Spatial Distribution Maps of Horticultural and Agricultural Crops Land Generating at the Country Scale for Iran

Introduction There are only a limited number of maps available regarding the distribution of agricultural and horticultural lands in the country. The existence of these maps can play an important role in various fields such as agricultural planning and development, climate change assessment, yield gap, and food security analysis, livestock production systems management, ecosystem service, fertilizer use management, agricultural optimal cropping pattern determination and other studies related to agriculture, forestry, and rangeland. For example, one of the relevant applications of these maps is their use in combination with climate zoning maps and soil maps to select the target meteorological stations in plant production simulation studies with different practical purposes. The SPAM project (Spatial Production Allocation Model) in IFPRI (International Food Policy Institute) is the main source of these kinds of maps. The accuracy of SPAM maps is acceptable at providence or higher scales because they are prepared based on provincial data. However, these maps may not be precise on a smaller scale than the province. The SPAM data bank does not cover all main crops and none of the horticultural plants. Therefore, in this study, the land use maps of 33 main agricultural and horticultural plants were produced at the country scale using the simplified SPAM method. Materials and Methods: To generate these maps, a spatial overlay of land distribution (total rainfed land raster, total irrigated land raster, and total garden products raster for target crop) with polygon boundaries of cities was produced in ArcGIS. The value of the pixels in each county must be calculated in the created map. To do this, first, the number of pixels with land in each county was counted on this map. Then the area under cultivation of the target plant in each county was divided by the number of pixels counted in the same county. The number obtained in each county was considered as the pixel value of the land in that county for the target plant. The assumptions about the production of distribution maps for each plant in rainfed and irrigated Conditions are 1- To generate the distribution maps of each rainfed or irrigated annual plant; it is assumed that the distribution of the land of the given crop is the same as the distribution of all rainfed or irrigated lands within each county. It should be noted that this assumption can be incorrect if only the land distribution of the crop is considered in a particular crop year. Due to crop rotation, the distribution of a crop in the lands of a county changes every year. If the land distribution of a particular crop in a county is considered for a period of 3 years or more, the probability of target crop planting within each part of the county lands will be high. Because in the present study, information from the period 2014 to 2016 has been used, it is assumed that during this period, the distribution of lands the target rainfed and irrigated is similar to the distribution of total rainfed and irrigated lands in the given county. 2- In the case of horticultural crops that are perennial, it is assumed that the gardens of the target crop are distributed uniformly in the horticultural lands of that county. Therefore, the distribution of a particular horticultural crop in a county was similar to that of the total distribution of horticultural lands in that county.Results and DiscussionThe findings of this study suggest that SPAM2005 maps for various crops in Iran were less accurate than SPAM2010 maps at the province level. While the area of crops in SPAM maps was similar to statistical data at the province level, the spatial distribution of crops required modification. This issue became apparent when the grassland distribution map was compared with the crops SPAM maps, indicating that some crop land in SPAM maps was incorrectly located where grasslands should be. The approach used in this study resolved this problem and generated new spatial distribution maps for crops with higher accuracy.Conclusion This study utilized county-level data from over 400 counties to produce more accurate maps than the original SPAM maps. The advantages of the maps produced in this study compared to SPAM maps are (1) The use of up-to-date crop area information to generate the maps; (2) The use of crop area data at the county scale to increase map accuracy; (3) The application of country-specific land distribution maps to generate the maps; and (4) The production of distribution maps for all major horticultural and agricultural crops in the country, creating a comprehensive database in this field.