Abstract
Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (Delta Y). At global scale, Delta Y is 349% for wheat and 22 +/- 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30-47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts. There are big uncertainties in the contribution of irrigation to crop yields. Here, the authors use Bayesian model averaging to combine statistical and process-based models and quantify the contribution of irrigation for wheat and maize yields, finding that irrigation alone cannot close yield gaps for a large fraction of global rainfed agriculture.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Geowissenschaften > Department für Geographie |
| Themengebiete: | 500 Naturwissenschaften und Mathematik > 550 Geowissenschaften, Geologie |
| Sprache: | Englisch |
| Dokumenten ID: | 102935 |
| Datum der Veröffentlichung auf Open Access LMU: | 05. Jun. 2023, 15:41 |
| Letzte Änderungen: | 05. Jun. 2023, 15:41 |
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