Session 2.5 Session 2.5: Crop Model Improvement and Genetics Applications
For a complete list of all of the workshop abstracts click here (PDF).
If you are presenting a poster, please view the Poster Specifications here
Session 2.5: Oral Presentation
Title: Toward Next Generation Gene-based Crop Models: Implications on Experiments, Data, Modeling, and Modularity
Authors: James W. Jones1, C.E. Vallejos2, K. J. Boote1, M. J. Correll1, S.A. Gezan3, C. H. Porter1, G. Hoogenboom1, and M. Donatelli4
1 Agricultural and Biological Engineering Dept., University of Florida, USA, 2 Horticultural Sciences Dept., University of Florida, USA, 3 School of Forest Resources & Conservation, University of Florida, USA, 4 Council for Agricultural Research and Economics (CREA), Italy
Abstract: Studies have shown that some dynamic plant processes can be modeled to depend on genes (G), the environment (E), and GxE interactions. Most notably, phenological development of plants has been modeled by taking into account gene networks, RNA synthesis, and expression of first flower. However, prior research has been based primarily on empirical approaches to simulate the consequences of G, E, and GxE for processes such as first flower appearance, node addition rate, etc. Whereas one might argue that it is more desirable to model E responses as a function of G, we suggest that the two should coevolve. Characterization of dynamic plant functional E responses by specific G, including GxE, can lead to the discovery of underlying mechanisms using increasingly large genetic and phenotypic datasets. Furthermore, models developed from large datasets show promise for improving assumptions used in existing crop models. We use a bean dataset with 180 recombinant inbred lines grown over 5 environments to demonstrate that assumptions used in existing models about variations among genotypes may be wrong. We also show that development of process-oriented modules can improve previously used functional relationships, and that these modules can replace components in existing models. We focus on models for two different dynamic processes: Leaf Appearance Rate and Progress toward Flowering. Extending this effort to other crops and processes will contribute to next generation crop models. Implications of this approach on experiments, data collection, modeling processes, and modularity in crop models are discussed.
Session 2.5: Oral Presentation
Title: High-throughput phenotyping platform reveals genetic variability and quantitative trait loci of light-related parameters in maize models
Authors: Tsu-Wei Chen1,2*, C. Fournier1,3, S. Artzet1,3, N. Brichet1, J. Chopard1,3, C.
Pradal3, S. Alvarez-Prado1, L. Cabrera-Bosquet1, C. Welcker1, and F. Tardieu1
1 INRA, France, 2 Institut für Gartenbauliche Produktionssysteme, Leibniz Universität Hannover, Germany, 3 CIRAD, France
Abstract: Radiation interception efficiency (RIE) and radiation use efficiency (RUE) are the main driving forces of dry mass accumulation in many crop models, so parameters related to RIE and RUE, e.g. light extinction coefficient (k) and photosynthetic parameters, have strong influences on the results of simulations. In this work, we propose a new method to estimate the RIE- and RUE-related parameters in maize models by a high-throughput phenotyping platform, PHENOARCH (https://goo.gl /x3C6oN), where images of 330 maize lines were taken and used to reconstruct the 3D-structure of the plants. The 3D plants were used to construct a virtual canopy to calculate RIE based on the RATP light model. Leaf area index (LAI) was estimated by the reconstructed 3D-structure and k was calculated from RIE and LAI. Relationship between RIE and plant developmental stage was fitted to a sigmoidal function with three parameters: maximum RIE (RIEmax), maximum change of RIE (smax) and time taken to reach smax (ts). Between genotypes, significant differences in k, RIEmax, smax and ts were found and genome wide association analysis revealed 16 QTL for k, 77 for RIEmax, 1 for smax and 7 for ts. Further parameters including RUE and relative canopy photosynthetic capacity can be also estimated by our method. We conclude that 3D-structure of plants reconstructed in a phenotyping platform can be used to discover the genetic variability of light-related parameters for crop models.
Session 2.5: Oral Presentation
Title: A basic approach to predicting yields and optimizing inputs using artificial neural networks
Authors: Paul Koch Unaffiliated. The work based on on dissertation research completed in
1993 at the University of Nebraska.
Abstract: The purpose of this research was to investigate the degree to which a feedforward artificial neural network (ANN) with error back-propagation could model the relationship between water inputs and yield in a selected crop. At the outset of the effort, the number of crop growth scenarios needed to develop a useful ANN was unknown and presumed
to be quite large, perhaps even prohibitively so. In the interest of reaching some useful conclusions about ANN performance and data requirements expeditiously, multiple scenarios were initially generated using the mechanistic crop growth model CERES-Maize. The resulting data sets were then used to train and test ANNs in various configurations. An ANN was found to model simulated scenarios satisfactorily, and a further effort was undertaken to develop an ANN from a small set of available field data alone. Results generally showed that the ability of an ANN to model the relationship between water input and yield was highly dependent upon the interval over which the
inputs were summed. A summation interval of six days was found to be optimal or nearly so. ANNs that gave better yield predictions demonstrated expected intraseasonal variations in sensitivity to the amount of water provided. Further tests with the models demonstrated how ANNs might be employed to optimize the delivery of limited inputs across a growing season.
Session 2.5: Oral Presentation
Title: Importance of crop management for simulating crop phenology in large scale impact assessments
Authors: Ehsan Eyshi Rezaei1,2, S. Siebert1, and F. Ewert3 1 Institute of Crop Science and Resource Conservation, University of Bonn, Germany, 2 Center for Development Research (ZEF), Germany, 3 Leibniz Centre for Agricultural Landscape Research, Institute of Landscape Systems Analysis, Germany
Abstract: Phenological development is one of the most important processes influencing crop growth and yield. The responses of phenology to changes in climate and management (mainly sowing date and variety) are well documented. However, most studies have not considered crop management adequately. Crop models are often set up for current management conditions ignoring long-term past and future changes in management. Here we quantify the effects of change in management on phenology simulations of winter rapeseed and winter rye based on 54 years (1960-2013) observations across Germany. The crop model SIMPLACE<Phenology> was applied at a resolution of 1 km _ 1 km. We show that long-term changes in phenology trends are crop specific. The observed trend of advancement in the flowering stage of winter rapeseed is 30% steeper in comparison to the trend in the heading stage of winter rye. The crop model, which is calibrated based on observations for the last 10 years of the study period, was not able to reproduce the observed trend of flowering of winter rapeseed through the entire 54 year period, In contrast, the crop model results reproduced well the observed declining trend of heading date in winter rye. The documented changes in cultivars and management practices such as planting density of winter rapeseed were identified as the main reasons of the poor model performance. We conclude that it is essential to account for changes in crop management (especially cultivar change) in climate change impact assessments and that differences among crops need to be considered.
Session 2.5: Oral Presentation
Title: Assessing agricultural practices in highly variable environments: SARRA-H spatialized crop model for West Africa
Authors: Mathieu Castets1, C. Baron 1, S.B. Traore2, C. Jahel1, H. Songoti2, P. Degenne1, A. Alhassane2, and D. Lo-Seen1 1 CIRAD, UMR TETIS, Montpellier, France,2 AGRHYMET, Niamey, Niger
Abstract: In West Africa, environmental conditions that are highly variable in space and time, in particular due to climate, impose heavily constrained choices on agricultural practices. At large scale, this variability is expressed by an annual rainfall distribution that ranges from more than 1200 mm in the Guinean Zone with double rainy season, to almost no rainfall in a single rainy season near the Sahara. This large spatial variability is also expressed at finer scales due to the stormy character of rainfall events. The length of the rainy seasons, the distribution of rainfall events and their intensity within the season all show that the variability is also temporally very large.
During the course of the season, it is essential to take into account differences in agricultural practices while assessing potential productions at different spatial scales. The integration of the SARRA-H crop model in the spatial dynamics modelling platform Ocelet offers new opportunities for this assessment. Processes can be modelled at different spatial and temporal scales and multiple data sources, including vector and raster formats, can be managed efficiently. A working prototype has been developed and is being tested during the 2016 crop season at the AGRHYMET Regional Centre in Niamey, in the framework of its Food Security Early Warning System. The objective of this presentation is to show the crop monitoring capabilities of the spatialized version of the SARRA-H crop model that integrates common agricultural practices at the scale of West Africa.
Session 2.5: Oral Presentation
Title: Improved functions for simulating crop water use are necessary to simulate the impact of [CO2] on maize yields
Authors: J.L. Durand1, K. Delusca1, K. Boote2, J. Lizaso3, R. Manderscheid4, H.J. Weigel4, A.C. Ruane5, C. Rosenzweig5, J. Jones2, L. Ahuja6, S. Anapalli6, B. Basso7, C. Baron8,
P. Bertuzzi9, C. Biernath10, D. Deryng11, F. Ewert12, T. Gaiser12, S. Gayler13, F. Heinlein10, K.C. Kersebaum14, S.H. Kim15, C. Müller16, C. Nendel14, A. Olioso17, E. Priesack10, J.
Ramirez18, K. Waha16, D. Ripoche9, E. R. Rötter19, S. Seidel20, A. Srivastava12, F. Tao21,
D. Timlin22, T. Twine23, Wang24, H. Webber12, and Z. Zhao25
Abstract: Past increasing trends in maize yields observed around the world are at risk of slowing down due to rising temperatures and reduced water availability. However, the impact of increasing [CO2] on maize remains uncertain. FACE studies report significant positive responses to CO2 of maize yields (and other C4 crops) under dry conditions only. The aim of this work was to compare the simulations of different models using input data from a FACE experiment conducted in Braunschweig during 2 years under limiting and non-limiting water conditions. Twenty modelling groups using different maize models were
given the same instructions and input data. Following calibration (cultivar parameters) under non-limiting water conditions and under ambient [CO2] treatments of both years, simulations were undertaken for the other treatments: High [ CO2 ] (550 ppm) 2007 and 2008 in both irrigation regimes, and DRY AMBIENT 2007 and 2008. Only under severe
water deficits did models simulate an increase in yield for CO2 enrichment, which was associated with higher harvest index and, for those models which simulated it, higher grain number. However, the CO2 enhancement under water deficit simulated by the 20 models was 20 % at most and 10 % on average only. As in the experiment, the simulated impact of [ CO2 ] on water use was negligible, with a general displacement of the water deficit toward later phases of the crop along with longer green leaf area duration at reduced transpiration rate.
34. Poster Presentation: Session 2.5
Title: Crop model simulation slants for predicting and managing the climate risks in poor rainfed rice-wheat eco-system of Mid-Western Nepal: application of APSIM, DSSAT model and trade off economic analysis
Authors: Rajendra Darai1, D.B.T. Magar2, N. Subash3, and G. Baigorria4 1 NARC/GLRP, Nepal, 2 SARPOD, NARI, Nepal, 3 ICAR, India, 4 University of Nebraska-Lincoln, USA
Abstract: The Agriculture is the life line of Nepalese people which contributes to more than 34% GDP and employing >65% of total population. In Nepal, agriculture sector is in small fragmented subsistence rainfed farming system,must therefore be a special area of plan focus on other priorities such as resource use efficiency and technology to
ensure sustainability of natural resources, adaptation to climate change and improvements in total factor productivity. Obviously Nepalgunj, Banke district is the hub for mid and far western development region of Nepal. Whatsoever it is the hottest place of Nepal with temperature rising well above 46°C in the summer, while minimum
temperature goes down to 5°C or even below during the winter season. Annual average rainfall ranges from 1000mm to 1500mm in the district. The rice-wheat cropping system is currently practiced on about 0.5 million hectare of prime agricultural land in terai and lower basin of mid hills of Nepal. Both of the crops are grown in diverse agro-ecological environments and also seasonal diversity (rice-summer & wheat-winter) from terai to hills.
All five GCMs predicted higher mean monthly maximum and minimum temperatures during the mid-century period (2040–2069) under RCP8.5 compared to the baseline (1980–2010). However, rainfall projections are reduction trends in all GCM predictions except GCM IIXA. Both APSIM and DSSAT simulated higher wheat and rice yields compared to survey data after the cumulative probability level except one GCM. Moreover, DSSAT simulated higher rice yields than did APSIM. Differences between the DSSAT and APSIM projections are due to differences in sensitivity of the crop models to increases in CO2 and temperature. In the case of rice, out of the 20 GCMs there is an 80% positive response both in DSSAT-rice and wheat projected a pessimistic scenario. Overall,
DSSAT simulated more optimistic projections than APSIM for wheat.
35. Poster Presentation: Session 2.5
Title: Modeling the effects of genotypic and environmental variation on maize phenology
Authors: Kofikuma A. Dzotsi1 , M. Tollenaar, S. Kumudhini, et. al. 1 The Climate Corporation
Abstract: Crop phenology is a critical component of yield prediction because it affects the duration of growth and the timing of growth partitioning. Changes in maize genetic and crop-management technologies during the past three decades, such as increased duration of the grain-filling period in North American Corn-Belt maize hybrids, have led to significant yield improvement with phenology traits of new hybrids different from those of older hybrids. As the use of crop models in the assessment of global productivity in response to climate change becomes increasingly popular, limitations of simulation models developed 20 – 30 years ago (e.g. CERES-Maize) in describing the genotypic variation of maize hybrids across a wide range of relative maturities and environments has led to site-specific model calibration with the associated risk of compensation of model errors.
To overcome some of the limitations of the phenology routines in current process-based maize models, a maize model improvement effort was started by the AgMIP maize team. This effort involves the development of a new crop model called AgMaize that includes improved modeling approaches of the main crop processes. This poster introduces the phenology component of AgMaize and presents results of model performance comparison with CERES-Maize of DSSAT based on selected datasets shared through AgMIP.
36. Poster Presentation: Session 2.5
Title: Enhancing EcoMeristem model to better predict rice crop performance in response to increasing atmospheric CO2 concentrations
Authors: Damien Fumey1 , D. Fabre2 , L. Rouan2 , and D. Luquet2
1 ITK, France, 2 CIRAD, France
Abstract: Atmospheric CO2 is expected to reach near 800 ppm in 2100, accompanied by a rise of temperature. This will considerably impact crop performance due to a direct impact on leaf C assimilation, and finally on yield components’ elaboration (tillering, leaf area, panicle number, grain filling). Making crop models more predictive in future climate scenario is essential and implies firstly to better simulate the C gain generated by photosynthesis response to CO2. Crop models commonly compute biomass production using light interception (εi) and use (εb) efficiencies (Monteith’s approach). Few of them consider for so key crop architectural traits, leaf photosynthesis and stomatal conductance. EcoMeristem is a functional-structural crop model, simulating cereals’ plant growth and phenotypic plasticity at the organ level in response to plant C and water status. It is thus relevant to capture yield components’ regulation by climate parameters, particularly CO2. However it was initially developed using εi and εb. Also, a light interception model accounting for key crop architectural parameters and leaf photosynthesis model inspired from FvCB model accounting for key climate change and leaf parameters, were recently implemented and confronted to experimental data on rice.
This study aims to compare the original and the novel version of EcoMeristem in the way they simulate the regulation of yield elaboration for a few morphologically contrasted rice genotypes in response to radiation, temperature and CO2. Sensitivity analyses and simulation results will be presented and discussed with respect to the challenge of using crop modelling to support breeding in climate change context.
37. Poster Presentation: Session 2.5
Title: Modeling sorghum and millet genotypes responses to several fertilizer applications in order to optimize fertilizers use according to climate
Authors: Kyky K. Ganyo1 , B. Muller2,1 , G. Hoogenboom3 , and M. Adam2,4
1 ISRA-CERAAS, Sénégal, 2 CIRAD France, 3 University of Florida, USA, 4 INERA, Burkina Faso
Abstract: In Sahelian and Sudano-Sahelian areas rainfall uncertainty together with poor soil fertility strongly affect productions of millet and sorghum which are the main staple foods as elsewhere in the semi-arid tropics of Asia and Africa. However recent researches have shown that meteorological information and forecasts could help to improve cereal production by allowing providing pertinent advises about sowing dates and inputs use. In particular coupling weather forecasts with crop models to simulate crop responses to different fertilization strategies might help to define the right moments for fertilizer applications. Hence, we must improve our knowledge about Sahelian and Sudano-Sahelian millet and sorghum genotypes responses to different fertilizations patterns and crop simulation models capability to correctly simulate those Genotype*Water*Fertilizer interactions (GxWxF). That will then allow us assessing the impacts (virtual experiments) of different fertilization practices according to rainfalls seasons patterns. The main objective of this research is to capture those G*W*F interactions for some contrasted millet and sorghum West-African varieties with DSSAT Cropping System Simulation model, and then to develop fertilization recommendations for farmers according to weather forecasts. The study relies on a set of agronomical trials in Senegal carried out in different locations with respectively 4 and 2 contrasted sorghum and millet genotypes submitted to 5 fertilization modalities derived from standard recommended one and including 2 unconventional late fertilizer applications.
First results from the 2015 trials (first year) will be presented at the conference as well as preliminary results from DSSAT calibration. These results will help to identify potentials improvements for the model.
38. Poster Presentation: Session 2.5
Title: Effect of rainfall variability on the crop growing season characteristics: case of smallholder farming in Zimbabwe
Authors: Hillary Mugiyo1 1 University of Zimbabwe
Abstract: Rain-fed maize production has significantly declined in Zimbabwe especially in semi-arid and arid areas causing food insecurity. Erratic rainfall received associated with mid-season dry spells largely contribute to low and variable maize yields. This study involved a survey of current farmers’ cropping practices, analyses of climatic data from remote sensed data (daily rainfall and daily minimum and maximum temperature) of Wedza station and simulation of maize yield response to climate change using DSSAT crop growth simulation model. The climatic and maize yield data was analyzed using mean correlation and regression analyses to establish relationships between rainfall characteristics and maize yield in the study area. Survey results showed that maize was the staple food grown by 100% of the farming households while 8.7% also grew sorghum. The survey concludes that 56.2% of the farmers grew short season cultivars, 40.2% medium season cultivars and 3.6% long season cultivars. The result of the correlation analysis of climatic data and maize yield showed that number of rain days had strong positive relationship (r = 0.7) with maize yield. Non-significant yield differences (p > 0.05) between maize cultivar and planting date criteria were obtained. Highest yields were obtained under the combination of medium season maize cultivar and the DEPTH criterion in all simulations. The range of simulated district average yields of 0.4 t/ha to 1.8 t/ha formed the basis for the development of an operational decision support tool (cropping calendar). The study recommends the application of climate smart agriculture techniques.
39. Poster Presentation: Session 2.5
Title: Effect of planting dates on tillering of local varieties of sorghum in Burkina Faso, West Africa.
Authors: Moussa Sanon1, G. Hoogenboom2, S.B. Traoré3, and L. Somé1
1 Institut de l’Environnement et de Recherches Agricoles (INERA), Burkina Faso, 2 Department of Agricultural and Biological Engineering, University of Florida, USA, 3
Centre Regional Aghrymet (CRA), Niger
Abstract: Tillering is a complex process which depends upon several factors that interact together. In West Africa, sorghum usually grows in conditions characterized by a short rainy season with dry spells, a high potential evapotranspiration rate, low soil fertility, and a low level of adoption of suitable farming technologies. In those conditions, planting in low density is a strategy to insure a minimum yield. Thus, understanding the main factors that affect tillering production under limiting conditions of growth and development of sorghum could help obtaining better management strategies for a sustainable production. The objective of this study was to determine the effect of photoperiod on tillering behavior of sorghum varieties in Burkina Faso, West Africa. A planting date study using 11 local sorghum varieties was conducted from June to August of 2003, 2004, 2006 and 2007 at the experiment station of Di in northwestern Burkina Faso. The number of stems at harvest time, the number of stems with panicle, the maximum number of stems hill-1, thermal time from emergence to maximum number stems hill-1 and photoperiod at maximum number of stems hill-1 were used to discuss and to characterize the dynamic of tillering. Our results show that the maximum number of tillers and the thermal time from emergence to reach maximum number of tillers were both affected by photoperiod or planting dates. The longest photoperiod, warm temperature, and the onset of organic matter mineralization at the beginning of the rain season were conditions that stimulated tillering. Further work will include the use of these results to improve crop management in West Africa.
Keywords: Sorghum bicolor (L) Moench, tillering, photoperiod, planting date, Burkina Faso, West Africa.
40. Poster Presentation: Session 2.5
Title: Model improvements for simulating heat stress in irrigated wheat by considering canopy temperature in a semi-arid environment: a multi-model comparison
Authors: Heidi Webber1 , P. Martre2 , S. Asseng3, B. Kimball4, J. White4, M.
Ottman5, G. W. Wall4 , G. De Sanctis6, J. Doltra7, R. Grant8, B. Kassie3, A.
Maiorano2, J. E. Olesen9, D. Ripoche10, E. E. Rezaei1, M. A. Semenov11, P. Stratonovitch11, and F. Ewert1 1 University of Bonn, Germany, 2 INRA, 3
University of Florida, USA, 4 ARS-USDA, 5 University of Arizona, USA, 6 JRC, 7
CIFA, 8 University of Alberta, Canada, 9 Aarhus Univeristy, Denmark, 10 AgroClim-INRA, 11 Rothamsted Research
Abstract: To account for the more frequent occurrence of extreme high temperatures expected under climate change, many models have focused on simulation of heat stress effects. However, most attempts to model heat stress have used air temperature (Tair), rather than crop canopy temperature (Tc). Recent improvements in models include the simulation of canopy temperature and range from empirical (EMP) to complex iterative models solving an energy balance correcting for atmospheric stability conditions (EBSC). A greatly simplified variation of the energy balance models assumes neutral stability conditions (EBN), avoiding iteration. The objectives of this study are: (1) to compare these new EMP, EBN, and EBSC approaches to simulate Tc and grain yield, and (2) to assess if simulation of Tc improves the ability of crop models to capture heat stress impacts. Nine crop models (three of each type) simulated crop growth and development for irrigated spring wheat in Arizona for a series of planting dates. The simulations were conducted twice: (1) using Tc on processes sensitive to heat stress and (2) using Tair on processes sensitive to heat stress. The three EBSC models had the lowest RMSE (2.9°C) while the three EBN had the highest (6.7°C). The RMSE of the EMP models was 3.9°C. Despite their relatively poor simulation of Tc, the EBN models simulated grain yields with lower RMSE (1.7 t ha-1) than the others. The use of Tc versus Tair lead to some improvements in simulating grain yield for most models.
57. Poster Presentation: Session 2.5
Title: Daphne: a generic database to integrate multiscale agronomic and phenotypic information for crop modelling
Authors: Lauriane Rouan1, D. Pot1, M. Boulnemour1,2, and S. Auzoux1
1 UMR AGAP, CIRAD, Montpellier, France2 UR AÏDA, CIRAD, Montpellier, France
Abstract: Studies of genotype x environment x management (GXEXM) interactions commonly use Crop Simulation Models (CSM). The minimum datasets required for a successful model implementation are multi-scale, multi-species and multi-disciplinary. We observed that although they are organized differently, CSM input files and field experiment datasets shared the same measurements (yield, leaf area index, biomass, etc.) and a few similar tables corresponding to the minimum dataset (weather, soil, crop, and management data). Based on this analysis, we have designed the schema of DAPHNE. We used the relevant technology of metadata. Thus, in DAPHNE, all variable labels are stored in a metadata table including the units and methods of measurements and the observed and experimental units. The main advantage of this technology is that the addition of any variable does not imply to reconsider the structure of the database. Database query performance is also improved. DAPHNE already has a wide application in GXEXM experiments on sorghum and sugarcane. The genericness of the schema of DAPHNE can allow intercomparison of CSM that require the same datasets with no common data structure.