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Nondestructive proximal sensors can be an efficient source of information of N status in crops for localized and rapid adjustment of fertilization applications. The aim of this study was to compare two transmittance/reflectance-based sensors (SPAD, ASD) and a florescence-based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level. Measurements of leaves and canopies from six fertilization field trials in 2019 and 2020 were analyzed to establish relationships between sensor information and laboratory-determined N content in crops. Analyses included linear regression for single sensor variables and machine learning for multivariate approaches, to assess the relative accuracy of the proximal sensors to measure N. The ASD is time-intensive and requires post hoc analyses of the spectra. However, the spectral outputs of this device were clearly correlated with the N status of leaves and canopies. At the leaf level, SPAD showed higher accuracy than any of the single Multiplex variables to predict plant N. Multiplex performance could be improved by combining three of its variables. At the canopy level, interpolated SPAD values and the best-performing Multiplex variables showed similar accuracy. It could be concluded that the relationship sensor-N status is species specific. Despite the high standard deviation recorded in some raw Multiplex variable, the derived indices showed a comparable low standard deviation. At both, leaf and canopy levels an integrated sensor solution would combine the multidimensionality of Multiplex and ASD, and the accuracy and practicality of SPAD.

Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation ( DFG , Grant Agreement SFB 1253/1 2017 ), the Academy of Finland through projects AICropPro ( 316172 ) and DivCSA ( 316215 ) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' ( BOMA 03037514 ) of the Federal Ministry of Education and Research ( BMBF ), Germany, the Deutsche Forschungsgemeinschaft ( DFG , German Research Foundation ) under Germany's Excellence Strategy - EXC 2070–390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B ) funded by the Federal Ministry of Education and Research ( BMBF , Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research ( BMBF ) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy – BonaRes”, project “BonaRes (Module B): BonaRes Centre for Soil Research , subproject B” (grant 031B0511B ), the National Key Research and Development Program of China ( 2017YFD0300205 ), the National Science Foundation for Distinguished Young Scholars ( 31725020 ), the Priority Academic Program Development of Jiangsu Higher Education Institutions ( PAPD ), the 111 Project ( B16026 ), and China Scholarship Council , the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ‘Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133 ) and USDA / NIFA HATCH grant N. MCL02368 , the National Key Research and Development Program of China ( 2016YFD0300105 ), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries , the Academy of Finland through project AI-CropPro ( 315896 ), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies ( D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797 ). The order in which the donors are listed is arbitrary. Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AICropPro (316172) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' (BOMA 03037514) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2070?390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure ?Soil as a Sustainable Resource for the Bioeconomy ? BonaRes?, project ?BonaRes (Module B): BonaRes Centre for Soil Research, subproject B? (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project (B16026), and China Scholarship Council, the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ?Agricultural Landscapes under Global Climate Change ? Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, the National Key Research and Development Program of China (2016YFD0300105), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, the Academy of Finland through project AI-CropPro (315896), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797). The order in which the donors are listed is arbitrary. Publisher Copyright: © 2021 Elsevier Ltd Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.

Plant phenology, which describes the timing of plant development, is a major aspect of plant response to environment and for crops, a major determinant of yield. Since climate change is projected to alter crop phenology worldwide, there is a large effort to predict phenology as a function of environment. Many studies have focused on comparing model equations for describing how phenology responds to weather but the effect of crop model calibration, also expected to be important, has received much less attention. The objective here was to obtain a rigorous evaluation of prediction capability of wheat crop phenology models, and to analyze the role of calibration. The 27 participants in this multi-model study were provided experimental data for calibration and asked to submit predictions for sites and years not represented in those data. Participants were instructed to use and document their 99usual99 calibration approach. Overall, the models provided quite good predictions of phenology (median of mean absolute error of 6.1 days) and did much better than simply using the average of observed values as predictor. Calibration was found to compensate to some extent for differences between models, specifically for differences in simulated time to emergence and differences in the choice of input variables. Conversely, different calibration approaches led to major differences in prediction error between models with the same structure. Given the large diversity of calibration approaches and the importance of calibration, there is a clear need for guidelines and tools to aid with calibration. Arguably the most important and difficult choice for calibration is the choice of parameters to estimate. Several recommendations for calibration practices are proposed. Model applications, including model studies of climate change impact, should focus more on the data used for calibration and on the calibration methods employed.

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Model predictions depend not only on model structure but also on the parameter values. This study is thus an evaluation of modeling groups, which choose the structure and fix or estimate the parameters, rather than an evaluation just of model structures. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. For a given modeling group, MAE for the evaluation environments was significantly correlated with MAE for the calibration environments, which suggests that it would be of interest to test ensemble predictors that weight individual modeling groups based on performance for the calibration data. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. Finally, there was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

Climate change impact on crop production depends on the cultivated crop and its position within crop rotations and on site conditions, e.g. soils and hydrology, buffering adverse weather situations. We present a regional study across the federal state of Brandenburg/Germany based on gridded climate data and a digital soil map using the HERMES-to-Go model. The aim was to investigate defined crop rotations and common agricultural practices under current and future climate conditions regarding productivity and environmental effects. Two contrasting GCMs (HAD and MPI) were used to generate climate input for modelling for the RCPs 2.6 and 8.5.5 different types of crop production were simulated by defining crop rotations over 4-5 years for soil quality rating groups. While one rotation is comprised by the most common crops, another rotation modifies the first one by introducing a legume followed by a more demanding crop. The third rotation intends to produce higher value crops, e.g. potatoes than the first one, while the fourth rotation has its focus on fodder grass and cereal production. Building on this the fifth rotation replaces the fodder grass by alfalfa. All rotations are simulated in shifted phases to ensure that each crop is simulated for each year.Sowing, harvest and nitrogen fertilization were derived by algorithms based on soil and climate information to allow self-adaptation to changing climate conditions. The crop rotations are simulated under rainfed and irrigated conditions and with and without the implementation of cover crops to prevent winter fallow.We used the digital soil map 1:300.000 for Brandenburg with 99 soil map units. Within the soil map unit, up to three dominant soil types were considered to achieve at least 65% coverage. 276 soil types are defined by their soil profiles including soil organic matter content and texture down to 2 meters. Groundwater levels are estimated using the depth of reduction horizons as constant values over the year, to consider capillary rise depending on soil texture and distance between the root zone and the groundwater table.In total each climate scenario contains about 148.000 simulations of 30 years. Beside crop yields we analyse the outputs for trends in soil organic matter, groundwater recharge, nitrogen leaching and the effect on water and nitrogen management using algorithms for automatic management.Results indicate that spring crops were more negatively affected by climate change than winter crops especially on soils with low water holding capacity. However, few areas with more loamy soils and potential contribution of capillary rise from a shallow groundwater even benefited from climate change. Irrigation in most cases improved crop yield especially for spring crops. However, further analysis is required to assess if irrigation gains an economic benefit for all crop rotations. Nitrogen leaching can be reduced by implementing winter cover crops. Soil organic matter is assessed to decline for most sites and rotations. Only the rotations with multiyear grass or alfalfa can keep the level, but not on all sites.

Process-oriented agro-ecosystem models are increasingly applied to assess crop management options or impacts of climate change on agricultural production, food security and ecosystem services. Thereby, the aggregation of initial soil and climate information is a widely used approach for performing simulations at larger scales such as regions, nations or even globally. In this context, the ability of models to respond to different site conditions is essential for high quality impact assessment through the use of modelling tools. As part of a model inter-comparison the present study investigated models' yield response on variable site conditions using data sets from two well-documented fields, one located in Germany and one in Italy. The fields were sampled at 60 and 100 grid points, respectively, and soil and crop variables were recorded at varying intensity for the entire simulation period covering three growing seasons. The data was provided successively to the participating modelling groups in three calibration steps (a, b, and c) and the first growing season was considered for calibration. Model validation was based on these steps and each growing season as well as on the entire simulation period considering the soil state variables mineral nitrogen and water content (N, WC) as well as crop yield, biomass, and leaf area index (LAI). The WC was best depicted by the models, resulting in high correlation coefficients (r) up to 0.81 between simulated and observed values. The root mean square error (RMSE) of simulated N ranged from 20 kg ha(-1) to 1072 kg ha(-1) regarding all steps and growing seasons. The annual within-field variability of yields was better simulated by the models when observed subsoil information was provided. However, the RMSE ranged from 0.5 t ha(-1) to 3.5 t ha(-1) at the German field, and from 0.6 t ha(-1) to 5.9 t ha(-1) at the Italian field, respectively. It was found that intensified calibration did not necessarily lead to improved model output. Furthermore, single models showed specific inconsistencies in their algorithms when, for example, underestimated WC was associated with overestimated yields. In total, the sensitivity of models to spatially variable site conditions differed considerably. The importance of quality-assured soil and yield information for model improvement was highlighted.

The benefits of process-oriented modelling for management recommendations at the field scale are constrained by high spatial variability of soil properties and lack of dense information on soil types, variability and patterns. Geo-electrical mapping provides dense information about the soil, but sensor output is influenced by several factors. The agro-ecosystem model HERMES simulates nitrogen and water flow in the soil–plant–atmosphere system and was applied to 60 soil sampling points from a well-documented field with a wide range of soil texture in North Rhine-Westphalia, Germany. Validation of HERMES resulted in satisfactory root mean square errors for yield (0.62 t ha−1), water (36.08 mm) and nitrogen in the soil (74.93 kg ha−1) over the whole simulation period of 3 years. For the same field, values of electrical conductivity (ECa) ranged from 20 to 90 mS m−1. Clay and sand contents of three soil layers were highly correlated with the measured ECa. Derived regression models showed R2 values between 0.69 and 0.85, and cross-validation of the statistical equations resulted in a very good reflection of observed soil texture for two of the three soil layers. The subsequent calculation of soil texture at the mapping points of ECa produced an improved resolution of this key attribute to initialize model simulation. The temporal and spatial patterns derived from this work linking the ECa sensor to a process model allow the designation of sub-areas within the field according to the soil nitrogen and water supply. However, results could be improved by considering more than one up-scaled soil property for process-modelling.

Drained and cultivated fen soils are characterised by continuous alteration of soil structure, which considerably complicates the precise simulation of soil water dynamics. Recently estimated van Genuchten parameters considering the gradient of moorsh-forming process were tested by hydrological modelling using HYDRUS-1D. Simulation was conducted for altogether four fen soil profiles of two peatland test sites varying in profile structure, land-use history and climate. Two parameter sets were applied to characterise the hydraulic properties of the fen soil profiles. The first one consisted of so far available or locally measured parameters, the second one of the new parameters, respectively. The quality of model output was assessed by comparing simulated and observed soil water contents or pore water pressure heads in consideration of the applied parameter sets. Model performance varied considerably for the fen soil profile under grassland use, where the mean normalised root mean square error (RMSEN) was 16.0% for the original parameters and 52.9% for the optimised ones. Contrasting results appeared due to the complex stratigraphy at the corresponding plot. For the rewetted plot formerly used as grassland, both parameter sets achieved comparable simulation results for all observation depths (RMSEN = 17.4%, 18.4% and 23.6%). When the locally measured hydraulic parameters were applied to the unused fen soil profiles solely influenced by drainage RMSEN decreased in average from 38.5% to 27.4% and 30.5% to 25.7%, respectively. However, for specific observation depths model's performance improved by using the optimised parameters. The problem of wettability of moorshified fen soil horizons was identified for one soil profile. Additionally, the new parameters created a more robust reaction in simulation when the residual water content was adjusted.

Soil hydraulic properties of drained and cultivated fen soils vary considerably due to structural and physical alteration in the long-term. Although hydrological modelling is a key method to determine soil water conditions for a large number of aims, for instance the utilization of fen soils, general estimators of water retention that reflect the progressive nature of fen soil alteration are rare. Based on 405 horizons from 30 fen soil profiles at 12 different peatland sites in Germany soil hydraulic properties were estimated. Water retention and bulk density measurements were conducted fivefold per each horizon mapped after the German Soil Taxonomy. Considering the varying phases of moorsh-forming process, bulk densities and water retention data were clustered according to horizon category by applying the k-means method. This approach reduced the range of water contents measured at considered pressure heads (pF) and recommended their subdivision into three groups of graduated bulk density (high, moderate, low) per each horizon category. This subdivision resulted in 18 separated data sets for parameterisation of the selected van Genuchten model using the RETC software. Estimated water content at saturation ranged between 0.68 and 0.90 cm3 cm− 3 and 0.10 and 0.35 cm3 cm− 3 at a pF-value of 4.2. Shape parameter α varied stronger than n, especially for the strongly altered fen soil horizons. The continuing alteration of the moorshified, earthified, aggregated, and shrunken fen soil horizons classified by increasing bulk density levels was reflected by the estimated van Genuchten parameters. For horizons inventoried as unchanged peat substrates, less variability of bulk densities in combination with deviating van Genuchten parameters indicated the influence of additional factors on soil hydraulic properties.

A four-year (1999-2002) multivariable data set for one specific agricultural used field located in North Rhine-Westphalia is documented in detailed. The data set focusses on the small-scale heterogeneity of soil properties varying in the spatial and temporal dimension. Initial soil sampling was conducted at altogether 80 sampling points arranged in a regular and a nested grid along the 20 ha large field. Information about the soil inventory (soil texture, soil organic carbon) exists for three subsequent soil layers to a total depth of 90 cm and for every sampling point. Subsequently, the same points and layers were examined for the soil variables soil moisture and soil nitrogen biannually. Additional information about crop rotation, tillage, site-specific fertilization, yield performance and weather complete the data set was that used for model inter-comparison within the crop modelling part (CropM) of the international FACCE JPI MACSUR2 project.

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

This paper describes the impact of drainage and grassland use on selected soil parameters (e.g. organic carbon content, bulk density) of typical fen soils in Northeast Germany and, hence, their influence on grassland productivity. A broad investigation of site conditions has been realized at 23 grassland fields concerning soil properties, vegetation attributes and groundwater dynamics. Collectively selected fields represent characteristic types of grassland management and stages of moorsh-forming process. Concerning the entire sample, site conditions of agriculturally used peatlands vary strongly. Based on that complex data-set, the importance of defined potential impact factors on grassland production is tested and expressed in a linear regression model. Annual yield of the 2011 growing season, as a quantitative parameter of grassland productivity, does not indicate any correlation to site-specific conditions or cultivation strategy. In contrast, energy content as a qualitative measure varies strongly in relation to defined environmental parameters. Model performance is indicated with an adjusted R-square of 0.89 while the main impact factors are annual use frequency, mean summer water table (MSWT), maximum groundwater drawdown (GWmax), and organic carbon content (Corg).