Final Project

Cross Comparison of Crop Productivity and Climate Changes

The goal of this project is to test if indirect factors such as climate related variables have had an impact on crop productivity on Alberta crop progression in the past few years through accessing data that was web scraped from different URLS and website interactions. By using Alberta crop reports progression data, the main goal was to scrape a metric that when measured in comparison to past years and expectations, could determine the average rate of progression of crops through each stage of production and harvest. As different rates would tend to deviate in accordance to broad based meteorological factors that affect production, we would be able to quantify and measure how much change can be explained by changes in weather related factors.

As meteorological expectations have changed, current weather tendencies have shown to offer new forms of variability which can greatly affect crop production. Global warming being the greatest cause of the variability, results in potentially giving unfavorable growing conditions to Alberta based regions.

Problem was approached from the direction of required inputs versus outputs allocated. Rather than making a model that focuses on price based data. Types of variables were collected to show the changes in weather. Attempting to focus on the effect of global warming based weather changes.

Topic Influence

From Amanullah (2020) documentation of “Agronomy: Climate Change & Food Security”, he identifies some key factors that influence crop yields and productivity in harvest season. Factors can be split into direct and indirect effects that can be measured and modeled into a multi-factor model to explain variances. Direct effects include charges in crops genotype and phenotype that affect plant productivity, and indirect effects being expanded by changes of weather based variables. In their paper, they also make mention to socioeconomic factors such as trade supply that also create vulnerability.

Climate Integration

As price data is affected by means of supply and demand and yield based data on direct effects, the project was based around crop rates in hopes to compare with weather changes. The importance of such a project can be captured by the effect global warming has on expected yields and productivity. Further iterations of this project idea can be expanded to model other factors that influence Alberta agricultural pricing and yields.

When focusing on indirect effects, the most notable factors of productivity are sources of growth for the plant itself. Therefore, variables such as air temperature, humidity, precipitation and solar radiation are considered for the project. Weather points were selected on the basis to get the best spread across Alberta agricultural sections. 2 points per section at approximate opposite end to cover greatest ground distance.

Goal of the project would be to have a model that represents the production productivity in Alberta and the relationship which is shared between the indirect weather variables which are most influenced by long term climate variability. Hypothesis going into the project would be that the tested relationship would vary by agricultural sections. Statistics Canada (2020) published an associated article documenting the effect of global warming climate models on group parts of Canada. In the article, they conclude that there is evidence that in prairie regions:

• Increased frost free periods
• Decreased precipitation later in season
• Frequent weather variability of accumulated precipitation & heat overexposure
• Reduced stream flow and runoff
• Warmer climate granting more ideal pest environments

• Higher Co2 level may result in greater productivity of crops such as wheat, barley, canola, soybeans and potatoes

  Results that may be observed:

• agricultural sections with more sun intensity will waste productive time in seeding stages for perennial crops
• crops that are heat sensitive will take longer to be processes
• Greater weather variability can effect processes of seeding, harvesting and processing
• reduced runoff till later season can effect productivity and rate of harvest
• Greater pest risk can extend productive timeline

• Greater CO2 levels for annual crops can increase productivity in stages resulting in faster production

  Further Potential

  With a working model, it would enable farmers to have more certainty to pursue certain long term strategies to ensure perpetuation of optimal yields. Adding more variable data sets (direct or indirect) to build multifactor model of non-human uncertain inputs and how changes in these variables affect production productivity in Alberta. Methodology can be further elaborated by adjusting to national data. In conjunction with mmfair functions (https://schmidtpaul.github.io/MMFAIR/) and dsfair tests integrated to manuipulate data. Further industry statistics could be applied.

  References

  Amanullah. (2020). Agronomy: Climate change & food security. IntechOpen.
  • Alberta crop reports. Alberta.ca. (n.d.). Retrieved April 4, 2023, from https://www.alberta.ca/alberta-crop-reports.aspx
  • Gouvernement du Canada. (2020, January 31). Government of Canada. Language selection - Agriculture and Agri-Food Canada / Sélection de la langue - Agriculture et Agroalimentaire Canada. Retrieved April 15, 2023, from https://agriculture.canada.ca/en/environment/climate-scenarios-agriculture

  • Yield Alberta. Agriculture Financial Services Corporation. (2021, February 22). Retrieved April 9, 2023, from https://afsc.ca/resources/5684-2/

    ## Warning: package 'jsonlite' was built under R version 4.2.3

    ## Warning: package 'pdftools' was built under R version 4.2.3

    ## Warning: package 'RSelenium' was built under R version 4.2.3

    ## [1] "https://open.alberta.ca/publications/2830245-2021"

    ## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
    ## header and 'col.names' are of different lengths

    ## Warning in grep("Spring Wheat", thin):grep("Source: A", thin): numerical
    ## expression has 2 elements: only the first used
    
    ## Warning in grep("Spring Wheat", thin):grep("Source: A", thin): numerical
    ## expression has 2 elements: only the first used
    
    ## Warning in grep("Spring Wheat", thin):grep("Source: A", thin): numerical
    ## expression has 2 elements: only the first used
    
    ## Warning in grep("Spring Wheat", thin):grep("Source: A", thin): numerical
    ## expression has 2 elements: only the first used

    ## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
    ## header and 'col.names' are of different lengths
    
    ## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
    ## header and 'col.names' are of different lengths
    
    ## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
    ## header and 'col.names' are of different lengths

    ## Warning in grep("Spring Wheat", thin):grep("Source: A", thin): numerical
    ## expression has 2 elements: only the first used
    
    ## Warning in grep("Spring Wheat", thin):grep("Source: A", thin): numerical
    ## expression has 2 elements: only the first used
    
    ## Warning in grep("Spring Wheat", thin):grep("Source: A", thin): numerical
    ## expression has 2 elements: only the first used