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Ongoing climate change is already affecting crop production patterns worldwide. Our aim was to investigate how increasing temperature and CO2 as well as changes in precipitation could affect potential yields for different historical pedoclimatic conditions at high latitudes (i.e., >55°). The APSIM crop model was used to simulate the productivity of four annual crops (barley, forage maize, oats, and spring wheat) over five sites in Sweden ranging between 55 and 64°N. A first set of simulations was run using site-specific daily weather data acquired between 1980 and 2005. A second set of simulations was then run using incremental changes in precipitation, temperature and CO2 levels, corresponding to a range of potential future climate scenarios. All simulation sets were compared in terms of production and risk of failure. Projected future trends showed that barley and oats will reach a maximum increase in yield with a 1°C increase in temperature compared to the 1980–2005 baseline. The optimum temperature for spring wheat was similar, except at the northernmost site (63.8°N), where the highest yield was obtained with a 4°C increase in temperature. Forage maize showed best performances for temperature increases of 2–3°C in all locations, except for the northernmost site, where the highest simulated yield was reached with a 5°C increase. Changes in temperatures and CO2 were the main factors explaining the changes in productivity, with ~89% of variance explained, whereas changes in precipitation explained ~11%. At the northernmost site, forage maize, oats and spring wheat showed decreasing risk of crop failure with increasing temperatures. The results of this modeling exercise suggest that the cultivation of annual crops in Sweden should, to some degree, benefit from the expected increase of temperature in the coming decades, provided that little to no water stress affects their growth and development. These results might be relevant to agriculture studies in regions of similar latitudes, especially the Nordic countries, and support the general assumption that climate change should have a positive impact on crop production at high latitudes.
Julien Morel; Uttam Kumar; Mukhtar Ahmed; Göran Bergkvist; Marcos Lana; Magnus Halling; David Parsons. Quantification of the Impact of Temperature, CO2, and Rainfall Changes on Swedish Annual Crops Production Using the APSIM Model. Frontiers in Sustainable Food Systems 2021, 5, 1 .
AMA StyleJulien Morel, Uttam Kumar, Mukhtar Ahmed, Göran Bergkvist, Marcos Lana, Magnus Halling, David Parsons. Quantification of the Impact of Temperature, CO2, and Rainfall Changes on Swedish Annual Crops Production Using the APSIM Model. Frontiers in Sustainable Food Systems. 2021; 5 ():1.
Chicago/Turabian StyleJulien Morel; Uttam Kumar; Mukhtar Ahmed; Göran Bergkvist; Marcos Lana; Magnus Halling; David Parsons. 2021. "Quantification of the Impact of Temperature, CO2, and Rainfall Changes on Swedish Annual Crops Production Using the APSIM Model." Frontiers in Sustainable Food Systems 5, no. : 1.
Phenology algorithms in crop growth models have inevitable systematic errors and uncertainties. In this study, the phenology simulation algorithms in APSIM classical (APSIM 7.9) and APSIM next generation (APSIM-NG) were compared for spring barley models at high latitudes. Phenological data of twelve spring barley varieties were used for the 2014–2018 cropping seasons from northern Sweden and Finland. A factorial-based calibration approach provided within APSIM-NG was performed to calibrate both models. The models have different mechanisms to simulate days to anthesis. The calibration was performed separately for days to anthesis and physiological maturity, and evaluations for the calibrations were done with independent datasets. The calibration performance for both growth stages of APSIM-NG was better compared to APSIM 7.9. However, in the evaluation, APSIM-NG showed an inclination to overestimate days to physiological maturity. The differences between the models are possibly due to slower thermal time accumulation mechanism, with higher cardinal temperatures in APSIM-NG. For a robust phenology prediction at high latitudes with APSIM-NG, more research on the conception of thermal time computation and implementation is suggested.
Uttam Kumar; Julien Morel; Göran Bergkvist; Taru Palosuo; Anne-Maj Gustavsson; Allan Peake; Hamish Brown; Mukhtar Ahmed; David Parsons. Comparative Analysis of Phenology Algorithms of the Spring Barley Model in APSIM 7.9 and APSIM Next Generation: A Case Study for High Latitudes. Plants 2021, 10, 443 .
AMA StyleUttam Kumar, Julien Morel, Göran Bergkvist, Taru Palosuo, Anne-Maj Gustavsson, Allan Peake, Hamish Brown, Mukhtar Ahmed, David Parsons. Comparative Analysis of Phenology Algorithms of the Spring Barley Model in APSIM 7.9 and APSIM Next Generation: A Case Study for High Latitudes. Plants. 2021; 10 (3):443.
Chicago/Turabian StyleUttam Kumar; Julien Morel; Göran Bergkvist; Taru Palosuo; Anne-Maj Gustavsson; Allan Peake; Hamish Brown; Mukhtar Ahmed; David Parsons. 2021. "Comparative Analysis of Phenology Algorithms of the Spring Barley Model in APSIM 7.9 and APSIM Next Generation: A Case Study for High Latitudes." Plants 10, no. 3: 443.
APSIM Next Generation was used to simulate the phenological development and biomass production of silage maize for high latitudes (i.e., >55°). Weather and soil data were carefully specified, as they are important drivers of the development and growth of the crop. Phenology related parameters were calibrated using a factorial experiment of simulations and the minimization of the root mean square error of observed and predicted phenological scaling. Results showed that the model performed well in simulating the phenology of the maize, but largely underestimated the production of biomass. Several factors could explain the discrepancy between observations and predictions of above-ground dry matter yield, such as the current formalization of APSIM for simulating the amount of radiation absorbed by the crop at high latitudes, as the amount of diffuse light and intercepted light increases with latitude. Another factor that can affect the accuracy of the predicted biomass is the increased duration of the day length observed at high latitudes. Indeed, APSIM does not yet formalize the effects of extreme day length on the balance between photorespiration and photosynthesis on the final balance of biomass production. More field measurements are required to better understand the drivers of the underestimation of biomass production, with a particular focus on the light interception efficiency and the radiation use efficiency.
Julien Morel; David Parsons; Magnus A. Halling; Uttam Kumar; Allan Peake; Göran Bergkvist; Hamish Brown; Mårten Hetta. Challenges for Simulating Growth and Phenology of Silage Maize in a Nordic Climate with APSIM. Agronomy 2020, 10, 645 .
AMA StyleJulien Morel, David Parsons, Magnus A. Halling, Uttam Kumar, Allan Peake, Göran Bergkvist, Hamish Brown, Mårten Hetta. Challenges for Simulating Growth and Phenology of Silage Maize in a Nordic Climate with APSIM. Agronomy. 2020; 10 (5):645.
Chicago/Turabian StyleJulien Morel; David Parsons; Magnus A. Halling; Uttam Kumar; Allan Peake; Göran Bergkvist; Hamish Brown; Mårten Hetta. 2020. "Challenges for Simulating Growth and Phenology of Silage Maize in a Nordic Climate with APSIM." Agronomy 10, no. 5: 645.
On Réunion Island, a French overseas territory located in the western Indian Ocean, increasing pig livestock farming is generating large quantities of slurry. Most of it is spread on a little agricultural land due to the insular context. Considering the limitation of the quantities that can be spread on agricultural areas (European “Nitrate Directive” 91/676/EEC), the use of wastewater treatment systems using phytoremediation principles is an attractive option for the pig slurry treatment. A wastewater treatment system using bamboo groves was assessed for the pig slurry treatment. Three field plots were designed on an agricultural area and planted with 40 bamboo clumps on each plot. A total of 67 m3 of pig slurry was spread on two plots in two forms: raw slurry and centrifuged slurry. The latter plot was watered with tap water. The total amount of nitrogen, phosphorus and potassium was 5.3, 1.4 and 5.5 t·ha−1, respectively, for the raw slurry treatment and 4.2, 0.4 and 5.1 t·ha−1, respectively, for the centrifuged slurry treatment. The response of bamboo species to pig slurry application was determined using morphologic parameters, Chlorophyll a fluorescence measurements and biomass yield. Compared to the control, the biomass increased by 1.8 to 6 times, depending on the species and the form of slurry. Depending on the species, the average biomass ranged from 52 to 135 t.DM.ha−1 in two years of experiment.
Julien Piouceau; Frédéric Panfili; Grégory Bois; Matthieu Anastase; Frédéric Feder; Julien Morel; Véronique Arfi; Laurent Dufossé. Bamboo Plantations for Phytoremediation of Pig Slurry: Plant Response and Nutrient Uptake. Plants 2020, 9, 522 .
AMA StyleJulien Piouceau, Frédéric Panfili, Grégory Bois, Matthieu Anastase, Frédéric Feder, Julien Morel, Véronique Arfi, Laurent Dufossé. Bamboo Plantations for Phytoremediation of Pig Slurry: Plant Response and Nutrient Uptake. Plants. 2020; 9 (4):522.
Chicago/Turabian StyleJulien Piouceau; Frédéric Panfili; Grégory Bois; Matthieu Anastase; Frédéric Feder; Julien Morel; Véronique Arfi; Laurent Dufossé. 2020. "Bamboo Plantations for Phytoremediation of Pig Slurry: Plant Response and Nutrient Uptake." Plants 9, no. 4: 522.
The project aim was to estimate N uptake (Nup), dry matter yield (DMY) and crude protein concentration (CP) of forage crops both during typical harvest times and at a very early developmental stage. Canopy spectral reflectance of legume and grass mixtures was measured in Sweden using a commercialized radiometer (400–1000 nm range). In total, 377 plant samples were tested in-situ in different grass and legume mixtures (6 grass species and 2 clover species) across two years, two locations and five N rates. Two mathematical methods, namely partial least squares (PLS) and support vector machine (SVM) were used to build prediction models between Nup, DMY and CP, and canopy spectral reflectance. Of the total 377 samples, 251 were randomly selected and used for calibration, and the remaining 126 samples were used as an independent dataset for validation. Results showed that the performance of SVM was better than PLS (based on mean absolute error (MAE) for both calibration and validation datasets) for the estimation of all investigated variables. Results for the validation set showed that the MAEs of PLS and SVM for Nup estimation were 17 and 9.2 kg/ha, respectively. The MAEs of PLS and SVM for DMY estimation were 587 and 283 kg/ha, respectively. The MAEs of PLS and SVM for CP estimation were 2.8 and 1.8%, respectively. In addition, a subsample, which corresponded to an early developmental stage, was analysed separately with PLS and SVM as for the whole dataset. Results showed that SVM was better than PLS for the estimation of all investigated variables. The high performance of SVM to estimate legume and grass mixture N uptake and dry matter yield could provide support for varying management decisions including fertilization and timing of harvest.
Zhenjiang Zhou; Julien Morel; David Parsons; Sergey V. Kucheryavskiy; Anne-Maj Gustavsson. Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data. Computers and Electronics in Agriculture 2019, 162, 246 -253.
AMA StyleZhenjiang Zhou, Julien Morel, David Parsons, Sergey V. Kucheryavskiy, Anne-Maj Gustavsson. Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data. Computers and Electronics in Agriculture. 2019; 162 ():246-253.
Chicago/Turabian StyleZhenjiang Zhou; Julien Morel; David Parsons; Sergey V. Kucheryavskiy; Anne-Maj Gustavsson. 2019. "Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data." Computers and Electronics in Agriculture 162, no. : 246-253.
The detection of plant diseases, including fungi, is a major challenge for reducing yield gaps of crops across the world. We explored the potential of the PROCOSINE radiative transfer model to assess the effect of the fungus Pseudocercospora fijiensis on leaf tissues using laboratory-acquired submillimetre-scale hyperspectral images in the visible and near-infrared spectral range. The objectives were (i) to assess the dynamics of leaf biochemical and biophysical parameters estimated using PROCOSINE inversion as a function of the disease stages, and (ii) to discriminate the disease stages by using a Linear Discriminant Analysis model built from the inversion results. The inversion results show that most of the parameter dynamics are consistent with expectations: for example, the chlorophyll content progressively decreased as the disease spreads, and the brown pigments content increased. An overall accuracy of 78.7% was obtained for the discrimination of the six disease stages, with errors mainly occurring between asymptomatic samples and first visible disease stages. PROCOSINE inversion provides relevant ecophysiological information to better understand how P. fijiensis affects the leaf at each disease stage. More particularly, the results suggest that monitoring anthocyanins may be critical for the early detection of this disease.
Julien Morel; Sylvain Jay; Jean-Baptiste Féret; Adel Bakache; Ryad Bendoula; Francoise Carreel; Nathalie Gorretta. Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology. Scientific Reports 2018, 8, 1 -13.
AMA StyleJulien Morel, Sylvain Jay, Jean-Baptiste Féret, Adel Bakache, Ryad Bendoula, Francoise Carreel, Nathalie Gorretta. Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology. Scientific Reports. 2018; 8 (1):1-13.
Chicago/Turabian StyleJulien Morel; Sylvain Jay; Jean-Baptiste Féret; Adel Bakache; Ryad Bendoula; Francoise Carreel; Nathalie Gorretta. 2018. "Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology." Scientific Reports 8, no. 1: 1-13.
Sylvain Jay; Nathalie Gorretta; Julien Morel; Fabienne Maupas; Ryad Bendoula; Gilles Rabatel; Dan Dutartre; Alexis Comar; Frédéric Baret. Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment 2017, 198, 173 -186.
AMA StyleSylvain Jay, Nathalie Gorretta, Julien Morel, Fabienne Maupas, Ryad Bendoula, Gilles Rabatel, Dan Dutartre, Alexis Comar, Frédéric Baret. Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment. 2017; 198 ():173-186.
Chicago/Turabian StyleSylvain Jay; Nathalie Gorretta; Julien Morel; Fabienne Maupas; Ryad Bendoula; Gilles Rabatel; Dan Dutartre; Alexis Comar; Frédéric Baret. 2017. "Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery." Remote Sensing of Environment 198, no. : 173-186.
Julien Morel; Agnès Bégué; Pierre Todoroff; Jean-François Martiné; Valentine LeBourgeois; Michel Petit. Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. European Journal of Agronomy 2014, 61, 60 -68.
AMA StyleJulien Morel, Agnès Bégué, Pierre Todoroff, Jean-François Martiné, Valentine LeBourgeois, Michel Petit. Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. European Journal of Agronomy. 2014; 61 ():60-68.
Chicago/Turabian StyleJulien Morel; Agnès Bégué; Pierre Todoroff; Jean-François Martiné; Valentine LeBourgeois; Michel Petit. 2014. "Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation." European Journal of Agronomy 61, no. : 60-68.
Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1) an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2) the Kumar-Monteith efficiency model, and (3) a forced-coupling method with a sugarcane crop model (MOSICAS) and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1). Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.
Julien Morel; Pierre Todoroff; Agnès Bégué; Aurore Bury; Jean-François Martiné; Michel Petit. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island. Remote Sensing 2014, 6, 6620 -6635.
AMA StyleJulien Morel, Pierre Todoroff, Agnès Bégué, Aurore Bury, Jean-François Martiné, Michel Petit. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island. Remote Sensing. 2014; 6 (7):6620-6635.
Chicago/Turabian StyleJulien Morel; Pierre Todoroff; Agnès Bégué; Aurore Bury; Jean-François Martiné; Michel Petit. 2014. "Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island." Remote Sensing 6, no. 7: 6620-6635.
Coupling remotely sensed data with crop model is known to improve the estimation of crop variables by the model. The recalibration coupling approach tends to reduce the differences between observation and simulation by optimizing the value of one of the model's parameter. In this study, we used this approach with a sugarcane model and Crop Water Stress Index calculated using remotely sensed thermal infrared data in order to optimize the value of the root depth parameter thanks to measured and simulated AET/MET ratio. The effect of the root depth recalibration has also been assessed on the yield estimation, which showed good trends with a significant enhancement of the estimated yield.
Julien Morel; Valentine LeBourgeois; Jean-Francois Martine; Pierre Todoroff; Agnès Bégué; Michel Petit. Recalibrating a sugarcane crop model using thermal infrared data. 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS 2013, 2806 -2809.
AMA StyleJulien Morel, Valentine LeBourgeois, Jean-Francois Martine, Pierre Todoroff, Agnès Bégué, Michel Petit. Recalibrating a sugarcane crop model using thermal infrared data. 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS. 2013; ():2806-2809.
Chicago/Turabian StyleJulien Morel; Valentine LeBourgeois; Jean-Francois Martine; Pierre Todoroff; Agnès Bégué; Michel Petit. 2013. "Recalibrating a sugarcane crop model using thermal infrared data." 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS , no. : 2806-2809.
Coupling remote sensing data with crop model has been shown to improve accuracy of the model yield estimation. MOSICAS model simulates sugarcane yield in controlled conditions plot, based on different variables, including the interception efficiency index (i). In this paper, we assessed the use of remote sensing data to sugarcane growth modeling by 1) comparing the sugarcane yield simulated with and without satellite data integration in the model, and 2) comparing two approaches of satellite data forcing. The forcing variable is the interception efficiency index (Εi). The yield simulations are evaluated on a data set of cane biomass measured on four on-farm fields, over three years, in Reunion Island. Satellite data are derived from a SPOT 10 m resolution time series acquired during the same period. Three types of simulations have been made: a raw simulation (where the only input data are daily precipitations, daily temperatures and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of Εi have been replaced by NDVI computed Εi for each available satellite image), and complete forcing method (where all MOSICAS simulated Εi have been replaced by NDVI computed Εi). Results showed significant improvements of the yield's estimation with complete forcing approach (with an estimation of the yield 8.3 % superior to the observed yield), but minimal differences between the yields computed with raw simulations and those computed with partial forcing approach (with a mean overestimation of respectively 34.7 and 35.4 %). Several enhancements can be made, especially by optimizing MOSICAS parameters, or by using other remote sensing index, like NDWI.
Julien Morel; Jean-François Martiné; Agnes Begue; Pierre Todoroff; Michel Petit. A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable. SPIE Remote Sensing 2012, 8531, 85310E .
AMA StyleJulien Morel, Jean-François Martiné, Agnes Begue, Pierre Todoroff, Michel Petit. A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable. SPIE Remote Sensing. 2012; 8531 ():85310E.
Chicago/Turabian StyleJulien Morel; Jean-François Martiné; Agnes Begue; Pierre Todoroff; Michel Petit. 2012. "A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable." SPIE Remote Sensing 8531, no. : 85310E.