Session 2.1: Scenarios and RAPs
For a complete list of all of the workshop abstracts click here (PDF).
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Session 1.6: Oral Presentation
Title: Soil Data Aggregation Effects in Regional Yield Simulations
Authors: Holgen Hoffmann1, G. Zhao1, S. Asseng1, M. Bindi3, D. Cammarano4, J.
Constantin5, E. Coucheney6, R. Dechow7, L. Doro8, H. Eckersten9, T. Gaiser1, B. Grosz7,
E. Haas10, B. Kassie2, KC Kersebaum11, R. Kiese10, S. Klatt10, M. Kuhnert12, E. Lewan6,
M. Moriondo13, C. Nendel11, H. Raynal5, P.P. Roggero8, R. Rötter14, S. Siebert1, C. Sosa6,
X. Specka11, F. Tao14, E. Teixeira15, G. Trombi3, J. Yeluripati16, E. Vanuytrecht17, D. Wallach5, E. Wang18, L. Weihermüller19, Z. Zhao18, and
F. Ewert1
1 INRES, University of Bonn, Germany, 2 University of
Florida, USA, 3 University of Florence, Italy, 4 Institute of Biochemical Plant Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Germany, 5 INRA, France, 6 Department of Crop Production Ecology,Swedish University of Agricultural Sciences, Sweden, 7 Thünen-Institute of Climate-Smart-Agriculture, Germany, 8 Universitá degli Studi di Sassari, Italy, 10 Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Germany, 11 University
of Abendeen, Scotland, UK, 12 CNR-Ibimet, Italy, 13 Environmental Impacts Group, Natural Resources Institute Finland (Luke), Finland, 14 Canterbury Agriculture and Science Centre, New Zealand, 15 Agrosphere Institute (IGB-3), Germany, 16 The James Hutton
Institute, Scotland, UK
Abstract: Large-scale yield simulations often use data of coarse spatial resolution as input for
process-based models. However, using aggregated data as input for process-based models entails the risks of introducing errors due to aggregation (AE). Such AE depend on the aggregation method, on the type of aggregated data as well as on its spatial heterogeneity. However, previous studies indicated that AE in Central Europe might be largely driven by aggregating soil data. AE in yield could therefore be assessed prior to simulation for those regions with a distinct relationship between spatial yield variability and soil heterogeneity. The present study investigates the AE for soil data and its contribution to the total AE for soil and climate data for a range of different crop models. Soil
data is aggregated by area majority in order to maintain physical consistency among soil variables. AE are assessed for climate and soil data in North Rhine-Westphalia, German, upscaling from 1 to 100 km resolution. We present a model comparison on AE for a range of environmental conditions differing in climate and soil for two crops grown under water-limited conditions. Winter wheat and silage maize yields of 1982-2011 were simulated with crop models after calibration to average regional sowing date, harvest date and crop yield.
Results point to the importance of estimating AE for soil data. Ways to generalize from these results to other regions are discussed.
Session 1.6: Oral Presentation
Title: The AgMIP Coordinated Climate Crop Modeling Project (C3MP) – Differences In Climate Response Across 1100+ Crop Modeling Sets
Authors: Alex C. Ruane1, S. P. McDermid2, and T. Mavromatis3 1 NASA Goddard Institute for Space Studies, New York, USA, 2 New York University, New York, USA, 3 Aristotle University of Thessaloniki, Greece
Abstract: The ways in which crops respond to fundamental changes in carbon dioxide concentration ([CO2]), temperature (ΔT), and precipitation (ΔP) hold the key to first order impacts of climate change on agricultural systems. This response may vary across
locations, crop models, crop species, cultivars, and management systems. The AgMIP Coordinated Climate-Crop Modeling Project (C3MP) enlisted crop modelers around the world to run a set of standardized carbon dioxide ([CO2]), temperature, and rainfall change experiments in their own crop model configurations. More than 100 crop modelers participated, examining over 15 species and 20 crop models, with simulation sets in more than 50 countries.
C3MP sites for maize, spring wheat, winter wheat, rice, soybeans, and peanuts provide the largest number of simulation sets and allow the most extensive evaluation. Ensemble mean responses reveal well-known features such as the lower response to elevated [CO2]
in C4 crops as compared to C3, but also show fundamental differences in temperature response due in part to the geographical locations where certain crops are most prevalent (e.g., wheat tends to be grown in cooler climates than maize). Uncertainty across simulation sets reveals heightened differences between simulation sets at extreme climate changes, particularly the high temperature conditions in which heat and water stress can be particularly damaging. C3MP results also demonstrate a strong interaction between mean climate change and climate variability, resulting in larger extremes under
future climate conditions.
Session 1.6: Oral Presentation
Title: Integrated crop model uses remote sensing data to simulate crop growth and carbon flux data
Authors: Jonghan Ko, S. Jeong, and J. Choi Chonnam National University, South Korea
Abstract: A hybrid technique that incorporates crop modeling and remote sensing has the potential to strengthen the individual capabilities of both, i.e., the continuous crop growth reproduction using crop modeling and the detailed observation of crop conditions using remote sensing. In this study, we integrated a crop model able to use remote sensing data to simulate crop growth and carbon flux information. The current model was developed in order to extend the previously developed hybrid crop model (i.e.,GRAMI-Rice) for possible applications in studying the influence of climate change on crops. To construct the model, a leaf-and-canopy carbon flux model was discretely formulated based on pre-existing modeling study outcomes. The carbon flux model was then unified with GRAMI-Rice, such that the assimilated model could simulate time-series information of hourly and daily carbon fluxes, as well as crop growth variables, and we found that the individual carbon model effectively reproduced the carbon fluxes of paddy rice (Oryza sativa). The integrated model will be further investigated for its capability to reproduce seasonal information regarding carbon fluxes and crop growth variables using operational satellite remote sensing data. We will also investigate the capability of the model to simulate the effects of climate change on crops. The current ongoing efforts show promise for simulating crop growth, determining remedial measures for securing future food crop production, and extending the applicability of current and future operational satellites for monitoring croplands.
Session 1.6: Oral Presentation
Title: Using Earth Observation and ancillary data sources as alternative to household surveys for regional integrated assessments For maize production in the Free state of South Africa.
Authors: Wiltrud Durand, D. Cammarano, O. Crespo, and A. Fourie
Abstract: Various regions of the world are undertaking regional assessments following AgMIP protocols and integrated assessment procedures. These protocols were created to link climate, crop and economic modelling through information technology components to assess the impact of Climate Change on future agricultural systems. It relies on household surveys to supply the necessary data inputs to the crop and economic models. In South Africa no such detailed survey data set could be obtained and an alternative method had to be developed. Taking into account that, although, most dynamic crop models have been developed and tested for plot scale (homogeneous fields), applications related to climate change, often require broader spatial scales that can incorporate considerable heterogeneity. This prompted the approach to use satellite imagery, producer independent crop estimate survey (PICES) and crop type classification to develop a maize crop field level land cover and linking this to regional enterprise budgets. Using Landsat and Spot images, 14 million hectare of field boundaries was digitized. The field crop boundaries were used as basis for an aerial-survey, identifying fields planted with crops. The identified crop type per field was used for satellite image classification. For the maize crop field level land cover all fields that were identified to have been panted to maize were integrated into one data basis. To establish crop management input for crop modelling, samples obtained from objective yield surveying were used to calculate the proportion of fields with certain row widths, planting dates and plant populations. The same proportion was used to assign the management strategies to all the fields within the Free State using GIS. Fertilization was based on the average modelled 50 year yield potential of each field. The soil properties required for crop yield modelling were derived using the identified soil series suitable for maize production from Terrain Units of land type maps within a GIS
framework. This assigned each field a unique soil description. Pedo-transfer functions were used to calculate soil model inputs. Two sources of climate data, quinary catchments database and MERRA were linked to generate a continual coverage of climate data for the Free State Province for 29 (Global Circulation Models) GCMs and two Representative Concentration. Pathways RCPs (4.5 and 8.5) for baseline (1980-2010) and mid-century (2040-270) climate change predictions based on AgMIP methodology. Using two crop models DSSAT and APSIM the impact of 5 selected climate change scenario’s on production and economics were evaluated on dryland and irrigated maize systems using the Trade-off Analysis Multi-dimensional Model (TOA-MD) model with a
minimum data set approach based and stakeholder outlooks in the form of
representative agricultural pathways (RAPs).
16. Poster Presentation: Session 1.6
Title: Disentangling factors of landscape changes in Burkina Faso, the nexus between spatial modelling and remote sensing
Authors: Camille Jahel*1 , Louise Leroux*1 , A. Bégué1 , M. Castets1 , C. Baron1 , and
D. L. Seen1 1 CIRAD, UMR TETIS, *C. Jahel and L. Leroux have equally contributed to the abstract and are thus joined ‘‘lead authors’’
Abstract: Rural areas of West Burkina Faso have seen notable transformations these last two decades due to high population growth and farming systems evolution. Satellite images acquired frequently and covering large areas are essential for detecting such landscape changes and long term trends. However, these images generally have coarse spatial resolutions and can only provide information about changes in the main vegetation patterns. The factors causing these changes are more difficult to determine, although there are essential for monitoring landscape evolution. We hereby present a method based on multi-scalar modelling of past landscape dynamics crossed with changes in vegetation trends identified from coarse resolution satellite images. The aim of our presentation is to use the model to simulate and illustrate how land cover and land use
changes may impact vegetation response by improving the qualification and
understanding of the observed trends. The cropping systems dynamics of the study area, the Tuy province of West Burkina Faso, were modelled with the Ocelet Modelling Platform over the last fifteen years through a multi-scalar model. The model was validated at local scale with information derived from high resolution images. At the same time, vegetation trends were analysed using Ordinary Least Square regressions based on MODIS NDVI time series. Simulated cropland change maps were then used to decompose the remote sensing-based trends. This allowed the spatial identification of factors responsible for the vegetation changes. The original approach we proposed here opens new opportunities for the understanding and monitoring of landscape changes using time series of coarse
resolution satellite images.
15. Poster Presentation: Session 1.6
Title: Determination of leaf area index and biomass value in the wheat fields with different method approaches using the remote sensing, LAI measuring device and manuel measuring
Authors: Omer Vanli Istanbul Technical University
Abstract: Population growth that restricts arable land in the world has increased the need for effective and efficient farming practices. Sustainable agriculture will be achieved in case of Fields that based on different user may be categorized and can be used. Knowing
physiological properties of plant is an important issue to sustain agricultural
activities and have high crop yields.
Monitoring of wheat growth period carefully, besides to provide a more accurate determination of pesticides and fertilizers application time, helps to make yield estimation. The light utilization rate of leaves that is the organ where generate the majority of photosynthesis and the yield is closely related to leaf area index (LAI). LAI which vary according to
species and varieties of plants is also varies during the vegetation period. In this study, wheat samples that taken unit per area and selected in the 4 different Fields in the Islahiye and Nurdagı regions is calculated manually LAI and also is weighed and recorded biomass values. Secondly LAI values that measured in the same areas and used LAI meter were recorded. Biomass and LAI calculated from NDVI that is found from remote
sensing image in the same period were determined. Finally with the comparison
all of this processes accuracy tests of the LAI and biomass values were made.
Key Words: Crop Yield, Leaf Area Index(LAI), NDVI, Biomass