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Biotic stress consists of damage to plants through other living organisms.
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Manso, G. L., Knidel, H., Krohling, R., & Ventura, J. A. (2019) · 1904
Earlier work this paper cites.
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Kranz, J. (1988) · 1988
Earlier work this paper cites.
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Pretty, J. (2007) · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
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Barbedo, J. G. A. (2018b)
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