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Journal articles on the topic 'Crop growth model'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Kobayashi, K. "A Very Simple Model of Crop Growth: Derivation and Application." International Rice Research Notes 19, no. 3 (1994): 50–51. https://doi.org/10.5281/zenodo.6822405.

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This article 'A Very Simple Model of Crop Growth: Derivation and Application' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain details on the research reported.

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2

J. R. Williams, C. A. Jones, J. R. Kiniry, and D. A. Spanel. "The EPIC Crop Growth Model." Transactions of the ASAE 32, no. 2 (1989): 0497–511. http://dx.doi.org/10.13031/2013.31032.

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3

Sadasivam, R., C. Kailasam, R. Chandrababu, A. Arjunan, M. Nagarajan, and Rangasamy S. R. Sree. "Developing a Functional Model of Rice Panicle Growth." International Rice Research Newsletter 15, no. 5 (1990): 9. https://doi.org/10.5281/zenodo.7214485.

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This article 'Developing a Functional Model of Rice Panicle Growth' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping systems. This publication will report what scientists are doing to increase the production of rice in as much as this crop feeds the most densely populated and land scarce nations in the world.

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4

Rodriguez and Ober. "AquaCropR: Crop Growth Model for R." Agronomy 9, no. 7 (2019): 378. http://dx.doi.org/10.3390/agronomy9070378.

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The Food and Agriculture Organization (FAO) AquaCrop model, run either via a standalone graphical user interface (GUI) or via a matlab application programming interface (API) (AquaCrop-OS), has been successfully tested on many crop species and under multiple scenarios. However, with these current versions, it is difficult for users to adapt formulae, add functionality or incorporate the model into other applications such as decision support tools. Here, we report on the release of a version of AquaCrop written in R. Performance of the model was tested using published datasets of wheat (Triticu

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5

Liang, Jun Feng, Yan Qing Wang, Lin Li An, and Xu Ning Liu. "Application Research of Growth Management Model of Crop." Advanced Materials Research 989-994 (July 2014): 5551–54. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5551.

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In order to explore the growth mechanism of crop, further improve the level of crop growth management, the simulation research technology for management model of the crop growth is proposed, meanwhile ontology and visual model technology are used in the establishment process of crop growth model, so all kinds of states of crop growth at different stages can be predicted, then efficiency of crop growth management is greatly improved. The research on the crop growth model has important theory and practical significance in understanding the yield of the various parts of the crop, and help the man

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6

Kuijpers, Wouter J. P., Marinus J. G. van de Molengraft, Simon van Mourik, Albertus van ’t Ooster, Silke Hemming, and Eldert J. van Henten. "Model selection with a common structure: Tomato crop growth models." Biosystems Engineering 187 (November 2019): 247–57. http://dx.doi.org/10.1016/j.biosystemseng.2019.09.010.

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7

Shi, Pei-Jian, Xing-Yuan Men, Hardev S. Sandhu, et al. "The “general” ontogenetic growth model is inapplicable to crop growth." Ecological Modelling 266 (September 2013): 1–9. http://dx.doi.org/10.1016/j.ecolmodel.2013.06.025.

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8

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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9

Wang, Zhiqiang, Li Ye, Jingyi Jiang, Yida Fan, and Xiaoran Zhang. "Review of application of EPIC crop growth model." Ecological Modelling 467 (May 2022): 109952. http://dx.doi.org/10.1016/j.ecolmodel.2022.109952.

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10

Gallardo, M., R. L. Snyder, K. Schulbach, and L. E. Jackson. "Crop Growth and Water Use Model for Lettuce." Journal of Irrigation and Drainage Engineering 122, no. 6 (1996): 354–59. http://dx.doi.org/10.1061/(asce)0733-9437(1996)122:6(354).

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11

Thornton, P. K., J. B. Dent, and Z. Bacsi. "A framework for crop growth simulation model applications." Agricultural Systems 37, no. 4 (1991): 327–40. http://dx.doi.org/10.1016/0308-521x(91)90056-g.

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12

Díaz Iturry, Gabriel, Marco C. Matthies, Guy Pe’er, and Daniel Vedder. "AquaCrop.jl: A Process-Based Model of Crop Growth." Journal of Open Source Software 10, no. 110 (2025): 7944. https://doi.org/10.21105/joss.07944.

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13

Katterman, Matthew E., Peter M. Waller, Diaa Eldin M. Elshikha, et al. "WINDS Model Simulation of Guayule Irrigation." Water 15, no. 19 (2023): 3500. http://dx.doi.org/10.3390/w15193500.

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The WINDS (Water-Use, Irrigation, Nitrogen, Drainage, and Salinity) model uses the FAO56 dual crop coefficient and a daily time-step soil–water balance to simulate evapotranspiration and water content in the soil profile. This research calibrated the WINDS model for simulation of guayule under full irrigation. Using data from a furrow irrigated two-season guayule experiment in Arizona, this research developed segmented curves for guayule basal crop coefficient, canopy cover, crop height and root growth. The two-season guayule basal crop coefficient (Kcb) curve included first and second season

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14

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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15

Artru, S., B. Dumont, F. Ruget, et al. "How does STICS crop model simulate crop growth and productivity under shade conditions?" Field Crops Research 215 (January 2018): 83–93. http://dx.doi.org/10.1016/j.fcr.2017.10.005.

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16

Park, Jun, Jun-Yeong Kim, Sung-Wook Park, Se-Hoon Jung, and Chun-Bo Sim. "Development of ResNet based Crop Growth Stage Estimation Model." Korean Institute of Smart Media 11, no. 2 (2022): 53–62. http://dx.doi.org/10.30693/smj.2022.11.2.53.

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17

CAO, Wei-xing, Yan ZHU, Ting-bo DAI, et al. "Model-Based Crop Growth Prediction and Precision Management Technology." Agricultural Sciences in China 8, no. 7 (2009): i. http://dx.doi.org/10.1016/s1671-2927(09)60040-7.

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18

O'Callaghan, J. R., A. H. M. S. Hossain, M. H. Dahab, and G. C. L. Wyseure. "SODCOM: a solar driven computational model of crop growth." Computers and Electronics in Agriculture 11, no. 4 (1994): 293–308. http://dx.doi.org/10.1016/0168-1699(94)90021-3.

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19

Steinbuch, Luc, Dick J. Brus, Lenny G. J. van Bussel, and Gerard B. M. Heuvelink. "Geostatistical interpolation and aggregation of crop growth model outputs." European Journal of Agronomy 77 (July 2016): 111–21. http://dx.doi.org/10.1016/j.eja.2016.03.007.

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20

Hautala, Mikko, and Mikko Hakojärvi. "An analytical C3-crop growth model for precision farming." Precision Agriculture 12, no. 2 (2010): 266–79. http://dx.doi.org/10.1007/s11119-010-9174-5.

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21

Jain, R. C., Ranjana Agrawal, and K. N. Singh. "A Within Year Growth Model for Crop Yield Forecasting." Biometrical Journal 34, no. 7 (1992): 789–99. http://dx.doi.org/10.1002/bimj.4710340705.

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22

Pradeep, M., M. Srinivas, M. Sunil Kumar, T. V. Sridhar, and M. Ravi babu. "Evaluation of the CMS-CERES rice crop growth model." Andhra Agricultural Journal 71, no. 4 (2024): 362–68. https://doi.org/10.61657/aaj.2024.145.

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23

Liu, Fangliang, Yunhe Liu, Lijun Su, Wanghai Tao, Quanjiu Wang, and Mingjiang Deng. "Integrated Growth Model of Typical Crops in China with Regional Parameters." Water 14, no. 7 (2022): 1139. http://dx.doi.org/10.3390/w14071139.

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The analysis of common properties of growth for crops is the basis for further understanding crop growth in different regions. We used four typical crops of China, winter wheat, summer maize, rice, and cotton, to build an integrated model suitable for simulating the growth of different crops. The rates and characteristics of crop growth were systematically analysed based on semirelative and fully relative logistic models of crop growth, and a comprehensive, fully relative logistic model for the four crops was established. The spatial distributions of the maximum leaf area index (LAImax) and ma

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24

Biswal, A., P. Srikanth, C. S. Murthy, and P. V. N. Rao. "SPATIALISATION OF RICE GROWTH AND YIELD MODEL USING OPTICAL AND SAR DERIVED BIOPHYSICAL PARAMETERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 181–85. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-181-2019.

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<p><strong>Abstract.</strong> Integration of remote sensing derived biophysical parameters with process based crop growth simulation model is an emerging technology with diversified application for crop insurance as well as precision farming. Basically the crop growth simulation models are point based which simulate crop growth and yield as a function of soil, weather and crop management factors at a daily time scale. The temporal dimension of the crop growth model is supplemented by the spatial information on crop coverage and condition generated from remote sensing satellit

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25

Logachev, Maxim, and Dmitrii Goncharov. "Simulation model of crop yields." BIO Web of Conferences 93 (2024): 02015. http://dx.doi.org/10.1051/bioconf/20249302015.

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The article identifies the factors that affect the yield of crops grown in the open ground. Most factors are random in nature, which depending on a variety of conditions can positively or negatively affect crop growth processes. To predict the strategy for growing plants to maximize possible yield requires the development of simulation models that allow repeated virtual experiments, taking into account changes in a variety of parameters. This justifies the need for the study to be conducted. The use of structural analysis methods allowed to establish key objects and processes affecting all sta

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26

Franko, Uwe, and Wilfried Mirschel. "Integration of a Crop Growth Model with a Model of Soil Dynamics." Agronomy Journal 93, no. 3 (2001): 666–70. http://dx.doi.org/10.2134/agronj2001.933666x.

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27

Liu, Xing, Fei Chen, Michael Barlage, Guangsheng Zhou, and Dev Niyogi. "Noah-MP-Crop: Introducing dynamic crop growth in the Noah-MP land surface model." Journal of Geophysical Research: Atmospheres 121, no. 23 (2016): 13,953–13,972. http://dx.doi.org/10.1002/2016jd025597.

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28

Sangale, Bhagwan, U. M. Khodke H. W. Awari, and Vishal Ingle. "Crop Growth Simulation Modelling - A Review." International Journal of Current Microbiology and Applied Sciences 11, no. 1 (2022): 78–84. http://dx.doi.org/10.20546/ijcmas.2022.1101.010.

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Agriculture plays a key role in overall economic and social wellbeing of the specially developing countries. Now it is the right option to increase the quality and quantity of food production through the technological and managerial interventions like crop growth and yield prediction models. Agricultural models are mathematical equations that represent the reactions that occur within the plant and the interactions between the plant and its environment. The model simulates or imitates the behaviour of real crop by predicting the growth of its components, such as leaves, roots, stems and grains.

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29

Yu, Lingxue, Ye Liu, Tingxiang Liu, et al. "Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China." Computers and Electronics in Agriculture 201 (October 2022): 107323. http://dx.doi.org/10.1016/j.compag.2022.107323.

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30

Bourd?t, G. W., P. D. Jamieson, and G. A. Hurrell. "Evaluation of a mechanistic model of wheat and weed growth." Proceedings of the New Zealand Plant Protection Conference 52 (August 1, 1999): 203–8. http://dx.doi.org/10.30843/nzpp.1999.52.11566.

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A mechanistic crop growth model, where daily growth is the product of light use efficiency and intercepted radiation, was tested for its ability to simulate biomass growth in wheat and weeds. Wheat was sown at six densities (including 0), in September 1994 in Canterbury and the naturally occurring weed community was either left intact or removed by herbicide. Weed biomass growth was accurately simulated in the presence and absence of the crop, but late-season wheat growth was underestimated, particularly at low sowing densities. Herbicide treatment reduced early-season crop growth, but a grain

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31

Singels, A., and J. M. de Jager. "Refinement and validation of the PUTU wheat crop growth model 3. Grain growth." South African Journal of Plant and Soil 8, no. 2 (1991): 73–77. http://dx.doi.org/10.1080/02571862.1991.10634583.

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32

McMaster, Gregory S., James C. Ascough, Debora A. Edmunds, et al. "Simulating Unstressed Crop Development and Growth Using the Unified Plant Growth Model (UPGM)." Environmental Modeling & Assessment 19, no. 5 (2014): 407–24. http://dx.doi.org/10.1007/s10666-014-9402-x.

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33

Masutomi, Yuji, Keisuke Ono, Masayoshi Mano, Atsushi Maruyama, and Akira Miyata. "A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 1: Model description." Geoscientific Model Development 9, no. 11 (2016): 4133–54. http://dx.doi.org/10.5194/gmd-9-4133-2016.

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Abstract. Crop growth and agricultural management can affect climate at various spatial and temporal scales through the exchange of heat, water, and gases between land and atmosphere. Therefore, simulation of fluxes for heat, water, and gases from agricultural land is important for climate simulations. A land surface model (LSM) combined with a crop growth model (CGM), called an LSM-CGM combined model, is a useful tool for simulating these fluxes from agricultural land. Therefore, we developed a new LSM-CGM combined model for paddy rice fields, the MATCRO-Rice model. The main objective of this

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34

Scholberg, J. M. S., B. L. McNeal, J. W. Jones, S. J. Locascio, S. R. Olsen, and C. D. Stanley. "Adaptation of the CROPGRO Model to Simulate Yield of Fresh-market Tomato." HortScience 31, no. 4 (1996): 572f—572. http://dx.doi.org/10.21273/hortsci.31.4.572f.

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Modeling the growth of field-grown tomato (Lycopersicon esculentum Mill.) should assist researchers and commercial growers to outline optimal crop management strategies for specific locations and production systems. A generic crop-growth model (CROPGRO) was previously adapted to simulate the growth of fresh-market tomato under field conditions. Plant growth and development of field-grown tomato, and fruit yields, will be outlined and compared to model predictions for a number of locations in Florida, nitrogen fertilizer rates, and irrigation management practices. Possible application of the mo

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35

Connor, D. J., and E. Fereres. "A dynamic model of crop growth and partitioning of biomass." Field Crops Research 63, no. 2 (1999): 139–57. http://dx.doi.org/10.1016/s0378-4290(99)00032-5.

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36

Lenz-Wiedemann, V. I. S., K. Schneider, Y. Miao, and G. Bareth. "Development of a regional crop growth model for Northeast China." Procedia Environmental Sciences 13 (2012): 1946–55. http://dx.doi.org/10.1016/j.proenv.2012.01.188.

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37

Rao, N. H., and D. H. Rees. "Irrigation scheduling of rice with a crop growth simulation model." Agricultural Systems 39, no. 2 (1992): 115–32. http://dx.doi.org/10.1016/0308-521x(92)90104-v.

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38

Zhang, Yuliang, Zhiyong Wu, Vijay P. Singh, et al. "Coupled hydrology-crop growth model incorporating an improved evapotranspiration module." Agricultural Water Management 246 (March 2021): 106691. http://dx.doi.org/10.1016/j.agwat.2020.106691.

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39

Wang, DongWei, JinDi Wang, and ShunLin Liang. "Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model." Science China Earth Sciences 53, no. 5 (2010): 721–30. http://dx.doi.org/10.1007/s11430-009-0203-z.

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40

Ehumadu, Chikodi N. "Crop Water Simulation Using CropWAT Model for Climate Change Mitigation." FUOYE Journal of Engineering and Technology 9, no. 4 (2025): 575–79. https://doi.org/10.4314/fuoyejet.v9i4.3.

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Uncertainties caused by population increase and adverse climate change impact affect global food sufficiency and water availability. This can be tackled using crop models, an innovative, dynamic and robust approach. The study was conducted in the southwestern city of Ibadan, Oyo state, Nigeria. The study is aimed at determining the crop water requirement of Corchorus Olitorus and the feasibility of utilizing the CropWAT model for effective water management and mitigate climate change impact. An automatic weather station was used in obtaining meteorological data of the study area. Soil characte

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41

Masutomi, Yuji, Keisuke Ono, Takahiro Takimoto, Masayoshi Mano, Atsushi Maruyama, and Akira Miyata. "A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation." Geoscientific Model Development 9, no. 11 (2016): 4155–67. http://dx.doi.org/10.5194/gmd-9-4155-2016.

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Abstract. We conducted two types of validation for the simulations by MATCRO-Rice developed by Masutomi et al. (2016). In the first validation, we compared simulations with observations for latent heat flux (LHF), sensible heat flux (SHF), net carbon uptake by crop, and paddy rice yield from 2003 to 2006 at the site where model parameters are parameterized. In the second validation, we compared the observed and simulated paddy rice yields over Japan from 1991 to 2010 between observations and simulations. The 4-year average root mean square errors (RMSEs) of the first validation for LHF and SHF

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42

Rana, Shubham, Salvatore Gerbino, Ehsan Akbari Sekehravani, Mario Brandon Russo, and Petronia Carillo. "Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-Date Aerial Images and Ortho-Mosaics." Agronomy 14, no. 9 (2024): 2052. http://dx.doi.org/10.3390/agronomy14092052.

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Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SA

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43

Zi-zhen, Li, Wang Wan-xiong, and Xu Cai-lin. "Dynamic model of crop growth system and numerical simulation of crop growth process under the multi-environment external force action." Applied Mathematics and Mechanics 24, no. 6 (2003): 727–37. http://dx.doi.org/10.1007/bf02437875.

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Osborne, Tom, Julia Slingo, David Lawrence, and Tim Wheeler. "Examining the Interaction of Growing Crops with Local Climate Using a Coupled Crop–Climate Model." Journal of Climate 22, no. 6 (2009): 1393–411. http://dx.doi.org/10.1175/2008jcli2494.1.

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Abstract This paper examines to what extent crops and their environment should be viewed as a coupled system. Crop impact assessments currently use climate model output offline to drive process-based crop models. However, in regions where local climate is sensitive to land surface conditions more consistent assessments may be produced with the crop model embedded within the land surface scheme of the climate model. Using a recently developed coupled crop–climate model, the sensitivity of local climate, in particular climate variability, to climatically forced variations in crop growth througho

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45

Zeng, X., B. A. Drewniak, and E. M. Constantinescu. "Calibration of the Crop model in the Community Land Model." Geoscientific Model Development Discussions 6, no. 1 (2013): 379–98. http://dx.doi.org/10.5194/gmdd-6-379-2013.

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Abstract. Farming is using more terrestrial ground with increases in population and the expanding use of agriculture for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given

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46

Kirthiga, S. M., and N. R. Patel. "In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model." AgriEngineering 4, no. 4 (2022): 1054–75. http://dx.doi.org/10.3390/agriengineering4040066.

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In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 20

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47

Willocquet, Laetitia, Simone Bregaglio, Roberto Ferrise, KH Kim, and Serge Savary. "DYNAMO-A: A generic simulation model coupling crop growth and disease epidemic." PLOS One 20, no. 4 (2025): e0321261. https://doi.org/10.1371/journal.pone.0321261.

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Very few dynamic simulation models truly involve explicit, quantitative, two-way couplings of epidemiological and agrophysiological processes. Our aim is to develop a generic, transparent and simple, coupled disease-crop model, DYNAMO-A, where a polycyclic epidemic develops within the canopy of an annual crop. DYNAMO-A builds upon existing models, RICEPEST and WHEATPEST, respectively designed as crop loss simulation platforms for rice and wheat, and the generic model GENEPEST, which was designed for further crop-specific development and educational purposes. Two intertwined components constitu

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48

Kobayashi, Kent D. "PRINCIPLES OF MODELING CROP GROWTH AND DEVELOPMENT, MODEL TYPES, TECHNIQUES, AND SIMULATION." HortScience 28, no. 5 (1993): 512b—512. http://dx.doi.org/10.21273/hortsci.28.5.512b.

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Crop modeling encompasses the use of mathematics, statistics, and equations to describe quantitatively crop growth and development. It can be useful tool to describe, quantify, and predict crop growth, development, and phenology. Crop models fall into two general classes—statistical (empirical) or mechanistic (physiological)—or they may be a combination of the two. The model type depends upon several factors including the objectives of the modeler, understanding of underlying physiological processes, and availability of data. Model development proceeds from a preliminary conceptual model throu

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49

K.K. SINGH, R.K. MALL, R.S. SINGH, and A. K. SRIVASTAVA. "Evaluation of CANEGRO Sugarcane model in East Uttar Pradesh, India." Journal of Agrometeorology 12, no. 2 (2010): 181–86. http://dx.doi.org/10.54386/jam.v12i2.1301.

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The sugarcane crop growth simulation model was calibrated and validated in Eastern Uttar Pradesh (UP) region of Indo-Gangetic Plains of India using 12 years field experiment data conducted in several places. The results reveal that the CANEGRO Sugarcane model satisfactorily simulated the potential growth and yield of sugarcane crop. The model simulates the stalk height, stalk fresh mass and sucrose yield within ±15 % of range in comparison to the observed values. Therefore the validated CANEGRO Sugarcane model can be further used for applications such as prediction of crop growth, phenology, w

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Hakojärvi, Mikko, Mikko Hautala, and Laura Alakukku. "Testing the use of an analytical and mechanistic C3 - biomass accumulation model for precision fertilization." Agricultural and Food Science 23, no. 2 (2014): 89–105. http://dx.doi.org/10.23986/afsci.40938.

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Abstract:

A single and uniform fertilizer application may lead to ineffective crop nutrient uptake and use. In order to enhance nutrient use efficiency the application should be adjusted according to the need of the cultivated crop. This task is challenging because weather is unknown and unpredictable over the upcoming growing season. One solution is site-specific fertilizer application in several separate events throughout the season. Such a precision fertilization method requires information on the current crop state (e.g. the availability of water and nutrients in the soil) and a crop growth model th

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