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
Using aquatic animals as partners to increase yield and soil nitrogen in the paddy ecosystem.
DOI: 10.31220/agriRxiv.2021.00086
-
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; separatSubmit 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
Article Metrics
- Read: 51
- Download: 0
- Comment: 0



