资源科学, 2012, 34(1): 105-113.
	[XU L, HUANG Y.Measurement of irrigation water efficiency and analysis of influential factors: An empirical study of Mengcheng county in Anhui province. Resources Science, 2012, 34(1): 105-113.]
[23]	宋春晓, 马恒运, 黄季焜, 等. 气候变化和农户适应性对小麦灌溉效率影响: 基于中东部5省小麦主产区的实证研究. 农业技术经济, 2014, (2): 4-16.
	[SONG C X, MA H Y, HUANG J J, et al.Effects of climate change and farmer household adaptability on wheat irrigation efficiency: An empirical study based on the main wheat producing areas in 5 provinces of Eastern Central China. Journal of Agrotechnical Economics, 2014, (2): 4-16.]
[24]	COVENTRY D R, POSWAL R S, YADAV A, et al.A comparison of farming practices and performance for wheat production in Haryana, India. Agricultural Systems, 2015, 137: 139-153.
[25]	JIN J J, HE R, GONG H Z, et al.Farmers' risk preferences in rural China: Measurements and determinants. International Journal of Environmental Research & Public Health, 2017, 14(7): 713.
[26]	王兵, 唐文狮, 吴延瑞, 等. 城镇化提高中国绿色发展效率了吗?. 经济评论, 2014, (4): 38-49.
	[WANG B, TANG W S, WU Y R, et al.Does urbanization increase China's green development efficiency?. Economic Review, 2014, (4): 38-49.]
[27]	张晓敏, 张秉云, 陈晓宇, 等. 我国主要牧区牧业生产效率及影响因素研究: 基于DEA-CLAD的两阶段模型. 中国农业大学学报, 2017, 22(4): 171-178.
	[ZHANG X M, ZHANG B Y, CHEN X Y, et al.Study of the efficiency of livestock production and its influence factors in Chinese main pastoral area: Based on the two-phase modeling of DEA-CLAD. Journal of China Agricultural University, 2017, 22(4): 171-178.]
[28]	石敏俊, 王磊, 王晓君. 黑河分水后张掖市水资源供需格局变化及驱动因素. 资源科学, 2011, 33(8): 1489-1497.
	[SHI M J, WANG L, WANG X J.With the Heihe River water resources in Zhangye city after the water supply and demand pattern changes and driving factors. Resources Science, 2011, 33(8): 1489-1497.]
[29]	CHARNES A, COOPER W W, RHODES E.Measuring the efficiency of decision making units. European Journal of Operational Research, 1978, 2: 429-444.
[30]	COOPER W W, PARK K S, YU G.IDEA and AR-IDEA: Models for dealing with imprecise data in DEA. Management Science, 1999, 45(4): 597-607.
[31]	FÄRE R, GROSSKOPF S, LOVELL C A K. Production Frontiers. Cambridge: Cambridge University Press, 1994.
[32]	HU J L, WANG S C.Total-factor energy efficiency of regions in China. Energy Policy, 2006, 34(17): 3206-3217.
[33]	沈陈华, 冯电军, 王旭姣, 等. 农地细碎化度测度指数计算的改进. 资源科学, 2012, 34(12): 2242-2248.
	[SHEN C H, FENG D J, WANG X J, et al.Simpson index calculation of land fragmentation. Resources Science, 2012, 34(12): 2242-2248.]
[34]	ZHOU D Y, WANG X J, SHI M J.Human driving forces of oasis expansion in Northwestern China during the last decade: A case study of the Heihe River Basin. Land Degradation & Development, 2017, 28(2): 412-420.
[35]	吕晓, 黄贤金, 钟太洋, 等. 中国农地细碎化问题研究进展. 自然资源学报, 2011, 26(3): 530-540.
	[LYU X, HUANG X J, ZHONG T Y, et al.Review on the research of farmland fragmentation in China. Journal of Natural Resources, 2011, 26(3): 530-540.]

投入产出指标	北部荒漠灌区玉米套小麦
投入产出指标	平均值	最大值	最小值	标准差
玉米劳动投入/（天/亩）	6.36	35.00	0.80	5.00
小麦劳动投入/（天/亩）	5.10	25.00	1.14	4.00
灌溉用水/（m³/亩）	440.97	854.00	254.17	424.97
玉米种子投入/（元/亩）	49.80	180.00	27.00	45.00
小麦种子投入/（元/亩）	67.76	91.80	30.60	61.20
农药投入/（元/亩）	39.06	300.00	0	30.00
地膜投入/（元/亩）	10.30	65.00	0	0
化肥投入/（元/亩）	246.87	567.43	0	242.87
农机投入/（元/亩）	25.02	210.00	0	0
玉米产量/（kg/亩）	465.66	700.00	225.00	500.00
小麦产量/（kg/亩）	313.95	500.00	200.00	300.00

变量类型	影响因素	变量名	具体含义
农户人口社会学特征	户主年龄	lnAGE	户主实际年龄的对数,反映农户种植经验的影响
	户主受教育水平	D1	D1=1户主接受过初等及以上教育;否则D1=0
	家庭农业劳动力人数	lnAL	家庭农业劳动力人数的对数
农户生产管理特征	农户耕地面积	lnFLA	农户总耕地面积的对数,反映农户生产规模的影响
	农地细碎化程度	lnSI	用辛普森多样性指数的对数（Simpson Index,lnSI）表示农地细碎化程度,反映农户耕地空间集中程度对灌溉技术效率的影响
	耕地质量	D2	D2=1好地;D2=0差地
	灌溉水源	D3	D3=1井水灌溉;D3=0河水灌溉
	灌溉次数	lnIN	作物灌溉次数的对数和作物灌溉次数对数的二次项,验证灌溉次数与灌溉技术效率之间的关系
	灌溉次数的二次项	(lnIN)²	作物灌溉次数的对数和作物灌溉次数对数的二次项,验证灌溉次数与灌溉技术效率之间的关系
	种子类型	D4	D4=1有使用杂交种的情况;否则D4=0
	化肥使用量	D5	D5=1作物化肥使用量大于灌区内同种作物的平均使用量;否则D5=0
农户耕作需求及对风险态度	土地流入	D6	D6=1农户有土地流入情况;否则D6=0。反映耕作需求的影响
	家庭农业收入占比	lnAIR	家庭农业收入占比的对数,反映农户对农业收入依赖程度的影响
	作物种类数	lnCT	农户种植作物种类数的对数,反映农户对待风险的态度的影响
	作物价格	lnCP	作物市场价格的对数,反映作物市场价格波动的影响

灌区类型	作物类型	平均值	最大值	最小值	标准差	变异系数
平原灌区	制种玉米	0.6553	1.0000	0.3068	0.6442	0.2580
平原灌区	大田玉米	0.6185	1.0000	0.1399	0.5939	0.4558
北部荒漠灌区	棉花	0.5158	1.0000	0.1708	0.4503	0.4316
	制种西瓜	0.6518	1.0000	0.1765	0.6120	0.3366
	玉米—小麦	0.7701	1.0000	0.2381	0.7857	0.2659
沿山灌区	小麦	0.8552	1.0000	0.2163	1.0000	0.2757
	马铃薯	0.6925	1.0000	0.1906	0.6653	0.4084
	大麦	0.7450	1.0000	0.2138	0.7949	0.3371
	大田玉米	0.6404	1.0000	0.0861	0.6523	0.4714

灌溉技术效率	平原灌区制种玉米		平原灌区大田玉米		北部荒漠区棉花		北部荒漠区制种西瓜		北部荒漠区玉米—小麦
灌溉技术效率	DMU	占比/%	DMU	占比/%	DMU	占比/%	DMU	占比/%	DMU	占比/%
0~0.25	0	0	12	9.23	9	7.83	2	2.27	1	0.50
0.25~0.50	57	17.43	41	31.54	56	48.70	22	25.00	22	10.95
0.50~0.75	204	62.39	30	23.08	32	27.83	34	38.64	72	35.82
0.75~1.00	66	20.18	47	36.15	18	15.65	30	34.09	106	52.74
合计	327	100.00	128	100.00	115	100.00	88	100.00	201	100.00
灌溉技术效率	沿山灌区小麦		沿山灌区马铃薯		沿山灌区大麦		沿山灌区大田玉米
灌溉技术效率	DMU	占比/%	DMU	占比/%	DMU	占比/%	DMU	占比/%
0~0.25	1	0.39	2	2.50	4	3.48	14	15.38
0.25~0.50	30	11.72	23	28.75	18	15.65	19	20.88
0.50~0.75	45	17.58	19	23.75	25	21.74	23	25.27
0.75~1.00	180	70.31	36	45.00	68	59.13	35	38.46
合计	256	100.00	80	100.00	115	100.00	91	100.00