Site-Specific Nutrient Management

The concept of nitrogen gap (NG), i.e., its recognition and amelioration, forms the core of this book entitled Site-Specific Nutrient Management (SSNM). Determination of the presence of an NG between fields on a farm and/or within a particular field, together with its size, requires a set of highly reliable diagnostic tools. The necessary set of diagnostic tools, based classically on pedological and agrochemical methods, should be currently supported by remote-sensing methods. A combination of these two groups of methods is the only way to recognize the factors responsible for yield gap (YG) appearance and to offer a choice of measures for its effective amelioration. The NG concept is discussed in the two first papers (Grzebisz and Łukowiak, Agronomy 2021, 11, 419; Łukowiak et al., Agronomy 2020, 10, 1959). Crop productivity depends on a synchronization of plant demand for nitrogen and its supply from soil resources during the growing season. The action of nitrate nitrogen (N–NO3), resulting in direct plant crop response, can be treated by farmers as a crucial growth factor. The expected outcome also depends on the status of soil fertility factors, including pools of available nutrients and the activity of microorganisms. Three papers are devoted to these basic aspects of soil fertility management (Sulewska et al., Agronomy 2020, 10, 1958; Grzebisz et al., Agronomy 2020, 10, 1701; Hlisnikovsky et al., Agronomy 2021, 11, 1333). The resistance of a currently cultivated crop to seasonal weather variability depends to a great extent on the soil fertility level. This aspect is thoroughly discussed for three distinct soil types and climates with respect to their impact on yield (Hlisnikovsky et al., Agronomy 2020, 10, 1160—Czech Republic; Wang et al., Agronomy 2020, 10, 1237—China; Łukowiak and Grzebisz et al., Agronomy 2020, 10, 1364—Poland). In the fourth section of this book, the division a particular field into homogenous production zones is discussed as a basis for effective nitrogen management within the field. This topic is presented for different regions and crops (China, Poland, and the USA) (Cammarano et al., Agronomy 2020, 10, 1767; Panek et al., Agronomy 2020, 10, 1842; Larson et al., Agronomy 2020, 10, 1858)

Sugarbeet Model Development for Soil and Water Quality Assessment

Sugarbeet (Beta vulgaris) is considered as one of the most viable alternatives to corn for biofuel production as it may be qualified as “advanced” biofuel feedstocks under the ‘EISA 2007’. Production of deep rooted sugarbeet may play a significant role in enhancing utilization of deeper layer soil water and nutrients, and thus may significantly affect soil health and water quality through recycling of water and nutrients. A model can be useful in predicting the sugarbeet growth, and its effect on soil and water quality. A sugarbeet model was developed by adopting and modifying the Crop Environment and Resource Synthesis-Beet (CERES-Beet) model. It was linked to the Cropping System Model (CSM) of the Decision Support System for Agrotechnology (DSSAT) and was termed as CSM-CERES-Beet. The CSM-CERES-Beet model was then linked to the plant growth module of the Root Zone Water Quality Model (RZWQM2) to simulate crop growth, soil water and NO3-N transport in crop fields. For both DSSAT and RZWQM2, parameter estimation (PEST) software was used for model calibration, evaluation, predictive uncertainty analysis, sensitivity, and identifiability. The DSSAT model was evaluated with two sets of experimental data collected in two different regions and under different environmental conditions, one in Bucharest, Romania and the other in Carrington, ND, USA, while RZWQM2 was evaluated for only Carrington, ND experimental data. Both DSSAT and RZWQM2 performed well in simulating leaf area index, leaf or top weight, and root weight for the datasets used (d-statistic = 0.783-0.993, rRMSE = 0.006-1.014). RZWQM2 was also used to evaluate soil water and NO3-N contents and did well (d-statistic = 0.709-0.992, rRMSE = 0.066-1.211). The RZWQM2 was applied for simulating the effects of crop rotation and tillage operations on sugarbeet production. Hypothetical crop rotation and tillage operation scenarios identified wheat as the most suitable previous year crop for sugarbeet and moldboard plow as the most suitable tillage operation method. Both DSSAT and RZWQM2 enhanced with CSM-CERES-Beet may be used to simulate sugarbeet production under different management scenarios for different soils and under different climatic conditions in the Red River Valley.USDA National Institute of Food and Agriculture Foundational Program (Award No.: 2013-67020-21366

A process-based crop growth model for assessing Global Change effects on biomass production and water demand - A component of the integrative Global Change decision support system DANUBIA -

Spatial and temporal changes in crop water demand are of fundamental significance when examining potential impacts of Global Change on water resources on the regional scale. Carried out within the project GLOWA-Danube, this study investigates the response of crops to changing environmental conditions as well as to agricultural management. As a component of the integrative Global Change decision support system DANUBIA, a process-based crop growth model was developed by combining the models GECROS and CERES. The object-oriented, generic model comprises sugar beet, spring barley, maize, winter wheat and potato. The modelled processes are valid for all crops and mainly comprise phenological development, photosynthesis, transpiration, respiration, nitrogen demand, root growth, soil layer-specific water and nitrogen uptake, allocation of carbon and nitrogen as well as leaf area development and senescence. Attention is given to crop-specific differences through assignment to crop categories (e.g. C4 photosynthesis type) and a set of crop-specific parameters. The model was validated by comparing simulated data with several sets of field measurements, covering a wide range of meteorological and pedological conditions in Germany. Furthermore, the responsiveness of the model to Global Change effects was examined in terms of increased air temperatures and atmospheric carbon dioxide concentrations. The results show that the model efficiently simulates crop development and growth and adequately responds to Global Change effects. The crop growth model is therefore a suitable tool for numerically assessing the consequences of Global Change on biomass production and water demand, taking into account the complex interplay of water, carbon and nitrogen fluxes in agro-ecosystems. Within DANUBIA, the model will contribute to the development of effective strategies for a sustainable management of water resources in the Upper Danube Basin

Adaptation of boreal field crop production to climate change

Peer reviewe

Analysis of the influence of environmental variables on carbon content of sugar beet crop and estimation of nitrogen content in leaves by vegetation indices

Esta Tesis trata de conocer el papel del cultivo de la remolacha azucarera como sumidero de CO2 cuantificando, por una parte, la absorción de dicho gas de efecto invernadero y dilucidando, por otra, si este proceso de asimilación está determinado ontogénicamente o si bien está influenciado por condiciones ambientales. Una vez demostrado que el factor localización (clima, suelo y campaña) es determinante e identificada la influencia de radiación y temperatura, se plantea la necesidad de, a través de un escenario de cambio climático del AR5 de IPCC y el modelo de simulación de cultivos Aquacrop de FAO, conocer los posibles futuros comportamientos y tendencias del cultivo. Todo ello y junto al uso de índices de vegetación RGB (existentes y dos nuevos propuestos), como herramientas para la detección de cambios críticos en el cultivo que afecten a su capacidad como sumidero, se genera el conocimiento necesario para ulteriores medidas de adaptación.Departamento de Ingeniería Agrícola y Foresta

Assessing the agricultural system and the Carbon cycle under climate change in Europe using a dynamic global vegetation model

Several recent studies predicted changes in the climatic conditions in Europe driven by the increased atmospheric CO2 concentration due anthropogenic activities. The climate change can affect the agriculture through many aspects of crop production over the European continent. Not only plant productivity, but also geographical shifts of cultivation areas, changes in crop phenology, in land use, and in soil carbon stocks have to be taken into account for assessments of the next future. This study provides a potentially powerful baseline to perform integrated assessments on the impacts of the changing climate by assessing crop production with a single integrated framework for large-scale studies. Not only crops and natural vegetation in a single Dynamic Global Vegetation Model, the LPJ-C, but also potential and water-limited crop production are included within the same biosphere scheme. The LPJ-C is extended to simulate not only natural biomes, but also crops. We perform an optimization procedure, which provides a set of crop parameters used in the regional assessment over Europe. Further, we used the resulting modelling framework to study the changes of potential production of maize and wheat together with the shift in their potential growing area. The results show that wheat yield will suffer from a decline, but fertilization due to the CO2 enriched atmosphere will compensate this effect. For maize, cultivation will clearly expand towards north and east. Since maize, as a C4 plant, is mostly unaffected by the CO2 fertilization effect, the shorter growing season will lead to a lower net primary productivity, while the mean over the continent will increase according to the large geographical spread. Furthermore, LPJ-C is able to reproduce the observed relative increase of water use efficiency under water-limited conditions and a CO2 fertilization effect. The improved water use efficiency of wheat leads to a relatively smaller transpiration per unit of biomass, so that precipitation will partially satisfy the transpiration demand. On the other hand, wheat will suffer from an increase of yield variability and a higher frequency of extreme crop failures. Even though maize potential distribution will be enlarged, the yield will be affected by strong losses, unless largely improved irrigation will satisfy the increased water demand. We perform also the coupling of LPJ-C with the land-use model KLUM, as a connection between a profit maximization procedure for land allocation and a process-based description of crop production. The coupled system showed that temperature would play a major role in the soil carbon dynamics over the expected northward shift of crops. However, important changes have to be expected for distribution of "warm" cereals as rice and maiz