西北地区主要粮食作物种植的水、碳足迹及布局优化

doi:10.3864/j.issn.0578-1752.2024.06.009

摘要/Abstract

摘要：

【目的】对西北地区主要粮食作物碳足迹和水足迹进行评估，并基于碳足迹和水足迹建立多目标种植结构优化模型，对各省（区）粮食作物进行空间布局调整，为减少碳排放量和强化水资源管理提供理论依据。【方法】以西北地区小麦、玉米和水稻种植面积、产量和农资投入量等为基础数据，利用生命周期法（LCA）对碳足迹进行评估；以蒸散量和有效降水量为基础数据，利用Penman-Monteith公式对水足迹进行评估；基于熵权法对西北五省（区）三大粮食作物布局进行多目标优化。【结果】 1999—2020年西北地区小麦、玉米、水稻的碳足迹和水足迹在不同省（区）表现出明显的差异性。西北地区3种粮食作物小麦、玉米、水稻碳排放呈现出东西部较高，中部较低的分布特点，其单位产量碳足迹（PCF）的范围分别为：0.36—0.63、0.33—0.56、0.57—0.97 t CO₂-eq·t^-1，单位面积碳足迹（FCF）分别为：（2.46±0.77）、（3.21±0.49）、（5.57±0.91）t CO₂-eq·hm^-2。2010—2018年西北地区小麦、玉米绿水总量呈平稳上升趋势，水稻绿水总量变化不显著。平均绿水总量较高的地区分布在陕西、甘肃和宁夏。蓝水总量在2010—2015年呈上升趋势，2016—2018年呈现下降趋势，平均蓝水总量较高的地区分布在甘肃、宁夏和新疆。玉米是西北地区三大粮食作物中蓝水足迹最小的作物，平均蓝水足迹为0.45 m³·kg^-1；水稻的蓝水足迹最大，平均蓝水足迹为0.77 m³·kg^-1。基于碳足迹和水足迹进行粮食作物种植结构优化，以各省（区）不同作物种植面积为决策变量，并根据不同权重设置重点减少碳排放（生态效益）和重点增加绿水利用（水资源效益）的优化情景。情景1碳排放总量减少1.9%，绿水利用总量增加5.0%；情景2碳排放总量减少11.8%；情景3绿水利用总量增加6.7%。【结论】西北地区三大粮食作物碳排放和水总量时空差异显著。碳足迹方面，该地区三大粮食作物平均FCF呈现增加，PCF呈现降低的变化趋势。水足迹方面，该地区三大粮食作物绿水足迹高于全国均值，其中玉米绿水足迹最大，水稻绿水足迹最小。在粮食作物安全保障的前提下，小麦种植面积增加6.7%、玉米和水稻种植面积分别减少5.8%和8.0%，经济、资源和生态效益均有一定的改进。综上所述，多目标优化后可以提高绿水资源利用，减少碳排放量，缓解环境压力。

关键词: 碳足迹, 水足迹, 种植结构优化, 粮食作物, 西北地区

Abstract:

【Objective】 This paper assessed the carbon footprint and water footprint of major food crops in the northwest region, and established a multi-objective planting structure optimization model based on the carbon footprint and water footprint to adjust the spatial distribution of food crops in each province, so as to provide a theoretical basis for reducing carbon emissions and enhancing water resource management. 【Method】 Based on the planting area, yield, and agricultural inputs of wheat, maize, and rice in the northwest region, the carbon footprint was evaluated using Life Cycle Analysis (LCA); Based on evapotranspiration and effective precipitation, the water footprint was evaluated by Penman-Monteith formula; Multi objective optimization of the layout of three major grain crops in the five northwest provinces was performed based on entropy weight method. 【Result】 The data showed that the carbon footprint and water footprint of wheat, maize and rice in northwest China from 1999 to 2020 showed significant differences in different provinces. In the northwest region, the carbon emissions from the production of three grain crops, wheat, maize and rice showed a distribution pattern of higher levels in the eastern and western regions and lower levels in the central region; the range of carbon footprint per unit production (PCF) was 0.36-0.63, 0.33-0.56, 0.57-0.97 t CO₂-eq·t^-1, respectively; the carbon footprint per unit area (FCF) was (2.46±0.77), (3.21±0.49) and (5.57±0.91) t CO₂-eq·hm^-2, respectively. From 2010 to 2018, the total green water content of wheat, maize and rice in the northwest region showed a steady upward trend, with regions with higher average green water content distributed in Shaanxi, Gansu, and Ningxia. The total amount of blue water showed an upward trend from 2010 to 2015, and a downward trend from 2015 to 2018. The regions with higher average total blue water were distributed in Gansu, Ningxia, and Xinjiang. Among the three major grain crops in northwest China, maize consumes the least blue water footprint, with an average blue water footprint of 0.45 m³·kg^-1; the blue water footprint consumption of rice was the largest, and the average production blue water footprint was 0.77 m³·kg^-1. The optimization of food crop cultivation structure was performed, based on carbon and water footprints, using the cultivated area of different crops in each province as decision variables, and setting optimization scenarios focusing on reducing carbon emissions (ecological benefits) and increasing green water use (water resource benefits) according to different weights. In Scenario 1, the total carbon emissions decreased by 1.9% and the total green water increased by 5.0%; In scenario 2, the total carbon emissions decreased by 11.8%, while in scenario 3, the total utilization of green water increased by 6.7%. 【Conclusion】 There were significant spatial and temporal differences in carbon emissions and total water volume of the three major food crops in the Northwest Region. In terms of carbon footprint, the average FCF of the three major grain crops in the region shows an increasing trend, and the PCF shows a decreasing trend. In terms of water footprint, the green water footprint of the three major food crops in the region was higher than the national average, with maize having the largest green water footprint and rice having the smallest green water footprint. Under the premise of ensuring food crop security, the planting area of wheat has increased by 6.7%, while the planting area of maize and rice has decreased by 5.8% and 8.0%, respectively. The economic, resource, and ecological benefits have all been improved to a certain extent. In summary, multi-objective optimization could improve the utilization of green water resources, reduce carbon emissions, and alleviate environmental pressure.

Key words: carbon footprint, water footprint, optimization of planting structure, food crops, northwest region

王楚凡, 牛俊. 西北地区主要粮食作物种植的水、碳足迹及布局优化[J]. 中国农业科学, 2024, 57(6): 1137-1152.

WANG ChuFan, NIU Jun. Water and Carbon Footprint and Layout Optimization of Major Grain Crops in the Northwest China[J]. Scientia Agricultura Sinica, 2024, 57(6): 1137-1152.

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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2024.06.009

https://www.chinaagrisci.com/CN/Y2024/V57/I6/1137

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西北各地区粮食作物碳排放总量 XA：西安市Xi’an；TCA：铜川市Tongchuan；BJ：宝鸡市Baoji；XY：咸阳市Xianyang；WN：渭南市Weinan；YA：延安市Yan’an；HZ：汉中市Hanzhong；YLA：榆林市Yulin；AK：安康市Ankang；SL：商洛市Shangluo；LZ：兰州市Lanzhou；JYG：嘉峪关市Jiayuguan；JC：金昌市Jinchang；BY：白银市Baiyin；TS：天水市Tianshui；WW：武威市Wuwei；ZY：张掖市Zhangye；PL：平凉市Pingliang；JQ：酒泉市Jiuquan；QY：庆阳市Qingyang；DX：定西市Dingxi；LN：陇南市Longnan；LX：临夏回族自治州Linxia Hui Autonomous Prefecture；GN：甘南藏族自治州Gannan Tibetan Autonomous Prefecture；XZ：西宁市Xining；HD：海东市Haidong；HB：海北藏族自治州Haibei Tibetan Autonomous Prefecture；HNA：黄南藏族自治州Huangnan Tibetan Autonomous Prefecture；HNB：海南藏族自治州Hainan Tibetan Autonomous Prefecture；GL：果洛藏族自治州Guoluo Tibetan Autonomous Prefecture；YS：玉树藏族自治州Yushu Tibetan Autonomous Prefecture；HX：海西蒙古族藏族自治州Haixi Mongolian and Tibetan Autonomous Prefecture；YC：银川市Yinchuan；SZS：石嘴山市Shizuishan；WZ：吴忠市Wuzhong；GY：固原市Guyuan；ZW：中卫市Zhongwei；WLMQ：乌鲁木齐市Urumqi；KLMY：克拉玛依市Kelamayi；TLF：吐鲁番市Tulufan；HM：哈密市Hami；CJ：昌吉回族自治州Changji Hui Autonomous Prefecture；BETL：博尔塔拉蒙古自治州Bortala Mongolian Autonomous Prefecture；BYGL：巴音郭楞蒙古自治州Bayingol Mongolian Autonomous Prefecture；AKS：阿克苏地区Aksu；KZL：克孜勒苏柯尔克孜自治州Kizilsu Kirgiz Autonomous Prefecture；KS：喀什地区Kashgar；HT：和田地区Hotan；YLB：伊犁哈萨克自治州Ili Kazakh Autonomous Prefecture；TCB：塔城地区Tarbagatay；ALT：阿勒泰地区Altay；SHZ：石河子市Shihezi；ALE：阿拉尔市Alaer；TMSK：图木舒克市Tumushuke"

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项目 Item	排放系数 Emission factor	参考文献Reference
氮肥Nitrogenous fertilizer	7.759 t CO₂-eq·t^-1	[25]
磷肥Phosphate fertilizer	2.332 t CO₂-eq·t^-1	[25]
钾肥Potassic fertilizer	0.66 t CO₂-eq·t^-1	[25]
农药 Pesticides	18.08 t CO₂-eq·t^-1	[26]
柴油 Diesel fuel	4.63 t CO₂-eq·L^-1	[27]
塑料薄膜 Plastic film	5.18 t CO₂-eq·t^-1	[28]
灌溉电力 Irrigation electricity	266.48 kg CO₂-eq·hm^-2	[29]
小麦种子 Wheat seeds	1.22 t CO₂-eq·t^-1	[30]
玉米种子 Maize seeds	1.16 t CO₂-eq·t^-1	[31]
水稻种子 Rice seeds	1.84 t CO₂-eq·t^-1	[32]
氮肥引起农田N₂O直接排放 Direct N₂O emission from N fertilizer	小麦Wheat 0.0105 kg N₂O-N·kg^-1N	[19]
	玉米Maize 0.0105 kg N₂O-N·kg^-1N	[19]
	水稻Rice 0.0042 kg N₂O-N·kg^-1N	[20-21]

省（区） Province	小麦 Wheat	玉米 Maize	水稻 Rice
陕西 Shaanxi	1.27	1.1	0.94
甘肃 Gansu	1.26	1.11	0.84
青海 Qinghai	1.31	1.1	0
宁夏 Ningxia	1.08	1.21	0.99
新疆 Xinjiang	1.36	1.15	0.74

省（区） Province	室内秸秆燃烧率 Domestic straw burning percentage (%)	田间秸秆燃烧率 In-field straw burning percentage (%)	秸秆还田比例 Proportion of straw returning (%)
陕西Shaanxi	10.24	8.4	33.4
甘肃 Gansu	23.1	9.9	6.5
青海 Qinghai	19.6	8.4	7.7
宁夏 Ningxia	11.2	4.8	19.6
新疆 Xinjiang	15	15	47.2

燃烧模式 Burning mode	作物 Crop	CO₂排放系数 CO₂ emission factors (g·kg^-1)	N₂O排放系数 N₂O emission factors (g·kg^-1)	CH₄排放系数 CH₄ emission factors (g·kg^-1)
室内秸秆燃烧 Domestic straw burning	小麦 Wheat	1246.7	1.19	8.3
	玉米 Maize	1491	1.86	3.91
	水稻 Rice	1147.4	1.92	4.8
田间秸秆燃烧 In-field straw burning	小麦 Wheat	1390	3.3	3.4
	玉米 Maize	1350	4.3	4.4
	水稻 Rice	1393	1.42	3.9

作物Crop	K_cb(tab)_ini	K_cb(tab)_mid	K_cb(tab)_end	L_ini	L_dep	L_mid	L_end
小麦 Wheat	0.15	1.1	0.15	0.15	0.25	0.4	0.2
玉米 Maize	0.15	1.15	0.5	0.17	0.28	0.33	0.22
水稻 Rice	1.0	1.15	0.6	0.17	0.18	0.44	0.21