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Статті в журналах з теми "STICS soil-crop model"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

Ravelojaona, Nomena, Guillaume Jégo, Noura Ziadi, Alain Mollier, Jean Lafond, Antoine Karam, and Christian Morel. "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 (September 30, 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 (northeastern Quebec, Canada). There is a good agreement between observed and predicted variables during the 31 successive barley cropping years. STICS predicted well biomass accumulation and plant N content with a low relative bias (|normalized mean error| = 0–13%) and small prediction error (normalized root mean square error = 6–25%). Overall, the STICS outputs reproduced the same trends as the field-observed data with various tillage systems and N sources. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. These ‘newly calibrated’ parameters in STICS for spring barley cropped under continental cold and humid climates require validation using independent observation datasets from other sites.

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 (June 1, 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 assimilation of LAI and yield into STICS to simulate crop and soil variables and (2) the interest of site specific application to maximize both the gross margin and the agro-environmental criterion.

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 (March 31, 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 an irrigated vineyard in Chile and an irrigated and a non-irrigated vineyard in France. The crop model gave a good estimation of the main stages of grapevine phenology (less than six days difference between simulated and observed values). Soil water content was the best simulated variable (R2 = 0.99), whereas grapevine evapotranspiration observed only in Chile (R2 = 0.43) and leaf area index observed only in France (R2= 0.80) were the worst simulated variables. Biomass production, yield and their components were correctly simulated (within the 95 % Student confidence interval around the mean observed value). A comparison of the fraction of transpirable soil water and vine water potential measurements with the water stress indices calculated by the STICS model showed that the time and duration of the grapevine water stress period was correctly estimated.</p><p style="text-align: justify;"><strong>Conclusions</strong>: Therefore, the STICS model was reasonably successful in simulating vine growth and development, and identifying critical periods concerning the vine water status.</p><p style="text-align: justify;"><strong>Significance of the study</strong>: The STICS model can be used to evaluate various water management strategies and their impacts on grape production.</p>

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, J. Bourges, P. Clastre, M. Launay, J. E. Bergez, M. Guerif, J. Soudais, and E. Justes. "A package of parameter estimation methods and implementation for the STICS crop-soil model." Environmental Modelling & Software 26, no. 4 (April 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, N. Viovy, A. Caubel, N. Huth, F. Marin, and J. F. Martiné. "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 (June 30, 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 number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugarcane biomass production with the agro-LSM ORCHIDEE–STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE–STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte Carlo sampling method associated with the calculation of partial ranked correlation coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugarcane cultivation in Australia and Brazil. The ten parameters driving most of the uncertainty in the ORCHIDEE–STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting different climate-mediated sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

7

Valade, A., P. Ciais, N. Vuichard, N. Viovy, N. Huth, F. Marin, and J. F. Martiné. "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 (January 31, 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 large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS' phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte-Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugar cane cultivation in Australia and Brazil. Ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting climate-mediated different sensitivities of modeled sugar cane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

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, Rémi Vezy, Olivier Roupsard, François Affholder, Moussa N’dienor, 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, Iñaki Garcia de Cortazar-Atauri, Marie Launay, Dominique Ripoche, Nicolas Beaudoin, 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

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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12

Dupraz, Christian, Kevin Wolz, Isabelle Lecomte, Grégoire Talbot, Grégoire Vincent, Rachmat Mulia, François Bussière, et al. "Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree–Crop Interactions." Sustainability 11, no. 8 (April 16, 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 designed to explore the interactions within agroforestry systems that mix trees with crops. The model couples the pre-existing STICS crop model to a new tree model that includes several plasticity mechanisms responsive to tree–tree and tree–crop competition for light, water, and nitrogen. Monoculture crop and tree systems can also be simulated, enabling calculation of the land equivalent ratio. The model’s 3D and spatially explicit form is key for accurately representing many competition and facilitation processes. Hi-sAFe is a novel tool for exploring agroforestry designs (e.g., tree spacing, crop type, tree row orientation), management strategies (e.g., thinning, branch pruning, root pruning, fertilization, irrigation), and responses to environmental variation (e.g., latitude, climate change, soil depth, soil structure and fertility, fluctuating water table). By improving our understanding of the complex interactions within agroforestry systems, Hi-sAFe can ultimately facilitate adoption of agroforestry as a sustainable land-use practice.

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 (July 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, Daniel Plaza-Bonilla, Abderrahim Bouhenache, Bahia Zemmouri, Peter Dokukin, and Mourad Latati. "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, C. NENDEL, C. ANGULO, F. EWERT, M. BINDI, et al. "Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization." Journal of Agricultural Science 154, no. 7 (December 22, 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, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.

18

Falconnier, Gatien N., Etienne-Pascal Journet, Laurent Bedoussac, Anthony Vermue, Florent Chlébowski, Nicolas Beaudoin, and Eric Justes. "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, B. Julier, B. Mary, F. Ruget, D. Ripoche, L. Rakotovololona, and G. Louarn. "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

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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22

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 (April 26, 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 and with specific episode durations (from five to nine days). The model was then run with this modified weather dataset, and the results were compared to the original unmodified runs. The results show that heatwaves can have a very strong impact on grapevine yields. However, these impacts strongly depend on the onset dates and duration of the heatwaves. The highest negative impacts may result in a decrease in the yield by up to −35% in some regions. The results show that regions with a peak vulnerability on 1 August will be more negatively impacted than other regions. Furthermore, the geographical representation of yield reduction hints at a latitudinal gradient in the heatwave impact, indicating stronger reductions in the cooler regions of Central Europe than in the warmer regions of Southern Europe. Despite some uncertainties inherent to the current modelling assessment, the present study highlights the negative impacts of heatwaves on viticultural yields in Europe, which is critical information for stakeholders within the winemaking sector for planning suitable adaptation measures.

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

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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25

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 (May 2008): 207–21. http://dx.doi.org/10.1016/j.scitotenv.2008.01.021.

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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 (July 1, 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, observed at drought stress release or after defoliation, relied on the possibility of remobilizing carbohydrates from storage roots to above-ground organs. This assumption was tested through a heuristic modelling approach, involving the STICS crop model and its existing sub-model on remobilization. The relevance of these formalizations for sugar beet was tested on the experimental data to validate the plant behaviour concerning remobilization. The model succeeded in reproducing leaf area index (LAI) dynamic trends and particularly leaf re-growth after drought stress release or defoliation, despite an over-estimation of the drought stress effect involving an inaccurate simulation of the changes in LAI. Nevertheless, the model's ability to forecast accurately above-ground and storage root dry weight, as well as trends in LAI dynamics, showed that the assumptions made about remobilization were able to explain sugar beet behaviour.

27

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 (February 2010): 177–85. http://dx.doi.org/10.1016/j.eja.2009.11.001.

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28

Coucheney, Elsa, Samuel Buis, Marie Launay, Julie Constantin, Bruno Mary, Iñaki García de Cortázar-Atauri, Dominique Ripoche, 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, Robélio Leandro Marchão, Alfredo José Barreto Luiz, Fabiana Piontekowski Ribeiro, and Artur Gustavo Müller. "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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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 (April 2, 2009): 171–85. http://dx.doi.org/10.1007/s11104-009-9966-4.

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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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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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Paleari, Livia, Fosco M. Vesely, Riccardo A. Ravasi, Ermes Movedi, Sofia Tartarini, Mattia Invernizzi, and Roberto Confalonieri. "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 (November 7, 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, radiation use efficiency, thermal time to first pod and maturity, seed weight, plant height) were derived for 24 bean varieties. The analysis of soil, climate and management in the study area led us to define 21 homogeneous contexts, for which ideotypes were identified using the crop model STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard), the E-FAST (Extended Fourier Amplitude Sensitivity Test) sensitivity analysis method, and the distributions of functional traits. For each context, the 24 cultivars were ranked according to the similarity (weighted Euclidean distance) with the ideotype. Context-specific ideotypes mainly differed for phenological adaptation to specific combinations of climate and management (sowing time) factors, and this reflected in the cultivar recommendation for the different contexts. Feedbacks from bean technicians in the study area confirmed the reliability of the results and, in turn, of the proposed methodology.

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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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Sansoulet, J., E. Pattey, R. Kröbel, B. Grant, W. Smith, G. Jégo, R. L. Desjardins, N. Tremblay, and G. Tremblay. "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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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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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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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 levels of salt, ranging from the river-water control (0.44 dS/m) to 2.50 dS/m, were applied over a 9-year period. Overall yield effects were smaller than expected, and did not conform well to the often used bent-stick model. Relative to the control, yield was initially higher (by up to 9%) in the intermediate salt treatments, and 3% lower in the highest treatment. However, relative yields of salinised trees decreased with time, and in the final year of the experiment, yield of the highest salt treatment was 9% lower than the control. Yield increases in the intermediate treatments resulted from increases in fruit number. All 3 salt treatments decreased average fruit weight by 4% and decreased juice content but increased juice sugar and acid content. Salt treatment strongly reduced trunk growth, and the effect increased with time. Our results show that with appropriate irrigation management, soils, and rootstocks, citrus trees can maintain productivity at salinity levels of 2.0 dS/m or more, but fresh fruit profitability is likely to be lower because of a reduction in average fruit size.

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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. Linear regression models, at the species, family or entire-database level depending on the data available, were built to predict dry biomass, N amount and C:N ratio. Dynamics of N mineralized and leaching from cover crop residues were predicted at 24 contrasting sites as a function of the biomass, carbon (C):N ratio and termination date. Results Correlations between fresh biomass, dry biomass and N amounts in experimental data were strong (r = 0.80-0.96), and predicted N amounts in fresh shoot biomass were relatively accurate. Percentages of N mineralized and leached simulated by STICS were explained mainly by the C:N ratio, site and number of months after termination, but to different degrees. Conclusion MERCI is an easy and robust decision-support tool for predicting N release in the field, and could thus be adopted by advisors and farmers to improve management of nutrient recycling in temperate arable cropping systems.

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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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Vezy, Rémi, Sebastian Munz, Noémie Gaudio, Marie Launay, Patrice Lecharpentier, Dominique Ripoche, and Eric Justes. "Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model." Agronomy for Sustainable Development 43, no. 5 (August 24, 2023). http://dx.doi.org/10.1007/s13593-023-00917-5.

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