Problem Definition and Significance of the Research Problem
Numerous climate prediction models have shown that there will be significant change in our climate in the upcoming future (Gov.ca, Pradeep, 2013). These climate changes will directly impact the way that agriculture practices are carried out world wide (Howden et al, 2007). The impacts of climate change will directly effect two important variable relating to crop production, temperature and prcipitation (Monteith et al, 1977). Indirectly and directly, climate change will produce massive environmental and economic impacts (Gepts et al, 2019). These factors combined together produce the real problem of potential food security(Fisher et al, 2005). This area of research will be highly important for future desicions regarding the agricultural sector. Agriculture production has the potential to benefit or be hugely limited by climate change and knowing which areas need further research is vital (Motha, 2005). By using climate prediction models, users can anticipate future changes and adapt the methods of how agriculture carried out in various parts of the world (Gepts et al, 2019). Particularly areas around the equator will need to adapt the most (Gepts et al, 2019). As Howden et al. (2007) states in regards to future adaptation “A crucial component of this approach is the implementation of adaptation assessment frameworks that are relevant, robust, and easily operated by all stakeholders, practitioners, policymakers, and scientists”. More diverse and relevant approaches are needed across the multitude of relevant disciplines such as: economics, GIS, geography and social science to further the knowledge in this area.
Current State of Knowledge and Research Gaps
Currently there is a vast depth of information pertaining to climate change. This area of research has seen increased emphasis as the reality of climate change and its effects have become more undeniable (Howden et al, 2007). Oreskes (2004), paper evaluated 928 scientific papers from the years 1993 to 2003 and found 100% of them supported the notion of climate change happening on a global scale. With climate change a certain, research has been done across a multitude of disciplines evaluating its current and predicted effects. This research has inspired governments and educational institutions around the world to start predicting and proposing methods of adapting to climate change (Gov.ca, Monteith et al, 1997, Oreskes, 2004). Pertaining to agriculture specifically climate prediction models have surfaced as being one of the most dominate tools and areas of emphasis for researchn (Matouq et al, 2014). Current models such as; CANESM2, HadGEM2-ES, IPSL-CM5A-LA, have found that the majority of current global land coverage is unsuitable for agriculture with 13.2% too cold, 26.5% too dry, 4.6% too steep, 2.0% too wet and 19.8% with too poor soils (Fisher et al, 2005). Prediction models have been created almost globally for variables such as temperature and precipitation (Matouq et al, 2014). The IPPC has stated that "Increased concentrations of greenhouse gases in the air keep more long-wave solar radiation in the lower atmosphere, increasing temperatures and altering precipitation" (IPCC, 2001). As well they have also stared that "Climate affects most aspects of our society, including human health, transportation infrastructure, employment, clean water, energy production and distribution, agriculture, and forestry" (IPCC, 2001). Alongside scientific research, the areas of economics and social impact have also been reviewed. Richard S. J. Tol’s review of the current literature in climate change found a diverse and complex amount of information on the its future, concluding that there is much left to be determined in these areas (Richard S. J. Tol, 2014).
The gaps in current research come down to specific impacts and regions. The world will be receiving various climate changes at different times and different places so the need to evaluate specific regions and time periods in a necessity (Gepts et al, 2019). Specifically pertaining to agriculture, some areas will be experiencing change in a positive way, with increased growing seasons, but most effects are predicted to be negative (Gepts et al 2019). Research is in need of these crucial “positive” areas that could help offset the losses in “negative” areas. In anticipation of these changes some regions have already begun some adaptation strategies to negate future negative effects. However, these new adaptation strategies will require further evaluation for effectiveness and possible unforeseen consequences outside of this projects scope (De Laporte et al, 2013). The combination of new policies and strategies being used and specific region and agriculture variability produces plenty of knowledge gaps throughout the literature. As Gepts states there is a need for “Development of assessment tools that incorporate the biophysical constraints that affect agricultural productivity and include climate and socioeconomic scenarios” (Gepts et al, 2019).
Importance of GIS Applications in this Research Project
The use of GIS in measuring, analyzing, and visualizing climate change has been of immense value in previous research studies and will most certainly be in this research project as well. The huge advantage GIS has is the ability to use data sets, tables and numbers and present them visually in the form of a map. This map then has a profound effect on the connections and interpretations a user can get from the data (Matouq et al, 2014). Relating specifically to this study, the information that will be projected are the predictions models of past, present and future temperature and precipitation. Having different time periods shown will allow the user to visually see the how the climate is changing and the areas of greatest impact. The biggest take away is the ability to combine the two models along with land use data to accurately predict the agricultural areas experiencing change in crop efficiency (De Laporte et al, 2013). This then allows the results to be interpreted easily as the data will be presented in a very user friendly format. This should help future decision makers in any relevant field make informed decisions with the results (Matouq et al 2014, De Laporte et al 2013).