Login / Signup

Viral infection can reduce the net nitrogen inputs of legume break crops and cover crops.

Katherine M MarchettoAlison G Power
Published in: Ecological applications : a publication of the Ecological Society of America (2020)
Legumes are used in crop rotations by both large-scale and smallholder farmers alike to increase soil fertility, especially before high-nitrogen-demanding crops such as corn (maize). Legume crop residues and green manures are rich in nitrogen due to mutualistic rhizobia, bacteria that live in their roots and convert atmospheric nitrogen into a biologically available form. Growers can obtain recommendations from local extension offices about how much less inorganic nitrogen fertilizer needs to be added to a subsequent crop following different legume break crops for the predominant soil type (the nitrogen fertilizer replacement value, or NFRV). Due to the intimate relationship between legumes and rhizobia, conditions that affect plant health can also affect the rhizobia and how much nitrogen they provide. We use a combination of empirical data and previously published values to estimate reductions in nitrogen inputs under outbreaks of plant viruses of varying severity. We also use historical fertilizer prices to examine the economic impacts of this lost fertilizer for farmers. We find that fertilizer losses are greatest for crops that fix large amounts of nitrogen, such as clover and alfalfa as opposed to common bean. The economic impact on farmers is controlled by the proportion of plants with viral infections and the price of synthetic fertilizer. In a year of high disease prevalence, attention is normally focused on the yield of the diseased crops. We suggest that farmers growing legumes as break crops should be concerned about yields of subsequent crops as well. Viral diseases can be difficult to diagnose in the field, so the easiest way for farmers to prevent unexpected yield losses in subsequent crops is to test their soil when it is feasible to do so.
Keyphrases
  • healthcare
  • climate change
  • sewage sludge
  • sars cov
  • public health
  • mental health
  • risk factors
  • systematic review
  • machine learning
  • social media
  • health information
  • deep learning
  • carbon dioxide
  • human health