Analysis of Spatiotemporal Variability of Corn Yields Using Empirical Orthogonal Functions

Kim, Seongyun; Daughtry, Craig S. T.; Russ, Andrew L.; Pedrera‐Parrilla, Aura; Pachepsky, Yakov

doi:10.3390/w12123339

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…This is expected considering that it is harder to determine maturity based on the PhenoCam images which were used as a reference for the remotely-sensed derived phenology. Additionally, the yield monitor data show considerable variability of grain yields within the maize field ( [48,69] see Figure 1a). However, our study used field averages for our calibration and evaluation (validation).…”

Section: Model Evaluation Resultsmentioning

confidence: 99%

Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA

et al. 2023

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Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

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Section: Model Evaluation Resultsmentioning

confidence: 99%

Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA

et al. 2023

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“…Currently, the variable application of agricultural inputs based on long‐term yield maps has been limited at BARC. One initial implementation of site‐specific farming technology at BARC involved the use of the SMS module to develop a base prescription map to control a variable rate fertilizer spreader for a nitrogen response study at the OPE3 experimental research site catchment D (Kim et al., 2020). As additional years of yield monitor data are added to the data archive, and the development of stable and robust yield base maps becomes possible, the impetus to apply site‐specific farming techniques at BARC increases.…”

Section: Applicationsmentioning

confidence: 99%

Development of a gridded yield data archive for farm management and research at the USDA Beltsville Agricultural Research Center

Dulaney,

Anderson,

Gao

et al. 2024

Agrosystems Geosci & Env

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High‐quality, gridded maps of crop yield, which discern the spatial variability in productivity across individual farm fields, are extremely valuable in numerous agricultural and remote sensing applications. The availability of these data was greatly facilitated by the development and adoption of grain yield monitors starting in the early 1990s. However, the utility of raw yield monitor data is limited if additional processing has not been carried out to ensure data quality. In addition, a readily accessible data repository that allows for examining long‐term yield variability in response to climate and management is often not available. We present a protocol for producing a multi‐year (7 years currently processed), gridded yield data archive derived from yield monitor data acquired from over 40 production fields at the Beltsville Agricultural Research Center (BARC) in Beltsville, MD. Production of the archive involved the projection of the yield monitor data into a geographic coordinate system suitable for mapping, the identification and removal of data outliers, and the production of gridded, spatially interpolated yield maps using block Kriging. All map products were produced in common, nonproprietary file formats for easy access. Preliminary assessments of the processed yield monitor data have underscored the necessity of data filtering and have shown the influence of previous land use practices and biophysical properties, such as topography and soil moisture availability, on yield response. We discuss the use of spatially interpolated, archival yield maps in the implementation of precision farming techniques such as the development of site‐specific variable rate prescriptions as well as remote sensing applications, including the selection of field sites for long‐term agricultural experiments and the assessment of crop modeling approaches. The gridded yield data archive will be housed at the National Agricultural Library as a dynamic dataset that will continue to expand in breadth and depth. It will be a part of and provide support to internal USDA yield monitoring investigations as well as complementary external efforts. All geospatial data delivered to NAL will adhere to the ISO 19115 Geospatial Metadata Standards to provide the user with a full understanding of the yield monitor and associated datasets.

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“…Results from such trials cannot be extended to other fields unless they have the same underlying conditions as the trial field [18]. In addition, it is quite possible for spatial variability within a field, even when selecting smaller experimental units, to mask any treatment effects being tested in OFE [19,20]. An example of a randomized compete block design (RCBD) to evaluate if a treatment impacted outcomes, such as yield.…”

Section: Figurementioning

confidence: 99%

Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

et al. 2021

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On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistical power when only three to four replications are implemented. Harvester-mounted yield monitor systems generate high resolution data (1-s intervals), allowing for development of more meaningful, easily implementable OFE designs. Here we explored statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Nitrogen-rich single treatment strips per field were implemented in 2018 and 2019 on three fields each on two farms in central New York. Least squares and generalized least squares approaches were evaluated for estimating treatment effects (assuming independence) versus spatial covariance for estimating standard errors. The analysis showed that estimates of treatment effects using the generalized least squares approach are unstable due to over-emphasis on certain data points, while assuming independence leads to underestimation of standard errors. We concluded that the least squares approach should be used to estimate treatment effects, while spatial covariance should be assumed when estimating standard errors for evaluation of zone-based treatment effects using the single-strip spatial evaluation approach.

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Analysis of Spatiotemporal Variability of Corn Yields Using Empirical Orthogonal Functions

Cited by 4 publications

References 25 publications

Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA

Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA

Development of a gridded yield data archive for farm management and research at the USDA Beltsville Agricultural Research Center

Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

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