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Accurate evaluation of crop performance and yield prediction at a sub-field scale is essential for achieving high yields while minimizing environmental impacts. Two important approaches for improving agronomic management and predicting future crop yields are the spatial stability of historic crop yields and in-season remote sensing imagery. However, the relative accuracies of these approaches have not been well characterized. In this study, we aim to first, assess the accuracies of yield stability and in-season remote sensing for predicting yield patterns at a sub-field resolution across multiple fields, second, investigate the optimal satellite image date for yield prediction, and third, relate bi-weekly changes in GCVI through the season to yield levels. We hypothesize that historical yield stability zones provide high accuracies in identifying yield patterns compared to within-season remote sensing images. To conduct this evaluation, we utilized biweekly Planet images with visible and near-infrared bands from June through September (2018–2020), along with observed historical yield maps from 115 maize fields located in Indiana, Iowa, Michigan, and Minnesota, USA. We compared the yield stability zones (YSZ) with the in-season remote sensing data, specifically focusing on the green chlorophyll vegetative index (GCVI). Our analysis revealed that yield stability maps provided more accurate estimates of yield within both high stable (HS) and low stable (LS) yield zones within fields compared to any single-image in-season remote sensing model. For the in-season remote sensing predictions, we used linear models for a single image date, as well as multi-linear and random forest models incorporating multiple image dates. Results indicated that the optimal image date for yield prediction varied between and within fields, highlighting the instability of this approach. However, the multi-image models, incorporating multiple image dates, showed improved prediction accuracy, achieving R2 values of 0.66 and 0.86 by September 1st for the multi-linear and random forest models, respectively. Our analysis revealed that most low or high GCVI values of a pixel were consistent across the season (77%), with the greatest instability observed at the beginning and end of the growing season. Interestingly, the historical yield stability zones provided better predictions of yield compared to the bi-weekly dynamics of GCVI. The historically high-yielding areas started with low GCVI early in the season but caught up, while the low-yielding areas with high initial GCVI faltered. In conclusion, the historical yield stability zones in the US Midwest demonstrated robust predictive capacity for in-field heterogeneity in stable zones. Multi-image models showed promise for assessing unstable zones during the season, but it is crucial to link these two approaches to fully capture both stable and unstable zones of crop yield. This study provides opportunities to achieve better precision management and yield prediction by integrating historical crop yields and remote sensing techniques. [ABSTRACT FROM AUTHOR]

Attaining high crop yields and increasing carbon storage in agricultural soils, while avoiding negative environmental impacts on water quality, soil erosion, and biodiversity, requires accurate and precise management of crop inputs and management practices. The long-term analysis of spatial and temporal patterns of crop yields provides insights on how yields vary in a field, with parts of field constantly producing either high yields or low yields and other parts that fluctuate from one year to the next. The concept of yield stability has shown to be informative on how plants translate the effects of environmental conditions (e.g., soil, climate, topography) across the field and over the years in the final yield, and as a valuable layer in developing prescription maps of variable fertilizer rate inputs. Using known relationships between soil health and crop yields, we hypothesize that areas with measured constantly low yield will return low carbon to the soil affecting its heath. On this premises, yield stability zones (YSZ) provide an effective and practical integrative measure of the small-scale variability of soil health on a field relative basis. We tested this hypothesis by measuring various metrics of soil health from commercial farmers' fields in the north central Midwest of the USA in samples replicated across YSZ, using a soil test suite commonly used by producers and stakeholders active in agricultural carbon credits markets. We found that the use of YSZ allowed us to successfully partition field-relative soil organic carbon (SOC) and soil health metrics into statistically distinct regions. Low and stable (LS) yield zones were statistically lower in normalized SOC when compared to high and stable (HS) and unstable (US) yield zones. The drivers of the yield differences within a field are a series of factors ranging from climate, topography and soil. LS zones occur in areas of compacted soil layers or shallow soils (edge of the field) on steeper slopes. The US zones occurring with high water flow accumulation, were more dependent on topography and rainfall. The differences in the components of the overall soil health score (SHS) between these YSZ increased with sample depth suggesting a deeper topsoil in the US and HS zones, driven by the accumulation of water, nutrients, and carbon downslope. Comparison of the field management provided initial evidence that zero tillage reduces the magnitude of the variance in SOC and soil health metrics between the YSZ. [ABSTRACT FROM AUTHOR]

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha−1 for maize, 0.39 Mg ha−1 for cotton, 0.34 Mg ha−1 for soybean, and 0.59 Mg ha−1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps. [ABSTRACT FROM AUTHOR]

Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (no-tillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10–22% for maize and 5–13% for wheat) rather than in early season (5–17% for maize and 2–18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4–2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses. [ABSTRACT FROM AUTHOR]

The perfect knowledge of yield before harvest has been a wish puzzling human being since the beginning of agriculture because seasonal forecast of crop yield plays a critical role in decision making for different stakeholders--from farmers to policy makers to governments for food security, to commodities traders. Different methods have been used to forecast yield with different levels of granularity, accuracy and timing. This chapter presents a critical review of the current seasonal crop yield forecasting methods found in the scientific literature. Extensive research has been conducted on crop yield forecast, particularly for wheat, maize, rice, barley, and soybean. Yield forecast are mainly based on field surveys, statistical regressions between historical yield and in-season variables (agrometeorological, or remotely sensed data), crop simulation models, or on integration between statistical modeling with dynamic process-based crop simulation models. A low number of studies rely on field surveys as a means to forecast yield, but they remain the main methods of yield forecast and estimation in several countries (i.e., USA). This chapter aims to report results found in peer-review journals for different crops, methods, geographies, and accuracies, and to end with a critical perspective on the advantage and disadvantage of the different methods currently employed by researchers and stakeholders. [ABSTRACT FROM AUTHOR]

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1% points, representing a relative change of −8.6%. [ABSTRACT FROM AUTHOR]

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. One way of estimating the projected impact of climate change on crops is to use crop models. There is a large variability in results of different crop models, but it has been observed that the mean or median of a multimodel ensemble (MME) often gives good agreement with observed data. We used empirical data from several MME studies, plus theoretical arguments, to better understand why and when the MME mean will be a good predictor. This should help modelers decide how to create and use MMEs for climate impact assessment. [ABSTRACT FROM AUTHOR]

Abstract: Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield‐scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

Syncronizing plant available water with soil nitrogen (N) remains a critical aspect of agronomic management to enhance crop yield, grain quality, farmers' profit, and environmental sustainability. Their interaction is essential expecially in landscapes characterized by a highly spatial and temporal range of pedoclimatic conditions. To support farmers in making more informed decisions, validate dynamic process based crop simulation models have been successfully used to optimize N fertlization. In this study, we aimed to develop a tactical N fertilizer management strategy to increase profitability, improve grain quality and reduce N losses. The SALUS model was tested against measured durum wheat grain yield and grain quality data collected across independent farmers' fields in Tuscany (Italy). The model was then used to optimize N fertilization under different potential plant extractable soil water (PESW) conditions at the second topdressing N fertilization timing. The model was tested against measurements of grain yield and protein concentration at harvest, as well as phenological stage, biomass, and plant N content during the growing season. Simulations were carried out for 30 years of available weather, using different N rates. The simulations allowed the identification of optimal N rates for each PESW condition and soil type concerning economic and environmental sustainability. Results showed higher yield and higher leaching for the silty clay soil (Quercia; QUE) than for the loamy soil (Arbia; ARB). No major differences were predicted for protein content across soils. Profitability and emissions increased as N rate increased. The N fertilization strategy locally adopted by farmers was also analyzed across different PESW conditions at 2nd topdressing fertilization in comparison to other adopted N management strategies (timing of application and fertilization rates). The model showed that the conventional fertilization strategy does not maximize socio-ecological benefits, but was also the lowest among all the strategies tested. The maximum economic benefit for farmers was reached by applying 90 kg N at 1st and 60 kg N at 2nd topdressing fertilization, both in dry (due to sufficient soil water storage in the soil) and wet years. These results provide valuable insights for developing strategies that balance sustainability, resilience, quality, and profitability in wheat crop production in central Italy. • Use of SALUS model to optimize nitrogen (N) fertilization. • Simulation of different potential plant extractable soil water (PESW) conditions. • Simulations were carried out for 30 years, using different N rates. • Identification of N fertilization strategy that maximizes economic returns. [ABSTRACT FROM AUTHOR]

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha) of the distribution. [ABSTRACT FROM AUTHOR]

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield ( GY) and grain protein concentration ( GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. [ABSTRACT FROM AUTHOR]

In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest. • A novel approach was developed to better predict in-season subfield corn yields. • Simulated plant water deficit was added in the model based on vegetation index. • Subfield predictions were conducted in 352 fields across US corn belt states with different environmental conditions. • In-season subfield maize yield prediction improved when crop water deficit was added remote sensing imagery-based model. [ABSTRACT FROM AUTHOR]

An objective for applying a Crop Simulation Model (CSM) in precision agriculture is to explain the spatial variability of crop performance and to help guide decisions related to the site-specific management of crop inputs. CSMs require inputs related to soil, climate, management, and crop genetic information to simulate crop yield. In practice, however, measuring these inputs at the desired high spatial resolution is prohibitively expensive. We propose a Bayesian modeling framework that melds a CSM with sparse data from a yield monitoring system to deliver location specific posterior predicted distributions of yield and associated unobserved spatially varying CSM parameter inputs. These products facilitate exploration of process-based explanations for yield variability. The proposed Bayesian melding model consists of a systemic component representing output from the physical model and a residual spatial process that compensates for the bias in the physical model. The spatially varying inputs to the systemic component arise from a multivariate Gaussian process, while the residual component is modeled using a univariate Gaussian process. Due to the large number of observed locations in the motivating dataset, we seek dimension reduction using low-rank predictive processes to ease the computational burden. The proposed model is illustrated using the Crop Environment Resources Synthesis (CERES)-Wheat CSM and wheat yield data collected in Foggia, Italy. [ABSTRACT FROM AUTHOR]

Process-oriented crop growth models simulate plant growth over homogeneous areas. The advent of precision farming has resulted in the need to extend the use of point-based crop models to account for spatial processes. Spatial processes include surface and subsurface water flow and spatial and temporal interaction of plant growth with soil water, nutrient and pest stress and management practices. Our research has focused on developing methods to account for spatial interactions in the CROPGRO-Soybean and CERES-Maize models. These methods introduce new challenges for accurately and economically defining spatial inputs for the models. In spite of these challenges, both models have been used to evaluate causes of spatial yield variability with reasonable success. The purpose of this paper is to present several examples of strategies that we have found useful in using these models to assess spatial and temporal yield variability over different environmental conditions and to analyze economic return of prescriptions. Strategies to overcome spatial resolution in point-based crop models include calibration techniques to run point-based models at small scales within a field, using remote sensing to target measurements of models inputs to areas of similar plant response, and linking point-based models to three-dimensional water flow models to better represent water transport. Each strategy is demonstrated using case studies and comparison of simulated and measured data are presented. A method to estimate break-even costs associated with variable soybean cultivar placement in a field is outlined and presented as a case study as well. Crop models can provide useful estimates of potential economic return of prescriptions, as well as estimate the sensitivity of a prescription to weather. They can also estimate the value of weather information on management prescriptions. [Copyright &y& Elsevier]

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability. [ABSTRACT FROM AUTHOR]

Crop yield is affected by many factors, including the availability of water and nutrients and planting distribution. Models developed to predict yield based only on water use can be misleading due to the dominance of crop management factors. The ability of crop models to predict the impact of management strategies on yield is illustrated in this paper. Studies on water use efficiency (WUE) have primarily dealt with crops grown under water limited conditions and have usually not considered crop management factors other than irrigation. Improvements in crop management strategies, such as supplemental irrigation, proper fertilizer use, plant population and row-spacing, and new cultivars, attempt to minimize a crop exposure to soil water deficits. Under these circumstances, factors other than water supply can become limiting and production related to evaporation may not be a useful approach. Crop simulation models like CERES and SALUS have proven capability to provide reasonable estimates of each evaporation component as affected by management. Using simulated transpiration to estimate growth through transpiration efficiency, or simulated growth to estimate transpiration has major limitations when management influences yield and water supply is fixed or non-limiting. The microenvironment in sparse canopies strongly influences transpiration and soil evaporation due to large possible variations in sensible heat arising from variations in wetness of the soil surface. Crop growth is related to photosynthesis and plant development rates and is influenced primarily by intercepted photosynthetically active radiation whereas evaporation is influenced by a wider spectrum of energy. Factors related to management such as plant population and nutrient supply influences biomass production but has less influence on evaporation rates. Our analysis of published data and simulation studies indicates the use of simulated transpiration to estimate growth, or growth simulations to estimate transpiration is an unreasonable approach when management influences yield and water supply is fixed or not-limiting. [ABSTRACT FROM AUTHOR]

Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions. • Multi-model assessment for long-term simulations of crop yield and soil carbon • Model ensemble uncertainty in simulating soil carbon increase with organic amendment • Model ensemble uncertainty in simulating yield increase in no-input treatments • Models largely differed in simulating root biomass and nitrogen mineralisation • More detailed experiments on soil crop feedback are needed to improve models [ABSTRACT FROM AUTHOR]

• Eddy covariance measurements were used to inter-compare 29 maize models in their ability to simulate evapotranspiration (ET). • There was a huge range among the models in their simulations of ET for the initial blind phase, which continued even as more information was supplied. • Medians of the 29 models were generally close to observations. • Both simple and complex models were among the best in their ability to simulate ET. • Widely used models were consistently among the best, presumably because large numbers of users with a wide range of conditions led to improvements. Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first "blind" phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET. [ABSTRACT FROM AUTHOR]

Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982–2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed. [ABSTRACT FROM AUTHOR]