IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i7p1442-d1197916.html
   My bibliography  Save this article

Agricultural Cultivation Structure in Arid Areas Based on Water–Carbon Nexus—Taking the Middle Reaches of the Heihe River as an Example

Author

Listed:
  • Boxuan Li

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China)

  • Meng Niu

    (China Urban Construction Design and Research Institute, Beijing 100120, China)

  • Jing Zhao

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Xi Zheng

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Ran Chen

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Xiao Ling

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Jinxin Li

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Yuxiao Wang

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China)

Abstract

China faces challenges of food security and sustainable agricultural production. However, current studies rarely address the spatial distribution patterns of water consumption and carbon emissions. We studied the irrigation water use efficiency and carbon emission differences of crops in arid areas and their spatial distribution using wheat and maize, two major food crops in the middle reaches of the Heihe River, as examples. Furthermore, we have optimized low-carbon cropping of crops under the multiple objectives of water conservation and economic development. The results show that: (1) The carbon emissions per unit of water consumption for maize are 0.03 × 10 −6 t mm −1 and 0.49 × 10 −6 t mm −1 for wheat. Irrigation water consumption per unit yield is 515.6 mm t −1 for maize and 426.7 mm t −1 for wheat. (2) The spatial distribution patterns of irrigation water consumption were opposites for maize and wheat. The former has lower irrigation water consumption in the planting area upstream of the Heihe River and higher in the lower reaches. In contrast, the pattern of wheat irrigation is the opposite. (3) After optimizing the cropping mix for both crops, the area planted with wheat should be reduced to 59% of the current size, while maize should be expanded to 104%. The results of the research hold immense importance in guiding the future grain crop planting patterns for water-saving agriculture and low-carbon agriculture development in arid zones worldwide, aligning with the United Nations’ Sustainable Development Goals.

Suggested Citation

  • Boxuan Li & Meng Niu & Jing Zhao & Xi Zheng & Ran Chen & Xiao Ling & Jinxin Li & Yuxiao Wang, 2023. "Agricultural Cultivation Structure in Arid Areas Based on Water–Carbon Nexus—Taking the Middle Reaches of the Heihe River as an Example," Land, MDPI, vol. 12(7), pages 1-18, July.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1442-:d:1197916
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/7/1442/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/7/1442/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. He, Li & Du, Yu & Wu, Shuang & Zhang, Zhaolong, 2021. "Evaluation of the agricultural water resource carrying capacity and optimization of a planting-raising structure," Agricultural Water Management, Elsevier, vol. 243(C).
    2. Zhuang, Renan & Abbott, Philip, 2007. "Price elasticities of key agricultural commodities in China," China Economic Review, Elsevier, vol. 18(2), pages 155-169.
    3. Man Li & Wenchao Xu & Tingju Zhu, 2019. "Agricultural Water Allocation under Uncertainty: Redistribution of Water Shortage Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(1), pages 134-153.
    4. Meng, Conghui & Du, Xiaoyun & Zhu, Mengcheng & Ren, Yitian & Fang, Kai, 2023. "The static and dynamic carbon emission efficiency of transport industry in China," Energy, Elsevier, vol. 274(C).
    5. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    6. Bauer, Siegfried & Kasnakoglu, Haluk, 1990. "Non-linear programming models for sector and policy analysis : Experiences with the Turkish agricultural sector model," Economic Modelling, Elsevier, vol. 7(3), pages 275-290, July.
    7. Dong, Zhaoyingzi & Xia, Chuyu & Fang, Kai & Zhang, Weiwen, 2022. "Effect of the carbon emissions trading policy on the co-benefits of carbon emissions reduction and air pollution control," Energy Policy, Elsevier, vol. 165(C).
    8. Liu, Qi & Niu, Jun & Wood, Jeffrey D. & Kang, Shaozhong, 2022. "Spatial optimization of cropping pattern in the upper-middle reaches of the Heihe River basin, Northwest China," Agricultural Water Management, Elsevier, vol. 264(C).
    9. Bartolini, F. & Bazzani, G.M. & Gallerani, V. & Raggi, M. & Viaggi, D., 2007. "The impact of water and agriculture policy scenarios on irrigated farming systems in Italy: An analysis based on farm level multi-attribute linear programming models," Agricultural Systems, Elsevier, vol. 93(1-3), pages 90-114, March.
    10. Zhao, Wenzhi & Liu, Bing & Zhang, Zhihui, 2010. "Water requirements of maize in the middle Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 97(2), pages 215-223, February.
    11. Acs, S. & Berentsen, P.B.M. & Huirne, R.B.M., 2007. "Conversion to organic arable farming in The Netherlands: A dynamic linear programming analysis," Agricultural Systems, Elsevier, vol. 94(2), pages 405-415, May.
    12. You, Liangzhi & Spoor, Max & Ulimwengu, John & Zhang, Shemei, 2011. "Land use change and environmental stress of wheat, rice and corn production in China," China Economic Review, Elsevier, vol. 22(4), pages 461-473.
    13. Gong, Xinghui & Zhang, Hongbo & Ren, Chongfeng & Sun, Dongyong & Yang, Jiantao, 2020. "Optimization allocation of irrigation water resources based on crop water requirement under considering effective precipitation and uncertainty," Agricultural Water Management, Elsevier, vol. 239(C).
    14. Jing Hou & Bo Hou, 2019. "Farmers’ Adoption of Low-Carbon Agriculture in China: An Extended Theory of the Planned Behavior Model," Sustainability, MDPI, vol. 11(5), pages 1-20, March.
    15. Xia, Chuyu & Chen, Bin, 2020. "Urban land-carbon nexus based on ecological network analysis," Applied Energy, Elsevier, vol. 276(C).
    16. Hanjra, Munir A. & Qureshi, M. Ejaz, 2010. "Global water crisis and future food security in an era of climate change," Food Policy, Elsevier, vol. 35(5), pages 365-377, October.
    17. Dong, Zhaoyingzi & Wang, Shaojian & Zhang, Weiwen & Shen, Huijun, 2022. "The dynamic effect of environmental regulation on firms’ energy consumption behavior-Evidence from China's industrial firms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    18. Kang, Shaozhong & Hao, Xinmei & Du, Taisheng & Tong, Ling & Su, Xiaoling & Lu, Hongna & Li, Xiaolin & Huo, Zailin & Li, Sien & Ding, Risheng, 2017. "Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice," Agricultural Water Management, Elsevier, vol. 179(C), pages 5-17.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kampas, Athanasios & Petsakos, Athanasios & Rozakis, Stelios, 2012. "Price induced irrigation water saving: Unraveling conflicts and synergies between European agricultural and water policies for a Greek Water District," Agricultural Systems, Elsevier, vol. 113(C), pages 28-38.
    2. Sandra Ricart & Anna Ribas & David Pavón, 2016. "Qualifying irrigation system sustainability by means of stakeholder perceptions and concerns: lessons from the Segarra‐Garrigues Canal, Spain," Natural Resources Forum, Blackwell Publishing, vol. 40(1-2), pages 77-90, February.
    3. Firouzabadi, Ali Ghadami & Baghani, Javad & Jovzi, Mehdi & Albaji, Mohammad, 2021. "Effects of wheat row spacing layout and drip tape spacing on yield and water productivity in sandy clay loam soil in a semi-arid region," Agricultural Water Management, Elsevier, vol. 251(C).
    4. Gao, Yukun & Zhao, Hongfang & Zhao, Chuang & Hu, Guohua & Zhang, Han & Liu, Xue & Li, Nan & Hou, Haiyan & Li, Xia, 2022. "Spatial and temporal variations of maize and wheat yield gaps and their relationships with climate in China," Agricultural Water Management, Elsevier, vol. 270(C).
    5. Zhang, Chenglong & Engel, Bernard A. & Guo, Ping, 2018. "An Interval-based Fuzzy Chance-constrained Irrigation Water Allocation model with double-sided fuzziness," Agricultural Water Management, Elsevier, vol. 210(C), pages 22-31.
    6. Hualin Xie & Yuyang Wen & Yongrok Choi & Xinmin Zhang, 2021. "Global Trends on Food Security Research: A Bibliometric Analysis," Land, MDPI, vol. 10(2), pages 1-21, January.
    7. Zhang, Chenglong & Guo, Ping, 2018. "FLFP: A fuzzy linear fractional programming approach with double-sided fuzziness for optimal irrigation water allocation," Agricultural Water Management, Elsevier, vol. 199(C), pages 105-119.
    8. Jiashan Yu & Jun Zhou & Jing Zhao & Ran Chen & Xueqi Yao & Xiaomin Luo & Sijia Jiang & Ziyang Wang, 2023. "Agroecological Risk Assessment Based on Coupling of Water and Land Resources—A Case of Heihe River Basin," Land, MDPI, vol. 12(4), pages 1-16, March.
    9. Su, Yuandong & Liang, Chao & Zhang, Li & Zeng, Qing, 2022. "Uncover the response of the U.S grain commodity market on El Niño–Southern Oscillation," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 98-112.
    10. Cheng, Minghan & Jiao, Xiyun & Jin, Xiuliang & Li, Binbin & Liu, Kaihua & Shi, Lei, 2021. "Satellite time series data reveal interannual and seasonal spatiotemporal evapotranspiration patterns in China in response to effect factors," Agricultural Water Management, Elsevier, vol. 255(C).
    11. Ito, Junichi & Ni, Jing, 2013. "Capital deepening, land use policy, and self-sufficiency in China's grain sector," China Economic Review, Elsevier, vol. 24(C), pages 95-107.
    12. Song Li & Fei Xue & Chuyu Xia & Jian Zhang & Ao Bian & Yuexi Lang & Jun Zhou, 2022. "A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou," Land, MDPI, vol. 11(6), pages 1-18, June.
    13. Zhang, Zepeng & Wang, Qingzheng & Guan, Qingyu & Xiao, Xiong & Mi, Jimin & Lv, Songjian, 2023. "Research on the optimal allocation of agricultural water and soil resources in the Heihe River Basin based on SWAT and intelligent optimization," Agricultural Water Management, Elsevier, vol. 279(C).
    14. Zhang, Wang & Tian, Yong & Sun, Zan & Zheng, Chunmiao, 2021. "How does plastic film mulching affect crop water productivity in an arid river basin?," Agricultural Water Management, Elsevier, vol. 258(C).
    15. Fang, Lan & Fu, Yong & Chen, Shaojian & Mao, Hui, 2021. "Can water rights trading pilot policy ensure food security in China? Based on the difference-in-differences method," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 23(6), pages 1415-1434.
    16. Cao, Zhaodan & Zhu, Tingju & Cai, Ximing, 2023. "Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia," Agricultural Water Management, Elsevier, vol. 277(C).
    17. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2021. "A comprehensive analysis of water productivity in natural vegetation and various crops coexistent agro-ecosystems," Agricultural Water Management, Elsevier, vol. 243(C).
    18. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    19. Zhang, Shulin & Su, Xiaoling & Singh, Vijay P & Ayantobo, Olusola Olaitan & Xie, Juan, 2018. "Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 208(C), pages 422-430.
    20. Pelai, Ricardo & Hagerman, Shannon M. & Kozak, Robert, 2020. "Biotechnologies in agriculture and forestry: Governance insights from a comparative systematic review of barriers and recommendations," Forest Policy and Economics, Elsevier, vol. 117(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1442-:d:1197916. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.