The aim of the mapspamc R
package is to facilitate
the creation of country level crop distribution maps. The model builds
on the global version of the Spatial Production Allocation model
(SPAM) (You and Wood 2006; You, Wood, and
Wood-Sichra 2009; You et al. 2014; Yu et al. 2020), which uses a
cross-entropy optimization approach to ‘pixelate’ national and
subnational crop statistics on a spatial grid at a resolution of 5 arc
minutes (~ 10 x 10 km). mapspamc provides the necessary
infrastructure to run SPAM at the country level and makes it possible to
incorporate national sources of information and potentially create maps
at a higher resolution of 30 arc seconds (~ 1 x 1 km)(Dijk et al.
2022).
The articles in the Background section provide general information on approaches to create crop distribution maps, the model, input data, country examples and an appendix with additional information on specific topics.
To install mapspamc:
install.packages("remotes")
remotes::install_github("michielvandijk/mapspamc")Running mapspamc requires the installation of several other pieces of
software, which are described in the
Installation section.
It takes some preparation before the crop distribution maps can be
generated. Most important and probably most time consuming is the
collection of input data. mapspamc requires a large variety of input
data, which can be grouped under three headers: (1) national crop
statistics, (2) data to construct the priors/fitness scores and (3) data
to determine the spatial constraints. The availability of data strongly
affects the structure of the model, how it will be solved and how long
it takes to solve. We highly recommend to start collecting input data
before running the model. The articles in the Background section give an
overview of the input data that are required
by the package and show were to download several country
examples.
Running mapspamc can be divided into six major steps which are split
into nine smaller steps in the Run mapspamc section.
- Design and process flow
- 1. Model setup
- 2.1. Pre-processing - Subnational statistics
- 2.2. Pre-processing - Spatial data
- 2.3. Pre-processing - cropland
- 2.4. Pre-processing - irrigated area
- 3. Model preparation
- 4. Running the model
- 5. Post-processing
- 6. Model validation
Dijk, Michiel van, Ulrike Wood-Sichra, Yating Ru, Amanda Palazzo, Petr Havlik, and Liangzhi You. 2022. “Generating multi-period crop distribution maps for Southern Africa using a data fusion approach.”
You, Liangzhi, and Stanley Wood. 2006. “An entropy approach to spatial disaggregation of agricultural production.” Agricultural Systems 90 (1): 329–47. https://doi.org/10.1016/j.agsy.2006.01.008.
You, Liangzhi, Stanley Wood, and Ulrike Wood-Sichra. 2009. “Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach.” Agricultural Systems 99 (2): 126–40. https://doi.org/10.1016/j.agsy.2008.11.003.
You, Liangzhi, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu. 2014. “Generating global crop distribution maps: From census to grid.” Agricultural Systems 127: 53–60. https://doi.org/10.1016/j.agsy.2014.01.002.
Yu, Qiangyi, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang. 2020. “A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps.” Earth System Science Data 12 (4): 3545–72. https://doi.org/10.5194/essd-12-3545-2020.