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Journal articles on the topic 'Crop modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Sirotenko, Oleg D. "Crop Modeling." Agronomy Journal 93, no. 3 (2001): 650–53. http://dx.doi.org/10.2134/agronj2001.933650ax.

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2

Sirotenko, Oleg D. "Crop Modeling." Agronomy Journal 93, no. 3 (2001): 650—a. http://dx.doi.org/10.2134/agronj2001.933650-ax.

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3

Poluektov, Ratmir A., and Alexandre G. Topaj. "Crop Modeling." Agronomy Journal 93, no. 3 (2001): 653–59. http://dx.doi.org/10.2134/agronj2001.933653x.

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4

Craufurd, Peter Q., Vincent Vadez, S. V. Krishna Jagadish, P. V. Vara Prasad, and M. Zaman-Allah. "Crop science experiments designed to inform crop modeling." Agricultural and Forest Meteorology 170 (March 2013): 8–18. http://dx.doi.org/10.1016/j.agrformet.2011.09.003.

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5

HAGIWARA, Kensuke, Naota HANASAKI, and Shinjiro KANAE. "MODELING WORLD BIOENERGY CROP POTENTIAL." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 67, no. 4 (2011): I_265—I_270. http://dx.doi.org/10.2208/jscejhe.67.i_265.

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6

Anderson, Ray, and Andrew French. "Crop Evapotranspiration." Agronomy 9, no. 10 (2019): 614. http://dx.doi.org/10.3390/agronomy9100614.

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Evapotranspiration (ET) is one of the largest components of the water cycle, and accurately measuring and modeling ET is critical for improving and optimizing agricultural water management. However, parameterizing ET in croplands can be challenging due to the wide variety of irrigation strategies and techniques, crop varieties, and management approaches that employ traditional tabular ET and make crop coefficient approaches obsolete. This special issue of Agronomy highlights nine approaches to improve the measurement and modeling of ET across a range of spatial and temporal resolutions and dif

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7

Phuoc, Le Huu, Irfan Suliansyah, Feri Arlius, Irawati Chaniago, Nguyen Thi Thanh Xuan, and Pham Van Quang. "Literature Review Crop Modeling and Introduction a Simple Crop Model." Journal of Applied Agricultural Science and Technology 7, no. 3 (2023): 197–216. http://dx.doi.org/10.55043/jaast.v7i3.123.

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Modeling science has been applied by many advanced countries in many fields, such as geology, meteorology, climate change, crop productivity, environment, erosion, and landslide. The crop model simulates the processes of agriculture. The writing of this article is descriptive qualitative using the Systematic Literature Review (SLR) method. So far, each model has its advantages and disadvantages but generally is based on the physiology of the growth and development of crops in relationship with soil, climate, solar radiation energy, and limiting factors to plant growth. There have been many mod

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8

Boote, K. J., and N. B. Pickering. "Modeling Photosynthesis of Row Crop Canopies." HortScience 29, no. 12 (1994): 1423–34. http://dx.doi.org/10.21273/hortsci.29.12.1423.

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9

Scholze, Marko, Alberte Bondeau, Frank Ewert, Chris Kucharik, Jörg Priess, and Pascalle Smith. "Advances in large-scale crop modeling." Eos, Transactions American Geophysical Union 86, no. 26 (2005): 245. http://dx.doi.org/10.1029/2005eo260002.

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10

Sinclair, Thomas R., and No'am G. Seligman. "Crop Modeling: From Infancy to Maturity." Agronomy Journal 88, no. 5 (1996): 698–704. http://dx.doi.org/10.2134/agronj1996.00021962008800050004x.

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11

Ben Dhiab, Ali, Mehdi Ben Mimoun, Jose Oteros, et al. "Modeling olive-crop forecasting in Tunisia." Theoretical and Applied Climatology 128, no. 3-4 (2016): 541–49. http://dx.doi.org/10.1007/s00704-015-1726-1.

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12

Máthé-Gáspár, G., and N. Fodor. "Modeling the phosphorus balance of different soilsusing the 4M crop model." Plant, Soil and Environment 58, No. 9 (2012): 391–98. http://dx.doi.org/10.17221/100/2012-pse.

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Our study focuses on the phosphorus (P) balance in two long-term fertilization experiments which were carried out in characteristic soils of Hungary with four fertilization treatments and four main crops. The objectives of this study are: (1) to quantify the P accumulation rate in the upper soil layers and (2) to calibrate and validate the P-balance module of the 4M crop model. The concentration of ammonium-lactate soluble P (AL-P) increased with time in both soils. The mean AL-P accumulation rates in the 0&ndash;20, 20&ndash;40 and 40&ndash;60 cm soil layers were 3.7, 0.7, 0.1 and

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13

Nagai, Takashi. "Quantitative Risk Assessment of Crop-Yield Variability by Crop and Prefecture by Using Crop Statistics." Agricultural Information Research 31, no. 4 (2023): 120–30. http://dx.doi.org/10.3173/air.31.120.

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14

Galán, C., H. García-Mozo, L. Vázquez, L. Ruiz, C. Díaz de la Guardia, and E. Domínguez-Vilches. "Modeling Olive Crop Yield in Andalusia, Spain." Agronomy Journal 100, no. 1 (2008): 98–104. http://dx.doi.org/10.2134/agronj2006.0345.

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15

A, Zaman, and Sagar Maitra. "Crop modeling: a tool for agricultural research." MOJ Food Processing & Technology 6, no. 4 (2018): 350–53. http://dx.doi.org/10.15406/mojfpt.2018.06.00186.

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16

Galán, C., H. García-Mozo, L. Vázquez, L. Ruiz, C. Díaz de la Guardia, and E. Domínguez-Vilches. "Modeling Olive Crop Yield in Andalusia, Spain." Agronomy Journal 100, no. 1 (2008): 98. http://dx.doi.org/10.2134/agrojnl2006.0345.

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17

Hoogenboom, Gerrit, Eric Justes, Christophe Pradal, et al. "iCROPM 2020: Crop Modeling for the Future." Journal of Agricultural Science 158, no. 10 (2020): 791–93. http://dx.doi.org/10.1017/s0021859621000538.

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AbstractDuring the past decade, the interest in using crop models for research, education, extension, outreach and in the private sector has rapidly increased. The iCROPM 2020 Symposium entitled ‘Crop Modeling for the Future’, held in February 2020, therefore, provided a great opportunity for over 400 scientists from 50 different countries to exchange information on crop model development, evaluation with experimental data and implementation. A key outcome was the understanding that crop models simulate the dynamics of the soil-plant-atmosphere continuum. Thus, the models can be used for a qua

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18

Kobayashi, Kent D. "COMPUTER SIMULATION PROGRAMS FOR TEACHING CROP MODELING." HortScience 27, no. 6 (1992): 671e—671. http://dx.doi.org/10.21273/hortsci.27.6.671e.

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The simulation programs Stella® (High Performance Systems) and Extend™ (Imagine That!) were used on Apple® Macintosh® computers in a graduate course on crop modeling to develop crop simulation models. Students developed models as part of their homework and laboratory assignments and their semester project Stella offered the advantage of building models using a relational diagram displaying state, rate, driving, and auxiliary variables. Arrows connecting the variables showed the relationships among the variables as information or material flows. Stella automatically kept track of differential e

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19

Thirunavukkarasu, M. "Hidden markov modeling for sorghum crop production." INTERNATIONAL JOURNAL OF AGRICULTURAL ENGINEERING 12, no. 2 (2019): 177–85. http://dx.doi.org/10.15740/has/ijae/12.2/177-185.

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20

Madden, L. V., and F. W. Nutter. "Modeling crop losses at the field scale." Canadian Journal of Plant Pathology 17, no. 2 (1995): 124–37. http://dx.doi.org/10.1080/07060669509500703.

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21

Kisekka, Isaya, Kendall C. DeJonge, Liwang Ma, Joel Paz, and Kyle Douglas-Mankin. "Crop Modeling Applications in Agricultural Water Management." Transactions of the ASABE 60, no. 6 (2017): 1959–64. http://dx.doi.org/10.13031/trans.12693.

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Abstract. This article introduces the fourteen articles that comprise the “Crop Modeling and Decision Support for Optimizing Use of Limited Water” collection. This collection was developed from a special session on crop modeling applications in agricultural water management held at the 2016 ASABE Annual International Meeting (AIM) in Orlando, Florida. In addition, other authors who were not able to attend the 2016 ASABE AIM were also invited to submit papers. The articles summarized in this introductory article demonstrate a wide array of applications in which crop models can be used to optimi

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22

AOYAGI, Shingo, Sho YAMAUCHI, and Keiji SUZUKI. "Hydroponic Crop Modeling and Growth Dataset Generation." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2024 (2024): 2A2—A03. https://doi.org/10.1299/jsmermd.2024.2a2-a03.

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23

Sinclair, Thomas R., and No’am Seligman. "Criteria for publishing papers on crop modeling." Field Crops Research 68, no. 3 (2000): 165–72. http://dx.doi.org/10.1016/s0378-4290(00)00105-2.

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24

Stockle, C. O., and P. Debaeke. "Modeling crop nitrogen requirements: a critical analysis." European Journal of Agronomy 7, no. 1-3 (1997): 161–69. http://dx.doi.org/10.1016/s1161-0301(97)00038-5.

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25

Monteith, John L. "The Quest for Balance in Crop Modeling." Agronomy Journal 88, no. 5 (1996): 695–97. http://dx.doi.org/10.2134/agronj1996.00021962008800050003x.

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26

Connor, D. J. "Modeling crop photosynthesis — from biochemistry to canopy." Field Crops Research 34, no. 2 (1993): 232–34. http://dx.doi.org/10.1016/0378-4290(93)90013-d.

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27

Barbier, Guillaume, Véronique Cucchi, and David R. C. Hill. "Model-driven engineering applied to crop modeling." Ecological Informatics 26 (March 2015): 173–81. http://dx.doi.org/10.1016/j.ecoinf.2014.05.004.

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28

McCauley, DJ. "Predictive Agriculture: Crop Modeling for the Future." CSA News 65, no. 5 (2020): 3–9. http://dx.doi.org/10.1002/csan.20140.

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29

Shiferaw, Andualem, Girma Birru, Tsegaye Tadesse, et al. "Optimizing Cover Crop Management in Eastern Nebraska: Insights from Crop Simulation Modeling." Agronomy 14, no. 7 (2024): 1561. http://dx.doi.org/10.3390/agronomy14071561.

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Cover crops (CCs) offer ecosystem benefits, yet their impact on subsequent crop yields varies with climate, soil, and management practices. Using the Decision Support System for Agrotechnology Transfer (DSSAT) at the University of Nebraska-Lincoln’s Eastern Nebraska Research, Education, and Extension Center (ENREEC), we identified optimal cereal rye management strategies focusing on planting, termination, and the intervals between CC termination and corn planting. Results showed minimal impact of CC management variations on corn yield, underscoring corn’s resilience to management changes. Dela

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30

Fathololoumi, Solmaz, Mohammad Karimi Firozjaei, and Asim Biswas. "Innovative Fusion-Based Strategy for Crop Residue Modeling." Land 11, no. 10 (2022): 1638. http://dx.doi.org/10.3390/land11101638.

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The purpose of this study was to present a new strategy based on fusion at the decision level for modeling the crop residue. To this end, a set of satellite imagery and field data, including the Residue Cover Fraction (RCF) of corn, wheat and soybean was used. Firstly, the efficiency of Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Partial-Least-Squares Regression (PLSR) in RCF modeling was evaluated. Furthermore, to increase the accuracy of RCF modeling, different algorithms results were combined based on their modeling error, which is c

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31

Drewniak, B., J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob. "Modeling agriculture in the Community Land Model." Geoscientific Model Development Discussions 5, no. 4 (2012): 4137–85. http://dx.doi.org/10.5194/gmdd-5-4137-2012.

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Abstract. The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above- and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types – maize, soybean, and spring wheat – into the coupled carbon-nitrogen version of the Community Land Model (CLM), to help address these questions. Here we present the new model, CLM-Crop, validated against observations from two AmeriFlux sites in the United Stat

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32

Brusenkov, Aleksey, Vasiliy Kapustin, Vladimir Nemtinov, and Yulia Nemtinova. "Analysis of root crop preparation system." E3S Web of Conferences 176 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202017603007.

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In recent years, the technical level of agricultural production has increased significantly, successfully introduced new technological techniques, means of mechanization and automation of production in crop and livestock. At the same time, further progress in this direction is impossible without systematically organized work on the development and implementation of modern automatic control systems for various processes. Works in this direction are based primarily on a detailed study of the properties of various objects of agricultural production, as the basis for the analysis and synthesis of

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33

Drewniak, B., J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob. "Modeling agriculture in the Community Land Model." Geoscientific Model Development 6, no. 2 (2013): 495–515. http://dx.doi.org/10.5194/gmd-6-495-2013.

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Abstract. The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above- and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types – maize, soybean, and spring wheat – into the coupled carbon–nitrogen version of the Community Land Model (CLM), to help address these questions. Here we present the new model, CLM-Crop, validated against observations from two AmeriFlux sites in the United Stat

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34

Benezoli, Victor Hugo, Hewlley Maria Acioli Imbuzeiro, Santiago Vianna Cuadra, et al. "Modeling oil palm crop for Brazilian climate conditions." Agricultural Systems 190 (May 2021): 103130. http://dx.doi.org/10.1016/j.agsy.2021.103130.

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35

Todorovic, P., and J. Gani. "Modeling the effect of erosion on crop production." Journal of Applied Probability 24, no. 4 (1987): 787–97. http://dx.doi.org/10.2307/3214205.

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This paper is concerned with a model for the effect of erosion on crop production. Crop yield in the year n is given by X(n) = YnLn, where is a sequence of strictly positive i.i.d. random variables such that E{Y1} <∞, and is a Markov chain with stationary transition probabilities, independent of . When suitably normalized, leads to a martingale which converges to 0 almost everywhere (a.e.) as n → ∞. In addition, for large n, the distribution of Ln is approximately lognormal. The conditional expectations and probabilities of , given the past history of the process, are determined. Finally, t

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36

Quinn, John, Kevin Leyton-Brown, and Ernest Mwebaze. "Modeling and Monitoring Crop Disease in Developing Countries." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1390–95. http://dx.doi.org/10.1609/aaai.v25i1.7811.

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Information about the spread of crop disease is vital in developing countries, and as a result the governments of such countries devote scarce resources to gathering such data. Unfortunately, current surveys tend to be slow and expensive, and hence also tend to gather insufficient quantities of data. In this work we describe three general methods for improving the use of survey resources by performing data collection with mobile devices and by directing survey progress through the application of AI techniques. First, we describe a spatial disease density model based on Gaussian process ordinal

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37

Barbier, Guillaume, Véronique Cucchi, François Pinet, and David R. C. Hill. "Domain-Specific Modeling for a Crop Model Factory." International Journal of Agricultural and Environmental Information Systems 4, no. 2 (2013): 37–49. http://dx.doi.org/10.4018/jaeis.2013040104.

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In the context of crop model design for industrial purposes, this paper proposes a domain-specific modeling approach to provide a crop model factory for the modelers in agronomy. The authors’ approach proposes to separate the concerns of representing the simulation process (process-based dynamics) and the plant data structure. They propose a refined and stabilized metamodel for the dynamics based on past preliminary work. This paper also explains how the Model-View-Controller design pattern can be applied to DSML environments to produce a more specialist-friendly interface. In addition, the au

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38

Martinez-Guanter, Jorge, Ángela Ribeiro, Gerassimos G. Peteinatos, et al. "Low-Cost Three-Dimensional Modeling of Crop Plants." Sensors 19, no. 13 (2019): 2883. http://dx.doi.org/10.3390/s19132883.

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Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction

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39

Priya, Satya, and Ryosuke SHIBASAKI. "Soil Erosion and Crop Production: A Modeling Approach." Proceedings of the Symposium on Global Environment 6 (1998): 175–80. http://dx.doi.org/10.2208/proge.6.175.

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40

Todorovic, P., and J. Gani. "Modeling the effect of erosion on crop production." Journal of Applied Probability 24, no. 04 (1987): 787–97. http://dx.doi.org/10.1017/s0021900200116687.

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This paper is concerned with a model for the effect of erosion on crop production. Crop yield in the year n is given by X(n) = YnLn, where is a sequence of strictly positive i.i.d. random variables such that E{Y 1} &lt;∞, and is a Markov chain with stationary transition probabilities, independent of . When suitably normalized, leads to a martingale which converges to 0 almost everywhere (a.e.) as n → ∞. In addition, for large n, the distribution of Ln is approximately lognormal. The conditional expectations and probabilities of , given the past history of the process, are determined. Final

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41

Söffker, Dirk, Friederike Kögler, and Lina Owino. "Crop Growth Modeling-a New Data-driven Approach." IFAC-PapersOnLine 52, no. 30 (2019): 132–36. http://dx.doi.org/10.1016/j.ifacol.2019.12.510.

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42

Júdez, L., J. M. de Miguel, J. Mas, and R. Bru. "Modeling crop regional production using positive mathematical programming." Mathematical and Computer Modelling 35, no. 1-2 (2002): 77–86. http://dx.doi.org/10.1016/s0895-7177(01)00150-9.

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43

Hoogenboom, Gerrit, Jeffrey W. White, and Carlos D. Messina. "From genome to crop: integration through simulation modeling." Field Crops Research 90, no. 1 (2004): 145–63. http://dx.doi.org/10.1016/j.fcr.2004.07.014.

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44

Reinmuth, Evelyn, Phillip Parker, Joachim Aurbacher, Petra Högy, and Stephan Dabbert. "Modeling perceptions of climatic risk in crop production." PLOS ONE 12, no. 8 (2017): e0181954. http://dx.doi.org/10.1371/journal.pone.0181954.

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45

Müller, Christoph, and Richard D. Robertson. "Projecting future crop productivity for global economic modeling." Agricultural Economics 45, no. 1 (2013): 37–50. http://dx.doi.org/10.1111/agec.12088.

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46

Adeloye, Adebayo J., Rabee Rustum, and Ibrahim D. Kariyama. "Neural computing modeling of the reference crop evapotranspiration." Environmental Modelling & Software 29, no. 1 (2012): 61–73. http://dx.doi.org/10.1016/j.envsoft.2011.10.012.

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47

Asthana, Dr Stuti, and Dr Rakesh Kumar Bhujade. "AI-Driven Predictive Modeling for Crop Disease Detection." International Journal of Environmental Sciences 11, no. 1 (2025): 54–64. https://doi.org/10.64252/pqah8r60.

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The efficient and safe production of crops is critical to addressing global food security challenges, which are exacerbated by rapid population growth. Among the numerous factors affecting agricultural productivity, plant diseases play a significant role in hindering crop yields. The accurate and timely detection of these diseases has become a pivotal area of research. Geographic Information Systems (GIS)-supported farming information systems have emerged as a powerful alternative to traditional farming methods, enabling more precise decision-making by overcoming the limitations of human-based

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48

Alade, Samuel M., and Olufemi D. Ninan. "Development of a Computational Model for Cassava Food Processing Using Coloured Petri Net." International Journal of Intelligent Systems and Applications 15, no. 1 (2023): 48–62. http://dx.doi.org/10.5815/ijisa.2023.01.05.

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A food system is composed of a complex network of activities and processes for production, distribution, transportation and consumption, which interact with each other, thus leading to changeable behaviour. Most existing empirical studies on cassava processing have focused on the technical efficiency analysis of the cassava crop processing techniques among processors indicating that the modelling of the events and operations involved in the processing of the cassava crop is highly limited. In this context, different strategies have been used to solve difficult environmental and agro-informatic

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49

Carturan, Bruno S., Nourridine Siewe, Christina A. Cobbold, and Rebecca C. Tyson. "Bumble bee pollination and the wildflower/crop trade-off: When do wildflower enhancements improve crop yield?" Ecological Modelling 484 (October 2023): 110447. http://dx.doi.org/10.1016/j.ecolmodel.2023.110447.

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50

Ivano, Yaroslav, and Sof'ya Petrova. "MODELING MULTILEVEL DYNAMICS OF AGRICULTURAL CROPS YIELD." Bulletin of KSAU, no. 12 (January 29, 2025): 66–77. https://doi.org/10.36718/1819-4036-2024-12-66-77.

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The objective of the study is to develop an algorithm for modeling crop yields based on the dynamic-stochastic and cyclical properties of long-term characteristic series. Tasks: identifying the properties of time series of agricultural crop bioproductivity based on their consideration as multi-level structures with cyclical fluctuations; using the properties of variability of long-term characteristic series to build forecasting or stochastic assessment models; implementing the modeling algorithm using the example of grain and leguminous crop yields in Russia and wheat bioproductivity in the US

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