The authors presented the formalized theory for identification of genotypes on phenotypes in modern breeding technologies. As a base the authors proposed the mathematical models of «genotype-environment» interaction, for which they solves an inverse informational problem during the estimation of sizes of no observed action of seven genetic-physiological system on selected quantitative traits to be improved.
The purpose of this work is to present a new method for estimating the parameters of the biomass of agricultural crops based on Earth remote sensing (ERS) data. The method includes mathematical models and algorithms estimation and has been tested on the example of spring wheat sowing. Sowing biomass parameters are the basis for making management decisions aimed at obtaining a given crop yield. Currently, for these purposes, vegetation indices are most widely used. It is impossible to estimate the physical parameters of the crop sowing biomass using these indices, due to their scalar form and lack of dimension. The paper develops a classical approach to the problem of estimating the parameters of the state of agricultural crops, in which remote sensing data are considered as an indirect measurement of the estimated parameters. The basis for the implementation of the estimation method is the dynamic model of biomass parameters and the remote sensing model, which reflects the relationship between the spectral reflection parameters and the estimated parameters of the crop biomass. The parameters of the dynamic model and the remote sensing model are refined by selective ground measurements in separate elementary sections of the field. The difference between this article and previous works of a similar nature lies in the fact that agricultural crops with a more complex morphological structure are considered as the object of evaluation. In addition, such an important feature of agricultural objects as their spatial distribution is considered here. To take it into account, a new type of mathematical models is used, in which spatial coordinates are introduced. Due to the significant complication of modeling and estimation algorithms based on such models, simpler approximation schemes are proposed. The advantage of the proposed approach is that the assessment is considered as a dynamic process that meets the content of the task of monitoring crops.
In agriculture, the volume and quality of the use of modern technologies, including systems for collecting, storing and processing data, is noticeably growing. This increases both the amount of data and the need for high-quality processing and reliable conclusions that you can rely on when making decisions. The lack of information for decision making leads to the fact that in the process of cultivating crops, up to 40 % of the crop is lost. Further automation of processes at all stages of the production cycle represents a higher level of digital integration, which affects the most complex organizational changes in the agricultural business, but their implementation can dramatically affect profit and competitiveness of products. The modernization of the agricultural sector is based on the transition to «intelligent agriculture». The greatest interest to science and practice is the intellectualization of agricultural technology management, where the basis is expert systems in which management decisions are made through knowledge bases (KB), formed through analytical control systems located in data centers. In this paper, we consider expert systems for the state control of spring wheat. To this type of management we attribute the task of preliminary formation of the sequence of technological operations on one growing season. In the cloud information system, the generated knowledge bases are transferred from the data center to local management systems at the request of consumers.
Evaluating the modern market of the Internet of things, one should consider equipment connected into a single network, solutions, applications along the entire chain of product creation, including the end user. In this paper, such a binding is based on cloud information technologies. Through these technologies, the intellectualization of agrotechnology management is implemented through the creation of expert management decision support systems (DSS). The aim of the work is to consider the methodology for constructing DSS of strategic management in precision farming systems, where this type of management has not been implemented to date. To this kind of management, we include the task of forming strategies for the application of mineral fertilizers and ameliorants of prolonged action for all the years of various types of crop rotations. To solve the problem, an algorithm for the formation of optimal strategies for the application of mineral fertilizers and ameliorants, which is implemented in an analytical automated control system of agricultural technologies (ASUAT), through which a knowledge base (BR) is transmitted from the cloud system to local DSS, is substantiated. To select the best option from the knowledge base, the pattern recognition method is used. The technique was tested on arbitrary initial conditions of local systems, including extreme combinations of initial conditions. Based on the analysis of the loss of optimality associated with the discrepancy of the initial conditions on the local DSS and the KB, a method for controlling the formation of the BR, aimed at reducing these losses, is substantiated.
A b s t r a c tImprovement of hereditary drought resistance in crops is recently being among the main objectives for food security of humanity because of global warming and the growing costs for bakery and forage grain. Analysis of complex properties of drought resistance in cereals (phenotyping) shows the limitations of a canonical genocentric approach and the approaches based on molecular genetics to solve the problem of significant hereditary improvement of drought resistance. The priority epigenetic approach that we propose is based on the theory of eco-genetic organization of quantitative traits (TEGOQT). In TEGOQT seven genetic-physiological systems (GPS) involved in harvest increasing, but not particular traits of productivity, are to be operated with. These GPS are attractions; micro-distribution of attractive plastic substances between grains and chaff in ear; adaptability to drought, cold, frost, heat, salt, etc; horizontal immunity; «payment» by dry biomass for a limitative factor of soil nutrition -N, P, K, etc; tolerance to plant density in phytocenosis; hereditary variability in duration of the phases of ontogenesis). In this paper we discuss drought adaptability as a part of GPS complex. It is shown that phenotyping evaluation is necessary to analyze drought tolerance, the complex property to which no less than 22 component characters contribute. This allows to construct a set of eco-genetic portraits (EGP) of parent plants at different types of drought simulated in an artificial climate control chamber. The EGP are histograms which reflect the values of all components of drought resistance for each parent thus allowing to select pairs complementary in the elements of the EGP. Based on a predictive EGP, it is possible to define, through mathematical models that reflect the contribution of each component to the resulting drought tolerance, the optimum combination of components for ensuring maximum positive additive effect, and thus to manage selection of the parents for hybridization in view to create new drought resistant varieties.
S u m m a r yThe authors considered the brand new approaches to choose the breeding pairs (varieties) for crossing and prediction of eco-genetic picture of new variety. The algorithm of control of breeding process on the basis of mathematical model for «genotype-environment» interaction was proposed, which consists in successive choose the variants of mutually complementary eco-genetic character of potential parents and prediction of resultant quantitative breeding determinants. The choose of the variants is performed until a required degree of coincidence between predictable and specified breeding determinants occurs. Under the control of ecological factors such choose is supplementing by optimization of environment parameters, and it provides the maximal usage of eco-genetic potential and the increase of reliability of identification of progeny genotypes on their phenotypes in the sequel.Keywords: mathematical modeling, plant breeding technologies, self-pollinators.Quantitative genetics (1) describes phenotypic diversity of any quantitative trait manifested in a splitting population as:, where vph -phenotypic variance, vg -genotypic variance, and ve -ecological variance caused by differences of environmental conditions for individual plants in population.For a breeder it is important to know genotypic variance of a productive trait, because selection of best genotypic changes and further improvement of these features at vegetative propagation (eg., potatoes, fruit-berry crops) increases productivity, resistance and quality of resulting clones. Today, rapid estimate of productivity (without changes of generations) is performed in a splitting population grown in artificially leveled background conditions with the use of standards (comparison of phenotypic diversity in a wild or splitting population (vph) with diversity of clones or pure lines derived from this population (ve) (1), as well as by Shrikhande's method (2-4) and the theory of background characters (5-10) that was found to be the most precise and reliable approach (11-13).In grain self-pollinating crops, genotypic variance vg can be easily assessed knowing the variance of mean values of traits manifested by cultivars grown on experimental plots in a breeding center (calculation of mean value of a trait over the plot eliminates all noises; the resulting mean corresponds to genotypic value of the trait, whose observed variance is vg). However, cereal crops can't be propagated by cloning or grafting, so, for a breeder, any prognoses about genetic improvement of their productivity are possible only upon the data on additive genetic variance (va) of these crops.It is known (1) that vg = va + vd + vi [2], where va -additive variance (caused by additively acting polygenes), vd -genetic variance caused by domination effects, and vivariance provided by effects of epistasis.Until recently, quantitative genetics could estimate v a only by the correlation "parent -offspring" covop = 1 / 2 va or correlations of relatives (half-sibs) covhs = 1 / 4 va (12). This app...
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