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Abstract Climate change adaptation requires building agricultural system resilience to warmer, drier climates. Increasing temporal plant diversity through crop rotation diversification increases yields of some crops under drought, but its potential to enhance crop drought resistance and the underlying mechanisms remain unclear. We conducted a drought manipulation experiment using rainout shelters embedded within a 36-year crop rotation diversity and no-till experiment in a temperate climate and measured a suite of soil and crop developmental and eco-physiological traits in the field and laboratory. We show that diversifying maize-soybean rotations with small grain cereals and cover crops mitigated maize water stress at the leaf and canopy scales and reduced yield losses to drought by 17.1 ± 6.1%, while no-till did not affect maize drought resistance. Path analysis showed a strong correlation between soil organic matter and lower maize water stress despite no significant differences in soil organic matter between rotations or tillage treatments. This positive relationship between soil organic matter and maize water status was not mediated by higher soil water retention or infiltration as often hypothesized, nor differential depth of root water uptake as measured with stable isotopes, suggesting that other mechanisms are at play. Crop rotation diversification is an underappreciated drought management tool to adapt crop production to climate change through managing for soil organic matter.
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Probabilistic estimates of drought impacts on agricultural production: Drought Impacts on Agriculture
Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980–2012, including the Millennium Drought in Australia (2001–2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25–45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.
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- Award ID(s):
- 1639318
- PAR ID:
- 10038077
- Date Published:
- Journal Name:
- Geophysical Research Letters
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/2017GL073606
