TY - JOUR A1 - Berg-Mohnicke, Michael A1 - Nendel, Claas T1 - A case for object capabilities as the foundation of a distributed environmental model and simulation infrastructure JF - Environmental modelling & software with environment data news N2 - With the advent of increasingly powerful computational architectures, scientists use these possibilities to create simulations of ever-increasing size and complexity. Large-scale simulations of environmental systems require huge amounts of resources. Managing these in an operational way becomes increasingly complex and difficult to handle for individual scientists. State-of-the-art simulation infrastructures usually provide the necessary re-sources in a centralised setup, which often results in an all-or-nothing choice for the user. Here, we outline an alternative approach to handling this complexity, while rendering the use of high-performance hardware and large datasets still possible. It retains a number of desirable properties: (i) a decentralised structure, (ii) easy sharing of resources to promote collaboration and (iii) secure access to everything, including natural delegation of authority across levels and system boundaries. We show that the object capability paradigm will cover these issues, and present the first steps towards developing a simulation infrastructure based on these principles. KW - Cap'n proto KW - Scientific collaboration KW - Co -development KW - Communication KW - protocol KW - Object capability Y1 - 2022 U6 - https://doi.org/10.1016/j.envsoft.2022.105471 SN - 1364-8152 SN - 1873-6726 VL - 156 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Ghafarian, Fatemeh A1 - Wieland, Ralf A1 - Lüttschwager, Dietmar A1 - Nendel, Claas T1 - Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data JF - Environmental modelling & software with environment data news N2 - Forest microclimate can buffer biotic responses to summer heat waves, which are expected to become more extreme under climate warming. Prediction of forest microclimate is limited because meteorological observation standards seldom include situations inside forests. We use eXtreme Gradient Boosting - a Machine Learning technique - to predict the microclimate of forest sites in Brandenburg, Germany, using seasonal data comprising weather features. The analysis was amended by applying a SHapley Additive explanation to show the interaction effect of variables and individualised feature attributions. We evaluate model performance in comparison to artificial neural networks, random forest, support vector machine, and multi-linear regression. After implementing a feature selection, an ensemble approach was applied to combine individual models for each forest and improve robustness over a given single prediction model. The resulting model can be applied to translate climate change scenarios into temperatures inside forests to assess temperature-related ecosystem services provided by forests. KW - cooling effect KW - machine learning KW - ensemble method KW - ecosystem services Y1 - 2022 U6 - https://doi.org/10.1016/j.envsoft.2022.105466 SN - 1364-8152 SN - 1873-6726 VL - 156 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Kothari, Kritika A1 - Battisti, Rafael A1 - Boote, Kenneth J. A1 - Archontoulis, Sotirios A1 - Confalone, Adriana A1 - Constantin, Julie A1 - Cuadra, Santiago A1 - Debaeke, Philippe A1 - Faye, Babacar A1 - Grant, Brian A1 - Hoogenboom, Gerrit A1 - Jing, Qi A1 - van der Laan, Michael A1 - Macena da Silva, Fernando Antonio A1 - Marin, Fabio R. A1 - Nehbandani, Alireza A1 - Nendel, Claas A1 - Purcell, Larry C. A1 - Qian, Budong A1 - Ruane, Alex C. A1 - Schoving, Celine A1 - Silva, Evandro H. F. M. A1 - Smith, Ward A1 - Soltani, Afshin A1 - Srivastava, Amit A1 - Vieira, Nilson A. A1 - Slone, Stacey A1 - Salmeron, Montserrat T1 - Are soybean models ready for climate change food impact assessments? JF - European journal of agronomy : the official journal of the European Society for Agronomy N2 - An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models. KW - Agricultural Model Inter-comparison and Improvement Project (AgMIP); KW - Model ensemble KW - Model calibration KW - Temperature KW - Atmospheric CO2 KW - concentration KW - Legume model Y1 - 2022 U6 - https://doi.org/10.1016/j.eja.2022.126482 SN - 1161-0301 SN - 1873-7331 VL - 135 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Hannigan, Sara A1 - Nendel, Claas A1 - Krull, Marcos T1 - Effects of temperature on the movement and feeding behaviour of the large lupine beetle, Sitona gressorius JF - Journal of pest science N2 - Even though the effects of insect pests on global agricultural productivity are well recognised, little is known about movement and dispersal of many species, especially in the context of global warming. This work evaluates how temperature and light conditions affect different movement metrics and the feeding rate of the large lupine beetle, an agricultural pest responsible for widespread damage in leguminous crops. By using video recordings, the movement of 384 beetles was digitally analysed under six different temperatures and light conditions in the laboratory. Bayesian linear mixed-effect models were used to analyse the data. Furthermore, the effects of temperature on the daily diffusion coefficient of beetles were estimated by using hidden Markov models and random walk simulations. Results of this work show that temperature, light conditions, and beetles' weight were the main factors affecting the flight probability, displacement, time being active and the speed of beetles. Significant variations were also observed in all evaluated metrics. On average, beetles exposed to light conditions and higher temperatures had higher mean speed and flight probability. However, beetles tended to stay more active at higher temperatures and less active at intermediate temperatures, around 20 degrees C. Therefore, both the diffusion coefficient and displacement of beetles were lower at intermediate temperatures. These results show that the movement behaviour and feeding rates of beetles can present different relationships in the function of temperature. It also shows that using a single diffusion coefficient for insects in spatially explicit models may lead to over- or underestimation of pest spread. KW - Agricultural pests KW - Diffusion KW - Hidden Markov models KW - Movement ecology Y1 - 2022 U6 - https://doi.org/10.1007/s10340-022-01510-7 SN - 1612-4758 SN - 1612-4766 SP - 389 EP - 402 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Ghafarian, Fatemeh A1 - Wieland, Ralf A1 - Nendel, Claas T1 - Estimating the Evaporative Cooling Effect of Irrigation within and above Soybean Canopy JF - Water N2 - Vegetation with an adequate supply of water might contribute to cooling the land surface around it through the latent heat flux of transpiration. This study investigates the potential estimation of evaporative cooling at plot scale, using soybean as example. Some of the plants' physiological parameters were monitored and sampled at weekly intervals. A physics-based model was then applied to estimate the irrigation-induced cooling effect within and above the canopy during the middle and late season of the soybean growth period. We then examined the results of the temperature changes at a temporal resolution of ten minutes between every two irrigation rounds. During the middle and late season of growth, the cooling effects caused by evapotranspiration within and above the canopy were, on average, 4.4 K and 2.9 K, respectively. We used quality indicators such as R-squared (R-2) and mean absolute error (MAE) to evaluate the performance of the model simulation. The performance of the model in this study was better above the canopy (R-2 = 0.98, MAE = 0.3 K) than below (R-2 = 0.87, MAE = 0.9 K) due to the predefined thermodynamic condition used to estimate evaporative cooling. Moreover, the study revealed that canopy cooling contributes to mitigating heat stress conditions during the middle and late seasons of crop growth. KW - canopy cooling effects KW - shading cooling KW - canopy-air temperature KW - energy KW - balance KW - the Penman-Monteith equation Y1 - 2022 U6 - https://doi.org/10.3390/w14030319 SN - 2073-4441 VL - 14 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Pohanková, Eva A1 - Hlavinka, Petr A1 - Kersebaum, Kurt-Christian A1 - Rodríguez, Alfredo A1 - Balek, Jan A1 - Bednařík, Martin A1 - Dubrovský, Martin A1 - Gobin, Anne A1 - Hoogenboom, Gerrit A1 - Moriondo, Marco A1 - Nendel, Claas A1 - Olesen, Jørgen E. E. A1 - Rötter, Reimund Paul A1 - Ruiz-Ramos, Margarita A1 - Shelia, Vakhtang A1 - Stella, Tommaso A1 - Hoffmann, Munir Paul A1 - Takáč, Jozef A1 - Eitzinger, Josef A1 - Dibari, Camilla A1 - Ferrise, Roberto A1 - Bláhová, Monika A1 - Trnka, Miroslav T1 - Expected effects of climate change on the production and water use of crop rotation management reproduced by crop model ensemble for Czech Republic sites JF - European journal of agronomy N2 - Crop rotation, fertilization and residue management affect the water balance and crop production and can lead to different sensitivities to climate change. To assess the impacts of climate change on crop rotations (CRs), the crop model ensemble (APSIM,AQUACROP, CROPSYST, DAISY, DSSAT, HERMES, MONICA) was used. The yields and water balance of two CRs with the same set of crops (winter wheat, silage maize, spring barley and winter rape) in a continuous transient run from 1961 to 2080 were simulated. CR1 was without cover crops and without manure application. Straw after the harvest was exported from the fields. CR2 included cover crops, manure application and crop residue retention left on field. Simulations were performed using two soil types (Chernozem, Cambisol) within three sites in the Czech Republic, which represent temperature and precipitation gradients for crops in Central Europe. For the description of future climatic conditions, seven climate scenarios were used. Six of them had increasing CO & nbsp;concentrations according RCP 8.5, one had no CO2 increase in the future. The output of an ensemble expected higher productivity by 0.82 t/ha/year and 2.04 t/ha/year for yields and aboveground biomass in the future (2051-2080). However, if the direct effect of a CO2 increase is not considered, the average yields for lowlands will be lower. Compared to CR1, CR2 showed higher average yields of 1.26 t/ha/year for current climatic conditions and 1.41 t/ha/year for future climatic conditions. For the majority of climate change scenarios, the crop model ensemble agrees on the projected yield increase in C3 crops in the future for CR2 but not for CR1. Higher agreement for future yield increases was found for Chernozem, while for Cambisol, lower yields under dry climate scenarios are expected. For silage maize, changes in simulated yields depend on locality. If the same hybrid will be used in the future, then yield reductions should be expected within lower altitudes. The results indicate the potential for higher biomass production from cover crops, but CR2 is associated with almost 120 mm higher evapotranspiration compared to that of CR1 over a 5-year cycle for lowland stations in the future, which in the case of the rainfed agriculture could affect the long-term soil water balance. This could affect groundwater replenishment, especially for locations with fine textured soils, although the findings of this study highlight the potential for the soil water-holding capacity to buffer against the adverse weather conditions. KW - Yields KW - Evapotranspiration KW - Winter wheat KW - Silage maize KW - Spring barley KW - Winter oilseed rape Y1 - 2022 U6 - https://doi.org/10.1016/j.eja.2021.126446 SN - 1161-0301 SN - 1873-7331 VL - 134 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Jänicke, Clemens A1 - Goddard, Adam A1 - Stein, Susanne A1 - Steinmann, Horst-Henning A1 - Lakes, Tobia A1 - Nendel, Claas A1 - Müller, Daniel T1 - Field-level land-use data reveal heterogeneous crop sequences with distinct regional differences in Germany JF - European journal of agronomy N2 - Crop cultivation intensifies globally, which can jeopardize biodiversity and the resilience of cropping systems. We investigate changes in crop rotations as one intensification metric for half of the croplands in Germany with annual field-level land-use data from 2005 to 2018. We proxy crop rotations with crop sequences and compare how these sequences changed among three seven-year periods. The results reveal an overall high diversity of crop sequences in Germany. Half of the cropland has crop sequences with four or more crops within a seven-year period, while continuous cultivation of the same crop is present on only 2% of the cropland. Larger farms tend to have more diverse crop sequences and organic farms have lower shares of cereal crops. In three federal states, crop rotations became less structurally diverse over time, i.e. the number of crops and the number of changes between crops decreased. In one state, structural diversity increased and the proportion of monocropping decreased. The functional diversity of the crop sequences, which measures the share of winter and spring crops as well as the share of leaf and cereal crops per sequence, remained largely stable. Trends towards cereal-or leaf -crop dominated sequences varied between the states, and no clear overall dynamic could be observed. However, the share of winter crops per sequence decreased in all four federal states. Quantifying the dynamics of crop sequences at the field level is an important metric of land-use intensity and can reveal the patterns of land-use intensification. KW - crop production KW - crop rotation KW - cropping diversity KW - IACS KW - intensification KW - land-use intensity Y1 - 2022 U6 - https://doi.org/10.1016/j.eja.2022.126632 SN - 1161-0301 SN - 1873-7331 VL - 141 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Zappa, Luca A1 - Schlaffer, Stefan A1 - Brocca, Luca A1 - Vreugdenhil, Mariette A1 - Nendel, Claas A1 - Dorigo, Wouter T1 - How accurately can we retrieve irrigation timing and water amounts from (satellite) soil moisture? JF - International journal of applied earth observation and geoinformation N2 - While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects. As detailed knowledge about the timing and the amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved potential to fill this gap. However, the spatial resolution and revisit time of current satellite products represent a major limitation to accurately estimating irrigation. This work aims to systematically quantify their impact on the retrieved irrigation information, hence assessing the value of satellite soil moisture for estimating irrigation timing and water amounts. In a real-world experiment, we modeled soil moisture using actual irrigation and meteorological data, obtained from farmers and weather stations, respectively. Modeled soil moisture was compared against various remotely sensed products differing in terms of spatio-temporal resolution to test the hypothesis that high-resolution observations can disclose the irrigation signal from individual fields while coarse-scale satellite products cannot. Then, in a synthetic experiment, we systematically investigated the effect of soil moisture spatial and temporal resolution on the accuracy of irrigation estimates. The analysis was further elaborated by considering different irrigation scenarios and by adding realistic amounts of random errors in the soil moisture time series. We show that coarse-scale remotely sensed soil moisture products achieve higher correlations with rainfed simulations, while high-resolution satellite observations agree significantly better with irrigated simulations, suggesting that high-resolution satellite soil moisture can inform on field-scale (similar to 40 ha) irrigation. A thorough analysis of the synthetic dataset showed that satisfactory results, both in terms of detection (F-score > 0.8) and quantification (Pearson's correlation > 0.8), are found for noise-free soil moisture observations either with a temporal sampling up to 3 days or if at least one-third of the pixel covers the irrigated field(s). However, irrigation water amounts are systematically underestimated for temporal samplings of more than one day, and decrease proportionally to the spatial resolution, i.e., coarsening the pixel size leads to larger irrigation underestimations. Although lower spatial and temporal resolutions decrease the detection and quantification accuracies (e.g., R between 0.6 and 1 depending on the irrigation rate and spatio-temporal resolution), random errors in the soil moisture time series have a stronger negative impact (Pearson R always smaller than 0.85). As expected, better performances are found for higher irrigation rates, i.e. when more water is supplied during an irrigation event. Despite the potentially large underestimations, our results suggest that high-resolution satellite soil moisture has the potential to track and quantify irrigation, especially over regions where large volumes of irrigation water are applied to the fields, and given that low errors affect the soil moisture observations. KW - remote sensing KW - soil moisture KW - irrigation KW - detection KW - quantification KW - sentinel-1 Y1 - 2022 U6 - https://doi.org/10.1016/j.jag.2022.102979 SN - 1569-8432 SN - 1872-826X VL - 113 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Wang, Enli A1 - He, Di A1 - Wang, Jing A1 - Lilley, Julianne M. A1 - Christy, Brendan A1 - Hoffmann, Munir P. A1 - O'Leary, Garry A1 - Hatfield, Jerry L. A1 - Ledda, Luigi A1 - Deligios, Paola A. A1 - Grant, Brian A1 - Jing, Qi A1 - Nendel, Claas A1 - Kage, Henning A1 - Qian, Budong A1 - Rezaei, Ehsan Eyshi A1 - Smith, Ward A1 - Weymann, Wiebke A1 - Ewert, Frank T1 - How reliable are current crop models for simulating growth and seed yield of canola across global sites and under future climate change? JF - Climatic change N2 - To better understand how climate change might influence global canola production, scientists from six countries have completed the first inter-comparison of eight crop models for simulating growth and seed yield of canola, based on experimental data from six sites across five countries. A sensitivity analysis was conducted with a combination of five levels of atmospheric CO2 concentrations, seven temperature changes, five precipitation changes, together with five nitrogen application rates. Our results were in several aspects different from those of previous model inter-comparison studies for wheat, maize, rice, and potato crops. A partial model calibration only on phenology led to very poor simulation of aboveground biomass and seed yield of canola, even from the ensemble median or mean. A full calibration with additional data of leaf area index, biomass, and yield from one treatment at each site reduced simulation error of seed yield from 43.8 to 18.0%, but the uncertainty in simulation results remained large. Such calibration (with data from one treatment) was not able to constrain model parameters to reduce simulation uncertainty across the wide range of environments. Using a multi-model ensemble mean or median reduced the uncertainty of yield simulations, but the simulation error remained much larger than observation errors, indicating no guarantee that the ensemble mean/median would predict the correct responses. Using multi-model ensemble median, canola yield was projected to decline with rising temperature (2.5-5.7% per degrees C), but to increase with increasing CO2 concentration (4.6-8.3% per 100-ppm), rainfall (2.1-6.1% per 10% increase), and nitrogen rates (1.3-6.0% per 10% increase) depending on locations. Due to the large uncertainty, these results need to be treated with caution. We further discuss the need to collect new data to improve modelling of several key physiological processes of canola for increased confidence in future climate impact assessments. KW - AgMIP KW - Brassica napus L. KW - Model calibration KW - Model improvement; KW - Multimodel ensemble KW - Sensitivity analysis Y1 - 2022 U6 - https://doi.org/10.1007/s10584-022-03375-2 SN - 0165-0009 SN - 1573-1480 VL - 172 IS - 1-2 PB - Springer Nature CY - Dordrecht ER - TY - JOUR A1 - Kamali, Bahareh A1 - Stella, Tommaso A1 - Berg-Mohnicke, Michael A1 - Pickert, Jürgen A1 - Groh, Jannis A1 - Nendel, Claas T1 - Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics JF - European journal of agronomy N2 - The dynamics of grassland ecosystems are highly complex due to multifaceted interactions among their soil, water, and vegetation components. Precise simulations of grassland productivity therefore rely on accurately estimating a variety of parameters that characterize different processes of these systems. This study applied three calibration schemes - a Single-Objective (SO-SUFI2), a Multi-Objective Pareto (MO-Pareto), and, a novel Uncertainty-Based Multi-Objective (MO-SUFI2) - to estimate the parameters of MONICA (Model for Nitrogen and Carbon Simulation) agro-ecosystem model in grassland ecosystems across Germany. The MO-Pareto model is based on a traditional Pareto optimality concept, while the MO-SUFI2 optimizes multiple target variables considering their level of prediction uncertainty. We used measurements of leaf area index, aboveground biomass, and soil moisture from experimental data at five sites with different intensities of cutting regimes (from two to five cutting events per season) to evaluate model performance. Both MO-Pareto and MO-SUFI2 outperformed SO-SUFI2 during calibration and validation. The comparison of the two MO approaches shows that they do not necessarily conflict with each other, but MO-SUFI2 provides complementary information for better estimations of model parameter uncertainty. We used the obtained parameter ranges to simulate grassland productivity across Germany under different cutting regimes and quantified the uncertainty associated with estimated productivity across regions. The results showed higher uncertainty in intensively managed grasslands compared to extensively managed grasslands, partially due to a lack of high-resolution input information concerning cutting dates. Furthermore, the additional information on the quantified uncertainty provided by our proposed MO-SUFI2 method adds deeper insights on confidence levels of estimated productivity. Benefiting from additional management data collected at high resolution and ground measurements on the composition of grassland species mixtures appear to be promising solutions to reduce uncertainty and increase model reliability. KW - intensively managed grasslands KW - extensively managed grasslands KW - grassland productivity KW - pareto optimality Y1 - 2022 U6 - https://doi.org/10.1016/j.eja.2022.126464 SN - 1161-0301 SN - 1873-7331 VL - 134 PB - Elsevier CY - Amsterdam ER -