Using aquatic animals as partners to increase yield and soil nitrogen in the paddy ecosystem.

DOI: 10.31220/agriRxiv.2021.00086
Statement: This article is a preprint and has not been peer-reviewed. It reports new research that has yet to be evaluated and so should not be used to guide clinical practice.
  •  

    Abstract: Abstract Whether species coculture can overcome the shortcomings of crop monoculture requires additional study. Here, we show how aquatic animals (i.e., carp, crabs, and soft-shelled turtles) benefit paddy ecosystems when cocultured with rice. Three separate field experiments and three separate mesocosm experiments were conducted. Each experiment included a rice monoculture (RM) treatment and a rice-aquatic animal (RA) coculture treatment. In the field experiments, rice yield was higher with RA than with RM, and RA also produced aquatic animal yields that averaged 0.85-2.66 t ha -1 . Compared to their corresponding RM, the three RAs had significantly higher apparent nitrogen (N)-use efficiency and lower weed infestation, while soil N contents were stable. Dietary reconstruction analysis based on 13 C and 15 N showed that 16.0-50.2% of aquatic animal foods were from naturally occurring organisms in the rice fields. Stable-isotope-labeling ( 13 C) in the field experiments indicated that the organic matter decomposition rate was greater with RA than with RM. Isotope 15 N labeling in the mesocosm experiments indicated that rice used 13.0-35.1% of the aquatic animal feed-N. All of these results suggest that rice-aquatic animal coculture increases food production, increases N-use efficiency, and maintains soil N content by reducing weeds and promoting decomposition and complementary N use. Our study supports the view that adding species to monocultures may enhance agroecosystem functions.

    Key words: rm; rice; ra; coculture; aquatic; separat

    Submit time: 1 January 2021

    Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity.
  • 图表

  • Akhil Wilson, Raji Sukumar, N. Hemalath. Machine learning model for rice yield prediction using KNN regression.. 2021. doi: 10.31220/agriRxiv.2021.00070

    Beckley Ikhajiagbe, Matthew Chidozie Ogwu, Ivhuobe Izuapa Omoayen. Implications of soil nitrogen enhancement on the yield performance of soybean ( Glycine max ) in cadmium-polluted soil.. 2021. doi: 10.31220/agriRxiv.2021.00101

    Tashin Ahmed, Chowdhury Rafeed Rahman, Md. Faysal Mahmud Abi. Rice grain disease identification using dual phase convolutional neural network based system aimed at small dataset.. 2021. doi: 10.31220/agriRxiv.2021.00062

    Anna Rini, N. Hemalatha, Raji Sukuma. Decision tree classification of digital soil, weather, crop mapping and yield prediction using linear regression with region influences.. 2021. doi: 10.31220/agriRxiv.2021.00072

    Samuel V.J. Robinson, Lan H. Nguyen, Paul Galper. Livin' on the edge: precision yield data shows evidence of ecosystem services from field boundaries.. 2021. doi: 10.31220/agriRxiv.2021.00089

    Todd S. Rosenstock, M. Mayzelle, Nictor Namoi, Peter Fantk. Climate impacts of natural farming: A cradle to gate comparison between conventional practice and Andhra Pradesh Community Natural Farming.. 2020. doi: 10.31220/agriRxiv.2020.00013

    Giovanni Cafà, J. Miguel Bonnin, Nicola Holden, Jacob Malone, Tim H. Mauchline, Ian M. Clark, Rodrigo Gouvêa Taketani, Matthew J. Rya. Cryopreservation of a soil microbiome using a Stirling 1 cycle approach - a genomic assessment.. 2021. doi: 10.31220/agriRxiv.2021.00066

    Akhil Wilson, N. Hemalatha, Raji Sukuma. Computational prediction model for pepper yield prediction using support vector regression.. 2021. doi: 10.31220/agriRxiv.2021.00069

    K. V. Daniel, Mark A. Bradford, Emma Fuller, Emily E. Oldfield, Stephen A. Woo. Soil organic matter effects on US maize production and crop insurance payouts under drought.. 2020. doi: 10.31220/agriRxiv.2020.00018

    Diego A. Delgadillo-Duran, César Augusto Vargas-García, Viviana Marcela Varón-Ramírez, Francisco Calderón, Andrea C. Montenegro, Paula H. Reyes‐Herrer. Using vis-NIRS and machine learning methods to diagnose sugarcane soil chemical properties.. 2021. doi: 10.31220/agriRxiv.2020.00028

  • ID Submit time Number Download
  • Public  Anonymous  To author only

Get Citation

Guoxian Liang, Lufeng Zhao, Junlong Ye, Zijun Ji, Jianjun Tang, Ke-yu Bai, Si-Jun Zheng, Liangliang Hu, Xin Che. Using aquatic animals as partners to increase yield and soil nitrogen in the paddy ecosystem.. 2021. agriXiv.2021.00086

Article Metrics

  • Read: 51
  • Download: 0
  • Comment: 0

Email This Article

User name:
Email:*请输入正确邮箱
Code:*验证码错误