Assessment of productivity of agrolandscapes on the regional scale on the basis of integration of the simulation model of agroecosystem and GIS
Evaluation of the productivity of agricultural landscapes plays an important role in solving the issue of ensuring sustainable development of rural territories. Dynamic simulation of agroecosystems is a modern, powerful and widespread scientific tool for analysis, monitoring and supporting decision-making in agronomy and agroecology. However, historically, most models of agroecosystems (the so-called crop models) are single-point, that is, they were developed for calculating the production process of agricultural sowing in one specific spatial location, for one specific crop and growing technology. For predictive estimates of the productivity of agroecosystems in the relatively large areas (on a national or regional scale), simpler and approximate calculation methods (empirical regression models) are traditionally used. This paper discusses the features, advantages, and prospects of using dynamic simulation models for regional (mesoscale) calculations. In particular, it raises issues of information support of models in regional projects and lists possible precedents for using mass computational experiments with a selected model or models of agroecosystems to support solutions on a regional scale. Standardized procedures for preparing arrays of spatially distributed source data, as well as processing and presenting the results in a GIS environment, are proposed. The practical applicability of the described methodology is demonstrated by the example of mass calculations of the production process of spring barley in the Republic of Crimea. As sources of initial information, a digitized electronic map of the Crimean soils and scenarios of actual weather realizations were used according to the records of 15 weather stations of the WMO network. The calculations for the vegetation seasons of 2012 and 2014 were carried out according to the grid of the selected 55 reference points located on the fields of agricultural enterprises of all regions of the republic. The obtained results make it possible to assess the degree of variability of the predicted yield values and to hypothesize that in this case it is more determined by the diversity of soil conditions than by the spatial heterogeneity of weather conditions at this territory.