Skip to main content

Advertisement

Log in

Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Understanding yield potential and yield-limiting factors is essential for improving profitability and grain yields while avoiding adverse environmental effects. In the USA, grain yield monitors are widely used but the information they provide is rarely used to understand within-field yield variations and associated yield constraints. The objectives of this research were to understand the influence of in-season precipitation on within-field spatio-temporal variation of maize (Zea mays L.) yield and to determine manageable yield variation in two contrasting Major Land Resource Areas of the Mid-Atlantic USA. It does this by assessing the association of grain yield monitor data and in-season precipitation information to be used for variable rate management. Maize yields, as evaluated by baseline functions, were more closely associated with in-season precipitation in the Coastal Plain than in the Piedmont. The study then used standardized principal component analysis (stdPCA) to reveal within-field yield patterns. These varied only under moisture-limited conditions in the Coastal Plain. In the Piedmont, the within-field yield pattern was more consistent under a range of in-season precipitation conditions. In Coastal Plain rainfed fields, the yield predictability increased at the end of June, indicating the possibility of predicting within-field spatial patterns in mid-season. These approaches were successful in deciding whether within-field site- and time-specific management is beneficial for a particular region or field. The presented method, combining stdPCA and geostatistical assessment, is useful in strategizing precision crop management, but do not reveal causes. Detailed soil information and topography could additionally be valuable for understanding constraints to crop yield.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  • Arslan, S., & Colvin, T. S. (2002). Grain yield mapping: Yield sensing, yield reconstruction, and errors. Precision Agriculture, 3(2), 135–154.

    Article  Google Scholar 

  • Belcher, B. N., & DeGaetano, A. T. (2005). A method to infer time of observation at US Cooperative Observer Network stations using model analyses. International Journal of Climatology, 25(9), 1237–1251.

    Article  Google Scholar 

  • Blackmore, S. (2000). The interpretation of trends from multiple yield maps. Computers and Electronics in Agriculture, 26(1), 37–51.

    Article  Google Scholar 

  • Blackmore, S., Godwin, R. J., & Fountas, S. (2003). The analysis of spatial and temporal trends in yield map data over six years. Biosystems Engineering, 84(4), 455–466.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Calviño, P. A., Andrade, F. H., & Sadras, V. O. (2003). Maize yield as affected by water availability, soil depth, and crop management. Agronomy Journal, 95(2), 275–281.

    Article  Google Scholar 

  • Calviño, P. A., & Sadras, V. O. (1999). Interannual variation in soybean yield: Interaction among rainfall, soil depth and crop management. Field Crops Research, 63(3), 237–246.

    Article  Google Scholar 

  • Cox, M. S., & Gerard, P. D. (2007). Soil management zone determination by yield stability analysis and classification. Agronomy Journal, 99(5), 1357–1365.

    Article  CAS  Google Scholar 

  • Eastman, J. R., & Filk, M. (1993). Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing, 59(6), 991–996.

    Google Scholar 

  • Florin, M. J., McBratney, A. B., & Whelan, B. M. (2009). Quantification and comparison of wheat yield variation across space and time. European Journal of Agronomy, 30(3), 212–219.

    Article  Google Scholar 

  • Foss, J. E., Fanning, D. S., Miller, F. P., & Wagner, D. P. (1978). Loess deposits of the Eastern Shore of Maryland. Soil Science Society of America Journal, 42(2), 329–334.

    Article  CAS  Google Scholar 

  • GRASS Development Team. (2015). Geographic Resources Analysis Support System (GRASS) Software, Version 6.4. Open Source Geospatial Foundation Project. Retrieved April 18, 2021, from http://grass.osgeo.org

  • Grassini, P., Eskridge, K. M., & Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications. https://doi.org/10.1038/ncomms3918

    Article  PubMed  Google Scholar 

  • Grassini, P., van Bussel, L. G. J., Wart, J. V., Wolf, J., Claessens, L., Yang, H., et al. (2015). How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research, 177, 49–63.

    Article  Google Scholar 

  • Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., van Rees, H., Marinoni, O., et al. (2012). Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia. Field Crops Research, 136, 85–96.

    Article  Google Scholar 

  • Hong, N., Scharf, P. C., Davis, J. G., Kitchen, N. R., & Sudduth, K. A. (2007). Economically optimal nitrogen rate reduces soil residual nitrate. Journal of Environmental Quality, 36(2), 354–362.

    Article  CAS  PubMed  Google Scholar 

  • Jiang, P., & Thelen, K. D. (2004). Effect of soil and topographic properties on crop yield in a north-central corn–soybean cropping system. Agronomy Journal, 96, 252–258.

    Article  Google Scholar 

  • Kaul, M., Hill, R. L., & Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85(1), 1–18.

    Article  Google Scholar 

  • Kettler, T. A., Doran, J. W., & Gilbert, T. L. (2001). Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Science Society of America Journal, 65, 849–852.

    Article  CAS  Google Scholar 

  • Kinoshita, R., van Es, H., Dantinne, J., & Twining, M. (2016). Within-field profitability informs agronomic management decision in the Mid-Atlantic USA. Agricultural & Environmental Letters. https://doi.org/10.2134/ael2016.09.0034

    Article  Google Scholar 

  • Koen, B. V. (1988). Toward a definition of the engineering method. European Journal of Engineering Education, 13(3), 307–315.

    Article  Google Scholar 

  • Kravchenko, A. N., & Bullock, D. G. (2000). Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92, 75–83.

    Article  Google Scholar 

  • Kumhálová, J., Kumhála, F., Kroulík, M., & Matějková, Š. (2011). The impact of topography on soil properties and yield and the effects of weather conditions. Precision Agriculture, 12, 813–830.

    Article  Google Scholar 

  • Lawes, R. A., Oliver, Y. M., & Robertson, M. J. (2009). Capturing the in-field spatial-temporal dynamic of yield variation. Crop & Pasture Science, 60, 834–843.

    Article  Google Scholar 

  • Lowenberg-DeBoer, J., & Erickson, B. (2019). Setting the record straight on precision agriculture adoption. Agronomy Journal, 111, 1552–1569.

    Article  Google Scholar 

  • Markewich, H. W., Pavich, M. J., & Buell, G. R. (1990). Contrasting soils and landscapes of the Piedmont and Coastal Plain, eastern United States. Geomorphology, 3(3), 417–447.

    Article  Google Scholar 

  • Mitášová, H., & Hofierka, J. (1993). Interpolation by regularized spline with tension: II. Application to terrain modeling and surface geometry analysis. Mathematical Geology, 25(6), 657–669.

    Article  Google Scholar 

  • Mueller, S. M., & Vyn, T. J. (2018). Physiological constraints to realizing maize grain yield recovery with silking-stage nitrogen fertilizer applications. Field Crops Research, 228, 102–109.

    Article  Google Scholar 

  • Neild, R. E., & Newman, J. E. (1990). Growing season characteristics and requirements in the corn belt. Purdue University, Cooperative Extension Service.

    Google Scholar 

  • Oliver, Y. M., Robertson, M. J., & Wong, M. T. F. (2010). Integrating farmer knowledge, precision agriculture tools, and crop simulation modelling to evaluate management options for poor-performing patches in cropping fields. European Journal of Agronomy, 32(1), 40–50.

    Article  Google Scholar 

  • Pebesma, E. J. (2004). Multivariate geostatistics in S: The gstat package. Computers & Geosciences, 30, 683–691.

    Article  Google Scholar 

  • Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Koppen-Geiger climate classification. Hydrology and Earth System Sciences, 11, 1633–1644.

    Article  Google Scholar 

  • Potgieter, A. B., Hammer, G. L., & Butler, D. (2002). Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO. Australian Journal of Agricultural Research, 53(1), 77–89.

    Article  Google Scholar 

  • Pringle, M. J., McBratney, A. B., Whelan, B. M., & Taylor, J. A. (2003). A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data. Agricultural Systems, 76(1), 273–292.

    Article  Google Scholar 

  • QGIS Development Team. (2015). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Retrieved April 18, 2021, from http://qgis.osgeo.org

  • R Core Team. (2014). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Retrieved April 18, 2021, from http://www.R-project.org/

  • Russo, D., & Bresler, E. (1981). Effect of field variability in soil hydraulic properties on solutions of unsaturated water and salt flows. Soil Science Society of America Journal, 45(4), 675–681.

    Article  Google Scholar 

  • Scharf, P. C., Shannon, D. K., Palm, H. L., Sudduth, K. A., Drummond, S. T., Kitchen, N. R., et al. (2011). Sensor-based nitrogen applications out-performed producer-chosen rates for corn in on-farm demonstrations. Agronomy Journal, 103(6), 1683–1691.

    Article  Google Scholar 

  • Sela, S., van Es, H. M., Moebius-Clune, B. N., Marjerison, R., Melkonian, J., Moebius-Clune, D., et al. (2016). Adapt-N outperforms grower-selected nitrogen rates in Northeast and Midwestern United States strip trials. Agronomy Journal, 108, 1726–1734.

    Article  CAS  Google Scholar 

  • Simonson, R. W. (1982). Loess in soils of Delaware, Maryland, and northeastern Virginia. Soil Science, 133(5), 167–178.

    Article  Google Scholar 

  • Soil Survey Staff. (2015). Official Soil Series Descriptions. Retrieved August 29, 2015, from https://soilseries.sc.egov.usda.gov/osdquery.aspx

  • Stanford, G. (1973). Rationale for optimum nitrogen fertilization in corn production. Journal of Environmental Quality, 2(2), 159–166.

    Article  Google Scholar 

  • Sudduth, K. A., & Drummond, S. T. (2007). Yield Editor: Software for removing errors from crop yield maps. Agronomy Journal, 99(6), 1471–1482.

    Article  Google Scholar 

  • Tremblay, N., Bouroubi, Y. M., Bélec, C., Mullen, R. W., Kitchen, N. R., Thomason, W. E., et al. (2012). Corn response to nitrogen is influenced by soil texture and weather. Agronomy Journal, 104(6), 1658–1671.

    Article  Google Scholar 

  • Troy, T. J., Kipgen, C., & Pal, I. (2015). The impact of climate extremes and irrigation on US crop yields. Environmental Research Letters. https://doi.org/10.1088/1748-9326/10/5/054013

    Article  Google Scholar 

  • USDA-NRCS. (2006). Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin. U.S. Department of Agriculture Handbook 296.

  • van Es, H. M., Kay, B. D., Melkonian, J. J., & Sogbedji, J. M. (2007). Nitrogen management under maize in humid regions: Case for a dynamic approach. In Bruulsema (Ed.), Managing crop nutrition for weather (pp. 6–13). International Plant Nutrition Institute.

  • van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis wtih local to global relevance: A review. Field Crops Research, 143, 4–17.

    Article  Google Scholar 

  • van Ittersum, M. K., & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research, 52(3), 197–208.

    Article  Google Scholar 

  • Ware, E. C. (2005). Corrections to Radar-Estimated Precipitation Using Observed Rain Gauge Data (MS thesis). Cornell University.

  • Warrick, A. W., Myers, D. E., & Nielsen, D. R. (1986). Geostatistical Methods Applied to Soil Science. In Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods (2nd ed., pp. 53–82). American Society of Agronomy: Soil Science Society of America.

  • Weaver, K. N. (1967). Generalized Geologic Map of Maryland. Maryland Geological Survey.

  • Wilks, D. S. (2008). High-resolution spatial interpolation of weather generator parameters using local weighted regressions. Agricultural and Forest Meteorology, 148(1), 111–120.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Jeff Melkonian, John Dantinne, and Mike Twinning who provided valuable suggestions.

Funding

Funding was provided by Willard Agri-Service of Frederick, Inc.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rintaro Kinoshita.

Ethics declarations

Conflict of interest

The authors declare 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kinoshita, R., Rossiter, D. & van Es, H. Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones. Precision Agric 22, 1952–1972 (2021). https://doi.org/10.1007/s11119-021-09820-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-021-09820-z

Keywords

Navigation