农田水利基础设施投资缺口对粮食生产效率损失的影响

doi:10.18402/resci.2022.01.13

摘要/Abstract

摘要：

粮食生产离不开农田水利基础设施支撑,农田水利基础设施投资不足会导致农户粮食生产陷入“效率损失”陷阱之中。本文以中国31个省份2009—2018年数据为样本,以秦岭—淮河为界,将全国划为南方、北方和秦岭—淮河3个区域,构建缺口测度模型和EBM模型,分别测度农田水利基础设施投资缺口和粮食生产效率损失,并基于Tobit随机效应面板模型对二者关系进行实证检验。结果表明：①中国农田水利基础设施投资缺口严重,且区域差异明显,呈现南方小小小北方小小小秦岭—淮河的空间特征;②南方和秦岭-淮河区域的粮食生产效率损失主要源于纯技术效率损失,而北方区域主要源于规模效率损失;③农田水利基础设施投资缺口对粮食生产综合效率损失和规模效率损失存在显著正向影响,农业经营收入和人口城镇化对粮食生产综合效率损失和纯技术效率损失存在显著负向影响;④农田水利基础设施投资缺口的粮食生产效率损失效应存在功能区差异。对此,应实行功能区策略,即在主产区走“农田水利+技术创新”路径、在非主产区走“农田水利+技术不掉队”路径,并与提升农业经营收入和城镇化水平协调配合,以提升中国粮食生产效率。

关键词: 农田水利基础设施, 投资缺口, 粮食生产, 效率损失, EBM测度模型, Tobit随机效应面板模型, 中国

Abstract:

Food production is inseparable from farmland water conservancy infrastructure. The dynamic asset poverty constraint framework indicates that insufficient investment in farmland water conservancy infrastructure will cause farmers’ food production to fall into the trap of low efficiency. Based on the data of 31 provinces in China’s mainland from 2009 to 2018, this study divided the country into three regions the south, the north, and the Qinling Mountains-Huaihe River reign, taking the Qingling-Huaihe River as the boundary and constructed a “gap” or deficiency measurement model for farmland water conservancy infrastructure investment and an EBM (epsilon-based measurement) model for food production efficiency loss, and analyzed the relationship between the two based on the Tobit random effect panel model in an empirical test. The results show that the deficiency of farmland water conservancy infrastructure investment is serious and has significant regional differences, which shows spatial characteristics of southern region less than northern region less than Qinling-Huaihe River boundary region, and the deficiency in China’s grain production bases is also serious. The loss of grain production efficiency in the south and the Qinling-Huaihe boundary regions is mainly caused by the loss of pure technical efficiency, while in the north is mainly caused by the loss of scale efficiency of grain production. The deficiency of farmland water conservancy infrastructure investment has significant positive impact on the loss of food production combined efficiency and scale efficiency, and agricultural operating income and urbanization level have significant positive impart on the pure technical efficiency. The effect of the deficiency of farmland water conservancy infrastructure investment on the loss of grain production efficiency differs between the main production areas and non-main production areas. Therefore, the functional zone strategy should be taken: taking the path of “farmland water conservancy and technological innovation” in the main production areas and “farmland water conservancy and technology catching up” in non-main production areas, and coordinate with the improvement of agricultural income and urbanization level to improve grain production efficiency in China.

Key words: farmland water conservancy infrastructure, investment deficiency, grain production, efficiency loss, EBM measurement model, Tobit random effect panel model, China

张长征, 李嘉雯, 孙杰. 农田水利基础设施投资缺口对粮食生产效率损失的影响[J]. 资源科学, 2022, 44(1): 169-180.

ZHANG Changzheng, LI Jiawen, SUN Jie. Influence of investment gap in farmland water conservancy infrastructure on the loss of grain production efficiency[J]. Resources Science, 2022, 44(1): 169-180.

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指标名称	计算方法	样本量	最小值	最大值	平均值	标准差
有效灌溉率	有效灌溉面积/粮食播种面积	310	0.21	1.00	0.54	0.21
节水灌溉率	节水灌溉面积/粮食播种面积	310	0.05	0.78	0.27	0.19
堤防保护率	堤防保护耕地面积/粮食播种面积	310	0.00	0.94	0.18	0.21
除涝率	除涝面积/粮食播种面积	310	0.01	1.00	0.34	0.27
单位播种面积小型水库库容/（m³/hm²）	小型水库库容量/粮食播种面积	310	4.18	2454.40	679.77	668.61
水灾发生率	水灾受灾面积/农作物总播种面积	310	0.00	0.58	0.07	0.08
旱灾发生率	旱灾受灾面积/农作物总播种面积	310	0.00	0.80	0.11	0.15

指标类型	指标名称	样本量	最小值	最大值	平均值	标准差
投入指标	粮食播种面积/千hm²	310	103.8	14902.7	5260.5	3885.1
	机械化水平/万kw	310	43.2	9863.7	2284.7	2240.1
	农药使用量/万t	310	0.07	11.7	3.8	3.1
	化肥使用量/万t	310	3.5	538.3	129.6	110.5
	劳动力/万人	310	9.7	1182	328.2	258.4
	年度农田水利投资额/百万元	310	498.6	36678.0	8618.7	6731.1
产出指标	粮食总产量/万t	310	63.7	9012.7	2832.1	2213.0

变量名称	变量符号	样本量	最小值	最大值	平均值	标准差
旱田占比	field	310	0.01	0.88	0.47	0.25
农村居民收入结构	revenue	310	0.06	6.65	1.39	1.04
人口城镇化水平	townpeople	310	0.22	0.90	0.55	0.14

南方区域			秦岭—淮河区域			北方区域
地区	G值	E值	地区	G值	E值	地区	G值	E值
福建	0.473	0.010	安徽	0.311	0.039	北京	0.142	0.001
广东	0.625	0.023	河南	0.328	0.055	甘肃	0.697	0.044
广西	0.694	0.038	江苏	0.000	0.024	河北	0.282	0.043
贵州	0.743	0.041	陕西	0.582	0.033	黑龙江	0.550	0.110
海南	0.758	0.007				吉林	0.626	0.052
湖北	0.553	0.040				辽宁	0.551	0.036
湖南	0.638	0.034				内蒙古	0.593	0.067
江西	0.655	0.025				宁夏	0.630	0.010
上海	0.000	0.001				青海	0.674	0.005
云南	0.726	0.055				山东	0.282	0.050
浙江	0.247	0.013				山西	0.661	0.035
重庆	0.746	0.021				天津	0.000	0.002
四川	0.630	0.053				西藏	0.533	0.029
						新疆	0.228	0.060
均值	0.576	0.028		0.305	0.040		0.461	0.039
方差	0.222	0.017		0.238	0.013		0.227	0.030

	南方区域				秦岭—淮河区域				北方区域
地区	综合	规模	纯技术	地区	综合	规模	纯技术	地区	综合	规模	纯技术
福建	0.308	0.059	0.263	安徽	0.246	0.016	0.234	北京	0.331	0.331	0.000
广东	0.321	0.010	0.313	河南	0.111	0.111	0.000	甘肃	0.424	0.040	0.400
广西	0.256	0.017	0.243	江苏	0.164	0.082	0.089	河北	0.276	0.044	0.243
贵州	0.07	-0.003	0.072	陕西	0.409	0.041	0.383	黑龙江	0.000	0.000	0.000
海南	0.375	0.254	0.151					吉林	0.000	0.000	0.000
湖北	0.146	0.022	0.127					辽宁	0.193	0.011	0.184
湖南	0.08	0.000	0.009					内蒙古	0.006	0.006	0.000
江西	0.059	0.004	0.055					宁夏	0.116	0.097	0.024
上海	0.000	0.000	0.000					青海	0.012	0.012	0.000
云南	0.431	0.006	0.427					山东	0.140	0.140	0.000
浙江	0.213	0.017	0.200					山西	0.474	0.046	0.448
重庆	0.287	0.013	0.277					天津	0.344	0.251	0.118
四川	0.048	0.000	0.044					西藏	0.272	0.272	0.000
								新疆	0.000	0.000	0.000
均值	0.200	0.031	0.168		0.233	0.063	0.177		0.185	0.089	0.101