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Journal articles on the topic 'STICS soil-crop model'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Ravelojaona, Nomena, Guillaume Jégo, Noura Ziadi, et al. "STICS Soil–Crop Model Performance for Predicting Biomass and Nitrogen Status of Spring Barley Cropped for 31 Years in a Gleysolic Soil from Northeastern Quebec (Canada)." Agronomy 13, no. 10 (2023): 2540. http://dx.doi.org/10.3390/agronomy13102540.

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Spring barley (Hordeum vulgare L.) is an increasingly important cash crop in the province of Quebec (Canada). Soil–crop models are powerful tools for analyzing and supporting sustainable crop production. STICS model has not yet been tested for spring barley grown over several decades. This study was conducted to calibrate and evaluate the STICS model, without annual reinitialization, for predicting aboveground biomass and N nutrition attributes at harvest during 31 years of successive cropping of spring barley grown in soil (silty clay, Humic Gleysol) from the Saguenay–Lac-Saint-Jean region (n

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2

Bourdin, F., F. J. Morell, D. Combemale, P. Clastre, M. Guérif, and A. Chanzy. "A tool based on remotely sensed LAI, yield maps and a crop model to recommend variable rate nitrogen fertilization for wheat." Advances in Animal Biosciences 8, no. 2 (2017): 672–77. http://dx.doi.org/10.1017/s2040470017000887.

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Inversing the STICS crop model with remote-sensing-derived leaf area index (LAI) and yield data from the previous crop is used to retrieve some soil permanent properties and crop emergence parameters. Spatialized nitrogen (N) fertilization recommendations are provided to farmers, for the second and third N applications, following the screening of eleven N application rates under a range of possible forthcoming climates, with the objective to maximize of the gross margin while respecting some environmental constraints. As a first field validation, we show (1) the improvement brought by the assi

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3

Valdés-Gómez, Héctor, Florian Celette, Iñaki García de Cortázar-Atauri, Francisco Jara-Rojas, Samuel Ortega-Farías, and Christian Gary. "Modelling soil water content and grapevine growth and development with the stics crop-soil model under two different water management strategies." OENO One 43, no. 1 (2009): 13. http://dx.doi.org/10.20870/oeno-one.2009.43.1.806.

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<p style="text-align: justify;"><strong>Background and aims</strong>: Many models have been developed to evaluate crop growth and development, but few are capable of simulating grapevine systems. The present study was carried out to evaluate the ability of the STICS model to represent grapevine phenology, biomass production, yield and soil water content in two situations differing with respect to rainfall distribution and water management strategies.</p><p style="text-align: justify;"><strong>Methods and results</strong>: Simulations were performed for

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4

Tribouillois, Hélène, Julie Constantin, and Eric Justes. "Analysis and modeling of cover crop emergence: Accuracy of a static model and the dynamic STICS soil-crop model." European Journal of Agronomy 93 (February 2018): 73–81. http://dx.doi.org/10.1016/j.eja.2017.12.004.

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5

Wallach, D., S. Buis, P. Lecharpentier, et al. "A package of parameter estimation methods and implementation for the STICS crop-soil model." Environmental Modelling & Software 26, no. 4 (2011): 386–94. http://dx.doi.org/10.1016/j.envsoft.2010.09.004.

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6

Valade, A., P. Ciais, N. Vuichard, et al. "Modeling sugarcane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values." Geoscientific Model Development 7, no. 3 (2014): 1225–45. http://dx.doi.org/10.5194/gmd-7-1225-2014.

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Abstract. Agro-land surface models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil–vegetation–atmosphere continuum. When developing agro-LSM models, particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of agro-LSM models is related to their usually large

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7

Valade, A., P. Ciais, N. Vuichard, et al. "Modeling sugar cane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values." Geoscientific Model Development Discussions 7, no. 1 (2014): 1197–244. http://dx.doi.org/10.5194/gmdd-7-1197-2014.

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Abstract. Agro-Land Surface Models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, a particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of Agro-LSM models is related to their usually larg

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8

Saadi, Sameh, Elizabeth Pattey, Guillaume Jégo, and Catherine Champagne. "Prediction of rainfed corn evapotranspiration and soil moisture using the STICS crop model in eastern Canada." Field Crops Research 287 (October 2022): 108664. http://dx.doi.org/10.1016/j.fcr.2022.108664.

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9

Sow, Sidy, Yolande Senghor, Khardiatou Sadio, et al. "Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth." Field Crops Research 306 (February 2024): 109206. http://dx.doi.org/10.1016/j.fcr.2023.109206.

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10

Demestihas, Constance, Daniel Plénet, Michel Génard, et al. "Analyzing ecosystem services in apple orchards using the STICS model." European Journal of Agronomy 94 (March 2018): 108–19. http://dx.doi.org/10.1016/j.eja.2018.01.009.

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11

Dupraz, Christian, Kevin Wolz, Isabelle Lecomte, et al. "Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree–Crop Interactions." Sustainability 11, no. 8 (2019): 2293. http://dx.doi.org/10.3390/su11082293.

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Agroforestry, the intentional integration of trees with crops and/or livestock, can lead to multiple economic and ecological benefits compared to trees and crops/livestock grown separately. Field experimentation has been the primary approach to understanding the tree–crop interactions inherent in agroforestry. However, the number of field experiments has been limited by slow tree maturation and difficulty in obtaining consistent funding. Models have the potential to overcome these hurdles and rapidly advance understanding of agroforestry systems. Hi-sAFe is a mechanistic, biophysical model des

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12

Bécel, C., N. M. Munier-Jolain, and B. Nicolardot. "Assessing nitrate leaching in cropping systems based on integrated weed management using the STICS soil–crop model." European Journal of Agronomy 62 (January 2015): 46–54. http://dx.doi.org/10.1016/j.eja.2014.09.005.

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13

Beaudoin, N., M. Launay, E. Sauboua, G. Ponsardin, and B. Mary. "Evaluation of the soil crop model STICS over 8 years against the “on farm” database of Bruyères catchment." European Journal of Agronomy 29, no. 1 (2008): 46–57. http://dx.doi.org/10.1016/j.eja.2008.03.001.

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14

Meyer, Nicolas, Jacques-Eric Bergez, Eric Justes, and Julie Constantin. "Influence of cover crop on water and nitrogen balances and cash crop yield in a temperate climate: A modelling approach using the STICS soil-crop model." European Journal of Agronomy 132 (January 2022): 126416. http://dx.doi.org/10.1016/j.eja.2021.126416.

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15

Kherif, Omar, Mounir Seghouani, Eric Justes, et al. "The first calibration and evaluation of the STICS soil-crop model on chickpea-based intercropping system under Mediterranean conditions." European Journal of Agronomy 133 (February 2022): 126449. http://dx.doi.org/10.1016/j.eja.2021.126449.

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16

Jégo, Guillaume, Gilles Bélanger, Gaëtan F. Tremblay, Qi Jing, and Vern S. Baron. "Calibration and performance evaluation of the STICS crop model for simulating timothy growth and nutritive value." Field Crops Research 151 (September 2013): 65–77. http://dx.doi.org/10.1016/j.fcr.2013.07.003.

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17

SALO, T. J., T. PALOSUO, K. C. KERSEBAUM, et al. "Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization." Journal of Agricultural Science 154, no. 7 (2015): 1218–40. http://dx.doi.org/10.1017/s0021859615001124.

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SUMMARYEleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology,

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18

Falconnier, Gatien N., Etienne-Pascal Journet, Laurent Bedoussac, et al. "Calibration and evaluation of the STICS soil-crop model for faba bean to explain variability in yield and N2 fixation." European Journal of Agronomy 104 (March 2019): 63–77. http://dx.doi.org/10.1016/j.eja.2019.01.001.

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19

Valdés-Gómez, H., C. Gary, N. Brisson, and F. Matus. "Modelling indeterminate development, dry matter partitioning and the effect of nitrogen supply in tomato with the generic STICS crop–soil model." Scientia Horticulturae 175 (August 2014): 44–56. http://dx.doi.org/10.1016/j.scienta.2014.05.030.

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20

Strullu, L., N. Beaudoin, P. Thiébeau, et al. "Simulation using the STICS model of C&N dynamics in alfalfa from sowing to crop destruction." European Journal of Agronomy 112 (January 2020): 125948. http://dx.doi.org/10.1016/j.eja.2019.125948.

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21

Fraga, Helder, Daniel Molitor, Luisa Leolini, and João A. Santos. "What Is the Impact of Heatwaves on European Viticulture? A Modelling Assessment." Applied Sciences 10, no. 9 (2020): 3030. http://dx.doi.org/10.3390/app10093030.

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Extreme heat events or heatwaves can be particularly harmful to grapevines, posing a major challenge to winegrowers in Europe. The present study is focused on the application of the crop model STICS to assess the potential impacts of heatwaves over some of the most renowned winemaking regions in Europe. For this purpose, STICS was applied to grapevines, using high-resolution weather, soil and terrain datasets from 1986 to 2015. To assess the impact of heatwaves, the weather dataset was artificially modified, generating periods with anomalously high temperatures (+5 °C), at specific onset dates

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22

Jégo, Guillaume, Elizabeth Pattey, S. Morteza Mesbah, Jiangui Liu, and Isabelle Duchesne. "Impact of the spatial resolution of climatic data and soil physical properties on regional corn yield predictions using the STICS crop model." International Journal of Applied Earth Observation and Geoinformation 41 (September 2015): 11–22. http://dx.doi.org/10.1016/j.jag.2015.04.013.

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23

Traoré, Amadou, Gatien N. Falconnier, Alassane Ba, Fagaye Sissoko, Benjamin Sultan, and François Affholder. "Modeling sorghum-cowpea intercropping for a site in the savannah zone of Mali: Strengths and weaknesses of the Stics model." Field Crops Research 285 (September 2022): 108581. http://dx.doi.org/10.1016/j.fcr.2022.108581.

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24

Seghouani, Mounir, Matthieu N. Bravin, Patrice Lecharpentier, and Alain Mollier. "Simulating phosphorus dynamics between the soil and the crop with the STICS model: Formalization and multi-site evaluation on maize in temperate area." European Journal of Agronomy 164 (March 2025): 127475. https://doi.org/10.1016/j.eja.2024.127475.

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25

Constantin, Julie, Christine Le Bas, and Eric Justes. "Large-scale assessment of optimal emergence and destruction dates for cover crops to reduce nitrate leaching in temperate conditions using the STICS soil–crop model." European Journal of Agronomy 69 (September 2015): 75–87. http://dx.doi.org/10.1016/j.eja.2015.06.002.

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26

LAUNAY, M., A. I. GRAUX, N. BRISSON, and M. GUERIF. "Carbohydrate remobilization from storage root to leaves after a stress release in sugar beet (Beta vulgaris L.): experimental and modelling approaches." Journal of Agricultural Science 147, no. 6 (2009): 669–82. http://dx.doi.org/10.1017/s0021859609990116.

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SUMMARYCarbohydrate remobilization from the sugar beet storage root to support leaf regrowth after release from water stress was demonstrated by experimental and modelling approaches. Experimental trials were carried out in northern France in 1994 and 1995 and in southern France in 2005, in conditions that involved a succession of soil moisture stresses and re-hydrations. Drought stress slowed leaf growth and the subsequent release of stress resulted in regrowth. A second trial showed that after total defoliation, sugar beet was able to produce new leaves. It was assumed that this leaf renewal

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27

Jégo, G., M. Martínez, I. Antigüedad, M. Launay, J. M. Sanchez-Pérez, and E. Justes. "Evaluation of the impact of various agricultural practices on nitrate leaching under the root zone of potato and sugar beet using the STICS soil–crop model." Science of The Total Environment 394, no. 2-3 (2008): 207–21. http://dx.doi.org/10.1016/j.scitotenv.2008.01.021.

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28

Coucheney, Elsa, Samuel Buis, Marie Launay, et al. "Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France." Environmental Modelling & Software 64 (February 2015): 177–90. http://dx.doi.org/10.1016/j.envsoft.2014.11.024.

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29

da Silva, Fernando Antônio Macena, Alexsandra Duarte de Oliveira, Arminda Moreira de Carvalho, et al. "Effects of agricultural management and of climate change on N2O emissions in an area of the Brazilian Cerrado: Measurements and simulations using the STICS soil-crop model." Agriculture, Ecosystems & Environment 363 (April 2024): 108842. http://dx.doi.org/10.1016/j.agee.2023.108842.

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30

Katerji, Nader, Marcello Mastrorilli, and Houssem Eddine Cherni. "Effects of corn deficit irrigation and soil properties on water use efficiency. A 25-year analysis of a Mediterranean environment using the STICS model." European Journal of Agronomy 32, no. 2 (2010): 177–85. http://dx.doi.org/10.1016/j.eja.2009.11.001.

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31

Justes, E., B. Mary, and B. Nicolardot. "Quantifying and modelling C and N mineralization kinetics of catch crop residues in soil: parameterization of the residue decomposition module of STICS model for mature and non mature residues." Plant and Soil 325, no. 1-2 (2009): 171–85. http://dx.doi.org/10.1007/s11104-009-9966-4.

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32

Lagacherie, P., S. Buis, J. Constantin, S. Dharumarajan, L. Ruiz, and M. Sekhar. "Evaluating the impact of using digital soil mapping products as input for spatializing a crop model: The case of drainage and maize yield simulated by STICS in the Berambadi catchment (India)." Geoderma 406 (January 2022): 115503. http://dx.doi.org/10.1016/j.geoderma.2021.115503.

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33

Lagacherie, P., S. Buis, J. Constantin, S. Dharumarajan, L. Ruiz, and M. Sekhar. "Evaluating the impact of using digital soil mapping products as input for spatializing a crop model: The case of drainage and maize yield simulated by STICS in the Berambadi catchment (India)." Geoderma 406 (January 2022): 115503. http://dx.doi.org/10.1016/j.geoderma.2021.115503.

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34

Paleari, Livia, Fosco M. Vesely, Riccardo A. Ravasi, et al. "Analysis of the Similarity between in Silico Ideotypes and Phenotypic Profiles to Support Cultivar Recommendation—A Case Study on Phaseolus vulgaris L." Agronomy 10, no. 11 (2020): 1733. http://dx.doi.org/10.3390/agronomy10111733.

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Cultivar recommendation is a key factor in cropping system management. Classical approaches based on comparative multi-environmental trials can hardly explore the agro-climatic and management heterogeneity farmers may have to face. Moreover, they struggle to keep up with the number of genotypes commercially released each year. We propose a new approach based on the integration of in silico ideotyping and functional trait profiling, with the common bean (Phaseoulus vulgaris L.) in Northern Italy as a case study. Statistical distributions for six functional traits (light extinction coefficient,

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35

Constantin, Julie, Magali Willaume, Clément Murgue, Bernard Lacroix, and Olivier Therond. "The soil-crop models STICS and AqYield predict yield and soil water content for irrigated crops equally well with limited data." Agricultural and Forest Meteorology 206 (June 2015): 55–68. http://dx.doi.org/10.1016/j.agrformet.2015.02.011.

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36

Sansoulet, J., E. Pattey, R. Kröbel, et al. "Comparing the performance of the STICS, DNDC, and DayCent models for predicting N uptake and biomass of spring wheat in Eastern Canada." Field Crops Research 156 (February 2014): 135–50. http://dx.doi.org/10.1016/j.fcr.2013.11.010.

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37

Watanabe, Masahisa, and Kenshi Sakai. "Novel power hop model for an agricultural tractor with coupling bouncing, stick-slip, and free-play dynamics." Biosystems Engineering 204 (April 2021): 156–69. http://dx.doi.org/10.1016/j.biosystemseng.2021.01.007.

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38

Yan, Xiaojie, Qiang Zhang, Laurie Connor, Nicolas Devillers, and Kristopher Dick. "A 2D stick model for simulation of sow walking on concrete floors and detection of sow lameness." Biosystems Engineering 226 (February 2023): 99–115. http://dx.doi.org/10.1016/j.biosystemseng.2022.12.011.

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39

Grieve, A. M., L. D. Prior, and K. B. Bevington. "Long-term effects of saline irrigation water on growth, yield, and fruit quality of 'Valencia' orange trees." Australian Journal of Agricultural Research 58, no. 4 (2007): 342. http://dx.doi.org/10.1071/ar06198.

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Citrus is regarded as a salt-sensitive crop, but its yield response to salinity is affected by variety, rootstock, duration of salt exposure, irrigation management, soil type, and climate. This study quantified the yield response of mature Valencia [Citrus sinensis (L. Osbeck)] orange trees on sweet orange (C. sinensis) rootstock to increased levels of sodium chloride in irrigation water in the Sunraysia area of the Murray Valley in south-eastern Australia. The orchard was planted on a loamy sand and trees were irrigated and fertilised with a well-managed under-tree microsprinkler system. Four

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40

Delandmeter, Mathieu, Gilles Colinet, Jérôme Pierreux, Jérôme Bindelle, and Benjamin Dumont. "Combining field measurements and process‐based modelling to analyse soil tillage and crop residues management impacts on crop production and carbon balance in temperate areas." Soil Use and Management 40, no. 3 (2024). http://dx.doi.org/10.1111/sum.13098.

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AbstractCrop residues management is an important issue in the context of climate change. They might be kept on the field and restituted to the soil to enhance its fertility or exported for other uses such as the production of energy through biomethanization. Furthermore, the choices regarding tillage operations impact the potential to incorporate residues, which in turn affects soil physical (e.g. structure, water retention), biological (e.g. organic matter, microorganisms) and chemical (e.g. nutrient release through mineralization) fertility. We combined measurements from a 14‐year field expe

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41

Sow, Sidy, Yolande Senghor, Khardiatou Sadio, et al. "Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth." Field Crops Research 306 (February 1, 2024). https://doi.org/10.1016/j.fcr.2023.109206.

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Context: Agroforestry systems provide critical benefits for food security and climate change mitigation. Yet, they are complex and heteregoneous sytems hard to optimize. The use of process-based crop models provides an opportunity to understand better the interactions between soil, crop, tree and climate and explore the impact of agroforestry on crop growth, for contrasting crop management. Objective: The objectives of this study were to i) calibrate the soil-crop STICS model for pearl millet (Pennisetum glaucum) in order to simulate millet potential growth and impact of water and nitrogen lim

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42

Constantin, Julie, Sébastien Minette, Gregory Vericel, Lionel Jordan-Meille, and Eric Justes. "MERCI: a simple method and decision-support tool to estimate availability of nitrogen from a wide range of cover crops to the next cash crop." Plant and Soil, September 20, 2023. http://dx.doi.org/10.1007/s11104-023-06283-1.

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Abstract Background and aims Cover crops can efficiently increase nitrogen (N) recycling in agroecosystems. By providing a green-manure effect for the next crop, they allow reduced mineral fertilisation. We developed a decision-support tool, called MERCI, to predict N available from cover crop residues over time, from a single measurement of fresh shoot biomass. Methods We coupled a large experimental database from France with a simulation experiment using the soil-crop model STICS. More than 25 000 measurements of 74 species of cover crops as a sole crop or bispecific mixtures were collected.

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43

Dueri, Sibylle, Joël Léonard, Florent Chlebowski, et al. "Data used for the simulation of six experiments on N2O emissions from arable crops and simulation results of three agroecosystem models (MONICA, SiriusQuality and STICS)." March 6, 2023. https://doi.org/10.5281/zenodo.7701075.

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The dataset contains data from six arable crop field experiments, that provided high-quality data on N<sub>2</sub>O emission for simulation woth agroecosystem models. The experimental sites were located in Ottawa, Canada, Grignon, France, New Delhi, India, Kingaroy, Australia, Santa Maria, Brazil, and Estrées-Mons, France. The dataset contains two growing seasons of ACBrio cultivar (spring wheat) in Ottawa, two growing seasons of Premio cultivar (winter wheat) in Grignon, three growing seasons of PBW343 cultivar (spring wheat) in New Delhi, one growing season of Hartog cultivar (winter

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44

Guinet, Maé, Bernard Nicolardot, and Anne-Sophie Voisin. "Nitrogen benefits of ten legume pre-crops for wheat assessed by field measurements and modelling." European Journal of Agronomy 120 (October 20, 2020). https://doi.org/10.1016/j.eja.2020.126151.

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The positive effect of grain legume pre-crops on the yield of the subsequent crop has been studied widely, whereas less information is available on the nitrogen (N) processes related to this positive effect, especially for a wide range of grain legume species. The objective was to quantify and understand the effect of grain legume compared to cereal pre-crops (sown in 2014 and 2016) on grain and shoot N yields and shoot N concentration of wheat (<em>Triticum aestivum</em>) grown the following year (2015 and 2017). Spring legumes (faba bean (<em>Vicia faba)</em>, fenugreek (<em>Trigolia foenum-

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45

Levavasseur, Florent, Bruno Mary, and Sabine Houot. "C and N dynamics with repeated organic amendments can be simulated with the STICS model." Nutrient Cycling in Agroecosystems, January 3, 2021. http://dx.doi.org/10.1007/s10705-020-10106-5.

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46

Chalhoub, Maha, Patricia Garnier, Yves Coquet, David Montagne, and Philippe C. Baveye. "Assessment of Future Soil Ecosystem Services of a Drained Soil Under Different Climate Change Scenarios." European Journal of Soil Science 76, no. 3 (2025). https://doi.org/10.1111/ejss.70144.

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ABSTRACTIn order to prepare for the necessary adaptation to climate change, it would be useful to have some idea of how the services provided by soils to human societies are going to evolve in the future. Unfortunately, since actual measurements of soil services remain elusive in general, modelling efforts needed to predict their future evolution are fraught with very large uncertainties. In one particular situation, however, on the Saclay plateau south of Paris (France), it has been possible to measure five soil services: three provisioning services (supply of water to nearby stream, provisio

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47

Vezy, Rémi, Sebastian Munz, Noémie Gaudio, et al. "Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model." Agronomy for Sustainable Development 43, no. 5 (2023). http://dx.doi.org/10.1007/s13593-023-00917-5.

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