Climate change impacts are expected to negatively affect crop productivity in several agricultural systems and agro-ecological zones of Africa, where the majority of the rural people derive their livelihood from rain-fed agriculture. In Uganda, mountainous and lake ecosystems are dominant growing areas for major annual and perennial crops, but are more susceptible to future changes in climate. This is likely to deteriorate agricultural livelihoods of these ecosystems through declining productivity of various crops. This study assessed the near-term future climate change effects of selected adaptation practices on yields of annual and perennial crops in coffee growing agro-ecological zones of Uganda. Based on a Cobb-Douglas logarithmic production function, the study examined whether future climate would increase crop productivity through the influence of adaptation practices at current and increased adoption levels in the near-term under RCP8.5 and RCP4.5 for five climate regimes. The study results showed that rainfall changes, particularly wetter conditions (cool-wet and hot-wet climate regimes) are expected to be the most damaging to coffee, banana, maize and beans yields than temperature changes with drier conditions (including ensemble mean, cool-dry and hot-dry) under various altitude gradients. Hence, current adaptation practices have significant potential to reduce crop yield losses especially if future climate becomes drier than wetter in the near-term. The study therefore, recommends that there is a need for further research to identify complementary adaptation practices e.g. through bioengineering, soil loss control and water draining efficiency technologies; that would boost positive crop productivity effects of current adaptation practices, as they are not sufficient on their own in the near-term future even with enhanced adoption rates. Also, plant breeding programs should focus on generating crop varieties that are drought tolerant but can also perform well in volatile hydrological conditions; and those that are more suitable for the various altitudinal changes in climate.

How to cite: Mulinde, C., Twinomuhangi, R., Kato, E., and Majaliwa, J. G. M.: Climate change effects of adaptation on annual and perennial crop yields in Uganda, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1210, https://doi.org/10.5194/egusphere-egu24-1210, 2024.

The positive effects of warm temperatures on crop yields reverses beyond a critical temperature threshold, where sharp decrease in yields is observed for all crops. In the light of warming trends observed globally, adaptation of crops to extreme climatic conditions could be crucial for ensuring a stable food supply in the future. While numerous studies have shown the potential positive impact of adaptation on food security, there is limited evidence showing observed changes in the sensitivity of major food crops to high temperatures at national and global levels. In this study, we use regression models to examine the spatiotemporal variations in critical temperature threshold for corn and soy in the US Midwest. Further, we also examine changes in yield response to exposure to temperatures beyond the critical temperature threshold. Overall, our work tests for the presence of adaptation in the observed yield trends of corn and soy in the US Midwest. 

How to cite: Raghuram, A. and Coffel, E.: Testing for adaptation in the observed corn and soy yields in the US Midwest, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20735, https://doi.org/10.5194/egusphere-egu24-20735, 2024.

Sorghum holds the fifth position worldwide in terms of both grain production and cultivation area. However, sorghum is still a minor crop in Europe where, on average, only 0.12% of the cropland area was used for sorghum production between 2017 and 2021. Nonetheless, its production is expanding in this region, with a 57% increase in total sorghum production during the last decade compared to the first decade of the 21^st century. Indeed, sorghum is considered a crop of interest for climate change adaptation in Europe due to its high heat tolerance compared to other crops, especially maize. In this study, we aimed to investigate the feasibility of expanding sorghum cultivation in Europe under current and future (middle and end of the 21^st century) climatic conditions. We also explored the possibility of replacing maize with locally-produced sorghum for feeding livestock in Europe.

To this end, we developed a machine-learning model that predicts sorghum yields from high-resolution climate data using a random forest algorithm. The model was trained on historical sorghum yield data collected in France, Italy, Spain, and the USA, covering the period from 2000 to 2020. The historical sorghum yield dataset comprises 11,644 data points at subnational ‎administrative levels‎. The set of predictors included monthly climate variables such as solar radiation, minimum and maximum temperature, rainfall, and relative humidity calculated over the growing season (April-November) from the ERA5-Land dataset. The model's performance was evaluated based on cross-validation (R²=0.83, RMSE=0.94 t ha^-1) for the 2000 to 2020 period.

In total, we ran the model for 30 future scenarios using bias-corrected climate data produced by five Global Climate Models of the Coupled Model Intercomparison Project phase 6 (CMIP6), following three Representative Concentration Pathways scenarios (SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5), and focusing on two periods (2041-2060 and 2081-2100). In almost all scenarios, sorghum yields decreased up to - 1.5 t ha^-1 in the southern part of Europe (e.g., center of Spain, south of France, and Italy) but increased substantially up to + 3 t ha^-1 in the northern part (e.g., north of Germany, Poland, and Lithuania) compared to historical yields. In all scenarios, at least 39% of European croplands were projected to support sorghum yields higher than 4.6 t ha^-1 (the average sorghum actual yield in Europe in the last decade). Our results showed that sorghum production could increase significantly in Europe under future climates. Regardless of the scenario, if sorghum was grown in one out of three years (respectively, one out of six years), at least 90% (respectively, 45%) of maize used as livestock feed could be replaced by sorghum in Europe. These results could provide valuable information for improving feed security in Europe in the face of climate change.

How to cite: Davoudkhani, M., Guilpart, N., Makowski, D., Viovy, N., Ciais, P., and Lauerwald, R.: Perspectives for expanding sorghum production in Europe in the face of climate change , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1367, https://doi.org/10.5194/egusphere-egu24-1367, 2024.

Understanding the microclimate dynamics within vineyard living labs is paramount for sustainable and optimised grape production. This study delves into a comprehensive approach, using a microclimate NicheMapR model, local station hourly data, ERA5 land data, and a high-resolution Digital Elevation Model to refine microclimate analyses. The key innovation lies in achieving an unprecedented 10-meter spatial resolution of climate variables, providing a perspective on the intricate interplay of climatic variables within each living lab. The initial phase of the study involves the incorporation of local station data to perform bias correction on ERA5 land data, achieved through quantile mapping techniques. This bias-corrected dataset serves as a robust foundation for subsequent analyses, ensuring that the microclimate model accurately reflects the unique characteristics of the vine living labs under study. Integrating a high-resolution DEM further enhances spatial precision, capturing subtle variations in terrain that can profoundly impact local microclimates, such as shade and horizon angles. Additionally, the 10-meter spatial resolution output from the microclimate model is used to bias correct EURO-CORDEX ensemble models, providing the development of future climatic scenarios. This approach ensures that the future projections are not only regionally specific but also representative of each living lab. An important output of the research is the determination of future climate extreme indices and bioclimatic indices specifically designed for viticulture. By analysing the ensemble models at the 10-meter scale, the study aims to provide invaluable insights into potential shifts in temperature extremes, precipitation patterns, and other climatic variables critical to grape cultivation within a specific living lab. In conclusion, this study presents a holistic and forward-looking approach to microclimate analysis in vine living labs. By integrating advanced geospatial technologies, bias-corrected ERA5 land data, high-resolution DEMs, and the microclimate NicheMapR model, this research expands the knowledge of present microclimates and provides viticulturists with insights into future climate scenarios.

How to cite: Fonseca, A., Cruz, J., Valente, J., Alves, F., Neto, A., Flores, R., and Santos, J.: Climate change projections coupled with microclimatic modelling for supporting decision making in viticulture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8495, https://doi.org/10.5194/egusphere-egu24-8495, 2024.

Farmers cultivating cereals in Germany have experienced unfavorable conditions during the recent decade due to more frequent droughts and heat episodes (Lüttger & Feike, 2018). These events are likely to aggravate in the future (Trnka et al., 2014). Drought and heat-related yield reductions were already being seen in cereal crops all over the country (Webber et al., 2020; Schmitt et al., 2022).

Franconia covers the northern part of Bavaria, the the most important region for wheat and silage maize cultivation. At the same time, Franconia is the driest region in Bavaria with mean annual precipitation <600 mm.  In the past decade, there has been a noticeable variability in crop yields. Particularly 2018 and 2020 had substantial yield shortfall due to lower rainfall amounts. However, not all regions experienced similar yield reductions. Therfore, further evaluation of the causes of yield variability in response to dry years is essential when choosing practices to increase plant resilience.

Previous studies investigating adaptation options of cereals to climate variability suggested practices such as early maturing cultivars, preceding sowing dates and breeding towards resistant varieties.

The objective of this study is to identify the challenges farmers in Franconia have faced in recent years regarding climate conditions. The temporal focus is from 2015 until the harvest of 2023. Based on that, by integrating farmer’s knowledge and experience we aim to identify successful adaptation strategies that reflect in higher and stable production under dry conditions – but also promise good yields in wet years.

Our approach is multi-faceted, including the evaluation of agricultural strategies applied by farmers, climate data analysis, and integration of satellite data and spatial characteristics. In addition, we use a long term experiment results on cereal cultivaiton methods to support the research findings. By conducting in-depth interviews with ~100 farmers in the region, we explore recent and local farming perspectives. With this combination of methods, we aim to dissect successful approaches and understand pivotal causes for sustainable productivity.

Eventually, we will be able to recommend a comprehensive set of scientifically sound and practical approaches for economic, climate resilient cereal farming under increasingly dry conditions in Northern Bavaria. 

Fig. 1. A flow chart of the data sources used in the research.

References:

How to cite: Shlomi, O., Schauberger, B., Wiesmeier, M., and Sümmerer, M.: Identifying effective strategies for cereal cultivation under dry climate in Bavaria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5647, https://doi.org/10.5194/egusphere-egu24-5647, 2024.

Agriculture is a major source of non-CO₂ greenhouse gas (GHG) emissions, namely methane (CH₄) and nitrous oxide (N₂O), and reactive trace gases, such as ammonia (NH₃). CH₄ emissions originate primarily from enteric fermentation of ruminants and during manure storage. N₂O emissions are produced in microbial processes of soils and manure. Emissions of NH₃ arise from livestock housing systems, manure storage and application to the soil as well as during grazing. Mitigating GHG emissions has emerged as a key priority for policy makers, researchers and stakeholders, evident in the ambitious emission reduction targets set at both the EU and national levels. However, mitigation measures at the farm level incur different marginal abatement costs (MACs) due to farm and regional specific characteristics. Farm specific calculations of MACs are still limited. Therefore, we aim at (i) modeling non-CO₂ GHG emissions, (ii) computing MACs of mitigation measures and (iii) identifying cost-efficient mitigation measures for the Austrian farms using the Farm Optimization Model FAMOS. FAMOS is a mixed-integer linear farm optimization model implemented in GAMS (General Algebraic Modeling Systems; https://www.gams.com/). It is extended with a non-CO₂ GHG emission accounting module that follows the guidelines for national GHG inventories provided by the Intergovernmental Panel on Climate Change. Country and farm-specific emission factors are used in the non-CO₂ GHG emission accounting. This module enhances the accuracy of emission calculations at the farm level. FAMOS maximizes farm net returns, defined as the sum of market revenues and policy payments minus the costs of production and investment, subject to the farm’s resource endowments such as available land, livestock housing capacity and farm family labor. Agronomic production relationships (e.g., fertilizer and feed balances), farm management practices (e.g., crop rotations, fertilization, irrigation, tillage, feeding and grazing strategies), and legal compliances (e.g., CAP measures and payments, fertilizer intensities as part of the Austrian agri-environmental OEPUL programme) are taken into account. The model uses farm level data from various data sources (e.g., Farm Structure Survey, Integrated Administration and Control System, Standard Gross Margin Catalogue) and is individually solved for each farm in Austria. The model results show that the MACs of mitigation measures differ between farm types and agricultural production regions. For instance, MACs are higher for specialized farms with few and labor-intensive management options. The MACs are lower for managerial measures (e.g., changes in fertilizer management), compared to technological (e.g., changes in livestock housing) and agronomic measures (e.g., cover cropping). Our analysis complements the existing research by calculating MACs of selected mitigation measures at farm level. These results may inform farmers, farm consultants and policy makers in fostering the implementation of cost-efficient mitigation strategies at farm level.

How to cite: Kröner, V., Falkner, K., Mitter, H., and Schmid, E.: Modeling of farm-specific marginal abatement costs of non-CO2 greenhouse gas mitigation measures in Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8911, https://doi.org/10.5194/egusphere-egu24-8911, 2024.

The relationship between food security and climate change is a central concern for policymakers and society at large. Temperature fluctuations and extreme weather events significantly impact agriculture, notably affecting yield production. Effective management measures that enhance resilience of crop production to abiotic stress are thus highly important. This requires an appropriate understanding of the predominant stressors and their temporal impact on yield formation under given pedo-climatic conditions. Designing future climate-smart management systems will strongly profit from an appropriate evaluation of current yield variability, identifying the main underlying environmental and management related factors. Therefore, the two key questions addressed in this study are:     

• At which temporal stage does crop development indicate differentiation in biomass growth that impacts the attainable final crop yield?
• Are the distinctive crop growth and yield patterns in a region predominantly driven to environmental site effects (soil type, rainfall, temperature) and to what extent farmers’ management decisions (pre-crop, cover crop, seeding time) can influence the site-specific natural drivers? 

In recent decades, multiple approaches have been used to analyze factors driving crop yields, from classical replicated field trials over plot scale agroecosystem models to remote sensing-based machine learning approaches. This work is centered on a georeferenced polygon dataset, containing fields from 0.1 to 16.6 hectares in Lower Austria focused on the country's predominant staple crop, winter wheat, between the years 2013 and 2020. In total, the dataset contains 541 entries with winter wheat yield data and detailed management history of the respective fields. Using different types of feature selection techniques, from classical machine learning (i.e., random forest) to recent techniques (i.e., guided regularized random forest), we aim to (i) analyze the temporal growth pattern and extract the yield determinant features as well as their specific timing from several remote sensing derived indices (e.g., Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI)), and (ii) the role of the site specific pedoclimatic information (e.g., surface air temperature, rainfall, soil data) as well as management data (e.g., previous crop, cover crop, seeding time, tillage type). 

Based on the most promising feature models, we will map the expected winter wheat yield variability for Lower Austria and evaluate yield predictability with regional winter wheat yield data from Lower Austria at NUTS3 level between 2015-2022. Since crop-specific crop yield maps are not currently available at the regional level, the validation data will be obtained by intersecting regional yield data and yearly land cover data.

From the results, we expect to provide an improved insight into yield-relevant time periods for winter wheat growth and their interplay with prevailing site-conditions such as soil type based on remote sensing indices. This can contribute to an improved understanding of winter wheat yield formation, thereby providing decision support for more targeted management adaptation and more realistic estimates of expectable management impacts over the unmanageable fate of natural site conditions.

How to cite: Bernardini, L. G., Bodner, G., Hofer, M., and Izquierdo-Verdiguier, E.: Learning from yields: Prevailing features for winter wheat yield variability and the role of farmers’ management decisions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10313, https://doi.org/10.5194/egusphere-egu24-10313, 2024.

Annual food caloric production is the product of caloric yield, cropping frequency (CF, number of production seasons per year) and cropland area. Existing studies have largely focused on crop yield, whereas how CF responds to climate change remains poorly understood. Here, we evaluate the global climate sensitivity of caloric yields and CF at national scale. We find a robust negative association between warming and both caloric yield and CF. By the 2050s, projected CF increases in cold regions are offset by larger decreases in warm regions, resulting in a net global CF reduction (−4.2 ± 2.5% in high emission scenario), suggesting that climate-driven decline in CF will exacerbate crop production loss and not provide climate adaptation alone. Although irrigation is effective in offsetting the projected production loss, irrigation areas have to be expanded by >5% in warm regions to fully offset climate-induced production losses by the 2050s.

How to cite: Zhu, P.: Warming reduces global agricultural production by decreasing cropping frequency and yields, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11585, https://doi.org/10.5194/egusphere-egu24-11585, 2024.

Institutional agencies play a crucial role in land use change, but modelling their decision-making processes is challenging due to the complexity of the environment they operate within and the bounded rationality of human organizations. Large Language Models (LLMs) offer a novel approach to simulating human decisions. This paper aims to investigate the challenges and opportunities that LLMs bring to land use change modelling by integrating LLM-powered institutional agents with the CRAFTY land use model, in which land users produce a range of ecosystem services. The study develops a structured prompt development approach for coupling LLM-powered agents with existing large-scale simulations. Four types of LLM-powered agents are examined, which use taxes to steer meat production toward a prescribed policy goal. The agents provide reasoning and policy action output in each simulation iteration. The study also uses a technique called quasi-multi-agent to simulate multiple roles involved in the policy processes. Unlike authentic multi-agent simulation, the LLM-powered quasi-multi-agent leverages the LLM's ability to generate contextually coherent text and allows the agents to work as a scriptwriter who composes conversations between different roles. This approach conserves computational resources and has the potential to manage conversational dynamics in policy discussions. The efficacy of these agents is benchmarked against two baseline scenarios: one without any policy intervention and another implementing optimal policy actions determined through a genetic algorithm.

The findings show that while LLM-powered agents perform better than the non-intervention scenario, they fall short of the performance achieved by optimal policy actions. However, LLM-powered agents demonstrate human-like decision-making, marked by policy consistency and transparent reasoning. The agents also generate real-world policymaking strategies, including incrementalism, considering delayed policy influence, proactive policy adjustments, and balancing multiple stakeholder interests. Agents equipped with experiential learning capabilities excel in achieving policy objectives through progressive policy actions. The order of reasoning and proposed policy actions in the prompts has a notable effect on the agents' performance. The research points to both promising opportunities and significant challenges in integrating LLMs into large-scale land-use simulations. The opportunities include exploring naturalistic institutional decision-making and its impact on land use change, using LLM's information retrieval to handle massive institutional documents, modelling institutional networks, and human-AI cooperation. However, challenges mainly lie in the scalability and reliability of LLMs due to the dependence on LLM providers, the paradox of pursuing realistic institutional behaviours versus abstraction and simplification in existing models, and the effectiveness and efficiency in scrutinizing massive textual output, detecting illogical content in prompts, and inaccurate formatting.

How to cite: Zeng, Y., Brown, C., Byari, M., Raymond, J., Hotz, R., and Rounsevell, M.: Exploring the Opportunities and Challenges of Using Large Language Models to Represent Institutional Agency in Land Use Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13421, https://doi.org/10.5194/egusphere-egu24-13421, 2024.

The implications of climate change on agro-pastoral farming systems in the Peruvian Andes are not fully understood. There is already a significant impact on agricultural productivity from current climate variability and extreme weather in the region. This is exacerbated by chronic poverty in many rural areas and the need for improved government-led strategic planning. Tools to assist with policy planning for climate change adaptations that achieve environmental and social resilience are vital, and these require collaboration with rural communities to incorporate the complexities of behavioural responses to climate change, market dynamics, and policy shifts in agricultural and water management. 

In this study we further develop a recent agricultural systems model (the TELLUS model; Pilditch et al., in review). The model is an agent-based simulation focussed on the behaviour of interacting populations of individual farming agents. TELLUS offers the opportunity to analyse the impact of interventions/policies in light of key scenarios and conditions of interest, with potential to uncover unforeseen emergent behaviours within farming systems (e.g., tipping points, amplifiers, system adaptations) and potential unintended consequences of scenarios and policies (e.g., increasing in equalities; increased system fragility). A difficulty in applying such models to specific case studies is in choosing valid parameter values, especially for model behaviour associated with human behaviour and decision-making.

Our work over recent years includes extensive fieldwork in the Cordillera Negra and Cordillera Blanca, involving interviews and workshops with farming communities, and collaboration with regional NGOs. These interactions have been instrumental in understanding local challenges and priorities. The challenge in terms of modelling this system is turning information gained from qualitative methods (e.g. interviews) into parameter values for the model. Our novel approach is to assess the extent to which modern AI systems, specifically, Large Language Models (LLMs) can help perform this task.  We leverage the reasoning abilities of LLMs to directly estimate relevant model parameters from automated interview transcription/translations. We will discuss the extent to which this integration has aided the creation of a TELLUS model tuned specifically to the Peruvian Andes context. Our approach will hopefully serve as a novel tool, combining empirical research, community involvement, and advanced computational modelling, to explore future climate scenarios and the potential effects of policy interventions.

How to cite: Singarayer, J., Bailey, R., McGuire, P., Araujo- Ferreira, F., Branch, N., Gonzalez, F., Santos Shupingahua, D., Walsh, D., Herrera, A., Wade, A., Rodda, H., Timana, M., and Lane, K.: An exploration of using large language models to integrate farmer behaviour into an agricultural systems model of the Peruvian Andes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18078, https://doi.org/10.5194/egusphere-egu24-18078, 2024.

Various inputs can be selected to establish a robust crop yield simulation based on statistical models. Typically, weather variables such as precipitation, temperature, relative humidity, etc., are used as inputs in these models. It is well known that drought is a major limiting factor for crop yield in Central Europe, as manifested in recent years. This study aimed to assess whether adding model-based drought indicators derived from a nationally calibrated and validated process-based agro-hydrological model (Soil and Water Assessment Tool - SWAT) could help increase the predictive power of crop yield prediction. The secondary objective was to assess future projections of crop yield. We considered two drought indicators: the Standardized Precipitation Index (SPI) and the Soil Moisture Index (SMI) with the following accumulation periods: 1970-2019. The ABSOLUT v1.2 (Assessing Best-predictive Sets fOr multiple Linear regressions throUgh exhaustive Testing) model was applied for the prediction of yield of major crops in Poland: winter wheat, spring barley, potatoes, sugar beet, and maize for 16 provinces of the country for the time period 1999-2019. ABSOLUT v1.2 is an adaptive algorithm that utilizes correlations between time-aggregated weather variables and crop yields for yield simulation. Future yield projections were derived based on bias-corrected EURO-CORDEX simulations driven by two Representative Concentration Pathways (RCPs), RCP4.5 and 8.5, corresponding to the radiative forcing levels of 4.5 W/m−2 and 8.5 W/m−2 in the year 2100, respectively. Our results indicate that incorporating drought indicators as predictors in statistical crop yield simulations slightly enhances the reliability of yield prediction in Poland. Projected crop yields reveal that in western parts of Poland, crop yields could experience a decrease of 8%, but in eastern parts, crop yields remain mostly unchanged.

How to cite: Eini, M., Conradt, T., and Piniewski, M.: Model-based drought indicators improve the reliability of crop yield simulations with a statistical model in Poland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14402, https://doi.org/10.5194/egusphere-egu24-14402, 2024.

Multiple cropping practices, i.e. planting and harvesting crops several times a year at the same plot of land, may increase global food production without further expanding cropland (Wu et al. 2014). Especially the combination of irrigation in the dry season to facilitate multiple harvests a year potentially facilitates more food production on the same amount of land. Global dynamic gridded vegetation models that inform global land-use models usually only model one growing season a year. Neglecting the yield that can be achieved in the second or third season leads to an underestimation of yields and irrigation water requirements and biased projections of the spatial allocation of rainfed and irrigated cropland.

With an update of our hydro-economic model (Beier et al. 2023), we are able to estimate multiple cropping potentials and model multiple cropping and irrigation expansion. It is the tandem of these two intensification measures that facilitates production gains without expanding cropland. We estimate multiple cropping potentials considering their interaction with irrigation and water availability limitations to determine how much cropland area can be managed in a multiple cropping system given local crop growth conditions (suitability for multiple cropping), the associated water requirements and locally limited water availability for irrigation. We obtain multiple cropping and irrigation potentials at a 0.5° spatial resolution using biophysical inputs from the global vegetation model LPJmL (Schaphoff et al. 2018, von Bloh et al. 2018). LPJmL provides crop-specific (irrigated and rainfed) crop yields and crop water requirements for the main growing season for 12 crop functional types and gross primary production (GPP) of grass for the entire year at a 0.5° spatial resolution. To derive a metric on the yield increase through multiple cropping, we need an aggregated approach that abstracts from the very high set of potential combinations of crops in multiple cropping. We therefore use the main-season-to-whole-year ratio of grass GPP to obtain the grid-cell-specific potential multiple cropping effect. This ratio is used to scale main season crop yields and crop water requirements. In terms of irrigation water availability, the spatial allocation of irrigation water takes upstream-downstream relationships into account and considers the monetary yield gain through irrigation to determine the location of potentially irrigated areas (Beier et al. 2023).

With this, we address the research question: What is the biophysical and economic multiple cropping production potential under consideration of local (spatially explicit) irrigation water availability constraints on current cropland?

References

Beier, F. et al. (2023a). Technical and Economic Irrigation Potentials within Land and Water Boundaries. Water Resources Research

Beier, F., et al. (2023b) ‘Mrwater: MadRat Based MAgPIE Water Input Data Library’. 10.5281/zenodo.5801680.

Schaphoff, S. et al. (2018). ‘LPJmL4 – a Dynamic Global Vegetation Model with Managed Land – Part 1: Model Description’. Geoscientific Model Development 11 (4)

Wu, W., et al. (2018) Global cropping intensity gaps: increasing food production without cropland expansion. Land Use Policy 76 (2018)

von Bloh, W. et al. (2018). Implementing the Nitrogen Cycle into the Dynamic Global Vegetation, Hydrology, and Crop Growth Model LPJmL (Version 5.0). Geoscientific Model Development 11 (7)

How to cite: Beier, F., Heinke, J., Bodirsky, B. L., Müller, C., Ostberg, S., Karstens, K., Abrahao, G., Popp, A., and Lotze-Campen, H.: Multiple cropping in global-scale Land-Use Models and the role of Irrigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18994, https://doi.org/10.5194/egusphere-egu24-18994, 2024.

We describe the methodological development and preliminary results of a new spatial modelling framework to support the evaluation and design of novel Food Value Chains (FVC). The sustainability of future FVCs will depend on how effectively these can be adapted to environmental (e.g., climate change) and socio-economic (e.g., resource access and dietary preferences) changes projected for coming decades. Our approach aims to account for the spatial and temporal complexity inherent to both biophysical (e.g., climate, genotypes and soils) and techno-economic (e.g., processing technologies and markets) components of FVCs to optimise supply- (e.g., production areas) and demand- (processing-plant locations) across landscapes. For that, we integrated georeferenced biophysical outputs of a process-based agricultural model (Agricultural Production Systems sIMulator, APSIM-NextGeneration) into a spatial techno-economic model (IIASA-BeWhere). We test the approach through a case-study to evaluate a novel (hypothetical) FVC to produce plant-based proteins from lucerne crops (Medicago sativa) across New Zealand’s agricultural landscapes. Results highlighted spatial protein production patterns driven by changes in crop canopy expansion and net carbon assimilation, with lower yields estimated in cooler and dryer environments, particularly when water supply was limited under rain-fed (non-irrigated) conditions with soils of low water holding capacity. Spatial variability in protein yields, production costs and emissions estimated by APSIM-NG running in the ATLAS framework were then used as inputs by BeWhere to optimise the location of production areas and protein-processing plants. We discuss potentials, limitations, and future development areas of this approach.

How to cite: Teixeira, E., Leduc, S., Tiwari, S., Kraxner, F., Guo, J., McNally, S., Yao, R., Yang, X., Johnstone, P., Sowersby, T., Edmonds, R., Maley, S., Sood, A., Bristow, J., and Moot, D.: Simulating future Food Value Chain components through the integration of biophysical and techno-economic spatial models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20927, https://doi.org/10.5194/egusphere-egu24-20927, 2024.

Achieving the second sustainable development goal, “Zero Hunger”, is challenging due to climate change, weather extremes and an unabated human population growth. The consequent increase in global food demand has put additional pressure on agricultural systems. Understanding spatial crop suitability alterations, yields and calories of the four major staple food crops around the globe is imperative for sustainable agricultural optimization, climate mitigation, and food security. This study uses three downscaled and bias-corrected shared socioeconomic scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from the latest state-of-art climate models in Coupled Model Intercomparison Project phase 6 (CMIP6) courtesy of Worldclim: an ecological crop requirement model (Eco Crop) and a machine-learning extreme gradient boosting model (XGBoost) to estimate future crop suitability and yields. Our results elucidate a northward spatial shift in climate suitability and shrinkage of optimal crop-growing regions as the unsuitable and marginal areas expand. Notably, more reduction of suitable regions is observed for all the crops under the highest emission and in far-future climate (2061-2100) scenarios as compared to the SSP1-2.6 and during the near-future period (2021-2060). Nevertheless, gain in suitable areas for soybeans and wheat has been observed at high latitudes, while the tropics are projected to experience a significant loss of arable land. The optimal zone for maize is projected to significantly reduce by approximately 75% in all emission scenarios. This translates to a maize yield loss of 17.3%, and 8.5% in near and far-future climate periods respectively under SSP5-8.5 scenario. Spatial consistency shows that most of the suitable and optimal zones for soybeans are currently not been used. This study sheds light on crop production optimization as farmers are advised to shift to more suitable climate regions for a given crop rather than agricultural extensification that triggers desertification. Due to the considerable loss of climate-suitable regions for rainfed agricultural systems, global efforts should be directed to irrigation systems to ensure global food security and peace.

Keywords: Eco crop, Climate suitability, CMIP6, Food security, Crop Yield, XGBoost

How to cite: Mumo, L., Franzke, C., and Lee, J.-Y.: Future changes of Climate Suitability of Global Rainfed Food Crops under different CMIP6 scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13942, https://doi.org/10.5194/egusphere-egu24-13942, 2024.

The projected warmer temperatures, together with the expected increase in seasonal dryness, frequency, and intensity of extreme climate events during sensitive phenological phases, may have strong effects on the regions’ suitability for grapevine cultivation determining a shift from currently suitable areas toward new ones. Furthermore, shortening phenological advancement is expected to affect the ripening period negatively, by affecting biochemical and physiological processes and thus impacting berry sugar-acid and flavonoid levels, colour, and aroma, especially for early ripening varieties. In this research, multiple climate, soil, topography, and land use data are analyzed and integrated into a multi-criteria evaluation (MCE) to classify suitable areas for grapevine according to FAO classification under actual and future climate conditions. In particular, through the adoption of machine learning techniques, some specific qualitative targets (BRIX, acidity, polyphenol content), functional to obtaining specific oenological objectives will be analyzed. The analysis is focused on the Cannonau terroir, in the region of Sardinia (Italy), and in particular the qualitative target data for land suitability model calibration and validation will be acquired from three wine cellars collecting production from single farmers located in three bioclimatic areas that can be considered as representatives of the whole Sardinia region (Nurra, Barbagia and Parteolla, located respectively in North-west, Center and South of Sardinia). A set of 8 bioclimatic, 5 pedological, and 3 topographic indicators with 1 land cover classification was selected and then divided into a range of values, according to the literature, each of which was associated with a suitability class (FAO). Bioclimatic indicators are obtained by the analysis of current and future climate scenarios from the regionalized climate models downscaled for the whole of Italy at 2.2 km spatial resolution. Considering main and secondary relevant and explanatory criteria with a hierarchical structure, after statistical autocorrelation analysis, different weights will be assigned, calculated, and associated with each factor using the analytical hierarchy (AHP) process and machine learning methods, depending on the importance of each factor in achieving specific production targets according to expert knowledge and literature. The performance of machine learning and statistical inference to define suitability as a function of environmental and bioclimatic characteristics (ANN, Random Forest, MaxEnt, Support Vector Machines), will be subsequently compared to GIS-based results to assess its applicability. The field measurements will be carried out in the pilot sites located in the north, center, and south of Sardinia and will be useful for obtaining pedological, phenological, and qualitative data for the calibration and validation of the model. This work aims to provide an assessment of the spatial variability of the environmental factors that drive terroir distribution, to preserve vineyard production and quality in a changing climate. The research is also a methodological contribution, with the integration of a machine learning approach to the multicriterial land suitability analysis techniques.

How to cite: Serra, E., Debolini, M., Marras, S., Mercenaro, L., Nieddu, G., Sirca, C., Trabucco, A., Deiana, P., and Spano, D.: Viticulture suitability for specific oenological objectives through machine learning integration in a multicriteria analysis: the case of Cannonau terroir in Sardinia (Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17998, https://doi.org/10.5194/egusphere-egu24-17998, 2024.

The European Commission has initiated the Green Deal aiming to make the European Union climate-neutral by 2050, with the Farm to Fork strategy being one of its components. Apart from making food systems fair, healthy and eco-friendly, the Farm to Fork strategy targets to reduce nutrient losses and fertilizer use. Previous research has criticized the strategy for its expected negative impacts on European economy, agriculture, and food supply. We add to this research by using an integrated modelling framework to assess the impacts of fertilizer and climate change scenarios on agricultural production and the environment in Austria. The integrated modelling framework consists of the crop rotation model CropRota, the biophysical process model EPIC, and the spatially explicit bottom-up economic land use optimization model BiomAT. Besides other bio-physical and economic datasets, we employ national nitrogen-balance calculations to differentiate between regional and crop specific fertilization intensities as well as mineral and organic fertilizers. We have developed two fertilizer scenarios: a f20 scenario, which considers a uniform 20% reduction of mineral N fertilizer on cropland and grassland, and a fcm scenario, which combines several fertilizer restrictions such as -20% of mineral N fertilizer, a maximum application of 175 kg N ha^-1 on cropland and grassland, and no mineral N fertilizer application on permanent grassland. In addition, we consider four climate change scenarios to support systematic analysis of potential effects of fertilizer reductions on land cover/use, fertilization intensities, potentially harmful nitrogen losses in air, water and soil sediments, and agricultural output. Our scenario results show a total reduction of N losses in air, water and soil sediment by 9% (f20) and 20% (fcm), yet imposed restrictions fall short of an intended 50% reduction. N loss reduction potentials are region, land cover/use and management specific. Magnitudes of N input reductions correspond well to potential N loss reductions to air. N losses to water and soil sediment seem to be determined by precipitation, temperature, and topographic factors. We conclude that agricultural measures need to be tailored to regional and topographic factors in order to effectively reduce nitrogen losses.

How to cite: Jost, E., Schönhart, M., Mitter, H., Zoboli, O., and Schmid, E.: Reducing nitrogen losses in agriculture: integrated modelling of fertilizer and climate change scenarios in Austria , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8088, https://doi.org/10.5194/egusphere-egu24-8088, 2024.

This scientific inquiry delves into the far-reaching implications of global warming and the continuous emission of anthropogenic greenhouse gases into the Earth's atmosphere. With a primary focus on the semi-arid regions of Morocco, the study broadens its perspective to conduct a comparative analysis of similar challenges faced by Spain, Egypt, Italy, Jordan, Turkey, and Iran. The paper aims to illuminate the intricate interplay between climate change and agriculture, underscoring the imperative for sustainable practices to alleviate the detrimental impacts on food security and economic stability. The methodology employed centers around the utilization of the DSSAT (Decision Support System for Agrotechnology Transfer) model, a reliable tool for simulating yield across different seasons. In this study, the performance of wheat varieties in the Mediterranean and MENA (Middle East and North Africa) regions was evaluated. Optimal yields were observed under treatments involving sprinkler or furrow irrigation and nitrogen application ranging from 60 to 120 kg/ha, resulting in an average yield trend of around 6 t/ha. The identified optimal seeding date was the 1^st of November, with conservation or adaptation practices demonstrating superior outcomes. This finding was further validated by MIROC5 climate change projections, estimating yields of up to 6.4 t/ha in Spain and a slight increase in Morocco and one of the sites in Jordan, alas a reduction of 20% in Italy and up to 88% in Iran at the end of the century. The study's significance lies in its evaluation of nutrient and water trends in the MENA and Mediterranean regions, offering farmers and policymakers valuable insights to guide a sustainable transition, both economically and ecologically.

How to cite: Tita, D., Devkota, K., Mahdi, K., and Devkota, M.: "Exploring pathways for the sustainable intensification of wheat production under current and future climate change scenarios in the Mediterranean region", EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3093, https://doi.org/10.5194/egusphere-egu24-3093, 2024.

Rising atmospheric CO₂ can enhance global crop yield directly through the CO₂ fertilization effect (physiological effects, ), but can also reduce it indirectly through CO₂-induced warming (radiative effects, ). The overall consequences of the two opposing CO₂ effects have constituted large uncertainties in projecting future crop yields. Here, we first employ a site-level CO₂ elevation experiment dataset to constrain the simulated  effect in yield projections of an ensemble of global crop models for four major cereal crops (wheat, maize, rice and soybean). Under well-watered and well-fertilized conditions, the constrained estimates show that elevated CO₂ will increase yield of major C3 crops (spring/winter wheat, rice and soybean) by 16.7 ± 2.7% 100 ppm^-1, 9.4 ± 2.7% 100 ppm^-1, 11.2 ± 2.7% 100 ppm^-1, and 12.9 ± 2.4% 100 ppm^-1, respectively, while no significant yield gain was found for maize (1.6 ± 1.7% 100 ppm^-1). Then, by combining CO₂induced warming, crop yield response to warming and the interactive term of the physiological effects and radiative effects, we assess the integrated effects of increasing atmospheric CO₂ on crop yield at global scale. The results show that the same level of increase in atmospheric CO₂ tends to induce larger  than the yield loss by  for both wheat and rice. But for soybean and maize,  largely offsets , resulting in statistically not significant integrated effects of CO₂ for soybean (4.2 ± 15.8%) and maize (-3.0 ± 4.6%).

How to cite: Sang, Y., Wang, X., Wang, C., and Müller, C.: CO2 fertilization effects can fully offset the yield loss due to CO2 induced warming for major C3 crops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6993, https://doi.org/10.5194/egusphere-egu24-6993, 2024.

Peruvian Andean rural farmers often have precarious livelihoods and already experience less predictable weather conditions than in recent decades. With the goal of investigating hydrological and agricultural resilience in a region with an uncertain climate future (with regard to both temperature and precipitation), we present here the results obtained from using the AquaCrop software to model both crop growth and the consequent harvest yields in the valleys of the Peruvian Andes, including the Rio Santa Valley in the Ancash region. The crop models are presented for 1970-2099 (the historical and the future during climate change), using RCP2.6 & RCP8.5 Regional Climate Models (RCMs) from CORDEX at a spatial resolution of 0.22 degrees. We chose the CORDEX RCM data that was dynamically downscaled from the CMIP5 GCMs instead of the CHELSA statistically-downscaled data, since the downscaling of the CORDEX RCM data produces more locally-heterogeneous climate averages, which are more consistent with the variable topography. The CORDEX RCM model data has subsequently been bias-corrected to monthly CHIRPS precipitation and monthly ECMWF ERA-Interim temperature extremes from 1981-2005 for locations in the Ancash region, including Yungay and Aija. For the various crops that we modelled (maize/corn, potatoes, dry beans, quinoa, wheat), we find significant interannual variability of the dry yields from crop harvest (without irrigation or fertilizers), particularly when the climate is transitioning to a warmer one for those crops that prefer warmer climates. Without the consideration of irrigation or fertilizers, the possibility of high yield interannual variability could make it difficult for the Peruvian Andean farmers to plan ahead, and maintaining a diversity of crops within the Rio Santa Valley and the wider Ancash region could be advantageous for these farmers.

How to cite: McGuire, P. C., Singarayer, J. S., Wade, A. J., Rodda, H. J. E., Branch, N. P., Joseph Mattam, D., Araujo-Ferreira, F., Capoen, E., Everhart, A. A., Florencio, C., Gonzalez, F., Herrera, A., Lane, K., Meddens, F. M., Santos Shupingahua, D., Timaná, M. E., and Walsh, D.: Crop modelling with AquaCrop during climate change in the Ancash region of the Peruvian Andes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9832, https://doi.org/10.5194/egusphere-egu24-9832, 2024.

Drought and heatwave events contribute to agricultural loss worldwide. The impact is further exacerbated with the occurrences of compound and cascading droughts and heatwaves. Here we present a study that evaluates the impact of compound and cascading droughts and heatwaves on Maize yield in Sinaloa Mexico, simulated using the WOFOST crop model. Drought and heatwave events were identified using the Standardized Precipitation Index (SPI-3) and threshold method, respectively. Results show that significant yield reductions are found during extreme drought events, emphasizing the vulnerability of maize farming to unfavorable drought conditions. While heatwaves alone did not show a significant impact on maize yields, the compound and cascading droughts and heatwaves amplify the loss of Maize yields up to 44% compared to normal conditions. This study highlights the need for adaptive strategies in agriculture to sustain food security during extreme events, especially in the context of a multi-hazard framework.

How to cite: Sutanto, S. J., Mora, S., Supit, I., and Wang, M.: Compound and cascading droughts and heatwaves decrease yields by more than half in Sinaloa, Mexico, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6266, https://doi.org/10.5194/egusphere-egu24-6266, 2024.

Soil management practices influence soil physical parameters and crop productivity. No-till farming, which is a key component of conservation agriculture, is considered a sustainable alternative to conventional agriculture. The extent to which soil conservation management practices can mitigate the impacts of extreme events (heavy precipitation events and drought) remains unknown, and is examined as part of the SoilX project. This study focuses on two different tillage management practices and their effects on soil hydraulic properties, soil structure, and crop yields under current and future climate conditions. An experimental study site that is a Long-Term Field Experiment (LTE) since 2006 located in Hollabrunn, Lower Austria, was used for soil sampling and crop modelling. The site is located in a Pannonian climate, with average annual (1991-2020) precipitation of 493 mm and a mean air temperature of 9.8 °C. The soil is classified as a silt loam calcareous Chernozem under the WRB or as Typic Vermudoll under the US Soil Taxonomy. The experimental layout comprised two soil tillage treatments (conventional tillage (CT) and no-tillage (NT), both with annual crops and winter cover crops) arranged in a randomized block design. The crop model APEX (Agricultural Policy/Environmental eXtender) model was set up for both treatments to assess the impacts of CT and NT on soil physical properties and their respective hydrological properties. Field soil samples were taken from both treatments (up to 50 cm depth) and analyzed for soil bulk density, soil organic matter (SOC), water stable aggregates (WSA), unsaturated infiltration rates (determined with TDI), water retention curves, and oxygen isotopes in soil pore water. These field measurements were used to parameterize the APEX model. Field operations between 2009 and 2023 also provided model inputs on crop cultivation cycles, tillage, fertilization, sowing, crop protection, and harvesting. The yield (dry matter Mg ha^-1) per plot was used for model calibration. From the soil samples obtained in 2023 differences between CT and NT were determined with respect to bulk density and soil water content, i.e. at 10 cm, the unsaturated infiltration rates were higher in CT. The future climate simulations (2050-2100) derived from regional climate models (RCMs) with different representation pathways (RCPs) were input in APEX to assess the impacts of climate change on the soil physical and hydraulic properties (SOC, infiltration rates, soil water storage) under CT and NT. The research results quantify differences in soil physical and hydraulic properties in a future climate, particularly focusing on the extreme events. The findings provide information on soil management strategies to potentially mitigate the adverse impacts of heavy precipitation events and droughts in agricultural cropping systems.

How to cite: Moura Lima, E., Böning, K., Ding, F., Stumpp, C., Holzkämper, A., and Mehdi-Schulz, B.: Assessing the impacts of future climate scenarios on soil management practices and their hydraulic proprieties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18816, https://doi.org/10.5194/egusphere-egu24-18816, 2024.

Faced with growing food security challenges influenced by global factors such as population growth, climate change, and soil erosion, the need for sustainable agricultural practices is particularly relevant in Africa. In Morocco, wheat is the most dominant crop, but its production is highly dependent on rainfall. In this research, we evaluate several crop growth models, including AquaCrop, among others, focusing on their ability to effectively improve crop production predictions and yield gap analysis in Morocco. This evaluation is essential to develop adaptive agricultural practices that can mitigate the adverse effects of climate change on crop yields. This study employs AquaCrop-OSPy (ACOSP), an open-source Python version of the AquaCrop model, to simulate various indicators of crop growth such as canopy cover (CC), actual evapotranspiration (ETcact), biomass, and grain yield (GY) for wheat under drip irrigation in the semi-arid Chichaoua region of Marrakech in Morocco. The model was first calibrated by using the field data collected over two wheat fields during the 2016/2017 cropping season. Key parameters affecting CC, ETcact, biomass, and GY were calibrated by comparing field measurements with the model outputs. Then, model validation was carried out on the same fields but during the 2017/2018 cropping season. The results demonstrated that ACOSP effectively simulates CC, ETcact, biomass, and GY across two growing seasons. The comparative analysis between observed and simulated parameters yielded the following average values: for CC, R²=95%, RMSE=8.5%, and MSE=1.1%; for ETcact, R²=76%, RMSE=0.61 mm/day, and MSE=0.40 mm/day; and biomass, R²=87%, RMSE=0.22 t/ha, and MSE=0.05 t/ha during the calibration season. GY recorded was 3.87 t/ha. In the validation season, the model achieved similar accuracy for CC R²=95%, RMSE=8.0%, MSE=1.0 %; and biomass R²=91%, RMSE=0.15 t/ha, MSE=0.05 t/ha; with a GY of 3.29 t/ha. These results confirm the model's reliability in simulating key growth parameters of wheat in a semi-arid environment. Two main aspects are addressed through this study: firstly, to provide valuable information for agricultural policy and decision-making in Morocco, and secondly, to enrich the international conversation on sustainable agricultural practices, particularly in arid and semi-arid regions. Leveraging the findings of efficient simulation of wheat growth and production using the ACOSP model, this research provides a solid basis for local, national, and international key actors in developing robust strategies to improve wheat production, thus enhancing the sustainability and resilience of Moroccan agriculture.

How to cite: Kaissi, O., Er-raki, S., Bouras, E., Belaqziz, S., and Chehbouni, A.: Assessment and comparison of crop growth models for estimating wheat production in a semi-arid region of Morocco, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13284, https://doi.org/10.5194/egusphere-egu24-13284, 2024.

The frequency and intensity of high temperature events will very likely increase in the future, which could have significant effects on agricultural production. A suitable tool to assess potential heat stress damages in crops is the climate-driven simulation of crop growth and development processes with computer models. While studies show that process-based models reproduce observed yield variabilities, the temperature sensitivities of underlying growth and development processes are often not in accordance with observational data, which can be a significant source of uncertainty especially in future projections. We intend to reduce these uncertainties by improving process responses to high temperatures in the dynamic global vegetation model LPJmL.

A common weakness of models, including LPJmL, is the use of air temperatures in crop related processes. Depending on climatic factors and water status, these can deviate strongly from canopy temperatures, which can have significant effects on the triggering of temperature-related process responses. As a first step, we thus implemented a combination of energy balance and empirical model in LPJmL that computes canopy temperatures based on equations of Penman and Monteith and empirical findings from Idso and Jackson. First preliminary results of future scenarios (SSP585) show that projected wheat yields are substantially higher or lower in some regions when using canopy temperatures compared to solely air temperature-driven LPJmL simulations. However, while the implemented approach assumes neutral atmospheric stability and thus requires little computing capacity, a comparison study showed that more complex methods that include stability correction factors better reproduce observed canopy temperatures. The difficulty with these complex canopy temperature computations is that the high computing costs can be a limitation for already computationally expensive global models. To solve this problem, we built a complex stand-alone model based on the Monin-Obukhov Similarity Theory for computing canopy temperatures with consideration of the stability conditions and from this derived two emulators that reproduce the results of the complex model with significantly less computing power. The two emulators describe upper and lower canopy temperature bounds under two extreme states of water stress as a function of air temperature, radiation, wind, vapor pressure deficit and leaf area index. For this, we chose parametric models with a third-order polynomial basis function that also include interaction terms of the different variables. To train the emulators, we used a global dataset that covers a broad range of combinations of different weather variables. These two emulators will be implemented in LPJmL to simulate canopy temperatures by first calculating upper and lower canopy temperature bounds and from this deriving final canopy temperatures through scaling with actual water stress. We will then compare the results to those of the approach that assumes neutral atmospheric conditions.

The computation of canopy temperatures is a first step towards better crop yield projections accounting for responses to high temperatures. The first preliminary results highlight the importance of improving the representation of canopy temperatures in global models to better estimate future agricultural yields and to identify potential risks to food security.

How to cite: Hemmen, M., von Bloh, W., Webber, H., Heinke, J., and Müller, C.: How to simulate canopy temperatures in a global, process-based model?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10424, https://doi.org/10.5194/egusphere-egu24-10424, 2024.

Most existed crop modelling studies are mainly cereal crops. Vegetables, the most economical and nutrient-dense crops, recieves insufficient attention, particularly on nutrient-uptake predictions. In open-field vegetable systems with shallower roots, shorter lifespan, and higher nutrient requirements, it is even more challenge to minize water pollution from fertilizers. To ensure both food and environment security, there is an urgent need of precise vegetable models to optimize productivity against fertilizer usage.

We adapted the WOrld FOod STudies (WOFOST) crop growth simulation model for chili pepper (Capsicum annuum L.)  and Chinese cabbage (Brassica rapa L.) to support better fertilizer management under various climate and soil conditions. We conducted field experiments with six various fertilizer strategies (etc., mixed synthetic and organic fertilizers, denitrification products, and slow-control-release fertilizers) in southwestern China from 2019 to 2021. In total about 20 parameters relevant to physiological development, dry matter accumulation, photosynthesis, and nutrient uptake were measured and used in model adaptation.

Our study shows that it is possible to model chili pepper’s growth without changing much from the WOFOST-generic model structure. We provide solutions by adapting user-defined developmental stages to mimic the growth from transplanting to fruiting and subsequently ripeness. As for WOFOST-Chinese cabbage, we further modify the phenological module to mimic the special vernalization habits of Chinese cabbage. Additionally, we design a new data re-analyzation method for accurate biomass partitioning predictions. Overall, both WOFOST-Chili and WOFOST-Chinese cabbage models show good model performance on biomass assimilation (rRMSE = 0.23/0.17 for chili/cabbage leaf dry weight; rRMSE = 0.06/0.17 for chili/cabbage storage organ dry weight) and nutrient uptake (rRMSE = 0.46/0.29 for chili/cabbage leaf N amount; rRMSE = 0.12/0.41 for chili/cabbage storage organ N amount). Besides, an improved leaf area index (LAI) simulation is found in WOFOST-Chinese cabbage (rRMSE = 0.11) than WOFOST-Chili (rRMSE = 0.76).

These findings improve our understanding of yield-nutrient interactions within crop models, provide insights on expanding application of original-designed-for-field crop models to different vegetable versions, also call for a refined dynamic nutrient simulation flow within soil module to evaluate mitigation effect of expanded fertilizer strategies under climate change.

How to cite: Tang, R., Supit, I., Hutjes, R., Zhang, F., Wang, X., Chen, X., Zhang, F., and Chen, X.: Comparison of developing WOFOST to model growth between typical fruit (chili pepper) and leafy (Chinese cabbage) vegetables, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14354, https://doi.org/10.5194/egusphere-egu24-14354, 2024.