Advances in application of climate prediction in agriculture

Hammer, GL, Hansen, JW, Phillips, JG, Mjelde, JW, Hill, H, Love, A and Potgieter, A (2001) Advances in application of climate prediction in agriculture. Agricultural Systems, 70 2-3: 515-553. doi:10.1016/S0308-521X(01)00058-0

Author Hammer, GL
Hansen, JW
Phillips, JG
Mjelde, JW
Hill, H
Love, A
Potgieter, A
Title Advances in application of climate prediction in agriculture
Journal name Agricultural Systems   Check publisher's open access policy
ISSN 0308-521X
Publication date 2001
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1016/S0308-521X(01)00058-0
Volume 70
Issue 2-3
Start page 515
End page 553
Total pages 39
Editor J.B. Dent
J.W. Jones
S.M. Andrews and P.K. Thornton
Place of publication Oxford, UK
Publisher Elsevier Science
Collection year 2001
Language eng
Subject C1
309900 Other Agricultural, Veterinary and Environmental Sciences
620100 Field Crops
Abstract Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keyword Agriculture, Multidisciplinary
Systems Analysis
El Nino
Nino-southern Oscillation
Sea-surface Temperature
Maize Yield Variability
Q-Index Code C1
Additional Notes This document is a journal review.

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collection: School of Agriculture and Food Sciences
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Citation counts: TR Web of Science Citation Count  Cited 95 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 14 Aug 2007, 16:54:51 EST