Relevant bibliographies by topics / DSSAT simulation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'DSSAT simulation'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "DSSAT simulation"

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Zhou, Shiwei, Zijin Liu, and Fu Chen. "Developing the DSSAT-CERES-Millet Model for Dynamic Simulation of Grain Protein and Starch Accumulation in Foxtail Millet (Setaria italica) Under Varying Irrigation and Nitrogen Regimes." Plants 14, no. 6 (2025): 910. https://doi.org/10.3390/plants14060910.

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Foxtail millet (Setaria italica), vital in northern China, has its quality and taste influenced by starch and protein. Existing models do not simulate the accumulation of these components during growth. To address this, we enhanced the DSSAT-CERES-Millet model (referred to as DSSAT) by integrating two newly developed modules: the protein simulation module and the starch simulation module. The protein simulation module uses a nitrogen-to-protein conversion coefficient to determine grain protein accumulation based on grain nitrogen accumulation simulated by the DSSAT model. In the starch simulat
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Shelia, Vakhtang, Jirka Šimůnek, Ken Boote, and Gerrit Hoogenbooom. "Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system." Journal of Hydrology and Hydromechanics 66, no. 2 (2018): 232–45. http://dx.doi.org/10.1515/johh-2017-0055.

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AbstractAccurate estimation of the soil water balance of the soil-plant-atmosphere system is key to determining the availability of water resources and their optimal management. Evapotranspiration and leaching are the main sinks of water from the system affecting soil water status and hence crop yield. The accuracy of soil water content and evapotranspiration simulations affects crop yield simulations as well. DSSAT is a suite of field-scale, process-based crop models to simulate crop growth and development. A “tipping bucket” water balance approach is currently used in DSSAT for soil hydrolog
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Paff, Kirsten, and Senthold Asseng. "A Crop Simulation Model for Tef (Eragrostis tef (Zucc.) Trotter)." Agronomy 9, no. 12 (2019): 817. http://dx.doi.org/10.3390/agronomy9120817.

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Tef is an Ethiopian staple grain that provides both food security and income for smallholders. As tef is nutritious and gluten free, it is also gaining popularity as a health food. A tef model was calibrated based on the Decision Support System for Agrotechnology Transfer’s (DSSAT) NWheat model and included parameter changes in phenology, photoperiod response, radiation use efficiency, and transpiration efficiency for both standard and elevated atmospheric CO2, based on published literature for tef and other C4 species. The new DSSAT-Tef model was compared with tef field experiments. DSSAT-Tef
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Alderman, Phillip D. "Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF." Geoscientific Model Development 14, no. 10 (2021): 6541–69. http://dx.doi.org/10.5194/gmd-14-6541-2021.

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Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatially explicit simulated outputs at grid points through an inefficient process of translation from binary spatially referenced inputs to point-specific text input files, followed by translation and aggregation back from point-specific text output files to binary spatially referenced outputs. The main objective of this paper was to document the design and implemen
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Liu, Hai Long, Ye Ping Zhu, Jing Yi Yang, Ping He, and Ji Yun Jin. "Simulation and Evaluation of DSSAT Model under Long-Term Continuous Maize Production for Agricultural Intelligent Decision Support." Advanced Materials Research 709 (June 2013): 551–54. http://dx.doi.org/10.4028/www.scientific.net/amr.709.551.

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DSSAT (Decision Support System for Agrotechnology Transfer) -CERES-Maize was employed to characterize maize yield and nitrogen dynamics in a 50-year maize production study at Woodslee, Ontario, Canada. The treatments selected for evaluation included continuous maize/corn with fertilization (CC-F) and continuous maize/corn without fertilization (CC-NF) treatments. The results showed that the DSSAT model could simulate yield of CC-F more precisely than that of CC-NF. The simulated cumulative nitrate loss from CC-F treatment from 1998 to 2000 was consistent with the actual measured value. For CC-
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Sachin, G., J. Mohammed Ahamed, K. Nagajothi, M. Rana, and B. S. Murugan. "AUTOMATION OF THE DSSAT CROP GROWTH SIMULATION MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 251–56. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-251-2019.

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<p><strong>Abstract.</strong> Crop Simulation Models (CSM) simulate the growth, development, and yield of crops using various inputs such as soil water, carbon and nitrogen processes, and management practices. DSSAT (Decision Support System for Agrotechnology Transfer) is a software program that comprises dynamic crop growth simulation models for over 42 crops. It incorporates modules for crop, soil, and weather to simulate long-term outcomes of crop management strategies. DSSAT-CSM requires various data for model operation. This includes data on the site where the model is t
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Ge, Jiankun, Zihui Yu, Xuewen Gong, Yinglu Ping, Jinyao Luo, and Yanbin Li. "Evaluation of Irrigation Modes for Greenhouse Drip Irrigation Tomatoes Based on AquaCrop and DSSAT Models." Plants 12, no. 22 (2023): 3863. http://dx.doi.org/10.3390/plants12223863.

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The improvement of the simulation accuracy of crop models in different greenhouse environments would be better applied to the automation management of greenhouse cultivation. Tomatoes under drip irrigation in a greenhouse were taken as the research object, and the cumulative evaporation capacity (Ep) of the 20 cm standard evaporation dish was taken as the basis for irrigation. Three treatments were set up in the experiment: high water treatment without mulch (NM-0.9 Ep), high water treatment with mulch (M-0.9 Ep), and low water treatment with mulch (M-0.5 Ep). AquaCrop and DSSAT models were us
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Kim, Jae Sung, and Isaya Kisekka. "FARMs: A Geospatial Crop Modeling and Agricultural Water Management System." ISPRS International Journal of Geo-Information 10, no. 8 (2021): 553. http://dx.doi.org/10.3390/ijgi10080553.

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To ensure agricultural sustainability and desirable environmental outcomes, stakeholders need systems-based model-driven decision support tools. The objective of this study was to develop a global scale web-based geospatial crop modeling application called Food, Agriculture, and Resource Management system (FARMs), to simplify the application of the crop simulation model —Decision Support System for Agrotechnology Transfer (DSSAT) without requiring users to create input weather, climate, and soil files. FARMs was built based on open source Geographic Information System (GIS) technologies and DS
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Khatua, Rajashree, S. Panneerselvam, V. Geethalakshmi, Kumar M., and P. Jeyakumar. "Calibration and Validation of DSSAT (CROPGRO) Model for Winter Irrigated Cotton in Coimbatore, Tamil Nadu, India." International Journal of Plant & Soil Science 35, no. 22 (2023): 752–60. http://dx.doi.org/10.9734/ijpss/2023/v35i224187.

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Crop simulation model is used for predicting the crop productivity under various crop management practices and find out the yield gap of cotton crop, that require determination of genetic coefficient of a crop cultivar. Successful use of a crop model depends on the accuracy of calibration and validation of different parameters. This paper aimed to evaluate the DSSAT model using experimental data on the different nutrient management practices for cotton and calibrated and validated the data by using DSSAT (CROPGRO) model. The field experiment was conducted at Cotton Breeding Station, TNAU, Coim
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Shen, Hongzheng, Fangping Xu, Rongheng Zhao, Xuguang Xing, and Xiaoyi Ma. "Optimization of Sowing Date, Irrigation, and Nitrogen Management of Summer Maize Using the DSSAT-CERES-Maize Model in the Guanzhong Plain, China." Transactions of the ASABE 63, no. 4 (2020): 789–97. http://dx.doi.org/10.13031/trans.13654.

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HighlightsGood applicability of DSSAT was validated in simulating summer maize yield in the Guanzhong Plain, China.Optimal sowing dates of summer maize were obtained for different climatic years.The optimal irrigation and nitrogen management strategy conserved water and nitrogen. Abstract. Agricultural system models play an important role in simulating crop growth processes and water and fertilizer regulation in arid regions. To solve the current problems of optimizing the sowing date in different climatic years and the fertilizer application in low-precipitation conditions in the Guanzhong Pl
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Dissertations / Theses on the topic "DSSAT simulation"

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Pinto, Helena Maria Soares. "Projeções de risco de produção de cana-de-açúcar no Estado de São Paulo baseadas em simulações multimodelos e cenários climáticos futuros." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-05112015-141817/.

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A cana-de-açúcar (Saccharum officinarum) é uma cultura de relevância socioeconômica para o Brasil por proporcionar divisas na balança comercial do país e figurar como pilar estratégico para a matriz energética brasileira. O Estado de São Paulo é o maior produtor nacional de cana e de seus derivados. Apesar da intensa discussão em torno das mudanças climáticas globais na última década, sabe-se que as projeções acerca do clima futuro e seu impacto na agricultura contêm uma série de incertezas oriundas dos diferentes componentes do sistema de análise (clima, culturas, práticas de manejo). Nesse s
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Leal, Daniel Philipe Veloso. "Parametrização do modelo CANEGRO (DSSAT) e caracterização biométrica de oito variedades de cana-de-açúcar irrigadas por gotejamento." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-26092016-145237/.

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O Brasil é um dos países de maior destaque do setor sucroenergético, uma vez que detém a maior produção mundial de cana-de-açúcar e pouco mais de um terço da cana cultivada no mundo. O presente experimento teve como principal finalidade caracterizar detalhadamente oito variedades de cana-de-açúcar, utilizando esta caracterização como suporte para parametrização do modelo CANEGRO/DSSAT que por sua vez é uma importante ferramenta na tomada de decisão dos cultivos de cana-de-açúcar, sendo assim os objetivos neste trabalho foi realizar uma análise agronômica e tecnológica de oito variedades de can
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Szesz, Junior Albino. "METODOLOGIA COMPUTACIONAL PARA DEFINIÇÃO DE PERÍODOS DE SEMEADURA DE CULTURAS AGRÍCOLAS." UNIVERSIDADE ESTADUAL DE PONTA GROSSA, 2015. http://tede2.uepg.br/jspui/handle/prefix/124.

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Made available in DSpace on 2017-07-21T14:19:23Z (GMT). No. of bitstreams: 1 Albino Szesz Junior.pdf: 2551855 bytes, checksum: ad2fa2da3cef74797c4e91a3da0e453b (MD5) Previous issue date: 2015-02-26<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>The aim of this work is to present the SEMEARE, a methodology for defining periods of sowing of crops, with four stages, including planning, modeling, experimentation and decision making. At the planning stage the goals and strategies for defining the sowing periods are defined and analyzed the agricultural context of the study are
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Barros, Allan Cunha. "Utilização do modelo CANEGRO para estimativa da produtividade de cana-de-açúcar irrigada em diferentes regiões do Brasil." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/11/11143/tde-18042011-160401/.

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Na produção da cana-de-açúcar, a irrigação começa a ser empregada mais intensivamente em novas áreas de expansão onde há insuficiência ou má distribuição temporal de chuvas. No entanto, a realização de experimentos, visando fornecer informações de produtividade na tomada de decisão, são onerosos e demoram muito tempo, por esse motivo, os modelos fisiológicos de simulação tornam-se ferramentas importantes, já que através deles é possível estimar a produtividade de uma cultura, em diferentes condições climáticas, sob diferentes épocas de plantio e colheita e sob diferentes métodos de manejo ado
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Matlaletsa, Tsoelipe Samuel. "Simulating nitrogen dynamics in sugarcane cropping systems using DSSAT-Canegro." Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/53523.

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Sugarcane is a high-biomass producing crop and often requires substantial amounts of nitrogen (N) fertiliser to achieve optimal yields. Nitrogen fertiliser represents a significant input cost for the sugar industry. This nutrient is highly challenging to manage due to its susceptibility to various kinds of losses following application, for example, leaching and denitrification. In addition to reduced profitability, N losses potentially lead to environmental degradation, for example, through eutrophication of water bodies. As a result of the complexities of N dynamics in sugarcane croppin
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Pequeno, Diego Noleto Luz. "Agronomic performance and adaptation of the CROPGRO - Perennial Forage Model to predict growth of three tropical forage grasses under irrigated and rainfed conditions." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11139/tde-21032014-151530/.

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Grasses of the genera Brachiaria and Cynodon are some of the most important pasture introductions in Brazil. Convert HD 364 brachiariagrass, a new Brachiaria hybrid, was released as an option for a broad range of environmental conditions, high nutritive value and forage production. Forage-based livestock systems are complex and interactions among animals, plants, and the environment exist at several levels of complexity, which can be evaluated using computer modeling. Herbage accumulation, crude protein (CP), neutral detergent fiber (NDF), in vitro organic matter digestibility (IVOMD), plant-p
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Silva, Camillo de Lellis Falcão da. "Novos algoritmos de simulação estocástica com atraso para redes gênicas." Universidade Federal de Juiz de Fora (UFJF), 2014. https://repositorio.ufjf.br/jspui/handle/ufjf/4828.

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Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-06-06T13:42:32Z No. of bitstreams: 1 camillodelellisfalcaodasilva.pdf: 1420414 bytes, checksum: f38c14f74131ea594b1e105fbfdb1619 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-06T14:07:56Z (GMT) No. of bitstreams: 1 camillodelellisfalcaodasilva.pdf: 1420414 bytes, checksum: f38c14f74131ea594b1e105fbfdb1619 (MD5)<br>Made available in DSpace on 2017-06-06T14:07:56Z (GMT). No. of bitstreams: 1 camillodelellisfalcaodasilva.pdf: 1420414 bytes, checksum: f38c14f74131ea594b1e105fbfdb1
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Jones, Matthew Robert. "Incorporating the Canegro sugarcane model into the DSSAT V4 cropping system model framework." Thesis, 2013. http://hdl.handle.net/10413/9387.

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Canegro is a leading sugarcane crop simulation model and has been used extensively in agronomic research and management. The model has been under development since the late 1980s at the South African Sugarcane Research Institute (SASRI). The Decision Support System for Agrotechnology Transfer (DSSAT) is a software package containing models for a wide range of field crops, and utilities for processing, storing and analysing model inputs and outputs. Canegro was included as part of version 3.1 of DSSAT in the mid-1990s. The SASRI Canegro model was subsequently developed further, but these change
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Books on the topic "DSSAT simulation"

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Kihara, Job. Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer Netherlands, 2012.

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Book chapters on the topic "DSSAT simulation"

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Nzimande, T. N. M., Vimbayi G. P. Chimonyo, E. M. Wimalasiri, and Tafadzwanashe Mabhaudhi. "Using AquaCrop, DSSAT and the SIMPLE to Estimate Water Use of Underutilised Cereal in South Africa." In Enhancing Water and Food Security Through Improved Agricultural Water Productivity. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1848-4_9.

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Abstract The study compares yield, biomass and water use (WU) for maize, sorghum and millet simulated using three crop models of varying complexity: AquaCrop, DSSAT and the SIMPLE model. A standard set of crop parameters was used to develop crop files for all three models. Similar soil, climate and management descriptions from the Ukulinga Research Farm were used across the models. The performance of the three models was observed to be statistically different. Based on the mean bias error, all models overestimated yield, but the lowest overestimation was with AquaCrop (0.22 t/ha), followed by
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Bowen, W. T., P. K. Thornton, and G. Hoogenboom. "The simulation of cropping sequences using DSSAT." In Understanding Options for Agricultural Production. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-3624-4_15.

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Feng, Haikuan, Zhenhai Li, Peng He, et al. "Simulation of Winter Wheat Phenology in Beijing Area with DSSAT-CERES Model." In Computer and Computing Technologies in Agriculture IX. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48354-2_27.

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de Vries, Michiel E., Abdoulaye Sow, Vincent B. Bado, and Nomé Sakane. "Simulation of Potential Yields of New Rice Varieties in the Senegal River Valley." In Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2960-5_9.

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Fatondji, Dougbedji, Andre Bationo, Ramadjita Tabo, James W. Jones, A. Adamou, and O. Hassane. "Water Use and Yield of Millet Under the Zai System: Understanding the Processes Using Simulation." In Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2960-5_6.

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Adamou, A., Ramadjita Tabo, Dougbedji Fatondji, O. Hassane, Andre Bationo, and T. Adam. "Effect of Integrated Soil Fertility Management Technologies on the Performance of Millet in Niger: Understanding the Processes Using Simulation." In Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2960-5_7.

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MacCarthy, D. S., P. L. G. Vlek, and B. Y. Fosu-Mensah. "The Response of Maize to N Fertilization in a Sub-humid Region of Ghana: Understanding the Processes Using a Crop Simulation Model." In Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2960-5_5.

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Jones, James W., J. Naab, Dougbedji Fatondji, K. Dzotsi, S. Adiku, and J. He. "Uncertainties in Simulating Crop Performance in Degraded Soils and Low Input Production Systems." In Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2960-5_4.

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Kalra, Naveen. "Perspective Chapter: Characterize the Impact of Climate Change and Its Variability on Agricultural Crops by Use of Crop Simulation Models." In Technologies and Innovations in Agriculture [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1008748.

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Rising trend in temperature and carbon dioxide concentration is noticed over the globe. In tropical and sub-tropical regions, inter-and intra- climatic variability is quite high. There is a need to characterize climatic variability through growth and yield of crops. Occurrence of extreme climatic/episodic events have increased in the last couple of decades. Crop growth models, such as WTGROWS (Wheat Growth Simulator), DSSAT (Decision Support System for Agrotechnology Transfer) and InfoCrop (for crops in tropical and sub-tropical regions) have been developed and successfully used for land use p
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Ivask, Mari, Eve Aruvee, and Kristjan Piirimäe. "Database of Environmental Decision Support Tools." In Transactional Environmental Support System Design. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2824-3.ch005.

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This chapter investigates modeling and data sources for environmental decision support for local authorities and private managers. A metadatabase of 198 computerized environmental decision tools was collected using a questionnaire with two sequential web-pages of 4+16 questions (http://tess.ttu.ee/). Half of the software applications are freely downloadable and well described online. The database contains 42 references reported as forestry tools, 50 as agriculture or apiculture tools, and 15 targeting to either amenity areas or tourism and access-based recreation. The most domain-general toolb
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Conference papers on the topic "DSSAT simulation"

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Resenes, Jonas de Abreu, Gerrit Hoogenboom, Alexandre Lazaretti Zanatta, Willingthon Pavan, and Carlos Amaral Holbig. "DSSAT-Lite: a web-based application for running crop models and analyzing results from DSSAT-CSM." In Congresso Brasileiro de Agroinformática. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbiagro.2021.18379.

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DSSAT is a set of tools that facilitate the creation and management of experiment, soil, and weather files. Its tools were built using programming languages like Visual Basic and Delphi, which are difficult to run remotely or in the cloud. In this work, we present DSSAT-Lite, a web-based application designed to facilitate the simulation of DSSAT-CSM models over HTTP. It provides simulation and visualization tools and integration with highly optimized third-party software for processing DSSAT files. Abstraction layers allow for a standardized methodology for reading multiple DSSAT files into a beau
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"APSIM and DSSAT models as decision support tools." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.c4.ahmed.

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Chinnachodteeranun, Rassarin, Kiyoshi Honda, Amor V. M. Ines, Kumpee Teeravech, Apichon Witayangkurn, and Takashi Seshimo. "Notice of Removal Web-based wheat simulation by DSSAT on sensor observation service standard API." In 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). IEEE, 2015. http://dx.doi.org/10.1109/sice.2015.7285540.

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Shi, Yuan, Yijun Guo, Yifan Li, Meijian Bai, and Yanan Liu. "Simulation of Winter Wheat Yields in North China Plain Based on DSSAT Model and Its Influencing Factors." In 15th International Conference on Hydroinformatics. The International Association for Hydro-Environment Engineering and Research (IAHR), 2024. http://dx.doi.org/10.3850/iahr-hic2483430201-60.

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Tao, Ran, Pan Zhao, Jing Wu, et al. "Optimizing Crop Management with Reinforcement Learning and Imitation Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/691.

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Crop management has a significant impact on crop yield, economic profit, and the environment. Although management guidelines exist, finding the optimal management practices is challenging. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system that optimizes nitrogen fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for
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Jang, Jiann-Woei, N. Bedrossian, and E. McCants. "DSAT - A Modular Simulation Development & Analysis Tool." In AIAA Modeling and Simulation Technologies Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4485.

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Jang, Jisung, Di Tian, and Q. Peter He. "Model-based Irrigation Control using Model Predictive Control and DSSAT Crop Simulator." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867276.

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Jackson, Mark, and R. McDonald. "Draper Simulation Analysis Tool (DSAT): Graphical Object Simulation Techniques and Tools for Simulink." In AIAA Modeling and Simulation Technologies Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-5458.

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"Simulating climate change impact on rice yield in Malaysia using DSSAT 4.5: shifting planting date as an adaptation strategy." In Integration of climate change adaptation and mitigation measures for small-scale rice cultivation. Food and Fertilizer Technology Center for the Asian and Pacific Region, 2015. https://doi.org/10.56669/hnns4807.

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