Comprehensive, detailed and spatially explicit information on the locations of crops is essential to inform agricultural and food policies. crop distribution maps are used to as crucial input for regional crop monitoring systems (Fritz et al. 2019; Becker-Reshef et al. 2019), to analyse national irrigation potential (You et al. 2014; Xie, You, and Takeshima 2017) and to assess the impact of climate change and socioeconomic development on land use change and wider developmental trade-offs (Ahmed et al. 2016).
Currently, the only source of information that provide spatial information on the location of crops are a number of global products (Monfreda, Ramankutty, and Foley 2008; Portmann, Siebert, and Döll 2010; You et al. 2014; see Anderson et al. 2015 for a comparison). These products use spatial information on land cover, suitability and irrigation to ‘grid’ (sub)national agricultural statistics at 5 arcmin resolution (~10x10 kilometer at the equator). Apart from the Spatial Production Allocation Model, which latest version covers the year 2010 (Yu et al. 2020), these datasets are rather outdated and cannot be used to track changes over time.
Global crop distribution maps are a useful starting point for national analysis but are of limited use when more detailed information is required. National decision makers often want to zoom in on subnational regions (e.g. bread basket areas) and investigate the shift in crops over time, which requires high(er) resolution maps. Also global products cannot easily be adapted when better information is available from local sources, such as national inventories of large-scale plantations, geo-coded data of irrigation schemes and national land cover maps.
An interesting alternative and new approach to create high resolution crop distribution maps are machine learning techniques, which can be used to identify the location of specific crops on satellite imagery. Although promising, these techniques are still under active development and the available studies predominantly target large-scale crops (e.g. soy bean and palm oil), which are easier to identify using machine learning classification approaches (Zhong et al. 2016; Song et al. 2017; Danylo et al. 2020).
The Spatial Production Allocation Model for Country level analysis (SPAMc) provides an approach to create plausible spatial estimates of physical and harvested crop area at national scale. The model builds on the global version of (SPAM) described in You and Wood (2006), You, Wood, and Wood-Sichra (2009), You et al. (2014) and Yu et al. (2020), which uses a cross-entropy framework to allocate subnational land use information on a 5 arcmin grid. We enhanced SPAM in several directions, including the possibility to increase the resolution of the maps to 30 arcsec (~1x1 kilometer at the equator). Van Dijk et al. (2020) provides an in-depth discussion of SPAMc including an example for Southern Africa.1
Ahmed, Kazi Farzan, Guiling Wang, Liangzhi You, and Miao Yu. 2016. “Potential impact of climate and socioeconomic changes on future agricultural land use in West Africa.” Earth System Dynamics 7 (1): 151–65. https://doi.org/10.5194/esd-7-151-2016.
Anderson, Weston, Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu. 2015. “An analysis of methodological and spatial differences in global cropping systems models and maps.” Global Ecology and Biogeography 24 (2): 180–91. https://doi.org/10.1111/geb.12243.
Becker-Reshef, Inbal, Brian Barker, Michael Humber, Estefania Puricelli, Antonio Sanchez, Ritvik Sahajpal, Katie McGaughey, et al. 2019. “The GEOGLAM crop monitor for AMIS: Assessing crop conditions in the context of global markets.” Global Food Security 23 (December): 173–81. https://doi.org/10.1016/j.gfs.2019.04.010.
Danylo, Olha, Johannes Pirker, Guido Lemoine, Guido Ceccherini, Linda See, Ian McCallum, Hadi, Florian Kraxner, Frédéric Achard, and Steffen Fritz. 2020. “Satellite reveals age and extent of oil palm plantations in Southeast Asia,” February. http://arxiv.org/abs/2002.07163.
Fritz, Steffen, Linda See, Juan Carlos Laso Bayas, François Waldner, Damien Jacques, Inbal Becker-Reshef, Alyssa Whitcraft, et al. 2019. “A comparison of global agricultural monitoring systems and current gaps.” Agricultural Systems 168 (January): 258–72. https://doi.org/10.1016/j.agsy.2018.05.010.
Monfreda, Chad, Navin Ramankutty, and Jonathan A. Foley. 2008. “Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000.” Global Biogeochemical Cycles 22 (1). https://doi.org/10.1029/2007GB002947.
Portmann, Felix T., Stefan Siebert, and Petra Döll. 2010. “MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling.” Global Biogeochemical Cycles 24: 1–24. https://doi.org/10.1029/2008GB003435.
Song, Xiao Peng, Peter V. Potapov, Alexander Krylov, Lee Ann King, Carlos M. Di Bella, Amy Hudson, Ahmad Khan, Bernard Adusei, Stephen V. Stehman, and Matthew C. Hansen. 2017. “National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey.” Remote Sensing of Environment 190 (March): 383–95. https://doi.org/10.1016/j.rse.2017.01.008.
Van Dijk, Michiel, Ulrike Wood-Sichra, Yating Ru, Amanda Palazzo, Petr Havlik, and Liangzhi You. 2020. “Mapping the change in crop distribution over time using a data fusion approach.”
Xie, Hua, Liangzhi You, and Hiroyuki Takeshima. 2017. “Invest in small-scale irrigated agriculture: A national assessment on potential to expand small-scale irrigation in Nigeria.” Agricultural Water Management 193 (November): 251–64. https://doi.org/10.1016/j.agwat.2017.08.020.
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, Wenbin Wu, and Peng Yang. 2020. “A cultivated planet in 2010: 2. the global gridded agricultural production maps.” Earth System Science Data. https://doi.org/https://doi.org/10.5194/essd-2020-11.
Zhong, Liheng, Lina Hu, Le Yu, Peng Gong, and Gregory S. Biging. 2016. “Automated mapping of soybean and corn using phenology.” ISPRS Journal of Photogrammetry and Remote Sensing 119 (September): 151–64. https://doi.org/10.1016/J.ISPRSJPRS.2016.05.014.
Note that the SPAMc version in mapspam2globiom is simpler version than the one described in Van Dijk et al. (2020). It is not possible to blend in detailed information on the location of crops (e.g. OpenStreetMap information, large-farm surveys and machine learning products). It also does not include the approach to backcast crop distribution maps to earlier periods. These features might be added in an update of the package.↩︎