Agroecology-based DSS for Climate-smart farming in the Central Russia conditions
Aggravating problems of food and environmental security are becoming the key issues of the global challenges of XXI century that was another time declared at the Paris Summit in December 2015. Current models of global changes in XXI century (IPCC, 2013) show a range of potential global warming from 0.3 (most optimistic version) to more than 4.5 °C at the end of the century, depending on our ability to reduce anthropogenic greenhouse gases emissions. However, at current level of greenhouse gases (GHG) emissions or in case of their future increasing with the same annual rate it is very likely that we will hit the worse case scenarios, with serious negative consequences on environment and more complicated ones on agriculture. For these reasons adaptation to global changes and contributing to climate change mitigation are among the main agroecological challenges addressed to current land-use and land management practice (Foresight, 2011; Beddington et al., 2012; FAO, 2013).
Recently actualized in RF agricultural intensification can essentially increase as GHG emission, including CO2 (due to non-compensated mineralization of soil organic carbon), CH4 (fast growing livestock) and N2O (applying more mineral fertilizers), as carbon sequestration in crop yield and soil organic matter due to more intensive photosynthesis and humification, higher efficiency of fertilizing. Current changes in agroecosystems functioning make important impacts on their farming profitability, regional and global biosphere processes, including C, nutrients’ and water balance, climate changes and agroecological potential of adaptation to them. These issues are particularly essential for Central region of Russia, which is characterized by the increased complexity of soil cover due to its comparatively young age, high current spatial differentiation, recent important transformations in land use and agricultural practices.
Modern systems of agroecological monitoring gradually develop the principal information basis for region-adapted decision support systems (DSS) on land-use agroecological optimizing (Vasenev, 2010). Developed and primary verified in frame of LAMP project (RF Government Grant # 11.G34.31.0079) regionally adapted and climate-smart DSS AKORD-R has framework databases on the actual features of local landscape, soil and soil cover patterns, agroclimate data and agrotechnology parameters, current and previous crops yield, organic and mineral fertilizer doses, technological costs used for profitability analysis.
Its region-adapted knowledge base (KnB) allows modeling not only basic agricultural crops yield and their production profitability in concrete landscape and agroclimate conditions, but also evaluate the different agrotechnologies efficiency for various scenarios of climate change. Climate-smart module of AKORD-R KnB is based on last techniques of the EU, summarized in “Climate Smart Agriculture” (FAO, 2013), after their adaptation to the climatic and soil-geomorphological conditions, land-use structure, and dominating agrotechnologies at the Central part of European Russia.
Together with the regional/ local systems of agroecological zoning, degradation risk and land quality assessment scales, crop (variety) and agrotechnology requirements, the previous farming practices results they allow to develop spatial and temporally differentiated most probable agroclimatic predictions and crop production models, the region-adapted framework systems of land agroecological functions, algorithms and standard data for their evaluation.
Sustainable climate-smart farming refers to the flexible balance among its land principal agroecological functions in changing landscape and agricultural conditions:
- Agroclimatic function of plant supply with photosynthetic active radiation, effective heat and available moisture;
- Agrochemical function of crop supply with available macro- and micro-nutrients;
- Agrophysical function of favorable condition support for effective work of the agricultural machines;
- Hydrophysical function of plant seasonal supply with available moisture and soil air exchange;
- Phyto-sanitary function of favorable condition support for crop without damage by pathogens, pests and weeds;
- Ecogeochemical function of land resistance to contamination;
- Ecopedomorphogenetic function of plant and farming support with soil-ecological condition quasi-homogeneity in space and time.
DSS AKORD-R includes the integral land evaluation algorithm consisted from four special modules. Primary algorithm (parametric analysis) describes the principal agroecological parameters assessment by their modelling or adapted to concrete soil and agricultural land type logistic equation. Second one (factor analysis) includes each agroecological function assessment as corrected harmonic mean from its parameters assessment values. Third algorithm (land analysis) determines homogeneous land unit assessment as combination of its functions values. Fourth one (field analysis) runs the heterogeneous land unit assessment as weighted average value corrected by soil cover patterns contrast and boundary complexity.
Its application is very useful for fast decision on new fields’ choice for most profitable crops in fast changing market, technology, climate and weather conditions. The same it is necessary to apply this tool for land agroecological typification and best available farming technologies transfer.
AKORD-R crop selection module include submodules on landscape (meso- and micro-relief, parent materials, ground water) ecological limiting factor assessment, predecessor set aftereffect analysis, predicted yield and scheduled costs evaluation.
Complex module of crop production modeling runs through PAR-based yield evaluation and analysis its limitation by inefficient soil available water supply or precipitation levels, soil macro- and micro-nutrients content or adverse acid-base conditions, unfavorable soil bulk density or phyto-sanitary state. PAR-based yield evaluation algorithm takes into consideration the vegetation period duration and its seasonal distribution, site geographical coordinates and its PAR value according to regional agroclimatic GIS, slope exposure and gradient, crop and variety features. Precipitation various predictions are based on their perennial statistics and global-change trends. Seasonal soil available water supply (SAWS) prediction takes into consideration not only seasonal precipitation, but also the previous fall characteristics and/or spring SAWS, the same as topsoil texture, structure, crop and slope parameters that determine soil infiltration rate and run-off in different precipitation and vegetation conditions.
Climate-smart module allows predicting crop development and yield in concrete land and various agro-climate and technology conditions for best available farming practice selection with minimum environmental and economic risks. Principal tasks of environmentally friendly and climate-smart land-use annual correction usually include the following target issues:
- Effective increase in production level and land-use efficiency (productivity/profitability),
- Improvement of consumer, technological and ecological quality of the end production (for example, increase in the content of protein or starch),
- Seasonal expansion and/or redistribution of the arable lands for different crops due to market needs dynamics,
- Agrotechnology adaptation to the current agrolandscape conditions, in particular connected with local climate change and/or forecrop impacts,
- Agroecologically based agrotechnology transfer through its consequences modelling, verification and adaptation to concrete regional and agrolandscape features,
- Support for production and farming system ecological certification,
- Prevention or minimizing the negative economic consequences of the drought, erosion, leaching and other problem agroecological situation development due to long-term farming practice and global changes.
Adapted to Central region of Russia climate-smart DSS AKORD-R is becoming the convenient framework tools for environmentally friendly and climate-smart agricultural land-use design taking into attention the regional and landscape-based features of the concrete soil cover patterns, land-use history, planned crops, farming subsystems and best available technologies, adapted to local agrolandscape conditions.