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Journal articles on the topic 'CSM-CERES- Maize'

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Published: 2 June 2025

Last updated: 31 July 2025

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1

BOGGIONE, IVALDO MARTINS, CAMILO DE LELIS TEIXEIRA DE ANDRADE, JOÃO CARLOS FERREIRA BORGES JÚNIOR, and JOÃO HERBERT MOREIRA VIANA. "MODELING APPLIED TO SOWING DATE OF IRRIGATED MAIZE." Revista Brasileira de Milho e Sorgo 17, no. 2 (2018): 201. http://dx.doi.org/10.18512/1980-6477/rbms.v17n2p201-215.

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ABSTRACT – In Brazil, the rainfed maize crop may undergo yield breaks due to uncertainties in the rainfall distribution. Irrigation can be a management alternative that, however, requires evaluation and planning to be helpful. The objective of this work was to analyze the simulated yield data of irrigated maize in counties of Minas Gerais state, Brazil. The CSM-CERES-Maize model was used to simulated weekly sowings of maize considering optimum agronomic conditions. A sprinkler irrigation scheme with 80% efficiency was used with automatic applications when the crop withdrew 50% of the soil avai

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Han, Z., B. Zhang, G. Hoogenboom, X. Li, and C. He. "Climate change impacts and adaptation strategies on rainfed and irrigated maize in the agro-pastoral ecotone of Northwestern China." Climate Research 83 (April 8, 2021): 75–90. http://dx.doi.org/10.3354/cr01635.

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The agro-pastoral ecotone of Northwestern China (APENC) is one of the major agricultural production areas in China and a region where climate change is evident. Maize is a widely cultivated crop in the APENC, but the potential impact of climate change on maize, and potential adaptation strategies in response to this, are poorly understood. In this study, we used the Cropping System Model (CSM)-CERES-Maize to evaluate the impacts of climate change on maize yield, as well as the feasibility of 2 adaptation strategies; namely, adjusting the planting date and supplying irrigation. CSM-CERES-Maize

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Mereu, Gallo, and Spano. "Optimizing Genetic Parameters of CSM-CERES Wheat and CSM-CERES Maize for Durum Wheat, Common Wheat, and Maize in Italy." Agronomy 9, no. 10 (2019): 665. http://dx.doi.org/10.3390/agronomy9100665.

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The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (D

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Yasin, Mubashra, Ashfaq Ahmad, Tasneem Khaliq, et al. "Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models." Environmental Science and Pollution Research 29, no. 13 (2021): 18967–88. http://dx.doi.org/10.1007/s11356-021-17050-z.

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AbstractFuture climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer’s fields for ten sowing dates during two consecutive growing years. Data about phenology, morphol

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Ali, Amied, and Bashrat Ali. "EVALUATION OF CERES-MAIZE MODEL FOR HYBRID MAIZE (Zea mays L.) PRODUCTION." AGRIBUSINESS JOURNAL 13, no. 1 (2019): 96–110. http://dx.doi.org/10.15408/aj.v13i1.11875.

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The trial was conducted at Agronomic Research farm, University of Sargodha during spring growing season, 2015, to calibrate and evaluate CERES-Maize model for simulating the impact of different sowing time on maize crop. The experiment was laid out in split plot design having three replications, keeping planting dates (25th Feb, 6thMar and 14thMar) in main plots and hybrids i.e. (DK-9108, DK-6525 and DK-6142) in the sub plots. The Calibration of CSM-CERES-Maize model showed the best possible closeness between simulated and observed days to flowering and physiological maturity, leaf area index

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Mubeen, Muhammad, Ashfaq Ahmad, Aftab Wajid, et al. "Application of CSM-CERES-Maize model in optimizing irrigated conditions." Outlook on Agriculture 45, no. 3 (2016): 173–84. http://dx.doi.org/10.1177/0030727016664464.

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Gobezie, Addey, Dereje Ademe, and Lakesh K. Sharma. "CERES-Maize (DSSAT) Model Applications for Maize Nutrient Management Across Agroecological Zones: A Systematic Review." Plants 14, no. 5 (2025): 661. https://doi.org/10.3390/plants14050661.

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Effective nutrient management is essential for boosting maize yield and quality and tackling factors that limit or reduce productivity. The Crop Environment Resource Synthesis (CERES)-Maize model embedded in the Decision Support Systems for Agrotechnology Transfer (DSSAT) cropping system model (CSM), known for its accurate predictions, serves as a valuable tool for guiding agricultural decisions, particularly in nutrient management, offering an efficient alternative to traditional long term field trials. This systematic review consolidates the current knowledge on nutrient management practices

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Sisay, Theodrose, Kindie Tesfaye, Mezegebu Getnet, Nigussie Dechassa, and Mengistu Ketema. "Calibration and Evaluation of CERES-Maize and CROPGRO-Dry Bean Crop Simulation Models of the DSSAT in the Great Rift Valley Region of Ethiopia." International Journal of Applied Agricultural Sciences 10, no. 4 (2024): 149–56. http://dx.doi.org/10.11648/j.ijaas.20241004.11.

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Maize (Zea mays L.) is one of the most principal cereal crops ranking first in production in Ethiopia, predominantly produced and consumed directly by the smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Common bean (Phaseolus vulgaris) is also the most important legume crops as the source of protein and export commodity in the GRV. However, the average maize and common bean yields in Ethiopia are still low due to abiotic, biotic and socioeconomic constraints. In this regard, Crop simulation models (CSMs) are used in predicting growth and yield of crops and associated yield gaps

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9

Shrestha, Umesh, Lal Prasad Amgain, Tika Bahadur Karki, and Khem Raj Dahal. "Simulation of growth and yield of rainfed maize under varied agronomic management and changing climatic scenario in Nawalparasi, Nepal." Journal of Maize Research and Development 1, no. 1 (2015): 123–33. http://dx.doi.org/10.3126/jmrd.v1i1.14250.

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Correction: Figure 3 was corrupted and so the PDF was replaced on 29th December 2016 with the corrected Figure 3.A field experiment and simulation modeling study in combination for different maize cultivars planted at different sowing dates were accomplished at Kawasoti-5, Nawalparasi during spring season of 2013 to assess the impact of climate change scenario as predicted by IPCC in rainfed spring maize by using CSM-CERES-Maize model. Result showed that RML-4/RML-17 produced higher kernel rows/ ear (13.77), kernel per row (30.42) and test weight (244.9 g). Significantly higher grain yield was

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Yang, Zhengyu, Gail G. Wilkerson, Gregory S. Buol, Daryl T. Bowman, and Ronnie W. Heiniger. "Estimating Genetic Coefficients for the CSM-CERES-Maize Model in North Carolina Environments." Agronomy Journal 101, no. 5 (2009): 1276–85. http://dx.doi.org/10.2134/agronj2008.0234x.

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11

Amgain, Lal Prasad, Bishal Dhakal, Umesh Shrestha, and Srijana Marasini. "Agronomic management and climate change scenario simulations on productivity of rice, maize and wheat in central Nepal using DSSAT ver 4.5 crop model." Journal of Agriculture and Natural Resources 2, no. 1 (2019): 193–214. http://dx.doi.org/10.3126/janr.v2i1.26068.

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Average productivity of 3.50 t/ha of rice, 2.50 t/ha of maize and 2.45 t/ha of wheat in Nepal have been very less than their potential productivity for which précised agronomic management and changing climatic scenarios have been reported the most challenging factors at present. Cropping system Model (CSM)-Crop Estimation through Resource and Environment Synthesis (CERES)- Rice, Maize and Wheat, embedded under Decision Support System for Agro-technology Transfer (DSSAT) ver. 4.5 was evaluated from a datasets of farmers’ field experimentations of the central Nepal (Terai-Nawalpur and mid-hill-K

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Hammad, Hafiz Mohkum, Farhat Abbas, Ashfaq Ahmad, Wajid Farhad, Jakarat Anothai, and Gerrit Hoogenboom. "Predicting water and nitrogen requirements for maize under semi-arid conditions using the CSM-CERES-Maize model." European Journal of Agronomy 100 (October 2018): 56–66. http://dx.doi.org/10.1016/j.eja.2017.10.008.

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13

Bhusal, TN, A. Lamsal, and LP Amgain. "Simulations of nitrogen dynamism in maize and rice by using CSM-CERES-maize and rice models under Chitwan, Nepal." Agronomy Journal of Nepal 1 (February 3, 2013): 94–102. http://dx.doi.org/10.3126/ajn.v1i0.7548.

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Crop modeling is being popular in the field of agriculture as a precision tool for resources allocations and their effective utilizations. Two separate field and simulation studies were accomplished in maize and rice that were laid out in the two factor factorial randomized complete block design at Agronomy Farm of Institute of Agriculture and Animal Sciences, Rampur, Chitwan Nepal during spring and rainy season of 2007 and 2008, respectively. CERESmaize and rice models were calibrated using the best performing treatments (7.41 x 104 plants /ha for all 3 varieties of maize i.e. Rampur Composit

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Bongiovani, Paola de Figueiredo, Jéfferson de Oliveira Costa, Carlos Alberto Quiloango-Chimarro, and Fabiani Denise Bender. "OFF-SEASON MAIZE YIELD FORECASTING FOR DIFFERENT SOWING DATES." ENERGIA NA AGRICULTURA 38, no. 3 (2023): 42–52. http://dx.doi.org/10.17224/energagric.2023v38n3p42-52.

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Brazil is the world's third-largest maize producer. Its yield is influenced by climate, soil conditions, management and their interactions. Identifying the most suitable sowing window and using yield forecasting systems allows for increased yields and better harvest management. Crop simulation models can be used to assess crop responses to various conditions. This study aimed to identify the most favorable planting date for off-season maize by using the DSSAT CSM-CERES-Maize model for Jataí, Goiás state. Using meteorological data from 1986 to 2015, eight productivity forecasting strategies wer

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JING, Q., J. SHANG, T. HUFFMAN, et al. "Using the CSM–CERES–Maize model to assess the gap between actual and potential yields of grain maize." Journal of Agricultural Science 155, no. 2 (2016): 239–60. http://dx.doi.org/10.1017/s0021859616000290.

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SUMMARYMaize in Canada is grown mainly in the south-eastern part of the country. No comprehensive studies on Canadian maize yield levels have been done so far to analyse the barriers of obtaining optimal yields associated with cultivar, environmental stress and agronomic management practices. The objective of the current study was to use a modelling approach to analyse the gaps between actual and potential (determined by cultivar, solar radiation and temperature without any other stresses) maize yields in Eastern Canada. The CSM–CERES–Maize model in DSSAT v4·6 was calibrated and evaluated with

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16

Bao, Yawen, Gerrit Hoogenboom, Ron McClendon, and George Vellidis. "A comparison of the performance of the CSM-CERES-Maize and EPIC models using maize variety trial data." Agricultural Systems 150 (January 2017): 109–19. http://dx.doi.org/10.1016/j.agsy.2016.10.006.

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17

Bagula, Espoir M., Jackson Gilbert M. Majaliwa, Gustave N. Mushagalusa, et al. "Climate Change Effect on Water Use Efficiency under Selected Soil and Water Conservation Practices in the Ruzizi Catchment, Eastern D.R. Congo." Land 11, no. 9 (2022): 1409. http://dx.doi.org/10.3390/land11091409.

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Concerns have been raised on the effectiveness and sustainability of Soil and Water Conservation (SWC) practices as adaptation options to climate change and high intra– and inter–annual rainfall variabilities in eastern Democratic Republic of Congo (DRC). This study was conducted in the Ruzizi Plain, a dryland area, to assess the performance of maize (Zea mays L.) under two Representative Concentration Pathways (RCP 4.5 and 8.5) and two SWC practices (tied ridges and conventional tillage). The AgMIP’s Regional Integrated Assessment (RIA) approach was used to simulate Water Use Efficiency (WUE)

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18

Fang, Hongliang, Shunlin Liang, Gerrit Hoogenboom, John Teasdale, and Michel Cavigelli. "Corn‐yield estimation through assimilation of remotely sensed data into the CSM‐CERES‐Maize model." International Journal of Remote Sensing 29, no. 10 (2008): 3011–32. http://dx.doi.org/10.1080/01431160701408386.

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19

Amaral, Tales Antônio, Camilo de Lelis Teixeira de Andrade, Maria Emília Borges Alves, and Denise Freitas Silva. "Applying CSM-CERES-Maize to define a sowing window for irrigated maize crop – The Riacho´s Farm case study." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 6, no. 2 (2011): 38–53. http://dx.doi.org/10.4136/ambi-agua.184.

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20

Amgain, Lal Prasad. "Application of csm- ceres-maize model for seasonal and multi-decadal predictions of maize yield in under subtropical condition of Chitwan, Nepal." Journal of Maize Research and Development 1, no. 1 (2015): 86–97. http://dx.doi.org/10.3126/jmrd.v1i1.14246.

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The average maize yield of 2.5 t/ha in sub-tropical terai and inner terai of Nepal has been very less than its potential yield of about 5.0 t/ha, for which changing climatic scenarios have been reported the critical factors. Cropping system Model (CSM)-Crop Estimation through Resource and Environment Synthesis (CERES)-Maize, embedded under Decision Support System for Agro-technology Transfer (DSSAT) ver. 4.2 was evaluated from a datasets of field experimentation by growing four diverse maize genotypes viz. full season OPV (Rampur Composite), Quality Protein Maize (Posilo Makai-1), Hybrid (Gaur

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Lizaso, J. I., K. J. Boote, C. M. Cherr, et al. "Developing a Sweet Corn Simulation Model to Predict Fresh Market Yield and Quality of Ears." Journal of the American Society for Horticultural Science 132, no. 3 (2007): 415–22. http://dx.doi.org/10.21273/jashs.132.3.415.

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Current production of sweet corn (Zea mays L.) in the United States is 4.0 million Mg with a value of $807 million. The fresh market component amounts to three-fourths of this value with California, Florida, and Georgia harvesting half of the U.S. fresh market production. Existing maize simulation models have limited potential to assist sweet corn production as a result of the distinctive nature of the marketed end product (i.e., fresh market ears versus dry mature kernels). The purpose of this study was to develop a sweet corn simulation model. The Cropping System Model-Crop-Environment Resou

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LIU, Hai-long, Jing-yi YANG, Ping HE, et al. "Optimizing Parameters of CSM-CERES-Maize Model to Improve Simulation Performance of Maize Growth and Nitrogen Uptake in Northeast China." Journal of Integrative Agriculture 11, no. 11 (2012): 1898–913. http://dx.doi.org/10.1016/s2095-3119(12)60196-8.

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Rani, P. Leela, G. Sreenivas, and D. Raji Reddy. "Application of CSM-CERES-Maize Model to Define a Sowing Window and Nitrogen Rates for Rainfed Maize in Semi-arid Environment." International Journal of Bio-resource and Stress Management 5, no. 2 (2014): 181. http://dx.doi.org/10.5958/0976-4038.2014.00553.3.

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Wei, Yanqi, Juliang Jin, Shangming Jiang, Shaowei Ning, Yi Cui, and Yuliang Zhou. "Simulated Assessment of Summer Maize Drought Loss Sensitivity in Huaibei Plain, China." Agronomy 9, no. 2 (2019): 78. http://dx.doi.org/10.3390/agronomy9020078.

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In an agricultural drought risk system, crop drought loss sensitivity evaluation is a fundamental link for quantitative agricultural drought loss risk assessment. Summer maize growth processes under various drought patterns were simulated using the Cropping System Model (CSM)-CERES-maize, which was calibrated and validated based on pit experiments conducted in the Huaibei Plain during 2016 and 2017 seasons. Then S-shaped maize drought loss sensitivity curve was built for fitting the relationship between drought hazard index intensity at a given stage and the corresponding dry matter accumulati

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Fang, Hongliang, Shunlin Liang, and Gerrit Hoogenboom. "Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation." International Journal of Remote Sensing 32, no. 4 (2011): 1039–65. http://dx.doi.org/10.1080/01431160903505310.

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Soler, Cecilia Manuela Tojo, Paulo César Sentelhas, and Gerrit Hoogenboom. "Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment." European Journal of Agronomy 27, no. 2-4 (2007): 165–77. http://dx.doi.org/10.1016/j.eja.2007.03.002.

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Dokoohaki, Hamze, Mahdi Gheysari, Sayed-Frahad Mousavi, and Gerrit Hoogenboom. "Effects of different irrigation regimes on soil moisture availability evaluated by CSM-CERES-Maize model under semi-arid condition." Ecohydrology & Hydrobiology 17, no. 3 (2017): 207–16. http://dx.doi.org/10.1016/j.ecohyd.2017.06.001.

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Amaral, T. A., A. C. R. Lima, C. L. T. Andrade, and S. D. A. Silva. "Parametrização e Avaliação do Modelo CSM-CERES-Maize para Cultivares de Milho Recomendadas para a Microrregião de Pelotas, RS." Revista Brasileira de Milho e Sorgo 14, no. 3 (2015): 371–91. http://dx.doi.org/10.18512/1980-6477/rbms.v14n3p371-391.

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Anar, Mohammad J., Zhulu Lin, Liwang Ma, and Amitava Chatterjee. "Modeling the Effects of Crop Rotation and Tillage on Sugarbeet Yield and Soil Nitrate Using RZWQM2." Transactions of the ASABE 64, no. 2 (2021): 461–74. http://dx.doi.org/10.13031/trans.13752.

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HighlightsFour crop growth modules in RZWQM2 were calibrated for four sugarbeet rotation sequences.Sugarbeet following wheat had a slightly higher yield (3% to 6.5%).Moldboard plow increased sugarbeet yield by 1% to 2%.The difference in N losses under different crop rotations and tillage operations was negligible.Abstract. Sugarbeet (Beta vulgaris) is considered to be one of the most viable alternatives to corn for biofuel production as it may be qualified as the feedstock for advanced biofuels (reducing greenhouse gas emission by 50%) under the Energy Independence and Security Act (EISA) of 2

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AMARAL, TALES ANTONIO DO, RICARDO NUNO FONSECA GARCIA PEREIRA BRAGA, ANA CLÁUDIA RODRIGUES DE LIMA, and CAMILO DE LELIS TEIXEIRA DE ANDRADE. "A MODELING APPROACH TO ESTABLISH STRATEGIES FOR MAIZE SILAGE PRODUCTION IN THE MICRO-REGION OF PELOTAS, BRAZIL." Revista Brasileira de Milho e Sorgo 16, no. 3 (2017): 536. http://dx.doi.org/10.18512/1980-6477/rbms.v16n3p536-555.

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ABSTRACT - The objective of this study was to evaluate some crop management strategies to improve silage production by family farmers in the micro-region of Pelotas, southern State of Rio Grande do Sul (RS), Brazil. The seasonal analysis tool of de CSM-CERES-Maize model was used to assess aboveground dry biomass production under rainfed conditions. The simulations comprised scenarios involving four cultivars (Amarelão, AL 30, AG 5011 and AG 122), six nitrogen (N) fertilization strategies, 52 sowing dates, and 21 years of daily weather data. Silage productivity and quality were assessed, and a

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Nilahyane, Abdelaziz, M. Islam, Abdel O. Mesbah, and Axel Garcia y Garcia. "Evaluation of Silage Corn Yield Gap: An Approach for Sustainable Production in the Semi-Arid Region of USA." Sustainability 10, no. 7 (2018): 2523. http://dx.doi.org/10.3390/su10072523.

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Water and nitrogen (N) play an important role in closing the yield gap of crops by reducing associated stresses and yield variability. Field research data coupled to the CSM-CERES-Maize model of Decision Support System Agrotechnology Transfer were used to advance our understanding of the effect of water and N on silage corn growth and yield. The objectives of the study were to determine: (i) the best combination of irrigation water and N for optimum biomass yield, and (ii) the yield gap of silage corn grown at different locations in Wyoming, USA. Field experiments were conducted under sub-surf

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Anothai, J., C. M. T. Soler, A. Green, T. J. Trout, and G. Hoogenboom. "Evaluation of two evapotranspiration approaches simulated with the CSM–CERES–Maize model under different irrigation strategies and the impact on maize growth, development and soil moisture content for semi-arid conditions." Agricultural and Forest Meteorology 176 (July 2013): 64–76. http://dx.doi.org/10.1016/j.agrformet.2013.03.001.

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Bigolin, Tiago, and Edson Talamini. "Impacts of Climate Change on Late Soybean Cultivation in Subtropical Southern Brazil." Crops 5, no. 2 (2025): 20. https://doi.org/10.3390/crops5020020.

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Soybeans are the most widely produced oilseed and the fifth most cultivated crop in the world. However, their growth and yield are significantly influenced by weather conditions. In Southern Brazil’s subtropical climate, farmers employ a double-cropping system, planting corn from late winter to early summer, followed by soybeans, which are sown after the corn harvest—typically in January—and harvested in autumn. This study argues that climate change has benefited late-sown soybeans in Rio Grande do Sul and will continue improving their growing conditions. The aim is to identify climate change’

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Suleiman, Ayman. "Peer review report 1 On “Evaluation of Two Evapotranspiration Approaches Simulated with the CSM-CERES-Maize Model under Different Irrigation Strategies and the Impact on Maize Growth, Development and Soil Moisture Content for Semi-arid Conditions”." Agricultural and Forest Meteorology 201 (January 2015): 546. http://dx.doi.org/10.1016/j.agrformet.2015.07.210.

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PazDr, Joel. "Peer review report 2 On “Evaluation of Two Evapotranspiration Approaches Simulated with the CSM-CERES-Maize Model under Different Irrigation Strategies and the Impact on Maize Growth, Development and Soil Moisture Content for Semi-arid Conditions”." Agricultural and Forest Meteorology 201 (January 2015): 217–18. http://dx.doi.org/10.1016/j.agrformet.2015.07.211.

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36

Mereu, Valentina, Andrea Gallo, and Donatella Spano. "Optimizing Genetic Parameters of CSM-CERES Wheat and CSM-CERES Maize for Durum Wheat, Common Wheat, and Maize in Italy." October 23, 2019. https://doi.org/10.3390/agronomy9100665.

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The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (D

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37

Langner, Josana Andreia, Nereu Augusto Streck, Angelica Durigon, et al. "Observed and estimated leaf appearance of landrace and improved maize cultivars." Ciência Rural 48, no. 1 (2017). http://dx.doi.org/10.1590/0103-8478cr20160775.

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ABSTRACT: The objective of this study was to compare the simulations of leaf appearance of landrace and improved maize cultivars using the CSM-CERES-Maize (linear) and the Wang and Engel models (nonlinear). The coefficients of the models were calibrated using a data set of total leaf number collected in the 11/04/2013 sowing date for the landrace varieties ‘Cinquentinha’ and ‘Bico de Ouro’ and the simple hybrid ‘AS 1573PRO’. For the ‘BRS Planalto’ variety, model coefficients were estimated with data from 12/13/2014 sowing date. Evaluation of the models was with independent data sets collected

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Umesh, Shrestha, Prasad Amgain Lal, Bahadur Karki Tika, and Raj Dahal Khem. "Simulation of growth and yield of rainfed maize under varied agronomic management and changing climatic scenario in Nawalparasi, Nepal." December 1, 2015. https://doi.org/10.5281/zenodo.34289.

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A field experiment and simulation modeling study in combination for different maize cultivars planted at different sowing dates were accomplished at Kawasoti-5, Nawalparasi during spring season of 2013 to assess the impact of climate change scenario as predicted by IPCC in rainfed spring maize by using CSM-CERES-Maize model. Result showed that RML-4/RML-17 produced higher kernel rows/ ear (13.77), kernel per row (30.42) and test weight (244.9 g). Significantly higher grain yield was also found for RML-4/RML-17 (6.03 t/ha) compared to Poshilo makai-1 (4.73 t/ha), Arun-2 (3.55 t/ha) and Local (2

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39

Amaral, Tales Antônio, Camilo De Lelis Teixeira de Andrade, Santiago Vianna Cuadra, José Eduardo B. A. Monteiro, Paulo Evaristo de O. Guimarães, and Roberto Dos Santos Trindade. "Applying the CSM-CERES-Maize for agricultural zoning of climate risk in Brazil." Agrometeoros 32 (October 31, 2024). http://dx.doi.org/10.31062/agrom.v32.e027716.

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40

Lal, Prasad Amgain. "Application of csm- ceres-maize model for seasonal and multi-decadal predictions of maize yield in under subtropical condition of Chitwan, Nepal." December 1, 2015. https://doi.org/10.5281/zenodo.34283.

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The average maize yield of 2.5 t/ha in sub-tropical terai and inner terai of Nepal has been very less than its potential yield of about 5.0 t/ha, for which changing climatic scenarios have been reported the critical factors. Cropping system Model (CSM)-Crop Estimation through Resource and Environment Synthesis (CERES)-Maize, embedded under Decision Support System for Agro-technology Transfer (DSSAT) ver. 4.2 was evaluated from a datasets of field experimentation by growing four diverse maize genotypes viz. full season OPV (Rampur Composite), Quality Protein Maize (Posilo Makai-1), Hybrid (Gaur

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Andrea, Maria Carolina Da Silva, João Danilo Barbieri, Rivanildo Dallacort, Rafael Cesar Tieppo, Paulo Sérgio Lourenço de Freitas, and Marco Antonio Camillo de Carvalho. "ADJUSTMENT AND EVALUATION OF CROPGRO-SOYBEAN AND CERES-MAIZE FOR DIFFERENT GENETIC MATERIAL IN A REGION OF MATO GROSSO STATE, BRAZIL." Tropical and Subtropical Agroecosystems 22, no. 1 (2019). http://dx.doi.org/10.56369/tsaes.2705.

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Abstract:

<p>Considering the importance of the use of crop models as an aid measure in the management of the agricultural production system, the objective of this study was to assess the performance of a soybean and maize models, CROPGRO-Soybean and CSM-CERES-Maize, respectively, for different genetic materials and water regime in the two annual cropping seasons in a region of Mato Grosso state, Brazil. Models were adjusted and evaluated, respectively, with field experiments under irrigated and rainfed conditions, by using crop production parameters and phenology. Model performance was generally v

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