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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    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).

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