Scale, context, and decision making in agricultural adaptation to climate variability and change

This work presents a framework for viewing agricultural adaptation, emphasizing the multiple spatial and temporal scales on which individuals and institutions process information on changes in their environment. The framework is offered as a means to gain perspective on the role of climate variability and change in agricultural adaptation, and developed for a case study of Australian agriculture. To study adaptation issues at the scale of individual farms we developed a simple modelling framework. The model highlights the decision making element of adaptation in light of uncertainty, and underscores the importance of decision information related to climate variability. Model results show that the assumption of perfect information for farmers systematically overpredicts adaptive performance. The results also suggest that farmers who make tactical planting decisions on the basis of historical climate information are outperformed by those who use even moderately successful seasonal forecast information. Analysis at continental scales highlights the prominent role of the decline in economic operating conditions on Australian agriculture. Examples from segments of the agricultural industry in Australia are given to illustrate the importance of appropriate scale attribution in adapting to environmental changes. In particular, adaptations oriented toward short time scale changes in the farming environment (droughts, market fluctuations) can be limited in their efficacy by constraints imposed by broad changes in the soil/water base and economic environment occuring over longer time scales. The case study also makes the point that adaptation must be defined in reference to some goal, which is ultimately a social and political exercise. Overall, this study highlights the importance of allowing more complexity (limited information, risk aversion, cross-scale interactions, mis-attribution of cause and effect, background context, identification of goals) in representing adaptation processes in climate change studies.

[1]  V. S. Tomar,et al.  Contribution of climatic variables in predicting maize yield under monsoon conditions , 1976 .

[2]  M. Rosenzweig Wealth, weather risk, and the composition and profitability of agricultural investments , 1989 .

[3]  W. Nordhaus,et al.  The Impact of Global Warming on Agriculture: A Ricardian Analysis: Reply , 1999 .

[4]  Stephen H. Schneider,et al.  Integrated assessment modeling of global climate change: Transparent rational tool for policy making or opaque screen hiding value‐laden assumptions? , 1997 .

[5]  C. Rosenzweig,et al.  Potential impact of climate change on world food supply , 1994, Nature.

[6]  Neville Nicholls,et al.  Recent apparent changes in relationships between the El Niño-Southern Oscillation and Australian rainfall and temperature , 1996 .

[7]  Daniel S. Wilks,et al.  Economic Value of Weather And Climate Forecasts: Forecast value: prescriptive decision studies , 1997 .

[8]  R. Moss,et al.  The regional impacts of climate change : an assessment of vulnerability , 1997 .

[9]  R. Kaufmann,et al.  A Biophysical Model of Corn Yield: Integrating Climatic and Social Determinants , 1997 .

[10]  R. Katz,et al.  Extreme events in a changing climate: Variability is more important than averages , 1992 .

[11]  B. Smit,et al.  Agricultural adaptation to climatic variation , 1996 .

[12]  D. Barrett,et al.  Climate change and Australian wheat yield , 1998, Nature.

[13]  J. Coppock THE GEOGRAPHY OF AGRICULTURE , 1968 .

[14]  P. Preckel,et al.  Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index , 1989 .

[15]  J. Houghton,et al.  Climate change 1995: the science of climate change. , 1996 .

[16]  R. Kaufmann The impact of climate change on US agriculture: a response to Mendelssohn et al. (1994) , 1998 .

[17]  N. Nicholls Increased Australian wheat yield due to recent climate trends , 1997, Nature.

[18]  E. Pasour Agriculture and the State , 2020 .

[19]  J. R. Kiniry,et al.  CERES-Maize: a simulation model of maize growth and development , 1986 .

[20]  B. R. Frank Constraints limiting innovation adoption in the north Queensland beef industry. II: Non-adoption is an intelligent response to environmental circumstances , 1995 .

[21]  B. R. Frank,et al.  Constraints limiting innovation adoption in the north Queensland beef industry. I: A socio-economic means of maintaining a balanced lifestyle , 1995 .

[22]  R. Talbot,et al.  Agriculture and the State: Market Processes and Bureaucracy , 1990, American Political Science Review.

[23]  Shiv Visvanathan,et al.  A Carnival for Science: Essays on Science, Technology and Development , 1997, The Journal of Asian Studies.

[24]  R. Hobbs Australia: State of the environment 1996 , 1997 .

[25]  D. Fisk,et al.  Climate Change and its Impacts: A Global Perspective , 1997 .

[26]  G. W. Yohe,et al.  Imbedding Dynamic Responses with Imperfect Information into Static Portraits of the Regional Impact of Climate Change , 1990, Economic Issues in Global Climate Change.

[27]  E. Runge Effects of Rainfall and Temperature Interactions During the Growing Season on Corn Yield1 , 1968 .

[28]  G. Watts,et al.  Climate Change 1995 , 1998 .

[29]  Neville Nicholls,et al.  An extended high-quality historical rainfall dataset for Australia , 1997 .

[30]  R. Moss,et al.  Climate change 1995 - impacts, adaptations and mitigation of climate change : scientific-technical analyses , 1997 .

[31]  Linda O. Mearns,et al.  Analysis of daily variability of precipitation in a nested regional climate model: comparison with observations and doubled CO2 results , 1995 .

[32]  W. McLennan Australians and the environment , 1996 .

[33]  P. Whetton,et al.  Implications of climate change due to the enhanced greenhouse effect on floods and droughts in Australia , 1993 .

[34]  Ian Bowler,et al.  The Geography of Agriculture in Developed Market Economies , 1992 .