Format

Send to

Choose Destination
Nat Plants. 2017 Jul 17;3:17102. doi: 10.1038/nplants.2017.102.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Author information

1
CSIRO Agriculture and Food, Black Mountain, Australian Capital Territory 2601, Australia.
2
UMR LEPSE, INRA, Montpellier SupAgro, 2 Place Viala, 34 060 Montpellier, France.
3
College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China.
4
Institute of Crop Science and Resource Conservation (INRES), University of Bonn, 53115 Bonn, Germany.
5
Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany.
6
Department of Crop Sciences, University of Goettingen, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), 37077 Göttingen, Germany.
7
Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Büsgenweg 1, 37077 Göttingen, Germany.
8
USDA, Agricultural Research Service, U.S. Arid-Land Agricultural Research Center, Maricopa, Arizona 85138, USA.
9
The School of Plant Sciences, University of Arizona, Tucson, Arizona 85721, USA.
10
Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT) Apdo, 06600 Mexico, D.F, Mexico.
11
CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, International Maize and Wheat Improvement Center (CIMMYT), New Delhi 110012, India.
12
AgWeatherNet Program, Washington State University, Prosser, Washington 99350-8694, USA.
13
Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University East Lansing, Michigan 48823, USA.
14
Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Biochemical Plant Pathology, Neuherberg, 85764, Germany.
15
Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida 32611, USA.
16
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS29JT, UK.
17
CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Km 17, Recta Cali-Palmira Apartado Aéreo 6713, Cali, Colombia.
18
GMO Unit, European Food Safety Authority (EFSA), Via Carlo Magno, 1A, 43126 Parma, Italy.
19
Cantabrian Agricultural Research and Training Centre (CIFA), 39600 Muriedas, Spain.
20
Dep. Agronomia, University of Cordoba, Apartado 3048, 14080 Cordoba, Spain.
21
IAS-CSIC, Cordoba 14080, Spain.
22
Institute of Soil Science and Land Evaluation, University of Hohenheim, 70599 Stuttgart, Germany.
23
Department of Plant Agriculture, University of Guelph, Guelph, Ontario N1G 2W1, Canada.
24
Department of Geographical Sciences, University of Maryland, College Park, Maryland 20742, USA.
25
Texas A&M AgriLife Research and Extension Center, Texas A&M University, Temple, Texas 76502, USA.
26
Department of Agroecology, Aarhus University, 8830 Tjele, Denmark.
27
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China.
28
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
29
Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, IARI PUSA, New Delhi 110 012, India.
30
Department of Economic Development, Landscape &Water Sciences, Jobs, Transport and Resources, Horsham 3400, Australia.
31
Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland.
32
INRA, US1116 AgroClim, 84 914 Avignon, France.
33
NASA Goddard Institute for Space Studies, New York, New York 10025, USA.
34
Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK.
35
Biological Systems Engineering, Washington State University, Pullman, Washington 99164-6120, USA.
36
PPS and WSG &CALM, Wageningen University, 6700AA Wageningen, The Netherlands.
37
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China.
38
CSIRO Agriculture and Food, St Lucia, Queensland 4067, Australia.
39
INRA, UMR 1248 Agrosystèmes et développement territorial (AGIR), 31 326 Castanet-Tolosan, France.
#
Contributed equally

Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

PMID:
28714956
DOI:
10.1038/nplants.2017.102
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for White Rose Research Online
Loading ...
Support Center