Browsing by Subject "Crop model"

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Bayesian multi-purpose modelling of plant growth and development across scales
(2024) Viswanathan, Michelle; Streck, Thilo
Crop models are invaluable tools for predicting the impact of climate change on crop production and assessing the fate of agrochemicals in the environment. To ensure robust predictions of crop yield, for example, models are usually calibrated to observations of plant growth and phenological development using different methods. However, various sources of uncertainty exist in the model inputs, parameters, equations, observations, etc., which need to be quantified, especially when model predictions influence decision-making. Bayesian inference is suitable for this purpose since it enables different uncertainties to be taken into account, while also incorporating prior knowledge. Thus, Bayesian methods are used for model calibration to improve the model and enhance prediction quality. However, this improvement in the model and its prediction quality does not always occur due to the presence of model errors. These errors are a result of incomplete knowledge or simplifying assumptions made to reduce model complexity and computational costs. For instance, crop models are used for regional scale simulations thereby assuming that these point-based models are able to represent processes that act at regional scale. Additionally, simple statistical assumptions are made about uncertainty in model errors during Bayesian calibration. In this work, the problems arising from such applications are analysed and other Bayesian approaches are investigated as potential solutions. A conceptually simple Bayesian approach of sequentially updating a maize phenology model, an important component in plant models, was investigated as yearly observation data were gathered. In this approach, model parameters and their uncertainty were estimated while accounting for observation uncertainty. As the model was calibrated to increasing amounts of observation data, the uncertainty in the model parameters reduced as expected. However, the prediction quality of the calibrated model did not always improve in spite of more data being available for potentially improving the model. This discrepancy was attributed to the presence of errors in the model structure, possibly due to missing environmental dependencies that were ignored during calibration. As a potential solution, the model was calibrated using Bayesian multi-level modelling which could account for model errors. Furthermore, this approach accounted for the hierarchical data structure of cultivars nested within maize ripening groups, thus simultaneously obtaining model parameter estimates for the species, ripening groups and cultivars. Applying this approach improved the model's calibration quality and further aided in identifying possible model deficits related to temperature effects in the post-flowering phase of development and soil moisture. As another potential solution, an alternative calibration strategy was tested which accounted for model errors by relaxing the strict statistical assumptions in classical Bayesian inference. This was done by first acknowledging that due to model errors, different data sets may yield diverse solutions to the calibration problem. Thus, instead of fitting the model to all data sets together and finding a compromise solution, a fit was found to each data set. This was implemented by modifying the likelihood, a term that accounts for information content of the data. An additive rather than the classical multiplicative strategy was used to combine likelihood values from different data sets. This approach resulted in conservative but more reliable predictions than the classical approach in most cases. The classical approach resulted in better predictions only when the prediction target represented an average of the calibration data. The above-mentioned results show that Bayesian methods with representative error assumptions lead to improved model performance and a more realistic quantification of uncertainties. This is a step towards the effective application of process-based crop models for developing suitable adaptation and mitigation strategies.
Developing cropping systems for the ancient grain chia (Salvia hispanica L.) in two contrasting environments in Egypt and Germany
(2020) Mack, Laura; Graeff-Hönninger, Simone
Chia (Salvia hispanica L.) seeds have been revived as functional “superfood” for human nourishment especially for vegan and vegetarian diets and are becoming increasingly widespread and present in new food products in Europe. The seeds are beneficial because of being gluten-free, containing antioxidants and a high concentration of α-linolenic acid, and having a high content of dietary fiber and high-quality protein. Chia is originally adapted to short-day conditions and grows naturally in tropical and subtropical environments. Nevertheless, it can survive under water stress and could, therefore, be cultivated in arid regions. Egypt has been classified as a water-scarce state. Due to its drought tolerance, chia might contribute to saving the scarce source “water” in Egypt and offer the chance to export these high value seeds, generating foreign exchange for reimporting e.g. wheat characterized by a higher water demand. Worldwide, the biggest problems and key challenges under climate change (CC) are water and food security in arid and semiarid regions. In the future, CC and water scarcity will significantly threaten agriculture and sustainable development. A rising population requires on the one hand an increase in food grain production, but also a change toward environmentally sound sustainable agriculture. Chia has been suggested as a favorably economic alternative for common field crops sustaining diversification and stabilization of the local agricultural economy. However, broad experience in growing chia in new environments is missing. The agronomic management has not been improved from formerly small-scale production systems. Most of the previous studies focused on seed characteristics. Information on fertilization, plant protection, and improved varieties is scarce, which are reasons for its low productivity in the countries of origin. Field experiments were conducted at the experimental station “Ihinger Hof” of the University of Hohenheim in southwestern Germany from 2015 to 2017 and in Egypt during the cropping season 2015 to 2016 at SEKEM’s experimental station located 50 km Northeast of Cairo. The present doctoral thesis was based on a project embedded in the graduate school Water-People-Agriculture (WPA) at the University of Hohenheim funded by the Anton-&-Petra-Ehrmann foundation that focuses on key water issues and water related challenges of todays society. On a final note, the main results of this thesis provide further information and expanded knowledge on chia cultivation in two contrasting environments (including a desert region) out of its center of origin. Overall, the current doctoral thesis presents a combined approach of experimental field research and crop modeling to support the optimization of farming practices of chia in new environments. A universal and nondestructive LA estimation model for chia was developed. Further, the CROPGRO model was adapted for chia to provide a preliminary model for a realistic simulation of crop growth variables. The approaches presented in this thesis may contribute to testing new environments for chia cultivation and to improving its production. Moreover, this study helped to develop further general model source codes to simulate the growth of tiny seeds. The adaptation to other Salvias should be much easier with this developed model. Future research requirements and issues requiring model improvement such as N-response and the development of code relationships that can simulate parameters of seed quality could improve the plant growth model for chia.
Proposal and extensive test of a calibration protocol for crop phenology models
(2023) Wallach, Daniel; Palosuo, Taru; Thorburn, Peter; Mielenz, Henrike; Buis, Samuel; Hochman, Zvi; Gourdain, Emmanuelle; Andrianasolo, Fety; Dumont, Benjamin; Ferrise, Roberto; Gaiser, Thomas; Garcia, Cecile; Gayler, Sebastian; Harrison, Matthew; Hiremath, Santosh; Horan, Heidi; Hoogenboom, Gerrit; Jansson, Per-Erik; Jing, Qi; Justes, Eric; Kersebaum, Kurt-Christian; Launay, Marie; Lewan, Elisabet; Liu, Ke; Mequanint, Fasil; Moriondo, Marco; Nendel, Claas; Padovan, Gloria; Qian, Budong; Schütze, Niels; Seserman, Diana-Maria; Shelia, Vakhtang; Souissi, Amir; Specka, Xenia; Srivastava, Amit Kumar; Trombi, Giacomo; Weber, Tobias K. D.; Weihermüller, Lutz; Wöhling, Thomas; Seidel, Sabine J.; Wallach, Daniel; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany; Palosuo, Taru; Natural Resources Institute Finland (Luke), Helsinki, Finland; Thorburn, Peter; CSIRO Agriculture and Food, Brisbane, Australia; Mielenz, Henrike; Institute for Crop and Soil Science, Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Braunschweig, Germany; Buis, Samuel; INRAE, UMR 1114 EMMAH, Avignon, France; Hochman, Zvi; CSIRO Agriculture and Food, Brisbane, Australia; Gourdain, Emmanuelle; ARVALIS - Institut du végétal Paris, Paris, France; Andrianasolo, Fety; ARVALIS - Institut du végétal Paris, Paris, France; Dumont, Benjamin; Plant Sciences & TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium; Ferrise, Roberto; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy; Gaiser, Thomas; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany; Garcia, Cecile; ARVALIS - Institut du végétal Paris, Paris, France; Gayler, Sebastian; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany; Harrison, Matthew; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Tasmania, Australia; Hiremath, Santosh; Aalto University School of Science, Espoo, Finland; Horan, Heidi; CSIRO Agriculture and Food, Brisbane, Australia; Hoogenboom, Gerrit; Global Food Systems Institute, University of Florida, Gainesville, USA; Jansson, Per-Erik; Royal Institute of Technology (KTH), Stockholm, Sweden; Jing, Qi; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada; Justes, Eric; PERSYST Department, CIRAD, Montpellier, France; Kersebaum, Kurt-Christian; Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany; Launay, Marie; INRAE, US 1116 AgroClim, Avignon, France; Lewan, Elisabet; Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden; Liu, Ke; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Tasmania, Australia; Mequanint, Fasil; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany; Moriondo, Marco; CNR-IBE, Firenze, Italy; Nendel, Claas; Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Padovan, Gloria; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy; Qian, Budong; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada; Schütze, Niels; Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany; Seserman, Diana-Maria; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Shelia, Vakhtang; Global Food Systems Institute, University of Florida, Gainesville, USA; Souissi, Amir; Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, Canada; Specka, Xenia; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Srivastava, Amit Kumar; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany; Trombi, Giacomo; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy; Weber, Tobias K. D.; Faculty of Organic Agriculture, Soil Science Section, University of Kassel, Witzenhausen, Germany; Weihermüller, Lutz; Institute of Bio- and Geosciences - IBG-3, Agrosphere, Forschungszentrum Jülich GmbH, Jülich, Germany; Wöhling, Thomas; Lincoln Agritech Ltd., Hamilton, New Zealand; Seidel, Sabine J.; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.
Reducing uncertainty in prediction of climate change impacts on crop production in Ethiopia
(2024) Rettie, Fasil Mequanint; Streck, Thilo
Ethiopia, with an economy heavily reliant on agriculture, is among the countries most vulnerable to climate change. It faces recurrent climate extreme events that result in devastating impacts and acute food shortages for millions of people. Studies that focus on their influence on agriculture, especially crop productivity, are of particular importance. However, only a few studies have been conducted in Ethiopia, and existing studies are spatially limited and show considerable spatial invariance in predicted impacts, as well as discrepancies in the sign and direction of impacts. Therefore, a robust, regionally focused, and multi-model assessment of climate change impacts is urgently needed. To guide policymaking and adaptation strategies, it is essential to quantify the impacts of climate change and distinguish the different sources of uncertainty. Against this backdrop, this study consisted of several key components. Using a multi-crop model ensemble, we began with a local climate change impact assessment on maize and wheat growth and yield across three sites in Ethiopia . We quantified the contributions of different sources of uncertainty in crop yield prediction. Our results projected a of 36 to 40% reduction in wheat grain yield by 2050, while the impact on maize was modest. A significant part of the uncertainty in the projected impact was attributed to differences in the crop growth models. Importantly, our study identified crop growth model-associated uncertainty as larger than the rest of the model components. Second, we produced a high-resolution daily projections database for rainfall and temperature to serve the requirement for impact modeling at regional and local levels using a statistical downscaling technique based on state-of-the-art GCMs under a range of emission scenarios called Shared Socioeconomic Pathways (SSPs). The evaluated results suggest that the downscaling strategy significantly reduced the biases between the GCM outputs and the observation data and minimized the errors in the projections. Third, we explored the magnitude and spatial patterns of trends in observed and projected changes in climate extremes indices based on downscaled high-resolution daily climate data to serve as a baseline for future national or regional-level impact assessment. Our results show largely significant and spatially consistent trends in temperature-derived extreme indices, while precipitation-related extreme indices are heterogeneous in terms of spatial distribution, magnitude, and statistical significance coverage. The projected changes in temperature-related indices are dominated by the uncertainties in the GCMs, followed by uncertainties in the SSPs. Unlike the temperature-related indices, the uncertainty from internal climate variability constitutes a considerable proportion of the total uncertainty in the projected trends. Fourth, we examined the regional-scale impact of climate change on maize and wheat yields by crop modeling, in which we calibrated and validated three process-based crop models to guide the design of national-level adaptation strategies in Ethiopia. Our analysis showed that under a high-emissions scenario, the national-level median wheat yield is expected to decrease by 4%, while maize yield is expected to increase by 2.5% by the end of the century. The CO2 fertilization effect on the crop simulations would offset the projected negative impact. Crop model spread followed by GCMs was identified as the largest contributor to overall uncertainty to the estimated yield changes. In summary, our study quantifies the impact of climate change and demonstrates the importance of a multi-model ensemble approach. We highlight the significant impacts of climate change on wheat yield in Ethiopia and the importance of crop model improvements to reduce overall uncertainty in the projected impact.
The role of crop management practices and adaptation options to minimize the impact of climate change on maize (Zea mays L.) production for Ethiopia
(2023) Feleke, Hirut Getachew; Savage, Michael J.; Fantaye, Kindie Tesfaye; Rettie, Fasil Mequanint
Climate change impact assessment along with adaptation measures are key for reducing the impact of climate change on crop production. The impact of current and future climate change on maize production was investigated, and the adaptation role of shifting planting dates, different levels of nitrogen fertilizer rates, and choice of maize cultivar as possible climate change adaptation strategies were assessed. The study was conducted in three environmentally contrasting sites in Ethiopia, namely: Ambo, Bako, and Melkassa. Future climate data were obtained from seven general circulation models (GCMs), namely: CanESM2, CNRM-CM5, CSIRO-MK3-6-0, EC-EARTH, HadGEM2-ES, IPSL-CM5A-MR, and MIROC5 for the highest representative concentration pathway (RCP 8.5). GCMs were bias-corrected at site level using a quantile-quantile mapping method. APSIM, AquaCrop, and DSSAT crop models were used to simulate the baseline (1995–2017) and 2030s (2021–2050) maize yields. The result indicated that the average monthly maximum air temperature in the 2030s could increase by 0.3–1.7 °C, 0.7–2.2 °C, and 0.8–1.8 °C in Ambo, Bako, and Melkassa, respectively. For the same sites, the projected increase in average monthly minimum air temperature was 0.6–1.7 °C, 0.8–2.3 °C, and 0.6–2.7 °C in that order. While monthly total precipitation for the Kiremt season (June to September) is projected to increase by up to 55% (365 mm) for Ambo and 75% (241 mm) for Bako respectively, whereas a significant decrease in monthly total precipitation is projected for Melkassa by 2030. Climate change would reduce maize yield by an average of 4% and 16% for Ambo and Melkassa respectively, while it would increase by 2% for Bako in 2030 if current maize cultivars were grown with the same crop management practice as the baseline under the future climate. At higher altitudes, early planting of maize cultivars between 15 May and 1 June would result in improved relative yields in the future climate. Fertilizer levels increment between 23 and 150 kg ha−1 would result in progressive improvement of yields for all maize cultivars when combined with early planting for Ambo. For a mid-altitude, planting after 15 May has either no or negative effect on maize yield. Early planting combined with a nitrogen fertilizer level of 23–100 kg ha−1 provided higher relative yields under the future climate. Delayed planting has a negative influence on maize production for Bako under the future climate. For lower altitudes, late planting would have lower relative yields compared to early planting. Higher fertilizer levels (100–150 kg ha−1) would reduce yield reductions under the future climate, but this varied among maize cultivars studied. Generally, the future climate is expected to have a negative impact on maize yield and changes in crop management practices can alleviate the impacts on yield.