Seasonal and Inter-Annual Climate Forecasting: The New Tool for Increasing Preparedness to Climate Variability and Change In Agricultural Planning And Operations
By Holger Meinke, Roger Stone. 2005
326 citations recorded by the source corpus.
Climate variability and change affects individuals and societies. Within agricultural systems, seasonal climate forecasting can increase preparedness and lead to better social, economic and environmental outcomes. However, climate forecasting is not the panacea to all our problems in agriculture. Instead, it is one of many risk management tools that sometimes play an important role in decision-making. Understanding when, where and how to use this tool is a complex and multi-dimensional problem. To do this effectively, we suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (e.g., climate science, agricultural systems science, rural sociology and many other disciplines) and people (scientist, policy makers and direct beneficiaries) as equal partners to reap the benefits from climate knowledge. Climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective. Any scientific breakthroughs in climate forecasting capabilities are much more likely to have an immediate and positive impact if they are conducted and delivered within such a framework. While knowledge and understanding of the socio-economic circumstances is important and must be taken into account, the general approach of integrated systems science is generic and applicable in developed as well as in developing countries. Examples of decisions aided by simulation output ranges from tactical crop management options, commodity marketing to policy decisions about future land use. We also highlight the need to better understand temporal- and spatial-scale variability and argue that only a probabilistic approach to outcome dissemination should be considered. We demonstrated how knowledge of climatic variability (CV), can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions.
Subfields: Ecology, Evolution, Behavior and Systematics, General Agricultural and Biological Sciences, Soil Science
View Paper: https://www.semanticscholar.org/paper/b951b8d8e30bc4bdbee2b6ccbe5d198e78bca6df
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In Ecology, Evolution, Behavior and Systematics
In General Agricultural and Biological Sciences
In Soil Science
Citations in the corpus
- Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia (2011, background): “Reliable intraseasonal forecasts may be valuable for decision making related to the scheduling of planting and harvesting, as well as within-season decisions, such as those related to fertilizer or pesticide application (Meinke and Stone, 2005; CliMag, 2009).”
- Constraints and opportunities in applying seasonal climate forecasts in agriculture (2007, background): “In addition, there can be more skill in forecasting some measure of plant or pasture growth or crop yield than in forecasting rainfall (Cobon 1999; Keogh et al. 2004; McIntosh et al. 2005; Meinke and Stone 2005).”
- Drought stress characterization of post-rainy season (rabi) sorghum in India (2013, background): “…occur at all locations, albeit with different frequencies, and the ability to forecast the season type remains probabilistic (Meinke et al., 2001; Meinke and Stone, 2005; Jagtap et al., 2002), this dilemma generates substantial trade-offs between production/profit likelihood and risk in choosing…”