Standortspezifische Modellierung von Pflanzenwachstum, Wasser- und N-Dynamik auf der Basis hochaufgelöster Bodensensordaten

Die Berücksichtigung von Bodenunterschieden innerhalb von Ackerschlägen bei der Bemessung von Düngergaben kann zu einer höheren Effizienz von Düngermaßnahmen führen, wenn einerseits Ertragspotentiale genutzt und andererseits Überdüngungen vermieden werden. Die technischen Möglichkeiten des Precision Agriculture werden jedoch bislang nur zögerlich genutzt, da die Erhebung der räumlichen Variabilität von Bodeneigenschaften mit erheblichem Aufwand verbunden ist und betriebswirtschaftlich wenig lohnend erscheint. Im BoNaRes Projekt I4S werden verschiedene Verfahren der Bodensensorik zur Erfassung wesentlicher Merkmale entwickelt und mit Modellen und Entscheidungsunterstützungsalgorithmen verknüpft. Erste Ergebnisse, die das Potential einer auf hochaufgelösten Bodendaten basierenden Simulation von Pflanzenwachstum sowie Bodenwasser und-Stickstoffdynamik im Vergleich mit hochaufgelösten Ertragskarten zeigen, werden vorgestellt. Diese basieren zunächst auf der bereits etablierten Messung der elektrischen Leitfähigkeit (EM-38) und der Nutzung von konventionell untersuchten Bodenproben in einem 50 m Raster. Hieraus lassen sich hochaufgelöste Karten zur Verteilung von Textur und Humusgehalt als Modelleingangsgrößen für 5000 Punkte innerhalb eines 20 ha Schlages ableiten. Die Konsistenz der Modellrechnungen wird anhand von Erträgen, Bodenwasser- und Nmin-Gehalten an 60 Rasterpunkten über drei Vegetationsperioden geprüft. Der Effekt unterschiedlicher Aggregierung sowohl von Boden als auch Ertragsdaten wird dargestellt

Description of the compiled experimental data available in the MACSUR CropM database

The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) have been collected through out the project together with data for additional analysis of abiotic factors influencing yields. A list of possible dataset was collated in the first year of project however very few of the existing datasets were found usable for the crop model simulation as they fell short of the requirements defined in the part 2.3. However database has been populated as planned with the results of the ongoing MACSUR studies and will serve in the same way for the MACSUR 2 duration

Strategien für die Landwirtschaft im Klimawandel: eine Modellstudie

Das dynamische, prozess-basierte Simulati-onsmodell MONICA wurde eingesetzt, um die Entwicklung des Bewässerungs- und des Stickstoffbedarfs von Winterweizen, Wintergerste und Silomais in einem angenommenen Klimaszenario (A1B) zu berechnen. Der Bewässerungsbedarf wurde mit einer automatischen Bewässe-rungsfunktion in Simulationen auf einem Sandstandort (531 mm durchschnittlicher Jahresniederschlag 1951 – 2003), der Stickstoffbedarf zusätzlich auf einem Lössstandort ermittelt (876 mm). Es ergaben sich ein signifikant erhöhtes Ertragsniveau bei Mais und Weizen unter Bewässerung im Zeitraum um das Jahr 2070 im Vergleich zur nicht bewässerten Kultur, jedoch eine kaum ertragssteigernde Wirkung bei Gerste. Der Stickstoffbedarf steigt in der Simulation um etwa 20 kg N ha–1 bei Weizen, bleibt bei Gerste und Mais jedoch auf heutigem Niveau

Analysing urban heat island patterns and simulating potential future changes

Climate change is interpreted as one of the most serious environmental problems for the 21st century. Changes in climate are now generally accepted. However, the rate of change has spatial characteristics and is highly uncertain. The Himalaya is experiencing abrupt change; so vulnerability and adaptation studies have become crucial. This pilot study presents initial findings of the research project entitled ‘Human Ecological Implications of Climate Change in the Himalaya.’ A study of climate change perceptions, vulnerability, and adaptation strategies of farming communities of the cool-wet temperate (Lumle) and the hot-wet sub-tropical (Meghauli) villages in Central Nepal was conducted. The findings are derived from the analysis of temperature and precipitation data of last 40 years, and primary data collected in September 2012. Focus Group Discussions, Key Informant Interviews, and Historical Timeline Calender were applied. The changes perceived by the communities are fairly consistent with the meteorological observations and are challenging the sustainability of social-ecological systems and communities’ livelihoods. Farming communities have adopted some strategies to minimize the vulnerability. But the adopted strategies have produced both negative and positive results. Strategies like flood control, shifting crop calendars, occupational changes and labour migrations have produced positive results in livelihood security. Occupational changes and labour migration have negatively impacted local agro-ecology and agricultural economies. Early-harvesting strategies to reduce losses from hailstorm have reduced the food and fodder security. Lack of irrigation for rice-seedlings is severely affecting the efficacy of shifting the rice-transplantation calendar. Conclusions suggest that while farmers have practiced strategies to better management of farms, livelihood sustainabilities are reaching thresholds due to the changing conditions.Rishikesh Pandey, Douglas K Bardsle

Climate-induced severe water scarcity events as harbinger of global grain price

The severe water scarcity (SWS) concept allows for consistent analysis of the supply and demand for water sourced grain production worldwide. Thus, the primary advantage of using SWS is its ability to simultaneously accommodate the spatial extent and temporal persistence of droughts using climatic data. The SWS concept was extended here to drivers of global grain prices using past SWS events and prices of three dominant grain crops: wheat, rice and maize. A significant relation between the SWS affected area and the prices of wheat was confirmed. The past price–SWS association was then used to project future wheat prices considering likely climate change scenarios until 2050 and expected SWS extent. The projected wheat prices increase with increasing SWS area that is in turn a function of greenhouse gas emissions. The need to act to reduce greenhouse gas emissions is again reinforced assuming the SWS-price relation for wheat is unaltered

Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

Simultaneous analysis of economic and environmental performance of horticultural crop production requires qualified assumptions on the effect of management options, and particularly of nitrogen (N) fertilisation, on the net returns of the farm. Dynamic soil-plant-environment simulation models for agro-ecosystems are frequently applied to predict crop yield, generally as dry matter per area, and the environmental impact of production. Economic analysis requires conversion of yields to fresh marketable weight, which is not easy to calculate for vegetables, since different species have different properties and special market requirements. Furthermore, the marketable part of many vegetables is dependent on N availability during growth, which may lead to complete crop failure under sub-optimal N supply in tightly calculated N fertiliser regimes or low-input systems. In this paper we present two methods for converting simulated total dry matter to marketable fresh matter yield for various vegetables and European growth conditions, taking into consideration the effect of N supply: (i) a regression based function for vegetables sold as bulk or bunching ware and (ii) a population approach for piecewise sold row crops. For both methods, to be used in the context of a dynamic simulation model, parameter values were compiled from a literature survey. Implemented in such a model, both algorithms were tested against experimental field data, yielding an Index of Agreement of 0.80 for the regression strategy and 0.90 for the population strategy. Furthermore, the population strategy was capable of reflecting rather well the effect of crop spacing on yield and the effect of N supply on product grading

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(sup 1) per degC. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

Designing new cereal cultivars as an adaptation measure using crop model ensembles

To date, crop models have been little used for characterising the types of cultivars suited to a changed climate, though simulations of altered management (e.g. sowing) are often reported. However, in neither case are model uncertainties evaluated at the same time

О перспективе извлечения йода из продукта утилизации окислителя ракетного топлива

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade