IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v258y2021ics0378377421004625.html
   My bibliography  Save this article

Runoff and soil conservation effects in Nagpur mandarin orchard under a sub-humid tropical climate of central India

Author

Listed:
  • Panigrahi, P.
  • Srivastava, A.K.
  • Pradhan, S.

Abstract

Limited availability of water and nutrients in soil affects the yield and longevity of citrus plantation. Conservation of rainfall runoff not only enhances water availability but improves the nutritional status in soil which might increase the orchard efficiency in citrus. Keeping this in view, a long term (7 years) experiment was imposed to study the response of Nagpur mandarin (Citrus reticulate Blanco) orchard in relation to fruit yield, water use, water productivity, sustainability of production and energy use efficiency to various conservation practices (CPs) on 12% land slope in a vertisol of central part of India. The CPs tested, against without conservation practice (WCP) were continuous trenching (CT) and staggered trenching (ST) alone and with grass mulch (GM). All the treatments were imposed in randomized block design (RBD) with 4 replications. The combined use of CT and GM conserved the highest rainfall runoff (48%), soil (51%), organic carbon (OC, 52%) and nutrients (43–53%) compared with WCP in mandarin orchard. The GM enhanced the available nutrients (N, P, K, and Cu) and OC contents in soil. The relationship between rainfall and runoff was linear (R2 = 0.85–0.93) in the plots with CPs, whereas in WCP plot, it was exponential (R2 = 0.88). However, the runoff was linearly related to soil loss under both CPs (R2 = 0.76–0.91) and WCP (R2 = 0.79). The empirical models developed were found reasonably accurate in predicting the runoff and soil loss with R2 value of 0.81–0.93 and 0.87–0.96, respectively. The soil water content was improved by 3–55% in different CPs compared with WCP. The CT with GM improved the fruit yield by 53%, utilizing 31% less water which resulted in 53% higher physical water productivity (PWP) and 123% higher irrigation water productivity (IWP) than that in WCP. The fruit quality (juice content, total soluble solids, acidity and ascorbic acid content) under CT with GM was better than that in WCP. The sustainability yield index (SYI) and energy use efficiency (EUE) under CT with GM were increased by 25.4% and 39.4%, respectively, compared with WCP. Overall, these results reveal that the CT with GM could be imposed to sustain and enhance yield and water productivity, by conserving rainwater and soil in citrus orchards of water scarce regions.

Suggested Citation

  • Panigrahi, P. & Srivastava, A.K. & Pradhan, S., 2021. "Runoff and soil conservation effects in Nagpur mandarin orchard under a sub-humid tropical climate of central India," Agricultural Water Management, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:agiwat:v:258:y:2021:i:c:s0378377421004625
    DOI: 10.1016/j.agwat.2021.107185
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421004625
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107185?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Y. & Tao, Y. & Wan, K.Y. & Zhang, G.S. & Liu, D.B. & Xiong, G.Y. & Chen, F., 2012. "Runoff and nutrient losses in citrus orchards on sloping land subjected to different surface mulching practices in the Danjiangkou Reservoir area of China," Agricultural Water Management, Elsevier, vol. 110(C), pages 34-40.
    2. Zhang, Mingkui & He, Zhenli & Calvert, David V. & Stoffella, Peter J., 2004. "Spatial and temporal variations of water quality in drainage ditches within vegetable farms and citrus groves," Agricultural Water Management, Elsevier, vol. 65(1), pages 39-57, February.
    3. Sheelabhadra Mohanty & Madan Jha & Ashwani Kumar & K. Sudheer, 2010. "Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(9), pages 1845-1865, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Binbin & Yan, Sihui & Li, Bin & Wu, Shufang & Feng, Hao & Gao, Xiaodong & Song, Xiaolin & Siddique, Kadambot H.M., 2023. "Combining organic and chemical fertilizer plus water-saving system reduces environmental impacts and improves apple yield in rainfed apple orchards," Agricultural Water Management, Elsevier, vol. 288(C).

    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. Panigrahi, P. & Srivastava, A.K. & Panda, D.K. & Huchche, A.D., 2017. "Rainwater, soil and nutrients conservation for improving productivity of citrus orchards in a drought prone region," Agricultural Water Management, Elsevier, vol. 185(C), pages 65-77.
    2. Yi Wang & Xinliang Liu & Yantai Gan & Yong Li & Ying Zhao, 2023. "Conversion of Forest Hillslopes into Tea Fields Increases Soil Nutrient Losses through Surface Runoff," Land, MDPI, vol. 12(2), pages 1-14, February.
    3. Haijiao Yu & Xiaohu Wen & Qi Feng & Ravinesh C. Deo & Jianhua Si & Min Wu, 2018. "Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 301-323, January.
    4. Li, Zhi-guo & Gu, Chi-ming & Zhang, Run-hua & Ibrahim, Mohamed & Zhang, Guo-shi & Wang, Li & Zhang, Run-qin & Chen, Fang & Liu, Yi, 2017. "The benefic effect induced by biochar on soil erosion and nutrient loss of slopping land under natural rainfall conditions in central China," Agricultural Water Management, Elsevier, vol. 185(C), pages 145-150.
    5. Sumaryanto & Sri Hery Susilowati & Fitri Nurfatriani & Herlina Tarigan & Erwidodo & Tahlim Sudaryanto & Henri Wira Perkasa, 2022. "Determinants of Farmers’ Behavior towards Land Conservation Practices in the Upper Citarum Watershed in West Java, Indonesia," Land, MDPI, vol. 11(10), pages 1-21, October.
    6. Sandra M. Guzman & Joel O. Paz & Mary Love M. Tagert, 2017. "The Use of NARX Neural Networks to Forecast Daily Groundwater Levels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1591-1603, March.
    7. Kostić, Srđan & Stojković, Milan & Guranov, Iva & Vasović, Nebojša, 2019. "Revealing the background of groundwater level dynamics: Contributing factors, complex modeling and engineering applications," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 408-421.
    8. Li, Hongying & Zhu, Ningyuan & Qiao, Jun & Tang, Jun, 2024. "Evaluating the long-term effects of best management practices on pollution reduction and soil quality improvement in sloping farmland of the Three Gorges Reservoir area," Agricultural Water Management, Elsevier, vol. 297(C).
    9. Hao Cheng & Chen Lin & Liangjie Wang & Junfeng Xiong & Lingyun Peng & Chenxi Zhu, 2020. "The Influence of Different Forest Characteristics on Non-point Source Pollution: A Case Study at Chaohu Basin, China," IJERPH, MDPI, vol. 17(5), pages 1-19, March.
    10. S. Mohanty & Madan Jha & S. Raul & R. Panda & K. Sudheer, 2015. "Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5521-5532, December.
    11. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    12. Raymond Kim & Daniel Loucks & Jery Stedinger, 2012. "Artificial Neural Network Models of Watershed Nutrient Loading," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2781-2797, August.
    13. Dai, Cuiting & Liu, Yaojun & Wang, Tianwei & Li, Zhaoxia & Zhou, Yiwen, 2018. "Exploring optimal measures to reduce soil erosion and nutrient losses in southern China," Agricultural Water Management, Elsevier, vol. 210(C), pages 41-48.
    14. repec:zbw:inwedp:542013 is not listed on IDEAS
    15. Dilip Kumar Roy & Sujit Kumar Biswas & Kowshik Kumar Saha & Khandakar Faisal Ibn Murad, 2021. "Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1653-1672, April.
    16. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.
    17. Thiago Victor Medeiros Nascimento & Celso Augusto Guimarães Santos & Camilo Allyson Simões Farias & Richarde Marques Silva, 2022. "Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2359-2377, May.
    18. Sunayana & Komal Kalawapudi & Ojaswikrishna Dube & Renuka Sharma, 2020. "Use of neural networks and spatial interpolation to predict groundwater quality," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 2801-2816, April.
    19. H. Zhang & Q. Liu & X. Yu & L. Wang, 2014. "Influences of mulching durations on soil erosion and nutrient losses in a peanut (Arachis hypogaea)-cultivated land," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 1175-1187, June.
    20. Minghao Mo & Zhao Liu & Jie Yang & Yuejun Song & Anguo Tu & Kaitao Liao & Jie Zhang, 2019. "Water and sediment runoff and soil moisture response to grass cover in sloping citrus land, Southern China," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 14(1), pages 10-21.
    21. Xuesong Zhang & Kaiguang Zhao, 2012. "Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2365-2382, June.

    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:eee:agiwat:v:258:y:2021:i:c:s0378377421004625. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.