Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes

Dürr, Carolyne; Constantin, Julie; Wagner, Marie-Hélène; Navier, Hélène; Demilly, Didier; Goertz, S.; Nési, Nathalie

doi:10.1016/j.eja.2016.06.001

Cited by 15 publications

(24 citation statements)

References 22 publications

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“…Several previous studies compared results of field observation and simulation using the SIMPLE crop emergence model and found its prediction similar to observed data (Dorsainvil et al 2005; Brunel-Muguet et al 2011; Constantin et al 2015; Dürr et al 2016). Therefore, prediction of germination and emergence rates reported in this study can be considered reliable.…”

Section: Discussionmentioning

confidence: 60%

“…We used the STICS soil-crop model based on the RCP 8.5 emission scenario to generate climate data. This model has been reported to be sensitive enough to generate realistic soil data such as soil moisture (Constantin et al 2015; Dürr et al 2016; Tribouillois et al 2018). Predictions of the STICS soil-crop model showed that during the sowing period of sugar beet, mean seedbed temperatures will increase over time and that a higher variability of rainfall will occur, without an overall increase of its cumulated values.…”

Section: Discussionmentioning

confidence: 99%

“…Briefly, the model predicts the germination and emergence process and their final rates in relation to environmental conditions during sowing. The model has previously been parameterized for a number of crop species -- including wheat, sugar beet, flax, mustard, French bean, oilseed rape (Dürr et al 2001; Dorsainvil et al 2005; Moreau-Valancogne et al 2008; Dürr et al 2016), and a plant model Medicago truncatula (Brunel et al 2009) – which allows to compare a range of plant species using the same set of parameters (Gardarin et al 2016).…”

Section: Methodsmentioning

confidence: 99%

“…These early phases of crop cycle are affected by several biotic and abiotic factors that may reduce seed germination and seedling emergence rates (Lamichhane et al 2018). More specifically to abiotic stresses, many factors including seedbed water content, temperature, and the frequency and quantity of cumulated rainfall profoundly impact crop establishment (Gallardo-Carrera et al 2007; Constantin et al 2015; Dürr et al 2016). Several studies reported that climate change will result in increased mean temperature and higher precipitation variability in many regions of the globe including Europe (Pendergrass et al 2017; Kjellström et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Will climate change affect sugar beet establishment of the 21^st century? Insights from a simulation study using a crop emergence model

Lamichhane

Constantin²,

Aubertot³

et al. 2019

Preprint

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9Ongoing climate change has been reported to have far-reaching impact on crop 10 development and yield in many regions of the globe including Europe. However, little is 11 known about the potential impact of climate change on specific stages of the crop cycle 12 including crop establishment, although it is a crucial stage of the annual crop cycles. For 13 the first time, we performed a simulation study to pinpoint how sugar beet sowing 14 conditions of the next eight decades will be altered under future climate change and if 15 these variations will affect sowing dates, germination and emergence as well as bolting 16 rates of this crop. We chose Northern France as an important study site, representative of 17 sugar beet growing basin in Northern Europe. Sugar beet emergence simulations were 18 performed for a period between 2020 and 2100, taking into account five sowing dates 19 (mid-February, 1 st March, mid-March, 1 st April and mid-April). Soil water contents and 20 temperatures in the 0-10 cm soil horizon were first simulated with the STICS soil-crop 21 model using the most pessimistic IPCC scenario (RCP 8.5) to feed the SIMPLE crop 22 emergence model. We also evaluated the probability of field access for the earlier sowings, 23 2 based on the amount of cumulated rainfall during February and March. When analyzed 24 by sowing date and for successive 20-year period from 2020 to 2100, there was a 25 significant increase in seedbed temperatures by 2°C after 2060 while no change in 26 cumulative rainfall was found before and after sowings, compared with the past. 27Emergence rate was generally higher for 2081-2100, while time to reach the maximum 28 emergence rate decreased by about one week, compared with other periods, due to higher 29 average seedbed temperatures. The rate of non-germinated seeds decreased, especially 30 for the earlier sowing dates, but the frequency of non-emergence due to water stress 31 increased after 2060 for all sowing dates, including the mid-February sowing. Bolting 32 remains a risk for sowings before mid-March although this risk will be markedly 33 decreased after 2060. The changes in seedbed conditions will be significant after 2060 in 34 terms of temperatures. However, the possibility of field access will be a main limiting 35 factor for earlier sowings, as no significant changes in cumulative rainfall, compared with 36 the past, will occur under future climate change. When field access is not a constraint, an 37 anticipation of the sowing date, compared to the currently practiced sowing (i.e. mid- 38March), will lead to decreased risks for the sugar beet crop establishment and bolting. The 39 use of future climate scenarios coupled with a crop model allows a precise insight into the 40 future sowing conditions, and provide helpful information to better project future farming 41 systems. 42 surface crust, temperature, water stress 44 phases of crop cycle are affected by several biotic and abiotic factors that may reduce seed 48 germination and seedling emergence rates (Lamich...

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Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Will climate change affect sugar beet establishment of the 21^st century? Insights from a simulation study using a crop emergence model

Lamichhane

Constantin²,

Aubertot³

et al. 2019

Preprint

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show abstract

“…In order to develop such strategies, field experiments have been used to investigate the impacts of different management strategies on crop yields and the environment [3][4][5]. Field experiments are sources of reliable data for establishing causal relationships between agricultural land management practices and real-world observed measurements [6,7]. However, field experiments are often expensive, time-consuming, and labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

Calibration and Validation of the EPIC Model for Maize Production in the Eastern Cape, South Africa

2019

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Crop models are useful tools to evaluate the effects of agricultural management on ecosystem services. However, before they can be applied with confidence, it is important to calibrate and validate crop models in the region of interest. In this study, the Environmental Policy Integrated Climate (EPIC) model was evaluated for its potential to simulate maize yield using limited data from field trials on two maize cultivars. Two independent fields at the Cradock Research Farm were used, one for calibration and one for validation. Before calibration, mean simulated yield was 8 t ha −1 while mean observed yield was 11.26 t ha −1 . Model calibration improved mean simulated yield to 11.23 t ha −1 with a coefficient of determination, (r 2 ) = 0.76 and a model efficiency (NSE) = 0.56. Validation with grain yield was satisfactory with r 2 = 0.85 and NSE = 0.61. Calibration of potential heat units (PHUs) and soil-carbon related parameters improved model simulations. Although the study only used grain yield to calibrate and evaluate the model, results show that the calibrated model can provide reasonably accurate simulations. It can be concluded that limited data sets from field trials on maize can be used to calibrate the EPIC model when comprehensive experimental data are not available.Agronomy 2019, 9, 494 2 of 16 used to test the effectiveness of alternative agricultural land management practices under varying climate change scenarios. However, to yield meaningful results, it is prudent to calibrate crop models in the region of intended use before their application [11].Model calibration is the procedure where model parameters are fine-tuned to increase the agreement between model simulations and real-world observations [12]. Calibration is important to increase model accurateness and decrease model prediction uncertainty [13]. Calibration is done by judiciously choosing model parameter values, adjusting them within recommended ranges, for example, from literature or expert opinions, and comparing the simulated outputs with observed data for a given set of conditions [14]. A successful calibration would be when the model reproduces observed data within a satisfactory degree of accuracy and precision for the intended model use [15,16]. Once calibrated and validated, the model can be reasonably applied in the area of interest. Calibrated crop growth models can, therefore, be useful tools to complement field experiments and support decision making for sustainable agricultural land management.The Environmental Policy Integrated Climate (EPIC) model [17], originally developed in the United States of America (USA), is a process-based, field-scale model with a daily time scale. It simulates the chemical processes occurring in the soil-water-plant interaction under different agricultural management regimes [18]. The main components of EPIC are weather simulation, crop growth, carbon and nutrient cycling, tillage, soil erosion, and hydrology [19]. Globally, the model has been applied to study crop yield responses to nutrients and...

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Integrated management of damping-off diseases. A review

Lamichhane

Dürr

Schwanck³

et al. 2017

Agron. Sustain. Dev.

207

133

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Damping-off is a disease that leads to the decay of germinating seeds and young seedlings, which represents for farmers one of the most important yield constraints both in nurseries and fields. As for other biotic stresses, conventional fungicides are widely used to manage this disease, with two major consequences. On the one hand, fungicide overuse threatens the human health and causes ecological concerns. On the other hand, this practice has led to the emergence of pesticide-resistant microorganisms in the environment. Thus, there are increasing concerns to develop sustainable and durable damping-off management strategies that are less reliant on conventional pesticides. Achieving such a goal requires a better knowledge of pathogen biology and disease epidemiology in order to facilitate the decision-making process. It also demands using all available non-chemical tools that can be adapted to regional and specific production situations. However, this still is not the case and major knowledge gaps must be filled. Here, we review up to 300 articles of the damping-off literature in order to highlight major knowledge gaps and identify future research priorities. The major findings are (i) damping-off is an emerging disease worldwide, which affects all agricultural and forestry crops, both in nurseries and fields; (ii) over a dozen of soil-borne fungi and fungus-like organisms are a cause of damping-off but only a few of them are frequently associated with the disease; (iii) damping-off may affect from 5 to 80% of the seedlings, thereby inducing heavy economic consequences for farmers; (iv) a lot of research efforts have been made in recent years to develop biocontrol solutions for damping-off and there are interesting future perspectives; and (v) damping-off management requires an integrated pest management (IPM) approach combining both preventive and curative tactics and strategies. Given the complex nature of damping-off and the numerous factors involved in its occurrence, we recommend further research on critical niches of complexity, such as seeds, seedbed, associated microbes and their interfaces, using novel and robust experimental and modeling approaches based on five research priorities described in this paper.

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Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes

Cited by 15 publications

References 22 publications

Will climate change affect sugar beet establishment of the 21^st century? Insights from a simulation study using a crop emergence model

Will climate change affect sugar beet establishment of the 21^st century? Insights from a simulation study using a crop emergence model

Calibration and Validation of the EPIC Model for Maize Production in the Eastern Cape, South Africa

Integrated management of damping-off diseases. A review

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Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes

Cited by 15 publications

References 22 publications

Will climate change affect sugar beet establishment of the 21st century? Insights from a simulation study using a crop emergence model

Will climate change affect sugar beet establishment of the 21st century? Insights from a simulation study using a crop emergence model

Calibration and Validation of the EPIC Model for Maize Production in the Eastern Cape, South Africa

Integrated management of damping-off diseases. A review

Contact Info

Product

Resources

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Will climate change affect sugar beet establishment of the 21^st century? Insights from a simulation study using a crop emergence model

Will climate change affect sugar beet establishment of the 21^st century? Insights from a simulation study using a crop emergence model