Browsing by Person "Memic, Emir"

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A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception
(2025) Otto, Dina; Munz, Sebastian; Memic, Emir; Hartung, Jens; Graeff-Hönninger, Simone; Otto, Dina; Institute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, Germany; Munz, Sebastian; Institute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, Germany; Memic, Emir; Institute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, Germany; Hartung, Jens; Department Sustainable Agriculture and Energy Systems, University of Applied Science, Freising, Germany; Graeff-Hönninger, Simone; Institute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, Germany
The precise determination of leaf shape is crucial for the quantification of morphological variations between individual leaf ranks and cultivars and simulating their impact on light interception in functional-structural plant models (FSPMs). Standard manual measurements on destructively collected leaves are time-intensive and prone to errors, particularly in maize ( Zea mays L.), which has large, undulating leaves that are difficult to flatten. To overcome these limitations, this study presents a new camera method developed as an image-based computer vision approach method for maize leaf shape analysis. A field experiment was conducted with seven commonly used silage maize cultivars at the experimental station Heidfeldhof, University of Hohenheim, Germany, in 2022. To determine the dimensions of fully developed leaves per rank and cultivar, three destructive measurements were conducted until flowering. The new camera method employs a GoPro Hero8 Black camera, integrated within an LI-3100C Area Meter, to capture high-resolution videos (1920 × 1080 pixels, 60 fps). A semi-automated software facilitates object detection, contour extraction, and leaf width determination, including calibration for accuracy. Validation was performed using pixel-counting and contrast analysis, comparing results against standard manual measurements to assess accuracy and reliability. Leaf width functions were fitted to quantify leaf shape parameters. Statistical analysis comparing cultivars and leaf ranks identified significant differences in leaf shape parameters (p < 0.01) for term alpha and term a . Simulations within a FSPM demonstrated that variations in leaf shape can alter light interception by up to 7%, emphasizing the need for precise parameterization in crop growth models. The new camera method provides a basis for future studies investigating rank-dependent leaf shape effects, which can offer an accurate representation of the canopy in FSPMs and improve agricultural decision-making.
Comprehensive evaluation of the DSSAT‐CSM‐CERES‐Wheat for simulating winter rye against multi‐environment data in Germany
(2024) Shawon, Ashifur Rahman; Memic, Emir; Kottmann, Lorenz; Uptmoor, Ralf; Hackauf, Bernd; Feike, Til; Shawon, Ashifur Rahman; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany; Memic, Emir; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Kottmann, Lorenz; Institute for Crop and Soil Science, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Braunschweig, Germany; Uptmoor, Ralf; Department of Agronomy, University of Rostock, Rostock, Germany; Hackauf, Bernd; Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Groß Lüsewitz, Germany; Feike, Til; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany
Crop models are valuable tools for simulating and assessing genotype‐by‐environment interactions. In most studies, these models are parameterized based on crop data from a few sites and years, which often limits their applicability to a broader geographic context. Therefore, we utilize countrywide multi‐environment variety trial data in this study to implement a genotype‐specific model parameterization for winter rye ( Secale cereale L.) in Germany. We use the Crop and Environment REsource Synthesis (CERES) model originally used for wheat available in the decision support system for agrotechnology transfer (DSSAT) framework and adapt and evaluate it for rye. Calibration and evaluation involved a comprehensive agronomic trial datasets for the rye cultivar Palazzo, encompassing 194 site‐years of experiments covering various cereal production regions in Germany. The parameterization followed a structured approach, encompassing phenology, growth, and yield‐specific coefficients. The parameterized CSM‐CERES‐Rye (where CSM is cropping system model) demonstrated reasonable accuracy in simulating critical crop parameters, including aboveground biomass, leaf area index, tiller, grain number, unit seed weight, and grain yield. The model is available for diverse model‐based assessments of rye cultivation, including evaluating crop management, analyzing crop rotations, and assessing rye's suitability across varied environments, making it valuable for sustainable agriculture and decision‐making.
Impact of calibration strategy and data on wheat simulation with the DSSAT‐Nwheat model
(2025) Shawon, Ashifur Rahman; Attia, Ahmed; Ko, Jonghan; Memic, Emir; Uptmoor, Ralf; Hackauf, Bernd; Feike, Til; Shawon, Ashifur Rahman; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany; Attia, Ahmed; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany; Ko, Jonghan; Department of Applied Plant Science, Chonnam National University, Gwangju, Republic of Korea; Memic, Emir; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Uptmoor, Ralf; Department of Agronomy, University of Rostock, Rostock, Germany; Hackauf, Bernd; Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Groß Lüsewitz, Germany; Feike, Til; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany
Cropping system models (CSMs) are valuable tools for analyzing genotype, environment, and management (G × E × M) interactions in crop production. To apply a CSM in a new region with specific soils, climate, and cultivars, proper calibration and evaluation are required. However, calibration methods vary widely, often depending on modelers' expertise and approach. This study compares three calibration strategies for the DSSAT‐Nwheat model using two datasets: one including yield components (1000‐kernel mass, ears per m 2 , grain number per m 2 ) alongside phenology and grain yield, and another excluding yield components. The datasets cover ∼100 site‐years of winter wheat ( Triticum aestivum ) data from German pre‐registration trials and field experiments. The calibration approaches were (1) stepwise calibration of phenology, biomass, and yield, (2) simultaneous calibration of multiple genetic coefficients, and (3) a hybrid approach combining elements of both. The Time‐Series cultivar coefficient estimator tool was used for implementation. Including yield component data improved model accuracy, reducing root mean square error (RMSE) by up to 10% for key variables such as phenology (3.4–5.5 days). Future wheat yield projections under selected climate scenarios varied by strategy and dataset, ranging from 6376 to 7473 kg ha −1 in fertile, wet soils and 6108 to 6757 kg ha −1 in poorer, dry soils. These results highlight the impact of calibration strategy and dataset choice on model performance. Transparent calibration practices are essential for improving CSM reliability in regional agricultural analysis under diverse environmental conditions.
Impact of construction measures and heat emissions from the operation of underground power cables on spelt (Triticum spelta L.) growth and yield
(2025) Trenz, Jonas; Ingwersen, Joachim; Schade, Alexander; Memic, Emir; Hartung, Jens; Graeff-Hönninger, Simone
Germany decided to promote the energy supply toward low or zero-carbon sources by the middle of the century. Therefore, massive infrastructural investments in grid expansion are needed. These grid expansions will be conducted with 525 kV High-Voltage Direct Current (HVDC) cables, buried at a depth of 1.5 m, passing mainly through arable land. The expected main effects of these cables on soils and crops are caused by construction measures (soil excavation and backfilling of soil material) and soil warming caused by heat dissipation using HVDC. To date, the impact of subsoil warming on crop growth and yield has not been studied in detail. This study investigates the effects of construction measures and subsoil warming on a field scale level for a 2-yr data set (2022 and 2023) in South Germany. The intricate dynamics between construction measures and subsoil heating on spelt (Triticum spelta L.) growth and yield were analyzed in three treatments: 1) Heated Trench (HT), 2) Unheated Trench (UT), and 3) Control. Construction measures were conducted by excavating the soil with a triple lift method (separated into three layers: A-, B-, and C-layer), storing them separately in ground heaps, and backfilling according to their natural layering. The triple lift method resulted in a 12.1 % decrease in bulk density (BD) for UT and 8.9 % for HT in the subsoil compared to the Control. The changes in soil properties affected spelt growth and yield, resulting in a yield increase of 14 % for the UT treatment. Additional subsoil warming in the HT treatment increased the topsoil temperature by 1.2 °C and spelt yield by 24 %. The triple lift method showed promising results, minimizing the impacts on soil compaction and maintaining the spelt growth and yield level.