Abstract
Harvester-mounted yield monitor systems are increasingly used to document corn (Zea mays L.) yield. The three most commonly used spatial estimation methods to convert point data gathered with yield monitors to regular, grid-based, raster maps include nearest neighbor (NN), inverse distance weighting (IDW) and kriging. Seven spatial estimation methods (NN, IDW using 10, 20, 30 and all data points and kriging with exponential and Matérn covariance functions) were evaluated to determine the method that most accurately captures intra-field spatial variability of corn silage and corn grain yield in New York. Yield monitor data from two dairy farms and two grain operations were cleaned using Yield Editor prior to spatial analyses. The dataset included 7–10 years of data per farm for a combined 7484 ha (245 fields) of silage and 6971 ha (253 fields) of grain. Data were split into training (80%) and cross-validation datasets (remaining 20% of the data). Normalized root mean squared error (NRMSE) was used to evaluate the accuracy of the spatial estimation methods. Kriging with the Matérn covariance function resulted in the most accurate corn silage and grain yield raster maps both at the farm and field level. There were statistically significant differences in NRMSE between kriging with the Matérn isotropic covariance function and all other models for both corn silage and grain, regardless of field size, year when data were obtained or farm that supplied the data. These results are beneficial to ensure accurate and precise spatial mapping of yield products toward optimized corn growth management.
Similar content being viewed by others
Abbreviations
- CV:
-
Coefficient of variation
- EC:
-
Electrical conductivity
- GP:
-
Gaussian process
- GNSS:
-
Global navigation satellite systems
- IDW:
-
Inverse distance weighting
- NDVI:
-
Normalized Difference Vegetation Index
- NN:
-
Nearest neighbor
- NRMSE:
-
Normalized root mean squared error
- US:
-
United States
References
Anderson-Cook, C. M., Alley, M. M., Roygard, J. K. F., Khosla, R., Noble, R. B., & Doolittle, J. A. (2002). Differentiating soil types using electromagnetic conductivity and crop yield maps. Soil Science Society of America Journal, 66, 1562–1570. https://doi.org/10.2136/sssaj2002.1562.
Arslan, S., & Colvin, T. (2002). An evaluation of the response of yield monitors and combines to varying yields. Precision Agriculture, 3, 107–122. https://doi.org/10.1023/A:1013887801918.
Basso, B., Bertocco, M., Sartori, L., & Martin, E. C. (2007). Analyzing the effects of climate variability on spatial pattern of yield in a maize–wheat–soybean rotation. European Journal of Agronomy, 26(2), 82–91. https://doi.org/10.1016/j.eja.2006.08.008.
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01.
Bazzi, C. L., Souza, E. G., Khosla, R., Uribe-Opazo, M. A., & Schenatto, K. (2015). Profit maps for precision agriculture. Ciencia e investigación agraria, 42(3), 305–330. https://doi.org/10.4067/S0718-16202015000300007.
Berman, J. D., Breysse, P. N., White, R. H., Darryn, W. W., & Curriero, F. C. (2015). Evaluating methods for spatial mapping: Applications for estimating ozone concentrations across the contiguous United States. Environmental Technology & Innovation, 3, 1–10. https://doi.org/10.1016/j.eti.2014.10.003.
Bhunia, G. S., Shit, P. K., & Maiti, R. (2018). Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences, 17(2), 114–126. https://doi.org/10.1016/j.jssas.2016.02.001.
Blackmore, S. (1999). Remedial correction of yield map data. Precision Agriculture, 1, 53–66. https://doi.org/10.1023/A:1009969601387.
Blackmore, S. (2000). The interpretation of trends from multiple yield maps. Computers and Electronics in Agriculture, 26(1), 37–51. https://doi.org/10.1016/S0168-1699(99)00075-7.
Bregt, A. K. (1992). Processing of soil survey data. Ph.D. thesis, Department of Environmental Sciences, Agricultural University of Wageningen, The Netherlands.
Brock, A., Brouder, S. M., Blumhoff, G., & Hofmann, B. S. (2005). Defining yield-based management zones for corn-soybean rotations. Agronomy Journal, 97(4), 1115–1128. https://doi.org/10.2134/agronj2004.0220.
Brus, D. J., DeGruijter, J. J., Marsman, B. A., Visschers, R., Bregt, A. K., & Breeuwsma, A. (1996). The performance of spatial interpolation methods and choropleth maps to estimate properties at points: A soil survey case study. Environmetrics, 7, 1–16. https://doi.org/10.1002/(sici)1099-095x(199601)7:1%3c1::aid-env157%3e3.0.co;2-y.
Buttafuoco, G., Castrignanò, A., Cucci, G., Lacolla, G., & Lucà, F. (2017). Geostatistical modelling of within-field soil and yield variability for management zones delineation: A case study in a durum wheat field. Precision Agriculture, 18, 37–58. https://doi.org/10.1007/s11119-016-9462-9.
Cox, M., & Gerard, P. (2007). Soil management zone determination by yield stability analysis and classification. Agronomy Journal, 99, 1357–1365. https://doi.org/10.2134/agronj2007.0041.
Declercq, F. A. N. (1996). Interpolation methods for scattered sample data: Accuracy, spatial patterns, processing time. Cartography and Geographic Information Systems, 23(3), 128–144. https://doi.org/10.1559/152304096782438882.
Diker, K., Heermann, D., & Brodahl, M. (2004). Frequency analysis of yield for delineating yield response zones. Precision Agriculture, 5, 435–444. https://doi.org/10.1007/s11119-004-5318-9.
Dobermann, A., & Ping, J. (2004). Geostatistical integration of yield monitor data and remote sensing improves yield maps. Agronomy Journal, 96, 285–297. https://doi.org/10.2134/agronj2004.0285.
Gallichand, J., & Marcotte, D. (1993). Mapping clay content for subsurface drainage in the Nile Delta. Geoderma, 58, 165–179. https://doi.org/10.1016/0016-7061(93)90040-r.
Grim, J. W., & Lynch, J. A. (1991). Statistical analysis of errors in estimating wet deposition using five surface estimation algorithms. Atmospheric Environment Part A, 26(2), 317–327. https://doi.org/10.1016/0960-1686(91)90303-o.
Guinness, J., & Katzfuss, M. (2019) GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation. R package version 0.2.1 Retrieved August 19, 2020, from https://CRAN.R-project.org/package=GpGp.
Heine, G. W. (1986). A controlled study of some two-dimensional interpolation methods. COGS Computer Contributions, 2(2), 60–72.
Hornung, A., Khosla, R., Reich, R., Inman, D., & Westfall, D. (2006). Comparison of site-specific management zones: Soil-color-based and yield-based. Agronomy Journal, 98(2), 405–417. https://doi.org/10.2134/agronj2005.0240.
Katzfuss, M., & Guinness, J. (2019). A general framework for Vecchia Approximations of Gaussian processes. Retrieved June 29, 2020, from non-peer reviewed preprint at https://arxiv.org/abs/1708.06302.
Kharel, T., Swink, S. N., Youngerman, C., Maresma, A., Czymmek, K. J., Ketterings, Q. M., et al. (2018). Processing/cleaning corn silage and grain yield monitor data for standardized yield maps across farms, fields, and years. Ithaca, NY, USA: Cornell University, Nutrient Management Spear Program, Department of Animal Science. Retrieved August 19, 2020, from http://nmsp.cals.cornell.edu/publications/extension/ProtocolYieldMonitorDataProcessing1_15_2020.pdf.
Kharel, T. P., Marema, A., Czymmek, K. J., Oware, E. K., & Ketterings, Q. M. (2019a). Combining spatial and temporal corn silage yield variability for management zone development. Agronomy Journal, 111(6), 2703–2711. https://doi.org/10.2134/agronj2019.02.0079.
Kharel, T. P., Swink, S. N., Maresma, A., Youngerman, C., Kharel, D., Czymmek, K. J., et al. (2019b). Yield monitor data cleaning is essential for accurate corn grain/silage yield determination. Agronomy Journal, 111(2), 509–516. https://doi.org/10.2134/agronj2018.05.0317.
Kitanidis, P. K., & Shen, K. F. (1996). Geostatistical interpolation of chemical data. Advances in Water Resources, 19, 369–378. https://doi.org/10.1016/0309-1708(96)00016-4.
Kitchen, N. R., Sudduth, K. A., & Drummond, S. T. (1999). Soil electrical conductivity as a crop productivity measure for claypan soils. Journal of Production Agriculture, 12(4), 607–617. https://doi.org/10.2134/jpa1999.0607.
Khosla, R., Inman, D. J., Westfall, D. G., Reich, R. M., Frasier, M., Mzuku, M., et al. (2008). A synthesis of multi-disciplinary research in precision agriculture: Site-specific management zones in the semi-arid western Great Plains of the USA. Precision Agriculture, 9, 85–100. https://doi.org/10.1007/s11119-008-9057-1.
Laslett, G. M., McBratnety, A. B., Pahl, P. J., & Hutchinson, M. F. (1987). Comparison of several spatial prediction methods for soil pH. Journal of Soil Science, 38, 325–341. https://doi.org/10.1111/j.1365-2389.1987.tb02148.x.
Laslett, G. M. (1994). Kriging and splines: An empirical comparison of their predictive performance in some applications. Journal of the American Statistical Association, 89, 391–409. https://doi.org/10.2307/2290840.
Laslett, G. M., & McBratney, A. B. (1990). Further comparisons of spatial methods for predicting soil pH. Soil Society of America Journal, 54, 1553–1558. https://doi.org/10.2136/sssaj1990.03615995005400060007x.
Lenth, R. V. (2016). Least-squares means: The R Package lsmeans. Journal of Statistical Software, 69(1), 1–33. https://doi.org/10.18637/jss.v069.i01.
Long, E., Ketterings, Q. M., Russell, D., Vermeylen, F., & DeGloria, S. D. (2016). Assessment of yield monitoring equipment for dry matter and yield of corn silage and alfalfa/grass. Precision Agriculture, 7, 546–563. https://doi.org/10.1007/s11119-016-9436-y.
Maestrini, B., & Basso, B. (2018a). Drivers of within-field spatial and temporal variability of crop yield across the US Midwest. Scientific Reports, 8, 106–112. https://doi.org/10.1038/s41598-018-32779-3.
Maestrini, B., & Basso, B. (2018b). Predicting spatial patterns of within-field crop yield variability. Field Crops Research, 219, 106–112. https://doi.org/10.1016/j.fcr.2018.01.028.
Pebesma, E. J. (2004). Multivariable geostatistics in S: The gstat package. Computers & Geosciences, 30, 683–691. https://doi.org/10.1016/j.cageo.2004.03.012.
Philips, D. L., Lee, E. H., Herstrom, A. A., Hogsett, W. E., & Tingey, D. T. (1997). Use of auxiliary data for spatial interpolation of ozone exposure in southeastern forests. Environmetrics, 8, 43–61. https://doi.org/10.1002/(sici)1099-095x(199701)8:1%3c43::aid-env237%3e3.0.co;2-g.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/
Ross, K., Morris, D., & Johannsen, C. J. (2008). A review of intra-field yield estimation from yield monitor data. Applied Engineering in Agriculture, 24, 309–317. https://doi.org/10.13031/2013.24496.
Rouhani, S. (1986). Comparative study of ground-water mapping techniques. Ground Water, 24(2), 207–216. https://doi.org/10.1111/j.1745-6584.1986.tb00996.x.
Souza, E. G., Bazzi, C. L., Khosla, R., Uribe-Opazo, M. A., & Reich, R. M. (2016). Interpolation type and data computation of crop yield maps is important for precision crop production. Journal of Plant Nutrition, 39(4), 531–538. https://doi.org/10.1080/01904167.2015.1124893.
Sudduth, K. A., & Drummond, S. T. (2007). Yield editor. Agronomy Journal, 99(6), 1471–1482. https://doi.org/10.2134/agronj2006.0326.
Sudduth, K. A., Drummond, S. T., & Myers, D. B. (2012). Yield editor 2.0: Software for automated removal of yield map errors. Paper No. 121338243. St. Joseph, MI, USA: ASABE. https://doi.org/10.13031/2013.41893.
Thylén, L., Algerbo, P.A., Giebel, A., Robert, P. C., Rust, R. H., & Larson, W. E. (2000). An expert filter removing erroneous yield data. In Robert, P. C., R. H. Rust, & W. E. Larson (Eds.), Proceedings of the 5th international conference on precision agriculture and other precision resources management. Madison, WI, USA: ASA/CSSA/SSSA – CDROM.
USDA. (2019a). United States Department of Agriculture - New York State Agriculture Overview. Retrieved August 19, 2020, from https://www.nass.usda.gov/Quick_Stats/Ag_Overview/stateOverview.php?state=NEW%20YORK.
USDA. (2019b). United States Department of Agriculture - Crop Production 2019 Summary. Retrieved from August 19, 2020, from https://www.nass.usda.gov/Publications/Todays_Reports/reports/cropan20.pdf.
Van Meirvenne, M., Scheldeman, K., Baert, G., & Hofman, G. (1994). Quantification of soil textural fractions of Bas-Zaire using soil map polygons and/or point observations. Geoderma, 62, 69–82. https://doi.org/10.1016/0016-7061(94)90028-0.
Vecchia, A. (1988). Estimation and model identification for continuous spatial processes. Journal of the Royal Statistical Society Series B (Methodological), 50(2), 297–312. https://doi.org/10.1111/j.2517-6161.1988.tb01729.x.
Vega, A., Córdoba, M., Castro-Franco, M., & Balzarini, M. (2019). Protocol for automating error removal from yield maps. Precision Agriculture, 20, 1033–1044. https://doi.org/10.1007/s11119-018-09632-8.
Weber, D., & Englund, E. (1994). Evaluation and comparison of spatial interpolators II. Mathematical Geology, 26(5), 589–603. https://doi.org/10.1007/bf02089243.
Wettschereck, D. (1994). A study of distance-based machine learning algorithms. Ph.D thesis, Oregon State University, Corvallis, OR, USA.
Yang, C., Bradford, J., & Wiegand, C. (2001). Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn. Transactions of the ASAE, 44(6), 1983–1994. https://doi.org/10.13031/2013.6997.
Acknowledgements
We thank the farmers for sharing whole-farm corn silage and grain yield data and Benjamin Lehman, John Steele, and Sheryl Swink for yield data processing.
Funding
This research was funded with grants from USDA-NIFA, the Northern New York Agricultural Development Program (NNYADP), New York State Corn Growers Association (NYSCGA).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cho, J.B., Guinness, J., Kharel, T.P. et al. Spatial estimation methods for mapping corn silage and grain yield monitor data. Precision Agric 22, 1501–1520 (2021). https://doi.org/10.1007/s11119-021-09793-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-021-09793-z