DOI: 10.31220/agriRxiv.2023.00202

•  

  Abstract: Abstract The crop rotation is a farm-scale management choice that dictates agronomic output, ecological impacts, and farm viability. Rotations have become less diverse recently. Re-diversifying may help agricultural systems meet the growing, dynamic demands from society and challenges from climate change. Using a long-term experiment, we tested whether diversified rotations could a) match the productivity (grain yield) of simplified rotations while b) stabilizing productivity against variable weather in the western Corn Belt. We identified rotation design choices that drive these productivity-stability dynamics. Although more productive rotations were more susceptible to weather ( r = 0.63; p < 0.001), this tradeoff was surmountable ( p < 0.001): the corn-oat-winter wheat-soybean ( Zea mays L., Avena sativa L., Triticum aestivum L., Glycine max (L.) Merr.) rotation was a) no less productive than the highly unstable, corn-soybean rotation ( p = 0.11); and b) only marginally less stable than the most stable rotation ( p = 0.06). Crop selection and sequencing were critical to this outcome. High productivity was due to a) overyielding of individual crops in diversified rotations that increased with time; b) sequencing to allow higher-yielding winter crops; and c) beneficial crop legacy effects. Stability was highest in rotations that included crops belonging to more weather-niches ( r 2 = 0.67; p < 0.001). Surprisingly, these weather-niches groups did not correspond to traditional categories like cool- vs warm-season crops. These results suggest four principles for the design, study, and implementation of diversified grain rotations that are stable under erratic weather and are as productive as current standard practices.

  Key words: weather; rotations; productivity; diversified; 001; crop

  Submit time: 30 September 2023

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

• 图表

• María‐Soledad Benítez, Patrick M. Ewing, Shannon L. Osborne, R. Michael Lehma. Rhizosphere microbial communities explain positive effects of diverse crop rotations on corn and soybean performance: microbiome explain benefits of diverse rotations.. 2020. doi: 10.31220/agriRxiv.2020.00023

  Patrick M.,Ewing, Bee K.,Chim, R. Michael,Lehman, Shannon L.,Osborn. Diversified grain rotations can be highly and reliably productive in unstable climates.. 2023. doi: 10.31220/agriRxiv.2023.00202

  Miranda Bane, Michael J. O. Pocock, Caroline Gibert, Matthieu Forster, Geoffroy Oudoire, Stéphane A. P. Derocles, David Boha. Farmer flexibility concerning future rotation planning is affected by the framing of climate predictions.. 2020. doi: 10.31220/agriRxiv.2020.00025

  Alexa Varah, Kwadjo Ahodo, Dylan Z. Childs, David Comont, Laura Crook, Robert P. Freckleton, Rob Goodsell, Helen L. Hicks, Richard T. Hull, Paul Neve, Ken Norri. Managing herbicide resistance doesn't pay - but acting pre-emptively does.. 2023. doi: 10.31220/agriRxiv.2023.00183

  Cristina Chiarella, Patrick Meyfroidt, Dilini Abeygunawardane, Piero Confort. Balancing the trade-offs between land productivity, labor productivity and labor intensity.. 2022. doi: 10.31220/agriRxiv.2022.00137

  Abdul Rahim Junejo, Shakeel Ahmed Soomro, Aftab Ahmed Memon, Wenquan Niu, Yasmin Junejo, Jahangeer Dahri, Jamshed Ali Channa, Zahid Ali Channa, N. Indianraj, Hyder Bux Khoso, Khadija Urooj, Aamir Hussain Bhutt. Effects of sub-surface wick irrigation system on crop yield, irrigation intervals, base period and crop water productivity of sponge gourd and bitter gourd.. 2023. doi: 10.31220/agriRxiv.2023.00178

  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

  Rohit Raj Jha, Gopal Prasad Khanal, Kavita Isvara. Factors affecting crop field use by Blackbuck in Krishnasaar Conservation Area, Nepal.. 2021. doi: 10.31220/agriRxiv.2021.00098

  Viviane Costa Silva, Mateus Silva Rocha, Gláucia Amorim Faria, Sílvio Fernando Alves Xavier, Tiago Almeida de Oliveira, Ana Patrícia Bastos Peixot. Boosting algorithms for prediction in agriculture: an application of feature importance and feature selection boosting algorithms for prediction crop damage.. 2021. doi: 10.31220/agriRxiv.2021.00092

  Marie-Cécile Dupas, Sophie Parison, Vincent Noël, Petros Chatzimpiros, Éric Herber. Variable renewable energy penetration impact on productivity: a case study of poultry farming.. 2022. doi: 10.31220/agriRxiv.2022.00157

• ID

  Submit time

  Number

  Download

• Public  Anonymous  To author only Submit Comments