Complexification methods of interval forecast estimates in the problems on short-term prediction

Вирішено завдання удосконалення методичної бази системи підтримки прийняття рішень у процесi короткострокового прогнозування показникiв організаційно-технічних систем шляхом розробки нових i адаптації існуючих методiв комплексування, здатних врахувати інтервальну невизначеність прогнозних оцінок. Актуальнiсть даного завдання обумовлена необхідністю врахування невизначеності первинної інформації, викликаної проявом НI-чинникiв. Проведений аналіз передумов i особливостей формалiзації невизначеності первинних даних в інтервальнiй формi, виявлені переваги iнтервального аналiзу для вирішення задачi комплексування інтервальних прогнозних оцінок. Викладено короткі відомості про базовий математичний апарат: iнтервальну арифметику та інтервальний аналiз. Вдосконалено методи комплексування прогнозних оцінок шляхом синтезу iнтервальних розширень, отриманих вiдповiдно до парадигми інтервального аналізу. В результатi досліджень встановлено, що введення аналiтичної функцiї переваг дозволило синтезувати модель комплексування в досить загальному виглядi, шляхом об'єднання в єдинiй формi класiв гiбридних i селективних моделей для генерації консолідованих прогнозiв на основi інтервальних прогнозних оцiнок. Це дозволяє отримувати комплексовані прогнози на основi інтервальних прогнозних оцінок, тим самим забезпечувати точність консолідованого короткострокового прогнозу. Проведено критичний аналіз запропонованих методiв i розроблено рекомендацiї щодо їх практичного використання. Сформульовано рекомендації щодо параметричного налаштування аналітичної функції переваг. На прикладi показано адаптивні властивості інтервальної моделі комплексування

Effective organic matter stock management in agricultural practices: modeling and observation

Long-term fertilization experiments (LTFE), with the focus on analyzing soil fertility indicators and their interrelation with crop have fundamental importance for monitoring, modeling, and controlling the status of soils. Validation datasets from LTFE provide the basis for understanding cropland responses to key natural and management drivers such as climate and productivity, land use changes, soil fertility and greenhouse gas emissions. RothC model was used for simulation soil organic carbon (SOC) stocks in several Russian LTFE with mineral and organic fertilization. RothC was able to adequately simulate long-term SOC stock changes in the arable layer of different treatments of fertilization experiments on Podzols, Albeluvisols and Chernozems. Annual C inputs sufficient for maintaining constant SOM stocks and additional C gain were estimated. Simulation of SOC dynamics for plots with no fertilization and the lowest SOC stock revealed that above ground NPP input is sufficient for maintaining constant SOM stocks after conversion to a grassland for forage production and returning FYM in the same plot. The changes in the observed trends for different fields with the same treatments are related to the initial level of soil fertility and different crop-climatic year combinations. This simulation has demonstrated the role of crop rotations and fallowing in SOC dynamics and revealed possible C sequestration in a short-term as it is highly yield-dependent. Agricultural soils are sensitive to small changes of extreme year’s pattern with more expressed loss of the initial C stock under intensive management systems

Assessment of crop yields in modern agriculture on the basis of GIS-Technologies

Information-analytical system of ensuring agricultural technologies was developed on the base of several GIS and models of crop yield.The system included creation of maps of potential yield (function of the natural factors) and possible (function of the real level of the fieldfertility) yield of various crops. These dateswere received in the mass field experiments with fertilizers and in available modern bases ofagrochemical, landscape, climatic parameters. The uneven distribution of natural properties- for example, soil quality, topography, microclimate - on the territory of any size determined a different degree of their suitability for growing different groups of crops. The methodology for calculating the yield of various cropswas based on independent objective assessment of different impact factors bythe methods of linear and nonlinear multiple regression.Modeling results were presented in the form of yield maps with using several GIS.Impact yield factors are divided into two big groups – natural (climate, topography, soils, etc.) and agrochemical (applicationof fertilizers, plant protection agents, intensity of cultivation technologies, etc.)

Monitoring of Soil Fertility (Agroecological Monitoring)

Monitoring the ecological status of agricultural land is a fundamental precondition for controlling its sustainable functions for human society and for maintaining the ecosystem's capacity. We analyze fundamentals, developments, and trends and present results of agroecological monitoring in Russia. This system has been developed and operated by the Pryanishnikov Institute of Agrochemistry in Moscow. Agroecological monitoring in Russia was installed in the 1970s and is based on a regular 5-year agrochemical survey of agricultural lands all over the country, more than 300 field experiments in all bioclimatic zones of the country, and more than 1000 reference monitoring plots. In trials with different inputs of fertilizers, the focus is on analyzing soil fertility indicators and their impact on productivity. Some of these experiments are long-term experiments and part of international networks. Their results are of fundamental importance for monitoring, modeling, and controlling the status of soils in future despite climate change. In a regular survey, we found tendencies toward decreasing soil fertility in some regions, for example with decreased contents of humus and plant-available minerals, and topsoil acidification. Nutrient withdrawals must be compensated for by regular fertilization regimes, nutrient mining must be avoided. We detected some gaps in knowledge on the topic of balancing elements and modeling the agroecosystem's response to climate and land use changes. We conclude that there is a need to implement modern measurement and modeling systems in some key long-term trials. The Pryanishnikov Institute has taken responsibility for coordinating running programs in different regions and administrative units of the Russian Federation, and for elaborating methodical guidelines and highly advanced monitoring technologies. National and international cooperation, research programs and networks are key for agroecological monitoring systems of the twenty-first century in addressing challenges for a highly productive, stable, sustainable, and environmentally safe food production

Evaluation and selection of indicators for land degradation and desertification monitoring : types of degradation, causes, and implications for management

International audienceIndicator-based approaches are often used to monitor land degradation and desertification from the global to the very local scale. However, there is still little agreement on which indicators may best reflect both status and trends of these phenomena. In this study, various processes of land degradation and desertification have been analyzed in 17 study sites around the world using a wide set of biophysical and socioeconomic indicators. The database described earlier in this issue by Kosmas and others (Environ Manage, 2013) for defining desertification risk was further analyzed to define the most important indicators related to the following degradation processes: water erosion in various land uses, tillage erosion, soil salinization, water stress, forest fires, and overgrazing.

Effective organic matter stock management in agricultural practices: modeling and observation - Langfassung

RothC soil carbon dynamic model was used for simulation SOC stocks in 6 Russian long-term fertilization experiments for estimation which agricultural practices lead to soil C accumulation. For all the treatments tested above ground NPP input is sufficient for maintaining constant SOM stocks and additional C gain

ASSESSMENT OF CROP YIELDS IN MODERN AGRICULTURE ON THE BASIS OF GIS-TECHNOLOGIES - Langfasssung

The uneven distribution of natural properties- for example, soil quality, topography, microclimate - on the territory of any size determined a different degree of their suitability for growing different groups of crops. Information-analytical system of ensuring agricultural technologies was developed on the base of several GIS and models of crop yield. The system included creation of maps of potential yield (function of the natural factors) and possible (function of the real level of the field fertility) yield of various crops. These dates were received in the field experiments with fertilizers and in available modern bases of agro-chemical, landscape, climatic parameters

Von J. v. Liebig bis E. A. Mitscherlich. Die Grundlage ressourceneffizienter Pflanzenernährung - Langfassung

We share the visions of the originator of the modern soil science VV Dokuchaev and the great innovators of agrochemistry J v. Liebig, EA Mitscherlich, DN Pryanishnikov, UU Uspanov and others. Their visions were to eliminate hunger and poverty of the population by stable crop yields based on innovative site-adapted soil management and farming

Von J. v. Liebig bis E. A. Mitscherlich. Die Grundlage ressourceneffizienter Pflanzenernährung

Mitscherlichs bedeutendste wissenschaftliche Leistung ist ein von ihm formuliertes Ertragsgesetz, das Wirkungsgesetz der Wachstumsfaktoren. Seine erste Veröffentlichung darüber erschien 1909 unter dem Titel Das Gesetz des Minimums und das Gesetz des abnehmenden Bodenertrages in der Zeitschrift Landwirtschaftliche Jahrbücher. Im Gegensatz zu dem von Carl Sprengel und Justus von Liebig aufgestellten Minimumgesetz, wonach von allen mineralischen Nährstoffen derjenige, der in geringster Menge im Boden vorhanden ist, den Pflanzenertrag maßgebend bestimmt, wies Mitscherlich nach, dass die Ertragshöhe von sämtlichen Wachstumsfaktoren abhängig ist. Nach seinen Forschungsergebnissen kann jeder einzelne Wachstumsfaktor mit einer ihm spezifischen Intensität (Wirkungsfaktor) die Ertragshöhe steigern. Mit zunehmender Annäherung an den Höchstertrag wird jedoch durch eine weitere Steigerung eines bestimmten Wachstumsfaktors im Vergleich zum Aufwand der Mehrertrag deutlich geringer. Die von Mitscherlich aus diesen Erkenntnissen abgeleitete Darstellung der Ertragssteigerungskurve als Logarithmische Verteilung fand in der Landbauwissenschaft weltweites Interesse. Sie gab der dynamisch-quantitativen Ertragsforschung neue Einsichten und führte zu einer kaum zu überblickenden Anzahl experimenteller Untersuchungen, aber auch zu kontroversen wissenschaftlichen Diskussionen. Mitscherlich hat umfangreichere Beiträge mit Forschungsergebnissen über sein Ertragsgesetz vor allem in der Zeitschrift Landwirtschaftliche Jahrbücher publiziert. Von mehreren Übersichtsarbeiten ist die 1956, kurz nach seinem Tode erschienene Schrift Ertragsgesetze hervorzuheben. Obgleich neuere Forschungsergebnisse zeigen, dass Mitscherlichs Ertragsgesetz uneingeschränkt nur für spezielle Versuchsbedingungen gilt, hat sein mathematisch orientiertes Forschungskonzept das Wissen um die Zusammenhänge von Wachstumsfaktoren und Ertragsbildung beträchtlich erweitert