A framework for assessing the value of seasonal climate forecasting in key agricultural decisions

An-Vo, Duc-Anh ORCID: https://orcid.org/0000-0001-7528-7139 and Radanielson, Ando Mariot and Mushtaq, Shahbaz and Reardon-Smith, Kate and Hewitt, Chris ORCID: https://orcid.org/0000-0002-4718-4009 (2021) A framework for assessing the value of seasonal climate forecasting in key agricultural decisions. Climate Services, 22:100234. pp. 1-14. ISSN 2405-8807

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Abstract

While climate information services are widely available, translating climate information into actionable solutions to reduce climate risk, which are readily taken up by producers, remains a critical challenge. Here, we apply a bio-economic approach to assess the potential economic value of seasonal climate forecasts (SCFs) as a basis for climate services for use in agricultural decision-making. We use a case study approach, quantifying the impacts of seasonal precipitation on rice cropping, a dominant farming system in the Greater Mekong Region (GMR) in Southeast Asia. We demonstrate values of seasonal precipitation forecasts for a range of forecast skill levels from low to perfect skill for three seasonal precipitation conditions (wet, normal and dry), as well as extreme conditions (extreme wet and extreme dry). Based on our integrated bio-economic assessment and seasonal variation in precipitation, we identify an optimal rice sowing window, which potentially results in improved yield and economic benefits compared with the currently applied sowing window. Applying this approach using common rice varieties grown by farmers – specifically, the medium growth duration Jasmine rice and the short duration Vietnamese long grain white rice variety OM 5451 – we find significant value in using seasonal precipitation forecasts to identify optimal sowing windows, ranging from an average of $135 ha−1 for precipitation forecasts at the current level (70% accuracy) of forecast skill to $220 ha−1 for perfect (100% accurate) precipitation forecasts.

We propose that such a framework can be used to examine the value of using seasonal climate forecasts, even at current skill levels, in farm adaptive operational decision-making. We envisage that demonstration of the value of using seasonal forecasts in crop production system decisions will build user confidence and help in upscaling the use of climate information in the region and more broadly.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Date Deposited: 21 May 2021 03:41
Last Modified: 21 May 2021 03:42
Uncontrolled Keywords: Climate risk management; Agricultural climate services; Cropping decisions; Adaptation; Bio-economic framework; Optimal sowing window
Fields of Research (2008): 01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010301 Numerical Analysis
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300210 Sustainable agricultural development
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9603 Climate and Climate Change > 960301 Climate Change Adaptation Measures
B Economic Development > 82 Plant Production and Plant Primary Products > 8204 Summer Grains and Oilseeds > 820402 Rice
Socio-Economic Objectives (2020): 26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2603 Grains and seeds > 260308 Rice
19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1901 Adaptation to climate change > 190101 Climate change adaptation measures (excl. ecosystem)
Identification Number or DOI: https://doi.org/10.1016/j.cliser.2021.100234
URI: http://eprints.usq.edu.au/id/eprint/42005

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