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Using the discrete element method to simulate the interaction between growing media and machinery is an effective method to design seedling machinery and improve the precision of facility horticultural operations. In order to further improve the accuracy of the study on the interaction between seedling block-forming machines and growing media, the growing media used in the production of seedling nursery blocks was taken as the research object, and the Plackett–Burman screening test and Box–Behnken test were conducted based on the discrete element method using the EEPA model to conduct the calibration of discrete element parameters of the growing media. Optimization was conducted with an actual repose angle as the target value, and the optimal combination is as follows: the interparticle collision-recovery coefficient is 0.5066, the collision-recovery coefficient between particles and the geometric model is 0.714, the interparticle dynamic-friction coefficient is 0.381, and the tangential stiffness factor is 0.375. Finally, the soil uniaxial closed compression test was conducted with optimized calibration parameters. The relative error between the maximum axial load on the punch and the measured value in the simulation process was 4.23%, which verified the accuracy and reliability of parameter calibration of the growing media and provided support for the simulation of growing media and optimization of seedling nursery block-forming machine. [ABSTRACT FROM AUTHOR]

Bean production is at risk due to climate change, declining water resources, and inadequate crop management. To address these challenges, dynamic models that predict crop growth and development can be used as fundamental tools to generate basic and applied knowledge such as production management and decision support. This study aimed to calibrate and evaluate the SIMPLE model under irrigation conditions for a semi-arid region in north-central Mexico and to simulate thermal time, biomass (Bio), and grain yield (GY) of common beans cv. 'Pinto Saltillo' using experimental data from four crop evapotranspiration treatments (ETct) (I50, I75, I100, and I125) applied during the 2020 and 2021 growing seasons. Both experiments were conducted in a randomized complete block design with three replicates. Model calibration was carried out by posing and solving an optimization problem with the differential-evolution algorithm with 2020 experimental data, while the evaluation was performed with 2021 experimental data. For Bio, calibration values had a root-mean-square error and Nash and Sutcliffe's efficiency of <0.58 t ha−1 and >0.93, respectively, while the corresponding evaluation values were <1.80 t ha−1 and >0.89, respectively. The I50 and I100 ETct had better fit for calibration, while I50 and I75 had better fit in the evaluation. On average, the model fitted for the predicted GY values had estimation errors of 37% and 22% for the calibration and evaluation procedures, respectively. Therefore, an empirical model was proposed to estimate the harvest index (HI), which produced, on average, a relative error of 6.9% for the bean-GY estimation. The SIMPLE model was able to predict bean biomass under irrigated conditions for these semi-arid regions of Mexico. Also, the use of both crop Bio and transpiration simulated by the SIMPLE model to calculate the HI significantly improved GY prediction under ETct. However, the harvest index needs to be validated under other irrigation levels and field experiments in different locations to strengthen the proposed model and design different GY scenarios under water restrictions for irrigation due to climate change. [ABSTRACT FROM AUTHOR]

In anticipation of climate changes, strategic soil management, encompassing reduced tillage and optimized crop residue utilization, emerges as a pivotal strategy for climate impact mitigation. Evaluating the transition from conventional to conservative cropping systems, especially the equilibrium shift in the medium to long term, is essential. ARMOSA, a robust crop simulation model, adeptly responds to varied soil management practices such as no tillage, minimum tillage, and specific straw management options such as chopping and incorporating crop residue into the soil (with or without prior nitrogen and water addition before ploughing). It effectively captures dynamic fluctuations in total organic carbon over an extended period. While challenges persist in precisely predicting grain yield due to climatic intricacies, ARMOSA stands out as a valuable and versatile tool. The model excels in comprehending and simulating wheat cultivar responses in dynamic agricultural ecosystems, shedding light on phenological patterns, biomass accumulation, and soil organic carbon dynamics. This research significantly advances our understanding of the intricate complexities associated with past wheat cultivation in diverse environmental conditions. ARMOSA's ability to inform decisions on conservation practices positions it as a valuable asset for researchers, agronomists, and policymakers navigating the challenges of sustainable agriculture amidst climate change. Its real-world significance lies in its potential to guide informed decisions, contributing to global efforts in sustainable agriculture and climate resilience. [ABSTRACT FROM AUTHOR]

Today, the post-harvest root stubble treatment of kale in Hunan mostly uses manual pulling and centralized treatments, which are inefficient and labor-intensive. In this study, to realize the direct mechanical crushing of kale root stubble and return it to the field after harvesting, we established an accurate simulation model of kale root stubble by creating a model of the root stubble of kale and calibrating the parameters of the simulation. This study took Jingfeng No. 1 kale stubble as the research object and used EDEM2021.2 simulation software to study the parameters of the kale stubble-crushing simulation model. The peak shear force of the sheared kale root stubble was used as the test data, and the most significant factors affecting the shear force were screened out through the Plackett–Burman test for the Design-Expert design. In addition, the steepest climb test and Box–Behnken test were used to accurately assess the factor data to obtain the best simulation value, which was 861.02 N. The relative error between the simulated and measured values was 0.61%. Finally, an accurate simulation stubble model was established by combining the best simulation parameters with the measured stubble length and diameter. This model provides a theoretical basis and technical support for more in-depth research on stubble simulation and mechanized stubble return. [ABSTRACT FROM AUTHOR]

The shape of corn seeds not being spherical affects their mobility. This study focuses on modeling the typically shaped corn seeds and calibrating the coefficient of rolling friction for different shape types to improve simulation reliability. By analyzing the corn seed shape characteristics and size statistics, this study establishes a classification system that enables the determination of the average value and quantity of different types of corn seed using the spherical granule cluster method. The discrete element method is used to model simplified corn models, and contact parameters are validated through two types of repose angle and a seed metering experiment. In the collapse repose experiment, the relative error between the simulation and the experiment was only 0.72%, while the relative error in another repose experiment was 0.2%. The verification experiment for the metering of seeds showed that the relative error between the simulation and the experiment was below 15% at both low and high speeds, and the multi-grain rate error was less than 10%. This shows that the method proposed in this paper is somewhat accurate. [ABSTRACT FROM AUTHOR]

In order to obtain accurate discrete element simulation model (DEM) parameters of compound fertilizer and solve the problem of challenging measurement of contact parameters of compound fertilizer particle, simulation calibration test was carried out by using EDEM simulation soft-ware. This study measured the intrinsic parameters and contact parameters of compound fertilizer particles through physical tests and established a simulation model that corresponds with the actual situation to calibrate the contact parameters of compound fertilizer particles. By using the Blackett-Burman test, the parameters that had a significant impact on the compound fertilizer's resting angle were determined by the fertilizer-fertilizer collision recovery coefficient, fertilizer-fertilizer rolling friction coefficient, and fertilizer-steel static friction coefficient. Utilizing the steepest ascent test, the ideal value intervals for the three key parameters were identified. Box-Burman response surface analysis was used to establish and optimize the regression model of the rest angle and significant parameters. With the actual rest angle as the target value, the best combination of significant parameters was found, which was used for the simulation verification test. The simulated rest angle was 20.61°, whereas the real rest angle was 19.95°, with a relative error of 3.31%. The results demonstrate that the calibration parameters are essentially accurate representations of the real characteristics, which can serve as a reference point for simulation research and optimization design of variable fertilizer spreader. [ABSTRACT FROM AUTHOR]

Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. We mainly aim to investigate the main source of the variability in the modelling errors for the flowering timings of two important varieties of vine in the Douro Demarcated Region (DDR) of Portugal; this is based on five phenology model simulations that use optimal parameters and that are estimated by three optimization algorithms (MLE, SA and SCE-UA). Our results indicate that the main source of the variability in calibration can be affected by the initially assumed parameter boundary. Restricting the initial parameter distribution to a narrow range impedes the algorithm from exploring the full parameter space and searching for optimal parameters. This can lead to the largest variation in different models. At an identified appropriate boundary, the difference between the two varieties represents the largest source of uncertainty, while the choice of algorithm for calibration contributes least to the overall uncertainty. The smaller variability among different models or algorithms (tools for analysis) compared to between different varieties could indicate the overall reliability of the calibration. All optimization algorithms show similar results in terms of the obtained goodness-of-fit: the RMSE (MAE) is 5–6 (4–5) days with a negligible mean bias and moderately good R2 (0.5–0.6) for the ensemble median predictor. Nevertheless, a similar predictive performance can result from differently estimated parameter values, due to the equifinality or multi-modal issue in which different parameter combinations give similar results. This mainly occurs for models with a non-linear structure compared to those with a near-linear one. Yet, the former models are found to outperform the latter ones in predicting the flowering timing of the two varieties in the DDR. Overall, our findings highlight the importance of carefully defining the initial parameter boundary and decomposing the total variance of prediction errors. This study is expected to bring new insights that will help to better inform users about the importance of choice when these factors are involved in calibration. Nonetheless, the importance of each factor can change depending on the specific situation. Details of how the optimization methods are applied and of the continuous model improvement are important. [ABSTRACT FROM AUTHOR]

To improve the accuracy of discrete element simulation parameters for the mechanized picking and collection of pears, the study calibrated the simulation parameters of pears by the method of combining a physical experiment and simulation. Based on the intrinsic parameters of four kinds of pears (Snow pears, Crisp pears, Huangguan pears and Qiuyue pears), their simulation models were constructed by the Hertz-Mindlin with a bonding model. The simulation parameters between pears and the contact material (PVC, EVA foam material) were calibrated by the methods of free fall collision, inclined sliding and rolling, respectively. The experiments of pear accumulation angle were carried out. It was obtained to process the image of pears with Matrix Laboratory software. In order to determine the optimal value interval of influencing factors of the pear accumulation angle, the steepest ascent experiment was carried out. Considering the coefficient of collision recovery, the coefficient of static friction and the coefficient of rolling friction between pears, five-level simulation experiments of the pear accumulation angle were designed for each factor by the method of orthogonal rotation combination. The regression model of the error between the measured value and the simulated value of the pear accumulation angle was established, and the influence of three factors on the pear accumulation angle was analyzed. The results showed that the static friction coefficient and rolling friction coefficient between pears have significant effects on the pear accumulation angle. Therefore, the optimal model of minimum error was constructed according to constraint condition, and the coefficient of collision recovery, coefficient of static friction and coefficient of rolling friction between pears were obtained. The accumulation angle verification experiments were carried out by the method of bottomless barrel lifting. The results showed that the relative error between the simulated and measured accumulation angle of four kinds of pears were 1.42%, 1.68%, 2.19% and 1.83%, respectively, which indicated that the calibrated simulation parameters were reliable. The research can provide a basis for the design and parameters optimization of harvesting machinery of pears. [ABSTRACT FROM AUTHOR]

Soil phosphorus (P) is essential for plant growth and influences biological processes. Determining the amounts of available P to plants has been challenging, and many different approaches exist. The traditional Hedley sequential extraction method and its subsequent modification are applied to determine different soil P forms, which is critical for understanding its dynamics and availability. However, quantifying organic and inorganic P (Po & Pi) in different extracts is labor-intensive and rarely used with large sample numbers. As an alternative, near-infrared spectroscopy (NIRS) has been employed to determine different P fractions at reasonable costs in a short time. This study aimed to test whether the analysis of P fractions with NIRS is an appropriate method to disentangle the effects of P limitation on high-altitude grassland ecosystems, particularly with fertilizer amendments. We explored NIRS in soils from the grassland soil samples on the northern Tibetan Plateau. First, we extracted the P fractions of 191 samples from the Haibei Alpine Meadow Ecosystem Research Station at four depth increments (0–10 cm, 10–20 cm, 20–40 cm, and 40–70 cm), including nutrient additions of nitrogen (N) and P. We compared the results of the Hedley extraction with the laboratory-based NIRS model. The fractionation data were correlated with the corresponding NIRS soil spectra; the coefficient of determination (R2) of the NIRS calibrations to predict P in P fractions ranged between 0.12 and 0.90; the ratio of (standard error of) prediction to the standard deviation (RPD) ranged between 1.07 and 3.21; the ratio of performance to inter-quartile distance (RPIQ) ranged from 0.3 to 4.3; and the model prediction quality was higher for Po than Pi fractions, and decreased with fertilizer amendment. However, the external-validation results were not precise enough for the labile P fractions (RPD < 1.4) due to the limited number of samples. The results indicate that using NIRS to predict the more stable P pools, combined with Hedley fractionation focusing on the labile P pool, can be a promising approach for soils in alpine grasslands on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Different methods have been proposed for in situ root-length density (RLD) measurement. One widely employed is the time-consuming sampling of soil cores or monoliths (MO). The profile wall (PW) method is a less precise, but faster and less laborious alternative. However, depth-differentiated functions to convert PW RLD estimates to MO RLD measurements have not yet been reported. In this study, we perform a regression analysis to relate PW results to MO results and determine whether calibration is possible for distinct crop groups (grasses, brassicas and legumes) consisting of pure and mixed stands, and whether soil depth affects this calibration. The methods were applied over two years to all crop groups and their absolute and cumulative RLD were compared using a linear (LR) and multiple linear (MLR) regression. PW RLD was found to highly underestimate MO RLD in absolute values and in highly rooted areas. However, a close agreement between both methods was found for cumulative root-length (RL) when applying MLR, highlighting the influence of soil depth. The level of agreement between methods varied strongly with depth. Therefore, the application of PW as the main RLD estimation method can provide reliable estimates of cumulative root distribution traits of cover crops. [ABSTRACT FROM AUTHOR]

A portable X-ray fluorescence probe (pXRF) is a tool that is used to measure many elements quickly and efficiently in various samples, without any pretreatment. However, each type of sample generally requires different calibrations to be accurate. To overcome this, our work evaluated the efficacy of determining several elements in forage plant samples using the 'Soil Nutrient and Metal' calibration in a commercially available pXRF probe, envisioning that a single calibration can be used to measure samples of different matrixes. For this, the net intensity of the pXRF probe was determined in place of the concentration values that are obtained directly from measurements. Elemental concentrations (P, K, Ca, Mg, S, Cu, Fe, Zn, and Mn) from forage plant samples, collected across Oklahoma, US, were assessed in a representative number of 'modeling' and 'validation' (independent dataset) samples. Linear regression (LR) associated with the d-index, polynomial regression (PR), and power regression (PwR) were tested for predictions, producing many statistical parameters associated with the models that were used for comparison goals. The pXRF elemental data provided highly reliable predictions of K, S, Zn, and Mn regardless of the regression model. Although all models can be reliable in prediction of Ca and Fe concentrations, the PwR provided better root mean square error (RMSE) values. The predictions of Mg concentrations were less reliable, although highly significant; however, the P and Cu predictions were not acceptable. Our work successfully showed that, once established, a single calibration curve that covers a wide range of concentrations of several elements in soils and plant tissues enables both soil and plant samples to be analyzed. This suggests that manufacturers can develop a new calibration model for a commercially available pXRF probe that covers a wide variety of heterogeneous samples. [ABSTRACT FROM AUTHOR]

Ginger and turmeric are tropical plant species with medicinal, beverage, and edible uses. Both species are typically propagated using seed rhizomes that often lack uniformity when sprouting, ultimately affecting the transplant growth and quality. Our objectives were to (1) develop a model to predict the effect of temperature on rhizome sprouting and transplant growth and (2) characterize the morphological factors affecting the sprouting of ginger and turmeric rhizomes. Two experiments were conducted where the rhizomes were placed in plastic bags with a moist substrate inside dark incubator chambers. Five temperature treatments (21, 25, 27, 30, and 32 °C) were used for calibrating the model, and six temperature treatments (14, 20, 25, 30, 35, and 40 °C) were used in the validation phase. The number of days for rhizomes to develop 1- and 5-cm sprouts were counted; after which, the total number of sprouts, total leaf length, and root quality were measured. A nonlinear regression analysis was used to develop temperature–response curves. Ginger and turmeric had optimal sprouting temperatures of 27.5 and 30.1 °C, respectively. Temperatures close to the optimal reduced the time to sprout and to subsequently reach the transplant stage. No sprouting was observed at 14 °C, and the minimum temperature to develop 5-cm sprouts was estimated at slightly above 17 °C in both species. Temperatures above 32 °C resulted in tissue damage and rhizome loss. The results from this study show the potential to produce uniform ginger and turmeric transplants using temperature treatments that accelerate sprouting. [ABSTRACT FROM AUTHOR]

Energy-dispersive X-ray fluorescence spectrometry (EDXRF) is a rapid and inexpensive method for soil analysis; however, analytical results are influenced by particle size effects and spectral interferences. The objective of this study was to optimise sample preparation and calibrations to improve the accuracy of EDXRF for soil tests. Methods of sample preparation were compared by calculating the recoveries of 13 elements in four International Soil-Analytical Exchange (ISE) standards prepared as loose powder (LP), pressed pellet (PP), and pressed pellet with wax binder (PPB). A matching library (ML) was created and evaluated against the fundamental parameters (FP) calibration using 20 ISE standards. Additionally, EDXRF results of 41 tillage soils were compared with Inductively coupled plasma optical emission spectrometry (ICP-OES) results. The PPB had most recoveries within the acceptable range of 80–120%; conversely, PP yielded the poorest element recoveries. For the calibration, the ML provided better recoveries of Ni, Pb, Cu, Mg, S, P, and Cr; however, for Zn, and Mn, it had the opposite effect. Furthermore, EDXRF results compared with ICP-OES separated by soil texture class for Al, K, Mn, and Fe. In conclusion, the EDXRF is suitable for quantifying both trace elements and macronutrients in contaminated soils and has the potential to provide screening or prediction of soil texture in agriculture. [ABSTRACT FROM AUTHOR]

The surface renewal method (SR) allows estimating the sensible heat flux (H) using high-frequency thermocouples. Traditionally, SR has been compared and calibrated using standard instruments such as the Eddy covariance system (EC). Calibration involves correcting H measured with SR (H'SR) by means of the calibration factor (α). However, several studies show that α is not constant and could depend on canopy architecture, measurement height, atmospheric stability, and weather conditions. In avocado orchards, there is not enough information about energy fluxes and the application of the SR method. Therefore, the objective of this study is to calibrate the SR method in a mature avocado orchard considering the effect of meteorological conditions on the determination of α. The components of the surface energy balance were measured using an EC system in a commercial avocado orchard (cv. Hass) located in the Aconcagua Valley, Valparaíso Region, Chile. To evaluate the effect of the meteorological conditions on the determination of α, the dataset was classified into nine categories based on solar radiation and wind intensity. The results show that α varies according to meteorological conditions, with significant differences for cloudy days. The use of the variable α reduced the error in estimating H, so, this methodology can be used to have a more precise approximation of the energy balance and therefore to the water requirements. [ABSTRACT FROM AUTHOR]

APSIM Next Generation was used to simulate the phenological development and biomass production of silage maize for high latitudes (i.e., >55°). Weather and soil data were carefully specified, as they are important drivers of the development and growth of the crop. Phenology related parameters were calibrated using a factorial experiment of simulations and the minimization of the root mean square error of observed and predicted phenological scaling. Results showed that the model performed well in simulating the phenology of the maize, but largely underestimated the production of biomass. Several factors could explain the discrepancy between observations and predictions of above-ground dry matter yield, such as the current formalization of APSIM for simulating the amount of radiation absorbed by the crop at high latitudes, as the amount of diffuse light and intercepted light increases with latitude. Another factor that can affect the accuracy of the predicted biomass is the increased duration of the day length observed at high latitudes. Indeed, APSIM does not yet formalize the effects of extreme day length on the balance between photorespiration and photosynthesis on the final balance of biomass production. More field measurements are required to better understand the drivers of the underestimation of biomass production, with a particular focus on the light interception efficiency and the radiation use efficiency. [ABSTRACT FROM AUTHOR]

Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards. [ABSTRACT FROM AUTHOR]

The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001–2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha−1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions. [ABSTRACT FROM AUTHOR]

Crop models are useful tools to evaluate the effects of agricultural management on ecosystem services. However, before they can be applied with confidence, it is important to calibrate and validate crop models in the region of interest. In this study, the Environmental Policy Integrated Climate (EPIC) model was evaluated for its potential to simulate maize yield using limited data from field trials on two maize cultivars. Two independent fields at the Cradock Research Farm were used, one for calibration and one for validation. Before calibration, mean simulated yield was 8 t ha−1 while mean observed yield was 11.26 t ha−1. Model calibration improved mean simulated yield to 11.23 t ha−1 with a coefficient of determination, (r2) = 0.76 and a model efficiency (NSE) = 0.56. Validation with grain yield was satisfactory with r2 = 0.85 and NSE = 0.61. Calibration of potential heat units (PHUs) and soil-carbon related parameters improved model simulations. Although the study only used grain yield to calibrate and evaluate the model, results show that the calibrated model can provide reasonably accurate simulations. It can be concluded that limited data sets from field trials on maize can be used to calibrate the EPIC model when comprehensive experimental data are not available. [ABSTRACT FROM AUTHOR]

An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals. [ABSTRACT FROM AUTHOR]