组态视角下新疆农业生产水足迹治理的多重路径

doi:10.12118/j.issn.1000-6060.2025.575

摘要/Abstract

摘要：

水资源对维系干旱区绿洲农业发展至关重要。针对新疆农业水资源结构性短缺与利用低效的复合困境，以2012—2024年新疆14个地州市为研究单元，测算棉花、玉米和小麦3种作物的生产水足迹，用对数平均迪氏指数分解法（LMDI）分解5类影响因子，动态定性比较分析（动态QCA）方法识别降低农业生产水足迹强度的组态路径及其时空异质性。结果表明：（1）全疆平均农业生产水足迹在时间上呈现先升后降的趋势，2016年达到峰值8.810 m³⋅kg^-1；空间上呈现东南高、西北低的分布格局，吐鲁番市均值高达25.609 m³⋅kg^-1，是阿勒泰地区的11.8倍。（2）技术进步、种植结构、节水效应、管理水平和农业经济发展水平是影响新疆农业生产水足迹强度的重要因素，并且有显著的动态演变特征。其中，管理水平与技术进步的驱动效应由局部正向逐渐演变为全域抑制。（3）降低新疆农业生产水足迹强度的等效路径分别为技术和种植结构双驱型、节水和管理协同型、技术主导型以及管理和种植结构双驱型。（4）所有组态均存在显著的空间异质性：农业基础较好的地区其组态路径显著多于薄弱地区。未来新疆需依据各地区的资源禀赋和发展阶段，对农业生产水足迹实施时空适配的治理策略。

关键词: 农业生产水足迹, LMDI模型, 动态QCA, 组态路径, 新疆

Abstract:

Water resources are crucial for the sustained development of oasis agriculture in arid regions. To resolve the complex dilemmas of structural shortages and inefficient use of agricultural water resources in Xinjiang, this study calculates the production water footprint of three crops (cotton, corn, and wheat) in 14 prefectures and cities in Xinjiang, China, from 2012 to 2024, identifies five types of influencing factors through the logarithmic mean Divisia index decomposition method, and identifies configurational paths for reducing the intensity of agricultural production water footprint and their spatiotemporal heterogeneity through dynamic qualitative comparative analysis (dynamic QCA). The results show that: (1) The average agricultural production water footprint across Xinjiang first increased and then decreased over time, reaching a peak of 8.810 m³·kg^-1 in 2016; spatially, it was higher in the southeast and lower in the northwest, with the average value in Turpan City as high as 25.609 m³·kg^-1, 11.8 times that of Altay Prefecture. (2) Technological progress, cropping structure, water-saving effects, management level, and agricultural economic development level are important factors in the intensity of agricultural production water footprint in Xinjiang and exhibit significant changes over time. In particular, the driving effects of management level and technological progress have gradually evolved from exerting partial positive effects to pan-regional inhibitory effects. (3) The equivalent paths for reducing the intensity of agricultural production water footprint in Xinjiang are the technology-cropping-structure dual-driving type, water-saving-management collaborative type, technology-led type, and management-cropping-structure dual-driving type. (4) All configurations demonstrate significant spatial heterogeneity: Regions with better agricultural foundations have significantly more configurational paths than those with weak foundations. In the future, spatiotemporally adaptive governance strategies based upon the resource endowments and development stages of each region should be implemented to reduce the agricultural production water footprint in Xinjiang.

Key words: agricultural production water footprint, LMDI model, dynamic QCA, configurational paths, Xinjiang

蒋志辉, 琚慧琦, 欧阳金琼, 苏旭峰. 组态视角下新疆农业生产水足迹治理的多重路径[J]. 干旱区地理, 2026, 49(9): 1951-1964.

JIANG Zhihui, JU Huiqi, OUYANG Jinqiong, SU Xufeng. Multiple paths for the governance of agricultural production water footprint in Xinjiang from the configurational perspective[J]. Arid Land Geography, 2026, 49(9): 1951-1964.

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作物	区域	开始生长日期（月-日）	结束生长日期（月-日）	平均作物系数^[21]	生育期天数^[22]
棉花	北疆	04-30	10-09	0.64	162
	南疆	04-11	10-15	0.64	187
	东疆	04-10	10-07	0.64	180
冬小麦	南疆	09-11	07-10	0.43	300
春小麦	北疆	04-01	07-21	0.53	111
小麦	东疆	04-01	07-21	0.53	111
玉米	全疆	04-21	09-11	0.57	143

因素	贡献度
因素	2012—2015年	2016—2020年	2021—2024年
技术进步效应	-0.037	0.109	-4.456
种植结构效应	-0.023	0.051	-0.267
节水效应	0.059	-0.176	3.330
管理水平效应	0.021	0.104	-1.001
农业经济发展水平	0.006	-0.173	1.029

变量名称		指标	校准			描述性分析
变量名称		指标	完全隶属	交叉点	完全不隶属	均值	标准差	最大值	最小值
结果变量	生产水足迹强度	Y	0.022	0.057	0.483	0.225	0.405	2.485	0.015
条件变量	技术水平	X₁	-1.399	-0.134	1.199	-0.084	1.402	7.900	-3.789
	种植结构	X₂	-1.152	-0.012	1.615	0.066	1.342	4.034	-5.966
	节水效应	X₃	-1.663	0.062	1.376	-0.066	1.465	3.945	-5.532
	管理水平	X₄	-1.108	0.119	1.192	0.092	0.965	2.236	-4.018
	农业经济发展水平	X₅	-1.279	-0.262	0.878	-0.333	1.311	1.937	-12.414

前因条件	低生产水足迹强度				高生产水足迹强度
前因条件	汇总一致性	汇总覆盖度	组间一致性调整距离	组内一致性调整距离	汇总一致性	汇总覆盖度	组间一致性调整距离	组内一致性调整距离
X₁	0.615	0.654	0.265	0.244	0.604	0.572	0.397	0.307
~X₁	0.598	0.629	0.269	0.236	0.636	0.595	0.374	0.297
X₂	0.626	0.672	0.327	0.253	0.589	0.564	0.296	0.297
~X₂	0.594	0.618	0.386	0.253	0.658	0.610	0.288	0.230
X₃	0.586	0.612	0.389	0.265	0.665	0.618	0.257	0.283
~X₃	0.634	0.680	0.389	0.282	0.582	0.556	0.350	0.295
X₄	0.617	0.676	0.222	0.277	0.590	0.577	0.249	0.303
~X₄	0.614	0.627	0.269	0.261	0.668	0.608	0.226	0.242
X₅	0.599	0.630	0.405	0.207	0.627	0.588	0.483	0.299
~X₅	0.609	0.647	0.393	0.261	0.605	0.573	0.456	0.291

因果组合	指标	2013年	2014年	2015年	2016年	2017年	2018年	2019年	2020年	2021年	2022年	2023年	2024年
X₁/Y	一致性	0.72	0.82	0.52	0.51	0.75	0.33	0.45	0.56	0.53	0.72	0.76	0.70
X₁/Y	覆盖度	0.63	0.56	0.79	0.59	0.56	0.93	0.92	0.63	0.64	0.68	0.63	0.68
~X₁/Y	一致性	0.58	0.48	0.74	0.72	0.56	0.90	0.73	0.60	0.65	0.47	0.38	0.45
~X₁/Y	覆盖度	0.55	0.69	0.55	0.63	0.55	0.54	0.51	0.66	0.75	0.74	0.97	0.79
X₂/Y	一致性	0.80	0.27	0.97	0.54	0.43	0.52	0.51	0.74	0.77	0.54	0.71	0.64
X₂/Y	覆盖度	0.63	0.52	0.54	0.64	0.59	0.61	0.62	0.68	0.76	0.72	0.94	0.80
~X₂/Y	一致性	0.60	0.97	0.20	0.64	0.89	0.75	0.75	0.43	0.40	0.65	0.46	0.55
~X₂/Y	覆盖度	0.64	0.58	0.93	0.55	0.55	0.64	0.67	0.60	0.59	0.70	0.55	0.69
X₃/Y	一致性	0.60	0.64	0.47	0.93	0.78	0.96	0.82	0.45	0.45	0.46	0.31	0.40
X₃/Y	覆盖度	0.65	0.59	0.56	0.55	0.53	0.57	0.62	0.56	0.77	0.72	0.72	0.78
~X₃/Y	一致性	0.82	0.71	0.80	0.19	0.56	0.25	0.39	0.69	0.73	0.73	0.86	0.78
~X₃/Y	覆盖度	0.64	0.66	0.69	0.57	0.64	0.77	0.63	0.69	0.66	0.69	0.73	0.71
X₄/Y	一致性	0.78	0.80	0.43	0.45	0.52	0.47	0.58	0.61	0.64	0.70	0.70	0.68
X₄/Y	覆盖度	0.65	0.55	0.70	0.84	0.60	0.72	0.64	0.68	0.71	0.67	0.72	0.74
~X₄/Y	一致性	0.64	0.40	0.79	0.81	0.79	0.84	0.67	0.58	0.53	0.46	0.45	0.53
~X₄/Y	覆盖度	0.65	0.56	0.57	0.55	0.53	0.62	0.66	0.65	0.67	0.71	0.72	0.78
X₅/Y	一致性	0.63	0.33	0.73	0.55	0.35	0.89	0.73	0.79	0.87	0.53	0.62	0.17
X₅/Y	覆盖度	0.58	0.62	0.57	0.59	0.90	0.56	0.52	0.66	0.63	0.79	0.73	1.00
~X₅/Y	一致性	0.74	0.93	0.56	0.71	0.88	0.33	0.52	0.40	0.24	0.65	0.56	0.90
~X₅/Y	覆盖度	0.67	0.57	0.78	0.67	0.45	0.78	0.97	0.66	0.75	0.64	0.73	0.63