Accurate Yield Estimation Research Articles

Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches

Fast, accurate and inexpensive estimates of crop yields at the field scale are useful for many applications. Based on the Google Earth Engine (GEE) platform, we recently developed a Scalable satellite-based Crop Yield Mapper (SCYM) that integrates crop simulations with satellite imagery and gridded weather data to generate 30m resolution yield estimates for multiple crops in different regions. Existing versions of SCYM typically capture one-third to half of the variation in reported county-scale yields. Using rainfed maize in the US Midwest as an example, this study tested multiple approaches for improving SCYM’s accuracy, including (i) calibrating the phenology parameters of the crop model (APSIM) used to generate training samples for SCYM; (ii) using an ensemble of three crop models (APSIM-Maize, CERES-Maize, and Hybrid-Maize) instead of a single model; (iii) using simulated biomass from the crop models instead of simulated yields to train SCYM, with the former assuming a constant harvest index (HI). Results show substantial improvement in performance, as assessed using reported county yields by USDA-NASS, both from calibrating APSIM phenology parameters and from training SCYM on simulated biomass rather than yields. Using a multi-model ensemble further improves SCYM, although the benefit is limited. The proposed preferred version of SCYM on average captures 75% of the yield variation for 2001–2015 in the 3I states (i.e. Illinois, Indiana and Iowa) where SCYM is trained, with RMSE typically less than 1t/ha, and explains 41% to 83% of multi-year yield variations when tested across nine Midwestern US states for 2008–2015. This level of accuracy is particularly notable given that only data from 2014 were used to calibrate phenology parameters. The yield estimates for multiple years in multiple states utilized 1184 Landsat tiles, but could be completed in about 2h per year by using the GEE platform. All approaches tested in this study do not require any site-specific measurements, and thus can be readily extended to other regions and crops.

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

AbstractTo accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index (LAI) values. The performances of the calibrated crop environment resource synthesis for wheat (CERES-Wheat) model for two different assimilation scenarios were compared by employing ensemble Kalman filter (EnKF)-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overall, the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehensively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination (R2) of 0.84, a root mean square error (RMSE) of 323 kg ha−1, and a relative errors (RE) of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha−1, and an RE of 4.52% were obtained at the pixel scale of 30 m×30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

Effects of sampling close relatives on some elementary population genetics analyses.

Many molecular ecology analyses assume the genotyped individuals are sampled at random from a population and thus are representative of the population. Realistically, however, a sample may contain excessive close relatives (ECR) because, for example, localized juveniles are drawn from fecund species. Our knowledge is limited about how ECR affect the routinely conducted elementary genetics analyses, and how ECR are best dealt with to yield unbiased and accurate parameter estimates. This study quantifies the effects of ECR on some popular population genetics analyses of marker data, including the estimation of allele frequencies, F-statistics, expected heterozygosity (He ), effective and observed numbers of alleles, and the tests of Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE). It also investigates several strategies for handling ECR to mitigate their impact and to yield accurate parameter estimates. My analytical work, assisted by simulations, shows that ECR have large and global effects on all of the above marker analyses. The naïve approach of simply ignoring ECR could yield low-precision and often biased parameter estimates, and could cause too many false rejections of HWE and LE. The bold approach, which simply identifies and removes ECR, and the cautious approach, which estimates target parameters (e.g., He ) by accounting for ECR and using naïve allele frequency estimates, eliminate the bias and the false HWE and LE rejections, but could reduce estimation precision substantially. The likelihood approach, which accounts for ECR in estimating allele frequencies and thus target parameters relying on allele frequencies, usually yields unbiased and the most accurate parameter estimates. Which of the four approaches is the most effective and efficient may depend on the particular marker analysis to be conducted. The results are discussed in the context of using marker data for understanding population properties and marker properties.

Improved Perturbation Vector Generation Method for Accurate SRAM Yield Estimation

Accurate yield estimation under parametric variation is one of the most integral parts for robust and nonwasted circuit design. In particular, due to the significant impact of disparity on the high-replication circuit, precise yield estimation is essential in SRAM design. In this paper, we propose an enhanced perturbation vector generation method to improve the accuracy of the yield estimation of the conventional direct SRAM yield computation method, which are access disturb margin (ADM) and write margin (WRM) first, by splitting the concave yield metric space, the estimation error caused by linear approximation can be significantly reduced with minor increase in simulation runtime. In addition, to compensate the inaccuracy of the conventional perturbation vector, a calibration method to reflect the multi-dc condition in SRAM assist operations is also proposed. Numerical results show that 37% improved estimation accuracy and 29% reduced estimation error can be achieved compared to the conventional ADM/WRM in the wide voltage range.

An Analysis of Fruit Detection and Yield Estimation Using on Tree Image

In this paper, we present a technique to evaluate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires more human resources, which do not always come cheap. Different methods have been used for estimation, yet separation of fruit from its background poses complicated and challenges, and render the exercise inaccurate. In this paper, we use Bicubic Interpolation technique to detect and count the number of fruits from its background, which segments the image accurately thus enabling more accurate yield estimation

Systematic cross-validation of photovoltaic energy yield models for dynamic environmental conditions

Abstract The continuous growth of photovoltaic installations globally carries hope for a sustainable future but also imposes challenges on all levels of energy production and distribution. Grid operators and designers have to cooperate more closely with monitoring service providers in order to sustain a flexible scheme of energy exchange. The basis of these calculations is accurate energy yield estimation models, which are able to capture all effects of the environment. Especially for locations with highly dynamic irradiance and environmental conditions, this remains a tough challenge. All photovoltaic energy yield models presented in this work aim at accommodating the inherent dynamism of these challenging locations at small time scales. A detailed, physics based electro-thermal energy yield model is validated along with other state-of-the-art models and performs 25% more accurately. Additionally, the results from the dynamic modeling are transferred to a neural network model, increasing the accuracy further up to six times better than any parametric solution.

Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model

To improve the accuracy of regional winter wheat yield estimation in the Guanzhong Plain, China, the field-measured leaf area index (LAI) and soil moisture at the depth of 0–20cm (θ) and both Landsat-retrieved LAI and θ were assimilated into the CERES-Wheat model with an ensemble Kalman filter (EnKF) algorithm. The correlation between the assimilated LAI and the measured yield at each main wheat growth stage, including the green-up, jointing, heading-filling and milk stages, was compared with that between assimilated θ and yield. Then, five types of assimilation schemes were investigated to test the effects of assimilating different state variables at each wheat growth stage on wheat yield estimation. The results showed that the correlations between LAI and wheat yield at the jointing and heading-filling stages were higher than those between θ and wheat yield; moreover, the correlations between θ and wheat yield were higher at the green-up and milk stages. Among the five assimilation schemes, the accuracy of the yield estimation obtained by assimilating LAI at the jointing and heading-filling stages as well as by assimilating θ at the green-up and milk stages was the highest (R2=0.76, root mean square error (RMSE)=548.97kgha−1), followed by the accuracy of the yield estimation obtained by assimilating LAI and θ simultaneously at each growth stage (R2=0.67, RMSE=610.67kgha−1). Conversely, the accuracy of the yield estimation obtained by assimilating LAI at the green-up and milk stages as well as by assimilating θ at the jointing and heading-filling stages was the lowest (R2=0.41, RMSE=928.95kgha−1). Thus, the assimilation of more yield-related state variables at each wheat growth stage in an agricultural data assimilation framework provides a reliable and promising method for improving wheat yield estimation.

Analysis of agriculture data using data mining techniques: application of big data

In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural planning intention is the accurate yield estimation for the numerous crops involved in the planning. Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. This paper focuses on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using data mining techniques like PAM, CLARA, DBSCAN and Multiple Linear Regression. Mining the large amount of existing crop, soil and climatic data, and analysing new, non-experimental data optimizes the production and makes agriculture more resilient to climatic change.

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models’ specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context.

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.

Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales

To improve the accuracy of winter wheat yield estimation, the Crop Environment Resource Synthesis for Wheat (CERES-Wheat) model with an assimilation strategy was performed by assimilating measured or remotely-sensed leaf area index (LAI) values. The performances of the crop model for two different assimilation methods were compared by employing particle filters (PF) and the proper orthogonal decomposition-based ensemble four-dimensional variational (POD4DVar) strategies. The uncertainties of wheat yield estimates due to different assimilation temporal scales (phenological stages and temporal frequencies) and spatial scale were also analyzed. The results showed that, compared with the crop model without assimilation and with PF-based assimilation, a better yield estimate performance resulted when the POD4DVar-based strategy was used at the field scale. When using this strategy, root mean square errors (RMSE) of 523 kg·ha−1, 543 kg·ha−1 and 172 kg·ha−1 and relative errors (RE) of 5.65%, 5.91% and 7.77% were obtained at the field plot scale, a pixel scale of 1 km and the county scale, respectively. Although the best yield estimates were obtained when all of the observed LAIs were assimilated into the crop model, an acceptable estimate of crop yield could also be achieved by assimilating fewer observations between jointing and anthesis periods of the crop growth season. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. Thus, it is important to consider reasonable spatio-temporal scales to obtain tradeoffs between accuracy and effectiveness in regional wheat estimates.

Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm

Timely and accurate estimation of winter wheat yield at a regional scale is crucial for national food policy and security assessments. Near-infrared reflectance is not sensitive to the leaf area index (LAI) and biomass of winter wheat at medium to high canopy cover (CC), and most of the vegetation indices displayed saturation phenomenon. However, LAI and biomass at medium to high CC can be efficiently estimated using imaging data from radar with stronger penetration, such as RADARSAT-2. This study had the following three objectives: (i) to combine vegetation indices based on our previous studies for estimating CC and biomass for winter wheat using HJ-1A/B and RADARSAT-2 imaging data; (ii) to combine HJ-1A/B and RADARSAT-2 imaging data with the AquaCrop model using the particle swarm optimization (PSO) algorithm to estimate winter wheat yield; and (iii) to compare the results from the assimilation of HJ-1A/B+RADARSAT-2 imaging data, HJ-1A/B imaging data, and RADARSAT-2 imaging data into the AquaCrop model using the PSO algorithm. Remote sensing data and concurrent LAI, biomass, and yield of sample fields were acquired in Yangling District, Shaanxi, China, during the 2014 winter wheat growing season. The PSO optimization algorithm was used to integrate the AquaCrop model and remote sensing data for yield estimation. The modified triangular vegetation index 2 (MTVI2)×radar vegetation index (RVI) and the enhanced vegetation index (EVI)×RVI had good relationships with CC and biomass, respectively. The results indicated that the predicted and measured yield (R2=0.31 and RMSE=0.94ton/ha) had agreement when the estimated CC from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model. When the estimated biomass from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model, the predicted yield showed agreement with the measured yield (R2=0.42 and RMSE=0.81ton/ha). These results show that using the biomass as the dynamic input variable provides a better yield estimation than using the CC as the dynamic variable. The predicted biomass and yield were more accurately estimated by combining the HJ-1A/B and RADARSAT-2 data with the AquaCrop model than by combining the only HJ-1A/B or RADARSAT-2 data with the AquaCrop model using the PSO algorithm. The results indicated that the PSO-based assimilation method could be used to estimate the winter wheat yield from the spot to the regional scale.

Stable and Transportable Seismic Yield Estimation from Local Full‐Envelope Template Matching

Abstract We developed a multifrequency, full‐envelope template approach to estimate surface explosion yield from seismic stations in the 5–50 km distance range. The new method is empirically based, transportable, and unlike other methods, it is designed to work for short‐duration coda that a simple exponential single‐scattering formulation in a homogeneous space does not model well. Our empirical full‐envelope template‐matching approach produces close fits to complicated short‐duration and high‐frequency waveforms observed at near‐field distances. Moreover, because the envelope templates capture the nuances of the initial P and S waves and their scattered counterparts, the method is robust and provides roughly a factor of three times less scatter in yield measurement than peak P ‐wave and integrated first P ‐wave estimates. In this article, we demonstrate that our improved empirical template matching achieves accurate explosive yield estimates that are within 50% of the design yields at 95% confidence for a set of chemical explosions ranging in yield from 50 kg to 45 tonnes.

An improved CROPR model for estimating cotton yield under soil aeration stress

Accurate estimation of crop yield under aeration stress is crucial for field water table management. In this study, the CROPR crop model was improved in two aspects: (i) a new aeration factor, which was related to a drainage index, was proposed and used to represent the condition of soil aeration; and (ii) a multiplicative structure, instead of the original additive structure, was used in the calculation of dry matter accumulation to include the after-effect of aeration stress. Four-year lysimeter experiments on cotton (Gossypium hirsutum L.) growth under aeration stress were conducted from 2008 to 2011 to calibrate and validate both the original and improved CROPR. The results indicated that the improved CROPR performed better than the original CROPR and was suitable for simulating cotton yield under aeration stress. In the calibration, with the improved CROPR, the root-mean-squared error (RMSE) of seed cotton yield was 832.84 kg ha–1 with a normalised value (NRMSE) of 15.87%, whereas with the original CROPR, the RMSE was 973.03 kg ha–1 with an NRMSE of 18.55%. In the validation, with the improved CROPR, the RMSE of seed cotton yield was 686.22 kg ha–1 with an NRMSE of 14.87%; with the original CROPR, the RMSE was 1019.02 kg ha–1 with an NRMSE of 22.08%.

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

In-field proximal sensing of most major crops nutrients still remains an economical and technical challenge. For this purpose, the use of effective multi-excitation fluorescence and reflectance wavelengths is explored in this work on Okra plant. Visible-near infrared (400 - 1000 nm) reflectance and multi-fluorescence data were collected at leaf scale in a chemically fertilized field by using an USB spectrometer mounted with an Arduino-based LED driver clip. N, P, K and Ca content of samples leaves were measured using reference methods. Average pods yield and leaves macronutrients content were calibrated using IRIV-PLS regression after spectra pretreatments. Single informative wavelengths bands in reflectance, red and far-red fluorescences were selected for building yield and macronutrient content models. We showed that flowering stage was more suitable for yield prediction. Moderately useful macronutrient models were found in Ca content (RPDval = 1.93, rP = 0.818) and potassium content with RPDval = 1.8, rP = 0.88. P and N yielding prediction performance of RPDval = 1.61 (rP = 0.718 ) and RPDval = 1.46 (rP = 0.56) respectively were less accurate. This study demonstrates potentiality of fluorescence and reflectance spectroscopy for accurate estimation of leaf macronutrient content and crop yield. High selectivity obtained from resulted spectral bands could lead to the development of reliable, rapid and cost-effective devices for nutrient diagnosis.

Monitoring wheat fields by RapidScan: accuracy and limitations

Simple active radiometer sensors, such as RapidScan, enable agronomic decision-making and phenotyping within commercial wheat fields and experiments. The objectives of this study were: 1 - to evaluate the accuracy of quantitative biomass and nitrogen uptake estimation by the RapidScan, and 2 - to evaluate yield loss estimation based on NDVI. The RapidScan sensor was used as an assessment tool in the following studies: (i) over 3 years, 518 wheat samples were monitored during the vegetative growth period for biomass and aboveground nitrogen uptake and (ii) wheat cultivars were tested in an additional 4 field experiments, which were scanned weekly and correlated with grain yield. Results showed that accurate biomass estimation is limited up to about 100 g DM m−2. Grain yield, actual and potential, estimation is highly affected by the emergence date. The results showed that the use of a proximal-sensing technique allows for rapid and accurate crop monitoring and yield estimation, but emphasizing limitations in future use as well.

Estimation of rice grain yield from dual-polarization Radarsat-2 SAR data by integrating a rice canopy scattering model and a genetic algorithm

Fast and accurate estimation of rice yield plays a role in forecasting rice productivity for ensuring regional or national food security. Microwave synthetic aperture radar (SAR) data has been proved to have a great potential for rice monitoring and parameters retrieval. In this study, a rice canopy scattering model (RCSM) was revised and then was applied to simulate the backscatter of rice canopy. The combination of RCSM and genetic algorithm (GA) was proposed for retrieving two important rice parameters relating to grain yield, ear length and ear number density, from a C-band, dual-polarization (HH and HV) Radarsat-2 SAR data. The stability of retrieved results of GA inversion was also evaluated by changing various parameter configurations.Results show that RCSM can effectively simulate backscattering coefficients of rice canopy at HH and HV mode with an error of<1dB. Reasonable selection of GA’s parameters is essential for stability and efficiency of rice parameter retrieval. Two rice parameters are retrieved by the proposed RCSM-GA technology with better accuracy. The rice ear length are estimated with error of<1.5cm, and ear number density with error of<23#/m2. Rice grain yields are effectively estimated and mapped by the retrieved ear length and number density via a simple yield regression equation. This study further illustrates the capability of C-band Radarsat-2 SAR data on retrieval of rice ear parameters and the practicability of radar remote sensing technology for operational yield estimation.

Sensing Technologies for Grain Crop Yield Monitoring Systems: A Review

Purpose: Yield monitoring systems are an essential component of precision agriculture. They indicate the spatial variability of crop yield in fields, and have become an important factor in modern harvesters. The objective of this paper was to review research trends related to yield monitoring sensors for grain crops. Methods: The literature was reviewed for research on the major sensing components of grain yield monitoring systems. These major components included grain flow sensors, moisture content sensors, and cutting width sensors. Sensors were classified by sensing principle and type, and their performance was also reviewed. Results: The main targeted harvesting grain crops were rice, wheat, corn, barley, and grain sorghum. Grain flow sensors were classified into mass flow and volume flow methods. Mass flow sensors were mounted primarily at the clean grain elevator head or under the grain tank, and volume flow sensors were mounted at the head or in the middle of the elevator. Mass flow methods used weighing, force impact, and radiometric approaches, some of which resulted in measurement error levels lower than 5% (R 2 = 0.99). Volume flow methods included paddle wheel type and optical type, and in the best cases produced error levels lower than 3%. Grain moisture content sensing was in many cases achieved using capacitive modules. In some cases, errors were lower than 1%. Cutting width was measured by ultrasonic distance sensors mounted at both sides of the header dividers, and the errors were in some cases lower than 5%. Conclusions: The design and fabrication of an integrated yield monitoring system for a target crop would be affected by the selection of a sensing approach, as well as the layout and mounting of the sensors. For accurate estimation of yield, signal processing and correction measures should be also implemented.

Wheat phenomics in the field by RapidScan: NDVI vs. NDRE

Phenomics is a relatively new approach by breeders, who use modern sensors for monitoring germplasm performance and as a selection tool. Plant physiologists can also make use of phenotyping in their studies, which usually focus on plant response to abiotic and biotic stresses. Simple active radiometer sensors such as RapidScan enable phenotyping within the framework of field experiments, and may open new horizons for breeders. The objectives of this study were to: (i) test the ability of vegetation indices (VIs) to distinguish between wheat cultivars during the entire growth period; and (ii) evaluate the accuracy of yield estimation based on only one or a few monitoring days. The following studies were conducted to test RapidScan as a tool for wheat phenotyping in the field: (i) response of 10 cultivars to water deficiency; (ii) response of 13 cultivars to late top N application. Plants were scanned weekly. The default VI parameters, NDVI and NDRE, could be used to show development during the growing season. However, due to low repeatability, it was almost impossible to use these indices for cultivar differentiation, unless there was wide variation in the cultivar phenotype. Nevertheless, combining the data from a few monitoring days improved cultivar classification and yield estimation. Although variation between fields and treatments affected the VI vs. crop parameter relationships more than the within-field variation, the results showed that the use of the proximal-sensing technique allows for rapid and quite accurate phenotyping. Thus, RapidScan can assist breeders during breeding programs for wide-scale in-field phenotyping.

From grid to field: Assessing quality of gridded weather data for agricultural applications

Abstract High quality measured weather data (MWD) are not available in many agricultural regions across the globe. As a result, many studies that dealt with global climate change, land use, and food security scenarios and emerging agricultural decision support tools have relied on gridded weather data (GWD) to estimate crop phenology and crop yields. An issue is the agreement of GWD with MWD and the degree to which this agreement may influence the utility of GWD for agricultural research. The objectives of this study were: (i) to compare the agreement of two widely used gridded weather databases (GWDs) (Daymet and PRISM) and MWD, (ii) to evaluate their robustness at simulating maize growth and development, and (iii) to examine how GWD compare relative to weather data interpolated from existing meteorological stations for which MWD are available. The U.S. Corn Belt, a region that accounts for 43 and 34% of respective global maize and soybean production, was used as a case of study because of its dense weather station network and high-quality MWD. Historical daily MWD were retrieved from 45 locations across the region, resulting in ca . 1300 site-years. To test the accuracy of GWDs, separate simulations of maize yield and development were performed, separately for the two GWDs and MWD, using a well-validated maize crop model. For both GWDs, small biases were observed for temperature and growing degree-days in relation with MWD. However, accuracy was much lower for relative humidity, precipitation, reference evapotranspiration, and degree of seasonal water deficit. There was close agreement in duration of vegetative and reproductive phases between GWD and MWD, with root mean square error (RMSE) ranging from 3 to 7 days for the different crop phases and GWDs. However, robustness of GWDs to reproduce maize yields simulated using MWD was lower as indicated by the RMSE (18 and 24% of average yield for Daymet and PRISM, respectively). There was also a high proportion of site-years (20 and 32% for Daymet and PRISM, respectively) exhibiting a yield deviation >15% in relation to the yield simulated using MWD. Data interpolation using a dense weather station network resulted in lower RMSE% for simulated phenology and yields relative to GWDs. Findings from this study indicate that GWD cannot replace MWD as a basis for field-scale agricultural applications. While GWD appear to be robust for applications that only require temperature for prediction of crop stages, GWD should not be used for applications that depend on accurate estimation of crop water balance, crop growth, and yield. We propose that the evaluation performed in this study should be taken as a routinary activity for any research or agricultural decision tool that relies on GWD.